.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/howto_simulation/plot_100_logparser_intro.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_howto_simulation_plot_100_logparser_intro.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_howto_simulation_plot_100_logparser_intro.py:


Logparser: Introduction
=======================

This simple example demonstrates how to analyze the OGS (OpenGeoSys) log output to
extract performance information regarding various computational parts of OGS.
Here we utilize the project file from the benchmark titled:
`OGS: Constant viscosity (Hydro-Thermal)
<https://www.opengeosys.org/docs/benchmarks/hydro-thermal/constant-viscosity>`_

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Complete example
================
For detailed explanation see all sections below.

.. GENERATED FROM PYTHON SOURCE LINES 19-36

.. code-block:: Python

    import pandas as pd

    from ogstools.examples import (
        log_const_viscosity_thermal_convection,
    )
    from ogstools.logparser import (
        fill_ogs_context,
        parse_file,
        time_step_vs_iterations,
    )

    records = parse_file(log_const_viscosity_thermal_convection)
    df_records = pd.DataFrame(records)
    df_log = fill_ogs_context(df_records)
    df_ts_it = time_step_vs_iterations(df_log)
    df_ts_it






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>iteration_number</th>
        </tr>
        <tr>
          <th>time_step</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>3</td>
        </tr>
        <tr>
          <th>6</th>
          <td>1</td>
        </tr>
        <tr>
          <th>7</th>
          <td>1</td>
        </tr>
        <tr>
          <th>8</th>
          <td>1</td>
        </tr>
        <tr>
          <th>9</th>
          <td>2</td>
        </tr>
        <tr>
          <th>10</th>
          <td>2</td>
        </tr>
        <tr>
          <th>11</th>
          <td>2</td>
        </tr>
        <tr>
          <th>12</th>
          <td>4</td>
        </tr>
        <tr>
          <th>13</th>
          <td>1</td>
        </tr>
        <tr>
          <th>14</th>
          <td>4</td>
        </tr>
        <tr>
          <th>15</th>
          <td>1</td>
        </tr>
        <tr>
          <th>16</th>
          <td>4</td>
        </tr>
        <tr>
          <th>17</th>
          <td>4</td>
        </tr>
        <tr>
          <th>18</th>
          <td>5</td>
        </tr>
        <tr>
          <th>19</th>
          <td>5</td>
        </tr>
        <tr>
          <th>20</th>
          <td>7</td>
        </tr>
        <tr>
          <th>21</th>
          <td>7</td>
        </tr>
        <tr>
          <th>22</th>
          <td>8</td>
        </tr>
        <tr>
          <th>23</th>
          <td>9</td>
        </tr>
        <tr>
          <th>24</th>
          <td>17</td>
        </tr>
        <tr>
          <th>25</th>
          <td>23</td>
        </tr>
        <tr>
          <th>26</th>
          <td>20</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

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The log file
-------------
Running `ogs` in the command line outputs the logs into the console output. With
`tee
<https://en.wikipedia.org/wiki/Tee_(command)>`_ in Linux and Mac
and
`Tee-Object
<https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.utility/tee-object?view=powershell-7.4>`_
in Windows Powershell the log gets directed into a file and into the console.

* **Linux/Mac**: ``ogs <your_prj_file> | tee <your_log_file>``
* **Windows**: ``ogs <your_prj_file> | Tee-Object -FilePath <your_log_file>``

For first step we recommend to use either **`ogs -l info`** or `ogs - l debug`.
Make sure the log file does not contain
`ANSI escape (e.g.color) code
<https://en.wikipedia.org/wiki/ANSI_escape_code>`_.
You can remove it with: ``cat ogs.log | sed 's/\x1b\[[0-9;]*m//g' > ogs.log``

.. GENERATED FROM PYTHON SOURCE LINES 61-69

Parsing steps
-------------
The functions :py:mod:`ogstools.logparser.parse_file` and
:py:mod:`ogstools.logparser.fill_ogs_context` are explained in
:ref:`sphx_glr_auto_examples_howto_simulation_plot_102_logparser_advanced.py`.
All predefined analyses need the result of fill_ogs_context.
Here `const_viscosity_thermal_convection_log` is string representing the
location of the ogs log file.

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.. code-block:: Python

    print(log_const_viscosity_thermal_convection)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /builds/ogs/tools/ogstools/ogstools/examples/logs/ConstViscosityThermalConvection.log




.. GENERATED FROM PYTHON SOURCE LINES 71-75

.. code-block:: Python

    records = parse_file(log_const_viscosity_thermal_convection)
    df_records = pd.DataFrame(records)
    df_log = fill_ogs_context(df_records)








.. GENERATED FROM PYTHON SOURCE LINES 76-88

Use predefined analyses
-----------------------
:py:mod:`ogstools.logparser.time_step_vs_iterations` is one of many predefined
analyses. All possibilities are shown here:
:ref:`sphx_glr_auto_examples_howto_simulation_plot_101_logparser_analyses.py`.

Here we are interested in every time step of the simulation and how many
iterations have been needed.
The predefined analyses only work with logs from `ogs` run with level `info` or finer (`debug`), like `ogs -l info` or `ogs - l debug`.
(see
`OGS Developer Guide - log and debug output
<https://www.opengeosys.org/docs/devguide/advanced/log-and-debug-output>`_

.. GENERATED FROM PYTHON SOURCE LINES 88-95

.. code-block:: Python


    df_ts_it = time_step_vs_iterations(df_log)
    # The result is a pandas.DataFrame. You may manipulate the dataframe to your
    # needs with pandas functionality.
    pd.set_option("display.max_rows", 8)  # for visualization only
    df_ts_it






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>iteration_number</th>
        </tr>
        <tr>
          <th>time_step</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
        </tr>
        <tr>
          <th>23</th>
          <td>9</td>
        </tr>
        <tr>
          <th>24</th>
          <td>17</td>
        </tr>
        <tr>
          <th>25</th>
          <td>23</td>
        </tr>
        <tr>
          <th>26</th>
          <td>20</td>
        </tr>
      </tbody>
    </table>
    <p>27 rows × 1 columns</p>
    </div>
    </div>
    <br />
    <br />

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Pandas to plot
--------------
You can directly use
`plot` `
<https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html>`_ from pandas.

.. GENERATED FROM PYTHON SOURCE LINES 101-102

.. code-block:: Python

    df_ts_it.plot(grid=True)



.. image-sg:: /auto_examples/howto_simulation/images/sphx_glr_plot_100_logparser_intro_001.png
   :alt: plot 100 logparser intro
   :srcset: /auto_examples/howto_simulation/images/sphx_glr_plot_100_logparser_intro_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Axes: xlabel='time_step'>




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.098 seconds)


.. _sphx_glr_download_auto_examples_howto_simulation_plot_100_logparser_intro.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_100_logparser_intro.ipynb <plot_100_logparser_intro.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_100_logparser_intro.py <plot_100_logparser_intro.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_100_logparser_intro.zip <plot_100_logparser_intro.zip>`