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# -*- encoding: utf-8 -*-
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"""
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@Author: packy945
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@FileName: fitting.py
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@DateTime: 2023/5/31 14:16
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@SoftWare: PyCharm
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import pylab as mpl
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from tkinter import *
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import mpl_toolkits.axisartist as axisartist
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import data as gl_data
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# import function
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mpl.rcParams['font.sans-serif'] = ['SimHei'] # 解决中文不显示问题
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plt.rcParams['axes.unicode_minus'] = False #解决负数坐标显示问题
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def draw_axis(low, high, step=250):
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fig = plt.figure(figsize=(4.4, 3.2))
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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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def generate_and_plot_sample_data(low, high):
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num_samples = 25#设置样本点数量
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gl_data.X = np.random.randint(low=low, high=high, size=num_samples)#随机生成x值
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gl_data.Y = np.random.randint(low=low, high=high, size=num_samples)#随机生成y值
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draw_axis(low, high)#绘制坐标轴
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plt.scatter(gl_data.X, gl_data.Y, color='red') # 绘制样本数据点
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plt.savefig(r"dot.png", facecolor='w')#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
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# 显示数据集
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def selfdata_show(x, y, low, high):# 显示数据集
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num_points = len(x) # 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(low, high)
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plt.scatter(x, y, c=colors)# 绘制样本数据点
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plt.savefig(r"dot.png", facecolor='w') # 保存为png文件
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plt.clf()
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set_phtot(1)
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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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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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def draw_line(xian_index, 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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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)
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func = create_func(code_str) # 获取当前函数func
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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 = curve_fit(func, 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 = [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
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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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plt.savefig(r"line.png", facecolor='w')#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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def draw_dots_and_line(xian_index, low, high, sx, sy):
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if gl_data.Quadrant:
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x = sx.tolist()
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y = sy.tolist()
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negative_x = [i for i, num in enumerate(x) if num < 0]
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negative_y = [i for i, num in enumerate(y) if num < 0]
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negative = list(set(negative_x + negative_y))
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for index in range(len(negative)):
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# print(negative[-index-1])
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x.pop(negative[-index - 1])
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y.pop(negative[-index - 1])
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sx = np.array(x)
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sy = np.array(y)
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print(sx)
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############## begin #####################
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draw_axis(low, high)
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popt = []
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pcov = []
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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)
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func = create_func(code_str) # 获取当前函数func
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print(code_str)
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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 = curve_fit(func, sx, sy)# 用curve_fit来对点进行拟合
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positive_mask = sy >= 0.0#给样本点分类
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negative_mask = sy < 0.0#给样本点分类
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positive_colors = ['red' if x >= 0.0 else 'red' for x in sx]#给样本点分类
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negative_colors = ['red' if x >= 0.0 else 'red' for x in sx]#给样本点分类
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#根据样本点类型决定样本点颜色
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plt.scatter(sx[positive_mask], sy[positive_mask], color=np.array(positive_colors)[positive_mask], lw=1)
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plt.scatter(sx[negative_mask], sy[negative_mask], color=np.array(negative_colors)[negative_mask], lw=1)
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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 = [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
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############## end #######################
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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.savefig(r"line.png", facecolor='w')#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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def set_phtot(index1):
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# 显示图像
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Canvas2 = gl_data.Canvas2
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if index1 == 1:
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gl_data.Img1=PhotoImage(file=r"dot.png")
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# print(gl_data.Img1)
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gl_data.Img1 = gl_data.Img1.zoom(2, 2)
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set_img1=Canvas2.create_image(2, 10, image=gl_data.Img1, anchor=NW)
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# Canvas2.itemconfigure(set_img1,int(420 * gl_data.MAGNIDICATION), int(300 * gl_data.MAGNIDICATION))
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Canvas2.update()
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print("已输出数据点")
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elif index1 == 2:
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gl_data.Img2=PhotoImage(file=r"line.png")
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# print(gl_data.Img2)
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gl_data.Img2 = gl_data.Img2.zoom(2, 2)
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set_img2=Canvas2.create_image(2, 10, image=gl_data.Img2, anchor=NW)
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# Canvas2.itemconfigure(set_img2, 0,0,int(420*gl_data.MAGNIDICATION), int(300*gl_data.MAGNIDICATION))
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Canvas2.update()
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print("已输出数据点和曲线")
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if __name__ == '__main__':
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# random_points()
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pass
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