imshow 的上/下三角形周围的边框

Question

这个问题与@bgbg 的question about how to visualize only the upper or lower triangle of a symmetric matrix in matplotlib 有关。使用他的代码（见最后），我们可以生成如下图：

现在我的问题是：我们怎样才能在这组块周围画一个黑色的边框？我问，因为我想绘制两组相关数据并将它们彼此相邻放置为一个上下三角形。然后，我们可以独立地在每个三角形周围绘制一个黑色边框，以将两个三角形分开并显示它们是不同的指标。所以，像这样，但不要混淆：

怎么做？

#Figure 1
import numpy as NP
from matplotlib import pyplot as PLT
from matplotlib import cm as CM

A = NP.random.randint(10, 100, 100).reshape(10, 10)
mask =  NP.tri(A.shape[0], k=-1)
A = NP.ma.array(A, mask=mask) # mask out the lower triangle
fig = PLT.figure()
ax1 = fig.add_subplot(111)
cmap = CM.get_cmap('jet', 10) # jet doesn't have white color
cmap.set_bad('w') # default value is 'k'
ax1.imshow(A, interpolation="nearest", cmap=cmap)
ax1.grid(True)
axis('off')

#Figure 2
A = NP.random.randint(10, 100, 100).reshape(10, 10)
mask =  NP.tri(A.shape[0], k=-1)
mask = NP.zeros_like(A)
mask[NP.arange(10), NP.arange(10)] = 1
A = NP.ma.array(A, mask=mask) # mask out the lower triangle
fig = PLT.figure()
ax1 = fig.add_subplot(111)
cmap = CM.get_cmap('jet', 10) # jet doesn't have white color
cmap.set_bad('w') # default value is 'k'
ax1.imshow(A, interpolation="nearest", cmap=cmap)
title("Correlation Data 1")
ylabel("Correlation Data 2")
yticks([])
xticks([])

Answer 1

您可以使用patches.Polygon 绘制边框：

import numpy as NP
from matplotlib import pyplot as PLT
import matplotlib.patches as patches

N = 10
A = NP.random.randint(10, 100, N * N).reshape(N, N)
mask = NP.tri(A.shape[0], k=-1)
mask = NP.zeros_like(A)
mask[NP.arange(N), NP.arange(N)] = 1
A = NP.ma.array(A, mask=mask)  # mask out the lower triangle
fig, ax = PLT.subplots()

cmap = PLT.get_cmap('jet', 10)  # jet doesn't have white color
cmap.set_bad('w')  # default value is 'k'
ax.imshow(A, interpolation="nearest", cmap=cmap, extent=[0, N, 0, N])

line = ([(0, N - 1), (0, 0), (N - 1, 0)] +
        [(N - 1 - i - j, i + 1) for i in range(N - 1) for j in (0, 1)])
lines = [line, [(N - x, N - y) for x, y in line]]
for line in lines:
    path = patches.Polygon(line, facecolor='none', edgecolor='black',
                           linewidth=5, closed=True, joinstyle='round')
    ax.add_patch(path)
ax.set_xlabel("Correlation Data 1")
ax.xaxis.set_label_position('top')
ax.set_ylabel("Correlation Data 2")
ax.set_yticks([])
ax.set_xticks([])
margin = 0.09
ax.set_xlim(-margin, N + margin)
ax.set_ylim(-margin, N + margin)
ax.set_frame_on(False)
PLT.show()