python - 如何从颜色条图像中提取颜色图并将其用于热图中

Question

我需要根据阈值生成色标，如下所示:

目前，我只能填充色标，如下所示:

def create_png(a_group,req_df):
    import matplotlib.pyplot as plt
    plt.figure(figsize=(15, 8))
    heat_plot = sns.heatmap(req_df, vmin=-120, vmax=-80, cmap="viridis", yticklabels=False,square=True,linecolor='black',linewidths=.5)
    fig = heat_plot.get_figure()

Answer 1

您可以从颜色列表创建一个ListedColormap。以下方法从颜色图中读取图像并提取彩色矩形中心的 45 种颜色。请注意，如果原始图像不通过 jpeg 保存为 ax png，质量会好一些。由于 jpeg 稍微混合了颜色，因此它们可能与原始文件略有不同。

import matplotlib.pyplot as plt
import numpy as np

img = plt.imread('desired_colormap.jpg')
# x and y positions of the rectangle centers are found via an image editor
xpos = 12  
ypos_124 = 768
ypos_80 = 22
val_min = -124
val_max = -80
# extract the color from each of 45 rectangles
colors = [img[int(ypos), xpos] / 255
          for ypos in np.linspace(ypos_124, ypos_80, val_max - val_min + 1)]

然后您可以使用此颜色列表创建颜色图并在 seaborn 中使用它。看起来有点迷幻。

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns
import numpy as np
from scipy.ndimage import gaussian_filter

# colors were read from image

# create some smooth test data, with the given range of values
data = gaussian_filter(np.random.rand(1000, 1000), 20)
data -= data.min()
data *= (val_max - val_min) / data.max()
data += val_min

fig, ax = plt.subplots(figsize=(12, 10))
sns.set_style('white')
sns.heatmap(data=data, xticklabels=[], yticklabels=[],
            cmap=ListedColormap(colors), vmin=val_min - 0.5, vmax=val_max + 0.5,
            cbar_kws={'ticks': np.arange(val_min, val_max + 1)}, ax=ax)
plt.tight_layout()
plt.show()

PS:如果你想保存列表供多次使用，你可以将颜色转换为十六进制(matplotlib.color.to_hex)。该列表将是

colors = ['#000000', '#000000', '#68696b', '#00008c', '#0001cd', '#0102fa', '#4467e5', '#6296ea', '#4683b2',
          '#4981b4', '#007f85', '#2d8a5d', '#619ca2', '#65cdaa', '#0afafb', '#02f99e', '#74fc02', '#adff31',
          '#f6fbd2', '#f0e7ac', '#f1e68a', '#fbff0a', '#fdda00', '#dba51e', '#f9a608', '#ff8a06', '#d16920',
          '#8d4312', '#fe804e', '#fe5945', '#fd4900', '#fe0300', '#fcb2c1', '#f0807c', '#fd6ba9', '#fc02f9',
          '#dd9ee4', '#ba55d7', '#9202d6', '#85038b', '#7f017c', '#480181', '#4c0084', '#4b007f', '#480185']