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

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

.. _sphx_glr_auto_examples_howto_postprocessing_plot_aggregate.py:


Aggregation of Meshseries Data
==============================

.. sectionauthor:: Florian Zill (Helmholtz Centre for Environmental Research GmbH - UFZ)

In this example we show how to aggregate data in a model over all timesteps as
well as plot differences between to timesteps. For this purpose we use
a component transport example from the ogs benchmark gallery
(https://www.opengeosys.org/docs/benchmarks/hydro-component/elder/).

To see this benchmark results over all timesteps have a look at
:ref:`sphx_glr_auto_examples_howto_plot_plot_animation.py`.

.. GENERATED FROM PYTHON SOURCE LINES 17-23

.. code-block:: Python

    import ogstools as ogs
    from ogstools import examples

    mesh_series = examples.load_meshseries_CT_2D_XDMF()
    saturation = ogs.variables.saturation








.. GENERATED FROM PYTHON SOURCE LINES 24-34

To read your own data as a mesh series you can do:

..  code-block:: python

  from ogstools.meshlib import MeshSeries
  mesh_series = MeshSeries("filepath/filename_pvd_or_xdmf")

You can also use a variable from the available presets instead of needing to
create your own:
:ref:`sphx_glr_auto_examples_howto_postprocessing_plot_variables.py`

.. GENERATED FROM PYTHON SOURCE LINES 36-43

You aggregate the data in MeshSeries over all timesteps given some
aggregation function, e.g. "min", "max", "var"
(see: :meth:`~ogstools.meshlib.mesh_series.MeshSeries.aggregate_over_time`).
The following code gets the maximum saturation for each point in the mesh over
all timesteps and plots it. Note: the data in the returned mesh has a suffix
equal to the aggregation functions name. The plot function will find the
correct data anyway if given the original variable

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: Python

    mesh = mesh_series.aggregate_over_time(saturation, "max")
    fig = mesh.plot_contourf(saturation)




.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_001.png
   :alt: plot aggregate
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 49-52

It is also possible to plot the time when the minimum or maximum occurs.
However, here we have to use a new variable for the plot to handle the
units correctly:

.. GENERATED FROM PYTHON SOURCE LINES 54-57

.. code-block:: Python

    mesh = mesh_series.time_of_max(saturation)
    fig = mesh.plot_contourf(ogs.variables.Scalar("max_Saturation_time", "s", "a"))




.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_002.png
   :alt: plot aggregate
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 58-59

Likewise we can calculate and visualize the variance of the saturation:

.. GENERATED FROM PYTHON SOURCE LINES 62-65

.. code-block:: Python

    mesh = mesh_series.aggregate_over_time(saturation, "var")
    fig = mesh.plot_contourf(saturation)




.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_003.png
   :alt: plot aggregate
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 66-67

Difference between the last and the first timestep:

.. GENERATED FROM PYTHON SOURCE LINES 69-72

.. code-block:: Python

    mesh = mesh_series.mesh(-1).difference(mesh_series.mesh(0), saturation)
    fig = mesh.plot_contourf(saturation)




.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_004.png
   :alt: plot aggregate
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 73-75

It's also possible to aggregate the data per timestep to return a timeseries
of e.g. the max or mean value of a variable in the entire domain.

.. GENERATED FROM PYTHON SOURCE LINES 77-78

.. code-block:: Python

    fig = mesh_series.plot_domain_aggregate(saturation, "mean", time_unit="a")



.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_005.png
   :alt: plot aggregate
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_aggregate_005.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.234 seconds)


.. _sphx_glr_download_auto_examples_howto_postprocessing_plot_aggregate.py:

.. only:: html

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

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

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

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

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

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

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