python - Merge colormaps in matplotlib -

i want merge 2 colormaps imshow plot. want use 'rdbu' range -0.4 0.4, 0.4 maximum value (say 1.5) want use gradient same blue color (say green example).

how can that?

this how far got far:

import numpy np import matplotlib.pyplot plt import matplotlib.colors colors matplotlib.mlab import bivariate_normal  n = 100 ''' custom norm: example customized normalization.  1 uses example above, , normalizes negative data differently positive. ''' x, y = np.mgrid[-3:3:complex(0, n), -2:2:complex(0, n)] z1 = (bivariate_normal(x, y, 1., 1., 1.0, 1.0))**2  \     - 0.4 * (bivariate_normal(x, y, 1.0, 1.0, -1.0, 0.0))**2 z1 = z1/0.03  # example of making own norm.  see matplotlib.colors. # joe kington: 1 gives 2 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 , kinds of edge cases make         # 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(1, 1)  minvalue = z1.min() maxvalue = 0.4  pcm = ax.imshow(z1,                 norm=midpointnormalize(midpoint=0.),                 vmin=minvalue, vmax=maxvalue,                 cmap='rdbu',                 origin='lower',                 aspect=1.0,                 interpolation='none') cbar = fig.colorbar(pcm, ax=ax, extend='both', ticks=[minvalue, 0.0, maxvalue])  fig.tight_layout()  plt.show() 

the purpose create colormapping, has several predefined values. start of colormap should @ vmin, white (which in middle of "rdbu" colormap) should @ 0, predefined point (0.4) shall upper end of rdbu colormap , color shall fade towards end color.

for purpose need 2 things. (a) colormap has colors in , (b) normalization allows map intermediate points respective colors.

(a) creating colormap

colormaps range between 0 , 1. may create colormap such colors "rdbu" colormap extend on first half of desired colormap, such 0 red, 0.25 white , 0.5 blue. second half of colormap ranges 0.5 (the same blue) on intermediate turquoise @ 0.75 green @ 1. (the intermediate turquoise chosen because direct transition blue green result in smeared brownish blue in middle, undesired.) steps accomplished via following code

colors = plt.cm.rdbu(np.linspace(0,1.,128))  colors = zip(np.linspace(0,0.5,128),colors)  colors += [ (0.75,"#1fa187"),(1., "#76d154")]  cmap = matplotlib.colors.linearsegmentedcolormap.from_list('mycmap', colors) 

such cmap desired colormap.

(b) creating normalization

unlike midpointnormalization, has 1 intermediate point, need 2 intermediate points: 1 being white color @ 0 value , 1 being end of first half of colormap. can hence use 2 values in custom normalization (here called low , up), such interpolation ranges on 4 points in total , low corresponds 0.25 value of colormap , up corresponds 0.5 value.

x, y = [self.vmin, self.low, self.up, self.vmax], [0, 0.25, 0.5, 1] 

complete code:

import numpy np import matplotlib.pyplot plt import matplotlib.colors matplotlib.mlab import bivariate_normal  n = 100 x, y = np.mgrid[-3:3:complex(0, n), -2:2:complex(0, n)] z1 = (bivariate_normal(x, y, 1., 1., 1.0, 1.0))**2  \     - 0.4 * (bivariate_normal(x, y, 1.0, 1.0, -1.0, 0.0))**2 z1 = z1/0.03   class twoinnerpointsnormalize(matplotlib.colors.normalize):     def __init__(self, vmin=none, vmax=none, low=none, up=none, clip=false):         self.low = low         self.up =         matplotlib.colors.normalize.__init__(self, vmin, vmax, clip)      def __call__(self, value, clip=none):         x, y = [self.vmin, self.low, self.up, self.vmax], [0, 0.25, 0.5, 1]         return np.ma.masked_array(np.interp(value, x, y))  colors = plt.cm.rdbu(np.linspace(0,1.,128))  colors = zip(np.linspace(0,0.5,128),colors)  colors += [ (0.75,"#1fa187"),(1., "#76d154")]  cmap = matplotlib.colors.linearsegmentedcolormap.from_list('mycmap', colors)   fig, ax = plt.subplots(1, 1)  norm = twoinnerpointsnormalize(vmin=-0.4, vmax=1.5, low=0., up=0.4) pcm = ax.imshow(z1, norm=norm, cmap=cmap,                 origin='lower', aspect=1.0, interpolation='none') cbar = fig.colorbar(pcm, ax=ax, ticks=[-0.4,0.0, 0.4,1.5])   fig.tight_layout() plt.show()