Python:从matplotlib热图及其图例中保留Numpy NaN值 [英] Python: Leave Numpy NaN values from matplotlib heatmap and its legend

问题描述

我有一个numpy数组，需要将其绘制为热图. numpy数组还将包含NaN值，我需要从绘图中排除这些值.在其他帖子中，有人告诉我numpy会自动掩盖绘图中的NaN值，但是它对我不起作用.这是示例代码

I have a numpy array that I need to plot as a heatmap. The numpy array would also contain NaN values which I need to exclude from plotting. I was told in other posts that numpy automatically masks the NaN values in the plot, but its somehow not working for me. Here is a sample code

column_labels = list('ABCDEFGH')
row_labels = list('WXYZ')
fig, ax = plt.subplots()
data = np.array([[ 0.96753494,  0.52349944,  0.0254628 ,  0.5104103 ],
         [ 0.07320069,  0.91278731,  0.97094436,  0.70533351],
         [ 0.30162006,  0.49068337,  0.41837729,  0.71139215],
         [ 0.19786101,  0.15882713,  0.59028841,  0.06242765],
         [ 0.51505872,  0.07798389,  0.58790067,  0.44782683],
         [ 0.68975694,  0.53535385,  0.15696023,  0.35641951],
         [ 0.66481995,  0.03576846,  0.9623601 ,  0.96006395],
         [ 0.45865404,  0.50433582,  0.18182575,  0.35126449],])

data[3,:] = np.nan
heatmap = ax.pcolor(data, cmap=plt.cm.seismic)

fig.colorbar(heatmap)
# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()

ax.set_xticklabels(row_labels, minor=False)
ax.set_yticklabels(column_labels, minor=False)
plt.show()

图看起来像

很明显，这与没有Nan的情节有很大不同，后者看起来像

Clearly this is very different from the plot without the Nan which looks like

我想完全避免使用图例中的NaN值，最好使用诸如 X 之类的符号进行标记.我怎么能达到同样的目的?

I want to avoid the NaN values completely from the legend and preferably mark it with some symbol such as X. How can I achieve the same?

推荐答案

nans会干扰pcolor确定data中包含的值的范围，因为

The nans interfere with pcolor determining the range of values contained in data since

In [72]: data.min(), data.max()
Out[72]: (nan, nan)

您可以通过使用np.nanmin和np.nanmax声明值的范围来解决问题，以找到data中的最小和最大非NaN值:

You can work around the problem by declaring the range of values yourself using np.nanmin and np.nanmax to find the minimum and maximum non-NaN values in data:

heatmap = ax.pcolor(data, cmap=plt.cm.seismic, 
                    vmin=np.nanmin(data), vmax=np.nanmax(data))

自

In [73]: np.nanmin(data), np.nanmax(data)
Out[73]: (0.025462800000000001, 0.97094435999999995)

---

import numpy as np
import matplotlib.pyplot as plt

column_labels = list('ABCDEFGH')
row_labels = list('WXYZ')
fig, ax = plt.subplots()
data = np.array([[ 0.96753494,  0.52349944,  0.0254628 ,  0.5104103 ],
         [ 0.07320069,  0.91278731,  0.97094436,  0.70533351],
         [ 0.30162006,  0.49068337,  0.41837729,  0.71139215],
         [ 0.19786101,  0.15882713,  0.59028841,  0.06242765],
         [ 0.51505872,  0.07798389,  0.58790067,  0.44782683],
         [ 0.68975694,  0.53535385,  0.15696023,  0.35641951],
         [ 0.66481995,  0.03576846,  0.9623601 ,  0.96006395],
         [ 0.45865404,  0.50433582,  0.18182575,  0.35126449],])

data[3,:] = np.nan
heatmap = ax.pcolor(data, cmap=plt.cm.seismic, 
                    vmin=np.nanmin(data), vmax=np.nanmax(data))
heatmap.cmap.set_under('black')

bar = fig.colorbar(heatmap, extend='both')

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()

ax.set_xticklabels(row_labels, minor=False)
ax.set_yticklabels(column_labels, minor=False)
plt.show() 

另一种选择(基于 Joe Kington's 解决方案)将是绘制 data是NaN的地方都有阴影线的矩形补丁.

Another option (based on Joe Kington's solution) would be to draw rectangular patches with hatch marks wherever data is NaN.

上面的示例显示了pcolor在NaN值为 尽管NaN值非常负.相反，如果您通过pcolor masked array ，pcolor使遮罩区域透明.因此，您可以画 轴背景补丁ax.patch上的阴影线以显示阴影线 在被遮盖的区域上.

The above example shows that pcolor colors in cells with NaN values as though the NaNs were very negative numbers. In contrast, if you pass pcolor a masked array, pcolor leaves the masked areas transparent. Thus, you can draw hatches on the axes background patch, ax.patch, to show hatch marks on the masked areas.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

column_labels = list('ABCDEFGH')
row_labels = list('WXYZ')
fig, ax = plt.subplots()
data = np.array([[ 0.96753494,  0.52349944,  0.0254628 ,  0.5104103 ],
         [ 0.07320069,  0.91278731,  0.97094436,  0.70533351],
         [ 0.30162006,  0.49068337,  0.41837729,  0.71139215],
         [ 0.19786101,  0.15882713,  0.59028841,  0.06242765],
         [ 0.51505872,  0.07798389,  0.58790067,  0.44782683],
         [ 0.68975694,  0.53535385,  0.15696023,  0.35641951],
         [ 0.66481995,  0.03576846,  0.9623601 ,  0.96006395],
         [ 0.45865404,  0.50433582,  0.18182575,  0.35126449],])

data[3,:] = np.nan
data = np.ma.masked_invalid(data)

heatmap = ax.pcolor(data, cmap=plt.cm.seismic, 
                    vmin=np.nanmin(data), vmax=np.nanmax(data))
# https://stackoverflow.com/a/16125413/190597 (Joe Kington)
ax.patch.set(hatch='x', edgecolor='black')
fig.colorbar(heatmap)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()

ax.set_xticklabels(row_labels, minor=False)
ax.set_yticklabels(column_labels, minor=False)
plt.show() 

如果您希望使用一种以上的填充标记，例如对NaN使用另一种填充标记，对负值使用另一种填充标记，则可以使用循环来添加填充阴影的矩形:

If you have wish to use more than one type of hatch mark, say one for NaNs and another for negative values, then you could use a loop to add the hatched Rectangles:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

column_labels = list('ABCDEFGH')
row_labels = list('WXYZ')
fig, ax = plt.subplots()
data = np.array([[ 0.96753494,  0.52349944,  0.0254628 ,  0.5104103 ],
         [ 0.07320069,  0.91278731,  0.97094436,  0.70533351],
         [ 0.30162006,  0.49068337,  0.41837729,  0.71139215],
         [ 0.19786101,  0.15882713,  0.59028841,  0.06242765],
         [ 0.51505872,  0.07798389,  0.58790067,  0.44782683],
         [ 0.68975694,  0.53535385,  0.15696023,  0.35641951],
         [ 0.66481995,  0.03576846,  0.9623601 ,  0.96006395],
         [ 0.45865404,  0.50433582,  0.18182575,  0.35126449],])
data -= 0.5
data[3,:] = np.nan
data = np.ma.masked_invalid(data)
heatmap = ax.pcolor(data, cmap=plt.cm.seismic, 
                    vmin=np.nanmin(data), vmax=np.nanmax(data))

# https://stackoverflow.com/a/16125413/190597 (Joe Kington)
ax.patch.set(hatch='x', edgecolor='black')

# draw a hatched rectangle wherever the data is negative
# http://matthiaseisen.com/pp/patterns/p0203/
mask = data < 0
for j, i in np.column_stack(np.where(mask)):
      ax.add_patch(
          mpatches.Rectangle(
              (i, j),     # (x,y)
              1,          # width
              1,          # height
              fill=False, 
              edgecolor='blue',
              snap=False,
              hatch='x' # the more slashes, the denser the hash lines 
          ))

fig.colorbar(heatmap)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()

ax.set_xticklabels(row_labels, minor=False)
ax.set_yticklabels(column_labels, minor=False)
plt.show() 

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