matplotlib의 반전 컬러 맵

matplotlib의 반전 컬러 맵

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plot_surface와 함께 사용하기 위해 주어진 컬러 맵의 색상 순서를 간단히 바꾸는 방법을 알고 싶습니다.

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표준 컬러 맵도 모두 역 버전입니다. 그들은 _r끝에 붙인 동일한 이름을 가지고 있습니다 . ( 여기에서 설명서 )

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matplotlib에서 색상 맵은 목록이 아니지만 색상 목록이로 포함되어 있습니다 colormap.colors. 그리고 모듈 matplotlib.colors은 ListedColormap()목록에서 컬러 맵을 생성 하는 기능 을 제공 합니다. 따라서 모든 색상 맵을 뒤집을 수 있습니다

colormap_r = ListedColormap(colormap.colors[::-1])

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a LinearSegmentedColormaps는 빨강, 녹색 및 파랑의 사전을 기반으로하므로 각 항목을 뒤집어 야합니다.

import matplotlib.pyplot as plt
import matplotlib as mpl
def reverse_colourmap(cmap, name = 'my_cmap_r'):
    """
    In: 
    cmap, name 
    Out:
    my_cmap_r

    Explanation:
    t[0] goes from 0 to 1
    row i:   x  y0  y1 -> t[0] t[1] t[2]
                   /
                  /
    row i+1: x  y0  y1 -> t[n] t[1] t[2]

    so the inverse should do the same:
    row i+1: x  y1  y0 -> 1-t[0] t[2] t[1]
                   /
                  /
    row i:   x  y1  y0 -> 1-t[n] t[2] t[1]
    """        
    reverse = []
    k = []   

    for key in cmap._segmentdata:    
        k.append(key)
        channel = cmap._segmentdata[key]
        data = []

        for t in channel:                    
            data.append((1-t[0],t[2],t[1]))            
        reverse.append(sorted(data))    

    LinearL = dict(zip(k,reverse))
    my_cmap_r = mpl.colors.LinearSegmentedColormap(name, LinearL) 
    return my_cmap_r

작동하는지 확인하십시오.

my_cmap        
<matplotlib.colors.LinearSegmentedColormap at 0xd5a0518>

my_cmap_r = reverse_colourmap(my_cmap)

fig = plt.figure(figsize=(8, 2))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap = my_cmap, norm=norm,orientation='horizontal')
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap = my_cmap_r, norm=norm, orientation='horizontal')

편집하다

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user3445587의 의견을 얻지 못했습니다. 무지개 컬러 맵에서 잘 작동합니다.

cmap = mpl.cm.jet
cmap_r = reverse_colourmap(cmap)

fig = plt.figure(figsize=(8, 2))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap = cmap, norm=norm,orientation='horizontal')
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap = cmap_r, norm=norm, orientation='horizontal')

But it especially works nice for custom declared colormaps, as there is not a default _r for custom declared colormaps. Following example taken from http://matplotlib.org/examples/pylab_examples/custom_cmap.html:

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))
         }

blue_red1 = mpl.colors.LinearSegmentedColormap('BlueRed1', cdict1)
blue_red1_r = reverse_colourmap(blue_red1)

fig = plt.figure(figsize=(8, 2))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])

norm = mpl.colors.Normalize(vmin=0, vmax=1)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap = blue_red1, norm=norm,orientation='horizontal')
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap = blue_red1_r, norm=norm, orientation='horizontal')

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The solution is pretty straightforward. Suppose you want to use the "autumn" colormap scheme. The standard version:

cmap = matplotlib.cm.autumn

To reverse the colormap color spectrum, use get_cmap() function and append '_r' to the colormap title like this:

cmap_reversed = matplotlib.cm.get_cmap('autumn_r')

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As of Matplotlib 2.0, there is a reversed() method for ListedColormap and LinearSegmentedColorMap objects, so you can just do

cmap_reversed = cmap.reversed()

Here is the documentation.

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There are two types of LinearSegmentedColormaps. In some, the _segmentdata is given explicitly, e.g., for jet:

>>> cm.jet._segmentdata
{'blue': ((0.0, 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65, 0, 0), (1, 0, 0)), 'red': ((0.0, 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89, 1, 1), (1, 0.5, 0.5)), 'green': ((0.0, 0, 0), (0.125, 0, 0), (0.375, 1, 1), (0.64, 1, 1), (0.91, 0, 0), (1, 0, 0))}

For rainbow, _segmentdata is given as follows:

>>> cm.rainbow._segmentdata
{'blue': <function <lambda> at 0x7fac32ac2b70>, 'red': <function <lambda> at 0x7fac32ac7840>, 'green': <function <lambda> at 0x7fac32ac2d08>}

We can find the functions in the source of matplotlib, where they are given as

_rainbow_data = {
        'red': gfunc[33],   # 33: lambda x: np.abs(2 * x - 0.5),
        'green': gfunc[13], # 13: lambda x: np.sin(x * np.pi),
        'blue': gfunc[10],  # 10: lambda x: np.cos(x * np.pi / 2)
}

Everything you want is already done in matplotlib, just call cm.revcmap, which reverses both types of segmentdata, so

cm.revcmap(cm.rainbow._segmentdata)

should do the job - you can simply create a new LinearSegmentData from that. In revcmap, the reversal of function based SegmentData is done with

def _reverser(f):
    def freversed(x):
        return f(1 - x)
    return freversed

while the other lists are reversed as usual

valnew = [(1.0 - x, y1, y0) for x, y0, y1 in reversed(val)] 

So actually the whole thing you want, is

def reverse_colourmap(cmap, name = 'my_cmap_r'):
     return mpl.colors.LinearSegmentedColormap(name, cm.revcmap(cmap._segmentdata)) 

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There is no built-in way (yet) of reversing arbitrary colormaps, but one simple solution is to actually not modify the colorbar but to create an inverting Normalize object:

from matplotlib.colors import Normalize

class InvertedNormalize(Normalize):
    def __call__(self, *args, **kwargs):
        return 1 - super(InvertedNormalize, self).__call__(*args, **kwargs)

You can then use this with plot_surface and other Matplotlib plotting functions by doing e.g.

inverted_norm = InvertedNormalize(vmin=10, vmax=100)
ax.plot_surface(..., cmap=<your colormap>, norm=inverted_norm)

This will work with any Matplotlib colormap.

참고URL : https://stackoverflow.com/questions/3279560/reverse-colormap-in-matplotlib