diff --git a/InnerModel.ipynb b/InnerModel.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm import tqdm\n",
+    "from sklearn.mixture import GaussianMixture"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 469,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cal_log_price(data):\n",
+    "    data = data.pct_change().apply(lambda x: np.log(1+x)).iloc[1:,:]\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 470,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%%**********************]  10 of 10 completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import yfinance as yf\n",
+    "\n",
+    "# S&P 500 섹터 지수 심볼 리스트\n",
+    "symbols = [   # S&P 500 지수\n",
+    "    \"XLK\",     # 기술 섹터\n",
+    "    \"XLC\",     # 소프트웨어 및 커뮤니케이션 서비스 섹터\n",
+    "    \"XLY\",     # 소비자 서비스 섹터\n",
+    "    \"XLP\",     # 소비자 필수 용품 섹터\n",
+    "    \"XLE\",     # 에너지 섹터\n",
+    "    \"XLF\",     # 금융 섹터\n",
+    "    \"XLV\",     # 건강 관리 섹터\n",
+    "    \"XLI\",     # 산업 섹터\n",
+    "    \"XLB\",     # 소재 섹터\n",
+    "    \"XLU\"      # 공공 서비스 섹터\n",
+    "]\n",
+    "\n",
+    "# 데이터 다운로드\n",
+    "data = yf.download(symbols, start=\"1999-01-01\", end=\"2024-01-01\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 471,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 다양한 컬럼 중에서 수정종가만 인덱싱\n",
+    "daily_data = pd.DataFrame(data[\"Adj Close\"]) \n",
+    "# 데이터 pandas 데이터 형식으로 변환\n",
+    "daily_data.index = pd.DatetimeIndex(daily_data.index).strftime('%Y-%m-%d %H:%M:%S')\n",
+    "#주간 데이터로 변환(들고 올 때 부터 주간으로 들고 올 수 있는데 이러면 날짜 인덱싱이 어려움)\n",
+    "weekly_data = daily_data.rolling(window=5).mean().iloc[4::5]\n",
+    "\n",
+    "weekly_data.index = pd.to_datetime(weekly_data.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 472,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_data.dropna(axis=1, inplace=True)\n",
+    "#log price 변환\n",
+    "weekly_data_log = cal_log_price(weekly_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 473,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_data_log = cal_log_price(daily_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 데이터 Maker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def datamaker(data,IS,OS,col_num=0,window=1000):\n",
+    "    return data.iloc[-window:,col_num]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Ticker</th>\n",
+       "      <th>XLB</th>\n",
+       "      <th>XLE</th>\n",
+       "      <th>XLF</th>\n",
+       "      <th>XLI</th>\n",
+       "      <th>XLK</th>\n",
+       "      <th>XLP</th>\n",
+       "      <th>XLU</th>\n",
+       "      <th>XLV</th>\n",
+       "      <th>XLY</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1999-01-08</th>\n",
+       "      <td>12.782729</td>\n",
+       "      <td>12.441175</td>\n",
+       "      <td>11.972251</td>\n",
+       "      <td>15.868192</td>\n",
+       "      <td>25.817183</td>\n",
+       "      <td>15.108136</td>\n",
+       "      <td>12.523823</td>\n",
+       "      <td>18.412255</td>\n",
+       "      <td>20.044230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999-01-15</th>\n",
+       "      <td>12.759545</td>\n",
+       "      <td>12.046898</td>\n",
+       "      <td>11.712992</td>\n",
+       "      <td>15.563337</td>\n",
+       "      <td>25.967894</td>\n",
+       "      <td>14.591667</td>\n",
+       "      <td>12.282904</td>\n",
+       "      <td>18.587856</td>\n",
+       "      <td>19.739965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999-01-25</th>\n",
+       "      <td>12.251303</td>\n",
+       "      <td>11.894115</td>\n",
+       "      <td>11.535521</td>\n",
+       "      <td>15.298338</td>\n",
+       "      <td>26.794476</td>\n",
+       "      <td>14.242132</td>\n",
+       "      <td>12.525118</td>\n",
+       "      <td>18.826332</td>\n",
+       "      <td>19.768049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999-02-01</th>\n",
+       "      <td>12.108639</td>\n",
+       "      <td>11.512976</td>\n",
+       "      <td>11.547868</td>\n",
+       "      <td>15.403941</td>\n",
+       "      <td>27.988422</td>\n",
+       "      <td>14.736001</td>\n",
+       "      <td>12.257001</td>\n",
+       "      <td>18.824168</td>\n",
+       "      <td>20.418716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999-02-08</th>\n",
+       "      <td>12.429634</td>\n",
+       "      <td>11.782402</td>\n",
+       "      <td>11.313298</td>\n",
+       "      <td>15.513528</td>\n",
+       "      <td>27.442085</td>\n",
+       "      <td>14.793390</td>\n",
+       "      <td>11.978519</td>\n",
+       "      <td>18.843674</td>\n",
+       "      <td>20.414037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-11-24</th>\n",
+       "      <td>79.811613</td>\n",
+       "      <td>83.247200</td>\n",
+       "      <td>34.883315</td>\n",
+       "      <td>105.060721</td>\n",
+       "      <td>183.567206</td>\n",
+       "      <td>68.796138</td>\n",
+       "      <td>61.279668</td>\n",
+       "      <td>128.687062</td>\n",
+       "      <td>167.142279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-01</th>\n",
+       "      <td>81.052455</td>\n",
+       "      <td>83.103746</td>\n",
+       "      <td>35.307606</td>\n",
+       "      <td>105.675447</td>\n",
+       "      <td>184.244595</td>\n",
+       "      <td>69.420767</td>\n",
+       "      <td>61.814176</td>\n",
+       "      <td>129.504434</td>\n",
+       "      <td>168.706067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-08</th>\n",
+       "      <td>80.797157</td>\n",
+       "      <td>81.211363</td>\n",
+       "      <td>35.672421</td>\n",
+       "      <td>107.688179</td>\n",
+       "      <td>183.758475</td>\n",
+       "      <td>69.464120</td>\n",
+       "      <td>62.238636</td>\n",
+       "      <td>130.922946</td>\n",
+       "      <td>170.873465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-15</th>\n",
+       "      <td>83.130417</td>\n",
+       "      <td>81.533640</td>\n",
+       "      <td>36.695483</td>\n",
+       "      <td>110.734050</td>\n",
+       "      <td>189.564053</td>\n",
+       "      <td>70.260176</td>\n",
+       "      <td>63.356779</td>\n",
+       "      <td>133.297699</td>\n",
+       "      <td>175.773999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-22</th>\n",
+       "      <td>84.705722</td>\n",
+       "      <td>84.017030</td>\n",
+       "      <td>37.132195</td>\n",
+       "      <td>112.219186</td>\n",
+       "      <td>191.274142</td>\n",
+       "      <td>70.502946</td>\n",
+       "      <td>62.315161</td>\n",
+       "      <td>133.629471</td>\n",
+       "      <td>179.568848</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1257 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Ticker            XLB        XLE        XLF         XLI         XLK  \\\n",
+       "Date                                                                  \n",
+       "1999-01-08  12.782729  12.441175  11.972251   15.868192   25.817183   \n",
+       "1999-01-15  12.759545  12.046898  11.712992   15.563337   25.967894   \n",
+       "1999-01-25  12.251303  11.894115  11.535521   15.298338   26.794476   \n",
+       "1999-02-01  12.108639  11.512976  11.547868   15.403941   27.988422   \n",
+       "1999-02-08  12.429634  11.782402  11.313298   15.513528   27.442085   \n",
+       "...               ...        ...        ...         ...         ...   \n",
+       "2023-11-24  79.811613  83.247200  34.883315  105.060721  183.567206   \n",
+       "2023-12-01  81.052455  83.103746  35.307606  105.675447  184.244595   \n",
+       "2023-12-08  80.797157  81.211363  35.672421  107.688179  183.758475   \n",
+       "2023-12-15  83.130417  81.533640  36.695483  110.734050  189.564053   \n",
+       "2023-12-22  84.705722  84.017030  37.132195  112.219186  191.274142   \n",
+       "\n",
+       "Ticker            XLP        XLU         XLV         XLY  \n",
+       "Date                                                      \n",
+       "1999-01-08  15.108136  12.523823   18.412255   20.044230  \n",
+       "1999-01-15  14.591667  12.282904   18.587856   19.739965  \n",
+       "1999-01-25  14.242132  12.525118   18.826332   19.768049  \n",
+       "1999-02-01  14.736001  12.257001   18.824168   20.418716  \n",
+       "1999-02-08  14.793390  11.978519   18.843674   20.414037  \n",
+       "...               ...        ...         ...         ...  \n",
+       "2023-11-24  68.796138  61.279668  128.687062  167.142279  \n",
+       "2023-12-01  69.420767  61.814176  129.504434  168.706067  \n",
+       "2023-12-08  69.464120  62.238636  130.922946  170.873465  \n",
+       "2023-12-15  70.260176  63.356779  133.297699  175.773999  \n",
+       "2023-12-22  70.502946  62.315161  133.629471  179.568848  \n",
+       "\n",
+       "[1257 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weekly_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 데이터 분석"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([22., 23., 24., 35., 43., 37., 57., 20., 20., 19.]),\n",
+       " array([12.49119492, 13.73718697, 14.98317902, 16.22917107, 17.47516312,\n",
+       "        18.72115517, 19.96714722, 21.21313927, 22.45913132, 23.70512337,\n",
+       "        24.95111542]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(weekly_data.iloc[100:400,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 4.,  4.,  1.,  1.,  0.,  2., 18., 26., 30., 14.]),\n",
+       " array([15.32787285, 16.76823811, 18.20860336, 19.64896862, 21.08933388,\n",
+       "        22.52969913, 23.97006439, 25.41042965, 26.85079491, 28.29116016,\n",
+       "        29.73152542]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(weekly_data.iloc[400:500,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering\n",
+    "### 다양한 클러스터 모델을 사용해서 IS(In Sample) OS(Out Sample) 기간의 군집이 차이나는지 확인 \n",
+    "IS OS를 분리해서 보는 이유는 전체적으로 봤을때 IS 길이가 더 길기에 구별됨에도 그 영향이 작게 보일 수 있기 때문이다"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GMM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 474,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_data.dropna(axis=1, inplace=True)\n",
+    "daily_data_log.dropna(axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 475,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_data = daily_data.mean(axis=1)\n",
+    "daily_data_log = daily_data_log.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 476,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2439"
+      ]
+     },
+     "execution_count": 476,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "position = daily_data.index.get_loc('2008-09-15 00:00:00')\n",
+    "position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 504,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cal_period(position):\n",
+    "    IS = np.array(daily_data[position-252:position]).reshape(-1,1)\n",
+    "    OS = np.array(daily_data[position:position+100]).reshape(-1,1)\n",
+    "    IS_OS = np.array(daily_data[position-252:position+100])\n",
+    "    IS_log = np.array(daily_data_log[position-252:position]).reshape(-1,1)\n",
+    "    OS_log  = np.array(daily_data_log[position:position+100]).reshape(-1,1)\n",
+    "    IS_OS_log = np.array(daily_data_log[position-252:position+100]).reshape(-1,1)\n",
+    "    return IS, OS,IS_OS ,IS_log, OS_log, IS_OS_log\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 505,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IS, OS,IS_OS ,IS_log, OS_log, IS_OS_log = cal_period(position = daily_data.index.get_loc('2020-03-02 00:00:00'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 501,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def GMM_AIC_BIC(data,title):\n",
+    "    aic_values = []\n",
+    "    bic_values = []\n",
+    "    X = data\n",
+    "    # 클러스터 개수를 변경하면서 AIC와 BIC 값을 계산합니다.\n",
+    "    for n_components in range(1, 11):\n",
+    "        gmm = GaussianMixture(n_components=n_components)\n",
+    "        gmm.fit(X)\n",
+    "        aic_values.append(gmm.aic(X))\n",
+    "        bic_values.append(gmm.bic(X))\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(range(1, 11), aic_values, marker='o', label='AIC')\n",
+    "    plt.plot(range(1, 11), bic_values, marker='s', label='BIC')\n",
+    "    plt.xlabel('Number of Components')\n",
+    "    plt.ylabel('Value')\n",
+    "    plt.title(title)\n",
+    "    plt.xticks(range(1, 11))\n",
+    "    plt.legend()\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n",
+    "    print('min AIC index:',np.argmin(aic_values))\n",
+    "    print('min AIC value:',np.min(aic_values))\n",
+    "    print('min BIC index:',np.argmin(bic_values))\n",
+    "    print('min BIC values:',np.min(bic_values))\n",
+    "def func_IS_OS(IS,OS):\n",
+    "    GMM_AIC_BIC(IS,'IS_2020')\n",
+    "    GMM_AIC_BIC(OS,'OS_Log_2020')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 502,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min AIC index: 1\n",
+      "min AIC value: -1763.281860793556\n",
+      "min BIC index: 1\n",
+      "min BIC values: -1745.6347153559989\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min AIC index: 2\n",
+      "min AIC value: -2058.4853363298835\n",
+      "min BIC index: 1\n",
+      "min BIC values: -2028.120011491342\n"
+     ]
+    }
+   ],
+   "source": [
+    "func_IS_OS(IS_log,IS_OS_log)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MCMC 모델링"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def GBM_PDF(s,mu,sigma,S0,t=1):\n",
+    "    import numpy as np\n",
+    "    \"\"\"\n",
+    "    기하 브라운 운동의 확률 밀도 함수를 계산\n",
+    "\n",
+    "    Parameters:\n",
+    "    s (float): 자산 가격\n",
+    "    S0 (float): 초기 자산 가격\n",
+    "    mu (float): 드리프트(평균 성장률)\n",
+    "    sigma (float): 변동성(표준 편차)\n",
+    "    t (float): 시간\n",
+    "    \"\"\"\n",
+    "    if s <= 0 or t <= 0:\n",
+    "        return 0.0\n",
+    "    \n",
+    "    term1 = 1 / (s * sigma * np.sqrt(2 * np.pi * t))\n",
+    "    exponent = -(np.log(s / S0) - (mu - 0.5 * sigma**2) * t)**2 / (2 * sigma**2 * t)\n",
+    "    term2 = np.exp(exponent)\n",
+    "    \n",
+    "    return term1 * term2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_likelihood(mu_proposal, sigma_proposal, data):\n",
+    "\n",
+    "    MLE_list = []\n",
+    "    \n",
+    "    data_1 = data[1:]\n",
+    "    for i,data_1 in enumerate(data_1):\n",
+    "        data_inital = data[i]\n",
+    "        MLE_list.append(np.log(GBM_PDF(data_1,mu_proposal,sigma_proposal,data_inital)))\n",
+    "    return np.sum(MLE_list)\n",
+    "\n",
+    "def metropolis_hastings(data, initial_params,iterations, proposal_width):\n",
+    "    mu_current, sigma_current = initial_params\n",
+    "    samples = []\n",
+    "    for _ in range(iterations):\n",
+    "        mu_proposal = np.random.normal(mu_current, proposal_width)\n",
+    "        sigma_proposal = np.abs(np.random.normal(sigma_current, proposal_width))\n",
+    "        \n",
+    "        # Calculate log-likelihoods\n",
+    "        ll_current = log_likelihood(mu_current, sigma_current, data)\n",
+    "        ll_proposal = log_likelihood(mu_proposal, sigma_proposal+0.0000001, data)\n",
+    "        \n",
+    "        # Calculate acceptance probability\n",
+    "        acceptance_prob = min(1, np.exp(ll_proposal - ll_current))\n",
+    "        \n",
+    "        # Accept or reject the proposal\n",
+    "        if np.random.rand() < acceptance_prob:\n",
+    "            mu_current = mu_proposal\n",
+    "            sigma_current = sigma_proposal\n",
+    "        \n",
+    "        samples.append((mu_current, sigma_current))\n",
+    "    \n",
+    "    return np.array(samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 518,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimated_para(data,initial_params,iterations,proposal_width):\n",
+    "    samples = metropolis_hastings(data, initial_params, iterations, proposal_width)\n",
+    "\n",
+    "    # Extract and plot samples\n",
+    "    mu_samples = samples[:, 0]\n",
+    "    sigma_samples = samples[:, 1]\n",
+    "\n",
+    "    estimated_mu = np.mean(mu_samples[int(iterations-iterations*0.4):iterations])\n",
+    "    estimated_sigma = np.mean(sigma_samples[int(iterations-iterations*0.4):iterations])\n",
+    "    return estimated_mu,estimated_sigma,mu_samples,sigma_samples\n",
+    "def ploting_para(estimated_mu,estimated_sigma,mu_samples,sigma_samples):\n",
+    "    print(f\"Estimated mu: {estimated_mu}\")\n",
+    "    print(f\"Estimated sigma: {estimated_sigma}\")\n",
+    "\n",
+    "    fig, ax = plt.subplots(2, 1, figsize=(10, 6))\n",
+    "\n",
+    "    ax[0].plot(mu_samples, label='mu samples')\n",
+    "\n",
+    "    ax[0].legend()\n",
+    "\n",
+    "    ax[1].plot(sigma_samples, label='sigma samples')\n",
+    "\n",
+    "    ax[1].legend()\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 416,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IS = weekly_data.iloc[500:1000,1]\n",
+    "OS = weekly_data.iloc[1000:1200,1]\n",
+    "IS_OS = weekly_data.iloc[500:1200,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 522,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/hy/_k7gcypd00s_2mmm5h2jktx00000gn/T/ipykernel_881/1553238095.py:8: RuntimeWarning: divide by zero encountered in log\n",
+      "  MLE_list.append(np.log(GBM_PDF(data_1,mu_proposal,sigma_proposal,data_inital)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "initial_params = [0, 0.1]\n",
+    "iterations = 1000\n",
+    "proposal_width = 0.1\n",
+    "\n",
+    "estimated_mu_IS_OS,estimated_sigma_IS_OS,mu_samples_IS_OS,sigma_samples_IS_OS = estimated_para(IS_OS,initial_params,iterations,proposal_width)\n",
+    "estimated_mu_OS,estimated_sigma_OS,mu_samples_OS,sigma_samples_OS = estimated_para(OS,initial_params,iterations,proposal_width)\n",
+    "estimated_mu_IS,estimated_sigma_IS,mu_samples_IS,sigma_samples_IS = estimated_para(IS,initial_params,iterations,proposal_width)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 523,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimated mu: 0.006282422543958439\n",
+      "Estimated sigma: 0.019716239984356624\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uK/LLO92kbgAAAADwFqHbgo6PdLfG9PKjvzK9HAAAAAC8R+i2IPOd7lZZSI3p5QAAAADQXIRuCzJHulvhXiykBgAAAADNR+i2IHOf7lbcMqyB0A0AAAAAXiN0W5DRimPdx0e6fX4rAAAAAGhzCN0W1Koj3fbGe5K6AQAAAMBbhG4LMlpxyzBzpJuhbgAAAADwWrNC95IlS9S7d2+FhYUpLS1NGzZsOG39ypUr1adPH4WFhSkxMVFr1qzx+P6VV17R8OHD1aVLF9lsNm3evPmkaxw+fFgTJ05Uly5ddN555+nGG29UZWVlc5rfZrTOlmFMLwcAAACA5vI6dK9YsUI5OTmaM2eOysrKlJSUpMzMTFVVVTVZv379eo0aNUrjx49XeXm5srKylJWVpS1btpg1NTU1Gjp0qB599NFT3veee+7R66+/rpUrV+qdd97R7t27dcMNN3jb/DbB3DKsFe7FPt0AAAAA0Hw2w8uXddPS0jRw4EAtXrxYkuR2uxUXF6fJkydr+vTpJ9VnZ2erpqZGq1evNo8NHjxYycnJys/P96jdsWOH4uPjVV5eruTkZPN4dXW1zj//fC1btkw33XSTJGnr1q3q27eviouLNXjw4B9st8vlUmRkpKqrqxUREeHNIwec9Lwi7ak+rNcmXaHLekT59F45KzbrlfJdmvnTPppw5YU+vRcAAAAAWMWZZkyvRrrr6upUWlqqjIyM4xew25WRkaHi4uImzykuLvaol6TMzMxT1jeltLRU9fX1Htfp06ePevbs6dV12hpba6xebmd6OQAAAAA0V7A3xfv27VNDQ4NiY2M9jsfGxmrr1q1NnuN0OpusdzqdZ3xfp9OpkJAQRUVFnfF1amtrVVtba/7scrnO+H6Bzh/7dDO9HAAAAAC812ZXL8/Ly1NkZKT5iYuL83eTWszxfbp9j9XLAQAAAKD5vArd0dHRCgoKOmnV8MrKSjkcjibPcTgcXtWf6hp1dXU6cODAGV9nxowZqq6uNj87d+484/sFutYc6baxejkAAAAANJtXoTskJEQpKSkqKioyj7ndbhUVFSk9Pb3Jc9LT0z3qJamwsPCU9U1JSUlRu3btPK6zbds2VVRUnPI6oaGhioiI8Pi0FY35tzXe6Q461kOYXg4AAAAA3vPqnW5JysnJ0dixY5WamqpBgwZp4cKFqqmp0bhx4yRJY8aMUffu3ZWXlydJmjJlioYNG6b58+dr5MiRWr58uTZt2qSlS5ea19y/f78qKiq0e/duSUcDtXR0hNvhcCgyMlLjx49XTk6OOnfurIiICE2ePFnp6elntHJ5W9Oa+Zd9ugEAAACg+bwO3dnZ2dq7d69yc3PldDqVnJysgoICc7G0iooK2e3HB9CHDBmiZcuWadasWZo5c6YSEhK0atUq9e/f36x57bXXzNAuSbfeeqskac6cOXrggQckSY8//rjsdrtuvPFG1dbWKjMzU0899VSzHtr6ju3T3SoLqfFONwAAAAA0l9f7dFtVW9qnO+XBQv27pk4FU3+kPg7fPsvvX/9Uz3+wQ7+56kLdN6KPT+8FAAAAAFbhk326ERha9Z1uppcDAAAAQLMRui2ocXJCq0wvP7ZR9zkyIQIAAAAAWhSh24KOj3T7XmOwb2CoGwAAAAC8Rui2oNbcp5vVywEAAACg+QjdFnR8qndrvtNN6gYAAAAAbxG6LciM3K0y0n3snoRuAAAAAPAaoduKGqeXt8KtbMeSfQOhGwAAAAC8Rui2oOMj3b6P3bzTDQAAAADNR+i2IHPLsFa4F9PLAQAAAKD5CN0W1KrvdB9L3W637+8FAAAAAG0NoduCzC3DWmGs28473QAAAADQbIRuCzKOjXW35urlbBkGAAAAAN4jdFtQa+bfxpFuMjcAAAAAeI/QbUF+eaeb1A0AAAAAXiN0W1HjO92tsmXY0V8b2DMMAAAAALxG6LYg853uVrgX08sBAAAAoPkI3RbGQmoAAAAAENgI3RbUqluG8U43AAAAADQboduCWnUhNVtj6Pb9vQAAAACgrSF0W5BhtOY73Ud/dZO6AQAAAMBrhG4LMuNvK6Rum43p5QAAAADQXIRuC2rNd7qDmF4OAAAAAM1G6LawVnmn+1gPYaQbAAAAALxH6LYY44Tw25r7dBO6AQAAAMB7hG6Lae3sa77T7W7d+wIAAABAW0DotpgTM7etFeaXBzHSDQAAAADNRui2mNafXt5431a4GQAAAAC0MYRui/Ec6fb9/RpH0xtI3QAAAADgtWaF7iVLlqh3794KCwtTWlqaNmzYcNr6lStXqk+fPgoLC1NiYqLWrFnj8b1hGMrNzVXXrl3Vvn17ZWRk6Msvv/So+eKLL3TdddcpOjpaERERGjp0qN5+++3mNN/STsy+rbFlWONIN9PLAQAAAMB7XofuFStWKCcnR3PmzFFZWZmSkpKUmZmpqqqqJuvXr1+vUaNGafz48SovL1dWVpaysrK0ZcsWs2bu3LlatGiR8vPzVVJSog4dOigzM1OHDx82a6699lodOXJEa9euVWlpqZKSknTttdfK6XQ247Gty5BH6va5IDv7dAMAAABAc3kduhcsWKA77rhD48aNU79+/ZSfn6/w8HA999xzTdY/8cQTGjFihKZNm6a+ffvqwQcf1OWXX67FixdLOjrKvXDhQs2aNUvXXXedLrvsMv3pT3/S7t27tWrVKknSvn379OWXX2r69Om67LLLlJCQoEceeUSHDh3yCO/nAo+R7tbYp/vYTQxGugEAAADAa16F7rq6OpWWliojI+P4Bex2ZWRkqLi4uMlziouLPeolKTMz06zfvn27nE6nR01kZKTS0tLMmi5duuiSSy7Rn/70J9XU1OjIkSN65plnFBMTo5SUFG8eoU1pjYXUGoN9A0PdAAAAAOC1YG+K9+3bp4aGBsXGxnocj42N1datW5s8x+l0NlnfOC288dfT1dhsNv3jH/9QVlaWOnbsKLvdrpiYGBUUFKhTp05N3re2tla1tbXmzy6Xy4snDVyeI92t8U4308sBAAAAoLkssXq5YRiaOHGiYmJi9N5772nDhg3KysrSz372M+3Zs6fJc/Ly8hQZGWl+4uLiWrnVvnHiO92ts2UY08sBAAAAoLm8Ct3R0dEKCgpSZWWlx/HKyko5HI4mz3E4HKetb/z1dDVr167V6tWrtXz5cl1xxRW6/PLL9dRTT6l9+/Z68cUXm7zvjBkzVF1dbX527tzpzaMGrFZ/p/tYD2H1cgAAAADwnlehOyQkRCkpKSoqKjKPud1uFRUVKT09vclz0tPTPeolqbCw0KyPj4+Xw+HwqHG5XCopKTFrDh06dLSxds/m2u12ud3uJu8bGhqqiIgIj09b4LFPd6tsGXZsn27mlwMAAACA17x6p1uScnJyNHbsWKWmpmrQoEFauHChampqNG7cOEnSmDFj1L17d+Xl5UmSpkyZomHDhmn+/PkaOXKkli9frk2bNmnp0qWSjr6XPHXqVD300ENKSEhQfHy8Zs+erW7duikrK0vS0eDeqVMnjR07Vrm5uWrfvr2effZZbd++XSNHjmyh3wprOHGad+uuXu77ewEAAABAW+N16M7OztbevXuVm5srp9Op5ORkFRQUmAuhVVRUeIxIDxkyRMuWLdOsWbM0c+ZMJSQkaNWqVerfv79Zc99996mmpkYTJkzQgQMHNHToUBUUFCgsLEzS0WntBQUF+t3vfqf/9//+n+rr63XppZfq73//u5KSks7298BSWjv7Htumm+nlAAAAANAMNuMcWSHL5XIpMjJS1dXVlp5qXv19vZJ+/5YkadtDIxQaHOTT+5VVfKsbnlqvnp3D9e59P/bpvQAAAADAKs40Y1pi9XKc4MSF1HinGwAAAAACGqHbwlrnne6jv54jEyIAAAAAoEURui3GX/t0M9ANAAAAAN4jdFuM5z7drTe9nIXUAAAAAMB7hG6L8dyn2/caF6JnpBsAAAAAvEfothh/7dPNSDcAAAAAeI/QbTEeI92tMr386K+EbgAAAADwHqHbYlo7+5oj3cwvBwAAAACvEbotpnH18taYWi4dD90MdAMAAACA9wjdVnMs/LZS5jZDdwOpGwAAAAC8Rui2mMbo2xrvcx+9z9FfeacbAAAAALxH6LYYo5VHuoPsjauXt9INAQAAAKANCfZ3A+AdQ62bfhunl9cdcev/PbauVe+NltO9U3s9c1uKwkP4vzwAAADQmvgTuMWYI92tNNQdFd5Oke3bqfr7en29r6Z1booW9/W+Gm3Yvl9XXRLj76YAAAAA5xRCt8WY73S30gTzsHZBKrp3mL7eS+C2qj+s/lRbdrnkOnzE300BAAAAzjmEbosxWvulbknR54Uq+rzQ1rshWlS3yPZHQ/f39f5uCgAAAHDOYSE1i/FD5obFRbRvJ0lyHSZ0AwAAAK2N0G1RrfVON6wvIuxo6D7I9HIAAACg1RG6Leb4SDepG2cmov3Rt0iYXg4AAAC0PkK3xTRuGcZIN85U40g3C6kBAAAArY/QbTG80w1vme90M9INAAAAtDpCt8WYW4Yx1I0z1DHs2PRyFlIDAAAAWh2h22IatwwjcuNMsZAaAAAA4D+EbotpHOkmdeNMsZAaAAAA4D+EbovhnW546/hCaoRuAAAAoLUF+7sBaB7e6caZalxI7XC9W+u/2qcgO30HAAAAga9/90h1CLV+ZLX+E5xz2DIM3ukYGiy7TXIb0n/9d4m/mwMAAACckTfuHqpLu0X6uxlnjdBtMUwvh7fsdpvuHHah/u9Tp7+bAgAAAJyx0OC28TZ0s0L3kiVLNG/ePDmdTiUlJenJJ5/UoEGDTlm/cuVKzZ49Wzt27FBCQoIeffRR/fSnPzW/NwxDc+bM0bPPPqsDBw7oiiuu0NNPP62EhASP67zxxhv6wx/+oI8//lhhYWEaNmyYVq1a1ZxHsCy2DENz3Deij+4b0cffzQAAAADOOV7/1cGKFSuUk5OjOXPmqKysTElJScrMzFRVVVWT9evXr9eoUaM0fvx4lZeXKysrS1lZWdqyZYtZM3fuXC1atEj5+fkqKSlRhw4dlJmZqcOHD5s1//u//6vbbrtN48aN00cffaQPPvhA//Vf/9WMR7Y2RroBAAAAwDpsRuPGz2coLS1NAwcO1OLFiyVJbrdbcXFxmjx5sqZPn35SfXZ2tmpqarR69Wrz2ODBg5WcnKz8/HwZhqFu3brp3nvv1W9/+1tJUnV1tWJjY/XCCy/o1ltv1ZEjR9S7d2/9/ve/1/jx45v1oC6XS5GRkaqurlZERESzrhEItjpdGrHwPUWfF6JNs37i7+YAAAAAwDnpTDOmVyPddXV1Ki0tVUZGxvEL2O3KyMhQcXFxk+cUFxd71EtSZmamWb99+3Y5nU6PmsjISKWlpZk1ZWVl2rVrl+x2uwYMGKCuXbvqmmuu8Rgt/0+1tbVyuVwen7bAYKNuAAAAALAMr0L3vn371NDQoNjYWI/jsbGxcjqbXqTJ6XSetr7x19PVfP3115KkBx54QLNmzdLq1avVqVMnXXXVVdq/f3+T983Ly1NkZKT5iYuL8+ZRA5Y5vZzMDQAAAAABzxLLwbndbknS7373O914441KSUnR888/L5vNppUrVzZ5zowZM1RdXW1+du7c2ZpN9hmjccswP7cDAAAAAPDDvArd0dHRCgoKUmVlpcfxyspKORyOJs9xOBynrW/89XQ1Xbt2lST169fP/D40NFQXXHCBKioqmrxvaGioIiIiPD5tASPdAAAAAGAdXoXukJAQpaSkqKioyDzmdrtVVFSk9PT0Js9JT0/3qJekwsJCsz4+Pl4Oh8OjxuVyqaSkxKxJSUlRaGiotm3bZtbU19drx44d6tWrlzeP0GbYGOsGAAAAgIDn9T7dOTk5Gjt2rFJTUzVo0CAtXLhQNTU1GjdunCRpzJgx6t69u/Ly8iRJU6ZM0bBhwzR//nyNHDlSy5cv16ZNm7R06VJJR/ebnjp1qh566CElJCQoPj5es2fPVrdu3ZSVlSVJioiI0J133qk5c+YoLi5OvXr10rx58yRJN998c0v8PlgGI90AAAAAYB1eh+7s7Gzt3btXubm5cjqdSk5OVkFBgbkQWkVFhez24wPoQ4YM0bJlyzRr1izNnDlTCQkJWrVqlfr372/W3HfffaqpqdGECRN04MABDR06VAUFBQoLCzNr5s2bp+DgYN122236/vvvlZaWprVr16pTp05n8/yWwzvdAAAAAGAdXu/TbVVtZZ/uj3Ye0HVLPlD3qPb6YPr/83dzAAAAAOCc5JN9uuF/58TfkAAAAABAG0HotphzZGICAAAAALQJhG6LaYzcLKQGAAAAAIGP0G0xrF4OAAAAANZB6LacxtXLSd0AAAAAEOgI3RbDSDcAAAAAWAeh22LMd7r92goAAAAAwJkgdFvM8ZFuYjcAAAAABDpCt8U0bhlG5AYAAACAwEfothhzl25SNwAAAAAEPEK3RZG5AQAAACDwEbothne6AQAAAMA6CN0WY4h3ugEAAADAKgjdVsM+3QAAAABgGYRuizm+TzepGwAAAAACHaHbYgxGugEAAADAMgjdFmMc3zQMAAAAABDgCN0Ww+rlAAAAAGAdhG6LOf5ONwAAAAAg0BG6LcY4NtTNQDcAAAAABD5Ct8WYI92EbgAAAAAIeIRuq2l8p5sJ5gAAAAAQ8AjdFtO4ejkj3QAAAAAQ+AjdFmOuXu7fZgAAAAAAzgCh22IMXuoGAAAAAMsgdFsMW4YBAAAAgHUQui2GLcMAAAAAwDqaFbqXLFmi3r17KywsTGlpadqwYcNp61euXKk+ffooLCxMiYmJWrNmjcf3hmEoNzdXXbt2Vfv27ZWRkaEvv/yyyWvV1tYqOTlZNptNmzdvbk7zLc344RIAAAAAQIDwOnSvWLFCOTk5mjNnjsrKypSUlKTMzExVVVU1Wb9+/XqNGjVK48ePV3l5ubKyspSVlaUtW7aYNXPnztWiRYuUn5+vkpISdejQQZmZmTp8+PBJ17vvvvvUrVs3b5vdZrCQGgAAAABYh9ehe8GCBbrjjjs0btw49evXT/n5+QoPD9dzzz3XZP0TTzyhESNGaNq0aerbt68efPBBXX755Vq8eLGko6PcCxcu1KxZs3Tdddfpsssu05/+9Cft3r1bq1at8rjWm2++qbfeekuPPfaY90/aZjROLyd2AwAAAECg8yp019XVqbS0VBkZGccvYLcrIyNDxcXFTZ5TXFzsUS9JmZmZZv327dvldDo9aiIjI5WWluZxzcrKSt1xxx166aWXFB4e7k2z2xRGugEAAADAOrwK3fv27VNDQ4NiY2M9jsfGxsrpdDZ5jtPpPG1946+nqzEMQ7fffrvuvPNOpaamnlFba2tr5XK5PD5tATuGAQAAAIB1WGL18ieffFIHDx7UjBkzzvicvLw8RUZGmp+4uDgftrD1HB/pJnUDAAAAQKDzKnRHR0crKChIlZWVHscrKyvlcDiaPMfhcJy2vvHX09WsXbtWxcXFCg0NVXBwsC666CJJUmpqqsaOHdvkfWfMmKHq6mrzs3PnTm8eNWAZYn45AAAAAFiFV6E7JCREKSkpKioqMo+53W4VFRUpPT29yXPS09M96iWpsLDQrI+Pj5fD4fCocblcKikpMWsWLVqkjz76SJs3b9bmzZvNLcdWrFihhx9+uMn7hoaGKiIiwuPTFvBONwAAAABYR7C3J+Tk5Gjs2LFKTU3VoEGDtHDhQtXU1GjcuHGSpDFjxqh79+7Ky8uTJE2ZMkXDhg3T/PnzNXLkSC1fvlybNm3S0qVLJR1dhXvq1Kl66KGHlJCQoPj4eM2ePVvdunVTVlaWJKlnz54ebTjvvPMkSRdeeKF69OjR7Ie3Mt7pBgAAAIDA53Xozs7O1t69e5Wbmyun06nk5GQVFBSYC6FVVFTIbj8+gD5kyBAtW7ZMs2bN0syZM5WQkKBVq1apf//+Zs19992nmpoaTZgwQQcOHNDQoUNVUFCgsLCwFnjEtsVcSI2xbgAAAAAIeDbDaJyw3La5XC5FRkaqurra0lPN/755l6Ys36whF3bRsjsG+7s5AAAAAHBOOtOMaYnVy3EyppcDAAAAQOAjdFsMW4YBAAAAgHUQui2mccswRroBAAAAIPARui3m3HgDHwAAAADaBkK3xZjTyxnqBgAAAICAR+i2mONbhgEAAAAAAh2h22Iad3hjoBsAAAAAAh+h22IY6QYAAAAA6yB0Ww3vdAMAAACAZRC6LcbcMszP7QAAAAAA/DBCt8UcX73cv+0AAAAAAPwwQrfFHN+mm9QNAAAAAIGO0G0xjHQDAAAAgHUQui2Gd7oBAAAAwDoI3RbDSDcAAAAAWAeh22KO79NN6gYAAACAQEfotppjQ92MdAMAAABA4CN0W4zxwyUAAAAAgABB6LYY3ukGAAAAAOsgdFuM0Ti9nHe6AQAAACDgEbotxpxeTuYGAAAAgIBH6LYoMjcAAAAABD5Ct8Ucf6eb2A0AAAAAgY7QbTHH9+kGAAAAAAQ6QrfFGOzTDQAAAACWQei2KDI3AAAAAAQ+QrfF8E43AAAAAFgHodtiDDXu0w0AAAAACHTNCt1LlixR7969FRYWprS0NG3YsOG09StXrlSfPn0UFhamxMRErVmzxuN7wzCUm5urrl27qn379srIyNCXX35pfr9jxw6NHz9e8fHxat++vS688ELNmTNHdXV1zWm+pRmspAYAAAAAluF16F6xYoVycnI0Z84clZWVKSkpSZmZmaqqqmqyfv369Ro1apTGjx+v8vJyZWVlKSsrS1u2bDFr5s6dq0WLFik/P18lJSXq0KGDMjMzdfjwYUnS1q1b5Xa79cwzz+jTTz/V448/rvz8fM2cObOZj21dxzM3qRsAAAAAAp3NMMyx0zOSlpamgQMHavHixZIkt9utuLg4TZ48WdOnTz+pPjs7WzU1NVq9erV5bPDgwUpOTlZ+fr4Mw1C3bt1077336re//a0kqbq6WrGxsXrhhRd06623NtmOefPm6emnn9bXX399Ru12uVyKjIxUdXW1IiIivHnkgPL0un/q0YKtuimlhx67OcnfzQEAAACAc9KZZkyvRrrr6upUWlqqjIyM4xew25WRkaHi4uImzykuLvaol6TMzEyzfvv27XI6nR41kZGRSktLO+U1paPBvHPnzt40v03gnW4AAAAAsI5gb4r37dunhoYGxcbGehyPjY3V1q1bmzzH6XQ2We90Os3vG4+dquY/ffXVV3ryySf12GOPnbKttbW1qq2tNX92uVynrLWS46uX+7cdAAAAAIAfZrnVy3ft2qURI0bo5ptv1h133HHKury8PEVGRpqfuLi4Vmyl7/FONwAAAAAEPq9Cd3R0tIKCglRZWelxvLKyUg6Ho8lzHA7Haesbfz2Ta+7evVs//vGPNWTIEC1duvS0bZ0xY4aqq6vNz86dO3/4AS2g8RV8RroBAAAAIPB5FbpDQkKUkpKioqIi85jb7VZRUZHS09ObPCc9Pd2jXpIKCwvN+vj4eDkcDo8al8ulkpISj2vu2rVLV111lVJSUvT888/Lbj9900NDQxUREeHxaQuYXg4AAAAA1uHVO92SlJOTo7Fjxyo1NVWDBg3SwoULVVNTo3HjxkmSxowZo+7duysvL0+SNGXKFA0bNkzz58/XyJEjtXz5cm3atMkcqbbZbJo6daoeeughJSQkKD4+XrNnz1a3bt2UlZUl6Xjg7tWrlx577DHt3bvXbM+pRtjbquNLzZO6AQAAACDQeR26s7OztXfvXuXm5srpdCo5OVkFBQXmQmgVFRUeo9BDhgzRsmXLNGvWLM2cOVMJCQlatWqV+vfvb9bcd999qqmp0YQJE3TgwAENHTpUBQUFCgsLk3R0ZPyrr77SV199pR49eni0x8sdzyyPkW4AAAAAsA6v9+m2qrayT/fCf3yhhf/4UqPTeurh6xP93RwAAAAAOCf5ZJ9u+B8j3QAAAABgHYRui2mclsCWYQAAAAAQ+AjdVsOWYQAAAABgGYRuizk+0g0AAAAACHSEbos5/k43sRsAAAAAAh2h22IMnROLzQMAAABAm0DoBgAAAADARwjdFsOWYQAAAABgHYRui2HLMAAAAACwDkK3xTDSDQAAAADWQei2mMaF1MjcAAAAABD4CN1Ww0g3AAAAAFgGodtizHe6Sd0AAAAAEPAI3RZjGEwvBwAAAACrIHRbjHF8+XIAAAAAQIAjdFsMW4YBAAAAgHUQui2GLcMAAAAAwDoI3RbDlmEAAAAAYB2EbothpBsAAAAArIPQbVG80w0AAAAAgY/QbTHmlmFkbgAAAAAIeIRui2HHMAAAAACwDkK3xRzfp5vYDQAAAACBLtjfDUDT3G5DZRXfynW43uP4v749JImRbgAAAACwAkJ3gHr9492asnzzKb8PthO7AQAAACDQEboD1JZd1ZKkmI6h6hoZ5vFdx7B2+ullXf3RLAAAAACAFwjdAWpP9WFJ0q+HXajxQ+P93BoAAAAAQHOwkFqAagzd/znKDQAAAACwjmaF7iVLlqh3794KCwtTWlqaNmzYcNr6lStXqk+fPgoLC1NiYqLWrFnj8b1hGMrNzVXXrl3Vvn17ZWRk6Msvv/So2b9/v0aPHq2IiAhFRUVp/Pjx+u6775rTfEvYc+B7SYRuAAAAALAyr0P3ihUrlJOTozlz5qisrExJSUnKzMxUVVVVk/Xr16/XqFGjNH78eJWXlysrK0tZWVnasmWLWTN37lwtWrRI+fn5KikpUYcOHZSZmanDhw+bNaNHj9ann36qwsJCrV69Wu+++64mTJjQjEcOfA1uQ5UHayVJXSPb+7k1AAAAAIDmshmGufPzGUlLS9PAgQO1ePFiSZLb7VZcXJwmT56s6dOnn1SfnZ2tmpoarV692jw2ePBgJScnKz8/X4ZhqFu3brr33nv129/+VpJUXV2t2NhYvfDCC7r11lv1+eefq1+/ftq4caNSU1MlSQUFBfrpT3+qf/3rX+rWrdsPttvlcikyMlLV1dWKiIjw5pFbnbP6sAbnFSnYbtO2h65RECuVAwAAAEBAOdOM6dVId11dnUpLS5WRkXH8Ana7MjIyVFxc3OQ5xcXFHvWSlJmZadZv375dTqfToyYyMlJpaWlmTXFxsaKioszALUkZGRmy2+0qKSlp8r61tbVyuVweHyu44akP9Iv/OfpMsRFhBG4AAAAAsDCvQve+ffvU0NCg2NhYj+OxsbFyOp1NnuN0Ok9b3/jrD9XExMR4fB8cHKzOnTuf8r55eXmKjIw0P3FxcWf4lP716W6Xvqo6+q56v26BPSIPAAAAADi9Nrtl2IwZM5STk2P+7HK5LBG8l45JldswFGSzKaVXJ383BwAAAABwFrwK3dHR0QoKClJlZaXH8crKSjkcjibPcTgcp61v/LWyslJdu3b1qElOTjZr/nOhtiNHjmj//v2nvG9oaKhCQ0PP/OECxLCLz/d3EwAAAAAALcSr6eUhISFKSUlRUVGRecztdquoqEjp6elNnpOenu5RL0mFhYVmfXx8vBwOh0eNy+VSSUmJWZOenq4DBw6otLTUrFm7dq3cbrfS0tK8eQQAAAAAAFqN19PLc3JyNHbsWKWmpmrQoEFauHChampqNG7cOEnSmDFj1L17d+Xl5UmSpkyZomHDhmn+/PkaOXKkli9frk2bNmnp0qWSJJvNpqlTp+qhhx5SQkKC4uPjNXv2bHXr1k1ZWVmSpL59+2rEiBG64447lJ+fr/r6ek2aNEm33nrrGa1cDgAAAACAP3gdurOzs7V3717l5ubK6XQqOTlZBQUF5kJoFRUVstuPD6APGTJEy5Yt06xZszRz5kwlJCRo1apV6t+/v1lz3333qaamRhMmTNCBAwc0dOhQFRQUKCwszKx5+eWXNWnSJF199dWy2+268cYbtWjRorN5dgAAAAAAfMrrfbqtykr7dAMAAAAAAptP9ukGAAAAAABnjtANAAAAAICPELoBAAAAAPARQjcAAAAAAD7i9erlVtW4XpzL5fJzSwAAAAAAVteYLX9obfJzJnQfPHhQkhQXF+fnlgAAAAAA2oqDBw8qMjLylN+fM1uGud1u7d69Wx07dpTNZvN3c07J5XIpLi5OO3fuZGszBBz6JwIVfROBjP6JQEXfRCCzQv80DEMHDx5Ut27dZLef+s3tc2ak2263q0ePHv5uxhmLiIgI2M4F0D8RqOibCGT0TwQq+iYCWaD3z9ONcDdiITUAAAAAAHyE0A0AAAAAgI8QugNMaGio5syZo9DQUH83BTgJ/ROBir6JQEb/RKCibyKQtaX+ec4spAYAAAAAQGtjpBsAAAAAAB8hdAMAAAAA4COEbgAAAAAAfITQHWCWLFmi3r17KywsTGlpadqwYYO/m4Q2Li8vTwMHDlTHjh0VExOjrKwsbdu2zaPm8OHDmjhxorp06aLzzjtPN954oyorKz1qKioqNHLkSIWHhysmJkbTpk3TkSNHWvNR0MY98sgjstlsmjp1qnmMvgl/2rVrl37xi1+oS5cuat++vRITE7Vp0ybze8MwlJubq65du6p9+/bKyMjQl19+6XGN/fv3a/To0YqIiFBUVJTGjx+v7777rrUfBW1IQ0ODZs+erfj4eLVv314XXnihHnzwQZ24jBN9E63l3Xff1c9+9jN169ZNNptNq1at8vi+pfrixx9/rB/96EcKCwtTXFyc5s6d6+tH8wqhO4CsWLFCOTk5mjNnjsrKypSUlKTMzExVVVX5u2low9555x1NnDhRH374oQoLC1VfX6/hw4erpqbGrLnnnnv0+uuva+XKlXrnnXe0e/du3XDDDeb3DQ0NGjlypOrq6rR+/Xq9+OKLeuGFF5Sbm+uPR0IbtHHjRj3zzDO67LLLPI7TN+Ev3377ra644gq1a9dOb775pj777DPNnz9fnTp1Mmvmzp2rRYsWKT8/XyUlJerQoYMyMzN1+PBhs2b06NH69NNPVVhYqNWrV+vdd9/VhAkT/PFIaCMeffRRPf3001q8eLE+//xzPfroo5o7d66efPJJs4a+idZSU1OjpKQkLVmypMnvW6IvulwuDR8+XL169VJpaanmzZunBx54QEuXLvX5850xAwFj0KBBxsSJE82fGxoajG7duhl5eXl+bBXONVVVVYYk45133jEMwzAOHDhgtGvXzli5cqVZ8/nnnxuSjOLiYsMwDGPNmjWG3W43nE6nWfP0008bERERRm1tbes+ANqcgwcPGgkJCUZhYaExbNgwY8qUKYZh0DfhX/fff78xdOjQU37vdrsNh8NhzJs3zzx24MABIzQ01PjLX/5iGIZhfPbZZ4YkY+PGjWbNm2++adhsNmPXrl2+azzatJEjRxq//OUvPY7dcMMNxujRow3DoG/CfyQZr776qvlzS/XFp556yujUqZPHf9fvv/9+45JLLvHxE505RroDRF1dnUpLS5WRkWEes9vtysjIUHFxsR9bhnNNdXW1JKlz586SpNLSUtXX13v0zT59+qhnz55m3ywuLlZiYqJiY2PNmszMTLlcLn366aet2Hq0RRMnTtTIkSM9+qBE34R/vfbaa0pNTdXNN9+smJgYDRgwQM8++6z5/fbt2+V0Oj36Z2RkpNLS0jz6Z1RUlFJTU82ajIwM2e12lZSUtN7DoE0ZMmSIioqK9MUXX0iSPvroI73//vu65pprJNE3EThaqi8WFxfryiuvVEhIiFmTmZmpbdu26dtvv22lpzm9YH83AEft27dPDQ0NHn8wlKTY2Fht3brVT63Cucbtdmvq1Km64oor1L9/f0mS0+lUSEiIoqKiPGpjY2PldDrNmqb6buN3QHMtX75cZWVl2rhx40nf0TfhT19//bWefvpp5eTkaObMmdq4caPuvvtuhYSEaOzYsWb/aqr/ndg/Y2JiPL4PDg5W586d6Z9otunTp8vlcqlPnz4KCgpSQ0ODHn74YY0ePVqS6JsIGC3VF51Op+Lj40+6RuN3J7724y+EbgCmiRMnasuWLXr//ff93RRAO3fu1JQpU1RYWKiwsDB/Nwfw4Ha7lZqaqj/+8Y+SpAEDBmjLli3Kz8/X2LFj/dw6nMv++te/6uWXX9ayZct06aWXavPmzZo6daq6detG3wT8hOnlASI6OlpBQUEnrbpbWVkph8Php1bhXDJp0iStXr1ab7/9tnr06GEedzgcqqur04EDBzzqT+ybDoejyb7b+B3QHKWlpaqqqtLll1+u4OBgBQcH65133tGiRYsUHBys2NhY+ib8pmvXrurXr5/Hsb59+6qiokLS8f51uv+uOxyOkxZLPXLkiPbv30//RLNNmzZN06dP16233qrExETddtttuueee5SXlyeJvonA0VJ90Qr/rSd0B4iQkBClpKSoqKjIPOZ2u1VUVKT09HQ/tgxtnWEYmjRpkl599VWtXbv2pOk5KSkpateunUff3LZtmyoqKsy+mZ6erk8++cTjX4qFhYWKiIg46Q+lwJm6+uqr9cknn2jz5s3mJzU1VaNHjzb/mb4Jf7niiitO2l7xiy++UK9evSRJ8fHxcjgcHv3T5XKppKTEo38eOHBApaWlZs3atWvldruVlpbWCk+BtujQoUOy2z3/iB8UFCS32y2JvonA0VJ9MT09Xe+++67q6+vNmsLCQl1yySUBMbVcEquXB5Lly5cboaGhxgsvvGB89tlnxoQJE4yoqCiPVXeBlnbXXXcZkZGRxrp164w9e/aYn0OHDpk1d955p9GzZ09j7dq1xqZNm4z09HQjPT3d/P7IkSNG//79jeHDhxubN282CgoKjPPPP9+YMWOGPx4JbdiJq5cbBn0T/rNhwwYjODjYePjhh40vv/zSePnll43w8HDjz3/+s1nzyCOPGFFRUcbf//534+OPPzauu+46Iz4+3vj+++/NmhEjRhgDBgwwSkpKjPfff99ISEgwRo0a5Y9HQhsxduxYo3v37sbq1auN7du3G6+88ooRHR1t3HfffWYNfROt5eDBg0Z5eblRXl5uSDIWLFhglJeXG998841hGC3TFw8cOGDExsYat912m7FlyxZj+fLlRnh4uPHMM8+0+vOeCqE7wDz55JNGz549jZCQEGPQoEHGhx9+6O8moY2T1OTn+eefN2u+//574ze/+Y3RqVMnIzw83Lj++uuNPXv2eFxnx44dxjXXXGO0b9/eiI6ONu69916jvr6+lZ8Gbd1/hm76Jvzp9ddfN/r372+EhoYaffr0MZYuXerxvdvtNmbPnm3ExsYaoaGhxtVXX21s27bNo+bf//63MWrUKOO8884zIiIijHHjxhkHDx5szcdAG+NyuYwpU6YYPXv2NMLCwowLLrjA+N3vfuexnRJ9E63l7bffbvLPmWPHjjUMo+X64kcffWQMHTrUCA0NNbp372488sgjrfWIZ8RmGIbhnzF2AAAAAADaNt7pBgAAAADARwjdAAAAAAD4CKEbAAAAAAAfIXQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoBgAAAADARwjdAAAAAAD4CKEbAAAAAAAfIXQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoBgAAAADAR4L93YDW4na7tXv3bnXs2FE2m83fzQEAAAAAWJhhGDp48KC6desmu/3U49nnTOjevXu34uLi/N0MAAAAAEAbsnPnTvXo0eOU358zobtjx46Sjv6GRERE+Lk1AAAAAAArc7lciouLM7PmqZwzobtxSnlERAShGwAAAADQIn7o9WUWUgMAAAAAwEcI3QAAAAAA+Mg5M70cAAAAQNvU0NCg+vp6fzcDbUy7du0UFBR01tchdAMAAACwJMMw5HQ6deDAAX83BW1UVFSUHA7HWW077bPQvWTJEs2bN09Op1NJSUl68sknNWjQoCZrP/30U+Xm5qq0tFTffPONHn/8cU2dOvWsrgkAAACgbWsM3DExMQoPDz+rYAScyDAMHTp0SFVVVZKkrl27NvtaPgndK1asUE5OjvLz85WWlqaFCxcqMzNT27ZtU0xMzEn1hw4d0gUXXKCbb75Z99xzT4tcEwAAAEDb1dDQYAbuLl26+Ls5aIPat28vSaqqqlJMTEyzp5r7ZCG1BQsW6I477tC4cePUr18/5efnKzw8XM8991yT9QMHDtS8efN06623KjQ0tEWuaVVut2F+AAAAADSt8R3u8PBwP7cEbVlj/zqbNQNafKS7rq5OpaWlmjFjhnnMbrcrIyNDxcXFrXbN2tpa1dbWmj+7XK5m3bu19c0tUO0Rt2w2acrVCZqacbG/mwQAAAAELKaUw5daon+1+Ej3vn371NDQoNjYWI/jsbGxcjqdrXbNvLw8RUZGmp+4uLhm3dtfDEMq+rzK380AAAAAAJyFNrtP94wZM1RdXW1+du7c6e8mnZHiGVfryVEDJEmGmGIOAAAAnEtuv/12ZWVl+bsZlrJu3TrZbLaAXcW+xaeXR0dHKygoSJWVlR7HKysr5XA4Wu2aoaGhp3w/PJB17hCijmFH/2cxyNwAAADAOeWJJ56QQRBoU1p8pDskJEQpKSkqKioyj7ndbhUVFSk9PT1grhnIGt8b4P9rAAAAwLklMjJSUVFR/m4GWpBPppfn5OTo2Wef1YsvvqjPP/9cd911l2pqajRu3DhJ0pgxYzwWRaurq9PmzZu1efNm1dXVadeuXdq8ebO++uqrM75mW9L4qj6ZGwAAAGh7/va3vykxMVHt27dXly5dlJGRoZqaGkknTy8/ePCgRo8erQ4dOqhr1656/PHHddVVV2nq1KlmTe/evfXQQw9pzJgxOu+889SrVy+99tpr2rt3r6677jqdd955uuyyy7Rp0ybznH//+98aNWqUunfvrvDwcCUmJuovf/nLadv9zTff6Gc/+5k6deqkDh066NJLL9WaNWskHd3Cbfz48YqPj1f79u11ySWX6IknnvA4v/HZ/vjHPyo2NlZRUVH6wx/+oCNHjmjatGnq3LmzevTooeeff948Z8eOHbLZbFq+fLmGDBmisLAw9e/fX++8885p2/r+++/rRz/6kdq3b6+4uDjdfffd5u+xJD311FNKSEhQWFiYYmNjddNNN532emfDJ/t0Z2dna+/evcrNzZXT6VRycrIKCgrMhdAqKipktx/P+7t379aAAQPMnx977DE99thjGjZsmNatW3dG12xL7OZIN7EbAAAAOFOGYej7+oZWv2/7dkFnvMr1nj17NGrUKM2dO1fXX3+9Dh48qPfee++Uf/bPycnRBx98oNdee02xsbHKzc1VWVmZkpOTPeoef/xx/fGPf9Ts2bP1+OOP67bbbtOQIUP0y1/+UvPmzdP999+vMWPG6NNPP5XNZtPhw4eVkpKi+++/XxEREXrjjTd022236cILL9SgQYOabMvEiRNVV1end999Vx06dNBnn32m8847T9LRmcg9evTQypUr1aVLF61fv14TJkxQ165ddcstt5jXWLt2rXr06KF3331XH3zwgcaPH6/169fryiuvVElJiVasWKFf//rX+slPfqIePXqY502bNk0LFy5Uv379tGDBAv3sZz/T9u3bm9yj/Z///KdGjBihhx56SM8995z27t2rSZMmadKkSXr++ee1adMm3X333XrppZc0ZMgQ7d+/X++9994Z/e/XHDbjHEl2LpdLkZGRqq6uVkREhL+bc1offLVPo/+7RJfEdtT/3XOlv5sDAAAABJzDhw9r+/btio+PV1hYmCTpUN0R9cv9v1Zvy2d/yFR4yJmNZ5aVlSklJUU7duxQr169Tvr+9ttv14EDB7Rq1SodPHhQXbp00bJly8yR2OrqanXr1k133HGHFi5cKOnoSPePfvQjvfTSS5Ikp9Oprl27avbs2frDH/4gSfrwww+Vnp6uPXv2nHJdrGuvvVZ9+vTRY4891uT3l112mW688UbNmTPnjJ510qRJcjqd+tvf/mY+27p16/T111+bg7B9+vRRTEyM3n33XUlHR8wjIyP13//937r11lu1Y8cOxcfH65FHHtH9998vSTpy5Iji4+M1efJk3XfffVq3bp1+/OMf69tvv1VUVJR+9atfKSgoSM8884zZlvfff1/Dhg1TTU2N1qxZo3Hjxulf//qXOnbseNpnaKqfNTrTjOmTkW6cnePTy8+Jvw8BAAAAzhlJSUm6+uqrlZiYqMzMTA0fPlw33XSTOnXqdFLt119/rfr6eo+R58jISF1yySUn1V522WXmPzfOBk5MTDzpWFVVlRwOhxoaGvTHP/5Rf/3rX7Vr1y7V1dWptrZW4eHhp2z73XffrbvuuktvvfWWMjIydOONN3rcd8mSJXruuedUUVGh77//XnV1dSeNyF966aUes55jY2PVv39/8+egoCB16dJFVVWe2yefuJZXcHCwUlNT9fnnnzfZzo8++kgff/yxXn75ZfOYYRhyu93avn27fvKTn6hXr1664IILNGLECI0YMULXX3/9aZ/9bBC6A9Gx1H1uzEEAAAAAWkb7dkH67A+ZfrnvmQoKClJhYaHWr1+vt956S08++aR+97vfqaSkRPHx8c1uQ7t27cx/bpzq3tQxt9stSZo3b56eeOIJLVy4UImJierQoYOmTp2qurq6U97jV7/6lTIzM/XGG2/orbfeUl5enubPn6/Jkydr+fLl+u1vf6v58+crPT1dHTt21Lx581RSUnLKdja2q6ljje1sju+++06//vWvdffdd5/0Xc+ePRUSEqKysjKtW7dOb731lnJzc/XAAw9o48aNPlnErs3u021ltmOp203qBgAAAM6YzWZTeEhwq3/O9H3uE9t5xRVX6Pe//73Ky8sVEhKiV1999aS6Cy64QO3atdPGjRvNY9XV1friiy/O+vfqgw8+0HXXXadf/OIXSkpK0gUXXHBG142Li9Odd96pV155Rffee6+effZZ83pDhgzRb37zGw0YMEAXXXSR/vnPf551Oxt9+OGH5j8fOXJEpaWl6tu3b5O1l19+uT777DNddNFFJ31CQkIkHR0tz8jI0Ny5c/Xxxx9rx44dWrt2bYu190SMdAcge+NIt3+bAQAAAKCFlZSUqKioSMOHD1dMTIxKSkq0d+/eJgNkx44dNXbsWHNl75iYGM2ZM0d2u93roP+fEhIS9Le//U3r169Xp06dtGDBAlVWVqpfv36nPGfq1Km65pprdPHFF+vbb7/V22+/bbY7ISFBf/rTn/R///d/io+P10svvaSNGzee1ej9iZYsWaKEhAT17dtXjz/+uL799lv98pe/bLL2/vvv1+DBgzVp0iT96le/Mhd9Kyws1OLFi7V69Wp9/fXXuvLKK9WpUyetWbNGbre7yWn7LYHQHYDM/wORugEAAIA2JSIiQu+++64WLlwol8ulXr16af78+brmmmuarF+wYIHuvPNOXXvttYqIiNB9992nnTt3nrSol7dmzZqlr7/+WpmZmQoPD9eECROUlZWl6urqU57T0NCgiRMn6l//+pciIiI0YsQIPf7445KkX//61yovL1d2drZsNptGjRql3/zmN3rzzTfPqp2NHnnkET3yyCPavHmzLrroIr322muKjo5usvayyy7TO++8o9/97nf60Y9+JMMwdOGFFyo7O1uSFBUVpVdeeUUPPPCADh8+rISEBP3lL3/RpZde2iJt/U+sXh6ANu7Yr5vzixUf3UFv//YqfzcHAAAACDinW1W6LaupqVH37t01f/58jR8/3t/N8bnG1cvLy8tPWpStNbB6eRtlrl5+bvx9CAAAAIBTKC8v19atWzVo0CBVV1ebW4Bdd911fm4ZzhShOwCZKwuSuQEAAIBz3mOPPaZt27YpJCREKSkpeu+99045tRqBh9AdgI6/0k3qBgAAAM5lAwYMUGlpqb+b4Te9e/e2/AxgtgwLQMenl/u1GQAAAACAs0ToDkCN08sJ3QAAAABgbYTuAGTu003qBgAAAE6LPzPDl1qifxG6A5Dt2ARz/vUBAAAANK1du3aSpEOHDvm5JWjLGvtXY39rDhZSC0DmQmqkbgAAAKBJQUFBioqKUlVVlSQpPDzcfE0TOFuGYejQoUOqqqpSVFSUgoKCmn0tQncAY/VyAAAA4NQcDockmcEbaGlRUVFmP2suQncAavwLOvbpBgAAAE7NZrOpa9euiomJUX19vb+bgzamXbt2ZzXC3YjQHYDsrF4OAAAAnLGgoKAWCUeAL/hsIbUlS5aod+/eCgsLU1pamjZs2HDa+pUrV6pPnz4KCwtTYmKi1qxZ4/F9ZWWlbr/9dnXr1k3h4eEaMWKEvvzyS18136+Ov4pC6gYAAAAAK/NJ6F6xYoVycnI0Z84clZWVKSkpSZmZmad812L9+vUaNWqUxo8fr/LycmVlZSkrK0tbtmyRdPQl9qysLH399df6+9//rvLycvXq1UsZGRmqqanxxSP4lbl6OZkbAAAAACzNZvhgY7u0tDQNHDhQixcvliS53W7FxcVp8uTJmj59+kn12dnZqqmp0erVq81jgwcPVnJysvLz8/XFF1/okksu0ZYtW3TppZea13Q4HPrjH/+oX/3qVz/YJpfLpcjISFVXVysiIqKFntQ3vqg8qOGPv6vOHUJUNvsn/m4OAAAAAOA/nGnGbPGR7rq6OpWWliojI+P4Tex2ZWRkqLi4uMlziouLPeolKTMz06yvra2VJIWFhXlcMzQ0VO+//36T16ytrZXL5fL4WIXdXEiNoW4AAAAAsLIWD9379u1TQ0ODYmNjPY7HxsbK6XQ2eY7T6TxtfZ8+fdSzZ0/NmDFD3377rerq6vToo4/qX//6l/bs2dPkNfPy8hQZGWl+4uLiWuDpWgvTywEAAACgLfDZQmotqV27dnrllVf0xRdfqHPnzgoPD9fbb7+ta665RnZ7048wY8YMVVdXm5+dO3e2cqubr3EhNR/M/AcAAAAAtKIW3zIsOjpaQUFBqqys9DheWVl5yk3FHQ7HD9anpKRo8+bNqq6uVl1dnc4//3ylpaUpNTW1yWuGhoYqNDT0LJ/GPxoXLydyAwAAAIC1tfhId0hIiFJSUlRUVGQec7vdKioqUnp6epPnpKene9RLUmFhYZP1kZGROv/88/Xll19q06ZNuu6661r2AQIA+3QDAAAAQNvQ4iPdkpSTk6OxY8cqNTVVgwYN0sKFC1VTU6Nx48ZJksaMGaPu3bsrLy9PkjRlyhQNGzZM8+fP18iRI7V8+XJt2rRJS5cuNa+5cuVKnX/++erZs6c++eQTTZkyRVlZWRo+fLgvHsGvmF4OAAAAAG2DT0J3dna29u7dq9zcXDmdTiUnJ6ugoMBcLK2iosLjXewhQ4Zo2bJlmjVrlmbOnKmEhAStWrVK/fv3N2v27NmjnJwcVVZWqmvXrhozZoxmz57ti+b7nblPt5/bAQAAAAA4Oz7ZpzsQWWmf7p37D+lHc99W+3ZB+vzBEf5uDgAAAADgP/htn260HPbpBgAAAABrI3QHILud6eUAAAAA0BYQugNQ45ZhpG4AAAAAsDZCdwAyVy8ndQMAAACApRG6A5C5ejmZGwAAAAAsjdAdgI690s1CagAAAABgcYTuQGROLwcAAAAAWBmhOwAxvRwAAAAA2gZCdwCy2Y7/s0HyBgAAAADLInQHIPsJqZvMDQAAAADWRegOQCcMdPNeNwAAAABYGKE7ADG9HAAAAADaBkJ3ALKdMNZN5AYAAAAA6yJ0B6ITRrrZqxsAAAAArIvQHYDsHtPL/dcOAAAAAMDZIXQHINuJL3UDAAAAACyL0B2APFYvZ6QbAAAAACzLZ6F7yZIl6t27t8LCwpSWlqYNGzactn7lypXq06ePwsLClJiYqDVr1nh8/91332nSpEnq0aOH2rdvr379+ik/P99Xzfcrj9XLWUoNAAAAACzLJ6F7xYoVysnJ0Zw5c1RWVqakpCRlZmaqqqqqyfr169dr1KhRGj9+vMrLy5WVlaWsrCxt2bLFrMnJyVFBQYH+/Oc/6/PPP9fUqVM1adIkvfbaa754BL+yn5C63WRuAAAAALAsn4TuBQsW6I477tC4cePMEenw8HA999xzTdY/8cQTGjFihKZNm6a+ffvqwQcf1OWXX67FixebNevXr9fYsWN11VVXqXfv3powYYKSkpJ+cATd6tinGwAAAACsq8VDd11dnUpLS5WRkXH8Jna7MjIyVFxc3OQ5xcXFHvWSlJmZ6VE/ZMgQvfbaa9q1a5cMw9Dbb7+tL774QsOHD2/pR/A7z+nlAAAAAACrCm7pC+7bt08NDQ2KjY31OB4bG6utW7c2eY7T6Wyy3ul0mj8/+eSTmjBhgnr06KHg4GDZ7XY9++yzuvLKK5u8Zm1trWpra82fXS5Xcx+p1dlOWEqNgW4AAAAAsC7LrF7+5JNP6sMPP9Rrr72m0tJSzZ8/XxMnTtQ//vGPJuvz8vIUGRlpfuLi4lq5xc3nuU83qRsAAAAArKrFR7qjo6MVFBSkyspKj+OVlZVyOBxNnuNwOE5b//3332vmzJl69dVXNXLkSEnSZZddps2bN+uxxx47aWq6JM2YMUM5OTnmzy6XyzLB+8R9usncAAAAAGBdLT7SHRISopSUFBUVFZnH3G63ioqKlJ6e3uQ56enpHvWSVFhYaNbX19ervr5edrtnc4OCguR2u5u8ZmhoqCIiIjw+VuGxT7ffWgEAAAAAOFstPtItHd3ea+zYsUpNTdWgQYO0cOFC1dTUaNy4cZKkMWPGqHv37srLy5MkTZkyRcOGDdP8+fM1cuRILV++XJs2bdLSpUslSRERERo2bJimTZum9u3bq1evXnrnnXf0pz/9SQsWLPDFI/iVjenlAAAAANAm+CR0Z2dna+/evcrNzZXT6VRycrIKCgrMxdIqKio8Rq2HDBmiZcuWadasWZo5c6YSEhK0atUq9e/f36xZvny5ZsyYodGjR2v//v3q1auXHn74Yd15552+eAS/8phe7sd2AAAAAADOjs04R4ZSXS6XIiMjVV1dbYmp5vEz3pBhSBt+d7ViOob5uzkAAAAAgBOcaca0zOrl5xpzrPuc+CsRAAAAAGibCN0BqnGKOZkbAAAAAKyL0B2gGke6z43J/wAAAADQNhG6A5T92Ei3m9QNAAAAAJZF6A5Ux4a6idwAAAAAYF2E7gB1fHo5sRsAAAAArIrQHaAat+omcwMAAACAdRG6A1TjO92EbgAAAACwLkJ3gDKnl/NWNwAAAABYFqE7QNkY6QYAAAAAyyN0B6jjI90AAAAAAKsidAcqcyE1YjcAAAAAWBWhO0A1LqTmJnMDAAAAgGURugNU45ZhTDAHAAAAAOsidAco851uMjcAAAAAWBahO0CZq5f7uR0AAAAAgOYjdAco+7GhbjdD3QAAAABgWYTugMU+3QAAAABgdT4L3UuWLFHv3r0VFhamtLQ0bdiw4bT1K1euVJ8+fRQWFqbExEStWbPG43ubzdbkZ968eb56BL+ymVuG+bcdAAAAAIDm80noXrFihXJycjRnzhyVlZUpKSlJmZmZqqqqarJ+/fr1GjVqlMaPH6/y8nJlZWUpKytLW7ZsMWv27Nnj8Xnuuedks9l04403+uIR/M5cSI23ugEAAADAsmyG0fJjqWlpaRo4cKAWL14sSXK73YqLi9PkyZM1ffr0k+qzs7NVU1Oj1atXm8cGDx6s5ORk5efnN3mPrKwsHTx4UEVFRWfUJpfLpcjISFVXVysiIqIZT9W6Bv+xSE7XYa2ePFT9u0f6uzkAAAAAgBOcacZs8ZHuuro6lZaWKiMj4/hN7HZlZGSouLi4yXOKi4s96iUpMzPzlPWVlZV64403NH78+FO2o7a2Vi6Xy+NjJUwvBwAAAADra/HQvW/fPjU0NCg2NtbjeGxsrJxOZ5PnOJ1Or+pffPFFdezYUTfccMMp25GXl6fIyEjzExcX5+WT+BfTywEAAADA+iy5evlzzz2n0aNHKyws7JQ1M2bMUHV1tfnZuXNnK7bw7Jn7dJO5AQAAAMCyglv6gtHR0QoKClJlZaXH8crKSjkcjibPcTgcZ1z/3nvvadu2bVqxYsVp2xEaGqrQ0FAvWx94yNwAAAAAYF0tPtIdEhKilJQUjwXO3G63ioqKlJ6e3uQ56enpJy2IVlhY2GT9//zP/yglJUVJSUkt2/AAYz/2v4yboW4AAAAAsKwWH+mWpJycHI0dO1apqakaNGiQFi5cqJqaGo0bN06SNGbMGHXv3l15eXmSpClTpmjYsGGaP3++Ro4cqeXLl2vTpk1aunSpx3VdLpdWrlyp+fPn+6LZAcUmppcDAAAAgNX5JHRnZ2dr7969ys3NldPpVHJysgoKCszF0ioqKmS3Hx9kHzJkiJYtW6ZZs2Zp5syZSkhI0KpVq9S/f3+P6y5fvlyGYWjUqFG+aHZAaVy9nAnmAAAAAGBdPtmnOxBZbZ/uq+a9rR3/PqS/3Zmu1N6d/d0cAAAAAMAJ/LZPN1qG/dhQt/uc+CsRAAAAAGibCN2B6tj08nNkIgIAAAAAtEmE7gDV+Eo3kRsAAAAArIvQHaBsNlYvBwAAAACrI3QHKHOkm9QNAAAAAJZF6A5QjQupEbkBAAAAwLoI3QHKZi6k5t92AAAAAACaj9Ad4AzGugEAAADAsgjdAYqF1AAAAADA+gjdAcp+bHq5m9QNAAAAAJZF6A5Q5jvd/m0GAAAAAOAsELoDlE2kbgAAAACwOkJ3gDo+0k3qBgAAAACrInQHqMaF1NxuPzcEAAAAANBshO4AdWygm3FuAAAAALAwQneAMqeXs3o5AAAAAFgWoTtAMdINAAAAANbns9C9ZMkS9e7dW2FhYUpLS9OGDRtOW79y5Ur16dNHYWFhSkxM1Jo1a06q+fzzz/Xzn/9ckZGR6tChgwYOHKiKigpfPYJfNb7TzUg3AAAAAFiXT0L3ihUrlJOTozlz5qisrExJSUnKzMxUVVVVk/Xr16/XqFGjNH78eJWXlysrK0tZWVnasmWLWfPPf/5TQ4cOVZ8+fbRu3Tp9/PHHmj17tsLCwnzxCH5nN6eX+7cdAAAAAIDmsxk+GEpNS0vTwIEDtXjxYkmS2+1WXFycJk+erOnTp59Un52drZqaGq1evdo8NnjwYCUnJys/P1+SdOutt6pdu3Z66aWXmtUml8ulyMhIVVdXKyIiolnXaE235Bdrw479emr05fppYld/NwcAAAAAcIIzzZgtPtJdV1en0tJSZWRkHL+J3a6MjAwVFxc3eU5xcbFHvSRlZmaa9W63W2+88YYuvvhiZWZmKiYmRmlpaVq1atUp21FbWyuXy+XxsRRGugEAAADA8lo8dO/bt08NDQ2KjY31OB4bGyun09nkOU6n87T1VVVV+u677/TII49oxIgReuutt3T99dfrhhtu0DvvvNPkNfPy8hQZGWl+4uLiWuDpWs/xhdRI3QAAAABgVZZYvdztdkuSrrvuOt1zzz1KTk7W9OnTde2115rTz//TjBkzVF1dbX527tzZmk0+a/ZjC6m5ydwAAAAAYFnBLX3B6OhoBQUFqbKy0uN4ZWWlHA5Hk+c4HI7T1kdHRys4OFj9+vXzqOnbt6/ef//9Jq8ZGhqq0NDQ5j6G37FPNwAAAABYX4uPdIeEhCglJUVFRUXmMbfbraKiIqWnpzd5Tnp6uke9JBUWFpr1ISEhGjhwoLZt2+ZR88UXX6hXr14t/ASBoTF0AwAAAACsq8VHuiUpJydHY8eOVWpqqgYNGqSFCxeqpqZG48aNkySNGTNG3bt3V15eniRpypQpGjZsmObPn6+RI0dq+fLl2rRpk5YuXWpec9q0acrOztaVV16pH//4xyooKNDrr7+udevW+eIR/M6mxn26/dwQAAAAAECz+SR0Z2dna+/evcrNzZXT6VRycrIKCgrMxdIqKipktx8fZB8yZIiWLVumWbNmaebMmUpISNCqVavUv39/s+b6669Xfn6+8vLydPfdd+uSSy7R//7v/2ro0KG+eAS/axzpdpO6AQAAAMCyfLJPdyCy2j7dY57boHe/2Kv5NyfpxpQe/m4OAAAAAOAEftunGy3j+JZhAAAAAACrInQHKFYvBwAAAADrI3QHKHOkm8wNAAAAAJZF6A5Q9mND3QYTzAEAAADAsgjdAer49HL/tgMAAAAA0HyE7oDVONINAAAAALAqQneAYqQbAAAAAKyP0B2g7MdCt5vUDQAAAACWRegOUDamlwMAAACA5RG6A5SNPcMAAAAAwPII3QHKfKfbv80AAAAAAJwFQneAsh1L3W43sRsAAAAArIrQHaDM2eV+bQUAAAAA4GwQugNU40g3r3QDAAAAgHURugMUI90AAAAAYH2E7gBlLqTGUDcAAAAAWBahO0DZmV4OAAAAAJbns9C9ZMkS9e7dW2FhYUpLS9OGDRtOW79y5Ur16dNHYWFhSkxM1Jo1azy+v/3222Wz2Tw+I0aM8FXz/e749HJSNwAAAABYlU9C94oVK5STk6M5c+aorKxMSUlJyszMVFVVVZP169ev16hRozR+/HiVl5crKytLWVlZ2rJli0fdiBEjtGfPHvPzl7/8xRfNDwzm9HL/NgMAAAAA0Hw+Cd0LFizQHXfcoXHjxqlfv37Kz89XeHi4nnvuuSbrn3jiCY0YMULTpk1T37599eCDD+ryyy/X4sWLPepCQ0PlcDjMT6dOnXzR/IBgO5a6ydwAAAAAYF0tHrrr6upUWlqqjIyM4zex25WRkaHi4uImzykuLvaol6TMzMyT6tetW6eYmBhdcskluuuuu/Tvf/+7pZsfMOzHRrrdDHUDAAAAgGUFt/QF9+3bp4aGBsXGxnocj42N1datW5s8x+l0NlnvdDrNn0eMGKEbbrhB8fHx+uc//6mZM2fqmmuuUXFxsYKCgk66Zm1trWpra82fXS7X2TxWq7MxvRwAAAAALK/FQ7ev3HrrreY/JyYm6rLLLtOFF16odevW6eqrrz6pPi8vT7///e9bs4ktymYupQYAAAAAsKoWn14eHR2toKAgVVZWehyvrKyUw+Fo8hyHw+FVvSRdcMEFio6O1ldffdXk9zNmzFB1dbX52blzp5dP4l/s0w0AAAAA1tfioTskJEQpKSkqKioyj7ndbhUVFSk9Pb3Jc9LT0z3qJamwsPCU9ZL0r3/9S//+97/VtWvXJr8PDQ1VRESEx8dKbMdSt5vMDQAAAACW5ZPVy3NycvTss8/qxRdf1Oeff6677rpLNTU1GjdunCRpzJgxmjFjhlk/ZcoUFRQUaP78+dq6daseeOABbdq0SZMmTZIkfffdd5o2bZo+/PBD7dixQ0VFRbruuut00UUXKTMz0xeP4He80w0AAAAA1ueTd7qzs7O1d+9e5ebmyul0Kjk5WQUFBeZiaRUVFbLbj+f9IUOGaNmyZZo1a5ZmzpyphIQErVq1Sv3795ckBQUF6eOPP9aLL76oAwcOqFu3bho+fLgefPBBhYaG+uIR/K7xjW6DTcMAAAAAwLJsxjny0rDL5VJkZKSqq6stMdV81qpP9OcPKzTl6gTd85OL/d0cAAAAAMAJzjRj+mR6Oc5e4+rl58jfiQAAAABAm0ToDlD2xne6/dsMAAAAAMBZIHQHqMbVyxnoBgAAAADrInQHOBZSAwAAAADrInQHKLYMAwAAAADrI3QHKPux1O0mdAMAAACAZRG6AxT7dAMAAACA9RG6A5TteOoGAAAAAFgUoTtAmauX+7kdAAAAAIDmI3QHqMaRbjcvdQMAAACAZRG6A5RNjHQDAAAAgNURugMUW4YBAAAAgPURugMUq5cDAAAAgPURugMUI90AAAAAYH2E7gBlb1y9nNQNAAAAAJZF6A5QbNMNAAAAANZH6A5U5ki3n9sBAAAAAGg2QneAYiE1AAAAALA+n4XuJUuWqHfv3goLC1NaWpo2bNhw2vqVK1eqT58+CgsLU2JiotasWXPK2jvvvFM2m00LFy5s4VYHjsZ3ut1kbgAAAACwLJ+E7hUrVignJ0dz5sxRWVmZkpKSlJmZqaqqqibr169fr1GjRmn8+PEqLy9XVlaWsrKytGXLlpNqX331VX344Yfq1q2bL5oeMFi9HAAAAACszyehe8GCBbrjjjs0btw49evXT/n5+QoPD9dzzz3XZP0TTzyhESNGaNq0aerbt68efPBBXX755Vq8eLFH3a5duzR58mS9/PLLateunS+aHjBs5j+RugEAAADAqlo8dNfV1am0tFQZGRnHb2K3KyMjQ8XFxU2eU1xc7FEvSZmZmR71brdbt912m6ZNm6ZLL730B9tRW1srl8vl8bESRroBAAAAwPpaPHTv27dPDQ0Nio2N9TgeGxsrp9PZ5DlOp/MH6x999FEFBwfr7rvvPqN25OXlKTIy0vzExcV5+ST+ZTPf6SZ1AwAAAIBVWWL18tLSUj3xxBN64YUXzDD6Q2bMmKHq6mrzs3PnTh+3smUx0g0AAAAA1tfioTs6OlpBQUGqrKz0OF5ZWSmHw9HkOQ6H47T17733nqqqqtSzZ08FBwcrODhY33zzje6991717t27yWuGhoYqIiLC42MltmNvdZO5AQAAAMC6glv6giEhIUpJSVFRUZGysrIkHX0fu6ioSJMmTWrynPT0dBUVFWnq1KnmscLCQqWnp0uSbrvttibf+b7ttts0bty4ln6EgNA40v3Wp06N2FXt8V1EWDs9fH1/JcR29EPLAAAAAABnqsVDtyTl5ORo7NixSk1N1aBBg7Rw4ULV1NSYAXnMmDHq3r278vLyJElTpkzRsGHDNH/+fI0cOVLLly/Xpk2btHTpUklSly5d1KVLF497tGvXTg6HQ5dccokvHsHvenYOlyS5Dh+Ry3nwpO//vnm3fpvZNp8dAAAAANoKn4Tu7Oxs7d27V7m5uXI6nUpOTlZBQYG5WFpFRYXs9uMz24cMGaJly5Zp1qxZmjlzphISErRq1Sr179/fF82zhGv6O7R68lAdOFTvcfyVsn/plfJdqv6+/hRnAgAAAAAChc0wzo2lulwulyIjI1VdXW2597tP9N/vfa2H3vhcP0/qpkWjBvi7OQAAAABwTjrTjGmJ1ctxXET7dpIk12FGugEAAAAg0BG6LSbyWOhmejkAAAAABD5Ct8UQugEAAADAOgjdFhMRdmx6+fdH/NwSAAAAAMAPIXRbTGR4Y+iu1zmyBh4AAAAAWBah22Iiwo7u8lbX4NbherefWwMAAAAAOB1Ct8WcFxqsILtNEiuYAwAAAECgI3RbjM1mM0e7WUwNAAAAAAIboduCzL26Cd0AAAAAENCC/d0AeK9x27Axz21Q8LGp5r7Ur1uE/jw+TcFB/B0NAAAAAHiD0G1Bl/fspI//Va1DdQ2tcr8Pv96vHf8+pItizmuV+wEAAABAW0HotqA5P+unX14RryNu369efsPT63XgUH2r3AsAAAAA2hpCtwXZbDb17BLeKvcKDT46pfxIA3uCAwAAAIC3eEkXpxVsP9pF6hsY6QYAAAAAbxG6cVrtgo4u1HbEzUg3AAAAAHiL0I3TCjq2Ojoj3QAAAADgPUI3TqvdsW3CGhjpBgAAAACv+Sx0L1myRL1791ZYWJjS0tK0YcOG09avXLlSffr0UVhYmBITE7VmzRqP7x944AH16dNHHTp0UKdOnZSRkaGSkhJfNR/HBDdOL2chNQAAAADwmk9C94oVK5STk6M5c+aorKxMSUlJyszMVFVVVZP169ev16hRozR+/HiVl5crKytLWVlZ2rJli1lz8cUXa/Hixfrkk0/0/vvvq3fv3ho+fLj27t3ri0fAMSykBgAAAADNZzMMo8WHMNPS0jRw4EAtXrxYkuR2uxUXF6fJkydr+vTpJ9VnZ2erpqZGq1evNo8NHjxYycnJys/Pb/IeLpdLkZGR+sc//qGrr776B9vUWF9dXa2IiIhmPtm55+b89dq441s9Nfpy/TSxq7+bAwAAAAAB4UwzZouPdNfV1am0tFQZGRnHb2K3KyMjQ8XFxU2eU1xc7FEvSZmZmaesr6ur09KlSxUZGamkpKSWazxOwkg3AAAAADRfcEtfcN++fWpoaFBsbKzH8djYWG3durXJc5xOZ5P1TqfT49jq1at166236tChQ+ratasKCwsVHR3d5DVra2tVW1tr/uxyuZrzOOc83ukGAAAAgOaz1OrlP/7xj7V582atX79eI0aM0C233HLK98Tz8vIUGRlpfuLi4lq5tW1D4+rlR9yMdAMAAACAt1o8dEdHRysoKEiVlZUexysrK+VwOJo8x+FwnFF9hw4ddNFFF2nw4MH6n//5HwUHB+t//ud/mrzmjBkzVF1dbX527tx5Fk917go29+lmpBsAAAAAvNXioTskJEQpKSkqKioyj7ndbhUVFSk9Pb3Jc9LT0z3qJamwsPCU9Sde98Qp5CcKDQ1VRESExwfeM0e6eacbAAAAALzW4u90S1JOTo7Gjh2r1NRUDRo0SAsXLlRNTY3GjRsnSRozZoy6d++uvLw8SdKUKVM0bNgwzZ8/XyNHjtTy5cu1adMmLV26VJJUU1Ojhx9+WD//+c/VtWtX7du3T0uWLNGuXbt08803++IRcIz5TrebkW4AAAAA8JZPQnd2drb27t2r3NxcOZ1OJScnq6CgwFwsraKiQnb78UH2IUOGaNmyZZo1a5ZmzpyphIQErVq1Sv3795ckBQUFaevWrXrxxRe1b98+denSRQMHDtR7772nSy+91BePgGMaVy8ndAMAAACA93yyT3cgYp/u5pn+vx9r+cad+u3wizXp/yX4uzkAAAAAEBD8tk832pbG6eUspAYAAAAA3iN047SOTy9nITUAAAAA8BahG6fVuGXYEUa6AQAAAMBrhG6cVvCxLcOYXg4AAAAA3iN047TamVuGMb0cAAAAALxF6MZpNb7TzUg3AAAAAHiP0I3Taly9/EgDI90AAAAA4C1CN07r+PRyRroBAAAAwFuEbpzW8enljHQDAAAAgLcI3TitxpHuBka6AQAAAMBrhG6cFluGAQAAAEDzEbpxWsF2tgwDAAAAgOYidOO02h0b6T7CSDcAAAAAeI3QjdNq3DKMhdQAAAAAwHuEbpxW4+rlbBkGAAAAAN4jdOO0zHe6GekGAAAAAK8RunFax6eXM9INAAAAAN7yWehesmSJevfurbCwMKWlpWnDhg2nrV+5cqX69OmjsLAwJSYmas2aNeZ39fX1uv/++5WYmKgOHTqoW7duGjNmjHbv3u2r5uMYcyE1Vi8HAAAAAK/5JHSvWLFCOTk5mjNnjsrKypSUlKTMzExVVVU1Wb9+/XqNGjVK48ePV3l5ubKyspSVlaUtW7ZIkg4dOqSysjLNnj1bZWVleuWVV7Rt2zb9/Oc/90XzcYLj08sZ6QYAAAAAb9kMw2jxNJWWlqaBAwdq8eLFkiS32624uDhNnjxZ06dPP6k+OztbNTU1Wr16tXls8ODBSk5OVn5+fpP32LhxowYNGqRvvvlGPXv2/ME2uVwuRUZGqrq6WhEREc18snNP6Tff6san16tn53C9e9+P/d0cAAAAAAgIZ5oxW3yku66uTqWlpcrIyDh+E7tdGRkZKi4ubvKc4uJij3pJyszMPGW9JFVXV8tmsykqKqpF2o2mtQtiITUAAAAAaK7glr7gvn371NDQoNjYWI/jsbGx2rp1a5PnOJ3OJuudTmeT9YcPH9b999+vUaNGnfJvFGpra1VbW2v+7HK5vHkMHNO4ZVg9W4ZZ2s79h/Txv6r93QwAAADgjA1NiFZk+3b+bsZZa/HQ7Wv19fW65ZZbZBiGnn766VPW5eXl6fe//30rtqxtYqTb+uob3Lr+qQ+077s6fzcFAAAAOGNv3D1Uke0j/d2Ms9bioTs6OlpBQUGqrKz0OF5ZWSmHw9HkOQ6H44zqGwP3N998o7Vr15523vyMGTOUk5Nj/uxyuRQXF+ft45zzghtXL2chNcsq++Zb7fuuTu3bBemyHtb/lxYAAADODR1CLDdG3KQWf4qQkBClpKSoqKhIWVlZko4upFZUVKRJkyY1eU56erqKioo0depU81hhYaHS09PNnxsD95dffqm3335bXbp0OW07QkNDFRoaetbPc65rXL289ohb//is8geqEYjWfLJHkvSTfrFaNGqAn1sDAAAAnFt88lcHOTk5Gjt2rFJTUzVo0CAtXLhQNTU1GjdunCRpzJgx6t69u/Ly8iRJU6ZM0bBhwzR//nyNHDlSy5cv16ZNm7R06VJJRwP3TTfdpLKyMq1evVoNDQ3m+96dO3dWSEiILx4DkkLbHR3prmtw61d/2uTn1uBsXHnx+f5uAgAAAHDO8Unozs7O1t69e5Wbmyun06nk5GQVFBSYi6VVVFTIbj++cPqQIUO0bNkyzZo1SzNnzlRCQoJWrVql/v37S5J27dql1157TZKUnJzsca+3335bV111lS8eA5JiOobpl1fEq7TiW383BWehW2SYfprY9OsdAAAAAHzHJ/t0ByL26QYAAAAAtBS/7dMNAAAAAACOInQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoBgAAAADARwjdAAAAAAD4SLC/G9BaDMOQdHQDcwAAAAAAzkZjtmzMmqdyzoTugwcPSpLi4uL83BIAAAAAQFtx8OBBRUZGnvJ7m/FDsbyNcLvd2r17tzp27Cibzebv5pySy+VSXFycdu7cqYiICH83B/BA/0Sgom8ikNE/EajomwhkVuifhmHo4MGD6tatm+z2U7+5fc6MdNvtdvXo0cPfzThjERERAdu5APonAhV9E4GM/olARd9EIAv0/nm6Ee5GLKQGAAAAAICPELoBAAAAAPARQneACQ0N1Zw5cxQaGurvpgAnoX8iUNE3EcjonwhU9E0EsrbUP8+ZhdQAAAAAAGhtjHQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCd4BZsmSJevfurbCwMKWlpWnDhg3+bhLauLy8PA0cOFAdO3ZUTEyMsrKytG3bNo+aw4cPa+LEierSpYvOO+883XjjjaqsrPSoqaio0MiRIxUeHq6YmBhNmzZNR44cac1HQRv3yCOPyGazaerUqeYx+ib8adeuXfrFL36hLl26qH379kpMTNSmTZvM7w3DUG5urrp27ar27dsrIyNDX375pcc19u/fr9GjRysiIkJRUVEaP368vvvuu9Z+FLQhDQ0Nmj17tuLj49W+fXtdeOGFevDBB3Xi2sn0TbSWd999Vz/72c/UrVs32Ww2rVq1yuP7luqLH3/8sX70ox8pLCxMcXFxmjt3rq8fzSuE7gCyYsUK5eTkaM6cOSorK1NSUpIyMzNVVVXl76ahDXvnnXc0ceJEffjhhyosLFR9fb2GDx+umpoas+aee+7R66+/rpUrV+qdd97R7t27dcMNN5jfNzQ0aOTIkaqrq9P69ev14osv6oUXXlBubq4/Hglt0MaNG/XMM8/osssu8zhO34S/fPvtt7riiivUrl07vfnmm/rss880f/58derUyayZO3euFi1apPz8fJWUlKhDhw7KzMzU4cOHzZrRo0fr008/VWFhoVavXq13331XEyZM8McjoY149NFH9fTTT2vx4sX6/PPP9eijj2ru3Ll68sknzRr6JlpLTU2NkpKStGTJkia/b4m+6HK5NHz4cPXq1UulpaWaN2+eHnjgAS1dutTnz3fGDASMQYMGGRMnTjR/bmhoMLp162bk5eX5sVU411RVVRmSjHfeeccwDMM4cOCA0a5dO2PlypVmzeeff25IMoqLiw3DMIw1a9YYdrvdcDqdZs3TTz9tREREGLW1ta37AGhzDh48aCQkJBiFhYXGsGHDjClTphiGQd+Ef91///3G0KFDT/m92+02HA6HMW/ePPPYgQMHjNDQUOMvf/mLYRiG8dlnnxmSjI0bN5o1b775pmGz2Yxdu3b5rvFo00aOHGn88pe/9Dh2ww03GKNHjzYMg74J/5FkvPrqq+bPLdUXn3rqKaNTp04e/12///77jUsuucTHT3TmGOkOEHV1dSotLVVGRoZ5zG63KyMjQ8XFxX5sGc411dXVkqTOnTtLkkpLS1VfX+/RN/v06aOePXuafbO4uFiJiYmKjY01azIzM+VyufTpp5+2YuvRFk2cOFEjR4706IMSfRP+9dprryk1NVU333yzYmJiNGDAAD377LPm99u3b5fT6fTon5GRkUpLS/Pon1FRUUpNTTVrMjIyZLfbVVJS0noPgzZlyJAhKioq0hdffCFJ+uijj/T+++/rmmuukUTfROBoqb5YXFysK6+8UiEhIWZNZmamtm3bpm+//baVnub0gv3dABy1b98+NTQ0ePzBUJJiY2O1detWP7UK5xq3262pU6fqiiuuUP/+/SVJTqdTISEhioqK8qiNjY2V0+k0a5rqu43fAc21fPlylZWVaePGjSd9R9+EP3399dd6+umnlZOTo5kzZ2rjxo26++67FRISorFjx5r9q6n+d2L/jImJ8fg+ODhYnTt3pn+i2aZPny6Xy6U+ffooKChIDQ0NevjhhzV69GhJom8iYLRUX3Q6nYqPjz/pGo3fnfjaj78QugGYJk6cqC1btuj999/3d1MA7dy5U1OmTFFhYaHCwsL83RzAg9vtVmpqqv74xz9KkgYMGKAtW7YoPz9fY8eO9XPrcC7761//qpdfflnLli3TpZdeqs2bN2vq1Knq1q0bfRPwE6aXB4jo6GgFBQWdtOpuZWWlHA6Hn1qFc8mkSZO0evVqvf322+rRo4d53OFwqK6uTgcOHPCoP7FvOhyOJvtu43dAc5SWlqqqqkqXX365goODFRwcrHfeeUeLFi1ScHCwYmNj6Zvwm65du6pfv34ex/r27auKigpJx/vX6f677nA4Tlos9ciRI9q/fz/9E802bdo0TZ8+XbfeeqsSExN122236Z577lFeXp4k+iYCR0v1RSv8t57QHSBCQkKUkpKioqIi85jb7VZRUZHS09P92DK0dYZhaNKkSXr11Ve1du3ak6bnpKSkqF27dh59c9u2baqoqDD7Znp6uj755BOPfykWFhYqIiLipD+UAmfq6quv1ieffKLNmzebn9TUVI0ePdr8Z/om/OWKK644aXvFL774Qr169ZIkxcfHy+FwePRPl8ulkpISj/554MABlZaWmjVr166V2+1WWlpaKzwF2qJDhw7Jbvf8I35QUJDcbrck+iYCR0v1xfT0dL377ruqr683awoLC3XJJZcExNRySaxeHkiWL19uhIaGGi+88ILx2WefGRMmTDCioqI8Vt0FWtpdd91lREZGGuvWrTP27Nljfg4dOmTW3HnnnUbPnj2NtWvXGps2bTLS09ON9PR08/sjR44Y/fv3N4YPH25s3rzZKCgoMM4//3xjxowZ/ngktGEnrl5uGPRN+M+GDRuM4OBg4+GHHza+/PJL4+WXXzbCw8ONP//5z2bNI488YkRFRRl///vfjY8//ti47rrrjPj4eOP77783a0aMGGEMGDDAKCkpMd5//30jISHBGDVqlD8eCW3E2LFjje7duxurV682tm/fbrzyyitGdHS0cd9995k19E20loMHDxrl5eVGeXm5IclYsGCBUV5ebnzzzTeGYbRMXzxw4IARGxtr3HbbbcaWLVuM5cuXG+Hh4cYzzzzT6s97KoTuAPPkk08aPXv2NEJCQoxBgwYZH374ob+bhDZOUpOf559/3qz5/vvvjd/85jdGp06djPDwcOP666839uzZ43GdHTt2GNdcc43Rvn17Izo62rj33nuN+vr6Vn4atHX/Gbrpm/Cn119/3ejfv78RGhpq9OnTx1i6dKnH926325g9e7YRGxtrhIaGGldffbWxbds2j5p///vfxqhRo4zzzjvPiIiIMMaNG2ccPHiwNR8DbYzL5TKmTJli9OzZ0wgLCzMuuOAC43e/+53Hdkr0TbSWt99+u8k/Z44dO9YwjJbrix999JExdOhQIzQ01OjevbvxyCOPtNYjnhGbYRiGf8bYAQAAAABo23inGwAAAAAAHyF0AwAAAADgI4RuAAAAAAB8hNANAAAAAICPELoBAAAAAPARQjcAAAAAAD5C6AYAAAAAwEcI3QAAAAAA+AihGwAAAAAAHyF0AwAAAADgI4RuAAAAAAB8hNANAAAAAICP/H8YVoSSaBOgGAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ploting_para(estimated_mu_IS_OS,estimated_sigma_IS_OS,mu_samples_IS_OS,sigma_samples_IS_OS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 507,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.stats import chi2\n",
+    "def para_hypo_test(estimated_mu_H0,estimated_sigma_H0,estimated_mu_H1,estimated_sigma_H1,OS):\n",
+    "    data = OS\n",
+    "    mu_0 = estimated_mu_H0\n",
+    "    sigma_0 = estimated_sigma_H0\n",
+    "\n",
+    "    # MLE estimates from the sample\n",
+    "    mu_hat = estimated_mu_H1\n",
+    "    sigma_hat = estimated_sigma_H1\n",
+    "\n",
+    "    \n",
+    "    # Log-likelihoods\n",
+    "    ll_null = log_likelihood(mu_0, sigma_0, data)\n",
+    "    ll_alternative = log_likelihood(mu_hat, sigma_hat, data)\n",
+    "\n",
+    "    # Likelihood ratio test statistic\n",
+    "    likelihood_ratio = -2 * (ll_null - ll_alternative)\n",
+    "\n",
+    "    # Critical value for chi-squared distribution with 2 degrees of freedom\n",
+    "    alpha = 0.1\n",
+    "    degrees_of_freedom = 1\n",
+    "    critical_value = chi2.ppf(1 - alpha, degrees_of_freedom)\n",
+    "\n",
+    "    print(\"Likelihood Ratio Test Statistic:\", likelihood_ratio)\n",
+    "    print(\"Critical Value (chi-squared with df=1):\", critical_value)\n",
+    "\n",
+    "    if likelihood_ratio > critical_value:\n",
+    "        print(\"Reject the null hypothesis.\")\n",
+    "        return \n",
+    "    else:\n",
+    "        print(\"Fail to reject the null hypothesis.\")\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 525,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/hy/_k7gcypd00s_2mmm5h2jktx00000gn/T/ipykernel_881/1553238095.py:8: RuntimeWarning: divide by zero encountered in log\n",
+      "  MLE_list.append(np.log(GBM_PDF(data_1,mu_proposal,sigma_proposal,data_inital)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Likelihood Ratio Test Statistic: 233.1168027026779\n",
+      "Critical Value (chi-squared with df=1): 2.705543454095404\n",
+      "Reject the null hypothesis.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# IS = weekly_data.iloc[500:1000,1]\n",
+    "# OS = weekly_data.iloc[1000:1500,1]\n",
+    "# IS_OS = weekly_data.iloc[500:1500,1]\n",
+    "IS, OS,IS_OS ,IS_log, OS_log, IS_OS_log = cal_period(position = daily_data.index.get_loc('2008-09-15 00:00:00'))\n",
+    "estimated_mu_IS_OS,estimated_sigma_IS_OS,mu_samples_IS_OS,sigma_samples_IS_OS = estimated_para(IS_OS,initial_params,iterations,proposal_width)\n",
+    "estimated_mu_OS,estimated_sigma_OS,mu_samples_OS,sigma_samples_OS = estimated_para(OS,initial_params,iterations,proposal_width)\n",
+    "estimated_mu_IS,estimated_sigma_IS,mu_samples_IS,sigma_samples_IS = estimated_para(IS,initial_params,iterations,proposal_width)\n",
+    "para_hypo_test(estimated_mu_IS,estimated_sigma_IS,estimated_mu_IS_OS,estimated_sigma_IS_OS,IS_OS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 513,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Likelihood Ratio Test Statistic: 2279.1862965485257\n",
+      "Critical Value (chi-squared with df=1): 2.705543454095404\n",
+      "Reject the null hypothesis.\n"
+     ]
+    }
+   ],
+   "source": [
+    "para_hypo_test(estimated_mu_IS,estimated_sigma_IS,estimated_mu_IS_OS,estimated_sigma_IS_OS,IS_OS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fromson",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}