{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7b4324e",
   "metadata": {},
   "source": [
    "# Time Series Clustering using DTW KMeans\n",
    "\n",
    "In this notebook, we will train a KMeans Clustering algorithm based on DTW distances between Time Series data. \n",
    "\n",
    "We leverage the [tslearn library](https://tslearn.readthedocs.io/en/stable/index.html) for clustering. The data used in this analysis is publicly available via UCI Archive under [Online Retail II Data Set](https://archive.ics.uci.edu/ml/datasets/Online+Retail+II).\n",
    "\n",
    "We have cleaned and preprocessed this dataset in the optional notebook: 01. Optional - Data Cleaning and Preparation. The reader may directly use the preprocessed data included in the repository under: `./data/df_pivoted.zip` for running this notebook.\n",
    "\n",
    "Tested with Python3, Pandas version 1.0.5.\n",
    "\n",
    "*References*\n",
    " * Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    " * Direct link to UCI dataset: https://archive.ics.uci.edu/ml/machine-learning-databases/00502/online_retail_II.xlsx\n",
    " * tslearn github repo: https://github.com/tslearn-team/tslearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fcbf3263",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a6a7fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# optional - suppress warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7721c77",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a25451d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivoted = pd.read_csv('./data/df_pivoted.zip', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48ae4f68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4605, 374) Index(['2009-12-01 00:00:00', '2009-12-02 00:00:00', '2009-12-03 00:00:00',\n",
      "       '2009-12-04 00:00:00', '2009-12-05 00:00:00', '2009-12-06 00:00:00',\n",
      "       '2009-12-07 00:00:00', '2009-12-08 00:00:00', '2009-12-09 00:00:00',\n",
      "       '2009-12-10 00:00:00',\n",
      "       ...\n",
      "       '2010-11-30 00:00:00', '2010-12-01 00:00:00', '2010-12-02 00:00:00',\n",
      "       '2010-12-03 00:00:00', '2010-12-04 00:00:00', '2010-12-05 00:00:00',\n",
      "       '2010-12-06 00:00:00', '2010-12-07 00:00:00', '2010-12-08 00:00:00',\n",
      "       '2010-12-09 00:00:00'],\n",
      "      dtype='object', length=374)\n"
     ]
    },
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       "           2009-12-01 00:00:00  2009-12-02 00:00:00  2009-12-03 00:00:00  \\\n",
       "StockCode                                                                  \n",
       "10002                     12.0                  0.0                  7.0   \n",
       "10080                      0.0                  1.0                  0.0   \n",
       "10109                      0.0                  0.0                  4.0   \n",
       "10120                     60.0                 10.0                  0.0   \n",
       "10125                      5.0                  0.0                  0.0   \n",
       "\n",
       "           2009-12-04 00:00:00  2009-12-05 00:00:00  2009-12-06 00:00:00  \\\n",
       "StockCode                                                                  \n",
       "10002                     73.0                  0.0                 49.0   \n",
       "10080                      3.0                  0.0                  0.0   \n",
       "10109                      0.0                  0.0                  0.0   \n",
       "10120                     30.0                  0.0                  0.0   \n",
       "10125                     46.0                  0.0                  8.0   \n",
       "\n",
       "           2009-12-07 00:00:00  2009-12-08 00:00:00  2009-12-09 00:00:00  \\\n",
       "StockCode                                                                  \n",
       "10002                      2.0                 12.0                  0.0   \n",
       "10080                      0.0                  0.0                  0.0   \n",
       "10109                      0.0                  0.0                  0.0   \n",
       "10120                      0.0                  1.0                  1.0   \n",
       "10125                     20.0                  1.0                 22.0   \n",
       "\n",
       "           2009-12-10 00:00:00  ...  2010-11-30 00:00:00  2010-12-01 00:00:00  \\\n",
       "StockCode                       ...                                             \n",
       "10002                      1.0  ...                 12.0                 60.0   \n",
       "10080                      0.0  ...                  0.0                  0.0   \n",
       "10109                      0.0  ...                  0.0                  0.0   \n",
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       "\n",
       "           2010-12-02 00:00:00  2010-12-03 00:00:00  2010-12-04 00:00:00  \\\n",
       "StockCode                                                                  \n",
       "10002                      1.0                  8.0                  0.0   \n",
       "10080                      0.0                  0.0                  0.0   \n",
       "10109                      0.0                  0.0                  0.0   \n",
       "10120                      0.0                  3.0                  0.0   \n",
       "10125                      0.0                  0.0                  0.0   \n",
       "\n",
       "           2010-12-05 00:00:00  2010-12-06 00:00:00  2010-12-07 00:00:00  \\\n",
       "StockCode                                                                  \n",
       "10002                      1.0                 25.0                  8.0   \n",
       "10080                      0.0                  0.0                  0.0   \n",
       "10109                      0.0                  0.0                  0.0   \n",
       "10120                      0.0                  0.0                  0.0   \n",
       "10125                      0.0                  3.0                  0.0   \n",
       "\n",
       "           2010-12-08 00:00:00  2010-12-09 00:00:00  \n",
       "StockCode                                            \n",
       "10002                     13.0                 44.0  \n",
       "10080                      0.0                  0.0  \n",
       "10109                      0.0                  0.0  \n",
       "10120                     12.0                  0.0  \n",
       "10125                     40.0                  0.0  \n",
       "\n",
       "[5 rows x 374 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare data to laod to tslearn time_series_dataset object\n",
    "df_pivoted.set_index('StockCode', inplace=True)\n",
    "\n",
    "print(df_pivoted.shape, df_pivoted.columns)\n",
    "\n",
    "df_pivoted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e522195a",
   "metadata": {},
   "source": [
    "## DTW KMeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d96ebce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Requirement already satisfied: tslearn in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (0.5.2)\n",
      "Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from tslearn) (1.5.3)\n",
      "Requirement already satisfied: joblib in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from tslearn) (1.1.0)\n",
      "Requirement already satisfied: numba in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from tslearn) (0.54.1)\n",
      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from tslearn) (1.20.3)\n",
      "Requirement already satisfied: Cython in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from tslearn) (0.29.24)\n",
      "Requirement already satisfied: scikit-learn in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from tslearn) (1.0.1)\n",
      "Requirement already satisfied: setuptools in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from numba->tslearn) (59.4.0)\n",
      "Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from numba->tslearn) (0.37.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from scikit-learn->tslearn) (3.0.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/python3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install tslearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04aaa397",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tslearn.utils import to_time_series_dataset\n",
    "from tslearn.preprocessing import TimeSeriesScalerMeanVariance\n",
    "from tslearn.clustering import TimeSeriesKMeans, silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ce08d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4605, 374, 1) (4605, 374, 1)\n",
      "CPU times: user 334 ms, sys: 34.4 ms, total: 368 ms\n",
      "Wall time: 355 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# convert dataframe to time_series_dataset\n",
    "X = to_time_series_dataset(df_pivoted.values)\n",
    "\n",
    "# normalize time series to zero mean and unit variance\n",
    "X_train = TimeSeriesScalerMeanVariance().fit_transform(X)\n",
    "\n",
    "print(X.shape, X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "362fc965",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create required directory structure\n",
    "dir_paths = ['./tsl', './tsl/models', './tsl/plots']\n",
    "\n",
    "for dir_path in dir_paths:\n",
    "    if not os.path.exists(dir_path):\n",
    "        os.makedirs(dir_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee9e7783",
   "metadata": {},
   "source": [
    "### Perform clustering\n",
    "\n",
    "With 12 cores, the clustering operation can take roughly half an hour. Your mileage may vary depending on your machine's configuration. `n_jobs = -1` ensures that the training uses all available cores on your machine.\n",
    "\n",
    "Other alternative to the distance metric is \"Soft-DTW\" which may produce higher separation at a higher compute cost. Please see the `tslearn` documentation [link](https://tslearn.readthedocs.io/en/stable/auto_examples/clustering/plot_kmeans.html#sphx-glr-auto-examples-clustering-plot-kmeans-py) for more details.\n",
    "\n",
    "Finding the optimal number of clusters to use - Adding more clusters decreases the inertia value but the information contained in each cluster also decreases. Hence we want to have a small cluster size with a relatively small inertia value. As with finding the optimal number of clusters, the elbow heuristic works well here. Also, clustering techniques are not advised for datasets with fewer than a thousand time series since this could have limiting effect on deep learning models.\n",
    "\n",
    "The next cell execution could take around 140 minutes with 16 vCPU (ml.c5.4xlarge). If you want you could skip it and move to a next. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67167ab6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2h 18min 17s, sys: 41min 20s, total: 2h 59min 37s\n",
      "Wall time: 1h 24min 44s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WCSSS')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# algorithm configuration\n",
    "algo = \"DTW_kmeans\"\n",
    "metric = \"dtw\"\n",
    "\n",
    "wcss = []\n",
    "\n",
    "for i in range(1,5):\n",
    "    model= TimeSeriesKMeans(n_clusters=i,metric=metric,n_jobs=-1,random_state=0)\n",
    "    model.fit_predict(X_train)\n",
    "    wcss.append(model.inertia_)\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.grid()\n",
    "plt.plot(range(1,5),wcss,marker='o',linestyle='--')\n",
    "plt.xlabel('number of clusters')\n",
    "plt.ylabel('WCSSS');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9aadd724",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 45min 55s, sys: 13min 14s, total: 59min 10s\n",
      "Wall time: 30min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# algorithm configuration\n",
    "algo = \"DTW_kmeans\"\n",
    "metric = \"dtw\"\n",
    "\n",
    "# cluster configuration\n",
    "N_CLUSTERS = 2\n",
    "\n",
    "model= TimeSeriesKMeans(n_clusters=N_CLUSTERS,\n",
    "                        metric=metric,\n",
    "                        n_jobs=-1,\n",
    "                        random_state=0)\n",
    "\n",
    "y_pred = model.fit_predict(X_train)\n",
    "\n",
    "model.to_pickle(f\"./tsl/models/{algo}.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d6e86f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# backup clustering results\n",
    "np.save(f\"./data/tls_{algo}_cluster_labels\", y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9287ee9b",
   "metadata": {},
   "source": [
    "Let us plot the different clusters to visually inspect the homogeneity of the cluster composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64bd629f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y/zY1/Lr9CSP6h6+tr7L3ZltjJ1GFsU4Jkui2/Vnwb8LhMHmMjGQorOPPvioT4FDhZfL7rMbvAQqbJlE9hrn0d99xOEy+hal3P2n6QL+M9pB49rYFkE9IdhWcXWjOj3No/cqIA8bBMfkuA5v3INRsyHOk+8r2kHjFLsJLfVFfh9SmI/YbB8PkxY/Wkcnb3x2qPMmAsf3uQlbKjGWc9rbgepXcMeN6/Ig2uEpjugsOgsnbJoK6OHkzjV23TlwKL9z0AKgmrXxbg8iKsTWKzMrpsYyqYwSuwjwZQ/VG3E84GCZfN2UtZtj+sbMy7r63aQhX3fs+N8GuNYf7lc25C/S76INd9/sIQVM/HEo3HQSTN7gabG0677vde7Tqjbo4dCunfNb5LrGNDVMmaPehvoZejhrukZ6+sc+0tcW6fbNRiDHvR8x+XzgIJm9HDbdl8n3CexGF/at13eRpd41t+ayHOirXzbPLCZ0GiY1OC+91ZS57JLtGOFi3X5iBK7ytoICDYPIuXAlcZ5L7GPM6uAqLepswoH3SvNugbYSV7QaWz90X7UeMuCrohckT0bcT0fuJ6HHn2cNE9FNE9Bv696E+yqpC7mtHZ657KHO7HHvdpl5lJp6kRVeTfO+HvhT9MNK2gscn5O83Rn7VhPQ+4FDcNIa+NPnvAPCq0rOvAvDTzPwSAD+t3weDO5bbjuv4wLS3uuii+wHtNPht0HEfd0ILjO2zXfTC5Jn5zQCeKj1+NYDv1M/fCeCz+yirlg6018ozl85QxAyc9y6RRSZ1qOBQbTHkZrKhMTTZ+9gqfSx2Z/nswQmrfWDoOgzpk/8gZv49ANC/z/YlIqLXEtFjRPTY7du3Ny606fyaNscf9IldDsJy0bJesT/09IF1oyCuPmu42ti0/RndlLr7GTtfeGXm1zPzLWa+9eijj66ZR75wlqJ+B2mSAknFQU2dd8y2SJxy/Yl2Q6J8vG+TADwU7HsVx81YVwOH0kNDMvn3EdFzAUD/vn+ogogAUDufNDthfMxA7DD8fdIMNt19O6I7Nt7x7HtWsSt4yK5tyns+96+kb+N+3qswog9t2g3J5H8IwBfo5y8A8IMDllU7epLUH1qXM3veMMxw/Xer0Oa+WvNHt9UK+1qYbcMIV2PXNy+3K1rR6SQ5hDuC05qx3Oak4Ste/Y1wqHXvK4TyewD8fwA+koieIKIvBvD3APwJIvoNAH9Cv28F5c5iVPttr3rHMszNtFqTsr6WH+3QvtblpBKm2Y3Gptj1LkKqLdos8LEy9auguXUJKLgC1VnBvrqvrvLCvmHSRybM/LkVP31qH/k3gQhAy8Ftk8BvWgNRCsyYQT1dnc4AqOb7NuFu12ZmBCEVfluHMN6wrdJMy2cEA1xX32ZM1FX9ak/vzWAbxoboFxc2BjYdS13KYwBhqSxfXycMBLS7OdsHdr7w2ifKsfI+CVzlugE2D6tcx6e/yUKv1aWOHhetXEArZVbT2Pb8Hebhr55bJ5yusCjd8p0+tLpNcmhzP29fiqdZwIOfa7QDTblp3uz64vk+cRBMnogQJ8D5Mje/o1T+yaSQTo0TYKmOyTKjZ8+zPmCRFH6h4r/Awt5ZuU2Icy3EaO6CdcJHfefdN2WRlgoZ+rAnW0zvWsQ6l4tXHbHcBRsxefu3yQIxrx6zXcY6Vz6uRwuv1aabLJJXKy05czd+4Uu/boz+Oucq9YGDYPLMjFgnbBJXJcoFQJLkDN12vVrb990F60RSmOYrwon1mT+TNtFEvmdxhR+/Mp/s3fp34pSxXFML6tP/uU4u6xZ9VV22PqtNImzSvXFT+d2qPIjiYKHYrrLnjY5CrhR2Wt/Cbk5iPRAmL1p71cj0PU7TXEPdlwHtwhYF6waR1bvrwGHkWn2Xuvd1dV4VM9/V8a5DhzR2xbqhjL24knbYFtsutqo8a4OmdhjC8h8CB8PkM6ZVkMJAlDDiNP9uJtkQ59Zsqona5C6fkGi/rZycydWMsSo+2/6aVrENuKSkzIjSfu/WLefUxtXgbbN1y+fuG+lcK81H20YunSbrrsX7Mg7t+xbuG2iRPTMjSvq7wrK2LLQLp70CPP4wmDzp6rcxLldDj50Jw1AG726A6rGXfAy3LnuvK6UmbXH3Khd+a5O363PcBF3n2C60nSjZTIh16Tcz332o2t2678zBrdPQFpbN2TbCaZkgU9r6xlXQytdBLyGU+4JyH62YU1Rk+OVz58ufu6KvidCW+Wd+wewvI0BRC6llVi1oWXc+pWkxRHMo2FHIQY26Uq4nY5iQOLecLDTUxuAWY/ASLt57vMvwv03CInMB2W8NKhWpigQpM4IrLAAORpN3IyVMC6ncANWTL22FebTRRrxulPU0Pa9Qa/Fem3yHigLoI5SSKz4X0rQgvystFlVR1TZtLao+UF1vXiuKqpjH+u+WsUk00qb16APrrF3tGw6CyZuPOUnrJ67rO016OrOmKVbdRb7xx0eb435ZkxbJZ/N0efRCu8wafbxtn23B97sp1hG+K7/vdxV7Q+cQw57SlcvdRMgcAg7LXcNAVNMxqTL3TTfVsS4eJqlfQFQNDp/lufFAYiDtWJ+ie2qVKNZEru+zTnN0G9QnZLOFcWcBnMg3GaWcroMyZYCcanTS/hhgGnZCm8AMlcBdRWWYMtSYbnBKVstq45Wpa7Mk5WwYpiy7VOUzg0DZ92J+xbHbtU/W7cNyVS2bodxqB6HJWz8lDCxtYZVLAwjFhddNBrJtlNh0oiZp8wp+7c48rSMzsIyxEkWUJqtMl7n9IpflXUdb2wsgzMoagrkV7vWtSVf327omeeNuX6wy9bbMdh2Ux7yLrsIlE8obU9VQDufu1jhdb0fvpjT6zmRyv7pW5lXT8A+EyQuXZwijczsrTXMm4A7acjhhF7QxF+OUkabcyZ0DWBhlTpvFwDcxKJeBmjArD9JNsVEW3BzZ0/fc6Uxvy/TFkND1oz3Wac9WmnhLQb4v8EUrraRB7kYsz/G6vPLnLRWRimR1Ctm+uxgPgskD8No67GjcPmbuCoDstzU0nZVyPXS0Rc7Yq3/3lRNzUZPfNiyO3zTX1d+r31kHUZJW7BvovmFr04gqb+gs+y9V736+Tv9nvEct07lKQ99o4yd3FR6gKLjKr7sL4j6ffNs2LAdPtGmDRcKIhz6caQMcBJM3TT5N8yMLgOqzSTZdMTd54narRTVswjDs3bZmtTvg4wTghnFWjkAyRAljmTDSmoG6om3pBHRptjZdTVvfD53PLUlZz9RxmKjRkda3Q5dNULKRzi2DC3nU7VXwWZVN5VUhYUbsSK7q6B6uzH+dIyx86CPqyj11sjZdWlGXimdVQt5XTJLWW5ZdXVWbyuGkhZtqXRwEkzdYvLgxD2NCZe03YSCK12fIxuDkc56PHeblSn/7t5KH87y8+3HFuqiAq0H6tMlV7dJXl3yCtJ1Q9l6ZRneXZFO55XwKWm/HjjHroUqrNrhuuzZ1ZV611iLdjBMlae3EzvqwIk2XGrpuhDxfj7ZaVRYzzi+5MC5TzgXYikVUQUdb5twGTVks47STyylO83Zad8OfFeUqJeU+Xuc+hSz/ynE57KU6B8XkmYXRu8x9Hsmi5AqjL+187VQO8gFhm5GqkDOe6l4sCyKf6d9IU4uBzSrckhq3js8FspLGmzcXmKgPZZ+nMWcXixiYx74jHKrboMuCri3ySZ5u/qt5+KrB+jyuOZagnJ496esF32ofNDGB2vxq0lSFy3bh40na8zEVNW4/SbCa3h6XlZV1XE517eV73ocFP6SL9WCYfCFqhvPOTpWxrTBmp2FTRwNo3dg16SySJNFD05o0zKYii4O2OKDN7MwYN2pcI9pGxohdjYchW8armHDdZMlosXaF53hip0/q6tnGiim4yeD0cS3jrMkQ9Yw0v2YxL7SLUOnirq3SyJsYTxs6KjX90mfrv3I7V6ElL65FWQnrWtZKHl2twZbp3fmwjlWzi0vcD4bJM8QfHzkLrZnJVRHv7frdkqSZCRnsgDNfeKK5Asx1ZOUUImb0RWPSPt9tnbbtlhcn+q8inRsVYBM4c9GgyDyqogvaavNNz63sKsRptaltEUtlv2UmnJ2yy5pc5k6zg+mqaGSsnIWf08aNUTRCCxfK9Wlp60xxob3+zfKv6zLZ8vd11k3q8i7Dp1y5zLTxsL2WFlJb+Nye5QMNM4VkDddQ1TrNUJdiHQyTB+uxu2n+PY6FebtnRKeQNFEs6W2ANflQXSTsZ0hlbd2Y6TLOD8xy05SZkZtP04UWXs3aM1FdwWX1X3EPpau0u2Xkg7y5cdyyXLTxr2Zn6Ps2VMEvAHx95iumYGlUTLI6CyJTGlr0idFlTMDHNOpcTG2Yahteti7Dc+eDfW+raTchLvm07RIcZpknizgvv80+kr5R4AXOM6BY73XpqnQBrZddKwy+45WI3g3gHuSy+JiZbw1Vlg0WY94J50zcmIRtmJqmeeVdzb9N55VNtrrDi1y3URuU3ShtUJV1mU5DkgBMwKQcQqrMPAzaMYgqZtr2vtSykHMtoaq6pAzYuWdeLdnSwa/BuBpgRm9LDcrGByFnQllFKvJIU2nrIhGr+xjKNNa1v5U98VQw04or6Cm6NKrLqMvDT5Mw7zb3wbp9CKcYK7LAXFvQ4PY5p8JoFjEw83A3WXCuZ7Z218S0VIa7o5YbBL7stEXrw9mG9OBs61iDP87MTw5dCDtak8vcXV90xkhKWlvK8n4r26ak4XiZHZQZaFlxUhx0biRKmgJp4NDBtu1/NV/T8rKh42ocSpN725U9A61aEZY2dRmdhwm4A9rocxmMy5R8TDM1Oivaygd3y7kxEGEi/jYxTb3VAnTpb7Hc+u9umVGCrF0DoHDKabmstii6eaqujfS3Q1V55ainJobCngYqvs/qXigeZ5Gi+eRRn0VglnbgjFGz6GweuGPK2idJgZA4E7pQAcxpvp5gY78gOJzyC2stRg/nY4pZTji10FyfYC0jSVKkAMKAWslJ13IJB/CtHIS7hpkRxfnCYVkTTpTJpub/zt5zGH+SM74uUtWEBlA9oeOk6N7JmK+TZmXBs+RCqfKXJ2WG7qHPnpmVUDXhXRM1TkUbimJ1N6VOeZ461rmXGPX+y6wP9P04Bi4irLiHyq6PAkPgIv1V6BoEUneKYmuBVf7O/udALtDWLatcTtVuaVYhFdWYruXxae3gc+2Z0KuPIlv9zXWPmEJUVkR8LtCVdRh2tPBsnsm1oF5Bifx9K8fNp2zdmxJR1T4u7H7pNujrtrU6bIPJM4CfJKK3ENFryz8S0WuJ6DEieuz27dtrFxKrtu5exJuUmJoxeD3apkBgzN2iayxdkthFBpwNiKRMi1NO2ddptPryLjwrPWfOB2qU6uUoafH3svbCkDRGRzlvly6j3R2AEh+uzx1haXWsM4GzdZHyb84kt3LT7B3WsjgTknWatquB+nzhmQBvwUR9WnSBobjCpkF4ruSLVaHd+8XZDQypPBZz+pzEnDPmNpS1Fno+put5ljiM35eH2+ZmLRsfcOmxulqUVB2dboSY+65Lt88aKdBWUcddYRtM/hOZ+eMAfDqALyeiT3Z/ZObXM/MtZr716KOPrl2IRZlYB0UpsIhWJXRi6Tzvt9biHWbuMkFj7mnp2QpzTk2LEq3VG6XjMD6n2BWkaXEfADMKF5VneTnWhuuusue2GF2YNMZtHbqycpFbRkA9rRnz9uRVqJM3XXFSlid22RKxNOUomjI9TYLVOw5qhJhr0dUkrVk/kV3Hy5ooL9flKDQWU/mYkPtbWWD76ZC0i8g5JqOUv4/21KHJjhFou4Mzs8jY/zyvxOrHssyubHdGLT2mrbNZApqx60opZpjnuw58Vs2Vja5h5vfq3/cDeAOAV/RdBhFl1/zZZp/yRHG/W3RMpo3rYk1k7pyGnjPmGCUlpun+85Rr77p/vfmzY7qmjrsJxcHmuipcpmkM2NLYM2PcPvPQ2q/AqDyMLzOrHe21XOfiO1z5G1DMo+42K5eBed0ZXEpTYy7XWR1ZGm5I4woXHT8rfIBzq7FMn/vXsjPro1oQ+IV/ucxKcu39xB+2647NlNtvFnT7JGUNN9XOtDuLm95vYwW5KAv7lTzbEF6DqjFWdhcmtf3VTtC1Vi7XxKBMnoiuEdEN+wzgTwJ4fIiy0hS5X12fMfyhe8zC1CJHa2LVwpeJR2p7EJnWrswuct7zMb2MkSU5k0nT1Q1IRjdQiq1nhwkYzfrXdw6HKyjMjWPtkSZ5GnetwJ2oLh322RVoibZf1ZHL5Udm+Vj+XKqHD+7aiff3ionoW79I3DKdyWpmvkuXPa/S7t3niQl69r9TBzdtWfhuMumr3DC+uhfTyIOogrnX0eQbw1n/1tBqv1XRBFh/5j9kyhnnFlt5HJXHbqHMGkHY1Oxly7EqfaJzO6oRcswiDO3noTT5oaNrPgjAG3QVfgLg3zDzj/ddCDMjioAkzjV02/3qmlsu87IzSArMsMWgNBizJAIQFjXHlEUIZBEynDMCiwTI/PcJMCH/YK/UWiH5xHFxwTNjxJzXEaTMVWlcpnKxCpnPPUUhxG+FAWrbTML8WZIAQbiqUWZuobLqUE7DksQ3qNnzTllQM+dtyypswmDVHWTvZALSqRMBmIbIXHhM9ZMWyPc7zJzYulTbg0K//9jqKjRxDxfWFL/HKSPQC1hcIW3t446NRusERQHhHv/gMlFmrIaF1tCYPa8p0+YhBTIm3eihrF4lJYrc3znX7hcxcOTGPzplW/08Ua1ZmjgFQuT1tt8tCq4qssng8pUgrE7HKJY7lDY/KJNn5t8C8LFDlqHlyHki0I5QKcrKbFMW5gbkAypx/q1XaO6TpwoXiDGVJC3F3Nr7CbK4/VNnAhmN5fGRKrMuP0tQnCip+hR9kzpNxYoJPRYE4DBqRXnh2pjqtBRC6VooPvO7rpnLAq7wm2aU5e2hNzamYNJvJY/iRMr6JHTGgL67ot0if7ZUS286ydNa3qQ05gt8Sp8Je6f8qjr62sR22rpx8TnTzStctdmsLh4/r19+2bbbfyYYXFg0SJ0LwMdEyzQRgIA4KzMjxkcfsDKeUoaESqZAGBaftZ3SMtZLN6CV5uEKPZwrBFWwd8Psu/8yczvosOXSxdo4iOv/iAhpKow+UQ39UsP+WP3sy1gGw4Ll84SARVg8nsAmf5NEdSewaRQrTE3nX8LCGGaWVvs6VrriWJiDuI441zQ9Pkdj2sbPCufWkO7ijfN3Ys43nWTaGbRtEuB0qhOZiulcTX4ZO9aH0hw6O1bsEnUgdwNVtpu2WRLk7WACt2xNGMouFNcrZRabMVcro6BxeoSd9U2cSP1cq8JNl6hmaZ8jRzBqk+dlpKvWhFmQ5OyBcK0F361argBKUxY3oN3wVd6bsNLCOVO05jSfsdsmEYBp0xgvtYW1gRsGn6gV4b7jaspeS037LKjYU5BZeqWNR2WtGtAItlAUKEY+99tYLJb33MZsWhy/K4IFMldnoTO/fUI1SVtr5HX+/D5xEEw+TRlpDCwBRAtgGQEgYJECaQRwkGubcSQx2GEAHHPuxrHwwMmkueHN32YadICi1hMnYjLSFNnCaeIMXvORJ6wxtXPg5lHOuAtMCvlnIB98rmspTQCExZAzM2FjLgox15KJ1N0UMXA0WWUGZhUEQb44nYVWhiXhg1zTDwHvQLc8oxgIp3l9LA83HZzn7uFrLlxhZO1Gpd+t3U0QAtpGDsM0bbBAa+mDy5jjRDXRIM+bWXY+FxiDjimfVmtCY5kwZiFloaPujk9btLVxVm6bMlLPM+vTuOKdyuxKwtHV7CVPBojqrQRX2iiMCftOAs3KcZ7FCRCRWE/ufEhceljbmnXelfJLubjJyJQtVxlz4+vjWIWsje8yzSj+dWFznSp+z+vInQTSJjiIzVBpCiyXMpCTNL8lKUnkqOEoARZJrpkmqtkvHC0pimWQtGlv02oZ+eDIJpNqezGKsfLlTUu2oy9mvZc2La4bmOBxmXzGVFGcfL5wOHeh1soy7SOOc+ZvQsittzuhrR3NMiiHiLpWTYqiUCmjLESy99JVLdd9J/ut9G7WjklJgJXKN/rrVuPcPH1JUofWcppUPxS0eKdOK23g9LeMIc6er1gcKDK3Mnzx9b66lMGe9izQCEdZaIAJb7fdE64+NM8V3LbprrzL1Ij3uTPcOWHfE6dv3HFQdeidb7wD+diNk6ILNtW5HHn6yMekq85gctvAgjUG5vGHoclnpjZyRp2mtmsyN01t4EUxsCBgsXRcDfp+lxbPtLhEBoEJl6nrOy39c90eFiufhUmmufsoZXHZZJqPqnkMgMoDy2EYiVNXc2+UmUXCmoe2E3vUDpsckTPhjGktY9Gs3PBNq1ekDZmXyXlUkLuYBmdyImfUgDBlgysMqnzBNmEy66OGgZXzrUpTfmQ+d7dMa0NG1W1hxc/GeEyYVfl8m2hyLZcys/K5ncoZcOn3RB/4NODsH4rHDkyoWFasbilX2JKTlw9uWmYg9bjsysqNKWgEtXQc+lKWcZQyEEVANBFmbVr8PF5dNDWrljSPTBOnXDFiFu9ApPmzHnOwVCstDEoKgQ5uU5JmgcyD7Hd3XOhfQqejgjrhIJg8s7hhIsi/hAFSTT2IpRPsXBPzo5r55dse3QQ3hj0ORIhY/pwAbJEWAbLjj4mAkxhIA1lAs515Kecask2+VBm6y0Czsp0JlLIexlbSOoBcoLmWwzLJF4rNT7yIgJiAKYnLwiaSaSEJOweCQSdzUpyEmUWgdZo4o8omUVY3ZdRm9Vid3Cgh08CszHJfFxe11M0WSH/boWtxCiTO+gQDK7t92aHLCqvV6J3f3VDUbLG6xNRdV6A7ruwsJbDjf3cZI5fat9QI9iwLH3Ro4iRvY5TqVWCoXGQqhYVdp5xsrqTq0iosInPB8W51toXVykVwa3ctzJhvSHl0TahRYYtE2scIc0N4V/JksaBtTFhgRGB95I4Dkogoq7vrrbP2M4GdbXJUmiaaTxSLy25aGo/mKiWnXwhisdkifws20xsOwl0DINuMEkE19RiYL4D5UjXmONc4oqVq3onjJkHJrVEDlxHYhihjgNmGKkfTsklikT42yWOHES910XQeifsm8817JnzmLlJGeZkAF6Xw0flS8jHaUjWL4yQXDItlfs6GK3QSp57ulv3Yceyatm2McxnJZLV2djdjmbvEdV+kabEermkblZhxmkq7pElRGLDbzlqOCbM4kTUZRnGdwz2OIdPSEuB8oTukAbh33bqbhtzjKuJEtEUbCHN1DZa19yTJF8PdPF1GZwLDddVl6ztuHa3NnDHlc2X4XEWZsC+lzfqolFdhM52+m22OKo3JjGan39z2LtQ9EQt7qW3njg3Xyikw7iR/Hqu71XZ1Z+OB8x3kZSFg5VrblNeufBZ31jduXzlttIxlfBnP8LrlPM+yne5pUZi6/dE3DkaTv4QsvC4gTCJUxslLUTbmugR/NgfOY4DOgOnUYWDpauPXwRbOgjDXYCK1Hk6UaSa6+BvFomGc6oIvU651ZAIgccI+E9GuoxQ4neX0WPwwU645RKlM3KUeJMbTXAOyepWFgy2iLhNgyZLHjHKzNpzIgJ7r4nLBHaQaq23eoVRi6M2S8S0kZQzEMYET528iP8luZc4no+WTLWQn4iY4muR1M0FNgX7WycehWibuBHIYEYd5nRaJCMQTANcdJrBysJz2VxRLHwQsE8jWISJ1AaZHDgPFKtOyBXdzHVj/M4Bgsspgl5G2cVqMWEnh2ZJQYpL2zLUmfH8z68dhYi5/thDZBKJlsy28U1Egu11vce9xCszsGYuFldq+DW3jiWrwE0elZuQuTNeqyBh/In08DUrC22lzi23PjA4di8T5WIaT3miMYpl7UBps8TtJiuM8ioBI91tMg9R7MJnQzVjae0kezMBpvoDfZv1jHRwEkwfy6IEYMkCPApXqCXB2CZyeqEm/FEFwBGQnV5pZZ4strk/YB4YMqDjOTdxUGWukGvNcTYuYJWxzEgN3WYRCOM3PmzGNZBHnA8eYTEiiHV9LJD0gk4PTnCHb5IwT0USTI8c/rYM5C/1S+lNIHhc6yM8jcbEcQRjpidWTgSQShpZoBE8mcBKxmoIwZ9imWfk0mGyCGs2JhL6ZJhYD2SQw368xkcwFwECgmlOUSpvMNVQ2cCae+UGtHeDUJ1JmlYY5M1gmwkhnAQq7gZNU2m4ZFaMx3OrZrmd23jEasudOO5gikYURsravjp+AHaGi9NrYmAZFV5gJ2wDqD3f6odD2zj9zFbjjDMgFiGsRpRYPbs+g7rBQXChhAJDTwJGu1di4NNdTCsC9QtG95yFbO3IYOHM+v+I0n6NmuS0jGS9RIqHJFoacsvjNQ3asDoiFFSKPoEoZmWs1q7uzjmZ8Ort0KMlPYr02zdskU9BiZOHSNgZct7ApjgUvgTswUBTKfeMgmDxRboYuIe6YQMMXlxAGcDEHTkLRIpaaziZ3eWNUG3dNwlJmoJMmExCpDLQsEgXIBnqi6dJJ7jZZaqTLYpK7lWzAGoNexsIAA6g/UDULmwTmw15w7v6JTQsnYR7HZq7qoLOLQaJI0iVaL1bG7x6vygRcRsKUozTf/JVSUeM9j7Ss0qUj5qqyTUFWZ1tXyBaVtR6hvptCJmKUqN82Le4szfotAeYAjifIGN9lJPRZnW2SmZtsFkp7LrStlupuMY0thVoWSd4Xc9X4r9s4Qc60l3HOrLNFay3PzvkxV89S2wlKk2l1y0jqTkFOw8Uyj+VP0nwxPgyFcSUsTDc0uh3Gc1QKC41Zxo/RXnaNGHPLrDZlbiHpFv1EmPiRMn8LlTWhRUB2qJ25GidBSdCp4LJgCHMPciodPgmBUMe6ud1CE4bIFSIOc8WMJ7kw5FSsYBMimVBntexU8SJWS8D2QTjzZqmC+8YsF9TWp1mkWSoRfRyo9RHkbiUTvCnL2iACyTMI8nqw9afW3fz2Q+AgmDyzaJUAslthzP0R6bOTCLiRiPYaQZn8BXBxikIY3zxuNpsynyeQSXQGsusHUzhuBx1oEwKWgTBCG8BRKpMYAOIwZxDGDFLKB1yaCIPLTobUQbeIdGByrrma2b1Ud5K5B8xdk+qEStNcYCyWMoHdhcJlqrdoQZmSmt6cACdTKTuYAEeqhcZKz7mz1yBJcndGipJ2k+SC1ZiYfY+SXINe6uSKU4DMHaLMIWHVIJWZWKDQMpVF98KJnGmu7cWp9Mk8UusrAWYObbZgbiY6kDO6FGrua/9nUVuOFWBMcqEx10kqbgNjwstEmKSr0cfW3o4GrUVgHslW/QnyMTKZIVtLKm8eMoYfJ9K3dsy2K+jZKSNJRWGxcW07sZdxHihgxz8EOl4nATINNkmkr4wpZ+sQQb5wamtFkQqhbH2DHbcT52MnCzFMc8vA6DPGaHW1zY9JmrtQl1HOeK2PZuoutaM5sr0NjoA2oW1rUKYgRKnwhxN2yk+BWaL7azivE2m4jNG3iHPXkrWFjYVAI5Wu6tk1WwGzaHKGeSQVu4S4ZhjAcgEsTlUqA7jQ5w8tc+3ZpHTTgf+mwc2XeeROzMDFQiyF44mGZwbiAnFdQGc6iS9jsS4WkQzeRSif55FOAM79sClUo1KNNyR551JpuFBmuzT3kw62eSS+UBM0tsN2HuXujDQGliSa7WUkt1eZphylupgYAnzkWCu6sB2xaGSZQHVcEdkRxEAhtNSY5DIBTpRJWB+aBZQxKRV0caTCDmrJOFrgUrXriaaP9K9pu+aXZYdJuP5dO2DNGLKdYlpY1COHOTuLyyBhYNmmFuSLsZyqNcG5Fj8JcysmTjSMV4VAwTeMvH5WlyQFrjmCOqCcQU5cV1xajOW+WBRdIZmbJMnraczJdqEu1ULMhDPnQtbaOYtc4dwCy9w91m5OuYCkmSf5eMiCDxL1q2s5aSj9bnc1zEJNE+bl2vlDWdCECVkGOAbOCZgs83DfhTJsC/2MWeaTu4M35dzdGrNaZ8ZfbG8J522TJDLH50vg5ChXUOw482CiIZY6NymVuWTWTdampaimvnEQTB5AYUefae8L/RxDNPeLc+Cu81uAXNNkFoZ27ai5sRky+BaxSHHzvy6UKR7PhQEfk5hz80QGFLM8O4uAyyVwby4MKmRgngLnS92ly/L5eApE01zLY0g+CcuEjCHPoxg4T4FrJ8rcVVOcx+reYMk3TiQKZx5Lo3AInM+Bc8gaxnSSMxRbfIpUw4tiICFhoonW1TTzOM6tIBMybrQMw3ElJMq4Y2ARSCfY9WrzSPIMtV8wUaGkmnaU5G4JANmtVXO1QmwislpJYVzUrpNEGK/tdk31nyuYzJdr9xPELO8EUCGGfJHU+n0ei7CEo7FGKoCiJUDHuXa4TMTMN61xGQPxNPf5UpJre5HSawI0sraFMNbzhR7VEef0LRNps4kGGrgunDiRBXaL5jHrI02kLYmkL8yVaAKGgtzNsVwC01nOpAKnrULV+EnnUgBJK+0vZ+XbZkR3/cSEeartNklkrNk4iadSVkjIjiwBdB0CuXJmkTNTkjFxPM2PLVgspW7H2nZRDNAs3/+QprngyxZUZ7mlaON6ngCnsSpkqYw1u+PZ6nSxFMv6WAVTrFZBolZQmggtCy3P3JV1m6c2wUEw+fJi2AVEgzW3DCBa/eWy6NY5gzLWJI+w8F3iUUac5JrfUSyDbqYTJkqFeS9CGexRAMznGk+bQpgapLyLuWpiukicANkOQNMKo5loY/MUOHEWxuwoBYu7tcESOX76ZSITJtOIU5mkUSILwfGRMlAG7l0C4YlMnIW5EnTgLyHvUKC0mdBhABPgMhU31NmlCrvjosvL1eSiRK0urQcScf2YBhhreWcL4ER97JeRTJxM49P+matVMo+FoU9UaCTaluZTtqMkolTdOJyb/1GSC9eZo72a1rlMxAc9CXJrIk1FUIfKMJcMQDXZLIw1zt1dQYLsqI2LhVheCx1nlxFww4SACo8JOcJVBfMiBh40qyOV+kydRWmzMkzbNEZq/WBnyGcLxUku+BeafxBI2YmOq0S1WQqBCx3zCQPzALjGucVrzPtI18EsYCBlYBaJJr5Q5eLCtZSS3K1CkDbMXCu64L1gcQfOo9wtk2rfBqy7ZUNVXiwfFbYm9MBSJ7PoTNhOEuEHFoVlFrlrHZjbziJs5qkocLHVX/soVsEUQ/o3SkXQ2tyLUvndrJwJcot9qhZUOdy0LxwEk3fjtwFhIglyBg+ItnqKVS09jsWVk5JjrifVZdnOtSTNff+Ras82WcJY3BtMRbNvmQLBMTBdimYxX0AkOIDFVBjAUt0vMeT9eQTcCyR/pLnWO1MNIYqVqekkn+uAXKrbZw4gOlHGkUi5tlMvOsq1iXkCnNtOvpke1WprAqaZqQC4XKoPNQCOZ6pxLoG7S4Aj4MZpbrqaaT+PAEyFsZjL43iW+y6jWBheHANpIMLzIWV65sO1xW5bezAf6XwJHJ+IFgjNwzTRRVTU+IxppSz9MTetTF1VCxUmc/3LmuZ4quUScBbrxivKN8LZBLX9Ctk6AudupWQpTGqZSBjvscMgIs4jqCLzryN3TWTrKmnuZksn6i6h3PqxcMwwyC0se9/cJObSuXDGb+aCiHMhxcjbOeK8Lc2yANRlogrSVPv2ZKpCMpYNgCcpEAfA5SL3m2frPkvgXMsPIZZ0morgnCstlwvgbApcQ95OxMjWA8513sWpuERs+6iNy1SZ7THnTNaE+RISMcSU05Zp15CyznQ8IJA2TxPgbAncuZBgjnCat2kW9aVz1ASlLcJHtivY3KKU1zMqM6eecBBMvsyUF8DKYU1LiIZfRqSTPUplgp/b4mYF5KB/YQpxLINoMhU3ysVctG8K5d80kAbONm/o5FiqBrJkDfeCMLjLhWpuetxCGgKnxxr2yQBNgCPVpkgnXayLS4Ey4qWmvXOhvmqd7EvKF6IiFkF0ucwHYErANMqZH8fAYuFoaRPg+EgG5KVq2UEgNACqleg7l7rZTNpLBMhCNaroSMqPUjGd5zFwqj7Ty1isntkEiGd5xMo8EcGXpMC1Wb7pyBjJ5RI4TXNT+zISbWsWA5enegQDxHVi1sn5TKOXEmFsZslFae4miZRJT5S5X2odSYVPQqIJLiB5TCfIFrhtY84yBYIISGbiJsoYfiJBAK7JHkW6kEc5k7EwzigV6+aBY2RHd1AqeVyDCnOHoQRJbnkkKlQBZeTKUKIEuHOmC/oJkEx1oTvWtQ3NbzbJhSyluZsHUPeOCo65/h5EudV2vhBLJTDFI8ktJbM6zTrFJNdoLQQxVqslioBkIjRcxjKvzJ9+ucitV2YRppio8I6RxdObO+dSrZjjVPKxHeopkK3TxKwuwjh3r1kE0jLWOZmKshPqetpEBYBF6CytbThfPyOIoJyq1W93Edia0RA4CCZf1uSB3C3j4qziWWbKxrn2U4VsoChDA4CjYzmfPUlkMEKZ7mwqTPZyng+iUM9emGu8/kSS42gh+V2oZpqwaD/XTmQwXyQyoM6U4SRHMukXejAboL74WJj8eSxMLQhzhnXvUgUMhFHFiVgTFxCGuzwSM95CLC9jYH4pA/D0GOCF5LVYAJdTAIGE6ZmAcV1OxlSsvRaJCMIokQmxiEVgRZHkFavGdm8ujDxbBE/EFXS+kElkmlLCuuisbXtxKe6WlNRKioB5mLu+limyBWNjdIEy3HsqHKOjnAmZNpYm4q5ZLmUj3eUlMDsVpk2paPN3Y2HM5hKL1TpYKnOaqkU0odw1uEjEjbQ0hpjmC+WXaoVNQhU+iQq6ObC4Lu2aqGUXI9eM51HuXmClJVVr0nbokh5dwRAazxIgnEnamW66Wizz/rO+i1WbD6w/l8DkOHddLlkUg6MgbweL/opiGVdzFWgXC2njO7oQb3HmdiRDlIoAvoyFkfJCXICnyhTnc5kTAQPXQhnj86UqXARdKJDPl5HwguOZjp0onzOLQBjrdKICyLGUbDe6bdZaaqMtI2Cu2ne0FC1+ogrKcZKPk4hl/F0sxUIxoTDXOXJN/f0X2ufTID8GoW8cLJP3wcf470AWHxfIfWRNvrFIB2wEgBYaETOXyWMLbpwI4ycG7imDN2a+vBTNK9ZnCwiDureQyXK2lPRHUJdOAFyoVnUSyqr9iWqsC83D4m0tNDNZSBnTheQXKqOaQ5h6CpkYc8h6RQJgOhfmtYiEgV0u1HcMtTygDDoC7p4L0w0gUTIWFWQ7js24ShmIlxpuqTTEQLa4dxkDk0gmjPlrLzWOfBEL7ffOgeVcJuXxBEhOpc/n6m5YAJgsgDsJMJsBd9WKOTqSOpi2uYiBO+cAT9Ra0QXw+VyFRpwvfEcsjZSdyQNgcaF5zEVIHk1yjY9Nq1dmeKkCZam++0jLW5pmmKgPVyf2PJG1jTSWCZ/q+IpZaLyMZTwszQUDYdpRIFbVMpE0xCKcjlWIU5i7UxapszNYv4epRFrZ2oLtgZjHUv/4WAZHxOLWZFJ3jPq9WWlcxPIlnSjd2m6X2s+xWnTnc1FCeAEsT4RWczGehmp96IJTlMr4D5RWC9GdqzU1YeAiAE6PVCGJNfIszNdb2P4uZQdrCHEPUSrMOmTgSK3GbFNhLHVIVeO/jESJ4Im4n6aBuroisXxsx/fZQuidq6I103ZLoOszEbAg6QOzGC0smCf1HoRNcBBMvmmHah0YOvGnwvDuXSLbqOJNb/68SJgjQzT1mTK/CwgznEG+M+WMFABOL9V/ijyMM4FYFDdUmzhHnubeBZAeiYYbR8CNG3KLFCXIdv5ZmOiJMsI7Dh0XkQzIiU6USwg9CwAPLaSsOVTbiUSA2OS8cwe4B2Hyp5cyISxiKVgiO1gqmcgkmGuZi0VuztvGpPMzIJ4CN050EiRqvcRAeCkTZnmhWvpUNPd7M+DOArh3pmsSCTBTd9B8qWsYC2k7ioQ+Zok0mgNIF7JASsrUP3BHFlnDADi/JlbIvYX42AFgegGcH4ura5HkFlsIef8ylnyPImmUaKmhqCoYwqkwqTsXosHdm0ubJ0vg2lwiLRZa52UsQtkWYc+XwNmFtJEt8pqFdKbtcw6p90Q30y0SFQaTXNuHWmfJEfBArEotSzvcmwOYAtFpLjyCIFdAjkPR6hdR7kpYLPIxYXTPJlKXm0B2BeVCNd25lmmRWUt1U1j0zyLSNaGp1OFyoYEJKZDOgJsxssXzpQrJxOaWtumlxksHiSo90Mgq1ZJDoycRhs7qrjmdyryxugUaBDGJNHBgqW4nXaOznbK/f0ctxSNZH5tBrKqzuewOnwbSj4DkcR6JsL4IgIuZjJFFLPlNNZosSKVPEl3rOj7KXZ994yCYvLlN1sVlKgzhHKJ9L2vyS1OddItcIz5VU3gOsQwAYaZHAO4sJV84z81F4xogC6iLRT/Hmu5kIYPkXDVgOhcmMA0lJPQC8i9QOs6WuYZuaxMX5zK4L5ELG4Ywx7slGsJLYcrzSOpitNyN1YeOnJkHqTDqONYIG027mKsmCBm4z5yLJZIscy19GQHLUOoVMHB+oa4qiEvr+FL8z++/K5aQtdvsArh3TfK8cyka04X+NonV16l0H2l/xdou55fAJQtjWCyF5nv3cmF7tBThcq6C5EJN70QZ+VzbK1QNeILiWs9M3TSXS6Ht/FI1uUAWDieBCIC7F7mlczaXyX6xyBkeAJyfytlKdy+E6UTaLhdRvjdhbq6IiWiwdy6A5bkI5mtL4MFJvlv1Ut0ZU+QuorkKmgDSWLYhyXaPRhFwMdXNcMuc7mAuAmGh7qEz/buEWiwxcHcu+RADdy8l/T1tW4uiOj+WOi/nQgcR8Mwl8MBMF/uX8luqVutxANy5q+sz2r8XMxnbiwt1cwKYxtLmlxpMwanEql+fSR0vU53jqYS3TkPgmQsR9mksNDFp3gzML9TaXYgL6FzrfgmA1V14OpNgi/OFCCE7BuKBa9KnTy+AeCH5Hx1Lv10u1dIkYLYEzh/AIDgIJv/UU5u9b8zpEqq1NVgG55fAnTRndtcX8n6ZjDvAyj2tZ5BJMy89XwL4AJDF+EP/3oUw3rv6LidAuJDdjnPOGUwK+XxvIZPc6jSHaOjHWF14vlP6/pTm88y5+OLvQZilpTWhYZYJQwqZxjntAHDGuVZyNhfXzh3IpLz2tJj0SSLPLtU1tVyo60zTnZ4Dz6ggO9P2ASSC46k74qKI4pxBm6CbaJvMte0vtALnkZwdtICU/fS5pD/XupB+vjcHLo6F2VxGurBNMnmf1j55BtKeof4z11vIwI1z4MZENL0LdXGlCfD0XeD4WCwzGycnEIVhPlehoq4xuhThB2Wsd62tF8DTZxK9dLlQ//tE3FnTqVoDuu4SXJOxcI30UL6FWB5ziNAwV1Ks2j/HQHis+czzQIAbU9maf+cyjwwiBu6GwCPX8gP/LpcyOE7U7XA+F8GQkDDAazM96VMXMROSut3R8OAJVDlRbXe5kO9nKoTuXaoGvcwtzxhAeE9cjJdL6ccYIujjJO9fgigA9+biKnn6TNdNdE/MJBRBdecCiOfaZyz0znU+qfGG6Rw4Z+AZpSOF9NNT51LOvaWsn0UQpn65FMZ+544I/HgJXAcQ6cY4G5/BMo+u6huDM3kiehWAfwqZD/83M/+9vsvIjnxdE086n8+Qm14+MANPXeSLuCmKDNCFzyDQOeVFmfFD8zatG5ABd6xC5ulS2jNoDD6KVsKTUK2nlH5Z+p5AmPW1M8lrUUpbTg8IwwpQXO94ChoyCWGmtxnZWf93WXzTDCC50MlzJozJLJ6F0oG7uSA1PAPggTnwyFIsOGuXCNIehDxM9hzCSOIYeGopzHIJYQbHF1LOXaetziFa2NlS/KvLpbqVkG+iM/o0+hUnKLbrk2fAA1OxTJ7UdDcgZvllJG1j9TQ32DMLZfaaV7BQK2IhjPsZp42fviuujmfOZUKZ1TINxU11Hkv9gnvA2UQWJd/3FPCBS2THMdw9F0373lx3REPdBwTcuC5lnGm7PHlPAgjOLuVZArHIppfAzXNlzAw8fU/afaa7Up+5RHZ0c5gCz7opTPSeCrIpgNt3xK1yT9vjKAGO1bVz7x7wZCxtNQEwO5fCz7T9lhBBCwjjfAa5kpY4n43xHwG4diG0n6nFeDPNN9c9+Yxo+CZAAFGmEsgcjCFRTCFEAJkCdKzjjc9FAbuMZaxYvz91V/z/Zzq+LxOAL4CTY1nEP9M1pWmyOR+rAvluiu8tc6IQwK8D+BMAngDwSwA+l5l/xZf+1q1b/Nhjj3Uu52d+JsWPDNRAI0aMGLEtfP0rg+ZEHhDRW5j5lu+39XJsj1cA+E1m/i1mXgL4XgCv7ruQkcGPGDFihB9DM/nnA3iP8/0JfZaBiF5LRI8R0WO3b98emJwRI0aMuL8wNJP3beIq+IeY+fXMfIuZbz366KNrFfLlL8kvurjZIv1BrDaPGDHioHB9oHyH5ndPAHih8/0FAN7bdyHPex7wF2fA00vgwSPgA2cAWBa0zhNZ+Ll2DER6Q9S1Uw0nI4mfjVKJiIg5PxP7pKZl7EKKZaRnRE8kOmGmOzeRSGx2mkh41IOnwDvfA/zKMxrqB+CDHwAefRh48kJCxsByPsrpEfDdv7a6iPslLwN+//3A296PbKfsgwAeerbQe28BfOxzgQ/ck7DDX9ZwtymADwXw4g+W6Io7S1loes4JcPMaJN6bZRHu7hx49EFZNJxZ7G4MvOgBieV9312pa0h56N6Fbpx67gO6u48kWmKxzDcBPXAsm5Qstj1BvlMWAfD0HAABH/qwhvYtpZzLpWw8uqbtGpDGuGt8+t2FhmOyhOhNUuAt7wXe57TbJz8AfMA2hAVyp8CDz9Ydkk8D0XWJfjiZAR/7PGmDG0fIDgz7d7+Vh7z+9T8k5V3qivQS0n7X9Zwi2+x1dyFnsFybSvucLZCdG/7giWyiOo8kpO/epYQUHk9l4e1JDalLYuCBUz1RUTdbHYUSvXFzImNtoZEudjyGnaA6nQhdz2go37UjiViZx9KPy1iiXU70iIj33JPxG8XAg8eyl+FoJnHlFnp57VQiT+5qXHeUSL9eLiS2PlrKouv5Qvr6RDcYEWTRGQHw0KkukkbITsV84Eh2o55prDqx5P/AVOasbRA6m+f7VwLdvPXgsYynaQhcP5VyTk6k7W7fk3Y5mUjZR3ru0p0L4Lk35Gym3z/LNzwtdKy9+wOyOP4ggIeOgA95FHjxw8iue2QNCT2aCr0zPb7E7ka4fozstrKZHliGVCKiFpFEHdleEpoAjxzn+Tz34Xb8riuGZvK/BOAlRPRiAL8L4DUAPq/vQoIAeOA6gAuZLC98WDqNSFbsg1AG5OS6Rl8QMD2W0K4bGh98ciSMchZIervf0Ydz3bE4j4RxHGsr2p2VIImJDknyDkLg2Q8C7zsH4olM1kceAh46kd1916eyi/BZ12TA3/i11fDGh65LZMCDZzIhpiHwyIkM7stIYm8fvS6DOgkkkuAScijbg9eEgT49B+hMJs6j14APeliYwlP3pK1mRzKwn3Ws1wHqFvjnPCB1uXmq28xZ2nc6BR5RxjudyOQOCHgklL9241FIkr+djriIZEKEJJuMHroJIAU+6Ga+MzVOgWfflNA027wSBnIOCljOgbmYA7cvJKLhxkTa83FHhTgG8JyHAcyBUOOtH4IwT7oOPMXAhW70uX4EPHoTeOSmjIkkBZ6+kDwm0qW4eU3bWg9Wm2l8bMI5Y53cFAY4mwAn0wB3LlLcXUhdT/XgN4K0QUrAwzd0cwwLA0rPhc6TGfCsG/Levbm0eUjAc0IZ49NQmG8CaY9pkF+9N9UD2J6dFndRBqSmtfbFVDfp2MmiIOn/2YnkcaK0LmNp+4uZhHdOlYYbJ8Ik786B5bH2TQo9TxfZyY4TEoExnRKWMeP6QubOtZm8M4+BkyXwQCQ0nirjJyA7M+fBG/nmsflS6L15KlE7R2F+d4BcvkF44JRxtpR2noV6HhJL2Of1Y+B4SgBxtpFtGQHJPeDoGblu8yQAHr4JfPAjwPMe1NBRFeSP3MzP+ElTCcGcTfQEzxTZNaCBCuYwkPQJ636IJUA3pdxJKMc13DgSuobAoEyemWMi+goAPwGJPvp2Zn5H3+UEAWESMo6mMmFmE+nw6RFwkujnQA8jYhmHE92ObLe1TCcS7z0LhcGd2qHe3oqlmJN0cBCKVhVop9s2daJ8QtstRNNQti/PQh2MKgiOJvJvqvn4Sg4D+ReQ7EAkDY/Lfler5PQI2X2VAaT8IJBJGUZS50AH5ZGeOR6EukVfJ/NM8w5NUOnEmU4ZcaSDU2kmym/+sXpPQ+BoQqKRantECWPi9I2d3QKdDJMpEAQBAjBOZkCSMmYh4XjJiBZSh6n2l00aZmAyl2cnU2G+oTIYQPp7GgjDmCmzCFI9UjfUi1K03ULSdgmkXlGq7WD10rpZHYnkdyJh6iGJ4AlUa7SLICbKEAFp72lICMDZefSyezRA6pxvbcxhpoxrNpW+mU4kv+kEmASEQA8yJ4jVkCD3hU5DwtGEsIxTOXcFUreZEpZqVF2UsFwFCan/dILsSjyC0BYGcj9rGEgdTKgfTwKkQSq3XCWq2KhiEEMm/EQVnqMpgYgQEEsZoVhf00mAKEmzs+wDyueTtXmgY2qiO4oDEuVoGgo9swBIQVnalIHZlHCUMphFgUu1j0LtI1M8iOTzbCpWxGQi4YwBybiZhtpnAWc3PoUkjUOE7ETXMJAxv4gZISuTD/I+T1JGwDLHYx3zCSM7zdVt974xuHuamX8UwI8OXQ44Z4SAo7VAGhvIz8pmTU82kDRdSHnatvBd2eXmaQh0AFahTbkEvXSklI/d6FNHh1vHqrTZNWT2TNP6yHbr7TK/SeAfrIV2DpDdNVqF0GmsbEJQOY0wwqVqU2UEUMGgfW19boS4bWMT1QTISh31e+AIkap2L9Dg5GVjgDSavzx2Ct/1cxAUvsKTZGXsUIt75AKi7NhsbZratFJl9l5TFwTC0H31sXFVfh6Sv51dhIEIBffAQLMQrT9E2BFSz6mCobrwAhLr3Sop7xLcE8Gyfgpk70A4KY6FNiCqbkkyrcR9hnxc2vchMPTC69ZQGPz6ZeowVpvAmZZCDlPru3VpdYCYhk4NLe4TENlvzuSoSjMJVvPosqBjmns26JEzjZV2oiITm4ZSv6b2DIJ8opZR96wqX1dYu0kmEKHoZuK2f0EIWh4NQtvWBaZhcQyV6XPzDVWzl/Sm0db3tUuTaZpewoyuYOVRI1xhQJTvzm7bL1JuUJhD5b9BS0ZJFWnLfVI1X33CJHDavvC8igbK2yGg6rnapj42h1yaCmPN+3wYNn8wTN5Q1kRrtWdHAGQN0WZAOgwwy6vFO4EjYIqaXRFtXHMFRl8SJrVmX2nClB6tPJ9UMdYKmqogArYoLDYd05kp77Str+qkz4mKv7vtZBpi3XixMt1J6pr9Va+ai8DFRM37QAsMSgncsRKuw8A7pKOgKKys3OJ3ytyFK3lUjOO2yPrGk4Ev34BQuJsVMK2/+NAY/8r7nvxcGsx1F5bebcNLAEdYVKT3Mf9h2LuWN2DeW0XZSKqbdEA+AGxy+yZi5bumpaw5sKdTh8ay5uLJ8AXu7xVp3c9HnjKb6PS6cGiVybsDsuzeaQMq/a1N28rtUGRQoWMhuMqvPXMnnkt/0fdOvU46H3P01a1g+bVx35Xafy2aqUJb9uQ3kKJZJmflwYp7qEmRUUx17WkWUmvarS02rWvgNGBV/1hZxn+GwsEweWtA3ykNExQbmFD064kUp0a/uft+WxO0Np0xmA4DyjVpXTe0qx37TMEuCD3+bbcM97NXs6vJ2xWu69JXpqkg4DxuINO27a8tSJuW3NX3Wiy//bhpzKuxrNVnRQFHhbWMNvmHFbRLmxR/qMratzi9LuqEW8YsW47vgEgZvD+Vm182LsNipnUMv6uC432/x7lQhYNg8u4CksGnhdrzKrSW9pSvsDelA5oXXH0Svg0aXUTOv8o0DRO3C3JXRn/DtUuf2N8y7W4WUyf9Om4jn1bmd2Gs3waNzH7tnJtxVOMrrHLvtf3dTdP0zPtuh7SGsgun+JvzOSh+blOHsuJYnZ4K71XRMBQOgsmXkfm7K2rnM4/W1caaJH2b9zdBWbAZZs5vZX+rvVc3SANdsPK1YZ3gqGrDdatZ2bYtP3sjfUwQ1GmNazD+LukCdQm5TKggeEq0uPRUGFrN5TcwWCoXXEKVj9nNyycEC2mq3ttQK94UQYBsoTWjg6rHVgElzb8qya7qd5BMHtDGbmhVt/IS9dBz+WivfaxTthvKvxIRgyZTtmhpmNXT5Beua9cuGtmmIz5jJm49auj1rR8UIm1qGGCbMNKqPOrgc634sqhb++lS5FDRG5XltU3Xgkl2yW8d2NgIKR8XJkwDdcl5x0hTvvpXFoaLAmRbOAgmbyv/5U4Y7hDldmiamEFFmrb9b77zlfQNWlH5txXfYMX7JrD68kEDzdphE6omXxUIOe2uWe6u0ZTT+/Koyvt+RdaOVGSU7m+9lDOQImb9XxgbzjivcvvkLsqqMqoJXp2Hw4ygg2DyPrRZnOnTVVJGUwRAQaNsYJpVdamK423jMmmqep1fu0xTGzRFk3SydirSroTGld/35OcKLNvhm/1WQUNVKGEtzdVkF9K6Fkpj3i3z7Qs+6wkojnOfoGybb9OzvkEoRtVlTN7+obkuAeV7U3z5t3k2NA6HyXN1h1QyvU1anDzugTV6NdMiOtISEHD6QHVx5b8ruyIraNk3VDGNSlobBJzPxVKliXkXxT1p27Zb1/btopx00RjXTWcCrgw7rmJdWL51GrGl6yI0u9Lgrg10yburNZmV2f2VtXAQTL6wcw95g4eeCexlbmu0tutfW6ez1jlWtEmTXRfraF99l1O5mNigUZVdR1V++ZVx4LzXWZAMCF94aRs61jX1++j3JoXB/RxUaFy7aGvXXeMKmYzR1zDvVeG/mrBRaHZIuwkOgslXglc7yt1J2AfKawFDMMu6LAsDpUYFqaTLo9m2eW8dxuPbldgFPv89eSyqcrJJhX84DJvdLuT82xY6rTHsyPyq9kF3tzTsvSE3BNWV6bo9u9BQt1i/TzhoJm/a2jYbvmrRsjI9Oi4+ljXbDky4spgGbXkleU26vgcUObTVnfvTtPvW1doMJwMd7eort/N7LT73harNb5tg04X5bHhXWXDB+mOtzmpzj8fYVqRPn4EM3vyHy3o/sE7bNY35rnmWB4s7qZr8kG6BLhMLVn/uRpNHu/XS0eDqqkneG1xG35Qmi4bwpCkL/EmDH5mcfNrWy3euyr4jqNrhvG5+GnLYBeXk22RMZSFKJHO0y9zcpOBW5WyAg2PyK76y8veyO2JIYjqgLR2uddKomXverStvXeHVeUKv2ejecmjVh22fbcK2KW9awehsh6zvPCA/jdXb6PvEvozbddEH/V3ycF16PqsOyOeVbaTsu40rj1cYuDMPjsn70LYNB7BaM3gbumMgf9Ng8Pmns/Ldd3vYQOCGEfayeLeh5lf5rIVriWrSkfOvbb6bwlfeYGVdMWnRi3BoGLd9tUlTNl3vrlgXB8PkV06hLH/vkyHVlLNx3jX+wtr39G/VwOmspZc+XxVmUGaQdRPJvU9gW/7XOnQND9x2lxQCGFxaB9RQtzXuiIQZBs731u/2VP5QOBgmvw62OUm6TNqqtHVRJNRQSBu3RRVD3DQqpi+sw/jMt5r93mM1dt8iPWJL2qv3nZpO8Z0quymyA8NK61LZZTYDjZdd4aCZvOu3zv4OVOM22n12N+QAZdalCYN+yt1FLHkfFgiVvrfOq8aF04WOpvwOBRuPLzRYVKUfu5RXOSfLGxH1c5Mr5Sp15WBMnoheR0S/S0Rv1X+fMVRZgF7K7dFW2/pu10XbvKr85a6G3bRp8PS0umz3WkNv+aUXfLTsSuKv00eVoXUlYVZeZKtyOTTl25WOddDVXdM7tuUa2dI7lXmZJl94VvzcOpy4P7IGw9AXeX8DM//DgcvIQOQ377ahQa07SDZB1dkqbdI3LuK2LK/y/Zb1b9SYSky6sdzS50KcPdUz0rI/vwq5L7/fTiaIpclps8VpsdVJh7ybng8VeNAcMEDY1XGCVZufukbXtLHcquq5zrEmXXDY7pp9KruB8W8a915LCxX/lj+v/ObQUMUU+4oM6MJUC+9VWSwlTT4MqzW2MiRiqFjhquRDbF6pams/A6k/Gnvbxwr3hUYLbt1822rmfTQbrY7DXWJoJv8VRPR2Ivp2InrIl4CIXktEjxHRY7dv3+6dgG2uznd/qV2y01n+uemqMK/ffGif1RooC5E6cnz0e484QK6N2s+zo6LQcjdAtemzbTZT0/n2K+lLaa8oX+8Ei2O3z0MspBeUmf6y3xk2qgMRvZGIHvf8ezWAbwbwYQBeDuD3APwjXx7M/HpmvsXMtx599NG16GCPj8Y11YHuOxfr0MY0Kz/3MbMAfo3wdPWRt5y6hVD3r9f14vjwt8kcqIu62iY/z5dageF8Dn0P3bRl62Zg7YxQfUT1ppZTV7r9Y6aaiKGsz+IzGmS8lueMWX917rt1adiFHN7IJ8/Mr2yTjoi+FcC/36SsrmjTCZsy/k0HWxAAQc8botrmcVW0vqbJ1vSu+7cMu7RlW97gJhfKRK+lTNMtEVTCtofEpmNwqDFs86ONS27ta0Mh46/tmsomGDK65rnO188B8PhQZfnL93+279s+8a4MWvuizvrHhFxym/uizvJwTf4eFOtBkB0D2yLtifO5jcW1MyvG+3ubPLqW2TJdt2yvLOrq6W6Gap1fjcK0L4rUkNE1X09EL4coSu8G8KUDllUAUb12RuuqhgOgy2FWvvBQr1ndJAgG1OTbZNvZdUDO4qsveqr0PSj99bpoOtBTEH6Uu1W2hVqXAfc3lPeFKQHNc3ioMoHVtY5Byxy+iOGYPDN//lB5d8EQGvsuF7rK2nbb9QF7FBCQVpwB0dRUu2QCtZEkFc/7XIcp5EvNWvmm6Cv75vDF6rRd6mjCjzzcuW0+7qu+fHzp+8CKkrCmC8aXVxkrYcxbkGSHsHgMoObMFldr9ZjqG50339KFUEXXpghcGpz8Aqo4atf9XDruuEBbxedCmh4EXatXK8ppind3/zbdHNWVrl1rvKFqLp33EAxI98anb5Y6bZchoO6dr4aVMdbL4tjmWbTBQTB5IvLvdqVVyVlmALuesF3h0nsMj5CiVSbv82fXXhzelpaW6Wrz6Cpo6wRSKZ8u9F2VcVDWvMs7fNfBrtendoU24cZ1bbtOu687PjfBwXTvOo23a5/bWoNkXUJavFtmmIOixk1QSNazpbBNX/o2sO2bz3zoQ6tdR8b37a4Jg/zfVnhDhYehbxwMk2+LbcwH1yUQWJxeT7CBUbeYUtWpdROk7PLZF9S5i6j0e6uQt1LjZDdJ9aARt9m30IShr4IbGttysxD6ZcR9jf9tWsFtcd8x+T7gvZl9zV7rGklZZlJHQ58+tGUMobm7vuuqK//2ia/uWjNvg75JrGLauzjRxlUehhD828bBMPlat0gPi4Rd0VRWG227VYKOdVrxzQf159S3zsget6SnqybWZqCW8/PtKD4q3eNXd0xE18lelceIIqqEcZt1oi4ux6bfq6ZS1eFwlZejNJS5axwOk++yYoL87s6h0LspWfO98YVytEALBr32tu2W3dCXW6LWBaX1rjt9c6VtnQQbWRU9DoChlRP38nFyntW+05BnHTNdB0O0gav8VTL8dfPu+HxIHAyTr8M2GrZpULuD9KgqUduCPAyyPCg7aaB12myX2Yz1mFsbK2wdlBnXOvXrEmu/D6a5D21j5QdhpP1nOWi+wHrt0NaFuwtr7yCZfJ3mu6uImiEX03yuD9fV0CqPFWa9IVFbhrVBOZzU/lSF2DZV0ybIUeGEyOJbV62tqjCZNafpE267tfW9b6WpD6Q/DQfJ5A3Z5Q76vW8TslCW51nfMbG1Gq/zt4phN5niO93R2uJ5W1dQuZ6bVMvWafs6P/9+xz4LRHL++TZE9YFC5N2W2uLgh279tu2ey6r57cbN5rKr6HGfBy3CcaosmX3s7E3u3F31pxe/24SyJiu7Xvpqj31mXF2xT1UZgpYmpcEYfCv3XH9kDYp9nPdrw9eB7rPJUPebOeVv0vFd3117cXS913rPY6Pyyy4T/etOUEtSt8i+zwKwjKGZXu9573qQrIst0b2t9rkKY7sbKhY7CH7z6AodQVFdfslaaUtP4b2WI6GvqJuuea5aYfmDKu2sLNRXfPYdyt8WvAtze0TfUKhypda6KIlauTCb8ql671CY46HUoxFBsLqrcegLdF20KWYy6UZMeRDXvu2uTTQU08ZtVCh3gza0V8MOVlZdcT7f+X3AIwvYhqXWSxmeTNoyJCJgEmx4KJqbX8X3IfnDtsblQTH5rW526pp+TcZaRtViTWF7PhX9z603KPmunStYCY72vKec03XTHc3yz+Xfs+/bIGrNcvZxIXzQMuusve2Rsbdje10cFJNvQhdtcWjYZqC6GHUfjlr0WJVLolHQNOa7/6O/YKmV23YN8jMLaQdV3//WHgZ19e5yyU7bPMu/N5WxrhuokMcWlY2DZPJVphfQnak2lkXtO7186lxrnumkO7nWInlF+Ne+MI2h6CgLsqpy2pRf6qqdYmjZarpPJ791S0a4D+3XBusw3XXWd/rYVd4VB8nkh0YvR6t2yKLM12t90h6N/cUfUu+y8OZzhUeGad+dJlxNXptgqBuG+sQ6zH3jKbBn3N9VEPrQ1PcJV3gq12ObJrartfRR5KNty61aIA3QqEkd2BzN4AonKgfGYzeaVBdU9ul2yThYNPZ52/Wrqn7qMKj69ipU4WCZvIsmrXRbZ3YU/H4173bpFF8510onLfZ9Nsmm+Qw5Edx0Dz24+nsQ5mncYKbm9YiWBFxVdKzfkAeGbbvcLO9136vYs7EvY2YjJk9Ef46I3kFEKRHdKv321UT0m0T0TiL6tM3IXIc2/7Z2N4JkyIOZ2mJd318vZa+hSfR9emSbrLrcfmTHEFQK2RaafNWk3TZ2MS772jey6b6IbaNvF80+VXFTTf5xAH8GwJvdh0T0UgCvAfBRAF4F4JuIaOuxLUOavn0u3K5bdtdoGfu+0un7NCI9aBMZVEjreWYIS4vSfWpdfeVxlddDhkAfwzNr0hqeYPcPeC/y3kfJ1BIbDSdm/lVmfqfnp1cD+F5mXjDzfwPwmwBesUlZ6+Lqds36aDp7o/LCkg6afR+Hf3WCh9F7N9MUDo9vzta99DxoOZH7Wntpi10IoF3xtHXccr2UW1p4bXt+zVXAUDrD8wG8x/n+hD5bARG9logeI6LHbt++3TshQw/Wjf3TG7w7a+PD7JhnFythyAuP66ywvn2eXY6CaJVfj3mVsUdbPbaCIZh5VSG+n/Zpb826aGTyRPRGInrc8+/Vda95nnmPjGbm1zPzLWa+9eijbeNKmuENh+qw0DYkjKm0HUCbHhtgWkpWbuX1ZvK3jQ98q+1ndHVQSbxz+VBUs4HQ5gC/XV8yvrWF1w7rdV38+X0rFG3QeA00M79yjXyfAPBC5/sLALx3jXw2Qqj3l3K6nfK6hOdtOlhDtIga8pSVneHTOBrb0bHpugdhs8ua61w4K/Uvp8XwTEt8ubu4jrodghJ9Tc0x7dF6WycbWvnQ8r2O6QN9Z4jxUXANbcHtOZS75ocAvIaIjojoxQBeAuAXByqrEgGpSwEeZuB5tg1sUqTv3eOac3Sr6ui7OrCNv3tfUUVqG63KDqlrulh9283RV3muoGu71lLL2Gj1oL8hse2QSbe/1z1CoQ4BAdMQmGxxcX3TEMrPIaInAHw8gB8hop8AAGZ+B4DvA/ArAH4cwJczc7IpsVcFVYzVPW7ggQer3q0eWb24ULY8abYNY2ZVE5grlGrfhPblM3Qdq/o4qKrUAOXXhj+69DTl1fmHzbFpFIwbULClJh8cje6aOjDzGwC8oeK3rwPwdZvkf5Wwa823Smvv4pv2nrffsuy2aftE2zUE+8nueQ30WeGCkeHI7MVq7Gt/wqYIaZ+dT+thm3O3yn03ZIjmRkz+KmNIX5jPHeL/sh4NPl9h5RgJVhdeq7TTqzKBa70JPisqzF13gdMWlp5qMg10Tk43GCjhjsPx8hNP+6Fi17tO1ym+7QKqy4LXrWbn+bxmOW1xX2+7CMnf+ZuGTdVpygH6DYMjWo2WqfIxm0bZqPV2NMW3og07hdRFSXnPxA+q601BMVZ+5Xf9tw9adBlZk7SgbR/p3wSbbCLskqbpnepd083vb8uCuG+ZfN9XAQY1oS5EstDSa6dWMa2ST9FFuM6ZsnuGgKr954DUMUCxik2bv+rSzOzvFW2zK0p2LdaJejH3XNV7pBMmU4Qa8guIKhdmM4sRw7ph2uK+ZPJBUH0d4CagoD6PNn7jNr/tftgUsWt6CidPwi/gfM8KeXie7XJfxTqa9zZo3DXP6nLExbr5Z5ZfY1p/ChNCYUMnNo3JvnCQTH6l7V0fLPKG7XLwVR842tQNVPG59e81GvBVRznu2EWVu6ZN3bNzTKo0wEa6urVwrSIwUGf5FuY21UDtku0+dyVbvkOjSwlVaUWbb2DyVG9d9IWDZPJ1GLLCW9GkBnp3Vz7bvosN9Sjh7Cj5kr+dnDS1dOkEDYJ8Y0wTDkVg9oVwDQbWxhXTVgP2CYSq99zAhPLzJlo2gQUEDIn7g8k7ISNDKwLlBRe3uJs32+Xhc/lk+VVMgiotPYuocd6rbIMeGFnX9u2Svk3SbPNbRVnuhCKSjSl1dPRN395ih64pF1XBEH2gHIZanqNNlnAZm9BJqN6z0TcOkslXtn2Pg6dO45iE3QeA5xKjQlnmcqrqsBsVz1sNVrecmmdlmjaN3c6EkCuA1s8uo8vQasNOg+/VFZJVCIh6DVEEmsu0NGiRrm/0ZfUVxludm6rh3bZlNb0zaXnkR2blab/vOw6SyVfBJ7HXRRWDazQH1yzcBt902i0yyBdSWNZYfBoNodmM3HSA2+t1+dQyYKzW2xbTy6QX6lx6Z1MmSWh/THFjXhXZrOP66FJmnZLhoo9NWQHabeu3tpAy80LNhbYOfIKlsGeio7Kx8ZrLFoTEQW2GqnNZbMsGrVpIafI1donN91XHy/A8z8KKl40+O8stCJB/KZWzlxumHAllVQxKrpmsPUqT2329jMwnXxs1RdlVgrQtG7wl2hyQ1kcAQl8CKAzyENmACCAGDXGIDKyMIu02ByzseUhk43Rg3nRQTN7gG9dBiYntC9rEzz/L806ZUZVdHr4s68I724aPDjjfvFinOJ8m74N7rEGbkLdtok9KwpImXIX88pQ13I09WTJyM1OeFxH5ZVRnd41f2FUpRmEATAfgjoRceJTXiIbCQTJ5YNUsKzOxPkz0rlpt1el9ZVq8IW1Y3eTTtFhI8J9SWVv1hnZpEz+8LZgWlFYwJZ8rx5dH1W8raQAMfcqezyIDNtsl3dWlUHaP9ImmXFuFHvZHjhd2DeA6a2tAww5qIkxDztIB26nPQWHbi1BdURfPXYcgcPyyNczK/e7V5lHf6QGK2sVQA8QGeFtfMJX+Ann8uzupMrdMbeG6vjGhxmNzszK2MFPcTXojBNvcMerOzaNgfS07IKqlO1+fo8ySHw8oWxPNq+TYyMHc9Hrd75tKcVfjCzxMyDS/yWzVqqm8GQr9XenXdtAGATAFMFmDs/kWUB9+KGfy5XA5+z4hgEsm84qlZ/lTPilH7Al66o+iwiCLUNbv29goKdE5jPW5QMtyBs19h/Ax15WIiz2YuG3McB8z8zEld6z4LiIm0jhkz3MTFG3bpK+2I7LbhvwZ+iwUHx3l16eltJNgdT2BiGShr0Z7Lp9auS3sw9jcV9S5Q+oQ0v5ZSUO5xQplDF7CjmALKKap5WZRv+V4GXCHwbQWPR7GRjBfokR6fNiL/a/OgnwDkI+OLpFI/TH6bhn5wuBcmEsqcwkFIkgsgsmd7G2svavMcJvalohwfEy4fgzcPN4eO9hFk67M1dLzTKBXva9pXGVhZYPVHg6Wg2XywKqvtvC8Z3OvXG5Tmj7KvXkz7z6XGYUh/PHxLepN1C6Cps+x3CYvLzOnYphbOcY5CPIdlO7kNbdUG+yb5rcOJgE1aow3j7oK2vUahoi8u1rL7rVdwOaHS9/q4n3R304N/vd9wEEzeaBiMvegna37fgdFubbs1paC8zmoGLwuTW1pG2pgy2JUNVMq1se/45ACV7g7E1L/hvBbYLV01f9cyMdH0xAMLDsErP+sW5W9LrzHdmzBldKYPfW3JrVPOHgmPxQC9ef6YBPPe0ASrTLYLqhLv65G3KWMVnS0eH9IVEXCkOdzdu53VV5U/NtYtkezmwbUOQa/LaMJg/3VJIcga5O6hh36YZtNOnRRGzF5IvpzRPQOIkqJ6Jbz/EVEdElEb9V/37I5qVcHsqAnXedt4A161dwp2Q7LkosiS1fF6Op8jj2MtoC6M7RN4KPZbntyfa52LklVHpsKzzqsE3dedi+N6B+mcNVtIhxagG5jvmwaQvk4gD8D4F94fnsXM798w/w7gQIg8ITVVHhs9gJhCMQdt+EGAUBaz6rx4YskCANgFjZrul3H9a4VScr+c565k5fyhXj73vb0gT6q1olRuFYe5XHUvKXjEsKA9vLYiqHGWHmObPuOiW1gIybPzL8K7MeKsk3ktIaUocls4/MDgOPQdem0yLcj3VWhYr7LM8qRAsZLutBXhy4M1fv+Bi+uLJqtYbeWtb0R7VF1jAgRNt6f0vR7OXtqMRBtzJcjaPrGhtXvjCF98i8mov9KRD9DRJ9UlYiIXktEjxHRY7dv3+6l4CbGFAbAyVEvRa0NomF2UXrdF+ZXbrqOzKEpDNpv696ljPcVXRU91fkCC+T7CkxwbovZr9Ompmztgc4FwBbGqfcxNGT1wlDDrT37TPrCtsNyGzV5InojgOd4fvoaZv7Bitd+D8AHM/MHiOgPAfgBIvooZr5bTsjMrwfwegC4devWVgScbB8ncE/ytGuHnZ52zL+lMGhDRhdS91F7XVm49tHocd00pqkqT/8Tdw/tjZtvV9hUC29CsI3zIxR1a1N9WLFV2MYGKBeNTJ6ZX9k1U2ZeAFjo57cQ0bsAfASAxzpTuCbcnYrbbFM3Lp/XvJFqMgGwdN71pPHlZ4c7ETk+l3Le4epz34JTW2yraTNrhJoniU3QqqNi3beDmglNAI719xtbqmhIQNKyLCJCAO69D+ry65PJrz3maDvjTnjH1Rfrg4hNInqUiEL9/KEAXgLgt4Yoq5aOLZfX1Jg7GS6lCeGbIGHjiV6ebLNNLfsXwkcBQM49rrVHLFP9dvcpUNs2Q9S9S5ZNh2EdAspHJdsO9iFg6y+H1KSbhlB+DhE9AeDjAfwIEf2E/vTJAN5ORG8D8O8AfBkzP7UZqV3oKn7epdvBpWU28HFwPg2XNlHTUT9ANmUu67xeeSOXx0XjRtPU0VAZWom86Q5ozm8NfbVZeZwNLdgOicEDm0fXvAHAGzzPvx/A92+Sd1+wyW4WJvV4KHjZdK0zZVv5y6ldJMoaindDwflOv3UiYbquBbRhvuW07vfyBC8fukYEEOfMezaTEwa7onA42ZYn/rYZzaExtja4X+p8WEcNUzvTZJd920fZbRaFfGl8t+MUNt3s6aBvOj2wapt8ZXpq51be9nrOiP7QttvKh9UFkLFxSN1+UMcaBKj2v1pI4K5ReTBW28U2tBdkgYaCuS4Or7uDioe5bQttrZvO6wVa7yrY8cJGQxuhuS1Y/+yrqyjQtZg+sO16+a5BdL+WffFVi/FXDYelyd8HqFoUqht8QXng8moeu1q3aFNs1/j0pk1xhfJ3vGbjQ+7OIoRgrwXWa3kd0gZEjSG9m26AGwplNx+RXNrhMn5SBSDkq8nQfTg4Jm8MIdniIGujBfYV/mtnomeMoJ9sd4awxTG4XW/Rsp27+8a818E2Ime6H57WP0276ip37NlFO4Ey+S2G7A+KA2XyhMTRfNydnPtkmhuayFn5fY0Fy9bv76EfuqseO53IwuuKtVKRN1Bznk+HReI+sS4jtbDWq4ZpGCBMuy+O94lDC500HIisao99NM+bUJbEQfm4AY/rZd067uMVabaxqe0E9F1jOA01ggjF9gn11qgqhHqgWxhuS6v21zNsecvJVWTwhm3udq3C1W29auy+VbeAoTuu73n1iZ/oKaMirS2YVi0YnU49edkirCfvbfKIdguvzanK99n67mUNyM7iIWXYbhnN+W8LAfmvZ7xKMEF61eBa+4eEg2fyrfzlG+RdlX3bge5LUrWpybsJSP/6FL0aZf9Kocm6IGWM1m7ToNma2ZSPDikMr7AyDsAWNK9eJcoUX8EqeHFwPnmD+eHTRP4OtQ4bVlxGYYdZJQ0lt4oucS7BKKcP9Ix4l8kHRDiZcbaINAvzkwB95RmTJLSLitj22O8aXXI8Azjqp+wwUHfNwatDgkNhbOvAqm7x8oeCg2TyBPHhprb4xvvpa14Hm64pTMJqRj4JgETXvvIBP0yjDdkVE08bFayuLoWb26eDe2dT7HKYXkUNvE/Y5fDBBiGU+9aChySwMlhYnnsF3BBoMyH6njMEv0bvg8+X7BN2vrzCIMBROMwZIZtceeYzqc0nb79NJqvtHgb+ek7UVx9W+G9Mi9+WJn9V/dmHAqJ8jqy7eXLfdkofJJM/ZNgAyjTtmrS+BbxpxfV/PsFRxeAr1yHWGNibTgbzvUtUimQWBKt1DKhC6Km7q4qOMFQf/5YWQ5tcBVXCaER3rG6Oyv9usthul6XsCw7SXXOlsA5jBHDU0HOho+EW3tUH7hVn5gJqOy79i8Xdq7LeBdeeRWmsRtK01YarJnTWToG6fxoWtkdcTUycgVJWoA4F9wWT31an+cw0if11Nnl4F2m7abREwMlxO1q8RyBQ8W8f6MrkhbbNCHAFluUUKlOehvK3MQ9U9BvZhdbsZfDBIXKDK4Khmt0CEMJA1q0OpXsPislXzbsho2sK5TRokK62nPnWaT0t2HBSV9aaedbBPcExrwOBwJ2ExjrmbNUrPoExDcWnauXUvVtnxVhk0x7s0zkYBHpmzD5v3NrEJ79vOCgmv7cnKZa+H8+KboIyzU15lt0TXYnL/PmkC6tJ++3khfUAyk8kXMf10q48ysKBQl1Mr0PgHLbvpnUFMDm/ZTdDVQyc6RQ4CuXvtrDNC8N3hX3yWRtc69d30mZfJG+76gfF5H3Mzxh/T/eErEAieZrS5Jq178KPrjsui4x2dccrUB+BUw7DdNNNgu4HVkmew41cYXrtxPckCDChdMWSsdA4SYPsJMVJIGfd+EBEmE0ZR1NgtiUmTyguIo/wY4jmmQSESVht8/clmPbuIu+rhDDwn3Vd3kvTdKT7pl1Q6xoIcibdRiNvlaaqzr7n7r9ydIH+V1eeo1hvDXWTwhV2cD5PSxvEJk5UUdmtV7cmQgFwOkHj8bp9YdN9EPcD1g2/bYK5HQ8NB8Xk28AmUUC6WQo5syPV9ta9FKFpAZUAzBxGE9g1NDUIoX5h/R5g1b1g2p8Lq2P5fJpJGCDg4hnaWQgiSF0Z1ZWoW3D0Chv4BUofkHh7hy6HjmmpH6vi5K3uVZiFwGkoLhtLP/LgdqBdaAQbIh9Ph2NNHQyTD1Rdz2KliTALGCEJH52w+HRPQkZsB1URgZwFoIlGwrQ5Dc8WIF2FeRLoglIp7SQAiIFnTQGcAg+EwPFEypoEQBAL8zetnQHcOgF+5xL46OcAL3tZgDhNEQA4DYAPvgacHknexwHw6APAwzccJgfgZAp8zHXgZFYME8vbKseR+jG4xYSsUqKqmJ/rtx8CPq3u+adAnBSja24eq8Ak4MhxvYTQYyEqOP0jDwY4uZbiVI+qHNLUbhp/s3DzM3e2jfLYA/I1kaGY6Caa/tGEwKoEXS3xVA1qM7ErXyb6BwA+C8ASwLsAfBEzP6O/fTWAL4a4w7+SmX+iKb9bt27xY489tjY9I0aMGHE/gojewsy3fL9t6mn8KQAvY+aPAfDrAL5aC3wpgNcA+CgArwLwTUR01ZSQESNGjLjy2IjJM/NPMnOsX38ewAv086sBfC8zL5j5vwH4TQCv2KSsESNGjBjRHX3GDPxPAH5MPz8fwHuc357QZysgotcS0WNE9Njt27d7JGfEiBEjRjQuvBLRGwE8x/PT1zDzD2qarwEQA/hue82T3uv8Z+bXA3g9ID75FjSPGDFixIiWaGTyzPzKut+J6AsAfCaAT+V8FfcJAC90kr0AwHvXJXLEiBEjRqyHjdw1RPQqAH8LwJ9m5gvnpx8C8BoiOiKiFwN4CYBf3KSsESNGjBjRHZvGyX8jgCMAP6Uxrz/PzF/GzO8gou8D8CsQN86XM/NQJwuMGDFixIgKbMTkmfnDa377OgBft0n+I0aMGDFiM2y0GapvENFtAL+9QRbPAvBkT+QMgX2nDxhp7AsjjZtj3+kD9ofGD2HmR30/7BWT3xRE9FjVrq99wL7TB4w09oWRxs2x7/QBV4PG8SqEESNGjDhgjEx+xIgRIw4Yh8bkX79rAhqw7/QBI419YaRxc+w7fcAVoPGgfPIjRowYMaKIQ9PkR4wYMWKEg5HJjxgxYsQB4yCYPBG9iojeSUS/SURftWt6DET0biL6ZSJ6KxE9ps8eJqKfIqLf0L8PbZmmbyei9xPR486zSpqI6Ku1Xd9JRJ+2QxpfR0S/q235ViL6jF3RSEQvJKL/SES/SkTvIKK/os/3ph1raNyndjwmol8korcpjV+rz/eiHWvo25s2bAVmvtL/IDeivQvAhwKYAXgbgJfumi6l7d0AnlV69vUAvko/fxWAv79lmj4ZwMcBeLyJJgAv1fY8AvBibedwRzS+DsDf9KTdOo0Angvg4/TzDciFOS/dp3asoXGf2pEAXNfPUwC/AOC/35d2rKFvb9qwzb9D0ORfAeA3mfm3mHkJ4Hshl5bsK14N4Dv183cC+OxtFs7MbwbwVEuadnL5SwWNVdg6jcz8e8z8X/TzPQC/CrkvYW/asYbGKuyCRmbmM/061X+MPWnHGvqqsJeXJR0Ck299QckOwAB+kojeQkSv1WcfxMy/B8hEBPDsnVGXo4qmfWvbryCit6s7x0z4ndJIRC8C8AchWt5etmOJRmCP2pGIQiJ6K4D3A/gpZt6rdqygD9ijNmzCITD51heU7ACfyMwfB+DTAXw5EX3yrgnqiH1q228G8GEAXg7g9wD8I32+MxqJ6DqA7wfwV5n5bl1Sz7Nd0bhX7cjMCTO/HHLnxCuI6GU1ybdOYwV9e9WGTTgEJr+3F5Qw83v17/sBvAFiur2PiJ4LAPr3/bujMEMVTXvTtsz8Pp1wKYBvRW4G74RGIppCmOd3M/P/q4/3qh19NO5bOxqY+RkAbwLwKuxZO5bp29c2rMIhMPlfAvASInoxEc0AvAZyaclOQUTXiOiGfQbwJwE8DqHtCzTZFwD4wd1QWEAVTXtz+YtNesXnQNoS2AGNREQAvg3ArzLzP3Z+2pt2rKJxz9rxUSJ6UD+fAHglgF/DnrRjFX371IatsOuV3z7+AfgMSPTAuyB3z+4DTR8KWWl/G4B3GF0AHgHw0wB+Q/8+vGW6vgdiYkYQzeOL62gC8DXaru8E8Ok7pPFfA/hlAG+HTKbn7opGAH8UYoa/HcBb9d9n7FM71tC4T+34MQD+q9LyOID/Q5/vRTvW0Lc3bdjm33iswYgRI0YcMA7BXTNixIgRIyowMvkRI0aMOGCMTH7EiBEjDhgjkx8xYsSIA8bI5EeMGDHigDEy+REjRow4YIxMfsSIESMOGP8/1K0gGjmO5osAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.23 s, sys: 364 ms, total: 7.59 s\n",
      "Wall time: 6.88 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "for yi in range(N_CLUSTERS):\n",
    "    X_sub = X_train[y_pred == yi]\n",
    "    ts_cnt = pd.Series(y_pred[y_pred == yi]).shape[0]\n",
    "    fig = plt.figure()\n",
    "    plt.title(f\"{algo} | Cluster ID: {yi} | TS Count: {ts_cnt}\")\n",
    "    for xx in X_sub:\n",
    "        plt.plot(xx.ravel(), color='xkcd:sky blue', alpha=0.025)\n",
    "    fig.savefig(f\"./tsl/plots/{algo}_cls_lbl_{yi}.png\", dpi=150)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a036937f",
   "metadata": {},
   "source": [
    "## Generate TTS for different clusters\n",
    "\n",
    "We can now split the TTS into clusters based on the labels for the different items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8facf29a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1722270, 3) item_id          object\n",
      "timestamp        object\n",
      "target_value    float64\n",
      "dtype: object\n"
     ]
    },
    {
     "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>item_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>target_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10002</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10080</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10109</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10120</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10125</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  item_id   timestamp  target_value\n",
       "0   10002  2009-12-01          12.0\n",
       "1   10080  2009-12-01           0.0\n",
       "2   10109  2009-12-01           0.0\n",
       "3   10120  2009-12-01          60.0\n",
       "4   10125  2009-12-01           5.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert data to TTS format expected by Forecast service\n",
    "df_pivoted.reset_index(inplace=True)\n",
    "\n",
    "df_tts = pd.melt(df_pivoted, id_vars=['StockCode'])\n",
    "df_tts.columns = ['item_id', 'timestamp', 'target_value']\n",
    "df_tts['timestamp'] = df_tts['timestamp'].str[:10]  # keep only the date part\n",
    "\n",
    "print(df_tts.shape, df_tts.dtypes)\n",
    "\n",
    "df_tts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c437a885",
   "metadata": {},
   "source": [
    "### Train - Hold-out Split\n",
    "\n",
    "Hold-out set offers a way for verifying model performance on unseen data. With this dataset, we are looking to forecast a week out (Forecast Horizon = 1 Week) and therefore leave out a week worth of data out from the TTS as holdout set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d28a9e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2009-12-01', '2010-12-09')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(df_tts['timestamp']), max(df_tts['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65803ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1722270, 3), (1690035, 3), (32235, 3))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_tts[df_tts['timestamp'] < '2010-12-03']\n",
    "df_test = df_tts[df_tts['timestamp'] > '2010-12-02']\n",
    "\n",
    "df_tts.shape, df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ba2c090c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4605, 4605, 4605)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify that we have adequate coverage across train and test\n",
    "df_tts.item_id.nunique(), df_train.item_id.nunique(), df_test.item_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fbdbb96",
   "metadata": {},
   "source": [
    "## Split data into clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adf72f6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 ... 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "# if restarting, reload the cluster labels\n",
    "y_pred = np.load(f\"./data/tls_{algo}_cluster_labels.npy\")\n",
    "\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e4a6deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4605, 2),\n",
       " item_id    object\n",
       " label       int64\n",
       " dtype: object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lookup dataframe with item_ids and corresponding labels\n",
    "df_lbl = pd.DataFrame()\n",
    "df_lbl['item_id'] = df_pivoted['StockCode']\n",
    "df_lbl['label'] = y_pred\n",
    "\n",
    "df_lbl.shape, df_lbl.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b03bc991",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1690035, 4) (1690035, 3)\n",
      "(32235, 4) (32235, 3)\n"
     ]
    }
   ],
   "source": [
    "# merge labels back to the TTS\n",
    "df_mrg = df_train.merge(df_lbl, how='left')\n",
    "df_mrg_test = df_test.merge(df_lbl, how='left')\n",
    "\n",
    "print(df_mrg.shape, df_train.shape)\n",
    "print(df_mrg_test.shape, df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01d4a5ad",
   "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>item_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>target_value</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1191247</th>\n",
       "      <td>35638A</td>\n",
       "      <td>2010-08-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320695</th>\n",
       "      <td>90188</td>\n",
       "      <td>2010-02-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111322</th>\n",
       "      <td>22322</td>\n",
       "      <td>2010-07-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531250</th>\n",
       "      <td>72778</td>\n",
       "      <td>2010-10-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668645</th>\n",
       "      <td>22450</td>\n",
       "      <td>2010-11-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_id   timestamp  target_value  label\n",
       "1191247  35638A  2010-08-16           0.0      0\n",
       "320695    90188  2010-02-08           0.0      0\n",
       "1111322   22322  2010-07-30           0.0      1\n",
       "1531250   72778  2010-10-29           0.0      1\n",
       "1668645   22450  2010-11-28           0.0      0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mrg.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d5b04b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create required directory structure\n",
    "dir_paths = ['./train']\n",
    "for i in range(N_CLUSTERS):\n",
    "    dir_paths.append(f\"./train/cls_{i+1}_DTW\")\n",
    "\n",
    "for dir_path in dir_paths:\n",
    "    if not os.path.exists(dir_path):\n",
    "        os.makedirs(dir_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a910bf10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1690035 1690035\n"
     ]
    }
   ],
   "source": [
    "# split and save TTS\n",
    "record_count = 0\n",
    "for i in range(N_CLUSTERS):\n",
    "    df_tmp = df_mrg[['item_id', 'timestamp', 'target_value']][df_mrg['label']==i]\n",
    "    df_tmp.to_csv(f\"./train/cls_{i+1}_DTW/tts_{i+1}_DTW.csv\", header=None, index=None)\n",
    "    df_tmp2 = df_mrg_test[['item_id', 'timestamp', 'target_value']][df_mrg_test['label']==i]\n",
    "    df_tmp2.to_csv(f\"./train/cls_{i+1}_DTW/test_{i+1}_DTW.csv\", header=None, index=None)\n",
    "    record_count += df_tmp.shape[0]\n",
    "    \n",
    "print(record_count, df_mrg.shape[0])  # verify that all time series are retained"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "985b9119",
   "metadata": {},
   "source": [
    "### Processing Complete\n",
    "\n",
    "These TTS files can now be uploaded to S3 and used to train Forecast models as described in the [Forecast Developers Guide](https://docs.aws.amazon.com/forecast/latest/dg/what-is-forecast.html)."
   ]
  }
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