使用matplotlib实现图形局部数据放大显示
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
axins = inset_axes(ax, width="40%", height="20%", loc=3,
bbox_to_anchor=(0.3, 0.1, 2, 2),
bbox_transform=ax.transAxes)
axins.plot(data1)
axins.plot(data2)
三、限制局部子坐标系数据范围
zone_left = 150
zone_right = 170
x_ratio = 0
y_ratio = 0.05
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 = 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
axins.set_xlim(xlim0, xlim1)
axins.set_ylim(ylim0, ylim1)
(-198439.93763, -134649.56637000002)
四、加上方框和连接线
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")
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)
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()
axins = inset_axes(ax, width="20%", height="20%", loc=3,
bbox_to_anchor=(0.3, 0.1, 2, 2),
bbox_transform=ax.transAxes)
axins.plot(data1)
axins.plot(data2)
zone_left = 150
zone_right = 170
x_ratio = 0
y_ratio = 0.05
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 = 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
axins.set_xlim(xlim0, xlim1)
axins.set_ylim(ylim0, ylim1)
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")
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)
xy = (xlim1,ylim0)
xy2 = (xlim1,ylim1)
con = ConnectionPatch(xyA=xy2,xyB=xy,coordsA="data",coordsB="data",
axesA=axins,axesB=ax)
axins.add_artist(con)