掩盖matplotlib中的contourf图的一部分

Question

我正在尝试使用contourf在matplotlib中生成一个填充的等高线图.在图底部附近的锯齿状图案中缺少数据.轮廓图不仅在原始数据被遮盖的地方变成空白,而且在口袋中也是空白的,其中轮廓算法不能干净地插入,因为没有足够的良好数据邻域.

我知道如何扩展数据集以在这些口袋中产生合理的轮廓.但是,如果我绘制扩展数据,我会在任何地方获得轮廓填充.我想掩盖原始数据缺失的黑色或白色区域.

在之前的一个帖子中,我通过绘制第一张图像然后用掩盖坏区域的另一张图像覆盖它来学习如何为图像做到这一点.模拟将是下面的代码片段,但它不适用于轮廓......我无法通过bad_data imshow来掩盖扩展的contourf图.可能吗？

谢谢,Eli

import matplotlib.pyplot as plt
lev = [0.0,0.1,0.2,0.5,1.0,2.0,4.0,8.0,16.0,32.0]           
norml = colors.BoundaryNorm(lev, 256)
# this is the contour plot, using extended_data so that the contours are plausibly extended
cs = plt.contourf(x,z,extended_data,levels = lev, cmap = cm.RdBu_r,norm = norml) 
# now the attempt to cover it up -- but imshow will not cover up the original plot as it will with another image
bad_data = np.ma.masked_where(~data.mask, data.mask, copy=True) 
plt.imshow(bad_data, interpolation='nearest', aspect = 'auto', cmap=cm.gray)
plt.show()

Answer 1

如果我错了,请纠正我,但据我所知你有这种情况:

import numpy as np
import matplotlib.pyplot as plt
# generate some data with np.nan values (the missing values)
d = np.random.rand(10, 10)
d[2, 2], d[3, 5] = np.nan, np.nan
# and in your case you actually have masked values too:
d = np.ma.array(d, mask=d < .2)
# now all of the above is just for us to get some data with missing (np.nan) and
# masked values

通过使用contourf绘制上述内容,

plt.contourf(d)
plt.show()

我明白了:

它不显示(空白)掩蔽值(d <.2)和np.nan值(d [2,2],d [3,5])!并且你希望matplotlib只显示掩码值.所以我们可以这样做:

# the following line is replaced by your interpolation routine for
# removing np.nan values
d[np.isnan(d)] = 1
# then because we use the masked array only the masked values will still be masked
# but the np.nan values which were replaced through the interpolation algorithm
# will show up if we do the contourf plot
plt.contourf(d)
plt.show()

我不知道在这种情况下使用蒙面数组的速度有多快,但无论如何这就是我要做的.如果你想要一个不同的颜色而不是空白点(白色)你需要为下面的轴的色块着色,因为contourf实际上不会绘制任何没有数据或掩盖数据的东西:

# make the background dark gray (call this before the contourf)
plt.gca().patch.set_color('.25')
plt.contourf(d)
plt.show()

要得到: