.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/10_normalizer/00_lognormal_kriging.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_10_normalizer_00_lognormal_kriging.py>`
        to download the full example code.

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

.. _sphx_glr_examples_10_normalizer_00_lognormal_kriging.py:


Log-Normal Kriging
------------------

Log Normal kriging is a term to describe a special workflow for kriging to
deal with log-normal data, like conductivity or transmissivity in hydrogeology.

It simply means to first convert the input data to a normal distribution, i.e.
applying a logarithic function, then interpolating these values with kriging
and transforming the result back with the exponential function.

The resulting kriging variance describes the error variance of the log-values
of the target variable.

In this example we will use ordinary kriging.

.. GENERATED FROM PYTHON SOURCE LINES 17-30

.. code-block:: Python


    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]
    # resulting grid
    gridx = np.linspace(0.0, 15.0, 151)
    # stable covariance model
    model = gs.Stable(dim=1, var=0.5, len_scale=2.56, alpha=1.9)








.. GENERATED FROM PYTHON SOURCE LINES 31-33

In order to result in log-normal kriging, we will use the :any:`LogNormal`
Normalizer. This is a parameter-less normalizer, so we don't have to fit it.

.. GENERATED FROM PYTHON SOURCE LINES 33-35

.. code-block:: Python

    normalizer = gs.normalizer.LogNormal








.. GENERATED FROM PYTHON SOURCE LINES 36-42

Now we generate the interpolated field as well as the mean field.
This can be done by setting `only_mean=True` in :any:`Krige.__call__`.
The result is then stored as `mean_field`.

In terms of log-normal kriging, this mean represents the geometric mean of
the field.

.. GENERATED FROM PYTHON SOURCE LINES 42-48

.. code-block:: Python

    krige = gs.krige.Ordinary(model, cond_pos, cond_val, normalizer=normalizer)
    # interpolate the field
    krige(gridx)
    # also generate the mean field
    krige(gridx, only_mean=True)








.. GENERATED FROM PYTHON SOURCE LINES 49-50

And that's it. Let's have a look at the results.

.. GENERATED FROM PYTHON SOURCE LINES 50-56

.. code-block:: Python

    ax = krige.plot()
    # plotting the geometric mean
    krige.plot("mean_field", ax=ax)
    # plotting the conditioning data
    ax.scatter(cond_pos, cond_val, color="k", zorder=10, label="Conditions")
    ax.legend()



.. image-sg:: /examples/10_normalizer/images/sphx_glr_00_lognormal_kriging_001.png
   :alt: Field 1D: (151,)
   :srcset: /examples/10_normalizer/images/sphx_glr_00_lognormal_kriging_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.054 seconds)


.. _sphx_glr_download_examples_10_normalizer_00_lognormal_kriging.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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