blocks|key|234882|text|staticmethod+colors.LinearSegmentedColormap.from_list可用于创建新的LinearSegmentedColormaps。下面，我在0.2到0.8之间的100个点上对原始色彩映射进行采样：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|234883|cmap(np.linspace(0.2,+0.8,+100))|code-block|syntax|javascript|234884|并使用这些颜色生成新的色彩映射表：|234885|import+matplotlib.pyplot+as+plt
import+matplotlib.colors+as+colors
import+numpy+as+np

def+truncate_colormap(cmap,+minval=0.0,+maxval=1.0,+n=100):
++++new_cmap+=+colors.LinearSegmentedColormap.from_list(
++++++++'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name,+a=minval,+b=maxval),
++++++++cmap(np.linspace(minval,+maxval,+n)))
++++return+new_cmap

arr+=+np.linspace(0,+50,+100).reshape((10,+10))
fig,+ax+=+plt.subplots(ncols=2)

cmap+=+plt.get_cmap('jet')
new_cmap+=+truncate_colormap(cmap,+0.2,+0.8)
ax[0].imshow(arr,+interpolation='nearest',+cmap=cmap)
ax[1].imshow(arr,+interpolation='nearest',+cmap=new_cmap)
plt.show()|234886|​|234887|📷|atomic|234888|234889|左边的图显示了使用原始色彩映射表(在本例中为jet)的图像。右边的图显示了使用new_cmap的相同图像。|style|CODE|234890|entityMap|0|LINK|mutability|MUTABLE|url|http://matplotlib.org/api/colors_api.html#matplotlib.colors.LinearSegmentedColormap.from_list|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/3e9cffcb71531c6bec23dd7a23c7b4b2.png|imageAlt^0|D|14|0|0|0|0|0|0|0|1|1|0|0|M|3|13|8|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@$A|1B|B|1C|1|1D]]|C|$]]|$1|D|3|E|5|F|7|1E|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1F|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|1G|8|@]|9|@]|C|$G|H]]|$1|M|3|N|5|6|7|1H|8|@]|9|@]|C|$]]|$1|O|3|P|5|Q|7|1I|8|@]|9|@$A|1J|B|1K|1|1L]]|C|$]]|$1|R|3|N|5|6|7|1M|8|@]|9|@]|C|$]]|$1|S|3|T|5|6|7|1N|8|@$A|1O|B|1P|U|V]|$A|1Q|B|1R|U|V]]|9|@]|C|$]]|$1|W|3|-4|5|6|7|1S|8|@]|9|@]|C|$]]]|X|$Y|$5|Z|10|11|C|$12|13]]|14|$5|15|10|16|C|$17|18|19|-4]]]]

The staticmethod <a href="http://matplotlib.org/api/colors_api.html#matplotlib.colors.LinearSegmentedColormap.from_list" rel="noreferrer">colors.LinearSegmentedColormap.from_list</a> can be used to create new LinearSegmentedColormaps. Below, I sample the original colormap at 100 points between 0.2 and 0.8:

<pre><code>cmap(np.linspace(0.2, 0.8, 100))
</code></pre>

and use these colors to generate a new colormap:

<pre><code>import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np

def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
 new_cmap = colors.LinearSegmentedColormap.from_list(
 'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
 cmap(np.linspace(minval, maxval, n)))
 return new_cmap

arr = np.linspace(0, 50, 100).reshape((10, 10))
fig, ax = plt.subplots(ncols=2)

cmap = plt.get_cmap('jet')
new_cmap = truncate_colormap(cmap, 0.2, 0.8)
ax[0].imshow(arr, interpolation='nearest', cmap=cmap)
ax[1].imshow(arr, interpolation='nearest', cmap=new_cmap)
plt.show()
</code></pre>

<img src="https://i.stack.imgur.com/jZfCW.png" alt="enter image description here">

The plot on the left shows the image using the original colormap (in this example, <code>jet</code>). The plot on the right shows the same image using <code>new_cmap</code>.

blocks|key|234981|text|以下是嵌入绘图函数的前一个答案的改编：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|234982|import+matplotlib.pyplot+as+plt
import+matplotlib.colors+as+colors
import+numpy+as+np

###################+Function+to+truncate+color+map+###################
def+truncate_colormap(cmapIn='jet',+minval=0.0,+maxval=1.0,+n=100):
++++'''truncate_colormap(cmapIn='jet',+minval=0.0,+maxval=1.0,+n=100)'''++++
++++cmapIn+=+plt.get_cmap(cmapIn)

++++new_cmap+=+colors.LinearSegmentedColormap.from_list(
++++++++'trunc({n},{a:.2f},{b:.2f})'.format(n=cmapIn.name,+a=minval,+b=maxval),
++++++++cmapIn(np.linspace(minval,+maxval,+n)))

++++arr+=+np.linspace(0,+50,+100).reshape((10,+10))
++++fig,+ax+=+plt.subplots(ncols=2)
++++ax[0].imshow(arr,+interpolation='nearest',+cmap=cmapIn)
++++ax[1].imshow(arr,+interpolation='nearest',+cmap=new_cmap)
++++plt.show()

++++return+new_cmap

cmap_mod+=+truncate_colormap(minval=.2,+maxval=.8)++#+calls+function+to+truncate+colormap|code-block|syntax|javascript|234983|​|234984|📷|atomic|offset|length|234985|234986|如果需要多次调用函数，使用嵌入绘图的紧凑函数会很有帮助。|234987|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/0a7b7d65f160df44724f5a89e9373187.jpeg|imageAlt^0|0|0|0|0|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|10|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|11|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|12|8|@]|9|@$L|13|M|14|1|15]]|A|$]]|$1|N|3|H|5|6|7|16|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|17|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|18|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X|Y|-4]]]]

Here is an adaptation of a previous answer which embeds the plotting function:

<pre><code>import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np

################### Function to truncate color map ###################
def truncate_colormap(cmapIn='jet', minval=0.0, maxval=1.0, n=100):
 '''truncate_colormap(cmapIn='jet', minval=0.0, maxval=1.0, n=100)''' 
 cmapIn = plt.get_cmap(cmapIn)

 new_cmap = colors.LinearSegmentedColormap.from_list(
 'trunc({n},{a:.2f},{b:.2f})'.format(n=cmapIn.name, a=minval, b=maxval),
 cmapIn(np.linspace(minval, maxval, n)))

 arr = np.linspace(0, 50, 100).reshape((10, 10))
 fig, ax = plt.subplots(ncols=2)
 ax[0].imshow(arr, interpolation='nearest', cmap=cmapIn)
 ax[1].imshow(arr, interpolation='nearest', cmap=new_cmap)
 plt.show()

 return new_cmap

cmap_mod = truncate_colormap(minval=.2, maxval=.8) # calls function to truncate colormap
</code></pre>

<img src="https://i.stack.imgur.com/mAGUl.jpg" alt="visual of output from truncate_colormap():">

Having a compact function with the plotting embedded is helpful if you need to call the function more than once.

blocks|key|18566|text|从答案中获得的可视化效果略有提高(受that+previous+answer的启发)|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|18567|import+numpy+as+np
import+matplotlib+as+mpl
import+matplotlib.pyplot+as+plt

def+truncate_colormap(cmap,+minval=0.0,+maxval=1.0,+n=100):
++++'''
++++https://stackoverflow.com/a/18926541
++++'''
++++if+isinstance(cmap,+str):
++++++++cmap+=+plt.get_cmap(cmap)
++++new_cmap+=+mpl.colors.LinearSegmentedColormap.from_list(
++++++++'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name,+a=minval,+b=maxval),
++++++++cmap(np.linspace(minval,+maxval,+n)))
++++return+new_cmap

cmap_base+=+'jet'
vmin,+vmax+=+0.2,+0.8
cmap+=+truncate_colormap(cmap_base,+vmin,+vmax)

fig,+ax+=+plt.subplots(nrows=2)
sm+=+mpl.cm.ScalarMappable(cmap=cmap_base)+
cbar+=+plt.colorbar(sm,+cax=ax[0],+orientation='horizontal')

sm+=+mpl.cm.ScalarMappable(cmap=cmap)+
cbar+=+plt.colorbar(sm,+cax=ax[1],+orientation='horizontal')
plt.show()|code-block|syntax|javascript|18568|​|18569|📷|atomic|18570|18571|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/53191379/5322720|1|https://stackoverflow.com/a/18926541/5322720|2|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/1ce4d2bcf3477a9472068b2743d65839.png|imageAlt^0|I|4|0|N|F|1|0|0|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@]|9|@$A|15|B|16|1|17]|$A|18|B|19|1|1A]]|C|$]]|$1|D|3|E|5|F|7|1B|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1C|8|@]|9|@]|C|$]]|$1|K|3|L|5|M|7|1D|8|@]|9|@$A|1E|B|1F|1|1G]]|C|$]]|$1|N|3|J|5|6|7|1H|8|@]|9|@]|C|$]]|$1|O|3|-4|5|6|7|1I|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]|W|$5|R|S|T|C|$U|X]]|Y|$5|Z|S|10|C|$11|12|13|-4]]]]

Slight improvement of visualization from a <a href="https://stackoverflow.com/a/18926541/5322720">previous answer</a>, (inspired by <a href="https://stackoverflow.com/a/53191379/5322720">that</a> answer)
<pre><code>import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
 '''
 https://stackoverflow.com/a/18926541
 '''
 if isinstance(cmap, str):
 cmap = plt.get_cmap(cmap)
 new_cmap = mpl.colors.LinearSegmentedColormap.from_list(
 'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
 cmap(np.linspace(minval, maxval, n)))
 return new_cmap

cmap_base = 'jet'
vmin, vmax = 0.2, 0.8
cmap = truncate_colormap(cmap_base, vmin, vmax)

fig, ax = plt.subplots(nrows=2)
sm = mpl.cm.ScalarMappable(cmap=cmap_base) 
cbar = plt.colorbar(sm, cax=ax[0], orientation='horizontal')

sm = mpl.cm.ScalarMappable(cmap=cmap) 
cbar = plt.colorbar(sm, cax=ax[1], orientation='horizontal')
plt.show()
</code></pre>
<a href="https://i.stack.imgur.com/mAB6H.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/mAB6H.png" alt="enter image description here" /></a>

blocks|key|18614|text|在我的CMasher包中，我提供了色彩映射表(+get_sub_cmap()-function，https://cmasher.readthedocs.io/user/usage.html#sub-colormaps)，它接受一个色彩映射表和一个范围，并返回一个包含请求范围的新色彩映射表。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|18615|因此，例如，如果您想要采用viridis色彩映射表的20%25到80%25之间的颜色，您可以使用：|18616|import+cmasher+as+cmr

cmap+=+cmr.get_sub_cmap('viridis',+0.2,+0.8)|code-block|syntax|javascript|18617|PS:不要使用jet+(或CMRmap)，因为它们在感知上不是一致的顺序。取而代之的是，使用matplotlib中正确的5个色彩映射表，或者使用cmocean或my+CMasher提供的色彩映射表。|18618|编辑:在最新版本的CMasher中，您还可以使用此相同的函数从任何颜色映射中创建离散/定性颜色映射，方法是为该函数提供要获取的分段数。例如，如果要创建20%25到80%25范围内的viridis的定性色彩映射，则可以使用：|18619|cmap+=+cmr.get_sub_map('viridis',+0.2,+0.8,+N=5)|18620|entityMap|0|LINK|mutability|MUTABLE|url|https://cmasher.readthedocs.io/user/usage.html#sub-colormaps^0|O|E|1C|1O|0|0|D|7|0|0|7|3|D|6|0|2E|7|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@$9|13|A|14|1|15]]|E|$]]|$1|F|3|G|5|6|7|16|8|@$9|17|A|18|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|19|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1A|8|@$9|1B|A|1C|B|C]|$9|1D|A|1E|B|C]]|D|@]|E|$]]|$1|O|3|P|5|6|7|1F|8|@$9|1G|A|1H|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|J|7|1I|8|@]|D|@]|E|$K|L]]|$1|S|3|-4|5|6|7|1J|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z]]]]

In my CMasher package, I provide the <code>get_sub_cmap()</code>-function (<a href="https://cmasher.readthedocs.io/user/usage.html#sub-colormaps" rel="nofollow noreferrer">https://cmasher.readthedocs.io/user/usage.html#sub-colormaps</a>), which takes a colormap and a range, and returns a new colormap containing the requested range.
So, for example, if you want to take the colors between 20% and 80% of the <code>viridis</code> colormap, you can do that with:
<pre class="lang-py prettyprint-override"><code>import cmasher as cmr

cmap = cmr.get_sub_cmap('viridis', 0.2, 0.8)
</code></pre>
PS: Do not use <code>jet</code> (or <code>CMRmap</code>), as they are not perceptually uniform sequential.
Instead, use the 5 proper colormaps in matplotlib or the colormaps provided by cmocean or my CMasher.
EDIT: In the latest version of CMasher, one can also use this same function to create a discrete/qualitative colormap out of any colormap by supplying the function with the number of segments to take.
For example, if you want to create a qualitative colormap of <code>viridis</code> in the 20% to 80% range, you can do this with:
<pre class="lang-py prettyprint-override"><code>cmap = cmr.get_sub_map('viridis', 0.2, 0.8, N=5)
</code></pre>

I would like to use a colormap from matplotlib e.g. CMRmap. But I don't want to use the "black" color at the beginning and the "white" color at the end. I'm interested to plot my data using the in-between colors. I think ppl use it quite often but I was searching over internet and could not manage to find any simple solution. I'll appreciate if someone suggest any solution.

how to extract a subset of a colormap as a new colormap in matplotlib?

我想使用matplotlib中的色彩映射，例如CMRmap。但我不想在开头使用“黑色”，在结尾使用“白色”。我对使用中间颜色绘制数据很感兴趣。我认为人们经常使用它，但我在互联网上搜索，没有找到任何简单的解决方案。如果有人提出任何解决方案，我将不胜感激。

问如何在matplotlib中提取色彩映射表的子集作为新的色彩映射表？
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