.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/howto_simulation/plot_101_logparser_analyses.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_101_logparser_analyses.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_howto_simulation_plot_101_logparser_analyses.py:


Logparser: Predefined Analyses
==============================

In this section, we showcase various predefined analyses available in the log parser.
We utilize project files from the following benchmarks:
`ogs: Constant viscosity (Hydro-Thermal)
<https://www.opengeosys.org/docs/benchmarks/hydro-thermal/constant-viscosity>`_
and for the **staggered scheme** we use a prj from
`ogs tests: HT StaggeredCoupling HeatTransportInStationaryFlow
<https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Parabolic/HT/StaggeredCoupling/HeatTransportInStationaryFlow/HeatTransportInStationaryFlow.prj>`_

.. GENERATED FROM PYTHON SOURCE LINES 16-33

.. code-block:: Python

    import pandas as pd

    from ogstools.examples import (
        log_const_viscosity_thermal_convection,
        log_staggered,
    )
    from ogstools.logparser import (
        analysis_convergence_coupling_iteration,
        analysis_convergence_newton_iteration,
        analysis_time_step,
        fill_ogs_context,
        parse_file,
        time_step_vs_iterations,
    )

    pd.set_option("display.max_rows", 8)  # for visualization only








.. GENERATED FROM PYTHON SOURCE LINES 34-37

The preprocessing of logs remains consistent across all examples and
thoroughly explained in
:ref:`sphx_glr_auto_examples_howto_simulation_plot_102_logparser_advanced.py`.

.. GENERATED FROM PYTHON SOURCE LINES 37-43

.. code-block:: Python


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








.. GENERATED FROM PYTHON SOURCE LINES 44-51

Analysis of iterations per time step
------------------------------------
For detailed explanation, refer to:
:ref:`sphx_glr_auto_examples_howto_simulation_plot_100_logparser_intro.py`.
(Section: Use predefined analyses)

:py:mod:`ogstools.logparser.analysis_time_step`

.. GENERATED FROM PYTHON SOURCE LINES 51-55

.. code-block:: Python

    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>...</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 />

.. GENERATED FROM PYTHON SOURCE LINES 56-63

Analysis of computational efficiency by time step
-------------------------------------------------
The resulting table represents performance metrics for different parts of the simulation,
organized by time step. It utilizes :py:mod:`ogstools.logparser.analysis_time_step`.
displaying metrics such
as output time [s], step size [s], time step solution time [s], assembly time [s],
Dirichlet time [s], and linear solver time [s].

.. GENERATED FROM PYTHON SOURCE LINES 63-69

.. code-block:: Python


    df_ts = analysis_time_step(df_log)
    df_ts = df_ts.loc[0]
    # Removing MPI_process (index=0) from result (all are 0) for serial log.
    df_ts






.. 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>output_time</th>
          <th>step_size</th>
          <th>time_step_solution_time</th>
          <th>assembly_time</th>
          <th>dirichlet_time</th>
          <th>linear_solver_time</th>
        </tr>
        <tr>
          <th>time_step</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.004476</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>NaN</td>
          <td>1.000000e-07</td>
          <td>0.142436</td>
          <td>0.048265</td>
          <td>0.000944</td>
          <td>0.092496</td>
        </tr>
        <tr>
          <th>2</th>
          <td>NaN</td>
          <td>1.000000e-05</td>
          <td>0.041676</td>
          <td>0.013362</td>
          <td>0.000503</td>
          <td>0.027244</td>
        </tr>
        <tr>
          <th>3</th>
          <td>NaN</td>
          <td>1.000000e-03</td>
          <td>0.033367</td>
          <td>0.019580</td>
          <td>0.000451</td>
          <td>0.012804</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>23</th>
          <td>NaN</td>
          <td>4.000000e+07</td>
          <td>0.531541</td>
          <td>0.177440</td>
          <td>0.004848</td>
          <td>0.343803</td>
        </tr>
        <tr>
          <th>24</th>
          <td>NaN</td>
          <td>2.000000e+08</td>
          <td>1.057740</td>
          <td>0.272943</td>
          <td>0.010794</td>
          <td>0.763421</td>
        </tr>
        <tr>
          <th>25</th>
          <td>NaN</td>
          <td>4.000000e+08</td>
          <td>3.149970</td>
          <td>0.666353</td>
          <td>0.014761</td>
          <td>2.459589</td>
        </tr>
        <tr>
          <th>26</th>
          <td>0.006782</td>
          <td>3.133499e+08</td>
          <td>2.406160</td>
          <td>0.550383</td>
          <td>0.012222</td>
          <td>1.833349</td>
        </tr>
      </tbody>
    </table>
    <p>27 rows × 6 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 70-71

Selecting specific metrics (3) and plotting using pandas plot function.

.. GENERATED FROM PYTHON SOURCE LINES 71-75

.. code-block:: Python

    df_ts[["assembly_time", "dirichlet_time", "linear_solver_time"]].plot(
        logy=True, grid=True
    )




.. image-sg:: /auto_examples/howto_simulation/images/sphx_glr_plot_101_logparser_analyses_001.png
   :alt: plot 101 logparser analyses
   :srcset: /auto_examples/howto_simulation/images/sphx_glr_plot_101_logparser_analyses_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <Axes: xlabel='time_step'>



.. GENERATED FROM PYTHON SOURCE LINES 76-100

Analysis of convergence criteria - Newton iterations
----------------------------------------------------
The :py:mod:`ogstools.logparser.analysis_convergence_newton_iteration`
function allows for the examination of convergence criteria based on
Newton iterations. The resulting table provides convergence metrics for monolithic processes.
For details, refer to the documentation on
<`convergence_criterion
<https://doxygen.opengeosys.org/d4/d58/classnumlib_1_1convergencecriterion>`_ > defined in in the prj file.

* **|x|** is a norm of a vector of the global component (e.g. pressure, temperature, displacement).
* **|dx|** is the change of a norm of the global component between 2 iteration of non linear solver.
* **|dx|/|x|** is the relative change of a norm of the global component

For this example we had defined in the prj-file:

.. code-block:: python

   <convergence_criterion>
     <type>DeltaX</type>
     <norm_type>NORM2</norm_type>
     <abstol>1.e-3</abstol>
   </convergence_criterion>

The resulting table contains `|x|`, `|dx|` and `|dx|/|x|` at different time steps, processes and non linear solver iterations.

.. GENERATED FROM PYTHON SOURCE LINES 103-107

.. code-block:: Python


    analysis_convergence_newton_iteration(df_log)







.. 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></th>
          <th></th>
          <th>dx</th>
          <th>dx_x</th>
          <th>x</th>
        </tr>
        <tr>
          <th>time_step</th>
          <th>process</th>
          <th>iteration_number</th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="2" valign="top">1</th>
          <th rowspan="2" valign="top">0</th>
          <th>1</th>
          <td>2.559100e+07</td>
          <td>2.530300e-02</td>
          <td>1.011400e+09</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.000000e+00</td>
          <td>0.000000e+00</td>
          <td>1.011400e+09</td>
        </tr>
        <tr>
          <th>2</th>
          <th>0</th>
          <th>1</th>
          <td>0.000000e+00</td>
          <td>0.000000e+00</td>
          <td>1.011400e+09</td>
        </tr>
        <tr>
          <th>3</th>
          <th>0</th>
          <th>1</th>
          <td>0.000000e+00</td>
          <td>0.000000e+00</td>
          <td>1.011400e+09</td>
        </tr>
        <tr>
          <th>...</th>
          <th>...</th>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th rowspan="4" valign="top">26</th>
          <th rowspan="4" valign="top">0</th>
          <th>17</th>
          <td>9.385500e-03</td>
          <td>9.286300e-12</td>
          <td>1.010700e+09</td>
        </tr>
        <tr>
          <th>18</th>
          <td>1.646800e-03</td>
          <td>1.629400e-12</td>
          <td>1.010700e+09</td>
        </tr>
        <tr>
          <th>19</th>
          <td>2.994300e-03</td>
          <td>2.962700e-12</td>
          <td>1.010700e+09</td>
        </tr>
        <tr>
          <th>20</th>
          <td>5.881000e-04</td>
          <td>5.818800e-13</td>
          <td>1.010700e+09</td>
        </tr>
      </tbody>
    </table>
    <p>136 rows × 3 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 108-116

Staggered
~~~~~~~~~
The resulting table provides convergence criteria for staggered coupled processes,
utilizing :py:mod:`ogstools.logparser.analysis_convergence_coupling_iteration`
Logs are generated from running
`ogs benchmark: HeatTransportInStationaryFlow
<https://gitlab.opengeosys.org/ogs/ogs/-/blob/master/Tests/Data/Parabolic/HT/HeatTransportInStationaryFlow/HeatTransportInStationaryFlow.prj>`_


.. GENERATED FROM PYTHON SOURCE LINES 116-123

.. code-block:: Python

    log = log_staggered
    records = parse_file(log)
    df_records = pd.DataFrame(records)
    df_log = fill_ogs_context(df_records)

    # Only for staggered coupled processes !
    analysis_convergence_coupling_iteration(df_log)





.. 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;
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        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th></th>
          <th>dx</th>
          <th>dx_x</th>
          <th>x</th>
        </tr>
        <tr>
          <th>time_step</th>
          <th>coupling_iteration</th>
          <th>coupling_iteration_process</th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="4" valign="top">1</th>
          <th rowspan="2" valign="top">1</th>
          <th>0</th>
          <td>0.35337</td>
          <td>0.17246</td>
          <td>2.049</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.00000</td>
          <td>0.00000</td>
          <td>1271200.000</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">2</th>
          <th>0</th>
          <td>0.00000</td>
          <td>0.00000</td>
          <td>2.049</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.00000</td>
          <td>0.00000</td>
          <td>1271200.000</td>
        </tr>
        <tr>
          <th>...</th>
          <th>...</th>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">49</th>
          <th rowspan="2" valign="top">1</th>
          <th>0</th>
          <td>0.00000</td>
          <td>0.00000</td>
          <td>8.293</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.00000</td>
          <td>0.00000</td>
          <td>1271200.000</td>
        </tr>
        <tr>
          <th rowspan="2" valign="top">50</th>
          <th rowspan="2" valign="top">1</th>
          <th>0</th>
          <td>0.00000</td>
          <td>0.00000</td>
          <td>8.379</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.00000</td>
          <td>0.00000</td>
          <td>1271200.000</td>
        </tr>
      </tbody>
    </table>
    <p>102 rows × 3 columns</p>
    </div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 0.180 seconds)


.. _sphx_glr_download_auto_examples_howto_simulation_plot_101_logparser_analyses.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_101_logparser_analyses.ipynb <plot_101_logparser_analyses.ipynb>`

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

      :download:`Download Python source code: plot_101_logparser_analyses.py <plot_101_logparser_analyses.py>`

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

      :download:`Download zipped: plot_101_logparser_analyses.zip <plot_101_logparser_analyses.zip>`