.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/real/Moureze.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_real_Moureze.py>`
        to download the full example code.

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

.. _sphx_glr_examples_real_Moureze.py:


Geomodeling benchmark: the "Moureze"-Model
==========================================

This model is part of a geomodeling benchmaring effort. More information (and, hopefully, publication) coming.

.. GENERATED FROM PYTHON SOURCE LINES 7-10

.. code-block:: Python


    import os








.. GENERATED FROM PYTHON SOURCE LINES 11-12

These two lines are necessary only if gempy is not installed

.. GENERATED FROM PYTHON SOURCE LINES 12-23

.. code-block:: Python


    # Importing gempy
    import gempy as gp
    import gempy_viewer as gpv

    # Aux imports
    import numpy as np
    import pandas as pd

    from gempy_engine.config import AvailableBackends








.. GENERATED FROM PYTHON SOURCE LINES 24-30

Loading surface points from repository:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

With pandas we can do it directly from the web and with the right args
we can directly tidy the data in gempy style:


.. GENERATED FROM PYTHON SOURCE LINES 32-53

.. code-block:: Python


    data_path = os.path.abspath('../../data/input_data/Moureze')
    Moureze_points = pd.read_csv(
        filepath_or_buffer=data_path + '/Moureze_Points.csv',
        sep=';',
        names=['X', 'Y', 'Z', 'G_x', 'G_y', 'G_z', '_'],
        header=0,
    )

    Sections_EW = pd.read_csv(
        filepath_or_buffer=data_path + '/Sections_EW.csv',
        sep=';',
        names=['X', 'Y', 'Z', 'ID', '_'], header=1
    ).dropna()

    Sections_NS = pd.read_csv(
        filepath_or_buffer=data_path + '/Sections_NS.csv',
        sep=';',
        names=['X', 'Y', 'Z', 'ID', '_'], header=1
    ).dropna()








.. GENERATED FROM PYTHON SOURCE LINES 54-56

Extracting the orientatins:


.. GENERATED FROM PYTHON SOURCE LINES 58-62

.. code-block:: Python

    mask_surfpoints = Moureze_points['G_x'] < -9999
    surface_points = Moureze_points[mask_surfpoints][::10]
    orientations = Moureze_points[~mask_surfpoints][::10]








.. GENERATED FROM PYTHON SOURCE LINES 63-65

Giving an arbitrary value name to the surface


.. GENERATED FROM PYTHON SOURCE LINES 67-70

.. code-block:: Python

    surface_points['surface'] = '0'
    orientations['surface'] = '0'








.. GENERATED FROM PYTHON SOURCE LINES 71-73

.. code-block:: Python

    surface_points.tail()






.. 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>G_x</th>
          <th>G_y</th>
          <th>G_z</th>
          <th>_</th>
          <th>surface</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>3370</th>
          <td>224.000000</td>
          <td>65.335228</td>
          <td>-120.0000</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>0.670953</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3386</th>
          <td>232.000000</td>
          <td>20.000000</td>
          <td>-85.1014</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>0.700194</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3396</th>
          <td>135.939468</td>
          <td>154.308319</td>
          <td>-154.0210</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>0.351625</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3411</th>
          <td>296.000000</td>
          <td>378.000000</td>
          <td>-156.4050</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>0.852947</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3424</th>
          <td>91.960609</td>
          <td>151.813431</td>
          <td>-83.2593</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>-99999.0</td>
          <td>0.950689</td>
          <td>0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 74-76

.. code-block:: Python

    orientations.tail()






.. 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>G_x</th>
          <th>G_y</th>
          <th>G_z</th>
          <th>_</th>
          <th>surface</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>3186</th>
          <td>139.936676</td>
          <td>207.712677</td>
          <td>-154.766</td>
          <td>-0.034360</td>
          <td>0.068784</td>
          <td>0.997040</td>
          <td>0.050783</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3254</th>
          <td>109.926086</td>
          <td>47.649914</td>
          <td>-135.337</td>
          <td>0.351890</td>
          <td>-0.766284</td>
          <td>-0.537571</td>
          <td>0.942560</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3315</th>
          <td>158.341888</td>
          <td>211.320877</td>
          <td>-147.312</td>
          <td>0.053863</td>
          <td>0.143364</td>
          <td>0.988203</td>
          <td>0.053091</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3371</th>
          <td>0.734351</td>
          <td>90.000000</td>
          <td>-100.000</td>
          <td>0.756724</td>
          <td>0.533905</td>
          <td>0.377245</td>
          <td>0.170960</td>
          <td>0</td>
        </tr>
        <tr>
          <th>3409</th>
          <td>47.970890</td>
          <td>129.885971</td>
          <td>-132.010</td>
          <td>-0.453523</td>
          <td>-0.849487</td>
          <td>0.269609</td>
          <td>0.016933</td>
          <td>0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 77-88

Data initialization:
~~~~~~~~~~~~~~~~~~~~

Suggested size of the axis-aligned modeling box:

Origin: -5 -5 -200

Maximum: 305 405 -50

Suggested resolution: 2m (grid size 156 x 206 x 76)


.. GENERATED FROM PYTHON SOURCE LINES 91-94

Only using one orientation because otherwhise it gets a mess
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


.. GENERATED FROM PYTHON SOURCE LINES 96-97

Number voxels

.. GENERATED FROM PYTHON SOURCE LINES 97-99

.. code-block:: Python

    np.array([156, 206, 76]).prod()





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

 .. code-block:: none


    np.int64(2442336)



.. GENERATED FROM PYTHON SOURCE LINES 100-136

.. code-block:: Python

    resolution_requ = [156, 206, 76]
    resolution = [77, 103, 38]
    resolution_low = [45, 51, 38]


    surface_points_table: gp.data.SurfacePointsTable = gp.data.SurfacePointsTable.from_arrays(
        x=surface_points['X'].values,
        y=surface_points['Y'].values,
        z=surface_points['Z'].values,
        names=surface_points['surface'].values.astype(str)
    )

    orientations_table: gp.data.OrientationsTable = gp.data.OrientationsTable.from_arrays(
        x=orientations['X'].values,
        y=orientations['Y'].values,
        z=orientations['Z'].values,
        G_x=orientations['G_x'].values,
        G_y=orientations['G_y'].values,
        G_z=orientations['G_z'].values,
        names=orientations['surface'].values.astype(str),
        name_id_map=surface_points_table.name_id_map  # ! Make sure that ids and names are shared
    )

    structural_frame: gp.data.StructuralFrame = gp.data.StructuralFrame.from_data_tables(
        surface_points=surface_points_table,
        orientations=orientations_table
    )

    geo_model: gp.data.GeoModel = gp.create_geomodel(
        project_name='Moureze',
        extent=[-5, 305, -5, 405, -200, -50],
        # resolution=resolution_low,
        refinement=5,
        structural_frame=structural_frame
    )








.. GENERATED FROM PYTHON SOURCE LINES 137-139

Now we can see how the data looks so far:


.. GENERATED FROM PYTHON SOURCE LINES 141-144

.. code-block:: Python

    gpv.plot_2d(geo_model, direction='y')





.. image-sg:: /examples/real/images/sphx_glr_Moureze_001.png
   :alt: Cell Number: mid Direction: y
   :srcset: /examples/real/images/sphx_glr_Moureze_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7fbc9ec7a3e0>



.. GENERATED FROM PYTHON SOURCE LINES 145-149

The default range is always the diagonal of the extent. Since in this
model data is very close we will need to reduce the range to 5-10% of
that value:


.. GENERATED FROM PYTHON SOURCE LINES 151-156

.. code-block:: Python

    geo_model.interpolation_options.kernel_options.range *= 0.2
    geo_model.interpolation_options.evaluation_options.verbose = True
    geo_model.interpolation_options.evaluation_options.octree_error_threshold = 1.5
    geo_model.interpolation_options.evaluation_options.number_octree_levels_surface = 5








.. GENERATED FROM PYTHON SOURCE LINES 157-167

.. code-block:: Python

    gp.compute_model(
        gempy_model=geo_model,
        engine_config=gp.data.GemPyEngineConfig(
            use_gpu=False, 
            dtype='float32',
            backend=AvailableBackends.PYTORCH
        )
    )





.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/real/images/sphx_glr_Moureze_002.png
         :alt: Voxel Scalar Values with Refinement Status
         :srcset: /examples/real/images/sphx_glr_Moureze_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/real/images/sphx_glr_Moureze_003.png
         :alt: Voxel Scalar Values with Refinement Status
         :srcset: /examples/real/images/sphx_glr_Moureze_003.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    Setting Backend To: AvailableBackends.PYTORCH
    Number of voxels marked by stats: 437 of torch.Size([512]).
     Number of voxels marked by corners : 408
    Total voxels: 437
    Dense Grid would be 512 voxels
    Chunking done: 30 chunks
    Number of voxels marked by stats: 3053 of torch.Size([3736]).
     Number of voxels marked by corners : 1989
    Total voxels: 3053
    Dense Grid would be 4096 voxels
    Chunking done: 25 chunks
    Chunking done: 197 chunks
    Chunking done: 35 chunks


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <b>Solutions:</b> 5 Octree Levels, 1 DualContouringMeshes
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 168-193

Time
~~~~

300k voxels 3.5k points
^^^^^^^^^^^^^^^^^^^^^^^

-  Nvidia 2080: 500 ms ± 1.3 ms per loop (mean ± std. dev. of 7 runs, 1
   loop each), Memory 1 Gb
-  CPU 14.2 s ± 82.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop
   each), Memory: 1.3 Gb

2.4 M voxels, 3.5k points
^^^^^^^^^^^^^^^^^^^^^^^^^

-  CPU 2min 33s ± 216 ms per loop (mean ± std. dev. of 7 runs, 1 loop
   each) Memory: 1.3 GB
-  Nvidia 2080: 1.92 s ± 6.74 ms per loop (mean ± std. dev. of 7 runs, 1
   loop each) 1 Gb

2.4 M voxels, 3.5k points 3.5 k orientations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-  Nvidia 2080: 2.53 s ± 1.31 ms per loop (mean ± std. dev. of 7 runs, 1
   loop each)


.. GENERATED FROM PYTHON SOURCE LINES 195-197

.. code-block:: Python


    gpv.plot_2d(geo_model, cell_number='mid', series_n=0, show_scalar=True)



.. image-sg:: /examples/real/images/sphx_glr_Moureze_004.png
   :alt: Cell Number: mid Direction: y
   :srcset: /examples/real/images/sphx_glr_Moureze_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7fbc9ec7aec0>



.. GENERATED FROM PYTHON SOURCE LINES 198-200

.. code-block:: Python

    gpv.plot_2d(geo_model, cell_number='mid', show_data=True, direction='y')




.. image-sg:: /examples/real/images/sphx_glr_Moureze_005.png
   :alt: Cell Number: mid Direction: y
   :srcset: /examples/real/images/sphx_glr_Moureze_005.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7fbcb5a16890>



.. GENERATED FROM PYTHON SOURCE LINES 201-202

sphinx_gallery_thumbnail_number = 4

.. GENERATED FROM PYTHON SOURCE LINES 202-203

.. code-block:: Python

    gpv.plot_3d(geo_model)



.. image-sg:: /examples/real/images/sphx_glr_Moureze_006.png
   :alt: Moureze
   :srcset: /examples/real/images/sphx_glr_Moureze_006.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gempy_viewer.modules.plot_3d.vista.GemPyToVista object at 0x7fbc9ec7add0>




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

   **Total running time of the script:** (0 minutes 10.680 seconds)


.. _sphx_glr_download_examples_real_Moureze.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_