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import mpl_toolkits.axisartist as axisartist
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from scipy.optimize import curve_fit
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from pandas import DataFrame
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import matplotlib.pyplot as plt
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import numpy as np
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import pylab as mpl
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from tkinter import *
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import pandas as pd
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import tkinter
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import sys
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import tkinter as tk
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from tkinter import filedialog
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import re
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import function
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import data as gl_data
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from X1 import *
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from X2 import *
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mpl.rcParams['font.sans-serif'] = ['SimHei'] # 解决matplotlib中文不显示问题
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plt.rcParams['axes.unicode_minus'] = False # 解决matplotlib负数坐标显示问题
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# 创建一个二维数组sampleData，用于存储样本数据
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sampleData = None
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global Q_root,F_root
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global root_window
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global label1,label2,label3
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# 编程4.6 -----------------------------------------
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# 定义一个处理文件的相关函数
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def askfile():
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# 选择需要加载的数据文件，并返回文件的路径
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filename = tkinter.filedialog.askopenfilename()
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if filename != '': # 若选中了一个文件，则对文件进行读取
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label3.config(text=filename) # 显示文件路径,其中label3为对应的按钮
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read_sample_data(filename) # 将文件读取并存到sampleData中
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# print_sample_data(filename)# 将文件读取并逐行输出
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selfdata_show(gl_data.SampleData[:,0], gl_data.SampleData[:,1], gl_data.LOW, gl_data.HIGH)
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else: # 若未选择文件，则显示为空
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label3.config(text='')
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def print_sample_data(file_path):#打开对应文件
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with open(file_path, 'r') as file:
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for line in file: # 逐行读取文件
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line = line.strip('\n') # 移除换行符
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sx, sy = line.split(' ') # 以空格分割x，y
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print(f'sx: {float(sx)}, sy: {float(sy)}') # 将sx、sy转换为浮点数并打印
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def read_sample_data(file_path):
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x, y = [], [] # 初始化x，y
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with open(file_path, 'r') as file:
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for line in file: # 逐行读文件
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line = line.strip('\n') # 移除换行符
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sx, sy = line.split(' ') # 以空格分割x，y
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x.append(float(sx)) # 将sx转换为浮点数并加入数组
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y.append(float(sy)) # 将sy转换为浮点数并加入数组
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# x，y数组转为array并赋值给全局变量sampleData
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x = np.array(x) # 列表转数组
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y = np.array(y)
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gl_data.SampleData = np.array(list(zip(x, y)))
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# 编程4.6 END-----------------------------------------
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# 编程4.7 -----------------------------------------
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def generate_and_plot_sample_data(sampleData, Low, High):
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sx,sy = sampleData[:, 0],sampleData[:, 1]
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draw_axis(Low, High) # 绘制坐标轴
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plt.scatter(sx, sy, color='red') # 绘制样本数据点
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plt.savefig(r"dot.png", facecolor='w', bbox_inches='tight') # 保存到本地
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plt.close() # 清除内存
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set_phtot(1) # 显示到主界面 4.7示例时没有编程该函数
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# if __name__ == '__main__':
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# num_samples = 25 #设置样本点数量
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# sampleData = random_points(num_samples, -1000, 1000) # 生成随机样本数据25个
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# generate_and_plot_sample_data(sampleData, gl_data.LOW, gl_data.HIGH)
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# 编程4.7 END-----------------------------------------
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# 编程4.8 -----------------------------------------
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def draw_dots_and_line(curveData, sampleData, low, high):
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x_fit, y_fit = curveData[:, 0], curveData[:, 1]
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x, y = sampleData[:, 0], sampleData[:, 1]
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draw_axis(low, high) # 画xy坐标轴
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positive_mask = y >= 0.0 # 给样本点分类
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negative_mask = y < 0.0 # 给样本点分类
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positive_colors = ['red' if xx >= 0.0 else 'blue' for xx in x] # 给样本点分类
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negative_colors = ['green' if xx >= 0.0 else 'purple' for xx in x] # 给样本点分类
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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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ax = draw_axis(low,high,step=250)
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# 根据样本点类型决定样本点颜色
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ax.scatter(x[positive_mask], y[positive_mask], color=np.array(positive_colors)[positive_mask], lw=1)
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ax.scatter(x[negative_mask], y[negative_mask], color=np.array(negative_colors)[negative_mask], lw=1)
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plt.plot(x_fit, y_fit, color='blue', label='Fitted Curve')# 绘制拟合曲线
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# plt.savefig(r"dot5.png", facecolor='w') # 保存到本地
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# plt.legend()
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# plt.show()
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# X5修改后展示到主界面中
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plt.legend(loc='center left', bbox_to_anchor=(1, 0.9), fontsize=9)
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plt.savefig(r"line.png", facecolor='w', bbox_inches='tight')# 将图片保存到本地
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plt.close()# 清除内存
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set_phtot(2)# 将图片显示到程序中
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# if __name__ == '__main__':
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# gl_data.SampleData = random_points(25,-1000, 1000)
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# # print("gl_data.SampleData",gl_data.SampleData)
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# draw_dots_and_line(gl_data.LOW, gl_data.HIGH, gl_data.SampleData)
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# if __name__ == '__main__':
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# LimitNum = 1000
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# # sampleData = random_points(20,0, 1000)
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# # x, y = sampleData[:, 0], sampleData[:, 1]
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# # 这里为了便于观察使用设计好的数据
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# x = np.array([0, 100, 200, 300, 400, 500, 600, 700, 800, 900])
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# y = np.array([10, 20, 10, 50, 80, 130, 210, 340, 550, 890])
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# sampleData = np.array(list(zip(x, y))) # 将两个一维数组拼接成二维数组
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# m = 3 # 使用三次函数拟合
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# theta, covariance_matrix = least_square_method(m, sampleData)
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# curveData = compute_curveData2(0,1000,1,theta,m, x.mean(), x.std())
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# draw_dots_and_line(curveData,sampleData,0,1000)
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# 编程4.8 END-----------------------------------------
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# 编程4.9 -----------------------------------------
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def input_num(root_tk):
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global top
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top = tk.Toplevel(root_tk)
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top.geometry("300x50")
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top.title('坐标点个数')
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label1 = Label(top, text="坐标点个数")
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label1.grid(row=0) # 这里的side可以赋值为LEFT RTGHT TOP BOTTOM
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num1 = IntVar()
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entry1 = Entry(top, textvariable=num1)
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num1.set(0)
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entry1.grid(row=0, column=1)
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Label(top, text=" ").grid(row=0, column=3)
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Button(top, text="确定", command=lambda: input_data(root_tk, int(entry1.get()))).grid(row=0, column=3)
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top.mainloop()
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def add_sample_data():
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global sample_x, sample_y, sample_data
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global entry_x,entry_y,label_status, numx
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try:
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x = float(entry_x.get())
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y = float(entry_y.get())
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except:
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label_status.config(text="输入不合法")
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return
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entry_x.delete(0, tk.END)
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entry_y.delete(0, tk.END)
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if min(x, y) < gl_data.LOW or max(x, y) > gl_data.HIGH:
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label_status.config(text="输入超过范围")
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return
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elif len(sample_data) < numx:
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label_status.config(text="点对已添加")
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sample_data.append((x, y))
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sample_x.append(x)
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sample_y.append(y)
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else:
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label_status.config(text="已达到最大数量")
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def check_sample_data():
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global label_status,numx,sample_x,sample_y,sample_data
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if len(sample_data) == numx:
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label_status.config(text="已达到最大数量")
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gl_data.X = np.array(sample_x)
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gl_data.Y = np.array(sample_y)
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print('已添加', sample_data)
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sys.exit()
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else:
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label_status.config(text="还需输入{}个点对".format(numx - len(sample_data)))
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print(sample_data)
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def input_data(root_tk, num):
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global top
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global sample_x,sample_y,sample_data
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global numx
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numx = num
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sample_x = []
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sample_y = []
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sample_data = []
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top.destroy()
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top = tk.Toplevel(root_tk)
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top.geometry("300x200")
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top.title('坐标')
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global entry_x, entry_y, label_status
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label_x = tk.Label(top, text="X 值：")
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label_x.pack()
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entry_x = tk.Entry(top)
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entry_x.pack()
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label_y = tk.Label(top, text="Y 值：")
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label_y.pack()
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entry_y = tk.Entry(top)
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entry_y.pack()
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button_add = tk.Button(top, text="添加", command=add_sample_data)
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button_add.pack()
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button_check = tk.Button(top, text="检查", command=check_sample_data)
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button_check.pack()
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label_status = tk.Label(top, text="")
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label_status.pack()
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top.mainloop()
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# 编程4.9 END-----------------------------------------
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# 编程4.10 -----------------------------------------
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def error_func(func, sampleData, theta): # 定义误差函数
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sx, sy = sampleData[:, 0], sampleData[:, 1]
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y_pred = func(theta, sx) # 使用func函数和参数theta计算预测值
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error = np.sum((sy - y_pred) ** 2) # 计算误差平方和
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ss_tot = np.sum((sy - np.mean(sy)) ** 2) # 计算总平方和
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r_squared = 1 - (error / ss_tot) # 计算决定系数(R^2)
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return error, r_squared
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# 这里是处理了标准化的error_func
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def error_func2(func, sampleData, theta): # 定义误差函数
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sx, sy = sampleData[:, 0], sampleData[:, 1]
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x_normalized = (sx - sx.mean()) / sx.std()
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# 使用func函数和参数theta计算预测值
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y_pred = func(theta, x_normalized)
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# 计算误差平方和
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error = np.sum((sy - y_pred) ** 2)
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# 计算总平方和
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ss_tot = np.sum((sy - np.mean(sy)) ** 2)
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# 计算决定系数(R^2)
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r_squared = 1 - (error / ss_tot)
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return error, r_squared
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# fit函数中修改使用方法为梯度下降法，并输出拟合结果
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def fit(sampleData, m):
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theta, covariance_matrix = least_square_method(m, sampleData) # 计算拟合结果
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# 计算拟合误差
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if m == 1: func = linear_function
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if m == 2: func = quadratic_function # 此处以m=2为例，省略其他函数
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if m == 3: func = qubic_function
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if m == 4: func = quartic_function
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error,r_squared = error_func2(func, sampleData, theta)
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fit_function_name = func.__name__ # 打印拟合函数的形式、系数、误差和优度
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print("拟合函数形式：{}".format(fit_function_name))
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print("标准化拟合系数：{}".format(theta))
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print("误差：{:.4f}".format(error))
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print("拟合优度（R^2）：{:.4f}".format(r_squared))
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return theta, covariance_matrix
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# if __name__ == '__main__':
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# # sampleData = random_points(20,0, 1000)
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# # x = sampleData[:,0]
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# # y = sampleData[:,1]
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# # 这里为了便于观察使用设计好的数据
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# x = np.array([0, 100, 200, 300, 400, 500, 600, 700, 800, 900])
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# y = np.array([10, 20, 10, 50, 80, 130, 210, 340, 550, 890])
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# sampleData = np.array(list(zip(x, y))) # 将两个一维数组拼接成二维数组
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# m = 2 # 假设选择使用二次函数拟合
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# theta, covariance_matrix = fit(sampleData, m)
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# ------fit_X4前提函数-------
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# 为了适应扩展的函数，需要根据用户输入的函数类型构造函数
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def fitting(letters,result):
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code_str = '''
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def func({}):
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return {}
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'''.format(letters,result)
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return code_str
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# 构造用户自定义的函数
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def create_func(code_str):
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# 创建一个空的命名空间
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namespace = {}
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# 使用exec函数执行字符串代码，并指定命名空间为locals
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exec(code_str, globals(), namespace)
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# 返回命名空间中的函数对象
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return namespace['func']
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# 简化版本的goodness_of_fit
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def goodness_of_fit_easy(y_fitting, y_no_fitting):
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"""
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计算拟合优度R^2
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:param y_fitting: List[int] or array[int] 拟合好的y值
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:param y_no_fitting: List[int] or array[int] 待拟合y值
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:return: 拟合优度R^2
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"""
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y_mean = sum(y_no_fitting) / len(y_no_fitting)
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# 计算SST
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sst = sum((y - y_mean) ** 2 for y in y_no_fitting)
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# 计算SSE
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sse = sum((y_fitting[i] - y_no_fitting[i]) ** 2 for i in range(len(y_fitting)))
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# 计算R^2
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r_squared = 1 - sse / sst
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return r_squared
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# ------fit_X4前提函数-------
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# 这里是为了后续的fit能够直接用到按钮中，而不是BF_Fit写一大堆
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def fit_X4(xian_index, sampleData):
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sx, sy = sampleData[:, 0], sampleData[:, 1]
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cur = function.Fun[xian_index] # 装载正在选择的函数
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func = cur.get_fun() # 获取当前函数func
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popt, pcov = gradient_descent_method_X5(func, sx, sy) # 用curve_fit来对数据进行拟合
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x_normalized = (sx - np.mean(sx)) / np.std(sx) # 标准化处理的x
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y_pred = func(x_normalized, *popt) # 获取y的预测值
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gl_data.CurveData = compute_curveData_X5_2(func, popt, sampleData) # 计算拟合曲线的数据
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rr = goodness_of_fit_easy(y_pred, sy) # 计算本次拟合的R*R值，用于表示拟合优度
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# 输出拟合后的各个参数值
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ans = '\n函数标准化系数：F(x) = '
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for i in range(cur.variable):
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if i == 4:
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ans += '\n'
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if i != 0:
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ans += ', '
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ans += chr(ord('a') + i) + '=' + '{:.2e}'.format(popt[i]) # str(round(gl_data.popt[i], 3))
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gl_data.Out = '函数形式：' + cur.name + ' '
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gl_data.Out += cur.demo
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gl_data.Out += ans
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gl_data.Out += '\n拟合优度(R\u00b2)：' + str(round(rr, 5))
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# 编程4.17-----------------------------------------
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# 选择
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def fit_X4(xian_index, sampleData):
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sx, sy = sampleData[:, 0], sampleData[:, 1]
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cur = function.Fun[xian_index] # 装载正在选择的函数
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func = cur.get_fun() # 获取当前函数func
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# params_count = func.__code__.co_argcount - 2 # 计算参数数量
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# if params_count <= 3 and len(sampleData) <= 20 : # 当参数和样本点数量较少时
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# popt, pcov = least_square_method(func, sx, sy) # 用最小二乘法来对数据进行拟合
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# else: # 当参数和样本点数量较多时
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# popt, pcov = gradient_descent_method_X5(func, sx, sy) # 用梯度下降法来对数据进行拟合
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popt, pcov = gradient_descent_method_X5(func, sx, sy)
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x_normalized = (sx - np.mean(sx)) / np.std(sx) # 标准化处理的x
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y_pred = func(x_normalized, *popt) # 获取y的预测值
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gl_data.CurveData = compute_curveData_X5_2(func, popt, sampleData) # 计算拟合曲线的数据
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rr = goodness_of_fit_easy(y_pred, sy) # 计算本次拟合的R*R值，用于表示拟合优度
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# 输出拟合后的各个参数值
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ans = '\n函数标准化系数：F(x) = '
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for i in range(cur.variable):
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if i == 4:
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ans += '\n'
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if i != 0:
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ans += ', '
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ans += chr(ord('a') + i) + '=' + '{:.2e}'.format(popt[i])
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gl_data.Out = '函数形式：' + cur.name + ' '
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gl_data.Out += cur.demo
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gl_data.Out += ans
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gl_data.Out += '\n拟合优度(R\u00b2)：' + str(round(rr, 5))
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# 编程4.17 END-----------------------------------------
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# 这里是为了后续的fit能够直接用到按钮中，而不是BF_Fit写一大堆
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def fit_X5(xian_index, sampleData):
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sx, sy = sampleData[:, 0], sampleData[:, 1]
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letters, result = gl_data.FITT_SAVE['variable'][xian_index], gl_data.FITT_SAVE['function'][xian_index]
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code_str = fitting(letters, result) # 获取当前选择函数的表达式
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func = create_func(code_str) # 构造当前函数的func方法
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popt, pcov = gradient_descent_method_X5(func, sx, sy) # 用curve_fit来对数据进行拟合
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x_normalized = (sx - np.mean(sx)) / np.std(sx)
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y_pred = func(x_normalized, *popt) # 获取y的预测值
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gl_data.CurveData = compute_curveData_X5_2(func, popt, sampleData) # 计算拟合曲线的数据
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rr = goodness_of_fit_easy(y_pred, sy) # 计算本次拟合的R*R值，用于表示拟合优度
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# 输出拟合后的各个参数值
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ans = '\n函数系数：F(x) = '
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letter = str(letters[2:]).split(',')
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pattern = re.compile('|'.join(letter))
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s = pattern.sub('{:.2g}', gl_data.FITT_SAVE['demo'][xian_index])
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print(pattern)
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print(gl_data.FITT_SAVE['demo'][xian_index])
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print("s:", s)
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ans += str(s.format(*popt))
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gl_data.Out = '函数形式：' + gl_data.FITT_SAVE['name'][xian_index] + ' '
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gl_data.Out += gl_data.FITT_SAVE['demo'][xian_index]
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gl_data.Out += ans
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gl_data.Out += '\n拟合优度(R\u00b2)：' + str(round(rr, 5))
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# 这里是使用curve_fit实现的，实际使用的方式
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def fit_XX(xian_index, sampleData):
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sx, sy = sampleData[:, 0], sampleData[:, 1]
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letters, result = gl_data.FITT_SAVE['variable'][xian_index], gl_data.FITT_SAVE['function'][xian_index]
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code_str = fitting(letters, result) # 获取当前选择函数的表达式
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|
func = create_func(code_str) # 构造当前函数的func方法
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popt, pcov = curve_fit(func, sx, sy) # 用curve_fit来对数据进行拟合
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|
y_pred = func(sx, *popt) # 获取y的预测值
|
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|
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|
gl_data.CurveData = compute_curveData_X5(func, popt) # 计算拟合曲线的数据
|
|
|
rr = goodness_of_fit_easy(y_pred, sy) # 计算本次拟合的R*R值，用于表示拟合优度
|
|
|
|
|
|
# 输出拟合后的各个参数值
|
|
|
ans = '\n函数系数：F(x) = '
|
|
|
letter = str(letters[2:]).split(',')
|
|
|
pattern = re.compile('|'.join(letter))
|
|
|
s = pattern.sub('{:.2g}', gl_data.FITT_SAVE['demo'][xian_index])
|
|
|
print(pattern)
|
|
|
print(gl_data.FITT_SAVE['demo'][xian_index])
|
|
|
print("s:", s)
|
|
|
ans += str(s.format(*popt))
|
|
|
gl_data.Out = '函数形式：' + gl_data.FITT_SAVE['name'][xian_index] + ' '
|
|
|
gl_data.Out += gl_data.FITT_SAVE['demo'][xian_index]
|
|
|
gl_data.Out += ans
|
|
|
gl_data.Out += '\n拟合优度(R\u00b2)：' + str(round(rr, 5))
|
|
|
|
|
|
# 编程4.10 END-----------------------------------------
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|
|
# 编程4.11 -----------------------------------------
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|
|
def change_Q(no):
|
|
|
gl_data.Quadrant = no #更改全局变量的象限显示
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|
|
if no: #若为一象限，则修改显示下限为0
|
|
|
gl_data.LOW = 0
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|
|
else: #若为四象限，则修改显示下限为-gl_data.MAXV
|
|
|
gl_data.LOW = -gl_data.MAXV
|
|
|
q_button_X3() #更新象限显示面板
|
|
|
|
|
|
|
|
|
def q_button_X3():
|
|
|
r = 7.5
|
|
|
rr = 2.5
|
|
|
for widget in Q_root.winfo_children():
|
|
|
widget.destroy()
|
|
|
q_cv = tk.Canvas(Q_root, width=450, height=500)
|
|
|
q_cv.place(x=0, y=0)
|
|
|
l = tk.Label(Q_root, text='坐标轴', bd=0, font=("微软雅黑", 16) , anchor=W)
|
|
|
l.place(x=20, y=0, width=80, height=50,)
|
|
|
# 四象限按钮
|
|
|
b1 = tk.Button(Q_root, text='四象限', bd=0, font=("微软雅黑", 16)
|
|
|
, command=lambda: change_Q(0), anchor=W)
|
|
|
b1.place(x=170, y=0, width=80, height=50,)
|
|
|
# 一象限按钮
|
|
|
b2 = tk.Button(Q_root, text='一象限', bd=0, font=("微软雅黑", 16)
|
|
|
, command=lambda: change_Q(1), anchor=W)
|
|
|
b2.place(x=320, y=0, width=80, height=50,)
|
|
|
# 绘制标记框
|
|
|
q_cv.create_oval(140 - r, 25 - r, 140 + r, 25 + r, fill="white", width=1, outline="black")
|
|
|
q_cv.create_oval(290 - r, 25 - r, 290 + r, 25 + r , fill="white", width=1, outline="black")
|
|
|
# 根据当前的象限选择值来填充标记框
|
|
|
if gl_data.Quadrant == 0:
|
|
|
q_cv.create_oval(140 - rr, 25 - rr, 140 + rr, 25 + rr, fill="black", width=1, outline="black")# 绘制象限
|
|
|
else:
|
|
|
q_cv.create_oval(290 - rr, 25 - rr, 290 + rr, 25 + rr, fill="black", width=1, outline="black")
|
|
|
if __name__ == '__main__':
|
|
|
# 象限选择相关界面
|
|
|
Q_root = tk.Tk()
|
|
|
Q_root.geometry("500x550") # 设置窗口的大小和位置
|
|
|
q_button_X3()
|
|
|
Q_root.mainloop()
|
|
|
|
|
|
# 编程4.11 END----------------------------------------- |
