仅绘制热图的上/下三角形

Question

在 maptplotlib 中，可以使用 imshow 函数创建相关矩阵的热图表示。根据定义，这样的矩阵围绕其主对角线对称，因此不需要同时呈现上下三角形。例如：
_{（来源：wisc.edu）}

上面的例子取自this site 不幸的是，我不知道如何在 matplotlib 中做到这一点。将矩阵的上/下部分设置为无会导致黑色三角形。我用谷歌搜索了“matplotlib 缺失值”，但找不到任何有用的东西

Answer 1

doug 提供的答案的问题在于它依赖于颜色图将零值映射到白色的事实。这意味着不包含白色的颜色图没有用处。解决的关键是cm.set_bad函数。您可以使用 None 或使用 NumPy 屏蔽数组和 set_bad 将矩阵中不需要的部分屏蔽为白色，而不是默认的黑色。采用 doug 的例子，我们得到以下结果：

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)
PLT.show()

Answer 2

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)
# create an upper triangular 'matrix' from A
A2 = NP.triu(A)
fig = PLT.figure()
ax1 = fig.add_subplot(111)
# use dir(matplotlib.cm) to get a list of the installed colormaps
# the "_r" means "reversed" and accounts for why zero values are plotted as white
cmap = CM.get_cmap('gray_r', 10)
ax1.imshow(A2, interpolation="nearest", cmap=cmap)
ax1.grid(True)
PLT.show()

Answer 3

我得到的最佳答案来自 seaborn。输出是一个平滑而简单的图形。此函数将三角形保存到本地

def get_lower_tri_heatmap(df, output="cooc_matrix.png"):
    mask = np.zeros_like(df, dtype=np.bool)
    mask[np.triu_indices_from(mask)] = True

    # Want diagonal elements as well
    mask[np.diag_indices_from(mask)] = False

    # Set up the matplotlib figure
    f, ax = plt.subplots(figsize=(11, 9))

    # Generate a custom diverging colormap
    cmap = sns.diverging_palette(220, 10, as_cmap=True)

    # Draw the heatmap with the mask and correct aspect ratio
    sns_plot = sns.heatmap(data, mask=mask, cmap=cmap, vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})
    # save to file
    fig = sns_plot.get_figure()
    fig.savefig(output)

Answer 4

您可以在一个上/下部分透明的白色矩阵上绘制

a =random((10,10))
imshow(a, interpolation='nearest')

b = ones(a.shape+(4,)) # «white» matrix with alpha=1
for i in range(a.shape[0]):
    for j in range(i, a.shape[1]):
        b[i,j,3] = 0   # upper triangle, alpha = 0
imshow(b, interpolation='nearest')

upper/lower triangle of a heatmap http://lh5.ggpht.com/_ZgVr3-a-Z00/S4P3_BWByKI/AAAAAAAAAXE/UsJpokz6LKE/pp.png

Answer 5

使用seaborn、matplotlib 和numpy，一个快速的解决方案是：

import matplotlib.pyplot as plt
import seaborn as sns

# Say your matrix object (e.g. np.array) is corr_mat

# Get the upper triangle without the diagonal 
corr_mat = np.triu(corr_mat, k=1)

# Plot the heatmap
ax = sns.heatmap(corr_mat)

化妆请参考seaborn在线文档。

Answer 6

您可以使用此代码：

from string import ascii_letters
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="white")

# Generate a large random dataset
rs = np.random.RandomState(33)
d = pd.DataFrame(data=rs.normal(size=(100, 26)),
                 columns=list(ascii_letters[26:]))

# Compute the correlation matrix
corr = d.corr()

# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})