在 matplotlib patchcollect 中设置颜色范围 [英] setting color range in matplotlib patchcollection

问题描述

我正在绘制 PatchCollection在matplotlib中，具有从文件中读取的坐标和色块颜色值.

I am plotting a PatchCollection in matplotlib with coords and patch color values read in from a file.

问题是matplotlib似乎会自动将颜色范围缩放到数据值的最小值/最大值.如何手动设置颜色范围?例如.如果我的数据范围是 10-30，但我想将其缩放到 5-50 的颜色范围(例如与另一个图进行比较)，我该怎么做?

The problem is that matplotlib seems to automatically scale the color range to the min/max of the data values. How can I manually set the color range? E.g. if my data range is 10-30, but I want to scale this to a color range of 5-50 (e.g. to compare to another plot), how can I do this?

我的绘图命令与api示例代码中的命令几乎相同: patch_collection.py

My plotting commands look much the same as in the api example code: patch_collection.py

colors = 100 * pylab.rand(len(patches))
p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.4)
p.set_array(pylab.array(colors))
ax.add_collection(p)
pylab.colorbar(p)

pylab.show()

推荐答案

在您的示例中，使用 p.set_clim([5，50])设置颜色缩放的最小值和最大值.matplotlib中具有颜色图的所有内容均具有 get_clim 和 set_clim 方法.

Use p.set_clim([5, 50]) to set the color scaling minimums and maximums in the case of your example. Anything in matplotlib that has a colormap has the get_clim and set_clim methods.

作为一个完整的例子:

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Circle
import numpy as np

# (modified from one of the matplotlib gallery examples)
resolution = 50 # the number of vertices
N = 100
x       = np.random.random(N)
y       = np.random.random(N)
radii   = 0.1*np.random.random(N)
patches = []
for x1,y1,r in zip(x, y, radii):
    circle = Circle((x1,y1), r)
    patches.append(circle)

fig = plt.figure()
ax = fig.add_subplot(111)

colors = 100*np.random.random(N)
p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.4)
p.set_array(colors)
ax.add_collection(p)
plt.colorbar(p)

plt.show()

现在，如果我们在调用 plt.show(...)，我们得到这个:

Now, if we just add p.set_clim([5, 50]) (where p is the patch collection) somewhere before we call plt.show(...), we get this:

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