在背景图片上绘制Seaborn热图

Question

我正在通过Jupyter中的seaborn创建一个热图，以显示将选择某个坐标点的人数。我目前使用以下代码创建了热图

cm = metrics.confusion_matrix(yVals, xVals)
fig, ax = plt.subplots(figsize=(10,10))
sns.heatmap(cm, annot=True, fmt="0.3f", linewidth=0.5, cbar=False,
              cmap="Reds", square=True, ax=ax)
plt.show()

如果需要查看图片的外观，也可以使用此图片的链接。我的问题是，如何将这个热图绘制在背景图像的上方，并使热图中的正方形越接近0，则它们越能显示背景图像，从而使其更加透明？还有一种方法可以将热图上的索引从1而不是0开始？

先感谢您！

Answer 1

您还需要缩放/翻转图像，以便它们一起绘制，因为地图的分辨率可能比热图要精细得多。我们让 Seaborn 做它的调整工作，然后将它匹配到imshow显示地图中。

您可以修改或创建颜色图以使透明度接近 0，我留下了代码来向您展示如何操作，但结果图并不理想，因为我无法在高温位置读取地图。如图所示，整个热图是半透明的。

留给读者：更改刻度以引用地图坐标，而不是热图索引。

# add alpha (transparency) to a colormap
import matplotlib.cm from matplotlib.colors 
import LinearSegmentedColormap 
wd = matplotlib.cm.winter._segmentdata # only has r,g,b  
wd['alpha'] =  ((0.0, 0.0, 0.3), 
               (0.3, 0.3, 1.0),
               (1.0, 1.0, 1.0))

# modified colormap with changing alpha
al_winter = LinearSegmentedColormap('AlphaWinter', wd) 

# get the map image as an array so we can plot it 
import matplotlib.image as mpimg 
map_img = mpimg.imread('tunis.png') 

# making and plotting heatmap 
import numpy.random as random 
heatmap_data = random.rand(8,9) 

import seaborn as sns; sns.set()

hmax = sns.heatmap(heatmap_data,
            #cmap = al_winter, # this worked but I didn't like it
            cmap = matplotlib.cm.winter,
            alpha = 0.5, # whole heatmap is translucent
            annot = True,
            zorder = 2,
            )

# heatmap uses pcolormesh instead of imshow, so we can't pass through 
# extent as a kwarg, so we can't mmatch the heatmap to the map. Instead, 
# match the map to the heatmap:

hmax.imshow(map_img,
          aspect = hmax.get_aspect(),
          extent = hmax.get_xlim() + hmax.get_ylim(),
          zorder = 1) #put the map under the heatmap

from matplotlib.pyplot import show 
show()

Answer 2

import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.image as mpimg 

file = "./iris.csv"
df = pd.read_csv(file)
import seaborn as sns
map_img = mpimg.imread('1538287373.02485_image.png') 
 # Custom it with the same argument as 1D density plot
hmax = sns.kdeplot(df.sepal_width, df.sepal_length, cmap="Reds", shade=True, bw=.15)
hmax.collections[0].set_alpha(0)

plt.imshow(map_img, zorder=0, extent=[0.5, 8.0, 1.0, 7.0])
plt.show()