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

.. only:: html

    .. meta::
        :keywords: codex

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

        Click :ref:`here <sphx_glr_download_gallery_images_contours_and_fields_colormap_normalizations_symlognorm.py>`
        to download the full example code

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

.. _sphx_glr_gallery_images_contours_and_fields_colormap_normalizations_symlognorm.py:


==================================
Colormap Normalizations SymLogNorm
==================================

Demonstration of using norm to map colormaps onto data in non-linear ways.

.. redirect-from:: /gallery/userdemo/colormap_normalization_symlognorm

.. GENERATED FROM PYTHON SOURCE LINES 12-20

Synthetic dataset consisting of two humps, one negative and one positive,
the positive with 8-times the amplitude.
Linearly, the negative hump is almost invisible,
and it is very difficult to see any detail of its profile.
With the logarithmic scaling applied to both positive and negative values,
it is much easier to see the shape of each hump.

See `~.colors.SymLogNorm`.

.. GENERATED FROM PYTHON SOURCE LINES 20-55

.. code-block:: default


    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.colors as colors


    def rbf(x, y):
        return 1.0 / (1 + 5 * ((x ** 2) + (y ** 2)))

    N = 200
    gain = 8
    X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
    Z1 = rbf(X + 0.5, Y + 0.5)
    Z2 = rbf(X - 0.5, Y - 0.5)
    Z = gain * Z1 - Z2

    shadeopts = {'cmap': 'PRGn', 'shading': 'gouraud'}
    colormap = 'PRGn'
    lnrwidth = 0.5

    fig, ax = plt.subplots(2, 1, sharex=True, sharey=True)

    pcm = ax[0].pcolormesh(X, Y, Z,
                           norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
                                                  vmin=-gain, vmax=gain, base=10),
                           **shadeopts)
    fig.colorbar(pcm, ax=ax[0], extend='both')
    ax[0].text(-2.5, 1.5, 'symlog')

    pcm = ax[1].pcolormesh(X, Y, Z, vmin=-gain, vmax=gain,
                           **shadeopts)
    fig.colorbar(pcm, ax=ax[1], extend='both')
    ax[1].text(-2.5, 1.5, 'linear')





.. image-sg:: /gallery/images_contours_and_fields/images/sphx_glr_colormap_normalizations_symlognorm_001.png
   :alt: colormap normalizations symlognorm
   :srcset: /gallery/images_contours_and_fields/images/sphx_glr_colormap_normalizations_symlognorm_001.png, /gallery/images_contours_and_fields/images/sphx_glr_colormap_normalizations_symlognorm_001_2_0x.png 2.0x
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Text(-2.5, 1.5, 'linear')



.. GENERATED FROM PYTHON SOURCE LINES 56-66

In order to find the best visualization for any particular dataset,
it may be necessary to experiment with multiple different color scales.
As well as the `~.colors.SymLogNorm` scaling, there is also
the option of using `~.colors.AsinhNorm` (experimental), which has a smoother
transition between the linear and logarithmic regions of the transformation
applied to the data values, "Z".
In the plots below, it may be possible to see contour-like artifacts
around each hump despite there being no sharp features
in the dataset itself. The ``asinh`` scaling shows a smoother shading
of each hump.

.. GENERATED FROM PYTHON SOURCE LINES 66-85

.. code-block:: default


    fig, ax = plt.subplots(2, 1, sharex=True, sharey=True)

    pcm = ax[0].pcolormesh(X, Y, Z,
                           norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
                                                  vmin=-gain, vmax=gain, base=10),
                           **shadeopts)
    fig.colorbar(pcm, ax=ax[0], extend='both')
    ax[0].text(-2.5, 1.5, 'symlog')

    pcm = ax[1].pcolormesh(X, Y, Z,
                           norm=colors.AsinhNorm(linear_width=lnrwidth,
                                                 vmin=-gain, vmax=gain),
                           **shadeopts)
    fig.colorbar(pcm, ax=ax[1], extend='both')
    ax[1].text(-2.5, 1.5, 'asinh')


    plt.show()



.. image-sg:: /gallery/images_contours_and_fields/images/sphx_glr_colormap_normalizations_symlognorm_002.png
   :alt: colormap normalizations symlognorm
   :srcset: /gallery/images_contours_and_fields/images/sphx_glr_colormap_normalizations_symlognorm_002.png, /gallery/images_contours_and_fields/images/sphx_glr_colormap_normalizations_symlognorm_002_2_0x.png 2.0x
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  3.374 seconds)


.. _sphx_glr_download_gallery_images_contours_and_fields_colormap_normalizations_symlognorm.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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