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

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

.. _sphx_glr_auto_examples_howto_plot_plot_observation_points.py:


How to plot data at observation points
======================================

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

In this example we plot the data values on observation points over all
timesteps. Since requested observation points don't necessarily coincide with
actual nodes of the mesh different interpolation options are available. See
:py:mod:`ogstools.meshlib.mesh_series.MeshSeries.probe` for more details.
Here we use a component transport example from the ogs benchmark gallery
(https://www.opengeosys.org/docs/benchmarks/hydro-component/elder/).

.. GENERATED FROM PYTHON SOURCE LINES 16-27

.. code-block:: Python



    import matplotlib.pyplot as plt
    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 33-43

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`

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Let's define 4 observation points and plot them on the mesh.

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.. code-block:: Python

    rows = np.array([np.linspace([0, 0, z], [120, 0, z], 4) for z in [60, 40]])
    fig = mesh_series.mesh(0).plot_contourf(si)
    fig.axes[0].scatter(rows[..., 0], rows[..., 2], s=50, fc="none", ec="r", lw=3)
    for i, pt in enumerate(rows.reshape(-1, 3)):
        fig.axes[0].annotate(str(i), (pt[0], pt[2] - 5), va="top", fontsize=32)




.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_observation_points_001.png
   :alt: plot observation points
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_observation_points_001.png
   :class: sphx-glr-single-img





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And now probe the points and plot the values over time.

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.. code-block:: Python

    labels = [
        [f"{i}: x={pt[0]: >5} z={pt[2]}" for i, pt in enumerate(pts)]
        for pts in rows
    ]
    ms_pts = [ot.MeshSeries.extract_probe(mesh_series, pts) for pts in rows]
    fig = ot.plot.line(ms_pts[0], "time", si, labels=labels[0], monospace=True)




.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_observation_points_002.png
   :alt: plot observation points
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_observation_points_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 66-71

You can also pass create your own matplotlib figure and pass the axes object.
Additionally, you can pass any keyword arguments which are known by
matplotlibs plot function to further customize the curves.
To show, how to customize a plot afterwards, we add the mean saturation of the
4 observation points per row to each subplot.

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.. code-block:: Python

    fig, axs = plt.subplots(nrows=2, figsize=[16, 10], sharey=True)
    ot.plot.line(ms_pts[0], "time", si, ax=axs[0], color="k", fontsize=20)
    ot.plot.line(ms_pts[1], "time", si, ax=axs[1], marker="o", fontsize=20)
    # add the mean of the observation point timeseries
    for index in range(2):
        values = si.transform(ms_pts[index])
        mean_values = np.mean((values), axis=-1)
        ts = ms_pts[index].timevalues
        fig.axes[index].plot(ts, mean_values, "rk"[index], lw=4)
        fig.axes[index].legend(labels[index] + ["mean"], fontsize=20)



.. image-sg:: /auto_examples/howto_plot/images/sphx_glr_plot_observation_points_003.png
   :alt: plot observation points
   :srcset: /auto_examples/howto_plot/images/sphx_glr_plot_observation_points_003.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 3.293 seconds)


.. _sphx_glr_download_auto_examples_howto_plot_plot_observation_points.py:

.. only:: html

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

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

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

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

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

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

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