在背景圖片上繪制 seaborn 熱圖

Question

我正在通過 Jupyter 中的 seaborn 創建一個熱圖，以顯示選擇某個坐標點的人數。 我目前使用以下代碼創建了熱圖

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()

我的問題是如何在背景圖像上繪制此熱圖，並使熱圖中的方塊越接近 0 越透明以顯示更多背景圖像？ 還有沒有辦法將熱圖上的索引從 1 而不是 0 開始？

如果需要查看它的外觀，這里還有指向圖片的鏈接。

Answer 1

您還需要縮放/翻轉圖像，以便它們一起繪制，因為地圖的分辨率可能比熱圖要精細得多。 我們讓 Seaborn 做它的調整工作，然后在顯示地圖的imshow中匹配它。

您可以修改或創建一個顏色圖，使透明度接近 0，我留下了代碼來向您展示如何操作，但結果圖並不理想，因為我無法在高溫位置讀取地圖。 如圖所示，整個熱圖是半透明的。

留給讀者：更改刻度以引用地圖坐標，而不是熱圖索引。

# 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()