import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib
import ipywidgets as widgets
from IPython.display import display, clear_output

# シミュレーション関数
def simulate_withdrawal(start_age, years, initial_assets, monthly_withdraw, annual_return, annual_risk, n_sim=1000):
    months = years * 12
    results = []
    for _ in range(n_sim):
        assets = initial_assets
        monthly_returns = np.random.normal(
            loc=(annual_return/100)/12,
            scale=(annual_risk/100)/np.sqrt(12),
            size=months
        )
        trajectory = []
        for r in monthly_returns:
            assets *= (1 + r)           
            assets -= monthly_withdraw  
            assets = max(0, assets)     
            trajectory.append(assets)
        results.append(trajectory)
    return pd.DataFrame(results).T  

# 実行関数
def run_simulation(b):
    clear_output(wait=True)
    display(ui)
    
    # 入力値取得
    start_age = age_slider.value
    years = years_slider.value
    initial_assets = assets_slider.value
    monthly_withdraw = withdraw_slider.value
    annual_return = return_slider.value
    annual_risk = risk_slider.value

    # シミュレーション実行
    df = simulate_withdrawal(start_age, years, initial_assets, monthly_withdraw, annual_return, annual_risk)
    mean_path = df.mean(axis=1)
    median_path = df.median(axis=1)
    best_path = df.max(axis=1)
    worst_path = df.min(axis=1)
    months = np.arange(1, years*12 + 1)

    # 最終結果テキスト
    final_assets = df.iloc[-1]
    print(
        f"最終結果:"
        f"  破産確率: {100 * (final_assets==0).mean():.1f}%"
        f"  平均資産額: {final_assets.mean():,.0f} 万円"
        f"  中央資産額: {final_assets.median():,.0f} 万円"
        f"  最良ケース: {final_assets.max():,.0f} 万円"
        f"  最悪ケース: {final_assets.min():,.0f} 万円"
    )

    # グラフ描画 (matplotlib)
    plt.figure(figsize=(10,6))
    #plt.plot(months, best_path, label="最良ケース", color="green")
    plt.plot(months, worst_path, label="最悪ケース", color="red")
    plt.plot(months, mean_path, label="平均ケース", color="blue")
    plt.plot(months, median_path, label="中央値ケース", color="orange")
    plt.title(f"資産取り崩しシミュレーション（開始年齢: {start_age}歳, 期間: {years}年）")
    plt.xlabel("経過月数")
    plt.ylabel("資産額 (万円)")
    plt.legend()
    plt.grid(True)
    plt.show()

# スライダーUI
age_slider = widgets.IntSlider(value=65, min=50, max=100, step=1, description="開始年齢")
years_slider = widgets.IntSlider(value=30, min=10, max=50, step=1, description="年数")
assets_slider = widgets.IntSlider(value=5000, min=1000, max=20000, step=100, description="資産(万円)")
withdraw_slider = widgets.IntSlider(value=20, min=10, max=100, step=1, description="月取崩(万円)")
return_slider = widgets.IntSlider(value=4, min=1, max=20, step=1, description="利回り%")
risk_slider = widgets.IntSlider(value=1, min=1, max=30, step=1, description="リスク%")

# 実行ボタン
run_button = widgets.Button(description="シミュレーション実行", button_style="success")
run_button.on_click(run_simulation)

# UIまとめて表示
ui = widgets.VBox([age_slider, years_slider, assets_slider,withdraw_slider, return_slider, risk_slider,run_button])
display(ui)

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import warnings
warnings.filterwarnings('ignore')

# パラメータ設定
g = 9.81   # 重力加速度
L1, L2 = 1.0, 1.0  # 振り子の長さ
m1, m2 = 1.0, 1.0  # 振り子の質量

# 初期条件（角度と角速度）
theta1 = np.pi/2
theta2 = np.pi/2 + 0.1
omega1 = 0.0
omega2 = 0.0
state = np.array([theta1, omega1, theta2, omega2])

# 時間設定
dt = 0.05
t_max = 25
steps = int(t_max/dt)

# 保存用リスト
x1_list, y1_list = [], []
x2_list, y2_list = [], []

# 二重振り子の運動方程式
def derivatives(state):
    theta1, omega1, theta2, omega2 = state
    delta = theta2 - theta1
    denom1 = (m1 + m2) * L1 - m2 * L1 * np.cos(delta)**2
    denom2 = (L2/L1) * denom1
    a1 = (m2*L1*omega1**2*np.sin(delta)*np.cos(delta) +
          m2*g*np.sin(theta2)*np.cos(delta) +
          m2*L2*omega2**2*np.sin(delta) -
          (m1+m2)*g*np.sin(theta1)) / denom1
    a2 = (-m2*L2*omega2**2*np.sin(delta)*np.cos(delta) +
          (m1+m2)*(g*np.sin(theta1)*np.cos(delta) -
                   L1*omega1**2*np.sin(delta) -
                   g*np.sin(theta2))) / denom2
    return np.array([omega1, a1, omega2, a2])

# Runge-Kutta 4次法で数値計算
for _ in range(steps):
    k1 = derivatives(state)
    k2 = derivatives(state + dt*k1/2)
    k3 = derivatives(state + dt*k2/2)
    k4 = derivatives(state + dt*k3)
    state += (dt/6)*(k1 + 2*k2 + 2*k3 + k4)
    theta1, omega1, theta2, omega2 = state

    # 座標変換
    x1 = L1 * np.sin(theta1)
    y1 = -L1 * np.cos(theta1)
    x2 = x1 + L2 * np.sin(theta2)
    y2 = y1 - L2 * np.cos(theta2)
    x1_list.append(x1)
    y1_list.append(y1)
    x2_list.append(x2)
    y2_list.append(y2)

fig, ax = plt.subplots(figsize=(6, 6))
ax.set_xlim(-2.2, 2.2)
ax.set_ylim(-2.2, 2.2)
ax.set_aspect('equal')
ax.axis("off")
line, = ax.plot([], [], 'o-', lw=2)       # 振り子本体
trace, = ax.plot([], [], 'r-', alpha=0.5) # 軌跡

# 軌跡用リスト
trace_x, trace_y = [], []
def update(i):
    # 点の位置（始点 → 中点 → 先端）
    thisx = [0, x1_list[i], x2_list[i]]
    thisy = [0, y1_list[i], y2_list[i]]
    line.set_data(thisx, thisy)
    trace_x.append(x2_list[i])
    trace_y.append(y2_list[i])
    trace.set_data(trace_x, trace_y)
    return line, trace

ani = FuncAnimation(fig, update, frames=len(x1_list), interval=40, blit=True)
ani.save("double_pendulum.mp4", fps=30, dpi=150)
plt.close(fig)

from IPython.display import Video
Video("double_pendulum.mp4", embed=True)

import numpy as np
import pandas as pd
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt

# データ定義
data = {
    2018 : [0,0,0,0,0,0,10038,10458,10709,11051,10188,10468],
    2019 : [8809,9854,10408,10565,10900,10031,10725,10978,10491,10890,11106,11680],
    2020 : [11873,11892,10825,9474,10655,11205,11465,11886,12707,12182,11767,12989],
    2021 : [13339,13407,14010,15217,15754,15924,16540,16712,17301,16677,18267,18005],
    2022 : [19291,18130,17601,19347,18798,18658,18052,19265,19394,18280,20283,19728],
    2023 : [17690,18714,19167,19388,20234,20666,22867,23260,23411,22911,22677,24155],
    2024 : [24154,25486,27473,28563,28573,29833,31693,29763,29789,29974,31336,32768],
    2025 : [33928,34065,32500,30512,28931,30867]
}

records = []
for year, prices in data.items():
    for month_idx, price in enumerate(prices):
        month = month_idx + 1
        if price == 0:
            continue
        date_str = f"{year}-{month:02d}-01"
        date = pd.to_datetime(date_str)
        records.append({"Date": date, "Price": price})

df = pd.DataFrame(records)
df = df.sort_values("Date")

# Plotlyでプロット
fig = px.line(df, x="Date", y="Price", title="Emaxis Slim value")
fig.show()