python - 如何在python中为热图的特定元素添加颜色边框或类似的突出显示？

Question

我想突出显示热图中的特定元素(在热图中的行和列)
他们是一种在两个轴上突出显示特定元素的方法。
期望的结果应该看起来像向量元素在两个轴上都突出显示的地方。
或类似于下面给出的示例。
我正在使用 seaborn 和 matplotlib。

编辑 1:
我正在使用树状图，这是我的代码，其中 **PairWise ** 表示我的数据矩阵。

import seaborn as sns
%matplotlib inline
pairwise_corr=PairWise.corr(method="spearman")
sns.clustermap(pairwise_corr,method="complete",annot=True,linewidth=0.5)

编辑 2:
这是列作为 b c d e 向量的数据

array([[191, 395,  63,  89, 247, 201],
       [379, 121, 187, 430, 265, 478],
       [ 38, 220, 406, 450, 349, 354],
       [165, 172, 469, 355,  56, 425],
       [482, 143, 411, 322, 184, 492],
       [ 16, 157, 172, 253, 320, 391],
       [328, 443, 418, 392,  95,  93],
       [208,  50, 362, 407,  43, 139],
       [ 68, 257, 447, 472, 357,  11],
       [164, 123, 469,  93, 296,   5],
       [ 54, 419, 264, 370, 474, 387],
       [171, 137, 108, 174, 339, 481],
       [470,  87, 139, 465, 189, 367],
       [115, 129, 237, 215,  19, 109],
       [196, 462, 256, 125, 329, 379],
       [241, 275, 472, 128, 176, 320],
       [394, 330, 262, 169, 491,  99],
       [406, 182, 190, 404,  71, 221],
       [143, 161,  69, 156, 319,  28],
       [ 95, 157,  52,  74, 380, 160],
       [180,  70,   4, 477, 392, 465],
       [406, 211, 116,  68, 324, 288],
       [477, 182, 459, 381, 244, 466],
       [337,  47, 383, 113, 124, 416],
       [386, 202,  14, 129, 325, 209],
       [444, 217,  39, 320, 474, 390],
       [ 66, 258, 241, 149,  80, 496],
       [ 50, 415, 410, 223, 429,  21],
       [203, 104, 148,  56, 378,  75],
       [277, 254, 172, 130, 142,  59],
       [266, 140, 389, 154, 207, 452],
       [ 76, 444,  25, 357,  30, 255],
       [421, 499, 322, 347,  44, 189],
       [142, 136,  50, 445, 234, 404],
       [487, 478, 282, 290, 172,  96],
       [ 75,  99,  82,  13, 148, 424],
       [ 61,  73, 181, 363,  96, 406],
       [ 71,  74, 490, 354, 364,  62],
       [409,  76, 475, 491, 267, 398],
       [422,  72, 272, 136, 140, 301],
       [128,  55, 100, 236, 255, 499],
       [ 13, 240, 254, 273,  59, 262],
       [ 45, 474, 153, 132, 422, 391],
       [264,  25, 382, 464, 440,  11],
       [120,  32, 497, 321,  81, 343],
       [165, 328, 284, 447, 279, 317],
       [  1, 247, 273, 145, 254,  47],
       [107, 135, 222, 140, 221, 207],
       [319, 387, 426,  86, 344, 481],
       [447, 344, 139, 336, 191, 442],
       [112, 173, 249, 371, 364, 246],
       [403,  20, 378, 387, 492, 308],
       [ 58, 206, 419, 378,  27, 487],
       [167, 249, 340, 253, 389,  38],
       [334,  27,  45, 112, 298, 359],
       [498, 233,  72, 111, 304, 117],
       [464, 285, 119, 433, 362,   7],
       [374,  36, 101,  21, 141, 430],
       [322,  80,  53, 184, 467, 330],
       [236, 461,   6, 353,  80, 193],
       [342, 178, 372, 227,  51, 460],
       [179, 448,  51, 309,  87, 403],
       [253, 329,  81,  28, 428, 490],
       [156, 128, 201,  74,  71, 418],
       [436, 393, 128, 370,  76, 115],
       [359, 157, 111, 425,  28,  92],
       [116, 377, 110,  37, 348, 329],
       [355, 282, 200, 205, 301, 198],
       [180, 390,  80, 183, 248, 116],
       [340, 113,  79, 479, 218, 310],
       [178, 334, 126, 470,  13, 405],
       [450,  50, 141, 145, 153, 108],
       [483, 120, 124,  94, 475, 259],
       [191, 366, 456, 400, 390, 378],
       [292, 245, 403, 321, 195, 138],
       [213, 474,  62, 351, 136,  38],
       [372, 314, 412, 191,  30, 244],
       [ 78, 473, 100, 448,  36,  19],
       [274,  78, 491,  29, 193, 243],
       [ 13, 177,  31, 471,  44, 488],
       [160, 310,   9,  43, 481, 498],
       [219, 465, 486,  91, 460, 429],
       [ 79, 326, 154, 337,  45,   8],
       [402, 124, 242, 479, 438, 170],
       [206, 354, 348, 133, 389,   0],
       [ 57, 239,  31,  42, 157, 458],
       [392,  71,  96, 134,  80, 436],
       [233, 268, 193, 129,  70, 385],
       [486, 111, 328, 219, 353, 371],
       [303, 476, 352,  30, 490, 428],
       [496,  59, 340, 332, 401, 389],
       [ 66, 302, 250, 322, 131, 328],
       [362, 407, 118, 237, 104,  72],
       [192, 493,   1,  42, 301, 103],
       [352, 402, 123, 435, 209, 314],
       [216,  42, 485,  72, 392, 251],
       [323,  36, 498,   9, 120, 327],
       [207, 178, 495, 144, 102, 276],
       [179, 476,  24,  51, 154,  69],
       [ 19,  62, 450, 199, 382, 139]])

任何资源或引用资料都会有所帮助。
谢谢

Answer 1

您可以添加所需大小的矩形。位置 (0, 1, 2, ...) 取决于要突出显示的标签的索引。设置 clip_on=False还有助于显示主情节之外的部分线条。

from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
import seaborn as sns
import numpy as np

labels = list('abcdef')
N = len(labels)
ax = sns.heatmap(np.random.uniform(0, 1, (N, N)), cmap='summer', annot=True, linewidths=.5,
                 xticklabels=labels, yticklabels=labels)
wanted_label = 'c'
wanted_index = labels.index(wanted_label)
x, y, w, h = 0, wanted_index, N, 1
for _ in range(2):
    ax.add_patch(Rectangle((x, y), w, h, fill=False, edgecolor='crimson', lw=4, clip_on=False))
    x, y = y, x # exchange the roles of x and y
    w, h = h, w # exchange the roles of w and h
ax.tick_params(length=0)
plt.show()

将此方法纳入 clustermap :

from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
import seaborn as sns; sns.set()
import numpy as np
import pandas as pd

data = np.random.uniform(0, 1, size=(20, 6))
# data = np.array([[...]])
columns = list('abcdef')
N = len(columns)
PairWise = pd.DataFrame(columns=columns, data=data)
pairwise_corr = PairWise.corr(method="spearman")
g = sns.clustermap(pairwise_corr, method="complete", annot=True, linewidth=0.5)

wanted_label = 'f'
# wanted_row = np.where(np.array(columns)[g.dendrogram_row.reordered_ind] == wanted_label)[0]
# wanted_col = np.where(np.array(columns)[g.dendrogram_col.reordered_ind] == wanted_label)[0]
wanted_row = g.dendrogram_row.reordered_ind.index(columns.index(wanted_label))
wanted_col = g.dendrogram_col.reordered_ind.index(columns.index(wanted_label))

xywh_row = (0, wanted_row, N, 1)
xywh_col = (wanted_col, 0, 1, N)
for x, y, w, h in (xywh_row, xywh_col):
    g.ax_heatmap.add_patch(Rectangle((x, y), w, h, fill=False, edgecolor='yellow', lw=4, clip_on=False))
g.ax_heatmap.tick_params(length=0)
plt.show()