I’m not quite sure if this qualifies within “not knowing the inner structure of the colormap”, but perhaps something like this would work to add a linear alpha to an existing colormap? import numpy as np import matplotlib.pylab as pl from matplotlib.colors import ListedColormap # Random data data1 = np.random.random((4,4)) # Choose colormap cmap = … Read more
Using vmin and vmax forces the range for the colors. Here’s an example: import matplotlib as m import matplotlib.pyplot as plt import numpy as np cdict = { ‘red’ : ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)), ‘green’: ( (0.0, 0.0, 0.0), (0.02, .45, .45), (1., .97, .97)), ‘blue’ : ( (0.0, … Read more
Hrm, it appears I can use a masked array to do this: masked_array = np.ma.array (a, mask=np.isnan(a)) cmap = matplotlib.cm.jet cmap.set_bad(‘white’,1.) ax.imshow(masked_array, interpolation=’nearest’, cmap=cmap) This should suffice, though I’m still open to suggestions. :]
I’m not quite sure if this qualifies within “not knowing the inner structure of the colormap”, but perhaps something like this would work to add a linear alpha to an existing colormap? import numpy as np import matplotlib.pylab as pl from matplotlib.colors import ListedColormap # Random data data1 = np.random.random((4,4)) # Choose colormap cmap = … Read more
The Matplotlib colormaps accept an argument (0..1, scalar or array) which you use to get colors from a colormap. For example: col = pl.cm.jet([0.25,0.75]) Gives you an array with (two) RGBA colors: array([[ 0. , 0.50392157, 1. , 1. ], [ 1. , 0.58169935, 0. , 1. ]]) You can use that to create N … Read more
The Matplotlib colormaps accept an argument (0..1, scalar or array) which you use to get colors from a colormap. For example: col = pl.cm.jet([0.25,0.75]) Gives you an array with (two) RGBA colors: array([[ 0. , 0.50392157, 1. , 1. ], [ 1. , 0.58169935, 0. , 1. ]]) You can use that to create N … Read more
I usually use the second one of these: from matplotlib.pyplot import cm import numpy as np #variable n below should be number of curves to plot #version 1: color = cm.rainbow(np.linspace(0, 1, n)) for i, c in zip(range(n), color): plt.plot(x, y, c=c) #or version 2: color = iter(cm.rainbow(np.linspace(0, 1, n))) for i in range(n): c … Read more
As of matplotlib version 3.0 there are built-in cyclic perceptually uniform colormaps. OK, just the one colormap for the time being, but with two choices of start and end along the cycle, namely twilight and twilight_shifted. A short example to demonstrate how they look: import matplotlib.pyplot as plt import numpy as np # example data: … Read more
You can get a tuple of rgba values for the segment with index i by calling cmap(i). There is also already a function that turns rgb values into hex. As Joe Kington wrote in the comments, you can use matplotlib.colors.rgb2hex. Therefore, a possible solution would be: from pylab import * cmap = cm.get_cmap(‘seismic’, 5) # … Read more
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 … Read more