.. 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.py: ======================= Colormap Normalizations ======================= Demonstration of using norm to map colormaps onto data in non-linear ways. .. code-block:: default import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as colors Lognorm: Instead of pcolor log10(Z1) you can have colorbars that have the exponential labels using a norm. .. code-block:: default N = 100 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)] # A low hump with a spike coming out of the top. Needs to have # z/colour axis on a log scale so we see both hump and spike. linear # scale only shows the spike. Z1 = np.exp(-(X)**2 - (Y)**2) Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2) Z = Z1 + 50 * Z2 fig, ax = plt.subplots(2, 1) pcm = ax[0].pcolor(X, Y, Z, norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()), cmap='PuBu_r') fig.colorbar(pcm, ax=ax[0], extend='max') pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r') fig.colorbar(pcm, ax=ax[1], extend='max') .. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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. .. code-block:: default 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') .. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none SymLogNorm: two humps, one negative and one positive, The positive with 5-times the amplitude. Linearly, you cannot see detail in the negative hump. Here we logarithmically scale the positive and negative data separately. Note that colorbar labels do not come out looking very good. .. code-block:: default X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)] Z1 = 5 * np.exp(-X**2 - Y**2) Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2) Z = (Z1 - Z2) * 2 fig, ax = plt.subplots(2, 1) pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03, vmin=-1.0, vmax=1.0), cmap='RdBu_r') fig.colorbar(pcm, ax=ax[0], extend='both') pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1)) fig.colorbar(pcm, ax=ax[1], extend='both') .. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Custom Norm: An example with a customized normalization. This one uses the example above, and normalizes the negative data differently from the positive. .. code-block:: default 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 # Example of making your own norm. Also see matplotlib.colors. # From Joe Kington: This one gives two different linear ramps: class MidpointNormalize(colors.Normalize): def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False): self.midpoint = midpoint colors.Normalize.__init__(self, vmin, vmax, clip) def __call__(self, value, clip=None): # I'm ignoring masked values and all kinds of edge cases to make a # simple example... x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1] return np.ma.masked_array(np.interp(value, x, y)) ##### fig, ax = plt.subplots(2, 1) pcm = ax[0].pcolormesh(X, Y, Z, norm=MidpointNormalize(midpoint=0.), cmap='RdBu_r') fig.colorbar(pcm, ax=ax[0], extend='both') pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z)) fig.colorbar(pcm, ax=ax[1], extend='both') .. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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. .. code-block:: default 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(Z1)) fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical') plt.show() .. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_005.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.214 seconds) .. _sphx_glr_download_gallery_userdemo_colormap_normalizations.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: colormap_normalizations.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: colormap_normalizations.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature Keywords: matplotlib code example, codex, python plot, pyplot `Gallery generated by Sphinx-Gallery `_