Create a colormap from a list of colors#

For more detail on creating and manipulating colormaps seeCreating Colormaps in Matplotlib.

Creating acolormap from a list of colorscan be done with theLinearSegmentedColormap.from_list method. You mustpass a list of RGB tuples that define the mixture of colors from 0 to 1.

Creating custom colormaps#

It is also possible to create a custom mapping for a colormap. This isaccomplished by creating dictionary that specifies how the RGB channelschange from one end of the cmap to the other.

Example: suppose you want red to increase from 0 to 1 over the bottomhalf, green to do the same over the middle half, and blue over the tophalf. Then you would use:

cdict={'red':((0.0,0.0,0.0),(0.5,1.0,1.0),(1.0,1.0,1.0),),'green':((0.0,0.0,0.0),(0.25,0.0,0.0),(0.75,1.0,1.0),(1.0,1.0,1.0),),'blue':((0.0,0.0,0.0),(0.5,0.0,0.0),(1.0,1.0,1.0),)}

If, as in this example, there are no discontinuities in the r, g, and bcomponents, then it is quite simple: the second and third element ofeach tuple, above, is the same -- call it "y". The first element ("x")defines interpolation intervals over the full range of 0 to 1, and itmust span that whole range. In other words, the values ofx divide the0-to-1 range into a set of segments, andy gives the end-point colorvalues for each segment.

Now consider the green,cdict['green'] is saying that for:

0 <=x <= 0.25,y is zero; no green.
0.25 <x <= 0.75,y varies linearly from 0 to 1.
0.75 <x <= 1,y remains at 1, full green.

If there are discontinuities, then it is a little more complicated. Label the 3elements in each row in thecdict entry for a given color as(x,y0,y1). Then for values ofx betweenx[i] andx[i+1] the color valueis interpolated betweeny1[i] andy0[i+1].

Going back to a cookbook example:

cdict={'red':((0.0,0.0,0.0),(0.5,1.0,0.7),(1.0,1.0,1.0),),'green':((0.0,0.0,0.0),(0.5,1.0,0.0),(1.0,1.0,1.0),),'blue':((0.0,0.0,0.0),(0.5,0.0,0.0),(1.0,1.0,1.0),)}

and look atcdict['red'][1]; becausey0!=y1, it is saying that forx from 0 to 0.5, red increases from 0 to 1, but then it jumps down, so thatforx from 0.5 to 1, red increases from 0.7 to 1. Green ramps from 0 to 1asx goes from 0 to 0.5, then jumps back to 0, and ramps back to 1 asxgoes from 0.5 to 1.

rowi:xy0y1//rowi+1:xy0y1

Above is an attempt to show that forx in the rangex[i] tox[i+1],the interpolation is betweeny1[i] andy0[i+1]. So,y0[0] andy1[-1] are never used.

importmatplotlib.pyplotaspltimportnumpyasnpimportmatplotlibasmplfrommatplotlib.colorsimportLinearSegmentedColormap# Make some illustrative fake data:x=np.arange(0,np.pi,0.1)y=np.arange(0,2*np.pi,0.1)X,Y=np.meshgrid(x,y)Z=np.cos(X)*np.sin(Y)*10

Colormaps from a list#

colors=[(1,0,0),(0,1,0),(0,0,1)]# R -> G -> Bn_bins=[3,6,10,100]# Discretizes the interpolation into binscmap_name='my_list'fig,axs=plt.subplots(2,2,figsize=(6,9))fig.subplots_adjust(left=0.02,bottom=0.06,right=0.95,top=0.94,wspace=0.05)forn_bin,axinzip(n_bins,axs.flat):# Create the colormapcmap=LinearSegmentedColormap.from_list(cmap_name,colors,N=n_bin)# Fewer bins will result in "coarser" colomap interpolationim=ax.imshow(Z,origin='lower',cmap=cmap)ax.set_title("N bins:%s"%n_bin)fig.colorbar(im,ax=ax)

N bins: 3, N bins: 6, N bins: 10, N bins: 100

Custom colormaps#

cdict1={'red':((0.0,0.0,0.0),(0.5,0.0,0.1),(1.0,1.0,1.0),),'green':((0.0,0.0,0.0),(1.0,0.0,0.0),),'blue':((0.0,0.0,1.0),(0.5,0.1,0.0),(1.0,0.0,0.0),)}cdict2={'red':((0.0,0.0,0.0),(0.5,0.0,1.0),(1.0,0.1,1.0),),'green':((0.0,0.0,0.0),(1.0,0.0,0.0),),'blue':((0.0,0.0,0.1),(0.5,1.0,0.0),(1.0,0.0,0.0),)}cdict3={'red':((0.0,0.0,0.0),(0.25,0.0,0.0),(0.5,0.8,1.0),(0.75,1.0,1.0),(1.0,0.4,1.0),),'green':((0.0,0.0,0.0),(0.25,0.0,0.0),(0.5,0.9,0.9),(0.75,0.0,0.0),(1.0,0.0,0.0),),'blue':((0.0,0.0,0.4),(0.25,1.0,1.0),(0.5,1.0,0.8),(0.75,0.0,0.0),(1.0,0.0,0.0),)}# Make a modified version of cdict3 with some transparency# in the middle of the range.cdict4={**cdict3,'alpha':((0.0,1.0,1.0),# (0.25, 1.0, 1.0),(0.5,0.3,0.3),# (0.75, 1.0, 1.0),(1.0,1.0,1.0),),}

Now we will use this example to illustrate 2 ways ofhandling custom colormaps.First, the most direct and explicit:

blue_red1=LinearSegmentedColormap('BlueRed1',cdict1)

Second, create the map explicitly and register it.Like the first method, this method works with any kindof Colormap, not justa LinearSegmentedColormap:

mpl.colormaps.register(LinearSegmentedColormap('BlueRed2',cdict2))mpl.colormaps.register(LinearSegmentedColormap('BlueRed3',cdict3))mpl.colormaps.register(LinearSegmentedColormap('BlueRedAlpha',cdict4))

Make the figure, with 4 subplots:

fig,axs=plt.subplots(2,2,figsize=(6,9))fig.subplots_adjust(left=0.02,bottom=0.06,right=0.95,top=0.94,wspace=0.05)im1=axs[0,0].imshow(Z,cmap=blue_red1)fig.colorbar(im1,ax=axs[0,0])im2=axs[1,0].imshow(Z,cmap='BlueRed2')fig.colorbar(im2,ax=axs[1,0])# Now we will set the third cmap as the default.  One would# not normally do this in the middle of a script like this;# it is done here just to illustrate the method.plt.rcParams['image.cmap']='BlueRed3'im3=axs[0,1].imshow(Z)fig.colorbar(im3,ax=axs[0,1])axs[0,1].set_title("Alpha = 1")# Or as yet another variation, we can replace the rcParams# specification *before* the imshow with the following *after*# imshow.# This sets the new default *and* sets the colormap of the last# image-like item plotted via pyplot, if any.## Draw a line with low zorder so it will be behind the image.axs[1,1].plot([0,10*np.pi],[0,20*np.pi],color='c',lw=20,zorder=-1)im4=axs[1,1].imshow(Z)fig.colorbar(im4,ax=axs[1,1])# Here it is: changing the colormap for the current image and its# colorbar after they have been plotted.im4.set_cmap('BlueRedAlpha')axs[1,1].set_title("Varying alpha")fig.suptitle('Custom Blue-Red colormaps',fontsize=16)fig.subplots_adjust(top=0.9)plt.show()