Matplotlib：颜色映射变换

日期2017-07-22（最后修改），2006-09-27（创建）

对颜色向量进行操作

是否想操作现有的颜色映射？这里有一个例程可以将函数应用于颜色映射的查找表

In [ ]
import matplotlib
import numpy as np
import matplotlib.pyplot as plt

def cmap_map(function, cmap):
    """ Applies function (which should operate on vectors of shape 3: [r, g, b]), on colormap cmap.
    This routine will break any discontinuous points in a colormap.
    """
    cdict = cmap._segmentdata
    step_dict = {}
    # Firt get the list of points where the segments start or end
    for key in ('red', 'green', 'blue'):
        step_dict[key] = list(map(lambda x: x[0], cdict[key]))
    step_list = sum(step_dict.values(), [])
    step_list = np.array(list(set(step_list)))
    # Then compute the LUT, and apply the function to the LUT
    reduced_cmap = lambda step : np.array(cmap(step)[0:3])
    old_LUT = np.array(list(map(reduced_cmap, step_list)))
    new_LUT = np.array(list(map(function, old_LUT)))
    # Now try to make a minimal segment definition of the new LUT
    cdict = {}
    for i, key in enumerate(['red','green','blue']):
        this_cdict = {}
        for j, step in enumerate(step_list):
            if step in step_dict[key]:
                this_cdict[step] = new_LUT[j, i]
            elif new_LUT[j,i] != old_LUT[j, i]:
                this_cdict[step] = new_LUT[j, i]
        colorvector = list(map(lambda x: x + (x[1], ), this_cdict.items()))
        colorvector.sort()
        cdict[key] = colorvector

    return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)

让我们试一试：我想要一个 jet 颜色映射，但更亮，这样我就可以在它上面绘制东西

In [ ]
light_jet = cmap_map(lambda x: x/2 + 0.5, matplotlib.cm.jet)

x, y = np.mgrid[1:2, 1:10:0.01]
plt.figure(figsize=[15, 1])
plt.imshow(y, cmap=light_jet, aspect='auto')
plt.axis('off')
plt.show()

类似地，如果需要更暗的 jet 颜色映射

In [ ]
dark_jet = cmap_map(lambda x: x*0.75, matplotlib.cm.jet)

x, y = np.mgrid[1:2, 1:10:0.01]
plt.figure(figsize=[15, 1])
plt.imshow(y, cmap=dark_jet, aspect='auto')
plt.axis('off')
plt.show()

作为比较，这是原始 jet 的样子：

对索引进行操作

好的，但是如果你想改变颜色映射的索引，而不是它的颜色呢。

In [ ]
def cmap_xmap(function, cmap):
    """ Applies function, on the indices of colormap cmap. Beware, function
    should map the [0, 1] segment to itself, or you are in for surprises.

    See also cmap_xmap.
    """
    cdict = cmap._segmentdata
    function_to_map = lambda x : (function(x[0]), x[1], x[2])
    for key in ('red','green','blue'):
        cdict[key] = map(function_to_map, cdict[key])
        cdict[key].sort()
        assert (cdict[key][0]<0 or cdict[key][-1]>1), "Resulting indices extend out of the [0, 1] segment."

    return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)

离散颜色映射

以下是如何离散化连续颜色映射。

In [ ]
def cmap_discretize(cmap, N):
    """Return a discrete colormap from the continuous colormap cmap.
    
        cmap: colormap instance, eg. cm.jet. 
        N: number of colors.
    """
    if type(cmap) == str:
        cmap = get_cmap(cmap)
    colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
    colors_rgba = cmap(colors_i)
    indices = np.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 range(N+1)]
    # Return colormap object.
    return matplotlib.colors.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)

例如，考虑一个具有 6 种颜色的离散化 jet 颜色映射

In [ ]
discretized_jet = cmap_discretize(matplotlib.cm.jet, 6)

x, y = np.mgrid[1:2, 1:10:0.01]
plt.figure(figsize=[15, 1])
plt.imshow(y, cmap=discretized_jet, aspect='auto')
plt.axis('off')
plt.show()

章节作者：GaelVaroquaux、DavidHuard、newacct、Unknown[88]、Robert Woodward、Christian Gagnon

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