Defining the midpoint of a colormap in matplotlib

Question

I want to set the middle point of a colormap, i.e., my data goes from -5 to 10 and I want zero to be the middle point. I think the way to do it is by subclassing normalize and using the norm, but I didn't find any example and it is not clear to me, what exactly have I to implement?

Answer 1

I know this is late to the game, but I just went through this process and came up with a solution that perhaps less robust than subclassing normalize, but much simpler. I thought it'd be good to share it here for posterity.

The function

import numpy as np import matplotlib import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid  def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'):     '''     Function to offset the "center" of a colormap. Useful for     data with a negative min and positive max and you want the     middle of the colormap's dynamic range to be at zero.      Input     -----       cmap : The matplotlib colormap to be altered       start : Offset from lowest point in the colormap's range.           Defaults to 0.0 (no lower offset). Should be between           0.0 and `midpoint`.       midpoint : The new center of the colormap. Defaults to            0.5 (no shift). Should be between 0.0 and 1.0. In           general, this should be  1 - vmax / (vmax + abs(vmin))           For example if your data range from -15.0 to +5.0 and           you want the center of the colormap at 0.0, `midpoint`           should be set to  1 - 5/(5 + 15)) or 0.75       stop : Offset from highest point in the colormap's range.           Defaults to 1.0 (no upper offset). Should be between           `midpoint` and 1.0.     '''     cdict = {         'red': [],         'green': [],         'blue': [],         'alpha': []     }      # regular index to compute the colors     reg_index = np.linspace(start, stop, 257)      # shifted index to match the data     shift_index = np.hstack([         np.linspace(0.0, midpoint, 128, endpoint=False),          np.linspace(midpoint, 1.0, 129, endpoint=True)     ])      for ri, si in zip(reg_index, shift_index):         r, g, b, a = cmap(ri)          cdict['red'].append((si, r, r))         cdict['green'].append((si, g, g))         cdict['blue'].append((si, b, b))         cdict['alpha'].append((si, a, a))      newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)     plt.register_cmap(cmap=newcmap)      return newcmap 

An example

biased_data = np.random.random_integers(low=-15, high=5, size=(37,37))  orig_cmap = matplotlib.cm.coolwarm shifted_cmap = shiftedColorMap(orig_cmap, midpoint=0.75, name='shifted') shrunk_cmap = shiftedColorMap(orig_cmap, start=0.15, midpoint=0.75, stop=0.85, name='shrunk')  fig = plt.figure(figsize=(6,6)) grid = AxesGrid(fig, 111, nrows_ncols=(2, 2), axes_pad=0.5,                 label_mode="1", share_all=True,                 cbar_location="right", cbar_mode="each",                 cbar_size="7%", cbar_pad="2%")  # normal cmap im0 = grid[0].imshow(biased_data, interpolation="none", cmap=orig_cmap) grid.cbar_axes[0].colorbar(im0) grid[0].set_title('Default behavior (hard to see bias)', fontsize=8)  im1 = grid[1].imshow(biased_data, interpolation="none", cmap=orig_cmap, vmax=15, vmin=-15) grid.cbar_axes[1].colorbar(im1) grid[1].set_title('Centered zero manually,\nbut lost upper end of dynamic range', fontsize=8)  im2 = grid[2].imshow(biased_data, interpolation="none", cmap=shifted_cmap) grid.cbar_axes[2].colorbar(im2) grid[2].set_title('Recentered cmap with function', fontsize=8)  im3 = grid[3].imshow(biased_data, interpolation="none", cmap=shrunk_cmap) grid.cbar_axes[3].colorbar(im3) grid[3].set_title('Recentered cmap with function\nand shrunk range', fontsize=8)  for ax in grid:     ax.set_yticks([])     ax.set_xticks([]) 

Results of the example:

Answer 2

Note that in matplotlib version 3.2+ the TwoSlopeNorm class was added. I think it covers your use case. It can be used like this:

from matplotlib import colors divnorm=colors.TwoSlopeNorm(vmin=-5., vcenter=0., vmax=10) pcolormesh(your_data, cmap="coolwarm", norm=divnorm) 

In matplotlib 3.1 the class was called DivergingNorm.