From 687d4bde4b852a17aa75d67edc35196b04b01af9 Mon Sep 17 00:00:00 2001 From: liaozhaorun Date: Sat, 1 Mar 2025 18:10:27 +0800 Subject: [PATCH] =?UTF-8?q?(data=20leak)super=E5=88=86=E7=B1=BB=E6=A8=A1?= =?UTF-8?q?=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/train/UpdateClassify.ipynb | 713 ++++++++++++++++++++++---------- 1 file changed, 497 insertions(+), 216 deletions(-) diff --git a/code/train/UpdateClassify.ipynb b/code/train/UpdateClassify.ipynb index 4836a46..d422c96 100644 --- a/code/train/UpdateClassify.ipynb +++ b/code/train/UpdateClassify.ipynb @@ -8,14 +8,17 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-02-21T15:14:44.093352Z", - "start_time": "2025-02-21T15:14:44.054242Z" + "end_time": "2025-03-01T10:01:24.391606Z", + "start_time": "2025-03-01T10:01:24.069449Z" } }, "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n" + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "import pandas as pd\n", + "\n", + "pd.set_option('display.max_columns', None)\n" ], "outputs": [], "execution_count": 1 @@ -25,8 +28,8 @@ "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:15:27.929196Z", - "start_time": "2025-02-21T15:14:44.100246Z" + "end_time": "2025-03-01T10:02:01.510095Z", + "start_time": "2025-03-01T10:01:24.392790Z" } }, "source": [ @@ -66,7 +69,7 @@ "money flow\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 8296325 entries, 0 to 8296324\n", + "RangeIndex: 8360947 entries, 0 to 8360946\n", "Data columns (total 21 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -92,7 +95,7 @@ " 19 sell_elg_vol float64 \n", " 20 net_mf_vol float64 \n", "dtypes: bool(1), datetime64[ns](1), float64(18), object(1)\n", - "memory usage: 1.2+ GB\n", + "memory usage: 1.3+ GB\n", "None\n" ] } @@ -104,8 +107,8 @@ "id": "a4eec8c93f6a7cc3", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:15:34.291625Z", - "start_time": "2025-02-21T15:15:32.199591Z" + "end_time": "2025-03-01T10:02:04.058688Z", + "start_time": "2025-03-01T10:02:01.684757Z" } }, "source": [ @@ -134,14 +137,15 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-02-21T15:15:34.921902Z", - "start_time": "2025-02-21T15:15:34.830089Z" + "end_time": "2025-03-01T10:02:04.116043Z", + "start_time": "2025-03-01T10:02:04.067264Z" } }, "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", + "\n", "def calculate_indicators(df):\n", " \"\"\"\n", " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", @@ -165,6 +169,7 @@ "\n", " return df\n", "\n", + "\n", "def generate_index_indicators(h5_filename):\n", " df = pd.read_hdf(h5_filename, key='index_data')\n", " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", @@ -210,14 +215,15 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-02-21T15:15:35.064287Z", - "start_time": "2025-02-21T15:15:34.954902Z" + "end_time": "2025-03-01T10:02:04.160778Z", + "start_time": "2025-03-01T10:02:04.134780Z" } }, "source": [ "import numpy as np\n", "import talib\n", "\n", + "\n", "def get_technical_factor(df):\n", " # 按股票和日期排序\n", " df = df.sort_values(by=['ts_code', 'trade_date'])\n", @@ -229,10 +235,12 @@ "\n", " # 计算 ATR\n", " df['atr_14'] = grouped.apply(\n", - " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), index=x.index)\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n", + " index=x.index)\n", " )\n", " df['atr_6'] = grouped.apply(\n", - " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), index=x.index)\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n", + " index=x.index)\n", " )\n", "\n", " # 计算 OBV 及其均线\n", @@ -320,7 +328,8 @@ "\n", " # 计算 act_factor5 和 act_factor6\n", " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", - " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(df['act_factor1']**2 + df['act_factor2']**2)\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", + " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", "\n", " # 根据 trade_date 截面计算排名\n", " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", @@ -374,8 +383,8 @@ "id": "c0ad4f64-a6c6-4e43-b7ba-3f2a36baaa64", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:15:38.325796Z", - "start_time": "2025-02-21T15:15:35.070272Z" + "end_time": "2025-03-01T10:02:11.090680Z", + "start_time": "2025-03-01T10:02:04.160778Z" } }, "source": [ @@ -384,18 +393,19 @@ " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", " industry_data = industry_data.reindex()\n", - " industry_data['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", "\n", " grouped = industry_data.groupby('ts_code', group_keys=False)\n", " industry_data['obv'] = grouped.apply(\n", " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", " )\n", " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", - " # industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", "\n", - " # industry_data = get_act_factor(industry_data, cat=False)\n", - " # industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", "\n", " # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", " factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", @@ -403,21 +413,278 @@ " for factor in factor_columns:\n", " if factor in industry_data.columns:\n", " # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", - " industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(lambda x: x - x.median())\n", + " industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " lambda x: x - x.median())\n", "\n", - " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(lambda x: x.rank(pct=True))\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", "\n", - "\n", " industry_data = industry_data.rename(\n", " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", - " \n", + "\n", " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", " return industry_data\n", "\n", + "\n", "industry_df = read_industry_data('../../data/sw_daily.h5')\n" ], - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\4080813444.py:11: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " industry_data['obv'] = grouped.apply(\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n", + "E:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\numpy\\lib\\nanfunctions.py:1215: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n" + ] + } + ], "execution_count": 6 }, { @@ -426,8 +693,8 @@ "metadata": { "scrolled": true, "ExecuteTime": { - "end_time": "2025-02-21T15:15:38.404286Z", - "start_time": "2025-02-21T15:15:38.358425Z" + "end_time": "2025-03-01T10:02:11.106136Z", + "start_time": "2025-03-01T10:02:11.102732Z" } }, "source": [ @@ -442,9 +709,10 @@ "cell_type": "code", "id": "5f3d9aece75318cd", "metadata": { + "scrolled": true, "ExecuteTime": { - "end_time": "2025-02-21T15:16:35.813375Z", - "start_time": "2025-02-21T15:15:38.437172Z" + "end_time": "2025-03-01T10:03:15.010037Z", + "start_time": "2025-03-01T10:02:11.119055Z" } }, "source": [ @@ -471,12 +739,34 @@ "print(df.info())" ], "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:15: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['atr_14'] = grouped.apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:19: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['atr_6'] = grouped.apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:25: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['obv'] = grouped.apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:28: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['maobv_6'] = grouped.apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:34: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['rsi_3'] = grouped.apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:37: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['rsi_6'] = grouped.apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:40: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['rsi_9'] = grouped.apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\18893331.py:146: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Index: 5538535 entries, 1962 to 5538534\n", + "Index: 5573859 entries, 1962 to 5573857\n", "Data columns (total 71 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -559,43 +849,13 @@ ], "execution_count": 8 }, - { - "cell_type": "code", - "id": "f4f16d63ad18d1bc", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-21T15:16:36.157453Z", - "start_time": "2025-02-21T15:16:36.019648Z" - } - }, - "source": [ - "feature_columns = [col for col in df.columns if col not in ['trade_date',\n", - " 'ts_code',\n", - " 'label']]\n", - "feature_columns = [col for col in feature_columns if 'future' not in col]\n", - "feature_columns = [col for col in feature_columns if 'score' not in col]\n", - "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", - "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", - "print(feature_columns)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['turnover_rate', 'pe_ttm', 'volume_ratio', 'cat_l2_code', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n" - ] - } - ], - "execution_count": 9 - }, { "cell_type": "code", "id": "0ebdfb92-d88b-4b5c-a715-675dab876fc0", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:16:36.234795Z", - "start_time": "2025-02-21T15:16:36.171454Z" + "end_time": "2025-03-01T10:03:15.119034Z", + "start_time": "2025-03-01T10:03:15.095810Z" } }, "source": [ @@ -606,18 +866,19 @@ "\n", " # 自动选择所有数值型特征\n", " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'industry' not in col]\n", "\n", " # 遍历所有数值型特征\n", " for feature in num_features:\n", " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", " continue\n", "\n", - " grouped_median = df.groupby(['trade_date', groupby_col])[feature].transform('median')\n", - " deviation_col_name = f'deviation_median_{feature}'\n", - " new_columns[deviation_col_name] = df[feature] - grouped_median\n", - " ret_feature_columns.append(deviation_col_name)\n", + " # grouped_median = df.groupby(['trade_date', groupby_col])[feature].transform('median')\n", + " # deviation_col_name = f'deviation_median_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_median\n", + " # ret_feature_columns.append(deviation_col_name)\n", "\n", - " grouped_mean = df.groupby(groupby_col)[feature].transform('mean')\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", " deviation_col_name = f'deviation_mean_{feature}'\n", " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", " ret_feature_columns.append(deviation_col_name)\n", @@ -631,18 +892,21 @@ " return df, ret_feature_columns\n" ], "outputs": [], - "execution_count": 10 + "execution_count": 9 }, { "cell_type": "code", "id": "fbb968383f8cf2c7", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:23:09.675611Z", - "start_time": "2025-02-21T15:22:31.966842Z" + "end_time": "2025-03-01T10:03:33.404454Z", + "start_time": "2025-03-01T10:03:15.217954Z" } }, "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", "def get_qcuts(series, quantiles):\n", " q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n", " return q[-1] # 返回窗口最后一个元素的分位数标签\n", @@ -651,106 +915,95 @@ "window = 5\n", "quantiles = 20\n", "\n", + "\n", "def calculate_risk_adjusted_target(df, days=5):\n", " df = df.sort_values(by=['ts_code', 'trade_date'])\n", "\n", " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", "\n", - " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(level=0, drop=True)\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", " df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n", " df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n", "\n", " return df['sharpe_ratio']\n", "\n", - "def get_label(df):\n", - " # labels = df['future_af13'] - df['act_factor1']\n", - " df['future_close'] = df.groupby('ts_code')['close'].shift(-4)\n", - " df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n", - " df['future_high'] = df.groupby('ts_code')['high'].shift(-1)\n", - " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", - " labels = df['future_return'] >= 0.03\n", - " # labels = df['future_af11']\n", - " # labels = df['ema_5'].shift(-1) - df['close']\n", - " # df['label'] = (df['future_af11'] - df['act_factor1']) / df['act_factor1']\n", - " # df['label'] = calculate_risk_adjusted_target(df, days=5)\n", - " # lower_percentile = df['label'].quantile(0.01) # 1%分位数\n", - " # upper_percentile = df['label'].quantile(0.99) # 99%分位数\n", - " # labels = df['label'].clip(lower=lower_percentile, upper=upper_percentile)\n", - " # labels = calculate_risk_adjusted_return(df, days=3, history_days=3, method='ratio')\n", - " return labels\n", "\n", - "df['label'] = get_label(df)\n", + "future_close = df.groupby('ts_code')['close'].shift(-4)\n", + "future_return = (future_close - df['close']) / df['close']\n", + "df['label'] = future_return\n", + "\n", "# df = df.apply(lambda x: x.astype('float32') if x.dtype in ['float64', 'float32'] else x)\n", "df = df.sort_values(by=['trade_date', 'ts_code'])\n", "train_data = df[(df['trade_date'] <= '2023-01-01') & (df['trade_date'] >= '2016-01-01')]\n", - "test_data = df[df['trade_date'] >= '2023-01-01']\n", + "test_data = df[(df['trade_date'] >= '2023-01-01') & (df['trade_date'] <= '2025-02-26')]\n", "\n", - "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", - "# train_data = train_data.rename(columns={'l2_code': 'cat_l2_code'})\n", - "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", - "# test_data = test_data.rename(columns={'l2_code': 'cat_l2_code'})\n", + "train_data = train_data.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x.nlargest(1000, 'return_20')\n", + ")\n", + "test_data = test_data.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x.nlargest(1000, 'return_20')\n", + ")\n", "\n", - "train_data = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", - "test_data = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "test_data = test_data.merge(index_data, on='trade_date', how='left')\n", "\n", - "# train_data = get_future_data(train_data)\n", - "\n", - "# df = df[['ts_code', 'trade_date', 'open', 'close']]\n", - "\n", - "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n" + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)" ], - "outputs": [], - "execution_count": 22 + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\1029324748.py:36: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " train_data = train_data.groupby('trade_date', group_keys=False).apply(\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_11956\\1029324748.py:39: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " test_data = test_data.groupby('trade_date', group_keys=False).apply(\n" + ] + } + ], + "execution_count": 10 }, { "cell_type": "code", "id": "de8c2f6c770d2439", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:23:09.967463Z", - "start_time": "2025-02-21T15:23:09.768435Z" + "end_time": "2025-03-01T10:03:33.451800Z", + "start_time": "2025-03-01T10:03:33.446956Z" } }, "source": [ - "print(train_data.columns)" + "feature_columns = [col for col in train_data.columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "print(feature_columns)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", - " 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio', 'is_st',\n", - " 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol',\n", - " 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol',\n", - " 'cat_l2_code', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6',\n", - " 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10',\n", - " 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15',\n", - " 'std_return_25', 'std_return_90', 'std_return_90_2',\n", - " 'std_return_5 / std_return_90', 'std_return_5 / std_return_25',\n", - " 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20',\n", - " 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4',\n", - " 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5',\n", - " 'act_factor6', 'rank_act_factor1', 'rank_act_factor2',\n", - " 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big',\n", - " 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol',\n", - " 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022',\n", - " 'alpha_003', 'alpha_007', 'alpha_013', 'future_close', 'future_open',\n", - " 'future_high', 'future_return', 'label'],\n", - " dtype='object')\n" + "['turnover_rate', 'pe_ttm', 'volume_ratio', 'cat_l2_code', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry_ema_5', 'industry_ema_13', 'industry_ema_20', 'industry_ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_obv_deviation', 'industry_return_5_deviation', 'industry_return_20_deviation', 'industry_act_factor1_deviation', 'industry_act_factor2_deviation', 'industry_act_factor3_deviation', 'industry_act_factor4_deviation', 'industry_return_5_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return']\n" ] } ], - "execution_count": 23 + "execution_count": 11 }, { "cell_type": "code", "id": "20ffa7229c9d2f86", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:23:27.677186Z", - "start_time": "2025-02-21T15:23:10.008115Z" + "end_time": "2025-03-01T10:03:47.388358Z", + "start_time": "2025-03-01T10:03:33.451800Z" } }, "source": [ @@ -765,7 +1018,7 @@ "train_data = train_data.reset_index(drop=True)\n", "\n", "test_data = test_data.dropna(subset=feature_columns_new)\n", - "test_data = test_data.dropna(subset=['label'])\n", + "# test_data = test_data.dropna(subset=['label'])\n", "test_data = test_data.reset_index(drop=True)\n", "\n", "print(len(train_data))\n", @@ -780,26 +1033,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "feature_columns size: 149\n", - "feature_columns size: 149\n", - "1171702\n", - "最小日期: 2017-03-21\n", + "feature_columns size: 155\n", + "feature_columns size: 155\n", + "1166756\n", + "最小日期: 2017-04-06\n", "最大日期: 2022-12-30\n", - "402634\n", + "408601\n", "最小日期: 2023-01-03\n", - "最大日期: 2025-02-12\n" + "最大日期: 2025-02-26\n" ] } ], - "execution_count": 24 + "execution_count": 12 }, { "cell_type": "code", "id": "35238cb4f45ce756", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:23:27.898697Z", - "start_time": "2025-02-21T15:23:27.709949Z" + "end_time": "2025-03-01T10:03:47.536921Z", + "start_time": "2025-03-01T10:03:47.432730Z" } }, "source": [ @@ -809,7 +1062,7 @@ " test_data[col] = test_data[col].astype('category')" ], "outputs": [], - "execution_count": 25 + "execution_count": 13 }, { "cell_type": "code", @@ -819,8 +1072,8 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-02-21T15:23:28.055133Z", - "start_time": "2025-02-21T15:23:27.962370Z" + "end_time": "2025-03-01T10:03:48.200997Z", + "start_time": "2025-03-01T10:03:47.581003Z" } }, "source": [ @@ -828,26 +1081,26 @@ "import lightgbm as lgb\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import optuna\n", - "from sklearn.model_selection import KFold\n", - "from sklearn.metrics import mean_absolute_error\n", - "import os\n", - "import json\n", - "import pickle\n", - "import hashlib\n", + "\n", "\n", "def train_light_model(train_data_df, test_data_df, params, feature_columns, callbacks, evals,\n", " print_feature_importance=True, num_boost_round=100,\n", " use_optuna=False):\n", " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " print(f'categorical_feature: {categorical_feature}')\n", + "\n", " X_train = train_data_df[feature_columns]\n", " y_train = train_data_df['label']\n", "\n", " X_val = test_data_df[feature_columns]\n", " y_val = test_data_df['label']\n", "\n", - " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", - " print(f'categorical_feature: {categorical_feature}')\n", + " scaler = StandardScaler()\n", + " numeric_columns = X_train.select_dtypes(include=['float64', 'int64']).columns\n", + " X_train.loc[:, numeric_columns] = scaler.fit_transform(X_train[numeric_columns])\n", + " X_val.loc[:, numeric_columns] = scaler.transform(X_val[numeric_columns])\n", + "\n", " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", " val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_feature)\n", " model = lgb.train(\n", @@ -861,7 +1114,7 @@ " # lgb.plot_tree(model, figsize=(20, 8))\n", " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", " plt.show()\n", - " return model\n", + " return model, scaler\n", "\n", "\n", "from catboost import CatBoostClassifier\n", @@ -869,7 +1122,6 @@ "\n", "\n", "def train_catboost(train_data_df, test_data_df, feature_columns, params=None, plot=False):\n", - "\n", " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", " X_train = train_data_df[feature_columns]\n", " y_train = train_data_df['label']\n", @@ -882,7 +1134,6 @@ " train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n", " val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n", "\n", - "\n", " model = CatBoostClassifier(**params)\n", " model.fit(train_pool,\n", " eval_set=val_pool, plot=plot)\n", @@ -890,15 +1141,15 @@ " return model" ], "outputs": [], - "execution_count": 26 + "execution_count": 14 }, { "cell_type": "code", "id": "4a4542e1ed6afe7d", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:23:28.135106Z", - "start_time": "2025-02-21T15:23:28.060117Z" + "end_time": "2025-03-01T10:03:48.253523Z", + "start_time": "2025-03-01T10:03:48.248246Z" } }, "source": [ @@ -911,49 +1162,49 @@ " 'min_data_in_leaf': 1024,\n", " 'max_depth': 32,\n", " 'max_bin': 1024,\n", - " 'feature_fraction': 0.7,\n", - " 'bagging_fraction': 0.7,\n", - " 'bagging_freq': 5,\n", + " # 'feature_fraction': 0.5,\n", + " # 'bagging_fraction': 0.5,\n", + " # 'bagging_freq': 5,\n", " # 'lambda_l1': 80,\n", " # 'lambda_l2': 65,\n", " 'verbosity': -1,\n", - " 'num_threads' : 16\n", + " 'num_threads': 16\n", "}" ], "outputs": [], - "execution_count": 27 + "execution_count": 15 }, { "cell_type": "code", "id": "beeb098799ecfa6a", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:24:42.785511Z", - "start_time": "2025-02-21T15:23:28.198166Z" + "end_time": "2025-03-01T10:05:12.645870Z", + "start_time": "2025-03-01T10:03:48.301057Z" } }, "source": [ "print('train data size: ', len(train_data))\n", - " \n", + "\n", "evals = {}\n", - "model = train_light_model(train_data, test_data, light_params, feature_columns_new,\n", - " [lgb.log_evaluation(period=500),\n", - " lgb.callback.record_evaluation(evals),\n", - " lgb.early_stopping(50, first_metric_only=True)\n", - " ], evals,\n", - " num_boost_round=1000, use_optuna=False,\n", - " print_feature_importance=True)" + "model, scaler = train_light_model(train_data, test_data, light_params, feature_columns_new,\n", + " [lgb.log_evaluation(period=500),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=1000, use_optuna=False,\n", + " print_feature_importance=True)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "train data size: 1171702\n", + "train data size: 1166756\n", "categorical_feature: [3, 34, 35, 36, 37]\n", "Training until validation scores don't improve for 50 rounds\n", "Early stopping, best iteration is:\n", - "[89]\ttrain's average_precision: 0.473589\tvalid's average_precision: 0.284686\n", + "[92]\ttrain's average_precision: 0.816465\tvalid's average_precision: 0.50322\n", "Evaluated only: average_precision\n" ] }, @@ -962,7 +1213,7 @@ "text/plain": [ "
" ], - "image/png": 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" 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" 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NZffu3URHR1OlShVatWrF7du330lcBu+kVyGE+EAEBwezadOmt06i4uLimDx5Mrt37+b27ds4OjrSo0cPxowZg5GRUe4OVgghhBD5ztbWVmt7xowZlCpVCg8PD1QqlZIoZli4cCG1atXi6tWrFCtWDDc3N8LCwpT6UqVKMXXqVHr06EFqaioGBgbcu3ePixcv8uOPP1KpUiXlOIsWLeLMmTPY29vnelySGAohxH9w7tw50tPT+e677yhdujR//fUX/v7+PHnyhK+//jq/hyeEEEKIdyg5OZnVq1cTGBiISqXKss3jx49RqVQULFjwlf08fvwYS0tLDAyep2fW1taUK1eOlStXUq1aNdRqNd999x2FCxemevXq7yIUSQyFEB++9PR0Zs+ezdKlS7l27RqFCxfm888/Z8yYMQQFBbFx40auX7+Ovb09vr6+jB8/HkNDQ5YvX87EiRMBlH/MQ0JC8PPzy/axvb298fb2VrZLlizJ+fPnWbx4cbYTwzNnzjBixAj+/PNPNBoNVapUYfny5ZQqVYr09HSmTJnC999/zz///EO5cuWYMWOG1jEPHz7M559/TkxMDG5ubowZMybTMc6ePcuwYcP4448/MDMzo1mzZnzzzTfY2NhkO1aA2tN3kWpglqN9PnRqfQ2zaoFb8A6S0rL+0P8Y6WrcoLuxS9y6FTfobuz/Je64GS21tjdt2sSjR49e+d3h2bNnjBw5ku7du2NpaZllm/v37zN58mQ+//xzpSxj5rFt27ZYWFigp6dH4cKF2b59+2sTzP9CEkMhxAdv1KhRLF26lG+++Yb69etz69Ytzp07B4CFhQXLly/H0dGR06dP4+/vj4WFBSNGjKBLly789ddfbN++nd9//x2AAgUK/OfxPH78GCsrq2y1vXHjBg0bNsTT05Pdu3djaWnJvn37SE1NBWD+/PnMmTOH7777jqpVq/Ljjz/Spk0bzpw5Q5kyZXjy5AmtWrWiUaNGrF69msuXL/Pll19qHePWrVt4eHjg7+/P3Llzefr0KUFBQXTu3Jndu3dnOa6kpCSSkpKU7fj4eADUehr09TVvc1o+WGo9jdZ/dYWuxg26G7vErVtxg+7G/l/iTklJ0dpetmwZzZs3x9bWNlNdSkoKXbt2JS0tjfnz52eqh+efrz4+PpQrV47Ro0crbTQaDQEBAdja2hIZGYmJiQk//vgjrVq1Yv/+/Tg4OLzVeF9HpdFodOtPghDio/Lvv/9ia2vLt99+y2efffbG9rNnz+ann37i6NGjwNtdY6hSqdi4cSOffPJJprrY2FiqVavGnDlzsjWe0aNHs27dOs6fP4+hoWGm+iJFijBgwABGjx6tlNWqVYuaNWvyv//9j++//55Ro0Zx7do1TE1NAViyZAlffPEFx48fp0qVKowfP55Dhw6xY8cOpY/r16/j5OTE+fPncXFxyXTc4OBgZTb1RWvWrFGOI4QQQuiyu3fvEhAQQFBQELVr19aqS01NZfbs2dy5c4dJkyZlOVv49OlTgoODUavVjB07VuveBCdPnmTixImsXr1a63P3iy++oEmTJnTo0CFbY0xMTKR79+7KUtXXkRlDIcQHLSYmhqSkJBo3bpxl/S+//MK8efO4ePEiCQkJpKamvvEfxrd18+ZNvL296dSpU7aSQoATJ07QoEGDLJPC+Ph4bt68Sb169bTK69Wrx8mTJ4Hn8VeuXFnrQ8Pd3V2rfXR0NJGRkZibm2c6RmxsbJaJ4ahRowgMDNQai5OTE15eXlhbW2crto9FSkoKERERNG3aNMv36WOlq3GD7sYucetW3KC7sedW3JMmTcLOzo5x48Yp1wZm9N+tWzf+/fdf9u3bl+lmNfD8c7Vly5YULlyYzZs3Z/rRNT09HXh+ycqLn9/m5uaUKVMGHx+fbI0xY8VPdkhiKIT4oJmYmLyy7uDBg3Tt2pWJEyfSvHlzChQowLp165gzZ06uj+PmzZt4eXnh7u7O999/n+39Xjf+DC9fzK7RaJSy7Cz6SE9Pp3Xr1sycOTNT3auWoqjVatRqdaZyQ0NDnfry8CJdjV1X4wbdjV3i1j26Gvt/iTs9PZ2VK1fSq1cvrc/y1NRUunXrxrFjx9i6dSt6enrcv38fACsrK4yMjPj3339p2bIliYmJhIaG8vTpU54+fQo8v+Opvr4+DRo0oFChQnz22WeMHz8eExMTli5dSlxcHG3atMn2uHMSnzzHUAjxQStTpgwmJibs2rUrU92+ffsoXrw4Y8aMoUaNGpQpU4YrV65otTEyMiItLe0/jeHGjRt4enpSrVo1QkJC0NPL/j+tlSpV4s8//8zyGgBLS0scHR3Zu3evVvn+/fspV64cAOXLl+fkyZPKBwo8T4hfVK1aNc6cOYOzszOlS5fWepmZ6daNZIQQQojc8Pvvv3P16lX69OmjVX79+nU2b97M9evXqVKlCg4ODspr//79wPOVPIcOHeL06dOULl1aq821a9cAsLGxYfv27SQkJNCoUSNq1KjB3r17+fXXX6lcufI7iUlmDIUQHzRjY2OCgoIYMWIERkZG1KtXj3/++YczZ85QunRprl69yrp166hZsybbtm1j48aNWvs7Oztz+fJlTpw4QdGiRbGwsMhypuxVbt68iaenJ8WKFePrr7/mn3/+Ueqy84yhgQMHsnDhQrp27cqoUaMoUKAABw8epFatWri6ujJ8+HAmTJhAqVKlqFKlCiEhIZw4cYLQ0FAAunfvzpgxY+jbty9jx44lLi4u091QBwwYwNKlS+nWrRvDhw/HxsaGixcvsm7dOpYuXYq+vn624xVCCCEENGvWLMtVO87Ozm9czePp6ZmtFT81atTQuj/AuyYzhkKID964ceP46quvGD9+POXKlaNLly7cvXuXtm3bMnToUAYOHEiVKlXYv38/48aN09q3Q4cOeHt74+Xlha2tLWvXrs3RsXfu3MnFixfZvXs3RYsW1frVLzusra3ZvXs3CQkJeHh4UL16dZYuXaos/Rg8eDBfffUVX331FRUrVmT79u1s3ryZMmXKAM+vNdiyZQtnz56latWqjBkzJtOSUUdHR/bt20daWhrNmzfHzc2NL7/8kgIFCuRodlMIIYQQHy+5K6kQQog3io+Pp0CBAty7d08nbz4THh6Oj4+PTl2Do6txg+7GLnHrVtygu7HrUtwZn9/ZuSup/FQshBBCCCGEEDpOEkMhhHhBaGgo5ubmWb4qVKiQ4/4CAgJe2V9AQMA7iEAIIYQQIufk5jNCCPGCNm3aZHpIbYa3WW4yadIkhg0blmXdu3qeohBCCCFETkliKIQQL7CwsMDCwiLX+rOzs8POzi7X+hNCCCGEeBdkKakQQgghhBBC6DhJDIUQQugUZ2dnVCpVpteAAQOUNjExMbRp04YCBQpgZWXFiBEjuHr1qlJ/+/ZtPv30U+zt7TEzM6NatWr88ssv+RGOEEIIkSskMRRCiPfc8uXLKViwYKZyZ2dn5s2bl+fj+dAdOXKEW7duKa+IiAgAOnXqBEBsbCz169enbNmyREVFcfToUTp37oyxsbHSx6effsr58+fZvHkzp0+fpn379nTp0oXjx4/nS0xCCCHEfyWJoRBCCJ1ia2uLvb298tq6dSulSpXCw8MDgDFjxuDj48OsWbOoWrUqJUuWpEaNGlrXih44cIBBgwZRq1YtSpYsydixYylYsCDHjh3Lr7CEEEKI/0QSQyGEyAWenp4MHDiQgQMHUrBgQaytrRk7diwajQaA5ORkRowYQZEiRTAzM6N27dpERUW9sd+oqCh69+7N48ePlSWPwcHBeHp6cuXKFYYOHaqUw//PLm7duhVXV1dMTU3p2LEjT548YcWKFTg7O1OoUCEGDRpEWlrauzwlH4Tk5GRWr15Nnz59UKlUpKens23bNlxcXGjevDl2dnbUq1ePgwcPau1Xv359fvrpJx48eEB6ejrr1q0jKSkJT0/P/AlECCGE+I/krqRCCJFLVqxYQd++fTl06BBHjx6lX79+FC9eHH9/f3r37k1cXBzr1q3D0dGRjRs34u3tzenTpylTpswr+6xbty7z5s1j/PjxnD9/HgBzc3MGDx5M5cqV6devH/7+/lr7JCYmsmDBAtatW8e///5L+/btad++PQULFiQ8PJxLly7RoUMH6tevT5cuXbI8blJSEklJScp2fHw8AA1n/k6qodl/PVX54q/g5pnKfvnlFx49eoSvry8pKSncvn2bhIQEZsyYwcSJE5kyZQq//fabkow3atQIgNWrV+Pr64u1tTUGBgaYmpry888/U6xYMVJSUvI6tHciI46PJZ6c0NXYJW7diht0N3ZdijsnMUpiKIQQucTJyYlvvvkGlUqFq6srp0+f5ptvvqFRo0asXbuW69ev4+joCMCwYcPYvn07ISEhTJs27ZV9GhkZUaBAAVQqFfb29lp1+vr6WFhYZCpPSUlh8eLFlCpVCoCOHTuyatUq7ty5g7m5OeXLl8fLy4vIyMhXJobTp09n4sSJmcrHVk3H1PTDnGkMDw/PVDZ79myqVq3KiRMnOHHiBA8ePACgevXqlClThps3b1K5cmVq1KjB1KlTefbsGQDff/89ly9fZuLEiVhaWnLo0CE6duzItGnTcHZ2zsuw3rmMazB1ka7GLnHrHl2NXRfiTkxMzHZbSQyFECKX1KlTR1nSCeDu7s6cOXM4evQoGo0GFxcXrfZJSUlYW1vn+jhMTU2VpBCgcOHCODs7Y25urlV29+7dV/YxatQoAgMDle34+HicnJyYclyPVEP9XB9zXnh5xvDKlSucOnWK9evX4+PjAzxfWtqvXz8aN26slKWkpLBixQpu3bqFj48PsbGxhIeHc/z4cSpUqADAgAED8Pb25syZM/Tv3z9vA3tHUlJSiIiIoGnTphgaGub3cPKUrsYucetW3KC7setS3BkrfrJDEkMhhMgD+vr6REdHo6+vnVS9mKzllpc/5FQqVZZl6enpr+xDrVajVqszlf8R1OSdJLP5YfXq1djZ2dG2bVsMDJ5/HBoaGlKzZk0uXryodc5u3ryJs7MzhoaGyrIctVqt1ebFPj4mhoaGH11M2aWrsUvcukdXY9eFuHMSnySGQgiRS16+QcnBgwcpU6YMVatWJS0tjbt379KgQYMc92tkZJTljWJeVS7eLD09nZCQEHr16qUkdBmGDx9Oly5daNiwIV5eXmzbto0jR44wY8YMAMqWLUvp0qX5/PPP+frrr7G2tmbTpk1ERESwdevW/AhHCCGE+M/krqRCCJFLrl27RmBgIOfPn2ft2rUsXLiQL7/8EhcXF3x9fenZsycbNmzg8uXLHDlyhJkzZ2Z53dvLnJ2dSUhIYNeuXdy7d0+5XsDZ2Zk//viDGzducO/evXcd3kfl999/5+rVq/Tp0ydTXbt27ViyZAmzZs2iYsWK/PjjjwQFBVGvXj3g+a+v4eHh2Nra0rp1aypVqsTKlStZsWKFsvxUCCGE+NDIjKEQQuSSnj178vTpU2rVqoW+vj6DBg2iX79+AISEhDBlyhS++uorbty4gbW1Ne7u7tlKJOrWrUtAQABdunTh/v37TJgwgeDgYCZNmsTnn39OqVKlSEpKUh6NId6sWbNmrz1fffr0UZLGlJSUTAl8mTJlCAsLe6djFEIIIfKSJIZCCJFLDA0NmTdvHosXL86ybuLEiVne6TM7Fi9enKnfOnXqcPLkSa0yPz8//Pz8tMqCg4MJDg7WKlu+fPlbjUMIIYQQHydZSiqEEEIIIYQQOk4SQyGEyGctWrTA3Nw8y9frnnEohBBCCJFbZCmpEELkgqioqLfed9myZTx9+jTLOisrq7fuVwghhBAiuyQxFEKIfFakSJH8HoIQQgghdJwsJRVCCCGEEEIIHSeJoRBCiI+es7MzKpUq02vAgAGkpKQQFBRExYoVMTMzw9HRkZ49e3Lz5k2tPpYtW4anpyeWlpaoVCoePXqUP8EIIYQQ74AkhkIIIT56R44c4datW8orIiICgE6dOpGYmMixY8cYN24cx44dY8OGDVy4cIE2bdpo9ZGYmIi3tzejR4/OjxCEEEKId0oSQyGEyCFPT0+GDBmS38MA4OTJk3Tr1g0nJydMTEwoV64c8+fPz9Tu9OnTeHh4YGJiQpEiRZg0adJrH/D+sbG1tcXe3l55bd26lVKlSuHh4UGBAgWIiIigc+fOuLq6UqdOHRYuXEh0dDRXr15V+hg8eDAjR46kTp06+RiJEEII8W7IzWeEEOIFycnJGBkZ5cmxUlJSMDQ0/E99REdHY2try+rVq3FycmL//v3069cPfX19Bg4cCEB8fDxNmzbFy8uLI0eOcOHCBfz8/DAzM+Orr77KjVA+KMnJyaxevZrAwEBUKlWWbR4/foxKpaJgwYJ5OzghhBAin0hiKITQaZ6enri5uWFkZMTKlSupUKECixcvZtiwYfzxxx+YmZnRrFkzvvnmG2xsbPDz82PPnj3s2bNHmZm7fPkyUVFRDBkyROu6s02bNtGuXTtlZi44OJhNmzYxePBgpkyZQlxcHGlpaejp6bF06VK2bdvGjh07KFKkCHPmzMm0lDErffr00douWbIkBw4cYMOGDUpiGBoayrNnz1i+fDlqtRo3NzcuXLjA3LlzX5scZaX29F2kGphlu31+i5vRMlPZpk2bePToEX5+flnu8+zZM0aOHEn37t2xtLQkJSXlHY9SCCGEyH+SGAohdN6KFSv44osv2LdvHw8ePMDDwwN/f3/mzp3L06dPCQoKonPnzuzevZv58+dz4cIF3NzcmDRpEvB8mWJ2Xbx4kfXr1xMWFoa+vr5SPnHiRGbNmsXs2bNZuHAhvr6+XLly5a2eY/j48WOt/Q4cOICHhwdqtVopa968OaNGjSIuLo4SJUpk6iMpKYmkpCRlOz4+HgC1ngZ9/Q9nCWpWSd2yZcto3rw5tra2mepTUlLo2rUraWlpzJ8/n5SUFKVNxn9TU1OV7Y85aXw5bl2iq7FL3LoVN+hu7LoUd05ilMRQCKHzSpcuzaxZswAYP3481apVY9q0aUr9jz/+iJOTExcuXMDFxQUjIyNMTU2xt7fP8bGSk5NZtWpVpmTSz8+Pbt26ATBt2jQWLlzI4cOH8fb2zlH/Bw4cYP369Wzbtk0pu337Ns7OzlrtChcurNRllRhOnz6diRMnZiofWzUdU9O0HI0pP4WHh2tt3717l127dhEUFJSpLjU1ldmzZ3Pnzh0mTZrE3r17teozblhz+vRpAHbu3Im5ufk7HP37ISNuXaSrsUvcukdXY9eFuBMTE7PdVhJDIYTOq1GjhvL/0dHRREZGZvmFPzY2FhcXl/90rOLFi2c5w1ipUiXl/83MzLCwsODu3bs56vvMmTO0bduW8ePH07RpU626l5eLZixvfdUy0lGjRhEYGKhsx8fH4+TkxJTjeqQa6me5z/vor+DmWtuTJk3Czs6OcePGYWDw/x+BKSkpdOvWjX///Zd9+/ZpvUcpKSlERETQtGlTDA0NMTN7vpS2WbNmH/U1iC/HrUt0NXaJW7fiBt2NXZfizljxkx2SGAohdF7GF32A9PR0WrduzcyZMzO1c3BweGUfenp6me7ymdXyjReP9aKXP5hUKhXp6emvHfeLzp49S6NGjfD392fs2LFadfb29ty+fVurLCPpzJg5fJlardZaeprhj6AmWFtbZ3tc75P09HRWrlxJr169MDExUcpTU1Pp1q0bx44dY+vWrejp6XH//n0ArKyslPfm/v373L9/n7i4OADOnTuHhYUFxYoVe6slvx8KQ0PDj/6L06voauwSt+7R1dh1Ie6cxCeJoRBCvKBatWqEhYXh7OysNaP0IiMjI9LStJdT2tra8u+///LkyRMl+Ttx4sS7Hi7wfKawUaNG9OrVi6lTp2aqd3d3Z/To0Vp3XN25cyeOjo6Zlph+zH7//XeuXr2a6YY9169fZ/PmzQBUqVJFqy4yMpJ69eoB8P333zNlyhSlrmHDhgCEhIS88kY2QgghxIdCnmMohBAvGDBgAA8ePKBbt24cPnyYS5cusXPnTvr06aMkg87Ozhw6dIi4uDju3btHeno6tWvXxtTUlNGjR3Px4kXWrFnD8uXL3/l4z5w5g5eXF02bNiUwMJDbt29z+/Zt/vnnH6VN9+7dUavV+Pn58ddff7Fx40amTZuW4zuSfuiaNWuGRqPJtBzY2dkZjUaT5cvT01NpN378+CzbSFIohBDiYyCJoRBCvMDR0ZF9+/aRlpZG8+bNcXNz48svv6RAgQLo6T3/J3PYsGHo6+tTvnx5bG1tuXr1KlZWVqxevZrw8HAqVqzI2rVrCQ4Ofufj/fnnn/nnn38IDQ3FwcFBedWsWVNpk/EA9+vXr1OjRg369+9PYGCg1jWEQgghhNBtspRUCKHToqKiMpWVKVOGDRs2vHIfFxcXDhw4kKn8k08+4ZNPPtEq8/f3V/4/ODg4y2Tx5WsTAa3nIb7Oq/p8WcWKFfnjjz+y1acQQgghdI/MGAohhBBCCCGEjpPEUAgh3mMBAQGYm5tn+QoICMjv4QkhhBDiIyFLSYUQ4j02adIkhg0blmWdpaVlHo9GCCGEEB8rSQyFEOI9Zmdnh52dXX4PQwghhBAfOVlKKoQQQgghhBA6ThJDIcQHx9PTkyFDhuR6vw0bNmTNmjXZart8+XIKFiyY62N414YNG8bgwYPzexh54saNG/To0QNra2tMTU2pUqUK0dHRSr2fnx8qlUrrVadOHa0+bt++jZ+fH35+fhQsWJBq1arxyy+/5HUoQgghxDsniaEQQgBbt27l9u3bdO3aNVvtu3TpwoULF97xqHLfiBEjCAkJ4fLly/k9lHfq4cOH1KtXD0NDQ3777TfOnj3LnDlzMiXz3t7e3Lp1S3mFh4dr1X/66adcuHCB0aNHc+zYMdq3b0+XLl04fvx4HkYjhBBCvHuSGAohBLBgwQJ69+6tPMT+TUxMTF577V9KSkpuDS1X2dnZ0axZM5YsWZLfQ3mnZs6ciZOTEyEhIdSqVQtnZ2caN25MqVKltNqp1Wrs7e2Vl5WVlVb9gQMH6N+/Py4uLpQsWZKxY8dSsGBBjh07lpfhCCGEEO+cJIZCiA/aw4cP6dmzJ4UKFcLU1JQWLVrw999/a7VZunQpTk5OmJqa0q5dO+bOnas1c3Tv3j1+//132rRpo7Xfo0eP6NevH4ULF8bY2Bg3Nze2bt0KZF5KGhwcTJUqVfjxxx8pWbIkarUajUbz2j5eJ6P/rVu34urqiqmpKR07duTJkyesWLECZ2dnChUqxKBBg0hLSwNg1KhRmZZCAlSqVIkJEyYo223atGHt2rVvHMOHbPPmzdSoUYNOnTphZ2dH1apVWbp0aaZ2UVFR2NnZ4eLigr+/P3fv3tWqr1+/Pr/88gv//vsv6enprFu3jqSkJDw9PfMoEiGEECJvyF1JhRAfND8/P/7++282b96MpaUlQUFB+Pj4cPbsWQwNDdm3bx8BAQHMnDmTNm3a8PvvvzNu3DitPvbu3YupqSnlypVTytLT02nRogX//vsvq1evplSpUpw9exZ9ff1XjuXixYusX7+esLAw9PX136qPFyUmJrJgwQLWrVvHv//+S/v27Wnfvj0FCxYkPDycS5cu0aFDB+rXr0+XLl3w9fVlxowZxMbGKjNjZ86c4fTp01rXxdWqVYtr165x5coVihcvnpPTTe3pu0g1MMvRPnkpbkZLAC5dusTixYsJDAxk9OjRHD58mMGDB6NWq+nZsycALVq0oFOnThQvXpzLly8zbtw4GjVqRHR0NGq1GoCffvqJzp078+mnn9K7d29MTU3ZuHFjpplHIYQQ4kMniaEQ4oOVkRDu27ePunXrAhAaGoqTkxObNm2iU6dOLFy4kBYtWijPAnRxcWH//v1as3ZxcXEULlxYaxnp77//zuHDh4mJicHFxQWAkiVLvnY8ycnJrFq1CltbWwB27tyZ4z5elJKSwuLFi5UkpGPHjqxatYo7d+5gbm5O+fLl8fLyIjIyki5duuDm5kalSpVYs2aNkvyGhoZSs2ZN5fgARYoUUeJ+VWKYlJREUlKSsh0fHw+AWk+Dvr4m2zHktYwlvOnp6VSvXp2JEycC4ObmxunTp1m0aBHdunUDoH379sp+rq6uVK5cmdKlS/Prr7/Srl07AEaPHs2DBw+YOHEizZo1Izw8nE6dOrF7924qVqyYx9HlrYxz+b4ui36XdDV2iVu34gbdjV2X4s5JjJIYCiE+WDExMRgYGFC7dm2lzNraGldXV2JiYgA4f/688iU/Q61atbQSw6dPn2JsbKzV5sSJExQtWlQroXqT4sWLK0nh2/bxIlNTU62ZqcKFC+Ps7Iy5ublW2YvLH319ffnxxx8ZN24cGo2GtWvXZrqDq4mJCfB8RvJVpk+friRVLxpbNR1T07S3iicvZNw8pmDBgpibm2vdTCY1NZW///470w1mXmRjY8O2bdtQq9XcunWLRYsWsWDBAooVK8adO3eoXr06xYsXZ/To0XzxxRfvPJ73QURERH4PId/oauwSt+7R1dh1Ie7Xfda/TBJDIcQHS6PJeuZKo9GgUqky/f+r9rOxseHhw4daZRnJU06YmWkvsXybPl5kaGiota1SqbIsS09PV7a7d+/OyJEjOXbsGE+fPuXatWuZ7rT64MEDAK0k9mWjRo0iMDBQ2Y6Pj8fJyQkvLy+sra3fOqa80qhRI65fv46Pj49Stnv3blxcXLTKXnT//n0ePHiAh4cHPj4+nD59GoB69epx7do1mjZtiqGhIf/73/8oWrToK/v5WKSkpBAREaHErUt0NXaJW7fiBt2NXZfizljxkx2SGAohPljly5cnNTWVQ4cOKUtJ79+/z4ULF5TrBcuWLcvhw4e19jt69KjWdtWqVbl9+zYPHz6kUKFCwPMbtly/fp0LFy689YxfbvSRU0WLFqVhw4aEhoby9OlTmjRpQuHChbXa/PXXXxgaGlKhQoVX9qNWq5Xr7F5kaGj4QXyIfvXVV9StW5fZs2fTuXNnDh8+zLJly/j+++8xNDQkISGB4OBgOnTogIODA3FxcYwePRobGxs6deqEoaEhFStWpHTp0nz55Ze0adMGV1dXtm3bxu+//87WrVs/iPOQGz6U9/xd0NXYJW7do6ux60LcOYlP7koqhPhglSlThrZt2+Lv78/evXs5efIkPXr0oEiRIrRt2xaAQYMGER4ezty5c/n777/57rvv+O2337RmEatWrYqtrS379u1Tyjw8PGjYsCEdOnQgIiKCy5cv89tvv7F9+/Zsjy83+ngbvr6+rFu3jp9//pkePXpkqv/zzz9p0KDBf57RfJ/VrFmTjRs3snbtWtzc3Jg8eTLz5s3D19cXAH19fU6fPk3btm1xcXGhV69euLi4cODAASwsLIDnH6bh4eHY2NgwdepUqlevzsqVK1mxYsVHP1sohBBC90hiKIT4oIWEhFC9enVatWqFu7s7Go2G8PBw5ReyevXqsWTJEubOnUvlypXZvn07Q4cO1bqmUF9fnz59+hAaGqrVd1hYGDVr1qRbt26UL1+eESNGKI+GyK7c6COnOnXqxP3790lMTOSTTz7JVL927Vr8/f3f6RjeB61ateL06dM8e/aMmJgYrZhNTEzYsWMHd+/eJTk5mStXrrB8+XKcnJy0+ihTpgzr169nxYoVPHr0iJMnT/Lpp5/mdShCCCHEO6fSvOoiHSGE+Ej5+/tz7tw5/vzzT6Xszp07VKhQgejo6Bw/wuFDsm3bNoYPH86pU6cwMMj+1QTx8fEUKFCAe/fufRDXGOamlJQUwsPD8fHx+eiXHL1IV+MG3Y1d4tatuEF3Y9eluDM+vx8/foylpeVr28qMoRDio/f1119z8uRJLl68yMKFC1mxYgW9evXSalO4cGF++OEHrl69mk+jzBtPnjwhJCQkR0mhEEIIIT5+8s1ACPHRO3z4MLNmzeLff/+lZMmSLFiwgM8++yxTu4zrEvNCixYttGYsXzR69GhGjx79To7buXPnd9KvEEIIIT5skhgKIT5669evz+8hZLJs2TKePn2aZZ2VlVUej0YIIYQQuk4SQyGEyAdFihTJ7yEIIYQQQijkGkMhhBBCCCGE0HGSGAohhPjo3Lhxgx49emBtbY2pqSlVqlQhOjpaqffz80OlUmm96tSpk2VfGo2GSZMmYWRkxKZNm/IoAiGEECJvyVJSIYT4j27fvs2nn37K/v37MTQ05NGjR/k9JJ328OFD6tWrh5eXF7/99ht2dnbExsZSsGBBrXbe3t6EhIQo20ZGRln2t2DBgnc5XCGEEOK9IDOGQgjxguDgYKpUqZKjfb755htu3brFiRMnuHDhQq6Mw9PTkyFDhuRKXy/SaDS0aNEClUr10c5+zZw5EycnJ0JCQqhVqxbOzs40btyYUqVKabVTq9XY29srr6xu+nPy5Enmz5/PoEGD8mr4QgghRL6QxFAIIf6j2NhYqlevTpkyZbCzs8vv4WhJTk7W2p43bx4qlSqfRpM3Nm/eTI0aNejUqRN2dnZUrVqVpUuXZmoXFRWFnZ0dLi4u+Pv7c/fuXa36xMREunXrxrx58yhUqFBeDV8IIYTIF7KUVAjx0dm+fTtTpkzhr7/+Ql9fH3d3d+bPn6/MGF2/fp1hw4axc+dOkpKSKFeuHP/73/+IiYlh4sSJAEryFBISgp+f3yuP5ezszJUrVwBYuXIlvXr1Yvny5cydO5eQkBAuXbqElZUVrVu3ZtasWZibmyv77tu3j9GjR3PkyBHUajW1atVi3bp1DB06lD179rBnzx7mz58PwOXLl3F2dmbPnj0MHz6ckydPYmVlRa9evZgyZYrywHpPT0/c3NwwMjJi5cqVVKhQgT179gDPZ7/mzp3LkSNHcHBweKtzW3v6LlINzN5q37wQN6Mlly5dYvHixQQGBjJ69GgOHz7M4MGDUavV9OzZE3j+HMlOnTpRvHhxLl++zLhx42jUqBHR0dGo1WoAhg4dSt26dWnTpg3h4eH5GZYQQgjxzkliKIT46Dx58oTAwEAqVqzIkydPGD9+PO3atePEiRMkJibi4eFBkSJF2Lx5M/b29hw7doz09HS6dOnCX3/9xfbt2/n9998BKFCgwGuPdeTIEXr27ImlpSXz58/HxMQEAD09PRYsWICzszOXL1+mf//+jBgxgkWLFgFw4sQJGjduTJ8+fViwYAEGBgZERkaSlpbG/PnzuXDhAm5ubkyaNAkAW1tbbty4gY+PD35+fqxcuZJz587h7++PsbExwcHByphWrFjBF198wb59+9BoNMD/z359++232Nvbv/EcJiUlkZSUpGzHx8cDoNbToK+vyeY7kfdSUlJIT0+nevXqSpLv5ubG6dOnWbRoEd26dQOgffv2yj6urq5UrlyZ0qVL8+uvv9KuXTu2bNnC7t27OXz4MCkpKUrb1NRUre2PWUacuhLvi3Q1dolbt+IG3Y1dl+LOSYySGAohPjodOnTQ2v7hhx+ws7Pj7Nmz7N+/n3/++YcjR44o15SVLl1aaWtubo6BgUG2kid4nrCp1WpMTEy09nnx+sASJUowefJkvvjiCyUxnDVrFjVq1FC2ASpUqKD8v5GREaamplp9Llq0CCcnJ7799ltUKhVly5bl5s2bBAUFMX78ePT09JR4Zs2apTXOjNmvtm3bZiuu6dOnK4nVi8ZWTcfUNC1bfeSH8PBwChYsiLm5udYsX2pqKn///fdrZ/5sbGzYtm0barWakJAQYmNjsbGx0WrTpUsXypUrx9SpU99ZDO+biIiI/B5CvtHV2CVu3aOrsetC3ImJidluK4mhEOKjExsby7hx4zh48CD37t0jPT0dgKtXr3LixAmqVq2a5Y1GclNkZCTTpk3j7NmzxMfHk5qayrNnz3jy5AlmZmacOHGCTp065ajPmJgY3N3dta4RrFevHgkJCVy/fp1ixYoBUKNGDa39Nm/ezO7duzl+/Hi2jzVq1CgCAwOV7fj4eJycnJhyXI9UQ/0cjTsv/RXcnEaNGnH9+nV8fHyU8t27d+Pi4qJV9qL79+/z4MEDPDw88PHxoVq1aty7dw94nlQeOHCAL7/8kq+//pqWLVtSokSJPIknP6WkpBAREUHTpk0xNDTM7+HkKV2NXeLWrbhBd2PXpbgzVvxkhySGQoiPTuvWrXFycmLp0qU4OjqSnp6Om5sbycnJylLPd+nKlSv4+PgQEBDA5MmTsbKyYu/evfTt21dZ0vE249BoNJluHJOxVPTFcjMz7WsAd+/eneXjGjp06ECDBg2IiorKdCy1Wq1ca/eiP4KaYG1tneOx56WvvvqKunXrMnv2bDp37szhw4dZtmwZ33//PYaGhiQkJBAcHEyHDh1wcHAgLi6O0aNHY2NjQ6dOnTA0NMTJyQknJyfg+ReImzdvAs9nf11cXPIzvDxnaGj40X9xehVdjV3i1j26GrsuxJ2T+OSupEKIj8r9+/eJiYlh7NixNG7cmHLlyvHw4UOlvlKlSpw4cYIHDx5kub+RkRFpaf9tqeTRo0dJTU1lzpw51KlTBxcXFyWxeHEcu3btemUfWY2jfPny7N+/X0kGAfbv34+FhQVFihR5ZV8jR47k1KlTnDhxQnnB88dsvPgcv49FzZo12bhxI2vXrsXNzY3Jkyczb948fH19AdDX1+f06dO0bdsWFxcXevXqhYuLCwcOHMDCwiKfRy+EEELkD5kxFEJ8VAoVKoS1tTXff/89Dg4OXL16lZEjRyr13bp1Y9q0aXzyySdMnz4dBwcHjh8/jqOjI+7u7srNYk6cOEHRokWxsLDIcubsdUqVKkVqaioLFy6kdevW7Nu3jyVLlmi1GTVqFBUrVqR///4EBARgZGREZGQknTp1wsbGBmdnZw4dOkRcXBzm5uZYWVnRv39/5s2bx6BBgxg4cCDnz59nwoQJBAYGKtcXZiXjOX0vK1as2Ee7JLJVq1a0atUqyzoTExN27NiR4z6Tk5M/+l+WhRBC6C6ZMRRCfFT09PRYt24d0dHRuLm5MXToUGbPnq3UGxkZsXPnTuzs7PDx8aFixYrMmDEDff3n18116NABb29vvLy8sLW1Ze3atTkeQ5UqVZg7dy4zZ87Ezc2N0NBQpk+frtXGxcWFnTt3cvLkSWrVqoW7uzu//vqr8tiJYcOGoa+vT/ny5bG1teXq1asUKVKE8PBwDh8+TOXKlQkICKBv376MHTv2P5wxIYQQQgiZMRRCfISaNGnC2bNntcpeXH5ZvHhxfvnllyz3VavVr6x7lU2bNmUqGzp0KEOHDtUq+/TTT7W2PTw82LdvX5Z9ZixtfJmHhweHDx9+5Viyul4wKy+eDyGEEEIImTEUQgghhBBCCB0niaEQQrxGaGgo5ubmWb5efO6gEEIIIcSHTJaSCiHEa7Rp04batWtnWSc3IhFCCCHEx0ISQyGEeA0LCwt5hIEQQgghPnqylFQIIYQQQgghdJwkhkIIIT46N27coEePHlhbW2NqakqVKlWIjo5W6v38/FCpVFqvOnXqaPWRlJTEoEGDcHBwoEuXLrRr147r16/ndShCCCFEnpClpEIIIT4qDx8+pF69enh5efHbb79hZ2dHbGwsBQsW1Grn7e1NSEiIsm1kZKRVP2TIELZs2cLq1auJiYlh8+bNtGrViujoaOW5l0IIIcTHQhJDIcR7xdPTkypVqjBv3rz8HsoHydnZmSFDhjBkyJD8Hkq+mTlzJk5OTlpJn7Ozc6Z2arUae3v7LPt4/PgxP/zwA6tWraJx48YkJSWxfPlySpYsye+//07z5s3f1fCFEEKIfCFLSYUQH52UlJT8HsJr5XR8Go2G1NTUdzSaj8/mzZupUaMGnTp1ws7OjqpVq7J06dJM7aKiorCzs8PFxQV/f3/u3r2r1EVHR5OSkkKzZs2UMkdHR9zc3Ni/f3+exCGEEELkJZkxFEK8N/z8/NizZw979uxh/vz5AISEhDBkyBAePXqktNu0aRPt2rVDo9EAEBwczKZNmxg8eDBTpkwhLi6OtLQ09PT0WLp0Kdu2bWPHjh0UKVKEOXPm0KZNG6WvPXv2MHz4cE6ePImVlRW9evViypQpGBgY8N133zFp0iSuXbuGnt7//47Wpk0bChUqxIoVKwDYsmULwcHBnDlzBkdHR3r16sWYMWMwMHj+T6xKpWLx4sX89ttv/P777wwbNoyJEye+8jxERUXh5eXF9u3bGTNmDKdOnWLHjh0UK1aMwMBADh48yJMnTyhXrhzTp0+nSZMmwPPZ1itXrjB06FCGDh0KoJyj/fv3M3LkSI4cOYKNjQ3t2rVj+vTpmJmZ5eg9qj19F6kGOdsnL8XNaMmlS5dYvHgxgYGBjB49msOHDzN48GDUajU9e/YEoEWLFnTq1InixYtz+fJlxo0bR6NGjYiOjkatVnP79m2MjIwoVKiQViJfuHBhbt++nV/hCSGEEO+MJIZCiPfG/PnzuXDhAm5ubkyaNAmAbdu2ZWvfixcvsn79esLCwrSu/5o4cSKzZs1i9uzZLFy4EF9fX65cuYKVlRU3btzAx8cHPz8/Vq5cyblz5/D398fY2Jjg4GA6derE4MGDiYyMpHHjxsDz69d27NjBli1bANixYwc9evRgwYIFNGjQgNjYWPr16wfAhAkTlHFMmDCB6dOn880332T7+rQRI0bw9ddfU7JkSQoWLMj169fx8fFhypQpGBsbs2LFClq3bs358+cpVqwYGzZsoHLlyvTr1w9/f3+ln9OnT9O8eXMmT57MDz/8wD///MPAgQMZOHCg1nLLFyUlJZGUlKRsx8fHA6DW06Cvr8nW+PNDSkoK6enpVK9eXUm+3dzcOH36NIsWLaJbt24AtG/fXtnH1dWVypUrU7p0aX799VfatWunzNCmpKQoiWFG3xqN5r2flc4NL8ata3Q1dolbt+IG3Y1dl+LOSYySGAoh3hsFChTAyMgIU1NT5dqv7CZRycnJrFq1CltbW61yPz8/JRmYNm0aCxcu5PDhw3h7e7No0SKcnJz49ttvUalUlC1blps3bxIUFMT48eOxsrLC29ubNWvWKInhzz//jJWVlbI9depURo4cSa9evQAoWbIkkydPZsSIEVqJYffu3enTp0+OzsekSZNo2rSpsm1tbU3lypWV7SlTprBx40Y2b97MwIEDsbKyQl9fHwsLC61r52bPnk337t2V6w7LlCnDggUL8PDwYPHixRgbG2c69vTp07Oc1RxbNR1T07QcxZGXwsPDKViwIObm5oSHhyvlqamp/P3331plL7OxsWHbtm2o1WquXLlCcnIy69evx9zcHICIiAhiY2OxsbF5bT8fm4iIiPweQr7R1dglbt2jq7HrQtyJiYnZbiuJoRDio1C8ePFMSSFApUqVlP83MzPDwsJCuZYsJiYGd3d3VCqV0qZevXokJCRw/fp1ihUrhq+vL/369WPRokWo1WpCQ0Pp2rWrkrBGR0dz5MgRpk6dqvSRlpbGs2fPSExMxNTUFIAaNWrkOKaX93ny5AkTJ05k69at3Lx5k9TUVJ4+fcrVq1df2090dDQXL14kNDRUKdNoNKSnp3P58mXKlSuXaZ9Ro0YRGBiobMfHx+Pk5ISXlxfW1tY5jiUvNWrUSJldzbB7925cXFy0yl50//59Hjx4gIeHBz4+PtSrV4/JkyejUqlo2rQpERERuLm5cfXqVb799lutaw8/VikpKURERNC0aVMMDQ3zezh5Sldjl7h1K27Q3dh1Ke6MFT/ZIYmhEOK9pqenp1wnlyGrZRGvulbu5X/wVSoV6enpwPPk6MWkMKMsox1A69atSU9PZ9u2bdSsWZM///yTuXPnKu3T09OZOHGi1tLEDC/OxOX0Wr6s9hk+fDg7duzg66+/pnTp0piYmNCxY0eSk5Nf2096ejqff/45gwcPzlRXrFixLPdRq9Wo1epM5YaGhu/9h+hXX31F3bp1mT17Np07d+bw4cMsW7aM77//HkNDQxISEggODqZDhw44ODgQFxfH6NGjsbGxoVOnThgaGmJjY0Pfvn0JCgrC2tqaS5cuMX/+fCpWrIi3t7dOPa7iQ3jP3xVdjV3i1j26GrsuxJ2T+CQxFEK8V4yMjEhL+/+lira2tvz77788efJESZROnDiRK8cqX748YWFhWgni/v37sbCwoEiRIgCYmJjQvn17QkNDuXjxIi4uLlSvXl3po1q1apw/f57SpUvnyphe588//8TPz4927doBkJCQQFxcnFabl89fxhjPnDmTJ2N8H9SsWZONGzcyatQoJk2aRIkSJZg3bx6+vr7A8+XJp0+fZuXKlTx69AgHBwe8vLz46aefsLCwUPr55ptvMDAwoHv37iQkJNCkSRO2bNmiU0mhEEII3SGJoRDiveLs7MyhQ4eIi4vD3Nyc2rVrY2pqyujRoxk0aBCHDx9m+fLluXKs/v37M2/ePAYNGsTAgQM5f/48EyZMIDAwUOsupL6+vrRu3ZozZ87Qo0cPrT7Gjx9Pq1atcHJyolOnTujp6XHq1ClOnz7NlClTcmWcGUqXLs2GDRto3bo1KpWKcePGKbOfGZydnfnjjz/o2rUrarUaGxsbgoKCqFOnDgMGDMDf3x8zMzNiYmKIiIhg4cKFuTrG90WrVq1o1apVlnUmJibs2LHjjX0YGxuzcOFC5s6dS3h4OD4+Ph/9L8tCCCF0lzzHUAjxXhk2bBj6+vqUL18eW1tb4uPjWb16NeHh4VSsWJG1a9cSHBycK8cqUqQI4eHhHD58mMqVKxMQEEDfvn0ZO3asVrtGjRphZWXF+fPn6d69u1Zd8+bN2bp1KxEREdSsWZM6deowd+5cihcvnitjfNE333xDoUKFqFu3Lq1bt6Z58+ZUq1ZNq82kSZOIi4ujVKlSyjWXlSpVYs+ePfz99980aNCAqlWrMm7cOBwcHHJ9jEIIIYT4MKk0L1+8I4QQQrwkPj6eAgUKcO/evff+5jO5LSUlRSdnDHU1btDd2CVu3YobdDd2XYo74/P78ePHWFpavratzBgKIYQQQgghhI6TxFAIIfJYQEAA5ubmWb4CAgLye3hCCCGE0EFy8xkhhMhjkyZNYtiwYVnWvWmZhxBCCCHEuyCJoRBC5DE7Ozvs7OzyexhCCCGEEApZSiqEEEIIIYQQOk4SQyH+A09PT4YMGZIrfcXFxaFSqf7zw9tzqx8hPhTBwcGoVCqtl729vVKfkJDAwIEDKVq0KCYmJpQrV47Fixdr9REbG0u7du2wtbXF0tKSzp07c+fOnbwORQghhMg3khgK8Z5wcnLi1q1buLm5ZXsfPz8/Pvnkk//cj8iZrM57fomLi6Nv376UKFECExMTSpUqxYQJE0hOTtZqd/XqVVq3bo2ZmRk2NjYMHjw4U5sPWYUKFbh165byOn36tFI3dOhQtm/fzurVq4mJiWHo0KEMGjSIX3/9FYAnT57QrFkzVCoVu3fvZt++fSQnJ9O6dWvS09PzKyQhhBAiT8k1hkK8J/T19bVmOfK7H12UnJyMkZFRnh0vLS0NlUqFnt7b/0Z37tw50tPT+e677yhdujR//fUX/v7+PHnyhK+//lo5TsuWLbG1tWXv3r3cv3+fXr16odFoWLhwYW6Fk68MDAxe+ef+wIED9OrVC09PTwD69evHd999x9GjR2nbti379u0jLi6O48ePKzf/CQkJwcrKit27d+Ph4ZFXYQghhBD5RmYMhcimJ0+e0LNnT8zNzXFwcGDOnDla9cnJyYwYMYIiRYpgZmZG7dq1iYqKAuDx48eYmJiwfft2rX02bNiAmZkZCQkJmZaApqWlac0Eubq6Mn/+fGXf4OBgVqxYwa+//qosn4uKispyKemePXuoVasWarUaBwcHRo4cSWpqqlLv6enJ4MGDGTFiBFZWVtjb2xMcHJztc6NSqfjuu+9o1aoVpqamlCtXjgMHDnDx4kU8PT0xMzPD3d2d2NhYrf22bNlC9erVMTY2pmTJkkycOFFrXHPnzqVixYqYmZnh5ORE//79SUhIUOqXL19OwYIF2bFjB+XKlcPc3Bxvb29u3bqVrXFnzPxNnz4dR0dHXFxcALhx4wZdunShUKFCWFtb07ZtW+Li4l573qOiolCpVDx69Ejp/8SJE6hUKmXfjPFu3bqV8uXLo1aruXLlCs7OzkybNo0+ffpgYWFBsWLF+P7777MVg7e3NyEhITRr1oySJUvSpk0bhg0bxoYNG5Q2O3fu5OzZs6xevZqqVavSpEkT5syZw9KlS4mPj8/Wcd53f//9N46OjpQoUYKuXbty6dIlpa5+/fps3ryZGzduoNFoiIyM5MKFCzRv3hyApKQkVCoVarVa2cfY2Bg9PT327t2b57EIIYQQ+UFmDIXIpuHDhxMZGcnGjRuxt7dn9OjRREdHU6VKFQB69+5NXFwc69atw9HRkY0bN+Lt7c3p06cpU6YMLVu2JDQ0FG9vb6XPNWvW0LZtW8zNzbl3757W8dLT0ylatCjr16/HxsaG/fv3069fPxwcHOjcuTPDhg0jJiaG+Ph4QkJCALCysuLmzZta/dy4cQMfHx/8/PxYuXIl586dw9/fH2NjY63kb8WKFQQGBnLo0CEOHDiAn58f9erVo2nTptk6P5MnT2bu3LnMnTuXoKAgunfvTsmSJRk1ahTFihWjT58+DBw4kN9++w2AHTt20KNHDxYsWECDBg2IjY2lX79+AEyYMAEAPT09FixYgLOzM5cvX6Z///6MGDGCRYsWKcdNTEzk66+/ZtWqVejp6dGjRw+GDRtGaGhotsa9a9cuLC0tiYiIQKPRkJiYiJeXFw0aNOCPP/7AwMCAKVOm4O3tzalTp1553vfv35+t4yUmJjJ9+nSWLVuGtbW1cnfSOXPmMHnyZEaPHs0vv/zCF198QcOGDSlbtmy2+n3R48ePsbKyUrYPHDiAm5sbjo6OSlnz5s1JSkoiOjoaLy+vTH0kJSWRlJSkbGckkA1n/k6qoVmOx/Su/BXcnOrVq/Pjjz9SpkwZ7t69y/Tp06lbty4nTpzA2tqaOXPmEBAQQNGiRTEwMEBPT48lS5ZQu3ZtUlJSqF69OmZmZgwfPpzJkyej0WgYPXo06enp3Lhxg5SUFADlv7pCV+MG3Y1d4tatuEF3Y9eluHMSo0qj0Wje4ViE+CgkJCRgbW3NypUr6dKlCwAPHjygaNGi9OvXj0GDBlGmTBmuX7+u9eW7SZMm1KpVi2nTprFx40Z69uzJnTt3MDU1JT4+nsKFCxMWFoaPjw9xcXGUKFGC48ePK8nmywYMGMCdO3f45ZdfgOczXo8ePWLTpk1Km5f7GTNmDGFhYcTExKBSqQBYtGgRQUFBPH78GD09PTw9PUlLS+PPP/9U+qlVqxaNGjVixowZbzw/KpWKsWPHMnnyZAAOHjyIu7s7P/zwA3369AFg3bp19O7dm6dPnwLQsGFDWrRowahRo5R+Vq9ezYgRIzIltxl+/vlnvvjiCyWJXr58Ob179+bixYuUKlVKiW3SpEncvn37jeP28/Nj+/btXL16VVlC+uOPPzJr1iyt85WcnEzBggXZtGkTzZo1y/K8R0VF4eXlxcOHDylYsCDwfMawatWqXL58GWdnZ2W8J06coHLlysq+zs7ONGjQgFWrVgGg0Wiwt7dn4sSJOX7gfWxsLNWqVWPOnDl89tlnwPOlk3FxcezcuVOrrVqtZvny5XTr1i1TP8HBwUycODFT+Zo1azA1Nc3RmPLas2fPCAgIoF27drRt25ZNmzaxc+dO/Pz8sLOz48yZM6xatYpRo0Yp78Px48dZsmQJd+/eRaVS0aBBA65du4aLi0uO3wMhhBDifZGYmEj37t15/PjxG5+VLDOGQmRDbGwsycnJuLu7K2VWVla4uroCcOzYMTQajbIUMUNSUhLW1tYAtGzZEgMDAzZv3kzXrl0JCwvDwsKCZs2avfK4S5YsYdmyZVy5coWnT5+SnJz8yqTxVWJiYnB3d1eSHIB69eqRkJDA9evXKVasGACVKlXS2s/BwYG7d+9m+zgv7l+4cGEAKlasqFX27Nkz4uPjsbS0JDo6miNHjjB16lSlTVpaGs+ePSMxMRFTU1MiIyOZNm0aZ8+eJT4+ntTUVJ49e8aTJ08wM3s+a2VqaqokhW8z7ooVK2pdVxgdHc3FixexsLDQavfs2bNMS2HfhpGRUaZzDdrnL+OumjmJA+DmzZt4e3vTqVMnJSl8sc+XaTSaLMsBRo0aRWBgoLIdHx+Pk5MTU47rkWqon6NxvUt/BTfPsnzp0qUYGhri5eVFp06d+Pnnn/Hx8VHqU1NT2bdvn/LDhI+PD2PGjOHevXsYGBhQsGBBnJyc8PDwoGnTpkRERNC0aVMMDQ3zJK73QUpKik7GDbobu8StW3GD7sauS3Hn5JIRSQyFyIY3Taynp6ejr69PdHQ0+vraX5rNzc2B5wlBx44dWbNmDV27dmXNmjV06dIFA4Os/xquX7+eoUOHMmfOHNzd3bGwsGD27NkcOnQox2N/+ct/Rjwvlr/8D6NKpcrRHRlf3D+j36zKMvpMT09n4sSJtG/fPlNfxsbGXLlyBR8fHwICApg8eTJWVlbs3buXvn37ai2LyGrcOVkIkZFgZkhPT6d69epZLkW1tbV9ZT8ZN5B58dhZLd8wMTHJMhn7r+f/5s2beHl54e7unun6RHt7+0x/bh4+fEhKSoqSxL9MrVZrXXOX4Y+gJsqPHe+rpKQkzp07R8OGDYHn74ORkZHWOTY0NESj0WQ67w4ODgDs3r2bu3fv0q5dO6WNoaHhR/8FIiu6GjfobuwSt+7R1dh1Ie6cxCeJoRDZULp0aQwNDTl48KAyw/bw4UMuXLiAh4cHVatWJS0tjbt379KgQYNX9uPr60uzZs04c+YMkZGRytLLrPz555/UrVuX/v37K2Uvz1gZGRmRlpb22rGXL1+esLAwrQRx//79WFhYUKRIkTfG/q5Uq1aN8+fPU7p06Szrjx49SmpqKnPmzFGSrvXr1+fJuH766Sfs7OxeueQiq/OekTTeunWLQoUKAeTZsyRv3LiBl5cX1atXJyQkJNNdTt3d3Zk6dSq3bt1SEp+dO3eiVqupXr16nozxXRo2bBitW7emWLFi3L17lylTphAfH0+vXr2wtLTEw8OD4cOHY2JiQvHixdmzZw8rV65k7ty5Sh8hISGUK1cOW1tbDhw4wJdffsnQoUNxdXXViWtQhBBCCLkrqRDZYG5uTt++fRk+fDi7du3ir7/+ws/PT/kC7uLigq+vLz179mTDhg1cvnyZI0eOMHPmTMLDw5V+PDw8KFy4ML6+vjg7O1OnTp1XHrN06dIcPXqUHTt2cOHCBcaNG8eRI0e02jg7O3Pq1CnOnz/PvXv3svwC279/f65du8agQYM4d+4cv/76KxMmTCAwMPA/PSbhvxo/fjwrV64kODiYM2fOEBMTw08//cTYsWMBKFWqFKmpqSxcuJBLly6xatUqlixZ8s7H5evri42NDW3btuXPP//k8uXL7Nmzhy+//JLr168DWZ/30qVL4+TkRHBwMBcuXGDbtm2Z7lz7Lty8eRNPT0+cnJz4+uuv+eeff7h9+7bWNZbNmjWjfPnyfPrppxw/fpxdu3YxbNgw/P3933i9wYfg+vXrdOvWDVdXV9q3b4+RkREHDx6kePHiwPPrW2vWrImvry/ly5dnxowZTJ06VevawfPnz/PJJ59Qrlw5Jk2axJgxY5THfQghhBC6QBJDIbJp9uzZNGzYkDZt2tCkSRPq16+vNdsSEhJCz549+eqrr3B1daVNmzYcOnQIJycnpY1KpaJbt26cPHkSX1/f1x4vICCA9u3b06VLF2rXrs39+/e1Zg8B/P39cXV1pUaNGtja2rJv375M/RQpUoTw8HAOHz5M5cqVCQgIoG/fvkoCll+aN2/O1q1biYiIoGbNmtSpU4e5c+cqX+arVKnC3LlzmTlzJm5uboSGhjJ9+vR3Pi5TU1P++OMPihUrRvv27SlXrhx9+vTh6dOnShKV1Xk3NDRk7dq1nDt3jsqVKzNz5kymTJnyzse7c+dOLl68yO7duylatCgODg7KK4O+vj7btm3D2NiYevXq0blzZz755JOPJvFZt24dN2/eJDk5mRs3bhAWFkb58uWVent7e0JCQrhx4wZPnz7l3LlzBAYGai3pnTFjBrdv3yY5OZkLFy5kqhdCCCE+dnJXUiGEEG8UHx9PgQIFuHfv3nt/jWFuS0lJITw8HB8fn4/+WpQX6WrcoLuxS9y6FTfobuy6FHfG53d27koqM4ZCCCGEEEIIoeMkMRRCvFZoaCjm5uZZvipUqJDfw3utV43b3Nxc65mN77Np06a9MoYWLVrk9/CEEEII8ZGQu5IKIV6rTZs21K5dO8u69335xevuCpqfd2TNiYCAADp37pxlnYmJSR6PRgghhBAfK0kMhRCvZWFhkelh7x+KVz0K40NiZWWFlZVVfg9DCCGEEB85WUoqhBBCCCGEEDpOEkMhhBAftODgYFQqldbL3t5eqU9ISGDgwIEULVoUExMTypUrx+LFi7X6SEpKYtCgQdjY2GBmZkabNm2U51YKIYQQukASQyHeA56engwZMiRX+oqLi0OlUr32+rq87EeIvFChQgVu3bqlvE6fPq3UDR06lO3bt7N69WpiYmIYOnQogwYN4tdff1XaDBkyhI0bN7Ju3Tr27t1LQkICrVq1Ii0tLT/CEUIIIfKcJIZCfGScnJy4desWbm5u2d7Hz8+PTz755D/3I96NqKgoVCoVjx490irPzR8UPnQGBgbY29srL1tbW6XuwIED9OrVC09PT5ydnenXrx+VK1fm6NGjADx+/JgffviBOXPm0KRJE6pWrcrq1as5ffo0v//+e36FJIQQQuQpSQyF+Mjo6+tjb2+PgcF/u7dUbvUjRF74+++/cXR0pESJEnTt2pVLly4pdfXr12fz5s3cuHEDjUZDZGQkFy5coHnz5gBER0eTkpJCs2bNlH0cHR1xc3Nj//79eR6LEEIIkR/kG58QeezJkyd88cUXbNiwAQsLC4YNG6ZVn5yczNixYwkNDeXRo0e4ubkxc+ZMPD09efz4Mfb29mzcuBFvb29lnw0bNvDpp59y584d7t27R4kSJTh+/DhVqlQhLS2Nfv36sXv3bm7fvk2xYsXo378/X375JfD8+qwVK1YAoFKpAIiMjMTZ2VmrH4A9e/YwfPhwTp48iZWVFb169WLKlClK8ujp6UmlSpUwNjZm2bJlGBkZERAQQHBwcLbOjUqlYsmSJWzZsoXdu3dTvHhxfvzxR2xtbfnss884cuQIlSpVYvXq1ZQqVUrZb8uWLQQHB3PmzBkcHR3p1asXY8aMUcY1d+5cQkJCuHTpElZWVrRu3ZpZs2Zhbm4OwPLlyxkyZAg//fQTQ4YM4dq1a9SvX5+QkBAcHBzeOG4/Pz8ePXpE1apV+d///sezZ8/o1q0bCxcuxMjICACNRsPs2bNZsmQJt27dwsXFhXHjxtGxY8fX9h0XF4eXlxcAhQoVAqBXr17K+7Fnzx7mz58PwOXLl5X227dvZ+TIkZw7dw53d3fWrVtHdHQ0gYGB3Lhxg5YtW/LDDz9gamqarfcmQ+3pu0g1MMvRPu9S3IyW1K5dm5UrV+Li4sKdO3eYMmUKdevW5cyZM1hbW7NgwQL8/f0pWrQoBgYG6OnpsWzZMurXrw/A7du3MTIyUs5vhsKFC3P79u38CEsIIYTIc5IYCpHHhg8fTmRkJBs3bsTe3p7Ro0cTHR2tJF+9e/cmLi6OdevW4ejoqCSBp0+fpkyZMrRs2ZLQ0FCtxHDNmjW0bdsWc3Nz7t27p3W89PR0ihYtyvr167GxsWH//v3069cPBwcHOnfuzLBhw4iJiSE+Pp6QkBDg+SMSbt68qdXPjRs38PHxwc/Pj5UrV3Lu3Dn8/f0xNjbWSvxWrFhBYGAghw4d4sCBA/j5+VGvXj2aNm2arfMzefJk5s6dy9y5cwkKCqJ79+6ULFmSUaNGUaxYMfr06cPAgQP57bffANixYwc9evRgwYIFNGjQgNjYWPr16wfAhAkTANDT02PBggU4Oztz+fJl+vfvz4gRI1i0aJFy3MTERL7++mtWrVqFnp4ePXr0YNiwYYSGhmZr3Lt27cLY2JjIyEji4uLo3bs3NjY2TJ06FYCxY8eyYcMGFi9eTJkyZfjjjz/o0aMHtra2eHh4vLJfJycnwsLC6NChA+fPn8fS0lJ5fuGFCxdwc3Nj0qRJANja2hIXFwc8T/i//fZbTE1N6dy5M507d0atVrNmzRoSEhJo164dCxcuJCgoKMvjJiUlkZSUpGzHx8cDoNbToK+vydY5yQspKSk0adJE2S5btiw1atSgbNmy/PjjjwwZMoRvvvmGAwcOsGHDBooVK8bevXvp378/tra2NG7cmNTUVKWvF6Wnp6PRaJTyl+s/droaN+hu7BK3bsUNuhu7LsWdkxhVGo3m/fmEF+Ijl5CQgLW1NStXrqRLly4APHjwgKJFi9KvXz8GDRpEmTJluH79Oo6Ojsp+TZo0oVatWkybNo2NGzfSs2dP7ty5g6mpKfHx8RQuXJiwsDB8fHyIi4vLNNP3sgEDBnDnzh1++eUX4P9nvDZt2qS0ebmfMWPGEBYWRkxMjDKzuGjRIoKCgnj8+DF6enp4enqSlpbGn3/+qfRTq1YtGjVqxIwZM954flQqFWPHjmXy5MkAHDx4EHd3d3744Qf69OkDwLp16+jduzdPnz4FoGHDhrRo0YJRo0Yp/axevZoRI0ZkSm4z/Pzzz3zxxRdKEr18+XJ69+7NxYsXlZnIRYsWMWnSpGzNGPn5+bFlyxauXbumzMAtWbKE4cOH8/jxY54+fYqNjQ27d+/G3d1d2e+zzz4jMTGRNWvWvLb/qKgovLy8ePjwIQULFlTKPT09qVKlCvPmzcvU9vfff6dx48YAzJgxg1GjRhEbG0vJkiUBCAgIIC4uju3bt2d5zODgYCZOnJipfM2aNTmeZcwPEyZMwMHBgd69e+Pr68vIkSOpUaOGUv/tt99y//59JkyYwKlTpxg/fjyrV69WZpHh+Q1pateuTbdu3fIjBCGEEOI/S0xMpHv37jx+/BhLS8vXtpUZQyHyUGxsLMnJyVrJgZWVFa6urgAcO3YMjUaDi4uL1n5JSUlYW1sD0LJlSwwMDNi8eTNdu3YlLCwMCwsLreujXrZkyRKWLVvGlStXePr0KcnJya9MGl8lJiYGd3d3JSkEqFevHgkJCVy/fp1ixYoBUKlSJa39HBwcuHv3braP8+L+hQsXBqBixYpaZc+ePSM+Ph5LS0uio6M5cuSIMjMHkJaWxrNnz0hMTMTU1JTIyEimTZvG2bNniY+PJzU1lWfPnvHkyRPMzJ4vizQ1NdVanprTcVeuXFkrYXJ3dychIYFr165x9+5dnj17lmnWNDk5mapVq2b7GDnx8nk0NTVVksKMssOHD79y/1GjRhEYGKhsx8fH4+TkxJTjeqQa6r+TMb+Nv4KbZypLSkpiwIABtG3bVpkRrFWrltYs+9atWwHw8fGhXr16TJ48GZVKhY+PDwC3bt3i6tWrfPvtt3h5eREREUHTpk0xNDTMm8DeAykpKToZN+hu7BK3bsUNuhu7LsWdseInOyQxFCIPvWmCPj09HX19faKjo9HX1/7ynTGTYWRkRMeOHVmzZg1du3ZlzZo1dOnS5ZU3iVm/fj1Dhw5lzpw5uLu7Y2FhwezZszl06FCOx/5iUvhiPC+Wv/wPrEqlIj09PdvHeXH/jH6zKsvoMz09nYkTJ9K+fftMfRkbG3PlyhV8fHwICAhg8uTJWFlZsXfvXvr27au1vCKrcefGgooX49+2bRtFihTRqler1f/5GFl5+Zzl9H1Rq9VZju2PoCbKjxTvi2HDhtG6dWuKFSvG3bt3mTJlCvHx8fTp0wdra2s8PDwYNWoUFhYWFC9enD179rB69Wrmzp2LoaEhNjY29O3bl6CgIAoXLoyVlRXDhg2jYsWKeHt7K+fJ0NDwo/8CkRVdjRt0N3aJW/foauy6EHdO4pPEUIg8VLp0aQwNDTl48KAyw/bw4UMuXLiAh4cHVatWJS0tjbt379KgQYNX9uPr60uzZs04c+YMkZGRytLLrPz555/UrVuX/v37K2WxsbFabYyMjN74vLby5csTFhamlSDu378fCwuLTMlOXqpWrRrnz5+ndOnSWdYfPXqU1NRU5syZg57e8xsxr1+/PtfHcfLkSZ4+fapc/3fw4EHMzc0pWrQohQoVQq1Wc/Xq1ddeT/gqGTewefk9ys77pguuX79Ot27duHfvHra2ttSpU4eDBw9SvHhx4Pny41GjRuHr68uDBw8oXrw4U6dOJSAgQOnjm2++wcDAgM6dO/P06VMaN27M8uXL0dfXz9EPG0IIIcSHShJDIfKQubk5ffv2Zfjw4VhbW1O4cGHGjBmjJCwuLi74+vrSs2dP5syZQ9WqVbl37x67d++mYsWKyjI3Dw8PChcujK+vL87OztSpU+eVxyxdujQrV65kx44dlChRglWrVnHkyBFKlCihtHF2dmbHjh2cP38ea2trChQokKmf/v37M2/ePAYNGsTAgQM5f/48EyZMIDAwUBl/fhg/fjytWrXCycmJTp06oaenx6lTpzh9+jRTpkyhVKlSpKamsnDhQlq3bs2+fftYsmRJro8jOTmZvn37MnbsWK5cucKECRMYOHAgenp6yt1nhw4dSnp6OvXr1yc+Pp79+/djbm6u3GX0VYoXL45KpWLr1q34+PhgYmKCubk5zs7OHDp0iLi4OMzNzbGyssr1uD4E69ate229vb29cmOlVzE2NmbhwoUsXLgwN4cmhBBCfDDkOYZC5LHZs2fTsGFD2rRpQ5MmTahfvz7Vq1dX6kNCQujZsydfffUVrq6utGnThkOHDuHk5KS0UalUdOvWjZMnT+Lr6/va4wUEBNC+fXu6dOlC7dq1uX//vtbsIYC/vz+urq7UqFEDW1tb9u3bl6mfIkWKEB4ezuHDh6lcuTIBAQFKIpSfmjdvztatW4mIiKBmzZrUqVOHuXPnKrNFVapUYe7cucycORM3NzdCQ0OZPn16ro+jcePGlClThoYNG9K5c2dat26tdbfWyZMnM378eKZPn065cuVo3rw5W7Zs0UrQX6VIkSJMnDiRkSNHUrhwYQYOHAg8X0Kpr69P+fLlsbW15erVq7kelxBCCCF0g9yVVAgh/qOs7ur6sYmPj6dAgQLcu3fvvbvG8F1LSUkhPDwcHx+fj/5alBfpatygu7FL3LoVN+hu7LoUd8bnd3buSiozhkIIIYQQQgih4yQxFELkidDQUMzNzbN8VahQIb+H91qvGre5ubnWMxvfVkBAwCv7f/EGKUIIIYQQ74rcfEYIkSfatGlD7dq1s6x735dxnDhx4pV1RYoUee0dZLNj0qRJDBs2LMu6Ny37EEIIIYTIDZIYCiHyhIWFBRYWFvk9jLfyqkdh5BY7Ozvs7Oze6TGEEEIIIV5HlpIKIYQQQgghhI6TxFDoJE9PT4YMGZIrfcXFxaFSqV673DAv+9EFfn5+fPLJJ++k708//ZRp06Zlq21UVBQqlYpHjx69k7G8K99++y1t2rTJ72HkiuDgYFQqldbL3t5eq01MTAxt2rShQIECWFhYUKdOHa1He3z//fd4enpiaWn5Qb6fQgghRG6QxFCI/8jJyYlbt27h5uaW7X2ySmzeph+Ru06dOsW2bdsYNGhQttrXrVuXW7duUaBAgXc8stzl7+/PkSNH2Lt3b34PJVdUqFCBW7duKa/Tp08rdbGxsdSvX5+yZcsSFRXFyZMnGTduHMbGxkqbxMREvL29GT16dH4MXwghhHgvyDWGQvxH+vr6mWYo8rMf8fa+/fZbOnXqlO1rIY2MjF77nqWlpaFSqdDTe79+g1Or1XTv3p2FCxdSv379/B7Of2ZgYPDK92HMmDH4+Pgwa9YspaxkyZJabTJWD0RFRb2rIQohhBDvvffr24oQ78CTJ0/o2bMn5ubmODg4MGfOHK365ORkRowYQZEiRTAzM6N27drKF8THjx9jYmLC9u3btfbZsGEDZmZmJCQkZFoCmpaWRt++fSlRogQmJia4uroyf/58Zd/g4GBWrFjBr7/+qix9i4qKynIp6Z49e6hVqxZqtRoHBwdGjhxJamqqUu/p6cngwYMZMWIEVlZW2NvbExwcnO1zo1Kp+O6772jVqhWmpqaUK1eOAwcOcPHiRTw9PTEzM8Pd3Z3Y2Fit/bZs2UL16tUxNjamZMmSTJw4UWtcc+fOpWLFipiZmeHk5ET//v1JSEhQ6pcvX07BggXZsWMH5cqVw9zcHG9vb27dupXtsb8oKSmJwYMHY2dnh7GxMfXr1+fIkSNabTZv3kyZMmUwMTHBy8uLFStWaC0bTE9P5+eff860xDIpKYkRI0bg5OSEWq2mTJky/PDDD0DmpaQZcW3dupXy5cujVqu5cuXKa/t4nYz+d+zYQdWqVTExMaFRo0bcvXuX3377jXLlymFpaUm3bt1ITEwE4LvvvqNIkSKkp6dr9dWmTRt69eqltb1p0yaePn2ao3P9Pvr7779xdHSkRIkSdO3alUuXLgHP39Nt27bh4uJC8+bNsbOzo3bt2mzatCl/ByyEEEK8h2TGUHz0hg8fTmRkJBs3bsTe3p7Ro0cTHR1NlSpVAOjduzdxcXGsW7cOR0dHNm7ciLe3N6dPn6ZMmTK0bNmS0NBQvL29lT7XrFlD27ZtMTc35969e1rHS09Pp2jRoqxfvx4bGxv2799Pv379cHBwoHPnzgwbNoyYmBji4+MJCQkBwMrKips3b2r1c+PGDXx8fPDz82PlypWcO3cOf39/jI2NtZK/FStWEBgYyKFDhzhw4AB+fn7Uq1ePpk2bZuv8TJ48mblz5zJ37lyCgoLo3r07JUuWZNSoURQrVow+ffowcOBAfvvtNwB27NhBjx49WLBgAQ0aNCA2NpZ+/foBMGHCBAD09PRYsGABzs7OXL58mf79+zNixAgWLVqkHDcxMZGvv/6aVatWoaenR48ePRg2bBihoaHZGveLRowYQVhYGCtWrKB48eLMmjWL5s2bc/HiRaysrIiLi6Njx458+eWXfPbZZxw/fjzT4yFOnTrFo0ePqFGjhlZ5z549OXDgAAsWLKBy5cpcvnw503v+osTERKZPn86yZcuwtrbGzs4ux328LDg4mG+//RZTU1M6d+5M586dUavVrFmzhoSEBNq1a8fChQsJCgqiU6dODB48mMjISBo3bgzAw4cP2bFjB1u2bFH6rFGjBikpKRw+fBgPD49sj6X29F2kGphlu/27EjejJQC1a9dm5cqVuLi4cOfOHaZMmULdunU5c+YMKSkpJCQkMGPGDKZMmcLMmTPZvn077du3JzIyMkdxCyGEEB+7XEsMHz16RMGCBXOrOyFyRUJCAj/88AMrV65UEqUVK1ZQtGhR4Pn1R2vXruX69es4OjoCMGzYMLZv305ISAjTpk3D19eXnj17kpiYiKmpKfHx8Wzbto2wsLAsj2loaMjEiROV7RIlSrB//37Wr19P586dMTc3x8TEhKSkpNcuQ1y0aBFOTk58++23qFQqypYty82bNwkKCmL8+PHK8sRKlSopCVmZMmX49ttv2bVrV7YTw969e9O5c2cAgoKCcHd3Z9y4cTRv3hyAL7/8kt69eyvtp06dysiRI5XZp5IlSzJ58mRGjBihjOPFG/uUKFGCyZMn88UXX2glhikpKSxZsoRSpUoBMHDgQCZNmpStMb/oyZMnLF68mOXLl9OiRQsAli5dSkREBD/88APDhw9nyZIluLq6Mnv2bABcXV3566+/mDp1qtJPXFwc+vr6Wo+NuHDhAuvXryciIoImTZoo8b5OSkoKixYtonLlym/dx8umTJlCvXr1AOjbty+jRo0iNjZW6adjx45ERkYSFBSElZUV3t7erFmzRkkMf/75Z6ysrJRtADMzMwoWLEhcXFyWCVJSUhJJSUnKdnx8PABqPQ36+pocjf9dSElJAVDOKUDZsmWpUaMGZcuW5ccff1T+XLdu3ZqBAwcCz69H3Lt3L4sWLaJu3bpafWbMeqekpCj9v3isF8t0ga7GDbobu8StW3GD7sauS3HnJMa3SgxnzpyJs7MzXbp0AaBz586EhYVhb29PeHi48oVIiPwWGxtLcnIy7u7uSpmVlRWurq4AHDt2DI1Gg4uLi9Z+SUlJWFtbA9CyZUsMDAzYvHkzXbt2JSwsDAsLC5o1a/bK4y5ZsoRly5Zx5coVnj59SnJysjJDmV0xMTG4u7ujUqmUsnr16pGQkMD169cpVqwY8DwxfJGDgwN3797N9nFe3L9w4cIAVKxYUavs2bNnxMfHY2lpSXR0NEeOHNFKqtLS0nj27JmSPEdGRjJt2jTOnj1LfHw8qampPHv2jCdPnmBm9ny2ydTUVEkK32bcGWJjY0lJSVESJ3ienNeqVYuYmBgAzp8/T82aNbX2q1Wrltb206dPUavVWuf7xIkT6Ovr52hmycjISOucvk0fL3v5PTI1NdVKLgsXLszhw4eVbV9fX/r168eiRYtQq9WEhobStWtX9PX1tfo1MTFRlqC+bPr06Vo/cGQYWzUdU9O0t44lt4SHh7+yzt7ent27d1OiRAn09fXR19fXam9kZMSpU6cy9ZFx05qdO3dibm6eqd+IiIhcGv2HRVfjBt2NXeLWPboauy7E/arP+ay8VWL43XffsXr1auD5CY2IiOC3335j/fr1DB8+nJ07d75Nt0LkOo3m9TMb6enp6OvrEx0dnelLc8YXQyMjIzp27MiaNWvo2rUra9asoUuXLhgYZP3XZ/369QwdOpQ5c+bg7u6OhYUFs2fP5tChQzke+4tJyovxvFhuaGio1UalUmW6vux1Xtw/o9+syjL6TE9PZ+LEibRv3z5TX8bGxly5cgUfHx8CAgKYPHkyVlZW7N27l759+2r9apXVuN/0fmUlq3OSUZ5R9rpzmcHGxobExESSk5MxMjICnidOOWViYqJ1rLfp42Uvvx9ves9bt26tXF9Xs2ZN/vzzT+bOnZup3wcPHmBra5vlMUeNGkVgYKCyHR8fj5OTE15eXsqPJu+jpKQkBgwYQNu2bWnbtq3yg4CPj4/S5scff6Ry5cpaZYDyo0WzZs20VsCkpKQQERFB06ZNM537j5muxg26G7vErVtxg+7GrktxZ6z4yY63Sgxv3bqFk5MTAFu3bqVz5840a9YMZ2dnateu/TZdCvFOlC5dGkNDQw4ePKjMsD18+JALFy7g4eFB1apVSUtL4+7duzRo0OCV/fj6+tKsWTPOnDlDZGQkkydPfmXbP//8k7p169K/f3+l7OWbtxgZGZGW9vpZl/LlyxMWFqaV1Ozfvx8LCwuKFCnyxtjflWrVqnH+/HlKly6dZf3Ro0dJTU1lzpw5ynLX9evXv7PxlC5dGiMjI/bu3Uv37t2B5//gHz16VFnSWrZs2UyzQ0ePHtXazpjRPXv2rPL/FStWJD09nT179mgtWcyJ3Ogjp0xMTGjfvj2hoaFcvHgRFxcXqlevrtUmNjaWZ8+eUbVq1Sz7UKvVqNXqTOWGhobv1YfosGHDaN26NcWKFePu3btMmTKF+Ph4+vTpg6GhISNGjKBLly54enri5eXF9u3b2bZtG1FRUUoct2/f5vbt28TFxQFw7tw5LCwsKFasGFZWVsqx3rfY84quxg26G7vErXt0NXZdiDsn8b3VXUkLFSrEtWvXANi+fbvyZUej0bzxy64Qecnc3Jy+ffsyfPhwdu3axV9//YWfn5+SsLi4uCjXEG7YsIHLly9z5MgRZs6cqZVIeHh4ULhwYXx9fXF2dqZOnTqvPGbp0qU5evQoO3bs4MKFC4wbNy7THTKdnZ05deoU58+f5969e1mu/+7fvz/Xrl1j0KBBnDt3jl9//ZUJEyYQGBiYr48/GD9+PCtXriQ4OJgzZ84QExPDTz/9xNixYwEoVaoUqampLFy4kEuXLrFq1SqWLFnyzsZjZmbGF198wfDhw9m+fTtnz57F39+fxMRE+vbtC8Dnn3/OuXPnCAoKUq75W758OfD/M422trZUq1ZN69l+zs7O9OrViz59+rBp0yYuX75MVFRUjhLd3Ojjbfj6+rJt2zZ+/PFHevTokan+zz//pGTJklrLeT9E169fp1u3bri6utK+fXuMjIw4ePAgxYsXB6Bdu3YsWbKEWbNmUbFiRZYtW0ZYWJjWYzqWLFlC1apV8ff3B6Bhw4ZUrVqVzZs350tMQgghRH54q2+X7du3p3v37jRt2pT79+8rN3w4ceLEK2cRhMgvs2fPpmHDhrRp04YmTZpQv359rdmTkJAQevbsyVdffYWrqytt2rTh0KFDyqw4PE8eunXrxsmTJ/H19X3t8QICAmjfvj1dunShdu3a3L9/X2v2EJ4/YNzV1ZUaNWpga2vLvn37MvVTpEgRwsPDOXz4MJUrVyYgIIC+ffsqCVh+ad68OVu3biUiIoKaNWtSp04d5s6dq3wRr1KlCnPnzmXmzJm4ubkRGhrK9OnT3+mYZsyYQYcOHfj000+pVq0aFy9eZMeOHRQqVAh4fgOcX375hQ0bNlCpUiUWL17MmDFjALRmxfr165fprqiLFy+mY8eO9O/fn7Jly+Lv78+TJ09yNL7c6COnGjVqhJWVFefPn1dmUl+0du1aJRH6kK1bt46bN2+SnJzMjRs3CAsLo3z58lpt+vTpw99//83Tp085ceIEbdu21aoPDg5Go9Fkevn5+eVhJEIIIUT+Umne4qKelJQU5s+fz7Vr1/Dz81OWIs2bNw9zc3M+++yzXB+oEELkpqlTp7JkyRJl9QPAs2fPcHV1Zd26dVo3LPrY/PXXXzRu3JgLFy5QoECBbO0THx9PgQIFuHfv3nt9jeG7kJKSQnh4OD4+Ph/9kqMX6WrcoLuxS9y6FTfobuy6FHfG5/fjx4+xtLR8bdu3usbQ0NAw0zPAQPsW9UII8T5ZtGgRNWvWxNramn379jF79mzlEQYZjI2NWblyZY6eMfghunnzJitXrsx2UiiEEEKIj99bX6i0atUq6tevj6OjI1euXAGezxj++uuvuTY4IcTbCw0NxdzcPMtXhQoV8nt4r/WqcZubm/Pnn3++VZ9///03bdu2pXz58kyePJmvvvqK4ODgTO08PDxo3br1f4wgewICAl4ZZ0BAwDs7brNmzZTnVAohhBBCwFvOGC5evJjx48czZMgQpk6dqtxwpmDBgsybNy/T9RtCiLzXpk2bV94l+H1fNnHixIlX1r3tHVm/+eYbvvnmm7cc0bsxadKkLFdfAG9c7iGEEEIIkZveKjFcuHAhS5cu5ZNPPmHGjBlKeY0aNV75JUcIkbcsLCywsLDI72G8FV25iZWdnR12dnb5PQwhhBBCiLdbSnr58uUsn32lVqvf+Z32hBBCCCGEEELkrrdKDEuUKJHlUq/ffvst023ChRBCiHchODgYlUql9bK3t1fq/fz8MtW//AzS27dv8+mnn2Jvb4+ZmRnVqlXjl19+yetQhBBCiHz3Vonh8OHDGTBgAD/99BMajYbDhw8zdepURo8ezfDhw3N7jLnO09Mz1+6gGhcXh0qleu01UXnZz/tKpVKxadMm4MONNSoqCpVKxaNHj/J7KOI1cvPvt3j/VahQgVu3bimv06dPa9V7e3tr1YeHh2vVf/rpp5w/f57Nmzdz+vRp5Rmkx48fz8swhBBCiHz3Volh7969mTBhAiNGjCAxMZHu3buzZMkS5s+fT9euXXN7jO81Jycnbt26hZubW7b38fPz45NPPvnP/Xyo3rdY36dEYvny5RQsWDC/h6HIzoxLdpQoUYLt27e/9Tjet6Tc2dmZefPm5fcwgOfnpm3btjg4OGBmZkaVKlUIDQ3N1Obl91GlUnHu3Ll8GnXuMTAwwN7eXnnZ2tpq1avVaq16KysrrfoDBw4waNAgatWqRcmSJRk7diwFCxbk2LFjeRmGEEIIke9ynBimpqayYsUKWrduzZUrV7h79y63b9/m2rVr9O3b912M8b2mr6+Pvb09BgZvdR+fXO/nQ6BLsWZIS0sjPT09T4+ZkpKSK/28acblTU6dOsX9+/fx8vLKlfG8S8nJyR/c8fbv30+lSpUICwvj1KlT9OnTh549e7Jly5ZMbc+fP6/1XpYpU+Y/Hz+//f333zg6OlKiRAm6du3KpUuXtOqjoqKws7PDxcUFf39/7t69q1Vfv359fvrpJx48eEB6ejrr1q0jKSkJT0/PPIxCCCGEyH85TgwNDAz44osvSEpKAsDGxua9vqvekydP6NmzJ+bm5jg4ODBnzhyt+uTkZEaMGEGRIkUwMzOjdu3aREVFAfD48WNMTEwyzXRs2LABMzMzEhISMi2LTEtLo2/fvpQoUQITExNcXV2ZP3++sm9wcDArVqzg119/VX61j4qKynJ55Z49e6hVqxZqtRoHBwdGjhxJamqqUu/p6cngwYMZMWIEVlZW2NvbZ/lctldRqVR89913tGrVClNTU8qVK8eBAwe4ePEinp6emJmZ4e7uTmxsrNZ+W7ZsoXr16hgbG1OyZEkmTpyoNa6///6bhg0bYmxsTPny5YmIiNDaP6fnDP5/lvXrr7/GwcEBa2trBgwYkO3kZ9GiRZQpUwZjY2MKFy5Mx44dlX737NnD/PnzlfcjLi4OgPDwcFxcXDAxMcHLy0spz46Mmb+tW7dSvnx51Go1V65cee2ft6ioKHr37s3jx4+VsWS8ny8uxc1QsGBBli9frnVO169fj6enJ8bGxqxevfo/nzd484zLm/z66680b94ctVr92nZXrlyhdevWFCpUCDMzMypUqEB4eDhxcXFKUlmoUCFUKhV+fn7Am/9+v4mzszNTpkzBz8+PAgUK4O/vDzxPtho2bIiJiQlOTk4MHjxYubGWp6cnV65cYejQocr7BM//blepUkWr/3nz5uHs7KxsZ7wf06dPx9HRERcXF+W927BhA15eXpiamlK5cmUOHDiQrRhGjx7N5MmTqVu3LqVKlWLw4MF4e3uzcePGTG3t7Oy03kt9ff0cnS+A2tN34TxyW76/AGrXrs3KlSvZsWMHS5cu5fbt29StW5f79+8D0KJFC0JDQ9m9ezdz5szhyJEjNGrUSPn8Avjpp59ITU3F2toatVrN559/zsaNGylVqlSOz40QQgjxIXurKZvatWtz/PhxihcvntvjyXXDhw8nMjKSjRs3Ym9vz+jRo4mOjla+wPXu3Zu4uDjWrVuHo6MjGzduxNvbm9OnT1OmTBlatmxJaGgo3t7eSp9r1qyhbdu2mJubc+/ePa3jpaenU7RoUdavX4+NjQ379++nX79+ODg40LlzZ4YNG0ZMTAzx8fGEhIQAYGVlxc2bN7X6uXHjBj4+Pvj5+bFy5UrOnTuHv78/xsbGWsnfihUrCAwM5NChQxw4cAA/Pz/q1atH06ZNs3V+Jk+ezNy5c5k7dy5BQUF0796dkiVLMmrUKIoVK0afPn0YOHAgv/32GwA7duygR48eLFiwgAYNGhAbG0u/fv0AmDBhAunp6bRv3x4bGxsOHjxIfHz8G5dpvumcZYiMjMTBwYHIyEguXrxIly5dqFKlivJl/lWOHj3K4MGDWbVqFXXr1uXBgwfKQ9Lnz5/PhQsXcHNzY9KkSQDY2tpy7do12rdvT0BAAF988QVHjx7lq6++ytY5zZCYmMj06dNZtmwZ1tbW2NnZvfbPW926dZk3bx7jx4/n/PnzwPMHvedEUFAQc+bMISQkBLVazZ49e976vGXImHEpWLAgHh4eTJ06NUc/Bm3evJkvv/zyje0GDBhAcnIyf/zxB2ZmZpw9exZzc3OcnJwICwujQ4cOnD9/HktLS0xMTIA3//3OjtmzZzNu3DjGjh0LwOnTp2nevDmTJ0/mhx9+4J9//mHgwIEMHDiQkJAQNmzYQOXKlenXr1+2z+GLdu3ahaWlJREREWg0GqV8zJgxfP3115QpU4YxY8bQrVs3Ll68+FYz648fP6ZcuXKZyqtWrcqzZ88oX748Y8eOfe0sblJSklYCFR8fD4BaT4O+vuZVu+WZlJQUmjRpomyXLVuWGjVqULZsWX788UeGDBlC+/btlXpXV1cqV65M6dKl+fXXX2nXrh3wPLF+8OAB27dvx9rams2bN9OpUyd2795NxYoVlWO9+F9doatxg+7GLnHrVtygu7HrUtw5ifGtEsP+/fvz1Vdfcf36dapXr46ZmZlWfaVKld6m21yXkJDADz/8wMqVK5VEacWKFRQtWhSA2NhY1q5dy/Xr13F0dARg2LBhbN++nZCQEKZNm4avry89e/YkMTERU1NT4uPj2bZtG2FhYVke09DQkIkTJyrbJUqUYP/+/axfv57OnTtjbm6OiYkJSUlJWnfPe9miRYtwcnLi22+/RaVSUbZsWW7evElQUBDjx49HT+/5ZG+lSpWYMGECAGXKlOHbb79l165d2U4Me/furSRfQUFBuLu7M27cOJo3bw7Al19+Se/evZX2U6dOZeTIkfTq1QuAkiVLMnnyZEaMGMGECRP4/fffiYmJIS4uTjnP06ZNo0WLFq8cw5vOWYZChQrx7bffoq+vT9myZWnZsiW7du1645fzq1evYmZmRqtWrbCwsKB48eLK41YKFCiAkZERpqamWu/H4sWLKVmyJN988w0qlQpXV1dOnz7NzJkzs3Ve4flfxEWLFlG5cmUge3/eChQokOnOijnx8pdhePvzBs9nXDp16kTx4sW5fPky48aNo1GjRkRHR79xBhCe/8Bx8uRJfHx83tj26tWrdOjQQfkyXrJkSaUuY5YyI0GFN//9zq5GjRppPX+1Z8+edO/eXflBo0yZMixYsAAPDw8WL16MlZUV+vr6WFhYvNX7ZGZmxrJlyzAyMgJQZqKHDRtGy5YtAZg4cSIVKlTg4sWLlC1bNkf9//LLLxw5coTvvvtOKXNwcOD777+nevXqJCUlsWrVKho3bkxUVBQNGzbMsp/p06dr/b3MMLZqOqamaTka07vwqiXN9vb27N69GxcXlyzrbWxs2LZtG2q1mlu3brFo0SIWLFjAs2fPuHHjBtWrV6d48eKMHj2aL774Qmvfl1c/6ApdjRt0N3aJW/foauy6EHdiYmK2275VYtilSxcABg8erJSpVCo0Gg0qlYq0tPz/0gDPv4gnJyfj7u6ulFlZWeHq6grAsWPH0Gg0mb5AJCUlYW1tDUDLli0xMDBg8+bNdO3albCwMCwsLGjWrNkrj7tkyRKWLVvGlStXePr0KcnJyTmawQCIiYnB3d1dWaYGUK9ePRISErh+/TrFihUDMifhDg4Oma6heZ0X9y9cuDCA8sU8o+zZs2fEx8djaWlJdHQ0R44cYerUqUqbtLQ0nj17RmJiIjExMRQrVkzry/mL5/9VsnPOKlSooLX0zcHBIdMdCLPStGlTihcvTsmSJfH29sbb25t27dphamr6yn1iYmKoU6eO1vnPThwvMjIy0jq/2fnz9l/VqFEjU9nbnjf4/7/rAG5ubtSoUYPixYuzbdu2TAloVjZv3ky9evWytfx08ODBfPHFF+zcuZMmTZrQoUOH1/7I9Ka/39n18jmLjo7m4sWLWjdw0Wg0pKenc/ny5Sxn4nKiYsWKSlL4ohdjdXBwAODu3bs5SgyjoqLw8/Nj6f+xd99RUVz/w8ffS6+iIFURsIFgQ4kCFtAgKtaosREVNRrsJViwYk9UiInGaIpYYk2wRbFgjQZRv7ZgQ0UJUUGiMaxYqPP8wcP8XAEFQUH3vs7Zoztz58793J1l9+4t88MPuLi4yNsdHR1V6sXDw4O///6bxYsXF9owDA4OZvz48fJzpVKJra0tc89pkKVd/CGope1iSNt829LT0xkxYgRdunQp8MeIBw8e8O+//+Ll5YWfn5/8PvDy8lJ5Xb/99luqVq0q55GZmUlUVBRt2rRBW1v7DUVU/qhr3KC+sYu41StuUN/Y1SnuvBE/RfFaDcNbt269zmFv3fPDtAqSk5ODpqYmZ86cyTfXJm8In46ODj169GDDhg307t2bDRs20KtXr0KHd23ZsoVx48YRGhqKh4cHxsbGLFq0iJMnTxa77M83Sp6P5/ntL17MCoWiWIucPH98Xr4FbcvLMycnh1mzZhXYKNDT0yuwzl+M40VFrbPXjdXY2JizZ89y5MgR9u/fz4wZMwgJCeH06dOFrgD6qmunKPT19VViL8r1Vpi8H16eV9DQgBd776Hk18jzrK2tsbOz4/r160VKv3PnTrp06VKktJ9++ilt27Zl9+7d7N+/nwULFhAaGsqoUaMKTF8arxHkr7OcnBw+++wzlR++8uT9IFMQDQ2N136N4OXvu6I4evQonTp1IiwsjP79+78yvbu7Oz///HOh+3V1dQvsFf59kk+p/ZBRUkFBQXTq1Ilq1aqRkpLC3LlzUSqVDBo0iPT0dEJCQujevTvW1tYkJCQwZcoUKleuzMcff4y2tjb16tWjZs2ajBw5ksWLF2NmZsb27ds5cOAAu3btyvfe0dbWfu+/QBREXeMG9Y1dxK1+1DV2dYi7OPG9VsPwXZhbCFCzZk20tbWJiYmRv9A9fPiQa9eu4eXlhaurK9nZ2aSkpNCiRYtC8/H398fX15dLly5x+PBh5syZU2jaY8eO4enpyfDhw+VtLy7eoqOj88peVWdnZyIiIlQaiNHR0RgbG1OlSpVXxv6mNGrUiLi4OGrWrFngfmdnZxITE7l79648XPJVi2gUpc5KSktLCx8fH3x8fJg5cyYVK1bk0KFDdOvWrcDXw9nZOd9iLzExMSUqQ1Gut8KuDXNzc5KSkuTn169fL9bQgNLy4MED/v77b7lH62XS0tI4fPgw3377bZHzt7W1JTAwkMDAQIKDg/nhhx8YNWqU3MP2fN286v39uho1asSlS5cKvcah4NfJ3Nyc5ORklffs27pX55EjR+jYsSNffvmlPOf3Vc6dO1ek17E8u337Nn369OH+/fuYm5vj7u5OTEwMdnZ2PH36lNjYWNauXct///2HtbU1rVq1YvPmzRgbGwO5H5aRkZFMnjyZTp06kZaWRs2aNVmzZk2Rhj8LgiAIwvvktRqGa9eufen+ovxa/TYYGRkxePBgJkyYgJmZGZaWlkydOlWen1e7dm15DmFoaCiurq7cv39fXnQg74uBl5cXlpaW+Pv7Y29v/9L7uNWsWVNeJc/BwYF169Zx+vRpHBwc5DT29vbs27ePuLg4zMzMMDExyZfP8OHDWbJkCaNGjWLkyJHExcUxc+ZMxo8fL5e/LMyYMYOOHTtia2vLxx9/jIaGBn/++SexsbHMnTsXHx8fHB0d5TpVKpVMnTr1pXkWpc5KYteuXdy8eZOWLVtSqVIlIiMjycnJkYfW2dvbc/LkSRISEjAyMsLU1JTAwEBCQ0MZP348n332GWfOnJFXAH1dRbne7O3tSUtL4+DBgzRo0AADAwMMDAxo3bo1y5Ytw93dnZycHCZNmvTGf+FKS0srtMclb+GOl9m7dy+1atVSmSv4MmPHjqV9+/bUrl2bhw8fcujQIXl4n52dHQqFgl27duHn54e+vv4r39+va9KkSbi7uzNixAiGDBmCoaEhV65cISoqiqVLlwK518zvv/9O79690dXVpXLlynh7e/PPP/+wcOFCevTowd69e9mzZw8VKlQoUXle5ciRI3To0IExY8bQvXt3kpOTgdzGa94Q3rzVUV1cXMjIyODnn38mIiKi0LnS74pNmzYVuk9fX599+/a9Mo9atWq98/UgCIIgCKXhtb5BjRkzRuUxfPhwAgICGDp0aLm5UXieRYsW0bJlSzp37oyPjw/NmzencePG8v7w8HD69+/P559/jqOjI507d+bkyZPY2trKaRQKBX369OHChQv4+/u/9HyBgYF069aNXr160bRpUx48eKDSEwYwZMgQHB0dcXNzw9zcnD/++CNfPlWqVCEyMpJTp07RoEEDAgMDGTx4sLxyYllp27Ytu3btIioqig8++AB3d3fCwsLkXmQNDQ22bdtGeno6TZo04dNPP1WZj1iQotRZSVSsWJGtW7fSunVr6tSpw4oVK9i4caM8BysoKAhNTU2cnZ0xNzcnMTGRatWqERERwW+//UaDBg1YsWIF8+fPL3FZXnW9eXp6EhgYSK9evTA3N2fhwoUAhIaGYmtrS8uWLenbty9BQUEvnSNZGjQ1NYmNjaVLly7Url2bAQMGULt2bU6cOCH3uLzMjh07ijyMFHJ7A0eMGEGdOnVo164djo6OLF++HMh9P8yaNYvJkydjaWnJyJEjgVe/v19H/fr1OXr0KNevX6dFixa4uroyffp0ld612bNnk5CQQI0aNeQbqtepU4fly5fz7bff0qBBA06dOqWyqM2bsnr1ankFXGtra/nx/HDvjIwMgoKCqF+/Pi1atOD48eNFnicqCIIgCIJ6UEilNFHn+vXrDBs2jAkTJsgrWgqCoJ6ys7OxsLBgz549NGnSpKyLI5QCpVKJiYkJ9+/fLzdzDN+WzMxMIiMj8fPze+/nojxPXeMG9Y1dxK1ecYP6xq5Oced9fqempr5yFFOpjUmsVasWX3zxRZHuVyYIwvvtwYMHjBs3jg8++KCsiyIIgiAIgiAUQalOVtPU1Mx3o3ahbKxfvx4jI6MCH88vY/8+OHbsWKGxFvcG8UXVvn37Qs9XGkNO34aS1tvLrrFWrVoxbdo0lVVZ33adlcV18Sa8D9eaIAiCIAjl32stPrNz506V55IkkZSUxLJly2jWrFmpFEwomc6dO9O0adMC971vXeZubm5vbfXHPD/++CNPnz4tcF9R7tlXHpS03op7jb3tOiuL6+JNeB+uNUEQBEEQyr/Xahh27dpV5blCocDc3JzWrVsTGhpaGuUSSsjY2LhIC4S8D/T19V96a4E3oSxvGVJaSlpvxb3G3nadlcV18Sa8D9eaIAiCIAjl32s1DF/35tiCIAiCIAiCIAhC+fNacwxnz55d4M21nz59yuzZs0tcKEEQBEF4lZCQEBQKhcrDysqqwLSfffYZCoWCJUuWqGxPTk6mX79+WFlZYWhoSKNGjfj111/fQukFQRAEoXx5rYbhrFmzSEtLy7f9yZMnzJo1q8SFEgRBeF0JCQkoFIr3Yn6h8GouLi4kJSXJj9jY2Hxptm/fzsmTJ7Gxscm3r1+/fsTFxbFz505iY2Ple6qeO3fubRRfEARBEMqN12oYSpKkstpgngsXLojFEARBeCMCAgLyzW8uK0eOHEGhUPDff/+VdVEAePToEWPHjsXOzg59fX08PT05ffq0ShpJkggJCcHGxgZ9fX28vb25dOlSGZW49GhpaWFlZSU/zM3NVfbfuXOHkSNHsn79+gIXRTpx4gSjRo2iSZMmVK9enWnTplGxYkXOnj37tkIQBEEQhHKhWA3DSpUqYWpqikKhoHbt2piamsoPExMT2rRpQ8+ePd9UWQVBEN4oSZLIysp6q+fMzMwscR6ffvopUVFRrFu3jtjYWHx9ffHx8eHOnTtymoULFxIWFsayZcs4ffo0VlZWtGnThkePHpX4/GXp+vXr2NjY4ODgQO/evbl586a8Lycnh379+jFhwoRCb9PTvHlzNm/ezL///ktOTg6bNm0iPT0db2/vtxSBIAiCIJQPxVp8ZsmSJUiSxKBBg5g1axYmJibyPh0dHezt7fHw8Cj1QgqCoD5+/fVXZs2axY0bNzAwMMDV1RVXV1fWrFkDII9WOHz4MN7e3pw6dYrPPvuMK1euULduXaZOnVrkcx05coRWrVqxd+9epk6dyp9//sm+ffvw9vZm0aJFrFixgqSkJGrXrs306dPp0aMHCQkJtGrVCsj9sQxgwIABrF69Gnt7e8aOHcvYsWPlczRs2JCuXbsSEhIil/+7775jz549HDhwgKCgIBQKBdu3b+fzzz9n+vTpPHz4kPbt2/PDDz+8cuXXp0+fEhERwY4dO2jZsiWQO/du+/btfPfdd8ydOxdJkliyZAlTp06lW7duAKxZswZLS0s2bNjAZ599VuQ6a7rgIFlahkVO/6YkfNGBpk2bsnbtWmrXrs29e/eYO3cunp6eXLp0CTMzM7788ku0tLQYPXp0ofls3ryZXr16YWZmhpaWFgYGBmzbto0aNWq8xWgEQRAEoewVq2E4YMAAABwcHPD09Hzv7ocnCELZSkpKok+fPixcuJCPPvqIR48ecezYMfr3709iYiJKpZLw8HAg9x5+jx8/pmPHjrRu3Zqff/6ZW7duMWbMmGKfd+LEiSxevJjq1atTsWJFpk2bxtatW/nuu++oVasWv//+O5988gnm5uY0b96ciIgIunfvTlxcHBUqVEBfX79Y55s5cyYLFizgq6++QlNTk/DwcOLj49m+fTu7du3i4cOH9OzZky+++IJ58+a9NK+srCyys7PR09NT2a6vr8/x48cBuHXrFsnJyfj6+sr7dXV18fLyIjo6usCGYXp6Ounp6fJzpVKZe5yGhKamVKx434TMzEx8fHzk505OTri5ueHk5MSqVato2bIlX3/9NSdPnlTpBc7OzlbppZ0yZQr//vsve/fuxczMjJ07d/Lxxx9z6NAh6tWrJ5/r+X/VhbrGDeobu4hbveIG9Y1dneIuToyvdbsKLy8v+f9Pnz7Nd8IKFSq8TraCIKi5pKQksrKy6NatG3Z2dgDyl3N9fX3S09NVVp1cvXo12dnZrFq1CgMDA1xcXLh9+zbDhg0r1nlnz55NmzZtAHj8+DFhYWEcOnRIHgFRvXp1jh8/zsqVK/Hy8pLnUltYWFCxYsVix9m3b18GDRqksi0nJ4fVq1fLPYT9+vXj4MGDr2wYGhsb4+HhwZw5c6hTpw6WlpZs3LiRkydPUqtWLSB35U0AS0tLlWMtLS3566+/Csx3wYIFBS4mNs01BwOD7KIF+gZFRkYWuN3KyopDhw5x9epVUlJSqF69urwvJyeHiRMn8uWXX/LDDz+QlJTE8uXL+eabb3j27Bl37tyhcePG2NnZMWXKlHzXUVRU1BuNqbxS17hBfWMXcasfdY1dHeIu6E4ShXmthuGTJ0+YOHEiW7Zs4cGDB/n2Z2eX/ZcGQRDePQ0aNODDDz+kXr16tG3bFl9fX3r06CEP2XzRlStXaNCgAQYGBvK21xnO7ubmJv//8uXLPHv2TG4o5snIyMDV1bXYeb/qfHns7e1Vho1aW1uTkpJSpPzWrVvHoEGDqFKlCpqamjRq1Ii+ffvmW0DlxUXDCltIDCA4OJjx48fLz5VKJba2trRq1QozM7MilettS09PZ8SIEXTp0oVhw4YxcuRIlf0dO3akb9++DBgwAEdHR3kFUy8vL+rUqSOn+/bbb6latSp+fn5A7q+tUVFRtGnTRq1Gyqhr3KC+sYu41StuUN/Y1SnuvBE/RfFaDcMJEyZw+PBhli9fTv/+/fn222+5c+cOK1eu5IsvvnidLAVBENDU1CQqKoro6Gj279/P0qVLmTp1KidPniwwvSSVzpBGQ8P/mzOXk5MDwO7du6lSpYpKOl1d3Zfmo6Ghka9MBQ3heP58eV78YFIoFHJZXqVGjRocPXqUx48fo1Qqsba2plevXjg4OADIvazJyclYW1vLx6WkpOTrRcyjq6tbYLza2trl5kM0KCiITp06Ua1aNVJSUpg7dy5KpZJBgwbJq5Q+T1tbmypVqlC3bl0gtze6Zs2ajBw5ksWLF2NmZsb27ds5cOAAu3btyhdneYr9bVLXuEF9Yxdxqx91jV0d4i5OfK91u4rffvuN5cuX06NHD7S0tGjRogXTpk1j/vz5rF+//nWyFARBAHIbRM2aNWPWrFmcO3cOHR0dtm3bho6OTr7RCM7Ozly4cIGnT5/K22JiYkp0fmdnZ3R1dUlMTKRmzZoqD1tbWyB3sS3IPzrC3NycpKQk+blSqeTWrVslKk9xGBoaYm1tzcOHD9m3bx9dunQBcueFW1lZqQyZycjI4OjRo3h6er618pW227dv06dPHxwdHenWrRs6OjrExMTIw5BfRVtbm8jISMzNzenUqRP169dn7dq1rFmzRu4tFARBEAR18Vo9hv/++6/8S3SFChX4999/gdxlv4s7t0cQBCHPyZMnOXjwIL6+vlhYWHDy5En++ecf6tSpw7Nnz9i3bx9xcXGYmZlhYmJC3759mTp1KoMHD2batGkkJCSwePHiEpXB2NiYoKAgxo0bR05ODs2bN0epVBIdHY2RkREDBgzAzs4OhULBrl278PPzQ19fHyMjI1q3bs3q1avp1KkTlSpVYvr06WhqapZS7RRu3759SJKEo6MjN27cYMKECTg6OjJw4EAgt7E9duxY5s+fT61atahVqxbz58/HwMCAvn37vvHyvSmbNm0qVvqEhIR822rVqkVEREQplUgQBEEQ3l2v1TCsXr06CQkJ2NnZ4ezszJYtW2jSpAm//fbbay3EIAiCALk/NP3+++8sWbIEpVKJnZ0doaGhtG/fHjc3N44cOYKbmxtpaWny7Sp+++03AgMDcXV1xdnZmS+//JLu3buXqBxz5szBwsKCBQsWcPPmTSpWrEijRo2YMmUKAFWqVGHWrFlMnjyZgQMH0r9/f1avXk1wcDA3b96kY8eOmJiYMGfOnLfSY5iamkpwcDC3b9/G1NSU7t27M2/ePJXhIxMnTuTp06cMHz6chw8f0rRpU/bv3//K22EIgiAIgqAeFNJrTNLJW2J99OjRHD58mA4dOpCdnU1WVhZhYWGvtVy8IAiCUH4plUpMTEy4f/9+uV185k3JzMwkMjISPz+/934uyvPUNW5Q39hF3OoVN6hv7OoUd97nd2pq6ivvHPFaPYbjxo2T/9+qVSuuXr3K//73P2rUqEGDBg1eJ0tBEARBEARBEAShjLzW4jPPe/bsGdWqVaNbt26iUSgIQrkSGBiIkZFRgY/AwMCyLl6RJCYmFhqDkZERiYmJZV1EQRAEQRDeA6/VY5idnc38+fNZsWIF9+7d49q1a1SvXp3p06djb2/P4MGDS7ucgiAIxTZ79myCgoIK3Peq4RTlhY2NDefPn3/pfkEQBEEQhJJ6rYbhvHnzWLNmDQsXLmTIkCHy9nr16vHVV1+JhqEgCOWChYUFFhYWZV2MEtHS0qJmzZplXQxBEARBEN5zrzWUdO3atXz//ff4+/urLMVev359rl69WmqFEwRBEARBEARBEN6812oY3rlzp8BfsHNycsjMzCxxoQRBENRFQkICCoXipcNFhfxCQkJQKBQqDysrK5X9Tk5OGBoaUqlSJXx8fDh58mSBeUmSRPv27VEoFGzfvv0tRSAIgiAI5ctrNQxdXFw4duxYvu2//PILrq6uJS6UIAiCULpOnDhB69atMTQ0pGLFinh7e/P06dOyLlaJuLi4kJSUJD9iY2PlfbVr12bZsmXExsZy/Phx7O3t8fX15Z9//smXz5IlS1AoFG+z6IIgCIJQ7rzWHMOZM2fSr18/7ty5Q05ODlu3biUuLo61a9eya9eu0i6jIAiCUAInTpygXbt2BAcHs3TpUnR0dLhw4QIaGiVemLpMaWlpqfQSPq9v374qz8PCwvjpp5/4888/+fDDD+XtFy5cICwsjNOnT2Ntbf1GyysIgiAI5VmxvhXcvHkTSZLo1KkTmzdvJjIyEoVCwYwZM7hy5Qq//fYbbdq0eVNlFQRBeKO8vb0ZNWoUY8eOpVKlSlhaWvL999/z+PFjBg4ciLGxMTVq1GDPnj1A7grNgwcPxsHBAX19fRwdHfn6669V8szJyWH27NlUrVoVXV1dGjZsyN69e/Od++rVq3h6eqKnp4eLiwtHjhyRj69atSorVqxQSX/27FkUCgU3b958ZVzjxo1j9OjRTJ48GRcXF2rVqkWPHj3Q1dV9zZoqH65fv46NjQ0ODg707t270LrIyMjg+++/x8TEROW2Sk+ePKFPnz4sW7as0AamIAiCIKiLYvUY1qpVi6SkJCwsLGjbti2rVq3ixo0b4gNVEIT3xpo1a5g4cSKnTp1i8+bNDBs2jO3bt/PRRx8xZcoUvvrqK/r160diYiLa2tpUrVqVLVu2ULlyZaKjoxk6dCjW1tb07NkTgK+//prQ0FBWrlyJq6srq1atonPnzly6dIlatWrJ550wYQJLlizB2dmZsLAwOnfuzK1btzAzM6N3796sX79e5d6LGzZswMPDg+rVq780npSUFE6ePIm/vz+enp7Ex8fj5OTEvHnzaN68eaHHpaenk56eLj9XKpUAtPzyAFnahq9Vt6XlYkhbGjduzKpVq6hVqxYpKSksWLAAT09Pzp8/j5mZGQC7d+/mk08+4cmTJ1hbW7Nnzx5MTEzkufBjxozB3d0dPz8/eVtWVla+ufJ5z9VtDr26xg3qG7uIW73iBvWNXZ3iLk6MCkmSpKIm1tDQIDk5WV7+vUKFCpw/f/6VX0wEQRDeBd7e3mRnZ8tzqLOzszExMaFbt26sXbsWgOTkZKytrTlx4gTu7u758hgxYgT37t3j119/BaBKlSqMGDGCKVOmyGmaNGnCBx98wLfffktCQgIODg588cUXTJo0CchtnDg4ODBq1CgmTpzIuXPnaNy4Mbdu3cLOzo6cnByqVavGlClTGD58+EtjiomJwcPDA1NTUxYvXkzDhg1Zu3Yty5cv5+LFiyqN0+eFhIQwa9asfNs3bNiAgYFBEWrz7Xr27BmBgYF89NFHdOnSRd728OFDlEol+/fvJzY2loULF1KxYkVOnTpFeHg4YWFh6OvrA9C1a1cmT55c4OsqCIIgCO+iJ0+e0LdvX1JTU195D+fXmmOYpxhtSkEQhHdC/fr15f9rampiZmZGvXr15G2WlpZAbk8cwIoVK/jxxx/566+/ePr0KRkZGTRs2BDI7WW7e/cuzZo1UzlHs2bNuHDhgso2Dw8P+f9aWlq4ublx5coVAFxdXXFycmLjxo1MnjyZo0ePkpKSIvdKvkxOTg4An332GQMHDpTzO3jwIKtWrWLBggUFHhccHMz48ePl50qlEltbW+ae0yBLW7PAY96WiyFtC9z+ww8/oK2tjZ+fX75948aNw9nZmb///pu+ffty8OBBkpOT+eSTT1TSLVy4kObNm3PgwAF5W2ZmJlFRUbRp0wZtbe3SDaYcU9e4QX1jF3GrV9ygvrGrU9x5I36KolgNw7wlwV/cJgiC8L548QNCoVCobMv7m5eTk8OWLVsYN24coaGheHh4YGxszKJFi/LdFuHFv5OSJBXpb+fzafz9/dmwYQOTJ09mw4YNtG3blsqVK78yj7wFVZydnVW216lTh8TExEKP09XVLXAO4u+TfOShmuVJeno6V69epWXLloV+yEuSRFZWFtra2kyZMoWhQ4eq7K9Xrx5fffUVnTp1KjAPbW3t9/4LREHUNW5Q39hF3OpHXWNXh7iLE1+xGoaSJBEQECB/WcgbumNoqDrfZOvWrcXJVhAE4Z107NgxPD09VYZzxsfHy/+vUKECNjY2HD9+nJYtW8rbo6OjadKkiUpeMTExcpqsrCzOnDnDyJEj5f19+/Zl2rRpnDlzhl9//ZXvvvuuSGW0t7fHxsaGuLg4le3Xrl2jffv2RQ+2nAkKCqJTp05Uq1aNlJQU5s6di1KpZMCAATx+/Jh58+bRuXNnrK2tefDgAcuXL+f27dt8/PHHAFhZWRU4P75atWo4ODi87XAEQRAEocwVq2E4YMAAlecvDsERBEFQJzVr1mTt2rXs27cPBwcH1q1bx+nTp1UaFhMmTGDmzJnUqFGDhg0bEh4ezvnz51m/fr1KXt9++y21atWiTp06fPXVVzx8+JBBgwbJ+x0cHPD09GTw4MFkZWXJ8+heRaFQyGVo0KABDRs2ZM2aNVy9elWeB/kuun37Nn369OH+/fuYm5vj7u5OTEwMdnZ2PHv2jKtXr7JmzRru37+PmZkZH3zwAceOHcPFxaWsiy4IgiAI5VKxGobh4eFvqhyCIAjvnMDAQM6fP0+vXr1QKBT06dOH4cOHy7ezABg9ejRKpZLPP/+clJQUnJ2d2blzZ75FX7744gu+/PJLzp07R40aNdixY0e+oaL+/v6MGDGC/v37ywumFMXYsWN59uwZ48aN499//6VBgwZERUVRo0aNklVAGdq0aVOh+/T09F5r5IqYNy8IgiCos2KtSioIgiCoJ6VSiYmJidwDp04yMzOJjIzEz8/vvZ+L8jx1jRvUN3YRt3rFDeobuzrFnff5XZRVSYt1g3tBEARBEARBEATh/SMahoIgCO+w9evXY2RkVOBDzKcTBEEQBKGoSnQfQ0EQBKFsde7cmaZNmxa4730fHiMIgiAIQukRDUNBEIR3mLGxMcbGxmVdDEEQBEEQ3nFiKKkgCIIgCIIgCIKaEw1DQRAE4Z0SEhKCQqFQeeTdrD4zM5NJkyZRr149DA0NsbGxoX///ty9e1c+PiEhId/xeY9ffvmlrMISBEEQhDJV7hqG3t7ejB07tlTyyvvwP3/+fLnIp7xSKBRs374deHdjPXLkCAqFgv/++6+siyKUUEBAAF27di3rYgjlnIuLC0lJSfIjNjYWgCdPnnD27FmmT5/O2bNn2bp1K9euXaNz587ysba2tirHJiUlMWvWLAwNDWnfvn1ZhSQIgiAIZarcNQxLU96Hf926dYt8TEFfSl8nn3dVeYu1NH8oKKnVq1dTsWLFsi6G7N69ewQEBGBjY4OBgQHt2rXj+vXrKmnS09MZNWoUlStXxtDQkM6dO3P79u23Xtby9oNDebquAA4ePIinpyfGxsZYW1szadIksrKyVNLExsbi5eWFvr4+VapUYfbs2UW+IfvWrVtp06YN5ubmVKhQAQ8PD/bt2/cmQnlrtLS0sLKykh/m5uYAmJiYEBUVRc+ePXF0dMTd3Z2lS5dy5swZEhMTAdDU1FQ51srKim3bttGrVy+MjIzKMixBEARBKDPvdcMw78NfS6tka+yUVj7vAnWKNU92djY5OTlv9ZyZmZklOl6SJLp27crNmzfZsWMH586dw87ODh8fHx4/fiynGzt2LNu2bWPTpk0cP36ctLQ0OnbsSHZ2dklDKJcyMjLe6vlK+joC/Pnnn/j5+dGuXTvOnTvHpk2b2LlzJ5MnT5bTKJVK2rRpg42NDadPn2bp0qUsXryYsLCwIp3j999/p02bNkRGRnLmzBlatWpFp06dOHfuXInLX1auX7+OjY0NDg4O9O7dm5s3bxaaNjU1FYVCUegPO2fOnOH8+fMMHjz4DZVWEARBEN4BUhlKS0uT+vXrJxkaGkpWVlbS4sWLJS8vL2nMmDGSJElSenq6NGHCBMnGxkYyMDCQmjRpIh0+fFiSJEn677//JD09PWnPnj0qeUZEREgGBgbSo0ePpFu3bkmAdO7cOUmSJCkrK0saNGiQZG9vL+np6Um1a9eWlixZIh87c+ZMCVB5HD58OF8+kiRJR44ckT744ANJR0dHsrKykiZNmiRlZmbK+728vKRRo0ZJEyZMkCpVqiRZWlpKM2fOLHLdANKKFSukDh06SPr6+pKTk5MUHR0tXb9+XfLy8pIMDAwkd3d36caNGyrH7dy5U2rUqJGkq6srOTg4SCEhISrlunbtmtSiRQtJV1dXqlOnjrR//34JkLZt2yZJklTsOpMkSRowYIDUpUsXadGiRZKVlZVkamoqDR8+XMrIyChSrN9++61Us2ZNSVdXV7KwsJC6d+8u5/vi63Hr1i1JkiRp9+7dUq1atSQ9PT3J29tbCg8PlwDp4cOHrzxfeHi4ZGJiIv32229SnTp1JE1NTenmzZsvvd4OHz6cryx5r+fz9ZfHxMRECg8PV6nTzZs3S15eXpKurq60atWqEtVbXFycBEgXL16Ut2VlZUmmpqbSDz/8IElS7ntEW1tb2rRpk5zmzp07koaGhrR3795XnqO4/v33X6lv375S5cqVJT09PalmzZrSqlWrJEmS8tWdl5eXXOZx48ZJJiYmkqmpqTRhwgSpf//+UpcuXYp0Ti8vL2nEiBHSuHHjJDMzM6lly5aSJEnSpUuXpPbt20uGhoaShYWF9Mknn0j//POPJEmFX1d518Xztm3bJj3/Z3LmzJlSgwYNpJ9++klycHCQFAqFlJOTIwHSDz/8IHXt2lXS19eXatasKe3YsaNIMQQHB0tubm75zqunpycplUpJkiRp+fLlkomJifTs2TM5zYIFCyQbGxspJyenSOd5kbOzszRr1qwip09NTZUAqcbnmyW7SbvK7CFJkhQZGSn9+uuv0p9//ilFRUVJXl5ekqWlpXT//v185X769KnUuHFjyd/fv9DYhg0bJtWpU6fQ/RkZGdL27duL/DftfaGucUuS+sYu4lavuCVJfWNXp7jzPr9TU1NfmbZMu4UmTJjA4cOH2bZtG1ZWVkyZMoUzZ87QsGFDAAYOHEhCQgKbNm3CxsaGbdu20a5dO2JjY6lVqxYdOnRg/fr1tGvXTs5zw4YNdOnSBSMjI+7fv69yvpycHKpWrcqWLVuoXLky0dHRDB06FGtra3r27ElQUBBXrlxBqVQSHh4OgKmpqcqiBQB37tzBz8+PgIAA1q5dy9WrVxkyZAh6enqEhITI6dasWcP48eM5efIkJ06cICAggGbNmtGmTZsi1c+cOXMICwsjLCyMSZMm0bdvX6pXr05wcDDVqlVj0KBBjBw5kj179gCwb98+PvnkE7755htatGhBfHw8Q4cOBWDmzJnk5OTQrVs3KleuTExMDEql8pXD6V5VZ3kOHz6MtbU1hw8f5saNG/Tq1YuGDRsyZMiQl+b/v//9j9GjR7Nu3To8PT35999/OXbsGABff/01165do27dusyePRsAc3Nz/v77b7p160ZgYCDDhg3jf//7H59//nmR6jTPkydPWLBgAT/++CNmZmZYWFi89Hrz9PRkyZIlzJgxg7i4OIBiDzmbNGkSoaGhhIeHo6ury9GjR1+73tLT0wHQ09OTt2lqaqKjo8Px48f59NNPOXPmDJmZmfj6+sppbGxsqFu3LtHR0bRt27ZY5X+V6dOnc/nyZfbs2UPlypW5ceMGT58+BeDUqVM0adKEAwcO4OLigo6ODgChoaGsWrWKn376CWdnZ0JDQ9m2bRutW7cu8nnXrFnDsGHD+OOPP5AkiaSkJLy8vBgyZAhhYWE8ffqUSZMm0bNnTw4dOlTodVVUN27cYMuWLURERKCpqSlvnzVrFgsXLmTRokUsXboUf39//vrrL0xNTV+aX3p6usrrCKCvr8+zZ884c+YM3t7enDhxAi8vL3R1deU0bdu2JTg4mISEBBwcHIpcfsh9Xz969OilZUtPT5evM8jttQTQ1ZDQ1CzaENY3ITMzEx8fH/m5k5MTbm5uODk5sWrVKpW/aZmZmfTu3Zvs7Gy+/vrrAnt4nz59yoYNG5gyZUqhPcB520ujh/hdoq5xg/rGLuJWr7hBfWNXp7iLE2OZNQzT0tL46aefWLt2rdxQWrNmDVWrVgUgPj6ejRs3cvv2bWxsbAAICgpi7969hIeHM3/+fPz9/enfvz9PnjzBwMAApVLJ7t27iYiIKPCc2trazJo1S37u4OBAdHQ0W7ZsoWfPnhgZGaGvr096erq8wl1Bli9fjq2tLcuWLUOhUODk5MTdu3eZNGkSM2bMQEMjd4Ru/fr1mTlzJgC1atVi2bJlHDx4sMgNw4EDB8qNr0mTJuHh4cH06dPlL/Rjxoxh4MCBcvp58+YxefJkBgwYAED16tWZM2cOEydOZObMmRw4cIArV66QkJAg1/P8+fNfutjCq+osT6VKlVi2bBmampo4OTnRoUMHDh48+MoGTmJiIoaGhnTs2BFjY2Ps7OxwdXUFcucK6ejoYGBgoPJ6fPfdd1SvXp2vvvoKhUKBo6MjsbGxfPnll0WqV8h9kyxfvpwGDRoARbveTExMVFY/LK6xY8fSrVs3lW2vW29OTk7Y2dkRHBzMypUrMTQ0JCwsjOTkZJKSkgBITk5GR0eHSpUqqRxraWlJcnLya8XwMomJibi6uuLm5gaAvb29vC+v4WVmZqZSf0uWLCE4OJju3bsDsGLFimLPfatZsyYLFy6Un8+YMYNGjRoxf/58eduqVauwtbXl2rVr1K5du8DrqqgyMjJYt25dvsZkQEAAffr0AXLfV0uXLuXUqVMqP1wVpG3btixZsoSNGzfSs2dPkpOTmTt3LoDKa/l8fULu65i3r7gNw9DQUB4/fqzyHn7RggULVN77eaa55mBgUHZDkSMjIwvcbmVlxaFDh6hduzYAWVlZLFq0iHv37jF79myOHz9e4HGHDx/m8ePHWFlZFZp3nqioqJIV/h2lrnGD+sYu4lY/6hq7OsT95MmTIqcts4ZhfHw8GRkZeHh4yNtMTU1xdHQE4OzZs0iSJH/I50lPT8fMzAyADh06oKWlxc6dO+nduzcREREYGxur9JC8aMWKFfz444/89ddfPH36lIyMDLmHsqiuXLmCh4cHCoVC3tasWTPS0tK4ffs21apVA3Ibhs+ztrYmJSWlyOd5/vi8L4H16tVT2fbs2TOUSiUVKlTgzJkznD59mnnz5slpsrOzefbsGU+ePOHKlStUq1ZNbhQCKvVfmKLUmYuLi0rvibW1tbxK4Mu0adMGOzs7qlevTrt27WjXrh0fffQRBgYGhR5z5coV3N3dVeq/KHE8T0dHR6V+i3K9lVReg+l5r1tv2traREREMHjwYExNTdHU1MTHx6dIKypKkqRSd4Vp37693HtrZ2fHpUuXXpp+2LBhdO/enbNnz+Lr60vXrl3x9PQsNH1qaipJSUkqr52WlhZubm5FXlQF8tfrmTNnOHz4cIE9uvHx8fle4+Kys7MrsIfx+evJ0NAQY2PjIr3ffX19WbRoEYGBgfTr1w9dXV2mT5/O8ePHVa6NF1+zvDoqymv5vI0bNxISEsKOHTuwsLAoNF1wcDDjx4+XnyuVSmxtbZl7ToMsbc1Cj3vTLobk7+lOT09nxIgRdOnSBT8/PzIzM+nTpw+PHj3ijz/+eGmPcFhYGJ06dZIb9QXJzMwkKiqKNm3aoK2tXSpxvAvUNW5Q39hF3OoVN6hv7OoUd96In6Ios4bhq7745eTkoKmpyZkzZ1S+HMH/DeHT0dGhR48ebNiwgd69e7NhwwZ69epV6MIpW7ZsYdy4cYSGhuLh4YGxsTGLFi3i5MmTxS57Ub6kvXihKRSKYi1y8vzxefkWtC0vz5ycHGbNmpWvVwpyhxwWVOev+lJZ1Dp73ViNjY05e/YsR44cYf/+/cyYMYOQkBBOnz5d6EIRxWk0FEZfX18l9qJcb4VRKBT5ylRQt72hoWG+bSW5Rho3bsz58+dJTU0lIyMDc3NzmjZtKjeUrKysyMjI4OHDhyq9hikpKS9tsOX58ccf5aGgRfmj2b59e/766y92797NgQMH+PDDDxkxYgSLFy8uUjyv68V6zcnJoVOnTgX2IFtbWxeaj4aGxmu/jlCy13L8+PGMGzeOpKQkKlWqREJCAsHBwXJPoJWVVb5e3rxGZ96PRkWxefNmBg8ezC+//KIyHLMgurq6KkNX8/w+yafUfix5XUFBQXTq1Ilq1aqRkpLC3LlzUSqVDBo0CIVCQZ8+fTh79iy7du1CQ0ODBw8eALk/PuYNY4bcYcHHjh0jMjKySNe4trb2e/8FoiDqGjeob+wibvWjrrGrQ9zFia/MGoY1a9ZEW1ubmJgYuYft4cOHXLt2DS8vL1xdXcnOziYlJYUWLVoUmo+/vz++vr5cunSJw4cPM2fOnELTHjt2DE9PT4YPHy5vi4+PV0mjo6PzyhUbnZ2diYiIUGkgRkdHY2xsTJUqVV4Z+5vSqFEj4uLiqFmzZoH7nZ2dSUxM5O7du/JwyRMnTrw0z6LUWUlpaWnh4+ODj48PM2fOpGLFihw6dIhu3boV+Ho4OzvL913MExMTU6IyFOV6K+zaMDc3l4f8Qe5qicXpti8pExMT+bz/+9//5PdA48aN0dbWlpfuh9yhiRcvXlQZelmY17mWzc3NCQgIICAggBYtWjBhwgQWL14sfxl/vv5MTEywtrYmJiaGli1bArnD/86cOUOjRo2Kfe48jRo1IiIiAnt7+0J/JCrotTQ3N+fRo0c8fvxYbvy9zdtrKBQK+X25ceNGbG1t5Xrw8PBgypQpZGRkyHW5f/9+bGxs8g0xLczGjRsZNGgQGzdupEOHDm8khrfl9u3b9OnTh/v372Nubo67uzsxMTHY2dmRkJDAzp07AfKNbDh8+DDe3t7y81WrVlGlSpWXjjIRBEEQBHVRZrerMDIyYvDgwUyYMIGDBw9y8eJFAgIC5Pl5tWvXlucQbt26lVu3bnH69Gm+/PJLlXkgXl5eWFpa4u/vj729Pe7u7oWes2bNmvzvf/9j3759XLt2jenTp3P69GmVNPb29vz555/ExcVx//79AnsMhg8fzt9//82oUaO4evUqO3bsYObMmYwfP14uf1mYMWMGa9euJSQkhEuXLnHlyhU2b97MtGnTAPDx8cHR0ZH+/ftz4cIFjh07xtSpU1+aZ1HqrCR27drFN998w/nz5/nrr79Yu3YtOTk58pBie3t7Tp48SUJCAvfv3ycnJ4fAwEDi4+MZP348cXFxbNiwgdWrV5eoHEW53uzt7UlLS+PgwYPcv39fbvy1bt2aZcuWcfbsWf73v/8RGBj4Vn59+uWXXzhy5Ih8y4o2bdrQtWtX+UuuiYkJgwcP5vPPP+fgwYOcO3eOTz75hHr16r2yt+h1zJgxgx07dnDjxg0uXbrErl27qFOnDgAWFhbo6+uzd+9e7t27R2pqKpA7T/aLL75g27ZtXL16leHDh/Pff/+VqBwjRozg33//pU+fPpw6dYqbN2+yf/9+Bg0aJDcGC7qumjZtioGBAVOmTOHGjRulcl0V1aJFi4iNjeXSpUvMmTOHL774gm+++Ubuve7bty+6uroEBARw8eJFtm3bxvz58xk/fnyRhpJu3LiR/v37Exoairu7O8nJySQnJ8uvw7tm06ZN3L17l4yMDO7cuUNERATOzs5A7msrSVKBj+cbhZA7F/Tvv/8u07/bgiAIglBelOmn4aJFi2jZsiWdO3fGx8eH5s2b07hxY3l/eHg4/fv35/PPP8fR0ZHOnTtz8uRJbG1t5TR5w4YuXLiAv7//S88XGBhIt27d6NWrF02bNuXBgwcqPWEAQ4YMwdHRETc3N8zNzfnjjz/y5VOlShUiIyM5deoUDRo0IDAwkMGDB8sNsLLStm1bdu3aRVRUFB988AHu7u6EhYVhZ2cH5A6V27ZtG+np6TRp0oRPP/1UZT5iQYpSZyVRsWJFtm7dSuvWralTpw4rVqxg48aNuLi4ALlDxjQ1NXF2dsbc3JzExESqVatGREQEv/32Gw0aNGDFihUqC428rlddb56engQGBtKrVy/Mzc3lXrfQ0FBsbW1p2bIlffv2JSgo6KVzJEtLUlIS/fr1w8nJidGjR9OvXz82btyokuarr76ia9eu9OzZk2bNmmFgYMBvv/2Wb7hsadDR0SE4OJj69evTsmVLNDU12bRpE5DbK/zNN9+wcuVKbGxs6NKlCwCff/45/fv3JyAgQB6q/NFHH5WoHDY2Nvzxxx9kZ2fTtm1b6taty5gxYzAxMZEbAAVdV6ampvz8889ERkZSr149eS7e27Bnzx5atGiBm5sbu3fvZseOHXTt2lXen3fT9tu3b+Pm5sbw4cMZP368yhzAl1m5ciVZWVmMGDECa2tr+TFmzJg3FJEgCIIgCO8ahVQaE7YEQRCE95pSqcTExIT79++X+RzDty0zM5PIyEj8/Pze+7koz1PXuEF9Yxdxq1fcoL6xq1PceZ/fqampVKhQ4aVpxfgZQRAEQRAEQRAENScahmVg/fr1GBkZFfjIG0L5vjh27FihsRb3BvFF1b59+0LPVxpDTt+Gt1Fv8+fPLzT/gm57ERgYWGj6wMDAUinT8xITE19aB4mJiaV+zjfhbdSbi4tLoedYv359qZxDEARBEIT3W5mtSqrOOnfuTNOmTQvc9751Z7u5ub3VlR1B9TYLLzI1NX2rZXldb6PeAgMDC73Bub6+fr5ts2fPJigoqMD0rxqa8DpsbGxeWgd5K3iWd2+j3iIjIwtcKAuKdzsLQRAEQRDUl2gYlgFjY2OMjY3Luhhvhb6+fqG3z3hTyvKWIaXlbdSbqalpsRrKFhYWL70hemnT0tJ669fOm/A26i1vgSlBEARBEITXJYaSCoIgCIIgCIIgqDnRMBQE4b2SkJCAQqF460OYhbcnJCQEhUKh8rCysgJyV5qbNGkS9erVw9DQEBsbG/r378/du3dV8vj+++/x9vamQoUKKBSKEt8/UxAEQRDedaJhKAjCOyEgIEDl3n5l6ciRI+WqMbFgwQI++OADjI2NsbCwoGvXrsTFxamkCQgIyNeYcnd3L6MSl5yLiwtJSUnyIzY2FoAnT55w9uxZpk+fztmzZ9m6dSvXrl2jc+fOKsc/efKEdu3aMWXKlLIoviAIgiCUO2KOoSAIwv8nSRLZ2dloab29P42ZmZklXnTq6NGjjBgxgg8++ICsrCymTp2Kr68vly9fxtDQUE7Xrl07wsPD5ec6OjolOm9Z0tLSknsJn2diYkJUVJTKtqVLl9KkSRMSExOpVq0aAGPHjgVyG/mCIAiCIIgeQ0EQyplff/2VevXqoa+vj5mZGT4+PkyYMIE1a9awY8cOubcr7wv9qVOncHV1RU9PDzc3N86dO1fkc+X1/O3btw83Nzd0dXU5duwYkiSxcOFCqlevjr6+Pg0aNODXX38FcoeqtmrVCoBKlSqhUCgICAgAwN7eniVLlqico2HDhoSEhMjPFQoFK1asoEuXLhgaGjJ37lxCQkJo2LAh69atw97eHhMTE3r37s2jR4+KFMfevXsJCAjAxcWFBg0aEB4eTmJiImfOnFFJp6uri5WVlfx4V1bpLcj169exsbHBwcGB3r17c/PmzULTpqamolAoqFix4tsroCAIgiC8Y0SPoSAI5UZSUhJ9+vRh4cKFfPTRRzx69Ihjx47Rv39/EhMTUSqVco+Xqakpjx8/pmPHjrRu3Zqff/6ZW7duMWbMmGKfd+LEiSxevJjq1atTsWJFpk2bxtatW/nuu++oVasWv//+O5988gnm5uY0b96ciIgIunfvTlxcHBUqVCjw9h4vM3PmTBYsWMBXX32FpqYm4eHhxMfHs337dnbt2sXDhw/p2bMnX3zxBfPmzSt2PKmpqUD+27McOXIECwsLKlasiJeXF/PmzSv2iqlNFxwkS8vw1QnfkIQvOtC0aVPWrl1L7dq1uXfvHnPnzsXT05NLly5hZmamkv7Zs2dMnjyZvn37vpHbqgiCIAjC+0I0DAVBKDeSkpLIysqiW7du8i0Y6tWrB+TewiM9PV1l+ODq1avJzs5m1apVGBgY4OLiwu3btxk2bFixzjt79mzatGkDwOPHjwkLC+PQoUN4eHgAUL16dY4fP87KlSvx8vKSG1x5jazi6tu3L4MGDVLZlpOTw+rVq+Vb2fTr14+DBw8Wu2EoSRLjx4+nefPm1K1bV97evn17Pv74Y+zs7Lh16xbTp0+ndevWnDlzBl1d3Xz5pKenk56eLj9XKpUA6GpIaGpKxSpTacrMzMTHx0d+7uTkhJubG05OTqxatUoeIpqXtnfv3mRnZ/P1118XeK/HrKwsOW1h94LM217Y/veVusYN6hu7iFu94gb1jV2d4i5OjKJhKAhCudGgQQM+/PBD6tWrR9u2bfH19aVHjx5UqlSpwPRXrlyhQYMGGBgYyNvyGnPF4ebmJv//8uXLPHv2TG4o5snIyMDV1bXYeb/qfHns7e1V7m9qbW1NSkpKsfMeOXIkf/75J8ePH1fZ3qtXL/n/devWxc3NDTs7O3bv3k23bt3y5bNgwQJmzZqVb/s01xwMDLKLXa7SEhkZWeB2KysrDh06RO3atYHcBt+iRYu4d+8es2fPzlcfefIWrdm/fz9GRkYvPfeLcxfVhbrGDeobu4hb/ahr7OoQ95MnT4qcVjQMBUEoNzQ1NYmKiiI6Opr9+/ezdOlSpk6dysmTJwtML0ml03P1/AItOTk5AOzevZsqVaqopCuoZ+15Ghoa+cpU0C91z58vz4sL0CgUCrksRTVq1Ch27tzJ77//TtWqVV+a1traGjs7O65fv17g/uDgYMaPHy8/VyqV2Nra0qpVq3zDNctaeno6I0aMoEuXLvj5+ZGZmUmfPn149OgRf/zxB+bm5oUem/da+Pr6Ftr7m5mZSVRUFG3atCnxQkHvEnWNG9Q3dhG3esUN6hu7OsWdN+KnKETDUBCEckWhUNCsWTOaNWvGjBkzsLOzY9u2bejo6JCdrdpT5ezszLp163j69Kk8zy8mJqZE53d2dkZXV5fExES8vLwKTJO3mueL5TE3NycpKUl+rlQquXXrVonKUxSSJDFq1Ci2bdvGkSNHcHBweOUxDx484O+//8ba2rrA/bq6ugU2hLW1tcv8QzQoKIhOnTpRrVo1UlJSmDt3LkqlkkGDBqFQKOjTpw9nz55l165daGho8ODBAyB3zmXea5ecnExycjIJCQkAXL16FWNjY6pVq1boojzlIfayoK5xg/rGLuJWP+oauzrEXZz4xKqkgiCUGydPnmT+/Pn873//IzExka1bt/LPP/9Qp04d7O3t+fPPP4mLi+P+/ftkZmbSt29fNDQ0GDx4MJcvXyYyMpLFixeXqAzGxsYEBQUxbtw41qxZQ3x8POfOnePbb79lzZo1ANjZ2aFQKNi1axf//PMPaWlpALRu3Zp169Zx7NgxLl68yIABA9DU1CxxvbzKiBEj+Pnnn9mwYQPGxsZyo+fp06cApKWlERQUxIkTJ0hISODIkSN06tSJypUr89FHH73x8pW227dv06dPHxwdHenWrRs6OjrExMRgZ2fH7du32blzJ7dv36Zhw4ZYW1vLj+joaDmPFStW4OrqypAhQwBo2bIlrq6u7Ny5s6zCEgRBEIQyJXoMBUEoNypUqMDvv//OkiVLUCqV2NnZERoaSvv27XFzc+PIkSO4ubmRlpbG4cOH8fb25rfffiMwMBBXV1ecnZ358ssv6d69e4nKMWfOHCwsLFiwYAE3b96kYsWKNGrUSL4ZepUqVZg1axaTJ09m4MCB9O/fn9WrVxMcHMzNmzfp2LEjJiYmzJkz5630GH733XcAeHt7q2wPDw8nICAATU1NYmNjWbt2Lf/99x/W1ta0atWKzZs3q8xrfFds2rSp0H329vZFGmIcEhKichsRQRAEQVB3Cqm0JukIgiAI7y2lUomJiQn3798vd3MM37TMzEwiIyPx8/N774ccPU9d4wb1jV3ErV5xg/rGrk5x531+p6amvvK2TWIoqSAIgiAIgiAIgpoTDUNBEN5bgYGBGBkZFfgIDAws6+IVSWJiYqExGBkZkZiYWNZFFARBEAThPSDmGAqC8N6aPXs2QUFBBe571XCK8sLGxobz58+/dL8gCIIgCEJJiYahIAjvLQsLCywsLMq6GCWipaVFzZo1y7oYgiAIgiC858RQUkEQBEEQBEEQBDUnGoaCIAjCOyMkJASFQqHysLKykvdLkkRISAg2Njbo6+vj7e3NpUuXVPLw9vbOl0fv3r3fdiiCIAiCUK6IhqHwzvP29mbs2LGlkldCQgIKheKlc7reZj7llUKhYPv27UDZxGpvb8+SJUtKPd+MjAxq1qzJH3/8UaT0ISEhNGzYsNTL8ab16NGDsLCwsi7Ga3NxcSEpKUl+xMbGyvsWLlxIWFgYy5Yt4/Tp01hZWdGmTRsePXqkkseQIUNU8li5cuXbDkMQBEEQyhUxx1AQnmNra0tSUhKVK1cu8jEBAQH8999/ckPpdfN5V71PsX7//ffY2dnRrFmzIqUPCgpi1KhRb7hUpW/GjBm0atWKTz/99J1ZhOd5WlpaKr2EeSRJYsmSJUydOpVu3boBsGbNGiwtLdmwYQOfffaZnNbAwKDAPARBEARBXYkeQ0F4jqamJlZWVmhplew3k9LK513wPsW6dOlSPv300yKnNzIyeunN3jMyMkqjWKWufv362Nvbs379+rIuymu5fv06NjY2ODg40Lt3b27evAnArVu3SE5OxtfXV06rq6uLl5cX0dHRKnmsX7+eypUr4+LiQlBQUL4eRUEQBEFQN6JhKLxTHj9+TP/+/TEyMsLa2prQ0FCV/RkZGUycOJEqVapgaGhI06ZNOXLkCACpqano6+uzd+9elWO2bt2KoaEhaWlp+YZFZmdnM3jwYBwcHNDX18fR0ZGvv/5aPjYkJIQ1a9awY8cOea7SkSNHChxeefToUZo0aYKuri7W1tZMnjyZrKwseb+3tzejR49m4sSJmJqaYmVlRUhISJHrRqFQsHLlSjp27IiBgQF16tThxIkT3LhxA29vbwwNDfHw8CA+Pl7luN9++43GjRujp6dH9erVmTVrlkq5rl+/TsuWLdHT08PZ2ZmoqCiV44tbZ5Dby9q1a1cWL16MtbU1ZmZmjBgxgszMzCLH+7zExES6dOmCkZERFSpUoGfPnty7d08lzdy5c7GwsMDY2JhPP/2UyZMnqwwDPXv2LDdu3KBDhw4qx92+fZvevXtjamqKoaEhbm5unDx5Esg/lDQvrgULFmBjY0Pt2rVfmcfL5OW/atUqqlWrhpGREcOGDSM7O5uFCxdiZWWFhYUF8+bNk4/p06dPvvlymZmZVK5cmfDwcHlb586d2bhx4yvL8KKmCw5iP3l3mTwAmjZtytq1a9m3bx8//PADycnJeHp68uDBA5KTkwGwtLRUKbOlpaW8D8Df35+NGzdy5MgRpk+fTkREhNzDKAiCIAjq6t3/iV9QKxMmTODw4cNs27YNKysrpkyZwpkzZ+Qv5wMHDiQhIYFNmzZhY2PDtm3baNeuHbGxsdSqVYsOHTqwfv162rVrJ+e5YcMGuVFx//59lfPl5ORQtWpVtmzZQuXKlYmOjmbo0KFYW1vTs2dPgoKCuHLlCkqlUv7SbWpqyt27d1XyuXPnDn5+fgQEBLB27VquXr3KkCFD0NPTU2n8rVmzhvHjx3Py5ElOnDhBQEAAzZo1o02bNkWqnzlz5hAWFkZYWBiTJk2ib9++VK9eneDgYKpVq8agQYMYOXIke/bsAWDfvn188sknfPPNN7Ro0YL4+HiGDh0KwMyZM8nJyaFbt25UrlyZmJgYlErlK+dzvqrO8hw+fBhra2sOHz7MjRs36NWrFw0bNmTIkCFFijWPJEl07doVQ0NDjh49SlZWFsOHD6dXr17yjwLr169n3rx5LF++nGbNmrFp0yZCQ0NxcHCQ8/n999+pXbu2ytDKtLQ0vLy8qFKlCjt37sTKyoqzZ8+Sk5NTaHkOHjxIhQoViIqKQpKk18rjefHx8ezZs4e9e/cSHx9Pjx49uHXrFrVr1+bo0aNER0czaNAgPvzwQ9zd3fH396dnz56kpaVhZGQE5L7Ojx8/pnv37nK+TZo0YcGCBaSnp6Orq5vvvOnp6aSnp8vPlUolALoaEpqaUpHKXtoyMzPx8fGRnzs5OeHm5oaTkxOrVq2iadOmAGRlZan8yJCdnS0fD7kN+DyOjo44ODjg7u7OqVOncHV1LfC8z/+rLtQ1blDf2EXc6hU3qG/s6hR3cWIUDUPhnZGWlsZPP/3E2rVr5YbSmjVrqFq1KpD7BXrjxo3cvn1bvul3UFAQe/fuJTw8nPnz5+Pv70///v158uQJBgYGKJVKdu/eTURERIHn1NbWZtasWfJzBwcHoqOj2bJlCz179sTIyAh9fX3S09NfOl9p+fLl2NrasmzZMhQKBU5OTty9e5dJkyYxY8YMNDRyO+/r16/PzJkzAahVqxbLli3j4MGDRW4YDhw4UG58TZo0CQ8PD6ZPn07btm0BGDNmDAMHDpTTz5s3j8mTJzNgwAAAqlevzpw5c5g4cSIzZ87kwIEDXLlyhYSEBLme58+fT/v27Qstw6vqLE+lSpVYtmwZmpqaODk50aFDBw4ePFjshuGBAwf4888/uXXrFra2tgCsW7cOFxcXTp8+zQcffMDSpUsZPHiwHPuMGTPYv38/aWlpcj4JCQn5bha/YcMG/vnnH06fPo2pqSnAK+8paGhoyI8//oiOjg6QO2+xuHk8Lycnh1WrVmFsbIyzszOtWrUiLi6OyMhINDQ0cHR05Msvv+TIkSO4u7vTtm1bDA0N2bZtG/369ZPj6NSpk0qjt0qVKqSnp5OcnIydnV2+8y5YsEDldcwzzTUHA4PsIpe/NEVGRha43crKikOHDsnxRUREUL16dXn/xYsXMTQ0LPR4SZLQ0tLil19+ISkpqdDzv9hbri7UNW5Q39hF3OpHXWNXh7ifPHlS5LSiYSi8M+Lj48nIyMDDw0PeZmpqiqOjI5A7FFCSJHn4Xp709HR5HliHDh3Q0tJi586d9O7dm4iICIyNjVXmJL1oxYoV/Pjjj/z11188ffqUjIyMYq9EeeXKFTw8PFAoFPK2Zs2akZaWxu3bt6lWrRqQ2zB8nrW1NSkpKUU+z/PH5w2nq1evnsq2Z8+eoVQqqVChAmfOnOH06dMqQxGzs7N59uwZT5484cqVK1SrVk1uFAIq9V+YotSZi4sLmpqaKrE+v7pkUV25cgVbW1u5UQjg7OxMxYoVuXLlCh988AFxcXEMHz5c5bgmTZpw6NAh+fnTp0/R09NTSXP+/HlcXV3lBl1R1KtXT24Uvm4ez7O3t8fY2Fh+bmlpiaampvxjQt62vOtEW1ubjz/+mPXr19OvXz8eP37Mjh072LBhg0q++vr6QOEfGMHBwYwfP15+rlQqsbW1Ze45DbK0NQs85k27GNI237b09HRGjBhBly5dGDhwICEhITx79gw/Pz8gd3j5gAEDmD9/vrwtX74XL5KVlUX79u1p0aJFvv2ZmZlERUXRpk0btLW1Szeockxd4wb1jV3ErV5xg/rGrk5x5434KQrRMBTeGZL08uFrOTk5aGpqcubMGZUGByAPqdPR0aFHjx5s2LCB3r17s2HDBnr16lXowilbtmxh3LhxhIaG4uHhgbGxMYsWLSrS/LAXy/58o/D5eJ7f/uIfJ4VCUeQhhy8en5dvQdvy8szJyWHWrFkFzq/S09MrsM5fjONFRa2zksaap6C6LWh7YfWfp3LlyvkapnmNp+IwNDQscR7PK6ieXlV3/v7+eHl5kZKSQlRUFHp6evl6ef/9918AzM3NCzyvrq5ugUNMf5/k89IFd960oKAgOnXqRLVq1UhJSWHu3LkolUoGDRqEjo4OY8eOZcGCBTg5OVGrVi3mz5+PgYEB/fr1Q1tbm/j4eNavX4+fnx+VK1fm8uXLfP7557i6uuLl5ZXvb8fztLW13/svEAVR17hBfWMXcasfdY1dHeIuTnyiYSi8M2rWrIm2tjYxMTFyD9vDhw+5du0aXl5euLq6kp2dTUpKSoG/+ufx9/fH19eXS5cucfjwYebMmVNo2mPHjuHp6anS2/Ti4i06OjryHKbCODs7ExERodJYiY6OxtjYmCpVqrwy9jelUaNGxMXFFTq00dnZmcTERO7evSsPszxx4sRL8yxKnZWmvDL+/fffcq/h5cuXSU1NpU6dOkDuPLJTp07JQysB/ve//6nk4+rqynfffafyGtWvX58ff/yRf//997V7/Eojj+Ly9PTE1taWzZs3s2fPHj7++GOVXkzI7SWrWrXqO3ebkdu3b9OnTx/u37+Pubk57u7uxMTEyMNhJ06cyNOnTxk+fDgPHz6kadOm7N+/X+511dHR4eDBg3z99dekpaVha2tLhw4dmDlz5ksbhYIgCILwvhMNQ+GdYWRkxODBg5kwYQJmZmZYWloydepUeUhd7dq15TmEoaGhuLq6cv/+fQ4dOkS9evXkYWReXl5YWlri7++Pvb097u7uhZ6zZs2a8gqIDg4OrFu3jtOnT6ssWmJvb8++ffuIi4vDzMwMExOTfPkMHz6cJUuWMGrUKEaOHElcXBwzZ85k/PjxKkMC37YZM2bQsWNHbG1t+fjjj9HQ0ODPP/8kNjaWuXPn4uPjg6Ojo1ynSqWSqVOnvjTPotRZafLx8aF+/fr4+/uzZMkSefEZLy8v3NzcABg1ahRDhgzBzc0NT09PNm/ezJ9//qkyD61Vq1Y8fvyYS5cuUbduXSB3hc/58+fLK41aW1tz7tw5bGxsijSktrTyKC6FQkHfvn1ZsWIF165d4/Dhw/nSHDt27KVDqMurTZs2vXS/QqEgJCSk0BV9bW1tOXr06BsomSAIgiC828TtKoR3yqJFi2jZsiWdO3fGx8eH5s2b07hxY3l/eHg4/fv35/PPP8fR0ZHOnTtz8uRJlflnCoWCPn36cOHCBfz9/V96vsDAQLp160avXr1o2rQpDx48yDdXbciQITg6OuLm5oa5uTl//PFHvnyqVKlCZGQkp06dokGDBgQGBjJ48GCmTZtWwhopmbZt27Jr1y6ioqL44IMPcHd3JywsTO590dDQYNu2baSnp9OkSRM+/fRTlfmIBSlKnZUmhULB9u3bqVSpEi1btsTHx4fq1auzefNmOY2/vz/BwcEEBQXRqFEjbt26RUBAgMqcQjMzM7p166Zybz8dHR3279+PhYUFfn5+1KtXjy+++KJYPUulkcfr8Pf35/Lly1SpUoVmzZqp7Hv27Bnbtm0r9kI/giAIgiC8vxTSqyZuCYIgvIfatGmDlZUV69atk7fFxsbi4+PDjRs3VBZ8ed98++237Nixg/379xf5GKVSiYmJCffv3y/TOYZlITMzk8jISPz8/N77uSjPU9e4QX1jF3GrV9ygvrGrU9x5n9+pqakqq5MXRAwlFQThvffkyRNWrFhB27Zt0dTUZOPGjRw4cCDfMtX16tVj4cKFJCQkqKzm+r7R1tZm6dKlZV0MQRAEQRDKETGUVBDeAevXr8fIyKjAh4uLS1kXr1QdO3as0FjzVpctLoVCQWRkJC1atKBx48b89ttvREREqNwsPc+AAQPeWqPQxcWl0DifH9Ja2oYOHSrf5kUQBEEQBAFEj6EgvBM6d+5M06ZNC9z3vg2BcHNz4/z586Wap76+PgcOHCjVPEtDZGQkmZmZBe7Luw+lIAiCIAjC2yAahoLwDjA2Nn6v57w9T19fv9DbZ7xv8hb5EQRBEARBKGtiKKkgCIIgCIIgCIKaEw1DQRAE4Z0REhKCQqFQeVhZWcn7JUkiJCQEGxsb9PX18fb25tKlSyp5fPbZZ9SoUQN9fX3Mzc3p0qULV69efduhCIIgCEK5IhqGgiAIwjvFxcWFpKQk+REbGyvvW7hwIWFhYSxbtozTp09jZWVFmzZtePTokZymcePGhIeHc+XKFfbt24ckSfj6+pKdnV0W4QiCIAhCuSDmGAqCIAjvFC0tLZVewjySJLFkyRKmTp1Kt27dAFizZg2WlpZs2LCBzz77DMhdlTWPvb09c+fOpUGDBiQkJFCjRo23E4QgCIIglDOix1AQBOEdZ29vz5IlS1S2NWzYkJCQECD3dh3fffcd7du3R19fHwcHB3755Ze3X9BScv36dWxsbHBwcKB3797cvHkTgFu3bpGcnIyvr6+cVldXFy8vL6KjowvM6/Hjx4SHh+Pg4ICtre1bKb8gCIIglEeix1AQBEENTJ8+nS+++IKvv/6adevW0adPH+rWrUudOnWKlU/TBQfJ0jJ8Q6V8uYQvOtC0aVPWrl1L7dq1uXfvHnPnzsXT05NLly6RnJwM5L/Vh6WlJX/99ZfKtuXLlzNx4kQeP36Mk5MTUVFR6OjovLVYBEEQBKG8EQ1DQRAENfDxxx/z6aefAjBnzhyioqJYunQpy5cvLzB9eno66enp8nOlUgmAroaEpqb05gtcgMzMTHx8fOTnTk5OuLm54eTkxKpVq+R7fWZlZancHzJv7uDz23r27Im3tzfJycmEhYXx8ccfc/ToUfT09Ao874vHqwN1jRvUN3YRt3rFDeobuzrFXZwYRcNQEARBDXh4eOR7fv78+ULTL1iwgFmzZuXbPs01BwODslmkJTIyssDtVlZWHDp0iAoVKgAQERFB9erV5f0XL17E0NCw0OMDAgL45JNPCAkJoWXLloWePyoqqgSlf3epa9ygvrGLuNWPusauDnE/efKkyGlFw1AQBOEdp6GhgSSp9uIV5RdChUJR6L7g4GDGjx8vP1cqldja2tKqVSvMzMxev7ClLD09nREjRtClSxcGDhxISEgIz549w8/PD4CMjAwGDBjA/Pnz5W0vysjIQENDA2dn5wLTZGZmEhUVRZs2bdDW1n6j8ZQn6ho3qG/sIm71ihvUN3Z1ijtvxE9RiIahIAjCO87c3JykpCT5uVKp5NatWyppYmJi6N+/v8pzV1fXQvPU1dVFV1c333Ztbe0y/RANCgqiU6dOVKtWjZSUFObOnYtSqWTQoEHo6OgwduxYFixYgJOTE7Vq1WL+/PkYGBjQr18/tLW1uXnzJps3b8bX1xdzc3Pu3LnDl19+ib6+Pp06dXppbGUde1lR17hBfWMXcasfdY1dHeIuTnyiYSgIgvCOa926NatXr6ZTp05UqlSJ6dOno6mpqZLml19+wc3NjebNm7N+/XpOnTrFTz/9VEYlfn23b9+mT58+3L9/H3Nzc9zd3YmJicHOzg6AiRMn8vTpU4YPH87Dhw9p2rQp+/fvx9jYGAA9PT2OHTvGkiVLePjwIZaWlrRs2ZLo6GgsLCzKMjRBEARBKFOiYSgIgvCOCw4O5ubNm3Ts2BETExPmzJmTr8dw1qxZbNq0ieHDh2NlZcX69etxdnYuoxK/vk2bNr10v0KhICQkRL5Vx4tsbGwKnWsoCIIgCOpMNAwFQRDecRUqVGDz5s0q2wYMGKDy3MbGhv3797/NYgmCIAiC8A4RN7gXBEEQBEEQBEFQc6JhKAiCIAiCIAiCoObEUFJBEIT33Iu3shAEQRAEQXiR6DEUBEEQBEEQBEFQc6JhKAiCIAiCIAiCoOZEw1AQBEEQBEEQBEHNiYahILwB3t7ejB07tlTySkhIQKFQcP78+XKRT3mlUCjYvn078P7HKuRasGABCoVC5b127949AgICsLGxwcDAgHbt2nH9+vV8x544cYLWrVtjaGhIxYoV8fb25unTp2+x9IIgCIJQvoiGoSCUc7a2tiQlJVG3bt0iHxMQEEDXrl1LnM+76n2MNSQkhIYNG+bb/nyDWJ2cPn2a77//nvr168vbJEmia9eu3Lx5kx07dnDu3Dns7Ozw8fHh8ePHcroTJ07Qrl07fH19OXXqFKdPn2bkyJFoaIiPREEQBEF9iVVJBaGc09TUxMrKqtzk8y5Qp1jVUVpaGv7+/vzwww/MnTtX3n79+nViYmK4ePEiLi4uACxfvhwLCws2btzIp59+CsC4ceMYPXo0kydPlo+tVavW2w1CEARBEMoZ8fOoIJTQ48eP6d+/P0ZGRlhbWxMaGqqyPyMjg4kTJ1KlShUMDQ1p2rQpR44cASA1NRV9fX327t2rcszWrVsxNDQkLS0t37DI7OxsBg8ejIODA/r6+jg6OvL111/Lx4aEhLBmzRp27NiBQqFAoVBw5MiRAodXHj16lCZNmqCrq4u1tTWTJ08mKytL3u/t7c3o0aOZOHEipqamWFlZERISUuS6USgUrFy5ko4dO2JgYECdOnU4ceIEN27cwNvbG0NDQzw8PIiPj1c57rfffqNx48bo6elRvXp1Zs2apVKu69ev07JlS/T09HB2diYqKkrl+OLWGfxfL+vixYuxtrbGzMyMESNGkJmZWaRY7e3tmTNnDn379sXIyAgbGxuWLl2qkiY1NZWhQ4diYWFBhQoVaN26NRcuXHhl3qtXr2bWrFlcuHBBfk1Xr16Nvb09AB999BEKhUJ+nte7uGrVKqpVq4aRkRHDhg0jOzubhQsXYmVlhYWFBfPmzStSbOXNiBEj6NChAz4+Pirb09PTAdDT05O3aWpqoqOjw/HjxwFISUnh5MmTWFhY4OnpiaWlJV5eXvJ+QRAEQVBXosdQEEpowoQJHD58mG3btmFlZcWUKVM4c+aMPOxv4MCBJCQksGnTJmxsbNi2bRvt2rUjNjaWWrVq0aFDB9avX0+7du3kPDds2ECXLl0wMjLi/v37KufLycmhatWqbNmyhcqVKxMdHc3QoUOxtramZ8+eBAUFceXKFZRKJeHh4QCYmppy9+5dlXzu3LmDn58fAQEBrF27lqtXrzJkyBD09PRUGn9r1qxh/PjxnDx5khMnThAQEECzZs1o06ZNkepnzpw5hIWFERYWxqRJk+jbty/Vq1cnODiYatWqMWjQIEaOHMmePXsA2LdvH5988gnffPMNLVq0ID4+nqFDhwIwc+ZMcnJy6NatG5UrVyYmJgalUvnK+ZyvqrM8hw8fxtramsOHD3Pjxg169epFw4YNGTJkSJFiXbRoEVOmTCEkJIR9+/Yxbtw4nJycaNOmDZIk0aFDB0xNTYmMjMTExISVK1fy4Ycfcu3aNUxNTQvNt1evXly8eJG9e/dy4MABAExMTOjQoQMWFhaEh4fTrl07NDU15WPi4+PZs2cPe/fuJT4+nh49enDr1i1q167N0aNHiY6OZtCgQXz44Ye4u7vnO2d6errc0AJQKpUAtPzyAFnahkWqj9J0MaQtAJs3b+bMmTOcOHGCzMxMJEkiJyeHzMxMatSogZ2dHZMmTWL58uUYGhqyZMkSkpOTuXv3LpmZmVy7dg3IbTx/+eWX1K9fn/Xr1/Phhx9y7ty5AnsO834cKOqPBO8LdY0b1Dd2Ebd6xQ3qG7s6xV2cGBWSuPOxILy2tLQ0zMzMWLt2Lb169QLg33//pWrVqgwdOpRRo0ZRq1Ytbt++jY2NjXycj48PTZo0Yf78+Wzbto3+/ftz7949DAwMUCqVWFpaEhERgZ+fHwkJCTg4OHDu3LkC55hBbg/KvXv3+PXXX4Hc3q///vtPZe7Zi/lMnTqViIgIrly5gkKhAHKH3U2aNInU1FQ0NDTw9vYmOzubY8eOyfk0adKE1q1b88UXX7yyfhQKBdOmTWPOnDkAxMTE4OHhwU8//cSgQYMA2LRpEwMHDpQX/mjZsiXt27cnODhYzufnn39m4sSJ3L17l/3798v1UrVqVQD27t1L+/bt2bZtG127dn3tOjty5Ajx8fFyA6tnz55oaGiwadOmV8Zqb29PnTp15AYuQO/evVEqlURGRnLo0CE++ugjUlJS0NXVldPUrFmTiRMnyo3fwoSEhLB9+/Z8C+ooFAo57ufTLlq0iOTkZIyNjQFo164dcXFxxMfHy3PpnJycCAgIUBlS+Xwes2bNyrd9w4YNGBgYvLI+3oR//vmHoKAgQkJCcHBwAGDq1Kk4ODjIw0Rv3LjBsmXLSEhIQENDgwYNGsjX94wZM7h69SqTJ0+me/fu9OvXT857zJgxuLm5qWwTBEEQhHfdkydP6Nu3L6mpqVSoUOGlaUWPoSCUQHx8PBkZGXh4eMjbTE1NcXR0BODs2bNIkkTt2rVVjktPT8fMzAyADh06oKWlxc6dO+nduzcREREYGxvj6+tb6HlXrFjBjz/+yF9//cXTp0/JyMgotAFUmCtXruDh4SF/aQZo1qwZaWlp3L59m2rVqgGoLO4BYG1tTUpKSpHP8/zxlpaWANSrV09l27Nnz1AqlVSoUIEzZ85w+vRplWGO2dnZPHv2jCdPnnDlyhWqVasmNwoBlfovTFHqzMXFRaXXzdramtjY2CLH+mI5PDw8WLJkCQBnzpyRf0h43tOnT/MNpS0N9vb2cqMQcutZU1NTZYEVS0vLQl/L4OBgxo8fLz9XKpXY2toy95wGWdqaBR7zJl0MacuOHTtITU0lKChI3p6dnc3ly5fZs2cPaWlp+Pn5MXr0aFJTU8nIyMDc3JxmzZrRuHFj/Pz8qFOnDpMnT6Zjx474+fnJ+fz8889oaWmpbMuTmZlJVFQUbdq0QVtb+63EWx6oa9ygvrGLuNUrblDf2NUp7rwRP0UhGoaCUAKv6nDPyclBU1OTM2fOqDQ4AIyMjADQ0dGhR48ebNiwgd69e7NhwwZ69eqFllbBb88tW7Ywbtw4QkND8fDwwNjYmEWLFnHy5Mlil/35RuHz8Ty//cU/mAqFgpycnCKf5/nj8/ItaFtenjk5OcyaNYtu3brly0tPT6/AOn8xjhcVtc5KGmtBno/P2tpanl/6vIoVK5boHAUpKJbixKerq6vSs5nn90k++Rq3b0vbtm3zNdQHDhyIk5MTkyZNUplbWLlyZSB3PuqZM2eYO3cu2tra1KpVCxsbG+Lj41Xq48aNG7Rv3/6lXxC0tbXf+y8QBVHXuEF9Yxdxqx91jV0d4i5OfKJhKAglULNmTbS1tYmJiZF72B4+fMi1a9fw8vLC1dWV7OxsUlJSaNGiRaH5+Pv74+vry6VLlzh8+LA89LIgx44dw9PTk+HDh8vbXuxx0tHRITs7+6Vld3Z2JiIiQqWBGB0djbGxMVWqVHll7G9Ko0aNiIuLo2bNmgXud3Z2JjExkbt378rDc0+cOPHSPItSZ6UhJiYm33MnJycgN67k5GS0tLTkRWKKo7DXVFtb+5Wv9fvC2Ng43y1IDA0NMTMzk7f/8ssvmJubU61aNWJjYxkzZgxdu3aVe+AVCgUTJkxg5syZNGjQgIYNG7JmzRquXr0qDysWBEEQBHUkGoaCUAJGRkYMHjyYCRMmYGZmhqWlJVOnTpWH69WuXRt/f3/69+9PaGgorq6u3L9/n0OHDlGvXj152JqXlxeWlpb4+/tjb29f4GIgeWrWrMnatWvZt28fDg4OrFu3jtOnT8tzriB3GOG+ffuIi4vDzMwMExOTfPkMHz6cJUuWMGrUKEaOHElcXBwzZ85k/PjxZXo/txkzZtCxY0dsbW35+OOP0dDQ4M8//yQ2Npa5c+fi4+ODo6OjXKdKpZKpU6e+NM+i1Flp+OOPP1i4cCFdu3YlKiqKX375hd27dwO580o9PDzo2rUrX375JY6Ojty9e5fIyEi6du2Km5vbS/O2t7fn1q1bnD9/nqpVq2JsbIyuri729vYcPHiQZs2aoaurS6VKlUo1pndNUlIS48eP5969e1hbW9O/f3+mT5+ukmbs2LE8e/aMcePG8e+//9KgQQOioqKoUaNGGZVaEARBEMqeuF2FIJTQokWLaNmyJZ07d8bHx4fmzZvTuHFjeX94eDj9+/fn888/x9HRkc6dO3Py5ElsbW3lNAqFgj59+nDhwgX8/f1fer7AwEC6detGr169aNq0KQ8ePFDpCQMYMmQIjo6OuLm5YW5uzh9//JEvnypVqhAZGcmpU6do0KABgYGBDB48mGnTppWwRkqmbdu27Nq1i6ioKD744APc3d0JCwvDzs4OAA0NDbZt20Z6ejpNmjTh008/feVtF4pSZ6Xh888/58yZM7i6ujJnzhxCQ0Np2zZ3NU2FQkFkZCQtW7Zk0KBB1K5dm969e5OQkCDPvXyZ7t27065dO1q1aoW5uTkbN24EIDQ0lKioKGxtbXF1dS31mMq7I0eOyPM4AUaPHs3ff/9NRkYGf/31F3PmzEFHRyffcZMnT+bvv//m8ePHREdH07x587dYakEQBEEof8SqpIIgCKXA3t6esWPHvvLWGe8qpVKJiYkJ9+/fL7M5hmUlMzOTyMhI/Pz83vu5KM9T17hBfWMXcatX3KC+satT3Hmf30VZlVT0GAqCIAiCIAiCIKg50TAUBOG1rF+/HiMjowIfLi4uZV28UnXs2LFCY81bXbakXFxcCs1//fr1pXIOQRAEQRCEwojFZwRBeC2dO3emadOmBe5734ZluLm55bux/IsSEhJKdI7IyEgyMzML3FeUOYiCIAiCIAglIRqGgiC8FmNjY5UbqL/P9PX1C719RmnJW1xHEARBEAShLIihpIIgCIIgCIIgCGpONAwFQRCEd9KCBQtQKBQqK8Heu3ePgIAAbGxsMDAwoF27dly/fl3lOG9vbxQKhcqjd+/eb7n0giAIglC+iIahIAjlSkJCAgqF4pVz+gT1dvr0ab7//nvq168vb5Mkia5du3Lz5k127NjBuXPnsLOzw8fHh8ePH6scP2TIEJKSkuTHypUr33YIgiAIglCuiIahIAhvRUBAAF27di3rYgC5N0VXKBT8999/ZV0UAB49esTYsWOxs7NDX18fT09PTp8+rZJGkiRCQkKwsbFBX18fb29vLl26VKT8//33X0aNGoWjoyMGBgZUq1aN0aNHk5qa+ibCeePS0tLw9/fnhx9+oFKlSvL269evExMTw3fffccHH3yAo6Mjy5cvJy0tjY0bN6rkYWBggJWVlfwwMTF522EIgiAIQrkiGoaCILw3JEkiKyvrrZ6zsJVEi+PTTz8lKiqKdevWERsbi6+vLz4+Pty5c0dOs3DhQsLCwli2bBmnT5/GysqKNm3a8OjRo1fmf/fuXe7evcvixYuJjY1l9erV7N27l8GDB5e47GVhxIgRdOjQAR8fH5Xt6enpAOjp6cnbNDU10dHR4fjx4ypp169fT+XKlXFxcSEoKKhI9SgIgiAI7zOxKqkgCKXq119/ZdasWdy4cQMDAwNcXV1xdXVlzZo1ACgUCgAOHz6Mt7c3p06d4rPPPuPKlSvUrVuXqVOnFvlcR44coVWrVuzdu5epU6fy559/sm/fPry9vVm0aBErVqwgKSmJ2rVrM336dHr06EFCQgKtWrUCkHubBgwYwOrVq7G3t2fs2LEqc9YaNmxI165dCQkJkcv/3XffsWfPHg4cOEBQUBAKhYLt27fz+eefM336dB4+fEj79u354YcfXrly69OnT4mIiGDHjh20bNkSgJCQELZv3853333H3LlzkSSJJUuWMHXqVLp16wbAmjVrsLS0ZMOGDXz22WcvPUfdunWJiIiQn9eoUYN58+bxySefkJWVhZZW0T8Kmi44SJaWYZHTl5aELzoAsGnTJs6ePZuvRxXAyckJOzs7goODWblyJYaGhoSFhZGcnExSUpKczt/fHwcHB6ysrLh48SLBwcFcuHCBqKiotxaPIAiCIJQ3omEoCEKpSUpKok+fPixcuJCPPvqIR48ecezYMfr3709iYiJKpZLw8HAATE1Nefz4MR07dqR169b8/PPP3Lp1izFjxhT7vBMnTmTx4sVUr16dihUrMm3aNLZu3cp3331HrVq1+P333/nkk08wNzenefPmRERE0L17d+Li4qhQoQL6+vrFOt/MmTNZsGABX331FZqamoSHhxMfH8/27dvZtWsXDx8+pGfPnnzxxRfMmzfvpXllZWWRnZ2t0ssFubfIyOvlunXrFsnJyfj6+sr7dXV18fLyIjo6+pUNw4KkpqZSoUKFQhuF6enpcg8cgFKpzD2vhoSmplTs85VUZmYmf//9N2PGjGH37t1oamqSmZmJJEnk5OTIPbebN29m6NChmJqaoqmpyYcffki7du3kPCB3WHMeR0dHHBwccHd359SpU7i6uhZ47uf/VRfqGjeob+wibvWKG9Q3dnWKuzgxioahIAilJikpiaysLLp16ybfl69evXpAbkMnPT0dKysrOf3q1avJzs5m1apVGBgY4OLiwu3btxk2bFixzjt79mzatGkDwOPHjwkLC+PQoUN4eHgAUL16dY4fP87KlSvx8vLC1NQUAAsLCypWrFjsOPv27cugQYNUtuXk5LB69Wq5h7Bfv34cPHjwlQ1DY2NjPDw8mDNnDnXq1MHS0pKNGzdy8uRJatWqBUBycjKQ/0b3lpaW/PXXX8Uu/4MHD5gzZ85LG5QLFixg1qxZ+bZPc83BwCC72OcsqcjISGJiYkhJSaFp06by9pycHI4dO8a3337LL7/8gqamJrNnz+bx48dkZWVhYmLChAkTqFmzJpGRkQXmLUkSWlpa/PLLLyo9iy9S1x5FdY0b1Dd2Ebf6UdfY1SHuJ0+eFDmtaBgKglBqGjRowIcffki9evVo27Ytvr6+9OjRQ2WBkOdduXKFBg0aYGBgIG/La8wVh5ubm/z/y5cv8+zZM7mhmCcjI6PA3qDX8fz58tjb26sMG7W2tiYlJaVI+a1bt45BgwZRpUoVNDU1adSoEX379uXs2bMq6fKG4eaRJCnftldRKpV06NABZ2dnZs6cWWi64OBgxo8fr3Kcra0tc89pkKWtWaxzloaLIW1p0aIFPXv2VNk+ZMgQHB0dCQoKom7duvmOu379OvHx8SxZsiTfNSHnffEiWVlZtG/fnhYtWuTbn5mZSVRUFG3atEFbW7t0AnoHqGvcoL6xi7jVK25Q39jVKe68ET9FIRqGgiCUGk1NTaKiooiOjmb//v0sXbqUqVOncvLkyQLTS1LpDEk0NPy/OW85OTkA7N69mypVqqik09XVfWk+Ghoa+cpU0BCM58+X58UPFoVCIZflVWrUqMHRo0d5/PgxSqUSa2trevXqhYODA4Dcy5qcnIy1tbV8XEpKSr5exJd59OgR7dq1w8jIiG3btr30w1BXV7fA+vp9kg9mZmZFPmdpMjU1lXt78xgZGWFubi43+n/55RfMzc2pVq0asbGxjBkzhq5du+Ln5wdAfHw869evx8/Pj8qVK3P58mU+//xzXF1d8fLyQlOz8Eavtrb2e/8FoiDqGjeob+wibvWjrrGrQ9zFiU+sSioIQqlSKBQ0a9aMWbNmce7cOXR0dNi2bRs6OjpkZ6sOQXR2dubChQs8ffpU3hYTE1Oi8zs7O6Orq0tiYiI1a9ZUedja2gKgo6MDkK885ubmKkMJlUolt27dKlF5isPQ0BBra2sePnzIvn376NKlC4C8UMrzQ14yMjI4evQonp6eRcpbqVTi6+uLjo4OO3fuzDen8X2RlJREv379cHJyYvTo0fTr10/lVhU6OjocPHiQtm3b4ujoyOjRo/H19eXAgQMvbRQKgiAIwvtO9BgKglBqTp48ycGDB/H19cXCwoKTJ0/yzz//UKdOHZ49e8a+ffuIi4vDzMwMExMT+vbty9SpUxk8eDDTpk0jISGBxYsXl6gMxsbGBAUFMW7cOHJycmjevDlKpZLo6GiMjIwYMGAAdnZ2KBQKdu3ahZ+fH/r6+hgZGdG6dWtWr15Np06dqFSpEtOnT38rjYV9+/YhSRKOjo7cuHGDCRMm4OjoyMCBA4HcxvbYsWOZP38+tWrVolatWsyfPx8DAwP69u37yvwfPXqEr68vT5484eeff0apVMpDS8zNzd/pBtGRI0dUno8ePZrRo0cXmt7W1pajR4++4VIJgiAIwrtHNAwFQSg1FSpU4Pfff2fJkiUolUrs7OwIDQ2lffv2uLm5ceTIEdzc3EhLS5NvV/Hbb78RGBiIq6srzs7OfPnll3Tv3r1E5ZgzZw4WFhYsWLCAmzdvUrFiRRo1asSUKVMAqFKlCrNmzWLy5MkMHDiQ/v37s3r1aoKDg7l58yYdO3bExMSEOXPmvJUew9TUVIKDg7l9+zampqZ0796defPmqQz/mDhxIk+fPmX48OE8fPiQpk2bsn///lfeDgPgzJkz8nDemjVrquy7desW9vb2pRqPIAiCIAjvHoVUWpN8BEEQhPeWUqnExMSE+/fvl9kcw7KSmZlJZGQkfn5+7/1clOepa9ygvrGLuNUrblDf2NUp7rzP77zbVL2MmGMoCIIgCIIgCIKg5kTDUBCEciswMBAjI6MCH4GBgWVdvCJJTEwsNAYjIyMSExNLfI7169cXmr+Li0spRCEIgiAIwvtOzDEUBKHcmj17NkFBQQXue9VwiPLCxsaG8+fPv3R/SXXu3Fnlpu/Pe9+HyAiCIAiCUDpEw1AQhHLLwsICCwuLsi5GiWhpaeVb8KW0GRsbF2kRGkEQBEEQhMKIoaSCIAiCIAiCIAhq7r1oGHp7ezN27NhSySshIQGFQvHSoV9vMx+h6BQKBdu3by/rYggltHr1aipWrFjWxRDKuQULFsj3d8yjUCgKfCxatEhO4+3tnW9/7969yyACQRAEQShf3ouGYWmytbUlKSmJunXrFvmYgIAAunbtWuJ8hPxCQkJo2LBhWRcDKH+N/czMTGbPnk2NGjXQ09OjQYMG7N27N1+65cuX4+DggJ6eHo0bN+bYsWNlUFqwt7dnyZIlZXLuF5Wn6wogPj6ejz76CHNzcypUqEDPnj25d++eSpqHDx/Sr18/TExMMDExoV+/fvz3339Fyv/ChQv06dMHW1tb9PX1qVOnDl9//fUbiOTtOH36NN9//z3169dX2Z6UlKTyWLVqFQqFIt99MYcMGaKSbuXKlW+z+IIgCIJQLomG4Qs0NTWxsrJCS6tk0y9LKx/h7cjMzHyr58vIyChxHtOmTWPlypUsXbqUy5cvExgYyEcffcS5c+fkNJs3b2bs2LFMnTqVc+fO0aJFC9q3b18qK2GWR2/7dZQkiaysrBLl8fjxY3x9fVEoFBw6dIg//viDjIwMOnXqRE5Ojpyub9++nD9/nr1797J3717Onz9Pv379inSOM2fOYG5uzs8//8ylS5eYOnUqwcHBLFu2rERlLwtpaWn4+/vzww8/UKlSJZV9VlZWKo8dO3bQqlUrqlevrpLOwMBAJZ2JicnbDEEQBEEQyifpHZOWlib169dPMjQ0lKysrKTFixdLXl5e0pgxYyRJkqT09HRpwoQJko2NjWRgYCA1adJEOnz4sCRJkvTff/9Jenp60p49e1TyjIiIkAwMDKRHjx5Jt27dkgDp3LlzkiRJUlZWljRo0CDJ3t5e0tPTk2rXri0tWbJEPnbmzJkSoPI4fPhwvnwkSZKOHDkiffDBB5KOjo5kZWUlTZo0ScrMzJT3e3l5SaNGjZImTJggVapUSbK0tJRmzpxZ5LoBpBUrVkgdOnSQ9PX1JScnJyk6Olq6fv265OXlJRkYGEju7u7SjRs3VI7buXOn1KhRI0lXV1dycHCQQkJCVMoVGhoq1a1bVzIwMJCqVq0qDRs2THr06JG8Pzw8XDIxMZH27t0rOTk5SYaGhlLbtm2lu3fvFqnchw8flj744APJwMBAMjExkTw9PaWEhAQpPDw8X92Gh4dLkiRJ165dk1q0aCHp6upKderUkfbv3y8B0rZt2155vrzXZvPmzZKXl5ekq6srrVq1SpIkSVq1apXk5OQk6erqSo6OjtK3336rUr/PP7y8vCRJklSuvzxdunSRBgwYID+3s7OT5syZIw0YMECqUKGC1L9//xLXm7W1tbRs2bJ85/X395efN2nSRAoMDFRJ4+TkJE2ePLlI5yiumTNnSra2tpKOjo5kbW0tjRo1SpKk3Dp6sf7yhIeHS7a2tpK+vr7UtWtXafHixZKJiUmRz9egQQPpp59+khwcHCSFQiHl5ORI//33nzRkyBDJ3NxcMjY2llq1aiWdP39ePl9B11VB79mHDx/K72lJyr1WAWnv3r1S48aNJW1tbenQoUMleu/u27dP0tDQkFJTU+Vt//77rwRIUVFRkiRJ0uXLlyVAiomJkdOcOHFCAqSrV68W6TwvGj58uNSqVasip09NTZUA6f79+691vtLSv39/aezYsZIkFfzey5OcnCxpaWlJ69evV9nu5eUlVa5cWTIzM5OcnZ2lzz//XFIqlS89Z0ZGhrR9+3YpIyOjVGJ4V6hr3JKkvrGLuNUrbklS39jVKe68z+/nv2cU5p3rzpowYQKHDx9m27ZtWFlZMWXKFM6cOSMPCxs4cCAJCQls2rQJGxsbtm3bRrt27YiNjaVWrVp06NCB9evX065dOznPDRs20KVLF4yMjLh//77K+XJycqhatSpbtmyhcuXKREdHM3ToUKytrenZsydBQUFcuXIFpVJJeHg4AKampty9e1clnzt37uDn50dAQABr167l6tWrDBkyBD09PUJCQuR0a9asYfz48Zw8eZITJ04QEBBAs2bNaNOmTZHqZ86cOYSFhREWFsakSZPo27cv1atXJzg4mGrVqjFo0CBGjhzJnj17ANi3bx+ffPIJ33zzDS1atCA+Pp6hQ4cCMHPmTAA0NDT45ptvsLe359atWwwfPpyJEyeyfPly+bxPnjxh8eLFrFu3Dg0NDT755BOCgoJYv379S8ublZVF165dGTJkCBs3biQjI4NTp06hUCjo1asXFy9eZO/evRw4cAAAExMTcnJy6NatG5UrVyYmJgalUvlac0wnTZpEaGgo4eHh6Orq8sMPPzBz5kyWLVuGq6sr586dY8iQIRgaGjJgwABOnTpFkyZNOHDgAC4uLujo6BTrfIsWLWL69OlMmzYNgOPHj792vQGkp6ejp6ensk1fX5/jx48Dub2SZ86cYfLkySppfH19iY6OLlbZi+LXX3/lq6++YtOmTbi4uJCcnMyFCxcA2Lp1Kw0aNGDo0KEMGTJEPubkyZMMGjSI+fPn061bN/bu3Stfd0V148YNtmzZQkREBJqamgB06NABU1NTIiMjMTExYeXKlXz44Ydcu3at0OvqxaGbLzNx4kQWL15M9erV5fmQr/veTU9PR6FQoKurK2/T09NDQ0OD48eP4+Pjw4kTJzAxMVG5JYW7uzsmJiZER0fj6OhY5LLnSU1NxdTUtNjHNV1wkCwtw2IfVxIJX3QAYNOmTZw9e5bTp0+/8pg1a9ZgbGxMt27dVLb7+/vj4OCAlZUVFy9eJDg4mAsXLhAVFfVGyi4IgiAI74p3qmGYlpbGTz/9xNq1a+UvW2vWrKFq1apA7jydjRs3cvv2bfneYEFBQezdu5fw8HDmz5+Pv78//fv358mTJxgYGKBUKtm9ezcREREFnlNbW5tZs2bJzx0cHIiOjmbLli307NkTIyMj9PX1SU9Px8rKqtCyL1++HFtbW5YtW4ZCocDJyYm7d+8yadIkZsyYgYZG7qje+vXry1+Ma9WqxbJlyzh48GCRG4YDBw6kZ8+eQG7Dx8PDg+nTp9O2bVsAxowZw8CBA+X08+bNY/LkyQwYMACA6tWrM2fOHCZOnCiX4/lGl4ODA3PmzGHYsGEqDcPMzExWrFhBjRo1ABg5ciSzZ89+ZXmVSiWpqal07NhRPrZOnTryfiMjI7S0tFTqdv/+/Vy5coWEhAT5tZ8/fz7t27cvUh3lGTt2rMqXxjlz5hAaGipvc3Bw4PLly6xcuZIBAwZgbm4OgJmZ2Utf68K0bt1a5Z58x48ff+16A2jbti1hYWG0bNmSGjVqcPDgQXbs2EF2djYA9+/fJzs7G0tLS5XjLC0tSU5OLnb5XyUxMRErKyt8fHzQ1tamWrVqNGnSBMj9sURTUxNjY2OVuvv6669p27at3HitXbs20dHRBc6VLExGRgbr1q2TX59Dhw4RGxtLSkqK3NhavHgx27dv59dff2Xo0KEFXlfFMXv27Hzvydd977q7u2NoaMikSZOYP38+kiQxadIkcnJySEpKAiA5ObnA23ZYWFi81mt54sQJtmzZwu7duwtNk56eTnp6uvxcqVQCoKshoakpFfucJZGZmcnff//NmDFj2L17N5qammRmZiJJEjk5OQUOIf7pp5/o06ePnDZPQECA/H9HR0ccHBxwd3fn1KlTuLq6Fnr+5/9VF+oaN6hv7CJu9Yob1Dd2dYq7ODG+Uw3D+Ph4MjIy8PDwkLeZmprKv5afPXsWSZKoXbu2ynHp6emYmZkBuT0JWlpa7Ny5k969exMREYGxsTG+vr6FnnfFihX8+OOP/PXXXzx9+pSMjIxiL1xx5coVPDw8UCgU8rZmzZqRlpbG7du3qVatGkC+xRSsra1JSUkp8nmePz6vQVCvXj2Vbc+ePUOpVFKhQgXOnDnD6dOnmTdvnpwmOzubZ8+eyY3nw4cPM3/+fC5fvoxSqSQrK4tnz57x+PFjDA1zew4MDAzkxk1xym1qakpAQABt27alTZs2+Pj40LNnT6ytrQs95sqVK1SrVk1uFAIq10RRubm5yf//559/+Pvvvxk8eLBKj1ZWVlapzT96/nx5XrfeILdRNWTIEJycnFAoFNSoUYOBAwfKPdd5nr/mIHde3IvbCjJ//nzmz58vP798+bJ8nRbk448/ZsmSJVSvXp127drh5+dHp06dXjrP9sqVK3z00Ucq2zw8PIrVMLSzs5MbhZA7ny4tLU1+z+d5+vQp8fHxRc73ZQp6LV/3vWtubs4vv/zCsGHD+Oabb9DQ0KBPnz40atRI7gGF/K8jFP21fN6lS5fo0qULM2bMeGmjdcGCBSo/iuWZ5pqDgUF2sc5ZUpGRkcTExJCSkqLSa5qTk8OxY8f49ttv+eWXX+T6unTpEteuXWPYsGFERka+NG9JktDS0uKXX36RG+KFUddeRXWNG9Q3dhG3+lHX2NUh7idPnhQ57TvVMJSkl/9KnZOTg6amJmfOnFH5QgW5PU8AOjo69OjRgw0bNtC7d282bNhAr169Cv3yumXLFsaNG0doaCgeHh4YGxuzaNEiTp48WeyyF/QFHVS/8Glra6ukUSgUKgtQvMrzx+flW9C2vDxzcnKYNWtWvuFWkDuc7a+//sLPz4/AwEDmzJmDqakpx48fZ/DgwSq/QBRU7le9XnnCw8MZPXo0e/fuZfPmzUybNo2oqCjc3d0LTF9QvsX9cgzIjVr4v/r44YcfVL54AvmupRdpaGjkK1NBv848f748Jak3c3Nztm/fzrNnz3jw4AE2NjZMnjwZBwcHACpXroympma+HqWUlJR8vYgFCQwMlHufAbkXvjC2trbExcUR5svJ3gAALGpJREFUFRXFgQMHGD58OIsWLeLo0aP54sxT1Fhf5sV6zcnJwdramiNHjuRL+7LbYOT12j9fpsJ+ZSvqa1nU966vry/x8fHcv38fLS0tKlasiJWVlfxaWllZFTjU9Z9//inSa5nn8uXLtG7dmiFDhshDmgsTHBzM+PHj5edKpRJbW1tatWqVr9H9NrRo0ULleoTc1UUdHR0JCgpSWQE6IiKCRo0aMWLEiFfme/HiRbKysmjfvj0tWrQoME1mZiZRUVG0adOm0Gv5faSucYP6xi7iVq+4QX1jV6e480b8FMU71TCsWbMm2traxMTEyD0XDx8+5Nq1a3h5eeHq6kp2djYpKSmFfsBD7hwTX19fLl26xOHDh5kzZ06haY8dO4anpyfDhw+Xt73Y66CjoyMP3yuMs7MzERERKg3E6OhojI2NqVKlyitjf1MaNWpEXFwcNWvWLHD///73P7KysggNDZW/OG/ZsqXUy+Hq6oqrqyvBwcF4eHiwYcMG3N3dC6xbZ2dnEhMTuXv3rtxYOXHiRInOb2lpSZUqVbh58yb+/v4FpsmbU/hieczNzVV6GrKzs7l48SKtWrUqUZmKSk9PjypVqpCZmUlERIT85VlHR4fGjRsTFRWl0isXFRVFly5dXpmvqalpseeg6evr07lzZzp37syIESNwcnIiNjaWRo0aFfpaxsTEqGx78XlxNWrUiOTkZLS0tLC3ty8wTUFlyet1TEpKkocUvu1bk1SuXBnIHQ6bkpJC586dgdxe1NTUVHmeK+TOz0xNTcXT07NIeV+6dInWrVszYMAAlREChdHV1VWZ95hHW1u7TD5EC7oejYyMMDc3VxkCqlQqiYiIIDQ0NF854+PjWb9+PX5+flSuXJnLly/z+eef4+rqipeX1yt/BCqr2MuausYN6hu7iFv9qGvs6hB3ceJ7pxqGRkZGDB48mAkTJmBmZoalpSVTp06VGyy1a9eW5xCGhobi6urK/fv3OXToEPXq1cPPzw8ALy8vLC0t8ff3x97evtCeKchtjK5du5Z9+/bh4ODAunXrOH36tPxLPuTen23fvn3ExcVhZmZW4NDD4cOHs2TJEkaNGsXIkSOJi4tj5syZjB8/Xi5/WZgxYwYdO3bE1taWjz/+GA0NDf78809iY2OZO3cuNWrUICsri6VLl9KpUyf++OMPVqxYUWrnv3XrFt9//z2dO3fGxsaGuLg4rl27Rv/+/QHkBW/Onz9P1apVMTY2xsfHB0dHR/l1ViqVTJ06tcRlCQkJYfTo0VSoUIH27duTnp7O//73Px4+fMj48eOxsLBAX1+fvXv3UrVqVfT09DAxMaF169aMHz+e3bt3U6NGDb766qsi31+uJE6ePMmdO3do2LAhd+7cISQkhJycHCZOnCinGT9+PP369cPNzQ0PDw++//57EhMTCQwMLPXyrF69muzsbJo2bYqBgQHr1q1DX18fOzs7IPe1/P333+nduze6urpUrlyZ0aNH4+npycKFC+natSv79+8v1jDSgvj4+ODh4UHXrl358ssvcXR05O7du0RGRtK1a1fc3NwKvK709fVxd3fniy++wN7envv377+yV620hIeHU6dOHczNzTlx4gRjxoxh3Lhx8jD5OnXq0K5dO4YMGSLfc2/o0KF07NixSAvPXLp0iVatWuHr68v48ePlXmRNTU2VYbjvg02bNiFJEn369Mm3T0dHh4MHD/L111+TlpaGra0tHTp0YObMma9sFAqCIAjCe6/0F0V9sx49eiR98sknkoGBgWRpaSktXLhQZcnyjIwMacaMGZK9vb2kra0tWVlZSR999JH0559/quQzYcIECZBmzJihsv3FJeufPXsmBQQESCYmJlLFihWlYcOGSZMnT5YaNGggH5OSkiK1adNGMjIyKvHtKl5124OX4YXbNRRUhrzl9h8+fChv27t3r+Tp6Snp6+tLFSpUkJo0aSJ9//338v6wsDDJ2tpa0tfXl9q2bSutXbtWJY+82y48b9u2bVJRLq/k5GSpa9eukrW1taSjoyPZ2dlJM2bMkLKzsyVJyq3/7t27SxUrVlS5XUVcXJzUvHlzSUdHR6pdu7a0d+/eYt+u4vl6ybN+/XqpYcOGko6OjlSpUiWpZcuW0tatW+X9P/zwg2RraytpaGjIt6vIyMiQhg0bJpmamkoWFhbSggULCrxdxVdffaVyrpLUmyTlXk916tSRdHV1JTMzM6lfv37SnTt38qX79ttvJTs7O0lHR0dq1KiRdPTo0SLlX1zbtm2TmjZtKlWoUEEyNDSU3N3dpQMHDsj7T5w4IdWvX1/S1dVVifGnn36SqlatKunr60udOnV6rdtVvEipVEqjRo2SbGxsJG1tbcnW1lby9/eXEhMTJUkq/Lq6fPmy5O7uLunr60sNGzaUb4Py4u0qnn//SFLJ37uTJk2SLC0tJW1tbalWrVpSaGiolJOTo5LmwYMHkr+/v2RsbCwZGxtL/v7++cpRmIJuqwNIdnZ2RTpeksrP7SrKgjota/48dY1bktQ3dhG3esUtSeobuzrFXZzbVSgkqRQm+QiCIAjvNaVSiYmJCffv3y+TOYZlKTMzk8jISPz8/N77IUfPU9e4QX1jF3GrV9ygvrGrU9x5n9+pqalUqFDhpWnLbgyjIAiCIAiCIAiCUC6IhuE7Yv369RgZGRX4cHFxKevivVRh5TYyMuLYsWOlfr758+cXer7i3uuwLL3pejt27NhLz/GisrgGXVxcCj3n+vXr38g5S9vbqLfAwMBCz/Em5pMKgiAIgvD+eacWn1FnnTt3zncbhTzlvQv8ZSs7vokVWV+8zcLz9PX1S/18b8qbrjc3N7dirbpZFtdgZGRkobeMKM5tGsrS26i32bNnExQUVOC+Vw0bEQRBEARBANEwfGcYGxtjbGxc1sV4LYXdCuNNeZ3bLJRHb7re9PX1i3WOsrgG81Y0fZe9jXqzsLDAwsLijZ5DEARBEIT3mxhKKgiCIAiCIAiCoOZEw1AQBEF45yxYsACFQsHYsWPlbQqFosDHokWL5DSfffYZNWrUQF9fH3Nzc7p06cLVq1fLIAJBEARBKF9Ew1B4r3l7e6t8cSyJhISE/9fencfVmPf/A3+daC8HSQupbJGylchSWUO22z0UmWS9+5LdyDqymzGMZW7LxJ1sj8ZMskxIiEFliUaSbnuGCIMjRuvn94df1+3SagaNc17Px+M8xvlcn/M5n9d1murddV2fCwqF4p2uy/uQ4/xdKRQK7N69G4D6Zy105coVtGnTBnp6emjevHlFT0ftnT17Ft9//z2aNm0qa8/IyJA9/vOf/0ChUOCf//yn1MfJyQmhoaFITU1FdHQ0hBDo1q0b8vPzP3YMIiKivxUWhkTlZGVlhYyMDDg4OJT7Nf7+/ujXr99fHudT9SlmLe4zK8vcuXNhaGiItLQ0HDly5L3Mw8bGBitXrnwvY70pOzsbzZs3/2QL9qysLPj6+iIkJATVqlWTbTM3N5c99uzZg44dO6Ju3bpSn9GjR8PNzQ02NjZo2bIlFi5ciDt37uDWrVsfOQkREdHfCwtDonKqVKkSzM3NUbnyX1uz6X2N8ynQlKzXr19H+/btYW1t/be7+XtOTo7s+bRp02BpaVlBs/nrxo4dCy8vL3Tp0qXUfg8ePEBUVBRGjBhRYp8XL14gNDQUtra2sLKyet9TJSIi+qSwMCS18eLFC/j5+cHIyAgWFhZYvny5bHtOTg6mTZuGWrVqwdDQEK1bt8axY8cAAM+ePYO+vj4OHjwoe82uXbtgaGiIrKysIqdF5ufnY8SIEbC1tYW+vj7s7OywatUq6bXBwcEICwvDnj17pGudjh07VuzplcePH4eLiwt0dXVhYWGB6dOnIy8vT9ru4eGB8ePHY9q0aahevTrMzc0RHBxc7n2jUCiwYcMG9OrVCwYGBmjcuDHi4+Nx7do1eHh4wNDQEK6urrh+/brsdfv27YOTkxP09PRQt25dzJs3Tzavq1evws3NDXp6erC3t0dMTIzs9e+6z4D/HbH75ptvYGFhARMTE4wdO7bE21a8bdu2bXB2doaxsTHMzc0xePBgZGZmyvqkpKTAy8sLVapUgbGxMTp06IDr16+X+JmVtW8TExMxf/58KBQK6XMJCgpCw4YNYWBggLp162LOnDlFMuzduxfOzs7Q09NDjRo10L9/fwCvP+/bt29j0qRJ0jwKRUREoEmTJtDV1YWNjU2Rr3MbGxssXLgQ/v7+UCqVGDVqlLTtwIEDOHToEL755pty7cvitF5yBDbToz7qo1B4eDjOnz+PJUuWlDnPsLAwGBsbS/v0TWvXrpXu83jw4EHExMRAR0fnT+8TIiIidaDef8YnjfLFF18gNjYWkZGRMDc3x8yZM5GYmChd8zVs2DDcunUL4eHhsLS0RGRkJLp3747k5GQ0aNAAXl5e2L59O7p37y6NuWPHDvTt2xdGRkZ49OiR7P0KCgpQu3Zt7Ny5EzVq1EBcXBxGjx4NCwsLDBw4EFOnTkVqaipUKhVCQ0MBvL6Vxr1792Tj3L17Fz179oS/vz+2bNmCK1euYNSoUdDT05MVf2FhYZg8eTJOnz6N+Ph4+Pv7o127dujatWu59s+CBQuwYsUKrFixAkFBQRg8eDDq1q2LGTNmoE6dOhg+fDgCAwNx4MABAEB0dDSGDBmC1atXS4XT6NGjAbw+dbKgoAD9+/dHjRo1kJCQAJVKVeb1nGXts0KxsbGwsLBAbGwsrl27Bm9vbzRv3lxW5JQkJycHCxYsgJ2dHTIzMzFp0iT4+/tj//790v52c3ODh4cHjh49iipVquDUqVPIy8sr8TMrTUZGBrp06YLu3btj6tSpMDIyAvD6NhWbN2+GpaUlkpOTMWrUKBgbG2PatGkAgKioKPTv3x+zZs3C1q1bkZOTg6io10XQrl270KxZM4wePVqWOTExEQMHDkRwcDC8vb0RFxeHMWPGwMTEBP7+/lK/ZcuWYc6cOZg9e7bU9uDBA4waNQq7d++GgYFBmfsxOzsb2dnZ0nOVSgUA0NUSqFRJlPn69yk3Nxd37tzBhAkTEBUVhUqVKiE3NxdCCBQUFBT7R4NNmzZh0KBBUt83DRw4EB4eHrh//z5WrFiBAQMG4Pjx49DT0yvx/d/8r6bQ1NyA5mZnbs3KDWhudk3K/S4ZFUKIj/sTnugDyMrKgomJCbZs2QJvb28AwO+//47atWtj9OjRGDduHBo0aIDffvtNdhpdly5d4OLigsWLFyMyMhJ+fn548OABDAwMoFKpYGZmhoiICPTs2RO3bt2Cra0tLly4UOICI2PHjsWDBw/w008/AXh99Ovp06fSYiwAiowza9YsREREIDU1VToytHbtWgQFBeHZs2fQ0tKCh4cH8vPzceLECWkcFxcXdOrUCUuXLi1z/ygUCsyePRsLFiwAACQkJMDV1RWbNm3C8OHDAbw+GjNs2DD88ccfAAA3Nzf06NEDM2bMkMbZtm0bpk2bhnv37uHQoUPSfqlduzYA4ODBg+jRowciIyPRr1+/P73Pjh07huvXr6NSpUoAXv8ir6WlhfDw8DKzvu3s2bNwcXHB8+fPYWRkhJkzZyI8PBxpaWnF3mC+uM+sLM2bN0e/fv1KPYq7bNky/PDDDzh37hwAoG3btqhbty62bdtWbH8bGxtMnDhRVmz7+vri4cOHOHTokNQ2bdo0REVFISUlRXpdixYtEBkZKfURQqBnz55o164dZs+eXa7PJTg4GPPmzSvSvmPHjnIVlu9bQkICli5dCi2t/53oUlBQIB1R/fHHH6Wvl5SUFMyaNQvffvstbG1tSx03NzcXQ4YMwdixY+Hm5vZBMxAREX1sL1++xODBg/Hs2TNUqVKl1L48Ykhq4fr168jJyYGrq6vUVr16ddjZ2QEAzp8/DyEEGjZsKHtddna2dE2Yl5cXKleujL1798LHxwcREREwNjZGt27dSnzf9evXY+PGjbh9+zb++OMP5OTkvPOqlKmpqXB1dZWdLtiuXTtkZWXht99+Q506dQCgyAqMFhYWRU6RLM2brzczMwMAODo6ytpevXoFlUqFKlWqIDExEWfPnsWiRYukPvn5+Xj16hVevnyJ1NRU1KlTRyoKAcj2f0nKs8+aNGki/ZJfmDU5OblcOS9cuIDg4GAkJSXh999/R0FBAQAgPT0d9vb2SEpKQocOHYotCt+nn376CStXrsS1a9eQlZWFvLw82TfkpKSkch0BfVNqair69u0ra2vXrh1WrlyJ/Px8aZ85OzvL+qxZswYqlUpW5JdlxowZmDx5svRcpVLBysoKCy9oIU+7UimvfP8uBXuiQ4cOsqPKADBq1CjY2dlh6tSpsgWOIiIi0LJlS4wdO7bMsXNycqClpQV7e3v07Nmz2D65ubmIiYlB165dP/jXzd+JpuYGNDc7c2tWbkBzs2tS7sIzfsqDhSGphbIOfBcUFKBSpUpITEyUFRwApFP/dHR08Nlnn2HHjh3w8fHBjh074O3tXeLCKTt37sSkSZOwfPlyuLq6wtjYGMuWLcPp06ffee5vFoVv5nmz/e1vXAqFQip6yuPN1xeOW1xb4ZgFBQWYN29esddo6enpFbvP387xtvLusz+b9cWLF+jWrRu6deuGbdu2wdTUFOnp6fD09JQWYdHX1y9znL8qISEBPj4+mDdvHjw9PaFUKhEeHi67HvDPzKO0r5U3GRoayp4fPXoUCQkJ0NXVlbU7OzvD19cXYWFhRcbQ1dUt0h8AfgnqUiEL7FSvXr3Iab1GRkYwNTVFixYtpDaVSoWIiAgsX768yNfRjRs38MMPP6Bbt24wNTXF3bt38dVXX0FfXx+9e/cu85cDbW1ttf8FojiamhvQ3OzMrXk0Nbsm5H6XfCwMSS3Ur18f2traSEhIkI6wPXnyBP/973/h7u6OFi1aID8/H5mZmejQoUOJ4/j6+qJbt25ISUlBbGysdOplcU6cOIG2bdtizJgxUtvbi7fo6OiUeX80e3t7REREyH7pj4uLg7GxMWrVqlVm9g+lZcuWSEtLQ/369Yvdbm9vj/T0dNy7d086PTc+Pr7UMcuzz/6KK1eu4NGjR1i6dKm0ymThqZuFmjZtirCwMOTm5hb7zbI8n1lZTp06BWtra8yaNUtqu337dpF5HDlyBMOGDSt2jOLmYW9vj5MnT8ra4uLi0LBhwyJ/8HjT6tWrsXDhQun5vXv34OnpiR9++AGtW7cud65PQXh4OIQQGDRoUJFtenp6OHHiBFauXIknT57AzMwMbm5uiIuLQ82aNStgtkRERH8fLAxJLRgZGWHEiBH44osvYGJiAjMzM8yaNUu6Hqlhw4bw9fWFn58fli9fjhYtWuDRo0c4evQoHB0dpVPI3N3dYWZmBl9fX9jY2KBNmzYlvmf9+vWxZcsWREdHw9bWFlu3bsXZs2dl1zTZ2NggOjoaaWlpMDExgVKpLDLOmDFjsHLlSowbNw6BgYFIS0vD3LlzMXnyZNn1VB/bl19+iV69esHKygoDBgyAlpYWLl68iOTkZCxcuBBdunSBnZ2dtE9VKpWsECpOefbZX1GnTh3o6OhgzZo1CAgIwKVLl4oU94GBgVizZg18fHwwY8YMKJVKJCQkwMXFBXZ2dsV+Zu/618T69esjPT0d4eHhaNWqFaKiomTX/AGvF/Dp3Lkz6tWrBx8fH+Tl5eHAgQPS4jQ2Njb45Zdf4OPjA11dXdSoUQNTpkxBq1atsGDBAnh7eyM+Ph7fffcd1q5dW+Z+eVPhUfJ69erJTgX+1BS3Yuzo0aOlRZLeZmlpKS1CRERERHK8XQWpjWXLlsHNzQ19+vRBly5d0L59ezg5OUnbQ0ND4efnhylTpsDOzg59+vTB6dOnZfcvUygUGDRoEH799Vf4+vqW+n4BAQHo378/vL290bp1azx+/Fh2JAz43zVQzs7OMDU1xalTp4qMU6tWLezfvx9nzpxBs2bNEBAQgBEjRshWlKwInp6e+PnnnxETE4NWrVqhTZs2WLFiBaytrQEAWlpaiIyMRHZ2NlxcXDBy5EjZ9YjFKc8++ytMTU2xefNm/Pjjj7C3t8fSpUuL3JrBxMQER48eRVZWFtzd3eHk5ISQkBCp+CvPZ1aWvn37YtKkSQgMDETz5s0RFxeHOXPmyPp4eHjgxx9/xN69e9G8eXN06tRJdkrt/PnzcevWLdSrVw+mpqYAXh/F3blzJ8LDw+Hg4IAvv/wS8+fPl61ISkRERPRncFVSIiIqk0qlglKpxKNHjyrkGsOKlJubi/3796Nnz55qfy3KmzQ1N6C52Zlbs3IDmptdk3IX/vwuz6qkPGJIRERERESk4VgYEn3itm/fDiMjo2IfTZo0qejpvVcnTpwoMWvhdXPv2+LFi0t8vx49enyQ9yQiIiL62Lj4DNEnrk+fPiWuLKlup0c4OzsjKSnpo75nQEBAkfvnFfoYt74gIiIi+hhYGBJ94oyNjWFsbFzR0/go9PX1S7x9xodS3P3ziIiIiNQNTyUlIiIiIiLScCwMiYiIiIiINBwLQyIiIiIiIg3HwpCIiIiIiEjDsTAkIiIiIiLScCwMiYiIiIiINBxvV0FERGUSQgAAnj9/rnb3xyxLbm4uXr58CZVKpVHZNTU3oLnZmVuzcgOam12TcqtUKgD/+zleGhaGRERUpsePHwMAbG1tK3gmRERE9K6eP38OpVJZah8WhkREVKbq1asDANLT08v8waJuVCoVrKyscOfOHVSpUqWip/PRaGpuQHOzM7dm5QY0N7sm5RZC4Pnz57C0tCyzLwtDIiIqk5bW60vSlUql2v8QLUmVKlU0Mrum5gY0Nztzax5Nza4pucv7B10uPkNERERERKThWBgSERERERFpOBaGRERUJl1dXcydOxe6uroVPZWPTlOza2puQHOzM7dm5QY0N7um5i6LQpRn7VIiIiIiIiJSWzxiSEREREREpOFYGBIREREREWk4FoZEREREREQajoUhERERERGRhmNhSEREZVq7di1sbW2hp6cHJycnnDhxoqKn9E5++eUX9O7dG5aWllAoFNi9e7dsuxACwcHBsLS0hL6+Pjw8PJCSkiLrk52djXHjxqFGjRowNDREnz598Ntvv8n6PHnyBJ9//jmUSiWUSiU+//xzPH369AOnK96SJUvQqlUrGBsbo2bNmujXrx/S0tJkfdQxNwCsW7cOTZs2lW5e7erqigMHDkjb1TX325YsWQKFQoGJEydKbeqaPTg4GAqFQvYwNzeXtqtrbgC4e/cuhgwZAhMTExgYGKB58+ZITEyUtqtjdhsbmyKft0KhwNixYwGoZ+aPQhAREZUiPDxcaGtri5CQEHH58mUxYcIEYWhoKG7fvl3RUyu3/fv3i1mzZomIiAgBQERGRsq2L126VBgbG4uIiAiRnJwsvL29hYWFhVCpVFKfgIAAUatWLRETEyPOnz8vOnbsKJo1ayby8vKkPt27dxcODg4iLi5OxMXFCQcHB9GrV6+PFVPG09NThIaGikuXLomkpCTh5eUl6tSpI7KysqQ+6phbCCH27t0roqKiRFpamkhLSxMzZ84U2tra4tKlS0II9c39pjNnzggbGxvRtGlTMWHCBKldXbPPnTtXNGnSRGRkZEiPzMxMabu65v7999+FtbW18Pf3F6dPnxY3b94Uhw8fFteuXZP6qGP2zMxM2WcdExMjAIjY2FghhHpm/hhYGBIRUalcXFxEQECArK1Ro0Zi+vTpFTSjv+btwrCgoECYm5uLpUuXSm2vXr0SSqVSrF+/XgghxNOnT4W2trYIDw+X+ty9e1doaWmJgwcPCiGEuHz5sgAgEhISpD7x8fECgLhy5coHTlW2zMxMAUAcP35cCKE5uQtVq1ZNbNy4USNyP3/+XDRo0EDExMQId3d3qTBU5+xz584VzZo1K3abOucOCgoS7du3L3G7Omd/04QJE0S9evVEQUGBxmT+EHgqKRERlSgnJweJiYno1q2brL1bt26Ii4uroFm9Xzdv3sT9+/dlGXV1deHu7i5lTExMRG5urqyPpaUlHBwcpD7x8fFQKpVo3bq11KdNmzZQKpV/i3317NkzAED16tUBaE7u/Px8hIeH48WLF3B1ddWI3GPHjoWXlxe6dOkia1f37FevXoWlpSVsbW3h4+ODGzduAFDv3Hv37oWzszMGDBiAmjVrokWLFggJCZG2q3P2Qjk5Odi2bRuGDx8OhUKhEZk/FBaGRERUokePHiE/Px9mZmaydjMzM9y/f7+CZvV+FeYoLeP9+/eho6ODatWqldqnZs2aRcavWbNmhe8rIQQmT56M9u3bw8HBAYD6505OToaRkRF0dXUREBCAyMhI2Nvbq33u8PBwnD9/HkuWLCmyTZ2zt27dGlu2bEF0dDRCQkJw//59tG3bFo8fP1br3Ddu3MC6devQoEEDREdHIyAgAOPHj8eWLVsAqPdnXmj37t14+vQp/P39AWhG5g+lckVPgIiI/v4UCoXsuRCiSNun7s9kfLtPcf3/DvsqMDAQFy9exMmTJ4tsU9fcdnZ2SEpKwtOnTxEREYGhQ4fi+PHj0nZ1zH3nzh1MmDABhw4dgp6eXon91DF7jx49pH87OjrC1dUV9erVQ1hYGNq0aQNAPXMXFBTA2dkZixcvBgC0aNECKSkpWLduHfz8/KR+6pi90KZNm9CjRw9YWlrK2tU584fCI4ZERFSiGjVqoFKlSkX+OpqZmVnkr7GfqsKVC0vLaG5ujpycHDx58qTUPg8ePCgy/sOHDyt0X40bNw579+5FbGwsateuLbWre24dHR3Ur18fzs7OWLJkCZo1a4ZVq1apde7ExERkZmbCyckJlStXRuXKlXH8+HGsXr0alStXlualjtnfZmhoCEdHR1y9elWtP3MLCwvY29vL2ho3boz09HQA6v//+e3bt3H48GGMHDlSalP3zB8SC0MiIiqRjo4OnJycEBMTI2uPiYlB27ZtK2hW75etrS3Mzc1lGXNycnD8+HEpo5OTE7S1tWV9MjIycOnSJamPq6srnj17hjNnzkh9Tp8+jWfPnlXIvhJCIDAwELt27cLRo0dha2sr266uuUsihEB2drZa5+7cuTOSk5ORlJQkPZydneHr64ukpCTUrVtXbbO/LTs7G6mpqbCwsFDrz7xdu3ZFbkPz3//+F9bW1gDU///z0NBQ1KxZE15eXlKbumf+oD7aMjdERPRJKrxdxaZNm8Tly5fFxIkThaGhobh161ZFT63cnj9/Li5cuCAuXLggAIgVK1aICxcuSLfcWLp0qVAqlWLXrl0iOTlZDBo0qNilzWvXri0OHz4szp8/Lzp16lTs0uZNmzYV8fHxIj4+Xjg6OlbY0ub/93//J5RKpTh27JhsWfeXL19KfdQxtxBCzJgxQ/zyyy/i5s2b4uLFi2LmzJlCS0tLHDp0SAihvrmL8+aqpEKob/YpU6aIY8eOiRs3boiEhATRq1cvYWxsLH2fUtfcZ86cEZUrVxaLFi0SV69eFdu3bxcGBgZi27ZtUh91zZ6fny/q1KkjgoKCimxT18wfGgtDIiIq07///W9hbW0tdHR0RMuWLaVbHnwqYmNjBYAij6FDhwohXi/pPnfuXGFubi50dXWFm5ubSE5Olo3xxx9/iMDAQFG9enWhr68vevXqJdLT02V9Hj9+LHx9fYWxsbEwNjYWvr6+4smTJx8ppVxxeQGI0NBQqY865hZCiOHDh0tfr6ampqJz585SUSiE+uYuztuFobpmL7xPnba2trC0tBT9+/cXKSkp0nZ1zS2EEPv27RMODg5CV1dXNGrUSHz//fey7eqaPTo6WgAQaWlpRbapa+YPTSGEEBVyqJKIiIiIiIj+FniNIRERERERkYZjYUhERERERKThWBgSERERERFpOBaGREREREREGo6FIRERERERkYZjYUhERERERKThWBgSERERERFpOBaGRERERGrKw8MDEydOrOhpENEngIUhERERaSR/f38oFIoij2vXrr2X8Tdv3oyqVau+l7H+rF27dmHBggUVOofSHDt2DAqFAk+fPq3oqRBpvMoVPQEiIiKiitK9e3eEhobK2kxNTStoNiXLzc2Ftrb2O7+uevXqH2A270dubm5FT4GI3sAjhkRERKSxdHV1YW5uLntUqlQJALBv3z44OTlBT08PdevWxbx585CXlye9dsWKFXB0dIShoSGsrKwwZswYZGVlAXh9JGzYsGF49uyZdCQyODgYAKBQKLB7927ZPKpWrYrNmzcDAG7dugWFQoGdO3fCw8MDenp62LZtGwAgNDQUjRs3hp6eHho1aoS1a9eWmu/tU0ltbGywcOFC+Pn5wcjICNbW1tizZw8ePnyIvn37wsjICI6Ojjh37pz0msIjn7t370bDhg2hp6eHrl274s6dO7L3WrduHerVqwcdHR3Y2dlh69atsu0KhQLr169H3759YWhoiJEjR6Jjx44AgGrVqkGhUMDf3x8AcPDgQbRv3x5Vq1aFiYkJevXqhevXr0tjFe6jXbt2oWPHjjAwMECzZs0QHx8ve89Tp07B3d0dBgYGqFatGjw9PfHkyRMAgBACX3/9NerWrQt9fX00a9YMP/30U6n7k0idsTAkIiIiekt0dDSGDBmC8ePH4/Lly9iwYQM2b96MRYsWSX20tLSwevVqXLp0CWFhYTh69CimTZsGAGjbti1WrlyJKlWqICMjAxkZGZg6deo7zSEoKAjjx49HamoqPD09ERISglmzZmHRokVITU3F4sWLMWfOHISFhb3TuN9++y3atWuHCxcuwMvLC59//jn8/PwwZMgQnD9/HvXr14efnx+EENJrXr58iUWLFiEsLAynTp2CSqWCj4+PtD0yMhITJkzAlClTcOnSJfzrX//CsGHDEBsbK3vvuXPnom/fvkhOTsb8+fMREREBAEhLS0NGRgZWrVoFAHjx4gUmT56Ms2fP4siRI9DS0sI//vEPFBQUyMabNWsWpk6diqSkJDRs2BCDBg2SivekpCR07twZTZo0QXx8PE6ePInevXsjPz8fADB79myEhoZi3bp1SElJwaRJkzBkyBAcP378nfYnkdoQRERERBpo6NCholKlSsLQ0FB6fPbZZ0IIITp06CAWL14s679161ZhYWFR4ng7d+4UJiYm0vPQ0FChVCqL9AMgIiMjZW1KpVKEhoYKIYS4efOmACBWrlwp62NlZSV27Ngha1uwYIFwdXUtcU7u7u5iwoQJ0nNra2sxZMgQ6XlGRoYAIObMmSO1xcfHCwAiIyNDygFAJCQkSH1SU1MFAHH69GkhhBBt27YVo0aNkr33gAEDRM+ePWW5J06cKOsTGxsrAIgnT56UmEEIITIzMwUAkZycLIT43z7auHGj1CclJUUAEKmpqUIIIQYNGiTatWtX7HhZWVlCT09PxMXFydpHjBghBg0aVOpciNQVrzEkIiIijdWxY0esW7dOem5oaAgASExMxNmzZ2VHCPPz8/Hq1Su8fPkSBgYGiI2NxeLFi3H58mWoVCrk5eXh1atXePHihTTOX+Hs7Cz9++HDh7hz5w5GjBiBUaNGSe15eXlQKpXvNG7Tpk2lf5uZmQEAHB0di7RlZmbC3NwcAFC5cmXZfBo1aoSqVasiNTUVLi4uSE1NxejRo2Xv065dO+kIYHGZSnP9+nXMmTMHCQkJePTokXSkMD09HQ4ODsVmsbCwkObdqFEjJCUlYcCAAcWOf/nyZbx69Qpdu3aVtefk5KBFixblmiORumFhSERERBrL0NAQ9evXL9JeUFCAefPmoX///kW26enp4fbt2+jZsycCAgKwYMECVK9eHSdPnsSIESPKXFRFoVDITtMEil+I5c3isrAwCgkJQevWrWX9Cq+JLK83F7FRKBQltr192mZhe0ltb28XQhRpK2/B3Lt3b1hZWSEkJASWlpYoKCiAg4MDcnJyysxSOG99ff0Sxy/sExUVhVq1asm26erqlmuOROqGhSERERHRW1q2bIm0tLRii0YAOHfuHPLy8rB8+XJoab1esmHnzp2yPjo6OtL1bG8yNTVFRkaG9Pzq1at4+fJlqfMxMzNDrVq1cOPGDfj6+r5rnL8sLy8P586dg4uLC4DX1wQ+ffoUjRo1AgA0btwYJ0+ehJ+fn/SauLg4NG7cuNRxdXR0AEC2nx4/fozU1FRs2LABHTp0AACcPHnynefctGlTHDlyBPPmzSuyzd7eHrq6ukhPT4e7u/s7j02kjlgYEhEREb3lyy+/RK9evWBlZYUBAwZAS0sLFy9eRHJyMhYuXIh69eohLy8Pa9asQe/evXHq1CmsX79eNoaNjQ2ysrJw5MgRNGvWDAYGBjAwMECnTp3w3XffoU2bNigoKEBQUFC5bkURHByM8ePHo0qVKujRoweys7Nx7tw5PHnyBJMnT/5QuwLA6yNz48aNw+rVq6GtrY3AwEC0adNGKhS/+OILDBw4EC1btkTnzp2xb98+7Nq1C4cPHy51XGtraygUCvz888/o2bMn9PX1Ua1aNZiYmOD777+HhYUF0tPTMX369Hee84wZM+Do6IgxY8YgICAAOjo6iI2NxYABA1CjRg1MnToVkyZNQkFBAdq3bw+VSoW4uDgYGRlh6NChf2o/EX3KuCopERER0Vs8PT3x888/IyYmBq1atUKbNm2wYsUKWFtbAwCaN2+OFStW4KuvvoKDgwO2b9+OJUuWyMZo27YtAgIC4O3tDVNTU3z99dcAgOXLl8PKygpubm4YPHgwpk6dCgMDgzLnNHLkSGzcuBGbN2+Go6Mj3N3dsXnzZtja2r7/HfAWAwMDBAUFYfDgwXB1dYW+vj7Cw8Ol7f369cOqVauwbNkyNGnSBBs2bEBoaCg8PDxKHbdWrVqYN28epk+fDjMzMwQGBkJLSwvh4eFITEyEg4MDJk2ahGXLlr3znBs2bIhDhw7h119/hYuLC1xdXbFnzx5Urvz6uMiCBQvw5ZdfYsmSJWjcuDE8PT2xb9++j7I/if6OFOLtk9yJiIiIiP6/zZs3Y+LEiXj69GlFT4WIPiAeMSQiIiIiItJwLAyJiIiIiIg0HE8lJSIiIiIi0nA8YkhERERERKThWBgSERERERFpOBaGREREREREGo6FIRERERERkYZjYUhERERERKThWBgSERERERFpOBaGREREREREGo6FIRERERERkYZjYUhERERERKTh/h/0va8un88HFgAAAABJRU5ErkJggg==" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 28 + "execution_count": 16 }, { "cell_type": "code", "id": "445dff84-70b2-4fc9-a9b6-1251993324d6", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:24:43.079719Z", - "start_time": "2025-02-21T15:24:42.890225Z" + "end_time": "2025-03-01T10:05:12.793641Z", + "start_time": "2025-03-01T10:05:12.787565Z" } }, "source": [ @@ -1007,90 +1258,120 @@ "# model = train_catboost(train_data, test_data, feature_columns_new, catboost_params, plot=True)" ], "outputs": [], - "execution_count": 29 + "execution_count": 17 }, { "cell_type": "code", - "id": "751a6df9-d90b-4053-8769-c6c3b6654406", + "id": "7bc246ddd6b2cdd1", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:24:43.172411Z", - "start_time": "2025-02-21T15:24:43.084720Z" + "end_time": "2025-03-01T10:05:12.823810Z", + "start_time": "2025-03-01T10:05:12.801658Z" } }, "source": [ "from tqdm import tqdm\n", "\n", "\n", - "def incremental_training(test_data: pd.DataFrame, model: lgb.Booster, days: int, back_days: int, feature_columns: list, params: dict):\n", + "def incremental_training(test_data: pd.DataFrame,\n", + " model,\n", + " scaler,\n", + " days: int,\n", + " back_days: int,\n", + " feature_columns: list,\n", + " params: dict,\n", + " model_type: str = 'lightgbm',\n", + " ):\n", + " if model_type not in ['lightgbm', 'catboost']:\n", + " raise ValueError(\"model_type must be either 'lightgbm' or 'catboost'\")\n", + "\n", " test_data = test_data.sort_values(by='trade_date')\n", - "\n", " scores = []\n", - "\n", " unique_trade_dates = sorted(test_data['trade_date'].unique())\n", - " for i in tqdm(range(0, len(unique_trade_dates), days)):\n", + "\n", + " new_model = None\n", + " for i in tqdm(range(0, len(unique_trade_dates))):\n", " # Get the current window of trade dates\n", - " current_dates = unique_trade_dates[i:i + days]\n", + " current_dates = [unique_trade_dates[i]]\n", " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", " X = window_data[feature_columns]\n", + " numeric_columns = X.select_dtypes(include=['float64', 'int64']).columns\n", + " X.loc[:, numeric_columns] = scaler.transform(X[numeric_columns])\n", "\n", - " window_scores = model.predict(X)\n", + " if new_model is not None:\n", + " window_scores = new_model.predict(X, prediction_type='RawFormulaVal')\n", + " else:\n", + " window_scores = model.predict(X, prediction_type='RawFormulaVal')\n", " scores.extend(window_scores)\n", "\n", - " current_dates = unique_trade_dates[max(0, i - back_days):i + days]\n", + " # # Prepare data for incremental training\n", + " # current_dates = unique_trade_dates[max(0, i - back_days):i + days]\n", + " # window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " # X_train = window_data[feature_columns]\n", + " current_dates = unique_trade_dates[max(0, i - days):i + 1]\n", " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", - "\n", - " # Incrementally train the model with the current window data\n", " X_train = window_data[feature_columns]\n", - " y_train = window_data['label'] # Assuming 'score' is what you're predicting\n", - " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", - " # print(f'categorical_feature: {categorical_feature}')\n", - " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", - "\n", - " model = lgb.train(params,\n", - " train_set=train_data,\n", - " num_boost_round=100,\n", - " init_model=model,\n", - " keep_training_booster=True)\n", + " y_train = window_data['label'] # Assuming 'label' is what you're predicting\n", + " # Incrementally train the model\n", + " if len(y_train.unique()) > 1:\n", + " numeric_columns = X.select_dtypes(include=['float64', 'int64']).columns\n", + " X_train.loc[:, numeric_columns] = scaler.transform(X_train[numeric_columns])\n", + " if model_type == 'lightgbm':\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", + " new_model = lgb.train(params,\n", + " train_set=train_data,\n", + " num_boost_round=100,\n", + " init_model=model,\n", + " keep_training_booster=True)\n", + " # print(f\"Number of trees: {model.num_trees()}\")\n", + " elif model_type == 'catboost':\n", + " from catboost import Pool\n", + " train_data = Pool(data=X_train, label=y_train,\n", + " cat_features=[col for col in feature_columns if col.startswith('cat')])\n", + " # model.set_params(**params)\n", + " model.fit(train_data, init_model=model)\n", + " else:\n", + " print(current_dates)\n", "\n", " # Add the scores as a new 'score' column to the test_data\n", " test_data['score'] = scores\n", - "\n", - " return test_data\n" + " return test_data" ], "outputs": [], - "execution_count": 30 + "execution_count": 18 }, { "cell_type": "code", - "id": "660a24f74501f98f", + "id": "34698ca4f5fb933", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:27:32.990879Z", - "start_time": "2025-02-21T15:24:43.206812Z" + "end_time": "2025-03-01T10:09:12.009268Z", + "start_time": "2025-03-01T10:05:12.875190Z" } }, "source": [ - "predictions_test = incremental_training(test_data, model, 5, 0, feature_columns_new, light_params)\n" + "predictions_test = incremental_training(test_data, model, scaler, 5, 0, feature_columns_new, light_params,\n", + " model_type='lightgbm')\n" ], "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 102/102 [02:47<00:00, 1.65s/it]\n" + "100%|██████████| 517/517 [03:58<00:00, 2.17it/s]\n" ] } ], - "execution_count": 31 + "execution_count": 19 }, { "cell_type": "code", "id": "36ccaa730ab46718", "metadata": { "ExecuteTime": { - "end_time": "2025-02-21T15:27:35.475780Z", - "start_time": "2025-02-21T15:27:33.233682Z" + "end_time": "2025-03-01T10:09:12.265271Z", + "start_time": "2025-03-01T10:09:12.167363Z" } }, "source": [ @@ -1098,7 +1379,7 @@ "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" ], "outputs": [], - "execution_count": 32 + "execution_count": 20 } ], "metadata": {