.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_userdemo_colormap_normalizations_power.py: ============================= Colormap Normalizations Power ============================= Demonstration of using norm to map colormaps onto data in non-linear ways. .. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_power_001.png :class: sphx-glr-single-img .. code-block:: default import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as colors N = 100 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)] ''' PowerNorm: Here a power-law trend in X partially obscures a rectified sine wave in Y. We can remove the power law using a PowerNorm. ''' X, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)] Z1 = (1 + np.sin(Y * 10.)) * X**(2.) fig, ax = plt.subplots(2, 1) pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=1./2.), cmap='PuBu_r') fig.colorbar(pcm, ax=ax[0], extend='max') pcm = ax[1].pcolormesh(X, Y, Z1, cmap='PuBu_r') fig.colorbar(pcm, ax=ax[1], extend='max') plt.show() .. _sphx_glr_download_gallery_userdemo_colormap_normalizations_power.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: colormap_normalizations_power.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: colormap_normalizations_power.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature Keywords: matplotlib code example, codex, python plot, pyplot `Gallery generated by Sphinx-Gallery `_