fitt/fitting.py

# -*- encoding: utf-8 -*-
"""
 @Author: packy945
 @FileName: fitting.py
 @DateTime: 2023/5/31 14:16
 @SoftWare: PyCharm
"""

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import pylab as mpl
from tkinter import *
import mpl_toolkits.axisartist as axisartist
import data as gl_data
# import function

mpl.rcParams['font.sans-serif'] = ['SimHei']  # 解决中文不显示问题
plt.rcParams['axes.unicode_minus'] = False       #解决负数坐标显示问题

def draw_axis(low, high, step=250):
    fig = plt.figure(figsize=(4.4, 3.2))
    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

def generate_and_plot_sample_data(low, high):
    num_samples = 25#设置样本点数量
    gl_data.X = np.random.randint(low=low, high=high, size=num_samples)#随机生成x值
    gl_data.Y = np.random.randint(low=low, high=high, size=num_samples)#随机生成y值
    draw_axis(low, high)#绘制坐标轴
    plt.scatter(gl_data.X, gl_data.Y, color='red')  # 绘制样本数据点
    plt.savefig(r"dot.png", facecolor='w')#保存到本地
    plt.close()#清除内存
    set_phtot(1)#显示到主界面

# 显示数据集
def selfdata_show(x, y, low, high):# 显示数据集
    num_points = len(x)  # 样本数据点的数量
    colors = np.random.rand(num_points, 3)# 生成随机颜色
    draw_axis(low, high)
    plt.scatter(x, y, c=colors)# 绘制样本数据点
    plt.savefig(r"dot.png", facecolor='w')  # 保存为png文件
    plt.clf()
    set_phtot(1)

def fitting(letters,result):
    code_str = '''
def func({}):
    return {}
    '''.format(letters,result)
    return code_str

def create_func(code_str):
    # 创建一个空的命名空间
    namespace = {}
    # 使用exec函数执行字符串代码，并指定命名空间为locals
    exec(code_str, globals(), namespace)
    # 返回命名空间中的函数对象
    return namespace['func']

def draw_line(xian_index, 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
    letters, result = gl_data.FITT_SAVE['variable'][xian_index], gl_data.FITT_SAVE['function'][xian_index]
    code_str = fitting(letters, result)
    func = create_func(code_str)  # 获取当前函数func
    # cur = function.Fun[xian_index]  # 装载正在选择的函数
    # func = cur.get_fun()#获取当前函数func
    popt, pcov = curve_fit(func, 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 = [func(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
    plt.savefig(r"line.png", facecolor='w')#将图片保存到本地
    plt.close()#清除内存
    set_phtot(2)#将图片显示到程序中

def draw_dots_and_line(xian_index, low, high, sx, sy):
    if gl_data.Quadrant:
        x = sx.tolist()
        y = sy.tolist()
        negative_x = [i for i, num in enumerate(x) if num < 0]
        negative_y = [i for i, num in enumerate(y) if num < 0]
        negative = list(set(negative_x + negative_y))
        for index in range(len(negative)):
            # print(negative[-index-1])
            x.pop(negative[-index - 1])
            y.pop(negative[-index - 1])
        sx = np.array(x)
        sy = np.array(y)
    print(sx)
    ############## begin #####################
    draw_axis(low, high)
    popt = []
    pcov = []
    letters, result = gl_data.FITT_SAVE['variable'][xian_index], gl_data.FITT_SAVE['function'][xian_index]
    code_str = fitting(letters, result)
    func = create_func(code_str)  # 获取当前函数func
    print(code_str)
    # cur = function.Fun[xian_index]  # 装载正在选择的函数
    # func = cur.get_fun()#获取当前函数func
    popt, pcov = curve_fit(func, sx, sy)# 用curve_fit来对点进行拟合
    positive_mask = sy >= 0.0#给样本点分类
    negative_mask = sy < 0.0#给样本点分类
    positive_colors = ['red' if x >= 0.0 else 'red' for x in sx]#给样本点分类
    negative_colors = ['red' if x >= 0.0 else 'red' for x in sx]#给样本点分类
    #根据样本点类型决定样本点颜色
    plt.scatter(sx[positive_mask], sy[positive_mask], color=np.array(positive_colors)[positive_mask], lw=1)
    plt.scatter(sx[negative_mask], sy[negative_mask], color=np.array(negative_colors)[negative_mask], lw=1)
    curve_x = np.arange(low, high)#按照步长生成的一串数字
    curve_y = [func(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
    ############## end #######################
    plt.plot(curve_x, curve_y, color='blue', label='Fitted Curve')#绘制拟合曲线
    plt.legend()
    plt.savefig(r"line.png", facecolor='w')#将图片保存到本地
    plt.close()#清除内存
    set_phtot(2)#将图片显示到程序中



def set_phtot(index1):
    # 显示图像
    Canvas2 = gl_data.Canvas2
    if index1 == 1:
        gl_data.Img1=PhotoImage(file=r"dot.png")
        # print(gl_data.Img1)
        gl_data.Img1 = gl_data.Img1.zoom(2, 2)
        set_img1=Canvas2.create_image(2, 10, image=gl_data.Img1, anchor=NW)
        # Canvas2.itemconfigure(set_img1,int(420 * gl_data.MAGNIDICATION), int(300 * gl_data.MAGNIDICATION))
        Canvas2.update()
        print("已输出数据点")
    elif index1 == 2:
        gl_data.Img2=PhotoImage(file=r"line.png")
        # print(gl_data.Img2)
        gl_data.Img2 = gl_data.Img2.zoom(2, 2)
        set_img2=Canvas2.create_image(2, 10, image=gl_data.Img2, anchor=NW)
        # Canvas2.itemconfigure(set_img2, 0,0,int(420*gl_data.MAGNIDICATION), int(300*gl_data.MAGNIDICATION))
        Canvas2.update()
        print("已输出数据点和曲线")
if __name__ == '__main__':
    # random_points()

    pass