使用matplotlib库实现图形局部数据放大显示的实践

一、绘制总体图形

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib.patches import connectionpatch
import  pandas as pd

max_episodes = 300
x_axis_data = []
for l in range(max_episodes):
    x_axis_data.append(l)

fig, ax = plt.subplots(1, 1)
data1 = pd.read_csv('./result/test_reward.csv')['test_reward'].values.tolist()[:max_episodes]
data2 = pd.read_csv('./result/test_reward_att.csv')['test_reward_att'].values.tolist()[:max_episodes]
ax.plot(data1,label="no att")
ax.plot(data2,label = "att")
ax.legend()

二、插入局部子坐标系

#插入子坐标系
axins = inset_axes(ax, width="40%", height="20%", loc=3,
                   bbox_to_anchor=(0.3, 0.1, 2, 2),
                   bbox_transform=ax.transaxes)
#在子坐标系中放入数据
axins.plot(data1)
axins.plot(data2)

三、限制局部子坐标系数据范围

#设置放大区间
zone_left = 150
zone_right = 170
# 坐标轴的扩展比例（根据实际数据调整）
x_ratio = 0  # x轴显示范围的扩展比例
y_ratio = 0.05  # y轴显示范围的扩展比例

# x轴的显示范围
xlim0 = x_axis_data[zone_left]-(x_axis_data[zone_right]-x_axis_data[zone_left])*x_ratio
xlim1 = x_axis_data[zone_right]+(x_axis_data[zone_right]-x_axis_data[zone_left])*x_ratio

# y轴的显示范围
y = np.hstack((data1[zone_left:zone_right], data2[zone_left:zone_right]))
ylim0 = np.min(y)-(np.max(y)-np.min(y))*y_ratio
ylim1 = np.max(y)+(np.max(y)-np.min(y))*y_ratio

# 调整子坐标系的显示范围
axins.set_xlim(xlim0, xlim1)
axins.set_ylim(ylim0, ylim1)

(-198439.93763, -134649.56637000002)

四、加上方框和连接线

# 原图中画方框
tx0 = xlim0
tx1 = xlim1
ty0 = ylim0
ty1 = ylim1
sx = [tx0,tx1,tx1,tx0,tx0]
sy = [ty0,ty0,ty1,ty1,ty0]
ax.plot(sx,sy,"blue")

# 画两条线
#第一条线
xy = (xlim0,ylim0)
xy2 = (xlim0,ylim1)
"""
xy为主图上坐标，xy2为子坐标系上坐标，axins为子坐标系，ax为主坐标系。
"""
con = connectionpatch(xya=xy2,xyb=xy,coordsa="data",coordsb="data",
        axesa=axins,axesb=ax)

axins.add_artist(con)
#第二条线
xy = (xlim1,ylim0)
xy2 = (xlim1,ylim1)
con = connectionpatch(xya=xy2,xyb=xy,coordsa="data",coordsb="data",
        axesa=axins,axesb=ax)
axins.add_artist(con)

五、总体实现代码

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib.patches import connectionpatch
import  pandas as pd

max_episodes = 300
x_axis_data = []
for l in range(max_episodes):
    x_axis_data.append(l)

fig, ax = plt.subplots(1, 1)
data1 = pd.read_csv('./result/test_reward.csv')['test_reward'].values.tolist()[:max_episodes]
data2 = pd.read_csv('./result/test_reward_att.csv')['test_reward_att'].values.tolist()[:max_episodes]
ax.plot(data1,label="no att")
ax.plot(data2,label = "att")
ax.legend()

#插入子坐标系
axins = inset_axes(ax, width="20%", height="20%", loc=3,
                   bbox_to_anchor=(0.3, 0.1, 2, 2),
                   bbox_transform=ax.transaxes)
#在子坐标系中放入数据
axins.plot(data1)
axins.plot(data2)

#设置放大区间
zone_left = 150
zone_right = 170
# 坐标轴的扩展比例（根据实际数据调整）
x_ratio = 0  # x轴显示范围的扩展比例
y_ratio = 0.05  # y轴显示范围的扩展比例

# x轴的显示范围
xlim0 = x_axis_data[zone_left]-(x_axis_data[zone_right]-x_axis_data[zone_left])*x_ratio
xlim1 = x_axis_data[zone_right]+(x_axis_data[zone_right]-x_axis_data[zone_left])*x_ratio

# y轴的显示范围
y = np.hstack((data1[zone_left:zone_right], data2[zone_left:zone_right]))
ylim0 = np.min(y)-(np.max(y)-np.min(y))*y_ratio
ylim1 = np.max(y)+(np.max(y)-np.min(y))*y_ratio

# 调整子坐标系的显示范围
axins.set_xlim(xlim0, xlim1)
axins.set_ylim(ylim0, ylim1)


# 原图中画方框
tx0 = xlim0
tx1 = xlim1
ty0 = ylim0
ty1 = ylim1
sx = [tx0,tx1,tx1,tx0,tx0]
sy = [ty0,ty0,ty1,ty1,ty0]
ax.plot(sx,sy,"blue")

# 画两条线
# 第一条线
xy = (xlim0,ylim0)
xy2 = (xlim0,ylim1)
"""
xy为主图上坐标，xy2为子坐标系上坐标，axins为子坐标系，ax为主坐标系。
"""
con = connectionpatch(xya=xy2,xyb=xy,coordsa="data",coordsb="data",
        axesa=axins,axesb=ax)

axins.add_artist(con)
# 第二条线
xy = (xlim1,ylim0)
xy2 = (xlim1,ylim1)
con = connectionpatch(xya=xy2,xyb=xy,coordsa="data",coordsb="data",
        axesa=axins,axesb=ax)
axins.add_artist(con)

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