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

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

.. _sphx_glr_auto_examples_howto_plot_plot_timeslice.py:


How to Create Time Slices
=========================

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

In this example we show how to create a filled contourplot of transient data
over a sampling line. 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 17-18

Let's load the data which we want to investigate.

.. GENERATED FROM PYTHON SOURCE LINES 20-28

.. code-block:: Python

    import numpy as np

    import ogstools as ot
    from ogstools import examples

    mesh_series = examples.load_meshseries_CT_2D_XDMF().scale(time=("s", "a"))
    si = ot.variables.saturation








.. GENERATED FROM PYTHON SOURCE LINES 29-30

Now we setup two sampling lines.

.. GENERATED FROM PYTHON SOURCE LINES 32-38

.. code-block:: Python

    pts_vert = np.linspace([25, 0, -75], [25, 0, 75], num=300)
    pts_diag = np.linspace([25, 0, 75], [100, 0, 0], num=300)
    fig = mesh_series.mesh(-1).plot_contourf(si, vmin=0)
    fig.axes[0].plot(pts_vert[:, 0], pts_vert[:, 2], "-k", linewidth=3)
    fig.axes[0].plot(pts_diag[:, 0], pts_diag[:, 2], "-.k", linewidth=3)




.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_001.png
   :alt: plot timeslice
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    [<matplotlib.lines.Line2D object at 0x780b2975fd90>]



.. GENERATED FROM PYTHON SOURCE LINES 39-41

Here, we first show a regular line sample plot for the vertical sampling line
for each timestep.

.. GENERATED FROM PYTHON SOURCE LINES 43-47

.. code-block:: Python

    ms_vert = ot.MeshSeries.extract_probe(mesh_series, pts_vert)
    labels = [f"{tv:.1f} a" for tv in ms_vert.timevalues]
    fig = ot.plot.line(ms_vert, si, "z", labels=labels, colors="coolwarm")




.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_002.png
   :alt: plot timeslice
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 48-52

As the above kind of plot is getting cluttered for lots of timesteps we
provide a function to create a filled contour plot over the transient data.
The function :meth:`~ogstools.meshlib.mesh_series.MeshSeries.plot_time_slice`
creates a heatmap over time and space.

.. GENERATED FROM PYTHON SOURCE LINES 54-56

.. code-block:: Python

    fig = ms_vert.plot_time_slice("time", "z", si, vmin=0, vmax=100)




.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_003.png
   :alt: plot timeslice
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 57-59

The stepping in this heatmap corresponds to the individual timesteps.
To create a smoother image, we can resample the MeshSeries to more timesteps.

.. GENERATED FROM PYTHON SOURCE LINES 61-64

.. code-block:: Python

    ms_vert_fine = ot.MeshSeries.resample(ms_vert, np.linspace(0, 4.2, 300))
    fig = ms_vert_fine.plot_time_slice("time", "z", si, vmin=0, vmax=100)




.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_004.png
   :alt: plot timeslice
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 65-67

You can also change the order of the arguments for spatial coordinate and time
to flip the x- and y-axis.

.. GENERATED FROM PYTHON SOURCE LINES 69-73

.. code-block:: Python

    ms_diag = ot.MeshSeries.extract_probe(mesh_series, pts_diag)
    ms_diag_fine = ot.MeshSeries.resample(ms_diag, np.linspace(0, 4.2, 300))
    fig = ms_diag_fine.plot_time_slice("x", "time", si, vmin=0, vmax=100)
    fig.axes[0].invert_yaxis()



.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_005.png
   :alt: plot timeslice
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_timeslice_005.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 3.864 seconds)


.. _sphx_glr_download_auto_examples_howto_plot_plot_timeslice.py:

.. only:: html

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

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

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

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

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

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

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