#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
#-----------------------------------------------------------------------------
#                     --- TDOUYA STUDIOS ---
#-----------------------------------------------------------------------------
#
# @Project : di08-tdd-cdg-python-learning
# @File    : monte_carlo.py
# @Author  : tianxin.xp@gmail.com
# @Date    : 2023/3/12 18:22
#
# 用Python實(shí)現(xiàn)蒙特卡洛模擬
#
#--------------------------------------------------------------------------"""
from datetime import datetime

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FuncFormatter, MultipleLocator
from scipy.stats import norm

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False


def to_percent(y, position):
    # 將縱軸用百分?jǐn)?shù)表示
    return '{:.0f}%'.format(100 * y)


class Activity:
    """ 活動(dòng)類，用于表示一個(gè)項(xiàng)目中的活動(dòng)

   Attributes:
       name (str): 活動(dòng)名稱
       optimistic (float): 樂(lè)觀時(shí)間
       pessimistic (float): 悲觀時(shí)間
       most_likely (float): 最可能時(shí)間
   """

    def __init__(self, name, optimistic, pessimistic, most_likely):
        """
            初始化活動(dòng)類

            Args:
                name (str): 活動(dòng)名稱
                optimistic (float): 樂(lè)觀時(shí)間
                pessimistic (float): 悲觀時(shí)間
                most_likely (float): 最可能時(shí)間
        """
        self.name = name
        self.optimistic = optimistic
        self.pessimistic = pessimistic
        self.most_likely = most_likely


class PMP:
    """
    PMP類用于進(jìn)行項(xiàng)目管理中的相關(guān)計(jì)算：
    方法：
    monte_carlo_simulation : 蒙特卡洛模擬試算，包括計(jì)算項(xiàng)目工期、平均值、標(biāo)準(zhǔn)差、繪制積累圖和概率密度曲線等功能。
    """

    def __init__(self, activities):
        """
        初始化PMP類，傳入活動(dòng)列表。
        :param activities: 活動(dòng)列表，包括活動(dòng)名稱、樂(lè)觀值、最可能值和悲觀值。
        """
        self.activities = activities

    def monte_carlo_simulation(self, n):
        """
        進(jìn)行蒙特卡洛模擬試算，計(jì)算項(xiàng)目工期、平均值、標(biāo)準(zhǔn)差、繪制積累圖和概率密度曲線等。
        :param n: 模擬次數(shù)。
        """
        # 模擬參數(shù)和變量
        t = []
        for activity in self.activities:
            t.append(np.random.triangular(activity.optimistic, activity.most_likely, activity.pessimistic, n))

        # 計(jì)算項(xiàng)目工期
        project_duration = sum(t)

        # 計(jì)算平均值和標(biāo)準(zhǔn)差
        mean_duration = np.mean(project_duration)
        std_duration = np.std(project_duration)

        # 繪制積累圖
        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10), gridspec_kw={'height_ratios': [3, 1]})

        ax1.hist(project_duration, bins=50, density=True, alpha=0.7, color='blue', cumulative=True)
        ax1.yaxis.set_major_locator(MultipleLocator(0.1))
        ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
        ax1.set_ylabel('完成概率')
        ax1.set_title('PMP蒙特卡洛模擬試算', fontsize=20)

        # 繪制概率密度曲線
        xmin, xmax = ax1.get_xlim()
        x = np.linspace(xmin, xmax, 100)
        p = norm.cdf(x, mean_duration, std_duration)
        ax1.plot(x, p, 'k', linewidth=2, drawstyle='steps-post')

        # 找到完成概率90%的點(diǎn)
        x_90 = norm.ppf(0.9, mean_duration, std_duration)

        # 繪制垂線
        ax1.axvline(x_90, linestyle='--', color='blue')
        ax1.axhline(0.9, linestyle='--', color='blue')

        # 隱藏右邊和上方的坐標(biāo)軸線
        ax1.spines['right'].set_visible(False)
        ax1.spines['top'].set_visible(False)

        # 添加表格
        col_labels = ['活動(dòng)名稱', '樂(lè)觀值', '最可能值', '悲觀值']

        cell_text = [[activity.name, activity.optimistic, activity.most_likely, activity.pessimistic] for activity in
                     self.activities]
        table = ax2.table(cellText=cell_text, colLabels=col_labels, loc='center')

        # 設(shè)置表格的字體大小和行高
        table.auto_set_font_size(False)
        table.set_fontsize(14)

        # # 設(shè)置表格的行高為1.5倍原來(lái)的高度
        for i in range(len(self.activities) + 1):
            table._cells[(i, 0)].set_height(0.2)
            table._cells[(i, 1)].set_height(0.2)
            table._cells[(i, 2)].set_height(0.2)
            table._cells[(i, 3)].set_height(0.2)

        ax2.axis('off')

        # 調(diào)整子圖之間的間距和邊距
        plt.subplots_adjust(hspace=0.3, bottom=0.05)

        # 保存圖表
        now = datetime.now().strftime('%Y%m%d%H%M%S')
        plt.savefig('monte_carlo_simulation_{}.png'.format(now))

        # 顯示圖形
        plt.show()


if __name__ == '__main__':
    # 模擬參數(shù)和變量
    n = 1000000  # 模擬次數(shù)

    # 活動(dòng)的工期分布
    activities = [
        Activity('活動(dòng)1', 5, 10, 7),
        Activity('活動(dòng)2', 3, 8, 5),
        Activity('活動(dòng)3', 2, 6, 4)
    ]

    # 進(jìn)行蒙特卡洛模擬
    pmp = PMP(activities)
    pmp.monte_carlo_simulation(n)