.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/06_conditioned_fields/00_condition_ensemble.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_06_conditioned_fields_00_condition_ensemble.py>`
        to download the full example code.

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

.. _sphx_glr_examples_06_conditioned_fields_00_condition_ensemble.py:


Conditioning with Ordinary Kriging
----------------------------------

Here we use ordinary kriging in 1D (for plotting reasons)
with 5 given observations/conditions,
to generate an ensemble of conditioned random fields.

.. GENERATED FROM PYTHON SOURCE LINES 9-20

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    import gstools as gs

    # condtions
    cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]
    cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
    gridx = np.linspace(0.0, 15.0, 151)








.. GENERATED FROM PYTHON SOURCE LINES 21-27

The conditioned spatial random field class depends on a Krige class in order
to handle the conditions.
This is created as described in the kriging tutorial.

Here we use a Gaussian covariance model and ordinary kriging for conditioning
the spatial random field.

.. GENERATED FROM PYTHON SOURCE LINES 27-33

.. code-block:: Python


    model = gs.Gaussian(dim=1, var=0.5, len_scale=1.5)
    krige = gs.krige.Ordinary(model, cond_pos, cond_val)
    cond_srf = gs.CondSRF(krige)
    cond_srf.set_pos(gridx)








.. GENERATED FROM PYTHON SOURCE LINES 34-36

To generate the ensemble we will use a seed-generator.
We can specify individual names for each field by the keyword `store`:

.. GENERATED FROM PYTHON SOURCE LINES 36-60

.. code-block:: Python


    seed = gs.random.MasterRNG(20170519)
    for i in range(100):
        cond_srf(seed=seed(), store=f"f{i}")
        label = "Conditioned ensemble" if i == 0 else None
        plt.plot(gridx, cond_srf[f"f{i}"], color="k", alpha=0.1, label=label)

    fields = [cond_srf[f"f{i}"] for i in range(100)]
    plt.plot(gridx, cond_srf.krige(only_mean=True), label="estimated mean")
    plt.plot(gridx, np.mean(fields, axis=0), linestyle=":", label="Ensemble mean")
    plt.plot(gridx, cond_srf.krige.field, linestyle="dashed", label="kriged field")
    plt.scatter(cond_pos, cond_val, color="k", zorder=10, label="Conditions")
    # 99 percent confidence interval
    conf = gs.tools.confidence_scaling(0.99)
    plt.fill_between(
        gridx,
        cond_srf.krige.field - conf * np.sqrt(cond_srf.krige.krige_var),
        cond_srf.krige.field + conf * np.sqrt(cond_srf.krige.krige_var),
        alpha=0.3,
        label="99% confidence interval",
    )
    plt.legend()
    plt.show()




.. image-sg:: /examples/06_conditioned_fields/images/sphx_glr_00_condition_ensemble_001.png
   :alt: 00 condition ensemble
   :srcset: /examples/06_conditioned_fields/images/sphx_glr_00_condition_ensemble_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 61-64

As you can see, the kriging field coincides with the ensemble mean of the
conditioned random fields and the estimated mean
is the mean of the far-field.


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

   **Total running time of the script:** (0 minutes 0.653 seconds)


.. _sphx_glr_download_examples_06_conditioned_fields_00_condition_ensemble.py:

.. only:: html

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

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      :download:`Download Jupyter notebook: 00_condition_ensemble.ipynb <00_condition_ensemble.ipynb>`

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

      :download:`Download Python source code: 00_condition_ensemble.py <00_condition_ensemble.py>`

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      :download:`Download zipped: 00_condition_ensemble.zip <00_condition_ensemble.zip>`


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

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