import sys
import numpy as np
import data as gl_data
import tkinter as tk
from tkinter import *
import tkinter.filedialog  # 注意次数要将文件对话框导入
import re
import inspect

from X1 import *
from X2 import *
from X3 import *


import function
import input

global Q_root,F_root
global root_window
global label1,label2,label3


def window():
    global root_window
    global label1, label2, label3
    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)
    # 使用按钮控件调用函数
    tk.Button(root_window, text="装载", relief=RAISED, command=lambda: askfile(label3)).place(x=370, y=12)
    label2 = tk.Label(root_window, text="拟合曲线类型", font=('Times', 12), bg="white",
                      width=20, height=3, # 设置标签内容区大小
                      padx=0, pady=0, borderwidth=0, )
    # 设置填充区距离、边框宽度和其样式（凹陷式）
    label2.place(x=450, y=4)
    gl_data.Canvas2 = tk.Canvas(root_window, bg='white', width=450, height=330)
    gl_data.Canvas2.place(x=4, y=60)


# 定义一个处理文件的相关函数
def askfile(label3):
    # 从本地选择一个文件，并返回文件的路径
    filename = tkinter.filedialog.askopenfilename()
    if filename != '':#若选中了一个文件，则对文件进行读取
        label3.config(text=filename)#显示文件路径
        read_sample_data(filename)#将文件读取并存到sampleData中
        # print_sample_data(filename)#将文件读取并逐行输出
        selfdata_show(gl_data.SampleData, gl_data.LOW, gl_data.HIGH)
    else:#若未选择文件，则显示为空
        label3.config(text='')

def print_sample_data(file_path):#打开对应文件
    with open(file_path, 'r') as file:
        for line in file:#逐行读取文件
            line = line.strip('\n')#移除换行符
            sx, sy = line.split(' ')#以空格分割x，y
            print(f'sx: {float(sx)}, sy: {float(sy)}')#将sx、sy转换为浮点数并打印


def read_sample_data(file_path):
    x, y = [], []#初始化x，y
    with open(file_path, 'r') as file:
        for line in file:#逐行读文件
            line = line.strip('\n')#移除换行符
            sx, sy = line.split(' ')#以空格分割x，y
            x.append(float(sx))#将sx转换为浮点数并加入数组
            y.append(float(sy))#将sy转换为浮点数并加入数组
    gl_data.SampleData = np.array(list(zip(x, y)))#x，y数组转为array并赋值给全局变量
    # x=np.array(x) 	# 列表转数组
    # y=np.array(y)
    # gl_data.SampleData = np.array(list(zip(x, y)))


# #################################拟合优度R^2的计算######################################
def __sst(y_no_fitting):
    """
    计算SST(total sum of squares) 总平方和
    :param y_no_predicted: List[int] or array[int] 待拟合的y
    :return: 总平方和SST
    """
    y_mean = sum(y_no_fitting) / len(y_no_fitting)
    s_list =[(y - y_mean)**2 for y in y_no_fitting]
    sst = sum(s_list)
    return sst


def __ssr(y_fitting, y_no_fitting):
    """
    计算SSR(regression sum of squares) 回归平方和
    :param y_fitting: List[int] or array[int]  拟合好的y值
    :param y_no_fitting: List[int] or array[int] 待拟合y值
    :return: 回归平方和SSR
    """
    y_mean = sum(y_no_fitting) / len(y_no_fitting)
    s_list =[(y - y_mean)**2 for y in y_fitting]
    ssr = sum(s_list)
    return ssr


def __sse(y_fitting, y_no_fitting):
    """
    计算SSE(error sum of squares) 残差平方和
    :param y_fitting: List[int] or array[int] 拟合好的y值
    :param y_no_fitting: List[int] or array[int] 待拟合y值
    :return: 残差平方和SSE
    """
    s_list = [(y_fitting[i] - y_no_fitting[i])**2 for i in range(len(y_fitting))]
    sse = sum(s_list)
    return sse


def goodness_of_fit(y_fitting, y_no_fitting):
    """
    计算拟合优度R^2
    :param y_fitting: List[int] or array[int] 拟合好的y值
    :param y_no_fitting: List[int] or array[int] 待拟合y值
    :return: 拟合优度R^2
    """
    sse = __sse(y_fitting, y_no_fitting)
    sst = __sst(y_no_fitting)
    rr = 1 - sse /sst
    return rr


# 简化版本的goodness_of_fit
def goodness_of_fit_easy(y_fitting, y_no_fitting):
    """
    计算拟合优度R^2
    :param y_fitting: List[int] or array[int] 拟合好的y值
    :param y_no_fitting: List[int] or array[int] 待拟合y值
    :return: 拟合优度R^2
    """
    y_mean = sum(y_no_fitting) / len(y_no_fitting)
    # 计算SST
    sst = sum((y - y_mean) ** 2 for y in y_no_fitting)
    # 计算SSE
    sse = sum((y_fitting[i] - y_no_fitting[i]) ** 2 for i in range(len(y_fitting)))
    # 计算R^2
    r_squared = 1 - sse / sst
    return r_squared


# 由于之前的函数修改了，这里需要对四个按钮的函数进行编程
def BF_generate_plot_dada(low, high):
    num_samples = 25  # 设置样本点数量
    sampleData = random_points(num_samples, low, high)  # 生成随机样本数据25个
    generate_and_plot_sample_data(sampleData, low, high)


def BF_plot_data(sampleData,low,high):
    selfdata_show(sampleData,low,high)


def BF_plot_data_and_curve(curveData, sampleData,low,high):
    # curveData = compute_curveData2(low,high,1,theta,m, x.mean(), x.std())
    draw_dots_and_line(curveData,sampleData,low,high)


# # X4版本
def BF_fit_X4(xian_index, sampleData):
    # gl_data.yvals_pow = []

    print(xian_index)

    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

    sx = sampleData[:,0]
    sy = sampleData[:,1]

    popt, pcov = curve_fit(func, sx, sy)# 用curve_fit来对点进行拟合
    yvals_pow = func(sx, *popt)

    # 这里直接套用会没有CurveData
    x_values = np.arange(gl_data.LOW, gl_data.HIGH, 1)
    y_values = func(x_values, *popt)
    gl_data.CurveData = np.column_stack((x_values, y_values))

    rr = goodness_of_fit(yvals_pow, 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) = '
    for i in range(cur.variable):
        if i == 4:
            ans += '\n'
        if i != 0:
            ans += ', '
        ans += chr(ord('a') + i) + '=' + '{:.2e}'.format(popt[i])  # str(round(gl_data.popt[i], 3))
    gl_data.Out = '函数形式：' + cur.name + '  '
    gl_data.Out += cur.demo
    gl_data.Out += ans
    gl_data.Out += '\n拟合优度(R\u00b2)：' + str(round(rr, 5))
    show_fit_X4()# 显示函数信息到输出框

# # X4版本
# 这里是梯度下降法更新后的新的按钮函数
def BF_fit_X4_testnew(xian_index, sampleData):
    # # print(xian_index)
    #
    # 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
    #
    # sx = sampleData[:,0]
    # sy = sampleData[:,1]
    #
    # popt, pcov = gradient_descent_method_X5(func, sx, sy)# 用curve_fit来对点进行拟合
    #
    # x_normalized = (sx - np.mean(sx)) / np.std(sx)
    # yvals_pow = func(x_normalized, *popt)
    #
    # # print("yvals_pow", yvals_pow)
    # # print("sy", sy)
    #
    # # 这里直接套用会没有CurveData
    # x_values = np.arange(gl_data.LOW, gl_data.HIGH, 1)
    # x_values_normalized = (x_values - np.mean(sx)) / np.std(sx)
    # y_values = func(x_values_normalized, *popt)
    # gl_data.CurveData = np.column_stack((x_values, y_values))
    #
    # rr = goodness_of_fit(yvals_pow, 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) = '
    # for i in range(cur.variable):
    #     if i == 4:
    #         ans += '\n'
    #     if i != 0:
    #         ans += ', '
    #     ans += chr(ord('a') + i) + '=' + '{:.2e}'.format(popt[i])  # str(round(gl_data.popt[i], 3))
    # gl_data.Out = '函数形式：' + cur.name + '  '
    # gl_data.Out += cur.demo
    # gl_data.Out += ans
    # gl_data.Out += '\n拟合优度(R\u00b2)：' + str(round(rr, 5))

    fit_X4(xian_index, sampleData)
    show_fit_X4()# 显示函数信息到输出框


# 编程4.18 -----------------------------------------
def fitting(letters,result):
    code_str = '''
def func({}):
    return {}
    '''.format(letters,result)
    print("code_str",code_str)
    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']

# 这里由于拓展的各种函数，因此使用的curve_fit
def BF_fit(root_window, screen_size, xian_index, sampleData):
    fit_XX(xian_index, sampleData)
    show_fit(root_window, screen_size)  # 显示函数信息到输出框，包括上述信息

# 这里是因为自己的梯度下降法可以使用了，所以整一个新的，同时按照X4中的工作，将BF_Fit修改为简单的函数调用形式，而不是一大片
def BF_fit_X5_new(root_window, screen_size, xian_index, sampleData):
    fit_X5(xian_index, sampleData)
    show_fit(root_window, screen_size)  # 显示函数信息到输出框，包括上述信息

# 编程4.18 END-----------------------------------------


def buttons():
    # 随机生成样本点并显示
    b_dot = tk.Button(root_window, text="生成\n数据集", relief=RAISED, bd=4, bg="white", font=("微软雅黑", 10),
                   command=lambda: BF_generate_plot_dada(gl_data.LOW, gl_data.HIGH))
    b_dot.place(x=455, y=410)
    # 显示当前样本点
    b_show = tk.Button(root_window, text="显示\n数据集", relief=RAISED, bd=4, bg="white", font=("微软雅黑", 10),
              command=lambda: BF_plot_data(gl_data.SampleData, gl_data.LOW, gl_data.HIGH))
    b_show.place(x=510, y=410)
    # 显示数据集与曲线
    b_line = tk.Button(root_window, text="显示数据\n集与曲线", relief=RAISED, bd=4, bg="white", font=("微软雅黑", 10),
                   command=lambda: BF_plot_data_and_curve(gl_data.CurveData, gl_data.SampleData, gl_data.LOW, gl_data.HIGH))
    b_line.place(x=565, y=410)
    # 手动输入数据集
    b_input = tk.Button(root_window, text="手动输入数据集", relief=RAISED, bd=4, bg="white", pady=7, font=("微软雅黑", 13),
                        command=lambda: input.input_num(root_window))
    b_input.place(x=633, y=410)
    # 拟合并输出拟合结果
    b_fit = tk.Button(root_window, text="拟合", relief=RAISED, bd=4, bg="white", pady=7, font=("微软雅黑", 13),
                   command=lambda: BF_fit_X4_testnew(gl_data.Xian_index, gl_data.SampleData))
    b_fit.place(x=771, y=410)


# 这里是X4版本的代码，X5修改了界面
def show_fit_X4():
    L = tk.Label(root_window, text='结果输出：', bg='white', font=("微软雅黑", 16)
                                   , anchor=W)
    L.place(x=20, y=480, width=100, height=30)
    ans = tk.Label(root_window, text=gl_data.Out, font=("微软雅黑", 14)
                                   , anchor=W, justify='left')
    ans.place(x=120, y=480, width=760, height=100)
    print(gl_data.Out)


def show_fit(root_window, screen_size):
    sout = str(gl_data.Out)
    ans = tk.Label(root_window, text=sout, font=("微软雅黑", 14)
                                   , anchor=W, justify='left')
    ans.place(x=int(120*screen_size), y=int(480*screen_size), width=int(760*screen_size), height=100)
    print(sout)


# 拟合按钮所对应的函数
def fit_data(xian_index, sampleData):
    # gl_data.yvals_pow = []
    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

    sx, sy = sampleData[:, 0], sampleData[:, 1]

    # 使用 inspect.signature 获取函数的签名信息
    sig = inspect.signature(func)
    # 获取所有参数的名称
    params = sig.parameters
    print(params)

    popt, pcov = curve_fit(func, sx, sy)# 用curve_fit来对点进行拟合
    yvals_pow = func(sx, *popt)

    rr = goodness_of_fit(yvals_pow, 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：'
    for i in range(cur.variable):
        if i == 4:
            ans += '\n'
        if i != 0:
            ans += ', '
        ans += chr(ord('a') + i) + '=' + '{:.2e}'.format(popt[i])  # str(round(gl_data.popt[i], 3))
    gl_data.Out = '函数形式：' + cur.name + '  '
    gl_data.Out += cur.demo
    gl_data.Out += ans
    gl_data.Out += '\n拟合优度(R\u00b2)：' + str(round(rr, 5))
    show_fit_X4()  # 显示函数信息到输出框


def change_Q_X4(no):
    gl_data.Quadrant = no #更改全局变量的象限显示
    if no:#若为一象限，则修改显示下限为0
        gl_data.LOW = 0
    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_X4()#更新象限显示面板


def q_button_X4():
    r = 7.5
    rr = 2.5
    for widget in Q_root.winfo_children():
        widget.destroy()
    q_cv = tk.Canvas(Q_root, width=400, height=50)
    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")
    else:
        q_cv.create_oval(290 - rr, 25 - rr, 290 + rr, 25 + rr
                     , fill="black", width=1, outline="black")


def change_f_X4(no):
    gl_data.Xian_index = no#设置全局函数编号为该函数
    f_button_X4()#重新绘制函数显示界面


def f_button_X4():
    r = 7.5#设置按钮大小
    rr = 2.5#设置按钮大小
    for widget in F_root.winfo_children():
        widget.destroy()#清空原有按钮
    f_cv = tk.Canvas(F_root, width=400, height=330)#新建画布
    f_cv.place(x=0, y=0)#放置画布
    f_cv.create_rectangle(2, 2, 398, 328, fill='white', outline="#0099ff")#设置画布边框及底色
    cur = 0#函数计数

    for fun in function.Fit_type_library:#遍历函数库
        f = function.Fit_type_library.get(fun)#获取函数具体信息
        if function.Show[f[0]] == 1:# 如果show为1则显示
            f_cv.create_oval(20 - r, 20 + 15 + cur * 30 - r, 20 + r, 20 + 15 + cur * 30 + r
                             , fill="white", width=1, outline="black")# 绘制标记框
            if f[0] == gl_data.Xian_index:# 若选择为当前函数则标记
                f_cv.create_oval(20 - rr, 20 + 15 + cur * 30 - rr, 20 + rr, 20 + 15 + cur * 30 + rr
                                 , fill="black", width=1, outline="black")
            # 绘制切换按钮,单击则将当前使用函数切换为该函数
            button = tk.Button(F_root, text=f[2] + ' ' + f[1], bd=0, bg="white", font=("微软雅黑", 12)
                               , command=lambda x=f[0]: change_f_X4(x), anchor=W)
            button.place(x=40, y=20 + cur * 30, width=300, height=30)
            cur += 1#计数加一


def close_window():
    sys.exit()


def main():
    global Q_root, F_root, root_window
    gl_data.X = []
    gl_data.X = []
    window()
    function.f_read()
    # 函数选择相关界面
    F_root = tk.Frame(root_window, width=400, height=330, bg='white', )
    F_root.place(x=490, y=60)
    # 象限选择相关界面
    Q_root = tk.Frame(root_window, width=400, height=50, bg='white', )
    Q_root.place(x=20, y=410)
    buttons()
    f_button_X4()
    q_button_X4()
    show_fit_X4()
    root_window.protocol("WM_DELETE_WINDOW", close_window)
    root_window.mainloop()


if __name__ == '__main__':
    main()

# X4除了没有使用自己定义的方法以外基本差不多了