On Thu, May 27, 2010 at 3:23 PM, Eric Firing <efir...@hawaii.edu> wrote: > > I'm not sure I understand the problem; could you provide a tiny example > to illustrate? >
Sure, let me focus just on the interpolation and I'll leave the filtering issue out. In the script below, I plot a 3x3 array with the center element having an "over" value. In the default case, because its value is over, the colormap will assign it the maximum color. I also plot the case when the "over" color is explicitly set to the minimum color and also to white. What I want is this: The center element should be an equal mixture of the 4 elements around it. This is partially achieved with "white" (and I suppose I could pick "grey" or "black"), but I think it might be nicer if it were a pure mixture, rather than a mixture of the surrounding colors and the "over" color. The script is attached below. Sorry it is a bit long, but I needed a discrete colormap. Can we get cmap_discrete() into matplotlib? ------------------ import matplotlib.pyplot as plt import matplotlib.colors import numpy as np from scipy import interpolate #### http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations #### Can this be added to matplotlib? def cmap_discretize(cmap, N): """Return a discrete colormap from the continuous colormap cmap. cmap: colormap instance, eg. cm.jet. N: Number of colors. Example x = resize(arange(100), (5,100)) djet = cmap_discretize(cm.jet, 5) imshow(x, cmap=djet) """ cdict = cmap._segmentdata.copy() # N colors colors_i = np.linspace(0,1.,N) # N+1 indices indices = np.linspace(0,1.,N+1) for key in ('red','green','blue'): # Find the N colors D = np.array(cdict[key]) I = interpolate.interp1d(D[:,0], D[:,1]) colors = I(colors_i) # Place these colors at the correct indices. A = np.zeros((N+1,3), float) A[:,0] = indices A[1:,1] = colors A[:-1,2] = colors # Create a tuple for the dictionary. L = [] for l in A: L.append(tuple(l)) cdict[key] = tuple(L) # Return colormap object. return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024) def draw(m, cm, norm, ncolors): ax = plt.gca() ai = ax.imshow(m, cmap=cm, norm=norm, interpolation='gaussian') cb = ax.figure.colorbar(ai) cb.set_ticks(np.linspace(.5, ncolors-.5, ncolors)) cb.set_ticklabels(['$%s$' % (i,) for i in np.arange(ncolors)]) return ai, cb if __name__ == '__main__': ncolors = 4 norm = plt.Normalize(vmax=ncolors) m = np.array([[0, 0, 1], [3, 10, 1], [3, 2, 2]]) for over in [None, 'min', (1,1,1,1)]: f = plt.figure() cm = cmap_discretize(plt.cm.jet, ncolors) if over == 'min': cm.set_over(cm(0.0)) elif over is not None: cm.set_over(over) ai, cb = draw(m, cm, norm, ncolors) plt.show() ------------------------------------------------------------------------------ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
