Diff for "Cookbook/Matplotlib/ColormapTransformations"

Differences between revisions 11 and 12

 Deletions are marked like this. Additions are marked like this. Line 79: Line 79: N: Number of colors. N: number of colors. Line 87: Line 87: cdict = cmap._segmentdata.copy()     # N colors     colors_i = linspace(0,1.,N)     # N+1 indices     indices = linspace(0,1.,N+1)     for key in ('red','green','blue'):         # Find the N colors         D = array(cdict[key])         I = interpolate.interp1d(D[:,0], D[:,1])         colors = I(colors_i)         # Place these colors at the correct indices.         A = 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) if type(cmap) == str:         cmap = get_cmap(cmap)     colors_i = concatenate((linspace(0, 1., N), (0.,0.,0.,0.)))     colors_rgba = cmap(colors_i)     indices = linspace(0, 1., N+1)     cdict = {}     for ki,key in enumerate(('red','green','blue')):         cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ] Line 108: Line 96: return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024) return matplotlib.colors.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)

# Operating on color vectors

Ever wanted to reverse a colormap, or to desaturate one ? Here is a routine to apply a function to the look up table of a colormap:

```   1 def cmap_map(function,cmap):
2     """ Applies function (which should operate on vectors of shape 3:
3     [r, g, b], on colormap cmap. This routine will break any discontinuous     points in a colormap.
4     """
5     cdict = cmap._segmentdata
6     step_dict = {}
7     # Firt get the list of points where the segments start or end
8     for key in ('red','green','blue'):         step_dict[key] = map(lambda x: x[0], cdict[key])
9     step_list = sum(step_dict.values(), [])
10     step_list = array(list(set(step_list)))
11     # Then compute the LUT, and apply the function to the LUT
12     reduced_cmap = lambda step : array(cmap(step)[0:3])
13     old_LUT = array(map( reduced_cmap, step_list))
14     new_LUT = array(map( function, old_LUT))
15     # Now try to make a minimal segment definition of the new LUT
16     cdict = {}
17     for i,key in enumerate(('red','green','blue')):
18         this_cdict = {}
19         for j,step in enumerate(step_list):
20             if step in step_dict[key]:
21                 this_cdict[step] = new_LUT[j,i]
22             elif new_LUT[j,i]!=old_LUT[j,i]:
23                 this_cdict[step] = new_LUT[j,i]
24         colorvector=  map(lambda x: x + (x[1], ), this_cdict.items())
25         colorvector.sort()
26         cdict[key] = colorvector
27
28     return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)
```

Lets try it out: I want a jet colormap, but lighter, so that I can plot things on top of it:

```light_jet = cmap_map(lambda x: x/2+0.5, cm.jet)
x,y=mgrid[1:2,1:10:0.1]
imshow(y, cmap=light_jet)
```

As a comparison, this is what the original jet looks like:

# Operating on indices

OK, but what if you want to change the indices of a colormap, but not its colors.

```   1 def cmap_xmap(function,cmap):
2     """ Applies function, on the indices of colormap cmap. Beware, function
3     should map the [0, 1] segment to itself, or you are in for surprises.
4
6     """
7     cdict = cmap._segmentdata
8     function_to_map = lambda x : (function(x[0]), x[1], x[2])
9     for key in ('red','green','blue'):         cdict[key] = map(function_to_map, cdict[key])
10         cdict[key].sort()
11         assert (cdict[key][0]<0 or cdict[key][-1]>1), "Resulting indices extend out of the [0, 1] segment."
12
13
14     return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)
```

# Discrete colormap

Here is how you can discretize a continuous colormap.

```   1 def cmap_discretize(cmap, N):
2     """Return a discrete colormap from the continuous colormap cmap.
3
4         cmap: colormap instance, eg. cm.jet.
5         N: number of colors.
6
7     Example
8         x = resize(arange(100), (5,100))
9         djet = cmap_discretize(cm.jet, 5)
10         imshow(x, cmap=djet)
11     """
12
13     if type(cmap) == str:
14         cmap = get_cmap(cmap)
15     colors_i = concatenate((linspace(0, 1., N), (0.,0.,0.,0.)))
16     colors_rgba = cmap(colors_i)
17     indices = linspace(0, 1., N+1)
18     cdict = {}
19     for ki,key in enumerate(('red','green','blue')):
20         cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]
21     # Return colormap object.
22     return matplotlib.colors.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)
```

So for instance, this is what you would get by doing cmap_discretize(cm.jet, 6).

Cookbook/Matplotlib/ColormapTransformations (last edited 2012-10-23 13:58:42 by macdems)