# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator"""
输出观测值和模型预测值之间的拟合曲线,即拟合模型
决定系数R2，有拟合曲线公式，有1:1线
"""
# R相关系数计算
def R2(target, predict):SSR = sum((predict - (target).mean())**2)SST = sum((target - (target).mean())**2)return SSR/SSTif __name__ == '__main__':# x: 是观测值; y: 是模型预测值x = np.random.random((100))y = (x+np.sin(np.random.random((100))))/2fig, ax = plt.subplots(figsize=(3, 3), dpi=100)# 绘制1:1对角线，linewidth线的粗细，ls线的格式，c线的颜色，ax.plot((0, 1), (0, 1), linewidth=1, transform=ax.transAxes, ls='--', c='k', label="1:1 line", alpha=0.5)# 绘制点，'o'点的形状，点的颜色，markersize点的大小ax.plot(x, y, 'o', c='black', markersize=5)# polyfit(x, y, 1)，1代表线性拟合# parameter返回的是线性拟合线的斜率和截距parameter = np.polyfit(x, y, 1)f = np.poly1d(parameter)ax.plot(x, f(x), 'r-', lw=1)# 计算相关系数Rr2 = R2(x,y)print(r2)# 那个框框的设置bbox = dict(boxstyle="round", fc='1', alpha=0.)bbox = bbox# 在图上安放R2和拟合曲线公式，0.05和0.87是位置偏移量，自己调试plt.text(0.05, 0.87, "$R^2=%.2f$\n$y=%.2fx+%.2f$" % ((r2), parameter[0], parameter[1]),transform=ax.transAxes, size=7, bbox=bbox)# 横轴的设置ax.set_xlabel('Measured values($g\cdot kg^{-1}$)', fontsize=7)ax.set_ylabel("Predicted values($g\cdot kg^{-1}$)", fontsize=7)# 设置图片titleax.tick_params(labelsize=7)ax.set_title("pre", fontsize=7)x_major_locator = MultipleLocator(0.1)ax.xaxis.set_major_locator(x_major_locator)y_major_locator = MultipleLocator(0.1)ax.yaxis.set_major_locator(y_major_locator)# 坐标轴ax.set(xlim=(-0.05, 1.05), ylim=(-0.05, 1.05))# plt.savefig("out.png", bbox_inches='tight')plt.show()