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from tkinter import *
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import tkinter as tk
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import mpl_toolkits.axisartist as axisartist
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import data as gl_data
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import numpy as np
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from X1 import random_points
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def window():
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root_window = Tk()
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root_window.title('函数拟合')
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root_window.geometry('900x600') # 设置窗口大小:宽x高,注,此处不能为 "*",必须使用 "x"
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# 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
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root_window["background"] = "white"
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root_window.resizable(0, 0) # 防止用户调整尺寸
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label1 = tk.Label(root_window, text="样本数据\n集文件", font=('Times', 8), bg="white",
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width=13, height=3, # 设置标签内容区大小
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padx=0, pady=0, borderwidth=0, )
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label1.place(x=10, y=4) # 设置填充区距离、边框宽度和其样式（凹陷式）
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label3 = tk.Label(root_window, text="", font=('Times', 8), bg="white", fg="black",
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width=39, height=2, padx=0, pady=0, borderwidth=0, relief="ridge", highlightcolor="blue")
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label3.place(x=122, y=10)
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return root_window
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# if __name__ == '__main__':
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# root_window = window()
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# root_window.mainloop()
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def draw_axis(low, high, step=250):
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fig = plt.figure(figsize=(4.4, 3.2)) # 设置显示大小
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ax = axisartist.Subplot(fig, 111) # 使用axisartist.Subplot方法创建一个绘图区对象ax
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fig.add_axes(ax) # 将绘图区对象添加到画布中
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ax.axis[:].set_visible(False)# 通过set_visible方法设置绘图区所有坐标轴隐藏
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ax.axis["x"] = ax.new_floating_axis(0, 0) # 添加新的x坐标轴
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ax.axis["x"].set_axisline_style("-|>", size=1.0) # 给x坐标轴加上箭头
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ax.axis["y"] = ax.new_floating_axis(1, 0) # 添加新的y坐标轴
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ax.axis["y"].set_axisline_style("-|>", size=1.0) # y坐标轴加上箭头
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ax.axis["x"].set_axis_direction("bottom") # 设置x、y轴上刻度显示方向
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ax.axis["y"].set_axis_direction("left") # 设置x、y轴上刻度显示方向
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plt.xlim(low, high) # 把x轴的刻度范围设置
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plt.ylim(low, high) # 把y轴的刻度范围设置
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ax.set_xticks(np.arange(low, high + 5, step)) # 把x轴的刻度间隔设置
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ax.set_yticks(np.arange(low, high + 5, step)) # 把y轴的刻度间隔设置
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# plt.show()
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# if __name__ == '__main__':
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# draw_axis(-1000, 1000)
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def selfdata_show(sampleData):
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num_points = len(sampleData) # 样本数据点的数量
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colors = np.random.rand(num_points, 3) # 生成随机颜色
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draw_axis(0, 1000)
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plt.scatter(sampleData[:, 0], sampleData[:, 1], c=colors) # 绘制样本数据点
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plt.show() #显示图片
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# if __name__ == '__main__':
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# sampleData = random_points() # 生成样本点
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# selfdata_show(sampleData) # 绘制样本数据点
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def quadratic_function(x, a, b, c, d): #构造三次函数y = a * X^3 + b * X^2 + C * x + d
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return a * x ** 3 + b * x **2 + c * x + d
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def draw_line(low, high, sx, sy):
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draw_axis(low, high) #绘制坐标轴
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popt = [] #初始化curve_fit
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pcov = [] #初始化curve_fit
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gl_data.yvals_pow = [] #初始化curve_fit
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popt, pcov = curve_fit(quadratic_function, sx, sy) # 用curve_fit来对点进行拟合
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curve_x = np.arange(low, high) #按照步长生成的一串数字
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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
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plt.plot(curve_x, curve_y, color='blue', label='Fitted Curve') #绘制拟合曲线
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plt.legend()
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plt.show() #显示函数图像
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if __name__ == '__main__':
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curve_data = [[-375,250],[-750,0],[-1000,-500],[0,0],[375,-250],[750,0],[1000,500]]
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x = np.array([point[0] for point in curve_data]) # 为二次函数.txt
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y = np.array([point[1] for point in curve_data])
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draw_line(-1000, 1000, x, y)
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# 下面是一个用于拟合曲线的最小二乘法的Python代码示例：
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# import numpy as np
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# import matplotlib.pyplot as plt
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#
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# # 定义x和y坐标数据
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# x = np.array([1, 2, 3, 4, 5])
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# y = np.array([2, 3, 4, 5, 6])
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#
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# # 定义多项式阶数m
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# m = 2
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#
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# # 构造A矩阵
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# A = np.vander(x, m + 1, increasing=True)
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#
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# # 计算拟合系数theta
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# theta = np.linalg.lstsq(A, y, rcond=None)[0]
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#
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# # 使用theta和x的幂次计算拟合曲线
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# x_fit = np.linspace(x.min(), x.max(), 100)
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# y_fit = np.polyval(theta[::-1], x_fit)
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#
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# # 创建图形对象并绘制拟合曲线和样本点
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# fig, ax = plt.subplots()
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# ax.plot(x_fit, y_fit, label='Fitted curve')
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# ax.scatter(x, y, label='Data points')
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#
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# # 添加标签
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# ax.set_xlabel('x')
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# ax.set_ylabel('y')
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# ax.set_title('Curve fitting using Least Squares')
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#
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# # 显示图形
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# plt.legend()
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# plt.show()
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