.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/10_normalizer/02_compare.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_02_compare.py>`
        to download the full example code.

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

.. _sphx_glr_examples_10_normalizer_02_compare.py:


Normalizer Comparison
---------------------

Let's compare the transformation behavior of the provided normalizers.

But first, we define a convenience routine and make some imports as always.

.. GENERATED FROM PYTHON SOURCE LINES 9-21

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    import gstools as gs


    def dashes(i=1, max_n=12, width=1):
        """Return line dashes."""
        return i * [width, width] + [max_n * 2 * width - 2 * i * width, width]









.. GENERATED FROM PYTHON SOURCE LINES 22-27

We select 4 normalizers depending on a single parameter lambda and
plot their transformation behavior within the interval [-5, 5].

For the shape parameter lambda, we create a list of 8 values ranging from
-1 to 2.5.

.. GENERATED FROM PYTHON SOURCE LINES 27-36

.. code-block:: Python


    lmbdas = [i * 0.5 for i in range(-2, 6)]
    normalizers = [
        gs.normalizer.BoxCox,
        gs.normalizer.YeoJohnson,
        gs.normalizer.Modulus,
        gs.normalizer.Manly,
    ]








.. GENERATED FROM PYTHON SOURCE LINES 37-38

Let's plot them!

.. GENERATED FROM PYTHON SOURCE LINES 38-64

.. code-block:: Python


    fig, ax = plt.subplots(2, 2, figsize=[8, 8])
    for i, norm in enumerate(normalizers):
        # correctly setting the data range
        x_rng = norm().normalize_range
        x = np.linspace(max(-5, x_rng[0] + 0.01), min(5, x_rng[1] - 0.01))
        for j, lmbda in enumerate(lmbdas):
            ax.flat[i].plot(
                x,
                norm(lmbda=lmbda).normalize(x),
                label=r"$\lambda=" + str(lmbda) + "$",
                color="k",
                alpha=0.2 + j * 0.1,
                dashes=dashes(j),
            )
        # axis formatting
        ax.flat[i].grid(which="both", color="grey", linestyle="-", alpha=0.2)
        ax.flat[i].set_ylim((-5, 5))
        ax.flat[i].set_xlim((-5, 5))
        ax.flat[i].set_title(norm().name)
    # figure formatting
    handles, labels = ax.flat[-1].get_legend_handles_labels()
    fig.legend(handles, labels, loc="lower center", ncol=4, handlelength=3.0)
    fig.suptitle("Normalizer Comparison", fontsize=20)
    fig.show()




.. image-sg:: /examples/10_normalizer/images/sphx_glr_02_compare_001.png
   :alt: Normalizer Comparison, BoxCox, YeoJohnson, Modulus, Manly
   :srcset: /examples/10_normalizer/images/sphx_glr_02_compare_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 65-68

The missing :any:`LogNormal` transformation is covered by the :any:`BoxCox`
transformation for lambda=0. The :any:`BoxCoxShift` transformation is
simply the :any:`BoxCox` transformation shifted on the X-axis.


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

   **Total running time of the script:** (0 minutes 0.219 seconds)


.. _sphx_glr_download_examples_10_normalizer_02_compare.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 02_compare.ipynb <02_compare.ipynb>`

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

      :download:`Download Python source code: 02_compare.py <02_compare.py>`

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

      :download:`Download zipped: 02_compare.zip <02_compare.zip>`


.. only:: html

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

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