[
  {
    "path": ".github/workflows/pylint.yml",
    "content": "name: Pylint\n\non: [push]\n\njobs:\n  build:\n\n    runs-on: ubuntu-latest\n\n    steps:\n      - uses: actions/checkout@v2\n      - name: Set up Python 3.8\n        uses: actions/setup-python@v1\n        with:\n          python-version: 3.8\n      - name: Install dependencies\n        run: |\n          python -m pip install --upgrade pip\n          pip install pylint numpy pandas matplotlib\n          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n      - name: Analysing the code with pylint\n        run: |\n          pylint `ls -R|grep .py$|xargs`\n"
  },
  {
    "path": ".github/workflows/python-package.yml",
    "content": "# This workflow will install Python dependencies, run tests and lint with a variety of Python versions\n# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions\n\nname: benford_py\n\non:\n  push:\n    branches: [ develop ]\n  pull_request:\n    branches: [ develop ]\n\njobs:\n  build:\n\n    runs-on: ubuntu-latest\n    strategy:\n      matrix:\n        python-version: [3.6, 3.7, 3.8, 3.9]\n\n    steps:\n    - uses: actions/checkout@v2\n    - name: Set up Python ${{ matrix.python-version }}\n      uses: actions/setup-python@v2\n      with:\n        python-version: ${{ matrix.python-version }}\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip\n        pip install flake8 pytest numpy pandas matplotlib\n        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n    - name: Lint with flake8\n      run: |\n        # stop the build if there are Python syntax errors or undefined names\n        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics\n        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide\n        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics\n    - name: Test with pytest\n      run: |\n        pytest\n"
  },
  {
    "path": ".gitignore",
    "content": "# Compiled python modules.\n*.pyc\n\n__pycache__/\n\n# ipython notebook checkpoints\n*.ipynb_checkpoints\n\n# text editor backups\n*~\n\n# VS Code\n.vscode/\n\n# Jupyter NB Checkpoints\n.ipynb_checkpoints/\n\n# Setuptools distribution folder.\n/dist/\n/build/\n\n# Python egg metadata, regenerated from source files by setuptools.\n/*.egg-info\n\n# Sphinx docs rendered files\n# /docs/build/\n_build\n_static\n_templates\n\n# pytest\n.pytest_cache/\n#VSCode\n.vscode/\n"
  },
  {
    "path": ".pylintrc",
    "content": "[MASTER]\ndisable=\n    F0001, # No module named XXXXXXXX\n\n\n\nignored-classes=SQLObject,Registrant,scoped_session\n"
  },
  {
    "path": ".readthedocs.yml",
    "content": "# .readthedocs.yml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details\n\n# Required\nversion: 2\n\n# Build documentation in the docs/ directory with Sphinx\nsphinx:\n  configuration: docs/source/conf.py\n\n# Build documentation with MkDocs\n#mkdocs:\n#  configuration: mkdocs.yml\n\n# Optionally build your docs in additional formats such as PDF and ePub\nformats: all\n\n# Optionally set the version of Python and requirements required to build your docs\npython:\n  version: 3.7\n  install:\n    - requirements: docs/requirements.txt"
  },
  {
    "path": "CITATION.cff",
    "content": "cff-version: 1.2.0\nmessage: \"If you use this software, please cite it as below.\"\nauthors:\n- family-names: \"Milcent\"\n  given-names: \"Marcel\"\n  orcid: \ntitle: \"Benford_py: a Python Implementation of Benford's Law Tests\"\nversion: 0.5.0\ndoi: \ndate-released: 2017\nurl: \"https://github.com/milcent/benford_py\""
  },
  {
    "path": "Demo.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Benford  for Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Current version: 0.4.3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Installation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Benford for python is a Package in PyPi, so you can install with *pip*:\\n\",\n    \"##### `$ pip install benford_py`\\n\",\n    \"### or\\n\",\n    \"##### `$ pip install benford-py`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Or you can cd into the site-packages subfolder of your python distribution (or environment) and clone from there:\\n\",\n    \"##### `$ git clone http://github.com/milcent/benford_py.git`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## [Documentation](https://benford-py.readthedocs.io)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Demo\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### This demo assumes you have (at least) some familiarity with Benford's Law.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### First let's import some libraries and the benford module.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import benford as bf\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Quick start\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Getting some public data with pandas, the S&P500 EFT quotes, up until Dec 2016.\\n\",\n    \"##### I have downloaded it and saved it in the data folder, so you should have it if you cloned the repo. Alternatively, you an downolad it [here](https://github.com/milcent/benford_py/blob/master/data/SPY.csv).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sp = pd.read_csv('data/SPY.csv', index_col='Date', parse_dates=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Creating simple and log return  columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Open</th>\\n\",\n       \"      <th>High</th>\\n\",\n       \"      <th>Low</th>\\n\",\n       \"      <th>Close</th>\\n\",\n       \"      <th>Volume</th>\\n\",\n       \"      <th>Adj_Close</th>\\n\",\n       \"      <th>p_r</th>\\n\",\n       \"      <th>l_r</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2016-12-23</th>\\n\",\n       \"      <td>225.429993</td>\\n\",\n       \"      <td>225.720001</td>\\n\",\n       \"      <td>225.210007</td>\\n\",\n       \"      <td>225.710007</td>\\n\",\n       \"      <td>36251400</td>\\n\",\n       \"      <td>225.710007</td>\\n\",\n       \"      <td>0.001464</td>\\n\",\n       \"      <td>0.001463</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2016-12-27</th>\\n\",\n       \"      <td>226.020004</td>\\n\",\n       \"      <td>226.729996</td>\\n\",\n       \"      <td>226.000000</td>\\n\",\n       \"      <td>226.270004</td>\\n\",\n       \"      <td>41054400</td>\\n\",\n       \"      <td>226.270004</td>\\n\",\n       \"      <td>0.002481</td>\\n\",\n       \"      <td>0.002478</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2016-12-28</th>\\n\",\n       \"      <td>226.570007</td>\\n\",\n       \"      <td>226.589996</td>\\n\",\n       \"      <td>224.270004</td>\\n\",\n       \"      <td>224.399994</td>\\n\",\n       \"      <td>59776300</td>\\n\",\n       \"      <td>224.399994</td>\\n\",\n       \"      <td>-0.008265</td>\\n\",\n       \"      <td>-0.008299</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2016-12-29</th>\\n\",\n       \"      <td>224.479996</td>\\n\",\n       \"      <td>224.889999</td>\\n\",\n       \"      <td>223.839996</td>\\n\",\n       \"      <td>224.350006</td>\\n\",\n       \"      <td>47719500</td>\\n\",\n       \"      <td>224.350006</td>\\n\",\n       \"      <td>-0.000223</td>\\n\",\n       \"      <td>-0.000223</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2016-12-30</th>\\n\",\n       \"      <td>224.729996</td>\\n\",\n       \"      <td>224.830002</td>\\n\",\n       \"      <td>222.729996</td>\\n\",\n       \"      <td>223.529999</td>\\n\",\n       \"      <td>101301800</td>\\n\",\n       \"      <td>223.529999</td>\\n\",\n       \"      <td>-0.003655</td>\\n\",\n       \"      <td>-0.003662</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                  Open        High         Low       Close     Volume  \\\\\\n\",\n       \"Date                                                                    \\n\",\n       \"2016-12-23  225.429993  225.720001  225.210007  225.710007   36251400   \\n\",\n       \"2016-12-27  226.020004  226.729996  226.000000  226.270004   41054400   \\n\",\n       \"2016-12-28  226.570007  226.589996  224.270004  224.399994   59776300   \\n\",\n       \"2016-12-29  224.479996  224.889999  223.839996  224.350006   47719500   \\n\",\n       \"2016-12-30  224.729996  224.830002  222.729996  223.529999  101301800   \\n\",\n       \"\\n\",\n       \"             Adj_Close       p_r       l_r  \\n\",\n       \"Date                                        \\n\",\n       \"2016-12-23  225.710007  0.001464  0.001463  \\n\",\n       \"2016-12-27  226.270004  0.002481  0.002478  \\n\",\n       \"2016-12-28  224.399994 -0.008265 -0.008299  \\n\",\n       \"2016-12-29  224.350006 -0.000223 -0.000223  \\n\",\n       \"2016-12-30  223.529999 -0.003655 -0.003662  \"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#adding '_' to facilitate handling the column\\n\",\n    \"sp.rename(columns={'Adj Close':'Adj_Close'}, inplace=True) \\n\",\n    \"sp['p_r'] = sp.Close/sp.Close.shift()-1        #simple returns\\n\",\n    \"sp['l_r'] = np.log(sp.Close/sp.Close.shift())  #log returns\\n\",\n    \"sp.tail()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### First Digits Test\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Let us see if the SPY log returns look like Benford.\\n\",\n    \"#### The `first_digits` function's first argument is the data to be analysed, which may be a pandas Series or a numpy 1D array.\\n\",\n    \"#### The `digs` argument tells the function which test to run: 1 for the _first_, 2 for the _first two_, and 3 for the _first three_ digits.The `decimals` parameter tells the function how many decimal places to consider when pre-processing the data. It defaults to 2, for dealing with currency, but since here we are dealing with tiny numbers, we will go with 8.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 1 after preparation.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"f1d = bf.first_digits(sp.l_r, digs=1, decimals=8) # digs=1 for the first digit (1-9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The *first_digits* function draws the plot (default) with blue bars fot the digits found frequencies and a red line corresponding to the expected Benford proportions. \\n\",\n    \"#### It also returns a pandas DataFrame object with Counts, Found proportions and Expected values for each digit in the data studied.\\n\",\n    \"#### Zeros and NANs are dropped before processing.\\n\",\n    \"#### By default, (`verbose`=True) it also gives informaiton on the sample size and on the number of records eventually discarded during pre-processing (more on this later) .\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Counts</th>\\n\",\n       \"      <th>Found</th>\\n\",\n       \"      <th>Expected</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>First_1_Dig</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1835</td>\\n\",\n       \"      <td>0.307473</td>\\n\",\n       \"      <td>0.301030</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>949</td>\\n\",\n       \"      <td>0.159015</td>\\n\",\n       \"      <td>0.176091</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>668</td>\\n\",\n       \"      <td>0.111930</td>\\n\",\n       \"      <td>0.124939</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>534</td>\\n\",\n       \"      <td>0.089477</td>\\n\",\n       \"      <td>0.096910</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>494</td>\\n\",\n       \"      <td>0.082775</td>\\n\",\n       \"      <td>0.079181</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>447</td>\\n\",\n       \"      <td>0.074899</td>\\n\",\n       \"      <td>0.066947</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>386</td>\\n\",\n       \"      <td>0.064678</td>\\n\",\n       \"      <td>0.057992</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>345</td>\\n\",\n       \"      <td>0.057808</td>\\n\",\n       \"      <td>0.051153</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>310</td>\\n\",\n       \"      <td>0.051944</td>\\n\",\n       \"      <td>0.045757</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"             Counts     Found  Expected\\n\",\n       \"First_1_Dig                            \\n\",\n       \"1              1835  0.307473  0.301030\\n\",\n       \"2               949  0.159015  0.176091\\n\",\n       \"3               668  0.111930  0.124939\\n\",\n       \"4               534  0.089477  0.096910\\n\",\n       \"5               494  0.082775  0.079181\\n\",\n       \"6               447  0.074899  0.066947\\n\",\n       \"7               386  0.064678  0.057992\\n\",\n       \"8               345  0.057808  0.051153\\n\",\n       \"9               310  0.051944  0.045757\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f1d\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### First Two Digists\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 10 after preparation.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x972 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"f2d = bf.first_digits(sp.l_r, digs=2, decimals=8) # Note the parameter digs=2!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The variable `f2d` is assigned a pandas DataFrame with the Counts of registries with the digitss of interest, in this case 10-99 (First Two), the Found proportions and the Expected, Benfors ones, as shown bellow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Counts</th>\\n\",\n       \"      <th>Found</th>\\n\",\n       \"      <th>Expected</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>First_2_Dig</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>298</td>\\n\",\n       \"      <td>0.049933</td>\\n\",\n       \"      <td>0.041393</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>232</td>\\n\",\n       \"      <td>0.038874</td>\\n\",\n       \"      <td>0.037789</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>203</td>\\n\",\n       \"      <td>0.034015</td>\\n\",\n       \"      <td>0.034762</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>238</td>\\n\",\n       \"      <td>0.039879</td>\\n\",\n       \"      <td>0.032185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>178</td>\\n\",\n       \"      <td>0.029826</td>\\n\",\n       \"      <td>0.029963</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"             Counts     Found  Expected\\n\",\n       \"First_2_Dig                            \\n\",\n       \"10              298  0.049933  0.041393\\n\",\n       \"11              232  0.038874  0.037789\\n\",\n       \"12              203  0.034015  0.034762\\n\",\n       \"13              238  0.039879  0.032185\\n\",\n       \"14              178  0.029826  0.029963\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f2d.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Counts</th>\\n\",\n       \"      <th>Found</th>\\n\",\n       \"      <th>Expected</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>First_2_Dig</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>95</th>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>0.005697</td>\\n\",\n       \"      <td>0.004548</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>96</th>\\n\",\n       \"      <td>29</td>\\n\",\n       \"      <td>0.004859</td>\\n\",\n       \"      <td>0.004501</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>97</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>0.005362</td>\\n\",\n       \"      <td>0.004454</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>98</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>0.005194</td>\\n\",\n       \"      <td>0.004409</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>99</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>0.005194</td>\\n\",\n       \"      <td>0.004365</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"             Counts     Found  Expected\\n\",\n       \"First_2_Dig                            \\n\",\n       \"95               34  0.005697  0.004548\\n\",\n       \"96               29  0.004859  0.004501\\n\",\n       \"97               32  0.005362  0.004454\\n\",\n       \"98               31  0.005194  0.004409\\n\",\n       \"99               31  0.005194  0.004365\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f2d.tail()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Assessing conformity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### There are some conformity tests to evaluate if the data studied is a good fit to Benford's Law. Let us not confuse conformity tests with the digits tests themselves. The latter are the First Digit Test, Second Digit Test, First Two Digits Tests, and so on, ie, the result of processing the data and finding the respective position digits, whilst the former are statistical tests applied to the results of the latter and compared to critical values to establish conformity or not with Benford's Law.\\n\",\n    \"#### These conformity tests cam be divided for didatic purposes in:\\n\",\n    \"- #### Tests which result in a single (scalar) statistic about the whole sample. Some examples are:\\n\",\n    \"    - #### the **Chi-Square** test;\\n\",\n    \"    - #### the **Kolmogorov-Smirnov** test;\\n\",\n    \"    - #### the Mean Absolute Deviation (**MAD**) test; and\\n\",\n    \"- #### Tests that render one statistic for each of the studied leading digits' proportions in relation to the expexted Benford's ones. That's the case of the **Z statistic** for the proportions. We will start with this one.\\n\",\n    \"\\n\",\n    \"#### There is one more way to divide such tests:\\n\",\n    \"- #### Those which depend on the confidence level and on the number of records in the sample:\\n\",\n    \"    - #### the **Kolmogorov-Smirnov** test; and\\n\",\n    \"    - #### the **Z statistic** for the proportions.\\n\",\n    \"- #### Those which do not:\\n\",\n    \"    - #### the **Chi-Square** test; and\\n\",\n    \"    - #### The Mean Absolute Deviation (**MAD**) test.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### As mentioned, the Z scores test needs a confidence level so as to set a threshold for the deviations to be deemed relevant (above it) or not (below it). In the digits functions, you can turn it on by setting the parameter `confidence`, which will tell the function which confidence level to consider after calculating the Z score for each proportion.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 10 after preparation.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"             Expected     Found   Z_score\\n\",\n      \"First_2_Dig                              \\n\",\n      \"67           0.006434  0.010389  3.740056\\n\",\n      \"13           0.032185  0.039879  3.331418\\n\",\n      \"10           0.041393  0.049933  3.279619\\n\",\n      \"66           0.006531  0.009886  3.137524\\n\",\n      \"82           0.005264  0.007540  2.340301\\n\",\n      \"72           0.005990  0.008210  2.138736\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x972 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# For a significance of 5% (p <= 0.05), a confidence of 95%\\n\",\n    \"f2d = bf.first_digits(sp.l_r, digs=2, decimals=8, confidence=95)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Some things happened:\\n\",\n    \"- #### It printed a DataFrame with the **significant positive deviations**, in descending order of the Z scores. So, for a confidence level of 95%, the data to be considered for further investigation would be the records with First Two Digits **67**, **13**, **10**, **66**, **82** and **72**, whose propostions displayed a Z score higher than 1.96 (95%) **and** were positive, ie, were higher than the expected proportions. The function can also return **all** the relevant deviations or just the **negative** ones, by setting the `high-Z` parameter to 'all' or 'neg', respectively (see below).\\n\",\n    \"- #### In the plot, it added upper and lower boundaries to the Benford Expected line based on the level of confidence. Accordingly, it changed the colors of the bars whose proportions fell lower or higher than the drawn boundaries, for better vizualization.\\n\",\n    \"\\n\",\n    \"#### The *confidence* parameter takes the following (discrete) values other than *None*: 80, 85, 90, 95, 99, 99.9, 99.99, 99.999, 99.9999 and 99.99999.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### When you set the confidence level, the resulting DataFrame gets one more column, with the Z scores (bellow).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Counts</th>\\n\",\n       \"      <th>Found</th>\\n\",\n       \"      <th>Expected</th>\\n\",\n       \"      <th>Z_score</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>First_2_Dig</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>67</th>\\n\",\n       \"      <td>62</td>\\n\",\n       \"      <td>0.010389</td>\\n\",\n       \"      <td>0.006434</td>\\n\",\n       \"      <td>3.740056</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>238</td>\\n\",\n       \"      <td>0.039879</td>\\n\",\n       \"      <td>0.032185</td>\\n\",\n       \"      <td>3.331418</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>298</td>\\n\",\n       \"      <td>0.049933</td>\\n\",\n       \"      <td>0.041393</td>\\n\",\n       \"      <td>3.279619</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>126</td>\\n\",\n       \"      <td>0.021113</td>\\n\",\n       \"      <td>0.028029</td>\\n\",\n       \"      <td>3.197832</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>66</th>\\n\",\n       \"      <td>59</td>\\n\",\n       \"      <td>0.009886</td>\\n\",\n       \"      <td>0.006531</td>\\n\",\n       \"      <td>3.137524</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"             Counts     Found  Expected   Z_score\\n\",\n       \"First_2_Dig                                      \\n\",\n       \"67               62  0.010389  0.006434  3.740056\\n\",\n       \"13              238  0.039879  0.032185  3.331418\\n\",\n       \"10              298  0.049933  0.041393  3.279619\\n\",\n       \"15              126  0.021113  0.028029  3.197832\\n\",\n       \"66               59  0.009886  0.006531  3.137524\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f2d.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### To get a feeling of a less compliant sample, let us try the SPY closing prices instead of its returns, now with a confidence level of 99%. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 6026 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 6026 registries.\\n\",\n      \"Discarded 0 records < 10 after preparation.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"             Expected     Found    Z_score\\n\",\n      \"First_2_Dig                               \\n\",\n      \"11           0.037789  0.135247  39.641478\\n\",\n      \"13           0.032185  0.111185  34.710863\\n\",\n      \"12           0.034762  0.115665  34.250363\\n\",\n      \"14           0.029963  0.084965  25.006243\\n\",\n      \"46           0.009340  0.030534  17.037044\\n\",\n      \"20           0.021189  0.045636  13.132371\\n\",\n      \"10           0.041393  0.074842  13.003059\\n\",\n      \"45           0.009545  0.021739   9.668882\\n\",\n      \"44           0.009760  0.019250   7.428122\\n\",\n      \"21           0.020203  0.029705   5.196428\\n\",\n      \"92           0.004695  0.008795   4.561719\\n\",\n      \"90           0.004799  0.007634   3.090972\\n\",\n      \"91           0.004746  0.007468   2.979729\\n\",\n      \"94           0.004596  0.007136   2.819974\\n\",\n      \"89           0.004853  0.007302   2.643275\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x972 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Note the decimals=2 parameter instead of 8, since now we are dealing\\n\",\n    \"# with two decimal places (price)\\n\",\n    \"f2d = bf.first_digits(sp.Close, digs=2, decimals=2, confidence=99)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Other digits tests and their Z scores \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### We can do all this with the *First Three Digits*, *Second Digit* and the *Last Two Digits* tests too.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 100 after preparation.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"             Expected     Found   Z_score\\n\",\n      \"First_3_Dig                              \\n\",\n      \"952          0.000456  0.001676  4.110387\\n\",\n      \"962          0.000451  0.001508  3.539604\\n\",\n      \"997          0.000435  0.001340  3.041483\\n\",\n      \"823          0.000527  0.001508  3.017908\\n\",\n      \"695          0.000624  0.001676  2.991625\\n\",\n      \"945          0.000459  0.001340  2.874850\\n\",\n      \"139          0.003113  0.005194  2.769750\\n\",\n      \"751          0.000578  0.001508  2.720614\\n\",\n      \"874          0.000497  0.001340  2.635545\\n\",\n      \"862          0.000504  0.001340  2.593616\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 2016x1512 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# First Three Digits Test, now with 99% confidence level\\n\",\n    \"# digs=3 for the first three digits\\n\",\n    \"f3d = bf.first_digits(sp.l_r, digs=3, decimals=8, confidence=99)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The First Three Digits plot is better seen and zoomed in and out without the inline plotting. Try `%matplotlib` jupyter magic to have it rendered in a separate window.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### There are also the Second Digit and Last Two Digits tests, as shown bellow. These are implemented with separate (and accordingly named) functions with the same parametres but `digs`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 10 after preparation.\\n\",\n      \"\\n\",\n      \"The entries with the significant deviations are:\\n\",\n      \"\\n\",\n      \"         Expected     Found   Z_score\\n\",\n      \"Sec_Dig                              \\n\",\n      \"0        0.119679  0.128686  2.123777\\n\",\n      \"3        0.104330  0.111595  1.814980\\n\",\n      \"2        0.108821  0.102547  1.535754\\n\",\n      \"5        0.096677  0.090818  1.509872\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Second Digit Test with confidence 85% and printing 'all' relevant deviations\\n\",\n    \"sd = bf.second_digit(sp.l_r, decimals=8, confidence=85, high_Z='all')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"\\n\",\n      \"Discarded 0 records < 1000 after preparation\\n\",\n      \"\\n\",\n      \"The entries with the significant negative deviations are:\\n\",\n      \"\\n\",\n      \"            Expected     Found   Z_score\\n\",\n      \"Last_2_Dig                              \\n\",\n      \"25          0.010101  0.006032  3.078727\\n\",\n      \"83          0.010101  0.007205  2.172566\\n\",\n      \"12          0.010101  0.007373  2.043114\\n\",\n      \"31          0.010101  0.007540  1.913662\\n\",\n      \"55          0.010101  0.007708  1.784211\\n\",\n      \"50          0.010101  0.007875  1.654759\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Last Two Digits Test with confidence 90% and printing only the negative deviations\\n\",\n    \"l2d = bf.last_two_digits(sp.l_r, decimals=8, confidence=90, high_Z='neg')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### **Note**: The Last Two Digits test is not very useful in an irrational numbers cenario such as this (log returns).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Remembering the Important Args:, and explaining some more\\n\",\n    \"- #### `digs`: only used in the First Digits function, to tell it which test to run: 1- First Digits; 2- Fist Two Digits; and 3- First Three Digits.\\n\",\n    \"- #### `decimals`: informs the number of decimal places to consider. Defaluts to 2, for currencies, but I set it to 8 here, since we are dealing with log returns (long floats). If the numbers are too small and you don't set it properly, you may have a division-by-zero error. If the sequence is of integers, set it to 0. You may also set it to *'infer'* if you don't know exactly or if the data has registries with different number of decimal places, and it will treat every registry separately (though with worse performance).\\n\",\n    \"- #### `sign`: tells which portion of the data to consider. *'pos'*: only the positive entries; *'neg'*: only the negative ones; *'all'*: all entries but zeros. Defaults to *'all'*.\\n\",\n    \"- #### `verbose`: gives information about the test during its run, like the number of registries analysed, the number of registries discarded according to each test (ie, < 10 for the First Digits), and shows the top Z scores of the resulting DataFrame if *confidence* is not None. Defaults to *True*.\\n\",\n    \"- #### `high_Z`: chooses which Z scores to be used when displaying results, according to the set confidence level. Defaluts to *'pos'*, which will return only values higher than the expexted frequencies; *'neg'* will return only values lower than the expexted frequencies; *'all'* will return both extremes (positive and negative); and an integer will return the first n entries, positive and negative, regardless of whether Z is higher than the confidence or not.\\n\",\n    \"- #### `limit_N`: sets a limit to the sample size for the calculation of the Z scores. This may be found useful if the sample is too big, due to the Z test power problem (More on this ahead). Defaults to None.\\n\",\n    \"- #### `show_plot`: draws the test plot. Defaults to True. If *`confidence`* is not None, the plot will highlight the bars outside the lower and upper boundaries, regardless of the *`high_Z`* value.\\n\",\n    \"- #### `MAD` and `MSE`: calculate, respectively, the Mean Absolute Deviation and the Mean Squared Error of the sample, for each test. Defaults to False. Both can be used inside the tests' functions or also separately, in their own functions, `mad()` and `mse()`.\\n\",\n    \"- #### `chi_square`: computes the Chi Square test for the sample, taking into account the confidence level chosen.\\n\",\n    \"- #### `KS`: computes the Kolmogorov-Smirnov test for the sample, taking into account the confidence level chosen.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### MAD\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The Mean Absolute Deviation, or MAD, is, as the name states, the average of all absolute deviations (or errors, or residues) between the found proportions and the Benford's expected ones. \\n\",\n    \"#### <a href=www.sciencedirect.com/science/article/pii/S0748575100000087>Drake and Nigrini (2000)</a> developed this model, later revised by <a href=www.wiley.com/WileyCDA/WileyTitle/productCd-0470890460.html>Nigrini (2001)</a>, using empirical data to set limits of conformity for the First, First Two, First Three and Second Digits tests.\\n\",\n    \"#### The MAD averages the proportions, so it is not directly influenced by the sample size. The lower the MAD, the better the confotmity.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.008337279258069716\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mad1 = bf.mad(sp.l_r, test=1, decimals=8) # test=1 : MAD for the First Digits\\n\",\n    \"mad1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Note that you must choose the *test* parameter, since there is one MAD for each test.\\n\",\n    \"- #### First Digit: `1` or *'F1D'*;\\n\",\n    \"- #### First Two Digits: `2` or *'F2D'*;\\n\",\n    \"- #### First Three Digits: `3` or *'F3D'*;\\n\",\n    \"- #### Second Digit: `22` or *'SD'*;\\n\",\n    \"- #### Last Two Digits: `-2` or *'L2D'*;\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.001432048977662544\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mad2 = bf.mad(sp.l_r, test=2, decimals=8) # test=2 : MAD for the First Two Digits\\n\",\n    \"mad2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.004150506450239337\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mad_sd = bf.mad(sp.l_r, test=22, decimals=8) # test=22 : MAD for the Second Digits\\n\",\n    \"mad_sd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Or you can set the *MAD* parameter to *True* when running the tests functions, and it will also give the corresponding conformity limits (as long as `verbose` is also True).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 10 after preparation.\\n\",\n      \"\\n\",\n      \"The Mean Absolute Deviation is 0.001432048977662544\\n\",\n      \"For the First Two Digits:\\n\",\n      \"            - 0.0000 to 0.0012: Close Conformity\\n\",\n      \"            - 0.0012 to 0.0018: Acceptable Conformity\\n\",\n      \"            - 0.0018 to 0.0022: Marginally Acceptable Conformity\\n\",\n      \"            - Above 0.0022: Nonconformity\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"f2d = bf.first_digits(sp.l_r, digs=2, decimals=8, MAD=True, show_plot=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 10 after preparation.\\n\",\n      \"\\n\",\n      \"The Mean Absolute Deviation is 0.004150506450239337\\n\",\n      \"For the Second Digits:\\n\",\n      \"            - 0.0000 to 0.008: Close Conformity\\n\",\n      \"            - 0.008 to 0.01: Acceptable Conformity\\n\",\n      \"            - 0.01 to 0.012: Marginally Acceptable Conformity\\n\",\n      \"            - Above 0.012: Nonconformity\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sd = bf.second_digit(sp.l_r, decimals=8, MAD=True, show_plot=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Both the Chi Square and the Kolmogorov-Smirnov tests can be performed by setting the Args: `chi-square` and `KS` to True (default to False).\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Initialized sequence with 5968 registries.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"Discarded 0 records < 10 after preparation.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"         Expected     Found   Z_score\\n\",\n      \"Sec_Dig                              \\n\",\n      \"0        0.119679  0.128686  2.123777\\n\",\n      \"\\n\",\n      \"The Mean Absolute Deviation is 0.004150506450239337\\n\",\n      \"For the Second Digits:\\n\",\n      \"            - 0.0000 to 0.008: Close Conformity\\n\",\n      \"            - 0.008 to 0.01: Acceptable Conformity\\n\",\n      \"            - 0.01 to 0.012: Marginally Acceptable Conformity\\n\",\n      \"            - Above 0.012: Nonconformity\\n\",\n      \"\\n\",\n      \"The Chi-square statistic is 13.9088.\\n\",\n      \"Critical Chi-square for this series: 16.919.\\n\",\n      \"\\n\",\n      \"The Kolmogorov-Smirnov statistic is 0.0090.\\n\",\n      \"Critical K-S for this series: 0.0176\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/milcent/anaconda3/lib/python3.7/site-packages/benford_py/benford/benford.py:854: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\\n\",\n      \"  verbose=self.verbose)\\n\",\n      \"/home/milcent/anaconda3/lib/python3.7/site-packages/benford_py/benford/benford.py:858: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\\n\",\n      \"  verbose=self.verbose)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sd = bf.second_digit(sp.l_r, decimals=8, MAD=True, confidence=95,\\n\",\n    \"                     chi_square=True, KS=True, show_plot=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Values of the Chi Square and the Kolmogorov-Smirnov statistics lower than their respective critical values indicate conformity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Mantissas\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The mantissa is the decimal part of a logarithm. In a Benford data set, the mantissas of the registries' logs are uniformly distributed, such that, when ordered, they should form a straight line in the interval \\\\[0,1), with slope 1/N, N being the sample size.\\n\",\n    \"#### The closest the mantissas **mean**, **variance**, **skewness** and **kurtosis** are to the reference values, the more compliant with Benford's the sample is.\\n\",\n    \"#### This can also be assessed visually:\\n\",\n    \"- #### with the ordered mantissas plot, as closest the red dotted line is to the blue, reference one; and\\n\",\n    \"- #### with the Arc Test plot ([PR-24 - thanks to @im-alexandre](https://github.com/milcent/benford_py/pull/24)), in whch a nicely compliant sample would have its mantissas, plotted by sine versus cosine, evenly distributed along a circle, with a Gravity Center as close to the origin as possible. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" #################  Mantissas Test  #################\\n\",\n      \"\\n\",\n      \"The Mantissas MEAN is      0.492058.\\tRef: 0.5\\n\",\n      \"The Mantissas VARIANCE is  0.089793.\\tRef: 0.08333\\n\",\n      \"The Mantissas SKEWNESS is  0.051333.\\tRef: 0.0\\n\",\n      \"The Mantissas KURTOSIS is  -1.280712.\\tRef: -1.2\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mant = bf.mantissas(sp.l_r)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The mantissas are accessed via the `Mantissa` column of the `data` DataFrame atribute of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Mantissa</th>\\n\",\n       \"      <th>mant_x</th>\\n\",\n       \"      <th>mant_y</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-01</th>\\n\",\n       \"      <td>0.149525</td>\\n\",\n       \"      <td>0.590196</td>\\n\",\n       \"      <td>0.807260</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-02</th>\\n\",\n       \"      <td>0.674633</td>\\n\",\n       \"      <td>-0.456043</td>\\n\",\n       \"      <td>-0.889958</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-03</th>\\n\",\n       \"      <td>0.978130</td>\\n\",\n       \"      <td>0.990574</td>\\n\",\n       \"      <td>-0.136982</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-04</th>\\n\",\n       \"      <td>0.379305</td>\\n\",\n       \"      <td>-0.725972</td>\\n\",\n       \"      <td>0.687724</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-05</th>\\n\",\n       \"      <td>0.157517</td>\\n\",\n       \"      <td>0.548933</td>\\n\",\n       \"      <td>0.835866</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            Mantissa    mant_x    mant_y\\n\",\n       \"Date                                    \\n\",\n       \"1993-02-01  0.149525  0.590196  0.807260\\n\",\n       \"1993-02-02  0.674633 -0.456043 -0.889958\\n\",\n       \"1993-02-03  0.978130  0.990574 -0.136982\\n\",\n       \"1993-02-04  0.379305 -0.725972  0.687724\\n\",\n       \"1993-02-05  0.157517  0.548933  0.835866\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mant.data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Let's now check how uniformly distributed are mantissas, this time with the `hist` method of the pandas DataFrame. (Maybe we eill implement it the future.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mant.data.Mantissa.hist(bins=50, figsize=(12,5));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Again, switching to the SPY closing prices for comparison\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" #################  Mantissas Test  #################\\n\",\n      \"\\n\",\n      \"The Mantissas MEAN is      0.341217.\\tRef: 0.5\\n\",\n      \"The Mantissas VARIANCE is  0.106994.\\tRef: 0.08333\\n\",\n      \"The Mantissas SKEWNESS is  0.873759.\\tRef: 0.0\\n\",\n      \"The Mantissas KURTOSIS is  -0.846793.\\tRef: -1.2\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mant_close = bf.mantissas(sp.Close, report=True, show_plot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mant_close.data.Mantissa.hist(bins=50, figsize=(12,5));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Benford Class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The tests' fnctions already make use of especial classes under the hood when processing the ingested data (bf.Source, bf.Mantissas...).\\n\",\n    \"#### The Benford Class came form the need of having an object instance that would comprise all we needed, from the original `data`, to the pre-processed `base`, to all the possible `tests`, along with their respective statistics, and independent, updatable confidence levels and critical values. All this is accessible via the Benford instance atributes or methods.\\n\",\n    \"#### See Figure Bellow:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![Benford Instance](\\\"https://github.com/milcent/benford_py/blob/master/img/Benford_Instance.png\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The call to the Benford class accepts data for ingestion in two ways:\\n\",\n    \"- #### A pandas Series or numpy array, just like the functions; and\\n\",\n    \"- #### A tuple, whose first element is a DataFrame and the second is a string with the name of the column that contains the records to be analyzed (example bellow).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ########## Benford Object Instantiated ########### \\n\",\n      \"\\n\",\n      \"Initial sample size: 6026.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"\\n\",\n      \"Number of discarded entries for each test:\\n\",\n      \"{'F1D': 0, 'F2D': 0, 'F3D': 0, 'SD': 0, 'L2D': 0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"benf = bf.Benford((sp, 'l_r'), decimals=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### There is no need to set the `digs` parameter or to appoint a specific test to be run. The call automatically runs the First Digit, Second Digit, First Two Digits, First Three Digits and Last Two Digits tests, along with all stats, and appends them as attributes of the Benford instance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"benford.benford.Benford\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(benf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The attribute `tests` holds a list of the tests run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['F1D', 'F2D', 'F3D', 'SD', 'L2D']\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"benf.tests # returns a list will all tests\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The attribute `data` is nothing but the raw input data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Open</th>\\n\",\n       \"      <th>High</th>\\n\",\n       \"      <th>Low</th>\\n\",\n       \"      <th>Close</th>\\n\",\n       \"      <th>Volume</th>\\n\",\n       \"      <th>Adj_Close</th>\\n\",\n       \"      <th>p_r</th>\\n\",\n       \"      <th>l_r</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-01-29</th>\\n\",\n       \"      <td>43.9687</td>\\n\",\n       \"      <td>43.9687</td>\\n\",\n       \"      <td>43.7500</td>\\n\",\n       \"      <td>43.9375</td>\\n\",\n       \"      <td>1003200</td>\\n\",\n       \"      <td>28.000838</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-01</th>\\n\",\n       \"      <td>43.9687</td>\\n\",\n       \"      <td>44.2500</td>\\n\",\n       \"      <td>43.9687</td>\\n\",\n       \"      <td>44.2500</td>\\n\",\n       \"      <td>480500</td>\\n\",\n       \"      <td>28.199990</td>\\n\",\n       \"      <td>0.007112</td>\\n\",\n       \"      <td>0.007087</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-02</th>\\n\",\n       \"      <td>44.2187</td>\\n\",\n       \"      <td>44.3750</td>\\n\",\n       \"      <td>44.1250</td>\\n\",\n       \"      <td>44.3437</td>\\n\",\n       \"      <td>201300</td>\\n\",\n       \"      <td>28.259704</td>\\n\",\n       \"      <td>0.002118</td>\\n\",\n       \"      <td>0.002115</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-03</th>\\n\",\n       \"      <td>44.4062</td>\\n\",\n       \"      <td>44.8437</td>\\n\",\n       \"      <td>44.3750</td>\\n\",\n       \"      <td>44.8125</td>\\n\",\n       \"      <td>529400</td>\\n\",\n       \"      <td>28.558465</td>\\n\",\n       \"      <td>0.010572</td>\\n\",\n       \"      <td>0.010516</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-04</th>\\n\",\n       \"      <td>44.9687</td>\\n\",\n       \"      <td>45.0937</td>\\n\",\n       \"      <td>44.4687</td>\\n\",\n       \"      <td>45.0000</td>\\n\",\n       \"      <td>531500</td>\\n\",\n       \"      <td>28.677956</td>\\n\",\n       \"      <td>0.004184</td>\\n\",\n       \"      <td>0.004175</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"               Open     High      Low    Close   Volume  Adj_Close       p_r  \\\\\\n\",\n       \"Date                                                                           \\n\",\n       \"1993-01-29  43.9687  43.9687  43.7500  43.9375  1003200  28.000838       NaN   \\n\",\n       \"1993-02-01  43.9687  44.2500  43.9687  44.2500   480500  28.199990  0.007112   \\n\",\n       \"1993-02-02  44.2187  44.3750  44.1250  44.3437   201300  28.259704  0.002118   \\n\",\n       \"1993-02-03  44.4062  44.8437  44.3750  44.8125   529400  28.558465  0.010572   \\n\",\n       \"1993-02-04  44.9687  45.0937  44.4687  45.0000   531500  28.677956  0.004184   \\n\",\n       \"\\n\",\n       \"                 l_r  \\n\",\n       \"Date                  \\n\",\n       \"1993-01-29       NaN  \\n\",\n       \"1993-02-01  0.007087  \\n\",\n       \"1993-02-02  0.002115  \\n\",\n       \"1993-02-03  0.010516  \\n\",\n       \"1993-02-04  0.004175  \"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"benf.data.head() # The raw input DataFrame now lives inside the Benford instance too\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The `base` object has the pre-processed data, with:\\n\",\n    \"- #### `Seq`: the sequence of records chosen to be analyzed;\\\\\\n\",\n    \"- #### `ZN`: the records transformed (multiplies) according to the `decimals` parameter;\\n\",\n    \"- #### `F1D`: the records first digits;\\n\",\n    \"- #### `F2D`: the records first two digits;\\n\",\n    \"- #### `F3D`: the records first three digits;\\n\",\n    \"- #### `SD`: the records second digits; and\\n\",\n    \"- #### `L2D`: the records last two digits.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Seq</th>\\n\",\n       \"      <th>ZN</th>\\n\",\n       \"      <th>F1D</th>\\n\",\n       \"      <th>F2D</th>\\n\",\n       \"      <th>F3D</th>\\n\",\n       \"      <th>SD</th>\\n\",\n       \"      <th>L2D</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-01</th>\\n\",\n       \"      <td>0.007087</td>\\n\",\n       \"      <td>708720</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>70</td>\\n\",\n       \"      <td>708</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>20</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-02</th>\\n\",\n       \"      <td>0.002115</td>\\n\",\n       \"      <td>211527</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>21</td>\\n\",\n       \"      <td>211</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>27</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-03</th>\\n\",\n       \"      <td>0.010516</td>\\n\",\n       \"      <td>1051647</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>105</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>47</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-04</th>\\n\",\n       \"      <td>0.004175</td>\\n\",\n       \"      <td>417537</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>41</td>\\n\",\n       \"      <td>417</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>37</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993-02-05</th>\\n\",\n       \"      <td>-0.000696</td>\\n\",\n       \"      <td>69579</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>69</td>\\n\",\n       \"      <td>695</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>79</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                 Seq       ZN  F1D  F2D  F3D  SD  L2D\\n\",\n       \"Date                                                 \\n\",\n       \"1993-02-01  0.007087   708720    7   70  708   0   20\\n\",\n       \"1993-02-02  0.002115   211527    2   21  211   1   27\\n\",\n       \"1993-02-03  0.010516  1051647    1   10  105   0   47\\n\",\n       \"1993-02-04  0.004175   417537    4   41  417   1   37\\n\",\n       \"1993-02-05 -0.000696    69579    6   69  695   9   79\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"benf.base.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Once we have the tests we want, we can use their `report` method to get the information we need.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ###############  First Digit Test  ############### \\n\",\n      \"\\n\",\n      \"Mean Absolute Deviation: 0.008337\\n\",\n      \"0.006000 < MAD <= 0.012000: Acceptable conformity.\\n\",\n      \"\\n\",\n      \"For confidence level 95%: \\n\",\n      \"\\n\",\n      \"\\tKolmogorov-Smirnov: 0.031075 \\n\",\n      \"\\tCritical value: 0.017605 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tChi square: 43.563426 \\n\",\n      \"\\tCritical value: 15.507000 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tCritical Z-score:1.96.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"             Expected     Found   Z_score\\n\",\n      \"First_1_Dig                              \\n\",\n      \"6            0.066947  0.074899  2.432260\\n\",\n      \"8            0.051153  0.057808  2.304522\\n\",\n      \"9            0.045757  0.051944  2.256091\\n\",\n      \"7            0.057992  0.064678  2.182305\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"benf.F1D.report()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ############  First Two Digits Test  ############# \\n\",\n      \"\\n\",\n      \"Mean Absolute Deviation: 0.001432\\n\",\n      \"0.001200 < MAD <= 0.001800: Acceptable conformity.\\n\",\n      \"\\n\",\n      \"For confidence level 95%: \\n\",\n      \"\\n\",\n      \"\\tKolmogorov-Smirnov: 0.031275 \\n\",\n      \"\\tCritical value: 0.017605 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tChi square: 158.705636 \\n\",\n      \"\\tCritical value: 112.022000 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tCritical Z-score:1.96.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"             Expected     Found   Z_score\\n\",\n      \"First_2_Dig                              \\n\",\n      \"67           0.006434  0.010389  3.740056\\n\",\n      \"13           0.032185  0.039879  3.331418\\n\",\n      \"10           0.041393  0.049933  3.279619\\n\",\n      \"66           0.006531  0.009886  3.137524\\n\",\n      \"82           0.005264  0.007540  2.340301\\n\",\n      \"72           0.005990  0.008210  2.138736\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x972 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"benf.F2D.report()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Now this may be a good time to discuss the *Power Problem*.\\n\",\n    \"#### Such a problem affects the tests that use the sample size in their computations. These tests become too \\\"nervous\\\" when the sample is large, and mey render false positives. It is the case with the Z scores, as well as the Chi-square and the Kolmogorov-smirnov tests.\\n\",\n    \"#### To circunvene this issue, one may increase the confidence level, that is, choosing a lower p-value, making it harder for it to reject the null hipothesis (conformity).\\n\",\n    \"#### The Benford class has an `update_confidence` method that allows changing the confidence/significance level, without having to ingest and pre-process the data all over again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"benf.update_confidence(99.999, tests=['F2D', 'L2D'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### As can be seen, this method can update the confidence level just for a part of the tests or, if the `tests` parameter is left unassigned (defaults to None), it will update all.\\n\",\n    \"#### The confidence of all tests can be recovered by the `all_confidences` attribute.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'F1D': 95, 'F2D': 99.999, 'F3D': 95, 'SD': 95, 'L2D': 99.999}\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"benf.all_confidences # Note different values for F2D and L2D\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Now let's check the same test, with the updated confidence\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ############  First Two Digits Test  ############# \\n\",\n      \"\\n\",\n      \"Mean Absolute Deviation: 0.001432\\n\",\n      \"0.001200 < MAD <= 0.001800: Acceptable conformity.\\n\",\n      \"\\n\",\n      \"For confidence level 99.999%: \\n\",\n      \"\\n\",\n      \"\\tKolmogorov-Smirnov: 0.031275 \\n\",\n      \"\\tCritical value: 0.031973 -- PASS\\n\",\n      \"\\n\",\n      \"\\tChi square: 158.705636 \\n\",\n      \"\\tCritical value: 157.702000 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tCritical Z-score:4.417.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"Empty DataFrame\\n\",\n      \"Columns: [Expected, Found, Z_score]\\n\",\n      \"Index: []\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x972 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"benf.F2D.report()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ###############  First Digit Test  ############### \\n\",\n      \"\\n\",\n      \"Mean Absolute Deviation: 0.008337\\n\",\n      \"0.006000 < MAD <= 0.012000: Acceptable conformity.\\n\",\n      \"\\n\",\n      \"For confidence level 95%: \\n\",\n      \"\\n\",\n      \"\\tKolmogorov-Smirnov: 0.031075 \\n\",\n      \"\\tCritical value: 0.017605 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tChi square: 43.563426 \\n\",\n      \"\\tCritical value: 15.507000 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tCritical Z-score:1.96.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"             Expected     Found   Z_score\\n\",\n      \"First_1_Dig                              \\n\",\n      \"6            0.066947  0.074899  2.432260\\n\",\n      \"8            0.051153  0.057808  2.304522\\n\",\n      \"9            0.045757  0.051944  2.256091\\n\",\n      \"7            0.057992  0.064678  2.182305\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"benf.F1D.report() # Note that th confidence remains at 95%\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ##############  Second Digit Test  ############### \\n\",\n      \"\\n\",\n      \"Mean Absolute Deviation: 0.004151\\n\",\n      \"MAD <= 0.008000: Close conformity.\\n\",\n      \"\\n\",\n      \"For confidence level 95%: \\n\",\n      \"\\n\",\n      \"\\tKolmogorov-Smirnov: 0.009007 \\n\",\n      \"\\tCritical value: 0.017605 -- PASS\\n\",\n      \"\\n\",\n      \"\\tChi square: 13.908828 \\n\",\n      \"\\tCritical value: 16.919000 -- PASS\\n\",\n      \"\\n\",\n      \"\\tCritical Z-score:1.96.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"         Expected     Found   Z_score\\n\",\n      \"Sec_Dig                              \\n\",\n      \"0        0.119679  0.128686  2.123777\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1008x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"benf.SD.report()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" #############  Last Two Digits Test  ############# \\n\",\n      \"\\n\",\n      \"Mean Absolute Deviation: 0.000997\\n\",\n      \"There is no conformity check for this test's MAD.\\n\",\n      \"\\n\",\n      \"For confidence level 99.999%: \\n\",\n      \"\\n\",\n      \"\\tKolmogorov-Smirnov: 0.017739 \\n\",\n      \"\\tCritical value: 0.031973 -- PASS\\n\",\n      \"\\n\",\n      \"\\tChi square: 98.139062 \\n\",\n      \"\\tCritical value: 170.798000 -- PASS\\n\",\n      \"\\n\",\n      \"\\tCritical Z-score:4.417.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"Empty DataFrame\\n\",\n      \"Columns: [Expected, Found, Z_score]\\n\",\n      \"Index: []\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"benf.L2D.report()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### The power problem can alsso be addressed by setting an arbitrarily lower **\\\"N\\\"** for the computations. This artificial sample size makes the tests more robust. Nigrini has proposed a size no larger than 2,500.\\n\",\n    \"#### This can be enforced by setting the parameter `lmimit_N` when calling the Benford class. Unfortunately, it is not yet implemented to be dinamically an independently update such as the confidence level.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ########## Benford Object Instantiated ########### \\n\",\n      \"\\n\",\n      \"Initial sample size: 6026.\\n\",\n      \"\\n\",\n      \"Test performed on 5968 registries.\\n\",\n      \"\\n\",\n      \"Number of discarded entries for each test:\\n\",\n      \"{'F1D': 0, 'F2D': 0, 'F3D': 0, 'SD': 0, 'L2D': 0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"benf = bf.Benford((sp, 'l_r'), decimals=8, limit_N=1800)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'F1D': 95, 'F2D': 95, 'F3D': 95, 'SD': 95, 'L2D': 95}\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"benf.all_confidences\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" ############  First Two Digits Test  ############# \\n\",\n      \"\\n\",\n      \"Mean Absolute Deviation: 0.001432\\n\",\n      \"0.001200 < MAD <= 0.001800: Acceptable conformity.\\n\",\n      \"\\n\",\n      \"For confidence level 95%: \\n\",\n      \"\\n\",\n      \"\\tKolmogorov-Smirnov: 0.031275 \\n\",\n      \"\\tCritical value: 0.032056 -- PASS\\n\",\n      \"\\n\",\n      \"\\tChi square: 158.705636 \\n\",\n      \"\\tCritical value: 112.022000 -- FAIL\\n\",\n      \"\\n\",\n      \"\\tCritical Z-score:1.96.\\n\",\n      \"\\n\",\n      \"The entries with the significant positive deviations are:\\n\",\n      \"\\n\",\n      \"Empty DataFrame\\n\",\n      \"Columns: [Expected, Found, Z_score]\\n\",\n      \"Index: []\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x972 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"benf.F2D.report()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Note the improvement of the Z scores and the Kolmogorov-Smirnov tests, which are affected by sample size\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Other digits tests\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### There are other tests that can be performed and checked for conformity. These are the Summation Tests and the Second Order Tests. Although we will leave their explanation for some other time, they are already implemented in the Benford class, and can be added with calls to the `summation` and `sec_order` methods, or at instantiation, by setting the Args: `summation` and `sec_order` to True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Added Summation DataFrames to F1D, F2D and F3D Tests.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"benf.summation()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Second order tests run in 5931 registries.\\n\",\n      \"\\n\",\n      \"Number of discarded entries for second order tests:\\n\",\n      \"{'F1D_sec': 17, 'F2D_sec': 110, 'F3D_sec': 997, 'SD_sec': 110, 'L2D_sec': 4411}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"benf.sec_order()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['F1D',\\n\",\n       \" 'F2D',\\n\",\n       \" 'F3D',\\n\",\n       \" 'SD',\\n\",\n       \" 'L2D',\\n\",\n       \" 'F1D_Summ',\\n\",\n       \" 'F2D_Summ',\\n\",\n       \" 'F3D_Summ',\\n\",\n       \" 'F1D_sec',\\n\",\n       \" 'F2D_sec',\\n\",\n       \" 'F3D_sec',\\n\",\n       \" 'SD_sec',\\n\",\n       \" 'L2D_sec']\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"benf.tests\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'F1D': 95,\\n\",\n       \" 'F2D': 95,\\n\",\n       \" 'F3D': 95,\\n\",\n       \" 'SD': 95,\\n\",\n       \" 'L2D': 95,\\n\",\n       \" 'F1D_Summ': None,\\n\",\n       \" 'F2D_Summ': None,\\n\",\n       \" 'F3D_Summ': None,\\n\",\n       \" 'F1D_sec': 95,\\n\",\n       \" 'F2D_sec': 95,\\n\",\n       \" 'F3D_sec': 95,\\n\",\n       \" 'SD_sec': 95,\\n\",\n       \" 'L2D_sec': 95}\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"benf.all_confidences\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### That's it for now.\\n\",\n    \"### If you have a data set that you think would be nice to study with Benford tests, share it and we can post a notebook with all tests and comments.\\n\",\n    \"### Thanks\\n\",\n    \"### Milcent\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "LICENSE.txt",
    "content": "BSD 3-Clause License\n\nCopyright (c) 2014-2021, Marcel Milcent.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
  },
  {
    "path": "MANIFEST.in",
    "content": "include README.md\ninclude README-pypi.md\ninclude LICENSE.txt"
  },
  {
    "path": "README-pypi.md",
    "content": "[![Downloads](https://pepy.tech/badge/benford-py)](https://pepy.tech/project/benford-py)\n\n# Benford for Python\n\n--------------------------------------------------------------------------------\n\n**Citing**\n\n\nIf you find *Benford_py* useful in your research, please consider adding the following citation:\n\n```bibtex\n@misc{benford_py,\n      author = {Marcel, Milcent},\n      title = {{Benford_py: a Python Implementation of Benford's Law Tests}},\n      year = {2017},\n      publisher = {GitHub},\n      journal = {GitHub repository},\n      howpublished = {\\url{https://github.com/milcent/benford_py}},\n}\n```\n\n--------------------------------------------------------------------------------\n\n`current version = 0.5.0`\n\n### See [release notes](https://github.com/milcent/benford_py/releases/) for features in this and in older versions\n\n### Python versions >= 3.6\n\n### Installation\n\nBenford_py is a package in PyPi, so you can install with pip:\n\n`pip install benford_py`\n\nor\n\n`pip install benford-py`\n\nOr you can cd into the site-packages subfolder of your python distribution (or environment) and git clone from there:\n\n`git clone https://github.com/milcent/benford_py`\n\nFor a quick start, please go to the [Demo notebook](https://github.com/milcent/benford_py/blob/master/Demo.ipynb), in which I show examples on how to run the tests with the SPY (S&P 500 ETF) daily returns.\n\nFor more fine-grained details of the functions and classes, see the [docs](https://benford-py.readthedocs.io/en/latest/index.html).\n\n### Background\n\nThe first digit of a number is [its leftmost digit](https://github.com/milcent/benford_py/blob/master/img/First_Digits.png)\n\nSince the first digit of any number can range from \"1\" to \"9\"\n(not considering \"0\"), it would be intuitively expected that the\nproportion of each occurrence in a set of numerical records would\nbe uniformly distributed at 1/9, i.e., approximately 0.1111,\nor 11.11%.\n\n[Benford's Law](https://en.wikipedia.org/wiki/Benford%27s_law),\nalso known as the Law of First Digits or the Phenomenon of\nSignificant Digits, is the finding that the first digits of the\nnumbers found in series of records of the most varied sources do\nnot display a uniform distribution, but rather are arranged in such\na way that the digit \"1\" is the most frequent, followed by \"2\",\n\"3\", and so in a successive and decremental way down to \"9\", \nwhich presents the lowest frequency as the first digit.\n\nThe expected distributions of the First Digits in a\nBenford-compliant data set are the ones shown [here](https://github.com/milcent/benford_py/blob/master/img/First.png)\n\nThe first record on the subject dates from 1881, in the work of\n[Simon Newcomb](https://github.com/milcent/benford_py/blob/master/img/Simon_Newcomb_APS.jpg), an American-Canadian astronomer and mathematician,\nwho noted that in the logarithmic tables the first pages, which\ncontained logarithms beginning with the numerals \"1\" and \"2\",\nwere more worn out, that is, more consulted.\n\nIn that same article, Newcomb proposed the [formula](https://github.com/milcent/benford_py/blob/master/img/formula.png) for the probability of a certain digit \"d\" \nbeing the first digit of a number, given by the following equation.\n\nIn 1938, the American physicist [Frank Benford](https://github.com/milcent/benford_py/blob/master/img/2429_Benford-Frank.jpg) revisited the \nphenomenon, which he called the \"Law of Anomalous Numbers,\" in \na survey with more than 20,000 observations of empirical data \ncompiled from various sources, ranging from areas of rivers to\nmolecular weights of chemical compounds, including cost data, \naddress numbers, population sizes and physical constants. All \nof them, to a greater or lesser extent, followed such \ndistribution.\n\nThe extent of Benford's work seems to have been one good reason \nfor the phenomenon to be popularized with his name, though \ndescribed by Newcomb 57 years earlier.\n\nDerivations of the original formula were also applied in the \nexpected findings of the proportions of digits in other \npositions in the number, as in the case of the second digit\n(BENFORD, 1938), as well as combinations, such as the first \ntwo digits of a number (NIGRINI, 2012, p.5).\n\nOnly in 1995, however, was the phenomenon proven by Hill. \nHis proof was based on the fact that numbers in data series\nfollowing the Benford Law are, in effect, \"second generation\"\ndistributions, ie combinations of other distributions.\nThe union of randomly drawn samples from various distributions\nforms a distribution that respects Benford's Law (HILL, 1995).\n\nWhen grouped in ascending order, data that obey Benford's Law \nmust approximate a geometric sequence (NIGRINI, 2012, page 21).\nFrom this it follows that the logarithms of this ordered series\nmust form a straight line. In addition, the mantissas (decimal\nparts) of the logarithms of these numbers must be uniformly\ndistributed in the interval [0,1] (NIGRINI, 2012, p.10).\n\nIn general, a series of numerical records follows Benford's Law\nwhen (NIGRINI, 2012, p.21):\n* it represents magnitudes of events or events, such as populations\nof cities, flows of water in rivers or sizes of celestial bodies;\n* it does not have pre-established minimum or maximum limits;\n* it is not made up of numbers used as identifiers, such as \nidentity or social security numbers, bank accounts, telephone numbers; and\n* its mean is less than the median, and the data is not\nconcentrated around the mean.\n\nIt follows from this expected distribution that, if the set of\nnumbers in a series of records that usually respects the Law\nshows a deviation in the proportions found, there may be\ndistortions, whether intentional or not.\n\nBenford's Law has been used in [several fields](http://www.benfordonline.net/). \nAfer asserting that the usual data type is Benford-compliant,\none can study samples from the same data type tin search of\ninconsistencies, errors or even [fraud](https://www.amazon.com.br/Benfords-Law-Applications-Accounting-Detection/dp/1118152859).\n\nThis open source module is an attempt to facilitate the \nperformance of Benford's Law-related tests by people using\nPython, whether interactively or in an automated, scripting way.\n\nIt uses the versatility of numpy and pandas, along with\nmatplotlib for vizualization, to deliver results like [this one](https://github.com/milcent/benford_py/blob/master/img/SPY-f2d-conf_level-95.png) and much more.\n\n\nIt has been a long time since I last tested it in Python 2. The death clock has stopped ticking, so officially it is for Python 3 now. It should work on Linux, Windows and Mac, but please file a bug report if you run into some trouble.\n\nAlso, if you have some nice data set that we can run these tests on, let'us try it.\n\nThanks!\n\nMilcent\n"
  },
  {
    "path": "README.md",
    "content": "[![Downloads](https://pepy.tech/badge/benford-py)](https://pepy.tech/project/benford-py)\n\n# Benford for Python\n\n--------------------------------------------------------------------------------\n\n**Citing**\n\n\nIf you find *Benford_py* useful in your research, please consider adding the following citation:\n\n```bibtex\n@misc{benford_py,\n      author = {Marcel, Milcent},\n      title = {{Benford_py: a Python Implementation of Benford's Law Tests}},\n      year = {2017},\n      publisher = {GitHub},\n      journal = {GitHub repository},\n      howpublished = {\\url{https://github.com/milcent/benford_py}},\n}\n```\n\n--------------------------------------------------------------------------------\n\n`current version = 0.5.0`\n\n### See [release notes](https://github.com/milcent/benford_py/releases/) for features in this and in older versions\n\n### Python versions >= 3.6\n\n### Installation\n\nBenford_py is a package in PyPi, so you can install with pip:\n\n`pip install benford_py`\n\nor\n\n`pip install benford-py`\n\nOr you can cd into the site-packages subfolder of your python distribution (or environment) and git clone from there:\n\n`git clone https://github.com/milcent/benford_py`\n\nFor a quick start, please go to the [Demo notebook](https://github.com/milcent/benford_py/blob/master/Demo.ipynb), in which I show examples on how to run the tests with the SPY (S&P 500 ETF) daily returns.\n\nFor more fine-grained details of the functions and classes, see the [docs](https://benford-py.readthedocs.io/en/latest/index.html).\n\n### Background\n\nThe first digit of a number is its leftmost digit.\n<p align=\"center\">\n  <img alt=\"First Digits\" src=\"https://github.com/milcent/benford_py/blob/master/img/First_Digits.png\">\n</p>\n\nSince the first digit of any number can range from \"1\" to \"9\"\n(not considering \"0\"), it would be intuitively expected that the\nproportion of each occurrence in a set of numerical records would\nbe uniformly distributed at 1/9, i.e., approximately 0.1111,\nor 11.11%.\n\n[Benford's Law](https://en.wikipedia.org/wiki/Benford%27s_law),\nalso known as the Law of First Digits or the Phenomenon of\nSignificant Digits, is the finding that the first digits of the\nnumbers found in series of records of the most varied sources do\nnot display a uniform distribution, but rather are arranged in such\na way that the digit \"1\" is the most frequent, followed by \"2\",\n\"3\", and so in a successive and decremental way down to \"9\", \nwhich presents the lowest frequency as the first digit.\n\nThe expected distributions of the First Digits in a\nBenford-compliant data set are the ones shown below:\n<p align=\"center\">\n  <img alt=\"Expected Distributions of First Digits\" src=\"https://github.com/milcent/benford_py/blob/master/img/First.png\">\n</p>\n\nThe first record on the subject dates from 1881, in the work of\nSimon Newcomb, an American-Canadian astronomer and mathematician,\nwho noted that in the logarithmic tables the first pages, which\ncontained logarithms beginning with the numerals \"1\" and \"2\",\nwere more worn out, that is, more consulted.\n\n<p align=\"center\">\n  <img alt=\"Simon Newcomb\" src=\"https://github.com/milcent/benford_py/blob/master/img/Simon_Newcomb_APS.jpg\">\n</p>\n<p align=\"center\">\n      Simon Newcomb, 1835-1909.\n</p>\n\nIn that same article, Newcomb proposed the formula for the\nprobability of a certain digit \"d\" being the first digit of a\nnumber, given by the following equation.\n\n<p align=\"center\">\n  <img alt=\"First digit equation\" src=\"https://github.com/milcent/benford_py/blob/master/img/formula.png\">\n</p>\n<p align=\"center\"> where: P (D = d) is the probability that\n  the first digit is equal to d, and d is an integer ranging \n  from 1 to 9.\n</p>\n\nIn 1938, the American physicist Frank Benford revisited the \nphenomenon, which he called the \"Law of Anomalous Numbers,\" in \na survey with more than 20,000 observations of empirical data \ncompiled from various sources, ranging from areas of rivers to\nmolecular weights of chemical compounds, including cost data, \naddress numbers, population sizes and physical constants. All \nof them, to a greater or lesser extent, followed such \ndistribution.\n\n<p align=\"center\">\n  <img alt=\"Frank Benford\" src=\"https://github.com/milcent/benford_py/blob/master/img/2429_Benford-Frank.jpg\">\n</p>\n<p align=\"center\">\n  Frank Albert Benford, Jr., 1883-1948.\n</p>\n\nThe extent of Benford's work seems to have been one good reason \nfor the phenomenon to be popularized with his name, though \ndescribed by Newcomb 57 years earlier.\n\nDerivations of the original formula were also applied in the \nexpected findings of the proportions of digits in other \npositions in the number, as in the case of the second digit\n(BENFORD, 1938), as well as combinations, such as the first \ntwo digits of a number (NIGRINI, 2012, p.5).\n\nOnly in 1995, however, was the phenomenon proven by Hill. \nHis proof was based on the fact that numbers in data series\nfollowing the Benford Law are, in effect, \"second generation\"\ndistributions, ie combinations of other distributions.\nThe union of randomly drawn samples from various distributions\nforms a distribution that respects Benford's Law (HILL, 1995).\n\nWhen grouped in ascending order, data that obey Benford's Law \nmust approximate a geometric sequence (NIGRINI, 2012, page 21).\nFrom this it follows that the logarithms of this ordered series\nmust form a straight line. In addition, the mantissas (decimal\nparts) of the logarithms of these numbers must be uniformly\ndistributed in the interval [0,1] (NIGRINI, 2012, p.10).\n\nIn general, a series of numerical records follows Benford's Law\nwhen (NIGRINI, 2012, p.21):\n* it represents magnitudes of events or events, such as populations\nof cities, flows of water in rivers or sizes of celestial bodies;\n* it does not have pre-established minimum or maximum limits;\n* it is not made up of numbers used as identifiers, such as \nidentity or social security numbers, bank accounts, telephone numbers; and\n* its mean is less than the median, and the data is not\nconcentrated around the mean.\n\nIt follows from this expected distribution that, if the set of\nnumbers in a series of records that usually respects the Law\nshows a deviation in the proportions found, there may be\ndistortions, whether intentional or not.\n\nBenford's Law has been used in [several fields](http://www.benfordonline.net/). \nAfer asserting that the usual data type is Benford-compliant,\none can study samples from the same data type tin search of\ninconsistencies, errors or even [fraud](https://www.amazon.com.br/Benfords-Law-Applications-Accounting-Detection/dp/1118152859).\n\nThis open source module is an attempt to facilitate the \nperformance of Benford's Law-related tests by people using\nPython, whether interactively or in an automated, scripting way.\n\nIt uses the versatility of numpy and pandas, along with\nmatplotlib for vizualization, to deliver results like the one\nbellow and much more.\n\n![Sample Image](https://github.com/milcent/benford_py/blob/master/img/SPY-f2d-conf_level-95.png)\n\nIt has been a long time since I last tested it in Python 2. The death clock has stopped ticking, so officially it is for Python 3 now. It should work on Linux, Windows and Mac, but please file a bug report if you run into some trouble.\n\nAlso, if you have some nice data set that we can run these tests on, let'us try it.\n\nThanks!\n\nMilcent\n"
  },
  {
    "path": "__init__.py",
    "content": "'''Benfords law module'''\n__version__ = \"0.5.0\"\n"
  },
  {
    "path": "benford/__init__.py",
    "content": "\"\"\"\nBenford_py for Python is a module for application of Benford's Law\nto a sequence of numbers.\n\nDependent on pandas, numpy and matplotlib\n\nAll logarithms ar in base 10: \"log10\"\n\nAuthor:  Marcel Milcent\n\nSDPX-License-Identifier: BSD-3-Clause\n\"\"\"\n\nfrom .benford import *\n\n__version__ = '0.5.0'\n"
  },
  {
    "path": "benford/benford.py",
    "content": "import warnings\nfrom pandas import Series, DataFrame\nfrom numpy import arange, log10, ones, abs, cos, sin, pi, mean\nfrom .constants import CONFS, DIGS, SEC_ORDER_DIGS, REV_DIGS, TEST_NAMES, \\\n    MAD_CONFORM, CRIT_CHI2, CRIT_KS\nfrom .checks import _check_digs_, _check_confidence_, _check_test_, \\\n    _check_num_array_, _check_high_Z_\nfrom .utils import _set_N_, input_data, prepare, \\\n    subtract_sorted, prep_to_roll, mad_to_roll, mse_to_roll, \\\n    get_mantissas\nfrom .expected import _get_expected_digits_ # First, Second, LastTwo\nfrom .viz import _get_plot_args, plot_digs, plot_sum, plot_ordered_mantissas,\\\n    plot_mantissa_arc_test, plot_roll_mse, plot_roll_mad\nfrom .reports import _inform_, _report_mad_, _report_test_, _deprecate_inform_,\\\n    _report_mantissa_\nfrom .stats import Z_score, chi_sq, chi_sq_2, kolmogorov_smirnov,\\\n    kolmogorov_smirnov_2, _bhattacharyya_distance_, _bhattacharyya_coefficient,\\\n    _kullback_leibler_divergence_, _mantissas_ks_\n\n\nclass Base(DataFrame):\n    \"\"\"Internalizes and prepares the data for Analysis.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.`\n\n    Raises:\n        TypeError: if not receiving `int` or `float` as input.\n    \"\"\"\n\n    def __init__(self, data, decimals, sign='all', sec_order=False):\n\n        DataFrame.__init__(self, {'seq': data})\n\n        if (self.seq.dtype != 'float') & (self.seq.dtype != 'int'):\n            raise TypeError(\"The sequence dtype was neither int nor \"\n                            \"float. Convert it to whether int of float, \"\n                            \"and try again.\")\n\n        if sign == 'all':\n            self.seq = self.seq.loc[self.seq != 0]\n        elif sign == 'pos':\n            self.seq = self.seq.loc[self.seq > 0]\n        else:\n            self.seq = self.seq.loc[self.seq < 0]\n\n        self.dropna(inplace=True)\n\n        ab = self.seq.abs()\n\n        if self.seq.dtype == 'int':\n            self['ZN'] = ab\n        else:\n            if decimals == 'infer':\n                self['ZN'] = ab.astype(str).str\\\n                               .replace('.', '', regex=False)\\\n                               .str.lstrip('0')\\\n                               .str[:5].astype(int)\n            else:\n                self['ZN'] = (ab * (10 ** decimals)).astype(int)\n        # First digits\n        for col in ['F1D', 'F2D', 'F3D']:\n            temp = self.ZN.loc[self.ZN >= 10 ** (REV_DIGS[col] - 1)]\n            self[col] = (temp // 10 ** ((log10(temp).astype(int)) -\n                                        (REV_DIGS[col] - 1)))\n            # fill NANs with -1, which is a non-usable value for digits,\n            # to be discarded later.\n            self[col] = self[col].fillna(-1).astype(int)\n        # Second digit\n        temp_sd = self.loc[self.ZN >= 10]\n        self['SD'] = (temp_sd.ZN // 10**((log10(temp_sd.ZN)).astype(int) -\n                                         1)) % 10\n        self['SD'] = self['SD'].fillna(-1).astype(int)\n        # Last two digits\n        temp_l2d = self.loc[self.ZN >= 1000]\n        self['L2D'] = temp_l2d.ZN % 100\n        self['L2D'] = self['L2D'].fillna(-1).astype(int)\n\n\nclass Test(DataFrame):\n    \"\"\"Transforms the original number sequence into a DataFrame reduced\n    by the ocurrences of the chosen digits, creating other computed\n    columns\n\n    Args:\n        base: The Base object with the data prepared for Analysis\n        digs: Tells which test to perform: 1: first digit; 2: first two digits;\n            3: furst three digits; 22: second digit; -2: last two digits.\n        confidence (int, float): confidence level to draw lower and upper limits when\n            plotting and to limit the top deviations to show.\n        limit_N (int): sets a limit to N as the sample size for the calculation of\n            the Z scores if the sample is too big. Defaults to None.\n\n    Attributes:\n        N: Number of records in the sample to consider in computations\n        ddf: Degrees of Freedom to look up for the critical chi-square value\n        chi_square: Chi-square statistic for the given test\n        KS: Kolmogorov-Smirnov statistic for the given test\n        MAD: Mean Absolute Deviation for the given test\n        confidence: Confidence level to consider when setting some critical values\n        digs (int): numerical representation of the test at hand. 1: F1D; 2: F2D;\n            3: F3D; 22: SD; -2: L2D.\n        sec_order (bool): True if the test is a Second Order one\n    \"\"\"\n\n    def __init__(self, base, digs, confidence, limit_N=None, sec_order=False):\n        # create a separated Expected distributions object\n        super(Test, self).__init__(_get_expected_digits_(digs))\n        # create column with occurrences of the digits in the base\n        self['Counts'] = base[DIGS[digs]].value_counts()\n        # create column with relative frequencies\n        self['Found'] = base[DIGS[digs]].value_counts(normalize=True)\n        self.fillna(0, inplace=True)\n        # create column with absolute differences\n        self['Dif'] = self.Found - self.Expected\n        self['AbsDif'] = self.Dif.abs()\n        self.limit_N = _set_N_(len(base), limit_N)\n        self['Z_score'] = Z_score(self, self.limit_N)\n        self.ddf = len(self) - 1\n        self.chi_square = chi_sq_2(self)\n        self.KS = kolmogorov_smirnov_2(self)\n        self.MAD = self.AbsDif.mean()\n        self.MSE = (self.AbsDif ** 2).mean()\n        self.bhattacharyya_coefficient = _bhattacharyya_coefficient(\n            self.Found.values, self.Expected.values)\n        self.bhattacharyya_distance = _bhattacharyya_distance_(\n            self.Found.values, self.Expected.values)\n        self.kullback_leibler_divergence = _kullback_leibler_divergence_(\n            self.Found.values, self.Expected.values)\n        self.confidence = confidence\n        self.digs = digs\n        self.sec_order = sec_order\n\n        if sec_order:\n            self.name = TEST_NAMES[SEC_ORDER_DIGS[digs]]\n        else:\n            self.name = TEST_NAMES[DIGS[digs]]\n\n    def update_confidence(self, new_conf, check=True):\n        \"\"\"Sets a new confidence level for the Benford object, so as to be used to\n        produce critical values for the tests\n\n        Args:\n            new_conf: new confidence level to draw lower and upper limits when\n                plotting and to limit the top deviations to show, as well as to\n                calculate critical values for the tests' statistics.\n            check: checks the value provided for the confidence. Defaults to True\n        \"\"\"\n        if check:\n            self.confidence = _check_confidence_(new_conf)\n        else:\n            self.confidence = new_conf\n\n    @property\n    def critical_values(self):\n        \"\"\"dict: a dictionary with the critical values for the test at hand,\n            according to the current confidence level.\"\"\"\n        crit_ks = CRIT_KS[self.confidence] / (self.limit_N ** 0.5) if self.confidence\\\n            else None\n        return {'Z': CONFS[self.confidence],\n                'KS': crit_ks,\n                'chi2': CRIT_CHI2[self.ddf][self.confidence],\n                'MAD': MAD_CONFORM[self.digs]}\n\n    def show_plot(self, save_plot=None, save_plot_kwargs=None):\n        \"\"\"Draws the test plot.\n        \n        Args:\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when save_plot is a string with the figure file\n                path/name.\n        \"\"\"\n        x, figsize, text_x = _get_plot_args(self.digs)\n        plot_digs(self, x=x, y_Exp=self.Expected, y_Found=self.Found,\n                    N=self.limit_N, figsize=figsize, conf_Z=CONFS[self.confidence],\n                    text_x=text_x, save_plot=save_plot, save_plot_kwargs=save_plot_kwargs\n                    )\n\n    def report(self, high_Z='pos', show_plot=True,\n               save_plot=None, save_plot_kwargs=None):\n        \"\"\"Handles the report especific to the test, considering its statistics\n        and according to the current confidence level.\n\n        Args:\n            high_Z (int): chooses which Z scores to be used when displaying results,\n                according to the confidence level chosen. Defaluts to 'pos',\n                which will highlight only values higher than the expexted\n                frequencies; 'all' will highlight both extremes (positive and\n                negative); and an integer, which will use the first n entries,\n                positive and negative, regardless of whether Z is higher than\n                the critical value or not.\n            show_plot: calls the show_plot method, to draw the test plot\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n        \"\"\"\n        high_Z = _check_high_Z_(high_Z)\n        _report_test_(self, high_Z, self.critical_values)\n        if show_plot:\n            self.show_plot(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n\nclass Summ(DataFrame):\n    \"\"\"Gets the base object and outputs a Summation test object\n\n    Args:\n       base: The Base object with the data prepared for Analysis\n       test: The test for which to compute the summation\n    \"\"\"\n\n    def __init__(self, base, test):\n        super(Summ, self).__init__(base.abs()\n                                   .groupby(test)[['seq']]\n                                   .sum())\n        self['Percent'] = self.seq / self.seq.sum()\n        self.columns.values[0] = 'Sum'\n        self.expected = 1 / len(self)\n        self['AbsDif'] = (self.Percent - self.expected).abs()\n        self.index = self.index.astype(int)\n        #: Mean Absolute Deviation for the test\n        self.MAD = self.AbsDif.mean()\n        self.MSE = (self.AbsDif ** 2).mean()\n        #: Confidence level to consider when setting some critical values\n        self.confidence = None\n        # (int): numerical representation of the test at hand\n        self.digs = REV_DIGS[test]\n        # (str): the name of the Summation test.\n        self.name = TEST_NAMES[f'{test}_Summ']\n\n    def show_plot(self, save_plot=None, save_plot_kwargs=None):\n        \"\"\"Draws the Summation test plot\n        \n        Args:\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when save_plot is a string with the figure file\n                path/name.\n        \"\"\"\n        figsize=(2 * (self.digs ** 2 + 5), 1.5 * (self.digs ** 2 + 5))\n        plot_sum(self, figsize, self.expected,\n                 save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    \n    def report(self, high_diff=None, show_plot=True,\n               save_plot=None, save_plot_kwargs=None):\n        \"\"\"Gives the report on the Summation test.\n\n        Args:\n            high_diff: Number of records to show after ordering by the absolute\n                differences between the found and the expected proportions\n            show_plot: calls the show_plot method, to draw the Summation test plot\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n        \"\"\"\n        _report_test_(self, high_diff)\n        if show_plot:\n            self.show_plot(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n\nclass Mantissas:\n    \"\"\"Computes and holds the mantissas of the logarithms of the records\n\n    Args:\n        data: sequence to compute mantissas from. numpy 1D array, pandas\n            Series of pandas DataFrame column.\n        confidence: confidence level for computing the critical values to\n            compare with some statistics\n    \"\"\"\n\n    def __init__(self, data, confidence=95, limit_N=None):\n\n        data = Series(_check_num_array_(data))\n        data = data.dropna().loc[data != 0].abs()\n        self.limit_N = _set_N_(len(data), limit_N)\n        #: (DataFrame): pandas DataFrame with the mantissas\n        self.data = DataFrame({'Mantissa': get_mantissas(data.abs())})\n        self.confidence = confidence\n\n    @property\n    def stats(self):\n        # (dict): Dictionary with the mantissas statistics\n        ks, crit_ks = _mantissas_ks_(self.data.Mantissa.values,\n                                     self.confidence, self.limit_N)\n        return {'Mean': self.data.Mantissa.mean(),\n                'Var': self.data.Mantissa.var(),\n                'Skew': self.data.Mantissa.skew(),\n                'Kurt': self.data.Mantissa.kurt(),\n                'KS': ks,\n                'KS_critical': crit_ks}\n\n\n    def update_confidence(self, new_conf, check=True):\n        \"\"\"Sets a new confidence level for the Benford object, so as to be used to\n        produce critical values for the tests\n\n        Args:\n            new_conf: new confidence level to draw lower and upper limits when\n                plotting and to limit the top deviations to show, as well as to\n                calculate critical values for the tests' statistics.\n            check: checks the value provided for the confidence. Defaults to True\n        \"\"\"\n        if check:\n            self.confidence = _check_confidence_(new_conf)\n        else:\n            self.confidence = new_conf\n\n\n    def report(self, show_plot=True, save_plot=None, save_plot_kwargs=None):\n        \"\"\"Displays the Mantissas test stats.\n\n        Args:\n            show_plot: shows the Ordered Mantissas plot and the Arc Test plot.\n                Defaults to True.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n        \"\"\"\n        _report_mantissa_(self.stats, confidence=self.confidence)\n\n        if show_plot:\n            self.show_plot(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n            self.arc_test(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n    def show_plot(self, figsize=(12, 6), save_plot=None, save_plot_kwargs=None):\n        \"\"\"Plots the ordered mantissas and a line with the expected\n        inclination.\n\n        Args:\n            figsize (tuple): figure size dimensions\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when save_plot is a string with the figure file\n                path/name.\n        \"\"\"\n        plot_ordered_mantissas(self.data.Mantissa, figsize=figsize,\n                               save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n    def arc_test(self, grid=True, figsize=12,\n                 save_plot=None, save_plot_kwargs=None):\n        \"\"\"Adds two columns to Mantissas's DataFrame equal to their \"X\" and \"Y\"\n        coordinates, plots its to a scatter plot and calculates the gravity\n        center of the circle.\n\n        Args:\n            grid: show grid of the plot. Defaluts to True.\n            figsize (int): size of the figure to be displayed. Since it is a square,\n                there is no need to provide a tuple, like is usually the case with\n                matplotlib.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n        \"\"\"\n        self.data['mant_x'] = cos(2 * pi * self.data.Mantissa)\n        self.data['mant_y'] = sin(2 * pi * self.data.Mantissa)\n        self.gravity_center = (self.data.mant_x.mean(), self.data.mant_y.mean())\n\n        plot_mantissa_arc_test(self.data, self.gravity_center,\n                               grid=grid, figsize=figsize,\n                               save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n\nclass Benford(object):\n    \"\"\"Initializes a Benford Analysis object and computes the proportions for\n    the digits. The tets dataFrames are atributes, i.e., obj.F1D is the First\n    Digit DataFrame, the obj.F2D,the First Two Digits one, and so one, F3D for\n    First Three Digits, SD for Second  Digit and L2D for Last Two Digits.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a tuple with a pandas DataFrame and the name (str)\n            of the chosen column. Values must be integers or floats.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n        confidence (int, float): confidence level to draw lower and upper limits when\n            plotting and to limit the top deviations to show, as well as to\n            calculate critical values for the tests' statistics. Defaults to 95.\n        mantissas (bool): opts for also running the mantissas Test. Defaulst to\n            True\n        sec_order: runs the Second Order tests, which are the Benford's tests\n            performed on the differences between the ordered sample (a value minus\n            the one before it, and so on). If the original series is Benford-\n            compliant, this new sequence should aldo follow Beford. The Second\n            Order can also be called separately, through the method sec_order().\n        summation: creates the Summation DataFrames for the First, First Two, and\n            First Three Digits. The summation tests can also be called separately,\n            through the method summation().\n        limit_N (int): sets a limit to N as the sample size for the calculation of\n            the Z scores if the sample is too big. Defaults to None.\n        verbose: gives some information about the data and the registries used\n            and discarded for each test.\n\n    Attributes:\n        data: the raw data provided for the analysis\n        chosen: the column of the DataFrame to be analysed or the data itself\n        sign (str): which number sign(s) to include in the analysis\n        confidence: current confidence level\n        limit_N (int): sample size to use in computations\n        verbose (bool): verbose or not\n        base: the Base, pre-processed object\n        tests (:obj:`list` of :obj:`str`): keeps track of the tests the\n            instance has\n    \"\"\"\n\n    def __init__(self, data, decimals=2, sign='all', confidence=95,\n                 mantissas=True, sec_order=False, summation=False,\n                 limit_N=None, verbose=True):\n        self.data, self.chosen = input_data(data)\n        self.decimals = decimals\n        self.sign = sign\n        self.confidence = _check_confidence_(confidence)\n        self.limit_N = limit_N\n        self.verbose = verbose\n        self.base = Base(self.chosen, decimals, sign)\n        self.tests = []\n\n        # Create a DatFrame for each Test\n        for key, val in DIGS.items():\n            test = Test(self.base.loc[self.base[val] != -1],\n                        digs=key, confidence=self.confidence,\n                        limit_N=self.limit_N)\n            setattr(self, val, test)\n            self.tests.append(val)\n        # dict with the numbers of discarded entries for each test column\n        self._discarded = {key: val for (key, val) in\n                           zip(DIGS.values(),\n                               [len(self.base[col].loc[self.base[col] == -1])\n                                for col in DIGS.values()])}\n\n        if self.verbose:\n            print('\\n', ' Benford Object Instantiated '.center(50, '#'), '\\n')\n            print(f'Initial sample size: {len(self.chosen)}.\\n')\n            print(f'Test performed on {len(self.base)} registries.\\n')\n            print(\n                f'Number of discarded entries for each test:\\n{self._discarded}')\n\n        if mantissas:\n            self.mantissas()\n\n        if sec_order:\n            self.sec_order()\n\n        if summation:\n            self.summation()\n\n    def update_confidence(self, new_conf, tests=None):\n        \"\"\"Sets (a) new confidence level(s) for the Benford object, so as to be\n        used to produce critical values for the tests.\n\n        Args:\n            new_conf: new confidence level to draw lower and upper limits when\n                plotting and to limit the top deviations to show, as well as to\n                calculate critical values for the tests' statistics.\n            tests (:obj:`list` of :obj:`str`): list of tests names (strings) to\n                have their confidence updated. If only one, provide a one-element\n                list, like ['F1D']. Defauts to None, in which case it will use\n                the instance .test list attribute.\n\n        Raises:\n            ValueError: if the test argument is not a `list` or `None`.\n        \"\"\"\n        self.confidence = _check_confidence_(new_conf)\n        if tests is None:\n            tests = self.tests\n        else:\n            if not isinstance(tests, list):\n                raise ValueError('tests must be a list or None.')\n        for test in tests:\n            try:\n                getattr(self, test).update_confidence(\n                            self.confidence, check=False)\n            except AttributeError as e:\n                if test in ['F1D_Summ', 'F2D_Summ', 'F3D_Summ']:\n                    pass\n                else:\n                    print(e,\n                        f\"\\n\\n{test} not in Benford instance tests - \"\n                        \"review test's name.\")\n\n    @property\n    def all_confidences(self):\n        \"\"\"dict: a dictionary with a confidence level for each computed tests,\n        when applicable.\"\"\"\n        con_dic = {}\n        for key in self.tests:\n            try:\n                con_dic[key] = getattr(self, key).confidence\n            except AttributeError:\n                continue\n        return con_dic\n\n    def mantissas(self):\n        \"\"\"Adds a Mantissas object to the tests, with all its statistics and\n        plotting capabilities.\n        \"\"\"\n        self.Mantissas = Mantissas(self.base.seq.values,\n                                   self.confidence, self.limit_N)\n        self.tests.append('Mantissas')\n        if self.verbose:\n            print('\\nAdded Mantissas test.')\n\n    def sec_order(self):\n        \"\"\"Runs the Second Order tests, which are the Benford's tests\n        performed on the differences between the ordered sample (a value minus\n        the one before it, and so on). If the original series is Benford-\n        compliant, this new sequence should aldo follow Beford. The Second\n        Order can also be called separately, through the method sec_order().\n        \"\"\"\n        #: Base instance of the differences between the ordered sample\n        self.base_sec = Base(subtract_sorted(self.chosen),\n                             decimals=self.decimals, sign=self.sign)\n        for key, val in DIGS.items():\n            test = Test(self.base_sec.loc[self.base_sec[val] != -1],\n                        digs=key, confidence=self.confidence,\n                        limit_N=self.limit_N, sec_order=True)\n            setattr(self, SEC_ORDER_DIGS[key], test)\n            self.tests.append(f'{val}_sec')\n            # No need to populate crit_vals dict, since they are the\n            # same and do not depend on N\n            self._discarded_sec = {key: val for (key, val) in zip(\n                                   SEC_ORDER_DIGS.values(),\n                                   [sum(self.base_sec[col] == -1) for col in\n                                    DIGS.values()])}\n        if self.verbose:\n            print(f'\\nSecond order tests run in {len(self.base_sec)} '\n                  'registries.\\n\\nNumber of discarded entries for second order'\n                  f' tests:\\n{self._discarded_sec}')\n\n    def summation(self):\n        \"\"\"Creates Summation test DataFrames from Base object\"\"\"\n        for test in ['F1D', 'F2D', 'F3D']:\n            t = f'{test}_Summ'\n            setattr(self, t, Summ(self.base, test))\n            self.tests.append(t)\n\n        if self.verbose:\n            print('\\nAdded Summation DataFrames to F1D, F2D and F3D Tests.')\n\n\nclass Source(DataFrame):\n    \"\"\"Prepares the data for Analysis. pandas DataFrame subclass.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n        sec_order: choice for the Second Order Test, which cumputes the\n            differences between the ordered entries before running the Tests.\n        verbose (bool): tells the number of registries that are being subjected to\n            the analysis; defaults to True.\n\n    Raises:\n        ValueError: if the `sign` arg is not in ['all', 'pos', 'neg']\n        TypeError: if not receiving `int` or `float` as input.\n    \"\"\"\n\n    def __init__(self, data, decimals=2, sign='all', sec_order=False,\n                 verbose=True, inform=None):\n\n        if sign not in ['all', 'pos', 'neg']:\n            raise ValueError(\"The -sign- argument must be \"\n                             \"'all','pos' or 'neg'.\")\n\n        DataFrame.__init__(self, {'seq': data})\n\n        if self.seq.dtype != 'float' and self.seq.dtype != 'int':\n            raise TypeError('The sequence dtype was neither int nor float.\\n'\n                            'Convert it to whether int or float, and try again.')\n\n        if sign == 'pos':\n            self.seq = self.seq.loc[self.seq > 0]\n        elif sign == 'neg':\n            self.seq = self.seq.loc[self.seq < 0]\n        else:\n            self.seq = self.seq.loc[self.seq != 0]\n\n        self.dropna(inplace=True)\n        #: (bool): verbose or not\n        self.verbose = _deprecate_inform_(verbose, inform)\n        if self.verbose:\n            print(f\"\\nInitialized sequence with {len(self)} registries.\")\n\n        if sec_order:\n            self.seq = subtract_sorted(self.seq.copy())\n            self.dropna(inplace=True)\n            self.reset_index(inplace=True)\n            if verbose:\n                print('Second Order Test. Initial series reduced '\n                      f'to {len(self.seq)} entries.')\n\n        ab = self.seq.abs()\n\n        if self.seq.dtype == 'int':\n            self['ZN'] = ab\n        else:\n            if decimals == 'infer':\n                # There is some numerical issue with Windows that required\n                # implementing it differently (and slower)\n                self['ZN'] = ab.astype(str)\\\n                               .str.replace('.', '', regex=False)\\\n                               .str.lstrip('0').str[:5]\\\n                               .astype(int)\n            else:\n                self['ZN'] = (ab * (10 ** decimals)).astype(int)\n\n    def mantissas(self, report=True, show_plot=True, figsize=(15, 8),\n                  save_plot=None, save_plot_kwargs=None):\n        \"\"\"Calculates the mantissas, their mean and variance, and compares them\n        with the mean and variance of a Benford's sequence.\n\n        Args:\n            report: prints the mamtissas mean, variance, skewness and kurtosis\n                for the sequence studied, along with reference values.\n            show_plot: plots the ordered mantissas and a line with the expected\n                inclination. Defaults to True.\n            figsize: tuple that sets the figure dimensions.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n        \"\"\"\n        self['Mant'] = get_mantissas(self.seq.abs())\n        if report:\n            p = self[['seq', 'Mant']]\n            p = p.loc[p.seq > 0].sort_values('Mant')\n            print(f\"The Mantissas MEAN is {p.Mant.mean()}. Ref: 0.5.\")\n            print(f\"The Mantissas VARIANCE is {p.Mant.var()}. Ref: 0.083333.\")\n            print(f\"The Mantissas SKEWNESS is {p.Mant.skew()}. \\tRef: 0.\")\n            print(f\"The Mantissas KURTOSIS is {p.Mant.kurt()}. \\tRef: -1.2.\")\n\n        if show_plot:\n            plot_ordered_mantissas(self.Mant, figsize=figsize,\n                                   save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n    def first_digits(self, digs, confidence=None, high_Z='pos',\n                     limit_N=None, MAD=False, MSE=False, chi_square=False,\n                     KS=False, show_plot=True, save_plot=None, save_plot_kwargs=None,\n                     simple=False, bhat_coeff = False, bhat_dist=False,\n                     kl_diverg=False, ret_df=False):\n        \"\"\"Performs the Benford First Digits test with the series of\n        numbers provided, and populates the mapping dict for future\n        selection of the original series.\n\n        Args:\n            digs (int): number of first digits to consider. Must be 1 (first digit),\n                2 (first two digits) or 3 (first three digits).\n            verbose (bool): tells the number of registries that are being subjected to\n                the analysis; defaults to True\n            confidence (int, float): confidence level to draw lower and upper limits when\n                plotting and to limit the top deviations to show, as well as to\n                calculate critical values for the tests' statistics. Defaults to None.\n            high_Z (int): chooses which Z scores to be used when displaying results,\n                according to the confidence level chosen. Defaluts to 'pos',\n                which will highlight only values higher than the expexted\n                frequencies; 'all' will highlight both extremes (positive and\n                negative); and an integer, which will use the first n entries,\n                positive and negative, regardless of whether Z is higher than\n                the confidence or not.\n            limit_N (int): sets a limit to N as the sample size for the calculation of\n                the Z scores if the sample is too big. Defaults to None.\n            MAD (bool): calculates the Mean Absolute Difference between the\n                found and the expected distributions; defaults to False.\n            MSE (bool): calculates the Mean Square Error of the sample; defaults to\n                False.\n            bhat_coeff (bool): computes the Bhattacharyya Coefficient between\n                the found and the expected (Benford) digits distribution; defaults\n                to Fasle\n            bhat_dist (bool): calculates the Bhattacharyya Distance between\n                the found and the expected (Benford) digits distribution; defaults\n                to Fasle\n            kl_diverg (bool): calculates the Kulback-Laibler Divergence between\n                the found and the expected (Benford) digits distribution;\n                defaults to False\n            show_plot (bool): draws the test plot. Defaults to True.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n            ret_df: returns the test DataFrame. Defaults to False. True if run by\n                the test function.\n\n        Returns:\n            DataFrame with the Expected and Found proportions, and the Z scores of\n                the differences\n        \"\"\"\n        # Check on the possible values for confidence levels\n        confidence = _check_confidence_(confidence)\n        # Check on possible digits\n        _check_digs_(digs)\n\n        temp = self.loc[self.ZN >= 10 ** (digs - 1)]\n        temp[DIGS[digs]] = (temp.ZN // 10 ** ((log10(temp.ZN).astype(\n                                                   int)) - (digs - 1))).astype(\n                                                       int)\n        n, m = 10 ** (digs - 1), 10 ** (digs)\n        x = arange(n, m)\n\n        if simple:\n            self.verbose = False\n            show_plot = False\n            df = prepare(temp[DIGS[digs]], digs, limit_N=limit_N,\n                         simple=True)\n        else:\n            N, df = prepare(temp[DIGS[digs]], digs, limit_N=limit_N,\n                            simple=False)\n\n        if self.verbose:\n            print(f\"\\nTest performed on {len(temp)} registries.\\n\"\n                  f\"Discarded {len(self) - len(temp)} records < {10 ** (digs - 1)}\"\n                  \" after preparation.\")\n            if confidence is not None:\n                _inform_(df, high_Z=high_Z, conf=CONFS[confidence])\n\n        # Mean absolute difference\n        if MAD:\n            self.MAD = df.AbsDif.mean()\n            if self.verbose:\n                _report_mad_(digs, self.MAD)\n\n        # Mean Square Error\n        if MSE:\n            self.MSE = (df.AbsDif ** 2).mean()\n\n        # Chi-square statistic\n        if chi_square:\n            self.chi_square = chi_sq(df, ddf=len(df) - 1,\n                                     confidence=confidence,\n                                     verbose=self.verbose)\n        # KS test\n        if KS:\n            self.KS = kolmogorov_smirnov(df, confidence=confidence, N=len(temp),\n                                         verbose=self.verbose)\n\n        if bhat_coeff:\n            self.bhat_coeff = _bhattacharyya_coefficient(\n                                df.Found.values, df.Expected.values)\n\n        if bhat_dist:\n            self.bhat_dist = _bhattacharyya_distance_(\n                                df.Found.values, df.Expected.values)\n        \n        if kl_diverg:\n            self.kl_diverg = _kullback_leibler_divergence_(\n                                df.Found.values, df.Expected.values)\n\n        # Plotting the expected frequncies (line) against the found ones(bars)\n        if show_plot:\n            plot_digs(df, x=x, y_Exp=df.Expected, y_Found=df.Found, N=N,\n                       figsize=(2 * (digs ** 2 + 5), 1.5 * (digs ** 2 + 5)),\n                       conf_Z=CONFS[confidence], save_plot=save_plot,\n                       save_plot_kwargs=save_plot_kwargs)\n        if ret_df:\n            return df\n\n    def second_digit(self, confidence=None, high_Z='pos',\n                     limit_N=None, MAD=False, MSE=False, chi_square=False,\n                     KS=False, bhat_coeff=False, bhat_dist=False, kl_diverg=False,\n                     show_plot=True, save_plot=None, save_plot_kwargs=None,\n                     simple=False, ret_df=False):\n        \"\"\"Performs the Benford Second Digit test with the series of\n        numbers provided.\n\n        Args:\n            verbose (bool): tells the number of registries that are being subjected to\n                the analysis; defaults to True\n            MAD (bool): calculates the Mean Absolute Difference between the\n                found and the expected distributions; defaults to False.\n            confidence (int, float): confidence level to draw lower and upper limits when\n                plotting and to limit the top deviations to show, as well as to\n                calculate critical values for the tests' statistics. Defaults to None.\n            high_Z (int): chooses which Z scores to be used when displaying results,\n                according to the confidence level chosen. Defaluts to 'pos',\n                which will highlight only values higher than the expexted\n                frequencies; 'all' will highlight both extremes (positive and\n                negative); and an integer, which will use the first n entries,\n                positive and negative, regardless of whether Z is higher than\n                the confidence or not.\n            limit_N (int): sets a limit to N as the sample size for the calculation of\n                the Z scores if the sample is too big. Defaults to None.\n            MSE (bool): calculates the Mean Square Error of the sample; defaults to\n                False.\n            bhat_coeff (bool): computes the Bhattacharyya Coefficient between\n                the found and the expected (Benford) digits distribution; defaults\n                to Fasle\n            bhat_dist (bool): calculates the Bhattacharyya Distance between\n                the found and the expected (Benford) digits distribution; defaults\n                to Fasle\n            kl_diverg (bool): calculates the Kulback-Laibler Divergence between\n                the found and the expected (Benford) digits distribution;\n                defaults to False\n            show_plot (bool): draws the test plot.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n            ret_df: returns the test DataFrame. Defaults to False. True if run by\n                the test function.\n\n        Returns:\n            DataFrame with the Expected and Found proportions, and the Z scores of\n                the differences\n        \"\"\"\n        confidence = _check_confidence_(confidence)\n\n        conf = CONFS[confidence]\n\n        temp = self.loc[self.ZN >= 10, :]\n        temp['SD'] = (temp.ZN // 10 ** ((log10(temp.ZN)).astype(\n                      int) - 1)) % 10\n\n        if simple:\n            self.verbose = False\n            show_plot = False\n            df = prepare(temp['SD'], 22, limit_N=limit_N, simple=True)\n        else:\n            N, df = prepare(temp['SD'], 22, limit_N=limit_N, simple=False)\n\n        if self.verbose:\n            print(f\"\\nTest performed on {len(temp)} registries.\\nDiscarded \"\n                  f\"{len(self) - len(temp)} records < 10 after preparation.\")\n            if confidence is not None:\n                _inform_(df, high_Z, conf)\n\n        # Mean absolute difference\n        if MAD:\n            self.MAD = df.AbsDif.mean()\n            if self.verbose:\n                _report_mad_(22, self.MAD)\n        # Mean Square Error\n        if MSE:\n            self.MSE = (df.AbsDif ** 2).mean()\n\n        # Chi-square statistic\n        if chi_square:\n            self.chi_square = chi_sq(df, ddf=9, confidence=confidence,\n                                     verbose=self.verbose)\n        # KS test\n        if KS:\n            self.KS = kolmogorov_smirnov(df, confidence=confidence, N=len(temp),\n\n                                         verbose=self.verbose)\n        if bhat_coeff:\n            self.bhat_coeff = _bhattacharyya_coefficient(\n                                df.Found.values, df.Expected.values)\n\n        if bhat_dist:\n            self.bhat_dist = _bhattacharyya_distance_(\n                                df.Found.values, df.Expected.values\n                            )\n        \n        if kl_diverg:\n            self.kl_diverg = _kullback_leibler_divergence_(\n                                df.Found.values, df.Expected.values\n                            )\n\n        # Plotting the expected frequncies (line) against the found ones(bars)\n        if show_plot:\n            plot_digs(df, x=arange(0, 10), y_Exp=df.Expected,\n                       y_Found=df.Found, N=N, figsize=(10, 6), conf_Z=conf,\n                       save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n        if ret_df:\n            return df\n\n    def last_two_digits(self, confidence=None, high_Z='pos',\n                        limit_N=None, MAD=False, MSE=False, chi_square=False,\n                        KS=False, bhat_coeff=False, bhat_dist=False, kl_diverg=False,\n                        show_plot=True, save_plot=None, save_plot_kwargs=None,\n                        simple=False, ret_df=False):\n        \"\"\"Performs the Benford Last Two Digits test with the series of\n        numbers provided.\n\n        Args:\n            verbose (bool): tells the number of registries that are being subjected to\n                the analysis; defaults to True\n            MAD (bool): calculates the Mean Absolute Difference between the\n                found and the expected distributions; defaults to False.\n            confidence (int, float): confidence level to draw lower and upper limits when\n                plotting and to limit the top deviations to show, as well as to\n                calculate critical values for the tests' statistics. Defaults to None.\n            high_Z (int): chooses which Z scores to be used when displaying results,\n                according to the confidence level chosen. Defaluts to 'pos',\n                which will highlight only values higher than the expexted\n                frequencies; 'all' will highlight both extremes (positive and\n                negative); and an integer, which will use the first n entries,\n                positive and negative, regardless of whether Z is higher than\n                the confidence or not.\n            limit_N (int): sets a limit to N as the sample size for the calculation of\n                the Z scores if the sample is too big. Defaults to None.\n            MSE (bool): calculates the Mean Square Error of the sample; defaults to\n                False.\n            bhat_coeff (bool): computes the Bhattacharyya Coefficient between\n                the found and the expected (Benford) digits distribution; defaults\n                to Fasle\n            bhat_dist (bool): calculates the Bhattacharyya Distance between\n                the found and the expected (Benford) digits distribution; defaults\n                to Fasle\n            kl_diverg (bool): calculates the Kulback-Laibler Divergence between\n                the found and the expected (Benford) digits distribution;\n                defaults to False\n            show_plot (bool): draws the test plot.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n        \n        Returns:\n            DataFrame with the Expected and Found proportions, and the Z scores of\n                the differences\n        \"\"\"\n        confidence = _check_confidence_(confidence)\n        conf = CONFS[confidence]\n\n        temp = self.loc[self.ZN >= 1000]\n        temp['L2D'] = temp.ZN % 100\n\n        if simple:\n            self.verbose = False\n            show_plot = False\n            df = prepare(temp['L2D'], -2, limit_N=limit_N, simple=True)\n        else:\n            N, df = prepare(temp['L2D'], -2, limit_N=limit_N, simple=False)\n\n        if self.verbose:\n            print(f\"\\nTest performed on {len(temp)} registries.\\n\\nDiscarded \"\n                  f\"{len(self) - len(temp)} records < 1000 after preparation\")\n            if confidence is not None:\n                _inform_(df, high_Z, conf)\n\n        # Mean absolute difference\n        if MAD:\n            self.MAD = df.AbsDif.mean()\n            if self.verbose:\n                _report_mad_(-2, self.MAD)\n        # Mean Square Error\n        if MSE:\n            self.MSE = (df.AbsDif ** 2).mean()\n\n        # Chi-square statistic\n        if chi_square:\n            self.chi_square = chi_sq(df, ddf=99, confidence=confidence,\n                                     verbose=self.verbose)\n        # KS test\n        if KS:\n            self.KS = kolmogorov_smirnov(df, confidence=confidence, N=len(temp),\n                                         verbose=self.verbose)\n\n        if bhat_coeff:\n            self.bhat_coeff = _bhattacharyya_coefficient(\n                                df.Found.values, df.Expected.values)\n\n        if bhat_dist:\n            self.bhat_dist = _bhattacharyya_distance_(\n                                df.Found.values, df.Expected.values)\n        \n        if kl_diverg:\n            self.kl_diverg = _kullback_leibler_divergence_(\n                                df.Found.values, df.Expected.values)\n\n        # Plotting expected frequencies (line) versus found ones (bars)\n        if show_plot:\n            plot_digs(df, x=arange(0, 100), y_Exp=df.Expected,\n                       y_Found=df.Found, N=N, figsize=(15, 5),\n                       conf_Z=conf, text_x=True, save_plot=save_plot,\n                       save_plot_kwargs=save_plot_kwargs)\n        if ret_df:\n            return df\n\n    def summation(self, digs=2, top=20, show_plot=True, save_plot=None,\n                  save_plot_kwargs=None, ret_df=False):\n        \"\"\"Performs the Summation test. In a Benford series, the sums of the\n        entries begining with the same digits tends to be the same.\n\n        Args:\n            digs: tells the first digits to use. 1- first; 2- first two;\n                3- first three. Defaults to 2.\n            top: choses how many top values to show. Defaults to 20.\n            show_plot: plots the results. Defaults to True.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension. Only available when\n                plot=True.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when plot=True and save_plot is a string with the\n                figure file path/name.\n        \n        Returns:\n            DataFrame with the Expected and Found proportions, and their\n                absolute differences\n        \"\"\"\n        _check_digs_(digs)\n\n        if digs == 1:\n            top = 9\n        # Call the dict for F1D, F2D, F3D\n        d = DIGS[digs]\n        if d not in self.columns:\n            self[d] = self.ZN.astype(str).str[:digs].astype(int)\n        # Call the expected proportion according to digs\n        li = 1. / (9 * (10 ** (digs - 1)))\n\n        df = self.groupby(d).sum()\n        # s.drop(0, inplace=True)\n        df['Percent'] = df.ZN / df.ZN.sum()\n        df.columns.values[1] = 'Summ'\n        df = df[['Summ', 'Percent']]\n        df['AbsDif'] = (df.Percent - li).abs()\n\n        if self.verbose:\n            # N = len(self)\n            print(f\"\\nTest performed on {len(self)} registries.\\n\")\n            print(f\"The top {top} diferences are:\\n\")\n            print(df[:top])\n\n        if show_plot:\n            plot_sum(df, figsize=(\n                       2 * (digs ** 2 + 5), 1.5 * (digs ** 2 + 5)), li=li,\n                       save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n        if ret_df:\n            return df\n\n    def duplicates(self, top_Rep=20, inform=None):\n        \"\"\"Performs a duplicates test and maps the duplicates count in descending\n        order.\n\n        Args:\n            verbose (bool): tells how many duplicated entries were found and prints the\n                top numbers according to the top_Rep argument. Defaluts to True.\n            top_Rep: int or None. Chooses how many duplicated entries will be\n                shown withe the top repititions. Defaluts to 20. If None, returns\n                al the ordered repetitions.\n\n        Returns:\n            DataFrame with the duplicated records and their occurrence counts,\n                in descending order (if verbose is False; if True, prints to\n                terminal).\n\n        Raises:\n            ValueError: if the `top_Rep` arg is not int or None.\n        \"\"\"\n        if top_Rep is not None and not isinstance(top_Rep, int):\n            raise ValueError('The top_Rep argument must be an int or None.')\n\n        dup = self[['seq']][self.seq.duplicated(keep=False)]\n        dup_count = dup.groupby(self.seq).count()\n\n        dup_count.index.names = ['Entries']\n        dup_count.rename(columns={'seq': 'Count'}, inplace=True)\n\n        dup_count.sort_values('Count', ascending=False, inplace=True)\n\n        # self.maps['dup'] = dup_count.index[:top_Rep].values  # array\n\n        if self.verbose:\n            print(f'\\nFound {len(dup_count)} duplicated entries.\\n'\n                  f'The entries with the {top_Rep} highest repitition counts are:')\n            print(dup_count.head(top_Rep))\n        else:\n            return dup_count\n\nclass Roll_mad(object):\n    \"\"\"Applies the MAD to sequential subsets of the Series, returning another\n    Series.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.\n        window: size of the subset to be used.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n\n    \"\"\"\n\n    def __init__(self, data, test, window, decimals=2, sign='all'):\n\n        #: the test (F1D, SD, F2D...) used for the MAD calculation and critical values\n        self.test = _check_test_(test)\n\n        if not isinstance(data, Source):\n            data = Source(data, sign=sign, decimals=decimals, verbose=False)\n\n        Exp, ind = prep_to_roll(data, self.test)\n\n        self.roll_series = data[DIGS[test]].rolling(\n                                window=window).apply(mad_to_roll, \n                                    args=(Exp, ind), raw=False)\n        self.roll_series.dropna(inplace=True)\n\n    def show_plot(self, figsize=(15, 8), save_plot=None, save_plot_kwargs=None):\n        \"\"\"Shows the rolling MAD plot\n\n        Args:\n            figsize: the figure dimensions.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when save_plot is a string with the figure file\n                path/name.\n        \"\"\"\n        plot_roll_mad(self, figsize=figsize,\n                      save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n\nclass Roll_mse(object):\n    \"\"\"Applies the MSE to sequential subsets of the Series, returning another\n    Series.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.\n        window: size of the subset to be used.\n            decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. 'pos': only the positive\n            entries; 'neg': only negative entries; 'all': all entries but zeros.\n            Defaults to 'all'.\n    \"\"\"\n\n    def __init__(self, data, test, window, decimals=2, sign='all'):\n\n        test = _check_test_(test)\n\n        if not isinstance(data, Source):\n            data = Source(data, sign=sign, decimals=decimals, verbose=False)\n\n        Exp, ind = prep_to_roll(data, test)\n\n        self.roll_series = data[DIGS[test]].rolling(\n                                window=window).apply(mse_to_roll, \n                                    args=(Exp, ind), raw=False)\n        self.roll_series.dropna(inplace=True)\n\n    def show_plot(self, figsize=(15, 8), save_plot=None, save_plot_kwargs=None):\n        \"\"\"Shows the rolling MSE plot\n\n        Args:\n            figsize: the figure dimensions.\n            save_plot (str): string with the path/name of the file in which the generated\n                plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n                is infered by the file name extension.\n            save_plot_kwargs (dict): any of the kwargs accepted by\n                matplotlib.pyplot.savefig()\n                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n                Only available when save_plot is a string with the figure file\n                path/name.\n        \"\"\"\n        plot_roll_mse(self.roll_series, figsize=figsize,\n                      save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n\n\ndef first_digits(data, digs, decimals=2, sign='all', verbose=True,\n                 confidence=None, high_Z='pos', limit_N=None,\n                 MAD=False, MSE=False, chi_square=False, KS=False,\n                 show_plot=True, save_plot=None, save_plot_kwargs=None,\n                 inform=None):\n    \"\"\"Performs the Benford First Digits test on the series of\n    numbers provided.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. 'pos': only the positive\n            entries; 'neg': only negative entries; 'all': all entries but zeros.\n            Defaults to 'all'.\n        digs (int): number of first digits to consider. Must be 1 (first digit),\n            2 (first two digits) or 3 (first three digits).\n        verbose (bool): tells the number of registries that are being subjected to\n            the analysis and returns tha analysis DataFrame sorted by the\n            highest Z score down. Defaults to True.\n        MAD (bool): calculates the Mean Absolute Difference between the\n            found and the expected distributions; defaults to False.\n        confidence (int, float): confidence level to draw lower and upper limits when\n            plotting and to limit the top deviations to show. Defaults to None.\n        high_Z (int): chooses which Z scores to be used when displaying results,\n            according to the confidence level chosen. Defaluts to 'pos',\n            which will highlight only values higher than the expexted\n            frequencies; 'all' will highlight both extremes (positive and\n            negative); and an integer, which will use the first n entries,\n            positive and negative, regardless of whether Z is higher than\n            the confidence or not.\n        limit_N (int): sets a limit to N as the sample size for the calculation of\n            the Z scores if the sample is too big. Defaults to None.\n        MSE (bool): calculates the Mean Square Error of the sample; defaults to\n            False.\n        chi_square: calculates the chi_square statistic of the sample and\n            compares it with a critical value, according to the confidence\n            level chosen and the series's degrees of freedom. Defaults to\n            False. Requires confidence != None.\n        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative\n            distribution of the sample with the Benford's, according to the\n            confidence level chosen. Defaults to False. Requires confidence\n            != None.\n        show_plot (bool): draws the test plot.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \n    Returns:\n        DataFrame with the Expected and Found proportions, and the Z scores of\n            the differences if the confidence is not None.\n    \"\"\"\n    verbose = _deprecate_inform_(verbose, inform)\n\n    if not isinstance(data, Source):\n        data = Source(data, decimals=decimals, sign=sign, verbose=verbose)\n\n    data = data.first_digits(digs, confidence=confidence, high_Z=high_Z,\n                             limit_N=limit_N, MAD=MAD, MSE=MSE,\n                             chi_square=chi_square, KS=KS, show_plot=show_plot,\n                             save_plot=save_plot, save_plot_kwargs=save_plot_kwargs,\n                             ret_df=True)\n\n    if confidence is not None:\n        data = data[['Counts', 'Found', 'Expected', 'Z_score']]\n        return data.sort_values('Z_score', ascending=False)\n    else:\n        return data[['Counts', 'Found', 'Expected']]\n\n\ndef second_digit(data, decimals=2, sign='all', verbose=True,\n                 confidence=None, high_Z='pos', limit_N=None,\n                 MAD=False, MSE=False, chi_square=False, KS=False,\n                 show_plot=True, save_plot=None, save_plot_kwargs=None,\n                 inform=None):\n    \"\"\"Performs the Benford Second Digits test on the series of\n    numbers provided.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. 'pos': only the positive\n            entries; 'neg': only negative entries; 'all': all entries but zeros.\n            Defaults to 'all'.\n        verbose (bool): tells the number of registries that are being subjected to\n            the analysis and returns tha analysis DataFrame sorted by the\n            highest Z score down. Defaults to True.\n        MAD (bool): calculates the Mean Absolute Difference between the\n            found and the expected distributions; defaults to False.\n        confidence (int, float): confidence level to draw lower and upper limits when\n            plotting and to limit the top deviations to show. Defaults to None.\n        high_Z (int): chooses which Z scores to be used when displaying results,\n            according to the confidence level chosen. Defaluts to 'pos',\n            which will highlight only values higher than the expexted\n            frequencies; 'all' will highlight both extremes (positive and\n            negative); and an integer, which will use the first n entries,\n            positive and negative, regardless of whether Z is higher than\n            the confidence or not.\n        limit_N (int): sets a limit to N as the sample size for the calculation of\n            the Z scores if the sample is too big. Defaults to None.\n        MSE (bool): calculates the Mean Square Error of the sample; defaults to\n            False.\n        chi_square: calculates the chi_square statistic of the sample and\n            compares it with a critical value, according to the confidence\n            level chosen and the series's degrees of freedom. Defaults to\n            False. Requires confidence != None.\n        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative\n            distribution of the sample with the Benford's, according to the\n            confidence level chosen. Defaults to False. Requires confidence\n            != None.\n        show_plot (bool): draws the test plot.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n\n    Returns:\n        DataFrame with the Expected and Found proportions, and the Z scores of\n            the differences if the confidence is not None.\n    \"\"\"\n    verbose = _deprecate_inform_(verbose, inform)\n\n    if not isinstance(data, Source):\n        data = Source(data, sign=sign, decimals=decimals, verbose=verbose)\n\n    data = data.second_digit(confidence=confidence, high_Z=high_Z,\n                             limit_N=limit_N, MAD=MAD, MSE=MSE,\n                             chi_square=chi_square, KS=KS, show_plot=show_plot,\n                             save_plot=save_plot, save_plot_kwargs=save_plot_kwargs,\n                             ret_df=True)\n    if confidence is not None:\n        data = data[['Counts', 'Found', 'Expected', 'Z_score']]\n        return data.sort_values('Z_score', ascending=False)\n    else:\n        return data[['Counts', 'Found', 'Expected']]\n\n\ndef last_two_digits(data, decimals=2, sign='all', verbose=True,\n                    confidence=None, high_Z='pos', limit_N=None,\n                    MAD=False, MSE=False, chi_square=False, KS=False,\n                    show_plot=True, save_plot=None, save_plot_kwargs=None,\n                    inform=None):\n    \"\"\"Performs the Last Two Digits test on the series of\n    numbers provided.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column,with values being\n            integers or floats.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. 'pos': only the positive\n            entries; 'neg': only negative entries; 'all': all entries but zeros.\n            Defaults to 'all'.\n        verbose (bool): tells the number of registries that are being subjected to\n            the analysis and returns tha analysis DataFrame sorted by the\n            highest Z score down. Defaults to True.\n        confidence (int, float): confidence level to draw lower and upper limits when\n            plotting and to limit the top deviations to show. Defaults to None.\n        high_Z (int): chooses which Z scores to be used when displaying results,\n            according to the confidence level chosen. Defaluts to 'pos',\n            which will highlight only values higher than the expexted\n            frequencies; 'all' will highlight both extremes (positive and\n            negative); and an integer, which will use the first n entries,\n            positive and negative, regardless of whether Z is higher than\n            the confidence or not.\n        limit_N (int): sets a limit to N as the sample size for the calculation of\n            the Z scores if the sample is too big. Defaults to None.\n        MAD (bool): calculates the Mean Absolute Difference between the\n            found and the expected distributions; defaults to False.\n        MSE (bool): calculates the Mean Square Error of the sample; defaults to\n            False.\n        chi_square: calculates the chi_square statistic of the sample and\n            compares it with a critical value, according to the confidence\n            level chosen and the series's degrees of freedom. Defaults to\n            False. Requires confidence != None.\n        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative\n            distribution of the sample with the Benford's, according to the\n            confidence level chosen. Defaults to False. Requires confidence\n            != None.\n        show_plot (bool): draws the test plot.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n\n    Returns:\n        DataFrame with the Expected and Found proportions, and the Z scores of\n            the differences if the confidence is not None.\n    \"\"\"\n    verbose = _deprecate_inform_(verbose, inform)\n\n    if not isinstance(data, Source):\n        data = Source(data, decimals=decimals, sign=sign, verbose=verbose)\n\n    data = data.last_two_digits(confidence=confidence, high_Z=high_Z,\n                                limit_N=limit_N, MAD=MAD,\n                                MSE=MSE, chi_square=chi_square, KS=KS,\n                                show_plot=show_plot, save_plot=save_plot,\n                                save_plot_kwargs=save_plot_kwargs, ret_df=True)\n\n    if confidence is not None:\n        data = data[['Counts', 'Found', 'Expected', 'Z_score']]\n        return data.sort_values('Z_score', ascending=False)\n    else:\n        return data[['Counts', 'Found', 'Expected']]\n\n\ndef mantissas(data, report=True, show_plot=True, arc_test=True,\n              save_plot=None, save_plot_kwargs=None, inform=None):\n    \"\"\"Extraxts the mantissas of the records logarithms\n\n    Args:\n        data: sequence to compute mantissas from, numpy 1D array, pandas Series\n            of pandas DataFrame column.\n        report: prints the mamtissas mean, variance, skewness and kurtosis\n            for the sequence studied, along with reference values.\n        show_plot: plots the ordered mantissas and a line with the expected\n            inclination. Defaults to True.\n        arc_test: draws the Arc Test plot. Defaluts to True.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \n    Returns:\n        Series with the data mantissas.\n    \"\"\"\n    report = _deprecate_inform_(report, inform)\n\n    mant = Mantissas(data)\n    if report:\n        mant.report(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    if show_plot:\n        mant.show_plot(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    if arc_test:\n        mant.arc_test(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    return mant\n\n\ndef summation(data, digs=2, decimals=2, sign='all', top=20, verbose=True,\n              show_plot=True, save_plot=None, save_plot_kwargs=None, inform=None):\n    \"\"\"Performs the Summation test. In a Benford series, the sums of the\n    entries begining with the same digits tends to be the same.\n    Works only with the First Digits (1, 2 or 3) test.\n\n    Args:\n        digs: tells the first digits to use: 1- first; 2- first two;\n            3- first three. Defaults to 2.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        top: choses how many top values to show. Defaults to 20.\n        show_plot: plots the results. Defaults to True.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \n    Returns:\n        DataFrame with the Summation test, whether sorted in descending order\n            (if verbose == True) or not.\n    \"\"\"\n    verbose = _deprecate_inform_(verbose, inform)\n\n    if not isinstance(data, Source):\n        data = Source(data, sign=sign, decimals=decimals, verbose=verbose)\n\n    data = data.summation(digs=digs, top=top,\n                          show_plot=show_plot, save_plot=save_plot,\n                          save_plot_kwargs=save_plot_kwargs, ret_df=True)\n    if verbose:\n        return data.sort_values('AbsDif', ascending=False)\n    else:\n        return data\n\n\ndef mad(data, test, decimals=2, sign='all', verbose=False):\n    \"\"\"Calculates the Mean Absolute Deviation of the Series\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: informs which base test to use for the mad.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n\n    Returns:\n        float: the Mean Absolute Deviation of the Series\n    \"\"\"\n    data = _check_num_array_(data)\n    test = _check_test_(test)\n    start = Source(data, sign=sign, decimals=decimals, verbose=verbose)\n    if test in [1, 2, 3]:\n        start.first_digits(digs=test, MAD=True, MSE=True, simple=True)\n    elif test == 22:\n        start.second_digit(MAD=True, MSE=False, simple=True)\n    else:\n        start.last_two_digits(MAD=True, MSE=False, simple=True)\n    return start.MAD\n\n\ndef mse(data, test, decimals=2, sign='all', verbose=False):\n    \"\"\"Calculates the Mean Squared Error of the Series\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: informs which base test to use for the mad.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n\n    Returns:\n        float: the Mean Squared Error of the Series\n    \"\"\"\n    data = _check_num_array_(data)\n    test = _check_test_(test)\n    start = Source(data, sign=sign, decimals=decimals, verbose=verbose)\n    if test in [1, 2, 3]:\n        start.first_digits(digs=test, MAD=False, MSE=True, simple=True)\n    elif test == 22:\n        start.second_digit(MAD=False, MSE=True, simple=True)\n    else:\n        start.last_two_digits(MAD=False, MSE=True, simple=True)\n    return start.MSE\n\n\ndef bhattacharyya_distance(data, test, decimals, sign=\"all\", verbose=False):\n    \"\"\"Computes the Bhattacharyya Distance between the Found and the Expected\n    (Benford) digits distributions, according toe the test chosen\n    (First, Second, First Two...)\n\n    Args:\n        data (ndarray, Series): sequence to be evaluated, with values being\n            integers or floats.\n        test (int, str): informs which base test to be used.\n        decimals (int): number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign (str, optional): tells which portion of the data to consider.\n            pos: only the positive entries; neg: only negative entries; all:\n            all entries but zeros. Defaults to \"all\".\n\n    Returns:\n        float: the Bhattacharyya Distance between the distributions\n    \"\"\"\n    data = _check_num_array_(data)\n    test = _check_test_(test)\n    start = Source(data, sign=sign, decimals=decimals, verbose=verbose)\n    if test in [1, 2, 3]:\n        start.first_digits(digs=test, MAD=False, bhat_dist=True, simple=True)\n    elif test == 22:\n        start.second_digit(MAD=False, bhat_dist=True, simple=True)\n    else:\n        start.last_two_digits(MAD=False, bhat_dist=True, simple=True)\n    return start.bhat_dist\n\n\ndef kullback_leibler_divergence(data, test, decimals, sign=\"all\",\n                                verbose=False):\n    \"\"\"Computes the Kulback-Leibler Divergence between the Found and the\n    Expected (Benford) digits distributions, according toe the test chosen\n    (First, Second, First Two...).\n\n    Args:\n        data (ndarray, Series): sequence to be evaluated, with values being\n            integers or floats.\n        test (int, str): informs which base test to be used.\n        decimals (int): number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign (str, optional): tells which portion of the data to consider.\n            pos: only the positive entries; neg: only negative entries; all:\n            all entries but zeros. Defaults to \"all\".\n\n    Returns:\n        float: the Kulback-Leibler Divergence between the distributions\n    \"\"\"\n    data = _check_num_array_(data)\n    test = _check_test_(test)\n    start = Source(data, sign=sign, decimals=decimals, verbose=verbose)\n    if test in [1, 2, 3]:\n        start.first_digits(digs=test, MAD=False, kl_diverg=True, simple=True)\n    elif test == 22:\n        start.second_digit(MAD=False, kl_diverg=True, simple=True)\n    else:\n        start.last_two_digits(MAD=False, kl_diverg=True, simple=True)\n    return start.kl_diverg\n\n\ndef mad_summ(data, test, decimals=2, sign='all', verbose=False):\n    \"\"\"Calculate the Mean Absolute Deviation of the Summation Test\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: informs which base test to use for the summation mad.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n\n    Returns:\n        float: the Mean Absolute Deviation of the Summation Test\n    \"\"\"\n    data = _check_num_array_(data)\n    test = _check_digs_(test)\n\n    start = Source(data, sign=sign, decimals=decimals, verbose=verbose)\n    temp = start.loc[start.ZN >= 10 ** (test - 1)]\n    temp[DIGS[test]] = (temp.ZN // 10 ** ((log10(temp.ZN).astype(\n                                                int)) - (test - 1))).astype(\n                                                    int)\n    li = 1. / (9 * (10 ** (test - 1)))\n\n    df = temp.groupby(DIGS[test]).sum()\n    return mean(abs(df.ZN / df.ZN.sum() - li))\n\n\ndef rolling_mad(data, test, window, decimals=2, sign='all',\n                show_plot=False, save_plot=None, save_plot_kwargs=None):\n    \"\"\"Applies the MAD to sequential subsets of the records.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.\n        window: size of the subset to be used.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n        show_plot (bool): draws the test plot.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \n    Returns:\n        Series with sequentially computed MADs.\n    \"\"\"\n    data = _check_num_array_(data)\n    r_mad = Roll_mad(data, test, window, decimals, sign)\n    if show_plot:\n        r_mad.show_plot(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    return r_mad.roll_series\n\n\ndef rolling_mse(data, test, window, decimals=2, sign='all',\n                show_plot=False, save_plot=None, save_plot_kwargs=None):\n    \"\"\"Applies the MSE to sequential subsets of the records.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.\n        window: size of the subset to be used.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n        show_plot (bool): draws the test plot.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \n    Returns:\n        Series with sequentially computed MSEs.\n    \"\"\"\n    data = _check_num_array_(data)\n    r_mse = Roll_mse(data, test, window, decimals, sign)\n    if show_plot:\n        r_mse.show_plot(save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    return r_mse.roll_series\n\n\ndef duplicates(data, top_Rep=20, verbose=True, inform=None):\n    \"\"\"Performs a duplicates test and maps the duplicates count in descending\n    order.\n\n    Args:\n        data: sequence to take the duplicates from. pandas Series or\n            numpy Ndarray.\n        verbose (bool): tells how many duplicated entries were found and prints the\n            top numbers according to the top_Rep argument. Defaluts to True.\n        top_Rep: chooses how many duplicated entries will be\n            shown withe the top repititions. int or None. Defaluts to 20.\n            If None, returns al the ordered repetitions.\n\n    Returns:\n        DataFrame with the duplicated records and their respective counts\n\n    Raises:\n        ValueError: if the `top_Rep` arg is not int or None.\n    \"\"\"\n    verbose = _deprecate_inform_(verbose, inform)\n\n    if top_Rep is not None and not isinstance(top_Rep, int):\n        raise ValueError('The top_Rep argument must be an int or None.')\n\n    if not isinstance(data, Series):\n        try:\n            data = Series(data)\n        except ValueError:\n            print('\\ndata must be a numpy Ndarray or a pandas Series.')\n\n    dup = data.loc[data.duplicated(keep=False)]\n    dup_count = dup.value_counts()\n\n    dup_count.index.names = ['Entries']\n    dup_count.name = 'Count'\n\n    if verbose:\n        print(f'\\nFound {len(dup_count)} duplicated entries.\\n'\n              f'The entries with the {top_Rep} highest repitition counts are:')\n        print(dup_count.head(top_Rep))\n\n    return dup_count\n\n\ndef second_order(data, test, decimals=2, sign='all', verbose=True, MAD=False,\n                 confidence=None, high_Z='pos', limit_N=None, MSE=False,\n                 show_plot=True, save_plot=None, save_plot_kwargs=None, inform=None):\n    \"\"\"Performs the chosen test after subtracting the ordered sequence by itself.\n    Hence Second Order.\n\n    Args:\n        data: sequence of numbers to be evaluated. Must be a numpy 1D array,\n            a pandas Series or a pandas DataFrame column, with values being\n            integers or floats.\n        test: the test to be performed - 1 or 'F1D': First Digit; 2 or 'F2D':\n            First Two Digits; 3 or 'F3D': First three Digits; 22 or 'SD':\n            Second Digits; -2 or 'L2D': Last Two Digits.\n        decimals: number of decimal places to consider. Defaluts to 2.\n            If integers, set to 0. If set to -infer-, it will remove the zeros\n            and consider up to the fifth decimal place to the right, but will\n            loose performance.\n        sign: tells which portion of the data to consider. pos: only the positive\n            entries; neg: only negative entries; all: all entries but zeros.\n            Defaults to all.\n        verbose (bool): tells the number of registries that are being subjected to\n            the analysis and returns tha analysis DataFrame sorted by the\n            highest Z score down. Defaults to True.\n        MAD (bool): calculates the Mean Absolute Difference between the\n            found and the expected distributions; defaults to False.\n        confidence (int, float): confidence level to draw lower and upper limits when\n            plotting and to limit the top deviations to show. Defaults to None.\n        high_Z (int): chooses which Z scores to be used when displaying results,\n            according to the confidence level chosen. Defaluts to 'pos',\n            which will highlight only values higher than the expexted\n            frequencies; 'all' will highlight both extremes (positive and\n            negative); and an integer, which will use the first n entries,\n            positive and negative, regardless of whether Z is higher than\n            the confidence or not.\n        limit_N (int): sets a limit to N as the sample size for the calculation of\n            the Z scores if the sample is too big. Defaults to None.\n        MSE (bool): calculates the Mean Square Error of the sample; defaults to\n            False.\n        chi_square: calculates the chi_square statistic of the sample and\n            compares it with a critical value, according to the confidence\n            level chosen and the series's degrees of freedom. Defaults to\n            False. Requires confidence != None.\n        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative\n            distribution of the sample with the Benford's, according to the\n            confidence level chosen. Defaults to False. Requires confidence\n            != None.\n        show_plot (bool): draws the test plot.\n        save_plot (str): string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs (dict): any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \n    Returns:\n        DataFrame of the test chosen, but applied on Second Order pre-\n            processed data.\n    \"\"\"\n    test = _check_test_(test)\n\n    verbose = _deprecate_inform_(verbose, inform)\n\n    data = Source(data, decimals=decimals, sign=sign,\n                  sec_order=True, verbose=verbose)\n    if test in [1, 2, 3]:\n        data.first_digits(digs=test, MAD=MAD,\n                          confidence=confidence, high_Z=high_Z,\n                          limit_N=limit_N, MSE=MSE, show_plot=show_plot,\n                          save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    elif test == 22:\n        data.second_digit(MAD=MAD, confidence=confidence, high_Z=high_Z,\n                          limit_N=limit_N, MSE=MSE, show_plot=show_plot,\n                          save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    else:\n        data.last_two_digits(MAD=MAD, confidence=confidence, high_Z=high_Z,\n                             limit_N=limit_N, MSE=MSE, show_plot=show_plot,\n                             save_plot=save_plot, save_plot_kwargs=save_plot_kwargs)\n    return data\n"
  },
  {
    "path": "benford/checks.py",
    "content": "from pandas import Series\nfrom numpy import array, ndarray\nfrom .constants import DIGS, REV_DIGS, CONFS\n\n\ndef _check_digs_(digs):\n    \"\"\"Checks the possible values for the digs parameter of the\n    First Digits tests\n    \"\"\"\n    if digs not in [1, 2, 3]:\n        raise ValueError(\"The value assigned to the parameter -digs- \"\n                         f\"was {digs}. Value must be 1, 2 or 3.\")\n\n\ndef _check_test_(test):\n    \"\"\"Checks the test chosen, both for int or str values\n    \"\"\"\n    if isinstance(test, int):\n        if test in DIGS.keys():\n            return test\n        else:\n            raise ValueError(f'Test was set to {test}. Should be one of '\n                             f'{DIGS.keys()}')\n    elif isinstance(test, str):\n        if test in REV_DIGS.keys():\n            return REV_DIGS[test]\n        else:\n            raise ValueError(f'Test was set to {test}. Should be one of '\n                             f'{REV_DIGS.keys()}')\n    else:\n        raise ValueError('Wrong value chosen for test parameter. Possible '\n                         f'values are\\n {list(DIGS.keys())} for ints and'\n                         f'\\n {list(REV_DIGS.keys())} for strings.')\n\n\ndef _check_decimals_(decimals):\n    \"\"\"\"\"\"\n    if isinstance(decimals, int):\n        if (decimals < 0):\n            raise ValueError(\n                \"Parameter -decimals- must be an int >= 0, or 'infer'.\")\n    else:\n        if decimals != 'infer':\n            raise ValueError(\n                \"Parameter -decimals- must be an int >= 0, or 'infer'.\")\n    return decimals\n\n\ndef _check_sign_(sign):\n    \"\"\"\"\"\"\n    if sign not in ['all', 'pos', 'neg']:\n        raise ValueError(\"Parameter -sign- must be one of the following: \"\n                         \"'all', 'pos' or 'neg'.\")\n    return sign\n\n\ndef _check_confidence_(confidence):\n    \"\"\"\"\"\"\n    if confidence not in CONFS.keys():\n        raise ValueError(\"Value of parameter -confidence- must be one of the \"\n                         f\"following:\\n {list(CONFS.keys())}\")\n    return confidence\n\n\ndef _check_high_Z_(high_Z):\n    \"\"\"\"\"\"\n    if not high_Z in ['pos', 'all']:\n        if not isinstance(high_Z, int):\n            raise ValueError(\"The parameter -high_Z- should be 'pos', \"\n                             \"'all' or an int.\")\n    return high_Z\n\n\ndef _check_num_array_(data):\n    \"\"\"\"\"\"\n    if (not isinstance(data, ndarray)) & (not isinstance(data, Series)):\n        print('\\n`data` not a numpy NDarray nor a pandas Series.'\n              ' Trying to convert...')\n        try:\n            data = array(data)\n        except:\n            raise ValueError('Could not convert data. Check input.')\n        print('\\nConversion successful.')\n\n        try:\n            data = data.astype(float)\n        except:\n            raise ValueError('Could not convert data. Check input.')\n    else:\n        if data.dtype not in [int, float]:\n            try:\n                data = data.astype(float)\n            except:\n                raise ValueError('Could not convert data. Check input.')\n    return data\n"
  },
  {
    "path": "benford/constants.py",
    "content": "DIGS = {1: 'F1D', 2: 'F2D', 3: 'F3D', 22: 'SD', -2: 'L2D'}\n\nSEC_ORDER_DIGS = {key: f'{val}_sec' for key, val in DIGS.items()}\n\nREV_DIGS = {'F1D': 1, 'F2D': 2, 'F3D': 3, 'SD': 22, 'L2D': -2}\n\nLEN_TEST = {1: 9, 2: 90, 3: 900, 22: 10, -2: 100}\n\nTEST_NAMES = {'F1D': 'First Digit Test', 'F2D': 'First Two Digits Test',\n         'F3D': 'First Three Digits Test', 'SD': 'Second Digit Test',\n         'L2D': 'Last Two Digits Test',\n         'F1D_sec': 'First Digit Second Order Test',\n         'F2D_sec': 'First Two Digits Second Order Test',\n         'F3D_sec': 'First Three Digits Second Order Test',\n         'SD_sec': 'Second Digit Second Order Test',\n         'L2D_sec': 'Last Two Digits Second Order Test',\n         'F1D_Summ': 'First Digit Summation Test',\n         'F2D_Summ': 'First Two Digits Summation Test',\n         'F3D_Summ': 'First Three Digits Summation Test',\n         'Mantissas': 'Mantissas Test'\n         }\n\n# Critical values for Mean Absolute Deviation\nMAD_CONFORM = {1: [0.006, 0.012, 0.015], 2: [0.0012, 0.0018, 0.0022],\n            3: [0.00036, 0.00044, 0.00050], 22: [0.008, 0.01, 0.012],\n            -2: None, 'F1D': 'First Digit', 'F2D': 'First Two Digits',\n            'F3D': 'First Three Digits', 'SD': 'Second Digits'}\n\n# Color for the plotting\nCOLORS = {'m': '#00798c', 'b': '#E2DCD8', 's': '#9c3848',\n          'af': '#edae49', 'ab': '#33658a', 'h': '#d1495b',\n          'h2': '#f64740', 't': '#16DB93'}\n\n# Critical Z-scores according to the confindence levels\nCONFS = {None: None, 80: 1.285, 85: 1.435, 90: 1.645, 95: 1.96,\n         99: 2.576, 99.9: 3.29, 99.99: 3.89, 99.999: 4.417,\n         99.9999: 4.892, 99.99999: 5.327}\n\nP_VALUES = {None: 'None', 80: '0.2', 85: '0.15', 90: '0.1', 95: '0.05',\n            99: '0.01', 99.9: '0.001', 99.99: '0.0001', 99.999: '0.00001',\n            99.9999: '0.000001', 99.99999: '0.0000001'}\n\n# Critical Chi-Square values according to the tests degrees of freedom\n# and confidence levels\nCRIT_CHI2 = {8: {80: 11.03, 85: 12.027, 90: 13.362, 95: 15.507,\n                 99: 20.090, 99.9: 26.124, 99.99: 31.827, None: None,\n                 99.999: 37.332, 99.9999: 42.701, 99.99999: 47.972},\n             9: {80: 12.242, 85: 13.288, 90: 14.684, 95: 16.919,\n                 99: 21.666, 99.9: 27.877, 99.99: 33.72, None: None,\n                 99.999: 39.341, 99.9999: 44.811, 99.99999: 50.172},\n             89: {80: 99.991, 85: 102.826, 90: 106.469, 95: 112.022,\n                  99: 122.942, 99.9: 135.978, 99.99: 147.350,\n                  99.999: 157.702, 99.9999: 167.348, 99.99999: 176.471,\n                  None: None},\n             99: {80: 110.607, 85: 113.585, 90: 117.407,\n                  95: 123.225, 99: 134.642, 99.9: 148.230,\n                  99.99: 160.056, 99.999: 170.798, 99.9999: 180.792,\n                  99.99999: 190.23, None: None},\n             899: {80: 934.479, 85: 942.981, 90: 953.752, 95: 969.865,\n                   99: 1000.575, 99.9: 1035.753, 99.99: 1065.314,\n                   99.999: 1091.422, 99.9999: 1115.141,\n                   99.99999: 1137.082, None: None}\n             }\n\n# Critical Kolmogorov-Smirnov values according to the confidence levels\n# These values are yet to be divided by the square root of the sample size\nCRIT_KS = {80: 1.073, 85: 1.138, 90: 1.224, 95: 1.358, 99: 1.628,\n           99.9: 1.949, 99.99: 2.225, 99.999: 2.47,\n           99.9999: 2.693, 99.99999: 2.899, None: None}\n"
  },
  {
    "path": "benford/expected.py",
    "content": "from pandas import DataFrame\nfrom numpy import array, arange, log10\nfrom .checks import _check_digs_\nfrom .viz import plot_expected\n\n\nclass First(DataFrame):\n    \"\"\"Holds the expected probabilities of the First, First Two, or\n    First Three digits according to Benford's distribution.\n\n    Args:\n        digs: 1, 2 or 3 - tells which of the first digits to consider:\n            1 for the First Digit, 2 for the First Two Digits and 3 for\n            the First Three Digits.\n        plot: option to plot a bar chart of the Expected proportions.\n            Defaults to True.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \"\"\"\n\n    def __init__(self, digs, plot=True, save_plot=None, save_plot_kwargs=None):\n        _check_digs_(digs)\n        dig_name = f'First_{digs}_Dig'\n        exp_array, dig_array = _gen_first_digits_(digs)\n \n        DataFrame.__init__(self, {'Expected': exp_array}, index=dig_array)\n        self.index.names = [dig_name]\n\n        if plot:\n            plot_expected(self, digs, save_plot=save_plot,\n                          save_plot_kwargs=save_plot_kwargs)\n\n\nclass Second(DataFrame):\n    \"\"\"Holds the expected probabilities of the Second Digits\n    according to Benford's distribution.\n\n    Args:\n        plot: option to plot a bar chart of the Expected proportions.\n            Defaults to True.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \"\"\"\n    def __init__(self, plot=True, save_plot=None, save_plot_kwargs=None):\n\n        exp, sec_digs = _gen_second_digits_()\n\n        DataFrame.__init__(self, {'Expected': exp, 'Sec_Dig': sec_digs})\n        self.set_index(\"Sec_Dig\", inplace=True)\n\n        if plot:\n            plot_expected(self, 22, save_plot=save_plot,\n                          save_plot_kwargs=save_plot_kwargs)\n\n\nclass LastTwo(DataFrame):\n    \"\"\"Holds the expected probabilities of the Last Two Digits\n    according to Benford's distribution.\n\n    Args:\n        plot: option to plot a bar chart of the Expected proportions.\n            Defaults to True.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension. Only available when\n            plot=True.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n            Only available when plot=True and save_plot is a string with the\n            figure file path/name.\n    \"\"\"\n    def __init__(self, num=False, plot=True, save_plot=None, save_plot_kwargs=None):\n        exp, l2d = _gen_last_two_digits_(num=num)\n        DataFrame.__init__(self, {'Expected': exp,\n                                  'Last_2_Dig': l2d})\n        self.set_index('Last_2_Dig', inplace=True)\n        if plot:\n            plot_expected(self, -2, save_plot=save_plot,\n                          save_plot_kwargs=save_plot_kwargs)\n\n\ndef _get_expected_digits_(digs):\n    \"\"\"Chooses the Exxpected class to be used in a test\n\n    Args:\n        digs: the int corresponding to the Expected class to be instantiated\n\n    Returns:\n        the Expected instance forthe propoer test to be performed\n    \"\"\"\n    if digs in [1, 2, 3]:\n        return First(digs, plot=False)\n    elif digs == 22:\n        return Second(plot=False)\n    else:\n        return LastTwo(num=True, plot=False)\n\n\ndef _gen_last_two_digits_(num=False):\n    \"\"\"Creates two arrays, one with the possible last two digits and one with\n    thei respective probabilities\n\n    Args:\n        num: returns numeric (ints) values. Defaluts to False,\n            which returns strings.\n\n    Returns:\n        exp (np.array): Array with the (constant) probabilities of occurrence of\n            each pair of last two digits \n        l2d (np.array): Array of ints or str, in any case representing all 100\n            possible combinations of last two digits\n    \"\"\"\n    exp = array([1 / 99.] * 100)\n    l2d = arange(0, 100)\n    if num:\n        return exp, l2d\n    l2d = l2d.astype(str)\n    l2d[:10] = array(['00', '01', '02', '03', '04', '05',\n                    '06', '07', '08', '09'])\n    return exp, l2d\n\ndef _gen_first_digits_(digs):\n    \"\"\"Creates two arrays, one with the possible digits combinations and the\n    other with their respective expected probabilities according to Benford\n\n    Args:\n        digs (int): 1, 2 or 3, for generation of the first, first two, or first\n            three digits\n\n    Returns:\n        (tuple of arrays): the expected probabilities array and the digits\n            combination array. \n    \"\"\"\n    dig_array = arange(10 ** (digs - 1), 10 ** digs)\n    exp_prob = log10(1 + (1. / dig_array))\n    return exp_prob, dig_array\n\ndef _gen_second_digits_():\n    \"\"\"Creates two arrays, one with he possible second digits combinations and\n    the other with their respective expected probabilities according to Benford\n\n    Returns:\n        (tuple of arrays): the expected probabilities array and the second\n        digits array.\n    \"\"\"\n    exp_f2d, _ = _gen_first_digits_(2)\n    sec_digs = range(10)\n    sec_digs_in_f2d = array(list(range(10)) * 9)\n    exp = array([exp_f2d[sec_digs_in_f2d == i].sum() for i in sec_digs])\n    return exp, array(sec_digs)"
  },
  {
    "path": "benford/reports.py",
    "content": "import warnings\nfrom .constants import MAD_CONFORM\n\n\ndef _inform_(df, high_Z, conf):\n    \"\"\"Selects and sorts by the Z_stats chosen to be considered, informing or not.\n    \"\"\"\n\n    if isinstance(high_Z, int):\n        if conf is not None:\n            dd = df[['Expected', 'Found', 'Z_score'\n                     ]].sort_values('Z_score', ascending=False).head(high_Z)\n            print(f'\\nThe entries with the top {high_Z} Z scores are:\\n')\n        # Summation Test\n        else:\n            dd = df[['Expected', 'Found', 'AbsDif'\n                     ]].sort_values('AbsDif', ascending=False\n                                    ).head(high_Z)\n            print(f'\\nThe entries with the top {high_Z} absolute deviations '\n                  'are:\\n')\n    else:\n        if high_Z == 'pos':\n            m1 = df.Dif > 0\n            m2 = df.Z_score > conf\n            dd = df[['Expected', 'Found', 'Z_score'\n                     ]].loc[m1 & m2].sort_values('Z_score', ascending=False)\n            print('\\nThe entries with the significant positive '\n                  'deviations are:\\n')\n        elif high_Z == 'neg':\n            m1 = df.Dif < 0\n            m2 = df.Z_score > conf\n            dd = df[['Expected', 'Found', 'Z_score'\n                     ]].loc[m1 & m2].sort_values('Z_score', ascending=False)\n            print('\\nThe entries with the significant negative '\n                  'deviations are:\\n')\n        else:\n            dd = df[['Expected', 'Found', 'Z_score'\n                     ]].loc[df.Z_score > conf].sort_values('Z_score',\n                                                           ascending=False)\n            print('\\nThe entries with the significant deviations are:\\n')\n    print(dd)\n\n\ndef _report_mad_(digs, MAD):\n    \"\"\"Reports the test Mean Absolut Deviation and compares it to critical values\n    \"\"\"\n    print(f'Mean Absolute Deviation: {MAD:.6f}')\n    if digs != -2:\n        mads = MAD_CONFORM[digs]\n        if MAD <= mads[0]:\n            print(f'MAD <= {mads[0]:.6f}: Close conformity.\\n')\n        elif MAD <= mads[1]:\n            print(f'{mads[0]:.6f} < MAD <= {mads[1]:.6f}: '\n                  'Acceptable conformity.\\n')\n        elif MAD <= mads[2]:\n            print(f'{mads[1]:.6f} < MAD <= {mads[2]:.6f}: '\n                  'Marginally Acceptable conformity.\\n')\n        else:\n            print(f'MAD > {mads[2]:.6f}: Nonconformity.\\n')\n    else:\n        print(\"There is no conformity check for this test's MAD.\\n\")\n\n\ndef _report_KS_(KS, crit_KS):\n    \"\"\"Reports the test Kolmogorov-Smirnov statistic and compares it to critical\n    values, depending on the confidence level\n    \"\"\"\n    result = 'PASS' if KS <= crit_KS else 'FAIL'\n    print(f\"\\n\\tKolmogorov-Smirnov: {KS:.6f}\",\n          f\"\\n\\tCritical value: {crit_KS:.6f} -- {result}\")\n\n\ndef _report_chi2_(chi2, CRIT_CHI2):\n    \"\"\"Reports the test Chi-square statistic and compares it to critical values,\n    depending on the confidence level\n    \"\"\"\n    result = 'PASS' if chi2 <= CRIT_CHI2 else 'FAIL'\n    print(f\"\\n\\tChi square: {chi2:.6f}\",\n          f\"\\n\\tCritical value: {CRIT_CHI2:.6f} -- {result}\")\n\n\ndef _report_Z_(df, high_Z, crit_Z):\n    \"\"\"Reports the test Z scores and compares them to a critical value,\n    depending on the confidence level\n    \"\"\"\n    print(f\"\\n\\tCritical Z-score:{crit_Z}.\")\n    _inform_(df, high_Z, crit_Z)\n\n\ndef _report_summ_(test, high_diff):\n    \"\"\"Reports the Summation Test Absolute Differences between the Found and\n    the Expected proportions\n\n    \"\"\"\n    if high_diff is not None:\n        print(f'\\nThe top {high_diff} Absolute Differences are:\\n')\n        print(test.sort_values('AbsDif', ascending=False).head(high_diff))\n    else:\n        print('\\nThe top Absolute Differences are:\\n')\n        print(test.sort_values('AbsDif', ascending=False))\n\n\ndef _report_bhattac_coeff_(bhattac_coeff):\n    \"\"\"\n    \"\"\"\n    print(f\"Bhattacharyya Coefficient: {bhattac_coeff:6f}\\n\")\n\n\ndef _report_bhattac_dist_(bhattac_dist):\n    \"\"\"\n    \"\"\"\n    print(f\"Bhattacharyya Distance: {bhattac_dist:6f}\\n\")\n\n\ndef _report_kl_diverg_(kl_diverg):\n    \"\"\"\n    \"\"\"\n    print(f\"Kullback-Leibler Divergence: {kl_diverg:6f}\\n\")\n\n\ndef _report_test_(test, high=None, crit_vals=None):\n    \"\"\"Main report function. Receives the Args: to report with, initiates\n    the process, and calls the right reporting helper function(s), depending\n    on the Test.\n    \"\"\"\n    print('\\n', f'  {test.name}  '.center(50, '#'), '\\n')\n    if not 'Summation' in test.name:\n        _report_mad_(test.digs, test.MAD)\n        _report_bhattac_coeff_(test.bhattacharyya_coefficient)\n        _report_bhattac_dist_(test.bhattacharyya_distance)\n        _report_kl_diverg_(test.kullback_leibler_divergence)\n        if test.confidence is not None:\n            print(f\"For confidence level {test.confidence}%: \")\n            _report_KS_(test.KS, crit_vals['KS'])\n            _report_chi2_(test.chi_square, crit_vals['chi2'])\n            _report_Z_(test, high, crit_vals['Z'])\n        else:\n            print('Confidence is currently `None`. Set the confidence level, '\n                  'so as to generate comparable critical values.')\n            if isinstance(high, int):\n                _inform_(test, high, None)\n    else:\n        _report_summ_(test, high)\n\n\ndef _report_mantissa_(stats, confidence):\n    \"\"\"Prints the mantissas statistics and their respective reference values\n\n    Args:\n        stats (dict): \n    \"\"\"\n    print(\"\\n\", '  Mantissas Test  '.center(52, '#'))\n    print(f\"\\nThe Mantissas MEAN is      {stats['Mean']:.6f}.\"\n          \"\\tRef: 0.5\")\n    print(f\"The Mantissas VARIANCE is  {stats['Var']:.6f}.\"\n          \"\\tRef: 0.08333\")\n    print(f\"The Mantissas SKEWNESS is  {stats['Skew']:.6f}.\"\n          \"\\tRef: 0.0\")\n    print(f\"The Mantissas KURTOSIS is  {stats['Kurt']:.6f}.\"\n          \"\\tRef: -1.2\")\n    print(\"\\nThe Kolmogorov-Smirnov statistic for the Mantissas distribution\"\n          f\" is {stats['KS']:.6f}.\\nThe critical value for the confidence \"\n          f\"level of {confidence}% is {stats['KS_critical']:.6f} -- \"\n          f\"{'PASS' if stats['KS'] < stats['KS_critical'] else 'FAIL'}\\n\")\n\n\ndef _deprecate_inform_(verbose, inform):\n    \"\"\"\n    Raises:\n        FutureWarning: if the arg `inform` is used (to be deprecated).    \n    \"\"\"\n    if inform is None:\n        return verbose\n    else:\n        warnings.warn('The parameter `inform` will be deprecated in future '\n                      'versions. Use `verbose` instead.',\n                      FutureWarning)\n        return inform\n"
  },
  {
    "path": "benford/stats.py",
    "content": "from numpy import abs as nabs, errstate, linspace, log, sqrt, where\nfrom .constants import CRIT_CHI2, CRIT_KS, MAD_CONFORM, DIGS\n\n\ndef Z_score(frame, N):\n    \"\"\"Computes the Z statistics for the proportions studied\n\n    Args:\n        frame: DataFrame with the expected proportions and the already calculated\n            Absolute Diferences between the found and expeccted proportions\n        N: sample size\n\n    Returns:\n        Series of computed Z scores\n    \"\"\"\n    return (frame.AbsDif - (1 / (2 * N))) / sqrt(\n           (frame.Expected * (1. - frame.Expected)) / N)\n\n\ndef chi_sq(frame, ddf, confidence, verbose=True):\n    \"\"\"Comnputes the chi-square statistic of the found distributions and compares\n    it with the critical chi-square of such a sample, according to the\n    confidence level chosen and the degrees of freedom - len(sample) -1.\n\n    Args:\n        frame: DataFrame with Found, Expected and their difference columns.\n        ddf: Degrees of freedom to consider.\n        confidence: Confidence level to look up critical value.\n        verbose: prints the chi-squre result and compares to the critical\n            chi-square for the sample. Defaults to True.\n\n    Returns:\n        The computed Chi square statistic and the critical chi square\n            (according) to the degrees of freedom and confidence level,\n            for comparison. None if confidence is None\n    \"\"\"\n    if confidence is None:\n        print('\\nChi-square test needs confidence other than None.')\n        return\n    else:\n        exp_counts = frame.Counts.sum() * frame.Expected\n        dif_counts = frame.Counts - exp_counts\n        found_chi = (dif_counts ** 2 / exp_counts).sum()\n        crit_chi = CRIT_CHI2[ddf][confidence]\n        if verbose:\n            print(f\"\\nThe Chi-square statistic is {found_chi:.4f}.\\n\"\n                  f\"Critical Chi-square for this series: {crit_chi}.\")\n        return (found_chi, crit_chi)\n\n\ndef chi_sq_2(frame):\n    \"\"\"Computes the chi-square statistic of the found distributions\n\n    Args:\n        frame: DataFrame with Found, Expected and their difference columns.\n\n    Returns:\n        The computed Chi square statistic \n    \"\"\"\n    exp_counts = frame.Counts.sum() * frame.Expected\n    dif_counts = frame.Counts - exp_counts\n    return (dif_counts ** 2 / exp_counts).sum()\n\n\ndef kolmogorov_smirnov(frame, confidence, N, verbose=True):\n    \"\"\"Computes the Kolmogorov-Smirnov test of the found distributions\n    and compares it with the critical chi-square of such a sample,\n    according to the confidence level chosen.\n\n    Args:\n        frame: DataFrame with Foud and Expected distributions.\n        confidence: Confidence level to look up critical value.\n        N: Sample size\n        verbose: prints the KS result and the critical value for the sample.\n            Defaults to True.\n\n    Returns:\n        The Suprem, which is the greatest absolute difference between the\n            Found and the expected proportions, and the Kolmogorov-Smirnov\n            critical value according to the confidence level, for ccomparison\n    \"\"\"\n    if confidence is None:\n        print('\\nKolmogorov-Smirnov test needs confidence other than None.')\n        return\n    else:\n        # sorting and calculating the cumulative distribution\n        ks_frame = frame.sort_index()[['Found', 'Expected']].cumsum()\n        # finding the supremum - the largest cumul dist difference\n        suprem = ((ks_frame.Found - ks_frame.Expected).abs()).max()\n        # calculating the crittical value according to confidence\n        crit_KS = CRIT_KS[confidence] / sqrt(N)\n\n        if verbose:\n            print(f\"\\nThe Kolmogorov-Smirnov statistic is {suprem:.4f}.\\n\"\n                  f\"Critical K-S for this series: {crit_KS:.4f}\")\n        return (suprem, crit_KS)\n\n\ndef kolmogorov_smirnov_2(frame):\n    \"\"\"Computes the Kolmogorov-Smirnov test of the found distributions\n\n    Args:\n        frame: DataFrame with Foud and Expected distributions.\n\n    Returns:\n        The Suprem, which is the greatest absolute difference between the\n            Found end th expected proportions\n    \"\"\"\n    # sorting and calculating the cumulative distribution\n    ks_frame = frame.sort_index()[['Found', 'Expected']].cumsum()\n    # finding the supremum - the largest cumul dist difference\n    return ((ks_frame.Found - ks_frame.Expected).abs()).max()\n\n\ndef _two_dist_ks_(dist1, dist2, cummulative=True):\n    \"\"\"Computes the Kolmogorov-Smirnov statistic between two distributions,\n    a found one (dist2) and an expected one (dist1).\n\n    Args:\n        dist1 (np.arrat): array with the expected distribution\n        dist2 (np.array): array with the found distribution\n        cummulative (bool): makes apply cummulutative sum to the\n            distributions (empirical cdf).\n\n    Returns:\n        tuple(floats): the KS statistic \n    \"\"\"\n    dist2.sort(); dist1.sort()\n    if not cummulative:\n        return nabs(dist2 - dist1).max()\n    return nabs(dist2.cumsum() - dist1.cumsum()).max()\n\n\ndef _mantissas_ks_(mant_dist, confidence, sample_size):\n    \"\"\"Computes the Kolmogorov-Smirnof statistic for the Mantissas, also\n    providing the KS critical value according the the sample size and\n    confidence level provided\n\n    Args:\n        mant_dist (np.array): array with the mantissas distribution found\n        confidence (float, int): level of confidence to compute the critical\n            value\n\n    Returns:\n        tuple(floats): the KS statistic and the critical value\n    \"\"\" \n    crit_ks = CRIT_KS[confidence] * sqrt(2 * sample_size / sample_size ** 2)\\\n                if confidence else None\n    # non-cummulative, uniformly distributed\n    expected = linspace(0, 1, len(mant_dist), endpoint=False)\n    ks = _two_dist_ks_(expected, mant_dist, cummulative=False)\n    return ks, crit_ks\n\n\ndef mad(frame, test, verbose=True):\n    \"\"\"Computes the Mean Absolute Deviation (MAD) between the found and the\n    expected proportions.\n\n    Args:\n        frame: DataFrame with the Absolute Deviations already calculated.\n        test: Test to compute the MAD from (F1D, SD, F2D...)\n        verbose: prints the MAD result and compares to limit values of\n            conformity. Defaults to True.\n\n    Returns:\n        The Mean of the Absolute Deviations between the found and expected\n            proportions. \n    \"\"\"\n    mad = frame.AbsDif.mean()\n\n    if verbose:\n        print(f\"\\nThe Mean Absolute Deviation is {mad}\")\n\n        if test != -2:\n            print(f\"For the {MAD_CONFORM[DIGS[test]]}:\\n\\\n            - 0.0000 to {MAD_CONFORM[test][0]}: Close Conformity\\n\\\n            - {MAD_CONFORM[test][0]} to {MAD_CONFORM[test][1]}: Acceptable Conformity\\n\\\n            - {MAD_CONFORM[test][1]} to {MAD_CONFORM[test][2]}: Marginally Acceptable Conformity\\n\\\n            - Above {MAD_CONFORM[test][2]}: Nonconformity\")\n        else:\n            pass\n    return mad\n\n\ndef mse(frame, verbose=True):\n    \"\"\"Computes the test's Mean Square Error\n\n    Args:\n        frame: DataFrame with the already computed Absolute Deviations between\n            the found and expected proportions\n        verbose: Prints the MSE. Defaults to True.\n\n    Returns:\n        Mean of the squared differences between the found and the expected proportions.\n    \"\"\"\n    mse = (frame.AbsDif ** 2).mean()\n\n    if verbose:\n        print(f\"\\nMean Square Error = {mse}\")\n\n    return mse\n\ndef _bhattacharyya_coefficient(dist_1, dist_2):\n    \"\"\"Computes the Bhattacharyya Coeficient between two probability\n    distributions, to be letar used to compute the Bhattacharyya Distance\n\n    Args:\n        dist_1 (np.array): The newly gathered distribution, to be compared\n            with an older / established distribution.\n        dist_2 (np.array): The older/ establhished distribution with which\n            the new one will be compared. \n    \n    Returns:\n        bhat_coef (float)\n    \"\"\"\n    return sqrt(dist_1 * dist_2).sum()\n\n\ndef _bhattacharyya_distance_(dist_1, dist_2):\n    \"\"\"Computes the Bhattacharyya Dsitance between two probability\n    distributions\n\n    Args:\n        dist_1 (np.array): The newly gathered distribution, to be compared\n            with an older / established distribution.\n        dist_2 (np.array): The older/ establhished distribution with which\n            the new one will be compared. \n    \n    Returns:\n        bhat_dist (float)\n    \"\"\"\n    with errstate(divide='ignore'):\n        bhat_dist =  -log(_bhattacharyya_coefficient(dist_1, dist_2))\n    return bhat_dist\n\n\ndef _kullback_leibler_divergence_(dist_1, dist_2):\n    \"\"\"Computes the Kullback-Leibler Divergence between two probability\n    distributions.\n\n    Args:\n        dist_1 (np.array): The newly gathered distribution, to be compared\n            with an older / established distribution.\n        dist_2 (np.array): The older/ establhished distribution with which\n            the new one will be compared. \n\n    Returns:\n        kulb_leib_diverg (float)        \n    \"\"\"\n    # ignore divide by zero warning in np.where\n    with errstate(divide='ignore'):\n        kl_d = (log((dist_1 / dist_2), where=(dist_1 != 0)) * dist_1).sum()\n    return kl_d\n"
  },
  {
    "path": "benford/utils.py",
    "content": "from pandas import Series, DataFrame\nfrom numpy import array, arange, log10, ndarray\nfrom .expected import _get_expected_digits_\nfrom .constants import DIGS, REV_DIGS\nfrom .stats import Z_score\nfrom .checks import _check_num_array_, _check_sign_, _check_decimals_\n\n\ndef _set_N_(len_df, limit_N):\n    \"\"\"\"\"\"\n    # Assigning to N the superior limit or the lenght of the series\n    if limit_N is None or limit_N > len_df:\n        return max(1, len_df)\n    # Check on limit_N being a positive integer\n    else:\n        if limit_N < 0 or not isinstance(limit_N, int):\n            raise ValueError(\"limit_N must be None or a positive integer.\")\n        else:\n            return max(1, limit_N)\n\n\ndef get_mantissas(arr):\n    \"\"\"Computes the mantissas, the non-integer part of the log of a number.\n\n    Args:\n        arr: array of integers or floats\n\n    Returns:\n        Array of floats withe logs mantissas\n    \"\"\"\n    log_a = abs(log10(arr))\n    return log_a - log_a.astype(int)  # the number - its integer part\n\n\ndef input_data(given):\n    \"\"\"Internalizes and transforms the input data\n\n    Args:\n        given: ndarray, Series or tuple with DataFrame and name of the\n            column to analyze\n\n    Returns:\n        The raw inputed data and the result of its first pre-processing,\n            when required.\n    \"\"\"\n    if type(given) == Series:\n        data = chosen = given\n    elif type(given) == ndarray:\n        data = given\n        chosen = Series(given)\n    elif type(given) == tuple:\n        if (type(given[0]) != DataFrame) | (type(given[1]) != str):\n            raise TypeError('The data tuple must be composed of a pandas '\n                            'DataFrame and the name (str) of the chosen '\n                            'column, in that order')\n        data = given[0]\n        chosen = given[0][given[1]]\n    else:\n        raise TypeError(\"Wrong data input type. Check docstring.\")\n    return data, chosen\n\n\ndef set_sign(data, sign=\"all\"):\n    \"\"\"\n    \"\"\"\n    sign = _check_sign_(sign)\n\n    if sign == 'all':\n        data.seq = data.seq.loc[data.seq != 0]\n    elif sign == 'pos':\n        data.seq = data.seq.loc[data.seq > 0]\n    else:\n        data.seq = data.seq.loc[data.seq < 0]\n\n    return data.dropna()\n\n\ndef get_times_10_power(data, decimals=2):\n    \"\"\"\"\"\"\n    decimals = _check_decimals_(decimals)\n\n    ab = data.seq.abs()\n\n    if data.seq.dtype == 'int':\n        data['ZN'] = ab\n    else:\n        if decimals == 'infer':\n            data['ZN'] = ab.astype(str).str\\\n                .replace('.', '', regex=False)\\\n                .str.lstrip('0')\\\n                .str[:5].astype(int)\n        else:\n            data['ZN'] = (ab * (10 ** decimals)).astype(int)\n    return data\n\n\ndef get_digs(data, decimals=2, sign=\"all\"):\n    \"\"\" \n    \"\"\"\n    df = DataFrame({'seq': _check_num_array_(data)})\n\n    df = set_sign(df, sign=sign)\n\n    df = get_times_10_power(df, decimals=decimals)\n\n    # First digits\n    for col in ['F1D', 'F2D', 'F3D']:\n        temp = df.ZN.loc[df.ZN >= 10 ** (REV_DIGS[col] - 1)]\n        df[col] = (temp // 10 ** ((log10(temp).astype(int)) -\n                                  (REV_DIGS[col] - 1)))\n        # fill NANs with -1, which is a non-usable value for digits,\n        # to be discarded later.\n        df[col] = df[col].fillna(-1).astype(int)\n    # Second digit\n    temp_sd = df.loc[df.ZN >= 10]\n    df['SD'] = (temp_sd.ZN // 10**((log10(temp_sd.ZN)).astype(int) -\n                                   1)) % 10\n    df['SD'] = df['SD'].fillna(-1).astype(int)\n    # Last two digits\n    temp_l2d = df.loc[df.ZN >= 1000]\n    df['L2D'] = temp_l2d.ZN % 100\n    df['L2D'] = df['L2D'].fillna(-1).astype(int)\n    return df\n\n\ndef get_found_proportions(data):\n    \"\"\"\n    \"\"\"\n    counts = data.value_counts()\n    # get their relative frequencies\n    proportions = data.value_counts(normalize=True)\n    # crate dataframe from them\n    return DataFrame({'Counts': counts, 'Found': proportions}).sort_index()\n\n\ndef join_expect_found_diff(data, digs):\n    \"\"\"\n    \"\"\"\n    dd =_get_expected_digits_(digs).join(data).fillna(0)\n    # create column with absolute differences\n    dd['Dif'] = dd.Found - dd.Expected\n    dd['AbsDif'] = dd.Dif.abs()\n    return dd\n\n\ndef prepare(data, digs, limit_N=None, simple=False):\n    \"\"\"Transforms the original number sequence into a DataFrame reduced\n    by the ocurrences of the chosen digits, creating other computed\n    columns\n    \"\"\"\n    df = get_found_proportions(data)\n    dd = join_expect_found_diff(df, digs)\n    if simple:\n        del dd['Dif']\n        return dd\n    else:\n        N = _set_N_(len(data), limit_N=limit_N)\n        dd['Z_score'] = Z_score(dd, N)\n        return N, dd\n\n\ndef subtract_sorted(data):\n    \"\"\"Subtracts the sorted sequence elements from each other, discarding zeros.\n    Used in the Second Order test\n    \"\"\"\n    temp = data.copy().sort_values(ignore_index=True)\n    temp = (temp - temp.shift(1)).dropna()\n    return temp.loc[temp != 0]\n\n\ndef prep_to_roll(start, test):\n    \"\"\"Used by the rolling mad and rolling mean, prepares each test and\n    respective expected proportions for later application to the Series subset\n    \"\"\"\n    if test in [1, 2, 3]:\n        start[DIGS[test]] = start.ZN // 10 ** ((\n            log10(start.ZN).astype(int)) - (test - 1))\n        start = start.loc[start.ZN >= 10 ** (test - 1)]\n\n        ind = arange(10 ** (test - 1), 10 ** test)\n        Exp = log10(1 + (1. / ind))\n\n    elif test == 22:\n        start[DIGS[test]] = (start.ZN // 10 ** ((\n            log10(start.ZN)).astype(int) - 1)) % 10\n        start = start.loc[start.ZN >= 10]\n\n        Expec = log10(1 + (1. / arange(10, 100)))\n        temp = DataFrame({'Expected': Expec, 'Sec_Dig':\n                          array(list(range(10)) * 9)})\n        Exp = temp.groupby('Sec_Dig').sum().values.reshape(10,)\n        ind = arange(0, 10)\n\n    else:\n        start[DIGS[test]] = start.ZN % 100\n        start = start.loc[start.ZN >= 1000]\n\n        ind = arange(0, 100)\n        Exp = array([1 / 99.] * 100)\n\n    return Exp, ind\n\n\ndef mad_to_roll(arr, Exp, ind):\n    \"\"\"Mean Absolute Deviation used in the rolling function\n    \"\"\"\n    prop = arr.value_counts(normalize=True).sort_index()\n\n    if len(prop) < len(Exp):\n        prop = prop.reindex(ind).fillna(0)\n\n    return abs(prop - Exp).mean()\n\n\ndef mse_to_roll(arr, Exp, ind):\n    \"\"\"Mean Squared Error used in the rolling function\n    \"\"\"\n    temp = arr.value_counts(normalize=True).sort_index()\n\n    if len(temp) < len(Exp):\n        temp = temp.reindex(ind).fillna(0)\n\n    return ((temp - Exp) ** 2).mean()\n"
  },
  {
    "path": "benford/viz.py",
    "content": "from numpy import array, arange, maximum, sqrt, ones\nimport matplotlib.pyplot as plt\nfrom matplotlib.text import Annotation\nfrom .constants import COLORS, MAD_CONFORM\n\n\ndef plot_expected(df, digs, save_plot=None, save_plot_kwargs=None):\n    \"\"\"Plots the Expected Benford Distributions\n\n    Args:\n        df: DataFrame with the Expected Proportions\n        digs: Test's digit\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n    \"\"\"\n    if digs in [1, 2, 3]:\n        y_max = (df.Expected.max() + (10 ** -(digs) / 3)) * 100\n        figsize = 2 * (digs ** 2 + 5), 1.5 * (digs ** 2 + 5)\n    elif digs == 22:\n        y_max = 13.\n        figsize = 14, 10.5\n    elif digs == -2:\n        y_max = 1.1\n        figsize = 15, 8\n    fig, ax = plt.subplots(figsize=figsize)\n    plt.title('Expected Benford Distributions', size='xx-large')\n    plt.xlabel(df.index.name, size='x-large')\n    plt.ylabel('Distribution (%)', size='x-large')\n    ax.set_facecolor(COLORS['b'])\n    ax.set_ylim(0, y_max)\n    ax.bar(df.index, df.Expected * 100, color=COLORS['t'], align='center')\n    ax.set_xticks(df.index)\n    ax.set_xticklabels(df.index)\n\n    if save_plot:\n        if not save_plot_kwargs:\n            save_plot_kwargs = {}\n        plt.savefig(save_plot, **save_plot_kwargs)\n\n    plt.show(block=False)\n\n\ndef _get_plot_args(digs):\n    \"\"\"Selects the correct arguments for the plotting functions, depending on the\n    the test (digs) chosen.\n    \"\"\"\n    if digs in [1, 2, 3]:\n        text_x = False\n        n, m = 10 ** (digs - 1), 10 ** (digs)\n        x = arange(n, m)\n        figsize = (2 * (digs ** 2 + 5), 1.5 * (digs ** 2 + 5))\n    elif digs == 22:\n        text_x = False\n        x = arange(10)\n        figsize = (14, 10)\n    else:\n        text_x = True\n        x = arange(100)\n        figsize = (15, 7)\n    return x, figsize, text_x\n\ndef plot_digs(df, x, y_Exp, y_Found, N, figsize, conf_Z, text_x=False,\n              save_plot=None, save_plot_kwargs=None):\n    \"\"\"Plots the digits tests results\n\n    Args:\n        df: DataFrame with the data to be plotted\n        x: sequence to be used in the x axis\n        y_Exp: sequence of the expected proportions to be used in the y axis\n            (line)\n        y_Found: sequence of the found proportions to be used in the y axis\n            (bars)\n        N: lenght of sequence, to be used when plotting the confidence levels\n        figsize: tuple to state the size of the plot figure\n        conf_Z: Confidence level\n        save_pic: file path to save figure\n        text_x: Forces to show all x ticks labels. Defaluts to True.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n        \n    \"\"\"\n    if len(x) > 10:\n        rotation = 90\n    else:\n        rotation = 0\n    fig, ax = plt.subplots(figsize=figsize)\n    plt.title('Expected vs. Found Distributions', size='xx-large')\n    plt.xlabel('Digits', size='x-large')\n    plt.ylabel('Distribution (%)', size='x-large')\n    if conf_Z is not None:\n        sig = conf_Z * sqrt(y_Exp * (1 - y_Exp) / N)\n        upper = y_Exp + sig + (1 / (2 * N))\n        lower_zeros = array([0]*len(upper))\n        lower = maximum(y_Exp - sig - (1 / (2 * N)), lower_zeros)\n        u = (y_Found < lower) | (y_Found > upper)\n        c = array([COLORS['m']] * len(u))\n        c[u] = COLORS['af']\n        lower *= 100.\n        upper *= 100.\n        ax.plot(x, upper, color=COLORS['s'], zorder=5)\n        ax.plot(x, lower, color=COLORS['s'], zorder=5)\n        ax.fill_between(x, upper, lower, color=COLORS['s'],\n                        alpha=.3, label='Conf')\n    else:\n        c = COLORS['m']\n    ax.bar(x, y_Found * 100., color=c, label='Found', zorder=3, align='center')\n    ax.plot(x, y_Exp * 100., color=COLORS['s'], linewidth=2.5,\n            label='Benford', zorder=4)\n    ax.set_xticks(x)\n    ax.set_xticklabels(x, rotation=rotation)\n    ax.set_facecolor(COLORS['b'])\n    if text_x:\n        ind = array(df.index).astype(str)\n        ind[:10] = array(['00', '01', '02', '03', '04', '05',\n                          '06', '07', '08', '09'])\n        plt.xticks(x, ind, rotation='vertical')\n    ax.legend()\n    ax.set_ylim(0, max([y_Exp.max() * 100, y_Found.max() * 100]) + 10 / len(x))\n    ax.set_xlim(x[0] - 1, x[-1] + 1)\n\n    if save_plot:\n        if not save_plot_kwargs:\n            save_plot_kwargs = {}\n        plt.savefig(save_plot, **save_plot_kwargs)\n\n    plt.show(block=False)\n\n\ndef plot_sum(df, figsize, li, text_x=False, save_plot=None, save_plot_kwargs=None):\n    \"\"\"Plots the summation test results\n\n    Args:\n        df: DataFrame with the data to be plotted\n        figsize: sets the dimensions of the plot figure\n        li: value with which to draw the horizontal line\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n    \"\"\"\n    x = df.index\n    rotation = 90 if len(x) > 10 else 0\n    fig = plt.figure(figsize=figsize)\n    ax = fig.add_subplot(111)\n    plt.title('Expected vs. Found Sums')\n    plt.xlabel('Digits')\n    plt.ylabel('Sums')\n    ax.bar(x, df.Percent, color=COLORS['m'],\n           label='Found Sums', zorder=3, align='center')\n    ax.set_xlim(x[0] - 1, x[-1] + 1)\n    ax.axhline(li, color=COLORS['s'], linewidth=2, label='Expected', zorder=4)\n    ax.set_xticks(x)\n    ax.set_xticklabels(x, rotation=rotation)\n    ax.set_facecolor(COLORS['b'])\n    if text_x:\n        ind = array(x).astype(str)\n        ind[:10] = array(['00', '01', '02', '03', '04', '05',\n                          '06', '07', '08', '09'])\n        plt.xticks(x, ind, rotation='vertical')\n    ax.legend()\n\n    if save_plot:\n        if not save_plot_kwargs:\n            save_plot_kwargs = {}\n        plt.savefig(save_plot, **save_plot_kwargs)\n\n    plt.show(block=False)\n\ndef plot_ordered_mantissas(col, figsize=(12, 12),\n                           save_plot=None, save_plot_kwargs=None):\n    \"\"\"Plots the ordered mantissas and compares them to the expected, straight\n        line that should be formed in a Benford-cmpliant set.\n\n    Args:\n        col (Series): column of mantissas to plot.\n        figsize (tuple): sets the dimensions of the plot figure.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n \n    \"\"\"\n    ld = len(col)\n    x = arange(1, ld + 1)\n    n = ones(ld) / ld\n    fig = plt.figure(figsize=figsize)\n    ax = fig.add_subplot(111)\n    ax.plot(x, col.sort_values(), linestyle='--',\n            color=COLORS['s'], linewidth=3, label='Mantissas')\n    ax.plot(x, n.cumsum(), color=COLORS['m'],\n            linewidth=2, label='Expected')\n    plt.ylim((0, 1.))\n    plt.xlim((1, ld + 1))\n    ax.set_facecolor(COLORS['b'])\n    ax.set_title(\"Ordered Mantissas\")\n    plt.legend(loc='upper left')\n\n    if save_plot:\n        if not save_plot_kwargs:\n            save_plot_kwargs = {}\n        plt.savefig(save_plot, **save_plot_kwargs)\n\n    plt.show(block=False);\n\ndef plot_mantissa_arc_test(df, gravity_center, grid=True, figsize=12,\n                           save_plot=None, save_plot_kwargs=None):\n    \"\"\"Draws thee Mantissa Arc Test after computing X and Y circular coordinates\n    for every mantissa and the center of gravity for the set\n\n    Args:\n        df (DataFrame): pandas DataFrame with the mantissas and the X and Y\n            coordinates.\n        gravity_center (tuple): coordinates for plottling the gravity center\n        grid (bool): show grid. Defaults to True.\n        figsize (int): figure dimensions. No need to be a tuple, since the\n            figure is a square.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n    \"\"\"\n    fig = plt.figure(figsize=(figsize, figsize))\n    ax = plt.subplot()\n    ax.set_facecolor(COLORS['b'])\n    ax.scatter(df.mant_x, df.mant_y, label=\"ARC TEST\",\n               color=COLORS['m'])\n    ax.scatter(gravity_center[0], gravity_center[1],\n               color=COLORS['s'])\n    text_annotation = Annotation(\n        \"  Gravity Center: \"\n        f\"x({round(gravity_center[0], 3)}),\"\n        f\" y({round(gravity_center[1], 3)})\",\n        xy=(gravity_center[0] - 0.65,\n            gravity_center[1] - 0.1),\n        xycoords='data')\n    ax.add_artist(text_annotation)\n    ax.grid(True, which='both')\n    ax.axhline(y=0, color='k')\n    ax.axvline(x=0, color='k')\n    ax.legend(loc='lower left')\n    ax.set_title(\"Mantissas Arc Test\")\n\n    if save_plot:\n        if not save_plot_kwargs:\n            save_plot_kwargs = {}\n        plt.savefig(save_plot, **save_plot_kwargs)\n\n    plt.show(block=False);\n\ndef plot_roll_mse(roll_series, figsize, save_plot=None, save_plot_kwargs=None):\n    \"\"\"Shows the rolling MSE plot\n\n    Args:\n        roll_series: pd.Series resultant form rolling mse.\n        figsize: the figure dimensions.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n    \"\"\"\n    fig, ax = plt.subplots(figsize=figsize)\n    ax.set_facecolor(COLORS['b'])\n    ax.plot(roll_series, color=COLORS['m'])\n\n    if save_plot:\n        if not save_plot_kwargs:\n            save_plot_kwargs = {}\n        plt.savefig(save_plot, **save_plot_kwargs)\n\n    plt.show(block=False)\n\ndef plot_roll_mad(roll_mad, figsize, save_plot=None, save_plot_kwargs=None):\n    \"\"\"Shows the rolling MAD plot\n\n    Args:\n        roll_mad: pd.Series resultant form rolling mad.\n        figsize: the figure dimensions.\n        save_plot: string with the path/name of the file in which the generated\n            plot will be saved. Uses matplotlib.pyplot.savefig(). File format\n            is infered by the file name extension.\n        save_plot_kwargs: dict with any of the kwargs accepted by\n            matplotlib.pyplot.savefig()\n            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n    \"\"\"\n    fig, ax = plt.subplots(figsize=figsize)\n    ax.set_facecolor(COLORS['b'])\n    ax.plot(roll_mad.roll_series, color=COLORS['m'])\n\n    if roll_mad.test != -2:\n        plt.axhline(y=MAD_CONFORM[roll_mad.test][0], color=COLORS['af'], linewidth=3)\n        plt.axhline(y=MAD_CONFORM[roll_mad.test][1], color=COLORS['h2'], linewidth=3)\n        plt.axhline(y=MAD_CONFORM[roll_mad.test][2], color=COLORS['s'], linewidth=3)\n\n    if save_plot:\n        if not save_plot_kwargs:\n            save_plot_kwargs = {}\n        plt.savefig(save_plot, **save_plot_kwargs)\n\n    plt.show(block=False)\n"
  },
  {
    "path": "data/SPY.csv",
    "content": "Date,Open,High,Low,Close,Volume,Adj Close\n1993-01-29,43.9687,43.9687,43.75,43.9375,1003200,28.000838\n1993-02-01,43.9687,44.25,43.9687,44.25,480500,28.19999\n1993-02-02,44.2187,44.375,44.125,44.3437,201300,28.259704\n1993-02-03,44.4062,44.8437,44.375,44.8125,529400,28.558465\n1993-02-04,44.9687,45.0937,44.4687,45.0,531500,28.677956\n1993-02-05,44.9687,45.0625,44.7187,44.9687,492100,28.658009\n1993-02-08,44.9687,45.125,44.9062,44.9687,596100,28.658009\n1993-02-09,44.8125,44.8125,44.5625,44.6562,122100,28.458857\n1993-02-10,44.6562,44.75,44.5312,44.7187,379600,28.498687\n1993-02-11,44.7812,45.125,44.7812,44.9375,19500,28.638126\n1993-02-12,44.875,44.875,44.5937,44.5937,42500,28.419026\n1993-02-16,44.4687,44.4687,43.4062,43.4687,374800,27.702077\n1993-02-17,43.4687,43.5312,43.2812,43.4375,210900,27.682194\n1993-02-18,43.9375,43.9375,42.8125,43.4062,378100,27.662247\n1993-02-19,43.4062,43.5625,43.3437,43.5625,34900,27.761855\n1993-02-22,43.6875,43.7812,43.5625,43.7187,513600,27.861399\n1993-02-23,43.8437,43.875,43.4687,43.6875,373700,27.841516\n1993-02-24,43.7187,44.25,43.7187,44.25,26300,28.19999\n1993-02-25,44.2187,44.375,44.125,44.3437,44500,28.259704\n1993-02-26,44.4375,44.4375,44.1875,44.4062,66200,28.299535\n1993-03-01,44.5625,44.5625,44.2187,44.2812,66500,28.219874\n1993-03-02,44.3125,44.9375,44.25,44.9375,182400,28.638126\n1993-03-03,45.0,45.1562,44.9375,45.125,280100,28.757617\n1993-03-04,45.1875,45.1875,44.875,44.875,89500,28.598295\n1993-03-05,44.9375,45.125,44.7187,44.75,40000,28.518634\n1993-03-08,44.8437,45.75,44.8437,45.75,50800,29.155922\n1993-03-09,45.6562,45.6875,45.5,45.5937,169300,29.056314\n1993-03-10,45.5937,45.6875,45.4062,45.6875,194400,29.116092\n1993-03-11,45.7187,45.8437,45.5,45.5625,70900,29.036431\n1993-03-12,45.1875,45.2187,44.8125,45.0937,643600,28.73767\n1993-03-15,45.0625,45.3125,45.0625,45.3125,310800,28.877109\n1993-03-16,45.3125,45.4375,45.3125,45.3125,30800,28.877109\n1993-03-17,45.25,45.25,44.9687,45.0312,21800,28.69784\n1993-03-18,45.2187,45.5,45.2187,45.3125,59300,28.877109\n1993-03-19,45.2812,45.2812,45.0312,45.0312,66900,28.833377\n1993-03-22,44.5937,44.875,44.5625,44.7812,183400,28.673303\n1993-03-23,44.9062,44.9375,44.8125,44.875,55200,28.733362\n1993-03-24,44.8125,45.0625,44.5937,44.875,37200,28.733362\n1993-03-25,44.9062,45.25,44.8437,45.1562,110100,28.913414\n1993-03-26,45.125,45.1562,44.875,44.9062,101500,28.75334\n1993-03-29,44.9375,45.3125,44.9375,45.0937,87100,28.873396\n1993-03-30,45.1562,45.2187,45.0937,45.2187,56000,28.953433\n1993-03-31,45.3437,45.4687,45.1875,45.1875,111600,28.933456\n1993-04-01,45.25,45.25,44.9375,45.0312,129500,28.833377\n1993-04-02,44.6562,44.6562,44.0937,44.0937,59400,28.233098\n1993-04-05,44.4375,44.4375,44.1875,44.3125,172200,28.373195\n1993-04-06,44.4062,44.4062,44.0625,44.1875,129700,28.293158\n1993-04-07,44.25,44.3437,44.1562,44.3437,28000,28.393173\n1993-04-08,44.5312,44.5312,44.0937,44.2812,180800,28.353154\n1993-04-12,44.7187,44.9375,44.6562,44.9062,348500,28.75334\n1993-04-13,44.875,45.1562,44.8437,45.0,146100,28.8134\n1993-04-14,45.0312,45.0625,44.9062,44.9375,119600,28.773381\n1993-04-15,44.9062,45.0312,44.75,44.9375,148600,28.773381\n1993-04-16,44.9687,45.0312,44.875,44.9375,47900,28.773381\n1993-04-19,44.9375,45.0625,44.7187,44.75,157000,28.653325\n1993-04-20,44.6875,44.75,44.25,44.5312,279500,28.513228\n1993-04-21,44.625,44.625,44.375,44.5,67900,28.493251\n1993-04-22,44.3125,44.6875,43.9375,43.9375,97700,28.133083\n1993-04-23,43.8437,43.9687,43.6875,43.75,106000,28.013027\n1993-04-26,43.7812,43.9375,43.2812,43.4062,62600,27.792893\n1993-04-27,43.3437,43.875,43.3437,43.875,156800,28.093065\n1993-04-28,43.8125,43.9062,43.7187,43.7812,85900,28.033005\n1993-04-29,43.875,43.9687,43.625,43.9687,85000,28.153061\n1993-04-30,44.125,44.2812,44.0312,44.0312,88500,28.193079\n1993-05-03,44.0937,44.3125,43.9062,44.3125,80500,28.373195\n1993-05-04,44.4062,44.625,44.3437,44.4687,149100,28.47321\n1993-05-05,44.4687,44.75,44.4687,44.5937,109000,28.553247\n1993-05-06,44.5312,44.5625,44.4062,44.4375,54700,28.453232\n1993-05-07,44.4687,44.4687,44.2812,44.3437,68000,28.393173\n1993-05-10,44.4062,44.6875,44.4062,44.4375,113900,28.453232\n1993-05-11,44.4375,44.625,44.3125,44.625,42600,28.573288\n1993-05-12,44.4375,44.5937,44.4375,44.5625,31000,28.533269\n1993-05-13,44.375,44.375,44.0,44.0312,129100,28.193079\n1993-05-14,44.0312,44.1562,43.9687,44.0,63500,28.173102\n1993-05-17,44.125,44.1562,43.9375,44.1562,34000,28.273117\n1993-05-18,44.1875,44.2187,43.9687,44.125,105200,28.253139\n1993-05-19,44.125,45.0312,43.8437,45.0312,50200,28.833377\n1993-05-20,45.0312,45.1562,44.9375,45.1562,98200,28.913414\n1993-05-21,45.1875,45.25,44.7187,44.75,221400,28.653325\n1993-05-24,44.8437,45.0937,44.8437,44.9375,30500,28.773381\n1993-05-25,45.125,45.125,45.0312,45.0312,191800,28.833377\n1993-05-26,45.1562,45.625,45.125,45.5937,102400,29.193545\n1993-05-27,45.6562,45.6562,45.375,45.4375,53800,29.09353\n1993-05-28,45.4062,45.4062,45.0,45.2187,79100,28.953433\n1993-06-01,45.375,45.8125,45.3125,45.6562,28300,29.233563\n1993-06-02,45.5312,45.75,45.3437,45.5937,20300,29.193545\n1993-06-03,45.5,45.5,45.3437,45.4375,21600,29.09353\n1993-06-04,45.3125,45.3125,45.0937,45.2812,32000,28.993452\n1993-06-07,45.375,45.375,45.125,45.125,121400,28.893437\n1993-06-08,45.0,45.0,44.7187,44.7187,104500,28.633284\n1993-06-09,44.875,45.0625,44.8125,44.875,43300,28.733362\n1993-06-10,44.8437,44.9375,44.75,44.9062,17900,28.75334\n1993-06-11,45.0625,45.1562,44.9062,45.0937,647400,28.873396\n1993-06-14,45.1562,45.1875,45.0312,45.0312,64200,28.833377\n1993-06-15,45.0937,45.125,44.9375,44.9375,142400,28.773381\n1993-06-16,44.9375,45.0312,44.8125,45.0312,330900,28.833377\n1993-06-17,45.125,45.1875,45.0312,45.1875,37400,28.933456\n1993-06-18,44.8437,44.8437,44.5,44.5,58500,28.695189\n1993-06-21,44.625,44.625,44.5312,44.5937,29300,28.75561\n1993-06-22,44.6562,44.6562,44.5625,44.625,137500,28.775794\n1993-06-23,44.625,44.625,44.2187,44.2187,227600,28.513797\n1993-06-24,44.3437,44.8125,44.3437,44.8125,243700,28.8967\n1993-06-25,44.7812,44.9062,44.75,44.7812,44800,28.876517\n1993-06-28,45.0,45.2812,44.9375,45.2812,439900,29.198935\n1993-06-29,45.2187,45.2187,45.0,45.0625,207500,29.057909\n1993-06-30,45.125,45.2187,45.0,45.0625,437600,29.057909\n1993-07-01,45.125,45.125,44.875,44.9375,605700,28.977305\n1993-07-02,44.7812,44.8125,44.5312,44.6875,285400,28.816096\n1993-07-06,44.625,44.75,44.1562,44.2187,246400,28.513797\n1993-07-07,44.1875,44.4062,44.1875,44.3437,343700,28.594402\n1993-07-08,44.375,44.9375,44.3125,44.8437,248200,28.916819\n1993-07-09,44.8437,44.9687,44.75,44.9687,378200,28.997424\n1993-07-12,44.9062,44.9687,44.8437,44.9375,373700,28.977305\n1993-07-13,44.9687,45.0937,44.6562,44.9062,389600,28.957122\n1993-07-14,44.9375,45.1875,44.9062,45.0625,617300,29.057909\n1993-07-15,45.0312,45.0312,44.7812,44.875,443800,28.937003\n1993-07-16,44.9062,44.9375,44.6875,44.75,216400,28.856398\n1993-07-19,44.75,44.75,44.5937,44.7187,188200,28.836215\n1993-07-20,44.6875,44.8437,44.4687,44.8437,68500,28.916819\n1993-07-21,44.7812,44.8125,44.6562,44.8125,142700,28.8967\n1993-07-22,44.75,44.8125,44.5,44.5,632400,28.695189\n1993-07-23,44.5937,44.7187,44.5625,44.7187,286200,28.836215\n1993-07-26,44.8437,45.0625,44.8437,44.9687,121300,28.997424\n1993-07-27,45.0,45.0312,44.7812,44.9375,92800,28.977305\n1993-07-28,44.8437,44.9375,44.7812,44.8437,30800,28.916819\n1993-07-29,44.9375,45.2187,44.875,45.0937,331000,29.078028\n1993-07-30,45.0937,45.0937,44.7812,44.8437,75300,28.916819\n1993-08-02,44.9062,45.125,44.9062,44.9687,41300,28.997424\n1993-08-03,45.0625,45.1875,44.8437,45.0,81600,29.017607\n1993-08-04,45.0,45.0937,44.875,45.0,434000,29.017607\n1993-08-05,45.0,45.0,44.8437,44.9062,36800,28.957122\n1993-08-06,45.0312,45.0625,44.9062,44.9687,402300,28.997424\n1993-08-09,45.0937,45.3437,45.0,45.2187,828200,29.158633\n1993-08-10,45.1875,45.2187,45.125,45.1875,604900,29.138514\n1993-08-11,45.1562,45.3125,45.1562,45.1875,542200,29.138514\n1993-08-12,45.3125,45.3125,44.9062,45.0625,303700,29.057909\n1993-08-13,45.0937,45.1562,45.0937,45.125,103500,29.098211\n1993-08-16,45.1562,45.5,45.1562,45.375,241800,29.25942\n1993-08-17,45.3437,45.5312,45.3437,45.5312,369300,29.360144\n1993-08-18,45.6875,45.875,45.6562,45.7812,414300,29.521353\n1993-08-19,45.8125,45.8125,45.7187,45.7812,28500,29.521353\n1993-08-20,45.6875,45.8125,45.6562,45.8125,80700,29.541536\n1993-08-23,45.625,45.75,45.625,45.7187,15600,29.481051\n1993-08-24,45.7187,46.2187,45.7187,46.2187,273400,29.803469\n1993-08-25,46.2187,46.4375,46.1562,46.25,242300,29.823652\n1993-08-26,46.2812,46.5312,46.0937,46.2812,120000,29.843771\n1993-08-27,46.1562,46.25,46.1562,46.25,25700,29.823652\n1993-08-30,46.2812,46.5,46.2812,46.4375,183500,29.944558\n1993-08-31,46.4062,46.5625,46.3437,46.5625,66500,30.025163\n1993-09-01,46.4062,46.5937,46.4062,46.5,136500,29.984861\n1993-09-02,46.5312,46.5937,46.3125,46.3437,472400,29.884073\n1993-09-03,46.3125,46.4375,46.25,46.375,630500,29.904256\n1993-09-07,46.375,46.4375,46.0,46.0625,196400,29.702745\n1993-09-08,46.0625,46.0625,45.5937,45.9062,269900,29.601957\n1993-09-09,45.7187,46.0312,45.7187,46.0,239200,29.662443\n1993-09-10,46.125,46.4375,46.0625,46.4062,106500,29.924375\n1993-09-13,46.5625,46.5625,46.4375,46.4375,66900,29.944558\n1993-09-14,46.3125,46.3125,46.0937,46.25,184500,29.823652\n1993-09-15,46.0625,46.4062,45.9062,46.375,101000,29.904256\n1993-09-16,46.3125,46.3437,46.1562,46.1875,54300,29.783349\n1993-09-17,45.875,45.9062,45.75,45.8125,200900,29.725603\n1993-09-20,45.875,45.9687,45.4375,45.4375,57800,29.482283\n1993-09-21,45.5,45.5625,44.8125,45.2812,318200,29.380867\n1993-09-22,45.4375,45.7187,45.375,45.6562,439700,29.624187\n1993-09-23,45.7812,45.9375,45.7187,45.9062,88500,29.786401\n1993-09-24,45.8437,45.875,45.7187,45.7812,53500,29.705294\n1993-09-27,46.125,46.2812,46.125,46.2812,274600,30.029721\n1993-09-28,46.3125,46.3125,46.1562,46.1875,158300,29.968923\n1993-09-29,46.1875,46.3125,45.9687,46.0312,221000,29.867507\n1993-09-30,46.0312,46.125,45.8437,45.9375,99300,29.80671\n1993-10-01,45.875,46.2187,45.8125,46.1562,22700,29.948614\n1993-10-04,46.2187,46.2187,46.0937,46.2187,1038500,29.989167\n1993-10-05,46.3125,46.3125,46.0,46.1562,436500,29.948614\n1993-10-06,46.1875,46.375,46.125,46.125,209200,29.92837\n1993-10-07,46.1875,46.1875,45.9687,46.0,59400,29.847263\n1993-10-08,46.125,46.1562,45.7187,46.0625,54400,29.887816\n1993-10-11,46.1562,46.25,46.1562,46.1562,467100,29.948614\n1993-10-12,46.2187,46.25,46.1875,46.2187,26200,29.989167\n1993-10-13,46.25,46.25,46.1562,46.2187,139100,29.989167\n1993-10-14,46.4062,46.8125,46.2812,46.8125,108100,30.374456\n1993-10-15,47.0312,47.1562,46.9062,47.0625,1502500,30.53667\n1993-10-18,47.0312,47.0312,46.875,46.9375,722400,30.455563\n1993-10-19,46.875,46.9687,46.5937,46.5937,880100,30.232487\n1993-10-20,46.75,46.75,46.5625,46.6562,230400,30.273041\n1993-10-21,46.6875,46.6875,46.5312,46.5937,56200,30.232487\n1993-10-22,46.6562,46.8437,46.375,46.375,390700,30.090583\n1993-10-25,46.4062,46.5625,46.2812,46.5,114500,30.17169\n1993-10-26,46.4687,46.5,46.3125,46.4687,186200,30.151381\n1993-10-27,46.4062,46.5312,46.4062,46.5,118400,30.17169\n1993-10-28,46.5625,46.9687,46.5625,46.8437,129600,30.394701\n1993-10-29,46.8125,46.875,46.7812,46.8437,80700,30.394701\n1993-11-01,46.7812,47.0,46.7812,46.9687,36400,30.475808\n1993-11-02,46.9062,47.0,46.6562,46.9375,262100,30.455563\n1993-11-03,46.9062,46.9062,46.125,46.3437,479100,30.070274\n1993-11-04,46.3437,46.3437,45.8125,45.8437,130400,29.745847\n1993-11-05,45.7187,46.0625,45.5312,46.0625,363200,29.887816\n1993-11-08,46.0937,46.25,45.9687,46.125,367600,29.92837\n1993-11-09,46.4375,46.4687,46.125,46.1562,246900,29.948614\n1993-11-10,46.1562,46.5,46.0312,46.5,46500,30.17169\n1993-11-11,46.5,46.625,46.3437,46.375,88900,30.090583\n1993-11-12,46.4687,46.75,46.4375,46.5937,108200,30.232487\n1993-11-15,46.6875,46.6875,46.4375,46.5625,243300,30.212243\n1993-11-16,46.6562,46.8125,46.4687,46.7812,492600,30.354148\n1993-11-17,46.8125,46.8125,46.4062,46.5312,39600,30.191934\n1993-11-18,46.4687,46.5625,46.2812,46.4062,240800,30.110827\n1993-11-19,46.25,46.375,46.2187,46.3125,106000,30.05003\n1993-11-22,46.1875,46.2187,45.875,46.0312,165300,29.867507\n1993-11-23,46.2812,46.3125,46.0312,46.2812,89700,30.029721\n1993-11-24,46.4062,46.5,46.3437,46.4687,77200,30.151381\n1993-11-26,46.5937,46.5937,46.4687,46.5,1019800,30.17169\n1993-11-29,46.625,46.7187,46.3125,46.3125,517500,30.05003\n1993-11-30,46.2812,46.5625,46.25,46.3437,230000,30.070274\n1993-12-01,46.5937,46.625,46.4062,46.4062,379200,30.110827\n1993-12-02,46.5,46.5625,46.4062,46.5312,352000,30.191934\n1993-12-03,46.5,46.7187,46.5,46.7187,306000,30.313594\n1993-12-06,46.7812,46.9375,46.7812,46.875,99500,30.41501\n1993-12-07,46.875,46.9062,46.7812,46.8437,88800,30.394701\n1993-12-08,46.8437,46.8437,46.7812,46.8437,146700,30.394701\n1993-12-09,46.8437,46.9062,46.625,46.6875,416500,30.29335\n1993-12-10,46.7187,46.7187,46.5,46.5937,412900,30.232487\n1993-12-13,46.5937,46.875,46.5312,46.875,273200,30.41501\n1993-12-14,46.9062,46.9062,46.5312,46.5312,41900,30.191934\n1993-12-15,46.5937,46.625,46.4687,46.4687,82600,30.151381\n1993-12-16,46.6562,46.6562,46.5625,46.625,78200,30.252796\n1993-12-17,46.4062,46.5937,46.375,46.5625,104700,30.419059\n1993-12-20,46.5312,46.6562,46.5,46.625,68800,30.45989\n1993-12-21,46.5625,46.5625,46.4062,46.4687,205700,30.35778\n1993-12-22,46.5937,46.8125,46.5,46.7812,410300,30.561935\n1993-12-23,46.75,46.8437,46.6875,46.75,533800,30.541551\n1993-12-27,46.75,47.0,46.75,47.0,447100,30.704875\n1993-12-28,46.9687,47.125,46.9375,47.0937,880600,30.766089\n1993-12-29,47.125,47.1562,47.0,47.0312,266700,30.725258\n1993-12-30,47.0,47.0,46.75,46.8437,219900,30.602766\n1993-12-31,46.9375,47.0,46.5625,46.5937,312900,30.439442\n1994-01-03,46.5937,46.6562,46.4062,46.4687,960900,30.35778\n1994-01-04,46.5312,46.6562,46.4687,46.6562,164300,30.480273\n1994-01-05,46.7187,46.7812,46.5312,46.75,710900,30.541551\n1994-01-06,46.8125,46.8437,46.6875,46.75,201000,30.541551\n1994-01-07,46.8437,47.0625,46.7187,47.0312,775500,30.725258\n1994-01-10,47.0937,47.5937,46.9687,47.5937,593700,31.092737\n1994-01-11,47.5625,47.5625,47.3437,47.5,295200,31.031523\n1994-01-12,47.5312,47.5312,47.1875,47.3437,158400,30.929413\n1994-01-13,47.2187,47.3125,47.1562,47.2187,244300,30.847751\n1994-01-14,47.375,47.5,47.375,47.4062,137200,30.970244\n1994-01-17,47.4062,47.4687,47.3125,47.4062,17700,30.970244\n1994-01-18,47.4687,47.5312,47.375,47.4687,166400,31.011075\n1994-01-19,47.4062,47.4687,47.25,47.3437,200800,30.929413\n1994-01-20,47.4062,47.5,47.375,47.4687,281100,31.011075\n1994-01-21,47.5312,47.5312,47.3437,47.375,85600,30.949861\n1994-01-24,47.3437,47.5625,47.1875,47.1875,373800,30.827368\n1994-01-25,47.2187,47.25,47.0937,47.1875,310400,30.827368\n1994-01-26,47.1875,47.3437,47.125,47.3125,145100,30.90903\n1994-01-27,47.4062,47.8125,47.3437,47.75,344500,31.194847\n1994-01-28,47.9375,48.0312,47.875,47.875,356500,31.276509\n1994-01-31,48.0625,48.3125,48.0,48.2187,313800,31.501046\n1994-02-01,48.1562,48.1562,47.9062,47.9687,303600,31.337723\n1994-02-02,48.125,48.2812,48.0937,48.2812,307600,31.541877\n1994-02-03,48.1875,48.1875,47.9062,48.0625,466100,31.399001\n1994-02-04,48.0625,48.125,46.9687,46.9687,1403200,30.684427\n1994-02-07,46.8437,47.3125,46.8437,47.1875,516400,30.827368\n1994-02-08,47.2812,47.2812,47.0312,47.2187,188200,30.847751\n1994-02-09,47.25,47.4375,47.1875,47.4062,144600,30.970244\n1994-02-10,47.375,47.4062,47.0,47.0,883900,30.704875\n1994-02-11,47.0312,47.2812,46.8125,47.1562,519400,30.80692\n1994-02-14,47.0625,47.375,47.0,47.2187,2742100,30.847751\n1994-02-15,47.3125,47.5,47.25,47.4687,374700,31.011075\n1994-02-16,47.5312,47.5312,47.3437,47.4375,287600,30.990692\n1994-02-17,47.6562,47.6875,47.0312,47.1562,342400,30.80692\n1994-02-18,47.1875,47.2187,46.75,46.875,313300,30.623213\n1994-02-22,47.0,47.3437,47.0,47.3437,154500,30.929413\n1994-02-23,47.4062,47.4375,47.0625,47.2187,391700,30.847751\n1994-02-24,47.0625,47.0625,46.5625,46.5937,770800,30.439442\n1994-02-25,46.6562,46.8125,46.5625,46.8125,531300,30.582382\n1994-02-28,46.9375,47.0625,46.8125,46.8125,333000,30.582382\n1994-03-01,46.8125,46.9062,46.375,46.625,423600,30.45989\n1994-03-02,46.0312,46.6875,45.9062,46.6875,581500,30.500721\n1994-03-03,46.625,46.625,46.4375,46.5625,223200,30.419059\n1994-03-04,46.5625,46.8125,46.4062,46.6875,595800,30.500721\n1994-03-07,46.8437,47.0,46.8437,46.9375,539800,30.664044\n1994-03-08,46.9687,46.9687,46.6875,46.75,880300,30.541551\n1994-03-09,46.8125,47.0,46.5937,46.9687,2500100,30.684427\n1994-03-10,46.9375,46.9375,46.4375,46.5937,207400,30.439442\n1994-03-11,46.6562,46.9062,46.4062,46.8437,576600,30.602766\n1994-03-14,47.0312,47.0312,46.8437,46.9062,345900,30.643596\n1994-03-15,47.0,47.0937,46.8437,46.875,748600,30.623213\n1994-03-16,46.9375,47.25,46.7812,47.25,455500,30.868199\n1994-03-17,47.0937,47.3125,47.0937,47.25,133000,30.868199\n1994-03-18,46.75,47.0312,46.7187,46.9687,365500,30.861432\n1994-03-21,46.8125,46.9375,46.7187,46.8437,324800,30.779299\n1994-03-22,46.8437,47.0625,46.75,46.9687,435700,30.861432\n1994-03-23,46.9375,47.0625,46.9062,46.9375,698500,30.840931\n1994-03-24,46.75,46.8437,46.1562,46.375,1200500,30.471333\n1994-03-25,46.4062,46.5312,45.9375,45.9375,100900,30.183867\n1994-03-28,46.0,46.0625,45.5937,46.0,1117200,30.224934\n1994-03-29,46.0,46.0312,45.0937,45.0937,338400,29.629437\n1994-03-30,45.125,45.25,44.4687,44.4687,1123900,29.218772\n1994-03-31,44.4687,44.6875,43.5312,44.5937,788800,29.300905\n1994-04-04,43.3437,44.0312,43.3437,43.9062,2627300,28.849174\n1994-04-05,44.3437,44.8125,44.3437,44.8125,1179000,29.444671\n1994-04-06,44.875,44.9062,44.5,44.8125,516500,29.444671\n1994-04-07,44.7812,45.125,44.5312,45.0312,666100,29.588371\n1994-04-08,44.9375,44.9375,44.4687,44.6875,242400,29.362538\n1994-04-11,44.8125,45.0625,44.703098,44.875,203300,29.485737\n1994-04-12,44.921799,45.0,44.7187,44.8125,1409200,29.444671\n1994-04-13,44.796799,44.9062,44.25,44.578098,364100,29.290654\n1994-04-14,44.5,44.828098,44.4062,44.5937,419900,29.300905\n1994-04-15,44.578098,44.7812,44.515598,44.5937,387300,29.300905\n1994-04-18,44.640598,44.765598,44.1562,44.296799,369100,29.105822\n1994-04-19,44.25,44.5312,43.984299,44.359299,472200,29.146889\n1994-04-20,44.4062,44.515598,44.078098,44.3125,508000,29.116139\n1994-04-21,44.5,44.9687,44.3437,44.9062,200300,29.506238\n1994-04-22,45.015598,45.046799,44.734299,44.859299,301800,29.47542\n1994-04-25,44.8437,45.359299,44.8437,45.328098,394600,29.783452\n1994-04-26,45.265598,45.3437,45.1875,45.2812,399000,29.752637\n1994-04-28,45.1875,45.25,44.8125,44.953098,287000,29.537053\n1994-04-29,44.875,45.1562,44.8125,45.0937,481900,29.629437\n1994-05-02,45.0937,45.7187,44.9375,45.375,275000,29.814269\n1994-05-03,45.390598,45.4062,45.0625,45.328098,183400,29.783452\n1994-05-04,45.421799,45.421799,45.078098,45.25,401900,29.732136\n1994-05-05,45.296799,45.375,45.1875,45.1875,659800,29.69107\n1994-05-06,44.9687,44.9687,44.5937,44.75,216300,29.403604\n1994-05-09,44.625,44.75,44.2812,44.359299,499300,29.146889\n1994-05-10,44.578098,44.8437,44.546799,44.703098,583400,29.372787\n1994-05-11,44.6562,44.7187,44.171799,44.296799,210600,29.105822\n1994-05-12,44.578098,44.625,44.4062,44.515598,305200,29.249587\n1994-05-13,44.640598,44.640598,44.296799,44.515598,321300,29.249587\n1994-05-16,44.609299,44.75,44.515598,44.5312,450400,29.259839\n1994-05-17,44.5625,45.1875,44.5,45.1875,470200,29.69107\n1994-05-18,45.2187,45.6875,45.015598,45.515598,824800,29.906651\n1994-05-19,45.421799,45.859299,45.421799,45.734299,531000,30.050351\n1994-05-20,45.6562,45.6875,45.5,45.5625,370400,29.937469\n1994-05-23,45.515598,45.5312,45.3437,45.484299,262700,29.886085\n1994-05-24,45.609299,45.8437,45.609299,45.671799,549600,30.009285\n1994-05-25,45.5,45.828098,45.3437,45.796799,738200,30.091418\n1994-05-26,45.7812,45.9375,45.734299,45.828098,369600,30.111984\n1994-05-27,45.75,45.875,45.671799,45.875,162000,30.142801\n1994-05-31,45.734299,45.9062,45.6562,45.8125,160000,30.101735\n1994-06-01,45.703098,46.015598,45.5625,46.015598,200500,30.235183\n1994-06-02,46.046799,46.046799,45.890598,45.9687,55100,30.204368\n1994-06-03,46.0,46.390598,45.859299,46.234299,550400,30.378883\n1994-06-06,46.328098,46.4687,46.1875,46.2187,99300,30.368634\n1994-06-07,46.140598,46.2187,46.0312,46.1562,120600,30.327568\n1994-06-08,46.265598,46.265598,45.7812,45.7812,131900,30.081169\n1994-06-09,45.984299,46.0312,45.890598,46.0312,80500,30.245435\n1994-06-10,46.078098,46.203098,46.078098,46.109299,83100,30.29675\n1994-06-13,46.046799,46.1875,46.0312,46.171799,109700,30.337817\n1994-06-14,46.3125,46.546799,46.3125,46.5312,161000,30.573967\n1994-06-15,46.546799,46.5625,46.3125,46.3437,142800,30.450767\n1994-06-16,46.3437,46.4687,46.296799,46.4375,44000,30.512399\n1994-06-17,46.1562,46.203098,45.8125,45.875,403800,30.342087\n1994-06-20,45.546799,45.625,45.4375,45.484299,137400,30.083674\n1994-06-21,45.453098,45.453098,44.9062,45.0937,139200,29.825329\n1994-06-22,45.2187,45.421799,45.171799,45.265598,279900,29.939024\n1994-06-23,45.3437,45.4062,44.9375,45.0,922500,29.763355\n1994-06-24,44.7812,44.7812,44.0,44.0625,353800,29.143285\n1994-06-27,44.234299,44.828098,44.015598,44.828098,371200,29.649658\n1994-06-28,44.8437,44.8437,44.25,44.609299,5382300,29.504942\n1994-06-29,44.671799,45.015598,44.625,44.75,311800,29.598003\n1994-06-30,44.828098,44.8437,44.3125,44.4687,271900,29.411949\n1994-07-01,44.6875,44.6875,44.375,44.5625,406900,29.473989\n1994-07-05,44.6562,44.859299,44.515598,44.796799,112000,29.628956\n1994-07-06,44.625,44.8125,44.4687,44.734299,174800,29.587618\n1994-07-07,44.734299,44.921799,44.6875,44.921799,66700,29.711632\n1994-07-08,44.640598,44.984299,44.625,44.9062,148400,29.701315\n1994-07-11,44.9375,45.015598,44.5312,44.75,124000,29.598003\n1994-07-12,44.765598,44.828098,44.515598,44.8125,257300,29.639341\n1994-07-13,44.828098,45.0312,44.828098,44.890598,532700,29.690996\n1994-07-14,45.109299,45.4687,45.078098,45.375,494200,30.011383\n1994-07-15,45.3437,45.484299,45.328098,45.390598,49100,30.0217\n1994-07-18,45.390598,45.578098,45.390598,45.4687,72300,30.073357\n1994-07-19,45.546799,45.5625,45.375,45.375,609500,30.011383\n1994-07-20,45.4062,45.4062,45.0625,45.171799,185300,29.876984\n1994-07-21,45.171799,45.296799,45.078098,45.265598,86300,29.939024\n1994-07-22,45.375,45.4062,45.25,45.3437,151600,29.990681\n1994-07-25,45.359299,45.453098,45.3125,45.4062,120900,30.032019\n1994-07-26,45.390598,45.421799,45.3125,45.359299,489600,30.000998\n1994-07-27,45.359299,45.359299,45.171799,45.359299,83200,30.000998\n1994-07-28,45.359299,45.578098,45.359299,45.453098,828200,30.063038\n1994-07-29,45.765598,46.046799,45.75,45.9062,459100,30.362723\n1994-08-01,45.9375,46.1562,45.890598,46.125,486300,30.507439\n1994-08-02,46.2812,46.375,46.0312,46.1875,505500,30.548777\n1994-08-03,46.171799,46.234299,46.0937,46.203098,144100,30.559094\n1994-08-04,46.171799,46.2187,45.921799,45.9375,231700,30.383425\n1994-08-05,45.703098,45.8437,45.6562,45.7812,138400,30.280047\n1994-08-08,45.828098,45.9375,45.7812,45.890598,328600,30.352404\n1994-08-09,45.7187,45.9687,45.7187,45.9687,105900,30.404061\n1994-08-10,46.0,46.1875,45.9375,46.140598,840300,30.517756\n1994-08-11,46.0312,46.234299,45.734299,45.9687,876200,30.404061\n1994-08-12,46.046799,46.3437,46.046799,46.328098,184100,30.64177\n1994-08-15,46.375,46.484299,46.3125,46.3125,325900,30.631453\n1994-08-16,46.3437,46.6875,46.1562,46.640598,1089500,30.84846\n1994-08-17,46.703098,46.734299,46.578098,46.609299,133700,30.827758\n1994-08-18,46.453098,46.5625,46.4062,46.453098,620000,30.724446\n1994-08-19,46.4687,46.515598,46.3125,46.421799,103200,30.703744\n1994-08-22,46.4375,46.4375,46.2812,46.375,79700,30.672791\n1994-08-23,46.578098,46.859299,46.515598,46.640598,268600,30.84846\n1994-08-24,46.6875,47.140598,46.671799,47.140598,254700,31.179164\n1994-08-25,47.0937,47.203098,46.890598,47.015598,147400,31.096488\n1994-08-26,47.125,47.7812,47.125,47.6875,339500,31.540889\n1994-08-29,47.8125,47.984299,47.640598,47.6562,350300,31.520187\n1994-08-30,47.640598,47.859299,47.578098,47.7812,36000,31.602863\n1994-08-31,47.703098,47.890598,47.5937,47.6562,356200,31.520187\n1994-09-01,47.5,47.5312,47.328098,47.5,294600,31.416875\n1994-09-02,47.7187,47.7187,47.2187,47.296799,99600,31.282476\n1994-09-06,47.265598,47.359299,47.125,47.3125,229800,31.292861\n1994-09-07,47.421799,47.421799,47.2187,47.265598,27900,31.26184\n1994-09-08,47.359299,47.5625,47.3437,47.5,284800,31.416875\n1994-09-09,47.0312,47.125,46.8125,47.0,488400,31.086171\n1994-09-12,47.0,47.0625,46.7812,46.859299,129400,30.99311\n1994-09-13,46.953098,47.125,46.859299,47.0,389200,31.086171\n1994-09-14,46.8437,47.109299,46.8437,47.046799,423500,31.117124\n1994-09-15,47.171799,47.640598,47.171799,47.640598,779800,31.509867\n1994-09-16,47.0,47.140598,46.8437,47.015598,571300,31.285618\n1994-09-19,47.125,47.328098,47.015598,47.0625,167300,31.316828\n1994-09-20,46.8125,46.859299,46.171799,46.171799,355600,30.724128\n1994-09-21,46.3125,46.3125,45.734299,46.171799,397500,30.724128\n1994-09-22,46.2812,46.2812,46.015598,46.0625,266400,30.651397\n1994-09-23,46.078098,46.171799,45.828098,45.9062,176600,30.547391\n1994-09-26,46.078098,46.171799,45.984299,46.140598,223000,30.703366\n1994-09-27,46.140598,46.265598,46.0,46.109299,479500,30.682539\n1994-09-28,46.359299,46.5312,46.328098,46.4687,324000,30.921695\n1994-09-29,46.4687,46.4687,46.0937,46.234299,195900,30.765717\n1994-09-30,46.2187,46.4375,46.171799,46.171799,5200,30.724128\n1994-10-03,46.203098,46.25,45.984299,46.0625,72600,30.651397\n1994-10-04,46.296799,46.296799,45.3437,45.375,84200,30.193914\n1994-10-05,45.296799,45.390598,45.0,45.390598,461900,30.204293\n1994-10-06,45.421799,45.453098,45.171799,45.25,345200,30.110735\n1994-10-07,45.25,45.578098,45.203098,45.453098,188700,30.245883\n1994-10-10,45.6562,46.015598,45.640598,45.9375,213600,30.568218\n1994-10-11,46.140598,46.75,46.140598,46.625,461400,31.025702\n1994-10-12,46.6875,46.75,46.578098,46.6875,162400,31.067291\n1994-10-13,47.2187,47.328098,46.796799,46.8437,1564500,31.171232\n1994-10-14,47.0,47.0625,46.6875,47.046799,72800,31.30638\n1994-10-17,47.0,47.0625,46.9062,46.953098,282300,31.244029\n1994-10-18,46.9062,46.9062,46.6875,46.8437,132100,31.171232\n1994-10-19,46.671799,47.234299,46.6562,47.0625,136700,31.316828\n1994-10-20,47.0625,47.0625,46.609299,46.75,290300,31.108881\n1994-10-21,46.625,46.6875,46.359299,46.5625,87800,30.984113\n1994-10-24,46.578098,46.765598,46.1562,46.171799,149600,30.724128\n1994-10-25,46.015598,46.3125,46.0,46.2187,91800,30.755338\n1994-10-26,46.390598,46.4375,46.171799,46.390598,201000,30.869724\n1994-10-27,46.5625,46.671799,46.453098,46.671799,210300,31.056843\n1994-10-28,46.765598,47.703098,46.765598,47.6562,192100,31.711894\n1994-10-31,47.546799,47.5937,47.4687,47.484299,36300,31.597506\n1994-11-01,47.2812,47.2812,46.9375,46.953098,435200,31.244029\n1994-11-02,46.8125,47.234299,46.6875,46.6875,115600,31.067291\n1994-11-03,46.8437,47.015598,46.7812,46.9375,87000,31.233649\n1994-11-04,47.1562,47.1562,46.328098,46.328098,124400,30.828134\n1994-11-07,46.4375,46.5937,46.3125,46.4687,115000,30.921695\n1994-11-08,46.625,46.984299,46.546799,46.828098,308300,31.16085\n1994-11-09,47.328098,47.328098,46.5,46.9062,318500,31.212821\n1994-11-10,46.9375,47.0312,46.546799,46.578098,172100,30.994492\n1994-11-11,46.515598,46.5625,46.2812,46.4062,302200,30.880106\n1994-11-14,46.609299,46.859299,46.609299,46.8437,181400,31.171232\n1994-11-15,46.796799,47.0937,46.328098,46.6875,316900,31.067291\n1994-11-16,46.765598,46.8437,46.609299,46.8437,106900,31.171232\n1994-11-17,46.875,46.875,46.3437,46.5312,104700,30.963285\n1994-11-18,46.546799,46.625,46.265598,46.4687,269300,30.921695\n1994-11-21,46.4375,46.578098,46.0,46.0,283100,30.609808\n1994-11-22,45.828098,46.0,45.0,45.0,483600,29.944377\n1994-11-23,45.0312,45.265598,44.609299,45.25,601600,30.110735\n1994-11-25,45.2812,45.5312,45.2812,45.4687,77300,30.256265\n1994-11-28,45.453098,45.6875,45.3437,45.671799,79900,30.391413\n1994-11-29,45.6562,45.75,45.5,45.6562,105500,30.381033\n1994-11-30,45.890598,45.953098,45.5937,45.5937,218800,30.339444\n1994-12-01,45.640598,45.640598,45.046799,45.140598,439000,30.037936\n1994-12-02,45.046799,45.5625,45.046799,45.5625,318500,30.318682\n1994-12-05,45.6562,45.8125,45.5,45.609299,139200,30.349823\n1994-12-06,45.4375,45.6562,45.359299,45.640598,249900,30.370651\n1994-12-07,45.359299,45.5,45.265598,45.3125,531900,30.152324\n1994-12-08,45.5,45.5625,44.75,44.875,261500,29.861198\n1994-12-09,44.875,45.078098,44.6875,45.046799,235900,29.975519\n1994-12-12,44.953098,45.3437,44.953098,45.3437,151100,30.173086\n1994-12-13,45.3125,45.5312,45.3125,45.515598,70900,30.287472\n1994-12-14,45.453098,45.953098,45.453098,45.75,203300,30.44345\n1994-12-15,45.8125,46.0312,45.8125,45.890598,130100,30.537009\n1994-12-16,45.671799,45.8437,45.625,45.75,266100,30.686181\n1994-12-19,45.75,45.8437,45.609299,45.8125,1120200,30.728102\n1994-12-20,45.859299,45.890598,45.671799,45.671799,675600,30.633728\n1994-12-21,45.703098,46.265598,45.703098,46.1562,544500,30.958634\n1994-12-22,46.1562,46.203098,45.9687,46.015598,222000,30.864327\n1994-12-23,45.984299,46.171799,45.984299,46.0625,125800,30.895786\n1994-12-27,46.2187,46.4062,46.1875,46.3125,95200,31.06347\n1994-12-28,46.359299,46.359299,45.9687,46.078098,358200,30.906248\n1994-12-29,46.25,46.25,46.0625,46.109299,220100,30.927176\n1994-12-30,46.203098,46.25,45.5625,45.5625,2209500,30.560418\n1995-01-03,45.703098,45.8437,45.6875,45.7812,324300,30.707108\n1995-01-04,45.984299,46.0,45.75,46.0,351800,30.853865\n1995-01-05,46.0312,46.109299,45.953098,46.0,89800,30.853865\n1995-01-06,46.0937,46.25,45.9062,46.046799,448400,30.885254\n1995-01-09,46.0312,46.0937,46.0,46.0937,36800,30.916713\n1995-01-10,46.203098,46.390598,46.140598,46.140598,229800,30.948169\n1995-01-11,46.296799,46.296799,45.8125,46.171799,222400,30.969097\n1995-01-12,46.125,46.2187,46.0312,46.1875,40300,30.979628\n1995-01-13,46.4375,46.734299,46.375,46.734299,170600,31.346386\n1995-01-16,46.7187,47.0312,46.7187,47.015598,105100,31.535063\n1995-01-17,46.921799,47.0625,46.859299,47.0312,89500,31.545528\n1995-01-18,47.0,47.078098,46.875,46.984299,84500,31.51407\n1995-01-19,46.828098,46.875,46.6875,46.7187,139100,31.335923\n1995-01-20,46.6562,46.671799,46.453098,46.546799,78700,31.220623\n1995-01-23,46.234299,46.6875,46.203098,46.6875,53700,31.314996\n1995-01-24,46.671799,46.75,46.640598,46.75,32400,31.356917\n1995-01-25,46.515598,47.046799,46.515598,46.875,15700,31.440759\n1995-01-26,46.8437,46.984299,46.75,46.921799,9800,31.472149\n1995-01-27,47.234299,47.234299,46.921799,47.109299,91200,31.597912\n1995-01-30,47.015598,47.046799,46.8437,46.9062,26600,31.461686\n1995-01-31,47.0,47.171799,46.921799,47.0937,127500,31.587449\n1995-02-01,47.1562,47.328098,47.0,47.078098,380200,31.576984\n1995-02-02,47.0625,47.359299,47.0625,47.359299,131700,31.765596\n1995-02-03,47.6562,48.109299,47.578098,48.0312,405100,32.216265\n1995-02-06,48.015598,48.375,47.9687,48.234299,405400,32.35249\n1995-02-07,48.3125,48.3125,48.140598,48.296799,702900,32.394411\n1995-02-08,48.234299,48.4687,48.203098,48.296799,521500,32.394411\n1995-02-09,48.2187,48.296799,48.125,48.296799,390700,32.394411\n1995-02-10,48.234299,48.359299,48.0937,48.359299,148300,32.436332\n1995-02-13,48.328098,48.4687,48.328098,48.375,79700,32.446863\n1995-02-14,48.5312,48.5312,48.25,48.4375,170200,32.488784\n1995-02-15,48.4375,48.765598,48.390598,48.703098,431500,32.666931\n1995-02-16,48.640598,48.6875,48.5,48.640598,99300,32.62501\n1995-02-17,48.5937,48.625,48.4375,48.453098,49100,32.499247\n1995-02-21,48.453098,48.5312,48.421799,48.4375,168000,32.488784\n1995-02-22,48.484299,48.796799,48.453098,48.796799,386400,32.729779\n1995-02-23,48.984299,49.1562,48.859299,48.875,402800,32.782231\n1995-02-24,48.9375,49.015598,48.8125,49.0,307600,32.866073\n1995-02-27,48.859299,49.0,48.5312,48.609299,280900,32.604016\n1995-02-28,48.5625,49.015598,48.5625,49.015598,493500,32.876536\n1995-03-01,48.9687,49.0312,48.6562,48.703098,242600,32.666931\n1995-03-02,48.6875,48.765598,48.546799,48.765598,488600,32.708852\n1995-03-03,48.609299,48.7812,48.484299,48.7812,290000,32.719317\n1995-03-06,48.453098,48.8125,48.390598,48.8125,85700,32.74031\n1995-03-07,48.640598,48.75,48.2187,48.4375,180900,32.488784\n1995-03-08,48.515598,48.625,48.3437,48.5625,155900,32.572626\n1995-03-09,48.546799,48.5937,48.421799,48.546799,63500,32.562095\n1995-03-10,48.5937,49.390598,48.5312,49.265598,192100,33.04422\n1995-03-13,49.234299,49.390598,49.171799,49.2187,261300,33.012764\n1995-03-14,49.359299,49.640598,49.359299,49.578098,223300,33.253825\n1995-03-15,49.5,49.578098,49.296799,49.484299,278500,33.19091\n1995-03-16,49.4375,49.8125,49.4375,49.7812,20400,33.390053\n1995-03-17,49.4375,49.625,49.4062,49.5625,89900,33.423297\n1995-03-20,49.625,49.625,49.4687,49.5625,91700,33.423297\n1995-03-21,49.5625,49.875,49.359299,49.4375,104400,33.339001\n1995-03-22,49.5312,49.5312,49.328098,49.484299,74900,33.370561\n1995-03-23,49.421799,49.6562,49.359299,49.515598,220500,33.391668\n1995-03-24,49.671799,50.2187,49.671799,50.2187,134000,33.865817\n1995-03-27,50.296799,50.421799,50.171799,50.421799,132100,34.00278\n1995-03-28,50.296799,50.421799,50.234299,50.421799,121900,34.00278\n1995-03-29,50.375,50.890598,50.125,50.4062,246100,33.992261\n1995-03-30,50.515598,50.515598,50.109299,50.3125,298400,33.929072\n1995-03-31,49.921799,50.171799,49.546799,50.109299,541300,33.79204\n1995-04-03,50.0937,50.234299,50.0625,50.234299,193300,33.876336\n1995-04-04,50.25,50.5625,50.25,50.5625,66900,34.097664\n1995-04-05,50.453098,50.609299,50.421799,50.5625,107200,34.097664\n1995-04-06,50.6875,50.796799,50.5937,50.75,352500,34.224108\n1995-04-07,50.859299,50.859299,50.4687,50.703098,361400,34.192479\n1995-04-10,50.625,50.796799,50.5937,50.796799,285400,34.255667\n1995-04-11,50.921799,50.9375,50.5937,50.625,250300,34.139812\n1995-04-12,50.703098,50.796799,50.609299,50.796799,150900,34.255667\n1995-04-13,50.890598,51.0937,50.859299,51.078098,243400,34.445366\n1995-04-17,51.265598,51.3125,50.625,50.7812,178900,34.245148\n1995-04-18,50.828098,50.828098,50.484299,50.609299,329500,34.129223\n1995-04-19,50.609299,50.7187,50.296799,50.5625,223000,34.097664\n1995-04-20,50.703098,50.7812,50.4375,50.640598,207900,34.150331\n1995-04-21,50.6562,50.9062,50.6562,50.890598,145000,34.318922\n1995-04-24,50.859299,51.484299,50.859299,51.484299,169000,34.719294\n1995-04-25,51.4062,51.484299,51.3125,51.3437,293200,34.624479\n1995-04-26,51.25,51.421799,51.125,51.390598,204400,34.656106\n1995-04-27,51.3125,51.546799,51.2812,51.515598,502200,34.740402\n1995-04-28,51.5,51.671799,51.171799,51.5937,130800,34.793071\n1995-05-01,51.546799,51.6562,51.453098,51.453098,518700,34.698254\n1995-05-02,51.5,51.640598,51.390598,51.5625,228400,34.772031\n1995-05-03,51.734299,52.2812,51.734299,52.2812,724700,35.256698\n1995-05-04,52.3437,52.6875,52.125,52.25,311400,35.235658\n1995-05-05,52.4687,52.4687,52.078098,52.1875,314900,35.19351\n1995-05-08,52.140598,52.734299,52.0937,52.5625,183100,35.446397\n1995-05-09,52.7812,52.8437,52.4375,52.515598,180600,35.414768\n1995-05-10,52.6562,52.671799,52.328098,52.578098,330300,35.456916\n1995-05-11,52.6562,52.7187,52.484299,52.7187,351700,35.551734\n1995-05-12,52.515598,52.875,52.5,52.75,94600,35.572841\n1995-05-15,52.890598,53.0,52.7812,53.0,147200,35.741433\n1995-05-16,52.984299,53.1562,52.890598,53.0312,221600,35.762473\n1995-05-17,53.0312,53.046799,52.7812,52.828098,189200,35.625508\n1995-05-18,52.734299,52.765598,52.0625,52.0625,577800,35.109214\n1995-05-19,51.984299,52.1875,51.9062,52.0937,363900,35.130254\n1995-05-22,52.265598,52.75,52.234299,52.671799,216000,35.520105\n1995-05-23,52.765598,53.1562,52.609299,53.1562,136800,35.846769\n1995-05-24,53.25,53.421799,52.796799,53.125,370800,35.825728\n1995-05-25,52.9687,53.2187,52.7187,53.171799,379900,35.857288\n1995-05-26,52.9687,52.984299,52.359299,52.5625,518600,35.446397\n1995-05-30,52.6875,52.828098,52.375,52.546799,61500,35.435809\n1995-05-31,52.453098,53.640598,52.453098,53.640598,564500,36.173431\n1995-06-01,53.4062,53.9062,53.25,53.5,810100,36.078616\n1995-06-02,53.2812,53.921799,53.0937,53.546799,112900,36.110175\n1995-06-05,53.546799,54.046799,53.5,53.875,257200,36.331503\n1995-06-06,53.828098,54.015598,53.7812,53.7812,165800,36.268248\n1995-06-07,53.671799,53.703098,53.4375,53.5,41900,36.078616\n1995-06-08,53.484299,53.640598,53.375,53.375,141100,35.99432\n1995-06-09,53.265598,53.328098,52.75,53.0625,304900,35.783581\n1995-06-12,53.234299,53.578098,53.203098,53.359299,378500,35.983732\n1995-06-13,53.609299,53.9687,53.5625,53.9375,120000,36.373651\n1995-06-14,53.859299,53.953098,53.6875,53.890598,389400,36.342022\n1995-06-15,53.9687,54.234299,53.875,54.125,274500,36.500095\n1995-06-16,53.703098,53.9687,53.703098,53.9687,325100,36.608425\n1995-06-19,54.125,54.609299,54.0937,54.578098,134600,37.021796\n1995-06-20,54.421799,54.609299,54.390598,54.4375,287800,36.926424\n1995-06-21,54.609299,54.6562,54.4062,54.4062,158200,36.905193\n1995-06-22,54.640598,55.1562,54.640598,55.125,297000,37.392774\n1995-06-23,55.0,55.0312,54.8437,55.015598,315400,37.318564\n1995-06-26,54.859299,54.953098,54.359299,54.359299,132900,36.873378\n1995-06-27,54.3437,54.703098,54.2187,54.25,127700,36.799238\n1995-06-28,54.25,54.75,54.078098,54.5312,212600,36.989984\n1995-06-29,54.5312,54.671799,54.0625,54.4375,89200,36.926424\n1995-06-30,54.546799,54.734299,54.296799,54.4062,714100,36.905193\n1995-07-03,54.4687,54.609299,54.4687,54.609299,9500,37.04296\n1995-07-05,54.765598,55.0625,54.640598,54.8125,409300,37.180797\n1995-07-06,54.828098,55.515598,54.7187,55.515598,202500,37.657727\n1995-07-07,55.4062,55.7812,55.375,55.765598,481700,37.827309\n1995-07-10,55.8125,55.953098,55.703098,55.796799,400400,37.848473\n1995-07-11,55.75,55.796799,55.453098,55.5312,420500,37.668311\n1995-07-12,55.640598,56.3125,55.5312,56.2187,203400,38.13466\n1995-07-13,56.140598,56.265598,56.0312,56.0937,215600,38.04987\n1995-07-14,55.796799,56.0937,55.75,56.046799,543700,38.018055\n1995-07-17,56.125,56.421799,56.078098,56.359299,171500,38.230032\n1995-07-18,56.265598,56.265598,55.828098,55.875,221200,37.901519\n1995-07-19,55.6562,55.7187,54.203098,55.265598,486600,37.488146\n1995-07-20,55.328098,55.578098,55.0312,55.4687,318900,37.625915\n1995-07-21,55.421799,55.625,55.1875,55.4062,94000,37.58352\n1995-07-24,55.484299,55.875,55.484299,55.8125,108400,37.859124\n1995-07-25,55.875,56.3125,55.703098,56.234299,107400,38.145241\n1995-07-26,56.203098,56.4687,56.171799,56.1875,167800,38.113496\n1995-07-27,56.453098,56.703098,56.453098,56.6562,187700,38.431428\n1995-07-28,56.703098,56.703098,56.25,56.296799,415600,38.187637\n1995-07-31,56.3437,56.390598,56.0312,56.1562,342500,38.092265\n1995-08-01,56.234299,56.234299,55.75,56.0625,141000,38.028705\n1995-08-02,56.390598,56.796799,55.8437,55.9375,240400,37.943915\n1995-08-03,55.546799,55.953098,55.453098,55.921799,1193600,37.933264\n1995-08-04,56.0312,56.078098,55.921799,55.984299,240500,37.975659\n1995-08-07,56.109299,56.2812,56.078098,56.109299,193100,38.06045\n1995-08-08,56.140598,56.3125,55.9375,56.109299,951900,38.06045\n1995-08-09,56.296799,56.296799,56.046799,56.0937,107100,38.04987\n1995-08-10,56.1562,56.2812,55.734299,55.9687,280800,37.965079\n1995-08-11,55.921799,55.984299,55.421799,55.6562,257200,37.753101\n1995-08-14,55.796799,56.1562,55.671799,56.1562,254800,38.092265\n1995-08-15,56.140598,56.140598,55.6562,56.046799,44400,38.018055\n1995-08-16,56.0,56.203098,55.9375,56.203098,374900,38.124077\n1995-08-17,56.234299,56.234299,55.9062,56.109299,353800,38.06045\n1995-08-18,56.390598,56.4062,56.125,56.171799,85400,38.102846\n1995-08-21,56.4062,56.640598,56.0,56.015598,266700,37.996891\n1995-08-22,56.0937,56.2187,55.8125,56.125,220200,38.071101\n1995-08-23,56.171799,56.203098,55.921799,55.921799,176500,37.933264\n1995-08-24,55.9375,56.0937,55.75,55.984299,167400,37.975659\n1995-08-25,56.078098,56.390598,56.078098,56.296799,195000,38.187637\n1995-08-28,56.453098,56.453098,56.0,56.0937,293000,38.04987\n1995-08-29,56.1562,56.25,55.765598,56.234299,1133100,38.145241\n1995-08-30,56.2812,56.421799,56.2187,56.359299,437400,38.230032\n1995-08-31,56.3437,56.5,56.296799,56.4062,491900,38.261847\n1995-09-01,56.390598,56.75,56.3437,56.6562,629900,38.431428\n1995-09-05,56.828098,57.2187,56.734299,57.1875,272200,38.791823\n1995-09-06,57.234299,57.359299,57.2187,57.296799,214500,38.865964\n1995-09-07,57.359299,57.390598,57.25,57.328098,258800,38.887195\n1995-09-08,57.546799,57.546799,57.171799,57.546799,107300,39.035545\n1995-09-11,57.640598,57.8125,57.640598,57.703098,260700,39.141568\n1995-09-12,57.7187,57.9687,57.578098,57.9687,139500,39.321733\n1995-09-13,57.9062,58.3125,57.890598,58.234299,239800,39.501895\n1995-09-14,58.4375,58.8125,58.2812,58.765598,457600,39.86229\n1995-09-15,58.4062,58.578098,58.203098,58.4375,431200,39.851312\n1995-09-18,58.234299,58.265598,57.9375,58.2187,307500,39.702102\n1995-09-19,58.328098,58.5312,58.125,58.5,549600,39.893934\n1995-09-20,58.5937,58.7812,58.546799,58.7812,290800,40.085698\n1995-09-21,58.703098,58.75,58.0625,58.296799,508500,39.755361\n1995-09-22,57.828098,58.375,57.7812,58.3125,449900,39.766069\n1995-09-25,58.375,58.375,58.015598,58.2187,130000,39.702102\n1995-09-26,58.390598,58.5312,58.0625,58.203098,466600,39.691463\n1995-09-27,57.953098,58.1875,57.5937,58.1562,654400,39.659481\n1995-09-28,58.203098,58.5937,58.1562,58.5937,456200,39.957833\n1995-09-29,58.546799,58.9062,58.4062,58.484299,606600,39.883226\n1995-10-02,58.484299,58.625,58.046799,58.1875,293100,39.680825\n1995-10-03,58.1875,58.3437,57.953098,58.25,839700,39.723447\n1995-10-04,58.2812,58.2812,58.109299,58.1875,248800,39.680825\n1995-10-05,58.25,58.359299,58.0937,58.359299,268800,39.797983\n1995-10-06,58.390598,58.578098,58.390598,58.4062,75300,39.829968\n1995-10-09,58.1875,58.2187,57.8125,57.921799,358200,39.499631\n1995-10-10,57.328098,57.890598,57.265598,57.890598,360800,39.478354\n1995-10-11,58.0937,58.0937,57.875,58.078098,228100,39.606219\n1995-10-12,58.125,58.4687,58.125,58.4375,199800,39.851312\n1995-10-13,58.703098,58.859299,58.625,58.625,488100,39.979177\n1995-10-16,58.453098,58.5625,58.3437,58.3437,443600,39.787346\n1995-10-17,58.4062,58.796799,58.296799,58.734299,146000,40.053713\n1995-10-18,59.015598,59.0937,58.7187,58.9062,228100,40.170941\n1995-10-19,58.875,59.1875,58.796799,59.1875,500600,40.362773\n1995-10-20,59.140598,59.1875,58.828098,58.828098,748400,40.11768\n1995-10-23,58.546799,58.8125,58.546799,58.703098,533000,40.032436\n1995-10-24,58.7812,58.8437,58.625,58.765598,172200,40.075058\n1995-10-25,58.8125,58.890598,58.2812,58.2812,302700,39.744724\n1995-10-26,58.375,58.421799,57.2812,57.75,606800,39.382473\n1995-10-27,57.6875,58.1875,57.421799,58.1875,905800,39.680825\n1995-10-30,58.328098,58.5937,58.328098,58.5625,488700,39.936556\n1995-10-31,58.7187,58.8437,58.3125,58.3125,508200,39.766069\n1995-11-01,58.2812,58.7812,58.234299,58.7812,415700,40.085698\n1995-11-02,58.6875,59.1562,58.6875,59.1562,326000,40.341428\n1995-11-03,59.234299,59.25,59.0625,59.234299,615000,40.394687\n1995-11-06,59.2187,59.2187,59.015598,59.0312,311200,40.256185\n1995-11-07,59.0,59.0,58.625,58.8125,358200,40.107043\n1995-11-08,58.890598,59.375,58.890598,59.3437,357200,40.469293\n1995-11-09,59.578098,59.578098,59.2812,59.5625,503700,40.618503\n1995-11-10,59.234299,59.609299,59.2187,59.5312,795600,40.597159\n1995-11-13,59.421799,59.6562,59.3125,59.4687,818500,40.554537\n1995-11-14,59.359299,59.484299,59.078098,59.078098,341800,40.288167\n1995-11-15,59.140598,59.6875,59.109299,59.671799,584700,40.693039\n1995-11-16,59.671799,60.0625,59.6562,60.0,577700,40.916855\n1995-11-17,60.0937,60.203098,60.015598,60.1875,267000,41.044721\n1995-11-20,60.2812,60.296799,59.859299,59.875,448800,40.831612\n1995-11-21,59.9375,60.359299,59.921799,60.359299,119200,41.161878\n1995-11-22,60.3125,60.375,60.171799,60.171799,351600,41.034013\n1995-11-24,60.234299,60.328098,60.1875,60.328098,62400,41.140601\n1995-11-27,60.453098,60.640598,60.3437,60.3437,273000,41.151241\n1995-11-28,60.296799,60.984299,60.1562,60.984299,479000,41.588095\n1995-11-29,61.0937,61.0937,60.8437,61.046799,549200,41.630717\n1995-11-30,61.046799,61.203098,60.8437,60.9062,286200,41.534837\n1995-12-01,60.984299,61.078098,60.8437,60.984299,465200,41.588095\n1995-12-04,61.125,61.734299,61.046799,61.734299,631700,42.099556\n1995-12-05,61.671799,62.203098,61.625,62.140598,567700,42.376631\n1995-12-06,62.296799,62.5312,62.0,62.2812,272800,42.472514\n1995-12-07,62.203098,62.2812,61.9062,61.953098,289700,42.248766\n1995-12-08,62.2187,62.234299,61.765598,62.1562,296900,42.387271\n1995-12-11,62.2812,62.546799,62.1562,62.421799,186500,42.568395\n1995-12-12,62.140598,62.359299,62.140598,62.234299,299400,42.44053\n1995-12-13,62.3125,62.703098,62.3125,62.625,390700,42.706968\n1995-12-14,62.6875,62.796799,62.015598,62.171799,395000,42.397908\n1995-12-15,61.875,61.875,61.5937,61.8125,416700,42.413484\n1995-12-18,61.265598,61.265598,60.609299,60.625,862300,41.598665\n1995-12-19,60.6562,61.3437,60.578098,61.265598,1022600,42.03822\n1995-12-20,61.453098,61.515598,60.671799,60.671799,1349800,41.630776\n1995-12-21,61.0312,61.171799,60.75,60.984299,857600,41.845203\n1995-12-22,61.3125,61.375,61.140598,61.203098,332800,41.995335\n1995-12-26,61.453098,61.5312,61.328098,61.5,432200,42.199058\n1995-12-27,61.6562,61.6562,61.359299,61.4687,151800,42.177581\n1995-12-28,61.3437,61.6562,61.25,61.4062,256200,42.134696\n1995-12-29,61.4687,61.5312,61.25,61.484299,339200,42.188284\n1996-01-02,61.4062,62.140598,61.3437,62.140598,514400,42.638613\n1996-01-03,62.3437,62.5,62.0,62.3125,610300,42.756566\n1996-01-04,62.390598,62.625,61.2187,61.7187,1129700,42.349122\n1996-01-05,61.3125,61.75,61.171799,61.5937,302400,42.263352\n1996-01-08,61.8125,61.9062,61.734299,61.828098,179900,42.424187\n1996-01-09,62.015598,62.0625,60.625,60.765598,415500,41.695138\n1996-01-10,60.6875,60.8125,59.640598,59.9687,787700,41.148336\n1996-01-11,60.0625,60.328098,59.7812,60.328098,513200,41.394942\n1996-01-12,60.484299,60.5,59.671799,60.234299,390400,41.33058\n1996-01-15,60.25,60.453098,59.859299,60.109299,154200,41.244809\n1996-01-16,60.4062,60.890598,59.890598,60.8437,454600,41.748729\n1996-01-17,60.703098,61.125,60.453098,60.6562,407600,41.620073\n1996-01-18,60.890598,60.9062,60.375,60.859299,425600,41.759432\n1996-01-19,60.859299,61.421799,60.796799,61.265598,169800,42.03822\n1996-01-22,61.2187,61.421799,61.109299,61.2812,288200,42.048926\n1996-01-23,61.25,61.421799,61.125,61.421799,362200,42.145399\n1996-01-24,61.515598,62.046799,61.484299,61.921799,1506500,42.488481\n1996-01-25,61.9687,62.0,61.625,61.703098,298400,42.338417\n1996-01-26,61.6562,62.265598,61.578098,62.234299,726300,42.702907\n1996-01-29,62.1875,62.484299,62.140598,62.484299,254800,42.874448\n1996-01-30,62.609299,63.140598,62.5312,63.015598,272100,43.239006\n1996-01-31,63.0,63.6875,62.9687,63.671799,498000,43.689267\n1996-02-01,63.609299,63.9062,63.5625,63.9062,373900,43.850105\n1996-02-02,63.875,64.015602,63.5,63.640598,496200,43.667859\n1996-02-05,63.5312,64.25,63.4375,64.156197,295300,44.021643\n1996-02-06,64.1875,64.828102,64.093697,64.765602,301600,44.439795\n1996-02-07,64.75,65.140602,64.6875,65.140602,585100,44.697106\n1996-02-08,65.046799,65.906197,64.921799,65.843697,1526600,45.179544\n1996-02-09,65.8125,66.375,65.515602,65.828102,804600,45.168844\n1996-02-12,65.953102,66.625,65.890602,66.328102,626500,45.511926\n1996-02-13,66.046799,66.6875,66.0,66.1875,1045900,45.41545\n1996-02-14,66.140602,66.359299,65.578102,65.609299,431400,45.018709\n1996-02-15,65.468697,65.906197,65.125,65.203102,889500,44.739992\n1996-02-16,65.078102,65.234299,64.734299,64.9375,606100,44.557745\n1996-02-20,64.4375,64.625,63.875,64.296799,492300,44.118119\n1996-02-21,64.421799,65.140602,64.421799,65.093697,434000,44.664922\n1996-02-22,65.453102,66.328102,65.406197,66.125,721400,45.372564\n1996-02-23,66.5,66.609299,65.328102,65.9375,1430400,45.243909\n1996-02-26,65.75,65.890602,64.968697,65.0,1408400,44.60063\n1996-02-27,65.156197,65.171799,64.5,64.796799,601000,44.461201\n1996-02-28,65.3125,65.734299,64.5,64.5,865900,44.257549\n1996-02-29,64.125,64.953102,63.875,63.875,914300,43.828696\n1996-03-01,64.640602,64.890602,63.609299,64.890602,1452600,44.525566\n1996-03-04,65.031197,65.656197,64.8125,65.265602,652600,44.782877\n1996-03-05,65.109299,65.875,65.078102,65.875,402000,45.201024\n1996-03-06,65.781197,65.968697,65.296799,65.296799,624700,44.804283\n1996-03-07,65.421799,65.656197,65.234299,65.625,534200,45.029483\n1996-03-08,64.1875,64.875,62.0,63.5,2289900,43.571385\n1996-03-11,63.25,64.328102,63.0625,64.234299,1511400,44.075234\n1996-03-12,63.8125,64.046799,62.9062,63.734299,1385300,43.732152\n1996-03-13,64.156197,64.4375,63.796799,64.171799,1019300,44.032349\n1996-03-14,64.265602,64.8125,64.234299,64.453102,725600,44.225369\n1996-03-15,64.0625,64.328102,63.75,64.125,869200,44.195664\n1996-03-18,64.718697,65.359299,64.578102,65.359299,917600,45.046356\n1996-03-19,65.9375,65.9375,65.0,65.218697,858300,44.949452\n1996-03-20,65.531197,65.531197,64.640602,65.140602,744300,44.895628\n1996-03-21,65.218697,65.25,64.765602,64.984299,650800,44.787902\n1996-03-22,65.015602,65.328102,64.9375,65.156197,344500,44.906376\n1996-03-25,65.75,65.8125,64.859299,65.031197,802400,44.820224\n1996-03-26,64.921799,65.515602,64.859299,65.343697,969400,45.035603\n1996-03-27,65.4375,65.484299,64.6875,64.75,809600,44.626421\n1996-03-28,64.515602,65.031197,64.5,65.0,1001300,44.798723\n1996-03-29,65.1875,65.1875,64.375,64.6875,457700,44.583345\n1996-04-01,65.0,65.453102,64.796799,65.4375,773400,45.100253\n1996-04-02,65.515602,65.5625,65.296799,65.5625,638800,45.186405\n1996-04-03,65.328102,65.609299,65.171799,65.5625,288000,45.186405\n1996-04-04,65.5625,65.718697,65.5,65.515602,934900,45.154082\n1996-04-08,63.875,64.390602,63.5937,64.390602,2217200,44.37872\n1996-04-09,64.640602,64.640602,64.015602,64.140602,1180900,44.206417\n1996-04-10,64.031197,64.3125,62.75,63.0,939500,43.420301\n1996-04-11,63.1562,63.4687,62.125,62.921799,1276000,43.366404\n1996-04-12,63.359299,63.8125,63.171799,63.7187,411300,43.915638\n1996-04-15,64.046799,64.328102,63.8437,64.25,484200,44.281815\n1996-04-16,64.375,64.656197,64.234299,64.4375,429700,44.411042\n1996-04-17,64.375,64.5625,63.8437,64.281197,525300,44.303316\n1996-04-18,64.4375,64.578102,64.093697,64.375,818600,44.367967\n1996-04-19,64.6875,64.859299,64.421799,64.531197,885100,44.475619\n1996-04-22,65.125,65.281197,64.593697,65.031197,828900,44.820224\n1996-04-23,64.921799,65.281197,64.781197,65.234299,225400,44.960205\n1996-04-24,65.5625,65.5625,64.859299,65.0,699000,44.798723\n1996-04-25,65.140602,65.5625,64.75,65.296799,475500,45.00328\n1996-04-26,65.468697,65.8125,65.234299,65.4375,563300,45.100253\n1996-04-29,65.156197,65.5625,65.156197,65.4375,219900,45.100253\n1996-04-30,65.4375,65.5625,65.125,65.390602,184400,45.067931\n1996-05-01,65.375,65.796799,65.281197,65.531197,561900,45.16483\n1996-05-02,65.015602,65.375,64.093697,64.406197,1299600,44.389468\n1996-05-03,64.781197,64.984299,63.75,64.3125,1329300,44.324891\n1996-05-06,64.5625,64.5625,63.6875,64.25,647500,44.281815\n1996-05-07,64.25,64.25,63.765598,63.984299,582100,44.098691\n1996-05-08,63.671799,64.781197,63.078098,64.781197,1694700,44.647922\n1996-05-09,64.5625,65.0625,64.5,64.734299,593400,44.615599\n1996-05-10,65.375,65.593697,65.0625,65.375,925100,45.057178\n1996-05-13,65.531197,66.640602,65.468697,66.359299,867500,45.735567\n1996-05-14,66.625,66.9375,66.5625,66.765602,632300,46.015596\n1996-05-15,66.781197,67.3125,66.640602,66.6875,466100,45.961767\n1996-05-16,66.3125,66.9375,66.281197,66.828102,514400,46.058672\n1996-05-17,67.0,67.296799,67.0,67.1875,427700,46.306373\n1996-05-20,67.656197,67.75,66.984299,67.640602,788500,46.618656\n1996-05-21,67.75,67.875,67.5,67.546799,352700,46.554005\n1996-05-22,67.625,68.1875,67.390602,68.1875,1218100,46.995584\n1996-05-23,68.281197,68.4375,67.531197,67.906197,967900,46.801706\n1996-05-24,67.968697,68.328102,67.9375,68.171799,659100,46.984762\n1996-05-28,68.375,68.375,67.3125,67.484299,456400,46.51093\n1996-05-29,67.625,67.6875,66.8125,67.031197,649600,46.198647\n1996-05-30,66.9375,67.718697,66.75,67.375,895800,46.4356\n1996-05-31,67.359299,67.640602,66.875,66.875,923500,46.090994\n1996-06-03,66.890602,67.109299,66.703102,67.0625,503100,46.220221\n1996-06-04,67.375,67.593697,67.25,67.531197,627100,46.543252\n1996-06-05,67.5625,68.203102,67.421799,68.125,428000,46.952508\n1996-06-06,68.5,68.5,67.5,67.625,523300,46.607903\n1996-06-07,66.1875,67.640602,66.1875,67.625,1422800,46.607903\n1996-06-10,67.531197,67.734299,67.296799,67.406197,548800,46.457101\n1996-06-11,67.656197,68.031197,67.343697,67.343697,688700,46.414025\n1996-06-12,67.5625,67.734299,67.109299,67.218697,1120600,46.327874\n1996-06-13,67.375,67.468697,66.828102,67.218697,1502000,46.327874\n1996-06-14,67.25,67.25,66.718697,66.9375,1468200,46.13407\n1996-06-17,66.968697,67.218697,66.718697,66.890602,1101000,46.101747\n1996-06-18,66.843697,67.031197,66.328102,66.343697,718000,45.724814\n1996-06-19,66.656197,67.0,66.421799,66.640602,552100,45.929445\n1996-06-20,66.703102,66.921799,66.1875,66.593697,1234400,45.897117\n1996-06-21,66.5625,66.8125,66.375,66.8125,573300,46.291912\n1996-06-24,66.968697,67.218697,66.843697,66.9375,416500,46.37852\n1996-06-25,67.125,67.171799,66.781197,66.859299,454400,46.324337\n1996-06-26,66.781197,66.921799,66.390602,66.406197,838200,46.0104\n1996-06-27,66.406197,67.046799,66.156197,66.875,1090500,46.335216\n1996-06-28,67.156197,67.5,66.968697,67.109299,1060800,46.497553\n1996-07-01,67.281197,67.703102,67.1875,67.6875,471300,46.898168\n1996-07-02,67.625,67.640602,67.328102,67.4375,389400,46.724952\n1996-07-03,67.406197,67.5,67.0625,67.296799,308300,46.627465\n1996-07-05,66.125,66.4375,65.375,65.578102,842900,45.436644\n1996-07-08,65.625,65.984299,65.125,65.3125,942000,45.252618\n1996-07-09,65.609299,65.8125,65.375,65.578102,716300,45.436644\n1996-07-10,65.5625,65.843697,64.921799,65.843697,1132300,45.620664\n1996-07-11,65.5625,65.6875,63.875,64.515602,1587400,44.700477\n1996-07-12,64.625,64.906197,64.078102,64.5625,1312000,44.73297\n1996-07-15,64.718697,64.718697,62.6562,62.6562,1951300,43.412166\n1996-07-16,62.8437,63.5,60.375,62.8125,4141000,43.52046\n1996-07-17,63.75,64.0,63.0,63.5625,1340500,44.040107\n1996-07-18,63.75,64.546799,63.328098,64.4375,1432500,44.646363\n1996-07-19,64.281197,64.375,63.625,63.9687,1216000,44.321549\n1996-07-22,63.5312,63.75,63.015598,63.5,519800,43.996803\n1996-07-23,63.5937,63.9375,62.578098,62.6875,1479800,43.433852\n1996-07-24,61.9375,63.1875,61.625,62.8125,2463400,43.52046\n1996-07-25,63.4375,63.5625,63.125,63.375,1989800,43.910196\n1996-07-26,63.5625,63.8125,63.390598,63.7187,642300,44.148333\n1996-07-29,63.625,63.7187,62.9375,63.015598,1297900,43.661179\n1996-07-30,63.4375,63.6875,62.9375,63.625,1868200,44.083411\n1996-07-31,63.671799,64.265602,63.515598,64.093697,767100,44.408154\n1996-08-01,64.156197,65.390602,64.0625,65.156197,1168600,45.144321\n1996-08-02,66.0,66.593697,65.75,66.5625,1098500,46.118697\n1996-08-05,66.578102,66.593697,65.968697,66.171799,710200,45.847994\n1996-08-06,66.156197,66.515602,65.796799,66.375,640000,45.988785\n1996-08-07,66.625,66.75,66.125,66.546799,917800,46.107818\n1996-08-08,66.5625,66.5625,66.1875,66.4375,925100,46.032089\n1996-08-09,66.671799,66.765602,66.0625,66.218697,648800,45.880488\n1996-08-12,66.25,66.859299,66.0,66.703102,764200,46.216115\n1996-08-13,66.578102,66.625,66.0,66.171799,805200,45.847994\n1996-08-14,66.1875,66.468697,66.0,66.406197,533800,46.0104\n1996-08-15,66.234299,66.625,66.203102,66.281197,632600,45.923792\n1996-08-16,66.468697,66.875,66.468697,66.843697,475100,46.313527\n1996-08-19,66.859299,66.921799,66.625,66.875,436400,46.335216\n1996-08-20,66.890602,66.906197,66.671799,66.828102,645700,46.302722\n1996-08-21,66.656197,66.8125,66.375,66.593697,266400,46.140312\n1996-08-22,66.75,67.343697,66.718697,67.218697,584800,46.573351\n1996-08-23,67.093697,67.203102,66.640602,66.859299,757100,46.324337\n1996-08-26,66.703102,66.781197,66.421799,66.531197,784700,46.097008\n1996-08-27,66.718697,66.843697,66.5,66.781197,389400,46.270223\n1996-08-28,66.9375,67.0,66.656197,66.671799,227600,46.194426\n1996-08-29,66.593697,66.593697,65.625,66.015602,680200,45.739771\n1996-08-30,66.0,66.015602,65.125,65.328102,1500900,45.263428\n1996-09-03,64.468697,65.843697,64.375,65.75,1782000,45.555745\n1996-09-04,65.75,66.75,65.484299,65.8125,930200,45.599049\n1996-09-05,65.5,65.765602,65.015602,65.015602,616600,45.046908\n1996-09-06,65.25,66.171799,65.140602,65.9375,804900,45.685657\n1996-09-09,66.1875,66.734299,66.078102,66.718697,719200,46.226919\n1996-09-10,66.625,66.906197,66.375,66.6875,669900,46.205304\n1996-09-11,66.5625,67.125,66.406197,67.0625,713500,46.465128\n1996-09-12,67.25,67.734299,67.156197,67.531197,713300,46.789871\n1996-09-13,68.3125,68.625,68.125,68.5625,2008800,47.504423\n1996-09-16,68.656197,69.156197,68.5625,68.8125,1220200,47.677638\n1996-09-17,68.875,68.984299,68.234299,68.656197,604700,47.569342\n1996-09-18,68.5625,68.75,68.218697,68.484299,225500,47.45024\n1996-09-19,68.375,68.796799,68.281197,68.75,410000,47.634334\n1996-09-20,68.593697,68.781197,68.375,68.640602,948400,47.80329\n1996-09-23,68.468697,68.75,68.156197,68.625,466800,47.792425\n1996-09-24,68.5625,69.25,68.3125,68.609299,1269800,47.78149\n1996-09-25,68.9375,68.9375,68.5,68.656197,1353300,47.814151\n1996-09-26,68.75,69.1875,68.390602,68.625,773200,47.792425\n1996-09-27,68.718697,68.718697,68.375,68.6875,407600,47.835952\n1996-09-30,68.781197,69.0625,68.578102,68.625,578200,47.792425\n1996-10-01,68.703102,69.046799,68.4375,69.0,561400,48.053586\n1996-10-02,69.25,69.593697,69.156197,69.468697,609000,48.379999\n1996-10-03,69.5,69.531197,69.203102,69.406197,342200,48.336473\n1996-10-04,69.6875,70.343697,69.5,70.343697,754100,48.989375\n1996-10-07,70.3125,70.625,70.25,70.421799,235100,49.043767\n1996-10-08,70.453102,70.6875,70.015602,70.140602,386200,48.847934\n1996-10-09,70.25,70.375,69.515602,69.593697,528800,48.467053\n1996-10-10,69.593697,69.859299,69.375,69.453102,1562200,48.369139\n1996-10-11,69.718697,70.3125,69.718697,70.3125,331300,48.967648\n1996-10-14,70.3125,70.6875,70.296799,70.406197,201600,49.032901\n1996-10-15,71.125,71.125,69.953102,70.3125,776900,48.967648\n1996-10-16,70.5,70.656197,69.9375,70.656197,590100,49.207008\n1996-10-17,70.875,71.031197,70.546799,70.843697,792500,49.337589\n1996-10-18,70.875,71.3125,70.718697,71.218697,695800,49.59875\n1996-10-21,71.281197,71.625,70.9375,71.125,879200,49.533497\n1996-10-22,71.0625,71.078102,70.593697,70.625,848900,49.185282\n1996-10-23,70.468697,70.890602,70.1875,70.859299,735900,49.348455\n1996-10-24,70.859299,70.984299,70.218697,70.25,379300,48.924121\n1996-10-25,70.234299,70.5625,70.125,70.3125,568300,48.967648\n1996-10-28,70.375,70.734299,69.8125,69.843697,975400,48.64116\n1996-10-29,70.0625,70.578102,69.703102,70.406197,815900,49.032901\n1996-10-30,70.625,70.625,70.093697,70.171799,495400,48.86966\n1996-10-31,70.265602,70.875,70.1875,70.843697,726300,49.337589\n1996-11-01,70.984299,71.156197,70.265602,70.593697,821300,49.163482\n1996-11-04,70.6875,71.0625,70.531197,71.0625,1608400,49.48997\n1996-11-05,71.296799,71.796799,71.281197,71.468697,764000,49.772857\n1996-11-06,71.593697,72.875,71.531197,72.843697,1665000,50.730446\n1996-11-07,72.625,73.296799,72.531197,73.015602,1446300,50.850166\n1996-11-08,73.031197,73.4375,72.75,73.421799,1355700,51.133053\n1996-11-11,73.25,73.515602,73.218697,73.343697,655900,51.078661\n1996-11-12,73.546799,73.593697,73.015602,73.125,719900,50.926354\n1996-11-13,73.203102,73.5625,73.015602,73.453102,481900,51.154854\n1996-11-14,73.1875,73.9375,73.156197,73.9375,558800,51.492203\n1996-11-15,74.125,74.5,73.718697,74.031197,2250900,51.557456\n1996-11-18,74.171799,74.25,73.734299,74.046799,1518200,51.568321\n1996-11-19,74.140602,74.593697,74.031197,74.578102,1029300,51.938336\n1996-11-20,74.5625,75.015602,74.3125,74.703102,730000,52.02539\n1996-11-21,74.796799,74.843697,74.375,74.593697,653000,51.949197\n1996-11-22,74.703102,75.25,74.703102,75.171799,965100,52.351804\n1996-11-25,75.25,76.156197,75.078102,76.125,2058800,53.015641\n1996-11-26,76.281197,76.6875,75.468697,75.875,2861800,52.841533\n1996-11-27,76.0625,76.1875,75.640602,75.734299,872000,52.743545\n1996-11-29,76.0,76.218697,75.859299,76.015602,1073400,52.939453\n1996-12-02,75.921799,76.125,75.390602,76.046799,1350600,52.961179\n1996-12-03,76.0625,76.578102,74.75,74.75,1777800,52.058051\n1996-12-04,74.875,75.0625,74.093697,74.953102,2365100,52.199497\n1996-12-05,74.843697,75.140602,74.531197,74.75,1697700,52.058051\n1996-12-06,73.25,74.75,72.656197,74.3125,3401800,51.753363\n1996-12-09,74.6875,75.421799,74.578102,75.406197,1864600,52.515045\n1996-12-10,75.5625,75.671799,74.968697,75.046799,1331600,52.26475\n1996-12-11,73.625,74.625,73.3125,74.359299,1847900,51.785955\n1996-12-12,74.781197,74.875,72.9375,73.125,2540200,50.926354\n1996-12-13,73.0625,73.578102,72.406197,73.3125,1678300,51.056935\n1996-12-16,73.5,73.6875,72.0625,72.375,1831100,50.404033\n1996-12-17,72.156197,73.281197,71.875,72.953102,2023200,50.80664\n1996-12-18,73.375,73.765602,73.265602,73.531197,1666800,51.209241\n1996-12-19,74.1875,75.125,73.890602,75.046799,2269300,52.26475\n1996-12-20,75.125,75.718697,74.75,74.843697,1543200,52.379452\n1996-12-23,75.093697,75.203102,74.328102,74.656197,1263700,52.24823\n1996-12-24,74.843697,75.203102,74.796799,75.203102,633000,52.630982\n1996-12-26,75.4375,75.828102,75.375,75.781197,1384300,53.035563\n1996-12-27,75.781197,76.031197,75.5,75.875,410400,53.101211\n1996-12-30,76.125,76.125,75.156197,75.218697,694100,52.641896\n1996-12-31,75.281197,75.375,73.843697,73.843697,1378100,51.679601\n1997-01-02,74.375,74.375,72.75,74.031197,2031900,51.810823\n1997-01-03,74.375,75.125,74.078102,75.093697,2123200,52.554415\n1997-01-06,75.093697,75.4375,74.3125,74.4375,1374100,52.095175\n1997-01-07,74.4375,75.468697,74.125,75.343697,939000,52.729378\n1997-01-08,75.75,75.781197,74.6875,74.6875,1802200,52.270138\n1997-01-09,75.0625,75.875,74.9375,75.3125,1415700,52.707545\n1997-01-10,74.25,76.25,74.25,76.125,2369500,53.276174\n1997-01-13,76.5,76.5,75.640602,76.015602,1364600,53.199611\n1997-01-14,76.6875,77.390602,76.5,76.968697,2111200,53.866636\n1997-01-15,76.718697,77.203102,76.375,76.781197,1583900,53.735414\n1997-01-16,77.031197,77.296799,76.5,77.093697,1308400,53.954117\n1997-01-17,77.203102,77.75,77.109299,77.5625,1604000,54.28221\n1997-01-20,77.75,78.093697,77.468697,77.656197,1889900,54.347784\n1997-01-21,77.375,78.546799,77.109299,78.281197,2785800,54.785191\n1997-01-22,78.3125,78.843697,77.875,78.843697,1201600,55.178857\n1997-01-23,79.0625,79.6875,76.906197,77.75,2601100,54.413432\n1997-01-24,77.875,77.906197,76.75,76.75,2176000,53.713581\n1997-01-27,76.875,77.234299,76.375,76.531197,2108500,53.560451\n1997-01-28,77.625,77.843697,76.0,76.75,4376000,53.713581\n1997-01-29,76.875,77.5,76.593697,77.5,1122700,54.238469\n1997-01-30,77.875,78.531197,77.406197,78.5,2126300,54.93832\n1997-01-31,78.9375,79.406197,78.375,78.406197,3208100,54.872672\n1997-02-03,78.718697,78.890602,78.359299,78.640602,755000,55.036721\n1997-02-04,78.75,79.203102,78.4375,79.125,654100,55.375727\n1997-02-05,79.234299,79.468697,77.125,77.640602,2255400,54.33687\n1997-02-06,77.781197,78.203102,77.406197,78.156197,2483100,54.697709\n1997-02-07,78.8125,79.25,77.781197,79.218697,2321000,55.441301\n1997-02-10,79.25,79.531197,78.421799,78.468697,1670000,54.916413\n1997-02-11,78.781197,79.375,78.125,79.375,1664300,55.55069\n1997-02-12,79.375,80.640602,79.265602,80.5,2675400,56.338023\n1997-02-13,80.8125,81.5625,80.6875,81.375,1104800,56.950393\n1997-02-14,81.1875,81.468697,80.906197,81.1875,1112900,56.819171\n1997-02-18,81.343697,81.9375,80.75,81.875,833100,57.300318\n1997-02-19,81.656197,82.0,81.156197,81.328102,648100,56.917571\n1997-02-20,81.031197,81.359299,80.156197,80.343697,1193900,56.228634\n1997-02-21,80.406197,80.765602,80.140602,80.375,1844400,56.250541\n1997-02-24,79.968697,81.375,79.968697,81.328102,787300,56.917571\n1997-02-25,81.406197,81.531197,80.875,81.390602,1653900,56.961312\n1997-02-26,81.3125,81.4375,79.625,80.593697,1597800,56.403596\n1997-02-27,80.8125,80.8125,79.375,79.390602,1813100,55.561609\n1997-02-28,79.25,79.875,79.0625,79.156197,2961200,55.39756\n1997-03-03,78.75,79.75,78.6875,79.6875,1210400,55.769394\n1997-03-04,79.906197,80.125,79.093697,79.25,1478700,55.463209\n1997-03-05,79.718697,80.593697,79.453102,80.593697,1254800,56.403596\n1997-03-06,80.625,80.906197,79.875,80.125,1528500,56.075579\n1997-03-07,80.4375,81.156197,80.25,80.843697,1859300,56.578559\n1997-03-10,80.9375,81.6875,80.546799,81.6875,1074900,57.169096\n1997-03-11,81.75,81.796799,81.25,81.25,624600,56.862911\n1997-03-12,81.3125,81.343697,80.296799,80.531197,1055600,56.359856\n1997-03-13,80.25,80.468697,79.0625,79.281197,2514100,55.485042\n1997-03-14,79.640602,80.031197,79.25,79.6875,1642700,55.769394\n1997-03-17,79.25,80.031197,78.4375,79.906197,3013500,55.922449\n1997-03-18,80.0,80.109299,78.718697,79.046799,1352500,55.320998\n1997-03-19,78.843697,79.5,78.0625,78.781197,1510000,55.135116\n1997-03-20,78.75,79.0,77.875,78.375,1808200,54.850839\n1997-03-21,78.6875,78.75,78.343697,78.4375,2810600,55.104806\n1997-03-24,78.5,79.578102,78.093697,79.531197,2145600,55.873163\n1997-03-25,79.343697,79.968697,78.531197,78.75,2125800,55.324348\n1997-03-26,79.0625,79.656197,78.6875,79.093697,1335300,55.565805\n1997-03-27,79.375,79.375,76.156197,77.0,2898500,54.094918\n1997-03-31,76.906197,77.125,75.25,75.375,4270700,52.953304\n1997-04-01,75.25,76.1875,75.046799,75.859299,3210900,53.293539\n1997-04-02,75.625,75.9375,74.4375,74.5,2672700,52.338589\n1997-04-03,74.4375,75.125,74.1875,74.906197,2284200,52.623955\n1997-04-04,74.5,75.859299,74.156197,75.843697,3706700,53.282578\n1997-04-07,76.1875,76.625,76.140602,76.140602,2596200,53.491164\n1997-04-08,76.125,76.781197,75.843697,76.6875,1822700,53.875377\n1997-04-09,77.0,77.046799,75.843697,76.0625,2453200,53.436294\n1997-04-10,76.093697,76.546799,75.6875,75.781197,1996500,53.23867\n1997-04-11,75.0625,75.3125,73.375,73.375,4221600,51.548241\n1997-04-14,73.734299,74.453102,73.3125,74.359299,3988500,52.239742\n1997-04-15,75.25,75.640602,74.5,75.625,2760400,53.128937\n1997-04-16,75.375,76.609299,75.25,76.5,2273300,53.743652\n1997-04-17,76.5,77.0,76.0,76.1875,1386000,53.524111\n1997-04-18,76.8125,76.984299,76.25,76.5625,1702100,53.78756\n1997-04-21,76.75,76.906197,75.468697,76.0625,2809400,53.436294\n1997-04-22,76.109299,77.734299,76.0,77.734299,3215000,54.610785\n1997-04-23,77.656197,78.031197,77.1875,77.8125,2047700,54.665724\n1997-04-24,78.046799,78.281197,76.875,77.421799,2689900,54.391244\n1997-04-25,77.0625,77.203102,76.421799,76.531197,1606200,53.765568\n1997-04-28,76.5,77.8125,76.375,77.281197,1666500,54.292467\n1997-04-29,78.593697,79.781197,78.296799,79.718697,3197400,56.004887\n1997-04-30,79.25,80.6875,79.218697,80.093697,3372200,56.268337\n1997-05-01,80.218697,80.531197,79.3125,80.0,2149500,56.202512\n1997-05-02,80.281197,81.671799,80.0625,81.4375,1448300,57.212401\n1997-05-05,81.656197,83.5625,81.296799,83.375,3636200,58.573555\n1997-05-06,82.968697,83.468697,82.5,83.3125,1721100,58.529647\n1997-05-07,83.0,83.0,81.406197,81.5,2327200,57.256309\n1997-05-08,81.3125,83.218697,81.1875,82.0625,2923700,57.651483\n1997-05-09,82.968697,83.125,81.656197,82.625,2558200,58.046657\n1997-05-12,82.9375,84.25,82.875,84.0,2357900,59.012637\n1997-05-13,83.9375,84.093697,83.015602,83.656197,1195100,58.771105\n1997-05-14,84.234299,84.5,83.4375,83.8125,2772000,58.880913\n1997-05-15,83.8125,84.593697,83.515602,84.375,884100,59.276087\n1997-05-16,84.125,84.3125,83.0,83.218697,1934200,58.463747\n1997-05-19,83.3125,83.906197,83.0625,83.468697,1375800,58.63938\n1997-05-20,83.281197,84.6875,82.75,84.468697,1756400,59.341911\n1997-05-21,84.6875,84.984299,83.656197,84.281197,1208000,59.210187\n1997-05-22,84.375,84.468697,83.5625,84.0,907600,59.012637\n1997-05-23,84.375,85.218697,84.156197,84.781197,644400,59.561453\n1997-05-27,84.375,85.531197,84.265602,85.125,1531000,59.802985\n1997-05-28,85.125,85.453102,84.515602,85.109299,716700,59.791955\n1997-05-29,85.093697,85.25,84.5,84.609299,1282700,59.440689\n1997-05-30,83.218697,85.5625,83.125,85.156197,2143300,59.824902\n1997-06-02,85.343697,85.5,84.718697,84.781197,1479100,59.561453\n1997-06-03,84.406197,85.4375,84.343697,84.5,1562100,59.363903\n1997-06-04,84.531197,84.75,84.078102,84.406197,1063100,59.298003\n1997-06-05,84.593697,85.3125,84.421799,84.718697,1180800,59.517544\n1997-06-06,84.593697,86.4375,84.593697,86.375,1511100,60.681149\n1997-06-09,86.4375,86.875,86.343697,86.8125,1823400,60.988507\n1997-06-10,86.843697,87.406197,86.5,87.078102,1217500,61.175101\n1997-06-11,87.0,87.406197,86.828102,87.281197,1673800,61.317781\n1997-06-12,87.875,89.0,87.5625,88.968697,4273300,62.503303\n1997-06-13,89.0625,90.0,88.906197,89.718697,2132900,63.030201\n1997-06-16,89.75,90.0,89.468697,89.75,800400,63.052193\n1997-06-17,89.4375,90.234299,88.9375,89.625,2048100,62.964376\n1997-06-18,89.125,89.625,88.968697,89.3125,1971900,62.744835\n1997-06-19,89.609299,90.5,89.375,90.234299,2029900,63.392428\n1997-06-20,89.9375,90.343697,89.5,89.578102,1396900,63.176475\n1997-06-23,89.406197,89.906197,87.343697,87.406197,3991500,61.644702\n1997-06-24,88.25,89.875,88.0,89.625,4895100,63.209551\n1997-06-25,89.484299,90.25,87.9375,89.0,4708900,62.768759\n1997-06-26,88.843697,89.625,87.6875,88.5625,2569900,62.460204\n1997-06-27,88.968697,89.718697,88.6875,88.906197,2617900,62.702602\n1997-06-30,88.906197,89.468697,87.875,88.3125,2384900,62.283888\n1997-07-01,88.5,89.5625,88.390602,89.343697,1749000,63.011157\n1997-07-02,89.5625,90.8125,89.218697,90.8125,1961800,64.047055\n1997-07-03,91.875,92.156197,91.468697,92.0625,2539100,64.928639\n1997-07-07,92.375,92.5,90.906197,91.125,1158600,64.267451\n1997-07-08,91.25,92.1875,91.1875,92.078102,3396500,64.939642\n1997-07-09,92.3125,92.375,90.031197,91.0625,5390300,64.223372\n1997-07-10,91.125,91.843697,90.484299,91.468697,2072200,64.509849\n1997-07-11,91.593697,92.1875,91.375,91.781197,2258900,64.730245\n1997-07-14,91.890602,92.3125,91.218697,92.0625,3186400,64.928639\n1997-07-15,92.3125,92.765602,91.468697,92.531197,2983100,65.259195\n1997-07-16,93.125,94.093697,92.890602,93.75,2621300,66.118777\n1997-07-17,93.625,93.8125,92.781197,93.281197,1794800,65.788145\n1997-07-18,93.015602,93.218697,91.0625,91.296799,3961900,64.388615\n1997-07-21,91.281197,91.593697,90.6875,91.3125,3810300,64.399689\n1997-07-22,91.6875,93.796799,91.625,93.734299,4243400,66.107703\n1997-07-23,93.9375,94.484299,93.531197,93.656197,3204300,66.05262\n1997-07-24,93.9375,94.265602,92.6875,94.093697,4152700,66.361175\n1997-07-25,94.5625,94.8125,93.625,94.031197,2364800,66.317095\n1997-07-28,94.25,94.5625,93.5,93.968697,2794700,66.273016\n1997-07-29,93.625,94.468697,93.421799,94.281197,2737100,66.493412\n1997-07-30,94.5,95.625,94.406197,95.406197,4839800,67.286838\n1997-07-31,95.406197,96.031197,95.031197,95.3125,2138700,67.220756\n1997-08-01,95.5,95.718697,93.8125,94.9375,7077700,66.956281\n1997-08-04,94.781197,95.546799,94.406197,95.1875,2339500,67.132598\n1997-08-05,95.093697,95.6875,94.906197,95.25,1466700,67.176677\n1997-08-06,95.4375,96.531197,95.0625,96.031197,2478200,67.727629\n1997-08-07,96.5,96.625,95.125,95.3125,3641900,67.220756\n1997-08-08,94.3125,94.75,92.406197,93.375,7113100,65.854302\n1997-08-11,93.75,94.281197,92.625,94.0625,6355900,66.339173\n1997-08-12,94.218697,94.546799,92.468697,92.5625,6132500,65.281272\n1997-08-13,93.6875,93.843697,91.468697,92.281197,6982300,65.082878\n1997-08-14,93.093697,93.3125,91.718697,92.625,3462700,65.325351\n1997-08-15,92.125,92.125,89.625,89.781197,4907100,63.319711\n1997-08-18,90.375,91.781197,89.343697,91.781197,5638900,64.730245\n1997-08-19,92.218697,92.906197,91.531197,92.843697,3802800,65.479591\n1997-08-20,92.9375,94.3125,92.609299,94.25,3564600,66.47141\n1997-08-21,94.125,94.25,92.093697,92.593697,5392600,65.303274\n1997-08-22,91.0,92.734299,90.5625,92.5625,8087800,65.281272\n1997-08-25,92.781197,93.406197,91.843697,92.218697,3888000,65.038799\n1997-08-26,92.0,92.5625,90.703102,90.859299,4290000,64.080061\n1997-08-27,91.0,91.968697,90.406197,91.406197,5484300,64.46577\n1997-08-28,91.1875,91.9375,90.0,90.015602,4287900,63.485029\n1997-08-29,90.125,91.109299,89.718697,90.375,2652300,63.738501\n1997-09-02,90.6875,93.359299,90.593697,93.3125,7294000,65.810223\n1997-09-03,93.296799,94.0,92.75,92.8125,2812500,65.457589\n1997-09-04,93.0625,93.718697,92.75,93.406197,2473700,65.876304\n1997-09-05,93.968697,94.4375,92.593697,93.140602,3663300,65.688989\n1997-09-08,93.718697,94.0,93.3125,93.546799,803500,65.975466\n1997-09-09,93.218697,94.281197,92.906197,93.593697,1885200,66.008541\n1997-09-10,93.125,93.5,91.656197,91.656197,3623700,64.642086\n1997-09-11,91.781197,91.906197,90.25,91.1875,7055900,64.31153\n1997-09-12,92.0,92.968697,90.9375,92.656197,6708000,65.347353\n1997-09-15,92.656197,93.3125,92.1875,92.5,2060500,65.237193\n1997-09-16,93.3125,95.281197,93.031197,94.8125,6979000,66.868123\n1997-09-17,95.375,95.5,94.5,94.8125,2220000,66.868123\n1997-09-18,95.0,96.375,94.968697,95.218697,5045400,67.1546\n1997-09-19,94.656197,95.359299,94.406197,95.031197,1566400,67.26821\n1997-09-22,95.5,96.203102,95.218697,95.5625,3259000,67.644295\n1997-09-23,95.5625,95.703102,94.75,95.125,1872800,67.334609\n1997-09-24,95.375,96.109299,94.281197,94.343697,2793100,66.78156\n1997-09-25,94.25,94.75,93.625,93.6875,6327800,66.317069\n1997-09-26,94.4375,94.8125,94.1875,94.468697,3995800,66.870042\n1997-09-29,94.5,95.5625,94.156197,95.375,2065000,67.511573\n1997-09-30,95.0,95.6875,94.375,94.375,4137600,66.803719\n1997-10-01,95.25,95.8125,94.781197,95.625,3567500,67.688536\n1997-10-02,95.531197,96.1875,95.3125,96.156197,2577500,68.064546\n1997-10-03,97.468697,97.75,95.343697,96.640602,6499900,68.407434\n1997-10-06,97.3125,97.609299,96.906197,97.281197,2272100,68.860881\n1997-10-07,97.531197,98.5,97.203102,98.1875,1832800,69.502412\n1997-10-08,98.359299,98.375,96.734299,97.5,4439400,69.015762\n1997-10-09,96.781197,97.609299,96.218697,97.156197,3607600,68.7724\n1997-10-10,96.3125,96.984299,96.25,96.875,2858100,68.573354\n1997-10-13,97.343697,97.546799,96.718697,96.968697,1760300,68.639677\n1997-10-14,97.468697,97.5,96.1875,97.0,2235700,68.661835\n1997-10-15,96.3125,97.0625,96.281197,96.781197,2601700,68.506955\n1997-10-16,97.3125,97.5,95.0,95.25,9488100,67.423091\n1997-10-17,95.015602,95.375,93.0,94.281197,8151300,66.73732\n1997-10-20,94.843697,95.75,94.156197,95.625,3636400,67.688536\n1997-10-21,96.218697,97.5,96.031197,97.484299,5225400,69.004648\n1997-10-22,97.343697,97.390602,96.531197,96.843697,4485200,68.551195\n1997-10-23,94.9375,95.75,94.281197,94.9375,8054100,67.201887\n1997-10-24,96.156197,96.156197,93.671799,94.0,6778700,66.538273\n1997-10-27,93.015602,93.9375,86.843697,87.1875,10840700,61.716018\n1997-10-28,84.375,92.875,84.375,92.218697,19548000,65.277371\n1997-10-29,92.5,93.75,91.281197,91.968697,10134400,65.100407\n1997-10-30,90.625,92.5625,89.75,89.9375,9772900,63.662617\n1997-10-31,91.8125,92.5,90.4375,92.0625,7072700,65.166806\n1997-11-03,93.1875,94.375,92.875,94.0,5548500,66.538273\n1997-11-04,93.875,94.4375,93.281197,94.0,3455700,66.538273\n1997-11-05,94.1875,95.281197,93.843697,94.3125,4774900,66.759478\n1997-11-06,94.031197,94.343697,93.5,93.953102,3679800,66.505077\n1997-11-07,92.375,93.25,91.4375,92.9375,10606800,65.786179\n1997-11-10,93.625,93.843697,92.0,92.375,4347900,65.388011\n1997-11-11,92.6875,93.0625,92.031197,92.406197,3212400,65.410093\n1997-11-12,91.625,92.718697,90.375,90.5,6775300,64.060785\n1997-11-13,91.468697,92.156197,90.093697,91.8125,8589600,64.989843\n1997-11-14,92.3125,93.390602,91.609299,93.0625,5827900,65.87466\n1997-11-17,94.375,95.3125,94.031197,94.781197,5149900,67.091247\n1997-11-18,94.8125,95.093697,93.890602,94.1875,3433600,66.670996\n1997-11-19,93.6875,95.0625,92.75,94.656197,4387900,67.002765\n1997-11-20,95.3125,96.531197,95.281197,96.093697,4822700,68.020305\n1997-11-21,96.625,96.8125,95.656197,96.75,5436500,68.484872\n1997-11-24,96.0,96.343697,94.625,95.0,4337200,67.246127\n1997-11-25,95.625,95.8125,94.656197,95.25,4525000,67.423091\n1997-11-26,95.875,95.984299,95.3125,95.531197,2681100,67.622137\n1997-11-28,95.75,96.25,95.593697,95.625,1564700,67.688536\n1997-12-01,96.218697,98.093697,96.031197,98.093697,4850900,69.436013\n1997-12-02,97.6875,97.984299,97.25,97.5,1974900,69.015762\n1997-12-03,97.625,98.531197,96.875,97.781197,3302500,69.214808\n1997-12-04,98.5,98.718697,97.375,97.6875,2872500,69.148485\n1997-12-05,97.375,99.0,97.125,98.9375,3458800,70.033302\n1997-12-08,98.968697,99.0,98.25,98.656197,2289200,69.834181\n1997-12-09,98.218697,98.593697,97.625,98.0625,1703000,69.41393\n1997-12-10,97.5625,97.843697,96.468697,97.218697,3558400,68.816641\n1997-12-11,96.3125,96.593697,95.3125,95.5625,5072800,67.644295\n1997-12-12,96.4375,96.5,94.906197,95.75,4478400,67.777018\n1997-12-15,96.25,97.109299,95.875,96.718697,4674700,68.462714\n1997-12-16,97.218697,97.8125,96.875,97.3125,2885800,68.88304\n1997-12-17,97.843697,97.875,96.703102,96.8125,2236300,68.529113\n1997-12-18,96.9375,96.9375,95.218697,95.875,4658300,67.8655\n1997-12-19,94.531197,95.875,92.375,94.781197,8556000,67.358226\n1997-12-22,95.4375,95.906197,94.5625,95.390602,5136800,67.791313\n1997-12-23,95.4375,95.625,93.531197,93.6875,4436500,66.580968\n1997-12-24,94.4375,94.4375,93.25,93.406197,2019200,66.381054\n1997-12-26,94.125,94.125,93.406197,93.781197,941800,66.647555\n1997-12-29,94.593697,95.718697,94.4375,95.625,2080000,67.957892\n1997-12-30,95.9375,97.25,95.843697,97.125,3616000,69.023898\n1997-12-31,96.875,97.625,96.6875,97.0625,4359500,68.979482\n1998-01-02,97.3125,97.656197,96.531197,97.5625,2360900,69.334817\n1998-01-05,97.843697,98.4375,96.781197,97.781197,4191800,69.490238\n1998-01-06,97.25,97.281197,96.1875,96.218697,3154900,68.379815\n1998-01-07,96.093697,96.718697,95.218697,96.468697,4424200,68.557483\n1998-01-08,96.3125,96.3125,95.375,95.625,3831000,67.957892\n1998-01-09,95.25,95.5,91.906197,92.3125,10258800,65.603795\n1998-01-12,91.125,94.1875,90.906197,94.0,12097900,66.803052\n1998-01-13,94.625,95.375,94.218697,95.3125,5224900,67.735808\n1998-01-14,95.6875,95.968697,94.718697,95.75,3770400,68.046726\n1998-01-15,95.5,95.75,94.8125,94.953102,2875400,67.480394\n1998-01-16,96.25,96.6875,95.656197,96.3125,4374800,68.446478\n1998-01-20,96.6875,98.015602,96.5,97.875,5091700,69.556902\n1998-01-21,97.218697,97.6875,96.156197,96.9375,4699400,68.890648\n1998-01-22,96.156197,96.875,95.875,96.078102,4543400,68.279899\n1998-01-23,96.5,96.781197,95.0,95.9375,6350300,68.179977\n1998-01-26,96.375,96.734299,95.406197,95.875,4362900,68.13556\n1998-01-27,95.8125,97.5,95.656197,96.843697,7044200,68.823984\n1998-01-28,97.406197,98.109299,97.1875,97.718697,4268600,69.445821\n1998-01-29,97.843697,99.5625,97.5625,98.25,8007700,69.823403\n1998-01-30,98.781197,98.968697,98.0,98.3125,3649100,69.86782\n1998-02-02,99.906197,100.5,99.75,99.9375,5756300,71.02266\n1998-02-03,100.0,100.8125,99.718697,100.6875,2759600,71.555663\n1998-02-04,100.281197,101.156197,99.9375,100.5625,3374000,71.466829\n1998-02-05,101.3125,101.593697,100.031197,100.5,5076200,71.422412\n1998-02-06,101.0,101.625,100.6875,101.625,5701200,72.221917\n1998-02-09,101.718697,101.75,100.718697,101.281197,2322200,71.977586\n1998-02-10,101.4375,102.468697,101.1875,102.25,3660400,72.666086\n1998-02-11,102.093697,102.343697,101.703102,102.156197,4073200,72.599423\n1998-02-12,101.718697,102.9375,100.875,102.593697,5024700,72.910341\n1998-02-13,102.1875,102.515602,101.875,102.0,2101300,72.488418\n1998-02-17,102.8125,103.093697,102.156197,102.5,3055500,72.843754\n1998-02-18,102.3125,103.484299,102.281197,103.4375,3007400,73.510008\n1998-02-19,103.25,103.406197,102.75,102.890602,3387800,73.121343\n1998-02-20,103.156197,103.718697,102.375,103.656197,3707000,73.665429\n1998-02-23,104.25,104.25,103.343697,104.0625,3227800,73.954177\n1998-02-24,103.906197,104.093697,102.9375,103.25,3386800,73.376757\n1998-02-25,103.75,104.875,103.625,104.531197,3481800,74.287266\n1998-02-26,104.4375,105.218697,104.1875,105.125,3187600,74.709265\n1998-02-27,104.968697,105.531197,104.531197,105.125,3442900,74.709265\n1998-03-02,105.25,105.75,104.625,104.906197,4252300,74.553767\n1998-03-03,104.531197,105.625,104.531197,105.5,3349200,74.975766\n1998-03-04,105.093697,105.406197,104.4375,104.8125,4404100,74.48718\n1998-03-05,103.5,104.4375,103.156197,103.843697,7268000,73.79868\n1998-03-06,104.5625,105.9375,104.4375,105.9375,6896300,75.286685\n1998-03-09,105.531197,106.218697,105.25,105.5625,3362800,75.020183\n1998-03-10,106.218697,106.843697,105.9375,106.5625,5481900,75.730854\n1998-03-11,106.968697,107.3125,106.781197,107.0625,3439600,76.086189\n1998-03-12,107.093697,107.593697,106.5,107.5,3191300,76.397108\n1998-03-13,107.843697,108.0,106.875,107.093697,2879400,76.10836\n1998-03-16,107.843697,108.375,107.531197,108.25,3223600,76.930111\n1998-03-17,108.3125,108.5625,107.656197,108.5625,4581900,77.152195\n1998-03-18,108.25,108.968697,108.031197,108.968697,1944100,77.440867\n1998-03-19,108.968697,109.375,108.656197,109.25,2554800,77.640782\n1998-03-20,109.5625,110.1875,108.875,109.875,3123300,78.309302\n1998-03-23,109.718697,110.3125,109.406197,109.625,4453100,78.131124\n1998-03-24,110.0625,110.8125,109.9375,110.5625,3333600,78.799292\n1998-03-25,111.406197,111.531197,109.1875,110.156197,4597600,78.509714\n1998-03-26,109.875,110.75,109.625,110.093697,3333500,78.46517\n1998-03-27,110.75,110.781197,109.0,109.625,2611300,78.131124\n1998-03-30,109.625,110.093697,108.968697,109.5625,3108700,78.086579\n1998-03-31,110.156197,111.1875,109.75,109.9375,5926500,78.353846\n1998-04-01,110.3125,111.078102,109.406197,110.828102,2929000,78.98859\n1998-04-02,110.9375,112.25,110.75,112.031197,3920900,79.846051\n1998-04-03,112.343697,112.8125,111.843697,112.593697,3787600,80.246951\n1998-04-06,113.25,113.375,111.6875,111.6875,4550500,79.601094\n1998-04-07,111.75,111.9375,110.156197,110.9375,5583700,79.066559\n1998-04-08,111.218697,111.281197,109.75,110.3125,4854800,78.621114\n1998-04-09,110.5625,111.281197,110.531197,111.1875,4481200,79.244737\n1998-04-13,111.375,111.375,110.0,110.875,4350200,79.022015\n1998-04-14,111.125,111.8125,110.906197,111.8125,3279100,79.690183\n1998-04-15,111.968697,112.125,111.156197,112.125,3867200,79.912905\n1998-04-16,111.3125,111.5,110.5,110.8125,7177900,78.97747\n1998-04-17,110.718697,112.4375,110.4375,112.281197,5688800,80.024229\n1998-04-20,112.0,112.5625,111.875,112.25,3688300,80.001994\n1998-04-21,112.4375,113.156197,111.906197,112.781197,4573200,80.380585\n1998-04-22,112.875,113.4375,112.8125,113.093697,2386100,80.603308\n1998-04-23,112.625,113.0,111.75,112.0,5062000,79.823816\n1998-04-24,111.75,112.468697,110.343697,110.8125,11039700,78.97747\n1998-04-27,109.375,109.6875,107.625,108.718697,14510200,77.48519\n1998-04-28,109.781197,109.8125,108.125,108.5625,6511400,77.373867\n1998-04-29,109.031197,109.968697,108.4375,109.3125,7703100,77.908401\n1998-04-30,110.5625,111.921799,110.406197,111.343697,8684500,79.356061\n1998-05-01,111.75,112.593697,111.3125,112.593697,4008800,80.246951\n1998-05-04,112.718697,113.3125,112.156197,112.3125,4537200,80.046539\n1998-05-05,112.0,112.156197,111.125,111.531197,5146400,79.489694\n1998-05-06,112.125,112.125,110.1875,110.218697,5526100,78.554259\n1998-05-07,110.5,110.5625,109.343697,109.343697,6940400,77.930635\n1998-05-08,110.0,111.375,110.0,111.125,7951000,79.200193\n1998-05-11,111.5625,112.218697,110.375,110.75,6336300,78.932925\n1998-05-12,110.8125,111.968697,110.343697,111.9375,6045200,79.779272\n1998-05-13,112.0625,112.5625,111.593697,112.218697,4441500,79.979684\n1998-05-14,111.531197,112.6875,111.343697,111.656197,4187500,79.578783\n1998-05-15,112.0,112.218697,110.8125,111.031197,6716100,79.133338\n1998-05-18,110.718697,111.593697,109.828102,110.593697,4847200,78.821526\n1998-05-19,111.0,111.6875,110.781197,111.343697,5918700,79.356061\n1998-05-20,112.093697,112.5,110.875,112.406197,5716600,80.113318\n1998-05-21,112.375,112.781197,111.3125,111.6875,6301500,79.601094\n1998-05-22,111.75,112.0625,110.9375,111.25,4862200,79.289282\n1998-05-26,112.093697,112.093697,109.1875,109.468697,6899300,78.019724\n1998-05-27,108.531197,109.906197,107.578102,109.625,10202600,78.131124\n1998-05-28,109.875,110.375,109.0625,110.125,4907600,78.48748\n1998-05-29,110.625,110.8125,109.031197,109.031197,4772600,77.707913\n1998-06-01,108.968697,110.218697,108.5625,109.531197,6092200,78.064269\n1998-06-02,110.0,110.343697,109.156197,109.625,6701100,78.131124\n1998-06-03,109.875,110.1875,107.875,107.875,6445100,76.883877\n1998-06-04,108.25,110.0625,108.0625,109.875,6566300,78.309302\n1998-06-05,110.375,112.0,109.875,112.0,8463200,79.823816\n1998-06-08,111.9375,112.468697,111.656197,111.875,4159900,79.734727\n1998-06-09,111.718697,112.421799,111.406197,112.218697,2725300,79.979684\n1998-06-10,111.625,113.0625,111.25,111.5,6186900,79.46746\n1998-06-11,111.4375,111.875,109.375,109.406197,8056600,77.97518\n1998-06-12,109.875,110.4375,108.25,110.4375,9779000,78.710203\n1998-06-15,108.8125,109.906197,107.5,107.531197,10234200,76.638844\n1998-06-16,108.406197,109.218697,107.75,109.1875,7471500,77.819312\n1998-06-17,110.156197,111.781197,109.9375,111.468697,12057700,79.44515\n1998-06-18,111.1875,111.4375,110.75,110.968697,3844600,79.088793\n1998-06-19,111.0625,111.234299,109.625,110.0625,3549100,78.692553\n1998-06-22,110.25,111.0625,110.0625,110.593697,5438300,79.072348\n1998-06-23,111.093697,112.0625,111.0,111.906197,5004600,80.01076\n1998-06-24,112.156197,113.6875,111.609299,113.406197,8291900,81.083231\n1998-06-25,113.906197,114.4375,112.8125,113.218697,5854800,80.949172\n1998-06-26,113.3125,113.843697,113.125,113.6875,4833100,81.284357\n1998-06-29,114.25,114.6875,113.796799,114.093697,5953400,81.57478\n1998-06-30,113.906197,114.1875,113.0625,113.3125,4284800,81.016239\n1998-07-01,114.0625,114.9375,113.625,114.625,3489500,81.954652\n1998-07-02,114.718697,114.875,114.25,114.843697,3503900,82.111015\n1998-07-06,114.781197,116.0,114.5625,116.0,3144400,82.93775\n1998-07-07,115.984299,116.125,115.281197,115.781197,4920700,82.78131\n1998-07-08,115.875,116.9375,115.75,116.625,6875000,83.384613\n1998-07-09,116.281197,116.718697,115.625,115.843697,6942500,82.825996\n1998-07-10,116.031197,116.9375,115.0625,116.468697,7849700,83.272859\n1998-07-13,116.5625,116.843697,116.0625,116.5,6555100,83.29524\n1998-07-14,116.9375,118.156197,116.9375,117.8125,6983600,84.233652\n1998-07-15,118.0625,118.281197,117.531197,117.593697,4977500,84.077212\n1998-07-16,117.6875,118.593697,117.0625,118.406197,6315600,84.658134\n1998-07-17,118.625,119.0,118.3125,118.5625,3539500,84.769888\n1998-07-20,118.75,119.234299,117.9375,118.5625,2338400,84.769888\n1998-07-21,119.0,119.0,116.281197,116.5,5518600,83.29524\n1998-07-22,116.343697,116.984299,115.531197,116.5,10417300,83.29524\n1998-07-23,116.3125,116.593697,113.906197,114.1875,15110900,81.641848\n1998-07-24,114.968697,115.093697,112.875,114.093697,11540500,81.57478\n1998-07-27,113.625,115.0,112.843697,114.906197,9600000,82.155702\n1998-07-28,114.4375,114.656197,111.875,112.968697,13693000,80.770427\n1998-07-29,113.718697,114.125,112.25,112.531197,7462400,80.457623\n1998-07-30,113.625,114.593697,113.406197,114.218697,6821600,81.664152\n1998-07-31,114.343697,114.5,111.3125,111.781197,7680500,79.921387\n1998-08-03,111.781197,112.421799,111.031197,111.3125,10486500,79.586278\n1998-08-04,112.218697,112.218697,107.0,107.0,15091600,76.502924\n1998-08-05,107.9375,108.875,105.593697,108.468697,21718400,77.553014\n1998-08-06,108.125,109.5,107.5625,108.9375,13084400,77.888199\n1998-08-07,109.6875,110.593697,108.468697,109.125,12083800,78.022258\n1998-08-10,108.75,109.656197,108.1875,108.5,6273000,77.575395\n1998-08-11,106.781197,107.4375,105.5,106.875,14287100,76.413552\n1998-08-12,107.75,108.875,107.5,108.6875,11158400,77.709454\n1998-08-13,108.8125,109.656197,107.375,107.375,9073900,76.771042\n1998-08-14,108.343697,108.718697,105.781197,106.125,8628500,75.877316\n1998-08-17,106.0,108.9375,105.5,108.375,11290000,77.486023\n1998-08-18,109.0,110.593697,108.781197,110.375,7603900,78.915984\n1998-08-19,111.093697,111.093697,109.625,110.093697,6258600,78.714857\n1998-08-20,109.6875,110.406197,109.156197,109.4375,7402800,78.24569\n1998-08-21,108.1875,108.718697,105.5,108.5625,16685400,77.620082\n1998-08-24,109.25,109.9375,108.3125,109.25,7201700,78.111631\n1998-08-25,110.375,111.25,108.640602,109.5,9688300,78.290376\n1998-08-26,108.281197,109.625,107.75,108.875,9339700,77.843513\n1998-08-27,107.0,107.5625,103.468697,103.75,24887800,74.179237\n1998-08-28,104.968697,105.718697,102.156197,103.375,23735900,73.91112\n1998-08-31,103.75,104.015602,95.0,96.0,22563100,68.638138\n1998-09-01,96.0625,100.5625,93.625,100.0625,24748500,71.542747\n1998-09-02,99.8125,101.75,98.781197,99.343697,13843200,71.028816\n1998-09-03,97.625,99.375,96.75,98.5625,17311300,70.470276\n1998-09-04,99.4375,99.8125,95.75,97.75,17783800,69.889354\n1998-09-08,100.875,103.0,99.8125,103.0,14746500,73.643002\n1998-09-09,102.75,103.125,100.4375,100.5,12109700,71.855551\n1998-09-10,98.4375,99.343697,96.8125,98.5,19889300,70.425589\n1998-09-11,98.1875,101.6875,97.0,101.6875,20379800,72.70459\n1998-09-14,102.875,104.453102,102.093697,103.4375,10686800,73.955806\n1998-09-15,102.875,104.3125,102.281197,104.0625,8337900,74.402669\n1998-09-16,104.75,105.25,103.156197,105.0,11024900,75.072963\n1998-09-17,102.25,103.031197,101.781197,102.0,12945200,72.928021\n1998-09-18,102.375,102.406197,101.093697,102.093697,7019200,73.252833\n1998-09-21,99.625,103.406197,98.9375,102.031197,8834600,73.207989\n1998-09-22,103.5,103.656197,102.156197,102.875,7270100,73.813423\n1998-09-23,103.875,107.0,103.781197,107.0,13688500,76.773135\n1998-09-24,106.3125,106.8125,103.25,104.375,11129500,74.889682\n1998-09-25,103.125,105.421799,102.625,104.25,10247400,74.799993\n1998-09-28,105.3125,106.3125,104.25,105.1875,8847800,75.472655\n1998-09-29,105.375,105.9375,103.375,104.9375,10618100,75.293279\n1998-09-30,103.5,104.3125,101.375,101.75,7908400,73.006229\n1998-10-01,100.031197,101.546799,98.093697,98.8125,13335600,70.898555\n1998-10-02,98.875,100.875,97.218697,100.718697,14351800,72.266262\n1998-10-05,99.593697,99.968697,96.343697,98.6875,12807200,70.808867\n1998-10-06,100.75,101.281197,97.531197,98.593697,12621000,70.741562\n1998-10-07,98.625,100.031197,95.75,97.125,14348500,69.687764\n1998-10-08,94.5625,96.75,92.218697,96.593697,20625000,69.30655\n1998-10-09,97.0,98.781197,94.0625,98.531197,12708500,70.696718\n1998-10-12,100.625,101.4375,99.6875,99.968697,9508500,71.728133\n1998-10-13,99.5625,100.406197,98.75,99.625,6256800,71.481528\n1998-10-14,98.9375,101.8125,98.843697,100.5625,8176900,72.15419\n1998-10-15,100.125,107.25,99.9375,105.968697,20541100,76.033168\n1998-10-16,106.125,106.75,105.0,106.0,16573900,76.055629\n1998-10-19,105.6875,106.8125,105.5,106.375,7915900,76.324694\n1998-10-20,107.5625,108.796799,106.093697,107.0,14611600,76.773135\n1998-10-21,106.875,107.625,105.875,106.625,9178400,76.50407\n1998-10-22,106.781197,108.468697,106.156197,108.218697,8277500,77.647557\n1998-10-23,107.968697,108.0,106.781197,106.843697,6237200,76.660986\n1998-10-26,107.718697,108.468697,106.781197,107.625,5838600,77.221576\n1998-10-27,108.4375,108.968697,106.281197,107.0,10089700,76.773135\n1998-10-28,106.5625,107.531197,106.031197,106.8125,5650600,76.638602\n1998-10-29,107.093697,109.4375,106.625,109.406197,9625600,78.499595\n1998-10-30,110.125,110.906197,109.5,110.0,9872800,78.925653\n1998-11-02,110.8125,111.875,110.1875,111.875,6501400,80.270976\n1998-11-03,111.593697,111.8125,110.75,111.0625,6866000,79.688003\n1998-11-04,112.468697,113.093697,111.093697,112.25,8233600,80.540041\n1998-11-05,111.531197,113.9375,111.125,113.781197,6841500,81.638683\n1998-11-06,113.468697,114.5625,113.3125,114.125,5224800,81.885364\n1998-11-09,113.9375,114.25,112.5,113.3125,6188700,81.302391\n1998-11-10,113.0,113.9375,112.25,113.0,14015200,81.07817\n1998-11-11,113.8125,114.0,111.890602,112.25,10922400,80.540041\n1998-11-12,112.3125,113.125,111.6875,112.125,5457400,80.450353\n1998-11-13,112.218697,113.218697,112.125,113.218697,5524600,81.235086\n1998-11-16,114.218697,114.359299,112.890602,114.0625,6023700,81.84052\n1998-11-17,113.656197,115.625,113.0,114.0625,9058900,81.84052\n1998-11-18,114.3125,115.0,113.5,114.75,4567700,82.333806\n1998-11-19,115.281197,115.906197,114.625,115.75,5249300,83.051312\n1998-11-20,116.359299,116.75,115.843697,116.625,5562000,83.679129\n1998-11-23,117.468697,119.625,117.156197,119.375,7151300,85.652271\n1998-11-24,119.0,119.656197,118.453102,118.625,5323700,85.114141\n1998-11-25,118.9375,119.1875,118.218697,118.625,4393500,85.114141\n1998-11-27,119.468697,119.718697,119.015602,119.5,4557900,85.741959\n1998-11-30,119.015602,119.375,116.125,116.125,8705400,83.320376\n1998-12-01,116.125,118.031197,115.218697,117.625,8950600,84.396635\n1998-12-02,117.218697,117.906197,116.0,117.281197,7495500,84.149954\n1998-12-03,117.25,118.3125,115.093697,115.343697,12145300,82.759787\n1998-12-04,116.625,118.5,116.5,118.375,10339500,84.934765\n1998-12-07,118.0625,119.468697,118.0,118.9375,4290000,85.338362\n1998-12-08,118.531197,119.75,117.5,118.406197,10102600,84.957148\n1998-12-09,118.6875,118.968697,117.875,118.625,5327900,85.114141\n1998-12-10,118.843697,118.843697,116.718697,116.890602,5966600,83.8697\n1998-12-11,116.4375,117.343697,115.5625,117.125,8198200,84.037882\n1998-12-14,116.156197,116.406197,113.75,113.75,9521400,81.6163\n1998-12-15,114.6875,116.75,114.531197,116.6875,9631500,83.723973\n1998-12-16,117.125,117.125,115.75,116.531197,7260000,83.611825\n1998-12-17,117.218697,118.593697,117.031197,118.390602,6914700,84.945959\n1998-12-18,118.3125,119.125,117.875,118.5,4802400,85.306915\n1998-12-21,119.25,121.343697,119.0,120.156197,8580700,86.499194\n1998-12-22,120.406197,121.218697,119.1875,120.6875,5461100,86.881674\n1998-12-23,121.1875,123.218697,120.8125,123.218697,7791000,88.703856\n1998-12-24,123.156197,123.25,122.5,122.6875,1507100,88.321453\n1998-12-28,123.25,123.3125,122.0,122.375,4203600,88.096487\n1998-12-29,122.718697,124.484299,122.125,124.3125,3935800,89.491274\n1998-12-30,123.9375,124.75,123.031197,123.3125,6810700,88.771384\n1998-12-31,123.3125,123.9375,122.468697,123.3125,6790500,88.771384\n1999-01-04,123.375,125.218697,121.718697,123.031197,9450400,88.568877\n1999-01-05,122.9375,124.875,122.9375,124.4375,8031000,89.58126\n1999-01-06,125.8125,127.75,125.75,127.4375,7737700,91.740929\n1999-01-07,126.375,127.218697,125.781197,126.8125,5504900,91.290998\n1999-01-08,128.1875,128.5,125.968697,127.75,6224400,91.965894\n1999-01-11,127.6875,127.6875,125.218697,126.531197,7578300,91.08849\n1999-01-12,126.218697,126.218697,123.75,124.25,7768800,89.44628\n1999-01-13,120.406197,125.125,120.375,123.375,10810600,88.816377\n1999-01-14,123.625,123.906197,120.906197,121.218697,11400700,87.264077\n1999-01-15,122.375,124.781197,122.0625,124.375,7817700,89.536267\n1999-01-19,125.296799,125.625,123.5,125.1875,6535100,90.121177\n1999-01-20,126.093697,127.9375,125.031197,126.1875,6534400,90.841067\n1999-01-21,125.578102,125.843697,122.593697,122.843697,6937500,88.433897\n1999-01-22,122.125,123.843697,121.781197,122.5625,7522300,88.231467\n1999-01-25,123.281197,124.0,121.906197,123.8125,5700300,89.131329\n1999-01-26,124.125,126.0625,123.625,126.0625,6047000,90.75108\n1999-01-27,126.375,126.625,124.156197,124.593697,7399400,89.693704\n1999-01-28,125.25,126.968697,125.1875,126.6875,5961700,91.201011\n1999-01-29,127.343697,128.296799,125.406197,127.656197,6456800,91.898366\n1999-02-01,128.6875,128.6875,126.906197,126.906197,9426800,91.358449\n1999-02-02,127.078102,127.218697,124.765602,126.125,9194500,90.796073\n1999-02-03,125.6875,127.9375,125.656197,127.406197,10290700,91.718394\n1999-02-04,127.375,127.5,124.781197,125.5,7761100,90.346142\n1999-02-05,125.656197,125.656197,123.218697,124.0625,7516100,89.311301\n1999-02-08,125.093697,125.093697,123.343697,124.3125,8528400,89.491274\n1999-02-09,124.375,124.5,121.515602,121.531197,8985700,87.489042\n1999-02-10,122.125,123.0,121.328102,122.3125,6936700,88.051494\n1999-02-11,123.0625,126.093697,122.5,125.125,9181800,90.076184\n1999-02-12,124.8125,125.5,122.625,123.625,10676600,88.996349\n1999-02-16,124.75,125.625,122.875,122.875,6915500,88.456432\n1999-02-17,123.1875,125.359299,122.25,122.75,7858100,88.366446\n1999-02-18,123.1875,124.375,122.218697,123.718697,9048900,89.063801\n1999-02-19,124.0,125.75,123.375,124.25,5312200,89.44628\n1999-02-22,124.4375,127.718697,124.281197,127.5625,10695800,91.830915\n1999-02-23,127.593697,128.5,126.593697,127.5,7770700,91.785922\n1999-02-24,127.843697,128.843704,125.0,125.25,7271000,90.16617\n1999-02-25,124.531197,125.281197,122.6875,124.0625,11633000,89.311301\n1999-02-26,124.75,124.843697,122.8125,123.5625,9621300,88.951356\n1999-03-01,123.656197,124.3125,122.375,123.906197,7607200,89.19878\n1999-03-02,124.5,125.3125,122.3125,122.8125,9651500,88.411439\n1999-03-03,123.093697,123.5625,121.781197,123.468697,7881400,88.883828\n1999-03-04,124.0625,125.484299,123.265602,125.031197,8256400,90.008656\n1999-03-05,127.5,128.125,126.5625,127.546799,10703100,91.819612\n1999-03-08,128.281204,128.796799,127.25,128.375,4802200,92.415825\n1999-03-09,128.125,129.9375,127.75,128.0625,7893400,92.190859\n1999-03-10,128.468704,129.25,127.781197,129.1875,3950000,93.000735\n1999-03-11,129.6875,131.1875,128.875,130.625,6583700,94.035577\n1999-03-12,131.0,131.031204,129.218704,129.375,5286500,93.135715\n1999-03-15,129.9375,131.25,129.5,131.218704,5394400,94.462978\n1999-03-16,131.125,131.656204,130.468704,130.718704,4547500,94.103033\n1999-03-17,130.6875,130.9375,129.625,130.156204,4524100,93.698095\n1999-03-18,129.781204,132.375,129.75,132.25,3506300,95.205397\n1999-03-19,132.3125,132.625,129.6875,129.6875,5526700,93.58713\n1999-03-22,130.0625,130.593704,129.421799,129.9375,4603800,93.767539\n1999-03-23,129.3125,129.531204,125.703102,126.1875,9713800,91.061405\n1999-03-24,126.843697,127.171799,125.625,126.906197,6280900,91.580042\n1999-03-25,128.0625,129.5,127.75,129.5,6639600,93.451823\n1999-03-26,128.625,129.125,127.718697,128.5625,6159700,92.77529\n1999-03-29,129.156204,131.4375,129.156204,131.156204,5863900,94.646999\n1999-03-30,129.9375,131.218704,129.5625,130.468704,5401400,94.150875\n1999-03-31,131.156204,131.609299,128.3125,128.375,7413600,92.639983\n1999-04-01,129.6875,129.6875,128.125,129.343704,7683600,93.339034\n1999-04-05,130.9375,132.593704,130.25,132.406204,5791100,95.549044\n1999-04-06,132.1875,132.984299,131.156204,132.093704,5381300,95.323533\n1999-04-07,132.6875,133.375,131.375,133.156204,6248600,96.090271\n1999-04-08,133.1875,134.9375,132.281204,134.843704,5909100,97.308031\n1999-04-09,134.4375,135.5,133.593704,134.875,4365800,97.330615\n1999-04-12,133.468704,136.406204,133.218704,136.3125,8213200,98.367966\n1999-04-13,136.25,136.468704,134.5,135.4375,10071400,97.736535\n1999-04-14,136.0625,136.0625,132.6875,133.156204,11551700,96.090271\n1999-04-15,133.4375,133.5625,131.0,132.656204,11150000,95.729453\n1999-04-16,132.906204,132.906204,131.1875,131.531204,6476200,94.917613\n1999-04-19,132.6875,134.531204,128.375,129.5,12487200,93.451823\n1999-04-20,129.8125,131.1875,129.0,131.125,9049600,94.624481\n1999-04-21,131.0625,135.625,130.406204,134.875,5772200,97.330615\n1999-04-22,135.125,136.375,134.390594,136.156204,6897400,98.255178\n1999-04-23,135.875,136.75,135.0,135.8125,4593100,98.007148\n1999-04-26,136.5,136.8125,135.468704,136.593704,3606500,98.570893\n1999-04-27,137.125,137.5,135.843704,137.25,5147200,99.0445\n1999-04-28,136.4375,137.25,135.0,135.375,5544300,97.691433\n1999-04-29,135.5625,136.0625,133.8125,134.343704,9824300,96.947213\n1999-04-30,135.093704,135.625,131.5,133.25,10991900,96.157957\n1999-05-03,133.4375,135.843704,133.093704,135.6875,10709700,97.916944\n1999-05-04,135.125,135.8125,133.125,133.75,10509300,96.518775\n1999-05-05,133.9375,135.0625,131.843704,134.8125,10032300,97.285513\n1999-05-06,134.4375,135.125,132.375,133.984299,13507200,96.687853\n1999-05-07,134.5,135.125,133.4375,135.0,8680700,97.420819\n1999-05-10,134.843704,135.718704,133.531204,134.328094,5580100,96.935948\n1999-05-11,135.3125,136.875,134.718704,135.6875,7000600,97.916944\n1999-05-12,135.75,137.218704,131.5,136.75,13865800,98.683682\n1999-05-13,137.25,138.0,136.8125,137.343704,4485700,99.11212\n1999-05-14,134.5625,135.875,133.5,133.781204,8370100,96.541293\n1999-05-17,133.625,134.484299,132.375,134.1875,6268100,96.83449\n1999-05-18,134.531204,134.984299,132.625,133.9375,8218600,96.654081\n1999-05-19,134.468704,135.0,133.25,134.906204,5052400,97.353133\n1999-05-20,135.125,135.593704,134.0625,134.093704,4670400,96.766804\n1999-05-21,134.125,134.6875,132.593704,133.343704,6418200,96.225577\n1999-05-24,133.843704,133.843704,130.390594,131.125,7952000,94.624481\n1999-05-25,131.375,132.343704,128.4375,129.0,15406400,93.091005\n1999-05-26,129.3125,131.0,128.093704,130.4375,12575900,94.128357\n1999-05-27,129.843704,130.281204,128.0,128.5625,14515400,92.77529\n1999-05-28,129.0,130.75,128.593704,130.203094,8748000,93.959201\n1999-06-01,130.125,130.156204,128.375,129.75,6705300,93.632232\n1999-06-02,129.75,130.1875,128.015594,129.875,6981900,93.722436\n1999-06-03,130.6875,130.875,129.781204,130.593704,6076600,94.241079\n1999-06-04,131.468704,133.359299,130.9375,133.218704,9787200,96.135373\n1999-06-07,133.4375,134.1875,132.906204,133.5625,5299100,96.383468\n1999-06-08,133.375,133.796799,131.593704,132.25,4895700,95.436321\n1999-06-09,132.406204,133.093704,131.8125,132.125,7409700,95.346117\n1999-06-10,131.4375,131.468704,129.593704,130.906204,7040700,94.46659\n1999-06-11,131.218704,132.218704,129.093704,129.8125,12824800,93.677334\n1999-06-14,130.6875,130.75,129.546799,129.9375,6823500,93.767539\n1999-06-15,130.468704,131.656204,130.078094,130.8125,5700900,94.39897\n1999-06-16,132.375,133.875,132.375,133.3125,7865800,96.203059\n1999-06-17,132.875,135.5625,132.625,134.5625,8162800,97.105104\n1999-06-18,134.0625,134.6875,133.375,134.3125,2875100,97.217293\n1999-06-21,134.468704,135.25,133.656204,134.625,4646400,97.443485\n1999-06-22,134.0,135.1875,133.5,133.6875,5862400,96.764909\n1999-06-23,133.0,133.625,132.125,133.031204,9629200,96.289873\n1999-06-24,132.875,132.9375,130.656204,132.031204,9176900,95.566059\n1999-06-25,132.593704,133.015594,131.25,131.703094,4087000,95.328569\n1999-06-28,132.6875,133.625,132.5,133.3125,5028900,96.493479\n1999-06-29,133.0,135.1875,132.781204,134.5625,6956200,97.398247\n1999-06-30,134.625,137.5,133.843704,137.0,16856100,99.162544\n1999-07-01,137.0,138.5,136.0625,138.031204,9939500,99.908944\n1999-07-02,138.125,139.5,137.906204,139.406204,3772800,100.904189\n1999-07-06,139.25,140.75,138.593704,139.375,11221100,100.881603\n1999-07-07,139.0625,139.718704,138.515594,139.5625,3230700,101.017318\n1999-07-08,139.0625,140.625,138.75,139.671799,7101500,101.09643\n1999-07-09,140.0,140.593704,139.5625,140.5,2976800,101.695894\n1999-07-12,140.9375,140.9375,139.5,139.875,4436300,101.24351\n1999-07-13,139.375,139.921799,138.656204,139.375,6477600,100.881603\n1999-07-14,140.0,140.218704,138.75,140.156204,4382500,101.447049\n1999-07-15,140.781204,141.375,140.3125,141.046799,3348600,102.091674\n1999-07-16,141.25,142.156204,140.75,141.8125,2281500,102.6459\n1999-07-19,142.1875,142.25,140.5625,140.875,4287000,101.967324\n1999-07-20,140.125,140.406204,137.531204,137.984299,7060100,99.874993\n1999-07-21,138.093704,138.906204,137.3125,137.828094,4793700,99.76193\n1999-07-22,137.4375,138.0,135.468704,136.015594,7804100,98.450017\n1999-07-23,136.656204,137.0,135.125,135.75,4603000,98.257776\n1999-07-26,134.875,136.125,134.625,134.75,4333400,97.533962\n1999-07-27,136.0,137.203094,135.375,135.875,5908500,98.348253\n1999-07-28,136.25,137.3125,135.593704,136.3125,3955500,98.664922\n1999-07-29,134.9375,135.25,133.3125,134.406204,7760600,97.285118\n1999-07-30,134.8125,135.343704,132.5625,132.75,5994300,96.086334\n1999-08-02,132.75,134.75,132.5,133.0625,6065500,96.312526\n1999-08-03,133.718704,133.843704,131.375,132.4375,5445400,95.860142\n1999-08-04,132.718704,133.875,130.4375,130.625,6497900,94.548228\n1999-08-05,130.875,131.9375,128.843704,131.8125,10322000,95.407758\n1999-08-06,131.218704,132.0,129.5,130.375,7537700,94.367275\n1999-08-09,130.593704,131.796799,129.75,130.093704,5565900,94.163669\n1999-08-10,129.875,130.156204,127.0,128.656204,9793300,93.123186\n1999-08-11,129.6875,130.531204,128.625,130.218704,8003400,94.254146\n1999-08-12,130.6875,131.8125,129.906204,130.140594,7036800,94.197609\n1999-08-13,131.625,133.25,131.125,133.156204,5857700,96.38035\n1999-08-16,133.125,133.968704,132.25,133.75,3927000,96.810148\n1999-08-17,134.4375,134.843704,133.125,134.625,4808000,97.443485\n1999-08-18,134.203094,134.375,133.25,133.656204,4355500,96.742257\n1999-08-19,132.375,133.234299,131.6875,132.5625,6448100,95.950618\n1999-08-20,133.0625,134.125,132.6875,133.906204,3663700,96.923211\n1999-08-23,134.8125,136.546799,134.656204,136.468704,5637800,98.777985\n1999-08-24,136.0625,137.968704,135.375,136.968704,9279500,99.139892\n1999-08-25,137.1875,138.781204,136.156204,138.375,6005300,100.157788\n1999-08-26,138.281204,138.421799,136.5,136.718704,4322300,98.958938\n1999-08-27,136.875,137.0625,135.0625,135.0625,6247400,97.760154\n1999-08-30,135.343704,135.5,132.0,132.5625,4652000,95.950618\n1999-08-31,132.9375,133.75,130.875,132.0625,11453700,95.588711\n1999-09-01,132.9375,133.734299,132.3125,133.6875,6863900,96.764909\n1999-09-02,132.125,132.671799,130.656204,132.109299,10896300,95.622585\n1999-09-03,134.875,136.5,134.6875,135.968704,9160800,98.416077\n1999-09-07,136.0625,136.625,135.218704,135.468704,4560800,98.05417\n1999-09-08,134.843704,136.0625,134.031204,134.8125,6159600,97.5792\n1999-09-09,134.75,135.25,133.6875,134.75,6177800,97.533962\n1999-09-10,136.25,136.359299,134.9375,135.875,2934500,98.348253\n1999-09-13,135.125,135.468704,134.5,134.953094,2320500,97.680965\n1999-09-14,134.0625,134.5625,133.375,134.0625,3736000,97.03634\n1999-09-15,135.4375,135.4375,131.875,131.9375,6984500,95.498235\n1999-09-16,132.5,132.8125,130.3125,132.453094,15377600,95.871429\n1999-09-17,132.625,133.9375,132.156204,133.75,8542300,97.08281\n1999-09-20,133.9375,134.0,133.093704,133.5625,2803900,96.946713\n1999-09-21,132.25,132.468704,130.156204,130.75,9335200,94.905252\n1999-09-22,131.25,131.843704,129.75,130.609299,13009100,94.803124\n1999-09-23,131.8125,131.8125,127.625,127.875,12204100,92.818425\n1999-09-24,127.75,128.375,126.3125,127.75,13792900,92.727694\n1999-09-27,128.75,129.75,128.281204,128.593704,6970300,93.340099\n1999-09-28,127.9375,128.8125,125.5625,128.343704,11063400,93.158635\n1999-09-29,128.4375,129.125,126.75,126.8125,7580300,92.047207\n1999-09-30,127.4375,129.4375,126.968697,128.75,7498900,93.453546\n1999-10-01,127.9375,128.5625,126.625,128.468704,11127100,93.249367\n1999-10-04,129.1875,130.875,128.75,130.75,6341600,94.905252\n1999-10-05,130.718704,131.968704,128.6875,130.625,11976000,94.81452\n1999-10-06,130.75,132.8125,130.6875,132.625,10308200,96.266226\n1999-10-07,132.859299,133.0,131.5,131.875,6535600,95.721836\n1999-10-08,131.75,133.875,131.234299,133.875,9674300,97.173542\n1999-10-11,133.593704,134.125,133.3125,133.656204,4282000,97.014728\n1999-10-12,133.125,133.3125,131.1875,131.593704,8686100,95.517657\n1999-10-13,130.6875,131.3125,128.1875,128.1875,10469300,93.045254\n1999-10-14,128.484299,129.1875,126.75,128.156204,10562200,93.022538\n1999-10-15,126.0,126.75,124.5,124.875,14238700,90.640867\n1999-10-18,124.9375,125.9375,123.4375,125.781197,9758800,91.298632\n1999-10-19,127.1875,128.25,125.9375,127.0,14392900,92.183304\n1999-10-20,127.75,129.468704,127.125,128.25,9759400,93.09062\n1999-10-21,127.218697,129.0,126.625,129.0,9105400,93.63501\n1999-10-22,129.75,131.218704,129.5625,130.093704,8309600,94.428878\n1999-10-25,129.3125,130.468704,128.75,129.4375,8114300,93.95257\n1999-10-26,130.1875,130.6875,127.8125,127.8125,6256300,92.773059\n1999-10-27,128.375,130.3125,128.25,130.125,5433300,94.451594\n1999-10-28,132.4375,134.859299,132.1875,134.5625,10509700,97.672566\n1999-10-29,135.843704,137.6875,135.718704,137.0,10988600,99.441832\n1999-11-01,136.5,137.0,135.5625,135.5625,4006500,98.398419\n1999-11-02,135.968704,137.25,134.593704,134.593704,6516900,97.695215\n1999-11-03,136.0,136.375,135.125,135.5,7222300,98.353053\n1999-11-04,136.75,137.359299,135.765594,136.531204,7907500,99.101555\n1999-11-05,138.625,139.109299,136.781204,137.875,7431500,100.076953\n1999-11-08,137.0,138.375,136.75,138.0,4649200,100.167685\n1999-11-09,138.5,138.6875,136.281204,136.703094,4533700,99.226322\n1999-11-10,136.25,138.390594,136.078094,137.718704,6405600,99.963505\n1999-11-11,138.1875,138.5,137.468704,138.5,4794100,100.530611\n1999-11-12,139.25,139.984299,137.125,139.75,11802900,101.437927\n1999-11-15,139.843704,140.25,139.406204,140.078094,2187500,101.676075\n1999-11-16,140.5625,143.0,140.093704,141.25,7544800,102.526706\n1999-11-17,142.25,142.9375,141.3125,141.625,9459000,102.798901\n1999-11-18,142.4375,143.0,141.625,142.625,4491000,103.524754\n1999-11-19,142.406204,142.968704,142.0,142.5,4832100,103.434022\n1999-11-22,142.4375,143.0,141.5,142.468704,4155400,103.411306\n1999-11-23,142.843704,142.843704,140.375,141.218704,5918000,102.50399\n1999-11-24,140.75,142.4375,140.0,141.968704,4459700,103.04838\n1999-11-26,142.468704,142.875,141.25,141.4375,1693900,102.662804\n1999-11-29,140.875,141.921799,140.4375,140.9375,7348600,102.299877\n1999-11-30,140.75,142.3125,139.0,139.281204,7682000,101.09765\n1999-12-01,139.3125,140.5,139.0,140.406204,6980200,101.914235\n1999-12-02,140.625,141.375,140.375,141.25,6698300,102.526706\n1999-12-03,143.031204,145.343704,143.031204,143.843704,10045400,104.409354\n1999-12-06,143.531204,143.718704,142.25,142.781204,3138900,103.638135\n1999-12-07,143.281204,143.3125,141.375,141.625,10714200,102.798901\n1999-12-08,141.343704,142.0625,140.5,140.718704,4611400,102.141064\n1999-12-09,141.8125,142.218704,139.375,141.406204,6474700,102.640088\n1999-12-10,142.281204,142.8125,140.875,141.875,5127300,102.980364\n1999-12-13,141.4375,142.718704,141.281204,142.125,4210800,103.161827\n1999-12-14,141.625,142.484299,140.625,140.75,5739000,102.16378\n1999-12-15,140.375,142.203094,140.031204,141.5,6879100,102.708169\n1999-12-16,142.1875,142.656204,141.156204,142.125,5854900,103.161827\n1999-12-17,143.0,143.3125,142.0625,142.6875,4775400,103.824338\n1999-12-20,142.5625,143.1875,141.093704,141.656204,4608700,103.073931\n1999-12-21,141.593704,144.0625,141.343704,143.8125,7981300,104.642926\n1999-12-22,143.625,144.1875,142.968704,144.1875,5377500,104.915789\n1999-12-23,145.015594,146.484299,145.0,146.484299,5721700,106.587019\n1999-12-27,146.5,146.781204,145.0625,146.281204,2691000,106.439241\n1999-12-28,145.875,146.5,145.484299,146.1875,4084500,106.371058\n1999-12-29,146.3125,146.8125,145.3125,146.8125,3001000,106.82583\n1999-12-30,147.125,147.5625,146.1875,146.640594,3641300,106.700745\n1999-12-31,146.843704,147.5,146.25,146.875,3172700,106.871307\n2000-01-03,148.25,148.25,143.875,145.4375,8164300,105.825332\n2000-01-04,143.531204,144.0625,139.640594,139.75,8089800,101.686912\n2000-01-05,139.9375,141.531204,137.25,140.0,12177900,101.86882\n2000-01-06,139.625,141.5,137.75,137.75,6227200,100.231643\n2000-01-07,140.3125,145.75,140.0625,145.75,8066500,106.052718\n2000-01-10,146.25,146.906204,145.031204,146.25,5741700,106.416535\n2000-01-11,145.8125,146.093704,143.5,144.5,7503700,105.143175\n2000-01-12,144.593704,144.593704,142.875,143.0625,6907700,104.097201\n2000-01-13,144.468704,145.75,143.281204,145.0,5158300,105.506992\n2000-01-14,146.531204,147.468704,145.968704,146.968704,7437300,106.939489\n2000-01-18,145.343704,146.625,145.1875,145.8125,6488500,106.098195\n2000-01-19,145.3125,147.0,145.0,147.0,6157900,106.962261\n2000-01-20,146.968704,146.968704,143.8125,144.75,5800100,105.325084\n2000-01-21,145.5,145.5,144.0625,144.4375,6244800,105.097698\n2000-01-24,145.656204,145.843704,139.406204,140.343704,7896900,102.118911\n2000-01-25,140.515594,141.9375,139.0,141.9375,9942500,103.278612\n2000-01-26,141.0,141.546799,140.093704,140.8125,5158100,102.460023\n2000-01-27,141.843704,142.218704,138.125,140.25,10922700,102.050729\n2000-01-28,139.4375,140.0625,135.531204,135.875,11916200,98.867328\n2000-01-31,135.8125,139.671799,135.0,139.5625,10768700,101.55048\n2000-02-01,139.75,141.6875,138.531204,140.9375,8419900,102.550977\n2000-02-02,141.281204,142.25,140.375,141.0625,6205900,102.641932\n2000-02-03,140.875,143.25,140.0,143.1875,7997500,104.188155\n2000-02-04,143.1875,144.0,142.125,142.593704,4925400,103.756089\n2000-02-07,142.5625,142.781204,141.4375,142.375,5845800,103.596952\n2000-02-08,143.968704,144.5625,143.625,144.3125,4936400,105.006744\n2000-02-09,144.468704,144.468704,141.265594,141.281204,8511500,102.801068\n2000-02-10,141.625,142.5625,140.875,141.5625,6690600,103.005749\n2000-02-11,141.843704,141.9375,138.031204,138.6875,9849800,100.9138\n2000-02-14,139.781204,139.781204,138.3125,139.5,8528800,101.505003\n2000-02-15,139.25,141.218704,137.796799,141.078094,11078300,102.653279\n2000-02-16,140.375,140.9375,138.796799,139.0,8845400,101.141186\n2000-02-17,140.4375,140.4375,138.218704,138.281204,7584200,100.618165\n2000-02-18,138.875,138.875,134.625,135.3125,9409200,98.458034\n2000-02-22,135.1875,136.343704,133.531204,134.968704,16415400,98.207876\n2000-02-23,135.625,137.468704,134.5,136.5625,12119000,99.367577\n2000-02-24,136.6875,137.031204,133.093704,133.8125,17375000,97.366582\n2000-02-25,135.1875,136.718704,133.125,133.328094,10559900,97.014112\n2000-02-28,133.375,136.6875,132.718704,136.125,13397800,99.049237\n2000-02-29,136.0625,137.4375,135.75,137.4375,8242500,100.004257\n2000-03-01,137.625,139.0,137.218704,138.4375,6868000,100.731891\n2000-03-02,138.6875,139.125,137.343704,138.531204,7600200,100.800074\n2000-03-03,140.4375,141.718704,139.718704,141.125,12770300,102.687409\n2000-03-06,140.8125,141.343704,138.75,139.75,11967100,101.686912\n2000-03-07,140.0,140.156204,135.218704,137.046799,20062000,99.719969\n2000-03-08,136.468704,137.843704,135.031204,136.875,11808500,99.594963\n2000-03-09,137.25,140.875,136.125,140.875,5500900,102.5055\n2000-03-10,140.1875,142.0,139.531204,140.125,7924600,101.959774\n2000-03-13,136.6875,140.468704,135.6875,138.593704,10540500,100.845551\n2000-03-14,139.281204,140.093704,136.156204,136.625,8263900,99.413054\n2000-03-15,136.875,140.4375,136.0625,139.8125,10300800,101.732389\n2000-03-16,141.625,146.843704,140.875,146.343704,25601400,106.484718\n2000-03-17,145.8125,148.0,145.4375,146.9375,10272900,107.188523\n2000-03-20,146.875,147.343704,144.781204,146.1875,12502300,106.641411\n2000-03-21,145.531204,149.75,144.5,149.1875,13612600,108.829862\n2000-03-22,149.5625,150.843704,148.6875,150.093704,8260000,109.490923\n2000-03-23,149.156204,153.468704,149.156204,152.656204,11654500,111.360225\n2000-03-24,152.875,155.75,151.718704,153.5625,11462900,112.021353\n2000-03-27,153.375,153.781204,151.8125,151.9375,8798600,110.835942\n2000-03-28,151.25,152.984299,150.593704,151.0625,6334400,110.197644\n2000-03-29,151.5625,152.484299,149.656204,151.218704,6747500,110.311592\n2000-03-30,150.156204,151.921799,147.125,148.6875,9491900,108.46512\n2000-03-31,149.625,152.3125,148.4375,150.375,9249100,109.696124\n2000-04-03,150.125,151.25,148.6875,151.25,8508200,110.334422\n2000-04-04,151.75,153.0,141.390594,150.125,19585500,109.513753\n2000-04-05,147.875,150.8125,147.625,149.1875,8387200,108.829862\n2000-04-06,150.25,151.6875,149.0,150.484299,6378500,109.775856\n2000-04-07,151.5625,152.125,150.5,151.4375,6023600,110.4712\n2000-04-10,151.75,153.109299,150.3125,150.843704,9624200,110.038036\n2000-04-11,150.0,151.625,148.375,150.406204,9006400,109.718887\n2000-04-12,150.375,151.156204,146.156204,146.281204,10779200,106.709766\n2000-04-13,147.468704,148.156204,143.781204,144.25,12225800,105.228036\n2000-04-14,142.625,142.8125,133.5,136.0,29604000,99.209795\n2000-04-17,135.1875,140.75,134.6875,140.75,23918200,102.674842\n2000-04-18,140.5625,144.468704,139.781204,144.468704,11069200,105.387577\n2000-04-19,144.5,145.125,142.531204,143.125,6553700,104.407366\n2000-04-20,143.5625,143.9375,142.375,143.8125,8537600,104.908887\n2000-04-24,141.5,143.3125,140.5,142.25,12893100,103.769068\n2000-04-25,144.625,148.156204,144.4375,148.156204,14102000,108.077548\n2000-04-26,147.968704,148.75,146.0,146.484299,7711100,106.85792\n2000-04-27,143.0,147.343704,143.0,146.0,15595300,106.504632\n2000-04-28,147.0,147.859299,145.0625,145.093704,8743400,105.843504\n2000-05-01,146.5625,148.484299,145.843704,147.0625,7328300,107.279709\n2000-05-02,145.5,147.125,144.125,144.125,9411900,105.13685\n2000-05-03,144.0,144.0,139.781204,140.75,12630700,102.674842\n2000-05-04,142.0,142.359299,140.75,141.8125,5963600,103.449919\n2000-05-05,141.0625,144.0,140.9375,143.531204,7862400,104.703686\n2000-05-08,142.75,143.375,141.843704,142.453094,5064100,103.917222\n2000-05-09,143.0625,143.406204,140.265594,141.3125,5620300,103.085177\n2000-05-10,140.5,140.968704,137.75,138.125,10293900,100.759948\n2000-05-11,140.125,141.5,139.125,141.281204,7091100,103.062347\n2000-05-12,141.8125,143.468704,141.625,142.8125,5960800,104.179403\n2000-05-15,142.75,145.609299,142.0,145.281204,4441300,105.980282\n2000-05-16,146.5625,147.718704,145.3125,146.6875,8192200,107.006152\n2000-05-17,145.6875,146.1875,144.468704,145.156204,5907200,105.889097\n2000-05-18,145.625,146.3125,143.375,143.375,4325600,104.589737\n2000-05-19,142.5625,143.234299,140.406204,141.125,6518400,102.948399\n2000-05-22,141.25,141.468704,137.0,140.0625,10839400,102.173322\n2000-05-23,140.4375,140.8125,137.5625,138.0,7979200,100.668762\n2000-05-24,138.0,140.6875,136.5,140.25,11081500,102.310101\n2000-05-25,140.6875,141.8125,137.718704,137.843704,8278900,100.554747\n2000-05-26,138.8125,139.6875,137.328094,138.0,4814000,100.668762\n2000-05-30,140.0,142.9375,139.468704,142.5,5362700,103.951439\n2000-05-31,142.5625,144.0,142.093704,142.8125,6056500,104.179403\n2000-06-01,143.6875,145.4375,143.0,145.3125,8961600,106.003112\n2000-06-02,148.9375,149.093704,147.484299,147.843704,8962200,107.849585\n2000-06-05,147.468704,148.218704,146.875,147.125,6998100,107.325302\n2000-06-06,146.625,147.781204,145.906204,146.468704,4858900,106.846544\n2000-06-07,146.625,148.0,146.0,147.484299,4919500,107.587404\n2000-06-08,147.5,147.75,146.0625,146.906204,5723100,107.165694\n2000-06-09,147.5,147.968704,145.625,146.593704,3085300,106.93773\n2000-06-12,146.968704,146.968704,144.875,144.875,3678900,105.683963\n2000-06-13,144.8125,147.75,144.625,147.593704,6558700,107.667214\n2000-06-14,148.25,148.875,147.1875,147.843704,6420500,107.849585\n2000-06-15,148.125,148.75,146.843704,148.156204,5881200,108.077548\n2000-06-16,148.3125,148.3125,145.875,146.593704,5567900,107.189497\n2000-06-19,146.468704,149.156204,146.25,148.468704,5106900,108.560499\n2000-06-20,148.1875,148.875,147.0,147.9375,6480000,108.172082\n2000-06-21,146.9375,148.4375,146.890594,147.875,3115000,108.126382\n2000-06-22,147.5625,147.6875,145.0,145.625,7490500,106.481179\n2000-06-23,145.8125,146.125,143.875,144.375,4863300,105.567178\n2000-06-26,145.375,146.25,144.875,146.234299,5201300,106.926699\n2000-06-27,145.984299,146.718704,145.015594,145.156204,4159500,106.138395\n2000-06-28,145.625,146.984299,145.3125,145.5625,5347700,106.435479\n2000-06-29,144.75,145.75,143.515594,144.1875,6345700,105.430078\n2000-06-30,143.9375,145.593704,143.890594,145.281204,7420200,106.229795\n2000-07-03,145.4375,147.4375,145.1875,147.281204,1436600,107.692198\n2000-07-05,146.375,146.656204,144.375,144.625,2748200,105.749978\n2000-07-06,144.9375,146.468704,144.218704,145.75,5963200,106.572579\n2000-07-07,146.6875,148.781204,146.25,148.093704,3034800,108.286299\n2000-07-10,147.875,148.906204,147.531204,147.843704,2816100,108.103499\n2000-07-11,147.468704,149.125,147.156204,148.156204,5431600,108.331999\n2000-07-12,149.281204,150.125,148.6875,149.125,5883000,109.040384\n2000-07-13,149.984299,150.375,149.1875,149.781204,5356000,109.520201\n2000-07-14,150.4375,151.25,149.671799,151.25,5341900,110.594186\n2000-07-17,150.984299,151.984299,150.6875,151.0,4208300,110.411386\n2000-07-18,150.625,150.625,149.343704,149.765594,4262100,109.508787\n2000-07-19,149.468704,149.906204,148.25,148.5625,8506800,108.629083\n2000-07-20,149.0,150.625,148.8125,150.625,4538900,110.137185\n2000-07-21,149.75,149.75,147.6875,147.6875,5656900,107.989282\n2000-07-24,148.125,148.859299,146.5625,146.843704,5628500,107.372297\n2000-07-25,147.75,147.843704,146.781204,147.3125,4757100,107.715081\n2000-07-26,146.968704,147.156204,145.640594,145.875,12062500,106.66398\n2000-07-27,145.9375,146.625,144.6875,145.375,7652600,106.298379\n2000-07-28,145.718704,145.906204,141.515594,142.093704,6229500,103.899092\n2000-07-31,142.9375,144.125,142.0625,143.0,5265500,104.561776\n2000-08-01,143.625,144.718704,143.125,143.875,3946600,105.201577\n2000-08-02,143.875,145.406204,143.625,144.593704,7439800,105.727095\n2000-08-03,142.875,145.8125,142.625,145.593704,4607100,106.458296\n2000-08-04,146.3125,146.718704,145.406204,146.375,3686600,107.02958\n2000-08-07,146.718704,148.4375,146.375,148.125,4159800,108.309182\n2000-08-08,147.5,148.8125,147.5,148.6875,3658700,108.720483\n2000-08-09,149.140594,149.218704,147.375,147.4375,5383800,107.806482\n2000-08-10,147.531204,147.859299,146.281204,146.718704,4193400,107.280897\n2000-08-11,146.625,148.0,145.5625,147.406204,5089400,107.783598\n2000-08-14,147.781204,149.5625,147.0625,149.281204,2966700,109.1546\n2000-08-15,149.343704,149.8125,148.5625,149.156204,5564600,109.0632\n2000-08-16,149.3125,149.9375,147.843704,148.625,5191600,108.674783\n2000-08-17,148.6875,150.4375,148.343704,150.1875,5652200,109.817285\n2000-08-18,150.375,150.375,149.218704,149.6875,4626400,109.451684\n2000-08-21,150.031204,150.718704,149.406204,150.5,2380600,110.045785\n2000-08-22,150.5625,151.3125,150.093704,150.25,3075300,109.862985\n2000-08-23,149.8125,151.281204,149.281204,150.843704,5483200,110.297102\n2000-08-24,151.156204,151.5,150.5,151.3125,4529000,110.639886\n2000-08-25,151.156204,151.625,150.9375,151.25,2822200,110.594186\n2000-08-28,151.25,152.906204,151.25,151.765594,5518700,110.97119\n2000-08-29,151.4375,151.875,150.906204,151.796799,3561900,110.994006\n2000-08-30,151.3125,151.5,150.343704,150.343704,3964800,109.931502\n2000-08-31,151.0625,153.093704,150.906204,152.343704,4863100,111.393904\n2000-09-01,153.25,153.593704,152.0,152.5,3191200,111.508188\n2000-09-05,151.875,152.203094,150.8125,151.281204,3470800,110.617003\n2000-09-06,151.1875,151.953094,149.531204,149.5625,4322200,109.360284\n2000-09-07,150.25,151.078094,149.828094,150.843704,4265500,110.297102\n2000-09-08,150.281204,150.5,149.328094,149.8125,3518200,109.543084\n2000-09-11,149.75,151.1875,148.6875,149.593704,3937500,109.383101\n2000-09-12,149.75,150.25,148.4375,148.5,4769100,108.583383\n2000-09-13,148.0,149.343704,147.656204,148.890594,7691600,108.868986\n2000-09-14,149.875,149.9375,148.156204,149.640594,3397100,109.417387\n2000-09-15,148.1875,148.25,146.0,146.0,4085700,107.023584\n2000-09-18,146.375,146.968704,144.203094,144.656204,5109300,106.03853\n2000-09-19,145.125,146.3125,144.703094,145.968704,6588200,107.000643\n2000-09-20,145.6875,146.031204,143.156204,144.890594,6508100,106.210347\n2000-09-21,144.468704,145.5625,142.625,142.6875,5972300,104.595395\n2000-09-22,142.625,145.3125,142.421799,145.281204,7791300,106.496679\n2000-09-25,145.9375,146.0625,143.718704,144.25,9726300,105.740767\n2000-09-26,144.375,145.0,142.406204,142.406204,5302400,104.389194\n2000-09-27,143.5625,143.968704,142.125,143.156204,7186200,104.938973\n2000-09-28,143.1875,146.328094,142.890594,145.0,7036400,106.290546\n2000-09-29,145.468704,145.968704,143.625,143.625,9333600,105.282618\n2000-10-02,144.281204,144.906204,143.140594,143.843704,5517800,105.442937\n2000-10-03,144.531204,145.75,142.281204,142.5,9347200,104.45795\n2000-10-04,142.875,144.25,141.75,143.6875,6148900,105.328433\n2000-10-05,143.406204,144.843704,143.3125,144.1875,4566900,105.694952\n2000-10-06,143.875,144.640594,139.75,141.0625,10014900,103.404208\n2000-10-09,141.3125,141.3125,139.375,140.0,4508000,102.625355\n2000-10-10,140.093704,141.25,137.6875,137.6875,6104700,100.930204\n2000-10-11,137.625,138.625,135.125,136.531204,10346000,100.082595\n2000-10-12,137.281204,137.593704,132.781204,133.125,12336900,97.585717\n2000-10-13,132.9375,137.656204,132.875,137.5625,11778800,100.838574\n2000-10-16,137.406204,138.234299,136.6875,138.1875,5659000,101.296723\n2000-10-17,138.4375,138.5625,134.406204,134.75,7831700,98.776904\n2000-10-18,132.625,136.125,130.156204,134.25,10897300,98.410385\n2000-10-19,136.843704,139.453094,136.4375,139.3125,8767300,102.121391\n2000-10-20,138.375,141.1875,138.375,139.906204,7373500,102.556599\n2000-10-23,139.9375,141.031204,138.9375,140.531204,5290000,103.014748\n2000-10-24,140.968704,141.9375,139.0,139.593704,5750700,102.327524\n2000-10-25,138.75,139.5625,136.125,136.3125,9137600,99.922276\n2000-10-26,137.125,137.656204,134.031204,136.6875,9345800,100.197165\n2000-10-27,137.875,139.281204,136.625,139.281204,9762900,102.09845\n2000-10-30,138.4375,141.093704,138.156204,140.531204,10154100,103.014748\n2000-10-31,141.015594,143.6875,140.0625,142.953094,7752400,104.790086\n2000-11-01,142.25,143.25,141.218704,142.468704,6753600,104.435009\n2000-11-02,143.156204,143.906204,142.515594,142.703094,11395100,104.606826\n2000-11-03,143.468704,143.75,142.375,142.781204,5187100,104.664084\n2000-11-06,143.156204,144.296799,143.031204,143.781204,4042500,105.397122\n2000-11-07,143.140594,144.0,142.5625,143.75,5231300,105.374248\n2000-11-08,144.0625,144.0625,140.5625,140.5625,6123300,103.037689\n2000-11-09,140.0,141.218704,137.25,140.031204,10635300,102.648228\n2000-11-10,139.0,139.468704,136.531204,136.625,8569500,100.15135\n2000-11-13,135.625,136.984299,133.015594,135.5625,17285300,99.372497\n2000-11-14,137.468704,139.625,137.0,139.125,7668900,101.983946\n2000-11-15,139.0625,140.109299,137.75,139.5625,8837700,102.30465\n2000-11-16,138.578094,139.875,137.3125,137.375,6684100,100.701129\n2000-11-17,137.3125,139.0,135.75,136.640594,6551100,100.162782\n2000-11-20,135.75,136.375,134.3125,134.6875,5458500,98.731089\n2000-11-21,134.875,136.1875,133.515594,135.375,7684300,99.235053\n2000-11-22,134.343704,134.875,132.125,132.140594,5736900,96.86411\n2000-11-24,133.625,134.968704,133.625,134.843704,3411600,98.845593\n2000-11-27,136.468704,136.6875,135.3125,136.031204,5992000,99.716075\n2000-11-28,135.125,136.593704,133.640594,133.6875,5336100,97.998051\n2000-11-29,134.375,135.906204,133.265594,133.4375,6914100,97.814791\n2000-11-30,132.5,133.5,129.75,132.281204,11201600,96.967182\n2000-12-01,133.1875,134.0625,131.0,132.218704,7587200,96.921367\n2000-12-04,131.875,133.875,131.5,133.343704,6996600,97.746035\n2000-12-05,134.875,138.25,134.406204,137.718704,8883400,100.953077\n2000-12-06,137.781204,138.343704,135.031204,135.515594,12888000,99.338114\n2000-12-07,134.875,135.875,133.656204,133.656204,6529100,97.97511\n2000-12-08,137.0625,139.468704,133.875,133.968704,10276300,98.204184\n2000-12-11,137.375,139.5625,136.718704,138.625,6405600,101.617427\n2000-12-12,138.1875,138.8125,137.375,138.031204,5022900,101.182152\n2000-12-13,139.25,139.406204,136.031204,136.140594,6070500,99.796263\n2000-12-14,135.875,136.5,134.031204,134.406204,7678400,98.524888\n2000-12-15,133.125,133.125,130.5625,130.968704,9065300,96.29954\n2000-12-18,132.0625,133.468704,131.765594,132.718704,7235400,97.586292\n2000-12-19,132.468704,134.968704,130.015594,130.015594,9616600,95.598731\n2000-12-20,128.625,128.9375,126.093697,126.25,9994300,92.82994\n2000-12-21,126.0,128.859299,125.531197,127.125,14331500,93.473316\n2000-12-22,129.0,131.109299,128.843704,130.9375,10182900,96.276596\n2000-12-26,130.843704,132.343704,130.281204,132.343704,4665300,97.310559\n2000-12-27,131.75,133.734299,131.25,133.3125,4854100,98.022902\n2000-12-28,132.8125,133.875,132.593704,133.718704,8358700,98.321578\n2000-12-29,134.0625,134.281204,131.1875,131.1875,8774600,96.460418\n2001-01-02,132.0,132.156204,127.5625,128.8125,8737500,94.714112\n2001-01-03,128.3125,136.0,127.656197,135.0,19431600,99.263698\n2001-01-04,134.9375,135.468704,133.0,133.546799,9219000,98.195179\n2001-01-05,133.468704,133.625,129.1875,129.1875,12911400,94.989844\n2001-01-08,129.875,130.1875,127.6875,130.1875,6625300,95.725131\n2001-01-09,131.046799,131.5,129.421799,129.843704,5702400,95.472343\n2001-01-10,129.0,132.125,128.8125,132.125,8746100,97.149749\n2001-01-11,131.093704,133.484299,131.093704,132.25,7245100,97.24166\n2001-01-12,132.6875,133.718704,131.281204,132.0,7244000,97.057838\n2001-01-16,132.0,133.1875,131.515594,132.843704,8542200,97.678203\n2001-01-17,134.843704,135.046799,132.640594,133.453094,7851400,98.126279\n2001-01-18,133.4375,135.703094,132.9375,134.781204,8107000,99.102821\n2001-01-19,136.1875,136.1875,133.875,134.015594,7782500,98.539878\n2001-01-22,134.25,135.781204,133.5625,134.906204,7050900,99.194731\n2001-01-23,134.468704,136.656204,134.156204,135.968704,8463100,99.975973\n2001-01-24,136.25,137.3125,135.843704,136.375,6199900,100.274717\n2001-01-25,136.25,137.25,135.656204,136.031204,10818300,100.021929\n2001-01-26,135.156204,136.125,134.453094,135.875,7136800,99.907074\n2001-01-29,135.5,136.899994,135.369995,136.600006,6705900,100.440161\n2001-01-30,136.300003,137.919998,135.789993,137.800003,7069100,101.322503\n2001-01-31,137.399994,138.699997,136.600006,137.020004,9706900,100.74898\n2001-02-01,137.100006,137.949997,136.25,137.929993,8239100,101.418083\n2001-02-02,137.399994,137.990005,134.75,134.800003,8276600,99.116643\n2001-02-05,134.800003,135.940002,134.75,135.789993,4352900,99.84457\n2001-02-06,135.300003,136.699997,135.220001,135.389999,7106700,99.55046\n2001-02-07,134.720001,135.399994,133.679993,134.690002,5748700,99.035761\n2001-02-08,134.800003,135.399994,133.100006,133.119995,5943300,97.881356\n2001-02-09,133.350006,133.350006,131.259995,131.839996,9913000,96.94019\n2001-02-12,131.699997,133.5,131.699997,133.350006,5804400,98.05048\n2001-02-13,133.699997,134.169998,132.0,132.259995,6587600,97.249009\n2001-02-14,132.649994,132.649994,130.660004,132.059998,8400100,97.101954\n2001-02-15,132.839996,133.520004,131.990005,133.339996,5929800,98.04312\n2001-02-16,131.0,131.289993,129.300003,130.399994,6434900,95.881375\n2001-02-20,131.039993,131.139999,128.100006,128.389999,5760000,94.403453\n2001-02-21,127.900002,128.839996,125.5,125.620003,10910800,92.366711\n2001-02-22,126.349998,126.540001,123.019997,125.809998,21281600,92.506412\n2001-02-23,125.080002,125.540001,121.800003,124.959999,20173000,91.881419\n2001-02-26,125.800003,127.620003,124.5,127.620003,11503700,93.837285\n2001-02-27,126.800003,127.839996,125.510002,126.440002,11415000,92.969646\n2001-02-28,126.75,126.839996,123.269997,123.949997,14825800,91.138778\n2001-03-01,124.050003,124.599998,121.75,124.599998,14672000,91.616716\n2001-03-02,122.5,125.650002,122.300003,123.610001,12564300,90.888784\n2001-03-05,124.150002,124.779999,123.809998,124.739998,5293200,91.719656\n2001-03-06,126.349998,127.75,125.489998,126.080002,6917000,92.704943\n2001-03-07,126.900002,127.040001,125.760002,126.980003,6371700,93.366702\n2001-03-08,126.599998,127.239998,126.139999,127.120003,6055000,93.469641\n2001-03-09,126.099998,126.099998,123.110001,123.360001,10020300,90.704962\n2001-03-12,122.339996,122.5,117.75,118.080002,13972900,86.822649\n2001-03-13,119.400002,120.440002,117.529999,120.019997,12888000,88.249102\n2001-03-14,117.050003,119.290001,115.75,117.650002,19883400,86.506476\n2001-03-15,118.449997,118.860001,117.360001,117.68,10370300,86.528534\n2001-03-16,117.129997,117.400002,114.809998,115.010002,58514600,84.793013\n2001-03-19,115.760002,117.690002,114.82,117.349998,10067800,86.518213\n2001-03-20,117.900002,118.459999,114.110001,114.199997,15083900,84.195823\n2001-03-21,114.18,115.260002,111.900002,112.260002,19004600,82.76553\n2001-03-22,112.019997,112.599998,108.040001,111.120003,28624800,81.925047\n2001-03-23,113.25,114.660004,111.5,114.480003,12861700,84.402262\n2001-03-26,115.699997,116.269997,114.769997,115.940002,9943800,85.478671\n2001-03-27,115.620003,118.650002,115.279999,118.309998,12880700,87.225989\n2001-03-28,116.900002,116.949997,114.900002,115.040001,10953300,84.81513\n2001-03-29,114.699997,116.5,112.139999,115.480003,12060300,85.139529\n2001-03-30,115.550003,116.690002,114.5,116.690002,9183600,86.031621\n2001-04-02,116.300003,117.379997,113.800003,114.199997,10561000,84.195823\n2001-04-03,113.980003,114.150002,110.059998,110.389999,12836000,81.38684\n2001-04-04,110.57,112.099998,109.300003,110.849998,14884300,81.725982\n2001-04-05,113.300003,115.489998,112.5,115.050003,21522800,84.822504\n2001-04-06,113.989998,114.400002,112.059998,113.300003,14937800,83.532288\n2001-04-09,114.0,114.989998,112.779999,114.559998,9034300,84.461239\n2001-04-10,115.449997,117.75,115.169998,116.650002,17873600,86.002129\n2001-04-11,118.779999,118.989998,116.139999,116.730003,12722300,86.061112\n2001-04-12,116.300003,118.940002,115.959999,118.849998,9233200,87.624113\n2001-04-16,118.290001,118.889999,116.910004,117.599998,7350000,86.70253\n2001-04-17,117.309998,119.660004,117.019997,119.260002,10924700,87.926395\n2001-04-18,121.059998,126.0,120.690002,124.0,32481600,91.421036\n2001-04-19,124.25,125.839996,123.580002,125.650002,13809900,92.637527\n2001-04-20,124.900002,125.400002,123.660004,124.5,7626700,91.789669\n2001-04-23,123.650002,123.889999,121.910004,122.239998,8451800,90.123446\n2001-04-24,122.519997,123.650002,121.099998,121.580002,10044700,89.636853\n2001-04-25,121.419998,123.669998,120.949997,123.169998,8249000,90.809104\n2001-04-26,123.730003,125.220001,123.5,123.720001,10590400,91.214603\n2001-04-27,124.919998,125.839996,124.199997,125.779999,7938700,92.73337\n2001-04-30,126.449997,127.269997,124.669998,126.660004,10766900,93.382168\n2001-05-01,125.07,127.150002,124.599998,127.050003,10578000,93.669701\n2001-05-02,127.410004,127.690002,126.0,126.82,9572900,93.500127\n2001-05-03,126.129997,126.150002,124.220001,125.209999,9926200,92.313128\n2001-05-04,123.650002,127.349998,123.440002,127.339996,12145300,93.883503\n2001-05-07,126.860001,127.379997,126.230003,126.239998,7185200,93.072511\n2001-05-08,126.860001,127.099998,125.559998,126.18,6952600,93.028277\n2001-05-09,125.25,126.550003,125.059998,125.650002,9507400,92.637527\n2001-05-10,127.260002,127.5,125.769997,126.019997,6872400,92.910312\n2001-05-11,126.0,126.489998,124.400002,125.150002,7734400,92.268894\n2001-05-14,124.900002,125.440002,124.459999,125.400002,7914000,92.45321\n2001-05-15,125.550003,126.5,124.849998,125.980003,9782200,92.880826\n2001-05-16,124.839996,129.199997,124.620003,128.949997,14909000,95.070503\n2001-05-17,129.0,130.080002,128.559998,129.149994,11824600,95.217954\n2001-05-18,129.089996,129.740005,128.100006,129.740005,6683100,95.65295\n2001-05-21,129.839996,131.839996,129.149994,131.649994,11531500,97.06112\n2001-05-22,131.830002,132.089996,131.070007,131.479996,8342700,96.935786\n2001-05-23,131.050003,131.050003,129.25,129.25,12330800,95.291685\n2001-05-24,129.470001,130.0,128.550003,129.630005,7902800,95.57185\n2001-05-25,129.649994,129.699997,127.900002,128.100006,7425000,94.443833\n2001-05-29,128.220001,128.350006,126.900002,127.080002,9003900,93.691818\n2001-05-30,126.589996,127.089996,125.0,125.300003,10041800,92.379485\n2001-05-31,125.43,126.760002,125.260002,125.949997,9874200,92.858704\n2001-06-01,126.199997,127.099998,125.120003,126.730003,8848300,93.433776\n2001-06-04,126.800003,127.43,126.080002,127.339996,5623500,93.883503\n2001-06-05,127.489998,129.229996,127.269997,128.800003,9115400,94.959917\n2001-06-06,128.830002,128.830002,127.360001,127.730003,12064900,94.171042\n2001-06-07,127.050003,128.350006,127.0,128.190002,7355300,94.510184\n2001-06-08,127.699997,127.870003,126.139999,127.0,8170600,93.632836\n2001-06-11,126.709999,126.989998,125.410004,126.099998,7012200,92.969295\n2001-06-12,124.860001,126.739998,124.040001,125.879997,9364400,92.807095\n2001-06-13,126.169998,126.580002,124.650002,124.800003,7629400,92.010852\n2001-06-14,124.18,124.300003,121.75,122.0,12603000,89.946503\n2001-06-15,120.910004,122.400002,120.400002,121.849998,16821100,90.091419\n2001-06-18,121.650002,122.440002,120.910004,121.260002,11368300,89.655197\n2001-06-19,122.379997,122.889999,120.889999,121.790001,7732300,90.047059\n2001-06-20,121.190002,122.860001,121.099998,122.43,8787200,90.520251\n2001-06-21,122.220001,124.309998,122.150002,123.82,12259100,91.547965\n2001-06-22,123.489998,123.589996,122.160004,122.849998,12212000,90.830782\n2001-06-25,123.279999,123.440002,121.5,121.720001,8406800,89.995303\n2001-06-26,120.900002,122.389999,120.57,121.550003,8005800,89.869613\n2001-06-27,121.599998,122.239998,120.910004,121.480003,10105100,89.817858\n2001-06-28,122.0,123.940002,121.93,122.150002,10269300,90.31323\n2001-06-29,122.800003,124.010002,122.260002,122.599998,9824200,90.645941\n2001-07-02,122.800003,124.32,122.620003,124.129997,8522200,91.777166\n2001-07-03,123.980003,124.099998,123.050003,124.099998,3303100,91.754986\n2001-07-05,123.07,123.650002,121.660004,121.68,5517900,89.965728\n2001-07-06,121.309998,121.489998,119.050003,119.050003,11665900,88.021205\n2001-07-09,119.489998,120.540001,119.199997,119.699997,8339300,88.501786\n2001-07-10,120.290001,120.639999,118.209999,118.260002,8630700,87.437107\n2001-07-11,118.099998,118.889999,117.089996,118.379997,15328600,87.525827\n2001-07-12,119.5,121.470001,119.309998,121.190002,12002800,89.603442\n2001-07-13,120.839996,122.32,120.620003,122.239998,10433800,90.37977\n2001-07-16,121.769997,122.279999,120.290001,120.709999,6915300,89.248545\n2001-07-17,120.199997,121.949997,119.830002,121.839996,7469800,90.084023\n2001-07-18,120.559998,121.639999,120.059998,121.010002,7709300,89.470356\n2001-07-19,122.18,122.980003,120.760002,122.07,10082900,90.254079\n2001-07-20,121.150002,121.940002,120.919998,121.339996,6705800,89.714342\n2001-07-23,121.800003,121.879997,118.949997,118.949997,8065200,87.947264\n2001-07-24,119.0,119.199997,116.75,117.800003,12269000,87.097\n2001-07-25,117.919998,119.480003,117.459999,119.099998,12088500,88.058169\n2001-07-26,119.059998,120.849998,118.559998,120.349998,12898200,88.982374\n2001-07-27,120.830002,121.349998,119.910004,120.809998,8478800,89.32248\n2001-07-30,121.190002,121.349998,120.300003,120.849998,8547700,89.352055\n2001-07-31,121.0,122.68,120.800003,121.349998,11918100,89.721737\n2001-08-01,121.970001,122.699997,121.550003,122.110001,11940800,90.283655\n2001-08-02,123.230003,123.25,121.889999,122.610001,11070100,90.653336\n2001-08-03,122.360001,122.360001,120.900002,121.940002,10816300,90.157964\n2001-08-06,121.349998,121.510002,120.099998,120.300003,8550100,88.945409\n2001-08-07,120.269997,121.199997,119.910004,120.769997,8865100,89.292905\n2001-08-08,120.120003,121.160004,118.43,118.529999,15183800,87.636733\n2001-08-09,118.699997,118.970001,117.860001,118.879997,14118500,87.895509\n2001-08-10,118.800003,119.839996,117.339996,119.290001,11173300,88.19865\n2001-08-13,119.599998,119.849998,118.809998,119.32,7431600,88.22083\n2001-08-14,120.139999,120.349998,118.800003,119.269997,13178100,88.18386\n2001-08-15,119.230003,119.610001,118.080002,118.239998,8520600,87.422317\n2001-08-16,117.800003,118.75,117.0,118.650002,10734700,87.725458\n2001-08-17,117.650002,117.870003,116.010002,116.75,11604600,86.320667\n2001-08-20,116.800003,117.900002,116.550003,117.830002,10417400,87.11918\n2001-08-21,117.800003,118.540001,115.800003,115.82,14480800,85.633059\n2001-08-22,116.75,117.43,115.779999,117.019997,11752300,86.520292\n2001-08-23,116.959999,117.519997,116.489998,116.599998,8744100,86.209761\n2001-08-24,117.209999,119.129997,116.739998,119.019997,11687700,87.999019\n2001-08-27,118.970001,119.199997,118.260002,118.309998,7425100,87.474072\n2001-08-28,118.279999,118.489998,116.580002,116.580002,12046600,86.194976\n2001-08-29,117.129997,117.18,115.169998,115.540001,16180000,85.426038\n2001-08-30,114.849998,115.739998,112.040001,113.32,17692600,83.78465\n2001-08-31,113.400002,114.769997,113.129997,114.150002,15985400,84.398323\n2001-09-04,113.849998,116.169998,113.370003,113.419998,24473400,83.858586\n2001-09-05,113.699997,114.190002,111.949997,113.699997,21477100,84.065606\n2001-09-06,112.650002,113.300003,110.769997,110.769997,21653000,81.899272\n2001-09-07,110.019997,111.25,108.629997,108.720001,33133900,80.38358\n2001-09-10,107.699997,110.349998,107.699997,110.050003,23408700,81.366935\n2001-09-17,101.0,106.400002,100.0,104.300003,32388700,77.115596\n2001-09-18,104.330002,105.300003,103.360001,104.050003,22029200,76.930755\n2001-09-19,104.099998,104.5,98.559998,101.949997,42771800,75.378087\n2001-09-20,100.400002,101.809998,98.559998,98.709999,36210900,72.982552\n2001-09-21,94.279999,98.989998,93.800003,97.279999,49782100,72.19514\n2001-09-24,99.720001,101.160004,99.059998,100.699997,25549600,74.733249\n2001-09-25,100.75,102.0,99.900002,101.75,25466200,75.512496\n2001-09-26,102.349998,102.400002,100.43,101.389999,18587500,75.245326\n2001-09-27,101.25,102.290001,100.0,102.269997,20536800,75.898405\n2001-09-28,102.980003,109.620003,102.5,104.440002,21687200,77.508848\n2001-10-01,103.900002,104.32,102.830002,104.269997,20457400,77.38268\n2001-10-02,104.0,105.580002,103.650002,105.580002,19833100,78.354884\n2001-10-03,104.599998,107.879997,104.349998,107.349998,32045800,79.668465\n2001-10-04,108.290001,108.970001,106.75,107.440002,32674100,79.73526\n2001-10-05,107.25,107.620003,105.519997,107.230003,29796100,79.579412\n2001-10-08,106.269997,107.300003,105.870003,106.529999,12970300,79.059912\n2001-10-09,106.610001,106.75,105.599998,105.959999,15976300,78.636894\n2001-10-10,105.800003,108.550003,105.519997,108.32,19987400,80.388339\n2001-10-11,108.949997,110.300003,108.949997,110.0,23006300,81.635131\n2001-10-12,109.150002,109.889999,107.300003,109.5,31360500,81.264062\n2001-10-15,108.629997,109.449997,108.059998,109.300003,16873000,81.115637\n2001-10-16,109.800003,110.610001,108.949997,109.989998,15877100,81.627708\n2001-10-17,111.07,111.150002,107.650002,107.650002,28542300,79.891109\n2001-10-18,107.82,108.160004,106.75,107.419998,16510000,79.720414\n2001-10-19,107.0,107.910004,106.010002,107.349998,21912500,79.668465\n2001-10-22,107.300003,109.57,107.209999,109.470001,17540600,81.241799\n2001-10-23,109.959999,110.279999,108.379997,108.910004,22062500,80.826203\n2001-10-24,108.980003,109.449997,108.220001,108.620003,16065200,80.610983\n2001-10-25,107.449997,110.599998,106.739998,110.57,27467900,82.058149\n2001-10-26,109.949997,111.790001,109.669998,110.32,18623300,81.872614\n2001-10-29,110.160004,110.550003,107.449997,107.449997,18727500,79.742678\n2001-10-30,107.349998,107.699997,105.559998,106.160004,26178600,78.785325\n2001-10-31,106.900002,107.860001,105.800003,105.800003,28124200,78.518155\n2001-11-01,106.599998,109.010002,105.699997,108.510002,29806800,80.529347\n2001-11-02,108.440002,109.379997,107.870003,109.25,17575900,81.078527\n2001-11-05,110.120003,111.089996,109.949997,110.68,15929500,82.139784\n2001-11-06,110.349998,112.480003,109.849998,112.400002,23245800,83.416262\n2001-11-07,111.769997,113.120003,111.639999,112.25,19716000,83.30494\n2001-11-08,112.870003,114.080002,111.900002,112.599998,22563500,83.564687\n2001-11-09,112.25,112.959999,111.440002,112.720001,15895800,83.653746\n2001-11-12,111.0,112.650002,110.0,112.029999,26068800,83.141669\n2001-11-13,113.440002,114.550003,113.18,114.550003,15296200,85.011859\n2001-11-14,115.169998,115.400002,113.709999,114.660004,17571300,85.093494\n2001-11-15,114.370003,115.18,113.93,114.870003,19470200,85.249343\n2001-11-16,115.080002,115.099998,113.400002,114.360001,18134900,84.870851\n2001-11-19,114.919998,115.849998,114.449997,115.769997,13625400,85.917262\n2001-11-20,115.370003,115.800003,114.639999,114.800003,16209700,85.197393\n2001-11-21,114.5,114.669998,113.510002,114.040001,11470200,84.633367\n2001-11-23,114.040001,115.75,114.0,115.68,6717100,85.850472\n2001-11-26,115.75,116.339996,115.07,115.93,13726000,86.036007\n2001-11-27,115.620003,116.900002,114.089996,115.43,19261400,85.664938\n2001-11-28,114.739998,115.169998,113.25,113.339996,20195500,84.113867\n2001-11-29,113.660004,114.919998,113.0,114.870003,16354700,85.249343\n2001-11-30,114.400002,114.910004,114.019997,114.050003,13680300,84.64079\n2001-12-03,113.650002,114.080002,113.010002,113.370003,15220400,84.136136\n2001-12-04,113.919998,115.300003,113.349998,115.290001,17239900,85.561039\n2001-12-05,115.610001,118.0,115.559998,117.400002,25204000,87.12695\n2001-12-06,117.339996,117.940002,116.93,117.339996,17972900,87.082418\n2001-12-07,116.900002,117.089996,115.699997,116.559998,18857800,86.503551\n2001-12-10,115.849998,116.389999,114.349998,114.379997,13862700,84.885691\n2001-12-11,114.900002,115.720001,113.900002,114.150002,20833300,84.715003\n2001-12-12,114.550003,114.779999,113.110001,114.279999,16171500,84.811478\n2001-12-13,113.449997,113.699997,112.040001,112.059998,19026700,83.163932\n2001-12-14,112.330002,113.489998,112.0,113.129997,16721900,83.958019\n2001-12-17,112.989998,114.360001,112.900002,114.300003,13925900,84.826324\n2001-12-18,114.629997,115.150002,114.339996,114.980003,13663700,85.330978\n2001-12-19,114.089996,115.919998,114.0,115.790001,20143400,85.932108\n2001-12-20,115.5,115.800003,114.550003,114.650002,14867900,85.086071\n2001-12-21,115.029999,115.07,114.199997,114.949997,14037700,85.602141\n2001-12-24,114.830002,115.040001,114.610001,114.730003,5728800,85.438314\n2001-12-26,114.650002,116.209999,114.650002,115.360001,10304800,85.907466\n2001-12-27,115.300003,116.080002,115.25,116.059998,9407300,86.428747\n2001-12-28,116.290001,116.75,115.919998,116.0,10593800,86.384068\n2001-12-31,116.150002,116.389999,114.230003,114.300003,14619500,85.118097\n2002-01-02,115.110001,115.75,113.809998,115.529999,18651900,86.034062\n2002-01-03,115.650002,116.949997,115.540001,116.839996,15743000,87.009605\n2002-01-04,117.169998,117.980003,116.550003,117.620003,20140700,87.590468\n2002-01-07,117.699997,117.989998,116.559998,116.790001,13106500,86.972374\n2002-01-08,116.790001,117.059998,115.970001,116.519997,12683700,86.771304\n2002-01-09,116.68,117.779999,115.339996,115.57,16610300,86.063851\n2002-01-10,115.690002,116.349998,115.300003,116.080002,12823400,86.443644\n2002-01-11,116.209999,116.279999,114.699997,114.940002,13708400,85.594698\n2002-01-14,114.650002,114.839996,113.959999,114.220001,12301100,85.05852\n2002-01-15,114.550003,115.389999,113.900002,115.150002,20219900,85.751082\n2002-01-16,114.300003,114.400002,112.690002,112.82,17067000,84.015953\n2002-01-17,113.760002,114.239998,113.400002,113.669998,17283400,84.648938\n2002-01-18,113.0,113.849998,112.669998,113.150002,17028000,84.261702\n2002-01-22,113.75,113.93,112.019997,112.370003,11689300,83.680844\n2002-01-23,112.629997,113.550003,112.019997,113.230003,12438900,84.321278\n2002-01-24,113.639999,114.25,113.32,113.580002,12142800,84.581919\n2002-01-25,113.120003,114.18,113.040001,113.550003,12810700,84.559579\n2002-01-28,113.900002,114.190002,112.919998,113.860001,10589200,84.790431\n2002-01-29,113.849998,114.129997,110.050003,110.279999,27720800,82.124439\n2002-01-30,110.389999,113.389999,108.400002,111.870003,34711800,83.308499\n2002-01-31,112.150002,113.300003,111.620003,113.18,19909200,84.284042\n2002-02-01,113.089996,113.300003,112.169998,112.650002,15838500,83.889357\n2002-02-04,112.230003,112.230003,109.440002,109.849998,24243400,81.804222\n2002-02-05,109.400002,110.489998,108.529999,109.169998,33614000,81.297832\n2002-02-06,109.650002,109.739998,108.059998,108.699997,29486000,80.947827\n2002-02-07,108.720001,109.860001,108.0,108.019997,23445400,80.441437\n2002-02-08,108.629997,110.75,108.300003,110.089996,19277800,81.982946\n2002-02-11,110.050003,111.639999,109.82,111.440002,18792400,82.988282\n2002-02-12,110.959999,111.709999,110.029999,111.089996,13942500,82.727636\n2002-02-13,111.480003,112.540001,111.349998,112.269997,16781100,83.606371\n2002-02-14,112.510002,112.970001,111.589996,112.059998,20453800,83.449986\n2002-02-15,112.150002,112.239998,110.709999,110.889999,18366800,82.5787\n2002-02-19,110.150002,110.290001,108.610001,108.760002,15988100,80.992512\n2002-02-20,109.050003,110.589996,107.82,110.589996,29242800,82.355291\n2002-02-21,109.93,110.629997,108.260002,108.300003,26288600,80.649955\n2002-02-22,108.349998,109.940002,107.870003,109.639999,26572900,81.647837\n2002-02-25,109.739998,111.809998,109.699997,111.449997,17458700,82.995725\n2002-02-26,111.599998,112.040001,110.57,111.220001,22346500,82.824449\n2002-02-27,111.959999,112.860001,110.650002,111.650002,28597900,83.144666\n2002-02-28,111.830002,112.75,111.029999,111.150002,23755400,82.772321\n2002-03-01,111.720001,113.849998,111.510002,113.739998,26273600,84.701066\n2002-03-04,113.900002,115.989998,113.650002,115.75,27184600,86.197895\n2002-03-05,115.330002,116.400002,114.970001,115.379997,22718900,85.922358\n2002-03-06,115.099998,117.150002,115.07,116.75,20143200,86.942585\n2002-03-07,117.360001,117.5,115.57,116.5,19330800,86.756413\n2002-03-08,117.379997,117.900002,116.480003,116.989998,19930100,87.12131\n2002-03-11,116.889999,117.900002,116.43,117.239998,15621800,87.307482\n2002-03-12,116.099998,117.25,115.940002,117.169998,17153600,87.255354\n2002-03-13,116.629997,116.75,115.639999,116.040001,17175300,86.413856\n2002-03-14,116.040001,116.43,115.629997,115.879997,11168100,86.294703\n2002-03-15,115.970001,116.949997,115.900002,116.650002,21220100,87.116961\n2002-03-18,117.099998,117.559998,116.099998,116.669998,17548900,87.131895\n2002-03-19,117.300003,117.739998,116.82,117.449997,17912000,87.714417\n2002-03-20,116.5,116.580002,115.190002,115.239998,17114500,86.063937\n2002-03-21,115.300003,115.900002,114.120003,115.290001,25846800,86.101281\n2002-03-22,115.5,115.940002,114.699997,115.040001,15235400,85.914575\n2002-03-25,115.089996,115.360001,113.300003,113.610001,17499600,84.846617\n2002-03-26,113.519997,115.019997,113.470001,114.269997,19947600,85.339518\n2002-03-27,114.029999,115.010002,113.760002,114.57,19020300,85.563567\n2002-03-28,114.970001,115.769997,114.5,114.519997,17532900,85.526224\n2002-04-01,114.230003,115.099998,113.5,114.57,17711000,85.563567\n2002-04-02,113.980003,114.949997,113.769997,113.940002,15669500,85.09307\n2002-04-03,114.010002,114.209999,112.160004,113.139999,25658500,84.495609\n2002-04-04,112.599998,113.400002,112.230003,112.669998,23549000,84.144601\n2002-04-05,113.190002,113.629997,112.18,112.690002,19404900,84.159541\n2002-04-08,111.32,113.029999,111.230003,112.93,16470100,84.338777\n2002-04-09,113.18,113.18,111.93,112.139999,15122700,83.748786\n2002-04-10,112.099998,113.540001,112.089996,113.410004,17199300,84.697255\n2002-04-11,112.889999,113.050003,110.5,110.589996,25453700,82.591207\n2002-04-12,111.019997,111.650002,110.040001,111.419998,14950600,83.211072\n2002-04-15,111.620003,111.860001,110.199997,110.57,17394900,82.576273\n2002-04-16,111.699997,113.32,111.669998,113.199997,15040900,84.540417\n2002-04-17,113.389999,113.669998,112.599998,112.959999,12920100,84.361181\n2002-04-18,112.900002,113.459999,111.150002,112.470001,25204800,83.995239\n2002-04-19,113.199997,113.239998,112.559998,112.879997,10499200,84.301433\n2002-04-22,112.379997,112.43,110.839996,111.0,13922900,82.897407\n2002-04-23,111.089996,111.480003,110.169998,110.519997,16967000,82.53893\n2002-04-24,110.559998,111.809998,109.400002,109.410004,18902700,81.709961\n2002-04-25,109.209999,109.739998,108.720001,109.470001,25451500,81.754768\n2002-04-26,109.790001,110.010002,107.290001,107.389999,19769800,80.201374\n2002-04-29,107.93,108.260002,106.629997,106.860001,17724400,79.805559\n2002-04-30,107.019997,108.639999,106.639999,107.860001,19473500,80.552382\n2002-05-01,107.970001,109.25,106.800003,109.18,24575600,81.538189\n2002-05-02,109.099998,109.910004,107.779999,108.760002,15666800,81.224524\n2002-05-03,108.599998,108.760002,107.199997,107.580002,18185500,80.343272\n2002-05-06,107.639999,107.989998,105.309998,105.470001,23630400,78.767474\n2002-05-07,106.099998,106.32,104.900002,105.099998,21910000,78.491148\n2002-05-08,107.050003,109.360001,106.790001,109.010002,27917400,81.41123\n2002-05-09,108.650002,109.099998,107.580002,107.75,18085600,80.470231\n2002-05-10,107.970001,108.050003,105.599998,105.720001,18958900,78.95418\n2002-05-13,106.220001,107.949997,105.790001,107.870003,14677700,80.559852\n2002-05-14,109.620003,110.370003,109.0,110.220001,34201200,82.314886\n2002-05-15,109.5,110.910004,109.290001,109.790001,29535300,81.993752\n2002-05-16,109.699997,110.480003,109.330002,110.360001,28092000,82.419441\n2002-05-17,110.660004,111.25,110.099998,110.900002,27823700,82.822726\n2002-05-20,110.639999,110.690002,109.489998,109.699997,13833800,81.926535\n2002-05-21,110.110001,110.480003,108.32,108.699997,16877200,81.179711\n2002-05-22,108.220001,109.120003,108.0,108.940002,15844200,81.358953\n2002-05-23,109.260002,110.360001,108.480003,110.099998,13879800,82.225265\n2002-05-24,109.980003,110.199997,108.610001,108.690002,11877000,81.172247\n2002-05-28,109.050003,109.129997,107.449997,108.099998,24236900,80.731618\n2002-05-29,107.620003,108.019997,107.129997,107.300003,14773300,80.134163\n2002-05-30,106.550003,107.510002,105.900002,107.0,18217900,79.910113\n2002-05-31,107.400002,108.559998,106.849998,107.220001,19826300,80.074415\n2002-06-03,107.089996,107.599998,104.129997,104.370003,26056300,77.94597\n2002-06-04,104.150002,105.199997,103.550003,104.629997,25856200,78.14014\n2002-06-05,104.949997,105.669998,104.349998,105.610001,19695900,78.872029\n2002-06-06,105.540001,105.599998,103.150002,103.459999,22998500,77.266358\n2002-06-07,101.779999,103.919998,101.720001,103.339996,24011600,77.176737\n2002-06-10,103.239998,104.459999,103.019997,103.739998,18759900,77.475467\n2002-06-11,104.129997,104.540001,101.730003,101.959999,19990700,76.146122\n2002-06-12,101.709999,102.809998,100.779999,102.580002,31266000,76.609155\n2002-06-13,102.129997,103.0,101.339996,101.550003,21043900,75.839928\n2002-06-14,100.309998,101.559998,98.5,101.400002,39267500,75.727903\n2002-06-17,101.919998,104.339996,101.849998,104.120003,17647200,77.759264\n2002-06-18,103.739998,105.029999,103.629997,104.970001,21628500,78.394063\n2002-06-19,103.5,104.43,102.239998,102.519997,21540700,76.564342\n2002-06-20,102.260002,103.050003,100.959999,101.209999,25691000,75.586005\n2002-06-21,100.470001,100.93,98.68,99.279999,31190700,74.404137\n2002-06-24,98.610001,100.690002,97.25,99.800003,37169700,74.793848\n2002-06-25,100.300003,100.889999,97.540001,97.559998,33355000,73.115104\n2002-06-26,95.199997,98.150002,95.190002,97.720001,37913600,73.235017\n2002-06-27,98.5,99.489998,96.57,99.43,31616400,74.516554\n2002-06-28,99.239998,100.5,98.879997,98.959999,28184200,74.164318\n2002-07-01,99.18,99.800003,96.889999,97.029999,20270200,72.717903\n2002-07-02,96.860001,97.199997,94.769997,94.970001,34213900,71.174064\n2002-07-03,94.620003,95.839996,93.720001,95.510002,30565800,71.578761\n2002-07-05,96.779999,99.529999,96.660004,99.309998,19013500,74.42662\n2002-07-08,98.980003,99.699997,97.559998,98.07,19118600,73.497319\n2002-07-09,97.730003,98.339996,95.010002,95.599998,28620400,71.646208\n2002-07-10,96.0,96.07,92.040001,92.120003,50522500,69.038169\n2002-07-11,91.760002,93.349998,90.32,92.870003,59476500,69.600247\n2002-07-12,93.330002,93.889999,91.519997,91.849998,39018600,68.835818\n2002-07-15,91.639999,92.400002,87.889999,92.339996,77317200,69.203041\n2002-07-16,91.120003,92.379997,89.870003,90.559998,53282400,67.869043\n2002-07-17,92.459999,93.300003,89.75,90.739998,48880600,68.003942\n2002-07-18,90.699997,91.099998,87.75,87.800003,32656700,65.8006\n2002-07-19,86.760002,87.550003,84.300003,84.709999,77572600,63.484836\n2002-07-22,84.099998,85.910004,81.449997,82.199997,78134000,61.603746\n2002-07-23,82.550003,83.239998,79.75,79.949997,74484100,59.917512\n2002-07-24,78.129997,85.120003,77.68,84.720001,107022800,63.492332\n2002-07-25,84.269997,85.849998,81.599998,84.0,87176600,62.952736\n2002-07-26,84.650002,85.93,83.800003,85.599998,41206800,64.151834\n2002-07-29,87.5,90.339996,87.300003,89.769997,53492900,67.276987\n2002-07-30,89.32,91.400002,88.720001,90.940002,47532200,68.153833\n2002-07-31,90.489998,91.550003,89.25,91.160004,44669900,68.31871\n2002-08-01,90.879997,91.349998,88.330002,88.779999,66571900,66.535045\n2002-08-02,88.5,88.910004,85.620003,86.790001,51772900,65.043667\n2002-08-05,86.489998,86.93,83.550003,83.769997,47191300,62.780363\n2002-08-06,85.230003,87.900002,85.110001,86.589996,64730000,64.893776\n2002-08-07,87.870003,88.5,85.769997,88.099998,43289400,66.025428\n2002-08-08,88.419998,91.099998,87.800003,90.949997,48339800,68.161323\n2002-08-09,90.099998,91.940002,89.349998,91.290001,41879200,68.416135\n2002-08-12,89.989998,91.269997,89.550003,90.620003,25841700,67.914013\n2002-08-13,90.150002,91.660004,88.650002,88.970001,49690500,66.67744\n2002-08-14,89.019997,92.629997,88.019997,92.220001,57423600,69.113112\n2002-08-15,92.839996,93.989998,92.199997,93.5,45551500,70.072391\n2002-08-16,92.82,94.080002,92.0,93.220001,36517300,69.862549\n2002-08-19,93.459999,95.75,93.099998,95.400002,33669900,71.496323\n2002-08-20,94.82,95.400002,93.620003,94.389999,30508300,70.739389\n2002-08-21,95.059998,95.779999,93.57,95.75,39628900,71.758625\n2002-08-22,95.489998,97.150002,95.07,96.68,38399800,72.455602\n2002-08-23,96.010002,96.150002,94.150002,94.599998,33716400,70.89677\n2002-08-26,94.910004,95.639999,93.5,95.260002,33830100,71.391402\n2002-08-27,95.699997,96.25,93.5,94.160004,35335700,70.567022\n2002-08-28,93.279999,93.489998,91.800003,92.099998,38970600,69.023177\n2002-08-29,91.269997,93.050003,90.809998,92.139999,42965700,69.053155\n2002-08-30,91.68,93.389999,91.400002,91.779999,30366700,68.783357\n2002-09-03,90.730003,91.0,88.150002,88.279999,76586400,66.160327\n2002-09-04,88.610001,90.25,88.059998,89.540001,51099500,67.104619\n2002-09-05,88.489998,89.43,87.5,88.779999,67250900,66.535045\n2002-09-06,89.75,90.57,89.339996,90.0,38622200,67.44936\n2002-09-09,89.099998,91.349998,88.800003,90.660004,33998400,67.943991\n2002-09-10,91.139999,91.779999,90.559998,91.699997,41416600,68.723401\n2002-09-11,92.470001,93.330002,91.099998,91.129997,27711200,68.296222\n2002-09-12,90.75,90.839996,88.989998,89.449997,43601700,67.037167\n2002-09-13,88.690002,89.900002,88.25,89.669998,41131000,67.202044\n2002-09-16,89.309998,89.889999,88.459999,89.889999,28167600,67.366921\n2002-09-17,90.889999,91.190002,87.75,87.830002,47609700,65.823082\n2002-09-18,87.010002,88.5,86.279999,86.949997,54719400,65.163574\n2002-09-19,85.989998,86.800003,84.699997,84.699997,48510500,63.47734\n2002-09-20,84.919998,85.199997,84.050003,84.349998,46325600,63.498421\n2002-09-23,83.650002,84.059998,82.690002,83.660004,46893800,62.978995\n2002-09-24,82.440002,83.650002,81.849998,82.309998,69507000,61.962715\n2002-09-25,83.370003,84.769997,82.040001,84.349998,59294400,63.498421\n2002-09-26,85.019997,85.970001,84.449997,85.730003,53638000,64.537285\n2002-09-27,85.0,85.629997,82.75,82.75,64648300,62.293947\n2002-09-30,82.0,82.800003,80.900002,81.790001,73096400,61.571263\n2002-10-01,82.43,85.769997,81.470001,85.720001,67198100,64.529755\n2002-10-02,84.690002,85.529999,82.599998,83.150002,56749100,62.595067\n2002-10-03,83.139999,84.599998,81.949997,82.309998,55547000,61.962715\n2002-10-04,82.800003,82.919998,79.580002,80.800003,68483700,60.825996\n2002-10-07,80.059998,81.199997,78.550003,79.129997,53188000,59.56882\n2002-10-08,79.809998,81.309998,78.199997,80.370003,79531000,60.502293\n2002-10-09,79.089996,79.699997,77.779999,78.099998,79956400,58.79344\n2002-10-10,77.940002,81.07,77.07,80.629997,76749000,60.698016\n2002-10-11,82.099998,84.730003,81.82,84.160004,82308300,63.355394\n2002-10-14,83.199997,84.849998,83.040001,84.629997,40631900,63.709204\n2002-10-15,86.989998,88.720001,86.849998,88.699997,82320300,66.773087\n2002-10-16,87.410004,87.800003,85.919998,86.550003,62977800,65.154578\n2002-10-17,88.870003,89.300003,87.849998,88.269997,68534100,66.449384\n2002-10-18,87.650002,89.110001,86.93,88.639999,47448700,66.727921\n2002-10-21,88.120003,90.5,87.57,90.169998,45859100,67.879699\n2002-10-22,89.050003,90.010002,88.519997,89.519997,40966800,67.39038\n2002-10-23,88.769997,90.269997,87.68,90.199997,54905800,67.902282\n2002-10-24,90.75,90.900002,88.099998,88.360001,54987400,66.517139\n2002-10-25,88.209999,90.389999,87.940002,90.199997,43672100,67.902282\n2002-10-28,91.150002,91.290001,88.849998,89.610001,39416100,67.458135\n2002-10-29,89.080002,89.489998,87.0,88.57,59508200,66.675225\n2002-10-30,88.68,89.959999,88.230003,89.43,41688600,67.322631\n2002-10-31,89.660004,90.300003,88.190002,88.519997,41620600,66.637583\n2002-11-01,88.349998,90.82,88.050003,90.269997,51878900,67.954978\n2002-11-04,91.800003,92.940002,90.900002,91.129997,49080600,68.602384\n2002-11-05,90.839996,92.07,90.839996,91.849998,37270800,69.144399\n2002-11-06,92.480003,93.07,90.790001,93.040001,65013100,70.040229\n2002-11-07,92.019997,92.220001,90.220001,90.760002,51572000,68.323853\n2002-11-08,90.529999,91.57,89.519997,89.650002,37905400,67.488248\n2002-11-11,89.510002,89.559998,87.800003,88.260002,33505100,66.44186\n2002-11-12,88.660004,89.93,88.370003,88.959999,37724500,66.968816\n2002-11-13,88.32,89.739998,87.449997,89.050003,63891500,67.036571\n2002-11-14,90.07,91.0,89.75,90.730003,31896800,68.30127\n2002-11-15,90.0,91.550003,89.949997,91.400002,39152100,68.805642\n2002-11-18,92.150002,92.150002,90.349998,90.480003,28934800,68.11307\n2002-11-19,90.019997,91.099998,89.760002,90.360001,32806600,68.022733\n2002-11-20,89.980003,92.419998,89.949997,92.370003,36688400,69.535856\n2002-11-21,92.599998,94.190002,92.43,94.089996,55128200,70.830662\n2002-11-22,93.480003,94.269997,93.269997,93.419998,32513800,70.326289\n2002-11-25,93.43,94.260002,92.769997,93.480003,33846400,70.371461\n2002-11-26,93.07,93.449997,91.620003,91.699997,42284700,69.031478\n2002-11-27,92.519997,94.650002,92.43,94.279999,37764000,70.973695\n2002-11-29,94.800003,94.949997,93.769997,93.980003,19385700,70.74786\n2002-12-02,95.470001,96.050003,93.220001,94.129997,49911900,70.860775\n2002-12-03,93.25,93.400002,92.349998,92.870003,34407700,69.912255\n2002-12-04,91.769997,93.139999,91.43,92.449997,64040800,69.596076\n2002-12-05,92.730003,92.800003,91.099998,91.43,36724900,68.828225\n2002-12-06,90.120003,92.169998,89.980003,92.029999,49824100,69.279902\n2002-12-09,91.07,91.459999,89.480003,89.5,36789600,67.375327\n2002-12-10,90.019997,91.099998,89.760002,90.699997,33319900,68.278681\n2002-12-11,90.419998,91.620003,90.160004,90.779999,39201000,68.338906\n2002-12-12,91.199997,91.489998,90.199997,90.769997,34465600,68.331376\n2002-12-13,89.910004,90.480003,89.269997,89.339996,36862200,67.254877\n2002-12-16,89.82,91.790001,89.660004,91.650002,37098400,68.993841\n2002-12-17,91.370003,91.739998,90.739998,90.849998,32353900,68.391602\n2002-12-18,90.32,90.400002,89.330002,89.800003,35612600,67.601168\n2002-12-19,89.349998,90.699997,88.599998,89.160004,39264200,67.119379\n2002-12-20,89.199997,90.019997,89.099998,89.989998,31176900,68.077101\n2002-12-23,89.589996,90.470001,89.309998,90.019997,22599300,68.099795\n2002-12-24,89.589996,89.860001,89.25,89.349998,10937000,67.592944\n2002-12-26,89.699997,90.610001,88.839996,89.389999,17485600,67.623205\n2002-12-27,88.959999,89.290001,87.379997,87.379997,22205700,66.102646\n2002-12-30,87.790001,88.470001,87.220001,88.110001,29968000,66.65489\n2002-12-31,87.989998,88.43,87.110001,88.230003,34036600,66.745672\n2003-01-02,88.849998,91.300003,88.540001,91.07,44516300,68.894119\n2003-01-03,90.910004,91.379997,90.5,91.349998,32222600,69.105937\n2003-01-06,91.239998,93.489998,91.169998,92.959999,40984500,70.323896\n2003-01-07,92.900002,93.370003,92.199997,92.730003,38640400,70.149905\n2003-01-08,92.199997,92.400002,91.050003,91.389999,38702200,69.136197\n2003-01-09,91.82,93.18,91.410004,92.809998,34804900,70.210421\n2003-01-10,91.949997,93.639999,91.800003,93.059998,37768900,70.399545\n2003-01-13,93.540001,93.860001,92.440002,93.029999,31649900,70.376851\n2003-01-14,92.690002,93.830002,92.410004,93.330002,30733000,70.603802\n2003-01-15,93.540001,93.57,91.910004,92.400002,33511800,69.90026\n2003-01-16,92.5,92.93,91.449997,92.019997,44812100,69.612788\n2003-01-17,90.989998,92.300003,90.150002,90.660004,35618300,68.583958\n2003-01-21,90.870003,90.919998,88.949997,89.25,36826200,67.517296\n2003-01-22,88.769997,89.800003,88.0,88.169998,42286500,66.700278\n2003-01-23,88.75,89.379997,87.949997,88.709999,55919800,67.108787\n2003-01-24,88.589996,88.68,86.169998,86.379997,68633900,65.346149\n2003-01-27,85.730003,86.800003,84.5,85.199997,57884400,64.453483\n2003-01-28,85.629997,86.400002,85.129997,85.830002,46929100,64.93008\n2003-01-29,85.419998,87.18,84.769997,86.480003,53712200,65.421803\n2003-01-30,86.790001,86.879997,84.400002,84.43,49845900,63.870984\n2003-01-31,84.150002,86.209999,84.150002,86.059998,55317000,65.104071\n2003-02-03,86.139999,86.809998,85.919998,86.230003,39696000,65.232679\n2003-02-04,85.309998,85.75,84.300003,85.379997,43633400,64.589653\n2003-02-05,85.75,86.540001,84.480003,84.849998,55270600,64.188711\n2003-02-06,84.370003,84.889999,83.650002,84.449997,53638000,63.886111\n2003-02-07,84.910004,84.989998,82.970001,83.419998,43165000,63.106921\n2003-02-10,83.459999,84.129997,82.650002,84.010002,45455900,63.553257\n2003-02-11,84.370003,84.879997,82.830002,83.43,46861000,63.114487\n2003-02-12,83.160004,83.620003,82.089996,82.099998,35873600,62.108346\n2003-02-13,82.150002,82.660004,81.0,82.349998,57934100,62.29747\n2003-02-14,82.370003,84.199997,81.82,84.150002,59580900,63.659166\n2003-02-18,84.529999,85.800003,84.389999,85.629997,39492700,64.778777\n2003-02-19,85.32,85.470001,84.279999,85.18,31432000,64.438356\n2003-02-20,85.209999,85.419998,84.050003,84.330002,29280100,63.795335\n2003-02-21,84.379997,85.739998,83.459999,85.18,60955800,64.438356\n2003-02-24,84.93,85.0,83.589996,83.800003,30628600,63.394393\n2003-02-25,82.949997,84.489998,82.220001,84.470001,56770600,63.901244\n2003-02-26,84.019997,84.529999,83.080002,83.239998,37787200,62.970751\n2003-02-27,83.699997,84.75,83.160004,84.339996,51126000,63.802896\n2003-02-28,84.470001,85.230003,84.160004,84.900002,43666800,64.226538\n2003-03-03,85.260002,85.779999,83.720001,84.089996,42923100,63.613772\n2003-03-04,83.949997,84.010002,82.650002,82.75,31440500,62.60007\n2003-03-05,82.610001,83.540001,82.360001,83.449997,43974700,63.129615\n2003-03-06,82.870003,83.519997,82.470001,82.75,41217700,62.60007\n2003-03-07,81.610001,83.989998,81.43,83.32,63538000,63.031272\n2003-03-10,82.599998,82.860001,81.099998,81.32,41014100,61.51828\n2003-03-11,81.480003,82.0,80.480003,80.519997,48102700,60.91308\n2003-03-12,80.379997,81.099998,79.379997,81.059998,62459800,61.321589\n2003-03-13,82.18,83.910004,81.529999,83.860001,72117800,63.439781\n2003-03-14,84.199997,84.769997,83.349998,84.129997,63951800,63.644033\n2003-03-17,83.459999,86.949997,83.220001,86.779999,88217500,65.648749\n2003-03-18,87.150002,87.349998,86.279999,87.290001,50792300,66.034564\n2003-03-19,87.279999,88.160004,86.68,87.959999,49630800,66.541415\n2003-03-20,87.330002,88.589996,86.349998,88.150002,67321600,66.685151\n2003-03-21,88.800003,89.879997,87.93,89.669998,71165300,68.108535\n2003-03-24,88.019997,88.139999,86.349998,86.690002,65398900,65.84509\n2003-03-25,86.739998,88.260002,86.440002,87.519997,61040100,66.475509\n2003-03-26,87.559998,87.849998,86.800003,87.080002,45740800,66.141312\n2003-03-27,86.400002,87.660004,85.989998,87.150002,53120200,66.19448\n2003-03-28,86.470001,87.279999,86.25,86.709999,32583000,65.860278\n2003-03-31,85.349998,86.589996,84.400002,84.739998,61119500,64.36397\n2003-04-01,85.25,86.389999,84.910004,86.040001,53574000,65.351383\n2003-04-02,87.540001,88.769997,87.5,88.120003,50431200,66.931241\n2003-04-03,88.870003,88.989998,87.650002,87.699997,48755500,66.612227\n2003-04-04,88.43,88.589996,87.620003,88.220001,36250100,67.007195\n2003-04-07,90.339996,90.849998,87.970001,88.050003,69776200,66.878073\n2003-04-08,88.300003,88.650002,87.709999,88.190002,39712700,66.984409\n2003-04-09,88.360001,89.099998,86.769997,87.029999,55647300,66.103333\n2003-04-10,87.089996,87.629997,85.709999,87.510002,41812400,66.467917\n2003-04-11,88.160004,88.699997,86.860001,87.150002,47730100,66.19448\n2003-04-14,87.470001,89.0,87.0,88.949997,36711700,67.56166\n2003-04-15,88.839996,89.779999,88.419998,89.779999,49709800,68.192085\n2003-04-16,89.910004,90.059998,88.029999,88.25,51788000,67.02998\n2003-04-17,88.300003,89.720001,88.190002,89.559998,37403100,68.024984\n2003-04-21,89.860001,90.160004,89.059998,89.650002,32052700,68.093347\n2003-04-22,89.099998,91.559998,88.889999,91.339996,59763600,69.376976\n2003-04-23,91.620003,92.349998,91.239998,92.18,44227100,70.014998\n2003-04-24,91.529999,92.080002,90.959999,91.360001,49692400,69.39217\n2003-04-25,91.300003,91.470001,90.019997,90.230003,43917200,68.533885\n2003-04-28,90.440002,92.190002,90.300003,91.790001,46432900,69.718776\n2003-04-29,92.139999,92.800003,91.400002,92.110001,52017100,69.96183\n2003-04-30,91.910004,92.57,91.410004,91.910004,48709100,69.809923\n2003-05-01,91.919998,92.730003,90.5,91.900002,50240400,69.802326\n2003-05-02,91.559998,93.470001,91.489998,93.209999,50201500,70.79733\n2003-05-05,93.470001,93.779999,92.5,93.029999,35437800,70.660612\n2003-05-06,93.040001,94.379997,93.0,93.910004,44401000,71.329016\n2003-05-07,93.419998,94.139999,92.970001,93.389999,41413100,70.934049\n2003-05-08,92.519997,93.330002,92.279999,92.449997,40570700,70.220073\n2003-05-09,92.830002,93.800003,92.610001,93.730003,33608100,71.192298\n2003-05-12,93.5,95.120003,93.279999,94.879997,35662300,72.065772\n2003-05-13,94.529999,95.18,94.260002,94.709999,39253600,71.93665\n2003-05-14,95.089996,95.239998,93.910004,94.510002,32195100,71.784743\n2003-05-15,94.889999,95.330002,94.25,95.110001,43879200,72.24047\n2003-05-16,94.889999,95.449997,94.260002,94.870003,38905000,72.05818\n2003-05-19,94.150002,94.419998,92.330002,92.650002,41606000,70.371986\n2003-05-20,92.82,93.029999,91.589996,92.459999,55404600,70.22767\n2003-05-21,92.110001,92.879997,91.910004,92.650002,49333800,70.371986\n2003-05-22,92.949997,94.050003,92.68,93.57,38421800,71.070767\n2003-05-23,93.529999,93.980003,93.139999,93.760002,26155900,71.215083\n2003-05-27,93.300003,95.839996,93.07,95.400002,43719200,72.460739\n2003-05-28,95.849998,96.470001,95.43,95.669998,37727100,72.665814\n2003-05-29,95.879997,96.82,95.080002,95.419998,50844200,72.475927\n2003-05-30,95.900002,97.089996,95.559998,96.949997,52529500,73.638033\n2003-06-02,97.529999,98.449997,96.669998,97.349998,50305500,73.941852\n2003-06-03,97.150002,97.839996,96.849998,97.75,38254500,74.245672\n2003-06-04,97.660004,99.349998,97.57,99.160004,49360700,75.316636\n2003-06-05,98.580002,99.650002,98.269997,99.650002,46262400,75.688812\n2003-06-06,100.400002,101.400002,99.129997,99.260002,60356800,75.392589\n2003-06-09,98.769997,99.099998,97.769997,98.25,37808500,74.625445\n2003-06-10,98.459999,99.260002,98.190002,99.25,29965900,75.384992\n2003-06-11,99.160004,100.389999,98.709999,100.300003,37610200,76.182518\n2003-06-12,100.75,100.900002,99.620003,100.610001,36442000,76.417976\n2003-06-13,100.610001,100.75,98.949997,99.559998,48628300,75.62045\n2003-06-16,99.959999,101.699997,99.800003,101.660004,36326300,77.215502\n2003-06-17,102.07,102.18,101.230003,101.660004,36801800,77.215502\n2003-06-18,101.290001,102.139999,101.0,101.57,35521000,77.14714\n2003-06-19,101.639999,101.730003,99.839996,100.019997,43551700,75.96984\n2003-06-20,100.389999,100.5,99.419998,99.440002,41545000,75.802143\n2003-06-23,99.449997,99.660004,97.919998,98.419998,34237500,75.024604\n2003-06-24,98.220001,99.089996,98.019997,98.519997,36213600,75.100832\n2003-06-25,98.529999,99.440002,97.529999,97.529999,47743400,74.346166\n2003-06-26,97.779999,98.980003,96.959999,98.800003,33477300,75.314278\n2003-06-27,98.75,99.190002,97.580002,97.660004,54208800,74.445268\n2003-06-30,98.220001,98.669998,97.470001,97.629997,33349000,74.422394\n2003-07-01,97.25,98.849998,96.43,98.529999,51322800,75.108456\n2003-07-02,98.769997,99.790001,98.57,99.769997,34662100,76.053695\n2003-07-03,99.07,99.849998,97.900002,98.739998,30792800,75.268537\n2003-07-07,99.650002,100.900002,99.650002,100.699997,31391100,76.762625\n2003-07-08,100.5,101.290001,100.169998,101.150002,30952500,77.105659\n2003-07-09,100.919998,101.400002,100.029999,100.580002,36528500,76.671154\n2003-07-10,99.839996,100.040001,98.629997,99.300003,49777700,75.695423\n2003-07-11,99.389999,100.449997,99.389999,100.239998,39976300,76.411972\n2003-07-14,101.199997,101.900002,100.449997,100.730003,42114900,76.785498\n2003-07-15,101.379997,101.459999,99.949997,100.510002,42573600,76.617794\n2003-07-16,100.809998,100.870003,99.230003,99.919998,39894200,76.168039\n2003-07-17,99.150002,99.879997,98.160004,98.5,52253500,75.085589\n2003-07-18,99.019997,99.800003,98.459999,99.510002,35698100,75.855503\n2003-07-21,99.449997,99.489998,97.849998,98.279999,34786000,74.917884\n2003-07-22,98.690002,99.410004,97.919998,99.169998,49968300,75.596322\n2003-07-23,99.209999,99.449997,98.279999,99.239998,37275400,75.649682\n2003-07-24,99.989998,100.339996,98.370003,98.489998,40896200,75.077964\n2003-07-25,98.660004,100.290001,98.040001,100.230003,43241100,76.404353\n2003-07-28,100.370003,100.980003,99.669998,99.860001,34382800,76.122304\n2003-07-29,100.139999,100.260002,98.68,99.400002,53472100,75.771651\n2003-07-30,99.599998,99.75,98.93,99.160004,28363300,75.588703\n2003-07-31,99.980003,100.910004,99.169998,99.389999,54937200,75.764026\n2003-08-01,99.190002,99.529999,98.239998,98.510002,49321000,75.093213\n2003-08-04,98.309998,99.0,97.0,98.510002,55214100,75.093213\n2003-08-05,98.410004,98.760002,96.339996,96.419998,61415600,73.500023\n2003-08-06,96.690002,98.059998,96.419998,96.980003,50096900,73.92691\n2003-08-07,97.169998,98.07,96.760002,98.0,43427400,74.704443\n2003-08-08,98.32,98.550003,97.760002,98.279999,27357300,74.917884\n2003-08-11,98.260002,99.040001,97.839996,98.650002,34631400,75.199933\n2003-08-12,98.709999,99.589996,98.419998,99.550003,43285600,75.885996\n2003-08-13,99.82,99.849998,98.529999,99.040001,36152000,75.497226\n2003-08-14,99.099998,99.75,98.449997,99.309998,35525100,75.703042\n2003-08-15,99.360001,99.790001,99.120003,99.620003,12567000,75.939356\n2003-08-18,99.93,100.599998,99.739998,100.480003,22873800,76.594926\n2003-08-19,100.690002,100.940002,100.0,100.860001,37437700,76.884594\n2003-08-20,100.290001,100.889999,100.160004,100.449997,21295300,76.572052\n2003-08-21,101.050003,101.519997,100.400002,100.769997,46494200,76.815985\n2003-08-22,101.75,101.82,99.730003,99.769997,52040300,76.053695\n2003-08-25,99.709999,100.830002,99.279999,99.93,23473000,76.175664\n2003-08-26,99.5,100.389999,98.830002,100.110001,45065800,76.312876\n2003-08-27,100.050003,100.360001,99.57,100.139999,18919500,76.335744\n2003-08-28,100.400002,101.0,99.660004,100.760002,27402400,76.808366\n2003-08-29,100.610001,101.480003,100.480003,101.440002,28706600,77.326724\n2003-09-02,101.639999,102.879997,101.050003,102.800003,49419100,78.363439\n2003-09-03,103.029999,103.699997,102.779999,103.360001,44912400,78.79032\n2003-09-04,103.099998,103.550003,102.760002,103.410004,28381000,78.828436\n2003-09-05,102.940002,103.550003,102.400002,102.830002,31637300,78.386307\n2003-09-08,103.040001,103.879997,102.93,103.68,32632800,79.034252\n2003-09-09,103.370003,103.459999,102.68,103.0,35053200,78.515895\n2003-09-10,102.529999,102.800003,101.550003,101.959999,45904900,77.723112\n2003-09-11,102.099998,102.760002,101.839996,102.260002,38396300,77.951802\n2003-09-12,101.910004,102.639999,101.349998,102.449997,42524800,78.096633\n2003-09-15,102.519997,102.629997,101.949997,102.089996,21312800,77.822208\n2003-09-16,102.230003,103.639999,102.169998,103.580002,37892600,78.958024\n2003-09-17,103.480003,103.790001,103.050003,103.379997,31885800,78.805563\n2003-09-18,103.400002,104.699997,103.169998,104.599998,30243600,79.735558\n2003-09-19,104.269997,104.599998,103.400002,103.669998,34331600,79.32999\n2003-09-22,102.849998,102.959999,102.029999,102.550003,36677400,78.472951\n2003-09-23,102.589996,103.290001,102.360001,102.940002,32489200,78.771385\n2003-09-24,103.120003,103.220001,101.07,101.110001,41694700,77.371038\n2003-09-25,101.410004,101.879997,100.199997,100.279999,52673100,76.735907\n2003-09-26,100.440002,100.660004,99.849998,99.949997,42914700,76.483384\n2003-09-29,100.300003,100.989998,99.790001,100.93,36771600,77.233299\n2003-09-30,100.480003,100.760002,99.25,99.949997,70764500,76.483384\n2003-10-01,100.239998,102.18,100.199997,102.080002,65797600,78.113299\n2003-10-02,101.93,102.559998,101.629997,102.449997,44591500,78.396425\n2003-10-03,103.669998,104.279999,103.080002,103.389999,48806900,79.11573\n2003-10-06,103.480003,103.989998,103.199997,103.860001,20226500,79.475383\n2003-10-07,103.260002,104.309998,102.910004,104.260002,42602900,79.781471\n2003-10-08,104.330002,104.389999,103.410004,104.0,30920800,79.582513\n2003-10-09,104.879997,105.220001,103.830002,104.279999,40066500,79.796773\n2003-10-10,104.269997,104.599998,103.910004,104.57,22682800,80.018686\n2003-10-13,104.709999,105.290001,104.510002,104.900002,23825600,80.271209\n2003-10-14,104.800003,105.43,104.360001,105.269997,38484400,80.554335\n2003-10-15,105.860001,105.889999,104.639999,104.989998,39021700,80.340076\n2003-10-16,104.68,105.730003,104.650002,105.410004,32772200,80.661471\n2003-10-17,105.470001,105.629997,103.980003,104.260002,32790300,79.781471\n2003-10-20,104.449997,105.040001,103.940002,105.040001,27657000,80.378339\n2003-10-21,104.82,105.279999,104.32,104.860001,26729100,80.240599\n2003-10-22,104.029999,104.190002,103.190002,103.540001,33914500,79.230514\n2003-10-23,102.889999,103.949997,102.839996,103.349998,45830500,79.085121\n2003-10-24,102.830002,103.580002,102.18,103.580002,51723600,79.261123\n2003-10-27,103.739998,104.18,103.269997,103.629997,32460200,79.299381\n2003-10-28,103.980003,105.150002,103.82,105.040001,34956200,80.378339\n2003-10-29,104.769997,105.43,103.870003,105.18,30955400,80.485469\n2003-10-30,105.790001,105.970001,104.800003,105.400002,39123100,80.653817\n2003-10-31,105.400002,105.739998,105.220001,105.300003,25761600,80.577297\n2003-11-03,105.75,106.610001,105.709999,105.989998,37589300,81.105292\n2003-11-04,105.989998,106.269997,105.580002,105.760002,31421600,80.929295\n2003-11-05,105.489998,105.970001,104.900002,105.839996,33558800,80.990508\n2003-11-06,105.599998,106.440002,105.099998,106.400002,28392300,81.419034\n2003-11-07,106.639999,106.720001,105.57,105.610001,31723200,80.814512\n2003-11-10,105.739998,105.839996,105.010002,105.18,25530800,80.485469\n2003-11-11,105.089996,105.330002,104.800003,105.150002,26558600,80.462513\n2003-11-12,105.230003,106.470001,105.160004,106.330002,28000000,81.365469\n2003-11-13,106.010002,106.540001,105.779999,106.360001,29714800,81.388424\n2003-11-14,106.400002,106.949997,105.290001,105.459999,49158500,80.699728\n2003-11-17,104.910004,105.139999,104.040001,104.93,44382100,80.294164\n2003-11-18,105.239998,105.449997,103.699997,103.839996,41155000,79.460075\n2003-11-19,104.029999,105.010002,103.919998,104.720001,29827000,80.13347\n2003-11-20,104.0,105.239998,103.75,103.779999,53578700,79.414164\n2003-11-21,104.239998,104.330002,103.620003,104.209999,30016000,79.743208\n2003-11-24,104.690002,105.779999,104.68,105.589996,28906400,80.799204\n2003-11-25,105.730003,106.419998,105.449997,105.989998,37580000,81.105292\n2003-11-26,106.419998,106.449997,105.389999,106.370003,33053600,81.396078\n2003-11-28,106.279999,106.660004,106.199997,106.449997,10507500,81.457291\n2003-12-01,106.849998,107.68,106.800003,107.599998,38699000,82.337291\n2003-12-02,107.379997,107.769997,107.07,107.330002,35352000,82.130685\n2003-12-03,107.650002,108.080002,107.07,107.160004,39078600,82.0006\n2003-12-04,107.169998,107.720001,106.940002,107.599998,36089500,82.337291\n2003-12-05,107.120003,107.800003,106.620003,106.849998,28824400,81.763379\n2003-12-08,106.739998,107.639999,106.68,107.57,32482900,82.314335\n2003-12-09,107.900002,107.93,106.540001,106.739998,43596100,81.679204\n2003-12-10,106.769997,106.980003,105.959999,106.730003,36915400,81.671556\n2003-12-11,106.68,108.099998,106.669998,107.93,45304000,82.589814\n2003-12-12,107.970001,108.199997,107.389999,108.139999,34142200,82.750509\n2003-12-15,109.169998,109.230003,107.480003,107.599998,38693400,82.337291\n2003-12-16,107.68,108.5,107.519997,108.160004,32894200,82.765816\n2003-12-17,108.059998,108.5,107.800003,108.5,23198800,83.025987\n2003-12-18,108.550003,109.730003,108.389999,109.720001,29353100,83.959552\n2003-12-19,109.300003,109.370003,108.580002,108.900002,41465100,83.725828\n2003-12-22,108.790001,109.660004,108.779999,109.660004,27611300,84.310143\n2003-12-23,109.480003,109.949997,109.379997,109.730003,24741200,84.363961\n2003-12-24,109.519997,109.879997,109.43,109.620003,8055800,84.279389\n2003-12-26,109.709999,110.080002,109.629997,109.699997,8308400,84.340891\n2003-12-29,110.099998,111.269997,109.779999,111.160004,22483700,85.463391\n2003-12-30,111.089996,111.269997,110.849998,111.18,19559500,85.478765\n2003-12-31,111.220001,111.519997,110.839996,111.279999,31501800,85.555647\n2004-01-02,111.739998,112.190002,110.730003,111.230003,38072300,85.517209\n2004-01-05,111.690002,112.519997,111.589996,112.440002,27959800,86.447495\n2004-01-06,112.160004,112.730003,112.0,112.550003,20472800,86.532067\n2004-01-07,112.389999,113.059998,111.889999,112.93,30170400,86.824221\n2004-01-08,113.25,113.410004,112.769997,113.379997,36438400,87.170193\n2004-01-09,112.830002,113.5,112.269997,112.389999,54084300,86.409051\n2004-01-12,112.550003,113.25,112.360001,113.220001,31564100,87.047183\n2004-01-13,113.089996,113.230003,111.760002,112.559998,54239700,86.539751\n2004-01-14,112.760002,113.660004,112.669998,113.5,30112800,87.262455\n2004-01-15,113.57,114.059998,112.580002,113.779999,38408700,87.477728\n2004-01-16,114.040001,114.309998,113.629997,114.230003,31922700,87.823706\n2004-01-20,114.529999,114.650002,113.82,114.199997,29863000,87.800636\n2004-01-21,114.129997,115.300003,113.720001,115.099998,30725000,88.492586\n2004-01-22,115.139999,115.379997,114.580002,114.800003,29888500,88.26194\n2004-01-23,115.0,115.370003,113.949997,114.43,44245300,87.97747\n2004-01-26,114.389999,115.93,114.379997,115.870003,30460600,89.08459\n2004-01-27,115.75,116.5,114.650002,114.68,35322800,88.169678\n2004-01-28,114.980003,115.279999,112.940002,113.370003,52621300,87.162509\n2004-01-29,113.559998,113.849998,112.559998,113.480003,60117100,87.247081\n2004-01-30,113.519997,113.720001,113.089996,113.480003,30984400,87.247081\n2004-02-02,113.699997,114.68,113.120003,113.970001,38832400,87.623808\n2004-02-03,113.739998,114.139999,113.440002,113.779999,25093500,87.477728\n2004-02-04,113.190002,113.730003,112.790001,112.849998,39332600,86.762713\n2004-02-05,113.169998,113.540001,112.779999,113.18,37226800,87.016429\n2004-02-06,113.419998,114.699997,113.199997,114.449997,37216000,87.992844\n2004-02-09,114.669998,114.870003,114.290001,114.480003,24851300,88.015914\n2004-02-10,114.279999,115.139999,114.260002,114.849998,27908100,88.300378\n2004-02-11,114.849998,116.389999,114.169998,116.07,42965700,89.238354\n2004-02-12,115.970001,116.269997,115.580002,115.650002,27814700,88.915446\n2004-02-13,115.82,116.199997,114.75,115.129997,44739900,88.51565\n2004-02-17,115.849998,116.43,115.769997,116.169998,23984300,89.315236\n2004-02-18,116.199997,116.599998,115.349998,115.660004,28618000,88.923136\n2004-02-19,116.330002,116.389999,115.059998,115.230003,51146200,88.592538\n2004-02-20,115.480003,115.559998,114.32,114.879997,46728800,88.323442\n2004-02-23,115.220001,115.260002,114.169998,114.589996,36357000,88.10048\n2004-02-24,114.269997,114.989998,113.029999,114.389999,43953000,87.946716\n2004-02-25,114.459999,115.059998,114.32,114.870003,31213600,88.315758\n2004-02-26,114.610001,115.290001,114.339996,114.940002,29683000,88.369576\n2004-02-27,115.190002,115.739998,114.629997,115.019997,39312000,88.431078\n2004-03-01,115.43,116.339996,115.25,116.160004,33130800,89.307552\n2004-03-02,115.940002,116.970001,115.230003,115.480003,38556400,88.784746\n2004-03-03,115.25,115.870003,114.919998,115.690002,31346200,88.9462\n2004-03-04,115.720001,116.099998,115.519997,115.989998,21060000,89.176846\n2004-03-05,115.419998,116.949997,115.279999,116.379997,55905600,89.47669\n2004-03-08,116.339996,116.620003,114.910004,114.959999,39281600,88.38495\n2004-03-09,115.099998,115.209999,114.239998,114.5,39746100,88.031288\n2004-03-10,114.720001,114.769997,112.559998,112.580002,67671800,86.555131\n2004-03-11,112.400002,113.269997,111.099998,111.120003,89134800,85.432637\n2004-03-12,111.730003,112.709999,111.580002,112.580002,54012200,86.555131\n2004-03-15,112.269997,112.349998,110.900002,111.199997,57677200,85.494139\n2004-03-16,111.779999,112.059998,110.839996,111.790001,59832600,85.947753\n2004-03-17,112.18,113.260002,112.099998,113.040001,41607300,86.908793\n2004-03-18,112.699997,113.269997,111.93,113.07,60014300,86.931857\n2004-03-19,112.410004,112.57,111.040001,111.059998,48636200,85.685839\n2004-03-22,110.540001,110.57,109.099998,109.650002,62752100,84.597988\n2004-03-23,110.25,110.400002,109.360001,109.459999,54080200,84.451396\n2004-03-24,109.580002,110.139999,108.849998,109.550003,51584300,84.520837\n2004-03-25,110.080002,111.300003,109.790001,111.0,49873600,85.639549\n2004-03-26,110.959999,111.790001,110.800003,111.029999,37409500,85.662694\n2004-03-29,111.629997,112.739998,111.580002,112.589996,44113600,86.866275\n2004-03-30,112.300003,113.07,112.220001,112.970001,39059900,87.159459\n2004-03-31,112.989998,113.400002,112.379997,113.099998,48517600,87.259756\n2004-04-01,113.07,113.870003,113.050003,113.779999,45103800,87.784394\n2004-04-02,114.809998,114.839996,113.900002,114.639999,50987700,88.447909\n2004-04-05,114.459999,115.379997,114.440002,115.269997,30251800,88.933969\n2004-04-06,114.830002,115.18,114.620003,114.900002,28420900,88.648507\n2004-04-07,114.949997,114.980003,114.110001,114.629997,45890500,88.440192\n2004-04-08,115.410004,115.410004,113.739998,114.370003,46929700,88.239599\n2004-04-12,114.580002,115.080002,114.57,114.82,23085200,88.586784\n2004-04-13,115.260002,115.300003,113.019997,113.209999,56210300,87.344624\n2004-04-14,112.610001,113.639999,112.550003,113.389999,62322300,87.483499\n2004-04-15,113.449997,113.779999,112.360001,112.959999,61602500,87.151742\n2004-04-16,113.379997,114.050003,112.980003,113.830002,47059200,87.822973\n2004-04-19,113.550003,113.989998,113.269997,113.830002,28277600,87.822973\n2004-04-20,114.080002,114.32,111.779999,111.919998,53299400,86.349353\n2004-04-21,112.199997,112.949997,111.870003,112.669998,50177300,86.927999\n2004-04-22,112.480003,114.669998,112.440002,114.25,62071500,88.147013\n2004-04-23,114.419998,114.57,113.790001,114.360001,29395700,88.231882\n2004-04-26,114.5,114.940002,113.599998,114.199997,35515200,88.108435\n2004-04-27,114.230003,115.120003,113.959999,114.300003,43485500,88.185592\n2004-04-28,113.889999,114.010002,112.5,112.82,50165800,87.043729\n2004-04-29,112.720001,113.32,111.160004,111.830002,69687600,86.279918\n2004-04-30,112.169998,112.379997,110.900002,110.959999,48681400,85.608687\n2004-05-03,111.370003,112.290001,111.349998,112.150002,33758000,86.526807\n2004-05-04,112.25,113.260002,111.660004,112.059998,51185100,86.457366\n2004-05-05,112.410004,112.959999,112.160004,112.779999,34405000,87.012867\n2004-05-06,112.019997,112.589996,111.0,111.809998,54997000,86.264484\n2004-05-07,111.220001,112.230003,109.959999,109.959999,60950000,84.83716\n2004-05-10,109.440002,109.75,108.360001,108.830002,75279400,83.965336\n2004-05-11,109.459999,110.050003,109.330002,109.75,48300600,84.67514\n2004-05-12,109.57,110.540001,108.059998,110.449997,90830500,85.215207\n2004-05-13,109.760002,110.809998,109.629997,109.989998,57393700,84.860305\n2004-05-14,109.949997,110.739998,109.269997,110.040001,54123100,84.898883\n2004-05-17,108.889999,109.5,108.410004,109.099998,55020400,84.173646\n2004-05-18,109.489998,109.940002,109.330002,109.650002,30193100,84.597988\n2004-05-19,110.5,111.18,109.150002,109.269997,54804100,84.304804\n2004-05-20,109.449997,109.870003,109.040001,109.620003,38082900,84.574843\n2004-05-21,109.970001,110.550003,109.470001,109.809998,47480400,84.72143\n2004-05-24,110.529999,110.760002,109.68,110.269997,40961500,85.076331\n2004-05-25,109.900002,111.980003,109.599998,111.849998,48668000,86.295346\n2004-05-26,111.660004,112.290001,111.510002,112.239998,35977000,86.596241\n2004-05-27,112.540001,113.029999,112.059998,112.870003,45306900,87.082308\n2004-05-28,112.730003,112.879997,112.360001,112.860001,23367200,87.074591\n2004-06-01,112.459999,112.860001,111.870003,112.709999,41044700,86.95886\n2004-06-02,113.029999,113.480003,112.459999,113.129997,39774200,87.2829\n2004-06-03,112.809998,113.190002,112.07,112.089996,38688300,86.480511\n2004-06-04,112.980003,113.580002,112.709999,112.980003,32739500,87.167176\n2004-06-07,113.43,114.809998,113.419998,114.699997,31643800,88.494198\n2004-06-08,114.370003,114.919998,114.169998,114.860001,32846500,88.617646\n2004-06-09,114.510002,114.699997,113.720001,113.790001,36737600,87.792111\n2004-06-10,114.040001,114.349998,113.93,114.349998,21711000,88.224165\n2004-06-14,113.82,113.849998,112.870003,113.220001,34633000,87.352341\n2004-06-15,113.900002,114.449997,113.510002,114.019997,37445000,87.969559\n2004-06-16,114.0,114.199997,113.699997,114.0,26633400,87.954131\n2004-06-17,113.849998,114.07,113.330002,113.830002,28402400,87.822973\n2004-06-18,113.269997,114.220001,113.18,113.629997,31799800,87.988683\n2004-06-21,113.75,114.139999,113.129997,113.199997,25284000,87.655715\n2004-06-22,113.129997,113.879997,112.669998,113.769997,37334000,88.09709\n2004-06-23,113.610001,114.839996,113.419998,114.75,35580000,88.85595\n2004-06-24,114.559998,114.93,114.260002,114.389999,35272100,88.577186\n2004-06-25,114.410004,114.940002,113.68,113.839996,32837900,88.151294\n2004-06-28,114.519997,114.610001,113.410004,113.449997,40824500,87.8493\n2004-06-29,113.529999,114.169998,113.419998,113.919998,28418100,88.213243\n2004-06-30,114.07,114.790001,113.650002,114.529999,52230600,88.685593\n2004-07-01,114.25,114.400002,112.580002,112.940002,57734700,87.454389\n2004-07-02,113.160004,113.290001,112.599998,112.879997,34615100,87.407925\n2004-07-06,112.370003,112.449997,111.629997,111.889999,38698200,86.641326\n2004-07-07,111.809998,112.57,111.75,112.220001,29839800,86.896861\n2004-07-08,111.809998,112.32,111.199997,111.440002,45291100,86.292874\n2004-07-09,111.720001,111.940002,111.379997,111.730003,27412900,86.517434\n2004-07-12,111.519997,112.040001,111.0,111.779999,35691300,86.556148\n2004-07-13,111.919998,112.019997,111.599998,111.860001,26752000,86.618097\n2004-07-14,111.260002,112.389999,111.120003,111.519997,54089400,86.354817\n2004-07-15,111.739998,111.910004,110.699997,110.800003,38403500,85.797294\n2004-07-16,111.57,111.669998,110.440002,110.709999,40871200,85.7276\n2004-07-19,110.75,110.959999,109.989998,110.239998,39592800,85.363658\n2004-07-20,110.529999,111.900002,110.25,111.639999,46679800,86.44774\n2004-07-21,111.82,112.059998,109.449997,109.580002,56241100,84.852594\n2004-07-22,109.360001,110.389999,108.769997,109.879997,72477100,85.084893\n2004-07-23,109.620003,109.709999,108.690002,108.959999,49610500,84.372498\n2004-07-26,109.190002,109.43,108.209999,108.75,49679100,84.209887\n2004-07-27,109.050003,110.110001,108.970001,109.769997,51295100,84.999715\n2004-07-28,109.550003,110.370003,108.589996,110.099998,65862300,85.25525\n2004-07-29,110.540001,110.870003,110.0,110.57,52200500,85.619192\n2004-07-30,110.32,110.900002,110.099998,110.839996,41581700,85.828263\n2004-08-02,110.190002,111.360001,110.050003,111.07,38263100,86.006364\n2004-08-03,110.93,111.059998,110.160004,110.209999,40948800,85.340428\n2004-08-04,109.889999,110.75,109.639999,110.199997,40763200,85.332683\n2004-08-05,110.290001,110.379997,108.269997,108.400002,50772000,83.938868\n2004-08-06,107.629997,107.959999,106.620003,106.849998,74729000,82.738633\n2004-08-09,107.019997,107.480003,106.870003,107.0,37476300,82.854785\n2004-08-10,107.309998,108.410004,107.260002,108.379997,55870600,83.923378\n2004-08-11,107.68,108.330002,107.099998,108.160004,52933200,83.753027\n2004-08-12,107.68,107.949997,106.629997,106.980003,50015900,82.839301\n2004-08-13,107.099998,107.349998,106.589996,107.190002,41634700,83.001913\n2004-08-16,107.139999,108.639999,107.099998,108.300003,45731900,83.861435\n2004-08-17,108.75,109.279999,108.529999,108.910004,40701600,84.333785\n2004-08-18,108.519997,110.169998,108.489998,110.029999,43165400,85.201046\n2004-08-19,109.809998,110.019997,109.18,109.709999,39881600,84.953256\n2004-08-20,109.610001,110.629997,109.510002,110.480003,44870900,85.549504\n2004-08-23,110.550003,110.769997,110.050003,110.199997,33745100,85.332683\n2004-08-24,110.639999,110.730003,109.849998,110.349998,30453100,85.448836\n2004-08-25,110.330002,111.269997,109.900002,111.099998,38551400,86.029594\n2004-08-26,110.959999,111.309998,110.849998,111.099998,26629500,86.029594\n2004-08-27,111.199997,111.629997,111.050003,111.449997,24902900,86.300613\n2004-08-30,111.220001,111.339996,110.449997,110.529999,26726500,85.588218\n2004-08-31,110.660004,111.160004,110.099998,111.110001,44125300,86.037339\n2004-09-01,110.949997,111.639999,110.480003,111.32,52778300,86.19995\n2004-09-02,111.239998,112.699997,111.239998,112.580002,42736600,87.175625\n2004-09-03,112.330002,112.82,112.010002,112.120003,30480500,86.819428\n2004-09-07,112.540001,113.129997,112.32,112.860001,37338800,87.39244\n2004-09-08,112.620003,113.059998,112.309998,112.580002,32963100,87.175625\n2004-09-09,112.57,112.879997,112.029999,112.480003,34314800,87.098192\n2004-09-10,112.519997,113.269997,112.080002,113.059998,27900600,87.547307\n2004-09-13,113.309998,113.739998,113.010002,113.43,44398100,87.833816\n2004-09-14,113.300003,113.690002,113.190002,113.660004,28048900,88.011918\n2004-09-15,113.300003,113.360001,112.68,112.800003,38295000,87.345982\n2004-09-16,112.849998,113.370003,112.800003,113.139999,23911700,87.609256\n2004-09-17,112.949997,113.360001,112.690002,113.150002,33683000,87.981711\n2004-09-20,112.669998,112.989998,112.279999,112.470001,37149400,87.452965\n2004-09-21,112.75,113.470001,112.540001,112.959999,40920800,87.833971\n2004-09-22,112.5,112.519997,111.470001,111.550003,49042100,86.737604\n2004-09-23,111.599998,111.699997,110.949997,110.949997,44068700,86.271059\n2004-09-24,111.169998,111.730003,111.129997,111.459999,34981100,86.66762\n2004-09-27,111.099998,111.199997,110.580002,110.75,39355100,86.115548\n2004-09-28,110.910004,111.510002,110.410004,111.279999,41662900,86.527658\n2004-09-29,111.209999,111.849998,111.0,111.839996,33325700,86.963094\n2004-09-30,111.550003,111.980003,111.260002,111.760002,43536700,86.900893\n2004-10-01,112.260002,113.650002,112.209999,113.650002,62824300,88.370494\n2004-10-04,114.099998,114.440002,113.800003,113.839996,33082400,88.518228\n2004-10-05,113.849998,114.160004,113.540001,113.900002,36910600,88.564886\n2004-10-06,113.769997,114.68,113.68,114.68,42297800,89.171387\n2004-10-07,114.379997,114.400002,113.360001,113.449997,39388800,88.214977\n2004-10-08,113.150002,113.769997,112.349998,112.510002,51872600,87.484068\n2004-10-11,112.779999,113.019997,112.639999,112.970001,20229100,87.841748\n2004-10-12,112.199997,112.830002,111.940002,112.529999,41754700,87.499617\n2004-10-13,113.0,113.07,111.32,111.540001,54212600,86.729827\n2004-10-14,111.68,111.93,110.580002,110.639999,64082200,86.030016\n2004-10-15,111.019997,111.739998,110.57,111.260002,63482200,86.512109\n2004-10-18,110.889999,111.900002,110.699997,111.68,43535100,86.838686\n2004-10-19,112.019997,112.230003,110.589996,110.739998,55851900,86.107771\n2004-10-20,110.379997,110.82,109.75,110.519997,57118500,85.936705\n2004-10-21,110.790001,111.32,110.209999,111.239998,53218300,86.496555\n2004-10-22,111.190002,111.25,109.860001,109.989998,48752400,85.524596\n2004-10-25,109.75,110.120003,109.349998,109.860001,43990900,85.423514\n2004-10-26,110.129997,111.599998,109.879997,111.540001,54337400,86.729827\n2004-10-27,111.379997,113.099998,111.120003,112.879997,73896000,87.771764\n2004-10-28,112.779999,113.559998,112.489998,113.220001,54413300,88.03614\n2004-10-29,113.120003,113.639999,112.900002,113.199997,48820200,88.020585\n2004-11-01,113.559998,113.839996,113.199997,113.510002,36720900,88.261635\n2004-11-02,113.669998,114.57,113.220001,113.550003,56210000,88.292739\n2004-11-03,115.029999,115.360001,114.239998,114.980003,76960200,89.40466\n2004-11-04,114.779999,116.669998,114.68,116.550003,55350300,90.62544\n2004-11-05,117.050003,117.639999,116.489998,117.279999,63287200,91.19306\n2004-11-08,116.980003,117.230003,116.720001,117.110001,33863800,91.060875\n2004-11-09,117.080002,117.5,116.760002,116.879997,44658100,90.882032\n2004-11-10,117.059998,117.550003,116.760002,116.970001,45265400,90.952016\n2004-11-11,117.18,118.120003,117.099998,117.860001,37863200,91.644051\n2004-11-12,117.970001,119.0,117.68,118.790001,55583700,92.367188\n2004-11-15,118.5,118.769997,118.230003,118.730003,35297900,92.594134\n2004-11-16,118.360001,118.410004,117.730003,117.879997,40028700,91.931239\n2004-11-17,118.370003,119.139999,118.07,118.580002,54494000,92.477153\n2004-11-18,118.529999,118.800003,118.230003,118.739998,31854300,92.601929\n2004-11-19,118.699997,118.720001,117.139999,117.419998,54276500,91.572499\n2004-11-22,117.169998,118.120003,117.029999,117.980003,37560200,92.009231\n2004-11-23,117.93,118.260002,117.370003,118.160004,41968800,92.149608\n2004-11-24,118.269997,118.589996,118.050003,118.440002,29724800,92.367971\n2004-11-26,118.510002,118.980003,118.300003,118.349998,15487700,92.297779\n2004-11-29,118.790001,119.010002,117.480003,117.809998,61460800,91.876648\n2004-11-30,118.0,118.239998,117.639999,117.889999,53685200,91.939039\n2004-12-01,118.160004,119.5,118.099998,119.230003,49898300,92.98407\n2004-12-02,119.099998,119.870003,119.010002,119.330002,60163500,93.062056\n2004-12-03,119.309998,120.139999,119.089996,119.25,49067900,92.999665\n2004-12-06,119.209999,119.639999,118.839996,119.209999,33030500,92.968469\n2004-12-07,119.489998,119.620003,118.040001,118.099998,52047200,92.102812\n2004-12-08,118.209999,118.82,118.010002,118.790001,43895100,92.640925\n2004-12-09,118.139999,119.459999,117.730003,119.209999,60922800,92.968469\n2004-12-10,118.919998,119.559998,118.849998,119.330002,47828600,93.062056\n2004-12-13,119.760002,120.400002,119.349998,120.370003,38541000,93.873123\n2004-12-14,120.18,120.959999,120.18,120.790001,41500700,94.200668\n2004-12-15,120.699997,121.110001,120.309998,120.879997,46699200,94.270853\n2004-12-16,120.720001,121.239998,120.040001,120.809998,51641800,94.216262\n2004-12-17,119.459999,119.970001,119.160004,119.440002,70761900,93.58785\n2004-12-20,119.75,120.290001,119.169998,119.470001,47187400,93.611356\n2004-12-21,119.599998,120.480003,119.459999,120.389999,33094200,94.332226\n2004-12-22,120.379997,121.080002,120.300003,120.68,31500700,94.559457\n2004-12-23,120.870003,121.279999,120.660004,120.769997,25646100,94.629974\n2004-12-27,121.199997,121.360001,120.389999,120.519997,29944100,94.434086\n2004-12-28,120.639999,121.330002,120.599998,121.18,23422900,94.951235\n2004-12-29,121.080002,121.400002,120.949997,121.360001,22650600,95.092275\n2004-12-30,121.400002,121.57,121.040001,121.129997,21076900,94.912055\n2004-12-31,121.300003,121.660004,120.800003,120.870003,28648800,94.708335\n2005-01-03,121.559998,121.760002,119.900002,120.300003,55748000,94.261708\n2005-01-04,120.459999,120.540001,118.440002,118.830002,69167600,93.109881\n2005-01-05,118.739998,119.25,118.0,118.010002,65667300,92.467366\n2005-01-06,118.440002,119.150002,118.260002,118.610001,47814700,92.937498\n2005-01-07,118.970001,119.230003,118.129997,118.440002,55847700,92.804295\n2005-01-10,118.339996,119.459999,118.339996,119.0,56563300,93.243084\n2005-01-11,118.639999,118.739998,117.989998,118.18,63099700,92.600569\n2005-01-12,118.400002,118.839996,117.519997,118.57,72720500,92.906155\n2005-01-13,118.639999,118.730003,117.5,117.620003,55537500,92.16178\n2005-01-14,117.970001,118.529999,117.760002,118.239998,42032500,92.64758\n2005-01-18,118.050003,119.620003,117.949997,119.470001,57391700,93.611356\n2005-01-19,119.43,119.519997,118.209999,118.220001,54378900,92.631912\n2005-01-20,117.889999,118.199997,117.290001,117.5,72049300,92.067751\n2005-01-21,117.790001,118.0,116.650002,116.779999,63160400,91.50359\n2005-01-24,117.089996,117.339996,116.370003,116.550003,58441900,91.323376\n2005-01-25,116.910004,117.470001,116.720001,116.879997,68245000,91.581945\n2005-01-26,117.32,117.599998,117.040001,117.230003,57195100,91.856194\n2005-01-27,117.190002,117.75,116.980003,117.43,55878800,92.012903\n2005-01-28,117.489998,117.550003,116.610001,117.43,60738900,92.012903\n2005-01-31,117.949997,118.25,117.709999,118.160004,52532700,92.584901\n2005-02-01,118.25,119.080002,118.099998,118.910004,49841200,93.172567\n2005-02-02,119.059998,119.589996,118.900002,119.269997,52468900,93.454641\n2005-02-03,119.059998,119.160004,118.57,118.959999,48837100,93.211741\n2005-02-04,119.0,120.43,118.980003,120.230003,50024600,94.20686\n2005-02-07,120.25,120.519997,119.959999,120.07,45412000,94.081488\n2005-02-08,120.169998,120.650002,120.07,120.209999,39263500,94.191185\n2005-02-09,120.419998,120.489998,119.25,119.309998,55279400,93.485984\n2005-02-10,119.660004,120.019997,119.260002,119.739998,45858600,93.822913\n2005-02-11,119.699997,121.040001,119.459999,120.769997,53133000,94.629974\n2005-02-14,120.690002,120.860001,120.480003,120.68,32432100,94.559457\n2005-02-15,120.800003,121.43,120.68,121.129997,43852700,94.912055\n2005-02-16,120.93,121.459999,120.669998,121.209999,55523000,94.974741\n2005-02-17,121.230003,121.330002,120.220001,120.230003,58124000,94.20686\n2005-02-18,120.239998,120.480003,119.900002,120.389999,47723300,94.332226\n2005-02-22,119.900002,120.470001,118.580002,118.599998,80697600,92.929661\n2005-02-23,118.93,119.57,118.620003,119.449997,68292600,93.595682\n2005-02-24,119.239998,120.32,118.980003,120.239998,68563600,94.214691\n2005-02-25,120.269997,121.669998,120.18,121.43,60899900,95.147124\n2005-02-28,121.150002,121.300003,120.040001,120.629997,69381300,94.520277\n2005-03-01,120.82,121.519997,120.779999,121.230003,47294400,94.990415\n2005-03-02,120.760002,121.93,120.650002,121.169998,64226500,94.943398\n2005-03-03,121.660004,121.900002,120.699997,121.220001,61230800,94.982578\n2005-03-04,122.050003,122.830002,121.790001,122.730003,56168500,96.165748\n2005-03-07,122.660004,123.25,122.400002,122.790001,43442400,96.21276\n2005-03-08,122.669998,123.0,122.110001,122.330002,44362000,95.852325\n2005-03-09,121.970001,122.290001,120.959999,120.970001,73263600,94.786689\n2005-03-10,121.199997,121.5,120.400002,121.239998,65149000,94.998246\n2005-03-11,121.309998,121.720001,120.160004,120.389999,57976500,94.332226\n2005-03-14,120.610001,121.160004,120.279999,121.139999,36336400,94.919892\n2005-03-15,121.419998,121.459999,120.080002,120.139999,62438500,94.136337\n2005-03-16,119.699997,120.160004,118.900002,119.120003,74874200,93.337113\n2005-03-17,119.309998,119.739998,118.980003,119.360001,62584200,93.525165\n2005-03-18,119.110001,119.529999,118.150002,118.540001,60232000,93.247485\n2005-03-21,118.709999,118.779999,117.760002,118.099998,61244300,92.901365\n2005-03-22,118.370003,118.93,116.900002,116.900002,92472400,91.957408\n2005-03-23,116.949997,117.720001,116.75,117.0,70817300,92.03607\n2005-03-24,117.459999,117.989998,117.059998,117.139999,51932500,92.146198\n2005-03-28,117.419998,117.940002,117.309998,117.309998,46765500,92.279924\n2005-03-29,117.139999,117.900002,116.25,116.529999,71160300,91.666351\n2005-03-30,116.779999,118.199997,116.769997,118.18,62002100,92.964297\n2005-03-31,118.190002,118.459999,117.870003,117.959999,64575400,92.791237\n2005-04-01,118.629997,118.989998,116.910004,117.43,95255300,92.374322\n2005-04-04,117.360001,117.860001,116.739998,117.629997,71581200,92.531646\n2005-04-05,117.779999,118.379997,117.669998,118.190002,46853900,92.972165\n2005-04-06,118.449997,118.949997,118.18,118.599998,53268200,93.294681\n2005-04-07,118.410004,119.260002,118.32,119.239998,46734600,93.798126\n2005-04-08,119.169998,119.209999,118.0,118.0,63772900,92.822703\n2005-04-11,118.290001,118.419998,117.830002,118.089996,44945000,92.893497\n2005-04-12,117.889999,119.059998,117.07,118.699997,86144800,93.373344\n2005-04-13,118.559998,118.800003,117.129997,117.300003,65949000,92.272062\n2005-04-14,117.400002,117.5,115.769997,115.769997,96119800,91.068508\n2005-04-15,115.739998,116.199997,114.099998,114.150002,128677300,89.794167\n2005-04-18,114.120003,114.959999,113.959999,114.5,100035200,90.069487\n2005-04-19,115.099998,115.529999,114.830002,115.410004,64930100,90.785326\n2005-04-20,115.379997,115.629997,113.550003,113.800003,107735900,89.518846\n2005-04-21,114.790001,116.209999,114.379997,116.010002,86952200,91.257305\n2005-04-22,115.739998,116.5,114.269997,115.57,88845800,90.911184\n2005-04-25,115.860001,116.5,115.720001,116.330002,52284100,91.509027\n2005-04-26,115.959999,116.769997,115.150002,115.199997,72626000,90.620128\n2005-04-27,114.860001,116.07,114.440002,115.650002,84131900,90.974116\n2005-04-28,115.269997,115.68,114.199997,114.199997,72481500,89.833495\n2005-04-29,115.07,115.870003,113.970001,115.75,103993800,91.052778\n2005-05-02,116.07,116.410004,115.519997,116.400002,56026400,91.564091\n2005-05-03,116.07,116.849998,115.690002,116.599998,86000300,91.721415\n2005-05-04,116.650002,117.75,116.279999,117.5,81055700,92.429386\n2005-05-05,117.669998,118.0,116.739998,117.459999,96906700,92.39792\n2005-05-06,117.93,117.989998,117.059998,117.089996,67415400,92.106864\n2005-05-09,117.209999,118.080002,117.050003,117.82,43750500,92.681109\n2005-05-10,117.360001,117.5,116.389999,116.599998,74613500,91.721415\n2005-05-11,116.93,117.400002,115.849998,117.239998,91647400,92.22486\n2005-05-12,117.32,117.589996,115.949997,115.949997,95086800,91.210103\n2005-05-13,116.300003,116.620003,114.800003,115.720001,85267000,91.02918\n2005-05-16,115.699997,116.839996,115.660004,116.800003,49207000,91.878745\n2005-05-17,116.410004,117.699997,116.160004,117.580002,61071800,92.492318\n2005-05-18,118.089996,119.080002,118.010002,118.790001,77944900,93.444144\n2005-05-19,119.019997,119.410004,118.699997,119.290001,61768100,93.83746\n2005-05-20,119.339996,119.389999,118.739998,119.120003,46345500,93.703734\n2005-05-23,119.209999,120.040001,119.190002,119.779999,51047900,94.222909\n2005-05-24,119.440002,119.830002,119.199997,119.5,50654100,94.002652\n2005-05-25,119.349998,119.870003,118.830002,119.410004,47608800,93.931858\n2005-05-26,119.790001,120.209999,119.620003,120.050003,43256200,94.435303\n2005-05-27,120.059998,120.25,119.800003,120.25,24596100,94.592627\n2005-05-31,120.080002,120.169998,119.400002,119.480003,43377200,93.986922\n2005-06-01,119.519997,120.919998,119.449997,120.5,69611000,94.789285\n2005-06-02,120.230003,120.839996,120.099998,120.760002,39704500,94.993812\n2005-06-03,120.550003,120.889999,119.730003,120.150002,60999400,94.513965\n2005-06-06,119.949997,120.199997,119.550003,120.040001,36046400,94.427435\n2005-06-07,120.389999,121.25,120.010002,120.129997,66501300,94.498229\n2005-06-08,120.43,120.589996,119.669998,119.910004,46881200,94.325175\n2005-06-09,119.739998,120.580002,119.440002,120.480003,56653300,94.773555\n2005-06-10,120.559998,120.650002,119.599998,120.199997,36465300,94.553293\n2005-06-13,119.940002,121.080002,119.809998,120.580002,49383200,94.852218\n2005-06-14,120.449997,121.199997,120.379997,120.860001,33857100,95.072474\n2005-06-15,121.160004,121.239998,120.230003,121.089996,53195600,95.253396\n2005-06-16,121.059998,121.639999,120.919998,121.400002,46564500,95.497256\n2005-06-17,121.540001,121.900002,121.220001,121.360001,51529400,95.851089\n2005-06-20,121.080002,121.839996,120.940002,121.400002,41019400,95.882682\n2005-06-21,121.5,121.650002,121.029999,121.470001,39879800,95.937968\n2005-06-22,121.68,121.940002,121.07,121.57,46310100,96.016948\n2005-06-23,121.32,121.599998,119.830002,119.860001,62185600,94.666377\n2005-06-24,119.879997,120.010002,118.839996,118.980003,58572500,93.971348\n2005-06-27,118.970001,119.410004,118.75,119.150002,48183800,94.105614\n2005-06-28,119.400002,120.239998,119.370003,120.150002,41174200,94.895422\n2005-06-29,120.370003,120.400002,119.760002,119.830002,42316500,94.642684\n2005-06-30,120.220001,120.32,118.949997,119.18,62288800,94.129308\n2005-07-01,119.449997,119.800003,119.209999,119.529999,49737500,94.405739\n2005-07-05,119.25,120.650002,119.190002,120.489998,51549000,95.163954\n2005-07-06,120.389999,120.650002,119.410004,119.480003,52363600,94.366252\n2005-07-07,118.290001,119.949997,118.260002,119.949997,103268800,94.737457\n2005-07-08,119.970001,121.32,119.720001,121.32,64491200,95.819496\n2005-07-11,121.330002,122.099998,121.309998,121.940002,49688300,96.309179\n2005-07-12,121.989998,122.629997,121.639999,122.260002,51871100,96.561917\n2005-07-13,122.269997,122.519997,121.989998,122.43,41182300,96.696183\n2005-07-14,122.980003,123.440002,122.489998,122.910004,63638800,97.075294\n2005-07-15,122.790001,123.040001,122.360001,122.839996,56075900,97.020001\n2005-07-18,122.5,122.629997,122.050003,122.349998,56598400,96.632997\n2005-07-19,122.709999,123.110001,122.410004,123.019997,59165700,97.162167\n2005-07-20,122.589996,123.730003,122.300003,123.440002,69477000,97.493891\n2005-07-21,123.550003,123.610001,122.470001,122.720001,101110900,96.925228\n2005-07-22,122.879997,123.559998,122.629997,123.540001,52607100,97.572871\n2005-07-25,123.410004,123.949997,122.849998,123.190002,57301600,97.296439\n2005-07-26,123.230003,123.529999,122.949997,123.339996,42758800,97.414905\n2005-07-27,123.5,123.889999,123.050003,123.790001,43181600,97.770323\n2005-07-28,123.959999,124.639999,123.639999,124.57,47880700,98.386372\n2005-07-29,124.410004,124.629997,123.5,123.739998,62358100,97.73083\n2005-08-01,123.830002,124.040001,123.449997,123.650002,40418200,97.65975\n2005-08-02,123.870003,124.599998,123.739998,124.389999,45147400,98.244206\n2005-08-03,124.25,124.739998,124.120003,124.720001,36837200,98.504844\n2005-08-04,124.230003,124.309998,123.57,123.720001,50855600,97.715036\n2005-08-05,123.449997,123.980003,122.669998,122.879997,53595500,97.051595\n2005-08-08,123.150002,123.410004,122.379997,122.650002,47616000,96.869942\n2005-08-09,123.059998,123.589996,122.870003,123.389999,47170000,97.454398\n2005-08-10,123.82,124.5,122.82,123.330002,72863700,97.407012\n2005-08-11,123.269997,124.029999,123.010002,123.82,58570200,97.794016\n2005-08-12,123.57,123.690002,122.75,123.059998,54776900,97.19376\n2005-08-15,123.220001,123.870003,122.830002,123.82,36208500,97.794016\n2005-08-16,123.440002,123.519997,122.089996,122.209999,71942100,96.522425\n2005-08-17,122.190002,122.870003,122.029999,122.199997,62275100,96.514525\n2005-08-18,122.050003,122.559998,121.839996,122.190002,53388600,96.506631\n2005-08-19,122.629997,122.82,122.199997,122.470001,39842100,96.727776\n2005-08-22,122.580002,123.230003,121.879997,122.470001,69912000,96.727776\n2005-08-23,122.5,122.610001,121.150002,122.239998,55168600,96.546118\n2005-08-24,121.940002,122.730003,121.089996,121.150002,79104600,95.68523\n2005-08-25,121.349998,121.669998,121.209999,121.589996,35631100,96.032742\n2005-08-26,121.480003,121.489998,120.68,120.760002,61956800,95.377206\n2005-08-29,120.410004,121.779999,120.379997,121.690002,56179200,96.111727\n2005-08-30,121.25,121.300003,120.389999,121.050003,74160200,95.606251\n2005-08-31,121.190002,122.660004,120.739998,122.580002,102945200,96.814656\n2005-09-01,122.519997,123.150002,121.139999,122.489998,74578700,96.74357\n2005-09-02,122.849998,122.879997,122.040001,122.269997,47653400,96.569811\n2005-09-06,122.660004,123.800003,122.650002,123.699997,57251300,97.699237\n2005-09-07,123.629997,124.129997,123.459999,123.910004,41749700,97.865102\n2005-09-08,123.660004,124.0,123.309998,123.5,39068700,97.541278\n2005-09-09,123.830002,124.739998,123.800003,124.599998,43093900,98.410065\n2005-09-12,124.449997,124.669998,124.269997,124.349998,33017600,98.212613\n2005-09-13,124.129997,124.419998,123.519997,123.660004,58427500,97.66765\n2005-09-14,123.739998,123.919998,123.019997,123.209999,57694600,97.312233\n2005-09-15,123.589996,123.650002,122.900002,123.150002,73156900,97.264846\n2005-09-16,123.300003,123.739998,122.870003,123.5,75424100,97.956492\n2005-09-19,123.470001,123.550003,122.639999,123.089996,53355300,97.631289\n2005-09-20,123.199997,123.610001,121.870003,122.050003,84480300,96.806398\n2005-09-21,121.790001,121.870003,120.779999,120.910004,94469100,95.902185\n2005-09-22,120.949997,121.660004,120.440002,121.339996,84597200,96.243242\n2005-09-23,121.239998,121.889999,120.900002,121.440002,59368100,96.322564\n2005-09-26,122.019997,122.239998,121.080002,121.580002,70415400,96.433607\n2005-09-27,121.519997,121.989998,121.019997,121.550003,66150800,96.409813\n2005-09-28,121.93,122.120003,121.199997,121.669998,58620500,96.504989\n2005-09-29,121.550003,122.860001,121.080002,122.660004,66607700,97.290232\n2005-09-30,122.620003,123.040001,121.739998,123.040001,47824200,97.591634\n2005-10-03,122.959999,123.339996,122.449997,122.599998,50994800,97.242638\n2005-10-04,122.790001,123.029999,121.160004,121.220001,60776300,96.148065\n2005-10-05,121.25,121.309998,119.57,119.629997,106052100,94.886922\n2005-10-06,119.779999,120.260002,118.169998,119.199997,140941800,94.545859\n2005-10-07,119.699997,120.050003,119.129997,119.610001,75661400,94.871061\n2005-10-10,119.68,119.709999,118.300003,118.599998,52677000,94.069958\n2005-10-11,118.989998,119.389999,118.32,118.43,75629800,93.93512\n2005-10-12,118.389999,119.129997,117.410004,117.5,100510400,93.197472\n2005-10-13,117.459999,118.080002,116.879997,117.43,99052900,93.14195\n2005-10-14,118.120003,118.809998,117.559998,118.669998,88651000,94.12548\n2005-10-17,118.800003,119.269997,118.449997,119.110001,68109300,94.474476\n2005-10-18,118.940002,118.959999,117.800003,117.82,74996900,93.451286\n2005-10-19,117.5,119.800003,117.120003,119.779999,116563800,95.005899\n2005-10-20,119.489998,119.809998,117.300003,117.669998,131966700,93.33231\n2005-10-21,118.290001,118.779999,117.510002,118.129997,96579500,93.697167\n2005-10-24,118.440002,120.089996,118.410004,119.959999,71308400,95.14867\n2005-10-25,119.720001,120.239998,118.940002,119.720001,76594500,94.95831\n2005-10-26,119.510002,120.540001,119.190002,119.370003,80855800,94.680702\n2005-10-27,119.199997,119.370003,117.93,118.099998,66623100,93.673373\n2005-10-28,118.43,119.949997,118.099998,119.800003,72322000,95.021765\n2005-10-31,120.290001,121.300003,120.129997,120.129997,77698900,95.283507\n2005-11-01,120.580002,120.900002,120.220001,120.489998,66365100,95.569049\n2005-11-02,120.169998,121.75,120.129997,121.75,74012300,96.568444\n2005-11-03,122.150002,122.660004,121.75,122.269997,84897600,96.98089\n2005-11-04,122.400002,122.459999,121.550003,122.110001,59156000,96.853986\n2005-11-07,122.370003,122.620003,121.849998,122.230003,46765400,96.949169\n2005-11-08,121.93,122.419998,121.790001,122.230003,42152800,96.949169\n2005-11-09,122.080002,122.949997,121.860001,122.389999,57666800,97.076073\n2005-11-10,122.339996,123.519997,121.75,123.339996,79048100,97.829582\n2005-11-11,123.349998,123.839996,122.43,123.760002,34867000,98.162718\n2005-11-14,123.790001,124.019997,123.379997,123.690002,45092200,98.107196\n2005-11-15,123.550003,124.089996,122.860001,123.239998,69592500,97.750266\n2005-11-16,123.370003,123.550003,122.980003,123.489998,51133000,97.948559\n2005-11-17,123.68,124.650002,123.139999,124.639999,55717500,98.860705\n2005-11-18,125.059998,125.279999,124.330002,125.129997,72437200,99.249357\n2005-11-21,125.150002,125.910004,124.980003,125.760002,50021200,99.749058\n2005-11-22,125.559998,126.519997,125.419998,126.300003,66438800,100.17737\n2005-11-23,126.25,127.410004,126.209999,127.029999,50854700,100.756381\n2005-11-25,126.980003,127.220001,126.809998,127.129997,15270000,100.835697\n2005-11-28,127.25,127.269997,126.040001,126.230003,54498500,100.121849\n2005-11-29,126.650002,126.980003,126.089996,126.089996,51738900,100.010799\n2005-11-30,126.160004,126.519997,125.290001,125.410004,56007200,99.47145\n2005-12-01,126.019997,127.029999,125.980003,126.690002,65468200,100.486706\n2005-12-02,126.769997,127.080002,126.5,126.849998,46699400,100.61361\n2005-12-05,126.699997,126.730003,126.18,126.580002,59273400,100.399457\n2005-12-06,127.050003,127.739998,126.629997,126.82,57935200,100.589816\n2005-12-07,126.769997,126.870003,125.68,126.080002,66816500,100.002872\n2005-12-08,126.220001,126.82,125.480003,126.0,62608600,99.939417\n2005-12-09,126.160004,126.779999,125.82,126.330002,50744500,100.201164\n2005-12-12,126.709999,126.860001,125.959999,126.449997,48389900,100.296341\n2005-12-13,126.419998,127.699997,126.290001,127.309998,88630900,100.978468\n2005-12-14,127.190002,128.089996,127.139999,127.809998,64375000,101.375053\n2005-12-15,127.839996,128.0,127.18,127.440002,55900300,101.081584\n2005-12-16,127.279999,127.360001,126.360001,126.360001,46238300,100.756248\n2005-12-19,126.730003,126.870003,125.690002,125.709999,48733000,100.237954\n2005-12-20,125.860001,126.589996,125.480003,125.830002,46603200,100.333641\n2005-12-21,126.220001,126.760002,125.800003,126.029999,51806900,100.493113\n2005-12-22,126.309998,126.690002,126.080002,126.690002,32247900,101.019383\n2005-12-23,126.779999,126.860001,126.419998,126.760002,27977300,101.075199\n2005-12-27,126.959999,127.050003,125.379997,125.470001,44499500,100.046585\n2005-12-28,125.739998,125.989998,125.5,125.75,30764300,100.269849\n2005-12-29,125.720001,125.959999,125.059998,125.190002,32788900,99.823321\n2005-12-30,124.800003,125.059998,124.360001,124.510002,44645600,99.281106\n2006-01-03,125.190002,127.0,124.389999,126.699997,73256700,101.027353\n2006-01-04,126.860001,127.489998,126.699997,127.300003,51899600,101.505782\n2006-01-05,127.150002,127.589996,126.879997,127.379997,47307500,101.569568\n2006-01-06,128.020004,128.580002,127.360001,128.440002,62885900,102.414789\n2006-01-09,128.419998,129.059998,128.380005,128.770004,43527400,102.677924\n2006-01-10,128.389999,128.979996,128.259995,128.899994,44960800,102.781574\n2006-01-11,129.020004,129.440002,128.729996,129.309998,49598900,103.108501\n2006-01-12,129.080002,129.279999,128.440002,128.800003,40509200,102.701844\n2006-01-13,128.570007,128.899994,128.199997,128.679993,44856700,102.606151\n2006-01-17,128.199997,128.419998,127.809998,128.330002,52066600,102.327077\n2006-01-18,127.580002,128.899994,127.160004,127.82,75067600,101.920414\n2006-01-19,128.130005,128.770004,127.809998,128.309998,81530400,102.311126\n2006-01-20,128.279999,128.309998,125.970001,125.970001,114957800,100.445273\n2006-01-23,126.209999,126.82,126.129997,126.419998,67017400,100.804089\n2006-01-24,126.629997,127.150002,126.419998,126.550003,53008800,100.907751\n2006-01-25,127.040001,127.18,125.839996,126.660004,87747700,100.995463\n2006-01-26,127.25,127.669998,126.760002,127.360001,71294000,101.553623\n2006-01-27,127.660004,128.660004,127.449997,128.539993,65771200,102.494519\n2006-01-30,128.440002,128.809998,128.350006,128.440002,33709600,102.414789\n2006-01-31,128.320007,128.539993,127.489998,127.5,72937000,101.665255\n2006-02-01,127.82,128.429993,127.720001,128.389999,63561000,102.374918\n2006-02-02,128.100006,128.139999,126.800003,126.900002,83626900,101.186831\n2006-02-03,126.580002,128.389999,126.139999,126.269997,86040400,100.684481\n2006-02-06,126.440002,126.800003,126.169998,126.599998,45511900,100.947616\n2006-02-07,126.379997,126.660004,125.400002,125.480003,71208100,100.054561\n2006-02-08,125.849998,128.100006,125.599998,126.620003,59422200,100.963567\n2006-02-09,126.919998,127.599998,126.370003,126.410004,62023300,100.796119\n2006-02-10,126.43,127.129997,125.449997,126.639999,64508700,100.979512\n2006-02-13,126.599998,126.790001,125.949997,126.410004,52308700,100.796119\n2006-02-14,126.459999,128.029999,126.209999,127.75,90964400,101.864598\n2006-02-15,127.68,128.320007,127.239998,128.199997,85471300,102.223414\n2006-02-16,128.339996,129.210007,128.179993,129.160004,61017900,102.988899\n2006-02-17,129.050003,129.160004,128.580002,128.809998,40342600,102.709813\n2006-02-21,129.110001,129.399994,128.289993,128.490005,46456300,102.45466\n2006-02-22,128.770004,129.649994,128.649994,129.270004,42326700,103.076611\n2006-02-23,129.270004,129.639999,128.279999,129.080002,43423200,102.925108\n2006-02-24,129.110001,129.479996,128.759995,129.410004,36777400,103.188243\n2006-02-27,129.399994,130.039993,129.279999,129.460007,35858600,103.228114\n2006-02-28,129.199997,129.910004,128.130005,128.229996,74394800,102.247335\n2006-03-01,128.600006,129.490005,128.5,129.369995,48641600,103.156341\n2006-03-02,128.899994,129.419998,128.610001,129.360001,60642300,103.148372\n2006-03-03,128.669998,130.070007,128.649994,128.759995,73402500,102.669942\n2006-03-06,129.139999,129.179993,127.849998,128.169998,57478400,102.199494\n2006-03-07,127.860001,128.059998,127.400002,127.970001,61780800,102.040022\n2006-03-08,127.699997,128.440002,127.18,128.240005,66692400,102.255316\n2006-03-09,128.279999,128.679993,127.379997,127.379997,56313600,101.569568\n2006-03-10,127.709999,128.839996,127.440002,128.589996,60490800,102.53439\n2006-03-13,128.839996,129.160004,128.529999,128.830002,45479100,102.725764\n2006-03-14,128.710007,130.229996,128.610001,130.179993,69877300,103.802213\n2006-03-15,130.149994,130.860001,129.850006,130.759995,53398900,104.264691\n2006-03-16,131.009995,131.470001,130.839996,131.029999,65526400,104.479986\n2006-03-17,130.679993,130.899994,130.380005,130.619995,47286800,104.567243\n2006-03-20,130.639999,130.899994,130.210007,130.410004,45538500,104.399136\n2006-03-21,130.369995,130.990005,129.449997,129.589996,87102700,103.742682\n2006-03-22,129.509995,130.509995,129.449997,130.380005,51605700,104.37512\n2006-03-23,130.259995,130.389999,129.660004,130.110001,46704200,104.15897\n2006-03-24,129.990005,130.570007,129.740005,130.210007,43209200,104.239029\n2006-03-27,130.029999,130.279999,129.740005,130.020004,32523000,104.086923\n2006-03-28,129.929993,130.529999,129.050003,129.220001,82079900,103.446485\n2006-03-29,129.410004,130.5,129.289993,130.029999,61505700,104.094924\n2006-03-30,130.110001,130.979996,129.550003,129.800003,70571700,103.910802\n2006-03-31,130.020004,130.240005,129.369995,129.830002,62925600,103.934818\n2006-04-03,130.070007,130.869995,129.490005,129.729996,61624700,103.854758\n2006-04-04,129.729996,130.729996,129.360001,130.559998,54809300,104.519213\n2006-04-05,130.610001,131.279999,130.380005,131.009995,50607200,104.879456\n2006-04-06,130.850006,131.210007,130.190002,130.869995,57906200,104.76738\n2006-04-07,131.059998,131.399994,129.350006,129.539993,80180900,103.702653\n2006-04-10,129.740005,130.080002,129.259995,129.740005,41496500,103.862772\n2006-04-11,129.850006,130.059998,128.25,128.639999,72799400,102.982167\n2006-04-12,128.770004,129.130005,128.610001,128.880005,43033700,103.174302\n2006-04-13,128.589996,129.25,128.309998,128.710007,51051800,103.038211\n2006-04-17,128.830002,129.309998,128.020004,128.660004,64167700,102.998181\n2006-04-18,128.929993,130.940002,128.929993,130.699997,92531800,104.631288\n2006-04-19,130.75,131.070007,130.240005,130.949997,87269000,104.831425\n2006-04-20,131.0,131.860001,130.600006,131.130005,86005500,104.975529\n2006-04-21,131.690002,131.789993,130.619995,131.149994,72342600,104.991531\n2006-04-24,130.889999,131.070007,130.380005,130.910004,52546400,104.799408\n2006-04-25,131.039993,131.119995,129.919998,130.369995,84359800,104.367107\n2006-04-26,130.5,131.139999,130.300003,130.399994,67262400,104.391122\n2006-04-27,129.899994,131.630005,129.589996,131.029999,124478600,104.89547\n2006-04-28,130.789993,131.75,130.710007,131.470001,55854400,105.247712\n2006-05-01,131.470001,131.800003,130.320007,130.399994,64990300,104.391122\n2006-05-02,131.009995,131.460007,130.740005,131.380005,49063500,105.175666\n2006-05-03,131.149994,131.320007,130.449997,130.889999,60821300,104.783394\n2006-05-04,131.080002,131.619995,130.970001,131.360001,42921400,105.159651\n2006-05-05,132.050003,132.800003,131.850006,132.520004,62588200,106.088287\n2006-05-08,132.509995,132.770004,132.360001,132.360001,30016700,105.960197\n2006-05-09,132.419998,132.770004,132.309998,132.619995,29864000,106.168334\n2006-05-10,132.410004,132.75,131.889999,132.550003,64378200,106.112302\n2006-05-11,132.509995,132.550003,130.520004,130.949997,80626900,104.831425\n2006-05-12,130.360001,130.720001,129.190002,129.240005,91726500,103.462499\n2006-05-15,128.789993,129.740005,128.610001,129.5,84029300,103.670636\n2006-05-16,129.759995,130.0,129.009995,129.309998,62137600,103.518531\n2006-05-17,128.669998,129.100006,126.769997,126.849998,144789500,101.54919\n2006-05-18,127.349998,127.75,126.110001,126.209999,87906300,101.036841\n2006-05-19,126.870003,127.489998,125.800003,127.099998,124309400,101.749326\n2006-05-22,126.279999,127.169998,125.5,126.129997,110852800,100.972796\n2006-05-23,127.18,127.629997,125.169998,125.169998,92006500,100.204273\n2006-05-24,125.68,126.889999,124.760002,126.169998,168405000,101.004818\n2006-05-25,126.919998,127.730003,126.43,127.730003,78977900,102.253674\n2006-05-26,128.009995,128.380005,127.510002,128.380005,62989700,102.774029\n2006-05-30,127.970001,128.0,126.050003,126.099998,72419900,100.948781\n2006-05-31,126.620003,127.510002,126.199997,127.510002,86926200,102.077553\n2006-06-01,127.379997,128.940002,127.269997,128.729996,73721700,103.054213\n2006-06-02,129.25,129.429993,128.320007,129.0,91702600,103.270364\n2006-06-05,128.850006,128.860001,126.769997,127.120003,86105100,101.76534\n2006-06-06,127.209999,127.379997,125.760002,126.809998,130498600,101.517167\n2006-06-07,126.910004,127.650002,125.790001,125.860001,108599400,100.756651\n2006-06-08,125.580002,126.5,123.870003,125.75,204957200,100.668591\n2006-06-09,126.360001,126.959999,125.290001,125.349998,94972200,100.348371\n2006-06-12,125.879997,125.93,123.82,123.989998,95815900,99.259629\n2006-06-13,123.739998,124.839996,122.550003,122.550003,185688800,98.106848\n2006-06-14,122.839996,123.629997,122.339996,123.5,163566400,98.867364\n2006-06-15,123.949997,126.360001,123.860001,126.120003,134057000,100.964795\n2006-06-16,125.290001,125.559998,124.459999,124.650002,94253500,100.229057\n2006-06-19,125.400002,125.480003,123.550003,123.669998,95804400,99.441052\n2006-06-20,124.010002,124.809998,123.720001,124.089996,65494700,99.778766\n2006-06-21,124.0,125.699997,123.959999,125.010002,75008200,100.518528\n2006-06-22,124.949997,125.059998,124.040001,124.459999,74566100,100.076279\n2006-06-23,124.330002,125.300003,124.029999,124.440002,54107000,100.0602\n2006-06-26,124.540001,125.059998,124.25,124.989998,37899600,100.502443\n2006-06-27,125.010002,125.290001,123.769997,123.910004,69780200,99.634037\n2006-06-28,124.190002,124.769997,123.650002,124.75,62368100,100.309464\n2006-06-29,125.199997,127.349998,125.169998,127.269997,110634800,102.335753\n2006-06-30,127.540001,127.660004,126.959999,127.279999,54227800,102.343796\n2006-07-03,127.43,128.009995,127.309998,127.800003,23914000,102.761923\n2006-07-05,127.290001,127.449997,126.519997,127.07,69653400,102.174939\n2006-07-06,127.199997,127.849998,127.080002,127.440002,50100300,102.472452\n2006-07-07,127.190002,127.559998,126.290001,126.610001,81626500,101.805061\n2006-07-10,126.940002,127.43,126.410004,126.849998,60964100,101.998039\n2006-07-11,126.610001,127.410004,125.940002,127.410004,73640800,102.448331\n2006-07-12,127.150002,127.400002,125.720001,126.050003,82561300,101.354776\n2006-07-13,125.449997,125.68,124.0,124.0,102405700,99.706402\n2006-07-14,124.129997,124.260002,122.830002,123.519997,103242500,99.320439\n2006-07-17,123.519997,124.099998,123.150002,123.339996,81159000,99.175703\n2006-07-18,123.709999,124.050003,122.389999,123.970001,122771000,99.68228\n2006-07-19,124.18,126.260002,123.720001,125.690002,133565300,101.065305\n2006-07-20,126.120003,126.300003,124.660004,124.830002,112259800,100.373793\n2006-07-21,125.150002,125.190002,123.82,123.949997,101560000,99.666195\n2006-07-24,124.440002,126.32,124.440002,126.209999,92884000,101.483426\n2006-07-25,125.980003,127.300003,125.720001,126.660004,95480700,101.845268\n2006-07-26,126.589996,127.449997,126.18,126.830002,84525800,101.98196\n2006-07-27,127.379997,127.690002,126.199997,126.709999,87257100,101.885468\n2006-07-28,127.040001,128.139999,126.860001,127.980003,82137000,102.906658\n2006-07-31,127.660004,127.959999,127.449997,127.849998,49593100,102.802123\n2006-08-01,127.339996,127.379997,126.599998,127.220001,65225600,102.295553\n2006-08-02,127.580002,128.460007,127.550003,128.080002,64770900,102.987065\n2006-08-03,127.339996,128.550003,127.150002,128.419998,63693800,103.260451\n2006-08-04,129.080002,129.429993,127.480003,128.199997,96294200,103.083551\n2006-08-07,127.900002,128.080002,127.400002,127.900002,45377300,102.84233\n2006-08-08,128.089996,128.460007,126.949997,127.410004,90901300,102.448331\n2006-08-09,128.179993,128.600006,126.610001,126.980003,78910600,102.102574\n2006-08-10,126.580002,127.5,126.279999,127.370003,69322300,102.416166\n2006-08-11,127.169998,127.199997,126.389999,127.010002,47482200,102.126696\n2006-08-14,127.629997,128.160004,126.919998,127.110001,57839000,102.207103\n2006-08-15,128.25,128.869995,127.900002,128.630005,68143000,103.429314\n2006-08-16,129.320007,129.889999,129.039993,129.699997,71737600,104.289677\n2006-08-17,129.580002,130.369995,129.490005,130.029999,70992800,104.555026\n2006-08-18,130.190002,130.690002,129.589996,130.690002,58288400,105.085725\n2006-08-21,130.179993,130.279999,129.800003,130.130005,42133600,104.63544\n2006-08-22,129.990005,130.520004,129.679993,130.119995,60839900,104.627391\n2006-08-23,130.179993,130.440002,129.190002,129.759995,66592100,104.33792\n2006-08-24,129.990005,130.100006,129.399994,129.649994,57983000,104.249471\n2006-08-25,129.639999,130.25,129.550003,129.809998,41756000,104.378127\n2006-08-28,129.649994,130.820007,129.639999,130.429993,52681500,104.876655\n2006-08-29,130.490005,130.830002,129.809998,130.580002,61817800,104.997275\n2006-08-30,130.869995,131.039993,130.550003,130.660004,50052200,105.061603\n2006-08-31,130.850006,130.990005,130.580002,130.639999,37510300,105.045518\n2006-09-01,131.139999,131.580002,130.839996,131.419998,48794500,105.672703\n2006-09-05,131.509995,131.850006,131.199997,131.669998,52348300,105.873724\n2006-09-06,131.110001,131.160004,130.330002,130.509995,53795600,104.940983\n2006-09-07,130.059998,130.570007,129.350006,129.910004,86269400,104.45854\n2006-09-08,130.080002,130.460007,129.830002,130.279999,45096300,104.756047\n2006-09-11,129.860001,130.690002,129.479996,130.410004,68496600,104.860582\n2006-09-12,130.559998,131.839996,130.369995,131.690002,69875600,105.889809\n2006-09-13,131.639999,132.449997,131.520004,132.220001,62898400,106.315972\n2006-09-14,131.960007,132.240005,131.75,132.229996,57805400,106.324009\n2006-09-15,132.309998,132.389999,131.679993,131.960007,76703100,106.573571\n2006-09-18,131.789993,132.389999,131.649994,132.139999,64154100,106.718936\n2006-09-19,132.119995,132.130005,131.070007,131.809998,92089100,106.45242\n2006-09-20,132.25,132.770004,132.059998,132.509995,75204100,107.017752\n2006-09-21,132.610001,132.75,131.419998,131.869995,88932500,106.500875\n2006-09-22,131.669998,131.679993,131.0,131.470001,65966800,106.177832\n2006-09-25,131.729996,132.850006,131.050003,132.479996,92299100,106.993524\n2006-09-26,132.5,133.600006,132.399994,133.580002,73962700,107.881911\n2006-09-27,133.490005,133.970001,133.270004,133.740005,82432200,108.011134\n2006-09-28,133.740005,133.990005,133.279999,133.690002,58597500,107.97075\n2006-09-29,133.800003,133.940002,133.479996,133.580002,47966600,107.881911\n2006-10-02,133.539993,133.830002,132.949997,133.080002,51687400,107.478101\n2006-10-03,132.889999,133.869995,132.649994,133.360001,73108100,107.704234\n2006-10-04,133.229996,135.0,133.080002,134.919998,80890500,108.96412\n2006-10-05,134.919998,135.410004,134.75,135.179993,60505900,109.174096\n2006-10-06,134.949997,135.100006,134.399994,135.009995,64983600,109.036802\n2006-10-09,134.850006,135.300003,134.639999,135.089996,41176800,109.101414\n2006-10-10,135.100006,135.449997,134.839996,135.270004,56403700,109.246792\n2006-10-11,134.839996,135.429993,134.300003,135.110001,104071800,109.117569\n2006-10-12,135.449997,136.389999,135.399994,136.279999,59158600,110.062484\n2006-10-13,136.160004,136.710007,136.039993,136.630005,53944000,110.345156\n2006-10-16,136.520004,137.050003,136.419998,136.839996,42273000,110.514749\n2006-10-17,136.470001,136.699997,135.669998,136.410004,90500600,110.167478\n2006-10-18,137.039993,137.369995,136.100006,136.589996,86848600,110.312844\n2006-10-19,136.389999,136.880005,136.229996,136.809998,64063200,110.490521\n2006-10-20,136.839996,136.949997,136.330002,136.839996,48094500,110.514749\n2006-10-23,136.559998,137.800003,136.389999,137.470001,66219900,111.023554\n2006-10-24,137.279999,137.929993,137.220001,137.880005,53234900,111.354681\n2006-10-25,137.740005,138.410004,137.509995,138.350006,78105400,111.734263\n2006-10-26,138.660004,139.0,137.979996,138.779999,66843700,112.081534\n2006-10-27,138.610001,138.75,137.630005,137.910004,80238000,111.378909\n2006-10-30,137.660004,138.199997,137.399994,137.809998,49717800,111.298142\n2006-10-31,138.070007,138.259995,137.25,137.789993,71274100,111.281986\n2006-11-01,138.220001,138.309998,136.720001,136.860001,83005600,110.530905\n2006-11-02,136.509995,137.009995,136.360001,136.779999,60693100,110.466294\n2006-11-03,137.270004,137.389999,135.619995,136.539993,71346400,110.27246\n2006-11-06,136.960007,138.279999,136.949997,138.080002,63303300,111.516202\n2006-11-07,138.199997,138.979996,138.0,138.610001,63318900,111.94424\n2006-11-08,138.0,139.050003,136.860001,138.910004,87517800,112.186529\n2006-11-09,139.009995,139.139999,137.899994,138.179993,95916300,111.596957\n2006-11-10,138.139999,138.339996,137.720001,138.240005,48991500,111.645425\n2006-11-13,138.179993,139.039993,138.070007,138.580002,59398200,111.920013\n2006-11-14,138.970001,139.740005,138.119995,139.619995,96704000,112.759932\n2006-11-15,139.570007,140.449997,139.529999,140.020004,76509600,113.082988\n2006-11-16,140.440002,140.679993,139.490005,140.380005,76728800,113.373731\n2006-11-17,139.929993,140.429993,139.729996,140.419998,56353800,113.406031\n2006-11-20,140.300003,140.740005,139.940002,140.5,69174200,113.470642\n2006-11-21,140.490005,140.669998,140.289993,140.639999,51367900,113.583708\n2006-11-22,140.75,141.160004,140.5,140.919998,45505300,113.809841\n2006-11-24,140.240005,140.839996,140.199997,140.350006,30998000,113.349504\n2006-11-27,140.279999,140.350006,138.380005,138.419998,84545100,111.79079\n2006-11-28,138.240005,139.160004,138.110001,139.020004,106652900,112.275367\n2006-11-29,139.470001,140.529999,139.080002,140.470001,90034900,113.446414\n2006-11-30,140.440002,141.050003,139.759995,140.529999,83994300,113.49487\n2006-12-01,140.529999,140.660004,138.970001,140.220001,126080000,113.244509\n2006-12-04,140.25,141.550003,140.229996,141.289993,87813200,114.108656\n2006-12-05,141.559998,141.960007,141.259995,141.899994,73374400,114.601305\n2006-12-06,141.869995,142.070007,141.5,141.779999,53253200,114.504395\n2006-12-07,142.029999,142.300003,141.110001,141.160004,62857400,114.003674\n2006-12-08,141.130005,141.899994,140.779999,141.419998,79625500,114.213651\n2006-12-11,141.419998,142.089996,141.339996,141.830002,39779400,114.544778\n2006-12-12,141.690002,141.869995,140.889999,141.720001,77451600,114.45594\n2006-12-13,142.229996,142.339996,141.559998,141.869995,55520200,114.577078\n2006-12-14,141.860001,143.240005,141.839996,143.119995,64755200,115.586603\n2006-12-15,142.639999,142.889999,142.240005,142.339996,70857400,115.597161\n2006-12-18,142.539993,142.880005,141.75,141.949997,48954600,115.280434\n2006-12-19,141.550003,142.559998,141.190002,142.220001,65023600,115.49971\n2006-12-20,142.279999,142.660004,142.009995,142.139999,41469600,115.434739\n2006-12-21,142.270004,142.429993,141.320007,141.619995,48698400,115.012433\n2006-12-22,141.639999,141.649994,140.669998,140.75,62069100,114.305893\n2006-12-26,140.809998,141.610001,140.779999,141.580002,32696900,114.979954\n2006-12-27,141.869995,142.600006,141.830002,142.509995,39727100,115.73522\n2006-12-28,142.410004,142.699997,141.990005,142.210007,37288800,115.491594\n2006-12-29,142.059998,142.539993,141.429993,141.619995,45461200,115.012433\n2007-01-03,142.25,142.860001,140.570007,141.369995,94807600,114.809403\n2007-01-04,141.229996,142.050003,140.610001,141.669998,69620600,115.053042\n2007-01-05,141.330002,141.399994,140.380005,140.539993,76645300,114.135342\n2007-01-08,140.820007,141.410004,140.25,141.190002,71655000,114.663228\n2007-01-09,141.309998,141.600006,140.399994,141.070007,75680100,114.565777\n2007-01-10,140.580002,141.570007,140.300003,141.539993,72428000,114.947462\n2007-01-11,141.580002,142.619995,141.5,142.160004,54476800,115.450985\n2007-01-12,142.149994,143.240005,142.110001,143.240005,55370600,116.328076\n2007-01-16,143.070007,143.440002,142.729996,142.960007,44871300,116.100684\n2007-01-17,142.850006,143.460007,142.729996,143.020004,50241400,116.149409\n2007-01-18,143.169998,143.259995,142.309998,142.539993,68177300,115.759582\n2007-01-19,142.539993,143.100006,142.460007,142.820007,56973000,115.986987\n2007-01-22,143.070007,143.100006,141.929993,142.380005,60253600,115.629652\n2007-01-23,142.259995,143.080002,142.059998,142.800003,54064400,115.970741\n2007-01-24,142.970001,143.979996,142.910004,143.949997,55834700,116.904674\n2007-01-25,143.860001,143.919998,142.009995,142.259995,73583800,115.53219\n2007-01-26,142.570007,142.649994,141.580002,142.130005,67255600,115.426622\n2007-01-29,142.190002,142.800003,141.740005,142.050003,66114600,115.361651\n2007-01-30,142.350006,142.860001,142.059998,142.789993,70407600,115.962612\n2007-01-31,142.630005,144.130005,142.399994,143.75,91868600,116.742253\n2007-02-01,144.149994,144.660004,143.910004,144.610001,69312400,117.440677\n2007-02-02,144.729996,144.949997,144.380005,144.809998,49607000,117.603098\n2007-02-05,144.699997,144.940002,144.339996,144.850006,45705300,117.63559\n2007-02-06,144.970001,145.029999,144.330002,144.889999,57081300,117.668069\n2007-02-07,145.119995,145.360001,144.570007,145.210007,55669700,117.927954\n2007-02-08,144.779999,145.119995,144.270004,145.020004,70641000,117.773649\n2007-02-09,145.059998,145.330002,143.389999,143.940002,79084400,116.896558\n2007-02-12,143.940002,144.039993,143.190002,143.449997,65657000,116.498614\n2007-02-13,143.770004,144.899994,143.759995,144.660004,64081800,117.481285\n2007-02-14,144.800003,145.899994,144.779999,145.610001,66039400,118.252797\n2007-02-15,145.669998,145.949997,145.429993,145.800003,38715200,118.407101\n2007-02-16,145.440002,145.759995,145.229996,145.729996,39841800,118.350247\n2007-02-20,145.559998,146.199997,145.0,146.039993,56911800,118.602002\n2007-02-21,145.610001,146.070007,145.350006,145.979996,63971600,118.553277\n2007-02-22,146.050003,146.419998,145.169998,145.869995,79067400,118.463943\n2007-02-23,145.740005,145.789993,145.029999,145.300003,71966200,118.001041\n2007-02-26,145.830002,145.949997,144.75,145.169998,69192800,117.895462\n2007-02-27,143.880005,144.199997,139.0,139.5,274466500,113.290743\n2007-02-28,140.389999,141.979996,139.800003,140.929993,177536300,114.452069\n2007-03-01,139.339996,141.25,138.050003,140.509995,212828600,114.11098\n2007-03-02,140.050003,140.660004,138.660004,138.669998,162574000,112.616682\n2007-03-05,137.929993,139.580002,137.330002,137.350006,143750400,111.54469\n2007-03-06,138.779999,140.119995,137.720001,139.699997,143333300,113.453164\n2007-03-07,139.589996,140.460007,139.399994,139.559998,115144900,113.339468\n2007-03-08,140.539993,141.160004,140.070007,140.740005,117891600,114.297776\n2007-03-09,141.309998,141.419998,140.080002,140.779999,107765100,114.330255\n2007-03-12,140.419998,141.339996,140.160004,140.990005,80366900,114.500806\n2007-03-13,140.179993,140.770004,138.039993,138.25,190605200,112.275593\n2007-03-14,138.429993,139.360001,136.75,139.279999,231853800,113.112075\n2007-03-15,138.970001,139.990005,138.800003,139.470001,132435900,113.26638\n2007-03-16,139.309998,139.630005,138.119995,138.529999,121531600,112.949212\n2007-03-19,139.259995,140.330002,139.149994,140.199997,96161200,114.31083\n2007-03-20,140.080002,141.050003,139.960007,140.970001,82147400,114.938646\n2007-03-21,141.100006,143.649994,140.820007,143.289993,152368700,116.830231\n2007-03-22,143.479996,143.679993,142.789993,143.179993,118942200,116.740543\n2007-03-23,143.279999,143.809998,143.149994,143.389999,74416800,116.911771\n2007-03-26,143.5,143.649994,142.089996,143.199997,113787500,116.756854\n2007-03-27,143.119995,143.160004,142.389999,142.860001,99864600,116.479641\n2007-03-28,142.139999,142.470001,141.259995,141.820007,152907900,115.631691\n2007-03-29,142.539993,142.610001,141.190002,141.970001,139432700,115.753988\n2007-03-30,142.240005,142.839996,140.559998,142.0,128194100,115.778447\n2007-04-02,142.160004,142.460007,141.479996,142.160004,79416400,115.908904\n2007-04-03,142.970001,143.979996,142.910004,143.690002,82417800,117.156375\n2007-04-04,143.690002,143.949997,143.160004,143.850006,63995200,117.286833\n2007-04-05,143.669998,144.440002,143.610001,144.240005,46822800,117.604816\n2007-04-09,144.649994,144.800003,144.149994,144.440002,50967400,117.767881\n2007-04-10,144.330002,144.850006,144.270004,144.610001,56620000,117.906488\n2007-04-11,144.820007,144.860001,143.539993,144.020004,106365700,117.425439\n2007-04-12,143.740005,144.919998,143.339996,144.660004,115534400,117.947257\n2007-04-13,144.899994,145.320007,144.360001,145.320007,84287000,118.485385\n2007-04-16,145.830002,146.860001,145.820007,146.699997,83064600,119.610548\n2007-04-17,146.970001,147.399994,146.649994,147.089996,108424100,119.92853\n2007-04-18,146.600006,147.699997,146.570007,147.270004,88345300,120.075298\n2007-04-19,146.550003,147.399994,146.360001,147.229996,102947700,120.042678\n2007-04-20,148.220001,148.619995,147.039993,148.619995,124114100,121.176001\n2007-04-23,148.369995,148.729996,147.970001,148.059998,77270800,120.719412\n2007-04-24,148.229996,148.399994,147.320007,148.119995,114471000,120.768331\n2007-04-25,148.729996,149.660004,148.020004,149.479996,108418800,121.877195\n2007-04-26,149.490005,149.800003,149.100006,149.649994,88741600,122.015802\n2007-04-27,149.089996,149.740005,148.839996,149.529999,108191100,121.917965\n2007-04-30,149.639999,149.740005,148.210007,148.289993,100874100,120.906937\n2007-05-01,148.419998,149.470001,147.669998,148.669998,134342700,121.216771\n2007-05-02,148.899994,149.949997,148.75,149.539993,87129800,121.926114\n2007-05-03,149.970001,150.399994,149.729996,150.350006,86569700,122.586551\n2007-05-04,150.75,151.119995,150.220001,150.919998,96409000,123.051289\n2007-05-07,150.880005,151.199997,150.809998,150.949997,63461400,123.075748\n2007-05-08,150.580002,150.919998,150.130005,150.75,80584000,122.912682\n2007-05-09,150.639999,152.820007,150.369995,151.160004,102070100,123.246975\n2007-05-10,150.729996,151.020004,149.270004,149.580002,153617800,121.958734\n2007-05-11,149.75,150.929993,149.720001,150.860001,113408900,123.00237\n2007-05-14,150.860001,151.300003,149.789993,150.529999,108027500,122.733306\n2007-05-15,150.699997,151.660004,150.190002,150.570007,180673300,122.765927\n2007-05-16,150.800003,151.630005,150.380005,151.600006,114166500,123.605727\n2007-05-17,151.380005,151.960007,151.110001,151.300003,101132800,123.361122\n2007-05-18,152.009995,152.619995,151.809998,152.619995,99182000,124.437366\n2007-05-21,152.580002,153.229996,152.5,152.539993,174664600,124.372137\n2007-05-22,152.699997,153.160004,152.380005,152.419998,82148800,124.2743\n2007-05-23,152.949997,153.5,152.360001,152.440002,133786600,124.290611\n2007-05-24,152.529999,153.210007,150.740005,151.059998,187593000,123.165436\n2007-05-25,151.5,152.020004,151.179993,151.690002,83309200,123.679105\n2007-05-29,151.940002,152.5,151.449997,152.240005,82020000,124.127545\n2007-05-30,151.460007,153.539993,151.339996,153.479996,129013600,125.13856\n2007-05-31,153.669998,153.889999,153.119995,153.320007,114866700,125.008115\n2007-06-01,153.880005,154.399994,153.509995,154.080002,107771700,125.62777\n2007-06-04,153.539993,154.389999,153.479996,154.100006,78008800,125.64408\n2007-06-05,153.740005,153.899994,152.860001,153.490005,126917900,125.146721\n2007-06-06,152.860001,152.949997,151.75,151.839996,164096800,123.801401\n2007-06-07,151.559998,152.5,149.059998,149.100006,232414600,121.567374\n2007-06-08,149.580002,151.190002,149.089996,151.039993,175886000,123.149126\n2007-06-11,150.929993,151.949997,150.699997,151.300003,102015600,123.361122\n2007-06-12,150.669998,151.539993,149.550003,149.649994,233898000,122.015802\n2007-06-13,150.5,152.070007,149.720001,151.889999,193208200,123.842171\n2007-06-14,152.059998,153.119995,152.029999,152.860001,146396500,124.633052\n2007-06-15,153.139999,153.660004,152.929993,153.070007,154030800,125.342185\n2007-06-18,153.380005,153.389999,152.660004,152.889999,88537500,125.194784\n2007-06-19,152.550003,153.380005,152.360001,153.270004,110851700,125.505953\n2007-06-20,153.580002,153.580002,150.960007,151.139999,177119700,123.761787\n2007-06-21,151.080002,152.110001,150.25,151.979996,205262000,124.449623\n2007-06-22,151.520004,151.770004,149.850006,150.550003,204964700,123.278666\n2007-06-25,150.240005,151.25,149.020004,149.830002,232014400,122.689089\n2007-06-26,150.210007,150.460007,148.279999,148.289993,198445700,121.428045\n2007-06-27,148.130005,150.570007,148.059998,150.399994,213638000,123.15583\n2007-06-28,150.380005,151.410004,149.669998,150.380005,157705000,123.139462\n2007-06-29,150.899994,151.649994,149.149994,150.429993,199701800,123.180395\n2007-07-02,150.869995,151.919998,150.770004,151.789993,103357000,124.294038\n2007-07-03,152.179993,152.5,151.990005,152.339996,54048400,124.744411\n2007-07-05,152.399994,152.559998,151.630005,152.179993,89279000,124.613391\n2007-07-06,152.380005,153.169998,151.929993,152.979996,81109000,125.268478\n2007-07-09,153.160004,153.360001,152.619995,153.100006,72348100,125.366749\n2007-07-10,152.289993,152.610001,150.770004,150.919998,180362600,123.581638\n2007-07-11,150.75,152.050003,150.520004,151.990005,175607600,124.457819\n2007-07-12,152.369995,154.75,152.339996,154.389999,133882500,126.423067\n2007-07-13,154.570007,155.460007,154.389999,154.850006,111794300,126.799746\n2007-07-16,154.990005,155.529999,154.580002,154.830002,98378700,126.783365\n2007-07-17,154.929993,155.479996,154.679993,154.75,126201300,126.717855\n2007-07-18,154.229996,154.800003,153.300003,154.470001,237887400,126.488577\n2007-07-19,155.199997,155.529999,154.75,155.070007,145212700,126.979895\n2007-07-20,154.889999,154.990005,152.830002,153.5,245502500,125.694286\n2007-07-23,154.179993,154.720001,153.300003,153.970001,121183900,126.079149\n2007-07-24,153.119995,154.279999,150.759995,151.300003,256732400,123.892807\n2007-07-25,152.020004,152.389999,150.25,151.610001,265214500,124.14665\n2007-07-26,150.190002,150.800003,146.389999,148.020004,467592500,121.206963\n2007-07-27,148.210007,148.869995,145.050003,145.110001,422987600,118.824091\n2007-07-30,145.929993,147.809998,145.289993,147.380005,283017500,120.682896\n2007-07-31,148.330002,149.460007,145.039993,145.720001,316976700,119.323593\n2007-08-01,145.179993,147.009995,143.949997,146.429993,467670000,119.904973\n2007-08-02,146.759995,147.759995,145.259995,147.600006,294758400,120.863045\n2007-08-03,147.279999,147.580002,143.199997,143.800003,359398200,117.751393\n2007-08-06,144.210007,146.830002,142.529999,146.210007,324980000,119.724837\n2007-08-07,145.940002,149.0,145.229996,147.770004,232568700,121.002249\n2007-08-08,148.410004,150.589996,147.339996,149.830002,274930600,122.689089\n2007-08-09,147.429993,148.949997,145.289993,145.389999,357622100,119.053369\n2007-08-10,144.389999,146.5,143.119995,144.710007,411018400,118.496554\n2007-08-13,146.5,146.889999,145.020004,145.229996,181917200,118.92235\n2007-08-14,145.699997,146.059998,142.720001,143.009995,264134500,117.10449\n2007-08-15,142.720001,144.460007,140.619995,141.039993,323834000,115.491344\n2007-08-16,139.789993,142.940002,137.0,142.100006,546743700,116.359341\n2007-08-17,145.5,145.809998,141.389999,144.710007,388218100,118.496554\n2007-08-20,145.169998,145.470001,143.289993,144.639999,187320400,118.439228\n2007-08-21,144.600006,145.970001,144.139999,144.929993,157066400,118.676691\n2007-08-22,146.009995,146.800003,145.330002,146.649994,173156700,120.085123\n2007-08-23,147.339996,147.649994,145.610001,146.520004,203915300,119.97868\n2007-08-24,146.479996,148.330002,146.279999,148.330002,128901900,121.460806\n2007-08-27,147.850006,148.330002,146.729996,146.949997,113024300,120.330782\n2007-08-28,146.160004,146.25,143.460007,143.720001,219790700,117.685883\n2007-08-29,144.369995,146.740005,143.960007,146.539993,207654200,119.995048\n2007-08-30,145.449997,147.190002,145.309998,146.149994,191817300,119.675695\n2007-08-31,147.649994,148.5,146.830002,147.589996,185477500,120.854849\n2007-09-04,147.449997,149.979996,147.399994,149.080002,120062000,122.074947\n2007-09-05,148.199997,148.360001,147.0,147.789993,166261800,121.018617\n2007-09-06,147.949997,148.610001,147.119995,148.130005,127878400,121.297037\n2007-09-07,146.479996,146.889999,145.259995,146.070007,235447600,119.610198\n2007-09-10,146.520004,146.720001,144.330002,145.789993,192305900,119.380907\n2007-09-11,146.240005,147.699997,146.130005,147.490005,162081900,120.772971\n2007-09-12,147.289993,148.440002,146.979996,147.869995,149554600,121.084127\n2007-09-13,148.550003,149.449997,148.199997,148.910004,154079000,121.935744\n2007-09-14,147.960007,149.089996,147.740005,148.899994,121911000,121.927547\n2007-09-17,148.309998,148.649994,147.630005,148.100006,109870800,121.272473\n2007-09-18,148.830002,152.5,148.130005,152.460007,263759500,124.842682\n2007-09-19,153.410004,154.389999,152.710007,153.360001,193779900,125.579647\n2007-09-20,153.339996,153.429993,152.110001,152.279999,175186800,124.695282\n2007-09-21,152.710007,153.119995,151.740005,151.970001,141457500,125.031793\n2007-09-24,152.419998,152.820007,151.360001,151.690002,139450200,124.801426\n2007-09-25,150.809998,151.660004,150.470001,151.389999,142289900,124.554602\n2007-09-26,152.25,152.770004,151.389999,152.190002,135547000,125.212796\n2007-09-27,152.910004,153.100006,152.190002,153.089996,102713300,125.953257\n2007-09-28,152.850006,153.190002,151.979996,152.580002,133372100,125.533664\n2007-10-01,152.600006,154.75,152.5,154.300003,148162300,126.948778\n2007-10-02,154.610001,154.649994,153.809998,154.089996,112978800,126.775997\n2007-10-03,153.809998,154.410004,153.009995,153.779999,119055900,126.52095\n2007-10-04,154.110001,154.259995,153.589996,154.020004,76864400,126.718412\n2007-10-05,155.029999,156.100006,154.630005,155.850006,134579700,128.224028\n2007-10-08,155.389999,155.490005,154.770004,155.020004,71280400,127.541152\n2007-10-09,155.600006,156.5,155.029999,156.479996,94054300,128.742345\n2007-10-10,156.039993,156.440002,155.410004,156.220001,101711100,128.528437\n2007-10-11,156.929993,157.520004,154.539993,155.470001,233529100,127.911382\n2007-10-12,155.460007,156.350006,155.270004,156.330002,124546700,128.618939\n2007-10-15,156.270004,156.360001,153.940002,155.009995,161151900,127.532916\n2007-10-16,154.410004,154.520004,153.470001,153.779999,166525700,126.52095\n2007-10-17,154.979996,155.089996,152.470001,154.25,216687300,126.907639\n2007-10-18,153.449997,154.190002,153.080002,153.690002,148367500,126.446906\n2007-10-19,153.089996,156.479996,149.660004,149.669998,297169900,123.139488\n2007-10-22,148.860001,150.759995,148.660004,150.539993,261989800,123.855268\n2007-10-23,151.460007,151.949997,150.25,151.759995,180085100,124.859012\n2007-10-24,151.210007,151.740005,148.839996,151.479996,326694200,124.628645\n2007-10-25,151.649994,152.289993,149.880005,151.839996,237374500,124.924832\n2007-10-26,153.059998,153.619995,151.899994,153.619995,176484000,126.389308\n2007-10-29,153.929993,154.440002,153.550003,154.130005,106841000,126.808914\n2007-10-30,153.449997,153.75,152.869995,153.059998,132981600,125.928576\n2007-10-31,153.979996,155.270004,152.839996,154.649994,220954400,127.23673\n2007-11-01,153.289993,153.410004,150.589996,151.029999,333040800,124.258415\n2007-11-02,151.529999,152.0,149.210007,151.199997,331228200,124.398279\n2007-11-05,149.639999,151.160004,148.970001,150.050003,226841000,123.452133\n2007-11-06,150.860001,152.110001,149.899994,152.070007,177800500,125.114072\n2007-11-07,150.440002,151.130005,147.550003,147.910004,306639700,121.69147\n2007-11-08,147.990005,148.399994,145.070007,147.160004,374509900,121.074415\n2007-11-09,145.690002,147.539993,144.889999,145.139999,277745100,119.412477\n2007-11-12,145.210007,146.610001,143.699997,143.699997,243087800,118.22773\n2007-11-13,145.369995,148.309998,145.220001,148.080002,191117400,121.831335\n2007-11-14,149.220001,149.399994,146.779999,147.669998,230558800,121.494008\n2007-11-15,146.570007,147.490005,144.520004,145.539993,263111100,119.741568\n2007-11-16,146.309998,146.470001,144.570007,145.789993,308770600,119.947253\n2007-11-19,145.279999,145.360001,143.190002,143.759995,267746000,118.277092\n2007-11-20,144.020004,145.529999,142.110001,144.639999,414767500,119.001107\n2007-11-21,143.080002,143.910004,141.669998,141.679993,259012400,116.565791\n2007-11-23,143.070007,144.339996,142.699997,144.130005,77688400,118.581514\n2007-11-26,144.429993,144.880005,140.660004,140.949997,214232000,115.965195\n2007-11-27,141.740005,143.229996,140.949997,142.570007,293897900,117.298042\n2007-11-28,144.190002,147.470001,144.139999,147.130005,258596900,121.049734\n2007-11-29,146.619995,147.720001,146.100006,147.179993,199409900,121.090861\n2007-11-30,149.039993,149.869995,147.330002,148.660004,222908000,122.308525\n2007-12-03,148.190002,148.449997,147.289993,147.679993,146430400,121.502231\n2007-12-04,146.660004,147.539993,146.309998,146.360001,136533900,120.416221\n2007-12-05,147.929993,149.199997,147.830002,148.809998,171130000,122.431931\n2007-12-06,148.630005,151.210007,148.570007,150.940002,154457400,124.184371\n2007-12-07,151.419998,151.5,150.550003,150.910004,148980100,124.15969\n2007-12-10,151.279999,152.25,150.860001,152.080002,123914300,125.122294\n2007-12-11,152.139999,152.889999,147.830002,147.910004,250346400,121.69147\n2007-12-12,151.059998,151.770004,147.199997,149.369995,322435600,122.892664\n2007-12-13,148.320007,149.389999,147.300003,149.059998,237551300,122.637616\n2007-12-14,147.929993,149.100006,147.100006,147.169998,159152900,121.082638\n2007-12-17,146.610001,146.869995,144.860001,145.070007,177269400,119.354892\n2007-12-18,146.100006,146.479996,143.960007,145.880005,245569300,120.021309\n2007-12-19,145.940002,146.889999,144.940002,145.880005,198917200,120.021309\n2007-12-20,146.839996,146.860001,145.179993,146.800003,214813800,120.778228\n2007-12-21,147.369995,148.419998,147.089996,148.130005,146084400,122.519286\n2007-12-24,148.820007,149.479996,148.479996,149.229996,45601400,123.429096\n2007-12-26,148.649994,149.679993,148.5,149.550003,67093100,123.693776\n2007-12-27,149.020004,149.029999,147.320007,147.669998,122981700,122.138812\n2007-12-28,148.539993,148.610001,146.899994,147.300003,116398100,121.832786\n2007-12-31,147.100006,147.610001,146.059998,146.210007,108126800,120.931243\n2008-01-02,146.529999,146.990005,143.880005,144.929993,204935600,119.872535\n2008-01-03,144.910004,145.490005,144.070007,144.860001,125133300,119.814645\n2008-01-04,143.339996,143.440002,140.910004,141.309998,232330900,116.878414\n2008-01-07,141.809998,142.229996,140.100006,141.190002,234991000,116.779165\n2008-01-08,142.080002,142.899994,138.440002,138.910004,326365700,114.893364\n2008-01-09,139.089996,140.789993,137.699997,140.369995,301824900,116.100932\n2008-01-10,139.679993,142.800003,139.369995,141.289993,335701200,116.861868\n2008-01-11,140.779999,141.899994,139.0,140.149994,267076600,115.918968\n2008-01-14,141.160004,141.860001,140.399994,141.279999,170365500,116.853602\n2008-01-15,139.789993,141.490005,137.899994,138.169998,239940100,114.2813\n2008-01-16,137.360001,139.119995,136.279999,136.979996,378802600,113.297042\n2008-01-17,137.809998,137.880005,132.929993,133.429993,397892600,110.360811\n2008-01-18,134.740005,135.020004,131.100006,132.059998,348561500,109.227679\n2008-01-22,127.209999,132.429993,126.0,130.720001,435923700,108.11936\n2008-01-23,127.089996,134.190002,126.839996,133.860001,511913000,110.716473\n2008-01-24,134.479996,135.460007,133.309998,134.990005,259949300,111.651108\n2008-01-25,136.509995,136.759995,132.600006,133.039993,269603900,110.03824\n2008-01-28,133.259995,135.520004,132.059998,135.240005,217934600,111.857884\n2008-01-29,136.100006,136.449997,134.880005,135.910004,168968300,112.412044\n2008-01-30,135.580002,138.539993,134.600006,134.910004,334939200,111.584938\n2008-01-31,133.399994,138.539993,133.199997,137.369995,343680800,113.619613\n2008-02-01,137.940002,139.610001,137.520004,139.580002,206843600,115.447523\n2008-02-04,139.210007,139.300003,137.639999,137.820007,124694300,113.991821\n2008-02-05,135.940002,136.25,133.669998,134.130005,286882500,110.939796\n2008-02-06,134.580002,135.25,132.410004,133.050003,250792900,110.046519\n2008-02-07,131.800003,134.789993,131.729996,133.929993,297368100,110.774364\n2008-02-08,133.089996,134.220001,132.100006,133.070007,221643500,110.063065\n2008-02-11,133.100006,134.229996,132.039993,133.75,188576300,110.625491\n2008-02-12,134.910004,136.309998,133.979996,134.990005,256654400,111.651108\n2008-02-13,136.009995,137.059998,135.139999,136.369995,181967800,112.792506\n2008-02-14,136.949997,137.0,134.789993,135.169998,215207200,111.799981\n2008-02-15,134.5,135.220001,133.910004,135.139999,154110300,111.775169\n2008-02-19,136.720001,136.889999,134.610001,135.520004,145190000,112.089473\n2008-02-20,133.990005,136.550003,133.759995,135.919998,220085700,112.420311\n2008-02-21,136.660004,137.009995,134.070007,134.789993,201051200,111.485676\n2008-02-22,134.970001,135.850006,132.860001,135.619995,205491000,112.172176\n2008-02-25,135.539993,137.649994,134.779999,137.330002,190107000,113.586534\n2008-02-26,136.75,138.949997,136.5,138.360001,212420700,114.438453\n2008-02-27,137.559998,139.139999,137.410004,138.220001,168395800,114.322658\n2008-02-28,137.240005,137.960007,136.550003,136.869995,170831100,113.206059\n2008-02-29,135.600006,135.679993,132.779999,133.820007,252715200,110.683395\n2008-03-03,133.139999,133.809998,132.240005,133.5,189483500,110.418715\n2008-03-04,132.229996,133.399994,130.990005,132.990005,282513100,109.996895\n2008-03-05,133.419998,134.770004,132.339996,133.830002,270681400,110.691661\n2008-03-06,132.979996,133.220001,130.550003,131.059998,247911700,108.400573\n2008-03-07,129.770004,131.740005,128.580002,129.710007,326434600,107.283987\n2008-03-10,129.839996,129.929993,127.589996,128.0,235683600,105.869629\n2008-03-11,130.720001,132.720001,128.949997,132.600006,341440600,109.674324\n2008-03-12,132.740005,133.770004,131.160004,131.360001,229161100,108.648707\n2008-03-13,129.610001,132.639999,128.600006,131.649994,351504200,108.888562\n2008-03-14,132.770004,132.809998,127.779999,129.610001,484687800,107.201271\n2008-03-17,126.57,129.259995,126.07,128.300003,405311100,106.117763\n2008-03-18,130.619995,133.690002,129.979996,133.630005,334416600,110.526242\n2008-03-19,134.139999,134.649994,130.039993,130.320007,345971600,107.788522\n2008-03-20,130.050003,132.910004,129.259995,132.080002,245320700,109.785063\n2008-03-24,133.309998,135.809998,133.240005,134.720001,208977300,111.979434\n2008-03-25,134.860001,135.550003,133.770004,134.850006,192947200,112.087494\n2008-03-26,134.460007,135.089996,133.110001,133.199997,196934300,110.716005\n2008-03-27,134.199997,134.440002,132.360001,132.779999,225153200,110.366902\n2008-03-28,132.990005,133.360001,131.059998,131.509995,180896100,109.311273\n2008-03-31,131.289993,132.729996,131.089996,131.970001,166692100,109.693631\n2008-04-01,133.610001,136.839996,133.509995,136.610001,254547300,113.550404\n2008-04-02,137.050003,137.669998,135.979996,136.699997,210910800,113.625209\n2008-04-03,135.960007,137.440002,135.710007,137.039993,175884800,113.907814\n2008-04-04,137.119995,137.960007,136.119995,136.889999,204446800,113.783139\n2008-04-07,137.869995,138.570007,136.740005,136.960007,154245500,113.84133\n2008-04-08,136.190002,136.919998,135.949997,136.820007,148937300,113.724962\n2008-04-09,136.610001,136.800003,134.889999,135.830002,195610600,112.902068\n2008-04-10,135.419998,136.669998,134.899994,136.020004,192967800,113.059998\n2008-04-11,134.490005,135.119995,133.009995,133.380005,222973300,110.865627\n2008-04-14,133.190002,133.539993,132.550003,132.929993,160522000,110.491577\n2008-04-15,133.580002,133.690002,132.330002,133.240005,172389200,110.74926\n2008-04-16,134.539993,136.910004,134.520004,136.850006,189268900,113.749897\n2008-04-17,136.020004,137.25,135.660004,137.050003,179665700,113.916135\n2008-04-18,138.940002,139.559998,138.259995,138.479996,218530600,115.104746\n2008-04-21,138.229996,138.979996,137.850006,138.550003,118587400,115.162936\n2008-04-22,138.190002,138.309998,136.899994,137.940002,162166000,114.655903\n2008-04-23,138.089996,138.779999,137.119995,137.720001,193309000,114.473038\n2008-04-24,138.080002,139.740005,137.039993,138.320007,229381300,114.971764\n2008-04-25,139.399994,139.889999,137.910004,139.600006,190788100,116.0357\n2008-04-28,139.880005,140.25,139.380005,139.630005,105610200,116.060635\n2008-04-29,139.389999,139.729996,138.610001,139.080002,125514100,115.603472\n2008-04-30,139.289993,140.589996,138.259995,138.259995,208395900,114.921881\n2008-05-01,138.380005,141.119995,138.270004,141.119995,187279500,117.299117\n2008-05-02,142.339996,142.369995,140.559998,141.509995,181585500,117.623285\n2008-05-05,141.050003,141.610001,140.410004,140.830002,118504500,117.058074\n2008-05-06,140.020004,142.199997,139.690002,142.050003,179339800,118.072141\n2008-05-07,141.889999,142.039993,139.130005,139.520004,199267300,115.969203\n2008-05-08,139.740005,140.320007,138.979996,139.160004,178321200,115.66997\n2008-05-09,138.600006,139.389999,138.449997,138.899994,152588200,115.453849\n2008-05-12,139.25,140.559998,138.729996,140.460007,147865900,116.750534\n2008-05-13,140.800003,140.889999,139.729996,140.479996,159132200,116.767149\n2008-05-14,141.070007,142.199997,140.460007,140.770004,181910800,117.008204\n2008-05-15,141.039993,142.630005,140.830002,142.529999,166927000,118.471114\n2008-05-16,142.860001,142.869995,141.610001,142.660004,204236800,118.579174\n2008-05-19,142.809998,144.300003,142.300003,143.050003,165664400,118.903342\n2008-05-20,142.270004,142.339996,141.0,141.889999,178552100,117.939146\n2008-05-21,141.809998,142.119995,139.0,139.490005,252724800,115.944268\n2008-05-22,139.429993,140.169998,139.0,139.509995,170820400,115.960883\n2008-05-23,139.050003,139.660004,137.520004,137.639999,181376400,114.40654\n2008-05-27,137.800003,139.0,137.529999,138.660004,168322900,115.254369\n2008-05-28,139.169998,140.0,138.0,139.300003,181288100,115.786337\n2008-05-29,139.130005,140.929993,139.080002,140.0,173927200,116.368176\n2008-05-30,140.470001,140.740005,139.940002,140.350006,117362000,116.659101\n2008-06-02,139.830002,139.860001,138.0,138.899994,181069900,115.453849\n2008-06-03,139.300003,139.619995,137.229996,138.089996,271965700,114.780578\n2008-06-04,137.699997,139.160004,137.460007,138.020004,246637700,114.722401\n2008-06-05,138.580002,140.889999,138.320007,140.779999,237867100,117.016512\n2008-06-06,139.550003,139.800003,136.220001,136.289993,384276300,113.284414\n2008-06-09,136.860001,137.5,135.410004,136.619995,228263900,113.558711\n2008-06-10,135.669998,137.100006,135.350006,135.940002,260234900,112.993501\n2008-06-11,135.970001,136.259995,133.929993,133.940002,283890100,111.331098\n2008-06-12,134.600006,135.869995,133.520004,134.449997,252791800,111.755006\n2008-06-13,135.169998,136.520004,134.419998,136.149994,244726900,113.168046\n2008-06-16,135.550003,136.929993,135.460007,136.229996,185832500,113.234543\n2008-06-17,137.070007,137.119995,135.369995,135.570007,191707700,112.68596\n2008-06-18,134.690002,135.520004,133.710007,134.25,265893000,111.588769\n2008-06-19,134.149994,135.240005,133.5,134.419998,304204900,111.730071\n2008-06-20,132.839996,133.089996,131.220001,131.580002,289275700,109.916512\n2008-06-23,132.089996,132.229996,131.320007,131.449997,165096400,109.807912\n2008-06-24,131.050003,132.440002,130.190002,131.190002,267300600,109.590723\n2008-06-25,131.720001,133.399994,131.240005,131.809998,287853900,110.108641\n2008-06-26,130.570007,131.419998,128.080002,128.229996,297775000,107.118055\n2008-06-27,128.279999,128.860001,127.040001,127.529999,303423400,106.533307\n2008-06-30,127.889999,128.910004,127.300003,127.980003,258842600,106.909222\n2008-07-01,126.519997,128.470001,125.93,128.380005,388622000,107.243367\n2008-07-02,128.789993,129.160004,125.949997,126.18,288064600,105.405573\n2008-07-03,127.110001,127.110001,124.989998,126.309998,239352500,105.514168\n2008-07-07,126.790001,127.339996,123.919998,125.019997,372427300,104.436554\n2008-07-08,124.989998,127.389999,124.199997,127.239998,375973700,106.291052\n2008-07-09,127.5,127.739998,124.389999,124.790001,336729400,104.244425\n2008-07-10,124.43,125.790001,123.580002,125.300003,436475700,104.67046\n2008-07-11,123.970001,125.900002,122.489998,123.839996,481124600,103.450831\n2008-07-14,125.260002,125.5,122.400002,122.720001,322720800,102.515233\n2008-07-15,121.800003,123.489998,120.019997,120.989998,502502500,101.070059\n2008-07-16,121.449997,124.57,121.099998,123.959999,371642900,103.551076\n2008-07-17,125.139999,126.260002,124.089996,125.199997,375490600,104.586919\n2008-07-18,126.169998,126.419998,125.150002,125.980003,267030100,105.238504\n2008-07-21,126.510002,126.800003,125.190002,126.050003,222863000,105.296979\n2008-07-22,125.150002,127.800003,124.849998,127.480003,296904200,106.491542\n2008-07-23,127.889999,129.149994,127.550003,128.169998,311698400,107.067936\n2008-07-24,128.339996,128.410004,125.160004,125.510002,248634500,104.845885\n2008-07-25,125.889999,126.489998,125.169998,125.480003,219131000,104.820825\n2008-07-28,125.510002,126.059998,123.419998,123.639999,205201300,103.283761\n2008-07-29,123.980003,126.379997,123.639999,126.279999,261505600,105.489108\n2008-07-30,127.110001,128.600006,126.279999,128.529999,354710000,107.368665\n2008-07-31,127.400002,128.570007,126.629997,126.830002,277402100,105.948558\n2008-08-01,127.120003,127.279999,125.459999,126.160004,248690900,105.388869\n2008-08-04,126.040001,126.139999,124.760002,124.989998,188239600,104.411494\n2008-08-05,126.019997,128.559998,124.970001,128.360001,251577600,107.226656\n2008-08-06,128.020004,129.300003,127.480003,128.929993,209555400,107.702804\n2008-08-07,127.959999,128.440002,126.540001,127.010002,246312500,106.098923\n2008-08-08,126.580002,129.929993,126.379997,129.369995,260811700,108.070364\n2008-08-11,129.470001,131.509995,129.229996,130.710007,249425800,109.189754\n2008-08-12,130.279999,130.699997,128.729996,129.350006,213200800,108.053666\n2008-08-13,128.789993,129.649994,127.669998,128.570007,256393200,107.402087\n2008-08-14,127.839996,130.279999,127.75,129.539993,239555300,108.212373\n2008-08-15,129.929993,130.5,129.300003,130.169998,181000800,108.738653\n2008-08-18,130.429993,130.479996,127.660004,128.389999,172275100,107.251716\n2008-08-19,127.419998,127.690002,126.529999,126.989998,194673700,106.082212\n2008-08-20,127.389999,127.949997,126.339996,127.580002,225498200,106.575077\n2008-08-21,126.75,128.440002,126.599998,127.800003,180609800,106.758857\n2008-08-22,128.669998,129.649994,127.800003,129.649994,167715300,108.304263\n2008-08-25,128.800003,129.649994,126.75,127.019997,171936900,106.107272\n2008-08-26,127.019997,127.870003,126.580002,127.389999,159117200,106.416357\n2008-08-27,127.550003,128.830002,127.300003,128.630005,171032800,107.452206\n2008-08-28,129.279999,130.339996,129.110001,130.190002,167537100,108.755364\n2008-08-29,129.729996,130.139999,128.509995,128.789993,189195800,107.585854\n2008-09-02,130.029999,130.710007,127.519997,127.989998,252364900,106.917571\n2008-09-03,127.879997,128.5,126.93,127.879997,251947000,106.825681\n2008-09-04,126.970001,127.230003,123.959999,124.029999,340042500,103.609551\n2008-09-05,123.290001,124.949997,122.0,124.419998,289503400,103.93534\n2008-09-08,128.039993,128.240005,124.419998,126.989998,364075300,106.082212\n2008-09-09,127.099998,127.360001,122.800003,123.220001,377326800,102.932912\n2008-09-10,123.889999,124.900002,122.550003,123.720001,298916600,103.350592\n2008-09-11,122.120003,125.739998,121.599998,125.510002,375369400,104.845885\n2008-09-12,124.290001,126.209999,123.830002,126.089996,297851200,105.330388\n2008-09-15,121.629997,125.650002,119.889999,120.089996,483607000,100.318235\n2008-09-16,117.199997,122.32,117.0,122.099998,581744300,101.997308\n2008-09-17,119.639999,121.849998,116.0,116.610001,624095600,97.41119\n2008-09-18,118.050003,121.790001,113.800003,120.07,776114700,100.301531\n2008-09-19,126.699997,128.0,123.330002,124.120003,501087800,104.284887\n2008-09-22,124.449997,124.75,120.360001,121.309998,249966500,101.923938\n2008-09-23,120.849998,122.019997,118.279999,118.550003,327470400,99.605007\n2008-09-24,119.349998,120.0,117.790001,118.93,311818400,99.924278\n2008-09-25,119.400002,121.910004,118.440002,120.790001,328253000,101.48704\n2008-09-26,118.830002,121.5,118.510002,120.849998,285917400,101.537449\n2008-09-29,119.139999,119.339996,110.970001,111.379997,459562300,93.580811\n2008-09-30,113.510002,116.800003,110.529999,115.989998,328154400,97.454106\n2008-10-01,115.269997,116.690002,113.949997,116.059998,332783000,97.512919\n2008-10-02,114.949997,115.110001,111.059998,111.849998,365337800,93.975703\n2008-10-03,112.860001,115.449997,109.68,110.339996,461798000,92.707008\n2008-10-06,107.150002,107.620003,100.639999,104.720001,610637500,87.985122\n2008-10-07,106.839996,107.330002,99.650002,100.029999,540012100,84.044609\n2008-10-08,97.519997,102.18,96.809998,97.510002,725414800,81.927323\n2008-10-09,99.660004,100.620003,90.25,90.699997,534485200,76.205597\n2008-10-10,86.760002,93.940002,83.580002,88.5,871026300,74.357173\n2008-10-13,93.870003,101.349998,89.949997,101.349998,455584000,85.153665\n2008-10-14,104.699997,105.529999,97.110001,99.849998,546268300,83.893374\n2008-10-15,97.459999,97.800003,89.709999,90.019997,484627500,75.634265\n2008-10-16,91.290001,94.769997,86.540001,93.769997,708811200,78.784993\n2008-10-17,91.989998,98.589996,91.650002,93.209999,476649000,78.314486\n2008-10-20,95.349998,99.099998,94.089996,98.809998,321294200,83.019572\n2008-10-21,96.970001,98.639999,95.220001,95.860001,356502000,80.541002\n2008-10-22,93.199997,95.860001,87.529999,90.639999,516168000,76.155188\n2008-10-23,90.290001,92.449997,85.809998,91.690002,634666400,77.037394\n2008-10-24,84.059998,89.919998,84.0,87.040001,545812600,73.130491\n2008-10-27,85.970001,89.510002,83.699997,83.949997,397288600,70.534288\n2008-10-28,87.339996,94.239998,84.529999,93.760002,639939500,78.776596\n2008-10-29,93.769997,97.169998,92.099998,93.080002,531270100,78.205263\n2008-10-30,95.779999,96.540001,92.900002,96.300003,414582100,80.910689\n2008-10-31,95.080002,98.57,94.480003,96.830002,411394000,81.355991\n2008-11-03,96.779999,97.690002,95.949997,97.110001,205419400,81.591244\n2008-11-04,99.059998,100.860001,96.709999,100.410004,346793400,84.363887\n2008-11-05,99.199997,100.709999,95.0,96.190002,387844100,80.818267\n2008-11-06,94.459999,95.440002,90.059998,90.860001,477721900,76.340032\n2008-11-07,91.650002,94.0,90.5,93.860001,380391000,78.860614\n2008-11-10,95.209999,95.529999,90.919998,92.629997,301773000,77.827172\n2008-11-11,90.760002,92.139999,88.650002,89.769997,418498200,75.424217\n2008-11-12,88.230003,88.949997,85.120003,85.82,454330600,72.105453\n2008-11-13,86.129997,91.730003,82.089996,91.169998,753141900,76.60049\n2008-11-14,89.410004,92.059998,86.519997,86.620003,540352300,72.777611\n2008-11-17,86.379997,88.559998,85.160004,85.470001,415254900,71.811386\n2008-11-18,85.150002,87.220001,82.910004,87.080002,523811800,73.164099\n2008-11-19,85.910004,86.870003,80.919998,81.5,558327600,68.475815\n2008-11-20,80.129997,82.510002,75.050003,75.449997,814180400,63.392638\n2008-11-21,77.459999,80.900002,74.339996,79.519997,718536500,66.812228\n2008-11-24,81.919998,86.989998,80.360001,85.029999,523305300,71.441699\n2008-11-25,87.300003,87.510002,83.82,85.660004,454112400,71.971025\n2008-11-26,84.300003,89.190002,84.239998,88.970001,370134200,74.752065\n2008-11-28,88.629997,90.129997,88.480003,90.089996,118308100,75.693079\n2008-12-01,87.510002,87.550003,81.860001,82.110001,369927100,68.988334\n2008-12-02,83.470001,85.489998,82.040001,85.269997,469508400,71.643344\n2008-12-03,83.400002,87.830002,83.139999,87.32,519863500,73.365744\n2008-12-04,86.059998,88.050003,83.739998,85.300003,444173800,71.668555\n2008-12-05,83.650002,88.419998,82.239998,87.93,471905300,73.878263\n2008-12-08,90.339996,92.379997,88.080002,91.0,412859300,76.457658\n2008-12-09,90.370003,92.129997,88.980003,89.5,370790000,75.197367\n2008-12-10,90.32,91.360001,89.0,90.110001,396187400,75.709886\n2008-12-11,89.540001,91.0,87.370003,87.940002,365061000,73.886667\n2008-12-12,85.550003,89.07,85.199997,88.989998,415060400,74.768866\n2008-12-15,89.019997,89.150002,86.290001,87.75,256694200,73.727028\n2008-12-16,91.879997,92.019997,88.18,91.879997,377699500,77.197027\n2008-12-17,90.839996,92.43,90.059998,90.989998,281819800,76.449254\n2008-12-18,91.400002,91.669998,88.209999,89.290001,374673300,75.020927\n2008-12-19,89.099998,90.620003,88.089996,88.190002,301451300,74.69822\n2008-12-22,88.580002,88.669998,85.489998,87.059998,243759500,73.74109\n2008-12-23,87.529999,87.93,85.800003,86.160004,221625200,72.978782\n2008-12-24,86.449997,86.870003,86.0,86.660004,62061600,73.402289\n2008-12-26,87.239998,87.300003,86.5,87.160004,74767700,73.825796\n2008-12-29,87.239998,87.330002,85.599998,86.910004,127795900,73.614043\n2008-12-30,87.510002,89.050003,86.879997,88.970001,168256300,75.358891\n2008-12-31,89.080002,90.970001,88.870003,90.239998,193987200,76.434596\n2009-01-02,90.440002,93.440002,89.849998,92.959999,227566300,78.738477\n2009-01-05,92.629997,93.660004,91.889999,92.849998,240349700,78.645305\n2009-01-06,93.639999,94.449997,92.68,93.470001,328260900,79.170456\n2009-01-07,92.0,92.260002,90.199997,90.669998,280899200,76.798813\n2009-01-08,90.160004,91.089996,89.669998,91.040001,263834400,77.112211\n2009-01-09,91.160004,91.32,85.360001,89.089996,330953600,75.460528\n2009-01-12,88.839996,88.910004,86.410004,86.949997,277858500,73.647917\n2009-01-13,86.730003,87.879997,86.199997,87.110001,356432300,73.783443\n2009-01-14,85.540001,85.75,83.160004,84.370003,435491600,71.462625\n2009-01-15,84.120003,85.25,81.720001,84.400002,532647300,71.488034\n2009-01-16,85.860001,85.989998,83.050003,85.059998,399237200,72.04706\n2009-01-20,84.230003,85.059998,80.050003,80.57,419855200,68.243967\n2009-01-21,81.940002,84.239998,80.470001,84.050003,364360700,71.19158\n2009-01-22,82.419998,84.040001,81.169998,82.75,427940300,70.090459\n2009-01-23,80.900002,83.989998,80.57,83.110001,386800600,70.395384\n2009-01-26,83.589996,85.360001,82.809998,83.68,317978800,70.878182\n2009-01-27,84.129997,85.150002,83.300003,84.529999,273789700,71.598144\n2009-01-28,86.400002,87.949997,86.07,87.389999,330007000,74.020606\n2009-01-29,86.110001,87.489998,84.470001,84.550003,294392500,71.615088\n2009-01-30,84.980003,85.400002,82.209999,82.830002,383383600,70.158221\n2009-02-02,81.57,83.18,81.309998,82.580002,288233300,69.946468\n2009-02-03,83.099998,84.360001,82.220001,83.739998,278385800,70.929001\n2009-02-04,84.300003,85.370003,83.040001,83.330002,322989300,70.581729\n2009-02-05,82.699997,85.290001,77.730003,84.57,417679400,71.632025\n2009-02-06,84.860001,87.339996,84.68,86.980003,366101700,73.673333\n2009-02-09,86.959999,87.739998,86.32,87.099998,240075200,73.774971\n2009-02-10,86.269997,87.029999,82.449997,83.110001,536212800,70.395384\n2009-02-11,83.449997,84.050003,82.400002,83.599998,324442500,70.81042\n2009-02-12,82.169998,83.82,81.050003,83.660004,469302200,70.861245\n2009-02-13,83.550003,84.239998,82.739998,82.760002,293998400,70.098931\n2009-02-17,80.160004,82.959999,79.169998,79.220001,478910100,67.100498\n2009-02-18,79.790001,79.940002,78.279999,79.029999,362964800,66.939563\n2009-02-19,79.839996,80.150002,78.019997,78.18,316867500,66.219602\n2009-02-20,76.730003,78.339996,75.769997,77.419998,477176600,65.575869\n2009-02-23,78.269997,78.269997,74.589996,74.650002,379641400,63.229642\n2009-02-24,75.290001,77.949997,74.699997,77.480003,426260900,65.626695\n2009-02-25,77.139999,78.419998,75.629997,76.870003,461985800,65.110015\n2009-02-26,77.82,79.669998,75.529999,75.620003,363353900,64.051247\n2009-02-27,74.010002,75.690002,73.809998,73.93,470510900,62.61979\n2009-03-02,72.519997,73.919998,70.370003,70.599998,426452600,59.79923\n2009-03-03,71.610001,71.699997,69.639999,70.07,443761000,59.350313\n2009-03-04,71.230003,72.870003,70.07,71.730003,462753100,60.756361\n2009-03-05,70.099998,71.730003,68.169998,68.800003,485549400,58.274607\n2009-03-06,69.400002,70.449997,67.099998,68.919998,490470000,58.376245\n2009-03-09,67.949997,70.0,67.730003,68.110001,379905300,57.690165\n2009-03-10,69.510002,72.370003,69.370003,72.169998,406227900,61.129043\n2009-03-11,73.0,73.75,71.830002,72.639999,356648300,61.52714\n2009-03-12,72.620003,75.75,71.970001,75.5,409702700,63.949603\n2009-03-13,76.010002,76.980003,74.730003,76.089996,337474700,64.449338\n2009-03-16,76.959999,77.970001,75.809998,75.860001,360644900,64.254529\n2009-03-17,76.07,78.360001,75.449997,78.18,356814300,66.219602\n2009-03-18,77.809998,80.900002,77.07,79.93,473273200,67.701878\n2009-03-19,80.93,81.0,78.690002,78.940002,428520400,66.863335\n2009-03-20,78.760002,78.910004,76.529999,76.709999,371078200,65.439545\n2009-03-23,78.739998,82.290001,78.309998,82.220001,419933300,70.140001\n2009-03-24,81.239998,82.360001,80.510002,80.599998,330271000,68.758014\n2009-03-25,81.230003,82.699997,79.059998,81.449997,441775100,69.483128\n2009-03-26,82.25,83.300003,81.32,83.110001,422025200,70.899239\n2009-03-27,82.050003,82.529999,81.309998,81.610001,322332300,69.619624\n2009-03-30,79.800003,79.870003,77.959999,78.790001,324108500,67.213947\n2009-03-31,79.559998,81.080002,79.050003,79.519997,364238300,67.836689\n2009-04-01,78.529999,81.419998,78.330002,81.059998,377018300,69.150429\n2009-04-02,83.080002,84.610001,81.129997,83.43,476230700,71.172224\n2009-04-03,83.489998,84.279999,82.669998,84.260002,284646300,71.880279\n2009-04-06,83.339996,84.279999,82.290001,83.599998,264866600,71.317245\n2009-04-07,82.25,82.650002,81.510002,81.650002,258947800,69.653748\n2009-04-08,82.059998,82.940002,81.540001,82.529999,230402800,70.404453\n2009-04-09,84.669998,85.82,84.330002,85.809998,269653500,73.202545\n2009-04-13,84.919998,86.540001,84.580002,85.830002,224847500,73.21961\n2009-04-14,85.029999,85.760002,84.080002,84.349998,276598800,71.957053\n2009-04-15,83.839996,85.419998,83.610001,85.25,250726100,72.724824\n2009-04-16,85.93,87.150002,84.769997,86.5,335202900,73.79117\n2009-04-17,86.830002,87.650002,86.139999,87.080002,262649000,74.285957\n2009-04-20,85.540001,87.050003,83.339996,83.43,293690100,71.172224\n2009-04-21,82.82,85.129997,82.75,85.059998,114090900,72.562737\n2009-04-22,84.290001,86.339996,84.07,84.540001,340395200,72.11914\n2009-04-23,84.709999,85.419998,83.629997,85.370003,324903700,72.827196\n2009-04-24,86.029999,87.309998,85.690002,86.660004,287703000,73.927666\n2009-04-27,85.68,87.010002,85.540001,85.839996,289581600,73.228136\n2009-04-28,84.970001,86.589996,84.760002,85.57,247926300,72.997808\n2009-04-29,86.519997,88.360001,86.300003,87.389999,311505700,74.550408\n2009-04-30,88.550003,89.019997,86.919998,87.419998,301419800,74.576\n2009-05-01,87.440002,88.209999,86.720001,87.889999,236110300,74.976947\n2009-05-04,88.550003,90.940002,88.379997,90.879997,287120000,77.527646\n2009-05-05,90.57,90.93,89.839996,90.57,243036300,77.263194\n2009-05-06,91.68,92.199997,90.610001,92.139999,291941000,78.602525\n2009-05-07,93.010002,93.150002,90.279999,90.860001,317728000,77.510587\n2009-05-08,92.029999,93.220001,91.440002,92.980003,299081700,79.319113\n2009-05-11,91.699997,92.110001,91.040001,91.239998,247923600,77.834754\n2009-05-12,91.629997,91.830002,89.849998,90.970001,282431300,77.604426\n2009-05-13,89.739998,90.010002,88.5,88.68,269619100,75.650879\n2009-05-14,88.720001,90.120003,88.5,89.440002,260098700,76.299219\n2009-05-15,89.370003,90.0,88.150002,88.709999,271502700,75.67647\n2009-05-18,89.550003,91.339996,88.57,91.230003,241447400,77.826228\n2009-05-19,91.18,91.970001,90.809998,91.120003,206102200,77.732389\n2009-05-20,91.949997,92.800003,90.410004,90.510002,285722200,77.212011\n2009-05-21,89.459999,89.800003,88.260002,89.209999,258988400,76.103009\n2009-05-22,89.459999,90.0,88.68,89.019997,166811900,75.940922\n2009-05-26,88.360001,91.559998,88.32,91.300003,236318500,77.885943\n2009-05-27,91.440002,91.75,89.529999,89.669998,246015800,76.495423\n2009-05-28,90.459999,91.339996,89.099998,90.919998,289095000,77.56177\n2009-05-29,91.419998,93.699997,90.68,92.529999,258641500,78.935224\n2009-06-01,93.669998,95.169998,93.43,94.769997,276246800,80.846115\n2009-06-02,94.400002,95.370003,94.230003,94.849998,230874500,80.914363\n2009-06-03,94.040001,94.129997,92.760002,93.650002,235310500,79.890673\n2009-06-04,94.0,94.669998,93.300003,94.529999,210102300,80.641379\n2009-06-05,95.489998,95.669998,93.800003,94.550003,284257900,80.658444\n2009-06-08,93.839996,95.099998,93.040001,94.160004,238565100,80.325744\n2009-06-09,94.690002,95.139999,94.019997,94.639999,225125500,80.735218\n2009-06-10,95.480003,95.489998,93.190002,94.400002,296100400,80.530481\n2009-06-11,94.580002,96.110001,94.559998,94.82,275414200,80.888772\n2009-06-12,94.400002,95.139999,94.0,95.080002,184361800,81.110574\n2009-06-15,93.959999,94.019997,92.400002,92.900002,224190500,79.250865\n2009-06-16,93.230003,93.290001,91.580002,91.639999,227319000,78.175986\n2009-06-17,91.599998,92.330002,90.830002,91.550003,223445200,78.099212\n2009-06-18,91.690002,92.669998,91.25,92.220001,211725100,78.670772\n2009-06-19,92.580002,92.699997,91.519997,92.040001,215655600,78.96074\n2009-06-22,91.139999,91.190002,89.25,89.279999,251913600,76.592946\n2009-06-23,89.470001,89.879997,88.849998,89.349998,188309800,76.652998\n2009-06-24,90.160004,91.080002,89.599998,90.120003,211577700,77.313582\n2009-06-25,89.669998,92.169998,89.57,92.080002,279411000,78.995057\n2009-06-26,91.769997,92.239998,91.269997,91.839996,167579000,78.789157\n2009-06-29,92.110001,92.82,91.599998,92.699997,168481300,79.526948\n2009-06-30,92.720001,93.059998,91.269997,91.949997,228888200,78.883526\n2009-07-01,92.339996,93.230003,92.209999,92.330002,173041100,79.209531\n2009-07-02,91.129997,92.360001,89.760002,89.809998,212309900,77.04763\n2009-07-06,88.940002,89.93,88.660004,89.800003,174499600,77.039055\n2009-07-07,89.709999,89.82,88.0,88.059998,197088900,75.546312\n2009-07-08,88.589996,88.800003,87.0,88.0,248050500,75.49484\n2009-07-09,88.610001,88.900002,87.910004,88.169998,163777600,75.640681\n2009-07-10,87.699997,88.489998,87.349998,87.959999,173520300,75.460524\n2009-07-13,88.309998,90.169998,87.589996,90.099998,217413500,77.29642\n2009-07-14,90.379997,90.690002,89.730003,90.610001,181487400,77.733949\n2009-07-15,91.809998,93.510002,90.68,93.260002,220877900,80.007374\n2009-07-16,93.0,94.510002,92.82,93.110001,231174500,79.878689\n2009-07-17,94.059998,94.32,93.540001,94.129997,138561700,80.75374\n2009-07-20,94.68,95.290001,94.190002,95.129997,164179400,81.611636\n2009-07-21,95.870003,95.900002,94.419998,95.57,217718300,81.989112\n2009-07-22,94.959999,96.129997,94.889999,95.550003,196068100,81.971957\n2009-07-23,95.610001,98.080002,95.529999,97.660004,258795500,83.782118\n2009-07-24,97.199997,98.139999,96.690002,98.059998,154003100,84.125271\n2009-07-27,97.879997,98.400002,97.339996,98.349998,159259400,84.374062\n2009-07-28,97.660004,98.370003,97.059998,97.889999,186685200,83.97943\n2009-07-29,97.440002,98.089996,96.980003,97.650002,194399300,83.773537\n2009-07-30,98.830002,99.830002,98.599998,98.669998,225575400,84.648588\n2009-07-31,98.650002,99.470001,98.379997,98.809998,207358000,84.768693\n2009-08-03,99.849998,100.529999,99.309998,100.440002,175776900,86.167067\n2009-08-04,99.989998,100.839996,99.779999,100.699997,176714600,86.390116\n2009-08-05,100.769997,100.860001,99.580002,100.410004,184726400,86.141332\n2009-08-06,100.870003,101.019997,99.419998,99.889999,193203800,85.695222\n2009-08-07,100.940002,102.029999,100.389999,101.199997,220640900,86.819064\n2009-08-10,100.739998,101.220001,100.269997,100.989998,130898700,86.638906\n2009-08-11,100.540001,100.610001,99.459999,99.730003,157301000,85.557962\n2009-08-12,99.559998,101.559998,99.510002,100.800003,219052400,86.47591\n2009-08-13,101.260002,101.610001,100.260002,101.57,176449500,87.136487\n2009-08-14,101.519997,101.599998,99.699997,100.790001,199616100,86.46733\n2009-08-17,98.849998,98.949997,98.110001,98.309998,237667500,84.339745\n2009-08-18,98.529999,99.440002,98.349998,99.089996,173461500,85.008903\n2009-08-19,98.309998,100.300003,98.209999,99.959999,192812800,85.755274\n2009-08-20,100.089996,101.220001,99.870003,100.989998,174131300,86.638906\n2009-08-21,101.82,103.129997,101.620003,102.970001,224605000,88.337543\n2009-08-24,103.389999,103.949997,102.589996,102.959999,191279000,88.328962\n2009-08-25,103.370003,104.260002,102.940002,103.160004,215310600,88.500545\n2009-08-26,102.839996,103.639999,102.489998,103.169998,194620700,88.50912\n2009-08-27,103.110001,103.720001,101.940002,103.400002,196230100,88.706438\n2009-08-28,104.230003,104.349998,102.669998,103.379997,147024400,88.689277\n2009-08-31,102.370003,102.580002,101.790001,102.459999,176051600,87.900014\n2009-09-01,101.949997,103.239998,99.989998,100.199997,321276800,85.961168\n2009-09-02,99.779999,100.440002,99.57,99.82,171805000,85.63517\n2009-09-03,100.400002,100.769997,99.589996,100.650002,143572300,86.347225\n2009-09-04,100.849998,102.089996,100.550003,102.059998,142687900,87.556855\n2009-09-08,103.0,103.050003,102.389999,102.940002,132909100,88.311807\n2009-09-09,103.120003,104.080002,102.800003,103.730003,154612500,88.989546\n2009-09-10,103.800003,104.860001,103.220001,104.790001,162902400,89.898913\n2009-09-11,104.989998,105.300003,104.279999,104.769997,152360100,89.881752\n2009-09-14,103.879997,105.459999,103.150002,105.279999,149593800,90.31928\n2009-09-15,105.449997,106.110001,104.760002,105.720001,196795900,90.696757\n2009-09-16,106.099998,107.339996,105.730003,107.32,206406300,92.069389\n2009-09-17,107.169998,108.059998,106.57,107.160004,229170900,91.932129\n2009-09-18,107.150002,107.160004,106.360001,106.720001,153799100,91.990744\n2009-09-21,105.889999,107.0,105.660004,106.449997,151892000,91.758005\n2009-09-22,107.080002,107.370003,106.599998,107.07,143126700,92.292437\n2009-09-23,107.32,108.029999,105.989998,106.18,225947400,91.525273\n2009-09-24,106.410004,106.639999,104.550003,105.010002,228636800,90.516755\n2009-09-25,104.779999,105.360001,104.089996,104.449997,204059000,90.034041\n2009-09-28,104.849998,106.550003,104.830002,106.32,118285800,91.64595\n2009-09-29,106.510002,107.019997,105.779999,106.0,133733900,91.370116\n2009-09-30,106.360001,106.459999,104.620003,105.589996,254383000,91.0167\n2009-10-01,103.0,105.730003,102.949997,102.970001,281840600,88.758311\n2009-10-02,102.019997,103.099998,101.989998,102.489998,224748800,88.344557\n2009-10-05,102.900002,104.32,102.599998,104.019997,149875000,89.663388\n2009-10-06,104.769997,106.110001,104.709999,105.510002,202491100,90.947747\n2009-10-07,105.269997,105.910004,105.07,105.800003,159200300,91.197722\n2009-10-08,106.550003,107.169998,105.849998,106.610001,183305800,91.895926\n2009-10-09,106.639999,107.260002,106.360001,107.260002,135008300,92.456215\n2009-10-12,107.760002,108.089996,107.279999,107.68,118031000,92.818246\n2009-10-13,107.389999,107.709999,106.760002,107.459999,157692700,92.628609\n2009-10-14,108.720001,109.419998,107.419998,109.309998,191421600,94.223275\n2009-10-15,108.779999,109.709999,108.730003,109.709999,173873600,94.568069\n2009-10-16,108.800003,109.269997,108.230003,108.889999,192069400,93.861244\n2009-10-19,109.07,110.129997,108.730003,109.790001,159530400,94.637029\n2009-10-20,109.949997,109.989998,108.68,109.209999,180921100,94.137078\n2009-10-21,109.040001,110.309998,108.150002,108.230003,225379300,93.292339\n2009-10-22,108.190002,109.68,107.5,109.330002,238444000,94.240518\n2009-10-23,109.690002,109.760002,107.629997,108.080002,240033200,93.163041\n2009-10-26,108.199997,109.309998,106.610001,106.910004,242028200,92.154523\n2009-10-27,107.029999,107.389999,106.160004,106.419998,253266300,91.732147\n2009-10-28,106.150002,106.480003,104.349998,104.410004,248821400,89.999567\n2009-10-29,105.190002,106.860001,104.940002,106.650002,198110600,91.930406\n2009-10-30,106.300003,106.620003,103.440002,103.559998,325608100,89.266877\n2009-11-02,104.129997,105.410004,103.080002,104.32,254222900,89.921986\n2009-11-03,103.739998,104.800003,103.540001,104.650002,228362600,90.206441\n2009-11-04,105.510002,106.330002,104.650002,104.919998,247996700,90.439174\n2009-11-05,105.660004,106.879997,105.440002,106.849998,180015300,92.1028\n2009-11-06,106.260002,107.400002,106.050003,107.129997,170954100,92.344154\n2009-11-09,107.949997,109.629997,107.870003,109.57,159495700,94.447392\n2009-11-10,109.309998,109.93,108.970001,109.589996,171899800,94.464629\n2009-11-11,110.309998,110.82,109.620003,110.150002,169466200,94.947344\n2009-11-12,110.0,110.57,108.75,109.029999,157144500,93.981921\n2009-11-13,109.309998,110.089996,108.75,109.620003,150963000,94.490494\n2009-11-16,110.379997,111.690002,110.32,111.209999,210922200,95.861043\n2009-11-17,110.919998,111.389999,110.5,111.339996,147134100,95.973098\n2009-11-18,111.260002,111.43,110.57,111.269997,156486800,95.912759\n2009-11-19,110.510002,111.309998,109.129997,109.82,208734600,94.662888\n2009-11-20,109.25,109.760002,109.010002,109.43,134196000,94.326715\n2009-11-23,110.720001,111.739998,110.599998,110.82,148010200,95.52487\n2009-11-24,111.0,111.199997,110.010002,110.989998,138420100,95.671405\n2009-11-25,111.169998,111.5,110.82,111.379997,109564800,96.007578\n2009-11-27,108.400002,110.32,108.290001,109.57,126001800,94.447392\n2009-11-30,109.480003,110.199997,108.120003,109.940002,160874800,94.766328\n2009-12-01,110.919998,111.660004,110.730003,111.300003,159613700,95.938624\n2009-12-02,111.279999,112.010002,110.919998,111.25,132315100,95.895523\n2009-12-03,111.550003,112.18,110.290001,110.379997,167324900,95.145596\n2009-12-04,111.839996,112.379997,110.040001,111.010002,274907800,95.688649\n2009-12-07,110.910004,111.529999,110.489998,110.839996,127973800,95.542107\n2009-12-08,110.040001,110.769997,109.269997,109.610001,169863700,94.481872\n2009-12-09,109.580002,110.18,109.019997,110.019997,155063400,94.835282\n2009-12-10,110.699997,111.120003,110.449997,110.639999,138014600,95.369713\n2009-12-11,111.110001,111.360001,110.610001,111.110001,124854000,95.774846\n2009-12-14,111.870003,112.0,111.129997,111.870003,107141500,96.429954\n2009-12-15,111.459999,111.919998,111.0,111.349998,120408800,95.98172\n2009-12-16,111.800003,112.129997,111.269997,111.519997,155358200,96.128255\n2009-12-17,110.720001,110.93,110.080002,110.18,183390100,94.973202\n2009-12-18,110.199997,110.300003,109.279999,110.209999,174591200,95.510505\n2009-12-21,110.760002,111.699997,110.760002,111.330002,118039600,96.481125\n2009-12-22,111.57,111.970001,111.43,111.730003,91707500,96.827776\n2009-12-23,112.0,112.110001,111.5,111.949997,111783100,97.018427\n2009-12-24,112.190002,112.599998,112.0,112.480003,39677500,97.477743\n2009-12-28,112.900002,112.989998,112.32,112.720001,87508500,97.68573\n2009-12-29,113.010002,113.029999,112.550003,112.559998,80572500,97.547068\n2009-12-30,112.230003,112.650002,112.169998,112.519997,73138400,97.512402\n2009-12-31,112.769997,112.800003,111.389999,111.440002,90637900,96.576454\n2010-01-04,112.370003,113.389999,111.510002,113.330002,118944600,98.214371\n2010-01-05,113.260002,113.68,112.849998,113.629997,111579900,98.474354\n2010-01-06,113.519997,113.989998,113.43,113.709999,116074400,98.543685\n2010-01-07,113.5,114.330002,113.18,114.190002,131091100,98.959667\n2010-01-08,113.889999,114.620003,113.660004,114.57,126402800,99.288981\n2010-01-11,115.080002,115.129997,114.239998,114.730003,106375700,99.427644\n2010-01-12,113.970001,114.209999,113.220001,113.660004,163333500,98.500358\n2010-01-13,113.949997,114.940002,113.370003,114.620003,161822000,99.332315\n2010-01-14,114.489998,115.139999,114.419998,114.93,115718800,99.600966\n2010-01-15,114.730003,114.839996,113.199997,113.639999,212283100,98.483022\n2010-01-19,113.620003,115.129997,113.589996,115.059998,139172700,99.713625\n2010-01-20,114.279999,114.449997,112.980003,113.889999,216490200,98.699678\n2010-01-21,113.919998,114.269997,111.559998,111.699997,344859600,96.801771\n2010-01-22,111.199997,111.739998,109.089996,109.209999,345942400,94.643882\n2010-01-25,110.209999,110.410004,109.410004,109.769997,186937500,95.129189\n2010-01-26,109.339996,110.470001,109.040001,109.309998,211168800,94.730543\n2010-01-27,109.169998,110.080002,108.330002,109.830002,271863600,95.181191\n2010-01-28,110.190002,110.25,107.910004,108.57,316104000,94.089244\n2010-01-29,109.040001,109.800003,107.220001,107.389999,310677600,93.066629\n2010-02-01,108.150002,109.07,107.5,109.059998,187865000,94.513888\n2010-02-02,109.260002,110.589996,108.879997,110.379997,216327900,95.657829\n2010-02-03,109.879997,110.480003,109.510002,109.830002,172730700,95.181191\n2010-02-04,108.980003,109.029999,106.419998,106.440002,356715700,92.24334\n2010-02-05,106.559998,106.879997,104.580002,106.660004,493585800,92.433998\n2010-02-08,106.739998,107.330002,105.809998,105.889999,224166900,91.766695\n2010-02-09,107.129997,108.150002,106.269997,107.220001,337820500,92.919305\n2010-02-10,107.050003,107.599998,106.110001,107.010002,240511500,92.737315\n2010-02-11,106.870003,108.25,106.25,108.129997,223591600,93.707928\n2010-02-12,106.989998,108.099998,106.510002,108.040001,304622100,93.629935\n2010-02-16,108.860001,109.849998,107.82,109.739998,159317500,95.103191\n2010-02-17,110.269997,110.410004,109.739998,110.260002,168845100,95.553839\n2010-02-18,110.080002,111.139999,110.040001,110.910004,193708600,96.117145\n2010-02-19,110.620003,111.57,110.360001,111.139999,222684900,96.316465\n2010-02-22,111.550003,111.580002,110.830002,111.160004,132346900,96.333801\n2010-02-23,110.860001,111.199997,109.519997,109.809998,207497000,95.163855\n2010-02-24,110.139999,111.0,109.860001,110.82,176350700,96.039146\n2010-02-25,109.239998,110.75,108.940002,110.669998,259634700,95.909151\n2010-02-26,110.769997,111.120003,110.110001,110.739998,173589300,95.969814\n2010-03-01,111.199997,112.0,111.169998,111.889999,147709700,96.966432\n2010-03-02,112.370003,112.739998,112.0,112.199997,160992400,97.235083\n2010-03-03,112.489998,112.970001,112.019997,112.300003,150785000,97.32175\n2010-03-04,112.449997,112.800003,112.029999,112.639999,135770400,97.616399\n2010-03-05,113.370003,114.339996,113.099998,114.25,176118800,99.011662\n2010-03-08,114.260002,114.519997,114.07,114.269997,114631200,99.028992\n2010-03-09,113.93,114.989998,113.870003,114.459999,154556700,99.193652\n2010-03-10,114.510002,115.279999,114.410004,114.970001,186088800,99.635632\n2010-03-11,114.699997,115.480003,114.349998,115.449997,160791100,100.051607\n2010-03-12,115.949997,115.970001,115.139999,115.459999,162074800,100.060275\n2010-03-15,115.260002,115.57,114.599998,115.489998,146816800,100.086273\n2010-03-16,115.809998,116.519997,115.489998,116.410004,168673000,100.883571\n2010-03-17,116.760002,117.480003,116.419998,117.099998,177468100,101.481536\n2010-03-18,117.110001,117.269997,116.57,117.040001,196509100,101.429541\n2010-03-19,115.970001,117.290001,115.519997,115.970001,226641100,100.916125\n2010-03-22,115.309998,116.800003,115.239998,116.589996,184477800,101.455639\n2010-03-23,116.760002,117.510002,116.379997,117.410004,182941600,102.169203\n2010-03-24,116.970001,117.43,115.580002,116.839996,196072600,101.673187\n2010-03-25,117.629997,118.169998,116.510002,116.650002,223396300,101.507855\n2010-03-26,116.870003,117.419998,116.120003,116.580002,205808500,101.446942\n2010-03-29,117.169998,117.529999,116.690002,117.32,134513500,102.090882\n2010-03-30,117.459999,117.830002,116.910004,117.400002,145772500,102.160499\n2010-03-31,116.949997,117.519997,116.610001,117.0,161078700,101.812421\n2010-04-01,117.800003,118.25,117.099998,117.800003,161215200,102.508577\n2010-04-05,118.25,118.839996,117.919998,118.760002,105847600,103.34396\n2010-04-06,118.419998,119.25,118.290001,119.040001,110384200,103.587613\n2010-04-07,118.800003,119.360001,117.809998,118.360001,184576300,102.995882\n2010-04-08,117.949997,118.970001,117.599998,118.769997,158704000,103.352657\n2010-04-09,119.019997,119.599998,118.800003,119.550003,133006500,104.031412\n2010-04-12,119.699997,120.050003,119.559998,119.739998,110279000,104.196744\n2010-04-13,119.620003,120.040001,119.0,119.830002,125043600,104.275065\n2010-04-14,120.269997,121.190002,120.080002,121.190002,161609000,105.458526\n2010-04-15,120.989998,121.57,120.949997,121.290001,144615300,105.545544\n2010-04-16,120.860001,121.290001,118.75,119.360001,366786700,103.866074\n2010-04-19,119.010002,119.93,118.470001,119.809998,217947800,104.257658\n2010-04-20,120.559998,120.980003,119.870003,120.879997,157708000,105.188762\n2010-04-21,120.949997,121.230003,119.989998,120.660004,192910100,104.997326\n2010-04-22,119.809998,121.169998,119.120003,121.019997,115360300,105.310589\n2010-04-23,120.940002,121.860001,120.629997,121.809998,177335500,105.998041\n2010-04-26,121.849998,122.120003,121.230003,121.349998,143457300,105.597753\n2010-04-27,120.650002,121.339996,118.25,118.480003,355853300,103.100308\n2010-04-28,119.050003,119.68,118.269997,119.379997,300674100,103.883475\n2010-04-29,120.099998,121.110001,120.07,120.860001,193775000,105.171361\n2010-04-30,120.879997,121.010002,118.779999,118.809998,270000900,103.387466\n2010-05-03,119.379997,120.68,119.199997,120.349998,182747900,104.727562\n2010-05-04,119.010002,119.029999,116.919998,117.519997,360353400,102.264918\n2010-05-05,116.559998,117.800003,115.970001,116.82,328973200,101.655786\n2010-05-06,116.260002,117.0,105.0,112.940002,647356600,98.279445\n2010-05-07,112.639999,113.769997,109.410004,111.260002,637558800,96.817523\n2010-05-10,115.809998,116.650002,114.910004,116.160004,396159600,101.081463\n2010-05-11,115.07,117.360001,114.910004,115.830002,317849800,100.794299\n2010-05-12,116.290001,117.620003,116.089996,117.449997,235607100,102.204005\n2010-05-13,117.129997,117.68,115.889999,115.989998,234452500,100.933526\n2010-05-14,115.120003,115.330002,112.870003,113.889999,345601400,99.106125\n2010-05-17,114.199997,114.519997,111.769997,113.949997,325739800,99.158334\n2010-05-18,114.879997,115.220001,112.029999,112.400002,360556800,97.809541\n2010-05-19,111.769997,112.769997,110.360001,111.760002,394742700,97.252619\n2010-05-20,109.379997,109.889999,107.470001,107.540001,530418300,93.580409\n2010-05-21,105.910004,109.379997,105.360001,109.110001,500909400,94.94661\n2010-05-24,108.519997,109.389999,107.610001,107.709999,269823000,93.72834\n2010-05-25,105.110001,107.870003,104.379997,107.82,396505200,93.824062\n2010-05-26,108.480003,109.470001,106.849998,107.169998,349719300,93.258436\n2010-05-27,109.190002,110.800003,108.779999,110.760002,300870500,96.382427\n2010-05-28,110.639999,110.720001,108.849998,109.370003,297933500,95.172861\n2010-06-01,108.349998,109.949997,107.370003,107.529999,277909400,93.571705\n2010-06-02,108.080002,110.339996,107.510002,110.330002,240243700,96.008245\n2010-06-03,110.650002,111.059998,109.580002,110.709999,226618300,96.338915\n2010-06-04,108.610001,109.330002,106.459999,106.82,398475600,92.95387\n2010-06-07,107.199997,107.610001,105.410004,105.489998,264609100,91.796514\n2010-06-08,105.57,106.830002,104.650002,106.620003,357774300,92.779834\n2010-06-09,107.239998,108.279999,105.599998,106.050003,268023300,92.283825\n2010-06-10,107.860001,109.279999,106.040001,109.150002,317890600,94.981418\n2010-06-11,108.190002,109.75,108.120003,109.68,214128200,95.442619\n2010-06-14,110.519997,111.120003,109.400002,109.510002,207196100,95.294688\n2010-06-15,110.279999,112.099998,110.089996,112.0,238268700,97.461463\n2010-06-16,111.419998,112.419998,111.199997,111.959999,216374000,97.426655\n2010-06-17,112.279999,112.330002,111.050003,112.139999,263185800,97.583289\n2010-06-18,111.830002,112.129997,111.370003,111.730003,174006600,97.689092\n2010-06-21,113.120003,113.199997,110.790001,111.410004,213140700,97.409306\n2010-06-22,111.410004,111.900002,109.410004,109.57,239355400,95.800532\n2010-06-23,109.639999,110.029999,108.480003,109.230003,254639900,95.503262\n2010-06-24,108.690002,108.830002,107.139999,107.419998,268523600,93.920717\n2010-06-25,107.739998,108.419998,106.769997,107.870003,238726500,94.314171\n2010-06-28,108.029999,108.32,107.139999,107.529999,169218600,94.016894\n2010-06-29,106.019997,107.510002,103.550003,104.209999,373649500,91.114113\n2010-06-30,103.919998,104.879997,102.879997,103.220001,284101700,90.248527\n2010-07-01,103.150002,103.489998,101.129997,102.760002,382924800,89.846335\n2010-07-02,103.110001,103.419998,101.620003,102.199997,233385200,89.356704\n2010-07-06,103.639999,104.370003,101.879997,102.870003,256935300,89.942512\n2010-07-07,103.129997,106.239998,103.019997,106.110001,253769400,92.775345\n2010-07-08,107.0,107.279999,105.910004,107.160004,210842100,93.693396\n2010-07-09,107.129997,107.970001,106.93,107.959999,144999900,94.392857\n2010-07-12,107.599998,108.239998,107.150002,108.029999,131283600,94.45406\n2010-07-13,109.150002,110.089996,108.93,109.660004,213025900,95.879225\n2010-07-14,109.309998,110.080002,108.860001,109.650002,184426800,95.87048\n2010-07-15,109.610001,110.059998,108.169998,109.68,232337900,95.896709\n2010-07-16,109.089996,109.209999,106.449997,106.660004,282693400,93.25623\n2010-07-19,107.050003,107.629997,106.220001,107.290001,186709000,93.807057\n2010-07-20,105.870003,108.559998,105.82,108.480003,258162400,94.847514\n2010-07-21,109.040001,109.07,106.629997,107.07,264527000,93.614703\n2010-07-22,108.339996,109.940002,108.330002,109.459999,274781300,95.704355\n2010-07-23,109.239998,110.57,108.93,110.410004,222020800,96.534974\n2010-07-26,110.599998,111.669998,110.290001,111.559998,184445700,97.54045\n2010-07-27,112.169998,112.290001,111.110001,111.550003,204855600,97.531712\n2010-07-28,111.32,111.660004,110.459999,110.830002,163056200,96.902192\n2010-07-29,111.519997,111.82,109.410004,110.290001,220149100,96.430052\n2010-07-30,109.169998,110.860001,108.980003,110.269997,220070600,96.412562\n2010-08-02,111.989998,112.940002,111.540001,112.760002,188263200,98.589653\n2010-08-03,112.480003,112.769997,111.849998,112.220001,146657300,98.117513\n2010-08-04,112.529999,113.110001,112.160004,112.970001,158171700,98.773261\n2010-08-05,112.25,112.910004,112.080002,112.849998,140473800,98.668339\n2010-08-06,111.739998,112.57,110.919998,112.389999,239728300,98.266147\n2010-08-09,112.919998,113.18,112.32,112.989998,120800400,98.790745\n2010-08-10,112.029999,112.980003,111.370003,112.379997,242916300,98.257402\n2010-08-11,110.650002,110.690002,109.120003,109.300003,273406900,95.564465\n2010-08-12,107.650002,109.019997,107.599998,108.629997,239542600,94.978658\n2010-08-13,108.290001,108.959999,108.18,108.309998,158698500,94.698872\n2010-08-16,107.57,108.610001,107.18,108.260002,147895300,94.65516\n2010-08-17,109.190002,110.389999,108.879997,109.589996,172270300,95.818016\n2010-08-18,109.540001,110.379997,108.910004,109.790001,182922100,95.992886\n2010-08-19,109.220001,109.489998,107.43,107.879997,265847600,94.322909\n2010-08-20,107.559998,107.940002,106.75,107.529999,209714200,94.016894\n2010-08-23,108.040001,108.57,107.07,107.120003,163490300,93.658422\n2010-08-24,105.949997,106.389999,104.970001,105.529999,280677800,92.268231\n2010-08-25,104.949997,106.339996,104.290001,105.940002,272234800,92.62671\n2010-08-26,106.440002,106.580002,104.879997,105.230003,224439500,92.005935\n2010-08-27,105.889999,106.970001,104.309998,106.860001,272649000,93.431094\n2010-08-30,106.580002,106.910004,105.300003,105.309998,167238600,92.075877\n2010-08-31,104.919998,105.980003,104.489998,105.309998,273933100,92.075877\n2010-09-01,106.730003,108.610001,106.660004,108.459999,256828100,94.830023\n2010-09-02,108.720001,109.489998,108.489998,109.470001,156112200,95.7131\n2010-09-03,110.540001,110.989998,109.949997,110.889999,212197300,96.95465\n2010-09-07,110.370003,110.510002,109.550003,109.639999,141973700,95.861735\n2010-09-08,109.860001,110.849998,109.809998,110.410004,149924400,96.534974\n2010-09-09,111.650002,111.68,110.620003,110.919998,147017900,96.980879\n2010-09-10,111.120003,111.610001,110.870003,111.480003,127819000,97.470509\n2010-09-13,112.580002,112.949997,112.129997,112.720001,178503500,98.554678\n2010-09-14,112.5,113.290001,112.080002,112.650002,209823600,98.493475\n2010-09-15,112.32,113.209999,111.980003,113.080002,168608400,98.869438\n2010-09-16,112.730003,113.120003,112.349998,113.050003,199962900,98.84321\n2010-09-17,113.040001,113.150002,112.18,112.489998,195836900,98.880124\n2010-09-20,112.879997,114.459999,112.519997,114.209999,214555200,100.392026\n2010-09-21,114.300003,114.839996,113.510002,113.980003,268389100,100.189857\n2010-09-22,113.800003,114.440002,113.099998,113.419998,191322400,99.697606\n2010-09-23,112.489998,113.669998,112.18,112.5,202354300,98.888916\n2010-09-24,113.75,114.900002,113.650002,114.82,209671800,100.928225\n2010-09-27,114.860001,114.989998,114.160004,114.269997,128761800,100.444765\n2010-09-28,114.419998,115.040001,113.18,114.669998,209207500,100.796371\n2010-09-29,114.379997,114.910004,114.019997,114.470001,179665800,100.620572\n2010-09-30,115.050003,115.790001,113.589996,114.129997,287106700,100.321704\n2010-10-01,114.989998,115.120003,113.93,114.610001,174638700,100.743633\n2010-10-04,114.370003,114.849998,113.18,113.75,166153200,99.987682\n2010-10-05,114.800003,116.32,114.669998,116.040001,229634100,102.000621\n2010-10-06,116.019997,116.330002,115.559998,116.029999,148626600,101.991829\n2010-10-07,116.5,116.529999,115.190002,115.889999,164860000,101.868768\n2010-10-08,116.050003,116.860001,115.610001,116.540001,177760100,102.440127\n2010-10-11,116.720001,116.970001,116.25,116.650002,103098300,102.536819\n2010-10-12,116.269997,117.349998,115.650002,117.010002,182210000,102.853264\n2010-10-13,117.660004,118.550003,117.379997,117.919998,194347200,103.653162\n2010-10-14,117.809998,118.010002,116.720001,117.459999,217764300,103.248817\n2010-10-15,118.279999,118.349998,116.760002,117.699997,243705000,103.459779\n2010-10-18,117.739998,118.669998,117.309998,118.279999,141204800,103.969607\n2010-10-19,117.190002,117.849998,116.019997,116.730003,280604700,102.607142\n2010-10-20,116.940002,118.440002,116.870003,117.870003,200051800,103.609216\n2010-10-21,118.400002,119.089996,117.209999,118.129997,221585500,103.837754\n2010-10-22,118.309998,118.529999,118.0,118.349998,108212400,104.031138\n2010-10-25,119.139999,119.760002,118.610001,118.699997,151145700,104.338791\n2010-10-26,118.099998,118.839996,117.870003,118.720001,158982900,104.356375\n2010-10-27,117.889999,118.510002,117.260002,118.379997,190024000,104.057507\n2010-10-28,119.059998,119.110001,117.830002,118.400002,168576000,104.075091\n2010-10-29,118.279999,118.720001,118.07,118.489998,144305500,104.154199\n2010-11-01,119.07,119.75,117.849998,118.529999,174074800,104.189361\n2010-11-02,119.419998,119.75,119.099998,119.470001,158345900,105.015635\n2010-11-03,119.68,120.019997,118.449997,119.949997,226702800,105.437557\n2010-11-04,121.279999,122.32,119.970001,122.260002,215039400,107.46808\n2010-11-05,122.339996,122.919998,122.18,122.720001,180654100,107.872425\n2010-11-08,122.339996,122.690002,121.940002,122.489998,156107100,107.67025\n2010-11-09,122.82,122.949997,121.120003,121.610001,186621600,106.896721\n2010-11-10,121.580002,122.160004,120.660004,122.099998,221387400,107.327435\n2010-11-11,121.050003,121.82,120.68,121.639999,158017600,106.92309\n2010-11-12,120.82,121.349998,119.650002,120.199997,239068800,105.65731\n2010-11-15,120.580002,121.050003,119.980003,120.029999,163940800,105.507879\n2010-11-16,119.290001,119.489998,117.589996,118.160004,299566200,103.86413\n2010-11-17,118.209999,118.709999,117.860001,118.220001,172308900,103.916869\n2010-11-18,119.360001,120.389999,119.349998,119.959999,197723700,105.446349\n2010-11-19,119.900002,120.339996,119.25,120.290001,156852900,105.736425\n2010-11-22,119.690002,120.239998,118.769997,120.190002,181361000,105.648525\n2010-11-23,118.769997,119.019997,117.989998,118.449997,222309000,104.119038\n2010-11-24,119.199997,120.230003,119.18,120.199997,140046100,105.65731\n2010-11-26,119.160004,119.809998,118.800003,118.800003,76007800,104.426698\n2010-11-29,118.5,119.480003,117.739998,119.160004,223642300,104.743143\n2010-11-30,117.980003,119.169998,117.809998,118.489998,233930700,104.154199\n2010-12-01,120.199997,121.239998,120.190002,121.010002,221037200,106.369315\n2010-12-02,121.199997,122.650002,121.129997,122.559998,191213600,107.73178\n2010-12-03,122.139999,123.029999,122.110001,122.889999,151288900,108.021856\n2010-12-06,122.629997,123.040001,122.5,122.760002,103050500,107.907587\n2010-12-07,123.940002,124.010002,122.760002,122.830002,206581000,107.969117\n2010-12-08,122.980003,123.379997,122.410004,123.279999,138019200,108.36467\n2010-12-09,123.970001,124.019997,123.150002,123.760002,123705100,108.786599\n2010-12-10,124.139999,124.599998,123.730003,124.480003,117571700,109.419489\n2010-12-13,125.050003,125.199997,124.519997,124.559998,133812700,109.489805\n2010-12-14,124.75,125.230003,124.290001,124.669998,147249600,109.586497\n2010-12-15,124.440002,124.93,123.889999,124.099998,160823100,109.08546\n2010-12-16,124.18,124.910004,123.75,124.82,185035200,109.718351\n2010-12-17,124.080002,124.459999,123.82,124.300003,141075300,109.835876\n2010-12-20,124.639999,124.900002,123.980003,124.599998,119085500,110.100962\n2010-12-21,124.989998,125.470001,124.870003,125.389999,94965500,110.799035\n2010-12-22,125.480003,125.82,125.410004,125.779999,78878100,111.143652\n2010-12-23,125.639999,125.779999,125.290001,125.599998,70053700,110.984598\n2010-12-27,125.129997,125.769997,125.040001,125.650002,58126000,111.028782\n2010-12-28,125.900002,125.949997,125.5,125.830002,55309100,111.187837\n2010-12-29,125.980003,126.199997,125.900002,125.919998,58033100,111.267361\n2010-12-30,125.800003,126.129997,125.529999,125.720001,76616900,111.090636\n2010-12-31,125.529999,125.870003,125.330002,125.75,91218900,111.117144\n2011-01-03,126.709999,127.599998,125.699997,127.050003,138725200,112.265873\n2011-01-04,127.330002,127.370003,126.190002,126.980003,137409700,112.204019\n2011-01-05,126.580002,127.720001,126.459999,127.639999,133975300,112.787215\n2011-01-06,127.690002,127.830002,127.010002,127.389999,122519000,112.566306\n2011-01-07,127.559998,127.769997,126.150002,127.139999,156034600,112.345397\n2011-01-10,126.580002,127.160004,126.199997,126.980003,122401700,112.204019\n2011-01-11,127.440002,127.739998,126.949997,127.43,110287000,112.601652\n2011-01-12,128.210007,128.720001,127.459999,128.580002,107929200,113.617834\n2011-01-13,128.630005,128.690002,128.050003,128.369995,129048400,113.432265\n2011-01-14,128.190002,129.330002,128.100006,129.300003,117677900,114.254052\n2011-01-18,129.179993,129.639999,129.029999,129.520004,114401300,114.448453\n2011-01-19,129.410004,129.539993,127.910004,128.25,151958400,113.326233\n2011-01-20,127.959999,128.399994,127.129997,128.080002,175745700,113.176016\n2011-01-21,128.880005,129.169998,128.229996,128.369995,151462900,113.432265\n2011-01-24,128.289993,129.25,128.259995,129.100006,113715500,114.077328\n2011-01-25,128.759995,129.279999,128.110001,129.169998,167552200,114.139176\n2011-01-26,129.490005,130.050003,129.229996,129.669998,141281500,114.580993\n2011-01-27,129.699997,130.210007,129.470001,129.990005,123302700,114.863763\n2011-01-28,130.139999,130.350006,127.510002,127.720001,295637300,112.857907\n2011-01-31,128.070007,128.779999,127.75,128.679993,149249200,113.706189\n2011-02-01,129.460007,130.970001,129.380005,130.740005,167194300,115.526489\n2011-02-02,130.399994,130.839996,130.330002,130.490005,118323600,115.305581\n2011-02-03,130.259995,130.979996,129.570007,130.779999,145886700,115.561829\n2011-02-04,130.830002,131.199997,130.229996,131.149994,134634800,115.88877\n2011-02-07,131.440002,132.399994,131.429993,131.970001,112439100,116.613357\n2011-02-08,132.089996,132.639999,131.729996,132.570007,99072800,117.143544\n2011-02-09,132.210007,132.630005,131.610001,132.270004,146436700,116.87845\n2011-02-10,131.600006,132.470001,131.300003,132.320007,162708500,116.922635\n2011-02-11,131.800003,133.279999,131.770004,133.110001,137710300,117.620701\n2011-02-14,133.029999,133.539993,132.880005,133.429993,101690700,117.903457\n2011-02-15,133.020004,133.220001,132.320007,133.009995,119575400,117.532332\n2011-02-16,133.460007,134.009995,133.190002,133.850006,130183500,118.274596\n2011-02-17,133.460007,134.429993,133.339996,134.25,109810500,118.628045\n2011-02-18,134.369995,134.690002,134.059998,134.529999,130002400,118.875461\n2011-02-22,133.119995,134.559998,131.470001,131.830002,233116400,116.489649\n2011-02-23,131.75,132.070007,130.210007,131.020004,227584000,115.773906\n2011-02-24,130.880005,131.440002,129.699997,130.929993,260431400,115.694369\n2011-02-25,131.479996,132.410004,131.399994,132.330002,141686900,116.931466\n2011-02-28,132.820007,133.320007,132.380005,133.149994,141585500,117.65604\n2011-03-01,133.570007,133.690002,130.889999,130.929993,258565500,115.694369\n2011-03-02,130.75,131.820007,130.350006,131.210007,200277400,115.941799\n2011-03-03,132.399994,133.619995,132.389999,133.470001,176480100,117.93881\n2011-03-04,133.369995,133.630005,131.600006,132.470001,277202300,117.055175\n2011-03-07,132.860001,133.160004,130.740005,131.429993,216790400,116.136187\n2011-03-08,131.639999,133.0,131.070007,132.580002,174615000,117.152375\n2011-03-09,132.320007,132.800003,131.600006,132.389999,153806000,116.984482\n2011-03-10,131.0,131.179993,129.809998,129.940002,301291800,114.819579\n2011-03-11,129.520004,131.309998,129.490005,130.839996,225621800,115.614845\n2011-03-14,129.990005,130.479996,129.059998,130.050003,234974100,114.916779\n2011-03-15,126.589996,129.330002,126.5,128.559998,359585400,113.600157\n2011-03-16,128.149994,128.570007,125.279999,126.18,468670300,111.497108\n2011-03-17,128.0,128.389999,127.099998,127.849998,254303700,112.972777\n2011-03-18,128.839996,128.880005,127.510002,127.760002,230435400,113.38368\n2011-03-21,129.350006,130.009995,129.199997,129.740005,153992600,115.140882\n2011-03-22,129.720001,129.889999,129.169998,129.289993,129538600,114.741508\n2011-03-23,128.929993,130.0,128.320007,129.660004,148603100,115.069882\n2011-03-24,130.399994,131.089996,129.669998,130.899994,159129800,116.170341\n2011-03-25,131.240005,131.869995,130.889999,131.300003,155642800,116.525339\n2011-03-28,131.580002,131.919998,130.940002,130.979996,109762400,116.241341\n2011-03-29,130.869995,131.899994,130.440002,131.860001,129798800,117.022322\n2011-03-30,132.550003,133.160004,132.360001,132.770004,135835000,117.829927\n2011-03-31,132.600006,132.960007,132.449997,132.589996,132537100,117.670174\n2011-04-01,133.410004,133.770004,132.830002,133.149994,153850100,118.167158\n2011-04-04,133.429993,133.669998,132.880005,133.259995,100768900,118.26478\n2011-04-05,133.0,133.830002,132.940002,133.240005,120791500,118.247041\n2011-04-06,133.880005,134.0,133.119995,133.660004,120411600,118.619778\n2011-04-07,133.419998,133.979996,132.660004,133.320007,170731500,118.31804\n2011-04-08,133.910004,133.990005,132.309998,132.860001,147945400,117.909796\n2011-04-11,133.0,133.449997,132.139999,132.460007,121385400,117.554812\n2011-04-12,131.720001,131.979996,130.990005,131.470001,161187400,116.676208\n2011-04-13,132.080002,132.179993,130.960007,131.460007,162059000,116.667338\n2011-04-14,130.699997,131.759995,130.270004,131.559998,161220400,116.756077\n2011-04-15,131.800003,132.369995,131.410004,132.039993,170006700,117.182061\n2011-04-18,130.589996,132.029999,129.509995,130.559998,210759300,115.868603\n2011-04-19,130.759995,131.350006,130.440002,131.309998,124258800,116.534209\n2011-04-20,132.880005,133.389999,132.789993,133.100006,156133800,118.122795\n2011-04-21,133.789993,133.839996,133.100006,133.779999,135935400,118.726271\n2011-04-25,133.679993,133.860001,133.199997,133.639999,65757100,118.602025\n2011-04-26,134.050003,135.059998,133.910004,134.789993,146600000,119.622615\n2011-04-27,135.050003,135.869995,134.5,135.669998,143031000,120.403596\n2011-04-28,135.429993,136.289993,135.410004,136.110001,124791100,120.794087\n2011-04-29,136.160004,136.570007,135.979996,136.429993,115094100,121.078071\n2011-05-02,137.070007,137.179993,135.949997,136.220001,126278700,120.891709\n2011-05-03,135.960007,136.190002,135.039993,135.729996,138375000,120.456842\n2011-05-04,135.669998,135.729996,134.229996,134.830002,182678500,119.658121\n2011-05-05,134.080002,134.949997,133.020004,133.610001,226900000,118.575402\n2011-05-06,134.940002,135.630005,133.679993,134.199997,222787200,119.099008\n2011-05-09,134.190002,135.110001,133.979996,134.720001,114104500,119.560498\n2011-05-10,135.169998,136.110001,135.0,135.869995,114806900,120.581088\n2011-05-11,135.669998,135.690002,133.820007,134.440002,193564200,119.312007\n2011-05-12,134.080002,135.360001,133.389999,135.080002,171550700,119.87999\n2011-05-13,135.149994,135.339996,133.559998,134.039993,157444900,118.957009\n2011-05-16,133.559998,134.610001,132.970001,133.190002,141675400,118.202664\n2011-05-17,132.690002,133.350006,132.119995,133.169998,192686200,118.184911\n2011-05-18,133.240005,134.5,132.949997,134.360001,135217900,119.241007\n2011-05-19,134.800003,135.029999,133.940002,134.679993,119489500,119.524992\n2011-05-20,134.330002,134.679993,133.360001,133.610001,182594900,118.575402\n2011-05-23,131.979996,132.720001,131.589996,132.059998,168700000,117.199814\n2011-05-24,132.440002,132.729996,131.699997,131.949997,147199600,117.102192\n2011-05-25,131.419998,132.940002,131.380005,132.389999,151050100,117.492682\n2011-05-26,132.029999,133.240005,131.779999,133.0,164850000,118.034042\n2011-05-27,133.369995,133.869995,132.960007,133.509995,120921900,118.486649\n2011-05-31,134.770004,134.919998,133.839996,134.899994,164731200,119.720237\n2011-06-01,134.509995,134.919998,131.759995,131.869995,233094300,117.031192\n2011-06-02,131.960007,132.240005,130.960007,131.729996,200466800,116.906946\n2011-06-03,130.149994,131.419998,130.080002,130.419998,234690200,115.744358\n2011-06-06,130.089996,130.360001,128.869995,129.039993,179951200,114.519639\n2011-06-07,129.699997,130.070007,128.850006,128.960007,161660500,114.448653\n2011-06-08,128.759995,129.190002,128.179993,128.419998,198696400,113.96941\n2011-06-09,128.770004,129.929993,128.460007,129.399994,160964400,114.83913\n2011-06-10,128.850006,128.929993,127.260002,127.599998,238629400,113.241681\n2011-06-13,127.889999,128.240005,127.050003,127.699997,207599800,113.330427\n2011-06-14,128.869995,129.770004,128.820007,129.320007,160570400,114.768144\n2011-06-15,128.240005,129.300003,126.68,127.019997,300958000,112.726945\n2011-06-16,127.059998,127.970001,126.32,127.300003,308032800,112.975443\n2011-06-17,127.93,127.940002,126.620003,127.050003,233284900,113.312574\n2011-06-20,126.620003,127.970001,126.580002,127.699997,159479000,113.892286\n2011-06-21,128.360001,129.699997,127.75,129.449997,193157300,115.453065\n2011-06-22,129.050003,129.809998,128.589996,128.669998,176703000,114.757405\n2011-06-23,127.160004,128.639999,126.190002,128.300003,334286500,114.427416\n2011-06-24,128.270004,128.369995,126.620003,126.809998,226129300,113.098519\n2011-06-27,126.889999,128.429993,126.639999,127.940002,168904700,114.106341\n2011-06-28,128.449997,129.630005,128.270004,129.610001,165556300,115.595768\n2011-06-29,130.199997,130.929993,129.630005,130.720001,244295500,116.585749\n2011-06-30,131.139999,132.179993,130.710007,131.970001,223322700,117.700591\n2011-07-01,132.089996,134.100006,131.779999,133.919998,202385700,119.439743\n2011-07-05,133.779999,134.080002,133.389999,133.809998,165936000,119.341636\n2011-07-06,133.490005,134.139999,133.110001,133.970001,143331600,119.484339\n2011-07-07,135.160004,135.699997,134.880005,135.360001,170464200,120.724043\n2011-07-08,133.830002,135.360001,133.389999,134.399994,194100500,119.867838\n2011-07-11,132.75,133.179993,131.660004,131.970001,195918600,117.700591\n2011-07-12,131.690002,132.779999,131.360001,131.399994,214675700,117.192217\n2011-07-13,132.089996,133.220001,131.520004,131.839996,204062600,117.584643\n2011-07-14,132.169998,132.779999,130.679993,130.929993,226111800,116.773035\n2011-07-15,131.660004,131.869995,130.770004,131.690002,220012800,117.450868\n2011-07-18,131.080002,131.279999,129.630005,130.610001,196872100,116.487642\n2011-07-19,131.339996,132.889999,131.309998,132.729996,166554900,118.378411\n2011-07-20,133.070007,133.149994,132.419998,132.649994,137145400,118.307059\n2011-07-21,133.399994,134.820007,132.669998,134.490005,245246300,119.948117\n2011-07-22,134.520004,134.720001,133.759995,134.580002,126019400,120.028383\n2011-07-25,133.300003,134.490005,133.160004,133.830002,136653800,119.359477\n2011-07-26,133.740005,133.960007,133.029999,133.330002,131278200,118.91354\n2011-07-27,132.589996,132.630005,130.429993,130.600006,249020100,116.478728\n2011-07-28,130.600006,131.770004,130.009995,130.220001,207939900,116.139812\n2011-07-29,128.910004,130.550003,127.970001,129.330002,307038400,115.346045\n2011-08-01,130.839996,130.960007,127.529999,128.779999,325790900,114.855512\n2011-08-02,127.809998,128.5,125.489998,125.489998,346653800,111.921246\n2011-08-03,125.660004,126.309998,123.529999,126.169998,370830800,112.52772\n2011-08-04,124.419998,124.620003,120.059998,120.260002,520721800,107.256749\n2011-08-05,121.760002,122.07,116.860001,120.080002,655619200,107.096212\n2011-08-08,116.910004,120.120003,112.019997,112.260002,702263900,100.121759\n2011-08-09,114.07,117.639999,110.269997,117.480003,717828700,104.777341\n2011-08-10,115.260002,116.279999,111.949997,112.290001,662607400,100.148514\n2011-08-11,113.260002,118.919998,112.32,117.330002,487979700,104.643559\n2011-08-12,118.400002,119.209999,117.279999,118.120003,313731600,105.34814\n2011-08-15,119.190002,120.739998,119.0,120.620003,258810600,107.577825\n2011-08-16,119.470001,120.690002,118.309998,119.589996,294095200,106.659189\n2011-08-17,120.25,121.199997,118.720001,119.669998,238201100,106.73054\n2011-08-18,116.5,119.709999,113.389999,114.510002,512956300,102.128475\n2011-08-19,112.959999,115.879997,112.5,112.639999,428281300,100.460668\n2011-08-22,115.169998,115.230003,112.410004,112.730003,275090600,100.540941\n2011-08-23,113.150002,116.57,112.580002,116.440002,331136600,103.849792\n2011-08-24,116.190002,118.239998,115.919998,118.080002,246869700,105.312464\n2011-08-25,118.730003,119.400002,115.870003,116.279999,312365400,103.707089\n2011-08-26,115.690002,118.510002,113.849998,117.970001,314495900,105.214358\n2011-08-29,119.559998,121.43,118.059998,121.360001,190977200,108.237809\n2011-08-30,120.830002,122.43,119.260002,121.68,241315700,108.523209\n2011-08-31,122.459999,123.510002,121.300003,122.220001,301828400,109.004821\n2011-09-01,122.290001,123.400002,120.779999,120.940002,254585900,107.863224\n2011-09-02,118.419998,120.870003,117.43,117.849998,255517200,105.10733\n2011-09-06,114.389999,117.160004,114.379997,116.989998,285130500,104.340318\n2011-09-07,118.760002,120.339996,118.360001,120.290001,209803200,107.283505\n2011-09-08,119.57,120.940002,118.769997,119.040001,250568200,106.168662\n2011-09-09,117.68,119.059998,115.279999,115.919998,380195100,103.386013\n2011-09-12,114.470001,116.760002,114.050003,116.669998,305793500,104.054919\n2011-09-13,117.050003,118.18,116.220001,117.739998,272514700,105.009224\n2011-09-14,118.339996,120.800003,116.720001,119.370003,319389500,106.462982\n2011-09-15,120.650002,121.470001,119.400002,121.43,326777200,108.30024\n2011-09-16,121.290001,121.970001,120.32,121.519997,284528300,108.94122\n2011-09-19,119.529999,120.93,118.720001,120.309998,241517000,107.85647\n2011-09-20,120.82,121.989998,120.010002,120.169998,218932200,107.730963\n2011-09-21,120.230003,120.599998,116.440002,116.629997,316251300,104.557394\n2011-09-22,113.25,114.209999,111.300003,112.860001,513911300,101.177637\n2011-09-23,112.110001,114.160004,112.019997,113.540001,307242500,101.78725\n2011-09-26,114.610001,116.400002,112.980003,116.239998,260673700,104.207764\n2011-09-27,118.529999,119.559998,116.839996,117.540001,311753900,105.373202\n2011-09-28,117.779999,118.489998,114.970001,115.139999,286696800,103.221629\n2011-09-29,117.050003,117.629997,113.93,116.050003,298108900,104.037436\n2011-09-30,114.449997,115.449997,113.07,113.150002,288392300,101.43762\n2011-10-03,112.489998,113.949997,109.809998,109.93,365136800,98.550927\n2011-10-04,108.349998,112.580002,107.43,112.339996,459177500,100.71146\n2011-10-05,112.620003,114.720001,111.580002,114.419998,284108000,102.576157\n2011-10-06,114.360001,116.660004,113.510002,116.489998,257800800,104.431886\n2011-10-07,117.169998,117.25,115.059998,115.709999,312657900,103.732627\n2011-10-10,117.68,119.629997,117.669998,119.580002,230666300,107.202038\n2011-10-11,118.870003,120.040001,118.75,119.699997,209088000,107.309612\n2011-10-12,120.599998,122.139999,120.330002,120.75,281544900,108.250927\n2011-10-13,120.040001,120.870003,119.120003,120.510002,212538800,108.035772\n2011-10-14,121.910004,122.599998,121.230003,122.57,211397600,109.882535\n2011-10-17,121.989998,122.550003,119.93,120.230003,202311600,107.784757\n2011-10-18,120.139999,123.5,119.199997,122.580002,318857900,109.891502\n2011-10-19,122.379997,123.080002,120.709999,121.129997,226601300,108.59159\n2011-10-20,121.43,122.099998,119.82,121.660004,262075600,109.066735\n2011-10-21,123.089996,124.120003,122.720001,123.970001,278999400,111.13762\n2011-10-24,124.169998,125.800003,124.059998,125.489998,203215600,112.500279\n2011-10-25,124.889999,124.949997,122.779999,123.050003,268596800,110.312852\n2011-10-26,124.349998,124.769997,122.209999,124.300003,289053800,111.433462\n2011-10-27,127.629997,129.419998,126.610001,128.630005,393220200,115.315257\n2011-10-28,128.0,128.850006,127.800003,128.600006,225906500,115.288364\n2011-10-31,127.160004,128.619995,125.32,125.5,228146700,112.509245\n2011-11-01,122.029999,123.510002,121.519997,122.0,416565800,109.371537\n2011-11-02,123.830002,124.400002,122.790001,123.989998,244717600,111.155547\n2011-11-03,125.269997,126.5,123.599998,126.25,291174800,113.181611\n2011-11-04,125.230003,125.699997,124.010002,125.480003,249401600,112.491319\n2011-11-07,125.389999,126.389999,124.199997,126.260002,196617200,113.190578\n2011-11-08,126.919998,128.020004,125.709999,127.879997,224426300,114.642884\n2011-11-09,124.889999,125.800003,122.860001,123.160004,337982000,110.411467\n2011-11-10,124.790001,124.940002,123.019997,124.32,231866500,111.451389\n2011-11-11,125.830002,126.989998,125.790001,126.660004,189924400,113.549175\n2011-11-14,126.190002,127.449997,124.919998,125.459999,159258300,112.473385\n2011-11-15,125.169998,126.75,124.720001,126.080002,184709400,113.02921\n2011-11-16,124.809998,126.339996,123.900002,124.080002,235782500,111.236234\n2011-11-17,123.849998,124.160004,121.230003,122.110001,331219600,109.470152\n2011-11-18,122.5,122.75,121.470001,121.980003,215580400,109.353611\n2011-11-21,120.199997,120.349998,118.650002,119.660004,229611600,107.273759\n2011-11-22,119.400002,120.099998,118.519997,119.190002,216494900,106.852408\n2011-11-23,118.07,119.190002,116.559998,116.559998,224329100,104.49464\n2011-11-25,116.379997,117.699997,116.199997,116.339996,99557000,104.297412\n2011-11-28,119.540001,120.18,118.82,119.709999,210686000,107.318579\n2011-11-29,120.050003,121.0,119.610001,120.050003,199241500,107.623388\n2011-11-30,123.489998,125.220001,120.0,124.989998,324439500,112.052035\n2011-12-01,124.849998,125.639999,124.43,124.970001,176954800,112.034108\n2011-12-02,126.120003,126.5,124.779999,124.860001,221109700,111.935494\n2011-12-05,126.839996,127.18,125.440002,126.220001,225263900,113.154718\n2011-12-06,126.209999,127.110001,125.760002,126.260002,178842100,113.190578\n2011-12-07,125.839996,127.260002,124.970001,126.730003,237802500,113.611929\n2011-12-08,125.900002,126.18,123.650002,123.949997,240862800,111.119686\n2011-12-09,124.510002,126.370003,124.400002,126.050003,209111400,113.002317\n2011-12-12,124.949997,124.970001,123.160004,124.209999,215826100,111.352775\n2011-12-13,124.860001,125.57,122.449997,123.050003,245159800,110.312852\n2011-12-14,122.559998,123.029999,121.470001,121.739998,238618800,109.138449\n2011-12-15,123.029999,123.199997,121.989998,122.18,199109200,109.532905\n2011-12-16,122.230003,122.949997,121.300003,121.589996,220481400,109.695297\n2011-12-19,122.059998,122.32,120.029999,120.290001,183903000,108.522475\n2011-12-20,122.18,124.139999,120.370003,123.93,225418100,111.806387\n2011-12-21,123.93,124.360001,122.75,124.169998,194230900,112.022906\n2011-12-22,124.629997,125.400002,124.230003,125.269997,119465400,113.015296\n2011-12-23,125.669998,126.43,125.410004,126.389999,92187200,114.025733\n2011-12-27,126.169998,126.82,126.059998,126.489998,86075700,114.115949\n2011-12-28,126.510002,126.529999,124.730003,124.830002,119107100,112.618344\n2011-12-29,125.239998,126.25,124.860001,126.120003,123507200,113.782149\n2011-12-30,126.019997,126.330002,125.5,125.5,95599000,113.222799\n2012-01-03,127.760002,128.380005,127.43,127.5,193697900,115.027146\n2012-01-04,127.199997,127.809998,126.709999,127.699997,127186500,115.207578\n2012-01-05,127.010002,128.229996,126.43,128.039993,173895000,115.514314\n2012-01-06,128.199997,128.220001,127.290001,127.709999,148050000,115.216602\n2012-01-09,128.0,128.179993,127.410004,128.020004,99530200,115.496281\n2012-01-10,129.389999,129.649994,128.949997,129.130005,115282000,116.497694\n2012-01-11,128.729996,129.369995,128.520004,129.199997,111540700,116.560839\n2012-01-12,129.570007,129.699997,128.539993,129.509995,118983700,116.840511\n2012-01-13,128.639999,129.050003,127.720001,128.839996,179836200,116.236056\n2012-01-17,130.080002,130.320007,128.899994,129.339996,132209200,116.687143\n2012-01-18,129.309998,130.839996,129.080002,130.770004,163395200,117.977258\n2012-01-19,131.220001,131.570007,130.800003,131.460007,126328900,118.59976\n2012-01-20,131.240005,131.949997,130.919998,131.949997,138230200,119.041817\n2012-01-23,131.509995,132.25,130.979996,131.610001,129295800,118.735081\n2012-01-24,130.800003,131.5,130.600006,131.460007,103083300,118.59976\n2012-01-25,131.259995,132.869995,130.75,132.559998,198613200,119.592143\n2012-01-26,133.149994,133.399994,131.360001,131.880005,184880500,118.978672\n2012-01-27,131.240005,132.050003,131.149994,131.820007,135259100,118.924543\n2012-01-30,130.509995,131.440002,130.059998,131.369995,147311800,118.518554\n2012-01-31,132.020004,132.179993,130.679993,131.320007,157212000,118.473457\n2012-02-01,132.289993,133.139999,132.130005,132.470001,166234500,119.510951\n2012-02-02,132.729996,133.020004,132.210007,132.679993,113090400,119.7004\n2012-02-03,134.0,134.619995,133.770004,134.539993,160598500,121.378443\n2012-02-06,133.979996,134.509995,133.830002,134.449997,107694500,121.297251\n2012-02-07,134.169998,135.020004,133.639999,134.789993,135528100,121.603987\n2012-02-08,134.860001,135.220001,134.309998,135.190002,139361400,121.964864\n2012-02-09,135.410004,135.589996,134.559998,135.360001,148602900,122.118232\n2012-02-10,134.160004,134.470001,133.839996,134.360001,167907500,121.216059\n2012-02-13,135.320007,135.520004,134.740005,135.360001,115841900,122.118232\n2012-02-14,135.0,135.270004,134.25,135.190002,165329500,121.964864\n2012-02-15,135.630005,135.830002,134.289993,134.559998,195195100,121.396491\n2012-02-16,134.570007,136.169998,134.330002,136.050003,186567800,122.740734\n2012-02-17,136.520004,136.630005,135.960007,136.410004,129869400,123.065517\n2012-02-21,136.729996,137.050003,136.050003,136.470001,134042300,123.119646\n2012-02-22,136.259995,136.550003,135.789993,136.029999,124455300,122.722687\n2012-02-23,135.960007,136.729996,135.5,136.630005,137704300,123.263997\n2012-02-24,136.929993,137.199997,136.630005,136.929993,105539100,123.534638\n2012-02-27,136.020004,137.529999,135.800003,137.160004,145728900,123.742148\n2012-02-28,137.199997,137.720001,136.929993,137.559998,129355900,124.103012\n2012-02-29,137.759995,138.190002,136.539993,137.020004,185934700,123.615844\n2012-03-01,137.309998,137.990005,136.929993,137.729996,145023500,124.25638\n2012-03-02,137.639999,137.820007,137.0,137.309998,120638300,123.877468\n2012-03-05,137.100006,137.199997,136.279999,136.75,140765000,123.372253\n2012-03-06,135.350006,135.429993,134.360001,134.75,202129900,121.567906\n2012-03-07,135.059998,135.910004,134.929993,135.690002,143692200,122.415951\n2012-03-08,136.520004,137.320007,136.240005,137.039993,116968900,123.633878\n2012-03-09,137.300003,137.929993,137.130005,137.570007,122836800,124.112042\n2012-03-12,137.550003,137.759995,137.089996,137.580002,104003500,124.121059\n2012-03-13,138.320007,140.130005,138.089996,140.059998,184090500,126.358446\n2012-03-14,140.100006,140.449997,139.479996,139.910004,145163600,126.223125\n2012-03-15,140.119995,140.779999,139.759995,140.720001,165118500,126.953884\n2012-03-16,140.360001,140.479996,140.0,140.300003,152893500,127.129676\n2012-03-19,140.210007,141.279999,140.110001,140.850006,125291100,127.628049\n2012-03-20,140.050003,140.610001,139.639999,140.440002,121729700,127.256533\n2012-03-21,140.520004,140.649994,139.919998,140.210007,122388400,127.048128\n2012-03-22,139.179993,139.550003,138.740005,139.199997,135216700,126.13293\n2012-03-23,139.320007,139.809998,138.550003,139.649994,120521000,126.540685\n2012-03-26,140.649994,141.610001,140.600006,141.610001,120164000,128.316701\n2012-03-27,141.740005,141.830002,141.080002,141.169998,119868500,127.918002\n2012-03-28,141.100006,141.320007,139.639999,140.470001,148562100,127.283716\n2012-03-29,139.639999,140.490005,139.089996,140.229996,164963700,127.06624\n2012-03-30,140.919998,141.050003,140.050003,140.809998,135486800,127.591796\n2012-04-02,140.639999,142.210007,140.360001,141.839996,151741100,128.525106\n2012-04-03,141.639999,141.880005,140.429993,141.259995,155806700,127.999551\n2012-04-04,140.220001,140.339996,139.339996,139.860001,146896000,126.730978\n2012-04-05,139.380005,140.199997,139.259995,139.789993,137439400,126.667542\n2012-04-09,138.029999,139.839996,137.839996,138.220001,127555900,125.244929\n2012-04-10,137.949997,138.339996,135.759995,135.899994,235360300,123.142707\n2012-04-11,137.289993,137.539993,136.75,137.0,154133000,124.139453\n2012-04-12,137.130005,138.899994,137.029999,138.789993,154321500,125.761415\n2012-04-13,138.470001,138.820007,137.009995,137.139999,169246700,124.26631\n2012-04-16,137.839996,138.039993,136.580002,137.050003,147825300,124.184762\n2012-04-17,137.839996,139.360001,137.699997,139.080002,147877600,126.0242\n2012-04-18,138.460007,139.080002,138.380005,138.610001,123884200,125.598319\n2012-04-19,138.630005,139.149994,137.070007,137.720001,198666700,124.791866\n2012-04-20,138.330002,138.830002,137.869995,137.949997,143199600,125.000271\n2012-04-23,136.539993,136.910004,135.940002,136.789993,171844900,123.94916\n2012-04-24,136.910004,137.660004,136.800003,137.309998,137484200,124.42035\n2012-04-25,138.649994,139.25,138.529999,139.190002,150252200,126.123874\n2012-04-26,138.889999,140.320007,138.809998,140.160004,136291600,127.002819\n2012-04-27,140.580002,140.789993,139.800003,140.389999,130725000,127.211224\n2012-04-30,140.110001,140.210007,139.490005,139.869995,115092200,126.740034\n2012-05-01,139.789993,141.660004,139.630005,140.740005,138832200,127.528374\n2012-05-02,139.919998,140.460007,139.460007,140.320007,121081000,127.147802\n2012-05-03,140.339996,140.449997,138.990005,139.25,143759700,126.17824\n2012-05-04,138.520004,139.300003,136.919998,137.0,193927300,124.139453\n2012-05-07,136.509995,137.559998,136.460007,137.100006,127765900,124.230071\n2012-05-08,136.279999,136.770004,134.919998,136.550003,213377700,123.731698\n2012-05-09,135.100006,136.610001,134.490005,135.740005,220752500,122.997737\n2012-05-10,136.679993,136.850006,135.710007,136.020004,150600000,123.251452\n2012-05-11,135.169998,136.869995,135.110001,135.610001,153032400,122.879936\n2012-05-14,134.309998,135.610001,133.910004,134.110001,163910000,121.520745\n2012-05-15,134.020004,134.809998,133.130005,133.339996,207629300,120.823023\n2012-05-16,133.940002,134.550003,132.800003,132.830002,207265500,120.360903\n2012-05-17,132.860001,133.020004,130.789993,130.860001,247992900,118.575831\n2012-05-18,131.369995,131.600006,129.550003,129.740005,319615900,117.560973\n2012-05-21,130.160004,132.020004,129.949997,131.970001,177861100,119.581633\n2012-05-22,132.309998,133.229996,131.339996,132.199997,197531200,119.790039\n2012-05-23,131.25,132.460007,129.990005,132.270004,204958400,119.853474\n2012-05-24,132.630005,132.839996,131.419998,132.529999,167357600,120.089062\n2012-05-25,132.479996,132.850006,131.779999,132.100006,135465600,119.699434\n2012-05-29,133.160004,133.929993,131.169998,133.699997,152883500,121.14923\n2012-05-30,132.559998,133.690002,131.490005,131.759995,162370400,119.39134\n2012-05-31,131.710007,132.449997,130.339996,131.470001,196186000,119.128569\n2012-06-01,129.410004,131.5,128.160004,128.160004,253240900,116.12929\n2012-06-04,128.389999,128.740005,127.139999,128.100006,202545800,116.074925\n2012-06-05,127.849998,129.259995,127.779999,129.070007,164149400,116.953869\n2012-06-06,129.970001,132.029999,129.929993,131.970001,184202800,119.581633\n2012-06-07,133.470001,133.529999,131.779999,132.050003,184772700,119.654125\n2012-06-08,131.710007,133.130005,131.289993,133.100006,143915400,120.605562\n2012-06-11,134.169998,134.25,131.279999,131.410004,169756100,119.074204\n2012-06-12,131.789993,133.009995,131.160004,132.919998,181931800,120.442451\n2012-06-13,132.529999,133.360001,131.619995,132.070007,172223900,119.672251\n2012-06-14,132.339996,134.0,131.979996,133.470001,230615500,120.940824\n2012-06-15,133.380005,134.259995,133.100006,134.139999,169444500,122.177715\n2012-06-18,133.580002,134.729996,133.279999,134.399994,131360900,122.414524\n2012-06-19,135.080002,136.25,134.369995,135.699997,137382600,123.598596\n2012-06-20,135.710007,136.100006,134.270004,135.479996,206451800,123.398214\n2012-06-21,135.639999,135.779999,132.330002,132.440002,205272200,120.62932\n2012-06-22,133.130005,133.710007,132.619995,133.460007,130029200,121.558362\n2012-06-25,132.050003,132.100006,130.850006,131.320007,146375700,119.609203\n2012-06-26,131.699997,132.380005,130.929993,131.979996,141634000,120.210335\n2012-06-27,132.419998,133.429993,131.970001,133.169998,108088000,121.294216\n2012-06-28,132.289993,132.990005,131.279999,132.789993,169242100,120.948099\n2012-06-29,135.199997,136.270004,134.850006,136.100006,212250900,123.962933\n2012-07-02,136.479996,136.649994,135.520004,136.509995,129524500,124.33636\n2012-07-03,136.479996,137.509995,136.339996,137.410004,80450000,125.156108\n2012-07-05,136.899994,137.800003,136.289993,136.789993,126177500,124.591389\n2012-07-06,135.470001,135.770004,134.850006,135.490005,151192100,123.407331\n2012-07-09,135.380005,135.570007,134.699997,135.320007,103780500,123.252493\n2012-07-10,136.009995,136.229996,133.679993,134.139999,167884800,122.177715\n2012-07-11,134.210007,134.600006,133.380005,134.160004,141733400,122.195935\n2012-07-12,133.380005,134.229996,132.600006,133.509995,143583200,121.603892\n2012-07-13,133.860001,135.889999,133.839996,135.75,129642600,123.64414\n2012-07-16,135.440002,135.830002,134.899994,135.429993,97525200,123.35267\n2012-07-17,135.970001,136.639999,134.550003,136.360001,138860300,124.199742\n2012-07-18,136.039993,137.639999,135.960007,137.369995,113349700,125.119667\n2012-07-19,137.649994,138.179993,137.210007,137.729996,129847300,125.447564\n2012-07-20,136.949997,137.160004,136.320007,136.470001,142904500,124.299933\n2012-07-23,134.470001,136.380005,133.839996,135.089996,145210900,123.042994\n2012-07-24,135.190002,135.25,133.029999,133.929993,173301200,121.986436\n2012-07-25,134.210007,134.559998,133.25,133.960007,129122300,122.013774\n2012-07-26,135.889999,136.460007,135.259995,136.169998,156526500,124.026683\n2012-07-27,136.889999,139.070007,136.139999,138.679993,236768900,126.312843\n2012-07-30,138.520004,139.339996,138.270004,138.679993,106782000,126.312843\n2012-07-31,138.490005,138.869995,137.710007,137.710007,120575900,125.429358\n2012-08-01,138.699997,138.729996,137.399994,137.589996,138293800,125.32005\n2012-08-02,136.550003,137.570007,135.580002,136.639999,199556600,124.454771\n2012-08-03,138.559998,139.639999,136.679993,139.350006,157825000,126.923106\n2012-08-06,139.720001,140.169998,139.559998,139.619995,86326200,127.169018\n2012-08-07,140.179993,140.919998,140.029999,140.320007,109545100,127.806605\n2012-08-08,139.850006,140.649994,139.809998,140.490005,89754700,127.961443\n2012-08-09,140.289993,140.889999,140.149994,140.610001,90291700,128.070737\n2012-08-10,140.039993,140.889999,139.809998,140.839996,99792700,128.280222\n2012-08-13,140.600006,140.839996,140.039993,140.770004,79426900,128.216472\n2012-08-14,141.289993,141.380005,140.369995,140.789993,102379400,128.234678\n2012-08-15,140.639999,141.190002,140.550003,140.949997,71085900,128.380413\n2012-08-16,141.149994,142.160004,140.800003,141.990005,112014200,129.327676\n2012-08-17,142.229996,142.300003,141.860001,142.179993,90813700,129.500721\n2012-08-20,141.979996,142.220001,141.589996,142.190002,78255700,129.509838\n2012-08-21,142.539993,143.089996,141.449997,141.759995,105581100,129.118177\n2012-08-22,141.399994,142.050003,141.070007,141.820007,133243500,129.172838\n2012-08-23,141.470001,141.479996,140.440002,140.660004,111466400,128.116281\n2012-08-24,140.309998,141.830002,140.220001,141.509995,99481200,128.890472\n2012-08-27,141.889999,142.080002,141.339996,141.539993,68785900,128.917795\n2012-08-28,141.179993,141.839996,140.970001,141.399994,75689600,128.790281\n2012-08-29,141.520004,141.889999,141.119995,141.509995,65421300,128.890472\n2012-08-30,140.899994,140.940002,140.190002,140.490005,96589900,127.961443\n2012-08-31,141.289993,141.820007,140.360001,141.160004,151970400,128.571692\n2012-09-04,141.039993,141.460007,140.130005,141.029999,120226200,128.453281\n2012-09-05,141.089996,141.470001,140.630005,140.910004,100660300,128.343987\n2012-09-06,141.759995,143.779999,141.75,143.770004,158272500,130.948939\n2012-09-07,144.009995,144.389999,143.880005,144.330002,107272100,131.458997\n2012-09-10,144.190002,144.440002,143.460007,143.509995,86458500,130.712116\n2012-09-11,143.600006,144.369995,143.559998,143.910004,88760000,131.076454\n2012-09-12,144.389999,144.550003,143.899994,144.389999,87640900,131.513645\n2012-09-13,144.369995,147.039993,143.990005,146.589996,225470200,133.517451\n2012-09-14,146.880005,148.110001,146.759995,147.240005,169777000,134.109494\n2012-09-17,146.940002,147.190002,146.369995,146.740005,119427800,133.654083\n2012-09-18,146.490005,146.809998,146.25,146.619995,98326600,133.544775\n2012-09-19,146.789993,147.169998,146.410004,146.699997,128318300,133.617642\n2012-09-20,146.029999,146.789993,145.630005,146.710007,154009800,133.626759\n2012-09-21,146.639999,146.669998,145.809998,145.869995,108737500,133.570897\n2012-09-24,145.149994,145.979996,145.039993,145.649994,95682000,133.369445\n2012-09-25,145.960007,146.240005,144.059998,144.100006,133165200,131.950145\n2012-09-26,144.070007,144.110001,142.949997,143.289993,146502200,131.208429\n2012-09-27,143.889999,144.970001,143.509995,144.639999,111830300,132.444609\n2012-09-28,144.089996,144.559998,143.460007,143.970001,150696100,131.831102\n2012-10-01,144.520004,145.690002,144.009995,144.350006,135911200,132.179066\n2012-10-02,144.919998,145.149994,143.830002,144.5,113422200,132.316413\n2012-10-03,144.889999,145.429993,144.130005,145.089996,121283100,132.856664\n2012-10-04,145.639999,146.339996,145.440002,146.130005,124311600,133.808984\n2012-10-05,146.910004,147.160004,145.699997,146.139999,124842100,133.818135\n2012-10-08,145.600006,146.119995,145.309998,145.639999,78415400,133.360293\n2012-10-09,145.529999,145.649994,144.149994,144.199997,148872900,132.041705\n2012-10-10,144.179993,144.320007,143.089996,143.279999,124247500,131.199277\n2012-10-11,144.279999,144.490005,143.330002,143.360001,123601500,131.272534\n2012-10-12,143.460007,143.949997,142.580002,142.889999,124181900,130.842161\n2012-10-15,143.229996,144.229996,142.770004,144.080002,107689100,131.931827\n2012-10-16,144.759995,145.639999,144.660004,145.539993,108815500,133.268719\n2012-10-17,145.639999,146.320007,145.419998,146.199997,128834100,133.873074\n2012-10-18,145.820007,146.520004,145.330002,145.820007,148108500,133.525124\n2012-10-19,145.550003,145.559998,143.050003,143.389999,185645200,131.300003\n2012-10-22,143.149994,143.669998,142.279999,143.410004,125578600,131.318321\n2012-10-23,141.860001,142.059998,140.830002,141.419998,192056300,129.496103\n2012-10-24,141.929993,142.100006,140.800003,141.020004,120179400,129.129835\n2012-10-25,142.020004,142.279999,140.570007,141.429993,134457400,129.505255\n2012-10-26,141.300003,141.839996,140.389999,141.350006,146023500,129.432013\n2012-10-31,141.850006,142.029999,140.679993,141.350006,103438500,129.432013\n2012-11-01,141.649994,143.009995,141.520004,142.830002,100995600,130.787222\n2012-11-02,143.679993,143.720001,141.410004,141.559998,137702200,129.624299\n2012-11-05,141.350006,142.169998,140.929993,141.850006,98378500,129.889855\n2012-11-06,142.279999,143.520004,142.130005,142.960007,107068100,130.906265\n2012-11-07,141.660004,141.679993,139.059998,139.720001,264304500,127.939442\n2012-11-08,139.699997,140.410004,137.929993,138.039993,181517300,126.401085\n2012-11-09,137.619995,139.440002,137.550003,138.160004,201055300,126.510977\n2012-11-12,138.589996,138.809998,137.960007,138.270004,97677500,126.611703\n2012-11-13,137.539993,139.25,137.360001,137.789993,123018300,126.172164\n2012-11-14,138.210007,138.429993,135.619995,135.929993,191505000,124.46899\n2012-11-15,135.979996,136.490005,135.179993,135.699997,178128400,124.258387\n2012-11-16,135.899994,136.639999,134.699997,136.369995,239483900,124.871894\n2012-11-19,137.899994,139.149994,136.410004,139.130005,151495800,127.399192\n2012-11-20,138.910004,139.419998,138.080002,139.190002,119807400,127.454131\n2012-11-21,139.309998,139.570007,139.029999,139.449997,81710800,127.692204\n2012-11-23,140.130005,141.399994,140.039993,141.350006,65409200,129.432013\n2012-11-26,140.649994,141.360001,140.190002,141.050003,100124400,129.157305\n2012-11-27,140.910004,141.389999,140.240005,140.330002,128646200,128.498011\n2012-11-28,139.759995,141.539993,139.0,141.460007,177086500,129.532739\n2012-11-29,141.990005,142.509995,141.369995,142.119995,151085900,130.13708\n2012-11-30,142.139999,142.419998,141.660004,142.149994,136568300,130.164549\n2012-12-03,142.800003,142.919998,141.339996,141.449997,124656300,129.523573\n2012-12-04,141.440002,141.869995,140.869995,141.25,127512200,129.340439\n2012-12-05,141.369995,142.160004,140.369995,141.5,147300500,129.56936\n2012-12-06,141.369995,142.039993,141.160004,141.979996,103220600,130.008884\n2012-12-07,142.529999,142.690002,141.669998,142.410004,108726400,130.402636\n2012-12-10,142.210007,142.809998,142.149994,142.470001,98840700,130.457575\n2012-12-11,143.059998,144.110001,142.990005,143.440002,152570400,131.34579\n2012-12-12,144.0,144.550003,143.309998,143.509995,145880100,131.409881\n2012-12-13,143.419998,143.830002,142.270004,142.630005,135715000,130.604088\n2012-12-14,142.320007,142.580002,141.880005,142.100006,137701700,130.118776\n2012-12-17,142.470001,143.850006,142.429993,143.770004,143238200,131.647968\n2012-12-18,144.0,145.5,143.789993,145.369995,177762800,133.113054\n2012-12-19,145.529999,145.580002,144.240005,144.289993,150895400,132.124113\n2012-12-20,144.380005,145.139999,143.979996,145.119995,168487000,132.884133\n2012-12-21,142.169998,144.089996,141.940002,142.789993,245883800,131.677922\n2012-12-24,142.479996,142.559998,142.190002,142.350006,53874600,131.272175\n2012-12-26,142.639999,142.710007,141.350006,141.75,106947700,130.718862\n2012-12-27,141.789993,142.080002,139.919998,141.559998,167920600,130.543646\n2012-12-28,140.639999,141.419998,139.869995,140.029999,148806700,129.132714\n2012-12-31,139.660004,142.559998,139.539993,142.410004,243935200,131.327504\n2013-01-02,145.110001,146.149994,144.729996,146.059998,192059000,134.693451\n2013-01-03,145.990005,146.369995,145.339996,145.729996,144761800,134.389131\n2013-01-04,145.970001,146.610001,145.669998,146.369995,116817700,134.979325\n2013-01-07,145.850006,146.110001,145.429993,145.970001,110002500,134.610459\n2013-01-08,145.710007,145.910004,144.979996,145.550003,121265100,134.223145\n2013-01-09,145.869995,146.320007,145.639999,145.919998,90745600,134.564347\n2013-01-10,146.729996,147.089996,145.970001,147.080002,130735400,135.634078\n2013-01-11,147.039993,147.149994,146.610001,147.070007,113917300,135.624861\n2013-01-14,146.889999,147.070007,146.429993,146.970001,89567200,135.532638\n2013-01-15,146.289993,147.210007,146.199997,147.070007,93172600,135.624861\n2013-01-16,146.770004,147.279999,146.610001,147.050003,104849500,135.606414\n2013-01-17,147.699997,148.419998,147.149994,148.0,133833500,136.482481\n2013-01-18,147.970001,148.490005,147.429993,148.330002,169906000,136.786801\n2013-01-22,148.330002,149.130005,147.979996,149.130005,111797300,137.524547\n2013-01-23,149.130005,149.5,148.860001,149.369995,104596100,137.745861\n2013-01-24,149.149994,150.139999,149.009995,149.410004,146426400,137.782756\n2013-01-25,149.880005,150.25,149.369995,150.25,147211600,138.557383\n2013-01-28,150.289993,150.330002,149.509995,150.070007,113357700,138.391398\n2013-01-29,149.770004,150.850006,149.669998,150.660004,105694400,138.93548\n2013-01-30,150.639999,150.940002,149.929993,150.070007,137447700,138.391398\n2013-01-31,149.889999,150.380005,149.600006,149.699997,108975800,138.050182\n2013-02-01,150.649994,151.419998,150.389999,151.240005,131173000,139.470346\n2013-02-04,150.320007,151.270004,149.429993,149.539993,159073600,137.90263\n2013-02-05,150.350006,151.479996,150.289993,151.050003,113912400,139.295129\n2013-02-06,150.520004,151.259995,150.410004,151.160004,138762800,139.39657\n2013-02-07,151.210007,151.350006,149.860001,150.960007,162490000,139.212137\n2013-02-08,151.220001,151.889999,151.220001,151.800003,103133700,139.986763\n2013-02-11,151.740005,151.899994,151.389999,151.770004,73775000,139.959099\n2013-02-12,151.779999,152.300003,151.610001,152.020004,65392700,140.189644\n2013-02-13,152.330002,152.610001,151.720001,152.149994,82322600,140.309518\n2013-02-14,151.690002,152.470001,151.520004,152.289993,80834300,140.438622\n2013-02-15,152.429993,152.589996,151.550003,152.110001,215226500,140.272637\n2013-02-19,152.369995,153.279999,152.160004,153.25,95105400,141.32392\n2013-02-20,153.139999,153.190002,151.259995,151.339996,160574800,139.562555\n2013-02-21,150.960007,151.419998,149.940002,150.419998,183257000,138.714152\n2013-02-22,151.149994,151.889999,150.490005,151.889999,106356600,140.069756\n2013-02-25,152.630005,152.860001,149.0,149.0,245824800,137.40466\n2013-02-26,149.720001,150.199997,148.729996,150.020004,186596200,138.345286\n2013-02-27,149.889999,152.330002,149.759995,151.910004,150781900,140.088204\n2013-02-28,151.899994,152.869995,151.410004,151.610001,126866000,139.811547\n2013-03-01,151.089996,152.339996,150.410004,152.110001,170634800,140.272637\n2013-03-04,151.759995,152.919998,151.520004,152.919998,99010200,141.019599\n2013-03-05,153.660004,154.699997,153.639999,154.289993,121431900,142.28298\n2013-03-06,154.839996,154.919998,154.160004,154.5,94469900,142.476644\n2013-03-07,154.699997,154.979996,154.520004,154.779999,86101400,142.734853\n2013-03-08,155.460007,155.649994,154.660004,155.440002,123477800,143.343494\n2013-03-11,155.320007,156.039993,155.130005,156.029999,83746800,143.887576\n2013-03-12,155.919998,156.100006,155.210007,155.679993,105755800,143.564808\n2013-03-13,155.759995,156.119995,155.229996,155.899994,92550900,143.767689\n2013-03-14,156.309998,156.800003,155.910004,156.729996,126329900,144.533099\n2013-03-15,155.850006,156.039993,155.309998,155.830002,138601100,144.342282\n2013-03-18,154.339996,155.639999,154.199997,154.970001,126704300,143.545681\n2013-03-19,155.300003,155.509995,153.589996,154.610001,167567300,143.212219\n2013-03-20,155.520004,155.949997,155.259995,155.690002,113759300,144.212604\n2013-03-21,154.759995,155.639999,154.100006,154.360001,128605000,142.980649\n2013-03-22,154.850006,155.600006,154.729996,155.600006,111163600,144.129242\n2013-03-25,156.009995,156.270004,154.350006,154.949997,151322300,143.527151\n2013-03-26,155.589996,156.229996,155.419998,156.190002,86856600,144.675744\n2013-03-27,155.259995,156.240005,155.0,156.190002,99950600,144.675744\n2013-03-28,156.089996,156.850006,155.75,156.669998,102932800,145.120355\n2013-04-01,156.589996,156.910004,155.669998,156.050003,99194100,144.546065\n2013-04-02,156.610001,157.210007,156.369995,156.820007,101504300,145.259305\n2013-04-03,156.910004,157.029999,154.820007,155.229996,154167400,143.786509\n2013-04-04,155.429993,156.169998,155.089996,155.860001,131885000,144.37007\n2013-04-05,153.949997,155.350006,153.770004,155.160004,159666000,143.721676\n2013-04-08,155.270004,156.220001,154.75,156.210007,86571200,144.694274\n2013-04-09,156.5,157.320007,155.979996,156.75,101922200,145.194459\n2013-04-10,157.169998,158.869995,157.130005,158.669998,135711100,146.972916\n2013-04-11,158.699997,159.710007,158.539993,159.190002,110142500,147.454585\n2013-04-12,158.679993,159.039993,157.919998,158.800003,116359900,147.093337\n2013-04-15,158.0,158.130005,155.100006,155.119995,217259000,143.684617\n2013-04-16,156.289993,157.490005,155.910004,157.410004,147507800,145.805807\n2013-04-17,156.289993,156.320007,154.279999,155.110001,226834800,143.675359\n2013-04-18,155.369995,155.410004,153.550003,154.139999,167583200,142.776866\n2013-04-19,154.5,155.550003,154.119995,155.479996,149687600,144.018079\n2013-04-22,155.779999,156.539993,154.75,156.169998,106553500,144.657214\n2013-04-23,156.949997,157.929993,156.169998,157.779999,166141300,146.148527\n2013-04-24,157.830002,158.300003,157.539993,157.880005,96781200,146.24116\n2013-04-25,158.339996,159.270004,158.100006,158.520004,131060600,146.833979\n2013-04-26,158.330002,158.600006,157.729996,158.240005,95918800,146.574622\n2013-04-29,158.669998,159.649994,158.419998,159.300003,88572800,147.556477\n2013-04-30,159.270004,159.720001,158.610001,159.679993,116010700,147.908454\n2013-05-01,159.330002,159.410004,158.100006,158.279999,138874200,146.611667\n2013-05-02,158.679993,159.889999,158.529999,159.75,96407600,147.9733\n2013-05-03,161.139999,161.880005,159.779999,161.369995,144202300,149.47387\n2013-05-06,161.490005,162.009995,161.419998,161.779999,66882100,149.853648\n2013-05-07,162.130005,162.649994,161.669998,162.600006,90359200,150.613205\n2013-05-08,162.419998,163.389999,162.330002,163.339996,97419200,151.298643\n2013-05-09,163.270004,163.699997,162.470001,162.880005,106738600,150.872562\n2013-05-10,162.990005,163.550003,162.509995,163.410004,103203000,151.36349\n2013-05-13,163.199997,163.809998,162.820007,163.539993,81843200,151.483897\n2013-05-14,163.669998,165.350006,163.669998,165.229996,119000900,153.049313\n2013-05-15,164.960007,166.449997,164.910004,166.119995,120718500,153.873702\n2013-05-16,165.779999,166.360001,165.089996,165.339996,109913600,153.151204\n2013-05-17,165.949997,167.039993,165.729996,166.940002,129801000,154.633259\n2013-05-20,166.779999,167.580002,166.610001,166.929993,85071200,154.623987\n2013-05-21,167.080002,167.800003,166.5,167.169998,95804200,154.846299\n2013-05-22,167.339996,169.070007,165.169998,165.929993,244031800,153.697706\n2013-05-23,164.160004,165.910004,163.940002,165.449997,211064400,153.253096\n2013-05-24,164.470001,165.380005,163.979996,165.309998,151573900,153.123417\n2013-05-28,167.039993,167.779999,165.809998,166.300003,143679800,154.04044\n2013-05-29,165.419998,165.800003,164.339996,165.220001,160363400,153.040055\n2013-05-30,165.350006,166.589996,165.220001,165.830002,107793800,153.605087\n2013-05-31,165.369995,166.309998,163.130005,163.449997,176850100,151.400535\n2013-06-03,163.830002,164.460007,162.660004,164.350006,168390700,152.234196\n2013-06-04,164.440002,165.100006,162.729996,163.559998,157631500,151.502426\n2013-06-05,163.089996,163.419998,161.130005,161.270004,211737800,149.38125\n2013-06-06,161.199997,162.740005,160.25,162.729996,200225500,150.733612\n2013-06-07,163.850006,164.949997,163.139999,164.800003,188337800,152.651019\n2013-06-10,165.309998,165.399994,164.369995,164.800003,105667100,152.651019\n2013-06-11,163.300003,164.539993,162.740005,163.100006,159505400,151.076345\n2013-06-12,164.220001,164.389999,161.600006,161.75,177361500,149.825861\n2013-06-13,161.660004,164.5,161.300003,164.210007,163587800,152.104517\n2013-06-14,164.029999,164.669998,162.910004,163.179993,141197500,151.150435\n2013-06-17,164.289993,165.220001,163.220001,164.440002,136295600,152.317558\n2013-06-18,164.529999,165.990005,164.520004,165.740005,114695600,153.521725\n2013-06-19,165.600006,165.889999,163.380005,163.449997,206149500,151.400535\n2013-06-20,161.860001,163.470001,158.979996,159.399994,321255900,147.649096\n2013-06-21,159.639999,159.759995,157.470001,159.070007,271956800,148.123084\n2013-06-24,157.410004,158.429993,155.729996,157.059998,222329000,146.2514\n2013-06-25,158.479996,160.100006,157.419998,158.570007,162262200,147.657494\n2013-06-26,159.869995,160.5,159.25,160.139999,134848000,149.119441\n2013-06-27,161.100006,161.820007,160.949997,161.080002,129483700,149.994755\n2013-06-28,160.630005,161.399994,159.860001,160.419998,160402900,149.380171\n2013-07-01,161.259995,162.479996,161.080002,161.360001,131954800,150.255484\n2013-07-02,161.119995,162.300003,160.5,161.210007,154863700,150.115813\n2013-07-03,160.479996,161.770004,160.220001,161.279999,75216400,150.180988\n2013-07-05,162.470001,163.080002,161.300003,163.020004,122416900,151.801249\n2013-07-08,163.860001,164.389999,163.080002,163.949997,108092500,152.667242\n2013-07-09,164.979996,165.330002,164.270004,165.130005,119298000,153.766043\n2013-07-10,164.970001,165.75,164.630005,165.190002,121410100,153.821912\n2013-07-11,167.110001,167.610001,165.179993,167.440002,135592200,155.917071\n2013-07-12,167.389999,167.929993,167.130005,167.509995,104212700,155.982246\n2013-07-15,167.970001,168.389999,167.679993,168.149994,69450600,156.578202\n2013-07-16,168.259995,168.360001,167.070007,167.520004,88702100,155.991567\n2013-07-17,168.160004,168.479996,167.729996,167.949997,92873900,156.391969\n2013-07-18,168.309998,169.270004,168.199997,168.869995,103620100,157.248654\n2013-07-19,168.520004,169.229996,168.309998,169.169998,103831700,157.528011\n2013-07-22,169.410004,169.740005,169.009995,169.5,79428600,157.835303\n2013-07-23,169.800003,169.830002,169.050003,169.139999,80829700,157.500077\n2013-07-24,169.789993,169.860001,168.179993,168.520004,112914000,156.922749\n2013-07-25,168.220001,169.080002,167.940002,168.929993,111088600,157.304523\n2013-07-26,168.220001,169.160004,167.520004,169.110001,107814600,157.472143\n2013-07-29,168.679993,169.059998,168.110001,168.589996,79695000,156.987924\n2013-07-30,169.100006,169.279999,168.190002,168.589996,85209600,156.987924\n2013-07-31,168.940002,169.850006,168.490005,168.710007,142388700,157.099676\n2013-08-01,169.990005,170.809998,169.899994,170.660004,110438400,158.915477\n2013-08-02,170.279999,170.970001,170.050003,170.949997,91116700,159.185514\n2013-08-05,170.570007,170.960007,170.350006,170.699997,54072700,158.952718\n2013-08-06,170.369995,170.740005,169.350006,169.729996,87495000,158.049471\n2013-08-07,169.190002,169.429993,168.550003,169.179993,84854700,157.537318\n2013-08-08,169.979996,170.179993,168.929993,169.800003,102181300,158.11466\n2013-08-09,169.580002,170.100006,168.720001,169.309998,91757700,157.658376\n2013-08-12,168.460007,169.309998,168.380005,169.110001,68593300,157.472143\n2013-08-13,169.410004,169.899994,168.410004,169.610001,80806000,157.937734\n2013-08-14,169.529999,169.800003,168.699997,168.740005,79829200,157.12761\n2013-08-15,167.410004,167.429993,166.089996,166.380005,152931800,154.930021\n2013-08-16,166.059998,166.630005,165.5,165.830002,130868200,154.417868\n2013-08-19,165.639999,166.210007,164.759995,164.770004,96437600,153.430817\n2013-08-20,165.039993,166.199997,164.860001,165.580002,89294400,154.185072\n2013-08-21,165.119995,166.029999,164.190002,164.559998,159530500,153.235263\n2013-08-22,164.899994,166.300003,164.889999,166.059998,101471400,154.632036\n2013-08-23,166.550003,166.830002,165.770004,166.619995,90888900,155.153495\n2013-08-26,166.789993,167.300003,165.889999,166.0,89702100,154.576167\n2013-08-27,164.360001,166.0,163.210007,163.330002,158619400,152.089913\n2013-08-28,163.259995,164.490005,163.050003,163.910004,108113000,152.630001\n2013-08-29,163.550003,165.039993,163.399994,164.169998,119200500,152.872103\n2013-08-30,164.509995,164.529999,163.169998,163.649994,134928900,152.387884\n2013-09-03,165.229996,165.580002,163.699997,164.389999,142375100,153.076964\n2013-09-04,164.429993,166.029999,164.130005,165.75,97389400,154.343372\n2013-09-05,165.850006,166.399994,165.729996,165.960007,63090500,154.538926\n2013-09-06,166.509995,166.979996,164.479996,166.039993,159756500,154.613408\n2013-09-09,166.449997,167.729996,166.449997,167.630005,87559300,156.093998\n2013-09-10,168.639999,168.899994,168.259995,168.869995,105847200,157.248654\n2013-09-11,168.639999,169.399994,168.350006,169.399994,94545900,157.742179\n2013-09-12,169.339996,169.559998,168.720001,168.949997,83209000,157.32315\n2013-09-13,169.130005,169.460007,168.740005,169.330002,72727800,157.677004\n2013-09-16,171.160004,171.240005,170.039993,170.309998,106299200,158.589558\n2013-09-17,170.460007,171.110001,170.460007,171.070007,82523300,159.297265\n2013-09-18,171.009995,173.520004,170.580002,173.050003,203460600,161.141001\n2013-09-19,173.520004,173.600006,172.589996,172.759995,146616900,160.87095\n2013-09-20,172.330002,172.330002,170.580002,170.720001,132867100,159.746226\n2013-09-23,170.490005,170.649994,169.389999,169.929993,104616500,159.006999\n2013-09-24,169.899994,170.529999,169.210007,169.529999,106333100,158.632716\n2013-09-25,169.639999,169.979996,168.889999,169.039993,117306500,158.174208\n2013-09-26,169.320007,170.169998,169.050003,169.690002,77146900,158.782435\n2013-09-27,168.839996,169.139999,168.470001,168.910004,99141800,158.052574\n2013-09-30,167.479996,168.539993,167.149994,168.009995,143937000,157.210417\n2013-10-01,168.139999,169.5,167.970001,169.339996,127160000,158.454927\n2013-10-02,168.350006,169.339996,167.830002,169.179993,113350000,158.305208\n2013-10-03,168.789993,168.940002,166.839996,167.619995,176698000,156.845486\n2013-10-04,167.75,169.059998,167.529999,168.889999,96878000,158.033855\n2013-10-07,167.419998,168.449997,167.25,167.429993,96295000,156.667697\n2013-10-08,167.399994,167.619995,165.360001,165.479996,178015000,154.843045\n2013-10-09,165.800003,166.199997,164.529999,165.600006,168973000,154.955341\n2013-10-10,167.289993,169.259995,167.229996,169.169998,195955000,158.295856\n2013-10-11,168.910004,170.320007,168.770004,170.259995,105040000,159.315788\n2013-10-14,169.210007,171.080002,169.080002,170.940002,112106000,159.952086\n2013-10-15,170.509995,171.149994,169.470001,169.699997,155485000,158.791787\n2013-10-16,170.720001,172.160004,170.639999,172.070007,161676000,161.009454\n2013-10-17,171.369995,173.320007,171.339996,173.220001,129389000,162.085527\n2013-10-18,173.860001,174.509995,173.509995,174.389999,138316000,163.180319\n2013-10-21,174.449997,174.75,174.009995,174.399994,104104000,163.189671\n2013-10-22,174.910004,175.929993,174.429993,175.410004,126663000,164.134758\n2013-10-23,174.809998,174.889999,173.960007,174.570007,105484000,163.348756\n2013-10-24,174.919998,175.369995,174.509995,175.149994,70350000,163.891461\n2013-10-25,175.509995,176.0,175.169998,175.949997,93625000,164.640041\n2013-10-28,175.889999,176.470001,175.699997,176.229996,84979000,164.902041\n2013-10-29,176.630005,177.240005,176.380005,177.169998,87401000,165.781621\n2013-10-30,177.380005,177.509995,175.660004,176.289993,140002000,164.958182\n2013-10-31,176.149994,176.889999,175.529999,175.789993,133795000,164.490322\n2013-11-01,176.020004,176.610001,175.220001,176.210007,142805000,164.883337\n2013-11-04,176.690002,176.899994,175.979996,176.830002,85677000,165.463479\n2013-11-05,176.139999,176.75,175.570007,176.270004,85825000,164.939478\n2013-11-06,177.029999,177.5,176.539993,177.169998,87348000,165.781621\n2013-11-07,177.5,177.639999,174.759995,174.929993,157000000,163.685602\n2013-11-08,174.869995,177.309998,174.850006,177.289993,136713000,165.893903\n2013-11-11,177.119995,177.529999,176.910004,177.320007,62614000,165.921988\n2013-11-12,176.940002,177.360001,176.369995,176.960007,83990000,165.585128\n2013-11-13,176.089996,178.429993,176.089996,178.380005,103844000,166.913849\n2013-11-14,178.539993,179.419998,178.25,179.270004,103435000,167.74664\n2013-11-15,179.559998,180.119995,179.330002,180.050003,102818000,168.476501\n2013-11-18,180.350006,180.5,179.020004,179.419998,104796000,167.886992\n2013-11-19,179.330002,179.869995,178.720001,179.029999,93891000,167.522062\n2013-11-20,179.389999,179.929993,177.979996,178.470001,124909000,166.998061\n2013-11-21,178.970001,180.050003,178.860001,179.910004,92841000,168.345501\n2013-11-22,179.979996,180.830002,179.770004,180.809998,81296000,169.187643\n2013-11-25,181.130005,181.169998,180.369995,180.630005,79486000,169.019221\n2013-11-26,180.720001,181.220001,180.410004,180.679993,86994000,169.065995\n2013-11-27,180.869995,181.240005,180.649994,181.119995,58800000,169.477715\n2013-11-29,181.320007,181.75,180.800003,181.0,55870900,169.365433\n2013-12-02,181.089996,181.429993,180.25,180.529999,99726000,168.925643\n2013-12-03,179.940002,180.389999,179.169998,179.75,116563000,168.195782\n2013-12-04,179.100006,180.479996,178.350006,179.729996,123033000,168.177063\n2013-12-05,179.410004,179.740005,178.770004,178.940002,106934000,167.43785\n2013-12-06,180.669998,181.110001,180.149994,180.940002,127728000,169.309292\n2013-12-09,181.470001,181.669998,181.160004,181.399994,70124000,169.739715\n2013-12-10,180.979996,181.360001,180.639999,180.75,80976000,169.131503\n2013-12-11,180.820007,180.850006,178.5,178.720001,130591000,167.231991\n2013-12-12,178.639999,178.860001,177.759995,178.130005,115565000,166.679919\n2013-12-13,178.5,178.660004,177.770004,178.110001,107808000,166.661201\n2013-12-16,178.949997,179.809998,178.899994,179.220001,96195000,167.699851\n2013-12-17,179.380005,179.410004,178.25,178.649994,89886000,167.166484\n2013-12-18,178.919998,181.729996,177.320007,181.699997,234906000,170.020434\n2013-12-19,181.179993,181.699997,180.710007,181.490005,136531200,169.823941\n2013-12-20,180.690002,181.990005,180.570007,181.559998,197087000,170.811767\n2013-12-23,182.449997,182.639999,182.070007,182.529999,85598000,171.724345\n2013-12-24,182.539993,183.009995,182.529999,182.929993,45368800,172.100659\n2013-12-26,183.339996,183.960007,183.320007,183.860001,63365000,172.975611\n2013-12-27,184.100006,184.179993,183.660004,183.850006,61814000,172.966208\n2013-12-30,183.869995,184.020004,183.580002,183.820007,56857000,172.937985\n2013-12-31,184.070007,184.690002,183.929993,184.690002,86119900,173.756477\n2014-01-02,183.979996,184.070007,182.479996,182.919998,119636900,172.091256\n2014-01-03,183.229996,183.600006,182.630005,182.889999,81390600,172.063033\n2014-01-06,183.490005,183.559998,182.080002,182.360001,108028200,171.56441\n2014-01-07,183.089996,183.789993,182.949997,183.479996,86144200,172.618102\n2014-01-08,183.449997,183.830002,182.889999,183.520004,96582300,172.655742\n2014-01-09,184.110001,184.130005,182.800003,183.639999,90683400,172.768634\n2014-01-10,183.949997,184.220001,183.009995,184.139999,102026400,173.239034\n2014-01-13,183.669998,184.179993,181.339996,181.690002,149892000,170.934075\n2014-01-14,182.289993,183.770004,181.949997,183.669998,105016100,172.796857\n2014-01-15,184.100006,184.940002,183.710007,184.660004,98525800,173.728255\n2014-01-16,184.279999,184.660004,183.830002,184.419998,72290600,173.502457\n2014-01-17,184.100006,184.449997,183.320007,183.639999,107848700,172.768634\n2014-01-21,184.699997,184.770004,183.050003,184.179993,88621200,173.27666\n2014-01-22,184.490005,184.570007,183.910004,184.300003,61270900,173.389566\n2014-01-23,183.369995,183.399994,181.820007,182.789993,132496900,171.968948\n2014-01-24,181.600006,181.660004,178.830002,178.889999,208677100,168.299831\n2014-01-27,179.059998,179.520004,177.119995,178.009995,180843100,167.471922\n2014-01-28,178.139999,179.300003,178.119995,179.070007,110463200,168.469182\n2014-01-29,177.580002,178.550003,176.880005,177.350006,216597300,166.851004\n2014-01-30,178.830002,179.809998,178.259995,179.229996,118938100,168.619699\n2014-01-31,177.009995,179.289993,176.919998,178.179993,194677900,167.631856\n2014-02-03,177.970001,178.369995,173.830002,174.169998,254837100,163.85925\n2014-02-04,174.949997,175.839996,174.110001,175.389999,165012400,165.007028\n2014-02-05,174.779999,175.559998,173.710007,175.169998,164230500,164.800051\n2014-02-06,175.580002,177.479996,175.220001,177.479996,132877600,166.973298\n2014-02-07,178.309998,179.869995,177.729996,179.679993,170787200,169.043057\n2014-02-10,179.699997,180.070007,179.210007,180.009995,92218800,169.353523\n2014-02-11,180.160004,182.440002,180.039993,181.979996,117814100,171.206901\n2014-02-12,182.25,182.830002,181.710007,182.070007,94717700,171.291584\n2014-02-13,180.839996,183.199997,180.830002,183.009995,100542200,172.175925\n2014-02-14,182.839996,184.360001,182.669998,184.020004,96498400,173.126143\n2014-02-18,184.179993,184.490005,183.649994,184.240005,80460900,173.33312\n2014-02-19,183.759995,184.949997,182.869995,183.020004,126524300,172.185342\n2014-02-20,183.270004,184.520004,182.600006,184.100006,104998100,173.201409\n2014-02-21,184.449997,184.889999,183.800003,183.889999,118116400,173.003834\n2014-02-24,184.279999,186.149994,184.199997,184.910004,114063900,173.963455\n2014-02-25,185.059998,185.589996,184.229996,184.839996,117085000,173.897592\n2014-02-26,185.110001,185.600006,184.330002,184.850006,98677200,173.907009\n2014-02-27,184.580002,185.869995,184.369995,185.820007,93880800,174.819587\n2014-02-28,185.789993,187.149994,185.050003,186.289993,150842000,175.26175\n2014-03-03,184.649994,185.449997,183.75,184.979996,167748500,174.029303\n2014-03-04,186.789993,187.979996,186.75,187.580002,167545900,176.475391\n2014-03-05,187.740005,188.070007,187.449997,187.75,88376900,176.635325\n2014-03-06,188.210007,188.610001,187.779999,188.179993,82516500,177.039863\n2014-03-07,188.960007,188.960007,187.429993,188.259995,114513500,177.115128\n2014-03-10,187.970001,188.229996,187.080002,188.160004,74939200,177.021057\n2014-03-11,188.440002,188.710007,186.800003,187.229996,99009100,176.146105\n2014-03-12,186.320007,187.350006,185.899994,187.279999,104824400,176.193148\n2014-03-13,187.839996,187.990005,184.660004,185.179993,155014300,174.217461\n2014-03-14,184.850006,185.800003,184.440002,184.660004,153919600,173.728255\n2014-03-17,185.589996,186.770004,185.509995,186.330002,98359500,175.29939\n2014-03-18,186.710007,187.910004,186.509995,187.660004,101804600,176.550657\n2014-03-19,187.679993,187.940002,185.470001,186.660004,176267300,175.609856\n2014-03-20,186.25,187.889999,185.919998,187.75,117241000,176.635325\n2014-03-21,187.710007,189.020004,186.029999,186.199997,163128000,175.950239\n2014-03-24,186.839996,187.070007,184.619995,185.429993,121411000,175.222621\n2014-03-25,186.369995,186.940002,185.270004,186.309998,103852000,176.054185\n2014-03-26,187.039993,187.339996,184.919998,184.970001,119843000,174.787951\n2014-03-27,184.75,185.339996,183.899994,184.580002,142383000,174.41942\n2014-03-28,185.110001,186.419998,185.0,185.490005,101642000,175.279331\n2014-03-31,186.669998,187.300003,185.520004,187.009995,99745000,176.715649\n2014-04-01,187.619995,188.360001,187.0,188.25,89193000,177.887396\n2014-04-02,188.490005,189.130005,188.139999,188.880005,78774000,178.482721\n2014-04-03,189.169998,189.220001,188.050003,188.630005,77435000,178.246482\n2014-04-04,189.660004,189.699997,186.100006,186.399994,169381000,176.139227\n2014-04-07,185.949997,186.259995,183.960007,184.339996,140803000,174.192626\n2014-04-08,184.259995,185.399994,183.589996,185.100006,112660000,174.9108\n2014-04-09,185.600006,187.149994,185.059998,187.089996,100254000,176.791247\n2014-04-10,187.080002,187.169998,182.929993,183.160004,172959000,173.077588\n2014-04-11,182.139999,183.419998,181.309998,181.509995,167251000,171.518407\n2014-04-14,182.929993,183.369995,181.440002,182.940002,132382000,172.869698\n2014-04-15,183.320007,184.330002,181.509995,184.199997,157093000,174.060333\n2014-04-16,185.470001,186.139999,184.649994,186.130005,105197000,175.8841\n2014-04-17,185.880005,186.910004,185.559998,186.389999,105255000,176.129783\n2014-04-21,186.440002,187.100006,186.210007,187.039993,68329000,176.743996\n2014-04-22,187.229996,188.399994,187.130005,187.889999,85790000,177.547212\n2014-04-23,187.820007,187.919998,187.300003,187.449997,73869000,177.13143\n2014-04-24,188.369995,188.389999,186.929993,187.830002,88170000,177.490517\n2014-04-25,187.220001,187.330002,185.869995,186.289993,100380000,176.035281\n2014-04-28,187.050003,187.690002,184.960007,186.880005,135121000,176.592815\n2014-04-29,187.479996,188.039993,187.080002,187.75,84098000,177.414919\n2014-04-30,187.440002,188.5,187.179993,188.309998,101508000,177.94409\n2014-05-01,188.220001,188.839996,187.729996,188.330002,93019000,177.962994\n2014-05-02,188.309998,189.139999,187.779999,188.059998,98122000,177.707852\n2014-05-05,187.139999,188.550003,186.619995,188.419998,75883000,178.048036\n2014-05-06,188.0,188.130005,186.740005,186.779999,85454000,176.498314\n2014-05-07,187.410004,187.970001,186.009995,187.880005,106500000,177.537768\n2014-05-08,187.710007,189.050003,187.080002,187.679993,93618000,177.348766\n2014-05-09,187.710007,188.039993,186.830002,187.960007,83679000,177.613366\n2014-05-12,188.800003,189.880005,188.0,189.789993,86940000,179.342617\n2014-05-13,190.039993,190.419998,189.770004,189.960007,66454000,179.503272\n2014-05-14,189.789993,189.880005,188.789993,189.059998,72367000,178.652805\n2014-05-15,188.679993,188.720001,186.479996,187.399994,154956000,177.08418\n2014-05-16,187.509995,188.130005,186.720001,188.050003,97458000,177.698408\n2014-05-19,187.690002,188.889999,187.520004,188.740005,63839000,178.350428\n2014-05-20,188.649994,188.669998,187.070007,187.550003,111644000,177.225931\n2014-05-21,188.089996,189.220001,188.059998,189.130005,89093000,178.718959\n2014-05-22,189.179993,189.979996,188.860001,189.589996,61549000,179.153629\n2014-05-23,189.759995,190.479996,189.589996,190.350006,61092800,179.871803\n2014-05-27,191.059998,191.580002,190.949997,191.520004,72010000,180.977396\n2014-05-28,191.520004,191.820007,191.059998,191.380005,66723000,180.845103\n2014-05-29,191.820007,192.399994,191.330002,192.369995,64377000,181.780597\n2014-05-30,192.190002,192.800003,192.029999,192.679993,76316000,182.07353\n2014-06-02,192.949997,192.990005,191.970001,192.899994,64656000,182.281421\n2014-06-03,192.429993,192.899994,192.25,192.800003,65047000,182.186934\n2014-06-04,192.470001,193.300003,192.270004,193.190002,55529000,182.555466\n2014-06-05,193.410004,194.649994,192.699997,194.449997,92103000,183.746101\n2014-06-06,194.869995,195.429993,194.779999,195.380005,78696000,184.624915\n2014-06-09,195.350006,196.050003,195.169998,195.580002,65119000,184.813903\n2014-06-10,195.339996,195.639999,194.919998,195.600006,57129000,184.832806\n2014-06-11,194.899994,195.119995,194.479996,194.919998,68772000,184.19023\n2014-06-12,194.690002,194.800003,193.110001,193.539993,106350000,182.88619\n2014-06-13,193.919998,194.320007,193.300003,194.130005,82017000,183.443724\n2014-06-16,193.889999,194.699997,193.660004,194.289993,87424000,183.594905\n2014-06-17,194.020004,194.970001,193.809998,194.830002,84834000,184.105188\n2014-06-18,194.830002,196.369995,194.399994,196.259995,105267000,185.456464\n2014-06-19,196.429993,196.600006,195.800003,196.479996,85929000,185.664354\n2014-06-20,196.029999,196.100006,195.699997,195.940002,100587000,186.0413\n2014-06-23,195.990005,196.050003,195.520004,195.880005,70611000,185.984333\n2014-06-24,195.529999,196.5,194.479996,194.699997,96237000,184.863938\n2014-06-25,194.289993,195.779999,194.25,195.580002,82782000,185.699486\n2014-06-26,195.610001,195.630005,194.130005,195.440002,84312000,185.566559\n2014-06-27,194.979996,195.880005,194.889999,195.820007,71445100,185.927367\n2014-06-30,195.699997,196.169998,195.529999,195.720001,70201200,185.832413\n2014-07-01,196.199997,197.630005,196.130005,197.029999,90470000,187.076231\n2014-07-02,197.050003,197.479996,196.960007,197.229996,52475000,187.266124\n2014-07-03,197.789993,198.289993,197.639999,198.199997,52938800,188.187122\n2014-07-07,197.820007,197.979996,197.220001,197.509995,61696000,187.531977\n2014-07-08,197.149994,197.220001,195.759995,196.240005,108143000,186.326147\n2014-07-09,196.729996,197.300003,196.309998,197.119995,72992000,187.16168\n2014-07-10,195.220001,196.860001,195.059998,196.339996,99040000,186.421086\n2014-07-11,196.220001,196.75,195.779999,196.610001,64243000,186.67745\n2014-07-14,197.610001,197.860001,197.440002,197.600006,58658000,187.617442\n2014-07-15,197.720001,198.100006,196.360001,197.229996,111307000,187.266124\n2014-07-16,198.110001,198.259995,197.419998,197.960007,79986400,187.959255\n2014-07-17,197.350006,198.100006,195.429993,195.710007,145398000,185.822923\n2014-07-18,196.350006,197.910004,196.240005,197.710007,124330000,187.721885\n2014-07-21,197.089996,197.5,196.429993,197.339996,67592000,187.370567\n2014-07-22,198.009995,198.559998,197.869995,198.199997,67678000,188.187122\n2014-07-23,198.5,198.850006,198.100006,198.639999,65612000,188.604895\n2014-07-24,198.830002,199.059998,198.449997,198.649994,56888000,188.614385\n2014-07-25,198.089996,198.259995,197.330002,197.720001,76837000,187.731375\n2014-07-28,197.759995,198.089996,196.619995,197.800003,69259000,187.807335\n2014-07-29,198.169998,198.449997,196.919998,196.949997,80466000,187.00027\n2014-07-30,197.649994,197.910004,196.160004,196.979996,104222000,187.028754\n2014-07-31,195.610001,195.779999,192.970001,193.089996,183479000,183.335273\n2014-08-01,192.559998,193.759995,191.570007,192.5,189261000,182.775083\n2014-08-04,192.869995,194.300003,192.050003,193.889999,91340000,184.094861\n2014-08-05,193.100006,193.600006,191.309998,192.009995,152690000,182.309832\n2014-08-06,191.110001,192.889999,191.080002,192.070007,94818000,182.366813\n2014-08-07,192.940002,193.130005,190.550003,191.029999,135733000,181.379345\n2014-08-08,191.460007,193.369995,190.949997,193.240005,117014000,183.477704\n2014-08-11,193.970001,194.660004,193.710007,193.800003,74544000,184.009411\n2014-08-12,193.610001,194.149994,192.940002,193.529999,73632000,183.753047\n2014-08-13,194.289993,195.059998,193.960007,194.839996,69047000,184.996865\n2014-08-14,195.160004,195.759995,194.979996,195.759995,57371000,185.870386\n2014-08-15,196.470001,196.649994,194.309998,195.720001,139951000,185.832413\n2014-08-18,196.800003,197.449997,196.690002,197.360001,75424000,187.389561\n2014-08-19,197.839996,198.539993,197.440002,198.389999,59135000,188.367525\n2014-08-20,198.119995,199.160004,198.080002,198.919998,72763000,188.870749\n2014-08-21,199.089996,199.759995,198.929993,199.5,67791000,189.42145\n2014-08-22,199.339996,199.690002,198.740005,199.190002,76107000,189.127113\n2014-08-25,200.139999,200.589996,199.149994,200.199997,63855000,190.086083\n2014-08-26,200.330002,200.820007,200.279999,200.330002,47298000,190.209521\n2014-08-27,200.429993,200.570007,199.940002,200.25,47874000,190.13356\n2014-08-28,199.589996,200.270004,199.389999,200.139999,58330000,190.029117\n2014-08-29,200.449997,200.729996,199.820007,200.710007,65907000,190.570328\n2014-09-02,200.970001,201.0,199.860001,200.610001,72426000,190.475374\n2014-09-03,201.380005,201.410004,200.220001,200.5,57462000,190.370931\n2014-09-04,200.839996,201.580002,199.660004,200.210007,85236000,190.095588\n2014-09-05,200.169998,201.190002,199.410004,201.110001,102177000,190.950115\n2014-09-08,200.919998,201.210007,200.0,200.589996,64146000,190.45638\n2014-09-09,200.410004,200.550003,198.910004,199.320007,88591000,189.25055\n2014-09-10,199.429993,200.199997,198.770004,200.070007,67251000,189.962661\n2014-09-11,199.270004,200.330002,199.119995,200.300003,66774400,190.181037\n2014-09-12,200.100006,200.119995,198.559998,199.130005,117409300,189.070146\n2014-09-15,199.160004,199.320007,198.380005,198.979996,76401000,188.927716\n2014-09-16,198.610001,200.839996,198.5,200.479996,116201000,190.351937\n2014-09-17,200.770004,201.679993,199.75,200.75,151266000,190.608301\n2014-09-18,201.360001,201.850006,201.100006,201.820007,94990000,191.624252\n2014-09-19,201.520004,201.899994,200.289993,200.699997,121649000,191.451587\n2014-09-22,200.350006,200.380005,198.729996,199.149994,125553000,189.973009\n2014-09-23,198.429993,199.259995,197.949997,198.009995,111393000,188.885542\n2014-09-24,198.039993,199.690002,197.520004,199.559998,107276000,190.36412\n2014-09-25,199.039993,199.050003,196.270004,196.339996,150300000,187.292498\n2014-09-26,196.699997,198.389999,196.419998,197.899994,103547000,188.78061\n2014-09-29,196.199997,197.889999,196.050003,197.539993,95112000,188.437199\n2014-09-30,197.690002,198.300003,196.610001,197.020004,131302000,187.941171\n2014-10-01,196.699997,196.770004,193.910004,194.350006,177798000,185.394209\n2014-10-02,194.179993,195.059998,192.350006,194.380005,157285000,185.422825\n2014-10-03,195.679993,196.940002,195.080002,196.520004,121569000,187.464212\n2014-10-06,197.339996,197.600006,195.580002,196.289993,104778000,187.2448\n2014-10-07,195.279999,195.720001,193.220001,193.259995,147913000,184.354426\n2014-10-08,193.369995,196.919998,192.360001,196.639999,186461000,187.578677\n2014-10-09,196.330002,196.600006,192.580002,192.740005,210705000,183.858398\n2014-10-10,192.690002,193.649994,190.490005,190.539993,221909000,181.759764\n2014-10-13,190.460007,191.149994,187.300003,187.410004,230939000,178.774007\n2014-10-14,188.419998,189.820007,187.039993,187.699997,215847000,179.050637\n2014-10-15,185.160004,187.690002,181.919998,186.429993,380715000,177.839155\n2014-10-16,183.059998,187.580002,182.889999,186.270004,270391000,177.686539\n2014-10-17,188.419998,189.75,187.619995,188.470001,214625000,179.785159\n2014-10-20,188.130005,190.449997,188.070007,190.300003,130011000,181.530833\n2014-10-21,191.679993,194.199997,191.479996,194.070007,154949000,185.127112\n2014-10-22,194.410004,194.910004,192.610001,192.690002,151822000,183.810699\n2014-10-23,194.619995,196.199997,194.259995,194.929993,154944000,185.947469\n2014-10-24,195.25,196.490005,194.490005,196.429993,117927000,187.378348\n2014-10-27,195.729996,196.449997,195.029999,196.160004,82954000,187.1208\n2014-10-28,196.820007,198.419998,196.729996,198.410004,106736000,189.267118\n2014-10-29,198.550003,199.119995,196.800003,198.110001,142557000,188.98094\n2014-10-30,197.580002,199.949997,197.399994,199.380005,113330000,190.192421\n2014-10-31,201.779999,201.820007,200.770004,201.660004,146903000,192.367356\n2014-11-03,201.919998,202.449997,201.309998,201.770004,93600000,192.472287\n2014-11-04,201.229996,201.600006,200.059998,201.070007,93343000,191.804547\n2014-11-05,202.539993,202.589996,201.449997,202.339996,91709000,193.016014\n2014-11-06,202.389999,203.259995,201.639999,203.149994,107089000,193.788686\n2014-11-07,203.169998,203.600006,202.610001,203.339996,89540000,193.969933\n2014-11-10,203.380005,204.039993,203.130005,203.979996,66319000,194.580441\n2014-11-11,204.059998,204.309998,203.649994,204.179993,54499400,194.771222\n2014-11-12,203.350006,204.240005,203.309998,203.960007,90120300,194.561373\n2014-11-13,204.160004,204.830002,203.210007,204.190002,85357900,194.78077\n2014-11-14,204.100006,204.490005,203.720001,204.240005,80417500,194.828469\n2014-11-17,203.850006,204.580002,203.649994,204.369995,80441000,194.952469\n2014-11-18,204.440002,205.919998,204.440002,205.550003,76068100,196.078101\n2014-11-19,205.309998,205.550003,204.300003,205.220001,82373000,195.763306\n2014-11-20,204.259995,205.710007,204.179993,205.580002,72840300,196.106717\n2014-11-21,207.639999,207.839996,205.979996,206.679993,142327300,197.15602\n2014-11-24,207.169998,207.389999,206.910004,207.259995,65880800,197.709295\n2014-11-25,207.539993,207.789993,206.800003,207.110001,79108300,197.566213\n2014-11-26,207.289993,207.759995,207.029999,207.639999,62167800,198.071789\n2014-11-28,207.490005,207.869995,206.910004,207.199997,57890100,197.652062\n2014-12-01,206.399994,206.539993,205.380005,205.759995,103968400,196.278416\n2014-12-02,205.809998,207.339996,205.779999,207.089996,74507200,197.54713\n2014-12-03,207.300003,208.149994,207.100006,207.889999,68952000,198.310269\n2014-12-04,207.539993,208.270004,206.699997,207.660004,91316600,198.090871\n2014-12-05,207.869995,208.470001,207.550003,208.0,91025500,198.4152\n2014-12-08,207.520004,208.119995,205.929993,206.610001,108588200,197.089253\n2014-12-09,204.369995,206.600006,203.910004,206.470001,125180100,196.955705\n2014-12-10,205.910004,205.979996,202.929993,203.160004,159856400,193.798235\n2014-12-11,203.880005,206.190002,203.710007,204.190002,159012800,194.78077\n2014-12-12,202.639999,203.820007,200.850006,200.889999,202330200,191.632834\n2014-12-15,201.979996,202.529999,198.779999,199.509995,189965800,190.316421\n2014-12-16,198.580002,202.399994,197.860001,197.910004,259543800,188.790159\n2014-12-17,198.440002,202.339996,198.289993,201.789993,253910100,192.491355\n2014-12-18,204.740005,212.970001,203.919998,206.779999,257633900,197.251418\n2014-12-19,206.429993,207.330002,205.610001,206.520004,245084600,198.090707\n2014-12-22,206.75,207.470001,206.460007,207.470001,148318900,199.001929\n2014-12-23,208.169998,208.229996,207.399994,207.75,122167900,199.270499\n2014-12-24,208.020004,208.339996,207.720001,207.770004,42963400,199.289687\n2014-12-26,208.309998,208.850006,208.25,208.440002,57326700,199.932338\n2014-12-29,208.220001,208.970001,208.139999,208.720001,79643900,200.200909\n2014-12-30,208.210007,208.369995,207.509995,207.600006,73540800,199.126627\n2014-12-31,207.990005,208.190002,205.389999,205.539993,130333800,197.150696\n2015-01-02,206.380005,206.880005,204.179993,205.429993,121465900,197.045185\n2015-01-05,204.169998,204.369995,201.350006,201.720001,169632600,193.48662\n2015-01-06,202.089996,202.720001,198.860001,199.820007,209151400,191.664176\n2015-01-07,201.419998,202.720001,200.880005,202.309998,125346700,194.052535\n2015-01-08,204.009995,206.160004,203.990005,205.899994,147217800,197.496002\n2015-01-09,206.399994,206.419998,203.509995,204.25,158567300,195.913355\n2015-01-12,204.410004,204.600006,201.919998,202.649994,144396100,194.378654\n2015-01-13,204.119995,205.479996,200.509995,202.080002,214553300,193.831927\n2015-01-14,199.649994,201.100006,198.570007,200.860001,192991100,192.661721\n2015-01-15,201.630005,202.009995,198.880005,199.020004,176613900,190.896826\n2015-01-16,198.770004,201.820007,198.550003,201.630005,211879600,193.400297\n2015-01-20,202.399994,202.720001,200.169998,202.059998,130991100,193.812739\n2015-01-21,201.5,203.660004,200.940002,203.080002,122942700,194.791111\n2015-01-22,203.990005,206.259995,202.330002,206.100006,174356000,197.687851\n2015-01-23,205.789993,206.100006,204.809998,204.970001,117516800,196.603968\n2015-01-26,204.710007,205.559998,203.850006,205.449997,92009700,197.064373\n2015-01-27,202.970001,204.119995,201.740005,202.740005,134044600,194.464992\n2015-01-28,204.169998,204.289993,199.910004,200.139999,168514300,191.971107\n2015-01-29,200.380005,202.300003,198.679993,201.990005,173585400,193.745604\n2015-01-30,200.570007,202.169998,199.130005,199.449997,197729700,191.309268\n2015-02-02,200.050003,202.029999,197.860001,201.919998,163107000,193.678454\n2015-02-03,203.0,204.850006,202.550003,204.839996,124212900,196.47927\n2015-02-04,203.919998,205.380005,203.509995,204.059998,134306700,195.731107\n2015-02-05,204.860001,206.300003,204.770004,206.119995,97953200,197.707024\n2015-02-06,206.559998,207.240005,204.919998,205.550003,125672000,197.160297\n2015-02-09,204.770004,205.639999,204.139999,204.630005,87219000,196.277849\n2015-02-10,205.880005,207.119995,204.679993,206.809998,96164200,198.368863\n2015-02-11,206.610001,207.449997,205.830002,206.929993,91087800,198.483961\n2015-02-12,207.889999,208.990005,206.970001,208.919998,97545900,200.392743\n2015-02-13,209.070007,209.839996,208.759995,209.779999,93670400,201.217641\n2015-02-17,209.399994,210.320007,209.100006,210.110001,76968200,201.534174\n2015-02-18,209.660004,210.220001,209.339996,210.130005,80652900,201.553362\n2015-02-19,209.410004,210.419998,209.240005,209.979996,91462500,201.409475\n2015-02-20,209.479996,211.330002,208.729996,211.240005,140896400,202.618057\n2015-02-23,210.940002,211.210007,210.479996,211.210007,74411100,202.589282\n2015-02-24,211.119995,212.050003,210.759995,211.809998,72472300,203.164784\n2015-02-25,211.660004,212.240005,211.220001,211.630005,73061700,202.992138\n2015-02-26,211.520004,211.710007,210.649994,211.380005,72697900,202.752342\n2015-02-27,211.259995,211.580002,210.600006,210.660004,108076000,202.061728\n2015-03-02,210.779999,212.059998,210.720001,211.990005,87491400,203.337445\n2015-03-03,211.470001,212.050003,210.080002,211.119995,110325800,202.502945\n2015-03-04,210.399994,210.490005,209.059998,210.229996,114497200,201.649271\n2015-03-05,210.619995,210.800003,209.850006,210.460007,76873000,201.869894\n2015-03-06,209.419998,209.940002,207.100006,207.5,188128000,199.030703\n2015-03-09,207.740005,208.789993,207.550003,208.360001,89818900,199.855602\n2015-03-10,206.710007,206.809998,204.929993,204.979996,157121300,196.613555\n2015-03-11,205.289993,205.5,204.399994,204.5,110145700,196.153151\n2015-03-12,205.259995,207.179993,205.199997,207.100006,93993500,198.647035\n2015-03-13,206.770004,207.929993,204.580002,205.830002,162410900,197.428867\n2015-03-16,206.710007,208.690002,205.860001,208.580002,136099200,200.066623\n2015-03-17,207.690002,208.419998,206.979996,207.960007,94510400,199.471934\n2015-03-18,207.389999,211.270004,206.619995,210.460007,228808500,201.869894\n2015-03-19,209.960007,210.470001,209.029999,209.5,117917300,200.949071\n2015-03-20,209.710007,211.020004,209.490005,210.410004,177715100,202.722807\n2015-03-23,210.419998,211.110001,210.0,210.0,71784500,202.327783\n2015-03-24,209.850006,210.399994,208.740005,208.820007,77805300,201.1909\n2015-03-25,209.070007,209.350006,205.710007,205.759995,159521700,198.242683\n2015-03-26,204.960007,206.369995,204.119995,205.270004,153067200,197.770594\n2015-03-27,205.130005,205.949997,204.899994,205.740005,118939000,198.223424\n2015-03-30,206.979996,208.610001,206.960007,208.25,96180400,200.641718\n2015-03-31,207.259995,208.100006,206.360001,206.429993,126768700,198.888203\n2015-04-01,206.389999,206.419998,204.509995,205.699997,137303600,198.184877\n2015-04-02,205.619995,206.979996,205.399994,206.440002,86900900,198.897847\n2015-04-06,205.369995,208.449997,205.210007,207.830002,114368200,200.237064\n2015-04-07,207.860001,208.759995,207.240005,207.279999,81236300,199.707155\n2015-04-08,207.550003,208.509995,207.080002,207.979996,89351900,200.381578\n2015-04-09,207.779999,209.179993,207.190002,208.899994,85548900,201.267964\n2015-04-10,209.199997,210.089996,208.960007,210.039993,72722900,202.366315\n2015-04-13,209.869995,210.630005,209.029999,209.089996,74436600,201.451025\n2015-04-14,208.850006,209.710007,208.100006,209.490005,75099900,201.83642\n2015-04-15,210.050003,211.039993,209.949997,210.429993,99529300,202.742066\n2015-04-16,210.029999,210.979996,209.789993,210.369995,68934900,202.68426\n2015-04-17,208.940002,209.229996,207.009995,207.949997,191113200,200.352675\n2015-04-20,209.059998,210.25,208.960007,209.850006,92189500,202.183269\n2015-04-21,210.669998,210.860001,209.240005,209.600006,72559800,201.942402\n2015-04-22,210.009995,210.850006,208.899994,210.630005,78264600,202.934771\n2015-04-23,210.149994,211.940002,210.009995,211.160004,102585900,203.445406\n2015-04-24,211.660004,211.970001,211.110001,211.649994,61327400,203.917495\n2015-04-27,212.330002,212.479996,210.539993,210.770004,79358100,203.069655\n2015-04-28,210.740005,211.5,209.330002,211.440002,86863500,203.715175\n2015-04-29,210.369995,211.289993,209.600006,210.570007,125684900,202.876965\n2015-04-30,209.880005,210.350006,207.619995,208.460007,161304900,200.844052\n2015-05-01,209.399994,210.770004,209.279999,210.720001,103399700,203.021479\n2015-05-04,211.229996,212.020004,211.100006,211.320007,70927200,203.599564\n2015-05-05,211.029999,211.460007,208.729996,208.899994,113326200,201.267964\n2015-05-06,209.559998,209.929993,206.759995,208.039993,135060200,200.439383\n2015-05-07,207.919998,209.380005,207.520004,208.869995,88244900,201.239062\n2015-05-08,210.880005,211.860001,210.779999,211.619995,155877300,203.888592\n2015-05-11,211.570007,211.889999,210.520004,210.610001,75708100,202.915497\n2015-05-12,209.610001,210.630005,208.619995,209.979996,119727600,202.308509\n2015-05-13,210.470001,211.220001,209.740005,210.020004,94667900,202.347056\n2015-05-14,211.240005,212.320007,210.910004,212.210007,95934000,204.457048\n2015-05-15,212.440002,212.610001,211.860001,212.440002,76510100,204.678641\n2015-05-18,212.240005,213.399994,212.160004,213.100006,74549700,205.314532\n2015-05-19,213.240005,213.570007,212.690002,213.029999,72114600,205.247082\n2015-05-20,213.149994,213.779999,212.5,212.880005,76857500,205.102568\n2015-05-21,212.710007,213.75,212.509995,213.5,64764600,205.699912\n2015-05-22,213.039993,213.539993,212.910004,212.990005,57433500,205.20855\n2015-05-26,212.399994,212.910004,210.199997,210.699997,124308600,203.002206\n2015-05-27,211.25,212.979996,210.759995,212.699997,93214000,204.929137\n2015-05-28,212.330002,212.589996,211.630005,212.460007,74974600,204.697914\n2015-05-29,212.380005,212.429993,210.820007,211.139999,124919600,203.426133\n2015-06-01,211.940002,212.339996,210.619995,211.570007,93338800,203.840431\n2015-06-02,211.020004,212.190002,210.270004,211.360001,91531000,203.638096\n2015-06-03,212.0,212.669998,211.330002,211.919998,87820900,204.177635\n2015-06-04,211.070007,211.860001,209.75,210.130005,151882800,202.453038\n2015-06-05,209.949997,210.580002,208.979996,209.770004,121704700,202.10619\n2015-06-08,209.639999,209.820007,208.389999,208.479996,89063300,200.863311\n2015-06-09,208.449997,209.100006,207.690002,208.449997,105034700,200.834408\n2015-06-10,209.369995,211.410004,209.300003,210.949997,134551300,203.243072\n2015-06-11,211.479996,212.089996,211.199997,211.630005,73876400,203.898236\n2015-06-12,210.639999,211.479996,209.679993,210.009995,135382400,202.337412\n2015-06-15,208.639999,209.449997,207.789993,209.110001,124384200,201.470299\n2015-06-16,208.929993,210.350006,208.720001,210.25,85308200,202.568649\n2015-06-17,210.589996,211.320007,209.360001,210.589996,126708600,202.896224\n2015-06-18,211.309998,213.339996,210.630005,212.779999,165867900,205.006216\n2015-06-19,211.460007,211.550003,210.360001,210.809998,130478700,204.096156\n2015-06-22,211.910004,212.589996,211.639999,211.889999,70696000,205.141762\n2015-06-23,212.139999,212.440002,211.570007,212.039993,68476800,205.286979\n2015-06-24,211.720001,212.169998,210.470001,210.5,92307300,203.796031\n2015-06-25,211.100006,211.25,209.770004,209.860001,97107400,203.176414\n2015-06-26,210.289993,210.580002,209.160004,209.820007,104174800,203.137694\n2015-06-29,208.050003,209.830002,205.330002,205.419998,202621300,198.877816\n2015-06-30,207.259995,207.320007,205.279999,205.850006,182925100,199.294129\n2015-07-01,207.729996,208.029999,206.559998,207.5,135979900,200.891574\n2015-07-02,208.070007,208.270004,206.809998,207.309998,104373700,200.707623\n2015-07-06,205.770004,207.649994,205.529999,206.720001,117975400,200.136417\n2015-07-07,206.960007,208.169998,204.110001,208.020004,173820200,201.395018\n2015-07-08,206.419998,206.759995,204.25,204.529999,164020100,198.016161\n2015-07-09,207.039993,207.350006,204.770004,204.899994,144113100,198.374373\n2015-07-10,207.289993,207.979996,204.949997,207.479996,129456900,200.872207\n2015-07-13,208.990005,209.899994,208.940002,209.770004,106069400,203.089284\n2015-07-14,209.720001,211.050003,209.649994,210.679993,81709600,203.970291\n2015-07-15,210.729996,211.279999,210.039993,210.610001,97914100,203.902528\n2015-07-16,211.869995,212.300003,211.580002,212.300003,106683300,205.538708\n2015-07-17,212.289993,212.550003,211.800003,212.479996,89030000,205.712968\n2015-07-20,212.75,213.179993,212.210007,212.589996,70446800,205.819465\n2015-07-21,212.429993,212.740005,211.389999,211.75,77965000,205.006221\n2015-07-22,210.929993,211.770004,210.889999,211.369995,88667900,204.638318\n2015-07-23,211.529999,211.649994,209.75,210.179993,90509100,203.486215\n2015-07-24,210.300003,210.369995,207.600006,208.0,117755000,201.37565\n2015-07-27,206.940002,207.550003,206.259995,206.789993,132361100,200.20418\n2015-07-28,207.789993,209.5,206.800003,209.330002,123544800,202.663295\n2015-07-29,209.479996,211.039993,209.309998,210.770004,105791300,204.057436\n2015-07-30,210.160004,211.020004,209.419998,210.820007,91304400,204.105847\n2015-07-31,211.419998,211.449997,210.160004,210.5,103266900,203.796031\n2015-08-03,210.460007,210.529999,208.649994,209.789993,113965700,203.108636\n2015-08-04,209.699997,210.25,208.800003,209.380005,81820800,202.711705\n2015-08-05,210.449997,211.309998,209.729996,210.070007,85786800,203.379732\n2015-08-06,210.289993,210.419998,207.649994,208.350006,116030800,201.71451\n2015-08-07,208.160004,208.339996,206.869995,207.949997,117858000,201.32724\n2015-08-10,209.279999,210.669998,209.279999,210.570007,80270700,203.863809\n2015-08-11,208.970001,209.470001,207.759995,208.669998,126081400,202.024311\n2015-08-12,207.110001,209.139999,205.360001,208.919998,172123700,202.266349\n2015-08-13,208.729996,209.550003,208.009995,208.660004,89383300,202.014634\n2015-08-14,208.429993,209.509995,208.259995,209.419998,72786500,202.750425\n2015-08-17,208.710007,210.589996,208.160004,210.589996,79072600,203.883161\n2015-08-18,210.259995,210.679993,209.699997,209.979996,71692700,203.292588\n2015-08-19,209.089996,210.009995,207.350006,208.320007,172946000,201.685466\n2015-08-20,206.509995,208.289993,203.899994,203.970001,194327900,197.473998\n2015-08-21,201.729996,203.940002,197.520004,197.830002,346588500,191.529545\n2015-08-24,187.490005,197.479996,182.399994,189.5,507244300,183.464835\n2015-08-25,195.429993,195.449997,186.919998,187.270004,369833100,181.30586\n2015-08-26,192.080002,194.789993,188.369995,194.460007,339257000,188.266877\n2015-08-27,197.020004,199.419998,195.210007,199.270004,274143900,192.923686\n2015-08-28,198.5,199.839996,197.919998,199.279999,160414400,192.933362\n2015-08-31,198.110001,199.130005,197.009995,197.669998,163298800,191.374637\n2015-09-01,193.119995,194.770004,190.729996,191.770004,256000400,185.662545\n2015-09-02,194.619995,195.460007,192.419998,195.410004,160269300,189.186618\n2015-09-03,196.259995,198.050003,194.960007,195.550003,152087800,189.322159\n2015-09-04,192.850006,193.860001,191.610001,192.589996,207081000,186.456422\n2015-09-08,195.940002,197.610001,195.169998,197.429993,116025700,191.142275\n2015-09-09,199.320007,199.470001,194.350006,194.789993,149347700,188.586354\n2015-09-10,194.559998,197.220001,194.25,195.850006,158611100,189.612608\n2015-09-11,195.380005,196.820007,194.529999,196.740005,119691200,190.474262\n2015-09-14,196.949997,197.009995,195.429993,196.009995,79452000,189.767501\n2015-09-15,196.610001,198.990005,195.960007,198.460007,113806200,192.139485\n2015-09-16,198.820007,200.410004,198.410004,200.179993,99581600,193.804693\n2015-09-17,200.020004,202.889999,199.279999,199.729996,276046600,193.369028\n2015-09-18,195.710007,198.679993,194.960007,195.449997,223657500,190.209099\n2015-09-21,196.440002,197.679993,195.210007,196.460007,105726200,191.192026\n2015-09-22,193.880005,194.460007,192.559998,193.910004,153890900,188.7104\n2015-09-23,194.110001,194.669998,192.910004,193.600006,92790600,188.408715\n2015-09-24,192.149994,193.449997,190.559998,192.899994,159378800,187.727473\n2015-09-25,194.639999,195.0,191.809998,192.850006,155054800,187.678826\n2015-09-28,191.779999,191.910004,187.639999,188.009995,178515900,182.968597\n2015-09-29,188.270004,189.740005,186.929993,188.119995,159045600,183.075648\n2015-09-30,190.369995,191.830002,189.440002,191.630005,163452000,186.491538\n2015-10-01,192.080002,192.490005,189.820007,192.130005,131079000,186.978131\n2015-10-02,189.770004,195.029999,189.119995,195.0,211003300,189.771169\n2015-10-05,196.460007,198.740005,196.330002,198.470001,126320800,193.148123\n2015-10-06,198.309998,198.979996,197.0,197.789993,110274500,192.486349\n2015-10-07,198.899994,199.830002,197.479996,199.410004,124307300,194.06292\n2015-10-08,198.949997,201.550003,198.589996,201.210007,153055200,195.814657\n2015-10-09,201.380005,201.899994,200.580002,201.330002,107069200,195.931434\n2015-10-12,201.419998,201.759995,200.910004,201.520004,56395600,196.116342\n2015-10-13,200.649994,202.160004,200.050003,200.25,88038700,194.880392\n2015-10-14,200.179993,200.869995,198.940002,199.289993,99106200,193.946128\n2015-10-15,200.080002,202.360001,199.639999,202.350006,134142200,196.924088\n2015-10-16,202.830002,203.289993,201.919998,203.270004,114580100,197.819417\n2015-10-19,202.5,203.369995,202.130005,203.369995,76523900,197.916726\n2015-10-20,202.850006,203.839996,202.550003,203.110001,78448500,197.663703\n2015-10-21,203.610001,203.789993,201.649994,201.850006,102038000,196.437495\n2015-10-22,202.979996,205.509995,201.850006,205.259995,174911700,199.756046\n2015-10-23,207.25,207.949997,206.300003,207.509995,144442300,201.945714\n2015-10-26,207.300003,207.369995,206.559998,207.0,69033000,201.449394\n2015-10-27,206.199997,207.0,205.789993,206.600006,77905800,201.060126\n2015-10-28,207.0,208.979996,206.210007,208.949997,135906700,203.347103\n2015-10-29,208.350006,209.270004,208.210007,208.830002,90525500,203.230325\n2015-10-30,209.059998,209.440002,207.740005,207.929993,131076900,202.35445\n2015-11-02,208.320007,210.619995,208.169998,210.389999,86270800,204.748492\n2015-11-03,209.970001,211.660004,209.699997,211.0,95246100,205.342136\n2015-11-04,211.350006,211.5,209.720001,210.360001,96224500,204.719298\n2015-11-05,210.429993,210.979996,209.089996,210.149994,78408700,204.514923\n2015-11-06,209.740005,210.320007,208.460007,210.039993,110471500,204.407872\n2015-11-09,209.309998,209.490005,206.949997,208.080002,131008700,202.500436\n2015-11-10,207.509995,208.600006,207.190002,208.559998,75874600,202.967561\n2015-11-11,208.880005,208.940002,207.660004,207.740005,67846000,202.169557\n2015-11-12,206.5,207.059998,204.820007,204.839996,121315200,199.34731\n2015-11-13,204.350006,204.669998,202.440002,202.539993,153577100,197.108981\n2015-11-16,202.320007,205.690002,202.179993,205.619995,117645200,200.106394\n2015-11-17,205.990005,207.039993,204.880005,205.470001,121123700,199.960422\n2015-11-18,206.039993,208.899994,205.990005,208.729996,121342500,203.133001\n2015-11-19,208.589996,209.050003,208.199997,208.550003,88220500,202.957835\n2015-11-20,209.449997,210.119995,208.860001,209.309998,94011500,203.69745\n2015-11-23,209.380005,209.979996,208.520004,209.070007,64931200,203.463895\n2015-11-24,207.869995,209.830002,207.410004,209.350006,98874400,203.736386\n2015-11-25,209.5,209.740005,209.009995,209.320007,51980100,203.707192\n2015-11-27,209.429993,209.800003,208.860001,209.559998,37317800,203.940747\n2015-11-30,209.75,209.889999,208.559998,208.690002,112822700,203.09408\n2015-12-01,209.440002,210.820007,209.110001,210.679993,97858400,205.03071\n2015-12-02,210.619995,211.0,208.229996,208.529999,108441300,202.938367\n2015-12-03,208.830002,209.149994,204.75,205.610001,166224200,200.096667\n2015-12-04,205.610001,209.970001,205.610001,209.619995,192913900,203.999135\n2015-12-07,209.229996,209.729996,207.199997,208.350006,102027100,202.763201\n2015-12-08,206.490005,208.289993,205.779999,206.949997,103372400,201.400732\n2015-12-09,206.190002,208.679993,204.179993,205.339996,162401500,199.833903\n2015-12-10,205.419998,207.429993,205.139999,205.869995,115196800,200.34969\n2015-12-11,203.350006,204.139999,201.509995,201.880005,211173300,196.466689\n2015-12-14,202.070007,203.050003,199.949997,202.899994,182385200,197.459328\n2015-12-15,204.699997,206.110001,202.869995,205.029999,154069600,199.532218\n2015-12-16,206.369995,208.389999,204.800003,208.029999,197017000,202.451774\n2015-12-17,208.399994,208.479996,204.839996,204.860001,173092500,199.366778\n2015-12-18,202.770004,202.929993,199.830002,200.020004,251393500,195.815048\n2015-12-21,201.410004,201.880005,200.089996,201.669998,99094300,197.430355\n2015-12-22,202.720001,203.850006,201.550003,203.5,111026200,199.221885\n2015-12-23,204.690002,206.070007,204.580002,206.020004,110987200,201.688912\n2015-12-24,205.720001,206.330002,205.419998,205.679993,48539600,201.356049\n2015-12-28,204.860001,205.259995,203.940002,205.210007,65899900,200.895943\n2015-12-29,206.509995,207.789993,206.470001,207.399994,92640700,203.039891\n2015-12-30,207.110001,207.210007,205.759995,205.929993,63317700,201.600793\n2015-12-31,205.130005,205.889999,203.869995,203.869995,102929500,199.584102\n2016-01-04,200.490005,201.029999,198.589996,201.020004,222353500,196.794026\n2016-01-05,201.399994,201.899994,200.050003,201.360001,110845800,197.126874\n2016-01-06,198.339996,200.059998,197.600006,198.820007,152112600,194.640278\n2016-01-07,195.330002,197.440002,193.589996,194.050003,213436100,189.970552\n2016-01-08,195.190002,195.850006,191.580002,191.919998,209817200,187.885326\n2016-01-11,193.009995,193.410004,189.820007,192.110001,187941300,188.071334\n2016-01-12,193.820007,194.550003,191.139999,193.660004,172330500,189.588752\n2016-01-13,194.449997,194.860001,188.380005,188.830002,221168900,184.86029\n2016-01-14,189.550003,193.259995,187.660004,191.929993,240795600,187.89511\n2016-01-15,186.770004,188.759995,185.520004,187.809998,314240200,183.861729\n2016-01-19,189.960007,190.110001,186.199997,188.059998,195244400,184.106473\n2016-01-20,185.029999,187.5,181.020004,185.649994,286547800,181.747134\n2016-01-21,186.210007,188.869995,184.639999,186.690002,195772900,182.765279\n2016-01-22,189.779999,190.759995,188.880005,190.520004,168319600,186.514764\n2016-01-25,189.919998,190.149994,187.410004,187.639999,130371700,183.695304\n2016-01-26,188.419998,190.529999,188.020004,190.199997,141036800,186.201484\n2016-01-27,189.580002,191.559998,187.059998,188.130005,185681700,184.175008\n2016-01-28,189.960007,190.199997,187.160004,189.110001,143798800,185.134402\n2016-01-29,190.020004,193.880005,189.880005,193.720001,210529300,189.647488\n2016-02-01,192.529999,194.580002,191.839996,193.649994,136061600,189.578953\n2016-02-02,191.960007,191.970001,189.539993,190.160004,182564900,186.162331\n2016-02-03,191.410004,191.779999,187.100006,191.300003,205054900,187.278365\n2016-02-04,190.710007,192.75,189.960007,191.600006,139531800,187.572061\n2016-02-05,190.990005,191.669998,187.199997,187.949997,180788300,183.998785\n2016-02-08,185.770004,186.119995,182.800003,185.419998,191526700,181.521973\n2016-02-09,183.360001,186.940002,183.199997,185.429993,184513100,181.531758\n2016-02-10,186.410004,188.339996,185.119995,185.270004,148214100,181.375133\n2016-02-11,182.339996,184.100006,181.089996,182.860001,219058900,179.015794\n2016-02-12,184.960007,186.649994,183.960007,186.630005,127632400,182.706542\n2016-02-16,188.770004,189.809998,187.630005,189.779999,120250700,185.790315\n2016-02-17,191.160004,193.320007,191.009995,192.880005,136009500,188.825151\n2016-02-18,193.199997,193.270004,191.720001,192.089996,102343000,188.05175\n2016-02-19,191.169998,192.179993,190.449997,192.0,114793000,187.963646\n2016-02-22,193.869995,194.949997,193.789993,194.779999,103640300,190.685202\n2016-02-23,194.0,194.320007,192.179993,192.320007,111455300,188.276926\n2016-02-24,190.630005,193.529999,189.320007,193.199997,150812200,189.138416\n2016-02-25,193.729996,195.550003,192.830002,195.539993,110728300,191.429219\n2016-02-26,196.570007,196.679993,194.899994,195.089996,129833700,190.988682\n2016-02-29,195.110001,196.229996,193.330002,193.559998,125918100,189.490848\n2016-03-01,195.009995,198.210007,194.449997,198.110001,141799700,193.945198\n2016-03-02,197.740005,199.059998,197.25,199.0,102415000,194.816487\n2016-03-03,198.789993,199.800003,198.110001,199.779999,95172200,195.580088\n2016-03-04,200.009995,201.350006,199.029999,200.429993,129293600,196.216418\n2016-03-07,199.339996,201.070007,199.25,200.589996,100219000,196.373058\n2016-03-08,199.320007,199.919998,198.210007,198.399994,123974900,194.229095\n2016-03-09,199.360001,199.789993,198.429993,199.380005,94801200,195.188503\n2016-03-10,199.960007,201.070007,197.380005,199.539993,156838700,195.345128\n2016-03-11,201.259995,202.809998,199.520004,202.759995,137964500,198.497437\n2016-03-14,202.160004,203.039993,201.770004,202.5,73612000,198.242908\n2016-03-15,201.360001,202.529999,201.050003,202.169998,93169100,197.919844\n2016-03-16,201.600006,203.820007,201.550003,203.339996,129303200,199.065245\n2016-03-17,203.240005,205.229996,202.770004,204.630005,134278500,200.328134\n2016-03-18,204.169998,204.779999,203.800003,204.380005,138372400,201.115364\n2016-03-21,204.070007,204.940002,203.800003,204.669998,72926700,201.400725\n2016-03-22,203.759995,205.229996,203.570007,204.559998,97471900,201.292481\n2016-03-23,204.110001,204.330002,203.009995,203.210007,81052500,199.964054\n2016-03-24,202.0,203.160004,201.740005,203.119995,84360900,199.87548\n2016-03-28,203.610001,203.860001,202.710007,203.240005,62408200,199.993574\n2016-03-29,202.759995,205.25,202.399994,205.119995,92922900,201.843534\n2016-03-30,206.300003,206.869995,205.589996,206.020004,86365300,202.729167\n2016-03-31,205.910004,206.410004,205.330002,205.520004,94584100,202.237153\n2016-04-01,204.350006,207.139999,203.979996,206.919998,114423500,203.614785\n2016-04-04,206.830002,207.070007,205.889999,206.25,63497000,202.955488\n2016-04-05,204.669998,206.259995,203.889999,204.190002,99662200,200.928396\n2016-04-06,204.190002,206.490005,203.979996,206.419998,91839800,203.122771\n2016-04-07,205.139999,205.559998,203.089996,203.949997,113859000,200.692224\n2016-04-08,205.339996,205.850006,203.869995,204.5,95040600,201.233442\n2016-04-11,205.25,206.070007,203.910004,204.020004,83757500,200.761113\n2016-04-12,204.220001,206.25,203.699997,205.919998,115350600,202.630758\n2016-04-13,207.0,208.100006,206.839996,208.0,96336400,204.677535\n2016-04-14,208.070007,208.600006,207.600006,208.009995,65212900,204.68737\n2016-04-15,208.009995,208.169998,207.399994,207.779999,75761600,204.461048\n2016-04-18,207.779999,209.279999,207.0,209.240005,75277700,205.897733\n2016-04-19,209.740005,210.199997,208.940002,209.899994,88316100,206.54718\n2016-04-20,209.949997,210.919998,209.389999,210.100006,81100300,206.743997\n2016-04-21,210.119995,210.25,208.649994,208.970001,85695000,205.632042\n2016-04-22,208.550003,209.289993,207.910004,208.970001,99251700,205.632042\n2016-04-25,208.259995,208.660004,207.539993,208.610001,66166500,205.277792\n2016-04-26,209.039993,209.520004,208.360001,208.919998,75864200,205.582838\n2016-04-27,208.470001,209.809998,208.050003,209.350006,77329400,206.005977\n2016-04-28,208.460007,209.759995,206.960007,207.449997,97216200,204.136317\n2016-04-29,206.720001,207.130005,205.029999,206.330002,142424100,203.034212\n2016-05-02,206.919998,208.179993,206.410004,207.970001,62188000,204.648015\n2016-05-03,206.520004,206.800003,205.279999,206.160004,101267500,202.86693\n2016-05-04,204.990005,205.850006,204.419998,205.009995,92243800,201.73529\n2016-05-05,205.559998,205.979996,204.470001,204.970001,67619200,201.695936\n2016-05-06,204.059998,205.770004,203.880005,205.720001,83784900,202.433956\n2016-05-09,205.570007,206.399994,205.360001,205.889999,74374900,202.601238\n2016-05-10,206.720001,208.470001,206.639999,208.449997,77472200,205.120344\n2016-05-11,207.910004,208.539993,206.5,206.5,81727000,203.201495\n2016-05-12,207.289993,207.490005,205.369995,206.559998,89586300,203.260534\n2016-05-13,206.210007,206.860001,204.380005,204.759995,96474600,201.489283\n2016-05-16,204.960007,207.339996,204.889999,206.779999,77486800,203.477021\n2016-05-17,206.460007,206.800003,204.229996,204.850006,114924900,201.577857\n2016-05-18,204.440002,206.300003,203.630005,204.910004,120062100,201.636896\n2016-05-19,204.059998,204.539993,202.779999,204.199997,115430500,200.938231\n2016-05-20,204.919998,206.100006,204.860001,205.490005,104990400,202.207634\n2016-05-23,205.509995,205.839996,204.990005,205.210007,58682600,201.932107\n2016-05-24,206.169998,208.240005,206.139999,207.869995,93537800,204.549607\n2016-05-25,208.669998,209.770004,207.869995,209.279999,76621400,205.937088\n2016-05-26,209.440002,209.710007,208.970001,209.339996,55280700,205.996127\n2016-05-27,209.529999,210.25,209.470001,210.240005,59329000,206.88176\n2016-05-31,210.559998,210.690002,209.179993,209.839996,109879400,206.48814\n2016-06-01,209.119995,210.479996,208.889999,210.270004,69936200,206.91128\n2016-06-02,209.800003,210.929993,209.240005,210.910004,63044700,207.541056\n2016-06-03,210.25,210.690002,208.860001,210.279999,101757100,206.921115\n2016-06-06,210.699997,211.770004,210.509995,211.350006,64887000,207.97403\n2016-06-07,211.529999,212.339996,211.5,211.679993,60974800,208.298746\n2016-06-08,211.839996,212.520004,211.690002,212.369995,66170900,208.977727\n2016-06-09,211.509995,212.220001,211.190002,212.080002,73786900,208.692365\n2016-06-10,210.460007,210.860001,209.429993,210.070007,113829200,206.714477\n2016-06-13,209.360001,210.369995,208.350006,208.449997,117751200,205.120344\n2016-06-14,208.0,208.740005,206.919998,208.039993,125059300,204.71689\n2016-06-15,208.039993,209.360001,207.529999,207.75,109124500,204.431528\n2016-06-16,207.75,208.570007,205.589996,208.369995,149533100,205.04162\n2016-06-17,207.169998,207.199997,205.75,206.520004,117055700,204.278004\n2016-06-20,208.820007,209.610001,207.75,207.850006,82789600,205.593567\n2016-06-21,208.300003,208.919998,207.779999,208.440002,72461700,206.177159\n2016-06-22,208.649994,209.5,207.929993,208.100006,95560500,205.840853\n2016-06-23,209.809998,210.869995,209.270004,210.809998,102731400,208.521425\n2016-06-24,203.630005,210.850006,202.720001,203.240005,333444400,201.033613\n2016-06-27,201.589996,201.600006,198.649994,199.600006,230775800,197.43313\n2016-06-28,201.479996,203.229996,201.119995,203.199997,159382400,200.994039\n2016-06-29,204.839996,206.929993,204.720001,206.660004,137328600,204.416484\n2016-06-30,207.210007,209.539993,206.559998,209.479996,165021900,207.205862\n2016-07-01,209.479996,210.490005,209.289993,209.919998,106055300,207.641087\n2016-07-05,208.949997,209.080002,207.710007,208.410004,109803700,206.147485\n2016-07-06,207.830002,209.800003,207.059998,209.660004,96021500,207.383915\n2016-07-07,209.869995,210.649994,208.630005,209.529999,85593800,207.255322\n2016-07-08,211.050003,212.940002,210.779999,212.649994,133971000,210.341446\n2016-07-11,213.190002,214.070007,212.949997,213.399994,73633900,211.083304\n2016-07-12,214.529999,215.300003,213.429993,214.949997,101275600,212.61648\n2016-07-13,215.440002,215.449997,214.350006,214.919998,87324100,212.586807\n2016-07-14,216.399994,216.669998,215.660004,216.119995,91230900,213.773777\n2016-07-15,216.779999,217.009995,215.309998,215.830002,107155400,213.486931\n2016-07-18,215.970001,216.600006,215.669998,216.410004,58725900,214.060637\n2016-07-19,215.919998,216.229996,215.630005,216.190002,54345700,213.843024\n2016-07-20,216.190002,217.369995,216.190002,217.089996,58159500,214.733247\n2016-07-21,216.960007,217.220001,215.75,216.270004,67777300,213.922157\n2016-07-22,216.410004,217.300003,216.100006,217.240005,62787500,214.881628\n2016-07-25,217.0,217.059998,215.970001,216.649994,55873100,214.298022\n2016-07-26,216.529999,217.169998,215.759995,216.75,70080500,214.396942\n2016-07-27,217.190002,217.270004,215.619995,216.520004,84083900,214.169443\n2016-07-28,216.289993,217.110001,215.75,216.770004,65035700,214.416729\n2016-07-29,216.460007,217.539993,216.130005,217.119995,79519400,214.76292\n2016-08-01,217.190002,217.649994,216.410004,216.940002,73311400,214.584882\n2016-08-02,216.649994,216.830002,214.570007,215.550003,92295500,213.209972\n2016-08-03,215.479996,216.25,215.130005,216.179993,53993600,213.833123\n2016-08-04,216.309998,216.779999,214.25,216.410004,46585500,214.060637\n2016-08-05,216.410004,218.229996,216.410004,218.179993,71892200,215.811411\n2016-08-08,218.399994,218.520004,217.740005,218.050003,39906500,215.682832\n2016-08-09,218.130005,218.759995,217.800003,218.179993,51251700,215.811411\n2016-08-10,218.309998,218.399994,217.229996,217.639999,57941100,215.27728\n2016-08-11,218.259995,218.940002,217.949997,218.649994,72504300,216.276309\n2016-08-12,218.289993,218.710007,217.990005,218.460007,61313500,216.088385\n2016-08-15,218.889999,219.5,218.880005,219.089996,49813500,216.711535\n2016-08-16,218.600006,218.679993,217.960007,217.960007,53213600,215.593813\n2016-08-17,218.0,218.529999,217.020004,218.369995,75134300,215.99935\n2016-08-18,218.339996,218.899994,218.210007,218.860001,52989300,216.484036\n2016-08-19,218.309998,218.75,217.740005,218.539993,75443000,216.167503\n2016-08-22,218.259995,218.800003,217.830002,218.529999,61368800,216.157617\n2016-08-23,219.25,219.600006,218.899994,218.970001,53399200,216.592843\n2016-08-24,218.800003,218.910004,217.360001,217.850006,71728900,215.485006\n2016-08-25,217.399994,218.190002,217.220001,217.699997,69224800,215.336626\n2016-08-26,217.919998,219.119995,216.25,217.289993,122506300,214.931073\n2016-08-29,217.440002,218.669998,217.399994,218.360001,70502200,215.989464\n2016-08-30,218.259995,218.589996,217.350006,218.0,58114500,215.633372\n2016-08-31,217.610001,217.75,216.470001,217.380005,85269500,215.020108\n2016-09-01,217.369995,217.729996,216.029999,217.389999,97844200,215.029994\n2016-09-02,218.389999,218.869995,217.699997,218.369995,79293900,215.99935\n2016-09-06,218.699997,219.119995,217.860001,219.029999,56702100,216.652189\n2016-09-07,218.839996,219.220001,218.300003,219.009995,76554900,216.632402\n2016-09-08,218.619995,218.940002,218.149994,218.509995,74102900,216.13783\n2016-09-09,216.970001,217.029999,213.25,213.279999,221589100,210.964611\n2016-09-12,212.389999,216.809998,212.309998,216.339996,168110900,213.991389\n2016-09-13,214.839996,215.149994,212.5,213.229996,182828800,210.915151\n2016-09-14,213.289993,214.699997,212.5,213.149994,134185500,210.836018\n2016-09-15,212.960007,215.729996,212.75,215.279999,134427900,212.942899\n2016-09-16,213.479996,213.690002,212.570007,213.369995,155236400,212.119742\n2016-09-19,214.130005,214.880005,213.029999,213.410004,80250500,212.159516\n2016-09-20,214.410004,214.589996,213.380005,213.419998,69665300,212.169452\n2016-09-21,214.240005,216.029999,213.440002,215.820007,110284400,214.555398\n2016-09-22,217.0,217.529999,216.710007,217.179993,76678700,215.907415\n2016-09-23,216.720001,216.880005,215.880005,215.990005,73630900,214.7244\n2016-09-26,215.020004,215.229996,214.009995,214.240005,89827300,212.984655\n2016-09-27,214.050003,215.679993,213.619995,215.570007,78494800,214.306863\n2016-09-28,215.830002,216.820007,214.710007,216.639999,87411000,215.370586\n2016-09-29,216.399994,216.869995,214.039993,214.679993,128070600,213.422064\n2016-09-30,215.649994,217.119995,215.360001,216.300003,117202900,215.032582\n2016-10-03,215.820007,216.039993,215.039993,215.779999,83512100,214.515624\n2016-10-04,215.910004,216.169998,213.990005,214.679993,119948100,213.422064\n2016-10-05,215.410004,216.130005,215.330002,215.630005,72816000,214.366509\n2016-10-06,215.369995,216.039993,214.740005,215.779999,62927400,214.515624\n2016-10-07,216.100006,216.300003,214.190002,215.039993,89788300,213.779955\n2016-10-10,216.160004,216.699997,215.990005,216.160004,51855000,214.893402\n2016-10-11,215.660004,215.740005,212.580002,213.429993,130367400,212.179388\n2016-10-12,213.589996,214.320007,213.009995,213.710007,73866100,212.457761\n2016-10-13,212.160004,213.589996,211.210007,213.009995,101357000,211.761851\n2016-10-14,214.149994,214.690002,213.029999,213.119995,93346200,211.871207\n2016-10-17,213.089996,213.389999,212.169998,212.380005,58275700,211.135553\n2016-10-18,214.240005,214.309998,213.270004,213.710007,76869700,212.457761\n2016-10-19,214.020004,214.639999,213.600006,214.279999,66519200,213.024414\n2016-10-20,213.869995,214.529999,213.110001,213.880005,73639800,212.626763\n2016-10-21,213.880005,214.080002,212.759995,213.979996,89089100,212.726168\n2016-10-24,215.0,215.320007,214.479996,214.889999,60146600,213.63084\n2016-10-25,214.679993,214.979996,213.979996,214.169998,66542300,212.915058\n2016-10-26,213.210007,214.419998,212.929993,213.740005,75705500,212.487584\n2016-10-27,214.580002,214.619995,213.080002,213.169998,77220200,211.920917\n2016-10-28,213.139999,213.929993,211.710007,212.539993,140623200,211.294604\n2016-10-31,212.929993,213.190002,212.360001,212.550003,61272500,211.304555\n2016-11-01,212.929993,212.990005,209.600006,211.009995,122781800,209.77357\n2016-11-02,210.649994,211.100006,209.229996,209.740005,103330800,208.511023\n2016-11-03,209.990005,210.240005,208.460007,208.779999,88939300,207.556641\n2016-11-04,208.910004,209.889999,208.380005,208.550003,109122100,207.327993\n2016-11-07,208.550003,213.190002,208.550003,213.149994,109794900,211.90103\n2016-11-08,212.690002,214.770004,212.380005,214.110001,106772100,212.855412\n2016-11-09,212.369995,217.100006,212.339996,216.380005,258429000,215.112115\n2016-11-10,217.300003,218.309998,215.220001,216.919998,172113300,215.648944\n2016-11-11,216.080002,216.699997,215.320007,216.419998,100552700,215.151874\n2016-11-14,217.029999,217.270004,215.720001,216.589996,94580000,215.320876\n2016-11-15,217.039993,218.279999,216.800003,218.279999,91652600,217.000975\n2016-11-16,217.559998,218.139999,217.419998,217.869995,65617700,216.593374\n2016-11-17,218.050003,219.059998,217.919998,218.990005,69797200,217.706822\n2016-11-18,219.070007,219.270004,218.289993,218.5,86265800,217.219687\n2016-11-21,219.169998,220.179993,219.0,220.149994,72402600,218.860013\n2016-11-22,220.509995,220.789993,219.729996,220.580002,67429000,219.287501\n2016-11-23,219.979996,220.759995,219.75,220.699997,56620200,219.406793\n2016-11-25,221.100006,221.559998,221.009995,221.520004,37872300,220.221996\n2016-11-28,221.160004,221.479996,220.360001,220.479996,76572500,219.188081\n2016-11-29,220.520004,221.440002,220.169998,220.910004,69886700,219.61557\n2016-11-30,221.630005,221.820007,220.309998,220.380005,113291800,219.088676\n2016-12-01,220.729996,220.729996,219.149994,219.570007,79040500,218.283425\n2016-12-02,219.669998,220.25,219.259995,219.679993,74840300,218.392766\n2016-12-05,220.649994,221.399994,220.419998,221.0,67837800,219.705039\n2016-12-06,221.220001,221.740005,220.660004,221.699997,59877400,220.400934\n2016-12-07,221.520004,224.669998,221.380005,224.600006,110738100,223.28395\n2016-12-08,224.570007,225.699997,224.259995,225.149994,99714400,223.830715\n2016-12-09,225.410004,226.529999,225.369995,226.509995,88005800,225.182747\n2016-12-12,226.399994,226.960007,225.759995,226.25,102016100,224.924276\n2016-12-13,227.020004,228.339996,227.0,227.759995,110477500,226.425423\n2016-12-14,227.410004,228.229996,225.369995,225.880005,142501800,224.556449\n2016-12-15,226.160004,227.809998,225.889999,226.809998,124972600,225.480992\n2016-12-16,226.009995,226.080002,224.669998,225.039993,156420200,225.039993\n2016-12-19,225.25,226.020004,225.080002,225.529999,90341100,225.529999\n2016-12-20,226.149994,226.570007,225.880005,226.399994,89838800,226.399994\n2016-12-21,226.25,226.449997,225.770004,225.770004,67909000,225.770004\n2016-12-22,225.600006,225.740005,224.919998,225.380005,56219100,225.380005\n2016-12-23,225.429993,225.720001,225.210007,225.710007,36251400,225.710007\n2016-12-27,226.020004,226.729996,226.0,226.270004,41054400,226.270004\n2016-12-28,226.570007,226.589996,224.270004,224.399994,59776300,224.399994\n2016-12-29,224.479996,224.889999,223.839996,224.350006,47719500,224.350006\n2016-12-30,224.729996,224.830002,222.729996,223.529999,101301800,223.529999\n"
  },
  {
    "path": "docs/Makefile",
    "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS    ?=\nSPHINXBUILD   ?= sphinx-build\nSOURCEDIR     = source\nBUILDDIR      = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n"
  },
  {
    "path": "docs/build/html/.buildinfo",
    "content": "# Sphinx build info version 1\n# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.\nconfig: 1ab0e725c448968d4851f0b695542647\ntags: 645f666f9bcd5a90fca523b33c5a78b7\n"
  },
  {
    "path": "docs/build/html/_modules/benford/benford.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>benford.benford &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"../../_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"../../_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"../../_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"../../\" src=\"../../_static/documentation_options.js\"></script>\n        <script src=\"../../_static/jquery.js\"></script>\n        <script src=\"../../_static/underscore.js\"></script>\n        <script src=\"../../_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"../../_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"../../genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"../../search.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"../../index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"../../search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"../../modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"../../index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"../../index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n          <li><a href=\"../index.html\">Module code</a> &raquo;</li>\n        \n      <li>benford.benford</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <h1>Source code for benford.benford</h1><div class=\"highlight\"><pre>\n<span></span><span class=\"kn\">import</span> <span class=\"nn\">warnings</span>\n<span class=\"kn\">from</span> <span class=\"nn\">pandas</span> <span class=\"kn\">import</span> <span class=\"n\">Series</span><span class=\"p\">,</span> <span class=\"n\">DataFrame</span>\n<span class=\"kn\">from</span> <span class=\"nn\">numpy</span> <span class=\"kn\">import</span> <span class=\"n\">arange</span><span class=\"p\">,</span> <span class=\"n\">log10</span><span class=\"p\">,</span> <span class=\"n\">ones</span><span class=\"p\">,</span> <span class=\"nb\">abs</span><span class=\"p\">,</span> <span class=\"n\">cos</span><span class=\"p\">,</span> <span class=\"n\">sin</span><span class=\"p\">,</span> <span class=\"n\">pi</span><span class=\"p\">,</span> <span class=\"n\">mean</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.constants</span> <span class=\"kn\">import</span> <span class=\"n\">CONFS</span><span class=\"p\">,</span> <span class=\"n\">DIGS</span><span class=\"p\">,</span> <span class=\"n\">SEC_ORDER_DIGS</span><span class=\"p\">,</span> <span class=\"n\">REV_DIGS</span><span class=\"p\">,</span> <span class=\"n\">TEST_NAMES</span><span class=\"p\">,</span> \\\n    <span class=\"n\">MAD_CONFORM</span><span class=\"p\">,</span> <span class=\"n\">CRIT_CHI2</span><span class=\"p\">,</span> <span class=\"n\">CRIT_KS</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.checks</span> <span class=\"kn\">import</span> <span class=\"n\">_check_digs_</span><span class=\"p\">,</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">,</span> <span class=\"n\">_check_test_</span><span class=\"p\">,</span> \\\n    <span class=\"n\">_check_num_array_</span><span class=\"p\">,</span> <span class=\"n\">_check_high_Z_</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.utils</span> <span class=\"kn\">import</span> <span class=\"n\">_set_N_</span><span class=\"p\">,</span> <span class=\"n\">input_data</span><span class=\"p\">,</span> <span class=\"n\">prepare</span><span class=\"p\">,</span> \\\n    <span class=\"n\">subtract_sorted</span><span class=\"p\">,</span> <span class=\"n\">prep_to_roll</span><span class=\"p\">,</span> <span class=\"n\">mad_to_roll</span><span class=\"p\">,</span> <span class=\"n\">mse_to_roll</span><span class=\"p\">,</span> \\\n    <span class=\"n\">get_mantissas</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.expected</span> <span class=\"kn\">import</span> <span class=\"n\">_get_expected_digits_</span> <span class=\"c1\"># First, Second, LastTwo</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.viz</span> <span class=\"kn\">import</span> <span class=\"n\">_get_plot_args</span><span class=\"p\">,</span> <span class=\"n\">plot_digs</span><span class=\"p\">,</span> <span class=\"n\">plot_sum</span><span class=\"p\">,</span> <span class=\"n\">plot_ordered_mantissas</span><span class=\"p\">,</span>\\\n    <span class=\"n\">plot_mantissa_arc_test</span><span class=\"p\">,</span> <span class=\"n\">plot_roll_mse</span><span class=\"p\">,</span> <span class=\"n\">plot_roll_mad</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.reports</span> <span class=\"kn\">import</span> <span class=\"n\">_inform_</span><span class=\"p\">,</span> <span class=\"n\">_report_mad_</span><span class=\"p\">,</span> <span class=\"n\">_report_test_</span><span class=\"p\">,</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">,</span>\\\n    <span class=\"n\">_report_mantissa_</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.stats</span> <span class=\"kn\">import</span> <span class=\"n\">Z_score</span><span class=\"p\">,</span> <span class=\"n\">chi_sq</span><span class=\"p\">,</span> <span class=\"n\">chi_sq_2</span><span class=\"p\">,</span> <span class=\"n\">kolmogorov_smirnov</span><span class=\"p\">,</span>\\\n    <span class=\"n\">kolmogorov_smirnov_2</span><span class=\"p\">,</span> <span class=\"n\">_bhattacharyya_distance_</span><span class=\"p\">,</span> <span class=\"n\">_bhattacharyya_coefficient</span><span class=\"p\">,</span>\\\n    <span class=\"n\">_kullback_leibler_divergence_</span><span class=\"p\">,</span> <span class=\"n\">_mantissas_ks_</span>\n\n\n<div class=\"viewcode-block\" id=\"Base\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Base\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Base</span><span class=\"p\">(</span><span class=\"n\">DataFrame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Internalizes and prepares the data for Analysis.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.`</span>\n\n<span class=\"sd\">    Raises:</span>\n<span class=\"sd\">        TypeError: if not receiving `int` or `float` as input.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n\n        <span class=\"n\">DataFrame</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"p\">{</span><span class=\"s1\">&#39;seq&#39;</span><span class=\"p\">:</span> <span class=\"n\">data</span><span class=\"p\">})</span>\n\n        <span class=\"k\">if</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">dtype</span> <span class=\"o\">!=</span> <span class=\"s1\">&#39;float&#39;</span><span class=\"p\">)</span> <span class=\"o\">&amp;</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">dtype</span> <span class=\"o\">!=</span> <span class=\"s1\">&#39;int&#39;</span><span class=\"p\">):</span>\n            <span class=\"k\">raise</span> <span class=\"ne\">TypeError</span><span class=\"p\">(</span><span class=\"s2\">&quot;The sequence dtype was neither int nor &quot;</span>\n                            <span class=\"s2\">&quot;float. Convert it to whether int of float, &quot;</span>\n                            <span class=\"s2\">&quot;and try again.&quot;</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">sign</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;all&#39;</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">!=</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n        <span class=\"k\">elif</span> <span class=\"n\">sign</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">&gt;</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">&lt;</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">(</span><span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n        <span class=\"n\">ab</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">dtype</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;int&#39;</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;ZN&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">ab</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"k\">if</span> <span class=\"n\">decimals</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;infer&#39;</span><span class=\"p\">:</span>\n                <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;ZN&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">ab</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">str</span>\\\n                               <span class=\"o\">.</span><span class=\"n\">replace</span><span class=\"p\">(</span><span class=\"s1\">&#39;.&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;&#39;</span><span class=\"p\">,</span> <span class=\"n\">regex</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\\\n                               <span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">lstrip</span><span class=\"p\">(</span><span class=\"s1\">&#39;0&#39;</span><span class=\"p\">)</span>\\\n                               <span class=\"o\">.</span><span class=\"n\">str</span><span class=\"p\">[:</span><span class=\"mi\">5</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n            <span class=\"k\">else</span><span class=\"p\">:</span>\n                <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;ZN&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">ab</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"n\">decimals</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n        <span class=\"c1\"># First digits</span>\n        <span class=\"k\">for</span> <span class=\"n\">col</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"s1\">&#39;F1D&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;F2D&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;F3D&#39;</span><span class=\"p\">]:</span>\n            <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">REV_DIGS</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)]</span>\n            <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">temp</span> <span class=\"o\">//</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">((</span><span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">))</span> <span class=\"o\">-</span>\n                                        <span class=\"p\">(</span><span class=\"n\">REV_DIGS</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)))</span>\n            <span class=\"c1\"># fill NANs with -1, which is a non-usable value for digits,</span>\n            <span class=\"c1\"># to be discarded later.</span>\n            <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">fillna</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n        <span class=\"c1\"># Second digit</span>\n        <span class=\"n\">temp_sd</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">10</span><span class=\"p\">]</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;SD&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">temp_sd</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">//</span> <span class=\"mi\">10</span><span class=\"o\">**</span><span class=\"p\">((</span><span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">temp_sd</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span> <span class=\"o\">-</span>\n                                         <span class=\"mi\">1</span><span class=\"p\">))</span> <span class=\"o\">%</span> <span class=\"mi\">10</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;SD&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;SD&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">fillna</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n        <span class=\"c1\"># Last two digits</span>\n        <span class=\"n\">temp_l2d</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">1000</span><span class=\"p\">]</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;L2D&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">temp_l2d</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">%</span> <span class=\"mi\">100</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;L2D&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;L2D&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">fillna</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span></div>\n\n\n<div class=\"viewcode-block\" id=\"Test\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Test\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Test</span><span class=\"p\">(</span><span class=\"n\">DataFrame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Transforms the original number sequence into a DataFrame reduced</span>\n<span class=\"sd\">    by the ocurrences of the chosen digits, creating other computed</span>\n<span class=\"sd\">    columns</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        base: The Base object with the data prepared for Analysis</span>\n<span class=\"sd\">        digs: Tells which test to perform: 1: first digit; 2: first two digits;</span>\n<span class=\"sd\">            3: furst three digits; 22: second digit; -2: last two digits.</span>\n<span class=\"sd\">        confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">            plotting and to limit the top deviations to show.</span>\n<span class=\"sd\">        limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">            the Z scores if the sample is too big. Defaults to None.</span>\n\n<span class=\"sd\">    Attributes:</span>\n<span class=\"sd\">        N: Number of records in the sample to consider in computations</span>\n<span class=\"sd\">        ddf: Degrees of Freedom to look up for the critical chi-square value</span>\n<span class=\"sd\">        chi_square: Chi-square statistic for the given test</span>\n<span class=\"sd\">        KS: Kolmogorov-Smirnov statistic for the given test</span>\n<span class=\"sd\">        MAD: Mean Absolute Deviation for the given test</span>\n<span class=\"sd\">        confidence: Confidence level to consider when setting some critical values</span>\n<span class=\"sd\">        digs (int): numerical representation of the test at hand. 1: F1D; 2: F2D;</span>\n<span class=\"sd\">            3: F3D; 22: SD; -2: L2D.</span>\n<span class=\"sd\">        sec_order (bool): True if the test is a Second Order one</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">base</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n        <span class=\"c1\"># create a separated Expected distributions object</span>\n        <span class=\"nb\">super</span><span class=\"p\">(</span><span class=\"n\">Test</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"n\">_get_expected_digits_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">))</span>\n        <span class=\"c1\"># create column with occurrences of the digits in the base</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">base</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">()</span>\n        <span class=\"c1\"># create column with relative frequencies</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">base</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">(</span><span class=\"n\">normalize</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">fillna</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n        <span class=\"c1\"># create column with absolute differences</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;Dif&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Found</span> <span class=\"o\">-</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Expected</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;AbsDif&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Dif</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span> <span class=\"o\">=</span> <span class=\"n\">_set_N_</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">base</span><span class=\"p\">),</span> <span class=\"n\">limit_N</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Z_score</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ddf</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"mi\">1</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chi_square</span> <span class=\"o\">=</span> <span class=\"n\">chi_sq_2</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">KS</span> <span class=\"o\">=</span> <span class=\"n\">kolmogorov_smirnov_2</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MSE</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhattacharyya_coefficient</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_coefficient</span><span class=\"p\">(</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhattacharyya_distance</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_distance_</span><span class=\"p\">(</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">kullback_leibler_divergence</span> <span class=\"o\">=</span> <span class=\"n\">_kullback_leibler_divergence_</span><span class=\"p\">(</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">confidence</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">digs</span> <span class=\"o\">=</span> <span class=\"n\">digs</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sec_order</span> <span class=\"o\">=</span> <span class=\"n\">sec_order</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">sec_order</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">name</span> <span class=\"o\">=</span> <span class=\"n\">TEST_NAMES</span><span class=\"p\">[</span><span class=\"n\">SEC_ORDER_DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]]</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">name</span> <span class=\"o\">=</span> <span class=\"n\">TEST_NAMES</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]]</span>\n\n<div class=\"viewcode-block\" id=\"Test.update_confidence\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Test.update_confidence\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">update_confidence</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">new_conf</span><span class=\"p\">,</span> <span class=\"n\">check</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Sets a new confidence level for the Benford object, so as to be used to</span>\n<span class=\"sd\">        produce critical values for the tests</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            new_conf: new confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">                plotting and to limit the top deviations to show, as well as to</span>\n<span class=\"sd\">                calculate critical values for the tests&#39; statistics.</span>\n<span class=\"sd\">            check: checks the value provided for the confidence. Defaults to True</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"k\">if</span> <span class=\"n\">check</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">(</span><span class=\"n\">new_conf</span><span class=\"p\">)</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">new_conf</span></div>\n\n    <span class=\"nd\">@property</span>\n    <span class=\"k\">def</span> <span class=\"nf\">critical_values</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;dict: a dictionary with the critical values for the test at hand,</span>\n<span class=\"sd\">            according to the current confidence level.&quot;&quot;&quot;</span>\n        <span class=\"n\">crit_ks</span> <span class=\"o\">=</span> <span class=\"n\">CRIT_KS</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">]</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span> <span class=\"o\">**</span> <span class=\"mf\">0.5</span><span class=\"p\">)</span> <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span>\\\n            <span class=\"k\">else</span> <span class=\"kc\">None</span>\n        <span class=\"k\">return</span> <span class=\"p\">{</span><span class=\"s1\">&#39;Z&#39;</span><span class=\"p\">:</span> <span class=\"n\">CONFS</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">],</span>\n                <span class=\"s1\">&#39;KS&#39;</span><span class=\"p\">:</span> <span class=\"n\">crit_ks</span><span class=\"p\">,</span>\n                <span class=\"s1\">&#39;chi2&#39;</span><span class=\"p\">:</span> <span class=\"n\">CRIT_CHI2</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ddf</span><span class=\"p\">][</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">],</span>\n                <span class=\"s1\">&#39;MAD&#39;</span><span class=\"p\">:</span> <span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">digs</span><span class=\"p\">]}</span>\n\n<div class=\"viewcode-block\" id=\"Test.show_plot\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Test.show_plot\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">show_plot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Draws the test plot.</span>\n<span class=\"sd\">        </span>\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when save_plot is a string with the figure file</span>\n<span class=\"sd\">                path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">text_x</span> <span class=\"o\">=</span> <span class=\"n\">_get_plot_args</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">digs</span><span class=\"p\">)</span>\n        <span class=\"n\">plot_digs</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"o\">=</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y_Exp</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"p\">,</span> <span class=\"n\">y_Found</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"p\">,</span>\n                    <span class=\"n\">N</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">conf_Z</span><span class=\"o\">=</span><span class=\"n\">CONFS</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">],</span>\n                    <span class=\"n\">text_x</span><span class=\"o\">=</span><span class=\"n\">text_x</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span>\n                    <span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"Test.report\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Test.report\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">report</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n               <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Handles the report especific to the test, considering its statistics</span>\n<span class=\"sd\">        and according to the current confidence level.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">                according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">                which will highlight only values higher than the expexted</span>\n<span class=\"sd\">                frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">                negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">                positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">                the critical value or not.</span>\n<span class=\"sd\">            show_plot: calls the show_plot method, to draw the test plot</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">high_Z</span> <span class=\"o\">=</span> <span class=\"n\">_check_high_Z_</span><span class=\"p\">(</span><span class=\"n\">high_Z</span><span class=\"p\">)</span>\n        <span class=\"n\">_report_test_</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">critical_values</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">show_plot</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div></div>\n\n\n<div class=\"viewcode-block\" id=\"Summ\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Summ\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Summ</span><span class=\"p\">(</span><span class=\"n\">DataFrame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Gets the base object and outputs a Summation test object</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">       base: The Base object with the data prepared for Analysis</span>\n<span class=\"sd\">       test: The test for which to compute the summation</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">base</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">):</span>\n        <span class=\"nb\">super</span><span class=\"p\">(</span><span class=\"n\">Summ</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"n\">base</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n                                   <span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)[[</span><span class=\"s1\">&#39;seq&#39;</span><span class=\"p\">]]</span>\n                                   <span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">())</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;Percent&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">/</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">columns</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"s1\">&#39;Sum&#39;</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">expected</span> <span class=\"o\">=</span> <span class=\"mi\">1</span> <span class=\"o\">/</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;AbsDif&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Percent</span> <span class=\"o\">-</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">expected</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">index</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n        <span class=\"c1\">#: Mean Absolute Deviation for the test</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MSE</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n        <span class=\"c1\">#: Confidence level to consider when setting some critical values</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"kc\">None</span>\n        <span class=\"c1\"># (int): numerical representation of the test at hand</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">digs</span> <span class=\"o\">=</span> <span class=\"n\">REV_DIGS</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]</span>\n        <span class=\"c1\"># (str): the name of the Summation test.</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">name</span> <span class=\"o\">=</span> <span class=\"n\">TEST_NAMES</span><span class=\"p\">[</span><span class=\"sa\">f</span><span class=\"s1\">&#39;</span><span class=\"si\">{</span><span class=\"n\">test</span><span class=\"si\">}</span><span class=\"s1\">_Summ&#39;</span><span class=\"p\">]</span>\n\n<div class=\"viewcode-block\" id=\"Summ.show_plot\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Summ.show_plot\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">show_plot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Draws the Summation test plot</span>\n<span class=\"sd\">        </span>\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when save_plot is a string with the figure file</span>\n<span class=\"sd\">                path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">),</span> <span class=\"mf\">1.5</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">))</span>\n        <span class=\"n\">plot_sum</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">expected</span><span class=\"p\">,</span>\n                 <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div>\n    \n<div class=\"viewcode-block\" id=\"Summ.report\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Summ.report\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">report</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">high_diff</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n               <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Gives the report on the Summation test.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            high_diff: Number of records to show after ordering by the absolute</span>\n<span class=\"sd\">                differences between the found and the expected proportions</span>\n<span class=\"sd\">            show_plot: calls the show_plot method, to draw the Summation test plot</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">_report_test_</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">high_diff</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">show_plot</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div></div>\n\n\n<div class=\"viewcode-block\" id=\"Mantissas\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Mantissas\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Mantissas</span><span class=\"p\">:</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes and holds the mantissas of the logarithms of the records</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence to compute mantissas from. numpy 1D array, pandas</span>\n<span class=\"sd\">            Series of pandas DataFrame column.</span>\n<span class=\"sd\">        confidence: confidence level for computing the critical values to</span>\n<span class=\"sd\">            compare with some statistics</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"mi\">95</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">))</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">data</span> <span class=\"o\">!=</span> <span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span> <span class=\"o\">=</span> <span class=\"n\">_set_N_</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">),</span> <span class=\"n\">limit_N</span><span class=\"p\">)</span>\n        <span class=\"c1\">#: (DataFrame): pandas DataFrame with the mantissas</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s1\">&#39;Mantissa&#39;</span><span class=\"p\">:</span> <span class=\"n\">get_mantissas</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">())})</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">confidence</span>\n\n    <span class=\"nd\">@property</span>\n    <span class=\"k\">def</span> <span class=\"nf\">stats</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n        <span class=\"c1\"># (dict): Dictionary with the mantissas statistics</span>\n        <span class=\"n\">ks</span><span class=\"p\">,</span> <span class=\"n\">crit_ks</span> <span class=\"o\">=</span> <span class=\"n\">_mantissas_ks_</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span>\n                                     <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span><span class=\"p\">)</span>\n        <span class=\"k\">return</span> <span class=\"p\">{</span><span class=\"s1\">&#39;Mean&#39;</span><span class=\"p\">:</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">(),</span>\n                <span class=\"s1\">&#39;Var&#39;</span><span class=\"p\">:</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"o\">.</span><span class=\"n\">var</span><span class=\"p\">(),</span>\n                <span class=\"s1\">&#39;Skew&#39;</span><span class=\"p\">:</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"o\">.</span><span class=\"n\">skew</span><span class=\"p\">(),</span>\n                <span class=\"s1\">&#39;Kurt&#39;</span><span class=\"p\">:</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"o\">.</span><span class=\"n\">kurt</span><span class=\"p\">(),</span>\n                <span class=\"s1\">&#39;KS&#39;</span><span class=\"p\">:</span> <span class=\"n\">ks</span><span class=\"p\">,</span>\n                <span class=\"s1\">&#39;KS_critical&#39;</span><span class=\"p\">:</span> <span class=\"n\">crit_ks</span><span class=\"p\">}</span>\n\n\n<div class=\"viewcode-block\" id=\"Mantissas.update_confidence\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Mantissas.update_confidence\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">update_confidence</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">new_conf</span><span class=\"p\">,</span> <span class=\"n\">check</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Sets a new confidence level for the Benford object, so as to be used to</span>\n<span class=\"sd\">        produce critical values for the tests</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            new_conf: new confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">                plotting and to limit the top deviations to show, as well as to</span>\n<span class=\"sd\">                calculate critical values for the tests&#39; statistics.</span>\n<span class=\"sd\">            check: checks the value provided for the confidence. Defaults to True</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"k\">if</span> <span class=\"n\">check</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">(</span><span class=\"n\">new_conf</span><span class=\"p\">)</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">new_conf</span></div>\n\n\n<div class=\"viewcode-block\" id=\"Mantissas.report\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Mantissas.report\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">report</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Displays the Mantissas test stats.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            show_plot: shows the Ordered Mantissas plot and the Arc Test plot.</span>\n<span class=\"sd\">                Defaults to True.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">_report_mantissa_</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">stats</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">show_plot</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">arc_test</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"Mantissas.show_plot\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Mantissas.show_plot\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">show_plot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">12</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">),</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Plots the ordered mantissas and a line with the expected</span>\n<span class=\"sd\">        inclination.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            figsize (tuple): figure size dimensions</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when save_plot is a string with the figure file</span>\n<span class=\"sd\">                path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">plot_ordered_mantissas</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">,</span>\n                               <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"Mantissas.arc_test\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Mantissas.arc_test\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">arc_test</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">grid</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"mi\">12</span><span class=\"p\">,</span>\n                 <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Adds two columns to Mantissas&#39;s DataFrame equal to their &quot;X&quot; and &quot;Y&quot;</span>\n<span class=\"sd\">        coordinates, plots its to a scatter plot and calculates the gravity</span>\n<span class=\"sd\">        center of the circle.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            grid: show grid of the plot. Defaluts to True.</span>\n<span class=\"sd\">            figsize (int): size of the figure to be displayed. Since it is a square,</span>\n<span class=\"sd\">                there is no need to provide a tuple, like is usually the case with</span>\n<span class=\"sd\">                matplotlib.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;mant_x&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">cos</span><span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">pi</span> <span class=\"o\">*</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;mant_y&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">pi</span> <span class=\"o\">*</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">Mantissa</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">gravity_center</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">mant_x</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">(),</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">mant_y</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">())</span>\n\n        <span class=\"n\">plot_mantissa_arc_test</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">gravity_center</span><span class=\"p\">,</span>\n                               <span class=\"n\">grid</span><span class=\"o\">=</span><span class=\"n\">grid</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">,</span>\n                               <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div></div>\n\n\n<div class=\"viewcode-block\" id=\"Benford\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Benford\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Benford</span><span class=\"p\">(</span><span class=\"nb\">object</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Initializes a Benford Analysis object and computes the proportions for</span>\n<span class=\"sd\">    the digits. The tets dataFrames are atributes, i.e., obj.F1D is the First</span>\n<span class=\"sd\">    Digit DataFrame, the obj.F2D,the First Two Digits one, and so one, F3D for</span>\n<span class=\"sd\">    First Three Digits, SD for Second  Digit and L2D for Last Two Digits.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a tuple with a pandas DataFrame and the name (str)</span>\n<span class=\"sd\">            of the chosen column. Values must be integers or floats.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n<span class=\"sd\">        confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">            plotting and to limit the top deviations to show, as well as to</span>\n<span class=\"sd\">            calculate critical values for the tests&#39; statistics. Defaults to 95.</span>\n<span class=\"sd\">        mantissas (bool): opts for also running the mantissas Test. Defaulst to</span>\n<span class=\"sd\">            True</span>\n<span class=\"sd\">        sec_order: runs the Second Order tests, which are the Benford&#39;s tests</span>\n<span class=\"sd\">            performed on the differences between the ordered sample (a value minus</span>\n<span class=\"sd\">            the one before it, and so on). If the original series is Benford-</span>\n<span class=\"sd\">            compliant, this new sequence should aldo follow Beford. The Second</span>\n<span class=\"sd\">            Order can also be called separately, through the method sec_order().</span>\n<span class=\"sd\">        summation: creates the Summation DataFrames for the First, First Two, and</span>\n<span class=\"sd\">            First Three Digits. The summation tests can also be called separately,</span>\n<span class=\"sd\">            through the method summation().</span>\n<span class=\"sd\">        limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">            the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">        verbose: gives some information about the data and the registries used</span>\n<span class=\"sd\">            and discarded for each test.</span>\n\n<span class=\"sd\">    Attributes:</span>\n<span class=\"sd\">        data: the raw data provided for the analysis</span>\n<span class=\"sd\">        chosen: the column of the DataFrame to be analysed or the data itself</span>\n<span class=\"sd\">        sign (str): which number sign(s) to include in the analysis</span>\n<span class=\"sd\">        confidence: current confidence level</span>\n<span class=\"sd\">        limit_N (int): sample size to use in computations</span>\n<span class=\"sd\">        verbose (bool): verbose or not</span>\n<span class=\"sd\">        base: the Base, pre-processed object</span>\n<span class=\"sd\">        tests (:obj:`list` of :obj:`str`): keeps track of the tests the</span>\n<span class=\"sd\">            instance has</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"mi\">95</span><span class=\"p\">,</span>\n                 <span class=\"n\">mantissas</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">summation</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                 <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chosen</span> <span class=\"o\">=</span> <span class=\"n\">input_data</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">decimals</span> <span class=\"o\">=</span> <span class=\"n\">decimals</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sign</span> <span class=\"o\">=</span> <span class=\"n\">sign</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">(</span><span class=\"n\">confidence</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span> <span class=\"o\">=</span> <span class=\"n\">limit_N</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">verbose</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span> <span class=\"o\">=</span> <span class=\"n\">Base</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chosen</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">tests</span> <span class=\"o\">=</span> <span class=\"p\">[]</span>\n\n        <span class=\"c1\"># Create a DatFrame for each Test</span>\n        <span class=\"k\">for</span> <span class=\"n\">key</span><span class=\"p\">,</span> <span class=\"n\">val</span> <span class=\"ow\">in</span> <span class=\"n\">DIGS</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span>\n            <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">Test</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">[</span><span class=\"n\">val</span><span class=\"p\">]</span> <span class=\"o\">!=</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">],</span>\n                        <span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">key</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">,</span>\n                        <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span><span class=\"p\">)</span>\n            <span class=\"nb\">setattr</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">)</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">tests</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">val</span><span class=\"p\">)</span>\n        <span class=\"c1\"># dict with the numbers of discarded entries for each test column</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_discarded</span> <span class=\"o\">=</span> <span class=\"p\">{</span><span class=\"n\">key</span><span class=\"p\">:</span> <span class=\"n\">val</span> <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"n\">key</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">)</span> <span class=\"ow\">in</span>\n                           <span class=\"nb\">zip</span><span class=\"p\">(</span><span class=\"n\">DIGS</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">(),</span>\n                               <span class=\"p\">[</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">])</span>\n                                <span class=\"k\">for</span> <span class=\"n\">col</span> <span class=\"ow\">in</span> <span class=\"n\">DIGS</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">()])}</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39; Benford Object Instantiated &#39;</span><span class=\"o\">.</span><span class=\"n\">center</span><span class=\"p\">(</span><span class=\"mi\">50</span><span class=\"p\">,</span> <span class=\"s1\">&#39;#&#39;</span><span class=\"p\">),</span> <span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">&#39;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s1\">&#39;Initial sample size: </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chosen</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s1\">.</span><span class=\"se\">\\n</span><span class=\"s1\">&#39;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s1\">&#39;Test performed on </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s1\"> registries.</span><span class=\"se\">\\n</span><span class=\"s1\">&#39;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span>\n                <span class=\"sa\">f</span><span class=\"s1\">&#39;Number of discarded entries for each test:</span><span class=\"se\">\\n</span><span class=\"si\">{</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_discarded</span><span class=\"si\">}</span><span class=\"s1\">&#39;</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">mantissas</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">mantissas</span><span class=\"p\">()</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">sec_order</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sec_order</span><span class=\"p\">()</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">summation</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">summation</span><span class=\"p\">()</span>\n\n<div class=\"viewcode-block\" id=\"Benford.update_confidence\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Benford.update_confidence\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">update_confidence</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">new_conf</span><span class=\"p\">,</span> <span class=\"n\">tests</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Sets (a) new confidence level(s) for the Benford object, so as to be</span>\n<span class=\"sd\">        used to produce critical values for the tests.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            new_conf: new confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">                plotting and to limit the top deviations to show, as well as to</span>\n<span class=\"sd\">                calculate critical values for the tests&#39; statistics.</span>\n<span class=\"sd\">            tests (:obj:`list` of :obj:`str`): list of tests names (strings) to</span>\n<span class=\"sd\">                have their confidence updated. If only one, provide a one-element</span>\n<span class=\"sd\">                list, like [&#39;F1D&#39;]. Defauts to None, in which case it will use</span>\n<span class=\"sd\">                the instance .test list attribute.</span>\n\n<span class=\"sd\">        Raises:</span>\n<span class=\"sd\">            ValueError: if the test argument is not a `list` or `None`.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">(</span><span class=\"n\">new_conf</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">tests</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n            <span class=\"n\">tests</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">tests</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">tests</span><span class=\"p\">,</span> <span class=\"nb\">list</span><span class=\"p\">):</span>\n                <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span><span class=\"s1\">&#39;tests must be a list or None.&#39;</span><span class=\"p\">)</span>\n        <span class=\"k\">for</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"n\">tests</span><span class=\"p\">:</span>\n            <span class=\"k\">try</span><span class=\"p\">:</span>\n                <span class=\"nb\">getattr</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">update_confidence</span><span class=\"p\">(</span>\n                            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">check</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n            <span class=\"k\">except</span> <span class=\"ne\">AttributeError</span> <span class=\"k\">as</span> <span class=\"n\">e</span><span class=\"p\">:</span>\n                <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"s1\">&#39;F1D_Summ&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;F2D_Summ&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;F3D_Summ&#39;</span><span class=\"p\">]:</span>\n                    <span class=\"k\">pass</span>\n                <span class=\"k\">else</span><span class=\"p\">:</span>\n                    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">e</span><span class=\"p\">,</span>\n                        <span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n\\n</span><span class=\"si\">{</span><span class=\"n\">test</span><span class=\"si\">}</span><span class=\"s2\"> not in Benford instance tests - &quot;</span>\n                        <span class=\"s2\">&quot;review test&#39;s name.&quot;</span><span class=\"p\">)</span></div>\n\n    <span class=\"nd\">@property</span>\n    <span class=\"k\">def</span> <span class=\"nf\">all_confidences</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;dict: a dictionary with a confidence level for each computed tests,</span>\n<span class=\"sd\">        when applicable.&quot;&quot;&quot;</span>\n        <span class=\"n\">con_dic</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"k\">for</span> <span class=\"n\">key</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">tests</span><span class=\"p\">:</span>\n            <span class=\"k\">try</span><span class=\"p\">:</span>\n                <span class=\"n\">con_dic</span><span class=\"p\">[</span><span class=\"n\">key</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"nb\">getattr</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">key</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">confidence</span>\n            <span class=\"k\">except</span> <span class=\"ne\">AttributeError</span><span class=\"p\">:</span>\n                <span class=\"k\">continue</span>\n        <span class=\"k\">return</span> <span class=\"n\">con_dic</span>\n\n<div class=\"viewcode-block\" id=\"Benford.mantissas\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Benford.mantissas\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">mantissas</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Adds a Mantissas object to the tests, with all its statistics and</span>\n<span class=\"sd\">        plotting capabilities.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Mantissas</span> <span class=\"o\">=</span> <span class=\"n\">Mantissas</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span>\n                                   <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">tests</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"s1\">&#39;Mantissas&#39;</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">Added Mantissas test.&#39;</span><span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"Benford.sec_order\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Benford.sec_order\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">sec_order</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Runs the Second Order tests, which are the Benford&#39;s tests</span>\n<span class=\"sd\">        performed on the differences between the ordered sample (a value minus</span>\n<span class=\"sd\">        the one before it, and so on). If the original series is Benford-</span>\n<span class=\"sd\">        compliant, this new sequence should aldo follow Beford. The Second</span>\n<span class=\"sd\">        Order can also be called separately, through the method sec_order().</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"c1\">#: Base instance of the differences between the ordered sample</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base_sec</span> <span class=\"o\">=</span> <span class=\"n\">Base</span><span class=\"p\">(</span><span class=\"n\">subtract_sorted</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chosen</span><span class=\"p\">),</span>\n                             <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sign</span><span class=\"p\">)</span>\n        <span class=\"k\">for</span> <span class=\"n\">key</span><span class=\"p\">,</span> <span class=\"n\">val</span> <span class=\"ow\">in</span> <span class=\"n\">DIGS</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span>\n            <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">Test</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base_sec</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base_sec</span><span class=\"p\">[</span><span class=\"n\">val</span><span class=\"p\">]</span> <span class=\"o\">!=</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">],</span>\n                        <span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">key</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">confidence</span><span class=\"p\">,</span>\n                        <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n            <span class=\"nb\">setattr</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">SEC_ORDER_DIGS</span><span class=\"p\">[</span><span class=\"n\">key</span><span class=\"p\">],</span> <span class=\"n\">test</span><span class=\"p\">)</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">tests</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s1\">&#39;</span><span class=\"si\">{</span><span class=\"n\">val</span><span class=\"si\">}</span><span class=\"s1\">_sec&#39;</span><span class=\"p\">)</span>\n            <span class=\"c1\"># No need to populate crit_vals dict, since they are the</span>\n            <span class=\"c1\"># same and do not depend on N</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_discarded_sec</span> <span class=\"o\">=</span> <span class=\"p\">{</span><span class=\"n\">key</span><span class=\"p\">:</span> <span class=\"n\">val</span> <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"n\">key</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">)</span> <span class=\"ow\">in</span> <span class=\"nb\">zip</span><span class=\"p\">(</span>\n                                   <span class=\"n\">SEC_ORDER_DIGS</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">(),</span>\n                                   <span class=\"p\">[</span><span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base_sec</span><span class=\"p\">[</span><span class=\"n\">col</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">col</span> <span class=\"ow\">in</span>\n                                    <span class=\"n\">DIGS</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">()])}</span>\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">Second order tests run in </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base_sec</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s1\"> &#39;</span>\n                  <span class=\"s1\">&#39;registries.</span><span class=\"se\">\\n\\n</span><span class=\"s1\">Number of discarded entries for second order&#39;</span>\n                  <span class=\"sa\">f</span><span class=\"s1\">&#39; tests:</span><span class=\"se\">\\n</span><span class=\"si\">{</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_discarded_sec</span><span class=\"si\">}</span><span class=\"s1\">&#39;</span><span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"Benford.summation\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Benford.summation\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">summation</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Creates Summation test DataFrames from Base object&quot;&quot;&quot;</span>\n        <span class=\"k\">for</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"s1\">&#39;F1D&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;F2D&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;F3D&#39;</span><span class=\"p\">]:</span>\n            <span class=\"n\">t</span> <span class=\"o\">=</span> <span class=\"sa\">f</span><span class=\"s1\">&#39;</span><span class=\"si\">{</span><span class=\"n\">test</span><span class=\"si\">}</span><span class=\"s1\">_Summ&#39;</span>\n            <span class=\"nb\">setattr</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">t</span><span class=\"p\">,</span> <span class=\"n\">Summ</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">base</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">))</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">tests</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">t</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">Added Summation DataFrames to F1D, F2D and F3D Tests.&#39;</span><span class=\"p\">)</span></div></div>\n\n\n<div class=\"viewcode-block\" id=\"Source\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Source\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Source</span><span class=\"p\">(</span><span class=\"n\">DataFrame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Prepares the data for Analysis. pandas DataFrame subclass.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n<span class=\"sd\">        sec_order: choice for the Second Order Test, which cumputes the</span>\n<span class=\"sd\">            differences between the ordered entries before running the Tests.</span>\n<span class=\"sd\">        verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">            the analysis; defaults to True.</span>\n\n<span class=\"sd\">    Raises:</span>\n<span class=\"sd\">        ValueError: if the `sign` arg is not in [&#39;all&#39;, &#39;pos&#39;, &#39;neg&#39;]</span>\n<span class=\"sd\">        TypeError: if not receiving `int` or `float` as input.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                 <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">sign</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;neg&#39;</span><span class=\"p\">]:</span>\n            <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span><span class=\"s2\">&quot;The -sign- argument must be &quot;</span>\n                             <span class=\"s2\">&quot;&#39;all&#39;,&#39;pos&#39; or &#39;neg&#39;.&quot;</span><span class=\"p\">)</span>\n\n        <span class=\"n\">DataFrame</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"p\">{</span><span class=\"s1\">&#39;seq&#39;</span><span class=\"p\">:</span> <span class=\"n\">data</span><span class=\"p\">})</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">dtype</span> <span class=\"o\">!=</span> <span class=\"s1\">&#39;float&#39;</span> <span class=\"ow\">and</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">dtype</span> <span class=\"o\">!=</span> <span class=\"s1\">&#39;int&#39;</span><span class=\"p\">:</span>\n            <span class=\"k\">raise</span> <span class=\"ne\">TypeError</span><span class=\"p\">(</span><span class=\"s1\">&#39;The sequence dtype was neither int nor float.</span><span class=\"se\">\\n</span><span class=\"s1\">&#39;</span>\n                            <span class=\"s1\">&#39;Convert it to whether int or float, and try again.&#39;</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">sign</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">&gt;</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n        <span class=\"k\">elif</span> <span class=\"n\">sign</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;neg&#39;</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">&lt;</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">!=</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">(</span><span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n        <span class=\"c1\">#: (bool): verbose or not</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">verbose</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">Initialized sequence with </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> registries.&quot;</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">sec_order</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">=</span> <span class=\"n\">subtract_sorted</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">copy</span><span class=\"p\">())</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">(</span><span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">reset_index</span><span class=\"p\">(</span><span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n            <span class=\"k\">if</span> <span class=\"n\">verbose</span><span class=\"p\">:</span>\n                <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;Second Order Test. Initial series reduced &#39;</span>\n                      <span class=\"sa\">f</span><span class=\"s1\">&#39;to </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s1\"> entries.&#39;</span><span class=\"p\">)</span>\n\n        <span class=\"n\">ab</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">dtype</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;int&#39;</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;ZN&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">ab</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"k\">if</span> <span class=\"n\">decimals</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;infer&#39;</span><span class=\"p\">:</span>\n                <span class=\"c1\"># There is some numerical issue with Windows that required</span>\n                <span class=\"c1\"># implementing it differently (and slower)</span>\n                <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;ZN&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">ab</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\\\n                               <span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">replace</span><span class=\"p\">(</span><span class=\"s1\">&#39;.&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;&#39;</span><span class=\"p\">,</span> <span class=\"n\">regex</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\\\n                               <span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">lstrip</span><span class=\"p\">(</span><span class=\"s1\">&#39;0&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"p\">[:</span><span class=\"mi\">5</span><span class=\"p\">]</span>\\\n                               <span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n            <span class=\"k\">else</span><span class=\"p\">:</span>\n                <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;ZN&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">ab</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"n\">decimals</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n\n<div class=\"viewcode-block\" id=\"Source.mantissas\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Source.mantissas\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">mantissas</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">report</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">),</span>\n                  <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Calculates the mantissas, their mean and variance, and compares them</span>\n<span class=\"sd\">        with the mean and variance of a Benford&#39;s sequence.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            report: prints the mamtissas mean, variance, skewness and kurtosis</span>\n<span class=\"sd\">                for the sequence studied, along with reference values.</span>\n<span class=\"sd\">            show_plot: plots the ordered mantissas and a line with the expected</span>\n<span class=\"sd\">                inclination. Defaults to True.</span>\n<span class=\"sd\">            figsize: tuple that sets the figure dimensions.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"s1\">&#39;Mant&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">get_mantissas</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">())</span>\n        <span class=\"k\">if</span> <span class=\"n\">report</span><span class=\"p\">:</span>\n            <span class=\"n\">p</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"p\">[[</span><span class=\"s1\">&#39;seq&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Mant&#39;</span><span class=\"p\">]]</span>\n            <span class=\"n\">p</span> <span class=\"o\">=</span> <span class=\"n\">p</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">p</span><span class=\"o\">.</span><span class=\"n\">seq</span> <span class=\"o\">&gt;</span> <span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"s1\">&#39;Mant&#39;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;The Mantissas MEAN is </span><span class=\"si\">{</span><span class=\"n\">p</span><span class=\"o\">.</span><span class=\"n\">Mant</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span><span class=\"si\">}</span><span class=\"s2\">. Ref: 0.5.&quot;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;The Mantissas VARIANCE is </span><span class=\"si\">{</span><span class=\"n\">p</span><span class=\"o\">.</span><span class=\"n\">Mant</span><span class=\"o\">.</span><span class=\"n\">var</span><span class=\"p\">()</span><span class=\"si\">}</span><span class=\"s2\">. Ref: 0.083333.&quot;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;The Mantissas SKEWNESS is </span><span class=\"si\">{</span><span class=\"n\">p</span><span class=\"o\">.</span><span class=\"n\">Mant</span><span class=\"o\">.</span><span class=\"n\">skew</span><span class=\"p\">()</span><span class=\"si\">}</span><span class=\"s2\">. </span><span class=\"se\">\\t</span><span class=\"s2\">Ref: 0.&quot;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;The Mantissas KURTOSIS is </span><span class=\"si\">{</span><span class=\"n\">p</span><span class=\"o\">.</span><span class=\"n\">Mant</span><span class=\"o\">.</span><span class=\"n\">kurt</span><span class=\"p\">()</span><span class=\"si\">}</span><span class=\"s2\">. </span><span class=\"se\">\\t</span><span class=\"s2\">Ref: -1.2.&quot;</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_ordered_mantissas</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">Mant</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">,</span>\n                                   <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"Source.first_digits\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Source.first_digits\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">first_digits</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span>\n                     <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                     <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                     <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_coeff</span> <span class=\"o\">=</span> <span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                     <span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Performs the Benford First Digits test with the series of</span>\n<span class=\"sd\">        numbers provided, and populates the mapping dict for future</span>\n<span class=\"sd\">        selection of the original series.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            digs (int): number of first digits to consider. Must be 1 (first digit),</span>\n<span class=\"sd\">                2 (first two digits) or 3 (first three digits).</span>\n<span class=\"sd\">            verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">                the analysis; defaults to True</span>\n<span class=\"sd\">            confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">                plotting and to limit the top deviations to show, as well as to</span>\n<span class=\"sd\">                calculate critical values for the tests&#39; statistics. Defaults to None.</span>\n<span class=\"sd\">            high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">                according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">                which will highlight only values higher than the expexted</span>\n<span class=\"sd\">                frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">                negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">                positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">                the confidence or not.</span>\n<span class=\"sd\">            limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">                the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">            MAD (bool): calculates the Mean Absolute Difference between the</span>\n<span class=\"sd\">                found and the expected distributions; defaults to False.</span>\n<span class=\"sd\">            MSE (bool): calculates the Mean Square Error of the sample; defaults to</span>\n<span class=\"sd\">                False.</span>\n<span class=\"sd\">            bhat_coeff (bool): computes the Bhattacharyya Coefficient between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution; defaults</span>\n<span class=\"sd\">                to Fasle</span>\n<span class=\"sd\">            bhat_dist (bool): calculates the Bhattacharyya Distance between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution; defaults</span>\n<span class=\"sd\">                to Fasle</span>\n<span class=\"sd\">            kl_diverg (bool): calculates the Kulback-Laibler Divergence between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution;</span>\n<span class=\"sd\">                defaults to False</span>\n<span class=\"sd\">            show_plot (bool): draws the test plot. Defaults to True.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">            ret_df: returns the test DataFrame. Defaults to False. True if run by</span>\n<span class=\"sd\">                the test function.</span>\n\n<span class=\"sd\">        Returns:</span>\n<span class=\"sd\">            DataFrame with the Expected and Found proportions, and the Z scores of</span>\n<span class=\"sd\">                the differences</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"c1\"># Check on the possible values for confidence levels</span>\n        <span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">(</span><span class=\"n\">confidence</span><span class=\"p\">)</span>\n        <span class=\"c1\"># Check on possible digits</span>\n        <span class=\"n\">_check_digs_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span>\n\n        <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)]</span>\n        <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">//</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">((</span><span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span>\n                                                   <span class=\"nb\">int</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span>\n                                                       <span class=\"nb\">int</span><span class=\"p\">)</span>\n        <span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"n\">m</span> <span class=\"o\">=</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">),</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span>\n        <span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"n\">m</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">simple</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n            <span class=\"n\">show_plot</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n            <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">prepare</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]],</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span>\n                         <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">prepare</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]],</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span>\n                            <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">Test performed on </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> registries.</span><span class=\"se\">\\n</span><span class=\"s2\">&quot;</span>\n                  <span class=\"sa\">f</span><span class=\"s2\">&quot;Discarded </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> records &lt; </span><span class=\"si\">{</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span>\n                  <span class=\"s2\">&quot; after preparation.&quot;</span><span class=\"p\">)</span>\n            <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n                <span class=\"n\">_inform_</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"n\">high_Z</span><span class=\"p\">,</span> <span class=\"n\">conf</span><span class=\"o\">=</span><span class=\"n\">CONFS</span><span class=\"p\">[</span><span class=\"n\">confidence</span><span class=\"p\">])</span>\n\n        <span class=\"c1\"># Mean absolute difference</span>\n        <span class=\"k\">if</span> <span class=\"n\">MAD</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n            <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n                <span class=\"n\">_report_mad_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span><span class=\"p\">)</span>\n\n        <span class=\"c1\"># Mean Square Error</span>\n        <span class=\"k\">if</span> <span class=\"n\">MSE</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MSE</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n\n        <span class=\"c1\"># Chi-square statistic</span>\n        <span class=\"k\">if</span> <span class=\"n\">chi_square</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chi_square</span> <span class=\"o\">=</span> <span class=\"n\">chi_sq</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">ddf</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">,</span>\n                                     <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span>\n                                     <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n        <span class=\"c1\"># KS test</span>\n        <span class=\"k\">if</span> <span class=\"n\">KS</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">KS</span> <span class=\"o\">=</span> <span class=\"n\">kolmogorov_smirnov</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">),</span>\n                                         <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">bhat_coeff</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhat_coeff</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_coefficient</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">bhat_dist</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhat_dist</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_distance_</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n        \n        <span class=\"k\">if</span> <span class=\"n\">kl_diverg</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">kl_diverg</span> <span class=\"o\">=</span> <span class=\"n\">_kullback_leibler_divergence_</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n\n        <span class=\"c1\"># Plotting the expected frequncies (line) against the found ones(bars)</span>\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_digs</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"o\">=</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y_Exp</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"p\">,</span> <span class=\"n\">y_Found</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"o\">=</span><span class=\"n\">N</span><span class=\"p\">,</span>\n                       <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">),</span> <span class=\"mf\">1.5</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">)),</span>\n                       <span class=\"n\">conf_Z</span><span class=\"o\">=</span><span class=\"n\">CONFS</span><span class=\"p\">[</span><span class=\"n\">confidence</span><span class=\"p\">],</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span>\n                       <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">ret_df</span><span class=\"p\">:</span>\n            <span class=\"k\">return</span> <span class=\"n\">df</span></div>\n\n<div class=\"viewcode-block\" id=\"Source.second_digit\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Source.second_digit\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">second_digit</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span>\n                     <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                     <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_coeff</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                     <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                     <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Performs the Benford Second Digit test with the series of</span>\n<span class=\"sd\">        numbers provided.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">                the analysis; defaults to True</span>\n<span class=\"sd\">            MAD (bool): calculates the Mean Absolute Difference between the</span>\n<span class=\"sd\">                found and the expected distributions; defaults to False.</span>\n<span class=\"sd\">            confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">                plotting and to limit the top deviations to show, as well as to</span>\n<span class=\"sd\">                calculate critical values for the tests&#39; statistics. Defaults to None.</span>\n<span class=\"sd\">            high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">                according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">                which will highlight only values higher than the expexted</span>\n<span class=\"sd\">                frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">                negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">                positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">                the confidence or not.</span>\n<span class=\"sd\">            limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">                the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">            MSE (bool): calculates the Mean Square Error of the sample; defaults to</span>\n<span class=\"sd\">                False.</span>\n<span class=\"sd\">            bhat_coeff (bool): computes the Bhattacharyya Coefficient between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution; defaults</span>\n<span class=\"sd\">                to Fasle</span>\n<span class=\"sd\">            bhat_dist (bool): calculates the Bhattacharyya Distance between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution; defaults</span>\n<span class=\"sd\">                to Fasle</span>\n<span class=\"sd\">            kl_diverg (bool): calculates the Kulback-Laibler Divergence between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution;</span>\n<span class=\"sd\">                defaults to False</span>\n<span class=\"sd\">            show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">            ret_df: returns the test DataFrame. Defaults to False. True if run by</span>\n<span class=\"sd\">                the test function.</span>\n\n<span class=\"sd\">        Returns:</span>\n<span class=\"sd\">            DataFrame with the Expected and Found proportions, and the Z scores of</span>\n<span class=\"sd\">                the differences</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">(</span><span class=\"n\">confidence</span><span class=\"p\">)</span>\n\n        <span class=\"n\">conf</span> <span class=\"o\">=</span> <span class=\"n\">CONFS</span><span class=\"p\">[</span><span class=\"n\">confidence</span><span class=\"p\">]</span>\n\n        <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">10</span><span class=\"p\">,</span> <span class=\"p\">:]</span>\n        <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"s1\">&#39;SD&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">//</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">((</span><span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span>\n                      <span class=\"nb\">int</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">))</span> <span class=\"o\">%</span> <span class=\"mi\">10</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">simple</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n            <span class=\"n\">show_plot</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n            <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">prepare</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"s1\">&#39;SD&#39;</span><span class=\"p\">],</span> <span class=\"mi\">22</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">prepare</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"s1\">&#39;SD&#39;</span><span class=\"p\">],</span> <span class=\"mi\">22</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">Test performed on </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> registries.</span><span class=\"se\">\\n</span><span class=\"s2\">Discarded &quot;</span>\n                  <span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> records &lt; 10 after preparation.&quot;</span><span class=\"p\">)</span>\n            <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n                <span class=\"n\">_inform_</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"p\">,</span> <span class=\"n\">conf</span><span class=\"p\">)</span>\n\n        <span class=\"c1\"># Mean absolute difference</span>\n        <span class=\"k\">if</span> <span class=\"n\">MAD</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n            <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n                <span class=\"n\">_report_mad_</span><span class=\"p\">(</span><span class=\"mi\">22</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span><span class=\"p\">)</span>\n        <span class=\"c1\"># Mean Square Error</span>\n        <span class=\"k\">if</span> <span class=\"n\">MSE</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MSE</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n\n        <span class=\"c1\"># Chi-square statistic</span>\n        <span class=\"k\">if</span> <span class=\"n\">chi_square</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chi_square</span> <span class=\"o\">=</span> <span class=\"n\">chi_sq</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">ddf</span><span class=\"o\">=</span><span class=\"mi\">9</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span>\n                                     <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n        <span class=\"c1\"># KS test</span>\n        <span class=\"k\">if</span> <span class=\"n\">KS</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">KS</span> <span class=\"o\">=</span> <span class=\"n\">kolmogorov_smirnov</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">),</span>\n\n                                         <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">bhat_coeff</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhat_coeff</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_coefficient</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">bhat_dist</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhat_dist</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_distance_</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span>\n                            <span class=\"p\">)</span>\n        \n        <span class=\"k\">if</span> <span class=\"n\">kl_diverg</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">kl_diverg</span> <span class=\"o\">=</span> <span class=\"n\">_kullback_leibler_divergence_</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span>\n                            <span class=\"p\">)</span>\n\n        <span class=\"c1\"># Plotting the expected frequncies (line) against the found ones(bars)</span>\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_digs</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"o\">=</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">),</span> <span class=\"n\">y_Exp</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"p\">,</span>\n                       <span class=\"n\">y_Found</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"o\">=</span><span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">),</span> <span class=\"n\">conf_Z</span><span class=\"o\">=</span><span class=\"n\">conf</span><span class=\"p\">,</span>\n                       <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">ret_df</span><span class=\"p\">:</span>\n            <span class=\"k\">return</span> <span class=\"n\">df</span></div>\n\n<div class=\"viewcode-block\" id=\"Source.last_two_digits\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Source.last_two_digits\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">last_two_digits</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span>\n                        <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                        <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_coeff</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                        <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                        <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Performs the Benford Last Two Digits test with the series of</span>\n<span class=\"sd\">        numbers provided.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">                the analysis; defaults to True</span>\n<span class=\"sd\">            MAD (bool): calculates the Mean Absolute Difference between the</span>\n<span class=\"sd\">                found and the expected distributions; defaults to False.</span>\n<span class=\"sd\">            confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">                plotting and to limit the top deviations to show, as well as to</span>\n<span class=\"sd\">                calculate critical values for the tests&#39; statistics. Defaults to None.</span>\n<span class=\"sd\">            high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">                according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">                which will highlight only values higher than the expexted</span>\n<span class=\"sd\">                frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">                negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">                positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">                the confidence or not.</span>\n<span class=\"sd\">            limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">                the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">            MSE (bool): calculates the Mean Square Error of the sample; defaults to</span>\n<span class=\"sd\">                False.</span>\n<span class=\"sd\">            bhat_coeff (bool): computes the Bhattacharyya Coefficient between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution; defaults</span>\n<span class=\"sd\">                to Fasle</span>\n<span class=\"sd\">            bhat_dist (bool): calculates the Bhattacharyya Distance between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution; defaults</span>\n<span class=\"sd\">                to Fasle</span>\n<span class=\"sd\">            kl_diverg (bool): calculates the Kulback-Laibler Divergence between</span>\n<span class=\"sd\">                the found and the expected (Benford) digits distribution;</span>\n<span class=\"sd\">                defaults to False</span>\n<span class=\"sd\">            show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">        </span>\n<span class=\"sd\">        Returns:</span>\n<span class=\"sd\">            DataFrame with the Expected and Found proportions, and the Z scores of</span>\n<span class=\"sd\">                the differences</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">confidence</span> <span class=\"o\">=</span> <span class=\"n\">_check_confidence_</span><span class=\"p\">(</span><span class=\"n\">confidence</span><span class=\"p\">)</span>\n        <span class=\"n\">conf</span> <span class=\"o\">=</span> <span class=\"n\">CONFS</span><span class=\"p\">[</span><span class=\"n\">confidence</span><span class=\"p\">]</span>\n\n        <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">1000</span><span class=\"p\">]</span>\n        <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"s1\">&#39;L2D&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">%</span> <span class=\"mi\">100</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">simple</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n            <span class=\"n\">show_plot</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n            <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">prepare</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"s1\">&#39;L2D&#39;</span><span class=\"p\">],</span> <span class=\"o\">-</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">prepare</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"s1\">&#39;L2D&#39;</span><span class=\"p\">],</span> <span class=\"o\">-</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">Test performed on </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> registries.</span><span class=\"se\">\\n\\n</span><span class=\"s2\">Discarded &quot;</span>\n                  <span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> records &lt; 1000 after preparation&quot;</span><span class=\"p\">)</span>\n            <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n                <span class=\"n\">_inform_</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"p\">,</span> <span class=\"n\">conf</span><span class=\"p\">)</span>\n\n        <span class=\"c1\"># Mean absolute difference</span>\n        <span class=\"k\">if</span> <span class=\"n\">MAD</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n            <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n                <span class=\"n\">_report_mad_</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MAD</span><span class=\"p\">)</span>\n        <span class=\"c1\"># Mean Square Error</span>\n        <span class=\"k\">if</span> <span class=\"n\">MSE</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">MSE</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n\n        <span class=\"c1\"># Chi-square statistic</span>\n        <span class=\"k\">if</span> <span class=\"n\">chi_square</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">chi_square</span> <span class=\"o\">=</span> <span class=\"n\">chi_sq</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">ddf</span><span class=\"o\">=</span><span class=\"mi\">99</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span>\n                                     <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n        <span class=\"c1\"># KS test</span>\n        <span class=\"k\">if</span> <span class=\"n\">KS</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">KS</span> <span class=\"o\">=</span> <span class=\"n\">kolmogorov_smirnov</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">),</span>\n                                         <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">bhat_coeff</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhat_coeff</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_coefficient</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">bhat_dist</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bhat_dist</span> <span class=\"o\">=</span> <span class=\"n\">_bhattacharyya_distance_</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n        \n        <span class=\"k\">if</span> <span class=\"n\">kl_diverg</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">kl_diverg</span> <span class=\"o\">=</span> <span class=\"n\">_kullback_leibler_divergence_</span><span class=\"p\">(</span>\n                                <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n\n        <span class=\"c1\"># Plotting expected frequencies (line) versus found ones (bars)</span>\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_digs</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"o\">=</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">100</span><span class=\"p\">),</span> <span class=\"n\">y_Exp</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"p\">,</span>\n                       <span class=\"n\">y_Found</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Found</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"o\">=</span><span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">5</span><span class=\"p\">),</span>\n                       <span class=\"n\">conf_Z</span><span class=\"o\">=</span><span class=\"n\">conf</span><span class=\"p\">,</span> <span class=\"n\">text_x</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span>\n                       <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">ret_df</span><span class=\"p\">:</span>\n            <span class=\"k\">return</span> <span class=\"n\">df</span></div>\n\n<div class=\"viewcode-block\" id=\"Source.summation\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Source.summation\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">summation</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">top</span><span class=\"o\">=</span><span class=\"mi\">20</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                  <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Performs the Summation test. In a Benford series, the sums of the</span>\n<span class=\"sd\">        entries begining with the same digits tends to be the same.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            digs: tells the first digits to use. 1- first; 2- first two;</span>\n<span class=\"sd\">                3- first three. Defaults to 2.</span>\n<span class=\"sd\">            top: choses how many top values to show. Defaults to 20.</span>\n<span class=\"sd\">            show_plot: plots the results. Defaults to True.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension. Only available when</span>\n<span class=\"sd\">                plot=True.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">                figure file path/name.</span>\n<span class=\"sd\">        </span>\n<span class=\"sd\">        Returns:</span>\n<span class=\"sd\">            DataFrame with the Expected and Found proportions, and their</span>\n<span class=\"sd\">                absolute differences</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">_check_digs_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">digs</span> <span class=\"o\">==</span> <span class=\"mi\">1</span><span class=\"p\">:</span>\n            <span class=\"n\">top</span> <span class=\"o\">=</span> <span class=\"mi\">9</span>\n        <span class=\"c1\"># Call the dict for F1D, F2D, F3D</span>\n        <span class=\"n\">d</span> <span class=\"o\">=</span> <span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">digs</span><span class=\"p\">]</span>\n        <span class=\"k\">if</span> <span class=\"n\">d</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">columns</span><span class=\"p\">:</span>\n            <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"n\">d</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"p\">[:</span><span class=\"n\">digs</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n        <span class=\"c1\"># Call the expected proportion according to digs</span>\n        <span class=\"n\">li</span> <span class=\"o\">=</span> <span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mi\">9</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)))</span>\n\n        <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"n\">d</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n        <span class=\"c1\"># s.drop(0, inplace=True)</span>\n        <span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s1\">&#39;Percent&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">/</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n        <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">columns</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"s1\">&#39;Summ&#39;</span>\n        <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"p\">[[</span><span class=\"s1\">&#39;Summ&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Percent&#39;</span><span class=\"p\">]]</span>\n        <span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s1\">&#39;AbsDif&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Percent</span> <span class=\"o\">-</span> <span class=\"n\">li</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"c1\"># N = len(self)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">Test performed on </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\"> registries.</span><span class=\"se\">\\n</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;The top </span><span class=\"si\">{</span><span class=\"n\">top</span><span class=\"si\">}</span><span class=\"s2\"> diferences are:</span><span class=\"se\">\\n</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">[:</span><span class=\"n\">top</span><span class=\"p\">])</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_sum</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span>\n                       <span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">),</span> <span class=\"mf\">1.5</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">)),</span> <span class=\"n\">li</span><span class=\"o\">=</span><span class=\"n\">li</span><span class=\"p\">,</span>\n                       <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">ret_df</span><span class=\"p\">:</span>\n            <span class=\"k\">return</span> <span class=\"n\">df</span></div>\n\n<div class=\"viewcode-block\" id=\"Source.duplicates\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Source.duplicates\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">duplicates</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">top_Rep</span><span class=\"o\">=</span><span class=\"mi\">20</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Performs a duplicates test and maps the duplicates count in descending</span>\n<span class=\"sd\">        order.</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            verbose (bool): tells how many duplicated entries were found and prints the</span>\n<span class=\"sd\">                top numbers according to the top_Rep argument. Defaluts to True.</span>\n<span class=\"sd\">            top_Rep: int or None. Chooses how many duplicated entries will be</span>\n<span class=\"sd\">                shown withe the top repititions. Defaluts to 20. If None, returns</span>\n<span class=\"sd\">                al the ordered repetitions.</span>\n\n<span class=\"sd\">        Returns:</span>\n<span class=\"sd\">            DataFrame with the duplicated records and their occurrence counts,</span>\n<span class=\"sd\">                in descending order (if verbose is False; if True, prints to</span>\n<span class=\"sd\">                terminal).</span>\n\n<span class=\"sd\">        Raises:</span>\n<span class=\"sd\">            ValueError: if the `top_Rep` arg is not int or None.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"k\">if</span> <span class=\"n\">top_Rep</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span> <span class=\"ow\">and</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">top_Rep</span><span class=\"p\">,</span> <span class=\"nb\">int</span><span class=\"p\">):</span>\n            <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span><span class=\"s1\">&#39;The top_Rep argument must be an int or None.&#39;</span><span class=\"p\">)</span>\n\n        <span class=\"n\">dup</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"p\">[[</span><span class=\"s1\">&#39;seq&#39;</span><span class=\"p\">]][</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"o\">.</span><span class=\"n\">duplicated</span><span class=\"p\">(</span><span class=\"n\">keep</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)]</span>\n        <span class=\"n\">dup_count</span> <span class=\"o\">=</span> <span class=\"n\">dup</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">seq</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">count</span><span class=\"p\">()</span>\n\n        <span class=\"n\">dup_count</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">names</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"s1\">&#39;Entries&#39;</span><span class=\"p\">]</span>\n        <span class=\"n\">dup_count</span><span class=\"o\">.</span><span class=\"n\">rename</span><span class=\"p\">(</span><span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;seq&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;Count&#39;</span><span class=\"p\">},</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n        <span class=\"n\">dup_count</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"s1\">&#39;Count&#39;</span><span class=\"p\">,</span> <span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n        <span class=\"c1\"># self.maps[&#39;dup&#39;] = dup_count.index[:top_Rep].values  # array</span>\n\n        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">Found </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">dup_count</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s1\"> duplicated entries.</span><span class=\"se\">\\n</span><span class=\"s1\">&#39;</span>\n                  <span class=\"sa\">f</span><span class=\"s1\">&#39;The entries with the </span><span class=\"si\">{</span><span class=\"n\">top_Rep</span><span class=\"si\">}</span><span class=\"s1\"> highest repitition counts are:&#39;</span><span class=\"p\">)</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">dup_count</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"p\">(</span><span class=\"n\">top_Rep</span><span class=\"p\">))</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"k\">return</span> <span class=\"n\">dup_count</span></div></div>\n\n<div class=\"viewcode-block\" id=\"Roll_mad\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Roll_mad\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Roll_mad</span><span class=\"p\">(</span><span class=\"nb\">object</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Applies the MAD to sequential subsets of the Series, returning another</span>\n<span class=\"sd\">    Series.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;</span>\n<span class=\"sd\">            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</span>\n<span class=\"sd\">        window: size of the subset to be used.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">window</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">):</span>\n\n        <span class=\"c1\">#: the test (F1D, SD, F2D...) used for the MAD calculation and critical values</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_test_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">Source</span><span class=\"p\">):</span>\n            <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n\n        <span class=\"n\">Exp</span><span class=\"p\">,</span> <span class=\"n\">ind</span> <span class=\"o\">=</span> <span class=\"n\">prep_to_roll</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">test</span><span class=\"p\">)</span>\n\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">roll_series</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span>\n                                <span class=\"n\">window</span><span class=\"o\">=</span><span class=\"n\">window</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">mad_to_roll</span><span class=\"p\">,</span> \n                                    <span class=\"n\">args</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">Exp</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">),</span> <span class=\"n\">raw</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">roll_series</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">(</span><span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n<div class=\"viewcode-block\" id=\"Roll_mad.show_plot\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Roll_mad.show_plot\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">show_plot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">),</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Shows the rolling MAD plot</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            figsize: the figure dimensions.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when save_plot is a string with the figure file</span>\n<span class=\"sd\">                path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">plot_roll_mad</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">,</span>\n                      <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div></div>\n\n\n<div class=\"viewcode-block\" id=\"Roll_mse\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Roll_mse\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Roll_mse</span><span class=\"p\">(</span><span class=\"nb\">object</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Applies the MSE to sequential subsets of the Series, returning another</span>\n<span class=\"sd\">    Series.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;</span>\n<span class=\"sd\">            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</span>\n<span class=\"sd\">        window: size of the subset to be used.</span>\n<span class=\"sd\">            decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. &#39;pos&#39;: only the positive</span>\n<span class=\"sd\">            entries; &#39;neg&#39;: only negative entries; &#39;all&#39;: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to &#39;all&#39;.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">window</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">):</span>\n\n        <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_test_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">Source</span><span class=\"p\">):</span>\n            <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n\n        <span class=\"n\">Exp</span><span class=\"p\">,</span> <span class=\"n\">ind</span> <span class=\"o\">=</span> <span class=\"n\">prep_to_roll</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">)</span>\n\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">roll_series</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span>\n                                <span class=\"n\">window</span><span class=\"o\">=</span><span class=\"n\">window</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">mse_to_roll</span><span class=\"p\">,</span> \n                                    <span class=\"n\">args</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">Exp</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">),</span> <span class=\"n\">raw</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">roll_series</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">(</span><span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n<div class=\"viewcode-block\" id=\"Roll_mse.show_plot\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.Roll_mse.show_plot\">[docs]</a>    <span class=\"k\">def</span> <span class=\"nf\">show_plot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">),</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"sd\">&quot;&quot;&quot;Shows the rolling MSE plot</span>\n\n<span class=\"sd\">        Args:</span>\n<span class=\"sd\">            figsize: the figure dimensions.</span>\n<span class=\"sd\">            save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">                plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">                is infered by the file name extension.</span>\n<span class=\"sd\">            save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">                matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">                https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">                Only available when save_plot is a string with the figure file</span>\n<span class=\"sd\">                path/name.</span>\n<span class=\"sd\">        &quot;&quot;&quot;</span>\n        <span class=\"n\">plot_roll_mse</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">roll_series</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">,</span>\n                      <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div></div>\n\n\n<div class=\"viewcode-block\" id=\"first_digits\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.first_digits\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">first_digits</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n                 <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                 <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                 <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                 <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Performs the Benford First Digits test on the series of</span>\n<span class=\"sd\">    numbers provided.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. &#39;pos&#39;: only the positive</span>\n<span class=\"sd\">            entries; &#39;neg&#39;: only negative entries; &#39;all&#39;: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to &#39;all&#39;.</span>\n<span class=\"sd\">        digs (int): number of first digits to consider. Must be 1 (first digit),</span>\n<span class=\"sd\">            2 (first two digits) or 3 (first three digits).</span>\n<span class=\"sd\">        verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">            the analysis and returns tha analysis DataFrame sorted by the</span>\n<span class=\"sd\">            highest Z score down. Defaults to True.</span>\n<span class=\"sd\">        MAD (bool): calculates the Mean Absolute Difference between the</span>\n<span class=\"sd\">            found and the expected distributions; defaults to False.</span>\n<span class=\"sd\">        confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">            plotting and to limit the top deviations to show. Defaults to None.</span>\n<span class=\"sd\">        high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">            according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">            which will highlight only values higher than the expexted</span>\n<span class=\"sd\">            frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">            negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">            positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">            the confidence or not.</span>\n<span class=\"sd\">        limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">            the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">        MSE (bool): calculates the Mean Square Error of the sample; defaults to</span>\n<span class=\"sd\">            False.</span>\n<span class=\"sd\">        chi_square: calculates the chi_square statistic of the sample and</span>\n<span class=\"sd\">            compares it with a critical value, according to the confidence</span>\n<span class=\"sd\">            level chosen and the series&#39;s degrees of freedom. Defaults to</span>\n<span class=\"sd\">            False. Requires confidence != None.</span>\n<span class=\"sd\">        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative</span>\n<span class=\"sd\">            distribution of the sample with the Benford&#39;s, according to the</span>\n<span class=\"sd\">            confidence level chosen. Defaults to False. Requires confidence</span>\n<span class=\"sd\">            != None.</span>\n<span class=\"sd\">        show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        DataFrame with the Expected and Found proportions, and the Z scores of</span>\n<span class=\"sd\">            the differences if the confidence is not None.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">verbose</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">Source</span><span class=\"p\">):</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">first_digits</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"n\">high_Z</span><span class=\"p\">,</span>\n                             <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"n\">MAD</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"n\">MSE</span><span class=\"p\">,</span>\n                             <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"n\">chi_square</span><span class=\"p\">,</span> <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"n\">KS</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"n\">show_plot</span><span class=\"p\">,</span>\n                             <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">,</span>\n                             <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[[</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">]]</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">,</span> <span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"p\">[[</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">]]</span></div>\n\n\n<div class=\"viewcode-block\" id=\"second_digit\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.second_digit\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">second_digit</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n                 <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                 <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                 <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                 <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Performs the Benford Second Digits test on the series of</span>\n<span class=\"sd\">    numbers provided.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. &#39;pos&#39;: only the positive</span>\n<span class=\"sd\">            entries; &#39;neg&#39;: only negative entries; &#39;all&#39;: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to &#39;all&#39;.</span>\n<span class=\"sd\">        verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">            the analysis and returns tha analysis DataFrame sorted by the</span>\n<span class=\"sd\">            highest Z score down. Defaults to True.</span>\n<span class=\"sd\">        MAD (bool): calculates the Mean Absolute Difference between the</span>\n<span class=\"sd\">            found and the expected distributions; defaults to False.</span>\n<span class=\"sd\">        confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">            plotting and to limit the top deviations to show. Defaults to None.</span>\n<span class=\"sd\">        high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">            according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">            which will highlight only values higher than the expexted</span>\n<span class=\"sd\">            frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">            negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">            positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">            the confidence or not.</span>\n<span class=\"sd\">        limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">            the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">        MSE (bool): calculates the Mean Square Error of the sample; defaults to</span>\n<span class=\"sd\">            False.</span>\n<span class=\"sd\">        chi_square: calculates the chi_square statistic of the sample and</span>\n<span class=\"sd\">            compares it with a critical value, according to the confidence</span>\n<span class=\"sd\">            level chosen and the series&#39;s degrees of freedom. Defaults to</span>\n<span class=\"sd\">            False. Requires confidence != None.</span>\n<span class=\"sd\">        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative</span>\n<span class=\"sd\">            distribution of the sample with the Benford&#39;s, according to the</span>\n<span class=\"sd\">            confidence level chosen. Defaults to False. Requires confidence</span>\n<span class=\"sd\">            != None.</span>\n<span class=\"sd\">        show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        DataFrame with the Expected and Found proportions, and the Z scores of</span>\n<span class=\"sd\">            the differences if the confidence is not None.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">verbose</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">Source</span><span class=\"p\">):</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">second_digit</span><span class=\"p\">(</span><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"n\">high_Z</span><span class=\"p\">,</span>\n                             <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"n\">MAD</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"n\">MSE</span><span class=\"p\">,</span>\n                             <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"n\">chi_square</span><span class=\"p\">,</span> <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"n\">KS</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"n\">show_plot</span><span class=\"p\">,</span>\n                             <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">,</span>\n                             <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[[</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">]]</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">,</span> <span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"p\">[[</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">]]</span></div>\n\n\n<div class=\"viewcode-block\" id=\"last_two_digits\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.last_two_digits\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">last_two_digits</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n                    <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                    <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                    <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n                    <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Performs the Last Two Digits test on the series of</span>\n<span class=\"sd\">    numbers provided.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column,with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. &#39;pos&#39;: only the positive</span>\n<span class=\"sd\">            entries; &#39;neg&#39;: only negative entries; &#39;all&#39;: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to &#39;all&#39;.</span>\n<span class=\"sd\">        verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">            the analysis and returns tha analysis DataFrame sorted by the</span>\n<span class=\"sd\">            highest Z score down. Defaults to True.</span>\n<span class=\"sd\">        confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">            plotting and to limit the top deviations to show. Defaults to None.</span>\n<span class=\"sd\">        high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">            according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">            which will highlight only values higher than the expexted</span>\n<span class=\"sd\">            frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">            negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">            positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">            the confidence or not.</span>\n<span class=\"sd\">        limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">            the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">        MAD (bool): calculates the Mean Absolute Difference between the</span>\n<span class=\"sd\">            found and the expected distributions; defaults to False.</span>\n<span class=\"sd\">        MSE (bool): calculates the Mean Square Error of the sample; defaults to</span>\n<span class=\"sd\">            False.</span>\n<span class=\"sd\">        chi_square: calculates the chi_square statistic of the sample and</span>\n<span class=\"sd\">            compares it with a critical value, according to the confidence</span>\n<span class=\"sd\">            level chosen and the series&#39;s degrees of freedom. Defaults to</span>\n<span class=\"sd\">            False. Requires confidence != None.</span>\n<span class=\"sd\">        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative</span>\n<span class=\"sd\">            distribution of the sample with the Benford&#39;s, according to the</span>\n<span class=\"sd\">            confidence level chosen. Defaults to False. Requires confidence</span>\n<span class=\"sd\">            != None.</span>\n<span class=\"sd\">        show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        DataFrame with the Expected and Found proportions, and the Z scores of</span>\n<span class=\"sd\">            the differences if the confidence is not None.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">verbose</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">Source</span><span class=\"p\">):</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">last_two_digits</span><span class=\"p\">(</span><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"n\">high_Z</span><span class=\"p\">,</span>\n                                <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"n\">MAD</span><span class=\"p\">,</span>\n                                <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"n\">MSE</span><span class=\"p\">,</span> <span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"n\">chi_square</span><span class=\"p\">,</span> <span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"n\">KS</span><span class=\"p\">,</span>\n                                <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"n\">show_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span>\n                                <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">,</span> <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[[</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">]]</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">,</span> <span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"p\">[[</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">]]</span></div>\n\n\n<div class=\"viewcode-block\" id=\"mantissas\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.mantissas\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mantissas</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">report</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">arc_test</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n              <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Extraxts the mantissas of the records logarithms</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence to compute mantissas from, numpy 1D array, pandas Series</span>\n<span class=\"sd\">            of pandas DataFrame column.</span>\n<span class=\"sd\">        report: prints the mamtissas mean, variance, skewness and kurtosis</span>\n<span class=\"sd\">            for the sequence studied, along with reference values.</span>\n<span class=\"sd\">        show_plot: plots the ordered mantissas and a line with the expected</span>\n<span class=\"sd\">            inclination. Defaults to True.</span>\n<span class=\"sd\">        arc_test: draws the Arc Test plot. Defaluts to True.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        Series with the data mantissas.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">report</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">report</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n\n    <span class=\"n\">mant</span> <span class=\"o\">=</span> <span class=\"n\">Mantissas</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">report</span><span class=\"p\">:</span>\n        <span class=\"n\">mant</span><span class=\"o\">.</span><span class=\"n\">report</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n        <span class=\"n\">mant</span><span class=\"o\">.</span><span class=\"n\">show_plot</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">arc_test</span><span class=\"p\">:</span>\n        <span class=\"n\">mant</span><span class=\"o\">.</span><span class=\"n\">arc_test</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">mant</span></div>\n\n\n<div class=\"viewcode-block\" id=\"summation\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.summation\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">summation</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">top</span><span class=\"o\">=</span><span class=\"mi\">20</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n              <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Performs the Summation test. In a Benford series, the sums of the</span>\n<span class=\"sd\">    entries begining with the same digits tends to be the same.</span>\n<span class=\"sd\">    Works only with the First Digits (1, 2 or 3) test.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        digs: tells the first digits to use: 1- first; 2- first two;</span>\n<span class=\"sd\">            3- first three. Defaults to 2.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        top: choses how many top values to show. Defaults to 20.</span>\n<span class=\"sd\">        show_plot: plots the results. Defaults to True.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        DataFrame with the Summation test, whether sorted in descending order</span>\n<span class=\"sd\">            (if verbose == True) or not.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">verbose</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">Source</span><span class=\"p\">):</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">summation</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">top</span><span class=\"o\">=</span><span class=\"n\">top</span><span class=\"p\">,</span>\n                          <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"n\">show_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span>\n                          <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">,</span> <span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">verbose</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"s1\">&#39;AbsDif&#39;</span><span class=\"p\">,</span> <span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">data</span></div>\n\n\n<div class=\"viewcode-block\" id=\"mad\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.mad\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mad</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Calculates the Mean Absolute Deviation of the Series</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: informs which base test to use for the mad.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        float: the Mean Absolute Deviation of the Series</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_test_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n    <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">first_digits</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"n\">test</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">second_digit</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">last_two_digits</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">MAD</span></div>\n\n\n<div class=\"viewcode-block\" id=\"mse\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.mse\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mse</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Calculates the Mean Squared Error of the Series</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: informs which base test to use for the mad.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        float: the Mean Squared Error of the Series</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_test_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n    <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">first_digits</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"n\">test</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">second_digit</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">last_two_digits</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">MSE</span></div>\n\n\n<div class=\"viewcode-block\" id=\"bhattacharyya_distance\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.bhattacharyya_distance\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">bhattacharyya_distance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s2\">&quot;all&quot;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Bhattacharyya Distance between the Found and the Expected</span>\n<span class=\"sd\">    (Benford) digits distributions, according toe the test chosen</span>\n<span class=\"sd\">    (First, Second, First Two...)</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data (ndarray, Series): sequence to be evaluated, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test (int, str): informs which base test to be used.</span>\n<span class=\"sd\">        decimals (int): number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign (str, optional): tells which portion of the data to consider.</span>\n<span class=\"sd\">            pos: only the positive entries; neg: only negative entries; all:</span>\n<span class=\"sd\">            all entries but zeros. Defaults to &quot;all&quot;.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        float: the Bhattacharyya Distance between the distributions</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_test_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n    <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">first_digits</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"n\">test</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">second_digit</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">last_two_digits</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">bhat_dist</span></div>\n\n\n<div class=\"viewcode-block\" id=\"kullback_leibler_divergence\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.kullback_leibler_divergence\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">kullback_leibler_divergence</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s2\">&quot;all&quot;</span><span class=\"p\">,</span>\n                                <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Kulback-Leibler Divergence between the Found and the</span>\n<span class=\"sd\">    Expected (Benford) digits distributions, according toe the test chosen</span>\n<span class=\"sd\">    (First, Second, First Two...).</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data (ndarray, Series): sequence to be evaluated, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test (int, str): informs which base test to be used.</span>\n<span class=\"sd\">        decimals (int): number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign (str, optional): tells which portion of the data to consider.</span>\n<span class=\"sd\">            pos: only the positive entries; neg: only negative entries; all:</span>\n<span class=\"sd\">            all entries but zeros. Defaults to &quot;all&quot;.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        float: the Kulback-Leibler Divergence between the distributions</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_test_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n    <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">first_digits</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"n\">test</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">second_digit</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">last_two_digits</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">kl_diverg</span></div>\n\n\n<div class=\"viewcode-block\" id=\"mad_summ\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.mad_summ\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mad_summ</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Calculate the Mean Absolute Deviation of the Summation Test</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: informs which base test to use for the summation mad.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        float: the Mean Absolute Deviation of the Summation Test</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_digs_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n\n    <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n    <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">test</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)]</span>\n    <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">//</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">((</span><span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span>\n                                                <span class=\"nb\">int</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"p\">(</span><span class=\"n\">test</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span>\n                                                    <span class=\"nb\">int</span><span class=\"p\">)</span>\n    <span class=\"n\">li</span> <span class=\"o\">=</span> <span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mi\">9</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">test</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)))</span>\n\n    <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n    <span class=\"k\">return</span> <span class=\"n\">mean</span><span class=\"p\">(</span><span class=\"nb\">abs</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">/</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span> <span class=\"o\">-</span> <span class=\"n\">li</span><span class=\"p\">))</span></div>\n\n\n<div class=\"viewcode-block\" id=\"rolling_mad\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.rolling_mad\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">rolling_mad</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">window</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span>\n                <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Applies the MAD to sequential subsets of the records.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;</span>\n<span class=\"sd\">            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</span>\n<span class=\"sd\">        window: size of the subset to be used.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n<span class=\"sd\">        show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        Series with sequentially computed MADs.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"n\">r_mad</span> <span class=\"o\">=</span> <span class=\"n\">Roll_mad</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">window</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n        <span class=\"n\">r_mad</span><span class=\"o\">.</span><span class=\"n\">show_plot</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">r_mad</span><span class=\"o\">.</span><span class=\"n\">roll_series</span></div>\n\n\n<div class=\"viewcode-block\" id=\"rolling_mse\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.rolling_mse\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">rolling_mse</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">window</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span>\n                <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Applies the MSE to sequential subsets of the records.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: tells which test to use. 1: Fisrt Digits; 2: First Two Digits;</span>\n<span class=\"sd\">            3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</span>\n<span class=\"sd\">        window: size of the subset to be used.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n<span class=\"sd\">        show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        Series with sequentially computed MSEs.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">_check_num_array_</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n    <span class=\"n\">r_mse</span> <span class=\"o\">=</span> <span class=\"n\">Roll_mse</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">window</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">show_plot</span><span class=\"p\">:</span>\n        <span class=\"n\">r_mse</span><span class=\"o\">.</span><span class=\"n\">show_plot</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">r_mse</span><span class=\"o\">.</span><span class=\"n\">roll_series</span></div>\n\n\n<div class=\"viewcode-block\" id=\"duplicates\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.duplicates\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">duplicates</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">top_Rep</span><span class=\"o\">=</span><span class=\"mi\">20</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Performs a duplicates test and maps the duplicates count in descending</span>\n<span class=\"sd\">    order.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence to take the duplicates from. pandas Series or</span>\n<span class=\"sd\">            numpy Ndarray.</span>\n<span class=\"sd\">        verbose (bool): tells how many duplicated entries were found and prints the</span>\n<span class=\"sd\">            top numbers according to the top_Rep argument. Defaluts to True.</span>\n<span class=\"sd\">        top_Rep: chooses how many duplicated entries will be</span>\n<span class=\"sd\">            shown withe the top repititions. int or None. Defaluts to 20.</span>\n<span class=\"sd\">            If None, returns al the ordered repetitions.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        DataFrame with the duplicated records and their respective counts</span>\n\n<span class=\"sd\">    Raises:</span>\n<span class=\"sd\">        ValueError: if the `top_Rep` arg is not int or None.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">verbose</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">top_Rep</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span> <span class=\"ow\">and</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">top_Rep</span><span class=\"p\">,</span> <span class=\"nb\">int</span><span class=\"p\">):</span>\n        <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span><span class=\"s1\">&#39;The top_Rep argument must be an int or None.&#39;</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">Series</span><span class=\"p\">):</span>\n        <span class=\"k\">try</span><span class=\"p\">:</span>\n            <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">)</span>\n        <span class=\"k\">except</span> <span class=\"ne\">ValueError</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">data must be a numpy Ndarray or a pandas Series.&#39;</span><span class=\"p\">)</span>\n\n    <span class=\"n\">dup</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">duplicated</span><span class=\"p\">(</span><span class=\"n\">keep</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)]</span>\n    <span class=\"n\">dup_count</span> <span class=\"o\">=</span> <span class=\"n\">dup</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">()</span>\n\n    <span class=\"n\">dup_count</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">names</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"s1\">&#39;Entries&#39;</span><span class=\"p\">]</span>\n    <span class=\"n\">dup_count</span><span class=\"o\">.</span><span class=\"n\">name</span> <span class=\"o\">=</span> <span class=\"s1\">&#39;Count&#39;</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">verbose</span><span class=\"p\">:</span>\n        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">Found </span><span class=\"si\">{</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">dup_count</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s1\"> duplicated entries.</span><span class=\"se\">\\n</span><span class=\"s1\">&#39;</span>\n              <span class=\"sa\">f</span><span class=\"s1\">&#39;The entries with the </span><span class=\"si\">{</span><span class=\"n\">top_Rep</span><span class=\"si\">}</span><span class=\"s1\"> highest repitition counts are:&#39;</span><span class=\"p\">)</span>\n        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">dup_count</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"p\">(</span><span class=\"n\">top_Rep</span><span class=\"p\">))</span>\n\n    <span class=\"k\">return</span> <span class=\"n\">dup_count</span></div>\n\n\n<div class=\"viewcode-block\" id=\"second_order\"><a class=\"viewcode-back\" href=\"../../api.html#benford.benford.second_order\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">second_order</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"s1\">&#39;all&#39;</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                 <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"s1\">&#39;pos&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n                 <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Performs the chosen test after subtracting the ordered sequence by itself.</span>\n<span class=\"sd\">    Hence Second Order.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        data: sequence of numbers to be evaluated. Must be a numpy 1D array,</span>\n<span class=\"sd\">            a pandas Series or a pandas DataFrame column, with values being</span>\n<span class=\"sd\">            integers or floats.</span>\n<span class=\"sd\">        test: the test to be performed - 1 or &#39;F1D&#39;: First Digit; 2 or &#39;F2D&#39;:</span>\n<span class=\"sd\">            First Two Digits; 3 or &#39;F3D&#39;: First three Digits; 22 or &#39;SD&#39;:</span>\n<span class=\"sd\">            Second Digits; -2 or &#39;L2D&#39;: Last Two Digits.</span>\n<span class=\"sd\">        decimals: number of decimal places to consider. Defaluts to 2.</span>\n<span class=\"sd\">            If integers, set to 0. If set to -infer-, it will remove the zeros</span>\n<span class=\"sd\">            and consider up to the fifth decimal place to the right, but will</span>\n<span class=\"sd\">            loose performance.</span>\n<span class=\"sd\">        sign: tells which portion of the data to consider. pos: only the positive</span>\n<span class=\"sd\">            entries; neg: only negative entries; all: all entries but zeros.</span>\n<span class=\"sd\">            Defaults to all.</span>\n<span class=\"sd\">        verbose (bool): tells the number of registries that are being subjected to</span>\n<span class=\"sd\">            the analysis and returns tha analysis DataFrame sorted by the</span>\n<span class=\"sd\">            highest Z score down. Defaults to True.</span>\n<span class=\"sd\">        MAD (bool): calculates the Mean Absolute Difference between the</span>\n<span class=\"sd\">            found and the expected distributions; defaults to False.</span>\n<span class=\"sd\">        confidence (int, float): confidence level to draw lower and upper limits when</span>\n<span class=\"sd\">            plotting and to limit the top deviations to show. Defaults to None.</span>\n<span class=\"sd\">        high_Z (int): chooses which Z scores to be used when displaying results,</span>\n<span class=\"sd\">            according to the confidence level chosen. Defaluts to &#39;pos&#39;,</span>\n<span class=\"sd\">            which will highlight only values higher than the expexted</span>\n<span class=\"sd\">            frequencies; &#39;all&#39; will highlight both extremes (positive and</span>\n<span class=\"sd\">            negative); and an integer, which will use the first n entries,</span>\n<span class=\"sd\">            positive and negative, regardless of whether Z is higher than</span>\n<span class=\"sd\">            the confidence or not.</span>\n<span class=\"sd\">        limit_N (int): sets a limit to N as the sample size for the calculation of</span>\n<span class=\"sd\">            the Z scores if the sample is too big. Defaults to None.</span>\n<span class=\"sd\">        MSE (bool): calculates the Mean Square Error of the sample; defaults to</span>\n<span class=\"sd\">            False.</span>\n<span class=\"sd\">        chi_square: calculates the chi_square statistic of the sample and</span>\n<span class=\"sd\">            compares it with a critical value, according to the confidence</span>\n<span class=\"sd\">            level chosen and the series&#39;s degrees of freedom. Defaults to</span>\n<span class=\"sd\">            False. Requires confidence != None.</span>\n<span class=\"sd\">        KS: calculates the Kolmogorov-Smirnov test, comparing the cumulative</span>\n<span class=\"sd\">            distribution of the sample with the Benford&#39;s, according to the</span>\n<span class=\"sd\">            confidence level chosen. Defaults to False. Requires confidence</span>\n<span class=\"sd\">            != None.</span>\n<span class=\"sd\">        show_plot (bool): draws the test plot.</span>\n<span class=\"sd\">        save_plot (str): string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs (dict): any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        DataFrame of the test chosen, but applied on Second Order pre-</span>\n<span class=\"sd\">            processed data.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">test</span> <span class=\"o\">=</span> <span class=\"n\">_check_test_</span><span class=\"p\">(</span><span class=\"n\">test</span><span class=\"p\">)</span>\n\n    <span class=\"n\">verbose</span> <span class=\"o\">=</span> <span class=\"n\">_deprecate_inform_</span><span class=\"p\">(</span><span class=\"n\">verbose</span><span class=\"p\">,</span> <span class=\"n\">inform</span><span class=\"p\">)</span>\n\n    <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">Source</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"n\">decimals</span><span class=\"p\">,</span> <span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"n\">sign</span><span class=\"p\">,</span>\n                  <span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"n\">verbose</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">first_digits</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"n\">MAD</span><span class=\"p\">,</span>\n                          <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"n\">high_Z</span><span class=\"p\">,</span>\n                          <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"n\">MSE</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"n\">show_plot</span><span class=\"p\">,</span>\n                          <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"n\">test</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">second_digit</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"n\">MAD</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"n\">high_Z</span><span class=\"p\">,</span>\n                          <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"n\">MSE</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"n\">show_plot</span><span class=\"p\">,</span>\n                          <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">last_two_digits</span><span class=\"p\">(</span><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"n\">MAD</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"n\">high_Z</span><span class=\"p\">,</span>\n                             <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"n\">MSE</span><span class=\"p\">,</span> <span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"n\">show_plot</span><span class=\"p\">,</span>\n                             <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">data</span></div>\n</pre></div>\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/_modules/benford/expected.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>benford.expected &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"../../_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"../../_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"../../_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"../../\" src=\"../../_static/documentation_options.js\"></script>\n        <script src=\"../../_static/jquery.js\"></script>\n        <script src=\"../../_static/underscore.js\"></script>\n        <script src=\"../../_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"../../_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"../../genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"../../search.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"../../index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"../../search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"../../modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"../../index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"../../index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n          <li><a href=\"../index.html\">Module code</a> &raquo;</li>\n        \n      <li>benford.expected</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <h1>Source code for benford.expected</h1><div class=\"highlight\"><pre>\n<span></span><span class=\"kn\">from</span> <span class=\"nn\">pandas</span> <span class=\"kn\">import</span> <span class=\"n\">DataFrame</span>\n<span class=\"kn\">from</span> <span class=\"nn\">numpy</span> <span class=\"kn\">import</span> <span class=\"n\">array</span><span class=\"p\">,</span> <span class=\"n\">arange</span><span class=\"p\">,</span> <span class=\"n\">log10</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.checks</span> <span class=\"kn\">import</span> <span class=\"n\">_check_digs_</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.viz</span> <span class=\"kn\">import</span> <span class=\"n\">plot_expected</span>\n\n\n<div class=\"viewcode-block\" id=\"First\"><a class=\"viewcode-back\" href=\"../../api.html#benford.expected.First\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">First</span><span class=\"p\">(</span><span class=\"n\">DataFrame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Holds the expected probabilities of the First, First Two, or</span>\n<span class=\"sd\">    First Three digits according to Benford&#39;s distribution.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        digs: 1, 2 or 3 - tells which of the first digits to consider:</span>\n<span class=\"sd\">            1 for the First Digit, 2 for the First Two Digits and 3 for</span>\n<span class=\"sd\">            the First Three Digits.</span>\n<span class=\"sd\">        plot: option to plot a bar chart of the Expected proportions.</span>\n<span class=\"sd\">            Defaults to True.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"n\">_check_digs_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span>\n        <span class=\"n\">dig_name</span> <span class=\"o\">=</span> <span class=\"sa\">f</span><span class=\"s1\">&#39;First_</span><span class=\"si\">{</span><span class=\"n\">digs</span><span class=\"si\">}</span><span class=\"s1\">_Dig&#39;</span>\n        <span class=\"n\">exp_array</span><span class=\"p\">,</span> <span class=\"n\">dig_array</span> <span class=\"o\">=</span> <span class=\"n\">_gen_first_digits_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span>\n \n        <span class=\"n\">DataFrame</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"p\">{</span><span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">:</span> <span class=\"n\">exp_array</span><span class=\"p\">},</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"n\">dig_array</span><span class=\"p\">)</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">names</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">dig_name</span><span class=\"p\">]</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_expected</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span>\n                          <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div>\n\n\n<div class=\"viewcode-block\" id=\"Second\"><a class=\"viewcode-back\" href=\"../../api.html#benford.expected.Second\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">Second</span><span class=\"p\">(</span><span class=\"n\">DataFrame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Holds the expected probabilities of the Second Digits</span>\n<span class=\"sd\">    according to Benford&#39;s distribution.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        plot: option to plot a bar chart of the Expected proportions.</span>\n<span class=\"sd\">            Defaults to True.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n\n        <span class=\"n\">exp</span><span class=\"p\">,</span> <span class=\"n\">sec_digs</span> <span class=\"o\">=</span> <span class=\"n\">_gen_second_digits_</span><span class=\"p\">()</span>\n\n        <span class=\"n\">DataFrame</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"p\">{</span><span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">:</span> <span class=\"n\">exp</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Sec_Dig&#39;</span><span class=\"p\">:</span> <span class=\"n\">sec_digs</span><span class=\"p\">})</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">set_index</span><span class=\"p\">(</span><span class=\"s2\">&quot;Sec_Dig&quot;</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_expected</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"mi\">22</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span>\n                          <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div>\n\n\n<div class=\"viewcode-block\" id=\"LastTwo\"><a class=\"viewcode-back\" href=\"../../api.html#benford.expected.LastTwo\">[docs]</a><span class=\"k\">class</span> <span class=\"nc\">LastTwo</span><span class=\"p\">(</span><span class=\"n\">DataFrame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Holds the expected probabilities of the Last Two Digits</span>\n<span class=\"sd\">    according to Benford&#39;s distribution.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        plot: option to plot a bar chart of the Expected proportions.</span>\n<span class=\"sd\">            Defaults to True.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension. Only available when</span>\n<span class=\"sd\">            plot=True.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">            Only available when plot=True and save_plot is a string with the</span>\n<span class=\"sd\">            figure file path/name.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">num</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n        <span class=\"n\">exp</span><span class=\"p\">,</span> <span class=\"n\">l2d</span> <span class=\"o\">=</span> <span class=\"n\">_gen_last_two_digits_</span><span class=\"p\">(</span><span class=\"n\">num</span><span class=\"o\">=</span><span class=\"n\">num</span><span class=\"p\">)</span>\n        <span class=\"n\">DataFrame</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"p\">{</span><span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">:</span> <span class=\"n\">exp</span><span class=\"p\">,</span>\n                                  <span class=\"s1\">&#39;Last_2_Dig&#39;</span><span class=\"p\">:</span> <span class=\"n\">l2d</span><span class=\"p\">})</span>\n        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">set_index</span><span class=\"p\">(</span><span class=\"s1\">&#39;Last_2_Dig&#39;</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n        <span class=\"k\">if</span> <span class=\"n\">plot</span><span class=\"p\">:</span>\n            <span class=\"n\">plot_expected</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"n\">save_plot</span><span class=\"p\">,</span>\n                          <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span></div>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_get_expected_digits_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Chooses the Exxpected class to be used in a test</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        digs: the int corresponding to the Expected class to be instantiated</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        the Expected instance forthe propoer test to be performed</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"n\">digs</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"k\">return</span> <span class=\"n\">First</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"n\">digs</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">Second</span><span class=\"p\">(</span><span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">LastTwo</span><span class=\"p\">(</span><span class=\"n\">num</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_gen_last_two_digits_</span><span class=\"p\">(</span><span class=\"n\">num</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Creates two arrays, one with the possible last two digits and one with</span>\n<span class=\"sd\">    thei respective probabilities</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        num: returns numeric (ints) values. Defaluts to False,</span>\n<span class=\"sd\">            which returns strings.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        exp (np.array): Array with the (constant) probabilities of occurrence of</span>\n<span class=\"sd\">            each pair of last two digits </span>\n<span class=\"sd\">        l2d (np.array): Array of ints or str, in any case representing all 100</span>\n<span class=\"sd\">            possible combinations of last two digits</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">exp</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"mi\">1</span> <span class=\"o\">/</span> <span class=\"mf\">99.</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"mi\">100</span><span class=\"p\">)</span>\n    <span class=\"n\">l2d</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">100</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">num</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">exp</span><span class=\"p\">,</span> <span class=\"n\">l2d</span>\n    <span class=\"n\">l2d</span> <span class=\"o\">=</span> <span class=\"n\">l2d</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n    <span class=\"n\">l2d</span><span class=\"p\">[:</span><span class=\"mi\">10</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"s1\">&#39;00&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;01&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;02&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;03&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;04&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;05&#39;</span><span class=\"p\">,</span>\n                    <span class=\"s1\">&#39;06&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;07&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;08&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;09&#39;</span><span class=\"p\">])</span>\n    <span class=\"k\">return</span> <span class=\"n\">exp</span><span class=\"p\">,</span> <span class=\"n\">l2d</span>\n\n<span class=\"k\">def</span> <span class=\"nf\">_gen_first_digits_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Creates two arrays, one with the possible digits combinations and the</span>\n<span class=\"sd\">    other with their respective expected probabilities according to Benford</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        digs (int): 1, 2 or 3, for generation of the first, first two, or first</span>\n<span class=\"sd\">            three digits</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        (tuple of arrays): the expected probabilities array and the digits</span>\n<span class=\"sd\">            combination array. </span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">dig_array</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">),</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"n\">digs</span><span class=\"p\">)</span>\n    <span class=\"n\">exp_prob</span> <span class=\"o\">=</span> <span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"n\">dig_array</span><span class=\"p\">))</span>\n    <span class=\"k\">return</span> <span class=\"n\">exp_prob</span><span class=\"p\">,</span> <span class=\"n\">dig_array</span>\n\n<span class=\"k\">def</span> <span class=\"nf\">_gen_second_digits_</span><span class=\"p\">():</span>\n    <span class=\"sd\">&quot;&quot;&quot;Creates two arrays, one with he possible second digits combinations and</span>\n<span class=\"sd\">    the other with their respective expected probabilities according to Benford</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        (tuple of arrays): the expected probabilities array and the second</span>\n<span class=\"sd\">        digits array.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">exp_f2d</span><span class=\"p\">,</span> <span class=\"n\">_</span> <span class=\"o\">=</span> <span class=\"n\">_gen_first_digits_</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">)</span>\n    <span class=\"n\">sec_digs</span> <span class=\"o\">=</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">)</span>\n    <span class=\"n\">sec_digs_in_f2d</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">(</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">))</span> <span class=\"o\">*</span> <span class=\"mi\">9</span><span class=\"p\">)</span>\n    <span class=\"n\">exp</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"n\">exp_f2d</span><span class=\"p\">[</span><span class=\"n\">sec_digs_in_f2d</span> <span class=\"o\">==</span> <span class=\"n\">i</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span> <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"n\">sec_digs</span><span class=\"p\">])</span>\n    <span class=\"k\">return</span> <span class=\"n\">exp</span><span class=\"p\">,</span> <span class=\"n\">array</span><span class=\"p\">(</span><span class=\"n\">sec_digs</span><span class=\"p\">)</span>\n</pre></div>\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/_modules/benford/stats.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>benford.stats &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"../../_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"../../_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"../../_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"../../\" src=\"../../_static/documentation_options.js\"></script>\n        <script src=\"../../_static/jquery.js\"></script>\n        <script src=\"../../_static/underscore.js\"></script>\n        <script src=\"../../_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"../../_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"../../genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"../../search.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"../../index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"../../search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"../../modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"../../index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"../../index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n          <li><a href=\"../index.html\">Module code</a> &raquo;</li>\n        \n      <li>benford.stats</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <h1>Source code for benford.stats</h1><div class=\"highlight\"><pre>\n<span></span><span class=\"kn\">from</span> <span class=\"nn\">numpy</span> <span class=\"kn\">import</span> <span class=\"nb\">abs</span> <span class=\"k\">as</span> <span class=\"n\">nabs</span><span class=\"p\">,</span> <span class=\"n\">errstate</span><span class=\"p\">,</span> <span class=\"n\">linspace</span><span class=\"p\">,</span> <span class=\"n\">log</span><span class=\"p\">,</span> <span class=\"n\">sqrt</span><span class=\"p\">,</span> <span class=\"n\">where</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.constants</span> <span class=\"kn\">import</span> <span class=\"n\">CRIT_CHI2</span><span class=\"p\">,</span> <span class=\"n\">CRIT_KS</span><span class=\"p\">,</span> <span class=\"n\">MAD_CONFORM</span><span class=\"p\">,</span> <span class=\"n\">DIGS</span>\n\n\n<div class=\"viewcode-block\" id=\"Z_score\"><a class=\"viewcode-back\" href=\"../../api.html#benford.stats.Z_score\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">Z_score</span><span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Z statistics for the proportions studied</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        frame: DataFrame with the expected proportions and the already calculated</span>\n<span class=\"sd\">            Absolute Diferences between the found and expeccted proportions</span>\n<span class=\"sd\">        N: sample size</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        Series of computed Z scores</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span> <span class=\"o\">-</span> <span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">N</span><span class=\"p\">)))</span> <span class=\"o\">/</span> <span class=\"n\">sqrt</span><span class=\"p\">(</span>\n           <span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Expected</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mf\">1.</span> <span class=\"o\">-</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"p\">))</span> <span class=\"o\">/</span> <span class=\"n\">N</span><span class=\"p\">)</span></div>\n\n\n<div class=\"viewcode-block\" id=\"chi_sq\"><a class=\"viewcode-back\" href=\"../../api.html#benford.stats.chi_sq\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">chi_sq</span><span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"p\">,</span> <span class=\"n\">ddf</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Comnputes the chi-square statistic of the found distributions and compares</span>\n<span class=\"sd\">    it with the critical chi-square of such a sample, according to the</span>\n<span class=\"sd\">    confidence level chosen and the degrees of freedom - len(sample) -1.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        frame: DataFrame with Found, Expected and their difference columns.</span>\n<span class=\"sd\">        ddf: Degrees of freedom to consider.</span>\n<span class=\"sd\">        confidence: Confidence level to look up critical value.</span>\n<span class=\"sd\">        verbose: prints the chi-squre result and compares to the critical</span>\n<span class=\"sd\">            chi-square for the sample. Defaults to True.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        The computed Chi square statistic and the critical chi square</span>\n<span class=\"sd\">            (according) to the degrees of freedom and confidence level,</span>\n<span class=\"sd\">            for comparison. None if confidence is None</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">Chi-square test needs confidence other than None.&#39;</span><span class=\"p\">)</span>\n        <span class=\"k\">return</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">exp_counts</span> <span class=\"o\">=</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Counts</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span> <span class=\"o\">*</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Expected</span>\n        <span class=\"n\">dif_counts</span> <span class=\"o\">=</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Counts</span> <span class=\"o\">-</span> <span class=\"n\">exp_counts</span>\n        <span class=\"n\">found_chi</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">dif_counts</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">/</span> <span class=\"n\">exp_counts</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n        <span class=\"n\">crit_chi</span> <span class=\"o\">=</span> <span class=\"n\">CRIT_CHI2</span><span class=\"p\">[</span><span class=\"n\">ddf</span><span class=\"p\">][</span><span class=\"n\">confidence</span><span class=\"p\">]</span>\n        <span class=\"k\">if</span> <span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">The Chi-square statistic is </span><span class=\"si\">{</span><span class=\"n\">found_chi</span><span class=\"si\">:</span><span class=\"s2\">.4f</span><span class=\"si\">}</span><span class=\"s2\">.</span><span class=\"se\">\\n</span><span class=\"s2\">&quot;</span>\n                  <span class=\"sa\">f</span><span class=\"s2\">&quot;Critical Chi-square for this series: </span><span class=\"si\">{</span><span class=\"n\">crit_chi</span><span class=\"si\">}</span><span class=\"s2\">.&quot;</span><span class=\"p\">)</span>\n        <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">found_chi</span><span class=\"p\">,</span> <span class=\"n\">crit_chi</span><span class=\"p\">)</span></div>\n\n\n<div class=\"viewcode-block\" id=\"chi_sq_2\"><a class=\"viewcode-back\" href=\"../../api.html#benford.stats.chi_sq_2\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">chi_sq_2</span><span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the chi-square statistic of the found distributions</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        frame: DataFrame with Found, Expected and their difference columns.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        The computed Chi square statistic </span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">exp_counts</span> <span class=\"o\">=</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Counts</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span> <span class=\"o\">*</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Expected</span>\n    <span class=\"n\">dif_counts</span> <span class=\"o\">=</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">Counts</span> <span class=\"o\">-</span> <span class=\"n\">exp_counts</span>\n    <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">dif_counts</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">/</span> <span class=\"n\">exp_counts</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span></div>\n\n\n<div class=\"viewcode-block\" id=\"kolmogorov_smirnov\"><a class=\"viewcode-back\" href=\"../../api.html#benford.stats.kolmogorov_smirnov\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">kolmogorov_smirnov</span><span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Kolmogorov-Smirnov test of the found distributions</span>\n<span class=\"sd\">    and compares it with the critical chi-square of such a sample,</span>\n<span class=\"sd\">    according to the confidence level chosen.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        frame: DataFrame with Foud and Expected distributions.</span>\n<span class=\"sd\">        confidence: Confidence level to look up critical value.</span>\n<span class=\"sd\">        N: Sample size</span>\n<span class=\"sd\">        verbose: prints the KS result and the critical value for the sample.</span>\n<span class=\"sd\">            Defaults to True.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        The Suprem, which is the greatest absolute difference between the</span>\n<span class=\"sd\">            Found and the expected proportions, and the Kolmogorov-Smirnov</span>\n<span class=\"sd\">            critical value according to the confidence level, for ccomparison</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;</span><span class=\"se\">\\n</span><span class=\"s1\">Kolmogorov-Smirnov test needs confidence other than None.&#39;</span><span class=\"p\">)</span>\n        <span class=\"k\">return</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"c1\"># sorting and calculating the cumulative distribution</span>\n        <span class=\"n\">ks_frame</span> <span class=\"o\">=</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()[[</span><span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">cumsum</span><span class=\"p\">()</span>\n        <span class=\"c1\"># finding the supremum - the largest cumul dist difference</span>\n        <span class=\"n\">suprem</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"n\">ks_frame</span><span class=\"o\">.</span><span class=\"n\">Found</span> <span class=\"o\">-</span> <span class=\"n\">ks_frame</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">())</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">()</span>\n        <span class=\"c1\"># calculating the crittical value according to confidence</span>\n        <span class=\"n\">crit_KS</span> <span class=\"o\">=</span> <span class=\"n\">CRIT_KS</span><span class=\"p\">[</span><span class=\"n\">confidence</span><span class=\"p\">]</span> <span class=\"o\">/</span> <span class=\"n\">sqrt</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">verbose</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">The Kolmogorov-Smirnov statistic is </span><span class=\"si\">{</span><span class=\"n\">suprem</span><span class=\"si\">:</span><span class=\"s2\">.4f</span><span class=\"si\">}</span><span class=\"s2\">.</span><span class=\"se\">\\n</span><span class=\"s2\">&quot;</span>\n                  <span class=\"sa\">f</span><span class=\"s2\">&quot;Critical K-S for this series: </span><span class=\"si\">{</span><span class=\"n\">crit_KS</span><span class=\"si\">:</span><span class=\"s2\">.4f</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n        <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">suprem</span><span class=\"p\">,</span> <span class=\"n\">crit_KS</span><span class=\"p\">)</span></div>\n\n\n<div class=\"viewcode-block\" id=\"kolmogorov_smirnov_2\"><a class=\"viewcode-back\" href=\"../../api.html#benford.stats.kolmogorov_smirnov_2\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">kolmogorov_smirnov_2</span><span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Kolmogorov-Smirnov test of the found distributions</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        frame: DataFrame with Foud and Expected distributions.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        The Suprem, which is the greatest absolute difference between the</span>\n<span class=\"sd\">            Found end th expected proportions</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"c1\"># sorting and calculating the cumulative distribution</span>\n    <span class=\"n\">ks_frame</span> <span class=\"o\">=</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()[[</span><span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">cumsum</span><span class=\"p\">()</span>\n    <span class=\"c1\"># finding the supremum - the largest cumul dist difference</span>\n    <span class=\"k\">return</span> <span class=\"p\">((</span><span class=\"n\">ks_frame</span><span class=\"o\">.</span><span class=\"n\">Found</span> <span class=\"o\">-</span> <span class=\"n\">ks_frame</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">())</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">()</span></div>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_two_dist_ks_</span><span class=\"p\">(</span><span class=\"n\">dist1</span><span class=\"p\">,</span> <span class=\"n\">dist2</span><span class=\"p\">,</span> <span class=\"n\">cummulative</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Kolmogorov-Smirnov statistic between two distributions,</span>\n<span class=\"sd\">    a found one (dist2) and an expected one (dist1).</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        dist1 (np.arrat): array with the expected distribution</span>\n<span class=\"sd\">        dist2 (np.array): array with the found distribution</span>\n<span class=\"sd\">        cummulative (bool): makes apply cummulutative sum to the</span>\n<span class=\"sd\">            distributions (empirical cdf).</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        tuple(floats): the KS statistic </span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">dist2</span><span class=\"o\">.</span><span class=\"n\">sort</span><span class=\"p\">();</span> <span class=\"n\">dist1</span><span class=\"o\">.</span><span class=\"n\">sort</span><span class=\"p\">()</span>\n    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">cummulative</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">nabs</span><span class=\"p\">(</span><span class=\"n\">dist2</span> <span class=\"o\">-</span> <span class=\"n\">dist1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">()</span>\n    <span class=\"k\">return</span> <span class=\"n\">nabs</span><span class=\"p\">(</span><span class=\"n\">dist2</span><span class=\"o\">.</span><span class=\"n\">cumsum</span><span class=\"p\">()</span> <span class=\"o\">-</span> <span class=\"n\">dist1</span><span class=\"o\">.</span><span class=\"n\">cumsum</span><span class=\"p\">())</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">()</span>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_mantissas_ks_</span><span class=\"p\">(</span><span class=\"n\">mant_dist</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"p\">,</span> <span class=\"n\">sample_size</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Kolmogorov-Smirnof statistic for the Mantissas, also</span>\n<span class=\"sd\">    providing the KS critical value according the the sample size and</span>\n<span class=\"sd\">    confidence level provided</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        mant_dist (np.array): array with the mantissas distribution found</span>\n<span class=\"sd\">        confidence (float, int): level of confidence to compute the critical</span>\n<span class=\"sd\">            value</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        tuple(floats): the KS statistic and the critical value</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span> \n    <span class=\"n\">crit_ks</span> <span class=\"o\">=</span> <span class=\"n\">CRIT_KS</span><span class=\"p\">[</span><span class=\"n\">confidence</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">sqrt</span><span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">sample_size</span> <span class=\"o\">/</span> <span class=\"n\">sample_size</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span>\\\n                <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"k\">else</span> <span class=\"kc\">None</span>\n    <span class=\"c1\"># non-cummulative, uniformly distributed</span>\n    <span class=\"n\">expected</span> <span class=\"o\">=</span> <span class=\"n\">linspace</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">mant_dist</span><span class=\"p\">),</span> <span class=\"n\">endpoint</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"n\">ks</span> <span class=\"o\">=</span> <span class=\"n\">_two_dist_ks_</span><span class=\"p\">(</span><span class=\"n\">expected</span><span class=\"p\">,</span> <span class=\"n\">mant_dist</span><span class=\"p\">,</span> <span class=\"n\">cummulative</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">ks</span><span class=\"p\">,</span> <span class=\"n\">crit_ks</span>\n\n\n<div class=\"viewcode-block\" id=\"mad\"><a class=\"viewcode-back\" href=\"../../api.html#benford.stats.mad\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mad</span><span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Mean Absolute Deviation (MAD) between the found and the</span>\n<span class=\"sd\">    expected proportions.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        frame: DataFrame with the Absolute Deviations already calculated.</span>\n<span class=\"sd\">        test: Test to compute the MAD from (F1D, SD, F2D...)</span>\n<span class=\"sd\">        verbose: prints the MAD result and compares to limit values of</span>\n<span class=\"sd\">            conformity. Defaults to True.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        The Mean of the Absolute Deviations between the found and expected</span>\n<span class=\"sd\">            proportions. </span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">mad</span> <span class=\"o\">=</span> <span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">verbose</span><span class=\"p\">:</span>\n        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">The Mean Absolute Deviation is </span><span class=\"si\">{</span><span class=\"n\">mad</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n\n        <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"o\">!=</span> <span class=\"o\">-</span><span class=\"mi\">2</span><span class=\"p\">:</span>\n            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;For the </span><span class=\"si\">{</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">DIGS</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]]</span><span class=\"si\">}</span><span class=\"s2\">:</span><span class=\"se\">\\n\\</span>\n<span class=\"s2\">            - 0.0000 to </span><span class=\"si\">{</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"si\">}</span><span class=\"s2\">: Close Conformity</span><span class=\"se\">\\n\\</span>\n<span class=\"s2\">            - </span><span class=\"si\">{</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"si\">}</span><span class=\"s2\"> to </span><span class=\"si\">{</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"si\">}</span><span class=\"s2\">: Acceptable Conformity</span><span class=\"se\">\\n\\</span>\n<span class=\"s2\">            - </span><span class=\"si\">{</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"si\">}</span><span class=\"s2\"> to </span><span class=\"si\">{</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"si\">}</span><span class=\"s2\">: Marginally Acceptable Conformity</span><span class=\"se\">\\n\\</span>\n<span class=\"s2\">            - Above </span><span class=\"si\">{</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">2</span><span class=\"p\">]</span><span class=\"si\">}</span><span class=\"s2\">: Nonconformity&quot;</span><span class=\"p\">)</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"k\">pass</span>\n    <span class=\"k\">return</span> <span class=\"n\">mad</span></div>\n\n\n<div class=\"viewcode-block\" id=\"mse\"><a class=\"viewcode-back\" href=\"../../api.html#benford.stats.mse\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mse</span><span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"p\">,</span> <span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the test&#39;s Mean Square Error</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        frame: DataFrame with the already computed Absolute Deviations between</span>\n<span class=\"sd\">            the found and expected proportions</span>\n<span class=\"sd\">        verbose: Prints the MSE. Defaults to True.</span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        Mean of the squared differences between the found and the expected proportions.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">mse</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">frame</span><span class=\"o\">.</span><span class=\"n\">AbsDif</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">verbose</span><span class=\"p\">:</span>\n        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;</span><span class=\"se\">\\n</span><span class=\"s2\">Mean Square Error = </span><span class=\"si\">{</span><span class=\"n\">mse</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n\n    <span class=\"k\">return</span> <span class=\"n\">mse</span></div>\n\n<span class=\"k\">def</span> <span class=\"nf\">_bhattacharyya_coefficient</span><span class=\"p\">(</span><span class=\"n\">dist_1</span><span class=\"p\">,</span> <span class=\"n\">dist_2</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Bhattacharyya Coeficient between two probability</span>\n<span class=\"sd\">    distributions, to be letar used to compute the Bhattacharyya Distance</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        dist_1 (np.array): The newly gathered distribution, to be compared</span>\n<span class=\"sd\">            with an older / established distribution.</span>\n<span class=\"sd\">        dist_2 (np.array): The older/ establhished distribution with which</span>\n<span class=\"sd\">            the new one will be compared. </span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        bhat_coef (float)</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">return</span> <span class=\"n\">sqrt</span><span class=\"p\">(</span><span class=\"n\">dist_1</span> <span class=\"o\">*</span> <span class=\"n\">dist_2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_bhattacharyya_distance_</span><span class=\"p\">(</span><span class=\"n\">dist_1</span><span class=\"p\">,</span> <span class=\"n\">dist_2</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Bhattacharyya Dsitance between two probability</span>\n<span class=\"sd\">    distributions</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        dist_1 (np.array): The newly gathered distribution, to be compared</span>\n<span class=\"sd\">            with an older / established distribution.</span>\n<span class=\"sd\">        dist_2 (np.array): The older/ establhished distribution with which</span>\n<span class=\"sd\">            the new one will be compared. </span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        bhat_dist (float)</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">with</span> <span class=\"n\">errstate</span><span class=\"p\">(</span><span class=\"n\">divide</span><span class=\"o\">=</span><span class=\"s1\">&#39;ignore&#39;</span><span class=\"p\">):</span>\n        <span class=\"n\">bhat_dist</span> <span class=\"o\">=</span>  <span class=\"o\">-</span><span class=\"n\">log</span><span class=\"p\">(</span><span class=\"n\">_bhattacharyya_coefficient</span><span class=\"p\">(</span><span class=\"n\">dist_1</span><span class=\"p\">,</span> <span class=\"n\">dist_2</span><span class=\"p\">))</span>\n    <span class=\"k\">return</span> <span class=\"n\">bhat_dist</span>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_kullback_leibler_divergence_</span><span class=\"p\">(</span><span class=\"n\">dist_1</span><span class=\"p\">,</span> <span class=\"n\">dist_2</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the Kullback-Leibler Divergence between two probability</span>\n<span class=\"sd\">    distributions.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        dist_1 (np.array): The newly gathered distribution, to be compared</span>\n<span class=\"sd\">            with an older / established distribution.</span>\n<span class=\"sd\">        dist_2 (np.array): The older/ establhished distribution with which</span>\n<span class=\"sd\">            the new one will be compared. </span>\n\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        kulb_leib_diverg (float)        </span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"c1\"># ignore divide by zero warning in np.where</span>\n    <span class=\"k\">with</span> <span class=\"n\">errstate</span><span class=\"p\">(</span><span class=\"n\">divide</span><span class=\"o\">=</span><span class=\"s1\">&#39;ignore&#39;</span><span class=\"p\">):</span>\n        <span class=\"n\">kl_d</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">log</span><span class=\"p\">((</span><span class=\"n\">dist_1</span> <span class=\"o\">/</span> <span class=\"n\">dist_2</span><span class=\"p\">),</span> <span class=\"n\">where</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dist_1</span> <span class=\"o\">!=</span> <span class=\"mi\">0</span><span class=\"p\">))</span> <span class=\"o\">*</span> <span class=\"n\">dist_1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n    <span class=\"k\">return</span> <span class=\"n\">kl_d</span>\n</pre></div>\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/_modules/benford/utils.html",
    "content": "\n\n<!DOCTYPE html>\n<!--[if IE 8]><html class=\"no-js lt-ie9\" lang=\"en\" > <![endif]-->\n<!--[if gt IE 8]><!--> <html class=\"no-js\" lang=\"en\" > <!--<![endif]-->\n<head>\n  <meta charset=\"utf-8\">\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n  \n  <title>benford.utils &mdash; benford_py 0.3.0 documentation</title>\n  \n\n  \n  \n  \n  \n\n  \n  <script type=\"text/javascript\" src=\"../../_static/js/modernizr.min.js\"></script>\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"../../\" src=\"../../_static/documentation_options.js\"></script>\n        <script type=\"text/javascript\" src=\"../../_static/jquery.js\"></script>\n        <script type=\"text/javascript\" src=\"../../_static/underscore.js\"></script>\n        <script type=\"text/javascript\" src=\"../../_static/doctools.js\"></script>\n        <script type=\"text/javascript\" src=\"../../_static/language_data.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"../../_static/js/theme.js\"></script>\n\n    \n\n  \n  <link rel=\"stylesheet\" href=\"../../_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"../../_static/pygments.css\" type=\"text/css\" />\n    <link rel=\"index\" title=\"Index\" href=\"../../genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"../../search.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"../../index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"../../search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"../../modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"../../index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"../../index.html\">Docs</a> &raquo;</li>\n        \n          <li><a href=\"../index.html\">Module code</a> &raquo;</li>\n        \n      <li>benford.utils</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <h1>Source code for benford.utils</h1><div class=\"highlight\"><pre>\n<span></span><span class=\"kn\">from</span> <span class=\"nn\">pandas</span> <span class=\"kn\">import</span> <span class=\"n\">Series</span><span class=\"p\">,</span> <span class=\"n\">DataFrame</span>\n<span class=\"kn\">from</span> <span class=\"nn\">numpy</span> <span class=\"kn\">import</span> <span class=\"n\">array</span><span class=\"p\">,</span> <span class=\"n\">arange</span><span class=\"p\">,</span> <span class=\"n\">log10</span><span class=\"p\">,</span> <span class=\"n\">ndarray</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.expected</span> <span class=\"kn\">import</span> <span class=\"n\">_test_</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.constants</span> <span class=\"kn\">import</span> <span class=\"n\">digs_dict</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.stats</span> <span class=\"kn\">import</span> <span class=\"n\">Z_score</span>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_set_N_</span><span class=\"p\">(</span><span class=\"n\">len_df</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;&quot;&quot;&quot;</span>\n    <span class=\"c1\"># Assigning to N the superior limit or the lenght of the series</span>\n    <span class=\"k\">if</span> <span class=\"n\">limit_N</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span> <span class=\"ow\">or</span> <span class=\"n\">limit_N</span> <span class=\"o\">&gt;</span> <span class=\"n\">len_df</span><span class=\"p\">:</span>\n        <span class=\"k\">return</span> <span class=\"n\">len_df</span>\n    <span class=\"c1\"># Check on limit_N being a positive integer</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"n\">limit_N</span> <span class=\"o\">&lt;</span> <span class=\"mi\">0</span> <span class=\"ow\">or</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"nb\">int</span><span class=\"p\">):</span>\n            <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span><span class=\"s2\">&quot;limit_N must be None or a positive integer.&quot;</span><span class=\"p\">)</span>\n        <span class=\"k\">else</span><span class=\"p\">:</span>\n            <span class=\"k\">return</span> <span class=\"n\">limit_N</span>\n\n\n<div class=\"viewcode-block\" id=\"get_mantissas\"><a class=\"viewcode-back\" href=\"../../benford.html#benford.utils.get_mantissas\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">get_mantissas</span><span class=\"p\">(</span><span class=\"n\">arr</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Computes the mantissas, the non-integer part of the log of a number.</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        arr: array of integers or floats</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        Array of floats withe logs mantissas</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">log_a</span> <span class=\"o\">=</span> <span class=\"nb\">abs</span><span class=\"p\">(</span><span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">arr</span><span class=\"p\">))</span>\n    <span class=\"k\">return</span> <span class=\"n\">log_a</span> <span class=\"o\">-</span> <span class=\"n\">log_a</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>  <span class=\"c1\"># the number - its integer part</span></div>\n\n\n<div class=\"viewcode-block\" id=\"input_data\"><a class=\"viewcode-back\" href=\"../../benford.html#benford.utils.input_data\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">input_data</span><span class=\"p\">(</span><span class=\"n\">given</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Internalizes and transforms the input data</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        given: ndarray, Series or tuple with DataFrame and name of the</span>\n<span class=\"sd\">            column to analyze</span>\n<span class=\"sd\">    </span>\n<span class=\"sd\">    Returns:</span>\n<span class=\"sd\">        The raw inputed data and the result of its first pre-processing,</span>\n<span class=\"sd\">            when required.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">given</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"n\">Series</span><span class=\"p\">:</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">chosen</span> <span class=\"o\">=</span> <span class=\"n\">given</span>\n    <span class=\"k\">elif</span> <span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">given</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"n\">ndarray</span><span class=\"p\">:</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">given</span>\n        <span class=\"n\">chosen</span> <span class=\"o\">=</span> <span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">given</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">given</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"nb\">tuple</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"p\">(</span><span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">given</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">])</span> <span class=\"o\">!=</span> <span class=\"n\">DataFrame</span><span class=\"p\">)</span> <span class=\"o\">|</span> <span class=\"p\">(</span><span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">given</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">])</span> <span class=\"o\">!=</span> <span class=\"nb\">str</span><span class=\"p\">):</span>\n            <span class=\"k\">raise</span> <span class=\"ne\">TypeError</span><span class=\"p\">(</span><span class=\"s1\">&#39;The data tuple must be composed of a pandas &#39;</span>\n                            <span class=\"s1\">&#39;DataFrame and the name (str) of the chosen &#39;</span>\n                            <span class=\"s1\">&#39;column, in that order&#39;</span><span class=\"p\">)</span>\n        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">given</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n        <span class=\"n\">chosen</span> <span class=\"o\">=</span> <span class=\"n\">given</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">][</span><span class=\"n\">given</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]]</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">raise</span> <span class=\"ne\">TypeError</span><span class=\"p\">(</span><span class=\"s2\">&quot;Wrong data input type. Check docstring.&quot;</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">chosen</span></div>\n\n\n<div class=\"viewcode-block\" id=\"prepare\"><a class=\"viewcode-back\" href=\"../../benford.html#benford.utils.prepare\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">prepare</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">limit_N</span><span class=\"p\">,</span> <span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Transforms the original number sequence into a DataFrame reduced</span>\n<span class=\"sd\">    by the ocurrences of the chosen digits, creating other computed</span>\n<span class=\"sd\">    columns</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"n\">_set_N_</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">),</span> <span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"n\">limit_N</span><span class=\"p\">)</span>\n\n    <span class=\"c1\"># get the number of occurrences of the digits</span>\n    <span class=\"n\">v</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">()</span>\n    <span class=\"c1\"># get their relative frequencies</span>\n    <span class=\"n\">p</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">(</span><span class=\"n\">normalize</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"c1\"># crate dataframe from them</span>\n    <span class=\"n\">dd</span> <span class=\"o\">=</span> <span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s1\">&#39;Counts&#39;</span><span class=\"p\">:</span> <span class=\"n\">v</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">:</span> <span class=\"n\">p</span><span class=\"p\">})</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span>\n    <span class=\"c1\"># join the dataframe with the one of expected Benford&#39;s frequencies</span>\n    <span class=\"n\">dd</span> <span class=\"o\">=</span> <span class=\"n\">_test_</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">join</span><span class=\"p\">(</span><span class=\"n\">dd</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">fillna</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n    <span class=\"c1\"># create column with absolute differences</span>\n    <span class=\"n\">dd</span><span class=\"p\">[</span><span class=\"s1\">&#39;Dif&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dd</span><span class=\"o\">.</span><span class=\"n\">Found</span> <span class=\"o\">-</span> <span class=\"n\">dd</span><span class=\"o\">.</span><span class=\"n\">Expected</span>\n    <span class=\"n\">dd</span><span class=\"p\">[</span><span class=\"s1\">&#39;AbsDif&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dd</span><span class=\"o\">.</span><span class=\"n\">Dif</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">()</span>\n    <span class=\"k\">if</span> <span class=\"n\">simple</span><span class=\"p\">:</span>\n        <span class=\"k\">del</span> <span class=\"n\">dd</span><span class=\"p\">[</span><span class=\"s1\">&#39;Dif&#39;</span><span class=\"p\">]</span>\n        <span class=\"k\">return</span> <span class=\"n\">dd</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"n\">confidence</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n            <span class=\"n\">dd</span><span class=\"p\">[</span><span class=\"s1\">&#39;Z_score&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Z_score</span><span class=\"p\">(</span><span class=\"n\">dd</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"p\">)</span>\n        <span class=\"k\">return</span> <span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">dd</span></div>\n\n<div class=\"viewcode-block\" id=\"subtract_sorted\"><a class=\"viewcode-back\" href=\"../../benford.html#benford.utils.subtract_sorted\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">subtract_sorted</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Subtracts the sorted sequence elements from each other, discarding zeros.</span>\n<span class=\"sd\">    Used in the Second Order test</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">sec</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">copy</span><span class=\"p\">()</span>\n    <span class=\"n\">sec</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n    <span class=\"n\">sec</span> <span class=\"o\">=</span> <span class=\"n\">sec</span> <span class=\"o\">-</span> <span class=\"n\">sec</span><span class=\"o\">.</span><span class=\"n\">shift</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n    <span class=\"n\">sec</span> <span class=\"o\">=</span> <span class=\"n\">sec</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">sec</span> <span class=\"o\">!=</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n    <span class=\"k\">return</span> <span class=\"n\">sec</span></div>\n\n<div class=\"viewcode-block\" id=\"prep_to_roll\"><a class=\"viewcode-back\" href=\"../../benford.html#benford.utils.prep_to_roll\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">prep_to_roll</span><span class=\"p\">(</span><span class=\"n\">start</span><span class=\"p\">,</span> <span class=\"n\">test</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Used by the rolling mad and rolling mean, prepares each test and</span>\n<span class=\"sd\">    respective expected proportions for later application to the Series subset</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"n\">test</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">start</span><span class=\"p\">[</span><span class=\"n\">digs_dict</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">//</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">((</span>\n            <span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">))</span> <span class=\"o\">-</span> <span class=\"p\">(</span><span class=\"n\">test</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">))</span>\n        <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">test</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)]</span>\n\n        <span class=\"n\">ind</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">test</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">),</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"n\">test</span><span class=\"p\">)</span>\n        <span class=\"n\">Exp</span> <span class=\"o\">=</span> <span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"n\">ind</span><span class=\"p\">))</span>\n\n    <span class=\"k\">elif</span> <span class=\"n\">test</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"p\">[</span><span class=\"n\">digs_dict</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">//</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">((</span>\n            <span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">))</span> <span class=\"o\">%</span> <span class=\"mi\">10</span>\n        <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">10</span><span class=\"p\">]</span>\n\n        <span class=\"n\">Expec</span> <span class=\"o\">=</span> <span class=\"n\">log10</span><span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">,</span> <span class=\"mi\">100</span><span class=\"p\">)))</span>\n        <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">:</span> <span class=\"n\">Expec</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Sec_Dig&#39;</span><span class=\"p\">:</span>\n                             <span class=\"n\">array</span><span class=\"p\">(</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">))</span> <span class=\"o\">*</span> <span class=\"mi\">9</span><span class=\"p\">)})</span>\n        <span class=\"n\">Exp</span> <span class=\"o\">=</span> <span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;Sec_Dig&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"o\">.</span><span class=\"n\">reshape</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">,)</span>\n        <span class=\"n\">ind</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">)</span>\n\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">start</span><span class=\"p\">[</span><span class=\"n\">digs_dict</span><span class=\"p\">[</span><span class=\"n\">test</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">%</span> <span class=\"mi\">100</span>\n        <span class=\"n\">start</span> <span class=\"o\">=</span> <span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">start</span><span class=\"o\">.</span><span class=\"n\">ZN</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">1000</span><span class=\"p\">]</span>\n\n        <span class=\"n\">ind</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">100</span><span class=\"p\">)</span>\n        <span class=\"n\">Exp</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"mi\">1</span> <span class=\"o\">/</span> <span class=\"mf\">99.</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"mi\">100</span><span class=\"p\">)</span>\n\n    <span class=\"k\">return</span> <span class=\"n\">Exp</span><span class=\"p\">,</span> <span class=\"n\">ind</span></div>\n\n<div class=\"viewcode-block\" id=\"mad_to_roll\"><a class=\"viewcode-back\" href=\"../../benford.html#benford.utils.mad_to_roll\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mad_to_roll</span><span class=\"p\">(</span><span class=\"n\">arr</span><span class=\"p\">,</span> <span class=\"n\">Exp</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Mean Absolute Deviation used in the rolling function</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">prop</span> <span class=\"o\">=</span> <span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">arr</span><span class=\"p\">)</span>\n    <span class=\"n\">prop</span> <span class=\"o\">=</span> <span class=\"n\">prop</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">(</span><span class=\"n\">normalize</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span>\n\n    <span class=\"k\">if</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">prop</span><span class=\"p\">)</span> <span class=\"o\">&lt;</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">Exp</span><span class=\"p\">):</span>\n        <span class=\"n\">prop</span> <span class=\"o\">=</span> <span class=\"n\">prop</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">(</span><span class=\"n\">ind</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">fillna</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n\n    <span class=\"k\">return</span> <span class=\"nb\">abs</span><span class=\"p\">(</span><span class=\"n\">prop</span> <span class=\"o\">-</span> <span class=\"n\">Exp</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span></div>\n\n<div class=\"viewcode-block\" id=\"mse_to_roll\"><a class=\"viewcode-back\" href=\"../../benford.html#benford.utils.mse_to_roll\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">mse_to_roll</span><span class=\"p\">(</span><span class=\"n\">arr</span><span class=\"p\">,</span> <span class=\"n\">Exp</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Mean Squared Error used in the rolling function</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">prop</span> <span class=\"o\">=</span> <span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">arr</span><span class=\"p\">)</span>\n    <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"n\">prop</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">(</span><span class=\"n\">normalize</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span>\n\n    <span class=\"k\">if</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">temp</span><span class=\"p\">)</span> <span class=\"o\">&lt;</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">Exp</span><span class=\"p\">):</span>\n        <span class=\"n\">temp</span> <span class=\"o\">=</span> <span class=\"n\">temp</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">(</span><span class=\"n\">ind</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">fillna</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n\n    <span class=\"k\">return</span> <span class=\"p\">((</span><span class=\"n\">temp</span> <span class=\"o\">-</span> <span class=\"n\">Exp</span><span class=\"p\">)</span> <span class=\"o\">**</span> <span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span></div>\n</pre></div>\n\n           </div>\n           \n          </div>\n          <footer>\n  \n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &copy; Copyright 2020, Marcel Milcent\n\n    </p>\n  </div>\n  Built with <a href=\"http://sphinx-doc.org/\">Sphinx</a> using a <a href=\"https://github.com/rtfd/sphinx_rtd_theme\">theme</a> provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/_modules/benford/viz.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>benford.viz &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"../../_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"../../_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"../../_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"../../\" src=\"../../_static/documentation_options.js\"></script>\n        <script src=\"../../_static/jquery.js\"></script>\n        <script src=\"../../_static/underscore.js\"></script>\n        <script src=\"../../_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"../../_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"../../genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"../../search.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"../../index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"../../search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"../../modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"../../index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"../../index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n          <li><a href=\"../index.html\">Module code</a> &raquo;</li>\n        \n      <li>benford.viz</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <h1>Source code for benford.viz</h1><div class=\"highlight\"><pre>\n<span></span><span class=\"kn\">from</span> <span class=\"nn\">numpy</span> <span class=\"kn\">import</span> <span class=\"n\">array</span><span class=\"p\">,</span> <span class=\"n\">arange</span><span class=\"p\">,</span> <span class=\"n\">maximum</span><span class=\"p\">,</span> <span class=\"n\">sqrt</span><span class=\"p\">,</span> <span class=\"n\">ones</span>\n<span class=\"kn\">import</span> <span class=\"nn\">matplotlib.pyplot</span> <span class=\"k\">as</span> <span class=\"nn\">plt</span>\n<span class=\"kn\">from</span> <span class=\"nn\">matplotlib.text</span> <span class=\"kn\">import</span> <span class=\"n\">Annotation</span>\n<span class=\"kn\">from</span> <span class=\"nn\">.constants</span> <span class=\"kn\">import</span> <span class=\"n\">COLORS</span><span class=\"p\">,</span> <span class=\"n\">MAD_CONFORM</span>\n\n\n<div class=\"viewcode-block\" id=\"plot_expected\"><a class=\"viewcode-back\" href=\"../../api.html#benford.viz.plot_expected\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">plot_expected</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">digs</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Plots the Expected Benford Distributions</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        df: DataFrame with the Expected Proportions</span>\n<span class=\"sd\">        digs: Test&#39;s digit</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"n\">digs</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">y_max</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">()</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"o\">-</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"mi\">3</span><span class=\"p\">))</span> <span class=\"o\">*</span> <span class=\"mi\">100</span>\n        <span class=\"n\">figsize</span> <span class=\"o\">=</span> <span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">),</span> <span class=\"mf\">1.5</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">)</span>\n    <span class=\"k\">elif</span> <span class=\"n\">digs</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">y_max</span> <span class=\"o\">=</span> <span class=\"mf\">13.</span>\n        <span class=\"n\">figsize</span> <span class=\"o\">=</span> <span class=\"mi\">14</span><span class=\"p\">,</span> <span class=\"mf\">10.5</span>\n    <span class=\"k\">elif</span> <span class=\"n\">digs</span> <span class=\"o\">==</span> <span class=\"o\">-</span><span class=\"mi\">2</span><span class=\"p\">:</span>\n        <span class=\"n\">y_max</span> <span class=\"o\">=</span> <span class=\"mf\">1.1</span>\n        <span class=\"n\">figsize</span> <span class=\"o\">=</span> <span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">8</span>\n    <span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">title</span><span class=\"p\">(</span><span class=\"s1\">&#39;Expected Benford Distributions&#39;</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"s1\">&#39;xx-large&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">xlabel</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"s1\">&#39;x-large&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">ylabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Distribution (%)&#39;</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"s1\">&#39;x-large&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_facecolor</span><span class=\"p\">(</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">])</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_ylim</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">y_max</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">bar</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Expected</span> <span class=\"o\">*</span> <span class=\"mi\">100</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;t&#39;</span><span class=\"p\">],</span> <span class=\"n\">align</span><span class=\"o\">=</span><span class=\"s1\">&#39;center&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xticks</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xticklabels</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">save_plot</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">save_plot_kwargs</span><span class=\"p\">:</span>\n            <span class=\"n\">save_plot_kwargs</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">(</span><span class=\"n\">block</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span></div>\n\n\n<span class=\"k\">def</span> <span class=\"nf\">_get_plot_args</span><span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Selects the correct arguments for the plotting functions, depending on the</span>\n<span class=\"sd\">    the test (digs) chosen.</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"n\">digs</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]:</span>\n        <span class=\"n\">text_x</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n        <span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"n\">m</span> <span class=\"o\">=</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">),</span> <span class=\"mi\">10</span> <span class=\"o\">**</span> <span class=\"p\">(</span><span class=\"n\">digs</span><span class=\"p\">)</span>\n        <span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"n\">m</span><span class=\"p\">)</span>\n        <span class=\"n\">figsize</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">),</span> <span class=\"mf\">1.5</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"n\">digs</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">))</span>\n    <span class=\"k\">elif</span> <span class=\"n\">digs</span> <span class=\"o\">==</span> <span class=\"mi\">22</span><span class=\"p\">:</span>\n        <span class=\"n\">text_x</span> <span class=\"o\">=</span> <span class=\"kc\">False</span>\n        <span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">)</span>\n        <span class=\"n\">figsize</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">14</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">text_x</span> <span class=\"o\">=</span> <span class=\"kc\">True</span>\n        <span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">100</span><span class=\"p\">)</span>\n        <span class=\"n\">figsize</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">7</span><span class=\"p\">)</span>\n    <span class=\"k\">return</span> <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">text_x</span>\n\n<div class=\"viewcode-block\" id=\"plot_digs\"><a class=\"viewcode-back\" href=\"../../api.html#benford.viz.plot_digs\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">plot_digs</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y_Exp</span><span class=\"p\">,</span> <span class=\"n\">y_Found</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">conf_Z</span><span class=\"p\">,</span> <span class=\"n\">text_x</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n              <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Plots the digits tests results</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        df: DataFrame with the data to be plotted</span>\n<span class=\"sd\">        x: sequence to be used in the x axis</span>\n<span class=\"sd\">        y_Exp: sequence of the expected proportions to be used in the y axis</span>\n<span class=\"sd\">            (line)</span>\n<span class=\"sd\">        y_Found: sequence of the found proportions to be used in the y axis</span>\n<span class=\"sd\">            (bars)</span>\n<span class=\"sd\">        N: lenght of sequence, to be used when plotting the confidence levels</span>\n<span class=\"sd\">        figsize: tuple to state the size of the plot figure</span>\n<span class=\"sd\">        conf_Z: Confidence level</span>\n<span class=\"sd\">        save_pic: file path to save figure</span>\n<span class=\"sd\">        text_x: Forces to show all x ticks labels. Defaluts to True.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">        </span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"k\">if</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span> <span class=\"o\">&gt;</span> <span class=\"mi\">10</span><span class=\"p\">:</span>\n        <span class=\"n\">rotation</span> <span class=\"o\">=</span> <span class=\"mi\">90</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">rotation</span> <span class=\"o\">=</span> <span class=\"mi\">0</span>\n    <span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">title</span><span class=\"p\">(</span><span class=\"s1\">&#39;Expected vs. Found Distributions&#39;</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"s1\">&#39;xx-large&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">xlabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Digits&#39;</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"s1\">&#39;x-large&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">ylabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Distribution (%)&#39;</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"s1\">&#39;x-large&#39;</span><span class=\"p\">)</span>\n    <span class=\"k\">if</span> <span class=\"n\">conf_Z</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n        <span class=\"n\">sig</span> <span class=\"o\">=</span> <span class=\"n\">conf_Z</span> <span class=\"o\">*</span> <span class=\"n\">sqrt</span><span class=\"p\">(</span><span class=\"n\">y_Exp</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">y_Exp</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">N</span><span class=\"p\">)</span>\n        <span class=\"n\">upper</span> <span class=\"o\">=</span> <span class=\"n\">y_Exp</span> <span class=\"o\">+</span> <span class=\"n\">sig</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">N</span><span class=\"p\">))</span>\n        <span class=\"n\">lower_zeros</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">upper</span><span class=\"p\">))</span>\n        <span class=\"n\">lower</span> <span class=\"o\">=</span> <span class=\"n\">maximum</span><span class=\"p\">(</span><span class=\"n\">y_Exp</span> <span class=\"o\">-</span> <span class=\"n\">sig</span> <span class=\"o\">-</span> <span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">N</span><span class=\"p\">)),</span> <span class=\"n\">lower_zeros</span><span class=\"p\">)</span>\n        <span class=\"n\">u</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">y_Found</span> <span class=\"o\">&lt;</span> <span class=\"n\">lower</span><span class=\"p\">)</span> <span class=\"o\">|</span> <span class=\"p\">(</span><span class=\"n\">y_Found</span> <span class=\"o\">&gt;</span> <span class=\"n\">upper</span><span class=\"p\">)</span>\n        <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;m&#39;</span><span class=\"p\">]]</span> <span class=\"o\">*</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">u</span><span class=\"p\">))</span>\n        <span class=\"n\">c</span><span class=\"p\">[</span><span class=\"n\">u</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;af&#39;</span><span class=\"p\">]</span>\n        <span class=\"n\">lower</span> <span class=\"o\">*=</span> <span class=\"mf\">100.</span>\n        <span class=\"n\">upper</span> <span class=\"o\">*=</span> <span class=\"mf\">100.</span>\n        <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">upper</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">],</span> <span class=\"n\">zorder</span><span class=\"o\">=</span><span class=\"mi\">5</span><span class=\"p\">)</span>\n        <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">lower</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">],</span> <span class=\"n\">zorder</span><span class=\"o\">=</span><span class=\"mi\">5</span><span class=\"p\">)</span>\n        <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">fill_between</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">upper</span><span class=\"p\">,</span> <span class=\"n\">lower</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">],</span>\n                        <span class=\"n\">alpha</span><span class=\"o\">=</span><span class=\"mf\">.3</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s1\">&#39;Conf&#39;</span><span class=\"p\">)</span>\n    <span class=\"k\">else</span><span class=\"p\">:</span>\n        <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;m&#39;</span><span class=\"p\">]</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">bar</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y_Found</span> <span class=\"o\">*</span> <span class=\"mf\">100.</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">c</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s1\">&#39;Found&#39;</span><span class=\"p\">,</span> <span class=\"n\">zorder</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"n\">align</span><span class=\"o\">=</span><span class=\"s1\">&#39;center&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y_Exp</span> <span class=\"o\">*</span> <span class=\"mf\">100.</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">],</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mf\">2.5</span><span class=\"p\">,</span>\n            <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s1\">&#39;Benford&#39;</span><span class=\"p\">,</span> <span class=\"n\">zorder</span><span class=\"o\">=</span><span class=\"mi\">4</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xticks</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xticklabels</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">rotation</span><span class=\"o\">=</span><span class=\"n\">rotation</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_facecolor</span><span class=\"p\">(</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">])</span>\n    <span class=\"k\">if</span> <span class=\"n\">text_x</span><span class=\"p\">:</span>\n        <span class=\"n\">ind</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n        <span class=\"n\">ind</span><span class=\"p\">[:</span><span class=\"mi\">10</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"s1\">&#39;00&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;01&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;02&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;03&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;04&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;05&#39;</span><span class=\"p\">,</span>\n                          <span class=\"s1\">&#39;06&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;07&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;08&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;09&#39;</span><span class=\"p\">])</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">xticks</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">,</span> <span class=\"n\">rotation</span><span class=\"o\">=</span><span class=\"s1\">&#39;vertical&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">legend</span><span class=\"p\">()</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_ylim</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"nb\">max</span><span class=\"p\">([</span><span class=\"n\">y_Exp</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">()</span> <span class=\"o\">*</span> <span class=\"mi\">100</span><span class=\"p\">,</span> <span class=\"n\">y_Found</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">()</span> <span class=\"o\">*</span> <span class=\"mi\">100</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"mi\">10</span> <span class=\"o\">/</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">))</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xlim</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">save_plot</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">save_plot_kwargs</span><span class=\"p\">:</span>\n            <span class=\"n\">save_plot_kwargs</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">(</span><span class=\"n\">block</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span></div>\n\n\n<div class=\"viewcode-block\" id=\"plot_sum\"><a class=\"viewcode-back\" href=\"../../api.html#benford.viz.plot_sum\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">plot_sum</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">li</span><span class=\"p\">,</span> <span class=\"n\">text_x</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Plots the summation test results</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        df: DataFrame with the data to be plotted</span>\n<span class=\"sd\">        figsize: sets the dimensions of the plot figure</span>\n<span class=\"sd\">        li: value with which to draw the horizontal line</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span>\n    <span class=\"n\">rotation</span> <span class=\"o\">=</span> <span class=\"mi\">90</span> <span class=\"k\">if</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span> <span class=\"o\">&gt;</span> <span class=\"mi\">10</span> <span class=\"k\">else</span> <span class=\"mi\">0</span>\n    <span class=\"n\">fig</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">figure</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">fig</span><span class=\"o\">.</span><span class=\"n\">add_subplot</span><span class=\"p\">(</span><span class=\"mi\">111</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">title</span><span class=\"p\">(</span><span class=\"s1\">&#39;Expected vs. Found Sums&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">xlabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Digits&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">ylabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Sums&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">bar</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">Percent</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;m&#39;</span><span class=\"p\">],</span>\n           <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s1\">&#39;Found Sums&#39;</span><span class=\"p\">,</span> <span class=\"n\">zorder</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"n\">align</span><span class=\"o\">=</span><span class=\"s1\">&#39;center&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xlim</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">axhline</span><span class=\"p\">(</span><span class=\"n\">li</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">],</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">,</span> <span class=\"n\">zorder</span><span class=\"o\">=</span><span class=\"mi\">4</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xticks</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xticklabels</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">rotation</span><span class=\"o\">=</span><span class=\"n\">rotation</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_facecolor</span><span class=\"p\">(</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">])</span>\n    <span class=\"k\">if</span> <span class=\"n\">text_x</span><span class=\"p\">:</span>\n        <span class=\"n\">ind</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n        <span class=\"n\">ind</span><span class=\"p\">[:</span><span class=\"mi\">10</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">([</span><span class=\"s1\">&#39;00&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;01&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;02&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;03&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;04&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;05&#39;</span><span class=\"p\">,</span>\n                          <span class=\"s1\">&#39;06&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;07&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;08&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;09&#39;</span><span class=\"p\">])</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">xticks</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">,</span> <span class=\"n\">rotation</span><span class=\"o\">=</span><span class=\"s1\">&#39;vertical&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">legend</span><span class=\"p\">()</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">save_plot</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">save_plot_kwargs</span><span class=\"p\">:</span>\n            <span class=\"n\">save_plot_kwargs</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">(</span><span class=\"n\">block</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"plot_ordered_mantissas\"><a class=\"viewcode-back\" href=\"../../api.html#benford.viz.plot_ordered_mantissas\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">plot_ordered_mantissas</span><span class=\"p\">(</span><span class=\"n\">col</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">12</span><span class=\"p\">,</span> <span class=\"mi\">12</span><span class=\"p\">),</span>\n                           <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Plots the ordered mantissas and compares them to the expected, straight</span>\n<span class=\"sd\">        line that should be formed in a Benford-cmpliant set.</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        col (Series): column of mantissas to plot.</span>\n<span class=\"sd\">        figsize (tuple): sets the dimensions of the plot figure.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\"> </span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">ld</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">col</span><span class=\"p\">)</span>\n    <span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">ld</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n    <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"n\">ones</span><span class=\"p\">(</span><span class=\"n\">ld</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">ld</span>\n    <span class=\"n\">fig</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">figure</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">fig</span><span class=\"o\">.</span><span class=\"n\">add_subplot</span><span class=\"p\">(</span><span class=\"mi\">111</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">col</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(),</span> <span class=\"n\">linestyle</span><span class=\"o\">=</span><span class=\"s1\">&#39;--&#39;</span><span class=\"p\">,</span>\n            <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">],</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s1\">&#39;Mantissas&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"o\">.</span><span class=\"n\">cumsum</span><span class=\"p\">(),</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;m&#39;</span><span class=\"p\">],</span>\n            <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s1\">&#39;Expected&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">ylim</span><span class=\"p\">((</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mf\">1.</span><span class=\"p\">))</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">xlim</span><span class=\"p\">((</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">ld</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">))</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_facecolor</span><span class=\"p\">(</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">])</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_title</span><span class=\"p\">(</span><span class=\"s2\">&quot;Ordered Mantissas&quot;</span><span class=\"p\">)</span>\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">legend</span><span class=\"p\">(</span><span class=\"n\">loc</span><span class=\"o\">=</span><span class=\"s1\">&#39;upper left&#39;</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">save_plot</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">save_plot_kwargs</span><span class=\"p\">:</span>\n            <span class=\"n\">save_plot_kwargs</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">(</span><span class=\"n\">block</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">);</span></div>\n\n<div class=\"viewcode-block\" id=\"plot_mantissa_arc_test\"><a class=\"viewcode-back\" href=\"../../api.html#benford.viz.plot_mantissa_arc_test\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">plot_mantissa_arc_test</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">gravity_center</span><span class=\"p\">,</span> <span class=\"n\">grid</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"mi\">12</span><span class=\"p\">,</span>\n                           <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Draws thee Mantissa Arc Test after computing X and Y circular coordinates</span>\n<span class=\"sd\">    for every mantissa and the center of gravity for the set</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        df (DataFrame): pandas DataFrame with the mantissas and the X and Y</span>\n<span class=\"sd\">            coordinates.</span>\n<span class=\"sd\">        gravity_center (tuple): coordinates for plottling the gravity center</span>\n<span class=\"sd\">        grid (bool): show grid. Defaults to True.</span>\n<span class=\"sd\">        figsize (int): figure dimensions. No need to be a tuple, since the</span>\n<span class=\"sd\">            figure is a square.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">fig</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">figure</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">))</span>\n    <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplot</span><span class=\"p\">()</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_facecolor</span><span class=\"p\">(</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">])</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">scatter</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">mant_x</span><span class=\"p\">,</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">mant_y</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s2\">&quot;ARC TEST&quot;</span><span class=\"p\">,</span>\n               <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;m&#39;</span><span class=\"p\">])</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">scatter</span><span class=\"p\">(</span><span class=\"n\">gravity_center</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"n\">gravity_center</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">],</span>\n               <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">])</span>\n    <span class=\"n\">text_annotation</span> <span class=\"o\">=</span> <span class=\"n\">Annotation</span><span class=\"p\">(</span>\n        <span class=\"s2\">&quot;  Gravity Center: &quot;</span>\n        <span class=\"sa\">f</span><span class=\"s2\">&quot;x(</span><span class=\"si\">{</span><span class=\"nb\">round</span><span class=\"p\">(</span><span class=\"n\">gravity_center</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"mi\">3</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\">),&quot;</span>\n        <span class=\"sa\">f</span><span class=\"s2\">&quot; y(</span><span class=\"si\">{</span><span class=\"nb\">round</span><span class=\"p\">(</span><span class=\"n\">gravity_center</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"mi\">3</span><span class=\"p\">)</span><span class=\"si\">}</span><span class=\"s2\">)&quot;</span><span class=\"p\">,</span>\n        <span class=\"n\">xy</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">gravity_center</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.65</span><span class=\"p\">,</span>\n            <span class=\"n\">gravity_center</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.1</span><span class=\"p\">),</span>\n        <span class=\"n\">xycoords</span><span class=\"o\">=</span><span class=\"s1\">&#39;data&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">add_artist</span><span class=\"p\">(</span><span class=\"n\">text_annotation</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">grid</span><span class=\"p\">(</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">which</span><span class=\"o\">=</span><span class=\"s1\">&#39;both&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">axhline</span><span class=\"p\">(</span><span class=\"n\">y</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s1\">&#39;k&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">axvline</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s1\">&#39;k&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">legend</span><span class=\"p\">(</span><span class=\"n\">loc</span><span class=\"o\">=</span><span class=\"s1\">&#39;lower left&#39;</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_title</span><span class=\"p\">(</span><span class=\"s2\">&quot;Mantissas Arc Test&quot;</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">save_plot</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">save_plot_kwargs</span><span class=\"p\">:</span>\n            <span class=\"n\">save_plot_kwargs</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">(</span><span class=\"n\">block</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">);</span></div>\n\n<div class=\"viewcode-block\" id=\"plot_roll_mse\"><a class=\"viewcode-back\" href=\"../../api.html#benford.viz.plot_roll_mse\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">plot_roll_mse</span><span class=\"p\">(</span><span class=\"n\">roll_series</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Shows the rolling MSE plot</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        roll_series: pd.Series resultant form rolling mse.</span>\n<span class=\"sd\">        figsize: the figure dimensions.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_facecolor</span><span class=\"p\">(</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">])</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">roll_series</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;m&#39;</span><span class=\"p\">])</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">save_plot</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">save_plot_kwargs</span><span class=\"p\">:</span>\n            <span class=\"n\">save_plot_kwargs</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">(</span><span class=\"n\">block</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span></div>\n\n<div class=\"viewcode-block\" id=\"plot_roll_mad\"><a class=\"viewcode-back\" href=\"../../api.html#benford.viz.plot_roll_mad\">[docs]</a><span class=\"k\">def</span> <span class=\"nf\">plot_roll_mad</span><span class=\"p\">(</span><span class=\"n\">roll_mad</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"p\">,</span> <span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n    <span class=\"sd\">&quot;&quot;&quot;Shows the rolling MAD plot</span>\n\n<span class=\"sd\">    Args:</span>\n<span class=\"sd\">        roll_mad: pd.Series resultant form rolling mad.</span>\n<span class=\"sd\">        figsize: the figure dimensions.</span>\n<span class=\"sd\">        save_plot: string with the path/name of the file in which the generated</span>\n<span class=\"sd\">            plot will be saved. Uses matplotlib.pyplot.savefig(). File format</span>\n<span class=\"sd\">            is infered by the file name extension.</span>\n<span class=\"sd\">        save_plot_kwargs: dict with any of the kwargs accepted by</span>\n<span class=\"sd\">            matplotlib.pyplot.savefig()</span>\n<span class=\"sd\">            https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</span>\n<span class=\"sd\">    &quot;&quot;&quot;</span>\n    <span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"n\">figsize</span><span class=\"p\">)</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_facecolor</span><span class=\"p\">(</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">])</span>\n    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">roll_mad</span><span class=\"o\">.</span><span class=\"n\">roll_series</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;m&#39;</span><span class=\"p\">])</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">roll_mad</span><span class=\"o\">.</span><span class=\"n\">test</span> <span class=\"o\">!=</span> <span class=\"o\">-</span><span class=\"mi\">2</span><span class=\"p\">:</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">axhline</span><span class=\"p\">(</span><span class=\"n\">y</span><span class=\"o\">=</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">roll_mad</span><span class=\"o\">.</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;af&#39;</span><span class=\"p\">],</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">)</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">axhline</span><span class=\"p\">(</span><span class=\"n\">y</span><span class=\"o\">=</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">roll_mad</span><span class=\"o\">.</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;h2&#39;</span><span class=\"p\">],</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">)</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">axhline</span><span class=\"p\">(</span><span class=\"n\">y</span><span class=\"o\">=</span><span class=\"n\">MAD_CONFORM</span><span class=\"p\">[</span><span class=\"n\">roll_mad</span><span class=\"o\">.</span><span class=\"n\">test</span><span class=\"p\">][</span><span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"n\">COLORS</span><span class=\"p\">[</span><span class=\"s1\">&#39;s&#39;</span><span class=\"p\">],</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">)</span>\n\n    <span class=\"k\">if</span> <span class=\"n\">save_plot</span><span class=\"p\">:</span>\n        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">save_plot_kwargs</span><span class=\"p\">:</span>\n            <span class=\"n\">save_plot_kwargs</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n        <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"n\">save_plot</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">save_plot_kwargs</span><span class=\"p\">)</span>\n\n    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">(</span><span class=\"n\">block</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span></div>\n</pre></div>\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/_modules/index.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>Overview: module code &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"../_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"../_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"../_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"../\" src=\"../_static/documentation_options.js\"></script>\n        <script src=\"../_static/jquery.js\"></script>\n        <script src=\"../_static/underscore.js\"></script>\n        <script src=\"../_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"../_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"../genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"../search.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"../index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"../search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"../modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"../index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"../index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n      <li>Overview: module code</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <h1>All modules for which code is available</h1>\n<ul><li><a href=\"benford/benford.html\">benford.benford</a></li>\n<li><a href=\"benford/expected.html\">benford.expected</a></li>\n<li><a href=\"benford/stats.html\">benford.stats</a></li>\n<li><a href=\"benford/viz.html\">benford.viz</a></li>\n</ul>\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/_sources/api.rst.txt",
    "content": "benford package\n===============\n\nbenford.benford module\n----------------------\n\n.. automodule:: benford.benford\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nbenford.expected module\n-----------------------\n\n.. automodule:: benford.expected\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nbenford.stats module\n--------------------\n\n.. automodule:: benford.stats\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nbenford.viz module\n------------------\n\n.. automodule:: benford.viz\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n"
  },
  {
    "path": "docs/build/html/_sources/index.rst.txt",
    "content": "Welcome to benford_py's documentation!\n======================================\n\n.. toctree::\n   :maxdepth: 3\n   :caption: Contents:\n\n   modules\n\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n\nOn GitHub\n---------\n\n`Package <https://github.com/milcent/benford_py>`_\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n`Demo Jupyter Notebook <https://github.com/milcent/benford_py/blob/master/Demo.ipynb>`_\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
  },
  {
    "path": "docs/build/html/_sources/modules.rst.txt",
    "content": "benford\n=======\n\n.. toctree::\n   :maxdepth: 2\n\n   api\n"
  },
  {
    "path": "docs/build/html/api.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>benford package &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"./\" src=\"_static/documentation_options.js\"></script>\n        <script src=\"_static/jquery.js\"></script>\n        <script src=\"_static/underscore.js\"></script>\n        <script src=\"_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"search.html\" />\n    <link rel=\"prev\" title=\"benford\" href=\"modules.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul class=\"current\">\n<li class=\"toctree-l1 current\"><a class=\"reference internal\" href=\"modules.html\">benford</a><ul class=\"current\">\n<li class=\"toctree-l2 current\"><a class=\"current reference internal\" href=\"#\">benford package</a><ul>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"#module-benford.benford\">benford.benford module</a></li>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"#module-benford.expected\">benford.expected module</a></li>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"#module-benford.stats\">benford.stats module</a></li>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"#module-benford.viz\">benford.viz module</a></li>\n</ul>\n</li>\n</ul>\n</li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n          <li><a href=\"modules.html\">benford</a> &raquo;</li>\n        \n      <li>benford package</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n          \n            <a href=\"_sources/api.rst.txt\" rel=\"nofollow\"> View page source</a>\n          \n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <div class=\"section\" id=\"benford-package\">\n<h1>benford package<a class=\"headerlink\" href=\"#benford-package\" title=\"Permalink to this headline\">¶</a></h1>\n<div class=\"section\" id=\"module-benford.benford\">\n<span id=\"benford-benford-module\"></span><h2>benford.benford module<a class=\"headerlink\" href=\"#module-benford.benford\" title=\"Permalink to this headline\">¶</a></h2>\n<dl class=\"py class\">\n<dt id=\"benford.benford.Base\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Base</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Base\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Base\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">pandas.core.frame.DataFrame</span></code></p>\n<p>Internalizes and prepares the data for Analysis.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.`</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Raises</dt>\n<dd class=\"field-even\"><p><strong>TypeError</strong> – if not receiving <cite>int</cite> or <cite>float</cite> as input.</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.benford.Test\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Test</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">base</span></em>, <em class=\"sig-param\"><span class=\"n\">digs</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Test\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Test\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">pandas.core.frame.DataFrame</span></code></p>\n<p>Transforms the original number sequence into a DataFrame reduced\nby the ocurrences of the chosen digits, creating other computed\ncolumns</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>base</strong> – The Base object with the data prepared for Analysis</p></li>\n<li><p><strong>digs</strong> – Tells which test to perform: 1: first digit; 2: first two digits;\n3: furst three digits; 22: second digit; -2: last two digits.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n</ul>\n</dd>\n</dl>\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.N\">\n<code class=\"sig-name descname\">N</code><a class=\"headerlink\" href=\"#benford.benford.Test.N\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Number of records in the sample to consider in computations</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.ddf\">\n<code class=\"sig-name descname\">ddf</code><a class=\"headerlink\" href=\"#benford.benford.Test.ddf\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Degrees of Freedom to look up for the critical chi-square value</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.chi_square\">\n<code class=\"sig-name descname\">chi_square</code><a class=\"headerlink\" href=\"#benford.benford.Test.chi_square\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Chi-square statistic for the given test</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.KS\">\n<code class=\"sig-name descname\">KS</code><a class=\"headerlink\" href=\"#benford.benford.Test.KS\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Kolmogorov-Smirnov statistic for the given test</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.MAD\">\n<code class=\"sig-name descname\">MAD</code><a class=\"headerlink\" href=\"#benford.benford.Test.MAD\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Mean Absolute Deviation for the given test</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.confidence\">\n<code class=\"sig-name descname\">confidence</code><a class=\"headerlink\" href=\"#benford.benford.Test.confidence\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Confidence level to consider when setting some critical values</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.digs\">\n<code class=\"sig-name descname\">digs</code><a class=\"headerlink\" href=\"#benford.benford.Test.digs\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>numerical representation of the test at hand. 1: F1D; 2: F2D;\n3: F3D; 22: SD; -2: L2D.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>int</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Test.sec_order\">\n<code class=\"sig-name descname\">sec_order</code><a class=\"headerlink\" href=\"#benford.benford.Test.sec_order\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>True if the test is a Second Order one</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>bool</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Test.update_confidence\">\n<code class=\"sig-name descname\">update_confidence</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">new_conf</span></em>, <em class=\"sig-param\"><span class=\"n\">check</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Test.update_confidence\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Test.update_confidence\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Sets a new confidence level for the Benford object, so as to be used to\nproduce critical values for the tests</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>new_conf</strong> – new confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show, as well as to\ncalculate critical values for the tests’ statistics.</p></li>\n<li><p><strong>check</strong> – checks the value provided for the confidence. Defaults to True</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Test.critical_values\">\n<em class=\"property\">property </em><code class=\"sig-name descname\">critical_values</code><a class=\"headerlink\" href=\"#benford.benford.Test.critical_values\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>a dictionary with the critical values for the test at hand,\naccording to the current confidence level.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>dict</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Test.show_plot\">\n<code class=\"sig-name descname\">show_plot</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Test.show_plot\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Test.show_plot\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Draws the test plot.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when save_plot is a string with the figure file\npath/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Test.report\">\n<code class=\"sig-name descname\">report</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Test.report\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Test.report\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Handles the report especific to the test, considering its statistics\nand according to the current confidence level.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe critical value or not.</p></li>\n<li><p><strong>show_plot</strong> – calls the show_plot method, to draw the test plot</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.benford.Summ\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Summ</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">base</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Summ\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Summ\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">pandas.core.frame.DataFrame</span></code></p>\n<p>Gets the base object and outputs a Summation test object</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>base</strong> – The Base object with the data prepared for Analysis</p></li>\n<li><p><strong>test</strong> – The test for which to compute the summation</p></li>\n</ul>\n</dd>\n</dl>\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Summ.MAD\">\n<code class=\"sig-name descname\">MAD</code><a class=\"headerlink\" href=\"#benford.benford.Summ.MAD\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Mean Absolute Deviation for the test</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Summ.confidence\">\n<code class=\"sig-name descname\">confidence</code><a class=\"headerlink\" href=\"#benford.benford.Summ.confidence\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Confidence level to consider when setting some critical values</p>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Summ.show_plot\">\n<code class=\"sig-name descname\">show_plot</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Summ.show_plot\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Summ.show_plot\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Draws the Summation test plot</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when save_plot is a string with the figure file\npath/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Summ.report\">\n<code class=\"sig-name descname\">report</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">high_diff</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Summ.report\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Summ.report\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Gives the report on the Summation test.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>high_diff</strong> – Number of records to show after ordering by the absolute\ndifferences between the found and the expected proportions</p></li>\n<li><p><strong>show_plot</strong> – calls the show_plot method, to draw the Summation test plot</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.benford.Mantissas\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Mantissas</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">95</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Mantissas\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Mantissas\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">object</span></code></p>\n<p>Computes and holds the mantissas of the logarithms of the records</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence to compute mantissas from. numpy 1D array, pandas\nSeries of pandas DataFrame column.</p></li>\n<li><p><strong>confidence</strong> – confidence level for computing the critical values to\ncompare with some statistics</p></li>\n</ul>\n</dd>\n</dl>\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Mantissas.data\">\n<code class=\"sig-name descname\">data</code><a class=\"headerlink\" href=\"#benford.benford.Mantissas.data\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>pandas DataFrame with the mantissas</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>(DataFrame)</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Mantissas.stats\">\n<em class=\"property\">property </em><code class=\"sig-name descname\">stats</code><a class=\"headerlink\" href=\"#benford.benford.Mantissas.stats\" title=\"Permalink to this definition\">¶</a></dt>\n<dd></dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Mantissas.update_confidence\">\n<code class=\"sig-name descname\">update_confidence</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">new_conf</span></em>, <em class=\"sig-param\"><span class=\"n\">check</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Mantissas.update_confidence\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Mantissas.update_confidence\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Sets a new confidence level for the Benford object, so as to be used to\nproduce critical values for the tests</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>new_conf</strong> – new confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show, as well as to\ncalculate critical values for the tests’ statistics.</p></li>\n<li><p><strong>check</strong> – checks the value provided for the confidence. Defaults to True</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Mantissas.report\">\n<code class=\"sig-name descname\">report</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Mantissas.report\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Mantissas.report\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Displays the Mantissas test stats.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>show_plot</strong> – shows the Ordered Mantissas plot and the Arc Test plot.\nDefaults to True.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Mantissas.show_plot\">\n<code class=\"sig-name descname\">show_plot</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"default_value\">(12, 6)</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Mantissas.show_plot\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Mantissas.show_plot\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Plots the ordered mantissas and a line with the expected\ninclination.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>figsize</strong> (<em>tuple</em>) – figure size dimensions</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when save_plot is a string with the figure file\npath/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Mantissas.arc_test\">\n<code class=\"sig-name descname\">arc_test</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">grid</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"default_value\">12</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Mantissas.arc_test\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Mantissas.arc_test\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Adds two columns to Mantissas’s DataFrame equal to their “X” and “Y”\ncoordinates, plots its to a scatter plot and calculates the gravity\ncenter of the circle.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>grid</strong> – show grid of the plot. Defaluts to True.</p></li>\n<li><p><strong>figsize</strong> (<em>int</em>) – size of the figure to be displayed. Since it is a square,\nthere is no need to provide a tuple, like is usually the case with\nmatplotlib.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.benford.Benford\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Benford</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">95</span></em>, <em class=\"sig-param\"><span class=\"n\">mantissas</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">summation</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Benford\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Benford\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">object</span></code></p>\n<p>Initializes a Benford Analysis object and computes the proportions for\nthe digits. The tets dataFrames are atributes, i.e., obj.F1D is the First\nDigit DataFrame, the obj.F2D,the First Two Digits one, and so one, F3D for\nFirst Three Digits, SD for Second  Digit and L2D for Last Two Digits.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a tuple with a pandas DataFrame and the name (str)\nof the chosen column. Values must be integers or floats.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show, as well as to\ncalculate critical values for the tests’ statistics. Defaults to 95.</p></li>\n<li><p><strong>mantissas</strong> (<em>bool</em>) – opts for also running the mantissas Test. Defaulst to\nTrue</p></li>\n<li><p><strong>sec_order</strong> – runs the Second Order tests, which are the Benford’s tests\nperformed on the differences between the ordered sample (a value minus\nthe one before it, and so on). If the original series is Benford-\ncompliant, this new sequence should aldo follow Beford. The Second\nOrder can also be called separately, through the method sec_order().</p></li>\n<li><p><strong>summation</strong> – creates the Summation DataFrames for the First, First Two, and\nFirst Three Digits. The summation tests can also be called separately,\nthrough the method summation().</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>verbose</strong> – gives some information about the data and the registries used\nand discarded for each test.</p></li>\n</ul>\n</dd>\n</dl>\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.data\">\n<code class=\"sig-name descname\">data</code><a class=\"headerlink\" href=\"#benford.benford.Benford.data\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>the raw data provided for the analysis</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.chosen\">\n<code class=\"sig-name descname\">chosen</code><a class=\"headerlink\" href=\"#benford.benford.Benford.chosen\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>the column of the DataFrame to be analysed or the data itself</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.sign\">\n<code class=\"sig-name descname\">sign</code><a class=\"headerlink\" href=\"#benford.benford.Benford.sign\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>which number sign(s) to include in the analysis</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>str</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.confidence\">\n<code class=\"sig-name descname\">confidence</code><a class=\"headerlink\" href=\"#benford.benford.Benford.confidence\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>current confidence level</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.limit_N\">\n<code class=\"sig-name descname\">limit_N</code><a class=\"headerlink\" href=\"#benford.benford.Benford.limit_N\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>sample size to use in computations</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>int</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.verbose\">\n<code class=\"sig-name descname\">verbose</code><a class=\"headerlink\" href=\"#benford.benford.Benford.verbose\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>verbose or not</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>bool</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.base\">\n<code class=\"sig-name descname\">base</code><a class=\"headerlink\" href=\"#benford.benford.Benford.base\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>the Base, pre-processed object</p>\n</dd></dl>\n\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Benford.tests\">\n<code class=\"sig-name descname\">tests</code><a class=\"headerlink\" href=\"#benford.benford.Benford.tests\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>keeps track of the tests the\ninstance has</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p><code class=\"xref py py-obj docutils literal notranslate\"><span class=\"pre\">list</span></code> of <code class=\"xref py py-obj docutils literal notranslate\"><span class=\"pre\">str</span></code></p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Benford.update_confidence\">\n<code class=\"sig-name descname\">update_confidence</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">new_conf</span></em>, <em class=\"sig-param\"><span class=\"n\">tests</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Benford.update_confidence\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Benford.update_confidence\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Sets (a) new confidence level(s) for the Benford object, so as to be\nused to produce critical values for the tests.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>new_conf</strong> – new confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show, as well as to\ncalculate critical values for the tests’ statistics.</p></li>\n<li><p><strong>tests</strong> (<code class=\"xref py py-obj docutils literal notranslate\"><span class=\"pre\">list</span></code> of <code class=\"xref py py-obj docutils literal notranslate\"><span class=\"pre\">str</span></code>) – list of tests names (strings) to\nhave their confidence updated. If only one, provide a one-element\nlist, like [‘F1D’]. Defauts to None, in which case it will use\nthe instance .test list attribute.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Raises</dt>\n<dd class=\"field-even\"><p><strong>ValueError</strong> – if the test argument is not a <cite>list</cite> or <cite>None</cite>.</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Benford.all_confidences\">\n<em class=\"property\">property </em><code class=\"sig-name descname\">all_confidences</code><a class=\"headerlink\" href=\"#benford.benford.Benford.all_confidences\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>a dictionary with a confidence level for each computed tests,\nwhen applicable.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>dict</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Benford.mantissas\">\n<code class=\"sig-name descname\">mantissas</code><span class=\"sig-paren\">(</span><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Benford.mantissas\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Benford.mantissas\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Adds a Mantissas object to the tests, with all its statistics and\nplotting capabilities.</p>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Benford.sec_order\">\n<code class=\"sig-name descname\">sec_order</code><span class=\"sig-paren\">(</span><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Benford.sec_order\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Benford.sec_order\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Runs the Second Order tests, which are the Benford’s tests\nperformed on the differences between the ordered sample (a value minus\nthe one before it, and so on). If the original series is Benford-\ncompliant, this new sequence should aldo follow Beford. The Second\nOrder can also be called separately, through the method sec_order().</p>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Benford.summation\">\n<code class=\"sig-name descname\">summation</code><span class=\"sig-paren\">(</span><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Benford.summation\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Benford.summation\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Creates Summation test DataFrames from Base object</p>\n</dd></dl>\n\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.benford.Source\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Source</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">sec_order</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Source\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Source\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">pandas.core.frame.DataFrame</span></code></p>\n<p>Prepares the data for Analysis. pandas DataFrame subclass.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n<li><p><strong>sec_order</strong> – choice for the Second Order Test, which cumputes the\ndifferences between the ordered entries before running the Tests.</p></li>\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis; defaults to True.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Raises</dt>\n<dd class=\"field-even\"><ul class=\"simple\">\n<li><p><strong>ValueError</strong> – if the <cite>sign</cite> arg is not in [‘all’, ‘pos’, ‘neg’]</p></li>\n<li><p><strong>TypeError</strong> – if not receiving <cite>int</cite> or <cite>float</cite> as input.</p></li>\n</ul>\n</dd>\n</dl>\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Source.verbose\">\n<code class=\"sig-name descname\">verbose</code><a class=\"headerlink\" href=\"#benford.benford.Source.verbose\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>verbose or not</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Type</dt>\n<dd class=\"field-odd\"><p>(bool)</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Source.mantissas\">\n<code class=\"sig-name descname\">mantissas</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">report</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"default_value\">(15, 8)</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Source.mantissas\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Source.mantissas\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Calculates the mantissas, their mean and variance, and compares them\nwith the mean and variance of a Benford’s sequence.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>report</strong> – prints the mamtissas mean, variance, skewness and kurtosis\nfor the sequence studied, along with reference values.</p></li>\n<li><p><strong>show_plot</strong> – plots the ordered mantissas and a line with the expected\ninclination. Defaults to True.</p></li>\n<li><p><strong>figsize</strong> – tuple that sets the figure dimensions.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Source.first_digits\">\n<code class=\"sig-name descname\">first_digits</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">digs</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">bhat_coeff</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Source.first_digits\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Source.first_digits\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Benford First Digits test with the series of\nnumbers provided, and populates the mapping dict for future\nselection of the original series.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>digs</strong> (<em>int</em>) – number of first digits to consider. Must be 1 (first digit),\n2 (first two digits) or 3 (first three digits).</p></li>\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis; defaults to True</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show, as well as to\ncalculate critical values for the tests’ statistics. Defaults to None.</p></li>\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe confidence or not.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>MAD</strong> (<em>bool</em>) – calculates the Mean Absolute Difference between the\nfound and the expected distributions; defaults to False.</p></li>\n<li><p><strong>MSE</strong> (<em>bool</em>) – calculates the Mean Square Error of the sample; defaults to\nFalse.</p></li>\n<li><p><strong>bhat_coeff</strong> (<em>bool</em>) – computes the Bhattacharyya Coefficient between\nthe found and the expected (Benford) digits distribution; defaults\nto Fasle</p></li>\n<li><p><strong>bhat_dist</strong> (<em>bool</em>) – calculates the Bhattacharyya Distance between\nthe found and the expected (Benford) digits distribution; defaults\nto Fasle</p></li>\n<li><p><strong>kl_diverg</strong> (<em>bool</em>) – calculates the Kulback-Laibler Divergence between\nthe found and the expected (Benford) digits distribution;\ndefaults to False</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot. Defaults to True.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n<li><p><strong>ret_df</strong> – returns the test DataFrame. Defaults to False. True if run by\nthe test function.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Expected and Found proportions, and the Z scores of</dt><dd><p>the differences</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Source.second_digit\">\n<code class=\"sig-name descname\">second_digit</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">bhat_coeff</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Source.second_digit\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Source.second_digit\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Benford Second Digit test with the series of\nnumbers provided.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis; defaults to True</p></li>\n<li><p><strong>MAD</strong> (<em>bool</em>) – calculates the Mean Absolute Difference between the\nfound and the expected distributions; defaults to False.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show, as well as to\ncalculate critical values for the tests’ statistics. Defaults to None.</p></li>\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe confidence or not.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>MSE</strong> (<em>bool</em>) – calculates the Mean Square Error of the sample; defaults to\nFalse.</p></li>\n<li><p><strong>bhat_coeff</strong> (<em>bool</em>) – computes the Bhattacharyya Coefficient between\nthe found and the expected (Benford) digits distribution; defaults\nto Fasle</p></li>\n<li><p><strong>bhat_dist</strong> (<em>bool</em>) – calculates the Bhattacharyya Distance between\nthe found and the expected (Benford) digits distribution; defaults\nto Fasle</p></li>\n<li><p><strong>kl_diverg</strong> (<em>bool</em>) – calculates the Kulback-Laibler Divergence between\nthe found and the expected (Benford) digits distribution;\ndefaults to False</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n<li><p><strong>ret_df</strong> – returns the test DataFrame. Defaults to False. True if run by\nthe test function.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Expected and Found proportions, and the Z scores of</dt><dd><p>the differences</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Source.last_two_digits\">\n<code class=\"sig-name descname\">last_two_digits</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">bhat_coeff</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">bhat_dist</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">kl_diverg</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">simple</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Source.last_two_digits\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Source.last_two_digits\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Benford Last Two Digits test with the series of\nnumbers provided.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis; defaults to True</p></li>\n<li><p><strong>MAD</strong> (<em>bool</em>) – calculates the Mean Absolute Difference between the\nfound and the expected distributions; defaults to False.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show, as well as to\ncalculate critical values for the tests’ statistics. Defaults to None.</p></li>\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe confidence or not.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>MSE</strong> (<em>bool</em>) – calculates the Mean Square Error of the sample; defaults to\nFalse.</p></li>\n<li><p><strong>bhat_coeff</strong> (<em>bool</em>) – computes the Bhattacharyya Coefficient between\nthe found and the expected (Benford) digits distribution; defaults\nto Fasle</p></li>\n<li><p><strong>bhat_dist</strong> (<em>bool</em>) – calculates the Bhattacharyya Distance between\nthe found and the expected (Benford) digits distribution; defaults\nto Fasle</p></li>\n<li><p><strong>kl_diverg</strong> (<em>bool</em>) – calculates the Kulback-Laibler Divergence between\nthe found and the expected (Benford) digits distribution;\ndefaults to False</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Expected and Found proportions, and the Z scores of</dt><dd><p>the differences</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Source.summation\">\n<code class=\"sig-name descname\">summation</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">top</span><span class=\"o\">=</span><span class=\"default_value\">20</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">ret_df</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Source.summation\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Source.summation\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Summation test. In a Benford series, the sums of the\nentries begining with the same digits tends to be the same.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>digs</strong> – tells the first digits to use. 1- first; 2- first two;\n3- first three. Defaults to 2.</p></li>\n<li><p><strong>top</strong> – choses how many top values to show. Defaults to 20.</p></li>\n<li><p><strong>show_plot</strong> – plots the results. Defaults to True.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Expected and Found proportions, and their</dt><dd><p>absolute differences</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Source.duplicates\">\n<code class=\"sig-name descname\">duplicates</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">top_Rep</span><span class=\"o\">=</span><span class=\"default_value\">20</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Source.duplicates\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Source.duplicates\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs a duplicates test and maps the duplicates count in descending\norder.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells how many duplicated entries were found and prints the\ntop numbers according to the top_Rep argument. Defaluts to True.</p></li>\n<li><p><strong>top_Rep</strong> – int or None. Chooses how many duplicated entries will be\nshown withe the top repititions. Defaluts to 20. If None, returns\nal the ordered repetitions.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the duplicated records and their occurrence counts,</dt><dd><p>in descending order (if verbose is False; if True, prints to\nterminal).</p>\n</dd>\n</dl>\n</p>\n</dd>\n<dt class=\"field-odd\">Raises</dt>\n<dd class=\"field-odd\"><p><strong>ValueError</strong> – if the <cite>top_Rep</cite> arg is not int or None.</p>\n</dd>\n</dl>\n</dd></dl>\n\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.benford.Roll_mad\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Roll_mad</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">window</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Roll_mad\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Roll_mad\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">object</span></code></p>\n<p>Applies the MAD to sequential subsets of the Series, returning another\nSeries.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</p></li>\n<li><p><strong>window</strong> – size of the subset to be used.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n</ul>\n</dd>\n</dl>\n<dl class=\"py attribute\">\n<dt id=\"benford.benford.Roll_mad.test\">\n<code class=\"sig-name descname\">test</code><a class=\"headerlink\" href=\"#benford.benford.Roll_mad.test\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>the test (F1D, SD, F2D…) used for the MAD calculation and critical values</p>\n</dd></dl>\n\n<dl class=\"py method\">\n<dt id=\"benford.benford.Roll_mad.show_plot\">\n<code class=\"sig-name descname\">show_plot</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"default_value\">(15, 8)</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Roll_mad.show_plot\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Roll_mad.show_plot\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Shows the rolling MAD plot</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>figsize</strong> – the figure dimensions.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when save_plot is a string with the figure file\npath/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.benford.Roll_mse\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">Roll_mse</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">window</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Roll_mse\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Roll_mse\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">object</span></code></p>\n<p>Applies the MSE to sequential subsets of the Series, returning another\nSeries.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</p></li>\n<li><p><strong>window</strong> – size of the subset to be used.\ndecimals: number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. ‘pos’: only the positive\nentries; ‘neg’: only negative entries; ‘all’: all entries but zeros.\nDefaults to ‘all’.</p></li>\n</ul>\n</dd>\n</dl>\n<dl class=\"py method\">\n<dt id=\"benford.benford.Roll_mse.show_plot\">\n<code class=\"sig-name descname\">show_plot</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"default_value\">(15, 8)</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#Roll_mse.show_plot\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.Roll_mse.show_plot\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Shows the rolling MSE plot</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>figsize</strong> – the figure dimensions.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when save_plot is a string with the figure file\npath/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.first_digits\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">first_digits</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">digs</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#first_digits\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.first_digits\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Benford First Digits test on the series of\nnumbers provided.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. ‘pos’: only the positive\nentries; ‘neg’: only negative entries; ‘all’: all entries but zeros.\nDefaults to ‘all’.</p></li>\n<li><p><strong>digs</strong> (<em>int</em>) – number of first digits to consider. Must be 1 (first digit),\n2 (first two digits) or 3 (first three digits).</p></li>\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis and returns tha analysis DataFrame sorted by the\nhighest Z score down. Defaults to True.</p></li>\n<li><p><strong>MAD</strong> (<em>bool</em>) – calculates the Mean Absolute Difference between the\nfound and the expected distributions; defaults to False.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show. Defaults to None.</p></li>\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe confidence or not.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>MSE</strong> (<em>bool</em>) – calculates the Mean Square Error of the sample; defaults to\nFalse.</p></li>\n<li><p><strong>chi_square</strong> – calculates the chi_square statistic of the sample and\ncompares it with a critical value, according to the confidence\nlevel chosen and the series’s degrees of freedom. Defaults to\nFalse. Requires confidence != None.</p></li>\n<li><p><strong>KS</strong> – calculates the Kolmogorov-Smirnov test, comparing the cumulative\ndistribution of the sample with the Benford’s, according to the\nconfidence level chosen. Defaults to False. Requires confidence\n!= None.</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Expected and Found proportions, and the Z scores of</dt><dd><p>the differences if the confidence is not None.</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.second_digit\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">second_digit</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#second_digit\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.second_digit\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Benford Second Digits test on the series of\nnumbers provided.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. ‘pos’: only the positive\nentries; ‘neg’: only negative entries; ‘all’: all entries but zeros.\nDefaults to ‘all’.</p></li>\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis and returns tha analysis DataFrame sorted by the\nhighest Z score down. Defaults to True.</p></li>\n<li><p><strong>MAD</strong> (<em>bool</em>) – calculates the Mean Absolute Difference between the\nfound and the expected distributions; defaults to False.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show. Defaults to None.</p></li>\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe confidence or not.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>MSE</strong> (<em>bool</em>) – calculates the Mean Square Error of the sample; defaults to\nFalse.</p></li>\n<li><p><strong>chi_square</strong> – calculates the chi_square statistic of the sample and\ncompares it with a critical value, according to the confidence\nlevel chosen and the series’s degrees of freedom. Defaults to\nFalse. Requires confidence != None.</p></li>\n<li><p><strong>KS</strong> – calculates the Kolmogorov-Smirnov test, comparing the cumulative\ndistribution of the sample with the Benford’s, according to the\nconfidence level chosen. Defaults to False. Requires confidence\n!= None.</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Expected and Found proportions, and the Z scores of</dt><dd><p>the differences if the confidence is not None.</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.last_two_digits\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">last_two_digits</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">chi_square</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">KS</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#last_two_digits\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.last_two_digits\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Last Two Digits test on the series of\nnumbers provided.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column,with values being\nintegers or floats.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. ‘pos’: only the positive\nentries; ‘neg’: only negative entries; ‘all’: all entries but zeros.\nDefaults to ‘all’.</p></li>\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis and returns tha analysis DataFrame sorted by the\nhighest Z score down. Defaults to True.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show. Defaults to None.</p></li>\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe confidence or not.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>MAD</strong> (<em>bool</em>) – calculates the Mean Absolute Difference between the\nfound and the expected distributions; defaults to False.</p></li>\n<li><p><strong>MSE</strong> (<em>bool</em>) – calculates the Mean Square Error of the sample; defaults to\nFalse.</p></li>\n<li><p><strong>chi_square</strong> – calculates the chi_square statistic of the sample and\ncompares it with a critical value, according to the confidence\nlevel chosen and the series’s degrees of freedom. Defaults to\nFalse. Requires confidence != None.</p></li>\n<li><p><strong>KS</strong> – calculates the Kolmogorov-Smirnov test, comparing the cumulative\ndistribution of the sample with the Benford’s, according to the\nconfidence level chosen. Defaults to False. Requires confidence\n!= None.</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Expected and Found proportions, and the Z scores of</dt><dd><p>the differences if the confidence is not None.</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.mantissas\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">mantissas</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">report</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">arc_test</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#mantissas\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.mantissas\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Extraxts the mantissas of the records logarithms</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence to compute mantissas from, numpy 1D array, pandas Series\nof pandas DataFrame column.</p></li>\n<li><p><strong>report</strong> – prints the mamtissas mean, variance, skewness and kurtosis\nfor the sequence studied, along with reference values.</p></li>\n<li><p><strong>show_plot</strong> – plots the ordered mantissas and a line with the expected\ninclination. Defaults to True.</p></li>\n<li><p><strong>arc_test</strong> – draws the Arc Test plot. Defaluts to True.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>Series with the data mantissas.</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.summation\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">summation</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">digs</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">top</span><span class=\"o\">=</span><span class=\"default_value\">20</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#summation\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.summation\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the Summation test. In a Benford series, the sums of the\nentries begining with the same digits tends to be the same.\nWorks only with the First Digits (1, 2 or 3) test.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>digs</strong> – tells the first digits to use: 1- first; 2- first two;\n3- first three. Defaults to 2.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>top</strong> – choses how many top values to show. Defaults to 20.</p></li>\n<li><p><strong>show_plot</strong> – plots the results. Defaults to True.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame with the Summation test, whether sorted in descending order</dt><dd><p>(if verbose == True) or not.</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.mad\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">mad</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#mad\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.mad\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Calculates the Mean Absolute Deviation of the Series</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – informs which base test to use for the mad.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>the Mean Absolute Deviation of the Series</p>\n</dd>\n<dt class=\"field-odd\">Return type</dt>\n<dd class=\"field-odd\"><p>float</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.mse\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">mse</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#mse\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.mse\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Calculates the Mean Squared Error of the Series</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – informs which base test to use for the mad.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>the Mean Squared Error of the Series</p>\n</dd>\n<dt class=\"field-odd\">Return type</dt>\n<dd class=\"field-odd\"><p>float</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.bhattacharyya_distance\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">bhattacharyya_distance</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#bhattacharyya_distance\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.bhattacharyya_distance\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the Bhattacharyya Distance between the Found and the Expected\n(Benford) digits distributions, according toe the test chosen\n(First, Second, First Two…)</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> (<em>ndarray</em><em>, </em><em>Series</em>) – sequence to be evaluated, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> (<em>int</em><em>, </em><em>str</em>) – informs which base test to be used.</p></li>\n<li><p><strong>decimals</strong> (<em>int</em>) – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> (<em>str</em><em>, </em><em>optional</em>) – tells which portion of the data to consider.\npos: only the positive entries; neg: only negative entries; all:\nall entries but zeros. Defaults to “all”.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>the Bhattacharyya Distance between the distributions</p>\n</dd>\n<dt class=\"field-odd\">Return type</dt>\n<dd class=\"field-odd\"><p>float</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.kullback_leibler_divergence\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">kullback_leibler_divergence</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#kullback_leibler_divergence\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.kullback_leibler_divergence\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the Kulback-Leibler Divergence between the Found and the\nExpected (Benford) digits distributions, according toe the test chosen\n(First, Second, First Two…).</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> (<em>ndarray</em><em>, </em><em>Series</em>) – sequence to be evaluated, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> (<em>int</em><em>, </em><em>str</em>) – informs which base test to be used.</p></li>\n<li><p><strong>decimals</strong> (<em>int</em>) – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> (<em>str</em><em>, </em><em>optional</em>) – tells which portion of the data to consider.\npos: only the positive entries; neg: only negative entries; all:\nall entries but zeros. Defaults to “all”.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>the Kulback-Leibler Divergence between the distributions</p>\n</dd>\n<dt class=\"field-odd\">Return type</dt>\n<dd class=\"field-odd\"><p>float</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.mad_summ\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">mad_summ</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#mad_summ\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.mad_summ\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Calculate the Mean Absolute Deviation of the Summation Test</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – informs which base test to use for the summation mad.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>the Mean Absolute Deviation of the Summation Test</p>\n</dd>\n<dt class=\"field-odd\">Return type</dt>\n<dd class=\"field-odd\"><p>float</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.rolling_mad\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">rolling_mad</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">window</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#rolling_mad\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.rolling_mad\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Applies the MAD to sequential subsets of the records.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</p></li>\n<li><p><strong>window</strong> – size of the subset to be used.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>Series with sequentially computed MADs.</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.rolling_mse\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">rolling_mse</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">window</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#rolling_mse\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.rolling_mse\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Applies the MSE to sequential subsets of the records.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – tells which test to use. 1: Fisrt Digits; 2: First Two Digits;\n3: First Three Digits; 22: Second Digit; and -2: Last Two Digits.</p></li>\n<li><p><strong>window</strong> – size of the subset to be used.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>Series with sequentially computed MSEs.</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.duplicates\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">duplicates</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">top_Rep</span><span class=\"o\">=</span><span class=\"default_value\">20</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#duplicates\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.duplicates\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs a duplicates test and maps the duplicates count in descending\norder.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence to take the duplicates from. pandas Series or\nnumpy Ndarray.</p></li>\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells how many duplicated entries were found and prints the\ntop numbers according to the top_Rep argument. Defaluts to True.</p></li>\n<li><p><strong>top_Rep</strong> – chooses how many duplicated entries will be\nshown withe the top repititions. int or None. Defaluts to 20.\nIf None, returns al the ordered repetitions.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>DataFrame with the duplicated records and their respective counts</p>\n</dd>\n<dt class=\"field-odd\">Raises</dt>\n<dd class=\"field-odd\"><p><strong>ValueError</strong> – if the <cite>top_Rep</cite> arg is not int or None.</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.benford.second_order\">\n<code class=\"sig-prename descclassname\">benford.benford.</code><code class=\"sig-name descname\">second_order</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">data</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">decimals</span><span class=\"o\">=</span><span class=\"default_value\">2</span></em>, <em class=\"sig-param\"><span class=\"n\">sign</span><span class=\"o\">=</span><span class=\"default_value\">'all'</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">MAD</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">high_Z</span><span class=\"o\">=</span><span class=\"default_value\">'pos'</span></em>, <em class=\"sig-param\"><span class=\"n\">limit_N</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">MSE</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">show_plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">inform</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/benford.html#second_order\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.benford.second_order\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Performs the chosen test after subtracting the ordered sequence by itself.\nHence Second Order.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>data</strong> – sequence of numbers to be evaluated. Must be a numpy 1D array,\na pandas Series or a pandas DataFrame column, with values being\nintegers or floats.</p></li>\n<li><p><strong>test</strong> – the test to be performed - 1 or ‘F1D’: First Digit; 2 or ‘F2D’:\nFirst Two Digits; 3 or ‘F3D’: First three Digits; 22 or ‘SD’:\nSecond Digits; -2 or ‘L2D’: Last Two Digits.</p></li>\n<li><p><strong>decimals</strong> – number of decimal places to consider. Defaluts to 2.\nIf integers, set to 0. If set to -infer-, it will remove the zeros\nand consider up to the fifth decimal place to the right, but will\nloose performance.</p></li>\n<li><p><strong>sign</strong> – tells which portion of the data to consider. pos: only the positive\nentries; neg: only negative entries; all: all entries but zeros.\nDefaults to all.</p></li>\n<li><p><strong>verbose</strong> (<em>bool</em>) – tells the number of registries that are being subjected to\nthe analysis and returns tha analysis DataFrame sorted by the\nhighest Z score down. Defaults to True.</p></li>\n<li><p><strong>MAD</strong> (<em>bool</em>) – calculates the Mean Absolute Difference between the\nfound and the expected distributions; defaults to False.</p></li>\n<li><p><strong>confidence</strong> (<em>int</em><em>, </em><em>float</em>) – confidence level to draw lower and upper limits when\nplotting and to limit the top deviations to show. Defaults to None.</p></li>\n<li><p><strong>high_Z</strong> (<em>int</em>) – chooses which Z scores to be used when displaying results,\naccording to the confidence level chosen. Defaluts to ‘pos’,\nwhich will highlight only values higher than the expexted\nfrequencies; ‘all’ will highlight both extremes (positive and\nnegative); and an integer, which will use the first n entries,\npositive and negative, regardless of whether Z is higher than\nthe confidence or not.</p></li>\n<li><p><strong>limit_N</strong> (<em>int</em>) – sets a limit to N as the sample size for the calculation of\nthe Z scores if the sample is too big. Defaults to None.</p></li>\n<li><p><strong>MSE</strong> (<em>bool</em>) – calculates the Mean Square Error of the sample; defaults to\nFalse.</p></li>\n<li><p><strong>chi_square</strong> – calculates the chi_square statistic of the sample and\ncompares it with a critical value, according to the confidence\nlevel chosen and the series’s degrees of freedom. Defaults to\nFalse. Requires confidence != None.</p></li>\n<li><p><strong>KS</strong> – calculates the Kolmogorov-Smirnov test, comparing the cumulative\ndistribution of the sample with the Benford’s, according to the\nconfidence level chosen. Defaults to False. Requires confidence\n!= None.</p></li>\n<li><p><strong>show_plot</strong> (<em>bool</em>) – draws the test plot.</p></li>\n<li><p><strong>save_plot</strong> (<em>str</em>) – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> (<em>dict</em>) – any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>DataFrame of the test chosen, but applied on Second Order pre-</dt><dd><p>processed data.</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n</div>\n<div class=\"section\" id=\"module-benford.expected\">\n<span id=\"benford-expected-module\"></span><h2>benford.expected module<a class=\"headerlink\" href=\"#module-benford.expected\" title=\"Permalink to this headline\">¶</a></h2>\n<dl class=\"py class\">\n<dt id=\"benford.expected.First\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.expected.</code><code class=\"sig-name descname\">First</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">digs</span></em>, <em class=\"sig-param\"><span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/expected.html#First\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.expected.First\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">pandas.core.frame.DataFrame</span></code></p>\n<p>Holds the expected probabilities of the First, First Two, or\nFirst Three digits according to Benford’s distribution.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>digs</strong> – 1, 2 or 3 - tells which of the first digits to consider:\n1 for the First Digit, 2 for the First Two Digits and 3 for\nthe First Three Digits.</p></li>\n<li><p><strong>plot</strong> – option to plot a bar chart of the Expected proportions.\nDefaults to True.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.expected.Second\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.expected.</code><code class=\"sig-name descname\">Second</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/expected.html#Second\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.expected.Second\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">pandas.core.frame.DataFrame</span></code></p>\n<p>Holds the expected probabilities of the Second Digits\naccording to Benford’s distribution.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>plot</strong> – option to plot a bar chart of the Expected proportions.\nDefaults to True.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py class\">\n<dt id=\"benford.expected.LastTwo\">\n<em class=\"property\">class </em><code class=\"sig-prename descclassname\">benford.expected.</code><code class=\"sig-name descname\">LastTwo</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">num</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">plot</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/expected.html#LastTwo\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.expected.LastTwo\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Bases: <code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">pandas.core.frame.DataFrame</span></code></p>\n<p>Holds the expected probabilities of the Last Two Digits\naccording to Benford’s distribution.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>plot</strong> – option to plot a bar chart of the Expected proportions.\nDefaults to True.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension. Only available when\nplot=True.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a>\nOnly available when plot=True and save_plot is a string with the\nfigure file path/name.</p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n</div>\n<div class=\"section\" id=\"module-benford.stats\">\n<span id=\"benford-stats-module\"></span><h2>benford.stats module<a class=\"headerlink\" href=\"#module-benford.stats\" title=\"Permalink to this headline\">¶</a></h2>\n<dl class=\"py function\">\n<dt id=\"benford.stats.Z_score\">\n<code class=\"sig-prename descclassname\">benford.stats.</code><code class=\"sig-name descname\">Z_score</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">frame</span></em>, <em class=\"sig-param\"><span class=\"n\">N</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/stats.html#Z_score\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.stats.Z_score\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the Z statistics for the proportions studied</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>frame</strong> – DataFrame with the expected proportions and the already calculated\nAbsolute Diferences between the found and expeccted proportions</p></li>\n<li><p><strong>N</strong> – sample size</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>Series of computed Z scores</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.stats.chi_sq\">\n<code class=\"sig-prename descclassname\">benford.stats.</code><code class=\"sig-name descname\">chi_sq</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">frame</span></em>, <em class=\"sig-param\"><span class=\"n\">ddf</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/stats.html#chi_sq\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.stats.chi_sq\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Comnputes the chi-square statistic of the found distributions and compares\nit with the critical chi-square of such a sample, according to the\nconfidence level chosen and the degrees of freedom - len(sample) -1.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>frame</strong> – DataFrame with Found, Expected and their difference columns.</p></li>\n<li><p><strong>ddf</strong> – Degrees of freedom to consider.</p></li>\n<li><p><strong>confidence</strong> – Confidence level to look up critical value.</p></li>\n<li><p><strong>verbose</strong> – prints the chi-squre result and compares to the critical\nchi-square for the sample. Defaults to True.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>The computed Chi square statistic and the critical chi square</dt><dd><p>(according) to the degrees of freedom and confidence level,\nfor comparison. None if confidence is None</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.stats.chi_sq_2\">\n<code class=\"sig-prename descclassname\">benford.stats.</code><code class=\"sig-name descname\">chi_sq_2</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">frame</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/stats.html#chi_sq_2\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.stats.chi_sq_2\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the chi-square statistic of the found distributions</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><p><strong>frame</strong> – DataFrame with Found, Expected and their difference columns.</p>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>The computed Chi square statistic</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.stats.kolmogorov_smirnov\">\n<code class=\"sig-prename descclassname\">benford.stats.</code><code class=\"sig-name descname\">kolmogorov_smirnov</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">frame</span></em>, <em class=\"sig-param\"><span class=\"n\">confidence</span></em>, <em class=\"sig-param\"><span class=\"n\">N</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/stats.html#kolmogorov_smirnov\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.stats.kolmogorov_smirnov\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the Kolmogorov-Smirnov test of the found distributions\nand compares it with the critical chi-square of such a sample,\naccording to the confidence level chosen.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>frame</strong> – DataFrame with Foud and Expected distributions.</p></li>\n<li><p><strong>confidence</strong> – Confidence level to look up critical value.</p></li>\n<li><p><strong>N</strong> – Sample size</p></li>\n<li><p><strong>verbose</strong> – prints the KS result and the critical value for the sample.\nDefaults to True.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>The Suprem, which is the greatest absolute difference between the</dt><dd><p>Found and the expected proportions, and the Kolmogorov-Smirnov\ncritical value according to the confidence level, for ccomparison</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.stats.kolmogorov_smirnov_2\">\n<code class=\"sig-prename descclassname\">benford.stats.</code><code class=\"sig-name descname\">kolmogorov_smirnov_2</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">frame</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/stats.html#kolmogorov_smirnov_2\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.stats.kolmogorov_smirnov_2\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the Kolmogorov-Smirnov test of the found distributions</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><p><strong>frame</strong> – DataFrame with Foud and Expected distributions.</p>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>The Suprem, which is the greatest absolute difference between the</dt><dd><p>Found end th expected proportions</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.stats.mad\">\n<code class=\"sig-prename descclassname\">benford.stats.</code><code class=\"sig-name descname\">mad</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">frame</span></em>, <em class=\"sig-param\"><span class=\"n\">test</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/stats.html#mad\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.stats.mad\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the Mean Absolute Deviation (MAD) between the found and the\nexpected proportions.</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>frame</strong> – DataFrame with the Absolute Deviations already calculated.</p></li>\n<li><p><strong>test</strong> – Test to compute the MAD from (F1D, SD, F2D…)</p></li>\n<li><p><strong>verbose</strong> – prints the MAD result and compares to limit values of\nconformity. Defaults to True.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p><dl class=\"simple\">\n<dt>The Mean of the Absolute Deviations between the found and expected</dt><dd><p>proportions.</p>\n</dd>\n</dl>\n</p>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.stats.mse\">\n<code class=\"sig-prename descclassname\">benford.stats.</code><code class=\"sig-name descname\">mse</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">frame</span></em>, <em class=\"sig-param\"><span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/stats.html#mse\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.stats.mse\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Computes the test’s Mean Square Error</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>frame</strong> – DataFrame with the already computed Absolute Deviations between\nthe found and expected proportions</p></li>\n<li><p><strong>verbose</strong> – Prints the MSE. Defaults to True.</p></li>\n</ul>\n</dd>\n<dt class=\"field-even\">Returns</dt>\n<dd class=\"field-even\"><p>Mean of the squared differences between the found and the expected proportions.</p>\n</dd>\n</dl>\n</dd></dl>\n\n</div>\n<div class=\"section\" id=\"module-benford.viz\">\n<span id=\"benford-viz-module\"></span><h2>benford.viz module<a class=\"headerlink\" href=\"#module-benford.viz\" title=\"Permalink to this headline\">¶</a></h2>\n<dl class=\"py function\">\n<dt id=\"benford.viz.plot_expected\">\n<code class=\"sig-prename descclassname\">benford.viz.</code><code class=\"sig-name descname\">plot_expected</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">df</span></em>, <em class=\"sig-param\"><span class=\"n\">digs</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/viz.html#plot_expected\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.viz.plot_expected\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Plots the Expected Benford Distributions</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>df</strong> – DataFrame with the Expected Proportions</p></li>\n<li><p><strong>digs</strong> – Test’s digit</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a></p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.viz.plot_digs\">\n<code class=\"sig-prename descclassname\">benford.viz.</code><code class=\"sig-name descname\">plot_digs</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">df</span></em>, <em class=\"sig-param\"><span class=\"n\">x</span></em>, <em class=\"sig-param\"><span class=\"n\">y_Exp</span></em>, <em class=\"sig-param\"><span class=\"n\">y_Found</span></em>, <em class=\"sig-param\"><span class=\"n\">N</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span></em>, <em class=\"sig-param\"><span class=\"n\">conf_Z</span></em>, <em class=\"sig-param\"><span class=\"n\">text_x</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/viz.html#plot_digs\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.viz.plot_digs\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Plots the digits tests results</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>df</strong> – DataFrame with the data to be plotted</p></li>\n<li><p><strong>x</strong> – sequence to be used in the x axis</p></li>\n<li><p><strong>y_Exp</strong> – sequence of the expected proportions to be used in the y axis\n(line)</p></li>\n<li><p><strong>y_Found</strong> – sequence of the found proportions to be used in the y axis\n(bars)</p></li>\n<li><p><strong>N</strong> – lenght of sequence, to be used when plotting the confidence levels</p></li>\n<li><p><strong>figsize</strong> – tuple to state the size of the plot figure</p></li>\n<li><p><strong>conf_Z</strong> – Confidence level</p></li>\n<li><p><strong>save_pic</strong> – file path to save figure</p></li>\n<li><p><strong>text_x</strong> – Forces to show all x ticks labels. Defaluts to True.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a></p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.viz.plot_sum\">\n<code class=\"sig-prename descclassname\">benford.viz.</code><code class=\"sig-name descname\">plot_sum</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">df</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span></em>, <em class=\"sig-param\"><span class=\"n\">li</span></em>, <em class=\"sig-param\"><span class=\"n\">text_x</span><span class=\"o\">=</span><span class=\"default_value\">False</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/viz.html#plot_sum\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.viz.plot_sum\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Plots the summation test results</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>df</strong> – DataFrame with the data to be plotted</p></li>\n<li><p><strong>figsize</strong> – sets the dimensions of the plot figure</p></li>\n<li><p><strong>li</strong> – value with which to draw the horizontal line</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a></p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.viz.plot_ordered_mantissas\">\n<code class=\"sig-prename descclassname\">benford.viz.</code><code class=\"sig-name descname\">plot_ordered_mantissas</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">col</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"default_value\">(12, 12)</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/viz.html#plot_ordered_mantissas\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.viz.plot_ordered_mantissas\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><dl class=\"simple\">\n<dt>Plots the ordered mantissas and compares them to the expected, straight</dt><dd><p>line that should be formed in a Benford-cmpliant set.</p>\n</dd>\n</dl>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>col</strong> (<em>Series</em>) – column of mantissas to plot.</p></li>\n<li><p><strong>figsize</strong> (<em>tuple</em>) – sets the dimensions of the plot figure.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a></p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.viz.plot_mantissa_arc_test\">\n<code class=\"sig-prename descclassname\">benford.viz.</code><code class=\"sig-name descname\">plot_mantissa_arc_test</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">df</span></em>, <em class=\"sig-param\"><span class=\"n\">gravity_center</span></em>, <em class=\"sig-param\"><span class=\"n\">grid</span><span class=\"o\">=</span><span class=\"default_value\">True</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"default_value\">12</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/viz.html#plot_mantissa_arc_test\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.viz.plot_mantissa_arc_test\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Draws thee Mantissa Arc Test after computing X and Y circular coordinates\nfor every mantissa and the center of gravity for the set</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>df</strong> (<em>DataFrame</em>) – pandas DataFrame with the mantissas and the X and Y\ncoordinates.</p></li>\n<li><p><strong>gravity_center</strong> (<em>tuple</em>) – coordinates for plottling the gravity center</p></li>\n<li><p><strong>grid</strong> (<em>bool</em>) – show grid. Defaults to True.</p></li>\n<li><p><strong>figsize</strong> (<em>int</em>) – figure dimensions. No need to be a tuple, since the\nfigure is a square.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a></p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.viz.plot_roll_mse\">\n<code class=\"sig-prename descclassname\">benford.viz.</code><code class=\"sig-name descname\">plot_roll_mse</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">roll_series</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/viz.html#plot_roll_mse\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.viz.plot_roll_mse\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Shows the rolling MSE plot</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>roll_series</strong> – pd.Series resultant form rolling mse.</p></li>\n<li><p><strong>figsize</strong> – the figure dimensions.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a></p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n<dl class=\"py function\">\n<dt id=\"benford.viz.plot_roll_mad\">\n<code class=\"sig-prename descclassname\">benford.viz.</code><code class=\"sig-name descname\">plot_roll_mad</code><span class=\"sig-paren\">(</span><em class=\"sig-param\"><span class=\"n\">roll_mad</span></em>, <em class=\"sig-param\"><span class=\"n\">figsize</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em>, <em class=\"sig-param\"><span class=\"n\">save_plot_kwargs</span><span class=\"o\">=</span><span class=\"default_value\">None</span></em><span class=\"sig-paren\">)</span><a class=\"reference internal\" href=\"_modules/benford/viz.html#plot_roll_mad\"><span class=\"viewcode-link\">[source]</span></a><a class=\"headerlink\" href=\"#benford.viz.plot_roll_mad\" title=\"Permalink to this definition\">¶</a></dt>\n<dd><p>Shows the rolling MAD plot</p>\n<dl class=\"field-list simple\">\n<dt class=\"field-odd\">Parameters</dt>\n<dd class=\"field-odd\"><ul class=\"simple\">\n<li><p><strong>roll_mad</strong> – pd.Series resultant form rolling mad.</p></li>\n<li><p><strong>figsize</strong> – the figure dimensions.</p></li>\n<li><p><strong>save_plot</strong> – string with the path/name of the file in which the generated\nplot will be saved. Uses matplotlib.pyplot.savefig(). File format\nis infered by the file name extension.</p></li>\n<li><p><strong>save_plot_kwargs</strong> – dict with any of the kwargs accepted by\nmatplotlib.pyplot.savefig()\n<a class=\"reference external\" href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html</a></p></li>\n</ul>\n</dd>\n</dl>\n</dd></dl>\n\n</div>\n</div>\n\n\n           </div>\n           \n          </div>\n          <footer>\n    <div class=\"rst-footer-buttons\" role=\"navigation\" aria-label=\"footer navigation\">\n        <a href=\"modules.html\" class=\"btn btn-neutral float-left\" title=\"benford\" accesskey=\"p\" rel=\"prev\"><span class=\"fa fa-arrow-circle-left\" aria-hidden=\"true\"></span> Previous</a>\n    </div>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/genindex.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>Index &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"./\" src=\"_static/documentation_options.js\"></script>\n        <script src=\"_static/jquery.js\"></script>\n        <script src=\"_static/underscore.js\"></script>\n        <script src=\"_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"#\" />\n    <link rel=\"search\" title=\"Search\" href=\"search.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n      <li>Index</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n          \n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n\n<h1 id=\"index\">Index</h1>\n\n<div class=\"genindex-jumpbox\">\n <a href=\"#A\"><strong>A</strong></a>\n | <a href=\"#B\"><strong>B</strong></a>\n | <a href=\"#C\"><strong>C</strong></a>\n | <a href=\"#D\"><strong>D</strong></a>\n | <a href=\"#F\"><strong>F</strong></a>\n | <a href=\"#K\"><strong>K</strong></a>\n | <a href=\"#L\"><strong>L</strong></a>\n | <a href=\"#M\"><strong>M</strong></a>\n | <a href=\"#N\"><strong>N</strong></a>\n | <a href=\"#P\"><strong>P</strong></a>\n | <a href=\"#R\"><strong>R</strong></a>\n | <a href=\"#S\"><strong>S</strong></a>\n | <a href=\"#T\"><strong>T</strong></a>\n | <a href=\"#U\"><strong>U</strong></a>\n | <a href=\"#V\"><strong>V</strong></a>\n | <a href=\"#Z\"><strong>Z</strong></a>\n \n</div>\n<h2 id=\"A\">A</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Benford.all_confidences\">all_confidences() (benford.benford.Benford property)</a>\n</li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Mantissas.arc_test\">arc_test() (benford.benford.Mantissas method)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"B\">B</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Benford.base\">base (benford.benford.Benford attribute)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Base\">Base (class in benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Benford\">Benford (class in benford.benford)</a>\n</li>\n      <li>\n    benford.benford\n\n      <ul>\n        <li><a href=\"api.html#module-benford.benford\">module</a>\n</li>\n      </ul></li>\n      <li>\n    benford.expected\n\n      <ul>\n        <li><a href=\"api.html#module-benford.expected\">module</a>\n</li>\n      </ul></li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li>\n    benford.stats\n\n      <ul>\n        <li><a href=\"api.html#module-benford.stats\">module</a>\n</li>\n      </ul></li>\n      <li>\n    benford.viz\n\n      <ul>\n        <li><a href=\"api.html#module-benford.viz\">module</a>\n</li>\n      </ul></li>\n      <li><a href=\"api.html#benford.benford.bhattacharyya_distance\">bhattacharyya_distance() (in module benford.benford)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"C\">C</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.stats.chi_sq\">chi_sq() (in module benford.stats)</a>\n</li>\n      <li><a href=\"api.html#benford.stats.chi_sq_2\">chi_sq_2() (in module benford.stats)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Test.chi_square\">chi_square (benford.benford.Test attribute)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Benford.chosen\">chosen (benford.benford.Benford attribute)</a>\n</li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Benford.confidence\">confidence (benford.benford.Benford attribute)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Summ.confidence\">(benford.benford.Summ attribute)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.Test.confidence\">(benford.benford.Test attribute)</a>\n</li>\n      </ul></li>\n      <li><a href=\"api.html#benford.benford.Test.critical_values\">critical_values() (benford.benford.Test property)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"D\">D</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Benford.data\">data (benford.benford.Benford attribute)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Mantissas.data\">(benford.benford.Mantissas attribute)</a>\n</li>\n      </ul></li>\n      <li><a href=\"api.html#benford.benford.Test.ddf\">ddf (benford.benford.Test attribute)</a>\n</li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Test.digs\">digs (benford.benford.Test attribute)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Source.duplicates\">duplicates() (benford.benford.Source method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.duplicates\">(in module benford.benford)</a>\n</li>\n      </ul></li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"F\">F</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.expected.First\">First (class in benford.expected)</a>\n</li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Source.first_digits\">first_digits() (benford.benford.Source method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.first_digits\">(in module benford.benford)</a>\n</li>\n      </ul></li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"K\">K</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.stats.kolmogorov_smirnov\">kolmogorov_smirnov() (in module benford.stats)</a>\n</li>\n      <li><a href=\"api.html#benford.stats.kolmogorov_smirnov_2\">kolmogorov_smirnov_2() (in module benford.stats)</a>\n</li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Test.KS\">KS (benford.benford.Test attribute)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.kullback_leibler_divergence\">kullback_leibler_divergence() (in module benford.benford)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"L\">L</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Source.last_two_digits\">last_two_digits() (benford.benford.Source method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.last_two_digits\">(in module benford.benford)</a>\n</li>\n      </ul></li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.expected.LastTwo\">LastTwo (class in benford.expected)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Benford.limit_N\">limit_N (benford.benford.Benford attribute)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"M\">M</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Summ.MAD\">MAD (benford.benford.Summ attribute)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Test.MAD\">(benford.benford.Test attribute)</a>\n</li>\n      </ul></li>\n      <li><a href=\"api.html#benford.benford.mad\">mad() (in module benford.benford)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.stats.mad\">(in module benford.stats)</a>\n</li>\n      </ul></li>\n      <li><a href=\"api.html#benford.benford.mad_summ\">mad_summ() (in module benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Mantissas\">Mantissas (class in benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Benford.mantissas\">mantissas() (benford.benford.Benford method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Source.mantissas\">(benford.benford.Source method)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.mantissas\">(in module benford.benford)</a>\n</li>\n      </ul></li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li>\n    module\n\n      <ul>\n        <li><a href=\"api.html#module-benford.benford\">benford.benford</a>\n</li>\n        <li><a href=\"api.html#module-benford.expected\">benford.expected</a>\n</li>\n        <li><a href=\"api.html#module-benford.stats\">benford.stats</a>\n</li>\n        <li><a href=\"api.html#module-benford.viz\">benford.viz</a>\n</li>\n      </ul></li>\n      <li><a href=\"api.html#benford.benford.mse\">mse() (in module benford.benford)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.stats.mse\">(in module benford.stats)</a>\n</li>\n      </ul></li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"N\">N</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Test.N\">N (benford.benford.Test attribute)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"P\">P</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.viz.plot_digs\">plot_digs() (in module benford.viz)</a>\n</li>\n      <li><a href=\"api.html#benford.viz.plot_expected\">plot_expected() (in module benford.viz)</a>\n</li>\n      <li><a href=\"api.html#benford.viz.plot_mantissa_arc_test\">plot_mantissa_arc_test() (in module benford.viz)</a>\n</li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.viz.plot_ordered_mantissas\">plot_ordered_mantissas() (in module benford.viz)</a>\n</li>\n      <li><a href=\"api.html#benford.viz.plot_roll_mad\">plot_roll_mad() (in module benford.viz)</a>\n</li>\n      <li><a href=\"api.html#benford.viz.plot_roll_mse\">plot_roll_mse() (in module benford.viz)</a>\n</li>\n      <li><a href=\"api.html#benford.viz.plot_sum\">plot_sum() (in module benford.viz)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"R\">R</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Mantissas.report\">report() (benford.benford.Mantissas method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Summ.report\">(benford.benford.Summ method)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.Test.report\">(benford.benford.Test method)</a>\n</li>\n      </ul></li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Roll_mad\">Roll_mad (class in benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Roll_mse\">Roll_mse (class in benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.rolling_mad\">rolling_mad() (in module benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.rolling_mse\">rolling_mse() (in module benford.benford)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"S\">S</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Test.sec_order\">sec_order (benford.benford.Test attribute)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Benford.sec_order\">sec_order() (benford.benford.Benford method)</a>\n</li>\n      <li><a href=\"api.html#benford.expected.Second\">Second (class in benford.expected)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Source.second_digit\">second_digit() (benford.benford.Source method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.second_digit\">(in module benford.benford)</a>\n</li>\n      </ul></li>\n      <li><a href=\"api.html#benford.benford.second_order\">second_order() (in module benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Mantissas.show_plot\">show_plot() (benford.benford.Mantissas method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Roll_mad.show_plot\">(benford.benford.Roll_mad method)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.Roll_mse.show_plot\">(benford.benford.Roll_mse method)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.Summ.show_plot\">(benford.benford.Summ method)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.Test.show_plot\">(benford.benford.Test method)</a>\n</li>\n      </ul></li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Benford.sign\">sign (benford.benford.Benford attribute)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Source\">Source (class in benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Mantissas.stats\">stats() (benford.benford.Mantissas property)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Summ\">Summ (class in benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Benford.summation\">summation() (benford.benford.Benford method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Source.summation\">(benford.benford.Source method)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.summation\">(in module benford.benford)</a>\n</li>\n      </ul></li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"T\">T</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Roll_mad.test\">test (benford.benford.Roll_mad attribute)</a>\n</li>\n  </ul></td>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Test\">Test (class in benford.benford)</a>\n</li>\n      <li><a href=\"api.html#benford.benford.Benford.tests\">tests (benford.benford.Benford attribute)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"U\">U</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Benford.update_confidence\">update_confidence() (benford.benford.Benford method)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Mantissas.update_confidence\">(benford.benford.Mantissas method)</a>\n</li>\n        <li><a href=\"api.html#benford.benford.Test.update_confidence\">(benford.benford.Test method)</a>\n</li>\n      </ul></li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"V\">V</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.benford.Benford.verbose\">verbose (benford.benford.Benford attribute)</a>\n\n      <ul>\n        <li><a href=\"api.html#benford.benford.Source.verbose\">(benford.benford.Source attribute)</a>\n</li>\n      </ul></li>\n  </ul></td>\n</tr></table>\n\n<h2 id=\"Z\">Z</h2>\n<table style=\"width: 100%\" class=\"indextable genindextable\"><tr>\n  <td style=\"width: 33%; vertical-align: top;\"><ul>\n      <li><a href=\"api.html#benford.stats.Z_score\">Z_score() (in module benford.stats)</a>\n</li>\n  </ul></td>\n</tr></table>\n\n\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/index.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>Welcome to benford_py’s documentation! &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"./\" src=\"_static/documentation_options.js\"></script>\n        <script src=\"_static/jquery.js\"></script>\n        <script src=\"_static/underscore.js\"></script>\n        <script src=\"_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"search.html\" />\n    <link rel=\"next\" title=\"benford\" href=\"modules.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"#\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"#\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"#\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n      <li>Welcome to benford_py’s documentation!</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n          \n            <a href=\"_sources/index.rst.txt\" rel=\"nofollow\"> View page source</a>\n          \n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <div class=\"section\" id=\"welcome-to-benford-py-s-documentation\">\n<h1>Welcome to benford_py’s documentation!<a class=\"headerlink\" href=\"#welcome-to-benford-py-s-documentation\" title=\"Permalink to this headline\">¶</a></h1>\n<div class=\"toctree-wrapper compound\">\n<p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"modules.html\">benford</a><ul>\n<li class=\"toctree-l2\"><a class=\"reference internal\" href=\"api.html\">benford package</a><ul>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"api.html#module-benford.benford\">benford.benford module</a></li>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"api.html#module-benford.expected\">benford.expected module</a></li>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"api.html#module-benford.stats\">benford.stats module</a></li>\n<li class=\"toctree-l3\"><a class=\"reference internal\" href=\"api.html#module-benford.viz\">benford.viz module</a></li>\n</ul>\n</li>\n</ul>\n</li>\n</ul>\n</div>\n</div>\n<div class=\"section\" id=\"indices-and-tables\">\n<h1>Indices and tables<a class=\"headerlink\" href=\"#indices-and-tables\" title=\"Permalink to this headline\">¶</a></h1>\n<ul class=\"simple\">\n<li><p><a class=\"reference internal\" href=\"genindex.html\"><span class=\"std std-ref\">Index</span></a></p></li>\n<li><p><a class=\"reference internal\" href=\"py-modindex.html\"><span class=\"std std-ref\">Module Index</span></a></p></li>\n<li><p><a class=\"reference internal\" href=\"search.html\"><span class=\"std std-ref\">Search Page</span></a></p></li>\n</ul>\n<div class=\"section\" id=\"on-github\">\n<h2>On GitHub<a class=\"headerlink\" href=\"#on-github\" title=\"Permalink to this headline\">¶</a></h2>\n<div class=\"section\" id=\"id1\">\n<h3><a class=\"reference external\" href=\"https://github.com/milcent/benford_py\">Package</a><a class=\"headerlink\" href=\"#id1\" title=\"Permalink to this headline\">¶</a></h3>\n</div>\n<div class=\"section\" id=\"id2\">\n<h3><a class=\"reference external\" href=\"https://github.com/milcent/benford_py/blob/master/Demo.ipynb\">Demo Jupyter Notebook</a><a class=\"headerlink\" href=\"#id2\" title=\"Permalink to this headline\">¶</a></h3>\n</div>\n</div>\n</div>\n\n\n           </div>\n           \n          </div>\n          <footer>\n    <div class=\"rst-footer-buttons\" role=\"navigation\" aria-label=\"footer navigation\">\n        <a href=\"modules.html\" class=\"btn btn-neutral float-right\" title=\"benford\" accesskey=\"n\" rel=\"next\">Next <span class=\"fa fa-arrow-circle-right\" aria-hidden=\"true\"></span></a>\n    </div>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/modules.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>benford &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"./\" src=\"_static/documentation_options.js\"></script>\n        <script src=\"_static/jquery.js\"></script>\n        <script src=\"_static/underscore.js\"></script>\n        <script src=\"_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"search.html\" />\n    <link rel=\"next\" title=\"benford package\" href=\"api.html\" />\n    <link rel=\"prev\" title=\"Welcome to benford_py’s documentation!\" href=\"index.html\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul class=\"current\">\n<li class=\"toctree-l1 current\"><a class=\"current reference internal\" href=\"#\">benford</a><ul>\n<li class=\"toctree-l2\"><a class=\"reference internal\" href=\"api.html\">benford package</a></li>\n</ul>\n</li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n      <li>benford</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n          \n            <a href=\"_sources/modules.rst.txt\" rel=\"nofollow\"> View page source</a>\n          \n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <div class=\"section\" id=\"benford\">\n<h1>benford<a class=\"headerlink\" href=\"#benford\" title=\"Permalink to this headline\">¶</a></h1>\n<div class=\"toctree-wrapper compound\">\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"api.html\">benford package</a><ul>\n<li class=\"toctree-l2\"><a class=\"reference internal\" href=\"api.html#module-benford.benford\">benford.benford module</a></li>\n<li class=\"toctree-l2\"><a class=\"reference internal\" href=\"api.html#module-benford.expected\">benford.expected module</a></li>\n<li class=\"toctree-l2\"><a class=\"reference internal\" href=\"api.html#module-benford.stats\">benford.stats module</a></li>\n<li class=\"toctree-l2\"><a class=\"reference internal\" href=\"api.html#module-benford.viz\">benford.viz module</a></li>\n</ul>\n</li>\n</ul>\n</div>\n</div>\n\n\n           </div>\n           \n          </div>\n          <footer>\n    <div class=\"rst-footer-buttons\" role=\"navigation\" aria-label=\"footer navigation\">\n        <a href=\"api.html\" class=\"btn btn-neutral float-right\" title=\"benford package\" accesskey=\"n\" rel=\"next\">Next <span class=\"fa fa-arrow-circle-right\" aria-hidden=\"true\"></span></a>\n        <a href=\"index.html\" class=\"btn btn-neutral float-left\" title=\"Welcome to benford_py’s documentation!\" accesskey=\"p\" rel=\"prev\"><span class=\"fa fa-arrow-circle-left\" aria-hidden=\"true\"></span> Previous</a>\n    </div>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/py-modindex.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>Python Module Index &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n  <!--[if lt IE 9]>\n    <script src=\"_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"./\" src=\"_static/documentation_options.js\"></script>\n        <script src=\"_static/jquery.js\"></script>\n        <script src=\"_static/underscore.js\"></script>\n        <script src=\"_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"_static/js/theme.js\"></script>\n\n    \n    <link rel=\"index\" title=\"Index\" href=\"genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"search.html\" />\n \n\n\n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"search.html\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n      <li>Python Module Index</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n\n   <h1>Python Module Index</h1>\n\n   <div class=\"modindex-jumpbox\">\n   <a href=\"#cap-b\"><strong>b</strong></a>\n   </div>\n\n   <table class=\"indextable modindextable\">\n     <tr class=\"pcap\"><td></td><td>&#160;</td><td></td></tr>\n     <tr class=\"cap\" id=\"cap-b\"><td></td><td>\n       <strong>b</strong></td><td></td></tr>\n     <tr>\n       <td><img src=\"_static/minus.png\" class=\"toggler\"\n              id=\"toggle-1\" style=\"display: none\" alt=\"-\" /></td>\n       <td>\n       <code class=\"xref\">benford</code></td><td>\n       <em></em></td></tr>\n     <tr class=\"cg-1\">\n       <td></td>\n       <td>&#160;&#160;&#160;\n       <a href=\"api.html#module-benford.benford\"><code class=\"xref\">benford.benford</code></a></td><td>\n       <em></em></td></tr>\n     <tr class=\"cg-1\">\n       <td></td>\n       <td>&#160;&#160;&#160;\n       <a href=\"api.html#module-benford.expected\"><code class=\"xref\">benford.expected</code></a></td><td>\n       <em></em></td></tr>\n     <tr class=\"cg-1\">\n       <td></td>\n       <td>&#160;&#160;&#160;\n       <a href=\"api.html#module-benford.stats\"><code class=\"xref\">benford.stats</code></a></td><td>\n       <em></em></td></tr>\n     <tr class=\"cg-1\">\n       <td></td>\n       <td>&#160;&#160;&#160;\n       <a href=\"api.html#module-benford.viz\"><code class=\"xref\">benford.viz</code></a></td><td>\n       <em></em></td></tr>\n   </table>\n\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n   \n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/search.html",
    "content": "\n\n<!DOCTYPE html>\n<html class=\"writer-html5\" lang=\"en\" >\n<head>\n  <meta charset=\"utf-8\" />\n  \n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n  \n  <title>Search &mdash; benford_py 0.3.3 documentation</title>\n  \n\n  \n  <link rel=\"stylesheet\" href=\"_static/css/theme.css\" type=\"text/css\" />\n  <link rel=\"stylesheet\" href=\"_static/pygments.css\" type=\"text/css\" />\n\n  \n  \n\n  \n  \n\n  \n\n  \n    \n  <!--[if lt IE 9]>\n    <script src=\"_static/js/html5shiv.min.js\"></script>\n  <![endif]-->\n  \n    \n      <script type=\"text/javascript\" id=\"documentation_options\" data-url_root=\"./\" src=\"_static/documentation_options.js\"></script>\n        <script src=\"_static/jquery.js\"></script>\n        <script src=\"_static/underscore.js\"></script>\n        <script src=\"_static/doctools.js\"></script>\n    \n    <script type=\"text/javascript\" src=\"_static/js/theme.js\"></script>\n\n    \n    <script type=\"text/javascript\" src=\"_static/searchtools.js\"></script>\n    <script type=\"text/javascript\" src=\"_static/language_data.js\"></script>\n    <link rel=\"index\" title=\"Index\" href=\"genindex.html\" />\n    <link rel=\"search\" title=\"Search\" href=\"#\" /> \n</head>\n\n<body class=\"wy-body-for-nav\">\n\n   \n  <div class=\"wy-grid-for-nav\">\n    \n    <nav data-toggle=\"wy-nav-shift\" class=\"wy-nav-side\">\n      <div class=\"wy-side-scroll\">\n        <div class=\"wy-side-nav-search\" >\n          \n\n          \n            <a href=\"index.html\" class=\"icon icon-home\"> benford_py\n          \n\n          \n          </a>\n\n          \n            \n            \n          \n\n          \n<div role=\"search\">\n  <form id=\"rtd-search-form\" class=\"wy-form\" action=\"#\" method=\"get\">\n    <input type=\"text\" name=\"q\" placeholder=\"Search docs\" />\n    <input type=\"hidden\" name=\"check_keywords\" value=\"yes\" />\n    <input type=\"hidden\" name=\"area\" value=\"default\" />\n  </form>\n</div>\n\n          \n        </div>\n\n        \n        <div class=\"wy-menu wy-menu-vertical\" data-spy=\"affix\" role=\"navigation\" aria-label=\"main navigation\">\n          \n            \n            \n              \n            \n            \n              <p class=\"caption\"><span class=\"caption-text\">Contents:</span></p>\n<ul>\n<li class=\"toctree-l1\"><a class=\"reference internal\" href=\"modules.html\">benford</a></li>\n</ul>\n\n            \n          \n        </div>\n        \n      </div>\n    </nav>\n\n    <section data-toggle=\"wy-nav-shift\" class=\"wy-nav-content-wrap\">\n\n      \n      <nav class=\"wy-nav-top\" aria-label=\"top navigation\">\n        \n          <i data-toggle=\"wy-nav-top\" class=\"fa fa-bars\"></i>\n          <a href=\"index.html\">benford_py</a>\n        \n      </nav>\n\n\n      <div class=\"wy-nav-content\">\n        \n        <div class=\"rst-content\">\n        \n          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<div role=\"navigation\" aria-label=\"breadcrumbs navigation\">\n\n  <ul class=\"wy-breadcrumbs\">\n    \n      <li><a href=\"index.html\" class=\"icon icon-home\"></a> &raquo;</li>\n        \n      <li>Search</li>\n    \n    \n      <li class=\"wy-breadcrumbs-aside\">\n        \n      </li>\n    \n  </ul>\n\n  \n  <hr/>\n</div>\n          <div role=\"main\" class=\"document\" itemscope=\"itemscope\" itemtype=\"http://schema.org/Article\">\n           <div itemprop=\"articleBody\">\n            \n  <noscript>\n  <div id=\"fallback\" class=\"admonition warning\">\n    <p class=\"last\">\n      Please activate JavaScript to enable the search functionality.\n    </p>\n  </div>\n  </noscript>\n\n  \n  <div id=\"search-results\">\n  \n  </div>\n\n           </div>\n           \n          </div>\n          <footer>\n\n  <hr/>\n\n  <div role=\"contentinfo\">\n    <p>\n        &#169; Copyright 2020, Marcel Milcent.\n\n    </p>\n  </div>\n    \n    \n    \n    Built with <a href=\"https://www.sphinx-doc.org/\">Sphinx</a> using a\n    \n    <a href=\"https://github.com/readthedocs/sphinx_rtd_theme\">theme</a>\n    \n    provided by <a href=\"https://readthedocs.org\">Read the Docs</a>. \n\n</footer>\n        </div>\n      </div>\n\n    </section>\n\n  </div>\n  \n\n  <script type=\"text/javascript\">\n      jQuery(function () {\n          SphinxRtdTheme.Navigation.enable(true);\n      });\n  </script>\n\n  \n  \n    \n  \n  <script type=\"text/javascript\">\n    jQuery(function() { Search.loadIndex(\"searchindex.js\"); });\n  </script>\n  \n  <script type=\"text/javascript\" id=\"searchindexloader\"></script>\n   \n\n\n</body>\n</html>"
  },
  {
    "path": "docs/build/html/searchindex.js",
    "content": "Search.setIndex({docnames:[\"api\",\"index\",\"modules\"],envversion:{\"sphinx.domains.c\":2,\"sphinx.domains.changeset\":1,\"sphinx.domains.citation\":1,\"sphinx.domains.cpp\":3,\"sphinx.domains.index\":1,\"sphinx.domains.javascript\":2,\"sphinx.domains.math\":2,\"sphinx.domains.python\":2,\"sphinx.domains.rst\":2,\"sphinx.domains.std\":1,\"sphinx.ext.viewcode\":1,sphinx:56},filenames:[\"api.rst\",\"index.rst\",\"modules.rst\"],objects:{\"benford.benford\":{Base:[0,1,1,\"\"],Benford:[0,1,1,\"\"],Mantissas:[0,1,1,\"\"],Roll_mad:[0,1,1,\"\"],Roll_mse:[0,1,1,\"\"],Source:[0,1,1,\"\"],Summ:[0,1,1,\"\"],Test:[0,1,1,\"\"],bhattacharyya_distance:[0,4,1,\"\"],duplicates:[0,4,1,\"\"],first_digits:[0,4,1,\"\"],kullback_leibler_divergence:[0,4,1,\"\"],last_two_digits:[0,4,1,\"\"],mad:[0,4,1,\"\"],mad_summ:[0,4,1,\"\"],mantissas:[0,4,1,\"\"],mse:[0,4,1,\"\"],rolling_mad:[0,4,1,\"\"],rolling_mse:[0,4,1,\"\"],second_digit:[0,4,1,\"\"],second_order:[0,4,1,\"\"],summation:[0,4,1,\"\"]},\"benford.benford.Benford\":{all_confidences:[0,2,1,\"\"],base:[0,3,1,\"\"],chosen:[0,3,1,\"\"],confidence:[0,3,1,\"\"],data:[0,3,1,\"\"],limit_N:[0,3,1,\"\"],mantissas:[0,2,1,\"\"],sec_order:[0,2,1,\"\"],sign:[0,3,1,\"\"],summation:[0,2,1,\"\"],tests:[0,3,1,\"\"],update_confidence:[0,2,1,\"\"],verbose:[0,3,1,\"\"]},\"benford.benford.Mantissas\":{arc_test:[0,2,1,\"\"],data:[0,3,1,\"\"],report:[0,2,1,\"\"],show_plot:[0,2,1,\"\"],stats:[0,2,1,\"\"],update_confidence:[0,2,1,\"\"]},\"benford.benford.Roll_mad\":{show_plot:[0,2,1,\"\"],test:[0,3,1,\"\"]},\"benford.benford.Roll_mse\":{show_plot:[0,2,1,\"\"]},\"benford.benford.Source\":{duplicates:[0,2,1,\"\"],first_digits:[0,2,1,\"\"],last_two_digits:[0,2,1,\"\"],mantissas:[0,2,1,\"\"],second_digit:[0,2,1,\"\"],summation:[0,2,1,\"\"],verbose:[0,3,1,\"\"]},\"benford.benford.Summ\":{MAD:[0,3,1,\"\"],confidence:[0,3,1,\"\"],report:[0,2,1,\"\"],show_plot:[0,2,1,\"\"]},\"benford.benford.Test\":{KS:[0,3,1,\"\"],MAD:[0,3,1,\"\"],N:[0,3,1,\"\"],chi_square:[0,3,1,\"\"],confidence:[0,3,1,\"\"],critical_values:[0,2,1,\"\"],ddf:[0,3,1,\"\"],digs:[0,3,1,\"\"],report:[0,2,1,\"\"],sec_order:[0,3,1,\"\"],show_plot:[0,2,1,\"\"],update_confidence:[0,2,1,\"\"]},\"benford.expected\":{First:[0,1,1,\"\"],LastTwo:[0,1,1,\"\"],Second:[0,1,1,\"\"]},\"benford.stats\":{Z_score:[0,4,1,\"\"],chi_sq:[0,4,1,\"\"],chi_sq_2:[0,4,1,\"\"],kolmogorov_smirnov:[0,4,1,\"\"],kolmogorov_smirnov_2:[0,4,1,\"\"],mad:[0,4,1,\"\"],mse:[0,4,1,\"\"]},\"benford.viz\":{plot_digs:[0,4,1,\"\"],plot_expected:[0,4,1,\"\"],plot_mantissa_arc_test:[0,4,1,\"\"],plot_ordered_mantissas:[0,4,1,\"\"],plot_roll_mad:[0,4,1,\"\"],plot_roll_mse:[0,4,1,\"\"],plot_sum:[0,4,1,\"\"]},benford:{benford:[0,0,0,\"-\"],expected:[0,0,0,\"-\"],stats:[0,0,0,\"-\"],viz:[0,0,0,\"-\"]}},objnames:{\"0\":[\"py\",\"module\",\"Python module\"],\"1\":[\"py\",\"class\",\"Python class\"],\"2\":[\"py\",\"method\",\"Python method\"],\"3\":[\"py\",\"attribute\",\"Python attribute\"],\"4\":[\"py\",\"function\",\"Python function\"]},objtypes:{\"0\":\"py:module\",\"1\":\"py:class\",\"2\":\"py:method\",\"3\":\"py:attribute\",\"4\":\"py:function\"},terms:{\"case\":0,\"class\":0,\"default\":0,\"float\":0,\"function\":0,\"int\":0,\"new\":0,\"return\":0,\"true\":0,The:0,Uses:0,_as_gen:0,about:0,absolut:0,accept:0,accord:0,add:0,after:0,aldo:0,all:0,all_confid:0,along:0,alreadi:0,also:0,analys:0,analysi:0,ani:0,anoth:0,api:0,appli:0,applic:0,arc:0,arc_test:0,arg:0,argument:0,arrai:0,atribut:0,attribut:0,avail:0,axi:0,bar:0,base:0,befor:0,beford:0,begin:0,being:0,benford:1,between:0,bhat_coeff:0,bhat_dist:0,bhattacharrya:[],bhattacharyya:0,bhattacharyya_dist:0,big:0,bool:0,both:0,calcul:0,call:0,can:0,capabl:0,ccomparison:0,center:0,chart:0,check:0,chi:0,chi_sq:0,chi_sq_2:0,chi_squar:0,choic:0,choos:0,chose:0,chosen:0,circl:0,circular:0,cmpliant:0,coeffici:0,col:0,column:0,comnput:0,compar:0,comparison:0,compliant:0,comput:0,conf_z:0,confid:0,conform:0,consid:0,content:1,coordin:0,core:0,count:0,creat:0,critic:0,critical_valu:0,cumput:0,cumul:0,current:0,data:0,datafram:0,ddf:0,decim:0,defalut:0,defaulst:0,defaut:0,degre:0,descend:0,deviat:0,dict:0,dictionari:0,difer:0,differ:0,dig:0,digit:0,dimens:0,discard:0,displai:0,distanc:0,distribut:0,diverg:0,down:0,draw:0,duplic:0,each:0,element:0,end:0,entri:0,equal:0,error:0,especif:0,evalu:0,everi:0,expecct:0,expect:[1,2],expext:0,extens:0,extraxt:0,extrem:0,f1d:0,f2d:0,f3d:0,fals:0,fasl:0,fifth:0,figsiz:0,figur:0,file:0,first:0,first_digit:0,fisrt:0,follow:0,forc:0,form:0,format:0,foud:0,foudn:[],found:0,frame:0,freedom:0,frequenc:0,from:0,furst:0,futur:0,gener:0,get:0,give:0,given:0,graviti:0,gravity_cent:0,greatest:0,grid:0,hand:0,handl:0,has:0,have:0,henc:0,high_diff:0,high_z:0,higher:0,highest:0,highlight:0,hold:0,horizont:0,how:0,html:0,http:0,inclin:0,includ:0,index:1,infer:0,inform:0,initi:0,input:0,instanc:0,integ:0,intern:0,its:0,itself:0,keep:0,kl_diverg:0,kolmogorov:0,kolmogorov_smirnov:0,kolmogorov_smirnov_2:0,kulback:0,kullback_leibler_diverg:0,kurtosi:0,kwarg:0,l2d:0,label:0,laibler:0,last:0,last_two_digit:0,lasttwo:0,leibler:0,len:0,lenght:0,level:0,like:0,limit:0,limit_n:0,line:0,list:0,logarithm:0,look:0,loos:0,lower:0,mad:0,mad_summ:0,mamtissa:0,mani:0,mantissa:0,map:0,matplotlib:0,mean:0,method:0,minu:0,modul:[1,2],mse:0,must:0,name:0,ndarrai:0,need:0,neg:0,new_conf:0,none:0,num:0,number:0,numer:0,numpi:0,obj:0,object:0,occurr:0,ocurr:0,one:0,onli:0,opt:0,option:0,order:0,org:0,origin:0,other:0,output:0,packag:2,page:1,panda:0,paramet:0,path:0,perform:0,place:0,plot:0,plot_dig:0,plot_expect:0,plot_mantissa_arc_test:0,plot_ordered_mantissa:0,plot_roll_mad:0,plot_roll_ms:0,plot_sum:0,plottl:0,popul:0,portion:0,pos:0,posit:0,pre:0,prepar:0,print:0,probabl:0,process:0,produc:0,properti:0,proport:0,provid:0,pyplot:0,rais:0,raw:0,receiv:0,record:0,reduc:0,refer:0,regardless:0,registri:0,relev:[],remov:0,repetit:0,repitit:0,report:0,represent:0,requir:0,respect:0,respectt:[],result:0,ret_df:0,right:0,roll:0,roll_mad:0,roll_ms:0,roll_seri:0,rolling_mad:0,rolling_ms:0,run:0,same:0,sampl:0,save:0,save_p:0,save_plot:0,save_plot_kwarg:0,savefig:0,scatter:0,score:0,search:1,sec_ord:0,second:0,second_digit:0,second_ord:0,select:0,separ:0,sequenc:0,sequenti:0,seri:0,set:0,should:0,show:0,show_plot:0,shown:0,sign:0,simpl:0,sinc:0,size:0,skew:0,smirnov:0,some:0,sort:0,sourc:0,squar:0,squre:0,stat:[1,2],state:0,statist:0,str:0,straight:0,string:0,studi:0,subclass:0,subject:0,subset:0,subtract:0,sum:0,summ:0,summat:0,suprem:0,take:0,tell:0,tend:0,termin:0,test:0,tet:0,text_x:0,tha:0,than:0,thee:0,them:0,thi:0,three:0,through:0,tick:0,toe:0,too:0,top:0,top_rep:0,track:0,transform:0,tupl:0,two:0,type:0,typeerror:0,updat:0,update_confid:0,upper:0,use:0,used:0,usual:0,valu:0,valueerror:0,varianc:0,verbos:0,viz:[1,2],well:0,were:0,when:0,whether:0,which:0,window:0,withe:0,work:0,y_exp:0,y_found:0,z_score:0,zero:0},titles:[\"benford package\",\"Welcome to benford_py\\u2019s documentation!\",\"benford\"],titleterms:{benford:[0,2],benford_pi:1,demo:1,document:1,expect:0,github:1,indic:1,jupyt:1,modul:0,notebook:1,packag:[0,1],stat:0,tabl:1,viz:0,welcom:1}})"
  },
  {
    "path": "docs/make.bat",
    "content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=source\r\nset BUILDDIR=build\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.http://sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\n\r\n:end\r\npopd\r\n"
  },
  {
    "path": "docs/requirements.txt",
    "content": "pandas\nnumpy\nmatplotlib"
  },
  {
    "path": "docs/source/api.rst",
    "content": "benford package\n===============\n\nbenford.benford module\n----------------------\n\n.. automodule:: benford.benford\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nbenford.expected module\n-----------------------\n\n.. automodule:: benford.expected\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nbenford.stats module\n--------------------\n\n.. automodule:: benford.stats\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nbenford.viz module\n------------------\n\n.. automodule:: benford.viz\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n"
  },
  {
    "path": "docs/source/conf.py",
    "content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('../..'))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'benford_py'\ncopyright = '2020, Marcel Milcent'\nauthor = 'Marcel Milcent'\n\n# The full version, including alpha/beta/rc tags\nrelease = '0.3.3'\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n    'sphinx.ext.autodoc',\n    'sphinx.ext.viewcode',\n    'sphinx.ext.napoleon'\n]\n\nmaster_doc = 'index'\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'sphinx_rtd_theme'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Show in order of code source, not alphabetical\nautodoc_member_order = 'bysource'\n"
  },
  {
    "path": "docs/source/index.rst",
    "content": "Welcome to benford_py's documentation!\n======================================\n\n.. toctree::\n   :maxdepth: 3\n   :caption: Contents:\n\n   modules\n\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n\nOn GitHub\n---------\n\n`Package <https://github.com/milcent/benford_py>`_\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n`Demo Jupyter Notebook <https://github.com/milcent/benford_py/blob/master/Demo.ipynb>`_\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
  },
  {
    "path": "docs/source/modules.rst",
    "content": "benford\n=======\n\n.. toctree::\n   :maxdepth: 2\n\n   api\n"
  },
  {
    "path": "setup.cfg",
    "content": "[metadata]\ndescription-file = README.md"
  },
  {
    "path": "setup.py",
    "content": "''' Setup for benford's module'''\nfrom os import path\nfrom setuptools import setup\n\nthis_directory = path.abspath(path.dirname(__file__))\nwith open(path.join(this_directory, 'README-pypi.md'), encoding='utf-8') as f:\n    long_description = f.read()\n\nsetup(name='benford_py',\n      version='0.5.0',\n      description='A library for testing data sets with Bendford\\'s Law',\n      long_description=long_description,\n      long_description_content_type='text/markdown',\n      url='https://github.com/milcent/benford_py',\n      download_url='https://github.com/milcent/benford_py/archive/v0.5.0.tar.gz',\n      author='Marcel Milcent',\n      author_email='marcelmilcent@gmail.com',\n      license='BSD 3-Clause',\n      packages=['benford'],\n      install_requires=[\n          'pandas',\n          'numpy',\n          'matplotlib',\n      ],\n      zip_safe=False,\n      classifiers=[\n          'Programming Language :: Python :: 3',\n          'License :: OSI Approved :: BSD License',\n          'Natural Language :: English',\n          'Operating System :: OS Independent',\n          'Development Status :: 3 - Alpha',\n          'Intended Audience :: Financial and Insurance Industry',\n          'Intended Audience :: Science/Research',\n          'Intended Audience :: Education',\n          'Intended Audience :: Other Audience',\n          'Topic :: Office/Business :: Financial :: Accounting',\n          'Topic :: Scientific/Engineering :: Mathematics',\n      ],)\n"
  },
  {
    "path": "tests/__init__.py",
    "content": ""
  },
  {
    "path": "tests/conftest.py",
    "content": "from random import choice\nimport pytest\nimport numpy as np\nimport pandas as pd\nfrom ..benford import utils as ut\nfrom ..benford.constants import CONFS, REV_DIGS\nfrom ..benford.expected import _get_expected_digits_\nfrom ..benford.stats import _two_dist_ks_\n\n\n@pytest.fixture\ndef gen_N():\n    return np.random.randint(0, 25000)\n\n\n@pytest.fixture\ndef gen_decimals():\n    return np.random.randint(0, 8)\n\n\n@pytest.fixture\ndef gen_N_lower(gen_N):\n    return np.random.randint(0, gen_N)\n\n\n@pytest.fixture\ndef gen_array(gen_N):\n    num = gen_N\n    return np.abs(np.random.rand(num) * np.random.randn(num) * \n                  np.random.randint(1, num, num))\n\n@pytest.fixture\ndef choose_digs_rand():\n    return choice([1, 2, 3, 22, -2])\n\n\n@pytest.fixture\ndef choose_test():\n    return choice([\"F1D\",\"F2D\",\"F3D\",\"SD\",\"L2D\"])\n\n\n@pytest.fixture\ndef choose_confidence():\n    return choice(list(CONFS.keys())[1:])\n\n\n@pytest.fixture\ndef gen_series(gen_array):\n    return pd.Series(gen_array)\n\n\n@pytest.fixture\ndef gen_data_frame(gen_array):\n    return pd.DataFrame({'seq': gen_array, 'col2': gen_array})\n\n\n@pytest.fixture\ndef gen_int_df(gen_data_frame):\n    return gen_data_frame.astype(int)\n\nsmall_arrays_type = [\n    (np.array([1, 2, 3, 4, 5.0, 6.3, .17]), float),\n    (np.array([1, 2, 3, 4, 5, 6, 7]), int),\n    (np.array(['1', '2', '3', '4', '5', '6', '7']), float),\n    (pd.Series([1, 2, 3, 4, 5.0, 6.3, .17]), float),\n    (pd.Series([1, 2, 3, 4, 5, 6, 7]), int),\n    (pd.Series(['1', '2', '3', '4', '5', '6', '7']), float)\n]\n\n@pytest.fixture(params=small_arrays_type)\ndef get_small_arrays(request):\n    return request.param\n\n\n@pytest.fixture\ndef small_str_foo_array():\n    return np.array(['foo', 'baar', 'baz', 'hixks'])\n\n\n@pytest.fixture\ndef small_str_foo_series():\n    return pd.Series(['foo', 'baar', 'baz', 'hixks'])\n\n\n@pytest.fixture\ndef gen_get_digs_df(gen_series, gen_decimals):\n    return ut.get_digs(gen_series, decimals=gen_decimals)\n\n\n@pytest.fixture\ndef gen_proportions_F1D(gen_get_digs_df):\n    return ut.get_found_proportions(gen_get_digs_df.F1D)\n\n\n@pytest.fixture\ndef gen_proportions_F2D(gen_get_digs_df):\n    return ut.get_found_proportions(gen_get_digs_df.F2D)\n\n\n@pytest.fixture\ndef gen_proportions_F3D(gen_get_digs_df):\n    return ut.get_found_proportions(gen_get_digs_df.F3D)\n\n\n@pytest.fixture\ndef gen_proportions_SD(gen_get_digs_df):\n    return ut.get_found_proportions(gen_get_digs_df.SD)\n\n\n@pytest.fixture\ndef gen_proportions_L2D(gen_get_digs_df):\n    return ut.get_found_proportions(gen_get_digs_df.L2D)\n\n\n@pytest.fixture\ndef gen_proportions_random_test(choose_test, gen_get_digs_df):\n    dig_str = choose_test\n    return ut.get_found_proportions(gen_get_digs_df[dig_str]), REV_DIGS[dig_str]\n\n\n@pytest.fixture\ndef gen_join_expect_found_diff_random_test(gen_proportions_random_test):\n    rand_test, rand_digs = gen_proportions_random_test\n    return ut.join_expect_found_diff(rand_test, rand_digs)\n\n\n@pytest.fixture\ndef gen_join_expect_found_diff_F1D(gen_proportions_F1D):\n    return ut.join_expect_found_diff(gen_proportions_F1D, 1)\n\n\n@pytest.fixture\ndef gen_join_expect_found_diff_F2D(gen_proportions_F2D):\n    return ut.join_expect_found_diff(gen_proportions_F2D, 2)\n\n\n@pytest.fixture\ndef gen_join_expect_found_diff_F3D(gen_proportions_F3D):\n    return ut.join_expect_found_diff(gen_proportions_F3D, 3)\n\n\n@pytest.fixture\ndef gen_join_expect_found_diff_SD(gen_proportions_SD):\n    return ut.join_expect_found_diff(gen_proportions_SD, 22)\n\n\n@pytest.fixture\ndef gen_join_expect_found_diff_L2D(gen_proportions_L2D):\n    return ut.join_expect_found_diff(gen_proportions_L2D, -2)\n\n\n@pytest.fixture\ndef gen_linspaced_zero_one(cuts:int=1000):\n    return np.linspace(0, 1, cuts)\n\n\n@pytest.fixture\ndef gen_mantissas_ks_dists(gen_array):\n    dist2 = ut.get_mantissas(gen_array)\n    dist1 = np.linspace(0, 1, len(dist2), endpoint=False)\n    return dist1, dist2\n\n\ndef gen_mantissa_distribution():\n    num =  np.random.randint(1500, 5000)\n    a = np.random.rand(num)\n    b = np.random.randint(1, 999, num)\n    c = np.random.randn(num)\n    abc = np.abs(a * b * c)\n    return ut.get_mantissas(abc)\n\n\nmant_ks_dists_types = [\n    (gen_mantissa_distribution(), np.random.choice([True, False]), np.float_) \n        for i in range(10)\n]\n\n@pytest.fixture(params=mant_ks_dists_types)\ndef get_mant_ks_types(request):\n    dist2, cummulative, ks_type = request.param\n    return np.linspace(0, 1, len(dist2), endpoint=False), \\\n        dist2, cummulative, ks_type\n\n\nmant_dists = [\n    (gen_mantissa_distribution(), np.random.choice([True, False]), 0)\n        for i in range(10)\n]\n\n@pytest.fixture(params=mant_dists)\ndef get_mant_ks_s(request):\n    dist2, cummulative, zero = request.param\n    return np.linspace(0, 1, len(dist2), endpoint=False), \\\n        dist2, cummulative, zero\n\ndef gen_mantissa_distribution_len():\n    mants = gen_mantissa_distribution()\n    return mants, len(mants)\n\nmant_ks_confidences = [\n    (*gen_mantissa_distribution_len(), conf) for conf in CONFS.keys()\n]\n\n@pytest.fixture(params=mant_ks_confidences)\ndef get_mant_ks_confs_limit_N(request):\n    mants, mants_lengths, confidence = request.param\n    cap_sample = mants_lengths - np.random.randint(500, 1400)\n    sample_size = np.random.choice([mants_lengths, cap_sample])\n    return mants, confidence, sample_size\n\n\n@pytest.fixture\ndef gen_random_digs_and_proportions(gen_linspaced_zero_one, choose_digs_rand):\n    exp = _get_expected_digits_(choose_digs_rand).Expected.values\n    rand_prop = np.random.choice(gen_linspaced_zero_one, len(exp))\n    return exp, rand_prop / rand_prop.sum()"
  },
  {
    "path": "tests/test_checks.py",
    "content": "from contextlib import suppress as do_not_raise\nimport pytest\nfrom pytest import raises\nfrom ..benford import checks as ch\nfrom ..benford.constants import CONFS, DIGS\n\n\nclass TestCheckDigs():\n\n    digs_to_raise = [\n        (x, raises(ValueError)) for x in \n        [0, 0.5, -3, -5, -1, 1.7, 22, 1000, \"One\", \"Two\", \"Second\", \"LastTwo\", \"Three\"]\n    ]\n\n    @pytest.mark.parametrize(\"dig, expectation\", digs_to_raise)\n    def test_digs_raise_msg(self, dig, expectation):\n        with expectation as context:\n            ch._check_digs_(dig)\n        assert str(context.value) == \"The value assigned to the parameter \" +\\\n                                f\"-digs- was {dig}. Value must be 1, 2 or 3.\"\n\n    @pytest.mark.parametrize(\"dig, expectation\", digs_to_raise)\n    def test_check_digs_raise(self, dig, expectation):\n        with expectation:\n            assert ch._check_digs_(dig) is not None\n\n    legit_digs = [\n        (y, do_not_raise()) for y in [1, 2, 3]\n    ]\n    @pytest.mark.parametrize(\"dig, expectation\", legit_digs)\n    def test_check_digs_no_raise(self, dig, expectation):\n        with expectation:\n            assert ch._check_digs_(dig) is None\n\n\nclass TestCheckTest():\n\n    digs_tests = [(d, d) for d in DIGS.keys()] +\\\n        [(val, key) for key, val in DIGS.items()]\n    \n    @pytest.mark.parametrize(\"dig, expected\", digs_tests)\n    def test_choose(self, dig, expected):\n        assert ch._check_test_(dig) == expected   \n    \n    test_check_raise = [\n        (y, raises(ValueError)) for y in [4, -3, 2.0, \"F4D\", False]] +\\\n        [(x, do_not_raise()) for x in DIGS.keys()] +\\\n        [(z, do_not_raise()) for z in DIGS.values()]\n\n    @pytest.mark.parametrize(\"dig, expectation\", test_check_raise)\n    def test_raise(self, dig, expectation):\n        with expectation:\n            assert ch._check_test_(dig) is not None\n\n    def test_None(self):\n        with pytest.raises(ValueError):\n            ch._check_test_(None)\n\n\nclass TestCheckDecimals():\n    \n    pos_int = zip(range(21), range(21))\n    \n    @pytest.mark.parametrize(\"pos_int, expected\", pos_int)\n    def test_positive_int(self, pos_int, expected):\n        assert ch._check_decimals_(pos_int) == expected\n\n    dec_errors = [(x, raises(ValueError)) for x in range(-15, 0)] +\\\n        [(y, do_not_raise()) for y in range(21)] +\\\n        [(z, raises(ValueError)) for z in [\"inf\", \"infe\", \"Infer\", []]]\n\n    @pytest.mark.parametrize(\"dec, expectation\", dec_errors)\n    def test_dec_raises(self, dec, expectation):\n        with expectation:\n            assert ch._check_decimals_(dec) is not None\n\n    def test_negative_int_msg(self):\n        with pytest.raises(ValueError) as context:\n            ch._check_decimals_(-2)\n        assert str(\n            context.value) == \"Parameter -decimals- must be an int >= 0, or 'infer'.\"\n\n    def test_infer(self):\n        assert ch._check_decimals_('infer') == 'infer'\n\n    def test_None_type(self):\n        with pytest.raises(ValueError):\n            ch._check_decimals_(None)\n\n\nclass TestCheckConfidence():\n\n    conf_errors = [\n        (x, raises(ValueError)) for x in\n        [93, \"95\", 76, \"80\", \"99\", 84, 99.8]\n    ] + [ # Except None ([:1]) due to comparison below\n        (y, do_not_raise()) for y in list(CONFS.keys())[1:] \n    ]\n    @pytest.mark.parametrize(\"conf, expectation\", conf_errors)\n    def test_conf_raises(self, conf, expectation):\n        with expectation:\n            assert ch._check_confidence_(conf) is not None\n\n    all_confidences = zip(CONFS.keys(), CONFS.keys())\n\n    @pytest.mark.parametrize(\"conf, expected\", all_confidences)\n    def test_all_confidences(self, conf, expected):\n        assert ch._check_confidence_(conf) == expected\n\n\nclass TestCheckHighZ():\n\n    z_errors = [\n        (x, raises(ValueError)) for x in\n        [5.0, 0.3, \"al\", \"poss\", \"po\", \"alll\", ]\n    ] + [\n        (y, do_not_raise()) for y in \n        [10, 20, 5, 2, \"pos\", \"all\"]\n    ]\n    @pytest.mark.parametrize(\"high_Z, expectation\", z_errors)\n    def test_high_Z_raises(self, high_Z, expectation):\n        with expectation:\n            assert ch._check_high_Z_(high_Z) is not None\n\n    high_Zs = [\n        (10, 10), (\"pos\", \"pos\"), (\"all\", \"all\")\n    ]\n    @pytest.mark.parametrize(\"z, expected\", high_Zs)\n    def test_high_zs(self, z, expected):\n        assert ch._check_high_Z_(z) == expected\n\n\nclass TestCheckNunmArray():\n    \n    arrays = [\n        ['1', '2', '3', '4', '5', '6', '7'],\n        [1, 2, 3, 4, 5, 6, 7],\n        [1, 2, 3, 4, 5.0, 6.3, .17],\n        [True, False, False, True, True, True, False, False]\n    ]\n\n    @pytest.mark.parametrize(\"arr\", arrays)\n    def test_arrays_to_float(self, arr):\n        assert ch._check_num_array_(arr).dtype == float\n\n    def test_small_arrays(self, get_small_arrays):\n        arr, expected = get_small_arrays\n        assert ch._check_num_array_(arr).dtype == expected\n    \n    def test_np_array_str(self, small_str_foo_array):\n        with pytest.raises(ValueError):\n            ch._check_num_array_(small_str_foo_array)\n\n    num_arr_raise = [\n        ({1, 2, 3, 4}, raises(ValueError)),\n        ({'a': 1, 'b': 2, 'c': 3, 'd': 4}, raises(ValueError)),\n        ([1, 2, 3, 4, 5.0, 6.3, .17], do_not_raise()),\n        (['foo', 'baar', 'baz', 'jinks'], raises(ValueError)),\n        ('alocdwneceo;u', raises(ValueError))\n    ]\n\n    @pytest.mark.parametrize(\"num_array, expectation\", num_arr_raise)\n    def test_num_array_raises(self, num_array, expectation):\n        with expectation:\n            print(num_array)\n            assert ch._check_num_array_(num_array) is not None\n"
  },
  {
    "path": "tests/test_expected.py",
    "content": "import pytest\nfrom ..benford import expected as ex\n\n\nclass TestGetExpectedDigits():\n\n    expected_types = [\n        (x, ex.First) for x in [1, 2, 3]\n    ] + [(22, ex.Second), (-2, ex.LastTwo)]\n\n    @pytest.mark.parametrize(\"dig, expec_type\", expected_types)\n    def test_expected_types(self, dig, expec_type):\n        assert type(ex._get_expected_digits_(dig)) == expec_type\n\n    expected_lenghts = [\n        (1, 9), (2, 90), (3, 900), (22, 10), (-2, 100)\n    ]\n\n    @pytest.mark.parametrize(\"dig, exp_len\", expected_lenghts)\n    def test_expected_lenghts(self, dig, exp_len):\n        assert len(ex._get_expected_digits_(dig)) == exp_len\n\n\nclass TestGenLastTwoDigits():\n\n    l2d_types = [([], \"<U21\"), ([True], \"int\")]\n\n    @pytest.mark.parametrize(\"num, arr_type\", l2d_types)\n    def test_types(self, num, arr_type):\n        _, lt = ex._gen_last_two_digits_(*num)\n        assert lt.dtype == arr_type\n\n\nclass TestGenDigits():\n\n    gen_digs = [\n        (\"_gen_first_digits_\", [1]), (\"_gen_first_digits_\", [2]),\n        (\"_gen_first_digits_\", [3]), (\"_gen_second_digits_\", []),\n        (\"_gen_last_two_digits_\", []), (\"_gen_last_two_digits_\", [True])\n    ]\n\n    @pytest.mark.parametrize(\"func, dig\", gen_digs)\n    def test_probs_sum_near_one(self, func, dig):\n        exp, _ = getattr(ex, func)(*dig)\n        assert exp.sum() > 0.999999\n\n    @pytest.mark.parametrize(\"func, dig\", gen_digs)\n    def test_no_negative_prob(self, func, dig):\n        exp, _ = getattr(ex, func)(*dig)\n        assert (exp < 0).sum() == 0\n\n    digs_sums = [\n        (\"_gen_first_digits_\", [1], 45), (\"_gen_first_digits_\", [2], 4905),\n        (\"_gen_first_digits_\", [3], 494550), (\"_gen_second_digits_\",  [], 45),\n        (\"_gen_last_two_digits_\", [True], 4950) \n    ]\n\n    @pytest.mark.parametrize(\"func, dig, exp_sum\", digs_sums)\n    def test_digs_sums(self, func, dig, exp_sum):\n        _, digits = getattr(ex, func)(*dig)\n        assert digits.sum() == exp_sum\n\n    digs_lengths = [\n        (\"_gen_first_digits_\", [1], 9), (\"_gen_first_digits_\", [2], 90),\n        (\"_gen_first_digits_\", [3], 900), (\"_gen_second_digits_\",  [], 10),\n        (\"_gen_last_two_digits_\", [], 100), (\"_gen_last_two_digits_\", [True], 100) \n    ]\n\n    @pytest.mark.parametrize(\"func, dig, exp_len\", digs_lengths)\n    def test_lengths(self, func, dig, exp_len):\n        exp, digits = getattr(ex, func)(*dig)\n        assert len(exp) == len(digits) == exp_len\n"
  },
  {
    "path": "tests/test_stats.py",
    "content": "import pytest\nfrom numpy import float_\nfrom ..benford import stats as st\nfrom ..benford.constants import CRIT_CHI2, CRIT_KS\n\n\ndef test_Z_score_F1D():\n    pass\n\nclass TestChiSquare():\n        \n    def test_conf_None(self, gen_join_expect_found_diff_F1D, capsys):\n        jefd_F1D = gen_join_expect_found_diff_F1D\n        chi = st.chi_sq(jefd_F1D, len(jefd_F1D) - 1, None)\n        out, _ = capsys.readouterr()\n        assert \"Chi-square test needs confidence other than None.\" in out\n        assert chi is None\n\n    def test_random_conf_F1D(self, gen_join_expect_found_diff_F1D, \n                             choose_confidence):\n        jefd_F1D = gen_join_expect_found_diff_F1D\n        ddf = len(jefd_F1D) - 1\n        confidence = choose_confidence\n        chis = st.chi_sq(jefd_F1D, ddf, choose_confidence, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][confidence]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n        \n\n    def test_random_conf_F2D(self, gen_join_expect_found_diff_F2D, \n                             choose_confidence):\n        jefd_F2D = gen_join_expect_found_diff_F2D\n        ddf = len(jefd_F2D) - 1\n        confidence = choose_confidence\n        chis = st.chi_sq(jefd_F2D, ddf, choose_confidence, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][confidence]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_random_conf_F3D(self, gen_join_expect_found_diff_F3D, \n                             choose_confidence):\n        jefd_F3D = gen_join_expect_found_diff_F3D\n        ddf = len(jefd_F3D) - 1\n        confidence = choose_confidence\n        chis = st.chi_sq(jefd_F3D, ddf, choose_confidence, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][confidence]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_random_conf_SD(self, gen_join_expect_found_diff_SD, \n                            choose_confidence):\n        jefd_SD = gen_join_expect_found_diff_SD\n        ddf = len(jefd_SD) - 1\n        confidence = choose_confidence\n        chis = st.chi_sq(jefd_SD, ddf, choose_confidence, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][confidence]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_random_conf_L2D(self, gen_join_expect_found_diff_L2D, \n                             choose_confidence):\n        jefd_L2D = gen_join_expect_found_diff_L2D\n        ddf = len(jefd_L2D) - 1\n        confidence = choose_confidence\n        chis = st.chi_sq(jefd_L2D, ddf, choose_confidence, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][confidence]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n    \n    def test_rand_test_rand_conf_verbose(self, choose_confidence,\n                            gen_join_expect_found_diff_random_test, capsys):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        conf = choose_confidence\n        chis = st.chi_sq(r_test, ddf, conf)\n        out, _ = capsys.readouterr()\n        assert f\"The Chi-square statistic is {chis[0]:.4f}.\" in out\n        assert f\"Critical Chi-square for this series: {chis[1]}.\" in out\n    \n    def test_rand_test_conf_80(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 80, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][80]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_85(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 85, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][85]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_90(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 90, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][90]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_95(self, gen_join_expect_found_diff_random_test,\n                               capsys):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 95, verbose=False)\n        out, _ = capsys.readouterr()\n        assert chis[1] == CRIT_CHI2[ddf][95]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n        assert f\"The Chi-square statistic is {chis[0]:.4f}.\" not in out\n        assert f\"Critical Chi-square for this series: {chis[1]}.\" not in out\n\n    def test_rand_test_conf_99(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 99, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][99]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_999(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 99.9, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][99.9]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_9999(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 99.99, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][99.99]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_99999(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 99.999, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][99.999]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_999999(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 99.9999, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][99.9999]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\n    def test_rand_test_conf_9999999(self, gen_join_expect_found_diff_random_test):\n        r_test = gen_join_expect_found_diff_random_test\n        ddf = len(r_test) - 1\n        chis = st.chi_sq(r_test, ddf, 99.99999, verbose=False)\n        assert chis[1] == CRIT_CHI2[ddf][99.99999]\n        assert chis[0] >= 0\n        assert isinstance(chis[0], float)\n\nclass TestBhattacharyya():\n\n    def test_coeff(self, gen_random_digs_and_proportions):\n        exp, rand_prop = gen_random_digs_and_proportions\n        bhat_coeff = st._bhattacharyya_coefficient(exp, rand_prop)\n        assert isinstance(bhat_coeff, float)\n        assert bhat_coeff >= 0\n        assert bhat_coeff <= 1\n    \n    def test_distance(self, gen_random_digs_and_proportions):\n        exp, rand_prop = gen_random_digs_and_proportions\n        bhat_dist = st._bhattacharyya_distance_(exp, rand_prop)\n        assert isinstance(bhat_dist, float)\n        assert bhat_dist >= 0\n\n\nclass TestKLDivergence():\n\n    def test_kld(self, gen_random_digs_and_proportions):\n        exp, rand_prop = gen_random_digs_and_proportions\n        kl_diverg = st._kullback_leibler_divergence_(exp, rand_prop)\n        assert isinstance(kl_diverg, float)\n        assert kl_diverg >= 0\n\n\nclass TestTwoDistKS():\n     \n    def test_type(self, get_mant_ks_types):\n        dist1, dist2, cummulative, ks_type = get_mant_ks_types\n        ks = st._two_dist_ks_(dist1, dist2, cummulative)\n        assert type(ks) == ks_type\n    \n    def test_more_equal_zero(self, get_mant_ks_s):\n        dist1, dist2, cummulative, zero = get_mant_ks_s\n        ks = st._two_dist_ks_(dist1, dist2, cummulative)\n        assert ks >= zero\n\nclass TestMantissasKS:\n\n    def test_confidence_limit_N(self, get_mant_ks_confs_limit_N):\n        mants, confidence, sample_size = get_mant_ks_confs_limit_N\n        ks, crit_ks = st._mantissas_ks_(mants, confidence, sample_size)\n        assert ks >= 0\n        if crit_ks is not None:\n            assert crit_ks >= 0\n    \n    \n"
  },
  {
    "path": "tests/test_utils.py",
    "content": "import pytest\nimport pandas as pd\nfrom ..benford import utils as ut\n\n\nclass Test_set_N_():\n        \n    def test_Limit_None(self, gen_N):\n        assert ut._set_N_(gen_N, None) == gen_N\n\n    def test_Limit_greater(self, gen_N, gen_N_lower):\n        assert ut._set_N_(gen_N, gen_N_lower) == gen_N_lower\n\n    def test_negative(self, ):\n        with pytest.raises(ValueError) as context:\n            ut._set_N_(-250, -1000)\n\n    def test_float(self, ):\n        with pytest.raises(ValueError) as context:\n            ut._set_N_(127.8, -100)\n\n    def test_zero(self, gen_N):\n        assert ut._set_N_(0, None) == 1\n        assert ut._set_N_(0, gen_N) == 1\n\n\nclass Test_get_mantissas():\n        \n    def test_less_than_1(self, gen_array):\n        assert sum(ut.get_mantissas(gen_array) > 1) == 0\n\n    def test_less_than_0(self, gen_array):\n        assert sum(ut.get_mantissas(gen_array) < 0) == 0\n\n\nclass Test_input_data():\n        \n    def test_Series(self, gen_series):\n        tup = ut.input_data(gen_series)\n        assert tup[0] is tup[1]\n\n    def test_array(self, gen_array):\n        tup = ut.input_data(gen_array)\n        assert tup[0] is gen_array\n        assert type(tup[1]) == pd.Series\n\n    def test_wrong_tuple(self, gen_array, gen_series, gen_data_frame):\n        with pytest.raises(TypeError) as context:\n            ut.input_data((gen_array, 'seq'))\n            ut.input_data((gen_series, 'col1'))\n            ut.input_data((gen_data_frame, 2))\n\n    def test_df(self, gen_data_frame):\n        tup = ut.input_data((gen_data_frame, 'seq'))\n        assert type(tup[0]) == pd.DataFrame\n        assert type(tup[1]) == pd.Series\n\n    def test_wrong_input_type(self, gen_array):\n        with pytest.raises(TypeError) as context:\n            ut.input_data(gen_array.tolist())\n\n\nclass Test_set_sign():\n        \n    def test_all(self, gen_data_frame):\n        sign_df = ut.set_sign(gen_data_frame, 'all')\n        assert len(sign_df.loc[sign_df.seq == 0]) == 0\n\n    def test_pos(self, gen_data_frame):\n        sign_df = ut.set_sign(gen_data_frame, 'pos')\n        assert sum(sign_df.seq <= 0) == 0\n\n    def test_neg(self, gen_data_frame):\n        sign_df = ut.set_sign(gen_data_frame, 'neg')\n        assert sum(sign_df.seq >= 0) == 0\n\n\nclass Test_get_times_10_power():\n        \n    def test_2(self, gen_data_frame):\n        pow_df = ut.get_times_10_power(gen_data_frame)\n        assert pow_df.ZN.dtype == int\n\n    def test_8(self, gen_data_frame):\n        pow_df = ut.get_times_10_power(gen_data_frame, 8)\n        assert pow_df.ZN.dtype == int\n        assert (pow_df.ZN == (pow_df.seq.abs() * 10 ** 8).astype(int)).all()\n\n    def test_0(self, gen_int_df):\n        pow_df = ut.get_times_10_power(gen_int_df)\n        assert pow_df.ZN.dtype == int\n        assert (pow_df.ZN == pow_df.seq.abs()).all()\n\n    def test_infer(self, gen_data_frame):\n        pow_df = ut.get_times_10_power(gen_data_frame, 'infer')\n        assert pow_df.ZN.dtype == int\n        assert (pow_df.ZN.astype(str).str.len() == 5).all()\n\n\nclass Test_get_digs():\n        \n    def test_dec_8(self, gen_array):\n        e_digs = ut.get_digs(gen_array, decimals=8)\n        cols = ['seq', 'ZN', 'F1D', 'F2D', 'F3D', 'SD', 'L2D']\n        assert e_digs.columns.str.contains('|'.join(cols)).all()\n        assert (e_digs[['F1D', 'F2D', 'F3D', 'SD', 'L2D']].dtypes == int).all()\n        assert e_digs.notna().all().all()\n\n    def test_dec_0(self, gen_array):\n        e_digs = ut.get_digs(gen_array, decimals=0)\n        cols = ['seq', 'ZN', 'F1D', 'F2D', 'F3D', 'SD', 'L2D']\n        assert e_digs.columns.str.contains('|'.join(cols)).all()\n        assert (e_digs[['F1D', 'F2D', 'F3D', 'SD', 'L2D']].dtypes == int).all()\n        assert e_digs.notna().all().all()\n\n    def test_dec_2(self, gen_array):\n        e_digs = ut.get_digs(gen_array, decimals=2)\n        cols = ['seq', 'ZN', 'F1D', 'F2D', 'F3D', 'SD', 'L2D']\n        assert e_digs.columns.str.contains('|'.join(cols)).all()\n        assert (e_digs[['F1D', 'F2D', 'F3D', 'SD', 'L2D']].dtypes == int).all()\n        assert e_digs.notna().all().all()\n\n    def test_dec_infer(self, gen_array):\n        e_digs = ut.get_digs(gen_array, decimals='infer')\n        cols = ['seq', 'ZN', 'F1D', 'F2D', 'F3D', 'SD', 'L2D']\n        assert e_digs.columns.str.contains('|'.join(cols)).all()\n        assert (e_digs[['F1D', 'F2D', 'F3D', 'SD', 'L2D']].dtypes == int).all()\n        assert e_digs.notna().all().all()\n\nclass Test_get_found_proportions():\n        \n    def test_F1D(self, gen_proportions_F1D):\n        prop_f1d = gen_proportions_F1D\n        # assert ((prop_f1d.index >= 1) & (prop_f1d.index <= 9)).all()\n        assert prop_f1d.Found.sum() > .99999\n        assert (prop_f1d.Found >= 0).all()\n        assert prop_f1d.Counts.dtype == int\n\n    def test_F2D(self, gen_proportions_F2D):\n        prop_f2d = gen_proportions_F2D\n        # assert ((prop_f2d.index >= 10) & (prop_f2d.index <= 99)).all()\n        assert prop_f2d.Found.sum() > .99999\n        assert (prop_f2d.Found >= 0).all()\n        assert prop_f2d.Counts.dtype == int\n\n    def test_F3D(self, gen_proportions_F3D):\n        prop_f3d = gen_proportions_F3D\n        # assert ((prop_f3d.index >= 100) & (prop_f3d.index <= 999)).all()\n        assert prop_f3d.Found.sum() > .99999\n        assert (prop_f3d.Found >= 0).all()\n        assert prop_f3d.Counts.dtype == int\n\n    def test_SD(self, gen_proportions_SD):\n        prop_sd = gen_proportions_SD\n        # assert ((prop_sd.index >= 0) & (prop_sd.index <= 9)).all()\n        assert prop_sd.Found.sum() > .99999\n        assert (prop_sd.Found >= 0).all()\n        assert prop_sd.Counts.dtype == int\n\n    def test_L2D(self, gen_proportions_L2D):\n        prop_l2d = gen_proportions_L2D\n        # assert ((prop_l2d.index >= 00) & (prop_l2d.index <= 99)).all()\n        assert prop_l2d.Found.sum() > .99999\n        assert (prop_l2d.Found >= 0).all()\n        assert prop_l2d.Counts.dtype == int\n\n\nclass Test_join_exp_found_diff():\n        \n    def test_F1D(self, gen_proportions_F1D):\n        jefd_F1D = ut.join_expect_found_diff(gen_proportions_F1D, 1)\n        assert len(jefd_F1D) == 9\n        assert (jefd_F1D.columns.str.contains('|'.join(\n            ['Expected', 'Counts', 'Found', 'Dif', 'AbsDif']))).all()\n        assert jefd_F1D.isna().sum().sum() == 0\n\n    def test_F2D(self, gen_proportions_F2D):\n        jefd_F2D = ut.join_expect_found_diff(gen_proportions_F2D, 2)\n        assert len(jefd_F2D) == 90\n        assert (jefd_F2D.columns.str.contains('|'.join(\n            ['Expected', 'Counts', 'Found', 'Dif', 'AbsDif']))).all()\n        assert jefd_F2D.isna().sum().sum() == 0\n\n    def test_F3D(self, gen_proportions_F3D):\n        jefd_F3D = ut.join_expect_found_diff(gen_proportions_F3D, 3)\n        assert len(jefd_F3D) == 900\n        assert (jefd_F3D.columns.str.contains('|'.join(\n            ['Expected', 'Counts', 'Found', 'Dif', 'AbsDif']))).all()\n        assert jefd_F3D.isna().sum().sum() == 0\n\n    def test_SD(self, gen_proportions_SD):\n        jefd_SD = ut.join_expect_found_diff(gen_proportions_SD, 22)\n        assert len(jefd_SD) == 10\n        assert (jefd_SD.columns.str.contains('|'.join(\n            ['Expected', 'Counts', 'Found', 'Dif', 'AbsDif']))).all()\n        assert jefd_SD.isna().sum().sum() == 0\n\n    def test_L2D(self, gen_proportions_L2D):\n        jefd_L2D = ut.join_expect_found_diff(gen_proportions_L2D, -2)\n        assert len(jefd_L2D) == 100\n        assert (jefd_L2D.columns.str.contains('|'.join(\n            ['Expected', 'Counts', 'Found', 'Dif', 'AbsDif']))).all()\n        assert jefd_L2D.isna().sum().sum() == 0\n\n\nclass Test_prepare():\n        \n    def test_F1D_simple(self, gen_series):\n        prep_F1D = ut.prepare(gen_series, 1, simple=True)\n        assert \"Dif\" not in prep_F1D.columns\n\n    def test_F2D_simple(self, gen_series):\n        prep_F2D = ut.prepare(gen_series, 2, simple=True)\n        assert \"Dif\" not in prep_F2D.columns\n\n    def test_F3D_simple(self, gen_series):\n        prep_F3D = ut.prepare(gen_series, 3, simple=True)\n        assert \"Dif\" not in prep_F3D.columns\n\n    def test_SD_simple(self, gen_series):\n        prep_SD = ut.prepare(gen_series, 22, simple=True)\n        assert \"Dif\" not in prep_SD.columns\n\n    def test_L2D_simple(self, gen_series):\n        prep_L2D = ut.prepare(gen_series, -2, simple=True)\n        assert \"Dif\" not in prep_L2D.columns\n\n    def test_F1D(self, gen_series):\n        ser = gen_series\n        lf = len(ser)\n        num, prep_F1D = ut.prepare(ser, 1)\n        assert \"Z_score\" in prep_F1D.columns\n        assert num == lf\n\n    def test_F2D(self, gen_series):\n        ser = gen_series\n        lf = len(ser)\n        num, prep_F2D = ut.prepare(ser, 2)\n        assert \"Z_score\" in prep_F2D.columns\n        assert num == lf\n\n    def test_F3D(self, gen_series):\n        ser = gen_series\n        lf = len(ser)\n        num, prep_F3D = ut.prepare(ser, 3)\n        assert \"Z_score\" in prep_F3D.columns\n        assert num == lf\n\n    def test_SD(self, gen_series):\n        ser = gen_series\n        lf = len(ser)\n        num, prep_SD = ut.prepare(ser, 22)\n        assert \"Z_score\" in prep_SD.columns\n        assert num == lf\n\n    def test_L2D(self, gen_series):\n        ser = gen_series\n        lf = len(ser)\n        num, prep_L2D = ut.prepare(ser, -2)\n        assert \"Z_score\" in prep_L2D.columns\n        assert num == lf\n\n    def test_F1D_N(self, gen_N, gen_series):\n        ser = gen_series\n        n_diff = gen_N\n        num, prep_F1D = ut.prepare(ser, 1, limit_N=n_diff)\n        assert \"Z_score\" in prep_F1D.columns\n        assert num == n_diff\n\n    def test_F2D_N(self, gen_N, gen_series):\n        ser = gen_series\n        n_diff = gen_N\n        num, prep_F2D = ut.prepare(ser, 2, limit_N=n_diff)\n        assert \"Z_score\" in prep_F2D.columns\n        assert num == n_diff\n\n    def test_F3D_N(self, gen_N, gen_series):\n        ser = gen_series\n        n_diff = gen_N\n        num, prep_F3D = ut.prepare(ser, 3, limit_N=n_diff)\n        assert \"Z_score\" in prep_F3D.columns\n        assert num == n_diff\n\n    def test_SD_N(self, gen_N, gen_series):\n        ser = gen_series\n        n_diff = gen_N\n        num, prep_SD = ut.prepare(ser, 22, limit_N=n_diff)\n        assert \"Z_score\" in prep_SD.columns\n        assert num == n_diff\n\n    def test_L2D_N(self, gen_N, gen_series):\n        ser = gen_series\n        n_diff = gen_N\n        num, prep_L2D = ut.prepare(ser, -2, limit_N=n_diff)\n        assert \"Z_score\" in prep_L2D.columns\n        assert num == n_diff\n\n\ndef test_subtract_sorted(gen_series):\n    ser = gen_series\n    sort = ut.subtract_sorted(ser)\n    assert len(ser) - len(sort) >= 1\n    assert (sort != 0).all()\n"
  }
]