Python 在背景图片上绘制海生热图

我正在通过位于朱皮特的seaborn制作一张热图，以显示选择某个坐标点的人数。我目前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()

我的问题是，如何在背景图像上绘制此热图，并使热图中的方块越接近0越透明，以更清晰地显示背景图像？还有一种方法可以将热图上的索引从1开始，而不是从0开始吗

如果需要查看图片的外观，这里还有一个链接

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您还需要缩放/翻转图像，以便它们一起打印，因为地图的分辨率可能比热图高得多。我们让Seaborn进行调整工作，然后在显示地图的

imshow

中进行匹配

您可以修改或创建一个颜色贴图，使其透明度接近0，我留下了代码来向您展示如何修改或创建颜色贴图，但生成的图形不是最佳的，因为我无法在高温位置读取贴图。如图所示，整个热图是半透明的

留给读者：将记号改为地图坐标，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()

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您需要在轴上添加一个

imshow

绘图和较低的

zorder

，并为热量使用自定义颜色贴图，其中alpha小于1。一般来说，使用matplotlib而不是seaborn来创建热图可能会使您拥有的选项更加透明，请参见。值得投票。可以改进代码，使其不产生语法错误。

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()