A. *_*Gup 9 python matplotlib heatmap
我试图绘制范围为-3到3的对数比率,并希望负比率为绿色,正值为红色,对数比率为0(中心)为白色.matplotlib中没有预先存在的颜色方案提供此选项,我无法弄清楚如何手动输出漂亮的渐变.
Imp*_*est 16
Usingmatplotlib.colors.LinearSegmentedColormap的from_list方法似乎比这里的其他一些答案更直观。
from matplotlib.colors import LinearSegmentedColormap
cmap=LinearSegmentedColormap.from_list('rg',["r", "w", "g"], N=256)
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或者进行更复杂的调整:
from matplotlib.colors import LinearSegmentedColormap
c = ["darkred","red","lightcoral","white", "palegreen","green","darkgreen"]
v = [0,.15,.4,.5,0.6,.9,1.]
l = list(zip(v,c))
cmap=LinearSegmentedColormap.from_list('rg',l, N=256)
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使用以下内容怎么样LinearSegmentedColormap:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
cmapGR = LinearSegmentedColormap(
'GreenRed',
{
'red': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green':((0.0, 1.0, 1.0),
(0.5, 1.0, 1.0),
( 1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 0.0, 0.0))
},)
plt.imshow(np.array([np.arange(200) for i in range(200)]), cmap=cmapGR)
plt.show()
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有关更多用途和其他示例,请参阅http://matplotlib.org/examples/pylab_examples/custom_cmap.html 。
您可以使用创建自己的LinearSegmentedColormap。我喜欢将红色和绿色通道的上限和下限设置为小于1.0,这样颜色就不会太亮(在这里我使用了0.8)。调整以适合您的口味。
有关更多详细信息,请参见matplotlib网站上的custom_cmap示例。
这是一个工作示例:
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
# This dictionary defines the colormap
cdict = {'red': ((0.0, 0.0, 0.0), # no red at 0
(0.5, 1.0, 1.0), # all channels set to 1.0 at 0.5 to create white
(1.0, 0.8, 0.8)), # set to 0.8 so its not too bright at 1
'green': ((0.0, 0.8, 0.8), # set to 0.8 so its not too bright at 0
(0.5, 1.0, 1.0), # all channels set to 1.0 at 0.5 to create white
(1.0, 0.0, 0.0)), # no green at 1
'blue': ((0.0, 0.0, 0.0), # no blue at 0
(0.5, 1.0, 1.0), # all channels set to 1.0 at 0.5 to create white
(1.0, 0.0, 0.0)) # no blue at 1
}
# Create the colormap using the dictionary
GnRd = colors.LinearSegmentedColormap('GnRd', cdict)
# Make a figure and axes
fig,ax = plt.subplots(1)
# Some fake data in the range -3 to 3
dummydata = np.random.rand(5,5)*6.-3.
# Plot the fake data
p=ax.pcolormesh(dummydata,cmap=GnRd,vmin=-3,vmax=3)
# Make a colorbar
fig.colorbar(p,ax=ax)
plt.show()
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