.. 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 20-31

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np
    import pyvista as pv

    import ogstools as ot
    from ogstools import examples

    mesh_series = examples.load_meshseries_CT_2D_XDMF().scale(time=("s", "a"))
    y = mesh_series[0].center[1]  # flat y coordinate of this 2D mesh is not 0
    si = ot.variables.saturation








.. GENERATED FROM PYTHON SOURCE LINES 32-33

Now we setup two sampling lines.

.. GENERATED FROM PYTHON SOURCE LINES 35-41

.. code-block:: Python

    pts_vert = np.linspace([25, y, -75], [25, y, 75], num=100)
    pts_diag = np.linspace([25, y, 75], [100, y, 0], num=100)
    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 0x7b2706103a30>]



.. GENERATED FROM PYTHON SOURCE LINES 42-44

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

.. GENERATED FROM PYTHON SOURCE LINES 46-54

.. code-block:: Python

    fig, ax = plt.subplots(figsize=[15, 8])
    for mesh, timevalue in zip(mesh_series, mesh_series.timevalues, strict=True):
        sample = pv.PolyData(pts_vert).sample(mesh)
        color = str(0.8 * timevalue / mesh_series.timevalues[-1])
        label = f"{timevalue:.1f} a"
        fig = ot.plot.line(
            sample, si, "z", ax=ax, label=label, color=color, fontsize=20
        )



.. 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 55-60

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`
automatically detects if the line lies on a cardinal direction and labels the
y-axes with the changing spatial dimension.

.. GENERATED FROM PYTHON SOURCE LINES 62-64

.. code-block:: Python

    fig = mesh_series.plot_time_slice(si, pts_vert)




.. 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 65-69

By default the plot is smoothened with interpolation. When deactivated, we
see the edges of the raw sampled data. When using the interpolation, be sure
to adjust the number of sampling points if the MeshSeries contains a lot of
small timesteps.

.. GENERATED FROM PYTHON SOURCE LINES 69-71

.. code-block:: Python

    fig = mesh_series.plot_time_slice(si, pts_vert, interpolate=False)




.. 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 72-76

If the line doesn't point in a cardinal direction the distance along the
line is used for the y-axis by default. You can however, specify if you want
to use spatial dimension via the argument "y_axis". This may be useful when
plotting data of an edge / boundary of the mesh.

.. GENERATED FROM PYTHON SOURCE LINES 76-77

.. code-block:: Python

    fig = mesh_series.plot_time_slice(si, pts_diag)



.. 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 1.597 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>`