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

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

.. _sphx_glr_auto_examples_howto_postprocessing_plot_sample_mesh_line.py:


Extract a 1D profile from 2D and plot it
========================================

.. sectionauthor:: Feliks Kiszkurno (Helmholtz Centre for Environmental Research GmbH - UFZ)

.. GENERATED FROM PYTHON SOURCE LINES 9-15

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np

    import ogstools as ogs
    from ogstools import examples








.. GENERATED FROM PYTHON SOURCE LINES 16-20

Single fracture
------------------
Define a profile line by providing a list of points in x, y, z coordinates
and load an example data set:

.. GENERATED FROM PYTHON SOURCE LINES 22-26

.. code-block:: Python

    mesh = examples.load_meshseries_HT_2D_XDMF().mesh(-1)

    profile_HT = np.array([[4, 2, 0], [4, 18, 0]])








.. GENERATED FROM PYTHON SOURCE LINES 27-31

.. code-block:: Python

    mesh_sp, mesh_kp = ogs.meshlib.sample_polyline(
        mesh, ["pressure", "temperature"], profile_HT
    )








.. GENERATED FROM PYTHON SOURCE LINES 32-35

It has returned a pandas DataFrame containing all information about the
profile and a numpy array with the position of the "knot-points".
Let's investigate the DataFrame first:

.. GENERATED FROM PYTHON SOURCE LINES 37-39

.. code-block:: Python

    mesh_sp.head(10)






.. 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>x</th>
          <th>y</th>
          <th>z</th>
          <th>pressure</th>
          <th>temperature</th>
          <th>dist</th>
          <th>dist_in_segment</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>4.0</td>
          <td>2.00</td>
          <td>0.0</td>
          <td>0.498440</td>
          <td>79.850025</td>
          <td>0.00</td>
          <td>0.00</td>
        </tr>
        <tr>
          <th>1</th>
          <td>4.0</td>
          <td>2.16</td>
          <td>0.0</td>
          <td>0.498725</td>
          <td>79.850031</td>
          <td>0.16</td>
          <td>0.16</td>
        </tr>
        <tr>
          <th>2</th>
          <td>4.0</td>
          <td>2.32</td>
          <td>0.0</td>
          <td>0.499150</td>
          <td>79.850021</td>
          <td>0.32</td>
          <td>0.32</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4.0</td>
          <td>2.48</td>
          <td>0.0</td>
          <td>0.499663</td>
          <td>79.849999</td>
          <td>0.48</td>
          <td>0.48</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>6</th>
          <td>4.0</td>
          <td>2.96</td>
          <td>0.0</td>
          <td>0.501203</td>
          <td>79.849934</td>
          <td>0.96</td>
          <td>0.96</td>
        </tr>
        <tr>
          <th>7</th>
          <td>4.0</td>
          <td>3.12</td>
          <td>0.0</td>
          <td>0.501717</td>
          <td>79.849913</td>
          <td>1.12</td>
          <td>1.12</td>
        </tr>
        <tr>
          <th>8</th>
          <td>4.0</td>
          <td>3.28</td>
          <td>0.0</td>
          <td>0.502230</td>
          <td>79.849891</td>
          <td>1.28</td>
          <td>1.28</td>
        </tr>
        <tr>
          <th>9</th>
          <td>4.0</td>
          <td>3.44</td>
          <td>0.0</td>
          <td>0.502936</td>
          <td>79.849931</td>
          <td>1.44</td>
          <td>1.44</td>
        </tr>
      </tbody>
    </table>
    <p>10 rows × 7 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 40-49

We can see the spatial coordinates of points on the profile ("x", "y", "z"
- columns), distances from the beginning of the profile ("dist") and within
current segment ("dist_in_segment"). Note, that since we defined our profile
on only two points, there is only one segment, hence in this special case
columns dist and dist_in_segment are identical. At the end of the DataFrame
we can can find two columns with the variables that we are interested in:
"temperature" and "pressure". Each occupies one column, as those are scalar
values. Using columns "dist", "pressure" and "temperature" we can easily
plot the data:

.. GENERATED FROM PYTHON SOURCE LINES 51-74

.. code-block:: Python

    fig, ax = plt.subplots(1, 1, figsize=(7, 5))
    ax = mesh.plot_linesample(
        x="dist",
        variable="pressure",
        profile_points=profile_HT,
        ax=ax,
        fontsize=15,
    )
    ax_twinx = ax.twinx()
    ax_twinx = mesh.plot_linesample(
        x="dist",
        variable="temperature",
        profile_points=profile_HT,
        ax=ax_twinx,
        fontsize=15,
    )
    ogs.plot.utils.color_twin_axes(
        [ax, ax_twinx],
        [ogs.variables.pressure.color, ogs.variables.temperature.color],
    )
    fig.tight_layout()





.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_001.png
   :alt: plot sample mesh line
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 75-77

What happens when we are interested in a vector variable?
We can see it in the following example using the Darcy velocity:

.. GENERATED FROM PYTHON SOURCE LINES 79-83

.. code-block:: Python

    mesh_sp, mesh_kp = ogs.meshlib.sample_polyline(
        mesh, "darcy_velocity", profile_HT
    )








.. GENERATED FROM PYTHON SOURCE LINES 84-86

.. code-block:: Python

    mesh_sp.head(5)






.. 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>x</th>
          <th>y</th>
          <th>z</th>
          <th>darcy_velocity_0</th>
          <th>darcy_velocity_1</th>
          <th>dist</th>
          <th>dist_in_segment</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>4.0</td>
          <td>2.00</td>
          <td>0.0</td>
          <td>1.169737e-93</td>
          <td>-2.231183e-94</td>
          <td>0.00</td>
          <td>0.00</td>
        </tr>
        <tr>
          <th>1</th>
          <td>4.0</td>
          <td>2.16</td>
          <td>0.0</td>
          <td>1.196810e-93</td>
          <td>-2.421097e-94</td>
          <td>0.16</td>
          <td>0.16</td>
        </tr>
        <tr>
          <th>2</th>
          <td>4.0</td>
          <td>2.32</td>
          <td>0.0</td>
          <td>1.237164e-93</td>
          <td>-2.637041e-94</td>
          <td>0.32</td>
          <td>0.32</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4.0</td>
          <td>2.48</td>
          <td>0.0</td>
          <td>1.285968e-93</td>
          <td>-2.869550e-94</td>
          <td>0.48</td>
          <td>0.48</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4.0</td>
          <td>2.64</td>
          <td>0.0</td>
          <td>1.334773e-93</td>
          <td>-3.102060e-94</td>
          <td>0.64</td>
          <td>0.64</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 87-95

Now we have two columns for the variable. The Darcy velocity is a vector,
therefore "sample_over_polyline" has split it into two columns and appended
the variable name with increasing integer. Note, that this suffix has no
physical meaning and only indicates order. It is up to user to interpret it
in a meaningful way. By the
`OpenGeoSys conventions <https://www.opengeosys.org/docs/userguide/basics/conventions/#a-namesymmetric-tensorsa--symmetric-tensors-and-kelvin-mapping>`_,
"darcy_velocity_0" will be in the x-direction and "darcy_velocity_1" in
y-direction.

.. GENERATED FROM PYTHON SOURCE LINES 98-103

Elder benchmark
------------------
In this example we will use a Variable object from the ogstools to
sample the data. This allows "sample_over_polyline" to automatically
convert from the "data_unit" to the "output_unit":

.. GENERATED FROM PYTHON SOURCE LINES 105-108

.. code-block:: Python

    profile_CT = np.array([[47.0, 1.17, 72.0], [-4.5, 1.17, -59.0]])
    mesh = examples.load_meshseries_CT_2D_XDMF().mesh(11)








.. GENERATED FROM PYTHON SOURCE LINES 109-113

.. code-block:: Python

    mesh_sp, mesh_kp = ogs.meshlib.sample_polyline(
        mesh, ogs.variables.saturation, profile_CT
    )








.. GENERATED FROM PYTHON SOURCE LINES 114-116

As before we can see the profile parameters and propertiy values in a
DataFrame:

.. GENERATED FROM PYTHON SOURCE LINES 118-121

.. code-block:: Python

    mesh_sp.head(5)







.. 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>x</th>
          <th>y</th>
          <th>z</th>
          <th>Si</th>
          <th>dist</th>
          <th>dist_in_segment</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>47.000</td>
          <td>1.17</td>
          <td>72.00</td>
          <td>79.601252</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>46.485</td>
          <td>1.17</td>
          <td>70.69</td>
          <td>73.478046</td>
          <td>1.407595</td>
          <td>1.407595</td>
        </tr>
        <tr>
          <th>2</th>
          <td>45.970</td>
          <td>1.17</td>
          <td>69.38</td>
          <td>70.034378</td>
          <td>2.815191</td>
          <td>2.815191</td>
        </tr>
        <tr>
          <th>3</th>
          <td>45.455</td>
          <td>1.17</td>
          <td>68.07</td>
          <td>67.833706</td>
          <td>4.222786</td>
          <td>4.222786</td>
        </tr>
        <tr>
          <th>4</th>
          <td>44.940</td>
          <td>1.17</td>
          <td>66.76</td>
          <td>67.245709</td>
          <td>5.630382</td>
          <td>5.630382</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 122-124

This time we will prepare more complicated plot showing both the mesh and
the profile.

.. GENERATED FROM PYTHON SOURCE LINES 126-130

.. code-block:: Python

    fig, ax = mesh.plot_linesample_contourf(
        ogs.variables.saturation, profile_CT, resolution=100
    )




.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_002.png
   :alt: plot sample mesh line
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 131-136

THM
------
It is also possible to obtain more than one variable at the same time using
more complex profiles. They can be constructed by providing more than
2 points. With those points:

.. GENERATED FROM PYTHON SOURCE LINES 138-146

.. code-block:: Python

    profile_THM = np.array(
        [
            [-1000.0, -175.0, 6700.0],  # Point A
            [-600.0, -600.0, 6700.0],  # Point B
            [100.0, -300.0, 6700.0],  # Point C
            [3500, -900.0, 6700.0],  # Point D
        ]
    )







.. GENERATED FROM PYTHON SOURCE LINES 147-157

the profile will run as follows:

.. math::

  \text{AB} \rightarrow \text{BC} \rightarrow \text{CD}

Point B will at the same time be the last point in the first segment AB
and first one in second segment BC, however in the returned array,
it will occur only once.
For this example we will use a different dataset:

.. GENERATED FROM PYTHON SOURCE LINES 159-161

.. code-block:: Python

    mesh = examples.load_meshseries_THM_2D_PVD().mesh(-1)








.. GENERATED FROM PYTHON SOURCE LINES 162-169

.. code-block:: Python

    ms_THM_sp, dist_at_knot = ogs.meshlib.sample_polyline(
        mesh,
        [ogs.variables.pressure, ogs.variables.temperature],
        profile_THM,
        resolution=100,
    )








.. GENERATED FROM PYTHON SOURCE LINES 170-172

Again, we can investigate the returned DataFrame, but this time we will
have a look at its beginning:

.. GENERATED FROM PYTHON SOURCE LINES 174-176

.. code-block:: Python

    ms_THM_sp.head(5)






.. 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>x</th>
          <th>y</th>
          <th>z</th>
          <th>pressure</th>
          <th>temperature</th>
          <th>dist</th>
          <th>dist_in_segment</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>-1000.000000</td>
          <td>-175.000000</td>
          <td>6700.0</td>
          <td>2.230181</td>
          <td>8.832194</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>-969.230769</td>
          <td>-207.692308</td>
          <td>6700.0</td>
          <td>2.551761</td>
          <td>8.828883</td>
          <td>44.894683</td>
          <td>44.894683</td>
        </tr>
        <tr>
          <th>2</th>
          <td>-938.461538</td>
          <td>-240.384615</td>
          <td>6700.0</td>
          <td>2.873396</td>
          <td>8.825926</td>
          <td>89.789366</td>
          <td>89.789366</td>
        </tr>
        <tr>
          <th>3</th>
          <td>-907.692308</td>
          <td>-273.076923</td>
          <td>6700.0</td>
          <td>3.195135</td>
          <td>8.825316</td>
          <td>134.684048</td>
          <td>134.684048</td>
        </tr>
        <tr>
          <th>4</th>
          <td>-876.923077</td>
          <td>-305.769231</td>
          <td>6700.0</td>
          <td>3.516919</td>
          <td>8.821331</td>
          <td>179.578731</td>
          <td>179.578731</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 177-178

and end:

.. GENERATED FROM PYTHON SOURCE LINES 180-182

.. code-block:: Python

    ms_THM_sp.tail(10)






.. 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>x</th>
          <th>y</th>
          <th>z</th>
          <th>pressure</th>
          <th>temperature</th>
          <th>dist</th>
          <th>dist_in_segment</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>92</th>
          <td>3075.000000</td>
          <td>-825.000000</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>32.327025</td>
          <td>4366.176575</td>
          <td>3020.968388</td>
        </tr>
        <tr>
          <th>93</th>
          <td>3122.222222</td>
          <td>-833.333333</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>35.663869</td>
          <td>4414.128454</td>
          <td>3068.920267</td>
        </tr>
        <tr>
          <th>94</th>
          <td>3169.444444</td>
          <td>-841.666667</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>40.358542</td>
          <td>4462.080333</td>
          <td>3116.872146</td>
        </tr>
        <tr>
          <th>95</th>
          <td>3216.666667</td>
          <td>-850.000000</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>44.984874</td>
          <td>4510.032212</td>
          <td>3164.824025</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>98</th>
          <td>3358.333333</td>
          <td>-875.000000</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>34.045579</td>
          <td>4653.887850</td>
          <td>3308.679663</td>
        </tr>
        <tr>
          <th>99</th>
          <td>3405.555556</td>
          <td>-883.333333</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>31.436554</td>
          <td>4701.839729</td>
          <td>3356.631542</td>
        </tr>
        <tr>
          <th>100</th>
          <td>3452.777778</td>
          <td>-891.666667</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>28.867132</td>
          <td>4749.791608</td>
          <td>3404.583421</td>
        </tr>
        <tr>
          <th>101</th>
          <td>3500.000000</td>
          <td>-900.000000</td>
          <td>6700.0</td>
          <td>0.0</td>
          <td>26.706150</td>
          <td>4797.743487</td>
          <td>3452.535300</td>
        </tr>
      </tbody>
    </table>
    <p>10 rows × 7 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 183-186

Note, that unlike in the first example, here the columns "dist" and
"dist_in_segment" are not identical, as this time profile consists of
multiple segments. The following figure illustrates the difference:

.. GENERATED FROM PYTHON SOURCE LINES 186-196

.. code-block:: Python

    plt.rcdefaults()
    ax: plt.Axes
    fig, ax = plt.subplots(1, 1, figsize=(7, 3))
    ax.plot(ms_THM_sp["dist"], label="dist")
    ax.plot(ms_THM_sp["dist_in_segment"], label="dist_in_segment")
    ax.set_xlabel("Point ID / -")
    ax.set_ylabel("Distance / m")
    ax.legend()
    fig.tight_layout()




.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_003.png
   :alt: plot sample mesh line
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 197-201

The orange line returns to 0 twice. It is because of how the overlap of nodal
points between segments is handled. A nodal point always belongs to the
segment it starts: point B is included in segment BC but not AB and point
C in CD but not in in BC. The following figure shows the profile on the mesh:

.. GENERATED FROM PYTHON SOURCE LINES 203-204

plt.rcdefaults()

.. GENERATED FROM PYTHON SOURCE LINES 204-209

.. code-block:: Python

    fig, ax = mesh.plot_linesample_contourf(
        [ogs.variables.pressure, ogs.variables.temperature],
        profile_THM,
        resolution=100,
    )



.. image-sg:: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_004.png
   :alt: plot sample mesh line
   :srcset: /auto_examples/howto_postprocessing/images/sphx_glr_plot_sample_mesh_line_004.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.864 seconds)


.. _sphx_glr_download_auto_examples_howto_postprocessing_plot_sample_mesh_line.py:

.. only:: html

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

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

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

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

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

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

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