.. _sphx_glr_gallery_userdemo_colormap_normalizations_bounds.py: ============================== Colormap Normalizations Bounds ============================== Demonstration of using norm to map colormaps onto data in non-linear ways. .. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_bounds_001.png :align: center .. code-block:: python 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)] Z1 = np.exp(-X**2 - Y**2) Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2) Z = (Z1 - Z2) * 2 ''' BoundaryNorm: For this one you provide the boundaries for your colors, and the Norm puts the first color in between the first pair, the second color between the second pair, etc. ''' fig, ax = plt.subplots(3, 1, figsize=(8, 8)) ax = ax.flatten() # even bounds gives a contour-like effect bounds = np.linspace(-1, 1, 10) norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256) pcm = ax[0].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r') fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical') # uneven bounds changes the colormapping: bounds = np.array([-0.25, -0.125, 0, 0.5, 1]) norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256) pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r') fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical') pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z)) fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical') plt.show() .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: colormap_normalizations_bounds.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: colormap_normalizations_bounds.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_