.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/11_plurigaussian/09_3d_tree_pgs.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_11_plurigaussian_09_3d_tree_pgs.py>`
        to download the full example code.

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

.. _sphx_glr_examples_11_plurigaussian_09_3d_tree_pgs.py:


Three dimensional PGS through decision trees
--------------------------------------------

Let's apply the decision tree approach to three dimensional PGS

.. GENERATED FROM PYTHON SOURCE LINES 7-21

.. code-block:: Python


    import numpy as np

    import gstools as gs

    dim = 3

    # no. of cells in all dimensions
    N = [60] * dim

    x = np.arange(N[0])
    y = np.arange(N[1])
    z = np.arange(N[2])








.. GENERATED FROM PYTHON SOURCE LINES 22-26

As we want to generate a three dimensional field, we generate three spatial
random fields (SRF) as our input. In this example, the number of fields
is equal to the number of dimensions, but as before, this is not a
requirement.

.. GENERATED FROM PYTHON SOURCE LINES 26-33

.. code-block:: Python


    model = gs.Gaussian(dim=dim, var=1, len_scale=[15, 15, 15])
    srf = gs.SRF(model)
    field1 = srf.structured([x, y, z], seed=20277519)
    field2 = srf.structured([x, y, z], seed=19727221)
    field3 = srf.structured([x, y, z], seed=21145612)








.. GENERATED FROM PYTHON SOURCE LINES 34-36

The decision tree will now utilise an ellipsoid as the decision boundary,
having a similar structure as the ellipse in the previous example.

.. GENERATED FROM PYTHON SOURCE LINES 36-66

.. code-block:: Python



    def ellipsoid(data, key1, key2, key3, c1, c2, c3, s1, s2, s3):
        return ((data[key1] - c1) / s1) ** 2 + ((data[key2] - c2) / s2) ** 2 + (
            (data[key3] - c3) / s3
        ) ** 2 <= 1


    config = {
        "root": {
            "type": "decision",
            "func": ellipsoid,
            "args": {
                "key1": "Z1",
                "key2": "Z2",
                "key3": "Z3",
                "c1": 0,
                "c2": 0,
                "c3": 0,
                "s1": 3,
                "s2": 1,
                "s3": 0.4,
            },
            "yes_branch": "phase1",
            "no_branch": "phase0",
        },
        "phase0": {"type": "leaf", "action": 0},
        "phase1": {"type": "leaf", "action": 1},
    }








.. GENERATED FROM PYTHON SOURCE LINES 67-69

As before, we initialise the PGS process, generate `P`, and plot to
visualise the results

.. GENERATED FROM PYTHON SOURCE LINES 69-79

.. code-block:: Python


    import pyvista as pv

    pgs = gs.PGS(dim, [field1, field2, field3])
    P = pgs(tree=config)

    grid = pv.ImageData(dimensions=N)
    grid.point_data["PGS"] = P.reshape(-1)
    grid.threshold(0.5, scalars="PGS").plot()








.. GENERATED FROM PYTHON SOURCE LINES 80-83

.. image:: ../../pics/3d_tree_pgs.png
   :width: 400px
   :align: center


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

   **Total running time of the script:** (0 minutes 20.500 seconds)


.. _sphx_glr_download_examples_11_plurigaussian_09_3d_tree_pgs.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 09_3d_tree_pgs.ipynb <09_3d_tree_pgs.ipynb>`

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

      :download:`Download Python source code: 09_3d_tree_pgs.py <09_3d_tree_pgs.py>`

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

      :download:`Download zipped: 09_3d_tree_pgs.zip <09_3d_tree_pgs.zip>`


.. only:: html

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

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