# -*- 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/SST

if __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))))/2

    fig, 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R
    r2 = 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title
    ax.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()