问题:从Matplotlib的颜色图中获取单个颜色

cmap例如,如果您有一个Colormap :

cmap = matplotlib.cm.get_cmap('Spectral')

如何从0到1之间获得特定的颜色,其中0是地图中的第一种颜色,而1是地图中的最后一种颜色?

理想情况下,我可以通过执行以下操作来获得地图中的中间颜色:

>>> do_some_magic(cmap, 0.5) # Return an RGBA tuple
(0.1, 0.2, 0.3, 1.0)

If you have a Colormap cmap, for example:

cmap = matplotlib.cm.get_cmap('Spectral')

How can you get a particular colour out of it between 0 and 1, where 0 is the first colour in the map and 1 is the last colour in the map?

Ideally, I would be able to get the middle colour in the map by doing:

>>> do_some_magic(cmap, 0.5) # Return an RGBA tuple
(0.1, 0.2, 0.3, 1.0)

回答 0

您可以使用下面的代码来执行此操作,而问题中的代码实际上与所需的代码非常接近,您所要做的就是调用cmap您拥有的对象。

import matplotlib

cmap = matplotlib.cm.get_cmap('Spectral')

rgba = cmap(0.5)
print(rgba) # (0.99807766255210428, 0.99923106502084169, 0.74602077638401709, 1.0)

对于[0.0,1.0]范围之外的值,它将分别返回底色和底色。默认情况下,这是该范围内的最小和最大颜色(即0.0和1.0)。可以使用cmap.set_under()和更改默认设置cmap.set_over()

对于“特殊”数字(例如)np.nannp.inf默认值是使用0.0值,可以使用cmap.set_bad()类似于“低于”和“高于”的方式更改此值。

最后,您可能需要对数据进行规范化以使其符合范围[0.0, 1.0]matplotlib.colors.Normalize只需使用下面的小示例所示,即可完成此操作,在该示例中,参数vminvmax描述应分别映射到0.0和1.0的数字。

import matplotlib

norm = matplotlib.colors.Normalize(vmin=10.0, vmax=20.0)

print(norm(15.0)) # 0.5

对数归一化器(matplotlib.colors.LogNorm)也可用于值范围较大的数据范围。

(感谢Joe Kingtontcaswell提出了有关如何改善答案的建议。)

You can do this with the code below, and the code in your question was actually very close to what you needed, all you have to do is call the cmap object you have.

import matplotlib

cmap = matplotlib.cm.get_cmap('Spectral')

rgba = cmap(0.5)
print(rgba) # (0.99807766255210428, 0.99923106502084169, 0.74602077638401709, 1.0)

For values outside of the range [0.0, 1.0] it will return the under and over colour (respectively). This, by default, is the minimum and maximum colour within the range (so 0.0 and 1.0). This default can be changed with cmap.set_under() and cmap.set_over().

For “special” numbers such as np.nan and np.inf the default is to use the 0.0 value, this can be changed using cmap.set_bad() similarly to under and over as above.

Finally it may be necessary for you to normalize your data such that it conforms to the range [0.0, 1.0]. This can be done using matplotlib.colors.Normalize simply as shown in the small example below where the arguments vmin and vmax describe what numbers should be mapped to 0.0 and 1.0 respectively.

import matplotlib

norm = matplotlib.colors.Normalize(vmin=10.0, vmax=20.0)

print(norm(15.0)) # 0.5

A logarithmic normaliser (matplotlib.colors.LogNorm) is also available for data ranges with a large range of values.

(Thanks to both Joe Kington and tcaswell for suggestions on how to improve the answer.)


回答 1

为了获得rgba整数值而不是float值,我们可以

rgba = cmap(0.5,bytes=True)

因此,为了简化基于Ffisegydd的答案的代码,代码将如下所示:

#import colormap
from matplotlib import cm

#normalize item number values to colormap
norm = matplotlib.colors.Normalize(vmin=0, vmax=1000)

#colormap possible values = viridis, jet, spectral
rgba_color = cm.jet(norm(400),bytes=True) 

#400 is one of value between 0 and 1000

In order to get rgba integer value instead of float value, we can do

rgba = cmap(0.5,bytes=True)

So to simplify the code based on answer from Ffisegydd, the code would be like this:

#import colormap
from matplotlib import cm

#normalize item number values to colormap
norm = matplotlib.colors.Normalize(vmin=0, vmax=1000)

#colormap possible values = viridis, jet, spectral
rgba_color = cm.jet(norm(400),bytes=True) 

#400 is one of value between 0 and 1000

回答 2

为了建立在Ffisegyddamaliammr的解决方案的基础上,这是一个示例,其中我们为自定义颜色图制作CSV表示形式:

#! /usr/bin/env python3
import matplotlib
import numpy as np 

vmin = 0.1
vmax = 1000

norm = matplotlib.colors.Normalize(np.log10(vmin), np.log10(vmax))
lognum = norm(np.log10([.5, 2., 10, 40, 150,1000]))

cdict = {
    'red':
    (
        (0., 0, 0),
        (lognum[0], 0, 0),
        (lognum[1], 0, 0),
        (lognum[2], 1, 1),
        (lognum[3], 0.8, 0.8),
        (lognum[4], .7, .7),
    (lognum[5], .7, .7)
    ),
    'green':
    (
        (0., .6, .6),
        (lognum[0], 0.8, 0.8),
        (lognum[1], 1, 1),
        (lognum[2], 1, 1),
        (lognum[3], 0, 0),
        (lognum[4], 0, 0),
    (lognum[5], 0, 0)
    ),
    'blue':
    (
        (0., 0, 0),
        (lognum[0], 0, 0),
        (lognum[1], 0, 0),
        (lognum[2], 0, 0),
        (lognum[3], 0, 0),
        (lognum[4], 0, 0),
    (lognum[5], 1, 1)
    )
}


mycmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', cdict, 256)   
norm = matplotlib.colors.LogNorm(vmin, vmax)
colors = {}
count = 0
step_size = 0.001
for value in np.arange(vmin, vmax+step_size, step_size):
    count += 1
    print("%d/%d %f%%" % (count, vmax*(1./step_size), 100.*count/(vmax*(1./step_size))))
    rgba = mycmap(norm(value), bytes=True)
    color = (rgba[0], rgba[1], rgba[2])
    if color not in colors.values():
        colors[value] = color

print ("value, red, green, blue")
for value in sorted(colors.keys()):
    rgb = colors[value]
    print("%s, %s, %s, %s" % (value, rgb[0], rgb[1], rgb[2]))

To build on the solutions from Ffisegydd and amaliammr, here’s an example where we make CSV representation for a custom colormap:

#! /usr/bin/env python3
import matplotlib
import numpy as np 

vmin = 0.1
vmax = 1000

norm = matplotlib.colors.Normalize(np.log10(vmin), np.log10(vmax))
lognum = norm(np.log10([.5, 2., 10, 40, 150,1000]))

cdict = {
    'red':
    (
        (0., 0, 0),
        (lognum[0], 0, 0),
        (lognum[1], 0, 0),
        (lognum[2], 1, 1),
        (lognum[3], 0.8, 0.8),
        (lognum[4], .7, .7),
    (lognum[5], .7, .7)
    ),
    'green':
    (
        (0., .6, .6),
        (lognum[0], 0.8, 0.8),
        (lognum[1], 1, 1),
        (lognum[2], 1, 1),
        (lognum[3], 0, 0),
        (lognum[4], 0, 0),
    (lognum[5], 0, 0)
    ),
    'blue':
    (
        (0., 0, 0),
        (lognum[0], 0, 0),
        (lognum[1], 0, 0),
        (lognum[2], 0, 0),
        (lognum[3], 0, 0),
        (lognum[4], 0, 0),
    (lognum[5], 1, 1)
    )
}


mycmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', cdict, 256)   
norm = matplotlib.colors.LogNorm(vmin, vmax)
colors = {}
count = 0
step_size = 0.001
for value in np.arange(vmin, vmax+step_size, step_size):
    count += 1
    print("%d/%d %f%%" % (count, vmax*(1./step_size), 100.*count/(vmax*(1./step_size))))
    rgba = mycmap(norm(value), bytes=True)
    color = (rgba[0], rgba[1], rgba[2])
    if color not in colors.values():
        colors[value] = color

print ("value, red, green, blue")
for value in sorted(colors.keys()):
    rgb = colors[value]
    print("%s, %s, %s, %s" % (value, rgb[0], rgb[1], rgb[2]))

回答 3

为了完整起见,这些是我到目前为止遇到的cmap选择:

重音,重音,蓝调,蓝调,BrBG,BrBG_r,BuGn,BuGn_r,BuPu,BuPu_r,CMRmap,CMRmap_r,Dark2,Dark2_r,GnBu,GnBu_r,Greens,Greens_r,Greys,Greys_r,Orange,Rr,OrRd,OrRd PRGn_r,成对,成对_r,Pastel1,Pastel1_r,Pastel2,Pastel2_r,PiYG,PiYG_r,PuBu,PuBuGn,PuBuGn_r,PuBu_r,PuOr,PuOr_r,PuRd,PuRd_r,Puror,PurOr_r,RdBu,RdBu,RdBu,RdBu,RdBu,RdBu,RdBu,RdBu,RdBu,RdBu,RdBu,RdBu RdYlBu,RdYlBu_r,RdYlGn,RdYlGn_r,Reds,Reds_r,Set1,Set1_r,Set2,Set2_r,Set3,Set3_r,Spectral,Spectral_r,Wistia,Wistia_r,YlGn,YlGnBu,YlGnBr_r,YlGnBr_r,YlGnBr_r,YlGnBr_r,YlGnBr_r,YlGnBr afmhot_r,秋季,autumn_r,二进制,binary_r,骨骼,bone_r,brg,brg_r,bwr,bwr_r,cividis,cividis_r,cool,cool_r,coolwarm,coolwarm_r,铜,copper_r,cubehelix,cubehelix_r,标志,flag_r,gist_eargist_gray,gist_gray_r,gist_heat,gist_heat_r,gist_ncar,gist_ncar_r,gist_rainbow,gist_rainbow_r,gist_stern,gist_stern_r,gist_yarg,gist_yarg_r,gnuplots,gn_lotv,gnuplot,gnuplot2,gnuplot2,gnuplot2,gnuplot2, jet_r,岩浆,岩浆_r,nipy_spectral,nipy_spectral_r,海洋,ocean_r,粉红色,pink_r,等离子,plasma_r,棱镜,prism_r,彩虹,rainbow_r,地震,地震_r,弹​​簧,spring_r,夏季,summer_r,tab10,tab10_r,tab20,tab20_r, tab20b,tab20b_r,tab20c,tab20c_r,terrain,terrain_r,twilight,twilight_r,twilight_shifted,twilight_shifted_r,viridis,viridis_r,冬天,winter_rgray_r,hot,hot_r,hsv,hsv_r,地狱,inferno_r,喷射,jet_r,岩浆,岩浆_r,nipy_spectral,nipy_spectral_r,海洋,ocean_r,粉红色,pink_r,等离子,plasma_r,棱镜,prism_r,彩虹,rainbow_r,地震,地震_r,春天,spring_r,夏天,summer_r,tab10,tab10_r,tab20,tab20_r,tab20b,tab20b_r,tab20c,tab20c_r,terrain,terrain_r,twilight,twilight_r,twilight_shifted,twilight_shifted_r,viridis,viridis_r,冬天,winter_rgray_r,hot,hot_r,hsv,hsv_r,地狱,inferno_r,喷射,jet_r,岩浆,岩浆_r,nipy_spectral,nipy_spectral_r,海洋,ocean_r,粉红色,pink_r,等离子,plasma_r,棱镜,prism_r,彩虹,rainbow_r,地震,地震_r,春天,spring_r,夏天,summer_r,tab10,tab10_r,tab20,tab20_r,tab20b,tab20b_r,tab20c,tab20c_r,terrain,terrain_r,twilight,twilight_r,twilight_shifted,twilight_shifted_r,viridis,viridis_r,冬天,winter_rviridis,viridis_r,冬天,winter_rviridis,viridis_r,冬天,winter_r

For completeness these are the cmap choices I encountered so far:

Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r


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