from tkinter import *
import tkinter as tk
import mpl_toolkits.axisartist as axisartist
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import data as gl_data
import numpy as np
from X1 import random_points

def window():
    root_window = Tk()
    root_window.title('函数拟合')

    root_window.geometry('900x600')  # 设置窗口大小:宽x高,注,此处不能为 "*",必须使用 "x"
    # 设置主窗口的背景颜色,颜色值可以是英文单词，或者颜色值的16进制数,除此之外还可以使用Tk内置的颜色常量
    root_window["background"] = "white"
    root_window.resizable(0, 0)  # 防止用户调整尺寸
    label1 = tk.Label(root_window, text="样本数据\n集文件", font=('Times', 8), bg="white",
                      width=13, height=3,  # 设置标签内容区大小
                      padx=0, pady=0, borderwidth=0, )
    label1.place(x=10, y=4)  # 设置填充区距离、边框宽度和其样式（凹陷式）
    label3 = tk.Label(root_window, text="", font=('Times', 8), bg="white", fg="black",
                      width=39, height=2, padx=0, pady=0, borderwidth=0, relief="ridge", highlightcolor="blue")
    label3.place(x=122, y=10)
    return root_window
# if __name__ == '__main__':
#     root_window = window()
#     root_window.mainloop()

def draw_axis(low, high, step=250):
    fig = plt.figure(figsize=(4.4, 3.2)) # 设置显示大小
    ax = axisartist.Subplot(fig, 111)	# 使用axisartist.Subplot方法创建一个绘图区对象ax
    fig.add_axes(ax)   # 将绘图区对象添加到画布中
    ax.axis[:].set_visible(False)# 通过set_visible方法设置绘图区所有坐标轴隐藏
    ax.axis["x"] = ax.new_floating_axis(0, 0)	# 添加新的x坐标轴
    ax.axis["x"].set_axisline_style("-|>", size=1.0) # 给x坐标轴加上箭头
    ax.axis["y"] = ax.new_floating_axis(1, 0)	# 添加新的y坐标轴
    ax.axis["y"].set_axisline_style("-|>", size=1.0) # y坐标轴加上箭头
    ax.axis["x"].set_axis_direction("bottom")	# 设置x、y轴上刻度显示方向
    ax.axis["y"].set_axis_direction("left")	# 设置x、y轴上刻度显示方向
    plt.xlim(low, high)  # 把x轴的刻度范围设置
    plt.ylim(low, high)  # 把y轴的刻度范围设置
    ax.set_xticks(np.arange(low, high + 5, step))  # 把x轴的刻度间隔设置
    ax.set_yticks(np.arange(low, high + 5, step))  # 把y轴的刻度间隔设置
    # plt.show()
# if __name__ == '__main__':
#     draw_axis(-1000, 1000)

def selfdata_show(sampleData):
    num_points = len(sampleData)  	# 样本数据点的数量
    colors = np.random.rand(num_points, 3)  # 生成随机颜色
    draw_axis(0, 1000)
    plt.scatter(sampleData[:, 0], sampleData[:, 1], c=colors)  # 绘制样本数据点
    plt.show()  #显示图片
# if __name__ == '__main__':
#     sampleData = random_points()  # 生成样本点
#     selfdata_show(sampleData) 	# 绘制样本数据点

def quadratic_function(x, a, b, c, d):	#构造三次函数y = a * X^3 + b * X^2 + C * x + d
    return a * x ** 3 + b * x **2 + c * x + d
def draw_line(low, high, sx, sy):
    draw_axis(low, high)	#绘制坐标轴
    popt = []	#初始化curve_fit
    pcov = []	#初始化curve_fit
    gl_data.yvals_pow = []	#初始化curve_fit
    popt, pcov = curve_fit(quadratic_function, sx, sy)  # 用curve_fit来对点进行拟合
    curve_x = np.arange(low, high)	#按照步长生成的一串数字
    curve_y = [quadratic_function (i, *popt) for i in curve_x]	# 根据x0(按照步长生成的一串数字)来计算y1值
    plt.plot(curve_x, curve_y, color='blue', label='Fitted Curve')   	#绘制拟合曲线
    plt.legend()
    plt.show()    #显示函数图像

if __name__ == '__main__':
    curve_data = [[-375,250],[-750,0],[-1000,-500],[0,0],[375,-250],[750,0],[1000,500]]
    x = np.array([point[0] for point in curve_data]) # 为二次函数.txt
    y = np.array([point[1] for point in curve_data])
    draw_line(-1000, 1000, x, y)

# 下面是一个用于拟合曲线的最小二乘法的Python代码示例：

# import numpy as np
# import matplotlib.pyplot as plt
#
# # 定义x和y坐标数据
# x = np.array([1, 2, 3, 4, 5])
# y = np.array([2, 3, 4, 5, 6])
#
# # 定义多项式阶数m
# m = 2
#
# # 构造A矩阵
# A = np.vander(x, m + 1, increasing=True)
#
# # 计算拟合系数theta
# theta = np.linalg.lstsq(A, y, rcond=None)[0]
#
# # 使用theta和x的幂次计算拟合曲线
# x_fit = np.linspace(x.min(), x.max(), 100)
# y_fit = np.polyval(theta[::-1], x_fit)
#
# # 创建图形对象并绘制拟合曲线和样本点
# fig, ax = plt.subplots()
# ax.plot(x_fit, y_fit, label='Fitted curve')
# ax.scatter(x, y, label='Data points')
#
# # 添加标签
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# ax.set_title('Curve fitting using Least Squares')
#
# # 显示图形
# plt.legend()
# plt.show()