Python：从 matplotlib 热图及其图例中保留 Numpy NaN 值 [重复]

Question

我有一个需要绘制为热图的 numpy 数组。 numpy 数组还将包含我需要从绘图中排除的 NaN 值。我在其他帖子中被告知 numpy 会自动掩盖图中的 NaN 值，但它对我不起作用。这是一个示例代码

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()

情节看起来像

显然这与没有 Nan 的情节非常不同，看起来像

我想完全避免图例中的 NaN 值，最好用 X 之类的符号对其进行标记。我怎样才能达到同样的效果？

Answer 1

nans 干扰 pcolor 确定 data 中包含的值的范围，因为

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

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

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

因为

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() 

---

另一个选项（基于Joe Kington's solution）是绘制 data 为 NaN 时带有阴影标记的矩形补丁。

上面的例子显示 pcolor 在单元格中的颜色为 NaN 为 尽管 NaN 是非常负数。相比之下，如果你通过pcolor a 蒙版数组，pcolor 使蒙版区域保持透明。因此，您可以绘制 坐标区背景补丁 ax.patch 上的影线以显示影线标记 在被遮罩的区域上。

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类型的阴影标记，例如一种用于 NaN，另一种用于负值，那么您可以使用循环来添加阴影矩形：

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()