带隐藏的无效值的pcolormesh [英] pcolormesh with masked invalid values

问题描述

我正在尝试将一维数组绘制为pcolormesh(因此颜色沿x轴变化，但每个x的y轴均不变).但是我的数据有一些错误的值，所以我使用了一个蒙版数组和一个自定义颜色表，其中蒙版值设置为蓝色:

I'm trying to plot a one-dimensional array as a pcolormesh (so the color varies along the x-axis, but is constant in the y-axis for each x). But my data has some bad values, so I'm using a masked array and a customized colormap with masked values set to blue:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import copy

a = np.array([3, 5, 10, np.inf, 5, 8])
a = np.ma.masked_where(np.isinf(a), a)
imdata = np.vstack((a, a))
myhot = copy.copy(cm.hot)
myhot.set_bad('b', 1)

fig, ax = plt.subplots()
im = ax.pcolormesh(imdata, cmap=myhot)
plt.colorbar(im)
plt.show()

如果我没有np.inf值，它可以很好地工作，但是如果我没有，则只能得到一个空白图.我似乎误解了set_bad的工作方式，因为我得到了另外的警告:

It works fine if I don't have the np.inf value, but I just get a blank plot if I do. I seem to have misunderstood something about the way set_bad works because I get an additional warning:

RuntimeWarning: invalid value encountered in true_divide
  resdat /= (vmax - vmin)

我应该怎么做才能获得想要的效果?

What should I be doing to get the effect I want?

推荐答案

您需要掩盖imdata，不一定是a:

You need to mask imdata, not necessarily a:

import numpy as np
import matplotlib.pyplot as plt

a = np.array([3, 5, 10, np.inf, 5, 8])
imdata = np.ma.masked_invalid(np.atleast_2d(a))
cmap = plt.cm.hot
cmap.set_bad('b', 1)
fig, ax = plt.subplots()
im = ax.pcolormesh(imdata, cmap=cmap)

plt.colorbar(im)
plt.show()

如果您在互动会话中查看imdata，您会看到

If you look at imdata in an interactive session, you'll see

In [185]: imdata
Out[185]: 
masked_array(data =
 [[  3.   5.  10.  inf   5.   8.]
 [  3.   5.  10.  inf   5.   8.]],
             mask =
 False,
       fill_value = 1e+20)

以上，mask=False表示未屏蔽任何内容.如果用np.ma.masked_invalid包装，则:

Above, mask=False means that nothing is masked. If you wrap that with np.ma.masked_invalid then:

In [186]: np.ma.masked_invalid(imdata)
Out[186]: 
masked_array(data =
 [[3.0 5.0 10.0 -- 5.0 8.0]
 [3.0 5.0 10.0 -- 5.0 8.0]],
             mask =
 [[False False False  True False False]
 [False False False  True False False]],
       fill_value = 1e+20)

遮罩a的问题是np.vstack不遵守遮罩. 或者，您可以使用np.ma.vstack.一般来说，只有 np.ma命名空间中的函数尊重掩码.

The problem with masking a is that np.vstack does not respect the mask. Alternatively, you could have used np.ma.vstack. Generally speaking, only functions in the np.ma namespace respect the mask.

但是，您实际上并不需要在这里使用vstack. np.atleast_2d可以. vstack创建形状为(2, N)的数组，而np.atleast_2d创建形状为(1, N)的数组.

However, you don't actually need to use vstack here; np.atleast_2d will do. vstack creates an array of shape (2, N), while np.atleast_2d creates an array of shape (1, N).

另一种替代方法是使用set_over代替set_bad.这将允许 您可以避免完全使用带掩码的数组:

Another alternative is to use set_over instead of set_bad. This would allow you to avoid needing a masked array altogether:

import numpy as np
import matplotlib.pyplot as plt

a = np.array([3, 5, 10, np.inf, 5, 8])
imdata = np.atleast_2d(a)
cmap = plt.cm.hot
cmap.set_over('b')
cmap.set_under('g')
fig, ax = plt.subplots()

b = a[np.isfinite(a)]
im = ax.pcolormesh(imdata, cmap=cmap, vmin=b.min(), vmax=b.max())

plt.colorbar(im, extend='both')
plt.show()

extend='both'与set_over 和set_under结合使用会在颜色栏上给您一些彩色的箭头，指示用于颜色栏范围之外的值的颜色.

The extend='both' in conjunction with set_over and set_under give you little colored arrows on the colorbar which indicate the color used for values beyond the colorbar's range.

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