以下代码展示如何在图中添加背景图,

首先生成图片


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from datetime import datetime

import numpy as np
import pandas as pd

from matplotlib import cm, colors
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_context('talk', font_scale=1.3)

## 自定义colormap
colors_pm = ['#009966', '#FFDE33', '#FF9A32', '#CC0033', '#660099', '#7D0023']
levels = [0, 35, 75, 115, 150, 250, 350]

cmap_pm = colors.ListedColormap(colors_pm)  
norm = colors.BoundaryNorm(levels, cmap_pm.N)

s = np.repeat(np.arange(350), 365).reshape((350, 365))

fig, ax = plt.subplots(figsize=(18, 12))

ax.imshow(s, cmap=cmap_pm, alpha=0.5, norm=norm, aspect='auto',
          extent=[xlims[0], xlims[-1], levels[-1], levels[0]])

# imshow显示的y轴可能是相反的,因此需要反转y轴
ax.invert_yaxis()

## 去除 x和y轴的ticklabels以及tick 
ax.set_xticks([])
ax.set_yticks([])
ax.tick_params(dict(length=0))

fig.savefig('img.png', dpi=300, bbox_inches='tight')



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## 读取图片
img = mpimg.imread('img.png')

date_range = pd.date_range(datetime(2015, 1, 1), datetime(2015, 12 ,31), freq='1d')
data = pd.DataFrame(np.random.random(365) * 300, index=date_range, columns=['pm2.5'])

s = np.repeat(np.arange(350), 365).reshape((350, 365))

fig, ax = plt.subplots(figsize=(18, 12))

# alpha 设置透明度,图片在图中的位置,主要由 extent 参数控制,transform 参数将坐标轴的范围归一化
ax.imshow(img, alpha=0.5, extent=[-.02, 1.02, -0.02, 1.02], transform=ax.transAxes)

## x轴时间需要另外设置
ax.plot(data['pm2.5'].values, color='k', linewidth=2) 

ax.set_yticks(levels)
_ = ax.set_ylabel('PM$_2.5$($\mu$g/m$^3$)', fontdict=dict(fontfamily='Times New Roman'))



以上示例展示了如何在图中添加背景图,添加背景图时可以添加多个需要的背景图片,关键在于设置 imshow 函数的 extend 参数。

上面实现的功能,可以通过以下代码完成:

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from datetime import datetime

import numpy as np
import pandas as pd

from matplotlib import cm, colors
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_context('talk', font_scale=1.3) # 调整图的配置


## 自定义 colormap
colors_pm = ['#009966', '#FFDE33', '#FF9A32', '#CC0033', '#660099', '#7D0023']
levels = [0, 35, 75, 115, 150, 250, 350]

cmap_pm = colors.ListedColormap(colors_pm)  
norm = colors.BoundaryNorm(levels, cmap_pm.N)

## 创建 DataFrame
date_range = pd.date_range(datetime(2015, 1, 1), datetime(2015, 12 ,31), freq='1d')
data = pd.DataFrame(np.random.random(365) * 300, index=date_range, columns=['pm2.5'])

s = np.repeat(np.arange(350), 365).reshape((350, 365))

fig, ax = plt.subplots(figsize=(18, 12))

xlims = mdates.date2num(data.index.values) # 转换datetime为timestamp

## imshow 默认 aspect 为 equal,应设置为 auto 才能使上述 figsize 参数生效
ax.imshow(s, cmap=cmap_pm, alpha=0.5, norm=norm, aspect='auto',
          extent=[xlims[0], xlims[-1], levels[-1], levels[0]])

ax.invert_yaxis() # 反转 y 轴

ax.xaxis_date() # 设置x轴刻度格式

# 不能使用 plot_date,因为imshow 暂时不支持 datetime axes
ax.plot(data['pm2.5'], color='k', linewidth=2) 

date_format = mdates.DateFormatter('%m/%d')
ax.xaxis.set_major_formatter(date_format)

ax.set_yticks(levels)
_ = ax.set_ylabel('PM$_2.5$($\mu$g/m$^3$)', fontdict=dict(fontfamily='Times New Roman'))

果然还是不喜欢多写字。

参考链接:
1. https://stackoverflow.com/questions/23139595/dates-in-the-xaxis-for-a-matplotlib-plot-with-imshow