python作图：heatmap

python 作图&＃xff1a;heatmap

导入几个库

import numpy as np
from numpy import random
import pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns
sns.set()


基本例子

我们以一个 $N\times N$ 矩阵为例。

N &＃61; 20
R &＃61; random.randn(N, N)


我们先绘制一个最基本的 heatmap&＃xff08;热图&＃xff09;&＃xff1a;

fig &＃61; plt.figure()
sns_plot &＃61; sns.heatmap(R)
# fig.savefig("heatmap.pdf", bbox_inches&＃61;&＃39;tight&＃39;) # 减少边缘空白
plt.show()


显示值

根据该 $N\times N$ 矩阵值的大小体现在热图中。查看值的分布&＃xff0c;可以采用 annot&＃61;True。

N &＃61; 5
R &＃61; random.randn(N, N)fig &＃61; plt.figure()
sns_plot &＃61; sns.heatmap(R, annot&＃61;True)plt.show()


新的调整

我们发现&＃xff0c;维度大的时候&＃xff0c;坐标轴刻度会太密集而看不清&＃xff0c;而且刻度上的字体很小。如何调整&＃xff0c;如下&＃xff1a;

• 刻度的步长调大 xticklabels
• 刻度字体大小 labelsize

N &＃61; 20
R &＃61; random.randn(N, N)# fig, ax &＃61; plt.subplots(figsize&＃61;(12,9))
fig &＃61; plt.figure()
# cmap&＃61;&＃39;RdBu_r&＃39; 颜色主题风格 xticklabels 代表步长
sns_plot &＃61; sns.heatmap(R, cmap&＃61;&＃39;YlGnBu&＃39;, xticklabels&＃61;8, yticklabels&＃61;8)sns_plot.tick_params(labelsize&＃61;15) # heatmap 刻度字体大小
# colorbar 刻度线设置
cax &＃61; plt.gcf().axes[-1]
cax.tick_params(labelsize&＃61;15) # colorbar 刻度字体大小# fig.savefig("heatmap.pdf", bbox_inches&＃61;&＃39;tight&＃39;)
plt.show()


另一方面&＃xff0c;Heatmap 中的刻度线很长&＃xff0c;不好看。所以可以调整刻度线的位置&＃xff08;内、外侧&＃xff09;以及四个方向边是否显示刻度线&＃xff1a;

fig &＃61; plt.figure()
# cmap&＃61;&＃39;RdBu_r&＃39;
sns_plot &＃61; sns.heatmap(R, cmap&＃61;&＃39;YlGnBu&＃39;, xticklabels&＃61;8, yticklabels&＃61;8)
# tick_params 中 direction&＃61;&＃39;in&＃39;表示刻度线位于内侧&＃xff0c;另外还有参数 out,inout
sns_plot.tick_params(labelsize&＃61;15, direction&＃61;&＃39;in&＃39;)cax &＃61; plt.gcf().axes[-1]
# colorbar 中 top&＃61;&＃39;off&＃39;, bottom&＃61;&＃39;off&＃39;, left&＃61;&＃39;off&＃39;, right&＃61;&＃39;off&＃39;表示上下左右侧的刻度线全部不显示
cax.tick_params(labelsize&＃61;15, direction&＃61;&＃39;in&＃39;, top&＃61;&＃39;off&＃39;, bottom&＃61;&＃39;off&＃39;, left&＃61;&＃39;off&＃39;, right&＃61;&＃39;off&＃39;)plt.show()


update&＃xff1a;不显示坐标

# xticklabels &＃61;False
sns_plot &＃61; sns.heatmap(R, xticklabels &＃61;False)


调整索引值

最后&＃xff0c;由于矩阵的原生索引 $x,y\in \{0,1,2,\cdots ,N-1\}$。
如果要指定新的索引 $\{1,2,\cdots ,N\}$&＃xff0c;可以利用 pandas 实现数据的包装整理。如下&＃xff1a;

import numpy as np
from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
sns.set()N &＃61; 16
R &＃61; random.randn(N, N)R &＃61; pd.DataFrame(R, columns&＃61;np.arange(1,N&＃43;1), index&＃61;np.arange(1,N&＃43;1))# fig, ax &＃61; plt.subplots(figsize&＃61;(12,9))
fig &＃61; plt.figure()
# cmap&＃61;&＃39;RdBu_r&＃39;
sns_plot &＃61; sns.heatmap(R, cmap&＃61;&＃39;YlGnBu&＃39;, xticklabels&＃61;5, yticklabels&＃61;5)sns_plot.tick_params(labelsize&＃61;15, direction&＃61;&＃39;in&＃39;)cax &＃61; plt.gcf().axes[-1]
cax.tick_params(labelsize&＃61;15, direction&＃61;&＃39;in&＃39;, top&＃61;&＃39;off&＃39;, bottom&＃61;&＃39;off&＃39;, left&＃61;&＃39;off&＃39;, right&＃61;&＃39;off&＃39;)# fig.savefig("heatmap.pdf", bbox_inches&＃61;&＃39;tight&＃39;)
plt.show()


cmap 风格大全

cmap 的参数如下&＃xff1a;


- Accent, Accent_r,
- Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r,
- CMRmap, CMRmap_r,
- Dark2, Dark2_r,
- GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r,
- OrRd, OrRd_r, Oranges, Oranges_r,
- PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r,
- RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r,
- Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r,
- Wistia, Wistia_r,
- YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r,
- afmhot, afmhot_r, autumn, autumn_r,
- binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r,
- cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r,
- flag, flag_r,
- gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r,
- hot, hot_r, hsv, hsv_r,
- icefire, icefire_r, inferno, inferno_r,
- jet, jet_r,
- magma, magma_r, mako, mako_r,
- nipy_spectral, nipy_spectral_r,
- ocean, ocean_r,
- pink, pink_r, plasma, plasma_r, prism, prism_r,
- rainbow, rainbow_r, rocket, rocket_r,
- seismic, seismic_r, spring, spring_r, summer, summer_r,
- tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r,
- viridis, viridis_r, vlag, vlag_r,
- winter, winter_r

参考&＃xff1a;https://matplotlib.org/examples/color/colormaps_reference.html