poc_signature_anomaly_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "indie-astronomy",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from header import *\n",
    "from preprocessor import *\n",
    "from mp_utility import *\n",
    "from tensor_utility import *\n",
    "from signal_utility import *\n",
    "from mscred import *\n",
    "\n",
    "SCALE_SIZES = [2,4,8,24,48]\n",
    "STEP_LEN = 4\n",
    "\n",
    "DATA_DIR = \"F:/quant_data/prices/correlation_test_2021_hourly/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "based-august",
   "metadata": {},
   "outputs": [],
   "source": [
    "SIGNAL_GROUPS = {\n",
    "    'CHI.IDXUSD' : \"000100\",\n",
    "    'EUS.IDXEUR' : \"000200\",\n",
    "    'USA500.IDXUSD' : \"000300\",\n",
    "    'USA30.IDXUSD' : \"000400\",\n",
    "    'USSC2000.IDXUSD' : \"000500\",\n",
    "    'GBR.IDXGBP' : \"000600\",\n",
    "    'IND.IDXUSD' : \"000700\",\n",
    "    'JPN.IDXJPY' : \"000800\",\n",
    "    'FRA.IDXEUR' : \"000900\",\n",
    "    'AUS.IDXAUD' : \"001000\",\n",
    "    'PLN.IDXPLN' : \"001100\",\n",
    "    'SGD.IDXSGD' : \"001200\",\n",
    "    'HKG.IDXHKD' : \"001300\",\n",
    "    'BRENT.CMDUSD' : \"010100\",\n",
    "    'DIESEL.CMDUSD' : \"010200\",\n",
    "    'GAS.CMDUSD' : \"010300\",\n",
    "    'LIGHT.CMDUSD' : \"010400\",\n",
    "    'COPPER.CMDUSD' : \"010500\",\n",
    "    'COTTON.CMDUSX' : \"010600\",\n",
    "    'SUGAR.CMDUSD' : \"010700\",\n",
    "    'SOYBEAN.CMDUSX' : \"010800\",\n",
    "    'COCOA.CMDUSD' : \"010900\",\n",
    "    'COFFEE.CMDUSX' : \"011000\",\n",
    "    'OJUICE.CMDUSX' : \"011100\",\n",
    "    'XAGUSD' : \"020100\",\n",
    "    'XAUUSD' : \"020200\",\n",
    "    'GLD.USUSD' : \"020300\",\n",
    "    'HSBA.GBGBX' : \"030100\",\n",
    "    'LYXBNK.FREUR' : \"030200\",\n",
    "    'LVC.FREUR' : \"030300\",\n",
    "    'TECDAXE.DEEUR' : \"030400\",\n",
    "    'SWEDA.SESEK' : \"030500\",\n",
    "    'TLSN.SESEK' : \"030600\",\n",
    "    'VOLVB.SESEK' : \"030700\",\n",
    "    'EURUSD' : \"040100\",\n",
    "    'EURJPY' : \"040200\",\n",
    "    'EURSEK' : \"040300\",\n",
    "    'EURGBP' : \"040400\",\n",
    "    'EURAUD' : \"040500\",\n",
    "    'EURCAD' : \"040600\",\n",
    "    'EURCHF' : \"040700\",\n",
    "    'EURCZK' : \"040800\",\n",
    "    'EURRUB' : \"040900\",\n",
    "    'EURHKD' : \"041000\",\n",
    "    'EURHUF' : \"041100\",\n",
    "    'EURDKK' : \"041200\",\n",
    "    'EURNOK' : \"041300\",\n",
    "    'EURNZD' : \"041400\",\n",
    "    'EURPLN' : \"041500\",\n",
    "    'EURSGD' : \"041600\",\n",
    "    'EURTRY' : \"041700\",\n",
    "    'USDCNH' : \"041800\",\n",
    "    'USDJPY' : \"041900\",\n",
    "    'USDHKD' : \"042000\",\n",
    "    'USDTHB' : \"042100\",\n",
    "    'USDCAD' : \"042200\",\n",
    "    'USDCHF' : \"042300\",\n",
    "    'USDCZK' : \"042400\",\n",
    "    'USDDKK' : \"042500\",\n",
    "    'USDNOK' : \"042600\",\n",
    "    'USDPLN' : \"042700\",\n",
    "    'USDRON' : \"042800\",\n",
    "    'USDSEK' : \"042900\",\n",
    "    'USDRUB' : \"043000\",\n",
    "    'USDHUF' : \"043100\",\n",
    "    'USDMXN' : \"043200\",\n",
    "    'USDILS' : \"043300\",\n",
    "    'USDSGD' : \"043400\",\n",
    "    'USDTRY' : \"043500\",\n",
    "    'USDZAR' : \"043600\",\n",
    "    'GBPAUD' : \"043700\",\n",
    "    'GBPCAD' : \"043800\",\n",
    "    'GBPCHF' : \"043900\",\n",
    "    'GBPNZD' : \"044000\",\n",
    "    'GBPUSD' : \"044100\",\n",
    "    'AUDNZD' : \"044200\",\n",
    "    'AUDSGD' : \"044300\",\n",
    "    'AUDUSD' : \"044400\",\n",
    "    'CADCHF' : \"044500\",\n",
    "    'CADHKD' : \"044600\",\n",
    "    'GBPJPY' : \"044700\",\n",
    "    'CADJPY' : \"044800\",\n",
    "    'CHFJPY' : \"044900\",\n",
    "    'TRYJPY' : \"045000\",\n",
    "    'SGDJPY' : \"045100\",\n",
    "    'NZDJPY' : \"045200\",\n",
    "    'HKDJPY' : \"045300\",\n",
    "    'CHFSGD' : \"045400\",\n",
    "    'NZDCAD' : \"045500\",\n",
    "    'NZDCHF' : \"045600\",\n",
    "    'NZDUSD' : \"045700\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "equal-myanmar",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for aa in list_files( DATA_DIR ):\n",
    "#     for tt in SIGNAL_GROUPS: \n",
    "#         if aa.startswith( tt ):\n",
    "#             os.rename( DATA_DIR + aa, DATA_DIR + SIGNAL_GROUPS[ tt ] + \"_\" + aa )\n",
    "#             print( \"Renamed\", aa, \"with\", SIGNAL_GROUPS[ tt ] + \"_\" + aa )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0926c11e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading:\n",
      "\tFile-0: 000100_CHI.IDXUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-1: 000200_EUS.IDXEUR_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-2: 000300_USA500.IDXUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-3: 000400_USA30.IDXUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-4: 000500_USSC2000.IDXUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-5: 000600_GBR.IDXGBP_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-6: 000700_IND.IDXUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-7: 000800_JPN.IDXJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-8: 000900_FRA.IDXEUR_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-9: 001000_AUS.IDXAUD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-10: 001100_PLN.IDXPLN_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-11: 001200_SGD.IDXSGD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-12: 001300_HKG.IDXHKD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-13: 010100_BRENT.CMDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-14: 010200_DIESEL.CMDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-15: 010300_GAS.CMDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-16: 010400_LIGHT.CMDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-17: 010500_COPPER.CMDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-18: 010600_COTTON.CMDUSX_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-19: 010700_SUGAR.CMDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-20: 010800_SOYBEAN.CMDUSX_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-21: 010900_COCOA.CMDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-22: 011000_COFFEE.CMDUSX_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-23: 011100_OJUICE.CMDUSX_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-24: 020100_XAGUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-25: 020200_XAUUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-26: 020300_GLD.USUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-27: 030100_HSBA.GBGBX_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-28: 030200_LYXBNK.FREUR_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-29: 030300_LVC.FREUR_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-30: 030400_TECDAXE.DEEUR_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-31: 030500_SWEDA.SESEK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-32: 030600_TLSN.SESEK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-33: 030700_VOLVB.SESEK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-34: 040100_EURUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-35: 040200_EURJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-36: 040300_EURSEK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-37: 040400_EURGBP_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-38: 040500_EURAUD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-39: 040600_EURCAD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-40: 040700_EURCHF_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-41: 040800_EURCZK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-42: 040900_EURRUB_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-43: 041000_EURHKD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-44: 041100_EURHUF_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-45: 041200_EURDKK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-46: 041300_EURNOK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-47: 041400_EURNZD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-48: 041500_EURPLN_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-49: 041600_EURSGD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-50: 041700_EURTRY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-51: 041800_USDCNH_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-52: 041900_USDJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-53: 042000_USDHKD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-54: 042100_USDTHB_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-55: 042200_USDCAD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-56: 042300_USDCHF_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-57: 042400_USDCZK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-58: 042500_USDDKK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-59: 042600_USDNOK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-60: 042700_USDPLN_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-61: 042800_USDRON_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-62: 042900_USDSEK_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-63: 043000_USDRUB_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-64: 043100_USDHUF_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-65: 043200_USDMXN_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-66: 043300_USDILS_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-67: 043400_USDSGD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-68: 043500_USDTRY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-69: 043600_USDZAR_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-70: 043700_GBPAUD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-71: 043800_GBPCAD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-72: 043900_GBPCHF_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-73: 044000_GBPNZD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-74: 044100_GBPUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-75: 044200_AUDNZD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-76: 044300_AUDSGD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-77: 044400_AUDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-78: 044500_CADCHF_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-79: 044600_CADHKD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-80: 044700_GBPJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-81: 044800_CADJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-82: 044900_CHFJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-83: 045000_TRYJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-84: 045100_SGDJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-85: 045200_NZDJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-86: 045300_HKDJPY_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-87: 045400_CHFSGD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-88: 045500_NZDCAD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-89: 045600_NZDCHF_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n",
      "\tFile-90: 045700_NZDUSD_Candlestick_1_Hour_BID_01.01.2021-12.06.2021.csv\n"
     ]
    }
   ],
   "source": [
    "file_data = load_csv_files( DATA_DIR )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fcb76fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Local time'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D7is9Bja9wQHdViClQI8kvGgtrQCIaS/yXAdCCCEkEVQ1dQEAMpItGrckTlA844GWFvQlLh6vdSsijwI9kvDK69phNjGU9ov8HL2mDgcOddjhdFGwRwghJDGcPa0EKVYTLFR+k1I1/ZDn5NH6giL1HEW9zVekQI8kvIr6dpTmpSEpCnMR5DX0OuyuiP9uQgghJFr6pScDoEAH8AxmaHe4UZDnpteglwI9kvDK69oxPArz8wBg5tA8APrr4SGEEKJfLkGAxazTevE9xDkHYyziyy8lMrqj8SUfHXrbNxTokYTmdAnYc6gjKoVYALHqJgC4BL199QkhhOiVU+AwU9qmgmI8/5heo5seUq+1qLd+fQr0SELb19gJh4tHpRALAJikCyUtsUAIISRRCJwr8/OMfvXyKTxC13OPYIZGOqXKrDrdDxTokYRWXidW3IzWiJ5Z+uILVIuFEELi1tq9Taht6dK6GXHD7uQwm0y6nHPUU5zrt6JiX+k1uOkp9zHCwHXWNUKBHkloFdIaesPyI19xEwDM0jeERvQIISQ+tdocOPu5nzD74e/Q3u3Uujmaa7M58M22g9hW26p1U+KCnkdress7mNFbcNMbVIyFkDhUXteOwqxkZKVYo/L75YvDaz/vwZA7PqO5eiRqbn9vIx1jhPRQe7cT17+xTvm5tcuhYWu0xznHXR+V+WwzOqq6GZgeg5ueUn9F9PZ1oUCPJLTy+vaozc8D3KmbLyyrBAB02qm3mETHe2urAAA2By3lQUi43l69Dz/ualB+1tk9Wo+9tXo/PtlYo/xMA1mqQhvaNiOu6C2Y6SsODgZ9DulRoEcSFucclXXtUZufB8Cnatm7a6qi9rcIAQC7kyaEEhKu0n6eaftGHr3aWtOK//tkC+aOyte6KXGFgwLeQOT9YuCvjZu8L7RtRcRRoEcSVl1bN9q6nVEN9ExegV5FfXvU/hYhANBNgR4hYTNyYKfW3u3EDW+uQ26aFY+fO1nr5sQV8RBhXj8TGQXB6lFf/e0MCvRIwqqQKm7GInUzPzMZADBzSF7U/hYhAI3oEdITDpd4hza5JBuAMW/iOee4+6PN2HOoA08umop+Gcm46ZgRuP3E0Tq8be0NLgYzFNH40GNg0xscqq4AnZ1DKNAjCau8PrpLKwCAPKCXnSoWe3G46CacRNfcf3yPLjvN0yMkHHaX+F05amR/jVuinXd+3Y8lG2pw6/GjMGtYPwDArSeMxvXzRwAwZvBLgqNjwhdj+uwLoECPJKyKunZkJFtQII22RYOcuil/9+XeY0Ki6cnvdmndBBLH/vLpVvxSeUjrZsSFNptYICszSpWX49222lbc//EWzBnZH9cdPcLneVpWwL1GmvKz3oZs+oDm6EnUVTd1dnxYtG4AIb1VXt+O4QUZUb2Qyamb8p+gET0SC4a/6JKAOrqdeGnFbjAGHC6N3hhZt0M8J6dYxH5rI313OrqduP7NdchOteLx86b4zCknIs71u0Zab6mDGUrflKtumnS5J2hEjySs8rp2jIji/DzAXXVz50ExTfTZH8qj+veIcY1UpSBTJzwJpLq5CwAV7ZEJUmTnXSFZ7zjnuGdxGfY0dOCJRVPRPyNwZoveRih6SimdT3zQXhHJnQHyYz2hQI8kpDabAwdbuzG8ID30i/vA+4b7YGt3VP8eMbZTJg5AktmkuwsNiZyqpk4A7pEsoxPkannSydooQc17a6rw0fpq3HLcKMweHnhkl27k/TDGIRKU9zXGKN+bYGiOHiFxpKK+AwBiNqKndvQ/vkdzpz2qf5cYj8C5eLPK6KJLAqtqkkf0qGAPYMwRvR0H2nDfx2U4akR/peAKCUxJ3TTOIRI22i8iHuCxHlCgRxKSsrRCFCtuAu5qm2p7DnVif2NXVP8uMR6BAyYmJRjp7UpDIkYO9Gw0ogfAvY6eSadpV946up247o21yEwR5+WFE+DqfZ+E4lE6nwCgS4w3zsX0Xj2m+FKgRxJSeX07rGaGwXlpUf07OalJymN10NfloN50ElkC5zBLvat0ESaB7G+UUjdpRA+AO3XTpKRu6hfnHPcuLsPuhg48sWiKsr5rMDRaI4/o0Y7wR4+BTW+55+jp6yxCgR5JSOV17Sjtlw6rObqHsNXiPgm2dDmUx3STRSJN4Fwa0WO6u9CQyHGnbtKIHmCs1M331lbhw/XVuOnYkThiuHHXDewrOrv6BjNGv+TIH1+PHa0U6JGEVFEf/YqbAJBsMQMAFk4p8tjuEvR2KiBaEwSx15kxuuiSwJRiLBToAfAzoqfTL8/Og224b0kZjhjeDzceM7JH79XpLgmbPOeZRq980UCnSP6O6HF3hAz0GGP/YYzVMcbKVNsmM8Z+ZoxtZox9whjLUj13J2OsnDG2gzF2omr7dOn15YyxJ5k0js4YS2aMvSNtX8UYGxLhz6hLrTYHyqpbtG6GJuxOAXsPdWJElOfnAUBeehI+u+ko/O03k3DqxIHKdgr0SKSJI3rihYaOLuJPe7cTTZ1iZgFlFYg45x4FJfT43em0O3HdG+uQkWzFvxaFNy9PRsENAE4BjTc9fk/6Sqncq7OdE86I3isATvLa9hKAOzjnEwF8BOB2AGCMjQOwCMB46T3PMsbM0nueA3A1gJHSf/LvvBJAE+d8BIDHAfyttx/GSC5+eTVOe2qF1s3QxL7GDrgEHvWlFWTji7KRbDGjJDdV2UaBHok0gXOYTQyMMd1daEhkVEtpm2YTo+UVJHLKs57dt2QLKurb8cSiKSjITNG6OQmHwzPQo/OrL6PvEuXz6/BcEjLQ45wvB9DotXk0gOXS468BnC09XgDgbc55N+d8N4ByADMZYwMBZHHOf+ZiXsVrABaq3vOq9Ph9AMcymjUb0sb9zQD0m6YSTLlUcXNEfmZM/65J1YsqGHC/k+gSpIIB4ogeHV/El1yIZXBeGqVuSsRqte6f9XZqfn9tFd5fW4UbjxmJI0f0bl6e0c8nSkVFurNUqL8nRr7lFgSOIXd8ho37m3U79t3bOXplAM6QHp8DYJD0uBjAftXrqqRtxdJj7+0e7+GcOwG0APC7+idj7GrG2BrG2Jr6+vpeNl1fYjWw9FN5A5o64mPtOHkNvWH5sRnRk6lPjE4a0SMRxqXUTdAcPRKAPD9veH4GbFT5F4B7/Uk93qzuOtiGexeX4fBhebj52J7Ny1Pob7eQCNLj96YnOlXn0S01Lbr8uvQ20LsCwPWMsbUAMgHIEYC/fRRoCROlyE2Q5zw3cv4i53wG53xGfn5+D5usT7FIIbQ5XLjgpVW46rU1Uf9b4Siva0dRdgrSky0x/bvqXlFK3SSR5hK4ex09QvyoaupCitWE4pwUGtGTcK8RPb0koXXZXbj+zXVITzbjyUVTDVFVNFp8Ujd1coz0idcuMGrnopwhBohzoGV6ypbrVaDHOd/OOT+Bcz4dwFsAKqSnquAe3QOAEgA10vYSP9s93sMYswDIhm+qKAkgFimEh6SRvDV7m6L+t8JRXtce9YXS/Rk7QKk5RIFeBLgEjrs/2ozK+vbQLzYAgUM1R4+OL+KrqqkLJblpSLGaqRiLRPDqINHLV+f+j8uwq64dj583BQVZfZuXp5d90luciyMKFCr7Mvp+UV9rTxw/QJfpvb0K9BhjBdL/TQDuAfC89NTHABZJlTSHQiy6sppzXgugjTF2uDT/7hIAS1TvuVR6/BsA33G6ywmq1eZezy0WgV6nqpdDa5xzVNS3Y3gMllbwtmBKEV66ZAYAMUj57au/4qHPtsa8HXqxrbYVb6zah2P+uQwPL92GNtVxbUSCqnognQCJP1XNnSjJTUWyxYRup0AdApDn6Olr/tWH66rw7poq3DB/BOaM7Fv2ko52S6+JI3q0J9RoVFMk30NPHpSDR86apGzX06k1nOUV3gLwM4DRjLEqxtiVAM5njO0EsB3iyNx/AYBzvgXAuwC2AvgCwPWcc7nb8VqI1TrLIY4ALpW2vwygH2OsHMCtAO6I0GfTrS82H1Aex2JgqdMePz3HtS02dNpdMVlawRtjDOOKxFE9gXN8s60O//5xd8zboRfqE+kLyyqxbl+zZm2JB2Kvs1SMRUcXGRI5+xu7xEDPagbngMNFB4rcQSJL9D1SXteGuz8qw6yhfZiX5yXR90mk0fnVzejxr0vKgL/9hNFITTLrcjmSkJOcOOfnB3jqiQCvfwjAQ362rwEwwc92G8SCLiRMizdUK49jkULY0N6tPNZqNE399wFo1gZ5ngQVY+k77x5Fo1cyda8Hxqi3lfhotTnQ0uXAoNw05TzU7XQhydLbqfb6wLmcupn4N2hddheuf2M90pLMePL8qbCY+/5vy6i4k1R1k4IaNd9jwpgHiXwP7T0HVk97w9hXiAR0oMWGnysPKT9/s/Vg1P/mBmkpBwC44pVfo/73glGWVtBgRA9wnwwECvT6zPtCY/Q0NA64F0w39q4gfizfKVaaLsoRUzcBGLYgS6vNgZvfXo+7P9qsq+UV/vzJFuysa8Pj501BYR/n5RE3DlAOqxd1NUQjB8DegZ4e9wUFegnm443VHheylq7oz2tSX3D2HurUdJmF8rp2ZKVY0D8jSZO/b2Y0ohcp3nswkW/QIsFdJl5fvYkkMuTF0mcOzUOyxQzAmIHettpWnPHUCizZUINf9zQqC6bLN2iJOhq+eH013v51P647ejjmjopwVfHE3CWRwz3jPKPvDjWjz110cTnQ89yup45nCvQSzOL1NZg8KAf/loqCDO0f/bXk5B6PP500BgDQYdeuOEtFfTtGFGRodnIym8W/S1U3I8/ou5QrNyPM8EEv8XWow45kiwkFmclItoqXbqOtpffB2iqc+exKdNpdGDMgE04Xh8DFm9VEvl2tqG/HXR9txswhefj9caMi+rsTe89EFu0LN+9AxqjXHJcgdpaZTeI5VY9HCAV6CWTnwTZsrW3FwilF6CeNaF3+yq+46a31Uf278uhVRoo4pdPm0K4XubyuQ7O0TcA9omf0+WSR4H2hMfo+ldOLxEPM2PuC+DrUbke/9CQwxtwjehqei2PJ5nDhzg8347b3NmLKoBx8dtMcjBuYBbtLkObouV+baKcRm8OF699YhxRr5OblEU8c3PAjV4Ewps90xXDJxVjMjObokTiweH01zCaG0yYVwa5K2fl4Yw2qmjqj9nflHo/MZDnQ06YXuaXTgYb2bk2LwfgrxqKnIf5YkvfhHSeLI8WG341UdZME0Gpz4IN1VahpsQGAMqJnhLX09jd24pznf8Zbq/fhd/OG439XzkJ+ZjKsZpM0oueVuplg350/f7IV2w+04bFzJ2NAdnTm5SVqOmukcK7PkZq+MPYR4RZojl6inUeCoUAvQQgCx5INNThqRH/kZyb7jH509XIJBIdLwEOfbUVzZ+B5d/INebrGgV55vbaFWAD/xVioxHnvOKX9liIVljB6wCz2Ootrghl8VxAv57/4i8fPRinG8v32Opz21ArsOdSBf18yA3ecPEYZ8TKbGZyCAK4UY0m8W/klG6rx1up9uPbo4Th6dEFU/oaRR2tknHvuB6Nfa9SMfnj4Bnr62yMU6CWItfuaUN3chYVTiwD49jb0Ntj4fHMt/v3jbjz8+faAr3G55EBPTBfq0ijQ03ppBcB/MZbmrtDFaerbunHz2+vx6aYa7DrYFrX2JRKnNFJslW5aaY6euwKa0dNYiactNa0AgFMnDQQA3RdjcQkcj321A5e/8iuKclLx6Y1H4fhxhR6vMTMGl+CeoydLlNGryvp23PXhZswozcVtx0d2Xh7xxMHFXAn93cP3mk/Va22aobmAxVh0tEco0EsQi9dXI9VqxgnjBgCAMkdveL5YjEW+ae4peSTQFeTGUpmjJ43oVdZ3YH9j9FJFA6moa0eS2YRBeWkx/9syk0lMEVIXY5n50Lch33faUz9iyYYa3PDmetzx4eZoNjFhyMeVVZoEracTa29wSPMlYNyLLgnuR2mJBWVET4fFWA61d+Oy/67Gk9+V45zpJfjouiNQ2s+36JjZxOAUuDhHz5RYI1c2hwvXv7keSRZTTOblUb8RCUgna1D2lqCM6Ok3HNLvJ9MRu1PAZ5trcfy4QiV9csyALCy+/kjcdcpYAL0v9//MD+UAgF3S+nT+uAQOs4kh1Sr2It//8RbM+fv3KKtu6dXf7K3yunYM7Z/us7BlrJkZw4frqkO/UGVCUbbymCp2SqTdYJLTYQ2+W8RFfZm4YLrB9wXx76o5wwAAKVZ9pm6u29eE055agVW7G/G3syfiH+dMRop03fFmNjEIgnuOniwRvjsPfroV22pb8di5U1CUkxrVv5VIAXC0+KZuateWeGH0jlWZfO/sU4xFR7uHAr0EsGxnPZo7HThzarHH9imDcpSLoLOXqZty1bYkc+CrgUMQYDYxnwvu3kOxHdWTl1bQmtnEUN3c1aP3ZKValce5adYgrzQO+UIjd2Ybfd6EPKInPjb2viCeTpkoZnJce/RwAO7UTb0sr8A5x6s/7cF5L/wMi5nhw2uPwHmHDQ76HrOJwcW5tGB64oxJfLKxBm+s2odr5g3D/DHRmZdHPKkXByee5H1i1OuvPKInD+jpsWOEAr0EsHhDNfLSk3DUyP4+zylVIF2969n9zfQSAMC8IAu0ulwcFj+BXrB0z0izOVzY19iJ4XEQ6PWG3SVgeH46JpdkG37kypuJlqwA4DlHj+I84m1EQYaS4qenYiwd3U7c9PYG3P/xFswdmY9Pb5iDCcXZId8njuiJ5w2xRHz836HtbujAnR9uxvTSXPzhhNEx+7tGP51wnhjHR6ws21mPhjZ3bQEj7xr5Ptai49RNi9YNIMG12Rz4ZutBnHfYIFj95PFbpZE4Ry+jB/ldwW4YnHLqZpJnoCfEMGLZe6gTAnfPSdSSel+dM70ES8sOhHyP3SnAajaJaXnRbFwCkeM6+QJs8DhPJJWJp11B1FwC90gtSrbqoxhLeV0bfve/daisb8ftJ47GtfOGK6ncoZiZuupm/KduyuvlWcwMT54/1e/1PBoSZ6yTxIIgcFz6n9XIlNZFNnKQB7hTN5URPR1+X/QbwurEF2UH0O0UsNArbVMm90K4elmMRR5Feeq78oCvcQkcVrNJKcai3t5TnHM8/Pk2fLqppkfvK6/TfmkFfwqyktHlcIVMe/h660FU1nfAxIybIuFN3g3uReg1bIzG5GOCQbzQ0DFC1AQOjwDIPaKXuKmbn26qwRlPr0RThx2vXzkL188fEXaQB4j7Q+DidciUAAUVH/psG7bWtuKf50xGcZTn5RFvXMqWiPejJPrkEaw2m1PjlsQHwQBz9GhEL84t2VCD0n5pmDoox+/zFnlEr5dz9NTr701/8Gt8c+s85KYnebxGHtHz1tPUTc45ht75ufLzaZOKwn5vRX07GAOG9Y+vQC8tyQKXwGF3Ccq8mUDsLgGMMcOnKMrkvSB3bBt5v7hHN0EjesSHIAUzMnfVzcQb0bM7BTy8dBv+u3IPpg3OwbMXTu/VQuHqpW48RvTi8Nvz6aYavP7LXlw9dxiOHVsY+g0RZvSOI+9iLEbm3UEvj2AZ9QiR94c8aKLH44RG9OLYwVYbfqpowILJRQF7ouSD0+niWLKhGjsO9GyNtqwUd2GQQx12fLa51uc1LkGAxU+g98f3N/Xob22q6n2VzvK6dhTnpPqkj2opLcmMNKk94faOiSN60WxV4lBGsdwVSAxL/uhyUQk6Roiai3t2tjHGkGQxJVzqZm1LFxa9+DP+u3IPLj9yCN6+enavgjzA3cnpFMQONOU0EmffnT0NHbjjg82YOjgHt58Yu3l5Mj3euPaUutAVEH/HSCz5q9Bu5EPE5ZW6KYvHDqPeohG9OPbJxhoIHFgQIG0T8LzY3fz2BgDAnkdO9Xnd+2urkJtmRX1bN06fXKQs0+AdON2zuAwXHV7qsS3QiF5P9eV3lNfFR8VNNc6BwizxJmXvoU70z0gO+voUqwkMDGv3NuFQezf6hXi9UbhTN/VzYu0pQZ26SfM4iRfvRcEBcVQvkVI3V5Y34Ka31sPmcOHpC6b2KKPDH3kUz+ESxNTNOLxb7Xa6cMNb62A2MTwVw3l5xL84PERizuWV/RWP35tY+vuX2wGoRvS0bEyU0FknBlo6HTjj6RX4bvvBHr1v8YZqTCrJxvD8wAGOvNi0PUTP7h/e24grX12DOz7cjC+3uIuHhFNQxSVwZURv8/+dgJ/uOCac5vuwS5VB5YIqjjArhQoCR2VDe9D9oIX8zGQMlHqjW7scQV9bnJOKUycWgTGxgMKCZ1bGoolxTRnFUlI3NWuK5jxSN0GpVsSTIHB4r4CTbDEnxIieIHA88305Ln55FfLSk7DkhqP6HOQB7pRvh8s7dTN+/PWzbSirbsWj50xGSW6aZu2Ip32iBXmNUiIOCvhj1EuOPO3JO+DV0/6gQC8GXvlpDzZVteC5HyrCfk95XTvKqluxYErg0TzAPaJ3ew/SKH/YUa88lofx7z1tnLLt+jfW4b4lZcp/G/c3K6NxmSlWjwVea1vCX09uyXpxkXF55KsrzDWgqpu7YHMIcTei9+ZVs5CTJs5nXLwh9ALq6rlXVU09W4dPj3yqbhr4dkT+7EyK9Iy7J4g/K8obsKWm1WNbssUU9+votXQ6cNVra/CPL3fgtElFWHz9kRE7j8vBXUN7t1SMRa7eGx/fns831+LVn/fit0cNxfHjYj8vj7j5pG4a+Axr9+pgZ4Chh/VmDc0DAGX5MD3uCgr0YuDxb3YC6FnFpyUbqmFiwOmTBwZ9XW/SIT/e6K546ZLSMscOzFS2fba5Fp9srFH+a+ly4LAheR6/49UrZgLo2aLpdW3dAIBppbkAAJs9vJuUinqx4ma8jeiV5KahNE/spQ11wyX2KCZ+OfTI8qx2RSN6IgZQpEcU8hwS73NHsjW+5+iVVbfgtKd/xPJd9fjzGePxxKIpypSBSKhptgEAKus73EPhcWLfoU786f1NmDwoB388aYzWzdHV6ERP2RwuHGq3u9coNTBB4Fixq0HrZsSVopxUDMrzrYKrp68MzdGLofzM8OZkcc6xeEM1jhzRHwWZwSeqey9iHuj3AcDcUflYvrPe4zl5/p13MZH1950Q9HfmpIpFXDq6wy/RO25gFpaWHVAFR+HdpMTb0grnTC/B51LRGpOJYXJJdsjPoqxXGOc98LGkLK9giq+eeC3JCz8buceZeArUiZRiMcdt1c13ft2He5dsQb/0JLxzzWxMG5wb8b+hntO7cX+z8ljrb06304Xr31wHxoCnz5+KJIu2/elGXlLgp4oG3P1RGXY3dODW40dp3RzNdDtdWLy+Gi8ur0RFfQcAICfNiuZOB4xeC83uEjzmzsZbZkAkUKAXAwunFGHxhhoMzgsvR3/dvibsb+zCLceGPjFlp1pDvmZ3g/jFXr6zHnNG9kdHtxOtNgdcLq5U1GzutCuv//GP80P+TrnaZLjpl4C8vACUC1+4xTcq6tuRm2ZFnteyD1p5+KyJeHDhBOXnZKs5ZFEEubxzPPfAayVeq+XFkpLGCqq6STwFOseKI3rx1XFkc7hw7+IyvLe2CnNG9se/zpsStaJTWSmety/xEs48/Pl2bK5uwQsXT8egMK/5JLKaOuz46+fb8N7aKgzOS8PrV87EnJH5+HBdFQDjnF9bOh3436q9eOWnPahv68a4gVl4YtEUPPt9BUwmhuZOsbZAvHx3tOB0CUhSB3o63BkU6MWAnBMd7sll8foapFhNOHHCgB7/LTnf2N/fv+fUsVhZ3gCXwDH9wa/hcHFcfuQQmBnDMWMKMaRfBV6+7LCwLk7ySOL6fc1hT6y3OwUkW0zKCE7YgV5dR9yM5gGAxWyCesm8ZIsp5PIKHOJk8HifUxNL6iUFAGNX3XTP0ZPmchp3VxAvgc4ZyXG2vMLeQx343f/WYVttK246ZgRuPm5URKo1B5IcIJtFy+/OF2W1eOWnPbjiyKE4cXzPr9/RYpTTCeccH2+swQOfbEVzlwO/mzccNx87UqkursebeH+qm7vwnxW78fbqfeiwuzBnZH88fu4UHDmiHxhj+M/KPUo2ltGL1Dhc3G81XD19ZyjQiwE5rS+cdCyHS8Cnm2pw/LgByOjFfIZVuxvFxXVVF9h2KQgZVZiJXyob4RS4UmnIJXCYzQz5mcn44fbQI3ky+cT58ordHoVcgul2ij0nrIdzssrr23Hi+PidzJ5iNaOh3R70NfKIXmOH+3WuCC1bkai8Uzdpjp60vAIodZO4BUoLT7aY0Ryi2m+sfLXlAG57byNMjOG/lx2G+WMKov431Wu7fnnLXBxotUk/afPd2d/Yidvf34TJJdm442Tt5+XJjHKF2XeoE3cv3owfdzVgckk2Xr9yFsYVZWndrJjaWtOKF5dX4JNN4tSS0ycNxFVzh2F8UbbH65LMDM1hVj3XO4dLgNW7pLHOUKAXA0p6TRjXn+U769HU6cDCKb0vP72/qROl/dKVn9uknpuMFAssJqZM7gc8l07oCfVC6+HqdgpItpoh/7lwRnAaO+xo7LDHXSEWtWSLKay5d96pm3d/tBmPnD0pmk2La3IwI4/o6SknvqfkT67nEb2mDjvW7WvCsWPD77RZWd6AwXlphk6BCzqip3GGgNMl4NGvduL5ZRWYWJyNZy+cFrN/q+PGFeLhpdtx/+njMHpAJg4qgV7s2Z0CbnhzHQDg6QumaT4vz0icLgEvr9iNx7/ZCTNjuP/0cbhk9hDDdKJyzrGivAEvLq/Ej7sakJ5kxmVHDMEVRw1FcY5vkRFAXDPOId+LMPfvMSK7U/A/oqej3UGBXpSt3duIleWHAITXz7h4Qw1y06yYOyq/139z3j9+wE3HjlR+Lq9rAwBkJltgNjNlSQWg96NK6gvZL5WHcPiwfiHf09xpR2aKpUcLZCsVN+ModdNbijX0elbu5bDd3v51v7EDPemf38Q8fzYi+SKrTATXsjFRcs3ra7F6TyPW33s8csOcb3vhS6uQZDZh50MnR7l18UvuKLz9xNEe25PDOO9EU31bN258ax1+qWzEBbMG477TxoVVHCxShudnYM8jp/ps1+I88sjS7dhY1YLnL4rTeXk6Pblu3N+MOz/cjK21rThubCEeWDDeY/mnQPSwNxwuAZ9tqsWLyyuxtbYV+ZnJ+ONJo3HhzFJkpwXviLdaTLCrFk43SkqrPw6X4FENWI/FiyjQi6Lq5i6c/dzPys+hekzau534eusB/GZ6id8ehkBevnQGUpPMuODfq5RtT367y+M1/TOSMSA7BRYTg1M1ZO8UOCym3vU+Pn3BVNzw5nos3VwbVqBXXicuei5/kVxh5OpVyBU343hEL8Uaej0rnV5nI6Knczb1yHNEj+nyeNlzSCwKFW5wIp8vvdd9MoI2mwMb9jdj3d5m/LhLrJQ8o9SzcqWWI3q/7mnE9W+sQ6vNgX+eMxlnTy/RpB1qWt2ffbnlAP6zcjcuO2IITurFvPpo0+F9K9q7nfjnVzvw6k970D8jGc9fNA0njh8Q8iZdD/PROrqdePvX/fjPit2obu7C8Px0/P3sSVgwtQjJlvA6WlyCgIZ2cbmrxN8jfeMMlNWmo2swBXpR8vove3Hv4jKPbaFu3r4sOwCbQ8CZU4Mvku5NToW6/Mgh+O/KPQDgt6cTEG+qD6hSXN5fW4WS3NA9YP6cNqkIf/l0W1iVN50uAXsOdeDYsYWqcvqh/0Z5XTtSrKaAKQjxID3JgrZup7hWXsALDdflBbcvvIuxuPQY3YTJZx09nVxlpjzwFZo7HdjzyKnKxTTcf2d1QNje7ezVnOVEwDlHZUMH1u1twrp9zVi3twk769qUeb2jCzNx6exSTCrJ8XifFsVYOOd4ecVuPLx0OwblpuLVK2Zi7MD4mgcVy2/O/sZO3P7eRkwszsadp8TPvDw9+2brQdy3pAw1LTZcdPhg/PGkMb2aSpJo6tpseGXlHvzvl71otTkxc0ge/nzGeBwzpsCjJkM45Cwz4pu6qcfbNH1eOeOAd5AHhL4ALd5QjUF5qb1ec+j+08djRmkehvQPnDpysNXmM7m/L+WvU6wmNHc6cPVrazB6QCZuO2G039ftbeyEw8UxoiCjR3P0yuvbMax/Ro9PZLGUk5YEu1NAl8OFtCT/XynO9XkC6Qt5xEY+ybpc+ghuekUuxsKYruboyeW7mzrsyne4y+5ES6cjZHrR0rJa5fHeQx0+BQUSVUe3Exv3N2PdPimw29ek7KfMFAumDc7FKRMHYlppDiYPygl4ExtOyngktdkc+NMHm/D55gM4YVwhHj13clzdYLvXv4rN37M7Bdzw1npwDjxzwbSwR1O0oIfTSV2rDf/3yRZ8vvkARhVm4IMLZmN6qW+V8XAk0ny08rp2/Ht5JT5aXw2HIOCk8QNw9dxhmBqhtSkbO+yGvTdp7LCjqdPud2qQngqiUaAXIxnJlqAXoLo2G1aWN+C6o0f0KUf41EkDgz7vryfntctn9vrvJVvM+GrrQQDAV1sP4tqjh/sNdipUi543SZUnw0rdrG/HlEGRX2w3klKsYqCyYX8zjhje3+9rOPyn0NidguEn7lukilcOA6boydyFacQRTr2lsf7rm53KSP5xjy0HAKy95zikJ1sCzuv6/Tsblcf7DnUmZKDHOcfeQ51SUNeEdXubsf1Aq1JhdkRBBk4YV4jppbmYNjgXw/PD79QSR/Rik7q540Abrv3fWuxt7MRdp4zBVXOGxd1cllg35+9fbMfG/c147sJpGNwvDuflSeLrX6nnBIHjrV/34ZGl29HtFPCHE0bh6rnDe3XdjLNDNiDOOdbsbcILyyrxzbaDSLaYcM6MEvx2zjAM7Z8e+hf0wLFjC/DllgMR/Z3xziVwvP3rPvzjyx1otzlxwjh3gbBEOUZ6ggK9GGEseA/BpxtrIXBg4dTeV9vsrVA968HIQY7sULsdaXm+h1W5XFQlPx3rpJLgoeI8m8OFqqYu/GbaoF63LxbkntwL/r0KA7JSPJ7LTLHgrasPB+A5P+CeU8fiL5+Jaa9GDfTUSwokmU1wGHh9BUG1L8RzhT7kplnR1OnAjoNt2Huo0+O56X/5BgBwmlfn1JyR/XHeYYM9tu1t9HxvvOqyu7CxqlkJ6tbva8IhqWMrI9mCKYNycMP8EZhWmoupg3L7dO5NtpjhcPGoL9OyeH017vxwMzJSLHjzt7MwK4z52FqKxWjN11sP4qUVu3Hp7FKcPDF45yrpvV0H23Dnh5uxZm8TZg/rh4fOnIBhcTxfv69cAsfXWw/gheWVWL+vGblpVtx07EhcMrsU/fuQeeXt29vm4dh/LgMAjCjIjNjvTQQb9jfj3sVl2FzdgtnD+uGBBeMxstB3H+ipr5UCvSgpyU3FiIIM/LBDnEjPEPzAWbyhGhOKs6L+pfvkhqNw+tMrlJ+3P3hSn36f96K1LV0O+AvLyuvaUZiVjMwUa9ipm5X1HeAcGF4Q2R6sSEtWBWo5aVZMlubS1LXZ8P2OelTUtUvz99zvkdchtDlcyE6Nn/QnLTDGYDEzd7lnA1KqbjIW8lyRSOTUwl8qGwO+Zmttq/L4YIsN22pbce4M8SxS2i8NrV0OnyAxHnDOUdXUJQV1YhrmttpWparxsP7pOHp0AaaV5mB6aS5GFmRGNCBLljrZ7E5BOZ9EUrfThb98ug2v/7IXM4fk4ekLpqLAqyMrnsh7NtpfnaqmTvzhvY2YUJyFu04dG+W/FhmJdj6xOVx49vtyPLesAunJFvzjN5Pwm+klERtFjrfdYXO48P7aKry8Yjd2N3RgcF4aHlgwHudMHxSV7/bw/AykJZlx/fwRyrZEO0Z6qrHDjr9/sR3vrNmPgsxkPHn+VJw+aaDPMaXDAT0K9KJBvgE4Yng/XHx4KQTO8emm2oA9jZX17dhU1YJ7YnDRmFiSjT2PnArOOQSOPt94eKddOQOMylTUd2CElActF98QQozgyKOAI+J4aQXAfcMFANNLc/HQmRMBiKWfv99RjzabU0zdVL0nVdpvnXZt18HSknqE28yYsYuxSP9n0pCeHvaEIPCQx/cjZ03Eopnu0bvr31znESydM70EX2+rw77Gjqi2NRw2hwtl1S1Yu7dJmV9X3yZWrktLMmNySQ6umTcM0wbnYurgXOSFuYREb8kdTN1OV8RvBqubu3DdG+uwcX8zrp47DLefOLpHlaA1EYM7NIdLwI1vrYcg8LiflyeLtxTbUH6uOIS7P9qMyoYOLJxShHtOGxfR0ax40tRhx+u/7MWrP+3BoQ47JpVk4+kLpuKk8QNgifL3besD7k7+RDtGesIlcLy1WkzT7Oh24qo5w3DTsSNDFvfSwzVYRoFeFMijeO+uqVKqX362udbvgbP9QCtO+tePAIDTJ8cubZMxBnMEvtvJXmmH/uZZcc5RUdeOs6eJ1USVQC/EN6m8rh0mBgzpF+8jeu6L/Q3HuHvIMlLEr1d7t9OntyxNujHrMnKg552uGOB4mP/oDxhXlIVnLpgWs7bFmse+QGIVCwjkzg83h3yN9znAJOWtyucRq9mE0rw0rNvXFI0mBlXTLI7WrZVG67bWtMAhFQwanJeGo0b0x7TBOZg6OBdjBmRG/cbMm3ze8S6u1VfLdtbjlrfXw+nieP6i6XG5ZEAw0fzq/OPLHVi/rxnPXDANpXF+XUo0zZ12/PXzbXh3TRUG5YkVXef1YT3heLa/sRMv/ViJd9dUocvhwvzR+bh67nAcPixP10FXrK3f14R7l5ShrLo1aJqmmh73PwV6UVDd3OWzzV86VpvNoQR5g/PSUBjHaTGBfC0VYpH5S7872NqN9m6nakRP3O4vdbOly4HGDjuG9k9HRX07BuWlxXQR3t5Qz7EbmO1eBiJTCvT8Lb0gf6YuhzNGrYw/SnCjrB3n/w5td0MHdjd04JkLYti4GOPuspu6mQze1i3OxR2Wn47KeveInNXMlIDphPGFHu9hADrsTtil84jFbMKQfmn4dFNNVAsXdTtd2FLTKqVgivPr5GVoki0mTC7JwZVHDVMCu/xM7UcY1CN6kSAIHE9+twtPfLsLowsz8dxF0yNe+CGaor1G2rfbDuLF5ZW4+PDSkEXP4k08VxDknOPjjTV48NOtaOp04Jp5w3DLsaOikrLo/ptR+9VBbapqxgvLK7F0cy3MJoYFU4px9dxhGBUi+IiFeD5GeupQezf+/sUOvLNmPwqzkvHU+VNxmp80zWD00Nkqo0AvCkZKAc2DC8Yr2xhjPl+kt1fvVx7/ds7Q2DQuyvwtblxeJxdiEfdLsAWyb3hzHX7c1YA9j5yKirr2uF4oXRbo1JGZLM69a5dSN9XkyqRddgPPS5P+z8B0VYCkV3xG9LRsTGQUZKYgM8XiEeQBwMo7jsHMh74FAL8pWQdbu3Hqk+I84iQzw+B+6RC4ODcqUoUYDrbalKBu7d4mlNW0KsFlcU4qDhuah+mDczCtNBdjB2bFZdqi3FkUiSUWmjrsuOWdDVi2sx5nTSvGQwsnRvVGO5qiccNa3dyF297biHEDs3B3gszLk8Vzv9H+xk7cs7gMy3bWY3JJNl69YmZUq+tqMVrDOccPO+rxwvIK/FLZiMxkC66aMwyXHzkUA7Ljo3M/no+RnnAJHG+u3odHpTTNa+YOw41hpGmqyYeIDi7BCgr0okh9U+Lv5u2tX/cBAG45biQumlUaw5ZFz/trqzB3ZL5HifAKr7l28sn24pdX45zpJfjHOZOV1/64qwGA+IWtbOjA3ARI3Qi0TESK1QSziaHNJo5seBRjUeboGXdET8Z0uqRAT6jn6PnrFEpEDpeAJD8BUkFm4JsbOWVTzoqwmE0olUrX723sXaDncAnYWtPqXrdub5Py+5MsJkwszsals0uVJQ7iueCImjKi18fUzY37m3HdG+tQ39aNv545EefPHJSQ6UtKkyP81XG4BNz45jo4XRzPXDgt7jNM/Im3U6vTJeA/K3fj8a93gTHg/tPH4ZLZQ6JaPTbW7E4BH2+swb+XV2LHwTYMyErBXaeMwaKZg+Nq/Um9WLevCfdJaZpHDBfTNHtT3FA/R6AbBXpRIBeVMKkulv5GLEYXZqKyvgO3HDcqhq2LrPNmDMI7a9wjk59uqsWiwwbjqJHu9eTK69qRmWJR0p3U5/L31lZ5BHqyqqZO2J1CQozoyalTowo928oYQ2aKBe3dToB7phbJveVdDiPP0XN/I8IZxfp220EcO7Yw+IsSlHuOnn6qbjpdHBYzw7/Om4JlO+vx0frqkO+RUzplFhNDaZ4Y6O0Ls/JmfVu3sm7d+r3N2FjVrIx6DcxOwbTBubj8yCGYVpqL8UVZCVFQwx+5CFRvUzc553hj1T488MlW5Gcm4/1rZ2OSVDE4EUXrBu3RL3dg3b5mPHX+1IRKZZXFW8y+qaoZd364GVtqWnHsmAI8sHACinNSQ78xoqJ3gm21OfDWqn3478o9ONBqw+jCTPzznMk4fXJRXC+llKjXnEPt3fjbF9vx7poqDMhKwdMXTMWpE3uWpulPou4PfyjQiwJB6mD17J1iHgfO5qoWLC1L/EUqjxrZ3yPQA8QTnVp5XTtGFGQoXzzvXrvGDrtPhbqNVS0A4n9pBQCYNjgXZhPDI2dP8nkuI9mipG4yBpw0fgC+2HLAY3kFmSBwCJzHvKiDVtTnURZGpckrX12DZbcfrcsiCPIInjiip4+LjFPgsJhMWDi1GAunFmP7gTZsUy2l4I93Mackiwn5mclItZpDLrFQ12rD+f/+BRVSqqjVzDC+KBsXzirFtNIcTBuci6KY31BGjxygeqdu3vz2ehwxvJ/PWoRqnXYn7v6oDB+tr8bRo/Px+LlTkBvlKqGxEsmvznfbD+KF5ZW4cNbgmBZL06OObif++dVOvPLTbvTPSMazF07DyRMGxHT0ONRf2lTVjNJ+6b1a8qi2pQv/XbkHb63ah7ZuJ2YP64eHz56Io0flx/0IeZw3zy+XwPHmqr34x5c70Gl34Zp5w3DTMSOR3oM0Tb8ScWeEQIFeFMgjeur7dfHYcV+CNlU3x7RN0TKpxDef3jv7ory+3aN6lsnri+SvUudNb60HAIzI136ScigFWSmo+Ospfp/LSLYoxVgA4PmLpwMQg1vAc3mFYXd9DgBYf+/xurnpCsqjGEt4k59XVTbqM9DzmKOnj9RNpyDAoirt++41hyvLEQR7j5rVbAJjDIPz0kIusVBe146K+g5cfuQQnDpxICYUZydkml24AhVjWbKhBks21AQM9Crr23Ht/9ZhZ10bbj1+FG6YP8Ij1T5RRfpmuqa5C7e+uxFjB2bh3tPGRfR3x5rWHUffbT+IexdvQXVzFy6cNRh/PGlM3K0fW9vShTOeXomLDy/FgwsnhP2+HQfa8OLySny8sRougeOUiQNxzdzhmOjn3ohExtq9YprmlppWHDmiH/58Ru/SNIPRwzVYRoFeFMjrw3mkbsLzZNvRrY+5Wf5SEW58az1OHF8LQLyXr2/r9lgLz/vmK9jcrOy0+LoY9FRmigVtNofPOnppQVI3K+rbMSM9LzYN1JB7FIvBFOYoVllNC87FoCi3LPY819HT/sYsEsQRPfdRn5liRaY0N2Vo/3RluRU1h9M3dRMABvdLw56G4IGeTQp4Fk4pxuRBOX1pekJQUjd7MEdv6eZa3P7+JljNDK9dMRNzRsb/HOieisR3R14vz+EU8MwFUxO8w0C7IL6uzYY/f7IVn22qxciCDLz/u9mYMUT7a5u/Y0TufF1Z0RDG+zl+rjyEF5dX4ocd9Ui1mnHhrFJcedRQDJJSzUnkNbR3429Lt+O9tWKa5jMXTMMpEyM7Kpz4XV6+KNCLArk4hzpF0Tsdq92mj0AvPyMZZ00txpVzhqKh3Y5L/7MaDhfHVlWK1pgBmR4jejlewVug9fR++MPR0WhyTGWmWFHXJpZpV5+Lki0mMOZ/HT0dZg4EJY9itXQ5sPNgW9BS02XVLbFrWAzJo5nKHD1tmxMRTpcAi8l/GvL3Ab7bDu8RPakjqTQvDct31kMQeMDRJ7mCbaJWi+wpZR29MOboOVwC/v7Fdvz7x92YMigHz144TVdprIC6Wp7vt8fpEvDkd+W4as5QpbMhmH9+tRNr9zbhiUVTIlbp1UgEgePtX/fj4aXb0O0QcNvxo3DNvOGaz1ELdm2VTz3BLr9Ol4ClZQfw4vJKbK5uQb/0JNx6/ChcfHhpwmfhxHPnokvgeGPVXjwqpWn+bt5w3HjMiL6naQYTx/ujpyjQiwLBXzEWr3SsNp2M6FnMJjx23hQAQHldm7L9u9uODvge79RNIUCkNyQBJ757y0i2oLJeXDCdeRTnYUi1mv0Gen2topco1BcWEwOWlh3A0rID2PPIqQHfs7W2FS6B66o6G6CaZyWnsergEHAJ3CN1MxxOr2IsctXO0n5p6HYKqGvrDliSXB4dT0nQ4io9FW7VzYOtNtzw5jr8uqcJl84uxd2njtP8hjsagh1pn2yqwZPf7kJzpx0PLAielvf9jjo8v6wC588cjAVTfEedE1Es71nL69pw54eb8eueJswamoe/njVRWVopnrmCRDqddifeW1OFl1ZUYn9jF4b2T8dDZ07A2dNKEny0VxTtNSj7Qp2medSI/vi/M8Z7ZIhFmh472inQiwI5bvGpuqnDET214fkZ+NNJY3D29OAXR7PXN8nf+fWEcfqorpghVd3k4D6n0lSr2W/qZiTWxUoE3ksKhMPmEFBZ346RcbDAbCS9uKwSgJjmLHYKJf4x4HD1vLCQ93xdef26wdK8zL2HOgIGenJho5Qk/QUx/oRaR29rTStabQ7c8OZ6dHQ78cSiKboJXILxdz3p6BaPDe+qrt5qW7pw6zsbMGZAJu4/PbHn5clidePa7XTh2e8r8OwP5UhLsuDvZ0/COTNK4r4QiczfMkkN7d147ac9eO2XvWjudGDa4Bzcfco4HD+uUHedjfGmob0bjyzdjvfXVmFgdkrMi/foaECPAr1oUEb01MVY4HngdOhw/TTGGK49enjo13ndh/mbo9fPz0LKiSgzxYJWm1MM8rzOTyleI3pmE4NL4L0ul55o1EsKyOua+SMHxP+97DBc/sqvKKtp0V2gJ6czHze2EP/7ZS9qW2zYUtMS1cWDo21FeQMG9HBNOt9AT/zSDJHX0jvUiVnD+vl9rxzopeqghz0cgYqxyE558kcAwLD8dLx51aygKdF6EGyh4zqpCFBykJFMp0vATW+tR7dTSNj18rTyS+Uh3PXRZlTWd2DBlCLce9o49I/ja7i/Y0S+D6mo78CjX+5AY6cdH6ytQrdTwHFjC/G7ecPiYn5htMRL8RGnS8Abq/bh0a92wOZw4dqjh+OG+VFO01SJ59HN3qJALwrkE4bZK1VPHc+06XBEL1zeI3r+UiaCXZATSWayBXan4FOJFBALsqhH9JLMJnQJLgON6LmXFFDznoeVbDXh3BklmDOyP5ItJpRVt+LMqbFsaXStLG/AC8vFEb1RhRmobRHndC5eX53QgZ5L4EEDeH+8R1zkEb2inFSYTQx7g1TelDtNjHKDHm7q5sc3HIWMGN0kaSvwDVprl7jkT7D98NjXO/Hrnib867wpCZFq2BPhVDTujZZOBx5eug1v/7ofJbmpePWKmR7z8eNNsJt49Yje09+XI8lswlnTivHbOcOimioYF4LENhe/vApD+6eHTHmOhDV7GnHvki3YVtuKOSPFNE2tvovxPGexp4xw9o85+YTBfFI33UdOc6fD531G4T1Hz99FqKK+PVbNiSr5xkLgvheZ1CSzx/IKSRYTuhwutHQZ69jwvsa4OIdJtdUlcDDGYDGbMHZglu4Kslz40irlsfqcEahIkZ7Jle/6ZySjob1bKaxiNZtQnJMadC29LocLFhNTgkO9s5hNMJtY0I6hpTfPMUiQ5+bvetIpZdCU5PovQLNsZz2e/aECiw4bhIVT9ZXeGo3xCc45PtlUiwc+2YKmTgeumTsMNx83EmlJiXusqWsF9EtPwtKb56CghxkJevTjrgb8uKshqoFefZuYpvnBOm3SNNUSJNO4R0JeERlj/2GM1THGylTbpjDGfmGMbWCMrWGMzVQ9dydjrJwxtoMxdqJq+3TG2GbpuSeZ9C/IGEtmjL0jbV/FGBsS4c8Yc/J1Rj2Kw5hnukBzlz2mbYon3oX4/N3Qzh9dEJvGRFmGqsKb9wnEe46eXCDhviVb/M4X0JtAPWben93hEpTRiwnFWdha0xqwgI+eBFt2JFGcP7NnS2HIgd0nNx6JBxd49uaW9kvDvsbAgZ7NIRgmbVOWbDEFTN287fhRGDswK8Yt0k6wGzQ5g8ZfRdYDLTb8/p0NGF2YiftPHx+t5unG/sZOXP7Kr7jprfUYmJ2KJdcfiTtPGZtQQZ6/U6s6s+je08ZRkBcDTpeAV1buxjH//AEfb6zGtUcPx7e3zcMpEwdqPrczXlJZIyGcrs9XAJzkte3vAP7MOZ8C4D7pZzDGxgFYBGC89J5nGWPymfU5AFcDGCn9J//OKwE0cc5HAHgcwN96+VnihpK6qV5eAcxzRK9DHLXRY/WzUHyqbvo5684cqo9c+MwU98XPpxhLklmZVwR4pqu+v3Z/tJumOeVf3WvHeAd6NoegpHJOKMpGW7cz6A2/XiR6MJtkMSE7tWclx9+9ZjZeumQGBman4uLZQzyeG5yXFnJEL9mAgZ4tQOrmuYfpb73JcPj71rQHqHLtdAm46e31sDlceObCaYZZmqM3nC4BL/1YiRMeX47Vuxtx32njsPj6IzGhOHHSy4PFDurrTqIvldAbse5XXLOnEac/vRL/98lWTBmUgy9umYs/nTRG8w4DHQ7ohQ70OOfLATR6bwYgdxVmA6iRHi8A8DbnvJtzvhtAOYCZjLGBALI45z9zMdp5DcBC1XtelR6/D+BYpnUo30cufwumq0b0HC4Bbd1OTBucg29vnadBC7XlPUdPvXTW/NFifn8iXTyCyVSlTfkb0VOnbqarTnB/+mCzksamW6q14/qpLqzqntVt0nqMX5QdAOA+LrbUuNdp1IulN8/x+DlYue9EwDnvcRrM0P7pOC5Axd3Sfmlo6XKgJUDau83hQqpBKm7Kki1mjxE9dWeiXuY5h6tJOl++s9q3k0we0fPuVPzXN7uwencj/rJwgm7nYQVfO457dDYGsrmqBQufXYm/fLYNRwzvh69vnYcrjhqqq8qT8rFx6exSzB3ZX+PWxFYs/xXr27px67sb8Jvnf0ZLpx3PXTgNr10xM+7mxSb45ddDb68EtwD4B2NsP4BHAdwpbS8GoD7LVknbiqXH3ts93sM5dwJoAeC3rBpj7GopVXRNfX19L5sefUrqpseInnu7PAdrwZRiDMpLi3HrtCdfeOTdo774mk0mXaUbZXiM6PnO0VNX3fROFbj57fW6TuFUL6/w9a3zcN4McQTCpSrIId+EXDhrMABgZGEGrGaGshr9zNObPzofw/PTfY57e4IX5eEcfosQ9dbgPGmJhQAFWWwOl/FSN60mjzl66psToxSlke2RRnu/2HLA5zl5RE+9f5bvrMczP5Tj3BklOGtaSUzaGG/uXVKGMfd+EbBYS0e3E3/5dCsWPLMCB1u78eyF0/DSpTNQnON/rmOi8JeWJxf8/c30QZqnDeqR0yXgvyt345hHf8AnG2tw3dHD8c1t83ByHKRpqgWr3puoehvoXQvg95zzQQB+D+Blabu/fy0eZHuw9/hu5PxFzvkMzvmM/Pz4rewk98R7ztFjyoeSC7HIJdWNhjGGVXcdi6cvmAbAu+fEd725RJbZgzl66v3w4MIJ+HFXA578dle0m6g5BiAvPUkpn7ztgHu0Tt4lw6Xe9mSLGaMKM3VVkMXFPY8TmXq0NxEJnPukafdFqWqJBX+6HC7DBTcpFrNH1U31qTTJIEVpZMFSnduVET3x54Ot4ry8kQUZ+PMZ0a8mqLVAoxNvrNoHAGjzk9r6/fY6nPD4cry0YjcWzRyMb26Nj7lTfRGs5UomlrG+NopoBja/7mnEaU+twJ8/2Yopg8U0zT/GQZqmP7S8gtulAG6WHr8H4CXpcRUA9cSAEohpnVXSY+/t6vdUMcYsEFNBvVNFE4qyjh7zHtETtzd3iikmOWnGywOXFWalIMUqnlHVI3qc66vqUbCKd2k+I3rAKRMH4NkLp4Nzjg37mvHkd7swdXAOjtZJcRo175uPH3eJo/QPfLIVX9wy1+M16kNiQlE2vt52UEoNTPyDxSUIsPgZ+qqoD7yUQCIQK81GzmAp+yHQ/Mwuu/ECPXFEz3/qpklHaXXhCJbq3GYTO1f/8N5GjCrMwEOfbUOn3YW3L9D/vLxwblzfXLUPv5snroFb12bDA59sxaebajGiIAPv/W42DtPx+nEyf7UVjEKsCh/531vf1o2Hl27Dh+uqUZyTiucvmoYTx2tTTbOnorUkiRZ623dRA0CeXHYMAHnY4WMAi6RKmkMhFl1ZzTmvBdDGGDtcmn93CYAlqvdcKj3+DYDveILvYbnX0KM3WzVHTx7RyzXoiJ5M/rJ7BHrQV6AXtBiLNKKnPtzlizJjDH9ZOAGjCzNxyzsbUNWkv+Ij8ueWjwN5xMZz7TXfpUomFGehscOurDeX6Jwu7vfmYltta8CKivHO+982EtKTLeifkYy9hwKkbjqNWnXTd0TvtuNHadMgDQWrUtuh6lA74+mVWLW7EQ8unICROl9EPlyPLN0OQeB4e/U+HPfPZfhqy0HcevwofHbTUboM8vxW3RR81z8mvaNO0/x0Yy2unz8cX986FydNSIAR4ThvXm+EHNFjjL0F4GgA/RljVQDuB3AVgCekETgbxGqa4JxvYYy9C2ArACeA6znn8hn2WogVPFMBLJX+A8S0z9cZY+UQR/IWReSTaUhQirG4tzFAuQo3ySN6PaxIpzcmJdBzb+Oc62roPNligsXE4BR8hypTpbQFm0NAapLZpwcpNcmM5y+ajtOfWoHr3liH9343G8kW/dzIBsrdlgsnAP5H9MZLBVnKqltQlOBzRQDAKXBldNvbxS+vxrvXzI5xi/qO++vsioAh/dKUuVjebHYXUg1WEj3ZYlbWiANU3xf9nELDVpCZIv0/OeRrfzO9BL+Zbpx5eeGUih921+cAgFlD8/DXsybGXXGMSAj2vZC/R0YbCQ+XwyWEtUbp6t2NuG9JGbYfaMPcUfn4v9PHYVgCHkuJPdzkKWSgxzk/P8BT0wO8/iEAD/nZvgaATzI859wG4JxQ7UgkflM3GVNOtnIxlmyDj+jJ51Ou4xE9xhgyUyxo6nT4GdETT5pdDpcY6AE+Uc+Q/ul49NzJuOb1tXjw0634y8KJsWh2THjflJbkpqme4x7zWtXHxNgBWTAxoKymFSeMHxCbxkZRRX07hvRL9/vc6t2JmcUuKCN6kf29g/ul4afyQ36fE+foGWuCjcXMPAo2cT8j4EZx7Bgxvf3qucM8tvsraPXAAuOsl9eTQ+HvZ0/COTNKDHn8VDeLGSJGywpQhAhstta0YvKgnIDP17XZ8PDn2/HRejlNczpOHF+YcMdSYrU2PPE3E1IH3JN6/VfdbO50wGxiyEox9u6XA2GPG5UIz+uJBxlyoOddjEWaG9JpdyIvPQkI8NlPHD8A18wdhheWV2J6aS7OnKqvnmh5BPeOk8dgd0MHlu2sR22LDUU5qaoRPfeeSU0yY3h+BrbopCBLc6cDGzqbtW5GRMnf6Eh3jpfmpePD1mrY/BResUkdJkaiZAtI9NQL3VPy9db7xrLd5llo5P7Tx8VlEYhoCnVcHDe2EP88dzKyU43R+exvf6RJ5w4jFskTr6/BD5KuAMtwOF0CXv15L/719U50OwXcMH8Erp8/wnDn4nhmrO7PGHGnLbm3qSe7NnXakZ1qTbiejkjzm7oJ6GtID0BGsnjh8F1eQU7dFE+g4mim/89++4mjMXNoHu78cDO2H9DHGnLePe0pVjOuO1osCLCrrh2Aeq6X53snFGfraokFvRGiMEcPcM/j3O+nIIsRq26aTczviJXOTqFhUcqie93Ft9o81128/MihsWpSXAh2LIyTlnR5/qJpBgnyQn8x9DR1JJLabL6VWVdVHsJpT63Ag59uxdTSXHz5+7n4w4mjEzrI0+N9OQV6UeC/6qY7dbO5y2HIXiNvflM3ddglnRlg5FZOEZHL6IvzE/2zmE14+oKpyEyx4tr/rVOqyCWiAy02DLnjMzz0+TZxg+pDy8URdh1s83iP934ZX5SFg63dqG/rjmJLYyPJYsI184aFfmECidZcscFBlljwN8qndxaTye+InhFvVuXrrfclxDvQI56OG1sAi8GW4vBHh7cePRJqHudVr61RHtdJy5Oc9+IvaLM58fxF0/Hq5YdhaH//UxASkZ6OB/p2R4Fc5lldSU89otfcaUeOIXrPgpNTbbw7pPV2i5IpLbHgfdMrp4rISyyEmp9YkJmCp8+fin2Nnfjj+5sSNij+ubLB42f1Z85LT0K/9CSUyyN6AX7HBKkgy5YEH9XjnMPuFJCssxutaBVjKZWWWNjrNaLndAlwuLjh5td4j+jJN2tGrCchf2bv6pv+RiKMJviVwoAHix/u+a0aN0QDwT6z+pzqdAl4ecVuHPPPZfhsUy1umD8C39w6DydNSIwlE8Khj0/hyViJ6jHirzfbe8H0AQarDudPoAuzTs4XigxpRM/7Y8mjD3LuezjzE2cN64c/nTQaf/18O15esRu/nZN4I0HeN//en3lEQQZ2SiN6yqHh9aJxRWLK0Zaa1oReY1AejQlVzazN5vC7qHq8UlI3I/x789KTkJFsUToCZDZpiQGjBXriHD338gpClEZSE4E8iundcdgqFT978eLpmFaaG+tmaS7Y6G5idhX2nb/RqwTtN426FKtJuUcZcbdYLP/o0fm4//TxuhrB8xZOpdpEoa9u5Djhbz0WzwXTHYavuAkEWEdPh8VY5NRNn2IsVs8RPfE1oT/9VXOG4aTxA/Dw0u0JWZUx2eJ52vH+zCMLM7Crrh2cc3cvq9dRkZVixZB+aShL8IIsDpd4k261BD4Vv/PrPkz8v69QWd8e8DXxxl2MJbLfZsYY+mUkYUV5vcd2+TtktKqbZhODy+Wb+m7E1E1ljp7XDVqrNKI3ekAm+meEXnrBSMTqxlq3InbC+axG2h9qjR12DLnjM9z6zoagr3vx4un472X6StNU0+O/v7GuijHif3kFz9TN3DRjr6EH+J9TwcF1kwIgU4qxeH0uJXVTKcYSXg8SYwx/P2cSBuWm4oY316GurecLh+862IYKjQKHpCBBDQCMLMhEm82JurZuJWLwd0iM10FBFod0k27xk2t307EjAQB/+mAzAOBga+LMR4zW8goAkJOWhBSLb8VNAMabo2f2qrop/V9np9CwBJqjJ89nzkqgEfFIC5bmb8BDJSijdpJU1HcAAD5cX+3xnPrI+fvZk3DCeP2kafrjrxp8oqNALwr8pc8wJn5h7E4BHXYXzdGDO3Xzzg83KxciPY/oeXMvrxB+6qYsK8WK5y6ajlabAze9tR5OlxD6TSrHP74cx/5zWY/eEy3en3lkgbi46q6D7QEXVQeACUXZ2N/YhZbOxC22II/o+Qt+vUc+0xKokhmXDsdo3BAMyk31uQjLgV4iV3vrDZ85evq5N+kx+VATvI6N1i5xRC/QeVj3gnwFOTdmp4C/70miznmPNs6B4pxUAMCZ04o1bk30ybU1KNAjQQkCh4l53uQwMHDO0dxlBwDkpNOIntxzcqDVhtd/2QtAnxeejADFWOSbUptqjl5PotyxA7Pw0MKJ+KWyEY9+tTMSTY0JwSsm9d4vIwqlQK+uTTXf1XfHTCiW5+kl7qieUxnRCx3oJZJoFgWxmj0rTQLuUXHvkT69s5hMSmcBANUIuM5OomFQRvS8trfaHEhLMlNlST84uKFGsIJ90mhVCk5Edqf7nMI5x/HjCrHnkVNDziXXA6tZPAAcLgr0SBAC574FJ6QRvWZp9IFG9DxPqPct2QJAnxeeUMsreMzR6+FnP3t6CS6YNRjPL6vAV1sO9L6RMeTyLr7j9ZnzM5KRnWoV5+kFqYQ2vkisvJnI6ZvKHD2z7wf0DvS8ixZp5fsddahq8l3eQE2OwyI9Rw/wv3ac/B0y/IieMqfVeAJX3XQYOm0TCFx0RY8dq70VLHvEaFq6EjdLpq/kTtdup/8F4hMRBXpRIHD/lQU5dwd6NEfPdx/tONDW41GtRKCM6Hl9MKvZBKuZodOhWkevF5/9vtPGYWJxNm57byP2NHT0ub3R5p0i4/2ZGWMYWZCB8oPtqnXBfOWlJ6EoOwVl1Ym7gLw70PM3oucZtMRDmMc5x+X//RVH/e37oKkt0ZyjZzExz1EsuKtuGm6Onon5X0dPZ+fQcLiLe3lub+1yGjdtEyFGsWDMYyUYI46GO5yeXxp1oBcP151YMkudrr8PUZQmkVCgFwUC5/DJxJKWV2jqlFI3qeqmT6C3evchXZ5UMgJU3QTEG1OPdfR68ftTrGY8e+E0mBjDtW+sU1JB41VHd+j2Feem4kCrLWRxiUQvyCKnh/gL9OQUVlk8DOipbwA+31wb8HXBUm77ymIOPKJntKqb3vvC6KMSTO5RVWnrdiCLMmj84lx/GTTBBDsfxcP5VSur93hW7/YY0TPYqK/8UfccCp61kkiMdVWMEXGOnr8RPa4UjqBAD/C+t221OcWTijbNiRo5bcjf50pVB3p9OKEOykvDv86bgm21rbhncVnYE8v3Hor9COB+r7Q/fxUnzYyBg6s+h/8dM6EoG7sbOtDenZiLIssjUxY/qZvTBufir2dOVKpvxqpv9bCHvsH9S8r8Prei3L3YfastcHqP/O8WjTl6FpMJNocL763ZrxTeUIqxGGxEz2wS5yu6i1nJI6l6O4uGx8SY3xG9LAOP6AEIeOrggP4uuL1k5LRnby1SLQlA7oA2zl7RUxEWGQV6UeDi3GMNPcB9A+8e0aPUTfXNSHaqFQdabNJ2rVoUHYGKsQBiJcUuR+/n6KnNH1OAm44ZgffXVuGdX/eH9Z6GdnvoF0WYPHq18f4TsOquY/0XSWBi0ZZQI3oTirPAObCtNjHTN+U41vt8Ibtg1mAcNkRc5DlW15/6tm68+rNYHKmqqROrKg8pz93w5nrlsXd1QzWl8nAUbhDMJoYOuwu3v78J76+tAmDcqptyJ4m8v93rF2rTHq0x+M7Ra7U5kGngOXpBg34ddqyGw3/VTfH/erv/6Il5o/IBeKVuGmytRT0Gegbv5oqs//2yF7/uacTmqhbfeUeQ5uh1OWAxMaQb7IbEH/Wo54CsFClVj4PprP9BSd30c0lNsZp7vI5eMDcfNwrr9zfjvo+3YEJxNiYUZyvPbalpwYfrqnHjMSP6/Hf6YvuBNgBAVooF2QFSqpRjI8gcPQDK59tS3YLDhuRFspkxoay5qTrk/3rmRKza7Q6u5OMm1qlFQ+74THn838sPw/zRBR7PB7sgRrPqpnoEeI80Im3UqptyKXCnIMBsMruDHCPdmamYpCkSam02J7JS6VbHH3GOnnGOlVDzFQFj7Q9vA7JSALhrSciMtEfipehZJOnrjlpj+xs7sXF/MwTOcdzYQo/nmJSK1txpR05akqFPJjL1TWBhdgrqWm26rAKWm5aEUycOxLTSXJ/n0pIik7opM5sY/nXeFPRLT8Lv/rcWh9rdi2z/86udeHnFbny15WDf/kgfydUkg30HTEw84bqrbvp/bUFmMvpnJKOsJjFH9Fx+Uu0umDUYTyyaqvysxLwaXoCqm7p8tgWrPh3NqpvqNFf5373L4CN6Lq8hPZ2dQsPGmOeNGuccrV1UdTNw1U1u2GOF+JKLFhm5GIu6uFW81zsIFwV6EXTnKWPxw+3z8cPt8/HYeVM8nmMAyuva8dbq/WhQ3XwbmXo0pzAzWSm+obdAz2xieObCaZjuJ9BLTVKP6EXms/fLSMYzF05DVVOXkmrX0unAd9vrAHjOrfKuXhgLDEBRdkqI1zAx0Atx48oYw4TiLJRVJ2ZBFjl4C5S6CagCvVg0KIB7FpcpSyokSam2wVI3/7Z0u/ggCt9ls2r4c3NVMwDAJnWWJPLag70hz02taxWvKaFSnfWOMc+Rb5tDgFPgxk7dDPKcHq+34fCbPaPDkZyemlCcjcxki1fqprGOkYHZqcrjMfd+kbDz/9WMdVXUEGPAwVYK8NTUgd6A7BTUt3XDKRirCliq1YxO1YhepO6Mpw3OxbiBWTjQKs57VBdAWb+/WXkcrz1WJpO4P8LJRJtQlI1dde1x+1mCCWfkS/4+aJ1Sct4LvwAA7FLngPd6iLLmTjs+3lgDIDojelZVKoB8UbY5BaRazYbLlBhZmAlATN0EVN8XA51D1UyMeYx8y51alLrpHzfYHL0Q0xUNFdD4s2BKEbJSrUrRQEBa29hAO2Z6aa5H8aZWHawpSIFejKi/KEtvnqNhS+KHvE+G56ejMCsFAgca2roNdbJNTbKoApTITnoeV5SFbocLZdUtOO2pFcr2zza5y+K/t6Yqcn8wgphUPc9dczPwjplQnAWXwLFDmvsXSzaHCy8sq/AYGeWc45WVuz0uloHIo2LB5rIpx4QGcZ56/bFmqZBUplRcKNAcPXWHVn5mcsTbZFalbspz1LrsLsMtrQC4R4LdmZvRW78wEXhX3ZRv0gyfuhmgU8ZoN/HBGC3o9Ycxhpw0q++InoZt0kKrzT2Kp4fiLNTNFSPqL8rYgVmatSPefPX7uSjITMaaPU0AgIOtNgzLT9e4VbGTajV5ztGL4O9OtpjQ7RTw3LKKgK/5bHMtnnAJ/itfasjExJsTd7n4wK8dXyQWZCmracHkQTkxaJ3bvYvL8N7aKjy/rAInTRgAAFi1uxGV9R3YVN2Cx86d4vMesYqZNLcqjHL4sYzz1DeEd548BtfMG45vtx3Ela+uQYd0nOakW9HW7QyYunnZf1cDAJ6+YKpSxS2S1H9XHuXscrgMt7QC4O4gkPdDqFRnvfOuuinfsBl6wfRgo1gGvIkHAmdpUtArZlq1eI9iGXi3OHUQ6MXX3Z2O0fnDv1GFmchJS0KhVO1JD1+qnkhLsqDTLt6MRDp1xGwS57llJAW/yflZVT4/Gr7acgDzH/0Bzh7MB1Tm6IXx2pLcVGSnWlFWHfuCLHLxD4vZhG+31eHbbXXY3yimyQYaRRh65+cYcsdnqG/rdi+vEGRIz2SKXdVN+es3bXAOrpk3HAB8qm1ecvgQAIFTN2ulZVL6pUd+NA8Ql3+Qye21OVxIMVghFsB9YyplbtIcPa85eu7UTYOP6AXaLi6SZhjBUzeNde8RSHaqFc0GLsbizSXEvo5BpBm3myvG5BuS644erm1D4lRhtvum0Ei9ailWM2wO94kkknNrTIyFlXaQnhzd08CfPtiEpk4Hmrsc6J8R3s2/iXleYIIdEowxjC/Kwpaa2BZkWbazHq/9vBdJFhN+vfs4ZbvN4cKYe79AQVbwz7qvsdO9vEKwzyf9PxZz9OS5XseqqgabvBr32zlD8dDn24IWYwGA8cXRyVxYWnZAeSyPQNocLsMtrQD4G9GTF302zjlUrdXmxO6GDvfPSuqmcW91Qh0JRj1WvBl1dNObd+qmuNaisfaMWOBN7DjWw+ADjejFiHzDnWHgC04w/dOTlVLhRjqlpCWZYXcJcLqEiJfPl+er9M9MAgD89qiheO7Cacrzr185E0D0c9DlXx+ssqQ3xhgEgYc9ijWhOBvba9tiWkX00v+IKYp2p+fflI9jZ7D1BwCc/dxP6OgW0yGDpm7GsOqmfCx4jzCOGSAW/UhPEguemE0s4IheSW4qzppaHLV5UZ/eeBSmDs7BcWMLlfZ2OVyGW1oBcBe7kf8plH8SI51EvSzbWa88bpNSN40+Ry8Qoy2GHQwVYxHJxVjk+xEe4doBieDPZ4xXHoe6jicCCvRiRO4VsJpol/tjMjEUSIUbjHRSkecVdTlcUUjdFG/c5cqEV88dhiH93fMf5b9dXtceuT/qR7dTDGZ6EoS5U7DCG6EYX5QFu0vAroPR/SzhUBax9vN53/11v8fPX20VR6eCLywu38zHYkRP/BsWrwa9fuUs3HTMCHx/+9EAxKB93d5m1DR7rq/ndAmob+tG/ygUYZEVZKXgo+uORLLVpIxk2RyCMefoSZcT79FeA51CPWQmWzC+yD2STKmbokCnDoNlbir87Q5uwJErf3JSk2B3CUqmkRFHOqeX5uE/l80AoI9iLBR1xEi3VFlRvdgv8VQgzdMz0h6S5xV12V0RP6GapFEXJT3QxDwKAckFWO78cHME/6ov+YLx2s97w36POBqpWkcvxI6ZUCwWZIl1+ibgHhmVMcZgMTG/KR+v/LTH4+cP11UDCDFHL0ojersOtuGsZ1cqcwoBdRVQz/bkZybj1hNGoyBT/I7aXQJ+rjyEIx75zuN1exs70e0UMFoq+x9NgsBRUS+m6Rm16qYyR8+rGEs0lrVIBJMH5Xispdhmc8JqZoZbX1EtWLaA0dZICxnIGWhfBCIve6VO3zTSMSKT12vVQ+om5RHGyHap9HubLfEXX4yWAXKgZ6CzSpp6RI9HttS1WUp/lJdT8E6dHCcFfbGav/L09+X4w4mjw3qtPEcv3OISQ/ulIz3JjC01rTinT63sucF5aT7bLGb/gV6g3sGg6+ix6Izofbu9Duv2NWPO37/HMGmkVxnR62WHlHxz0C8jKTKNDEKeq7dhf7M4R8+II3peyyvIAZ+BTqEexM4toK7Vhv/7ZAssJhOyUqyGuqb0BIex1q0NhoqxiORAr7nLjgHZKYbdK3JWix5G9CjQizGfsrVEMSDbeCN68rwiOXUzkkxMDDZW7W4EAOVG+P3fzUaK1YwkiwlnTSvGqsrGCP/l4N5bG3rtPp8RvRBHhcnEMK4oC2XVsR/R81fMxmoy+U1VdQao4BW02Iz0/0hnbsqFKk6bNNDjRnjGkFwcPaog0Ns8zBya5/GzTVqCIZZplPVt3bAZfHkF93wakVHjGjMTR3qf+q4cn28WOwKG9jfOcj2BcHC8uWofJhZnY2JJtnu7wUb0ZH47zQyYouhPTpo0oietA8u5MTsDlCkYVHWT9JR7cWziTa5SaKQLjxzoddpd4oUmgp/9UIddeXzc2ELlb80Y4r45T7aYYI9SAZPfvb7WY04ggLAWEQcAMEgLpoc/QjG+KBvvrtkPl8CDpkJGWrqf5SvMZv8VT+XiK8eNLcQ32w66Xx+kvUoxlggHeh3dTuSkWfH0BdNCvziA1bsbPUaiu6TzWyxH1zjn6KIRPQBUddNsEr93r//iThM38hp6gDt4uesjMUV/zyOnKs8ZrgBJ0OUVDLYvAnCP6EmBHoy5X/Q0omfcxHWNjCuixdIDkVM3jdSvJo9C2OxSMZYIfvaJxe6e2+PH+R+hsZr9jzxFwhdbDuD5ZRU4fpxYqn9wXhr2NnaEeJfIxMTczZ4sAD2hOBuddpdHefVY8Dc3zGIyweFVravb6cKBVnGNuXX7mjCqMEN5LljqplJZMRKNlXDO8Utlo98gNRzlD52sPK6o70BDezca2ruVNe5iXQHTqFU35cNGvhkx+oienAmgZvTOVZvThf2N7qJJddI5CJDPrwY9WPwwageJmt85elo1RkPuEb3ED/SM3dUVQ+fNGITPy2px/mGDtW5K3HIHesYhB3qddrnMfuR+d6FqHbdzpg/y+xqLyRT18sFyz9i+xk58vKEGgFgdLxgTg8eC6eHslwnSum1balowoiAjxKv7bki/NEwqyfE7/8dqZj5VN3cecFcE/eDaI3Dt/9YqP4czABnJdfSWbKjBjoNtvX6/xWzCFUcOxX9W7sZxjy3zeT4jymszqglcLPhjxBE9s9f8zRgUZo1r8oieWm2zLcCrjUFOYZWt3duEkycOlH4yVul895qkvs/FoqpxIsiWUjfl1H6j7haLVIzFpYPlFSjQi5FHzp6Ih8+a6LP4MHErlOfoGWgXpSV5FmOJpGqp9H2q1RzwuLOaWcARvYb2bqRazSEXVHcJHG+u2ouzppUgyU91O/Xvf2nFbgDAz3cdG/R3Mshz9MJfGGxEfgaSLSaUVbdgwZTikK/vjTOeXoGzp5Xg0iOGwMUDp4j6u+E8/ekVyuPSvDSPUbyw1tGL4OEhF4fqi7tPHYvxRVnotHsWmOqXkYyinNQ+//5wyct3GLHqpvy9blWKfMmpzgY6iarIlYbnjsrH8p31SLKY8JczJ2jdrLiigwGKXpPP1/6utUadr+gtI8kCEwOa1dMsDLhjaESP9BhjzIjflR4pNOLyCt7r6EXwd8u/66xpgYOeQNUhAWDGX74BIJ7wNtx3PDIDLDr8/fY63LtkC+5dssXv891eC4pnp1pDjviYmOcNSTjfHYvZhDEDMlFW3Rr6xb1w0UursKmqBZuqWnDqpIEQhMApl1azCQ7VB9ilGj3729lih8/WWnc7gy0mL6cTvbtmP06aMKCvHwMAcKClK/SLQjCbGM6eXhKB1vRNlwYFYOKF3M/wu/+txZ5HTu1RqrMeyZWG05PMGFGQgW9unad1k+KOuriE0dZIC3bzbtQ1Bb2ZTAzZqVa0dDlUc36NR648TXP0CImgjGQLMpIthgqI5RG9dpsz4j2K4aQ9Ws0muASurJ/mj0vguC9AEAe4FyM+fFge/nDCKPzhhFG46diRyvPdTgFD+rmXIAin8qx7SQHp55DvEI0vzkZZTUvERke31rRi3H1foKqpEyvKG5TtG/Y1S0Vf/L/PYvJM3axv71YenzvDN4026Bw96W98t72uh60PbKA04nbt0cMj9ju1IheAMWKgpx65u+ilVUqnipHOoWry+pW/VB6K2bIxiUadAm60Qhtyh9rHG2uU+cQy8fproJ0RRHaqFc1djrDXsdUjPVXdpECPxJXSfmkhUwX1RP6sm6tbxDWNInhGDWdpAnn+nCPEyWxbbeBRMrnH66ZjR+KGY6T/5o9Qnl+9uxEFWSk4f6b/eYL+yIGP/LvD3S8TirLRZnN6FB/oi3d+3YdOuwuvei10LkgL0QdK3bSY3cVYdhxoU0YwH1ww3u9nMQdZty4aBQKKpUDv8iOGRPx3x5o8v9WIxVjUHQQryhtwUCq0YdSiEiaTOKJnNpkMu2h8KOpMfaOVzpfP12+u2oeznlvp87xx9kRw8oiezEjHiIyqbhISJc9fNB13nTJW62bEjJy6mZViiXgajdxzG2xa6H9W7gEALNtRH3QUrMNrHpa/v6NOP7R6BS5tNqcy2jBvVH7QdgPuHkShh6kj6oIskSAHZd6jkAIX2xYoALWYGFyCgBeWVeDEfy3HNa+LhVdKct0jmzv+cpLyuCAz2ed3uNvQ6+YHJFciTNbBKJjyWSyJ/1l6yvu7Lc+HNWqMY2YMNS02NLR3Y8qgHK2bE5fU2RtGG9FTTyPw7gykBdPdstOSxNRNrRuiIT3N0aNAj8SVQXlp6J8R+KZXj/pnJMEhSJeZCF50LVJeYbCb+UZprb2rX1+LoXd+jrLqFuU/WZLZhNYuJx74ZCvabJ4Bz88Vh3Dv4jLp7wUuLvKXhROUyd1nTC4K2XaTd6AX5n4ZVZgJi4mhLEKBnkyuTHrE8H5Ku1wCDzi3Tp77+PDS7QCA2hZxpEVdFCfZYsaH1x2Be04dC2ugHFB4HhJD7vgMTar1EXurvr0bSRaTLtLbugw8oufd0y7fyBq15pf6+5Whg2M7Glzq1E2DzdFrDrKOK6dJeorsVCtaOu3uOXoG3C9K1U0dBHp0JiREYxaTCc2ddtidgnLTGgnnzihBVWMnblTNl/N2+4mj8Y8vdyg/n/bUCp/XDMpLRUV9B/6zcjdKclNxxVFDlefO//cvymPvVKlPbzwKb67eh/4ZyZg6KEeZYxYsDVTGlNRN6ecwr8ApVjNGFkauIIscaMqFVY4ZU4CfKg7hujfWAQi80LnVJK5PeM28YXhhWaWy3TswnDY4F9MG5wZtg3fQvHhDNS4/cmiAV4fH6eJIMpt0MSel08Bz9OxeFXPdazcm/r9rb6j7S4IVODIy9Y0rD5KVoEezhuV5/PzrnkYcNsS9zTh7IrgcuRiL9LMR94ueRvQo0CNEYxYzU9Y6eu3nvXhgQWTKgSdbzLgzRBrs6MJM5fEfThiFUaqfGWPITLHg1Z/2oKJeXIQ8WLVMuQdMNqE4G389c6Ly86SSbGyqasEpkwZ6v9WHshB0L3oUJxRl4bvtdRG9iXFJcxiTvZaPCDQPyGxicAqCT/DRm7XwvP/E377YjgFZKaq1sHpOTDvt9dvjwmVHDMErqrmTRlxeQV5aQvbgp1sBGLMHHgA6u937o6G9O8grjcu7GIuReGcLrao85BnoGfSLs+quY2FXpbXKc/R6mlGjJ8ocvQDLTyUS410ZCYkz6rS9WI9KqEekzpkxCCeMH6D8d/y4Qhw+rJ/HjYHNGXjEMTnMG+1wiiTIr5HTQntiQnE2DnXYcbC19zd6B1pseHjpNqU3T07d9J4HFrDqppnB4eLodgoe8xV39GL9OvXe+td5UzB2YBaufWMdnvx2V6+ri4oFKxL76r1gipgC/OaqfQCMOaLX7RBvQuR/S3kuaWL/y/be19sOKo8v1UGhoWjwSEUz+NpxG6vcKf5GXjC9MCsFg/Lc88ezU60QuFgNHDBmACwXSNPDiB4FeoRozKK64X7s3Mkx/dudqlTR7FT/6+SpO7QOtQeeHxbJRbLlAhuyHo3oSQVZ1PMMe+ryV37FC8sqsWKXuKTClhoxFdR7QfhAo2pWswlOQYDdKSDJbEKptLzEJUeU9rgt6ovs7OH98NZVh+PMqcV47OuduOntDT77KhwCT/zUNu9ANcWAgV5uehIAYKpX4REj3pgBQJ60PwBgWH6Ghi2JX3sPdeLRL3fAJc0LN2JFRQBYOKUIm6qalZ+NVpgmmOw08V6gOYylkPRKvi/zTo9PRBToERJH+pKO1xtdqiAh0I2yuqfziW93efQIl6rWxwu1CHpPjCjwvEnryY3r2IFZYAx9KsgizyOUi9VUN4sV2tSjryaGgPPrzCaGbbVteHnFbnTYXfjw2iOw5Poje1UZUh3PFGQmI8VqxmPnTsafThqDTzfV4NwXflbK6ofLpYO5Od43qEYM9KYMysEH1872mYeb2P+yvffYuVMAAP++ZIa2DYljr/y0B09/X47Vuxul9HatW6SNyYNycLC1GwekQllGK0wTTFaKGOiFs+atXsnX+q01reCc4+6PNmPD/mZtG9VLFOgRorGO7sBLF0SbnJcfbI07l1dKi3oEqTAzBQDw4x/nh/xbv5leAgAoyQ098hesCmUoaUkWDOufHpGCLO3Sv41cnVJePHVUYQYqHz414PtSrGYlIJ4yKAf9MpIxuZfl3uWAZkBWihKcMcZw7dHD8eLFM1BR144znl6BjT24CAlBFntPFN7zHY1YdRMAppfm+ewLo968Ty/NxZ5HTsXx4wq1bkrcW7OnEUYsNPnypTPw1PlTMakkBwCwURrVi/Q6tolMnm7gcBp3uRb5HiQ3LQnt3U68sWofFj7ju/ZiIkjwSz0hia9G6lF8YtGUmP/tTml9vFRr4NE4eQBPXv9OvRaR3SVgzsj+Hvn9gVx8eCkq/3pKWMtntNk8g9+eXmcmFGf3aS09dZEaAGiTAr6B2WKQev7MwUHfn5vmToP98Nojet0OwH2R9Ten7vhxhfjguiNgNZtw7gs/45ONNWH9ToHzhF9Q2nvmRJoBR/RkYwZ4Hq8J/k9LYmDZznpxFMtgx8qxYwtx+uQijC/KgsXEPNI3DbYrApKXKVGKoRl0z/TPSIKLc497nkScy0mBHiFxIjctKfSLIuzo0QUAgLOmFQd8jbzArrwulXpEzykIYY++McY81rkKxuGVF9/Tm5EJRdmolRZO7o30ZM+gQT63D8hKwa6HTsZlIQo95Ej/losOGxT2Zw5EHq3xrvgpGzMgC0uuPxKTSrJx41vr8dhXOzwWRfbHJYRXFCeeWbz2a1/3cyIbmJ2KU1Vp30a9MSPhW7evCXaXYNhRrBSrGaMHZGLjfrFDMAHv36NGvjYI8vJGxjxEYGIMgsA91l/s6sWceK1RoEeIxgZLo2E5af6LoUTTiIIM7HnkVEwozg74GjnQkEdM1L1bThf3ueGOhEWHDfIY4expEDxeKsgiF1HpqUBxksXMYA1j/bmbjx2Jf18yAw+fNTHo68KRn5mM4fnp+ONJYwK+pl9GMt747eE4d0YJnvyuHNe/uU4ZrfWHcw5Tgp/9xw7MwkWHiyOrxREsBJSoPAJdg96YkfA8df5UCFyswGnkQ2VSSQ42VTVDkAvTGHlnqMinkjV7G7VtiMYsJganwHH5K6uVbXKl40QS8lLPGPsPY6yOMVam2vYOY2yD9N8extgG1XN3MsbKGWM7GGMnqrZPZ4xtlp57kkl3SoyxZOn3lTPGVjHGhkT2IxIS3+QeovzM0CmNWpDnmqVJc6DUI3ougcNijvzVkTGGU1QjFD0ttDG+SAxce5u+GSg9I9zPajYxHD+uMCK95WlJFnx729E4acKAoK9Lspjwt7Mn4Z5Tx+LLLQdwzvM/o0YqIuPNxbkuqm7+ZeFElD90MpbdfrTWzdGc+tBM9NFaEl2nTBzo7lg08KEyZVA2Wm1O7DnUIY3oGXhnqMjXhn99swuAcfeKySSO6O1vdF9HE7EKZzh9uq8AOEm9gXN+Hud8Cud8CoAPAHwIAIyxcQAWARgvvedZxph8h/YcgKsBjJT+k3/nlQCaOOcjADwO4G99+DyEJJzbTxgNi4mFNXdNC3LIk+xvRE/gMEdpaMhqNuGBBePx2hUze/ze7FQrBuel4bNNtRhyx2ce8zDCoR7RK8pOcbcpzofBGGP47ZxhePmyw7DvUCfOeHol1u1r8nmdwPUTDFjMJlgSvbJMBKi/h/r4lyWRdunsUpw/cxDMJoa5I8U510ZO85ULsmyS1tPTySmxz7w7KI26X8wm5lOMTpcjepzz5QD8jt9Ko3LnAnhL2rQAwNuc827O+W4A5QBmMsYGAsjinP/Mxa7y1wAsVL3nVenx+wCOZZHoBickQZx72CCU//WUPlWajCZ5bpj8rfSZoxfFuVGXzB6CuVIRmJ6aUJylpG5+uK66R+8VOMeE4iz8ZeEEPHvRdGV7WnJiFPyYP7oAH153BNKTzVj04i/4aH2Vx/OCwA09p02P1KcPuoIS2ePnuddm/fOCCXj4rEkAgPljpEDPwMfKyIIMpFhNUtl8mqQn8740GLUzwMyYx3JSANDtNN4cvTkADnLOd0k/FwPYr3q+StpWLD323u7xHs65E0ALgH7+/hhj7GrG2BrG2Jr6+vo+Np0QEo77Tx+PeaPylR7gvYc6lJOfy8X9VoOMB3L6JtDzmxmBiwUuLjq8FFMG5eB/V87Cj3+c36t18LQysjATi687EtMG5+D372zE377YrhRpcQnc52JOEpv6e5joabkkco4eVeB3+9yR+WAMUe2oi3cWswmjCjOxZEM1raOn4n1Nb+6ya9QSbZlNzGfpmp8rD2nUmt7ra6B3PtyjeYD/70mgpVrkvRfsOc+NnL/IOZ/BOZ+Rn9+7Xn5CSM+MKMjAq1fMRHaqOKfjTx9sxt+/2A4AcERpjl4kqAvM2BwuDLnjM7z2856w3su5ZyB0VJhLSMSb3PQkvH7lLFwwazCe+6ECV722Bl+UHcBPFQ26Sd0kIvW/59TBuRq2hMSTQCP3/TKS8Z/LDsMFs0pj3KL4Ii8ObsSlJgLxTqpbWZ54wU0kmE0MTpdnOHLfki0atab3eh3oMcYsAM4C8I5qcxUA9crLJQBqpO0lfrZ7vEf6ndkIkCpKCNGOuiDKu2v2g3MuFmOJ03lr44uylMeH2sUeyX//WBnWe7mO5rBZzSY8tHAC7jl1LL7dXoff/W8tWm1O3Xw+IpKr3543Y5BhF48nvtKTzJhUko3Hzp3s89z80QUYoJqDbETD8tMBSAum05geAN/UzVlD87RpiMZMTBzRK8pOSejKzn25QzsOwHbOuTol82MAi6RKmkMhFl1ZzTmvBdDGGDtcmn93CYAlqvdcKj3+DYDveCKuSEiIzqVY3aeLpk4H/vXNLjhdQtymbqqL28hBzf7GLrR3B152QKaHBcXVGGO44sihHtu21vZu6QkSn6qa/FdYJcZmMZvw8Q1H4axpJaFfbEBmqYQ+QCN6Mu9r+s3HjdSoJdoymxh2N3TA5hRQlJOC1Xcdi5/vPEbrZvVYOMsrvAXgZwCjGWNVjLErpacWwTNtE5zzLQDeBbAVwBcAruecyzMXrwXwEsQCLRUAlkrbXwbQjzFWDuBWAHf06RMRQqLCe37a67/shVOIzjp6kXag1aY8nnD/lyFf7+Jcdxd9Kr6ibysrGgAAOemxX4+TkERlMYkFN2h4wc27kzMtyaJRS7RldwqoqO9AY4cd/dKTUZCVgoHZiTeyF/Jfj3N+foDtlwXY/hCAh/xsXwNggp/tNgDnhGoHIURb6hE9QJz35hR4XJe2P3/mYLy1ep9UVc2tprkLRTmp2HeoE3P/8T2umTsM81TVPVu7HEjt4dp9hGhp6c1z8dH6atwwf4TWTSEkYTS029FpdwUsJmFEeuvk7K3dhzqUx3VttiCvjG/GDNMJIT3mPaLXaRcH6+N5RO+eU8firdX7fLY/9vVOXD13GE54fDkA4IXllXhhuef8vXhdwD5SbjFoOo5eDe2fjluPH6V1MwhJKB+tF5feeX9tVULPw4okdepmv/QkDVuiLbtqzeDTJhVp2JK+oUCPEBKWJIsJ9502DltrW/H+WvfU3LEDs4K8S1spAUblTAxYs8e9kPgdJ4/B1EE5ys+MMUwqyfbzTn04d0YJbjmOggJCiLGdMbkIH28UawN2OxNvMexoUKdufvX7uRq2JD6kJZlx3mGDQr8wTlGgRwgJ2xVHDcWXWw54BHqnTBygYYuC81copjArGbUtNry7ZrOy7bwZg5BrgJ7L7FQrWrocuGT2EK2bQgghmttS06I8brM5NGxJ/FBfNvtl6DuzJZiTJwzA0rID+PXu45CenLjhUuK2nBCiCe/QyXvNnXg1c2geJhZnY+nmWvy4q0HZPmdkf0MEeQCw7t7j4RSEhFr4nRBCouWYMQWoqN8NgEb0ZHqqON0XTyyairtabQkd5AF9XzCdEGIwiRLYeXv3mtm497RxaLN5Lq/wxxPHaNSi2DObGAV5hBAiOWZModZNiDtyoJdsMXaIkGQxYVBemtbN6LPEDlMJITGXaGHeB9fOxqBc98na7vLstbVaEu0TEUIIiQSnQKN43uRAL0H7dIkXY4frhJAeG5iTonUTemR6aR4KstxtdgmeCyZZ43h5CEIIIdEza2g/HDe2QOtmxBWTdEmkFE59oDscQkiPjC/Kxqc3HoVzZ5Tg29vmad2cHnN6BXpJFOgRQoghJVlMeOnSw5CVYkGeQeZqh9I/IxljB2bh/tPHad0UEgGUukkI6bEJxdn4+28ma92MiEgy+DwEQggxum9vOxrJVroWAOKyREtvnqN1M0iEUKBHCDE0GtEjhBBjy8807jICRN/oDocQYmhWGtEjhBBCiA7RHQ4hxJDkRWEtfhZVJ4QQQghJdJS6SQgxpHevmY2G9m6kWGldOUIIIYToDwV6hBBDKs5NxYwheVo3gxBCCCEkKih1kxBiSLR+HiGEEEL0jO50CCGGRIEeIYQQQvSM7nQIIYZEyyoQQgghRM/oTocQYkhWM1XbJIQQQoh+UaBHCDEkC43oEUIIIUTH6E6HEEIIIYQQQnSGllcghBjKE4umoKy6RetmEEIIIYREFQV6hBBDWTClGAumFGvdDEIIIYSQqKLUTUIIIYQQQgjRGQr0CCGEEEIIIURnKNAjhBBCCCGEEJ2hQI8QQgghhBBCdIYCPUIIIYQQQgjRGQr0CCGEEEIIIURnKNAjhBBCCCGEEJ2hQI8QQgghhBBCdIYCPUIIIYQQQgjRGcY517oNvcIYqwewV+t2+NEfQIPWjSBxhY4JEgwdHyQUOkZIT9DxQoKh40N/Sjnn+f6eSNhAL14xxtZwzmdo3Q4SP+iYIMHQ8UFCoWOE9AQdLyQYOj6MhVI3CSGEEEIIIURnKNAjhBBCCCGEEJ2hQC/yXtS6ASTu0DFBgqHjg4RCxwjpCTpeSDB0fBgIzdEjhBBCCCGEEJ2hET1CCCGEEEII0RkK9AghhBBCCCFEZyjQI4QQQghJEIwxpnUbCCGJgQK9XqCTLFFjjOWpHtOxQXwwxo5mjPldzJQQAGCM3cYYO0F6TOcREkym/ICOFeIPHRdERoFeDzDGFjDGXgUwWeu2EO0xxk5ijC0H8C/G2D8BgFN1I6KiOkYuBNCtdXtI/GGMncAY+xLAnwBcAtB5hPjHGDueMbYCwKOMsT8CdKwQT3SfSrxZtG5AvGOMMc45Z4zNB/AgAAeA2YyxvZzzJo2bR2JM6iUzAbgSwBUAHgawHsBrjLGTOedLtWwf0Z50jDAA5wF4AcCVnPP3tG0ViSfSMWIFcB+AeRDPI0kADmOMWQE46QaeqDHGSgD8H4BHAPwA4G3GWD/O+Z/k+xQt20e0R/epxB8a0QvC6+S5G8CJAG4HMAvAJM0aRjQhHw+ccxeAFQCO4pwvAWADUAdgC2PMJL9Ww6YSjaiOEQFADYDXAJRLz53DGCuRbuTpGDEo1TFiB7CEcz6Hc/45gCYAizjnDrppJ4DPOWIMgM2c8084520AngHwe8bYSKkzms4nZDeAE0D3qUSFAr0AGGM3APiQMfZ7xtgAzvkeznkt5/w7AAcBzGOMFWvcTBIjXsfDQM75Vs65kzE2DcBiAEMgpl49Jr9Fm5YSraiOkVsZY/0hdgZsAvAcY2w7gHMBPAXgWfkt2rSUaMXPeeRXabuVc74MQCVj7GRtW0nigdexkgVgJ4CjGGOzpZcUANgC4B6t2ki0xRi7jjF2tvSYAdjPOT9A96lEjQI9PxhjZwK4FMCTEHtE7mGMTVG95A0AoyD2mKjfRzduOuTneLhbdTzIvfAzAfwRwGWMsRnSiA4xCK9jZCKAPwMYAeBTAN8DOJ9zfg7ElN+FjLHpdIwYS4DziDyPxikVddoLwKVRE0mc8HOs/A3iHN/HAVzDGFsJceTmLABTGGNDaBTYOBhjmYyx5yGmfr/KGLNI//7qkV26TyUAKNALZBaA5zjn30PMid8N4Cb5Sc75JgC/ApjAGDuGMfYnaTudaPXJ3/FwMwBwzndzzvdJjzsAvAsgS6N2Eu14HyN7ANzOOa8B8GfO+XoA4Jw3QhwBztCmmURDwc4jXDo2UgHMBwA5DZwYkr9j5c+c85cBXAXg95zzCwDsA7AaQKtWDSWxJ6XuLuOcD4DYmfiM9JQy3YjuU4mMLiQqqp6OSgAXAADnfC+AzwCkM8bOUL38LQC/BfAOgP5e7yc6EOJ4SPM6HsAYuwfAeABbY9lOop0gx8gnADIZY2dwzm2q198L8RjZHuu2Em308LryPwAzGWMpNOJrPEGOlY8B5DLGzpTmcK6WXvcggHQAbTFvLNGE6hj5WPr/LQDOl+ZquhhjFtVr6D6VUKDHGDPLj1U9He8D6GSMLZB+roVY5WocE2UAeALAZgCTOOe3e72fJKieHg/Se05mYsnrUQB+wzk/ELsWk1jr5TEyhzH2PcRj5GzO+cHYtZjEWm+uK9K2VABvg9I3DaOHx8po6T0jGWNLAEyAOLrniF2LSaz5O0Y45x2MMZN0v/EsgJek7U6pOE86xNRfuk81OEMGeoyx2YyxBwBAqqAob5cvtk0APgJwrVQhrQViqlWK9CWxAbiZc34q57w2xs0nEdaH4yFVen4bgN9xzi+h40GfInCM7AFwPef8YjpG9KkPx0iy6uZrCef833Tjrm99uQeRnj8A8XxyBnUa6VOwY8Q7rZtzfgeAodJ7Chljh0lTSW6i+1RiuECPMXYpgFchFlg5V9pmATx6OlIBfAmxF+1FxlgRgKkQ1yaRe0zqYt12Enl9PB7s0uv2cM7LYt12EhsROkb2c84ppVen+niMOOXfo76hI/oUoXuQNs55VazbTmIj1DHCORekzLJs1dv+BmAlgB8BpEmvpftUYrxAD0A1gGMAnATgUUAM3OShccbY/0HsSSsEcBvEErVvAmiGuFAp0Rc6HkgodIyQUOgYIeGiY4WEEuoYuR9ieu8E6eeTAdwIcXmn8VxcqoUQAGKFHq3bEFWMsYUQ58ls4px/Kn1RTJxzhzSv6nvO+b3SawsA/AvAvZzzCtXvSOOcd8a+9STS6HggodAxQkKhY4SEi44VEkpfjxHG2DgAbZzz/Zp8ABLXdBvoMcbyAfwbQB7E3rAHAFzDOf+IMZbEObczxsYD+AnAKO88d2mSK1U90wk6HkgodIyQUOgYIeGiY4WEEoFjxEzp3iQUPaduDgewknM+l3P+PMQUCLnqkF36gmwB8B6kdAhp+BvSa+gEqy90PJBQ6BghodAxQsJFxwoJpa/HCAV5JCRdBXqMsUsYY0czxtIArAXwmrTdDHFts83SzwyAXKL2twAuZYw1AZjsXc2IJC46HkgodIyQUOgYIeGiY4WEQscIiTWL1g3oK+nLMADisLcAoALAVRCXPzgoD20zxsYCyAWUylacMVYK4HGIVYqup8qJiY+OBxIKHSMkFDpGSLjoWCGh0DFCtJTQvQLSl4MDyARQzTk/FsB1ABoBvOj18hMgVimS86IBoAXAI5zzefTlSXx0PJBQ6BghodAxQsJFxwoJhY4RorWEHNFj4noiDwAwM8Y+B5AFwAUoJWhvAlDDGJvH3WVm2wHsZuIClGcxxk7hnO8DsFqDj0AiiI4HEgodIyQUOkZIuOhYIaHQMULiRcKN6DHG5kHMa84FUA7gQYiLiM5njM0ElCHvBwD8n/QeM4ArIPaUZAGYL315SIKj44GEQscICYWOERIuOlZIKHSMkHiSiCN6AoBHOeevAwBjbCqAoQDuA/AcgOnSRNWPIH6pSiF+zucBvMY5X6dNs0mU0PFAQqFjhIRCxwgJFx0rJBQ6RkjcSLgRPYi9JO9KvR8AsBLAYM75KxCHyG/kYlniEgAC53wv57yCc34LfXl0iY4HEgodIyQUOkZIuOhYIaHQMULiRsIFepzzTs55N3evH3I8gHrp8eUAxjLGPgXwFsQvm1zxiOgQHQ8kFDpGSCh0jJBw0bFCQqFjhMSTREzdBKDkM3MAhQA+lja3AbgLwAQAuznn1YCSC010jI4HEgodIyQUOkZIuOhYIaHQMULiQcKN6KkIAKwAGgBMknpH7oU4DL5C/vIQw6DjgYRCxwgJhY4REi46VkgodIwQzbFE7kRgjB0O4Cfpv/9yzl/WuElEQ3Q8kFDoGCGh0DFCwkXHCgmFjhGitUQP9EoAXAzgMc55t9btIdqi44GEQscICYWOERIuOlZIKHSMEK0ldKBHCCGEEEIIIcRXIs/RI4QQQgghhBDiBwV6hBBCCCGEEKIzFOgRQgghhBBCiM5QoEcIIYQQQgghOkOBHiGEEEIIIYToDAV6hBBCdIkx1h7L380Yy2GMXaf6uYgx9n602kAIIYQEQ8srEEII0SXGWDvnPCNWv5sxNgTAp5zzCdH4m4QQQkhP0IgeIYQQw2CMTWGM/cIY28QY+4gxlittH8EY+4YxtpExto4xNpwxlsEY+1b6eTNjbEGIX/8IgOGMsQ2MsX8wxoYwxsqk338ZY2wxY+wTxthuxtgNjLFbGWPrpfbkSa8bzhj7gjG2ljH2I2NsTHT3CCGEEL2iQI8QQoiRvAbgT5zzSQA2A7hf2v4GgGc455MBHAGgFoANwJmc82kA5gP4J2OMBfnddwCo4JxP4Zzf7uf5CQAuADATwEMAOjnnUwH8DOAS6TUvAriRcz4dwB8APNv7j0oIIcTILFo3gBBCCIkFxlg2gBzO+TJp06sA3mOMZQIo5px/BACcc5v0eiuAvzLG5gIQABQDKARwoJdN+J5z3gagjTHWAuATaftmAJMYYxkQg8z3VPFkci//FiGEEIOjQI8QQojRBRqluxBAPoDpnHMHY2wPgJQ+/J1u1WNB9bMA8XpsAtDMOZ/Sh79BCCGEAKDUTUIIIQbBOW8B0MQYmyNtuhjAMs55K4AqxthCAGCMJTPG0gBkA6iTgrz5AEpD/Ik2AJl9aF8rgN2MsXOkdjDG2OTe/j5CCCHGRoEeIYQQvUpjjFWp/rsVwKUA/sEY2wRgCoAHpNdeDOAmaftPAAZAnLc3gzG2BuLo3vZgf4xzfgjASsZYGWPsH71s84UArmSMbQSwBUCoAjCEEEKIX7S8AiGEEEIIIYToDI3oEUIIIYQQQojOUKBHCCGEEEIIITpDgR4hhBBCCCGE6AwFeoQQQgghhBCiMxToEUIIIYQQQojOUKBHCCGEEEIIITpDgR4hhBBCCCGE6Mz/A6by/TH6J29bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    file_data[ 0 ]\n",
    "    .set_index(\n",
    "        pd.to_datetime( file_data[ 0 ][ \"Local time\" ] )\n",
    "    )\n",
    "    .Close.plot( figsize = (15,5) )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "continuing-metropolitan",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Gen and use random reference data.\n",
    "# import random\n",
    "# random.seed( 1 )\n",
    "# def gen_test_random_walk( stdev, time_steps ):\n",
    "#     ts = np.zeros( time_steps )\n",
    "#     val = 0.0\n",
    "#     for ii in range( time_steps ):\n",
    "#         val += random.normalvariate( 0, stdev )\n",
    "#         ts[ ii ] = val\n",
    "#     return( ts )\n",
    "# file_data = []\n",
    "# for tt in range( 50 ):\n",
    "#     file_data.append( gen_test_random_walk( 100.5, 1000 ) )\n",
    "# plt.plot( file_data[ 0 ] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "optical-brown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def price_diff_histogram( data_price, freq_clipping = 1000 ):\n",
    "#     d_data_price = np.diff( data_price )\n",
    "    \n",
    "#     freq, bounds = np.histogram( d_data_price, bins = 128 )\n",
    "\n",
    "#     bound_labels = [str( round( 10000*tt ) ) for tt in bounds[ :-1 ][ freq > 1 ]]\n",
    "#     freq = np.clip( freq, 0, freq_clipping )[ freq > 1 ]\n",
    "\n",
    "#     plt.figure( figsize = (15,5) )\n",
    "#     plt.bar( bound_labels, freq )\n",
    "#     plt.xticks( rotation = 50 )\n",
    "#     plt.ylim( top = 400 )\n",
    "\n",
    "# price_diff_histogram( file_data[ 0 ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a08f871e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       17607.529\n",
       "1       17607.529\n",
       "2       17607.529\n",
       "3       17607.529\n",
       "4       17607.529\n",
       "          ...    \n",
       "2778    17761.729\n",
       "2779    17773.679\n",
       "2780    17786.729\n",
       "2781    17792.999\n",
       "2782    17785.479\n",
       "Name: Close, Length: 2783, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_price_data = [df.Close for df in file_data]\n",
    "close_price_data[ 0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "peripheral-wells",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Output result should not contain any row.\n",
    "\n",
    "probe = pd.DataFrame( [len( ss ) for ss in file_data] )\n",
    "probe[ probe[ 0 ] < np.mean( probe[ 0 ] ) ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "improving-velvet",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_train.shape: (2783, 91)\n",
      "data_train_split_in.shape: (1948, 91)\n",
      "data_eval_split_in.shape: (835, 91)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_train = np.asanyarray( close_price_data ).T\n",
    "print( \"data_train.shape:\", data_train.shape )\n",
    "\n",
    "TRAIN_SPLIT_PERCENTAGE = 0.7\n",
    "total_train_sample_points = int( TRAIN_SPLIT_PERCENTAGE*data_train.shape[ 0 ] )\n",
    "\n",
    "data_train_split_in = normalize_channels( data_train[ :total_train_sample_points, : ] )\n",
    "print( \"data_train_split_in.shape:\", data_train_split_in.shape )\n",
    "\n",
    "data_eval_split_in = normalize_channels( data_train[ total_train_sample_points:, : ] )\n",
    "print( \"data_eval_split_in.shape:\", data_eval_split_in.shape )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bridal-breed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sum_signal = np.sum( np.asanyarray( normalize_channels( file_data ) ), axis = 0 )\n",
    "plt.figure( figsize = (15,2) )\n",
    "plt.plot( data_train_split_in[ :, :1 ] )\n",
    "plt.show()\n",
    "\n",
    "plt.figure( figsize = (15,2) )\n",
    "plt.plot( data_eval_split_in[ :, :1 ] )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "square-recognition",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating multiscale signature matrices...\n",
      "\t current_scale: 2\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (1901, 91, 91, 1)\n",
      "\t current_scale: 4\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (1901, 91, 91, 1)\n",
      "\t current_scale: 8\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (1901, 91, 91, 1)\n",
      "\t current_scale: 24\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (1901, 91, 91, 1)\n",
      "\t current_scale: 48\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (1901, 91, 91, 1)\n",
      "\t (1901, 91, 91, 5)\n",
      "Generating multiscale signature matrices...\n",
      "\t current_scale: 2\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (788, 91, 91, 1)\n",
      "\t current_scale: 4\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (788, 91, 91, 1)\n",
      "\t current_scale: 8\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (788, 91, 91, 1)\n",
      "\t current_scale: 24\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (788, 91, 91, 1)\n",
      "\t current_scale: 48\n",
      "Pool initiated...\n",
      "Pool closed.\n",
      "\t\t (788, 91, 91, 1)\n",
      "\t (788, 91, 91, 5)\n"
     ]
    }
   ],
   "source": [
    "train_split_in = generate_multiscale_signature_matrix_series( \n",
    "    \"signature_matrix_func\", \n",
    "#     \"multi_ts_pearson_func\", \n",
    "    SCALE_SIZES, \n",
    "    data_train_split_in\n",
    ")\n",
    "##TODO: normalize each window instead. \n",
    "eval_split_in = generate_multiscale_signature_matrix_series( \n",
    "    \"signature_matrix_func\", \n",
    "#     \"multi_ts_pearson_func\", \n",
    "    SCALE_SIZES, \n",
    "    data_eval_split_in \n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "detailed-devon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_in.shape: (1898, 4, 91, 91, 5)\n",
      "train_out: (1898, 91, 91, 5)\n",
      "eval_in.shape: (785, 4, 91, 91, 5)\n",
      "eval_out: (785, 91, 91, 5)\n"
     ]
    }
   ],
   "source": [
    "# INPUT_OUTPUT_SAMPLE_GAP = STEP_LEN + max( SCALE_SIZES )\n",
    "INPUT_OUTPUT_SAMPLE_GAP = 0\n",
    "\n",
    "train_in = gen_rolling_window( train_split_in, STEP_LEN )\n",
    "train_out = train_in[ INPUT_OUTPUT_SAMPLE_GAP:, -1, ...]\n",
    "# train_out = np.pad( train_out, pad_width = [(0,INPUT_OUTPUT_SAMPLE_GAP), (0,0), (0,0), (0,0) ] )\n",
    "train_in = train_in[ :train_out.shape[ 0 ], ... ]\n",
    "print( \"train_in.shape:\", train_in.shape )\n",
    "print( \"train_out:\", train_out.shape )\n",
    "\n",
    "eval_in = gen_rolling_window( eval_split_in, STEP_LEN )\n",
    "eval_out = eval_in[ INPUT_OUTPUT_SAMPLE_GAP:, -1, ...]\n",
    "# eval_out = np.pad( eval_out, pad_width = [(0,INPUT_OUTPUT_SAMPLE_GAP), (0,0), (0,0), (0,0) ] )\n",
    "eval_in = eval_in[ :eval_out.shape[ 0 ], ... ]\n",
    "print( \"eval_in.shape:\", eval_in.shape )\n",
    "print( \"eval_out:\", eval_out.shape )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "starting-airline",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probe.shape: (91, 91)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# ## Load saved data. \n",
    "# with open( 'training.data', 'rb' ) as handle:\n",
    "#     [data_train_split_in, data_eval_split_in, \n",
    "#         train_in, train_out, \n",
    "#         eval_in, eval_out] = pickle.load( handle )\n",
    "\n",
    "probe = train_out[ 25, ..., 0 ]\n",
    "print( \"probe.shape:\", probe.shape )\n",
    "\n",
    "plt.figure( figsize = (10,10) )\n",
    "img_resi = plt.imshow( probe )\n",
    "plt.colorbar( img_resi )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d52cbfa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Dump to cache. \n",
    "# print( \"Dumping precomputed data...\")\n",
    "# with open( 'signature_matrix_training.data', 'wb' ) as handle:\n",
    "#     pickle.dump( [\n",
    "#         data_train_split_in, data_eval_split_in, \n",
    "#         train_in, train_out, \n",
    "#         eval_in, eval_out\n",
    "#     ], handle, protocol = pickle.HIGHEST_PROTOCOL )\n",
    "# print( \"Done\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "peripheral-niger",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "\n",
    "# fig, ax = plt.subplots( figsize=(18, 18) )\n",
    "# ax.grid()\n",
    "\n",
    "\n",
    "# ax.imshow( \n",
    "#     list( normalize_channels( np.diff( dataset ) )[ :, : ] ), \n",
    "#     aspect='auto', \n",
    "#     interpolation = \"Nearest\", \n",
    "#     cmap = \"jet\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "floppy-kinase",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "\n",
    "# ## (y > x) for the normal inner prod one. \n",
    "# pair_x_id = 20\n",
    "# pair_y_id = 30\n",
    "\n",
    "# plt.figure( figsize = (16, 8) )\n",
    "# plt.plot( signature_matrix[ :,pair_y_id, pair_x_id ], color = \"orange\" )\n",
    "# plt.plot( ts_data[ pair_x_id ], color = \"black\" )\n",
    "# plt.plot( ts_data[ pair_y_id ], color = \"gray\" )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "royal-latest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"input_8:0\", shape=(None, 4, 91, 91, 5), dtype=float32)\n",
      "Tensor(\"time_distributed_28/Reshape_1:0\", shape=(None, 4, 91, 91, 32), dtype=float32)\n",
      "Tensor(\"time_distributed_29/Reshape_1:0\", shape=(None, 4, 46, 46, 64), dtype=float32)\n",
      "Tensor(\"time_distributed_30/Reshape_1:0\", shape=(None, 4, 23, 23, 128), dtype=float32)\n",
      "Tensor(\"time_distributed_31/Reshape_1:0\", shape=(None, 4, 12, 12, 256), dtype=float32)\n",
      "Tensor(\"conv_lstm_0/strided_slice_26:0\", shape=(None, 91, 91, 32), dtype=float32)\n",
      "Tensor(\"conv_lstm_1/strided_slice_26:0\", shape=(None, 46, 46, 64), dtype=float32)\n",
      "Tensor(\"conv_lstm_2/strided_slice_26:0\", shape=(None, 23, 23, 128), dtype=float32)\n",
      "Tensor(\"conv_lstm_3/strided_slice_26:0\", shape=(None, 12, 12, 256), dtype=float32)\n",
      "Tensor(\"conv2d_transpose_28/Selu:0\", shape=(None, 24, 24, 128), dtype=float32)\n",
      "Tensor(\"conv2d_transpose_29/Selu:0\", shape=(None, 46, 46, 64), dtype=float32)\n",
      "Tensor(\"conv2d_transpose_30/Selu:0\", shape=(None, 92, 92, 32), dtype=float32)\n",
      "Tensor(\"conv2d_transpose_31/Selu:0\", shape=(None, 91, 91, 5), dtype=float32)\n",
      "Model: \"functional_15\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input (InputLayer)              [(None, 4, 91, 91, 5 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed_28 (TimeDistri (None, 4, 91, 91, 32 672         input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed_29 (TimeDistri (None, 4, 46, 46, 64 8256        time_distributed_28[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed_30 (TimeDistri (None, 4, 23, 23, 12 32896       time_distributed_29[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed_31 (TimeDistri (None, 4, 12, 12, 25 131328      time_distributed_30[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv_lstm_3 (ConvLSTM2D)        (None, 12, 12, 256)  2098176     time_distributed_31[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_28 (Conv2DTran (None, 24, 24, 128)  131200      conv_lstm_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv_lstm_2 (ConvLSTM2D)        (None, 23, 23, 128)  524800      time_distributed_30[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "slice_deconv_2 (Lambda)         (None, 23, 23, 128)  0           conv2d_transpose_28[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_21 (Concatenate)    (None, 23, 23, 256)  0           conv_lstm_2[0][0]                \n",
      "                                                                 slice_deconv_2[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv_lstm_1 (ConvLSTM2D)        (None, 46, 46, 64)   131328      time_distributed_29[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_29 (Conv2DTran (None, 46, 46, 64)   65600       concatenate_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_22 (Concatenate)    (None, 46, 46, 128)  0           conv_lstm_1[0][0]                \n",
      "                                                                 conv2d_transpose_29[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_30 (Conv2DTran (None, 92, 92, 32)   16416       concatenate_22[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv_lstm_0 (ConvLSTM2D)        (None, 91, 91, 32)   32896       time_distributed_28[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "slice_deconv_0 (Lambda)         (None, 91, 91, 32)   0           conv2d_transpose_30[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_23 (Concatenate)    (None, 91, 91, 64)   0           conv_lstm_0[0][0]                \n",
      "                                                                 slice_deconv_0[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_31 (Conv2DTran (None, 91, 91, 5)    1285        concatenate_23[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 3,174,853\n",
      "Trainable params: 3,174,853\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = mscred.build_mscred( input_tensor = train_in )\n",
    "\n",
    "def reduced_mean_loss( true_out, pred_out ):\n",
    "    mse = tf.math.reduce_mean( tf.math.abs( true_out - pred_out ), axis = [-1,-2,-3] )\n",
    "    return mse\n",
    "\n",
    "model.compile( \n",
    "    optimizer = 'adam', \n",
    "    loss = reduced_mean_loss\n",
    "#     loss = \"mae\"\n",
    ")\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "spread-pittsburgh",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model( model, show_shapes = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "meaningful-funeral",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.1490\n",
      "Epoch 00001: val_loss improved from inf to 0.07174, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 78s 164ms/step - loss: 0.1490 - val_loss: 0.0717\n",
      "Epoch 2/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0516\n",
      "Epoch 00002: val_loss improved from 0.07174 to 0.03819, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 78s 163ms/step - loss: 0.0516 - val_loss: 0.0382\n",
      "Epoch 3/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0314\n",
      "Epoch 00003: val_loss improved from 0.03819 to 0.02641, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0314 - val_loss: 0.0264\n",
      "Epoch 4/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0226\n",
      "Epoch 00004: val_loss improved from 0.02641 to 0.01948, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0226 - val_loss: 0.0195\n",
      "Epoch 5/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0186\n",
      "Epoch 00005: val_loss improved from 0.01948 to 0.01636, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 159ms/step - loss: 0.0186 - val_loss: 0.0164\n",
      "Epoch 6/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0161\n",
      "Epoch 00006: val_loss improved from 0.01636 to 0.01475, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0161 - val_loss: 0.0148\n",
      "Epoch 7/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0139\n",
      "Epoch 00007: val_loss improved from 0.01475 to 0.01286, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0139 - val_loss: 0.0129\n",
      "Epoch 8/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0133\n",
      "Epoch 00008: val_loss did not improve from 0.01286\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0133 - val_loss: 0.0134\n",
      "Epoch 9/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0122\n",
      "Epoch 00009: val_loss improved from 0.01286 to 0.01184, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 76s 159ms/step - loss: 0.0122 - val_loss: 0.0118\n",
      "Epoch 10/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0114\n",
      "Epoch 00010: val_loss improved from 0.01184 to 0.01108, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 159ms/step - loss: 0.0114 - val_loss: 0.0111\n",
      "Epoch 11/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0108\n",
      "Epoch 00011: val_loss improved from 0.01108 to 0.01069, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0108 - val_loss: 0.0107\n",
      "Epoch 12/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0109\n",
      "Epoch 00012: val_loss improved from 0.01069 to 0.00958, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 77s 163ms/step - loss: 0.0109 - val_loss: 0.0096\n",
      "Epoch 13/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0100\n",
      "Epoch 00013: val_loss improved from 0.00958 to 0.00920, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0100 - val_loss: 0.0092\n",
      "Epoch 14/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0100\n",
      "Epoch 00014: val_loss improved from 0.00920 to 0.00900, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 76s 161ms/step - loss: 0.0100 - val_loss: 0.0090\n",
      "Epoch 15/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0096\n",
      "Epoch 00015: val_loss did not improve from 0.00900\n",
      "475/475 [==============================] - 77s 161ms/step - loss: 0.0096 - val_loss: 0.0116\n",
      "Epoch 16/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0097\n",
      "Epoch 00016: val_loss improved from 0.00900 to 0.00839, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 76s 161ms/step - loss: 0.0097 - val_loss: 0.0084\n",
      "Epoch 17/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0093\n",
      "Epoch 00017: val_loss did not improve from 0.00839\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0093 - val_loss: 0.0084\n",
      "Epoch 18/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0091\n",
      "Epoch 00018: val_loss improved from 0.00839 to 0.00809, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0091 - val_loss: 0.0081\n",
      "Epoch 19/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0087\n",
      "Epoch 00019: val_loss improved from 0.00809 to 0.00809, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 159ms/step - loss: 0.0087 - val_loss: 0.0081\n",
      "Epoch 20/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0090\n",
      "Epoch 00020: val_loss did not improve from 0.00809\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0090 - val_loss: 0.0084\n",
      "Epoch 21/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0086\n",
      "Epoch 00021: val_loss improved from 0.00809 to 0.00795, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0086 - val_loss: 0.0079\n",
      "Epoch 22/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0085\n",
      "Epoch 00022: val_loss improved from 0.00795 to 0.00749, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 74s 157ms/step - loss: 0.0085 - val_loss: 0.0075\n",
      "Epoch 23/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0083\n",
      "Epoch 00023: val_loss improved from 0.00749 to 0.00744, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0083 - val_loss: 0.0074\n",
      "Epoch 24/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0082\n",
      "Epoch 00024: val_loss did not improve from 0.00744\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0082 - val_loss: 0.0085\n",
      "Epoch 25/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0081\n",
      "Epoch 00025: val_loss did not improve from 0.00744\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0081 - val_loss: 0.0089\n",
      "Epoch 26/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0083\n",
      "Epoch 00026: val_loss improved from 0.00744 to 0.00702, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0083 - val_loss: 0.0070\n",
      "Epoch 27/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0078\n",
      "Epoch 00027: val_loss did not improve from 0.00702\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0078 - val_loss: 0.0072\n",
      "Epoch 28/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0078\n",
      "Epoch 00028: val_loss improved from 0.00702 to 0.00672, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 76s 161ms/step - loss: 0.0078 - val_loss: 0.0067\n",
      "Epoch 29/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0078\n",
      "Epoch 00029: val_loss did not improve from 0.00672\n",
      "475/475 [==============================] - 76s 159ms/step - loss: 0.0078 - val_loss: 0.0096\n",
      "Epoch 30/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0078\n",
      "Epoch 00030: val_loss did not improve from 0.00672\n",
      "475/475 [==============================] - 76s 161ms/step - loss: 0.0078 - val_loss: 0.0068\n",
      "Epoch 31/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0074\n",
      "Epoch 00031: val_loss did not improve from 0.00672\n",
      "475/475 [==============================] - 76s 161ms/step - loss: 0.0074 - val_loss: 0.0068\n",
      "Epoch 32/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0073\n",
      "Epoch 00032: val_loss improved from 0.00672 to 0.00670, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0073 - val_loss: 0.0067\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0075\n",
      "Epoch 00033: val_loss improved from 0.00670 to 0.00665, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0075 - val_loss: 0.0067\n",
      "Epoch 34/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0074\n",
      "Epoch 00034: val_loss improved from 0.00665 to 0.00650, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0074 - val_loss: 0.0065\n",
      "Epoch 35/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0071\n",
      "Epoch 00035: val_loss did not improve from 0.00650\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0071 - val_loss: 0.0080\n",
      "Epoch 36/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0073\n",
      "Epoch 00036: val_loss improved from 0.00650 to 0.00636, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0073 - val_loss: 0.0064\n",
      "Epoch 37/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0069\n",
      "Epoch 00037: val_loss did not improve from 0.00636\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0069 - val_loss: 0.0081\n",
      "Epoch 38/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0072\n",
      "Epoch 00038: val_loss did not improve from 0.00636\n",
      "475/475 [==============================] - 75s 157ms/step - loss: 0.0072 - val_loss: 0.0076\n",
      "Epoch 39/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0071\n",
      "Epoch 00039: val_loss improved from 0.00636 to 0.00584, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0071 - val_loss: 0.0058\n",
      "Epoch 40/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0067\n",
      "Epoch 00040: val_loss did not improve from 0.00584\n",
      "475/475 [==============================] - 76s 159ms/step - loss: 0.0067 - val_loss: 0.0064\n",
      "Epoch 41/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0069\n",
      "Epoch 00041: val_loss did not improve from 0.00584\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0069 - val_loss: 0.0071\n",
      "Epoch 42/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0068\n",
      "Epoch 00042: val_loss did not improve from 0.00584\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0068 - val_loss: 0.0080\n",
      "Epoch 43/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0070\n",
      "Epoch 00043: val_loss did not improve from 0.00584\n",
      "475/475 [==============================] - 77s 161ms/step - loss: 0.0070 - val_loss: 0.0068\n",
      "Epoch 44/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0067\n",
      "Epoch 00044: val_loss did not improve from 0.00584\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0067 - val_loss: 0.0061\n",
      "Epoch 45/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0068\n",
      "Epoch 00045: val_loss improved from 0.00584 to 0.00578, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 77s 161ms/step - loss: 0.0068 - val_loss: 0.0058\n",
      "Epoch 46/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0064\n",
      "Epoch 00046: val_loss did not improve from 0.00578\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0064 - val_loss: 0.0062\n",
      "Epoch 47/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0065\n",
      "Epoch 00047: val_loss improved from 0.00578 to 0.00555, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0065 - val_loss: 0.0055\n",
      "Epoch 48/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0065\n",
      "Epoch 00048: val_loss did not improve from 0.00555\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0065 - val_loss: 0.0060\n",
      "Epoch 49/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0064\n",
      "Epoch 00049: val_loss improved from 0.00555 to 0.00551, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0064 - val_loss: 0.0055\n",
      "Epoch 50/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0064\n",
      "Epoch 00050: val_loss did not improve from 0.00551\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0064 - val_loss: 0.0069\n",
      "Epoch 51/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0064\n",
      "Epoch 00051: val_loss improved from 0.00551 to 0.00545, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 77s 161ms/step - loss: 0.0064 - val_loss: 0.0055\n",
      "Epoch 52/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0063\n",
      "Epoch 00052: val_loss did not improve from 0.00545\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0063 - val_loss: 0.0057\n",
      "Epoch 53/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0063\n",
      "Epoch 00053: val_loss did not improve from 0.00545\n",
      "475/475 [==============================] - 77s 161ms/step - loss: 0.0063 - val_loss: 0.0056\n",
      "Epoch 54/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0060\n",
      "Epoch 00054: val_loss did not improve from 0.00545\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0060 - val_loss: 0.0059\n",
      "Epoch 55/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0064\n",
      "Epoch 00055: val_loss did not improve from 0.00545\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0064 - val_loss: 0.0055\n",
      "Epoch 56/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0062\n",
      "Epoch 00056: val_loss improved from 0.00545 to 0.00529, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 75s 158ms/step - loss: 0.0062 - val_loss: 0.0053\n",
      "Epoch 57/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0061\n",
      "Epoch 00057: val_loss improved from 0.00529 to 0.00516, saving model to model_middle_poc_signature_matrix.h5\n",
      "475/475 [==============================] - 74s 156ms/step - loss: 0.0061 - val_loss: 0.0052\n",
      "Epoch 58/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0062\n",
      "Epoch 00058: val_loss did not improve from 0.00516\n",
      "475/475 [==============================] - 74s 157ms/step - loss: 0.0062 - val_loss: 0.0055\n",
      "Epoch 59/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0060\n",
      "Epoch 00059: val_loss did not improve from 0.00516\n",
      "475/475 [==============================] - 77s 161ms/step - loss: 0.0060 - val_loss: 0.0068\n",
      "Epoch 60/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0061\n",
      "Epoch 00060: val_loss did not improve from 0.00516\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0061 - val_loss: 0.0056\n",
      "Epoch 61/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0058\n",
      "Epoch 00061: val_loss did not improve from 0.00516\n",
      "475/475 [==============================] - 77s 161ms/step - loss: 0.0058 - val_loss: 0.0064\n",
      "Epoch 62/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0061\n",
      "Epoch 00062: val_loss did not improve from 0.00516\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0061 - val_loss: 0.0057\n",
      "Epoch 63/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0059\n",
      "Epoch 00063: val_loss did not improve from 0.00516\n",
      "475/475 [==============================] - 77s 162ms/step - loss: 0.0059 - val_loss: 0.0057\n",
      "Epoch 64/256\n",
      "475/475 [==============================] - ETA: 0s - loss: 0.0059\n",
      "Epoch 00064: val_loss did not improve from 0.00516\n",
      "475/475 [==============================] - 77s 163ms/step - loss: 0.0059 - val_loss: 0.0053\n",
      "Epoch 65/256\n",
      "358/475 [=====================>........] - ETA: 17s - loss: 0.0058\n",
      "Break!\n"
     ]
    }
   ],
   "source": [
    "checkpoint_model = keras.callbacks.ModelCheckpoint(\n",
    "    \"model_middle_poc_signature_matrix.h5\", \n",
    "    verbose = True, \n",
    "    save_best_only = True, \n",
    "    mode = 'auto', \n",
    "    save_freq = \"epoch\" \n",
    ")\n",
    "\n",
    "try:\n",
    "    model.fit( \n",
    "        train_in, train_out, \n",
    "        epochs = 256, \n",
    "        batch_size = 4, \n",
    "        validation_data = (eval_in, eval_out), \n",
    "#         shuffle = True\n",
    "#         validation_split  = 0.2,\n",
    "        verbose = 1, \n",
    "        callbacks = [checkpoint_model] \n",
    "    )\n",
    "    winsound.Beep( 880, 2000 )\n",
    "except KeyboardInterrupt:\n",
    "    print( \"\\nBreak!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "affiliated-multiple",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Load saved data. \n",
    "# with open( 'signature_matrix_training.data', 'rb' ) as handle:\n",
    "#     [data_train_split_in, data_eval_split_in, \n",
    "#         train_in, train_out, \n",
    "#         eval_in, eval_out] = pickle.load( handle )\n",
    "\n",
    "# model = keras.models.load_model( \"model_middle_poc_signature_matrix.h5\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "aquatic-respondent",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1/197 [..............................] - ETA: 0sWARNING:tensorflow:Callbacks method `on_predict_batch_end` is slow compared to the batch time (batch time: 0.0110s vs `on_predict_batch_end` time: 0.0180s). Check your callbacks.\n",
      "197/197 [==============================] - 5s 25ms/step\n"
     ]
    }
   ],
   "source": [
    "pred_out = model.predict( \n",
    "    eval_in, \n",
    "    batch_size = 4, \n",
    "    verbose = 1 \n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "handled-defensive",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(785, 91, 91)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TensorShape([785])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matrix_resi = np.abs( np.diff( eval_out, append = 0 ) )\n",
    "\n",
    "# matrix_resi = train_in[ :, 0, ..., 0 ]\n",
    "\n",
    "matrix_resi = np.abs( pred_out[ :, ] - eval_out[ :, : ] )\n",
    "matrix_resi = np.average( matrix_resi, axis = -1 )\n",
    "\n",
    "\n",
    "print( matrix_resi.shape )\n",
    "\n",
    "scalar_resi = reduced_mean_loss( pred_out, eval_out )\n",
    "scalar_resi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "established-baltimore",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3b416ee21714fb6897520aa14968664",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1471fb294ae24cea9617d709610b3bdd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TREND_LINE_DATA_ID = 34\n",
    "ZOOM_RANGE = int( 1/4*SCALE_SIZES[ -1 ] )\n",
    "time_series = data_eval_split_in[ INPUT_OUTPUT_SAMPLE_GAP:, TREND_LINE_DATA_ID ]\n",
    "# time_series = scalar_resi[ STEP_LEN + 1: ]\n",
    "\n",
    "\n",
    "CROP_PIXELS = 1\n",
    "matrix_series = matrix_resi[ :, CROP_PIXELS:-CROP_PIXELS, CROP_PIXELS:-CROP_PIXELS ] \n",
    "\n",
    "# matrix_series = matrix_resi \n",
    "\n",
    "# matrix_series = np.average( pred_out, axis = -1 ) \n",
    "\n",
    "# matrix_series = eval_in[ :, 0,..., 0 ] - eval_out[ ..., 0 ]\n",
    "\n",
    "\n",
    "animate_matrix_series( \n",
    "    matrix_series = matrix_series, \n",
    "    time_series = time_series, \n",
    "    time_interval = 100*4, \n",
    "    zoom_range = ZOOM_RANGE, \n",
    "    fig_size = (7,7), \n",
    "    ts_tick_freq = 2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "prospective-pathology",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "TOTAL_TAIL_CUT = 0\n",
    "STARTING_DAY = 8\n",
    "TOTAL_DAYS = 2\n",
    "TICK_FREQ = 1\n",
    "\n",
    "plt.figure( figsize = (15, 8) )\n",
    "raw_signal = data_eval_split_in[ \n",
    "    (INPUT_OUTPUT_SAMPLE_GAP + STARTING_DAY*24):(INPUT_OUTPUT_SAMPLE_GAP + (STARTING_DAY + TOTAL_DAYS)*24 - TOTAL_TAIL_CUT), \n",
    "    TREND_LINE_DATA_ID \n",
    "]\n",
    "plt.plot( \n",
    "    raw_signal, \n",
    "#     savitzky_golay( raw_signal, 7, 3 ), \n",
    "    linewidth = 0.5 \n",
    ")\n",
    "\n",
    "residual_signal = scalar_resi[ STARTING_DAY*24:(STARTING_DAY + TOTAL_DAYS)*24 - TOTAL_TAIL_CUT ]\n",
    "plt.plot( \n",
    "#     savitzky_golay( normalize_channels( residual_signal[...,np.newaxis] )[ :,0 ], 5, 3 ), \n",
    "#     savitzky_golay( residual_signal, 5, 3 )/8.0 - 0.8, \n",
    "    normalize_channels( residual_signal[...,np.newaxis] )[ :,0 ]/1.0 + 0.0, \n",
    "    linewidth = 0.5, color = \"red\", alpha = 0.5\n",
    ")\n",
    "plt.xticks( np.arange( 0, len( residual_signal ) + 1, TICK_FREQ ) )\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "manual-phase",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>60</th>\n",
       "      <td>042700_USDPLN_Candlestick_1_Hour_BID_01.01.202...</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>042800_USDRON_Candlestick_1_Hour_BID_01.01.202...</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>042900_USDSEK_Candlestick_1_Hour_BID_01.01.202...</td>\n",
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       "    <tr>\n",
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       "      <td>043200_USDMXN_Candlestick_1_Hour_BID_01.01.202...</td>\n",
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       "      <th>66</th>\n",
       "      <td>043300_USDILS_Candlestick_1_Hour_BID_01.01.202...</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>043400_USDSGD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>043500_USDTRY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>043600_USDZAR_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>043700_GBPAUD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>043800_GBPCAD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>043900_GBPCHF_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>044000_GBPNZD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>044100_GBPUSD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>044200_AUDNZD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>044300_AUDSGD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>044400_AUDUSD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>044500_CADCHF_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>044600_CADHKD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>044700_GBPJPY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>044800_CADJPY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>044900_CHFJPY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>045000_TRYJPY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>045100_SGDJPY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>045200_NZDJPY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>045300_HKDJPY_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>045400_CHFSGD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>045500_NZDCAD_Candlestick_1_Hour_BID_01.01.202...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>045600_NZDCHF_Candlestick_1_Hour_BID_01.01.202...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    0\n",
       "60  042700_USDPLN_Candlestick_1_Hour_BID_01.01.202...\n",
       "61  042800_USDRON_Candlestick_1_Hour_BID_01.01.202...\n",
       "62  042900_USDSEK_Candlestick_1_Hour_BID_01.01.202...\n",
       "63  043000_USDRUB_Candlestick_1_Hour_BID_01.01.202...\n",
       "64  043100_USDHUF_Candlestick_1_Hour_BID_01.01.202...\n",
       "65  043200_USDMXN_Candlestick_1_Hour_BID_01.01.202...\n",
       "66  043300_USDILS_Candlestick_1_Hour_BID_01.01.202...\n",
       "67  043400_USDSGD_Candlestick_1_Hour_BID_01.01.202...\n",
       "68  043500_USDTRY_Candlestick_1_Hour_BID_01.01.202...\n",
       "69  043600_USDZAR_Candlestick_1_Hour_BID_01.01.202...\n",
       "70  043700_GBPAUD_Candlestick_1_Hour_BID_01.01.202...\n",
       "71  043800_GBPCAD_Candlestick_1_Hour_BID_01.01.202...\n",
       "72  043900_GBPCHF_Candlestick_1_Hour_BID_01.01.202...\n",
       "73  044000_GBPNZD_Candlestick_1_Hour_BID_01.01.202...\n",
       "74  044100_GBPUSD_Candlestick_1_Hour_BID_01.01.202...\n",
       "75  044200_AUDNZD_Candlestick_1_Hour_BID_01.01.202...\n",
       "76  044300_AUDSGD_Candlestick_1_Hour_BID_01.01.202...\n",
       "77  044400_AUDUSD_Candlestick_1_Hour_BID_01.01.202...\n",
       "78  044500_CADCHF_Candlestick_1_Hour_BID_01.01.202...\n",
       "79  044600_CADHKD_Candlestick_1_Hour_BID_01.01.202...\n",
       "80  044700_GBPJPY_Candlestick_1_Hour_BID_01.01.202...\n",
       "81  044800_CADJPY_Candlestick_1_Hour_BID_01.01.202...\n",
       "82  044900_CHFJPY_Candlestick_1_Hour_BID_01.01.202...\n",
       "83  045000_TRYJPY_Candlestick_1_Hour_BID_01.01.202...\n",
       "84  045100_SGDJPY_Candlestick_1_Hour_BID_01.01.202...\n",
       "85  045200_NZDJPY_Candlestick_1_Hour_BID_01.01.202...\n",
       "86  045300_HKDJPY_Candlestick_1_Hour_BID_01.01.202...\n",
       "87  045400_CHFSGD_Candlestick_1_Hour_BID_01.01.202...\n",
       "88  045500_NZDCAD_Candlestick_1_Hour_BID_01.01.202...\n",
       "89  045600_NZDCHF_Candlestick_1_Hour_BID_01.01.202..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probe = pd.DataFrame( list_files( DATA_DIR ) )\n",
    "probe[ 60:90 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "collective-domestic",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure( figsize = (15,8))\n",
    "\n",
    "# plt.plot( data_raw[ 34, (train_split_in.shape[ 0 ] + 1): ] )"
   ]
  }
 ],
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