blocks|key|459291|text|您收到的错误是由于您定义jet的方式造成的。您正在创建名为‘Colormap’的基类jet，但这与获取'jet‘色彩映射的默认定义有很大不同。这个基类永远不应该被直接创建，只有子类应该被实例化。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|459292|你在你的例子中发现了Matplotlib中的一个错误行为。在运行此代码时，应该会生成更清晰的错误消息。|459293|这是您的示例的更新版本：|459294|import+matplotlib.pyplot+as+plt
import+matplotlib.colors+as+colors
import+matplotlib.cm+as+cmx
import+numpy+as+np

#+define+some+random+data+that+emulates+your+indeded+code:
NCURVES+=+10
np.random.seed(101)
curves+=+[np.random.random(20)+for+i+in+range(NCURVES)]
values+=+range(NCURVES)

fig+=+plt.figure()
ax+=+fig.add_subplot(111)
#+replace+the+next+line+
#jet+=+colors.Colormap('jet')
#+with
jet+=+cm+=+plt.get_cmap('jet')+
cNorm++=+colors.Normalize(vmin=0,+vmax=values[-1])
scalarMap+=+cmx.ScalarMappable(norm=cNorm,+cmap=jet)
print+scalarMap.get_clim()

lines+=+[]
for+idx+in+range(len(curves)):
++++line+=+curves[idx]
++++colorVal+=+scalarMap.to_rgba(values[idx])
++++colorText+=+(
++++++++'color:+(%254.2f,%254.2f,%254.2f)'%25(colorVal[0],colorVal[1],colorVal[2])
++++++++)
++++retLine,+=+ax.plot(line,
+++++++++++++++++++++++color=colorVal,
+++++++++++++++++++++++label=colorText)
++++lines.append(retLine)
#added+this+to+get+the+legend+to+work
handles,labels+=+ax.get_legend_handles_labels()
ax.legend(handles,+labels,+loc='upper+right')
ax.grid()
plt.show()|code-block|syntax|javascript|459295|结果是：|459296|​|459297|📷|atomic|459298|459299|使用ScalarMappable是对我的相关答案中提出的方法的改进：creating+over+20+unique+legend+colors+using+matplotlib|459300|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/ddfe8633f171f63d5d431ab923ca66aa.png|imageAlt|1|LINK|MUTABLE|url|https://stackoverflow.com/questions/8389636/creating-over-20-unique-legends-using-matplotlib/8391452#8391452^0|C|3|U|8|0|0|0|0|0|0|0|1|0|0|0|2|E|Y|1I|1|0^^$0|@$1|2|3|4|5|6|7|1C|8|@$9|1D|A|1E|B|C]|$9|1F|A|1G|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1H|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|1I|8|@]|D|@]|E|$]]|$1|J|3|K|5|L|7|1J|8|@]|D|@]|E|$M|N]]|$1|O|3|P|5|6|7|1K|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1L|8|@]|D|@]|E|$]]|$1|S|3|T|5|U|7|1M|8|@]|D|@$9|1N|A|1O|1|1P]]|E|$]]|$1|V|3|R|5|6|7|1Q|8|@]|D|@]|E|$]]|$1|W|3|X|5|6|7|1R|8|@$9|1S|A|1T|B|C]]|D|@$9|1U|A|1V|1|1W]]|E|$]]|$1|Y|3|-4|5|6|7|1X|8|@]|D|@]|E|$]]]|Z|$10|$5|11|12|13|E|$14|15|16|-4]]|17|$5|18|12|19|E|$1A|1B]]]]

The error you are receiving is due to how you define <code>jet</code>. You are creating the base class <code>Colormap</code> with the name 'jet', but this is very different from getting the default definition of the 'jet' colormap. This base class should never be created directly, and only the subclasses should be instantiated. 

What you've found with your example is a buggy behavior in Matplotlib. There should be a clearer error message generated when this code is run. 

This is an updated version of your example: 

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

# define some random data that emulates your indeded code:
NCURVES = 10
np.random.seed(101)
curves = [np.random.random(20) for i in range(NCURVES)]
values = range(NCURVES)

fig = plt.figure()
ax = fig.add_subplot(111)
# replace the next line 
#jet = colors.Colormap('jet')
# with
jet = cm = plt.get_cmap('jet') 
cNorm = colors.Normalize(vmin=0, vmax=values[-1])
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
print scalarMap.get_clim()

lines = []
for idx in range(len(curves)):
 line = curves[idx]
 colorVal = scalarMap.to_rgba(values[idx])
 colorText = (
 'color: (%4.2f,%4.2f,%4.2f)'%(colorVal[0],colorVal[1],colorVal[2])
 )
 retLine, = ax.plot(line,
 color=colorVal,
 label=colorText)
 lines.append(retLine)
#added this to get the legend to work
handles,labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc='upper right')
ax.grid()
plt.show()
</code></pre>

Resulting in:

<img src="https://i.stack.imgur.com/JvjS9.png" alt="enter image description here">

Using a <code>ScalarMappable</code> is an improvement over the approach presented in my related answer:
<a href="https://stackoverflow.com/questions/8389636/creating-over-20-unique-legends-using-matplotlib/8391452#8391452">creating over 20 unique legend colors using matplotlib</a>

blocks|key|673720|text|我认为将我认为更简单的方法与matplotlib的cm类型的对象结合起来使用numpy的linspace会更有好处。上面的解决方案可能是针对较旧版本的。我使用的是python+3.4.3、matplotlib+1.4.3和numpy+1.9.3，我的解决方案如下。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|673721|import+matplotlib.pyplot+as+plt

from+matplotlib+import+cm
from+numpy+import+linspace

start+=+0.0
stop+=+1.0
number_of_lines=+1000
cm_subsection+=+linspace(start,+stop,+number_of_lines)+

colors+=+[+cm.jet(x)+for+x+in+cm_subsection+]

for+i,+color+in+enumerate(colors):
++++plt.axhline(i,+color=color)

plt.ylabel('Line+Number')
plt.show()|code-block|syntax|javascript|673722|这将产生1000条唯一颜色的线条，这些线条覆盖了整个cm.jet色彩映射表，如下图所示。如果您运行此脚本，您会发现您可以放大各个行。|673723|​|673724|📷|atomic|offset|length|673725|673726|现在假设我希望我的1000行颜色只跨越400到600行之间的绿色部分。我只需将开始和停止值更改为0.4和0.6，这将导致仅使用0.4到0.6之间的20%25的cm.jet颜色映射。|673727|673728|673729|673730|因此，在一行摘要中，您可以相应地从matplotlib.cm色彩映射表创建rgba颜色列表：|673731|colors+=+[+cm.jet(x)+for+x+in+linspace(start,+stop,+number_of_lines)+]|673732|在本例中，我使用了通常调用的名为jet的映射，但您可以通过调用以下命令找到matplotlib版本中可用的色彩映射的完整列表：|673733|>>>+from+matplotlib+import+cm
>>>+dir(cm)|673734|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/2aea0259e4689e2f76f88dd466e9b0ad.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/75ead38dadb44d73ce034fd537d79ace.png^0|0|0|0|0|0|1|0|0|0|0|0|0|1|1|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1E|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1F|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1G|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|1H|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|1I|8|@]|9|@$N|1J|O|1K|1|1L]]|A|$]]|$1|P|3|J|5|6|7|1M|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1N|8|@]|9|@]|A|$]]|$1|S|3|J|5|6|7|1O|8|@]|9|@]|A|$]]|$1|T|3|L|5|M|7|1P|8|@]|9|@$N|1Q|O|1R|1|1S]]|A|$]]|$1|U|3|J|5|6|7|1T|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|1U|8|@]|9|@]|A|$]]|$1|X|3|Y|5|D|7|1V|8|@]|9|@]|A|$E|F]]|$1|Z|3|10|5|6|7|1W|8|@]|9|@]|A|$]]|$1|11|3|12|5|D|7|1X|8|@]|9|@]|A|$E|F]]|$1|13|3|-4|5|6|7|1Y|8|@]|9|@]|A|$]]]|14|$15|$5|16|17|18|A|$19|1A|1B|-4]]|1C|$5|16|17|18|A|$19|1D|1B|-4]]]]

I thought it would be beneficial to include what I consider to be a more simple method using numpy's linspace coupled with matplotlib's cm-type object. It's possible that the above solution is for an older version. I am using the python 3.4.3, matplotlib 1.4.3, and numpy 1.9.3., and my solution is as follows.

<pre><code>import matplotlib.pyplot as plt

from matplotlib import cm
from numpy import linspace

start = 0.0
stop = 1.0
number_of_lines= 1000
cm_subsection = linspace(start, stop, number_of_lines) 

colors = [ cm.jet(x) for x in cm_subsection ]

for i, color in enumerate(colors):
 plt.axhline(i, color=color)

plt.ylabel('Line Number')
plt.show()
</code></pre>

This results in 1000 uniquely-colored lines that span the entire cm.jet colormap as pictured below. If you run this script you'll find that you can zoom in on the individual lines. 

<a href="https://i.stack.imgur.com/i4sMR.png" rel="noreferrer"><img src="https://i.stack.imgur.com/i4sMR.png" alt="cm.jet between 0.0 and 1.0 with 1000 graduations"></a>

Now say I want my 1000 line colors to just span the greenish portion between lines 400 to 600. I simply change my start and stop values to 0.4 and 0.6 and this results in using only 20% of the cm.jet color map between 0.4 and 0.6. 

<a href="https://i.stack.imgur.com/bULCi.png" rel="noreferrer"><img src="https://i.stack.imgur.com/bULCi.png" alt="enter image description here"></a>

So in a one line summary you can create a list of rgba colors from a matplotlib.cm colormap accordingly:

<pre><code>colors = [ cm.jet(x) for x in linspace(start, stop, number_of_lines) ]
</code></pre>

In this case I use the commonly invoked map named jet but you can find the complete list of colormaps available in your matplotlib version by invoking:

<pre><code>&gt;&gt;&gt; from matplotlib import cm
&gt;&gt;&gt; dir(cm)
</code></pre>

blocks|key|673887|text|来自matplotlib的线条样式、标记和定性颜色的组合|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|673888|import+itertools
import+matplotlib+as+mpl
import+matplotlib.pyplot+as+plt
N+=+8*4%2B10
l_styles+=+['-','--','-.',':']
m_styles+=+['','.','o','%5E','*']
colormap+=+mpl.cm.Dark2.colors+++#+Qualitative+colormap
for+i,(marker,linestyle,color)+in+zip(range(N),itertools.product(m_styles,l_styles,+colormap)):
++++plt.plot([0,1,2],[0,2*i,2*i],+color=color,+linestyle=linestyle,marker=marker,label=i)
plt.legend(bbox_to_anchor=(1.05,+1),+loc=2,+borderaxespad=0.,ncol=4);|code-block|syntax|javascript|673889|​|673890|📷|atomic|673891|673892|更新:不仅支持ListedColormap，还支持LinearSegmentedColormap|673893|import+itertools
import+matplotlib.pyplot+as+plt
Ncolors+=+8
#colormap+=+plt.cm.Dark2#+ListedColormap
colormap+=+plt.cm.viridis#+LinearSegmentedColormap
Ncolors+=+min(colormap.N,Ncolors)
mapcolors+=+[colormap(int(x*colormap.N/Ncolors))+for+x+in+range(Ncolors)]
N+=+Ncolors*4%2B10
l_styles+=+['-','--','-.',':']
m_styles+=+['','.','o','%5E','*']
fig,ax+=+plt.subplots(gridspec_kw=dict(right=0.6))
for+i,(marker,linestyle,color)+in+zip(range(N),itertools.product(m_styles,l_styles,+mapcolors)):
++++ax.plot([0,1,2],[0,2*i,2*i],+color=color,+linestyle=linestyle,marker=marker,label=i)
ax.legend(bbox_to_anchor=(1.05,+1),+loc=2,+borderaxespad=0.,ncol=3,prop={'size':+8})|673894|673895|673896|673897|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/0ffbac7ec9519f8df2592f287aaa512d.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/c7a2e4aa41633c24aeef24168b7639f8.png^0|2|A|0|0|0|0|1|0|0|0|7|E|P|N|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1C|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1D|8|@]|D|@$9|1E|A|1F|1|1G]]|E|$]]|$1|P|3|L|5|6|7|1H|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1I|8|@$9|1J|A|1K|B|C]|$9|1L|A|1M|B|C]]|D|@]|E|$]]|$1|S|3|T|5|H|7|1N|8|@]|D|@]|E|$I|J]]|$1|U|3|L|5|6|7|1O|8|@]|D|@]|E|$]]|$1|V|3|N|5|O|7|1P|8|@]|D|@$9|1Q|A|1R|1|1S]]|E|$]]|$1|W|3|L|5|6|7|1T|8|@]|D|@]|E|$]]|$1|X|3|-4|5|6|7|1U|8|@]|D|@]|E|$]]]|Y|$Z|$5|10|11|12|E|$13|14|15|-4]]|16|$5|10|11|12|E|$13|17|15|-4]]]]

A combination of line styles, markers, and qualitative colors from <code>matplotlib</code>:

<pre><code>import itertools
import matplotlib as mpl
import matplotlib.pyplot as plt
N = 8*4+10
l_styles = ['-','--','-.',':']
m_styles = ['','.','o','^','*']
colormap = mpl.cm.Dark2.colors # Qualitative colormap
for i,(marker,linestyle,color) in zip(range(N),itertools.product(m_styles,l_styles, colormap)):
 plt.plot([0,1,2],[0,2*i,2*i], color=color, linestyle=linestyle,marker=marker,label=i)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,ncol=4);
</code></pre>

<a href="https://i.stack.imgur.com/exi38.png" rel="noreferrer"><img src="https://i.stack.imgur.com/exi38.png" alt="enter image description here"></a>

UPDATE: Supporting not only <code>ListedColormap</code>, but also <code>LinearSegmentedColormap</code>

<pre><code>import itertools
import matplotlib.pyplot as plt
Ncolors = 8
#colormap = plt.cm.Dark2# ListedColormap
colormap = plt.cm.viridis# LinearSegmentedColormap
Ncolors = min(colormap.N,Ncolors)
mapcolors = [colormap(int(x*colormap.N/Ncolors)) for x in range(Ncolors)]
N = Ncolors*4+10
l_styles = ['-','--','-.',':']
m_styles = ['','.','o','^','*']
fig,ax = plt.subplots(gridspec_kw=dict(right=0.6))
for i,(marker,linestyle,color) in zip(range(N),itertools.product(m_styles,l_styles, mapcolors)):
 ax.plot([0,1,2],[0,2*i,2*i], color=color, linestyle=linestyle,marker=marker,label=i)
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,ncol=3,prop={'size': 8})
</code></pre>

<a href="https://i.stack.imgur.com/K4daw.png" rel="noreferrer"><img src="https://i.stack.imgur.com/K4daw.png" alt="enter image description here"></a>

blocks|key|459684|text|你可以按照我删除的账号写的那样做(禁止新帖子:(有)。它相当简单，看起来也很漂亮。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|459685|我使用的3-rd这3个人中的一个通常，我也正在检查1和2的版本。|459686|from+matplotlib.pyplot+import+cm
import+numpy+as+np

#variable+n+should+be+number+of+curves+to+plot+(I+skipped+this+earlier+thinking+that+it+is+obvious+when+looking+at+picture+-+sorry+my+bad+mistake+xD):+n=len(array_of_curves_to_plot)
#version+1:

color=cm.rainbow(np.linspace(0,1,n))
for+i,c+in+zip(range(n),color):
+++ax1.plot(x,+y,c=c)

#or+version+2:+-+faster+and+better:

color=iter(cm.rainbow(np.linspace(0,1,n)))
c=next(color)
plt.plot(x,y,c=c)

#or+version+3:

color=iter(cm.rainbow(np.linspace(0,1,n)))
for+i+in+range(n):
+++c=next(color)
+++ax1.plot(x,+y,c=c)|code-block|syntax|javascript|459687|示例3：|459688|Ship+RAO+of+Roll+vs+Ikeda+damping+in+function+of+Roll+amplitude+A44|offset|length|459689|entityMap|0|LINK|mutability|MUTABLE|url|https://i.stack.imgur.com/KXUpB.png^0|0|0|0|0|0|1V|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|10|8|@]|9|@$M|11|N|12|1|13]]|A|$]]|$1|O|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V]]]]

U may do as I have written from my deleted account (ban for new posts :( there was). Its rather simple and nice looking.

Im using 3-rd one of these 3 ones usually, also I wasny checking 1 and 2 version.

<pre><code>from matplotlib.pyplot import cm
import numpy as np

#variable n should be number of curves to plot (I skipped this earlier thinking that it is obvious when looking at picture - sorry my bad mistake xD): n=len(array_of_curves_to_plot)
#version 1:

color=cm.rainbow(np.linspace(0,1,n))
for i,c in zip(range(n),color):
 ax1.plot(x, y,c=c)

#or version 2: - faster and better:

color=iter(cm.rainbow(np.linspace(0,1,n)))
c=next(color)
plt.plot(x,y,c=c)

#or version 3:

color=iter(cm.rainbow(np.linspace(0,1,n)))
for i in range(n):
 c=next(color)
 ax1.plot(x, y,c=c)
</code></pre>

example of 3: 

<a href="https://i.stack.imgur.com/KXUpB.png" rel="noreferrer">Ship RAO of Roll vs Ikeda damping in function of Roll amplitude A44</a>

How does one set the color of a line in matplotlib with scalar values provided at run time using a colormap (say <code>jet</code>)? I tried a couple of different approaches here and I think I'm stumped. <code>values[]</code> is a storted array of scalars. curves are a set of 1-d arrays, and labels are an array of text strings. Each of the arrays have the same length.

<pre><code>fig = plt.figure()
ax = fig.add_subplot(111)
jet = colors.Colormap('jet')
cNorm = colors.Normalize(vmin=0, vmax=values[-1])
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
lines = []
for idx in range(len(curves)):
 line = curves[idx]
 colorVal = scalarMap.to_rgba(values[idx])
 retLine, = ax.plot(line, color=colorVal)
 #retLine.set_color()
 lines.append(retLine)
ax.legend(lines, labels, loc='upper right')
ax.grid()
plt.show()
</code></pre>

Using Colormaps to set color of line in matplotlib

如何使用色彩映射表(比如jet)在matplotlib中使用在运行时提供的标量值来设置线条的颜色？我在这里尝试了几种不同的方法，我想我被难住了。values[]是一个存储的标量数组。曲线是一组一维的数组，标签是文本字符串的数组。每个数组具有相同的长度。fig = plt.figure()ax = fig.add_sub...

问在matplotlib中使用Colormap设置线条的颜色
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