使用渐变colorscheme在matlotlib色彩映射中屏蔽两组值

Question

我想在matplotlib色彩映射中区分NaN。然后：

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

# create a (4,5) matrix with values ranging from 0 to 19
np_data = np.arange(20).reshape((4,5)).astype(float)
# add a row with NaNs in the middle
np_data = np.insert(np_data,2,[np.nan for x in range(0,5)],axis=0)
# mask invalid data (NaNs)
np_data = np.ma.masked_invalid(np_data)

 # get figure and ax objects from plot
fig, ax = plt.subplots() 
 # Draw an "X" on transparent values (masked values)
ax.patch.set(hatch='x', edgecolor='blue')

# get a predefined color scheme
reds_cm = plt.get_cmap("Reds")
# Plot heatmap, add a colorbar and show it 
heatmap = ax.pcolor(np_data, cmap=reds_cm)
cbar = fig.colorbar(heatmap)
plt.show()

图：

现在NaN很容易在情节中识别出来。

现在，我希望能够轻松区分NaNs，0和其他值。

如果我现在屏蔽了0，我就无法告诉NaN和0分开。

如何区分色彩映射中的两组值？在这种情况下，一方面是NaN，另一方面是0。

Answer 1

如果您想告诉appart色彩映射的第一个或最后一个值，以下解决方案是一个很好的方法。您可以修改色彩映射，使这些值很容易变成不同的颜色

reds_cm = plt.get_cmap("name of colormap")
# init colormap such that its members are available
reds_cm._init()
# set the first value to black
reds_cm._lut[0,: ] = (0,0,0,1) #this is an RGBA tuple 
# set the last value to lightgreen
reds_cm._lut[-4:,: ] = np.array([149,238,58,255])/255.

这是一个完整的解决方案。

import numpy as np
import matplotlib.pyplot as plt

# create a (4,5) matrix with values ranging from 0 to 19
np_data = np.arange(20).reshape((4,5)).astype(float)
# add a row with NaNs in the middle
np_data = np.insert(np_data,2,[np.nan for x in range(0,5)],axis=0)
# mask invalid data (NaNs)
np_data = np.ma.masked_invalid(np_data)

 # get figure and ax objects from plot
fig, ax = plt.subplots() 
 # Draw an "X" on transparent values (masked values)
ax.patch.set(hatch='x', edgecolor='blue')

# get a predefined color scheme
reds_cm = plt.get_cmap("Reds")
# init colormap such that its members are available
reds_cm._init()
# set the first value to black
reds_cm._lut[0,: ] = (0,0,0,1)
# set the last value to lightgreen
reds_cm._lut[-4:,: ] = np.array([149,238,58,255])/255.

# Plot heatmap, add a colorbar and show it 
heatmap = ax.pcolor(np_data, cmap=reds_cm)
cbar = fig.colorbar(heatmap)
plt.show()

产生

Answer 2

我在一个不相关的问题中找到了来自@unutbu的this answer。我调整了他对我的问题的答案，并解决了新的舱口也包含在NaN细胞中的问题。为了避免这种情况，只需在屏蔽numpy数组之前获取值为0的单元格（我会评论他的答案，在上下文中指出这一点，但我没有所需的声誉）。我只包括从我的问题改变的代码。

# (previous imports)
# Import to add patches to "non transparent" cells
import matplotlib.patches as mpatches


# (generate np_data)

# Get mask positions of 0 values before masking NaNs so NaN cells aren't included
cells_with_0 = np_data == 0
# mask invalid data (NaNs)
np_data = np.ma.masked_invalid(np_data)

# (get color scheme, plot heatmap, plot colorbar)

#set the background color as gray so the transparent values (NaNs here) use that color
ax.patch.set_facecolor((0.6, 0.6, 0.6, 1.0))
# Draw an "X" on transparent values (masked values)
ax.patch.set(hatch='x', edgecolor='black')
# Put an x over cells which have value 0
for j, i in np.column_stack(np.where(cells_with_0)):
      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 
          ))

plt.show()

新热图：