Defining the midpoint of a colormap in matplotlib

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 npimport matplotlibimport matplotlib.pyplot as pltfrom mpl_toolkits.axes_grid1 import AxesGriddef 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.coolwarmshifted_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 cmapim0 = 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:

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

In matplotlib 3.1 the class was called DivergingNorm.

It's easiest to just use the vmin and vmax arguments to imshow (assuming you're working with image data) rather than subclassing matplotlib.colors.Normalize.

E.g.

import numpy as npimport matplotlib.pyplot as pltdata = np.random.random((10,10))# Make the data range from about -5 to 10data = 10 / 0.75 * (data - 0.25)plt.imshow(data, vmin=-10, vmax=10)plt.colorbar()plt.show()