diff --git a/notebooks/eda.ipynb b/notebooks/eda.ipynb
index 8abf50d..efe34a5 100644
--- a/notebooks/eda.ipynb
+++ b/notebooks/eda.ipynb
@@ -2796,6 +2796,195 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# identifying contiguous variables\n",
+    "\n",
+    "The objective of this section is to find all the variables of battery 1 and try to see if I can clean it up for multivariate data processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "import copy\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from pylab import rcParams\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rc\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "\n",
+    "from torch import nn, optim\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "import random\n",
+    "import datetime\n",
+    "# from arff2pandas import a2p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "from io import StringIO\n",
+    "import math\n",
+    "df = pl.read_csv('../data/battery_1.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['BATT_PACK_1_FAULT',\n",
+       " 'PACK1_CRIDATA_AVG_CELL_TEMP',\n",
+       " 'PACK1_CRIDATA_AVG_CELL_VOL',\n",
+       " 'PACK1_CRIDATA_BATT_VOL',\n",
+       " 'PACK1_CRIDATA_BUS_VOL',\n",
+       " 'PACK1_CRIDATA_CURR',\n",
+       " 'PACK1_CRIDATA_DISCHARGE_CURR_LIMIT',\n",
+       " 'PACK1_CRIDATA_SOC']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_condition =  df['PACK1_CRIDATA_BATT_VOL'].cast(pl.Float32) != 0\n",
+    "# filter_condition = [ True ] * len(df)\n",
+    "\n",
+    "voltage_data = (df['PACK1_CRIDATA_BATT_VOL']\n",
+    "                .filter(filter_condition)\n",
+    "                .cast(pl.Float32))\n",
+    "\n",
+    "def convert_values(values):\n",
+    "    numerical_values = []\n",
+    "    for value in values:\n",
+    "        if value == 'False':\n",
+    "            numerical_values.append(0)\n",
+    "        elif value == 'True':\n",
+    "            numerical_values.append(1)\n",
+    "        else:\n",
+    "            # numerical_values.append(np.nan)\n",
+    "            numerical_values.append(-1)\n",
+    "    return numerical_values\n",
+    "\n",
+    "\n",
+    "fault_data = convert_values(df['BATT_PACK_1_FAULT']\n",
+    "                            .filter(filter_condition))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'soc')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x2100 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start = 0\n",
+    "end = len(df)\n",
+    "#\n",
+    "\n",
+    "filter_condition =  df['PACK1_CRIDATA_BATT_VOL'].cast(pl.Float32) != 0\n",
+    "num_rows = 7\n",
+    "fig, axs = plt.subplots(nrows=num_rows, ncols=1, figsize=(10,3 * num_rows))\n",
+    "\n",
+    "numerical_values0 = convert_values(df['BATT_PACK_1_FAULT'][start:end])\n",
+    "numerical_values1 = (df['PACK1_CRIDATA_AVG_CELL_TEMP']\n",
+    "                     .filter(filter_condition)\n",
+    "                     .cast(pl.Float32)\n",
+    "                     .fill_null(strategy=\"mean\"))[start:end]\n",
+    "numerical_values2 = (df['PACK1_CRIDATA_AVG_CELL_VOL']\n",
+    "                     .filter(filter_condition)\n",
+    "                     .cast(pl.Float32)\n",
+    "                     .fill_null(strategy=\"mean\"))[start:end]\n",
+    "numerical_values3 = (df['PACK1_CRIDATA_BATT_VOL']\n",
+    "                     .filter(filter_condition)\n",
+    "                     .cast(pl.Float32)\n",
+    "                     .fill_null(strategy=\"mean\"))[start:end]\n",
+    "numerical_values4 = (df['PACK1_CRIDATA_BUS_VOL']\n",
+    "                     .filter(filter_condition)\n",
+    "                     .cast(pl.Float32)\n",
+    "                     .fill_null(strategy=\"mean\"))[start:end]\n",
+    "numerical_values5 = (df['PACK1_CRIDATA_CURR']\n",
+    "                     .filter(filter_condition)\n",
+    "                     .cast(pl.Float32)\n",
+    "                     .fill_null(strategy=\"mean\"))[start:end]\n",
+    "numerical_values6 = (df['PACK1_CRIDATA_SOC']\n",
+    "                     .filter(filter_condition)\n",
+    "                     .cast(pl.Float32)\n",
+    "                     .fill_null(strategy=\"mean\"))[start:end]\n",
+    "\n",
+    "axs[0].scatter(range(len(numerical_values0)), numerical_values0, s=10)\n",
+    "axs[0].set_title(\"fault\")\n",
+    "\n",
+    "axs[1].scatter(range(len(numerical_values1)), numerical_values1, s=10)\n",
+    "axs[1].set_title(\"avg_cell_temp\")\n",
+    "axs[1].set_ylim(-20, 100)\n",
+    "\n",
+    "axs[2].scatter(range(len(numerical_values2)), numerical_values2, s=10)\n",
+    "axs[2].set_title(\"avg cell vol\")\n",
+    "\n",
+    "axs[3].scatter(range(len(numerical_values3)), numerical_values3, s=10)\n",
+    "axs[3].set_title(\"batt vol\")\n",
+    "\n",
+    "axs[4].scatter(range(len(numerical_values4)), numerical_values4, s=10)\n",
+    "axs[4].set_title(\"bus vol\")\n",
+    "\n",
+    "axs[5].scatter(range(len(numerical_values5)), numerical_values5, s=10)\n",
+    "axs[5].set_title(\"curr\")\n",
+    "axs[5].set_ylim(-20, 20)\n",
+    "\n",
+    "axs[6].scatter(range(len(numerical_values6)), numerical_values6, s=10)\n",
+    "axs[6].set_title(\"soc\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2820,7 +3009,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.5"
   },
   "orig_nbformat": 4
  },
diff --git a/notebooks/model_save/model_23-09-07.pth b/notebooks/model_save/model_23-09-07.pth
new file mode 100644
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diff --git a/notebooks/model_save/model_23-09-08.pth b/notebooks/model_save/model_23-09-08.pth
new file mode 100644
index 0000000..5a471b2
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diff --git a/notebooks/model_save/multivar_model_23-09-08_01.pth b/notebooks/model_save/multivar_model_23-09-08_01.pth
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diff --git a/notebooks/multivar_anomaly_detection.ipynb b/notebooks/multivar_anomaly_detection.ipynb
new file mode 100644
index 0000000..27e2f08
--- /dev/null
+++ b/notebooks/multivar_anomaly_detection.ipynb
@@ -0,0 +1,1473 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multivariate Time Series Anomaly Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## boilerplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "import copy\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from pylab import rcParams\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rc\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "\n",
+    "from torch import nn, optim\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "import random\n",
+    "import datetime\n",
+    "# from arff2pandas import a2p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x7fe8283d0c30>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# %matplotlib inline\n",
+    "# %config InlineBackend.figure_format='retina'\n",
+    "\n",
+    "sns.set(style='whitegrid', palette='muted', font_scale=0.7)\n",
+    "\n",
+    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
+    "\n",
+    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
+    "\n",
+    "# rcParams['figure.figsize'] = 12, 8\n",
+    "\n",
+    "RANDOM_SEED = 42\n",
+    "np.random.seed(RANDOM_SEED)\n",
+    "torch.manual_seed(RANDOM_SEED) \n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "from io import StringIO\n",
+    "import math\n",
+    "df = pl.read_csv('../data/battery_1.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We only need 'PACK1_CRIDATA_BATT_VOL'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## visualize fault and non-fault regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "input_data = df.select(['PACK1_CRIDATA_AVG_CELL_TEMP', 'PACK1_CRIDATA_AVG_CELL_VOL', 'PACK1_CRIDATA_BATT_VOL', 'PACK1_CRIDATA_SOC'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# process explanatory variables\n",
+    "filter_condition =  df['PACK1_CRIDATA_BATT_VOL'].cast(pl.Float32) != 0\n",
+    "voltage_data = (df['PACK1_CRIDATA_BATT_VOL']\n",
+    "                .filter(filter_condition)\n",
+    "                .cast(pl.Float32))\n",
+    "\n",
+    "input_data = (df.select(\n",
+    "    pl.col('PACK1_CRIDATA_AVG_CELL_TEMP').cast(pl.Float32), \n",
+    "    pl.col('PACK1_CRIDATA_AVG_CELL_VOL').cast(pl.Float32),\n",
+    "    pl.col('PACK1_CRIDATA_BATT_VOL').cast(pl.Float32),\n",
+    "    pl.col('PACK1_CRIDATA_SOC').cast(pl.Float32),\n",
+    "    pl.col('PACK1_CRIDATA_CURR').cast(pl.Float32))\n",
+    ".filter(filter_condition))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def convert_values(values):\n",
+    "    numerical_values = []\n",
+    "    for value in values:\n",
+    "        if value == 'False':\n",
+    "            numerical_values.append(0)\n",
+    "        elif value == 'True':\n",
+    "            numerical_values.append(1)\n",
+    "        else:\n",
+    "            # numerical_values.append(np.nan)\n",
+    "            # numerical_values.append(-1)\n",
+    "            numerical_values.append(-1)\n",
+    "    return numerical_values\n",
+    "\n",
+    "\n",
+    "fault_data = convert_values(df['BATT_PACK_1_FAULT']\n",
+    "                            .filter(filter_condition))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'fault incidents')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9jbXXT167f9/ubGV18JR1MKj6PTMXd7TaHfJbf+tD46UvS6XP6/j7SI6x3tsJe56xkVL3KCuHQ37pRLm13HeMrH3J57c6WOsLyVqOk7xWBlX5dY6Szo22Mqy6nCg+0vrnN2vflxZpSFd2ty7V9BrSwDjpZIV1KeSpOs7IXtzRyqdP9Ol+9LPfT5RZKY+/VBXeGB2riNChBtadvbzWpa1bqh206hNtTauk0trm9PBYv5+skNK7Wn+L+WXW33ZStHXmv7RS2nzUyugrv/V++tqvSdZn2yHSWnY+OWltz+p6b50ipePVLk/tEmVdZlxYbuVbUmmN4zGsDLp7rIOMEXbmJZXWvE/a0+gYafW/qr/5Zda0Yux+lp4x/1GdpJ1F1md6vFz1Ztc50lRcpKFj5XWfMY80rHXOYb/Vt/6x1oHMjpGns46JOD3/MZ2t3LedOH2PWKRh/V2EQvVl9+KO0ldl0jU9rXXKv4usZfW8WGtZ+qqB5SXBa61nImR9fp0irQOUuaesdeO3O1vzyi2z+j4wzlpHl5vW3+v+apf19o2WhsRb66zSSutzKio31U1l6hXnUUqHCG04bB0E/XZnK5vzYq3++U1rPTcgtuY9dedGW7n9sJd0Z29rfyK/zLr65t8npNeacR/VOfb+g6nT67KkaCuLnNKal1XX5Ztx1t//kXIFfdA9yrAO7kYZNbfBXsM68BsTYR307hBpfb77T1l/W5L1mZiy1qWSlX+UTHWqLNWATl79JDlSY7qEvziSKJAkubdAqnJpJ+n9E9Yf8/fOkWb3ktKr3fj7f0OkyWe5z6PfP019aa8EKscbKvSbeuWI9OUpa4N852fWDu8F8dbC+8sDtadxtnuQCv2mTlZILxy2dr7LTevm41GdrLNUW4/X3OA1x8mxdZ95OVFuqtDeeXri4Olio6DM6kd5eblePlSunMqaD8Fo6v1VVfOTrHtbzo+zVkjZJaefbPJ5ifTSkSZNvkFVZ+iq/KiPdS9VbIS10kqqViAFo6DMlL9S+tkX1k7rh0XShq+kW3tJx8qlP1W7MXn/ZVLvaGu6uadMfeNf0g0J0q+/UaLdu3dr0KBBiouLU3GFqe/tkjJCdJnYvedKj6UYDV76WRenzlyFUn3vqepS26rLQ6uyPds0qu98SNZysOhzs9aDCeorVoN11U5TLx85PY/XvjYDDyb4bJSUUs9Z8Cr+SlNbjlvFZ3Qz+lGfz0tMpdgF0aoU6+BL33+dfr3q7yOYfIN1uMxUz2pncBv6G9x90tS33rN+f2GwNO2c8C2nZy6DleMNVZqm/GbzP5uG8q0+30hD8o+z5rVgn6kV+63hDV1yHkqVpqmof1i/35ok/eF8a56fFVtPDZMaPngZKuN3mNp81Pr9+BgpvoEDYqFcdoN15x5Tv8u1fr8+QfrLN1tuua1+CfndfaTHBzg776bk29j7tw+XmSqttNbZvz0oPXdI2j5CeuWI9NP/1H9v5p5R0oCzrGOdVPU+f5cqze7VtH6EY/kNRrC1QVSDryJkfplcprn/8dYY9q9qRcW6r6x/1UU2YT5dPYauTzz9/2sTar7+iwHWz8bsiHb1GOrqse4LqnJv3/rbm6YpU9ZO/ouHraNj/zhqHVWZ0PV0u7/kS7vOeBpWfZeldYwy1NFeWpefZ/38frX3WVzs103F1h9i/NbYoN9bfarP75Hzghun6n2Xm1YRUlYpfVws3b5HWthX6uaxngwjWUfktxy3ChSp5k2PZx7l/NUB6191jVldJdj3bf0+tfZrlaZZo0Cq/vyKXtGG/OOs34vPOAIdF2nolQut3w+UmjV2TJti5X5p5f6ay2TVGbjvdpNevtDqWPXl9j+XSt8I4z1QzVF2uRTVhB3Tm5OsHc0n7R2Yk2NDO/3qzjx0Vn1qwUzaE2FoTJdmdaFBMdVuNYwyrDPfTmvMPKpn5EB92GwRhqHoFuxX9SfXVV+0WqoL1oNsrDlXr0mqz78l+lJWbf0egucthdyvB1qXrSfHtnznqs8xuo08Y7n6fdOrBlj/JOmmJOtffftiLlw02h0KpBZyc88K7c/7Uh/H9NYlXSO15Zh0XULD91G47PkAQTMMQ4asS0hm2QXat7vUbjffLrJu+9TUmgaehNVaVL1vryGN6GQNG93FOjNYZX4DheWxclMFZVaRsuW49FRe/Y867d/8GlBSzZ0GqWkb7D4xhirHS6v3m4En8nwjRrUuw9t3idQ/1jpyfckH1iUMP+wl/T639jTrOzI3s6d16eX932i9xVHFuMY/8OToGOmdY9KkbtZZ4Sp1HVDYeEHzi6O61NjhD/nUGy/2jE60xNLQmPddvW3rXFJDq/puYPVjQOHYzkXWU7y2xHJd/T4UNxZIEYah5BBtXxo972q/t+KHzTbKtuHSG4XWQdDqlwS65aCKS7oRFhRILWim97AGpZ5T41TjDQ18D0UwG47mXh+5uF8zJxACF8WHuwfu0DnKUGf7LzI51iqgH/nCVJRhHWn6xX5pfFdrheVUZs3ZKN11rqG7zq09vMI0FVmtIIgwDL03/PTrT6RKN+82tTbfuiz0rWH1z2Pt+dblhuG89KC5mvI0yE5RhiZ3t343z/JX/52uDb7cZNV77YaDN9XPIPnNltmQN3UeX4fgnsa2pPrZyXAU27nVdkQbe2a0uaqfQXLD35GbVF81urF4dMKwjoaGdTz9Jc5V3HAQSqJAgks5ufLcPlz6vyPSj+vYoW1pP7TvhXFqx641W/iN0wvBz/o7Pz8njtpFBlEQPDXI0FODzj6tmEhDA9xzKXNYnO2uUac2rNWn64ajm9HhKJAaMZPqTRv70JG24j+XSsl1XH5bfREOR5HQr9oZkhrLdQvMu/oZJCe+OqM1q56G2wskp5cVN6xjJQokuFRT7kEK1kUdDV3U0cEZNEJUhKH7+4W7F5Dcv1HC2c8aO7Vhrb4v54ajm9UvDy2paJl7kBp1iZ3L8goH6zLY2ktsjXuQwlAk1Ffst0RX6npKICytqUCa6PABXbesM9pzDe+WzwB14PQ7WhrLnLNCcWnk2QqkUH2EZz5OuPrGwm3LiSl3X2LnlqPBbhHuR+fWKIqqD2+BeZdRINWr+mfx3e5h60ZQQr0Mn7mKYJ0RfhRILua2nRC0fVzy4ax3Gri/KlgtdQbpH0dr/r+ldyQbw+2X2Lktr3AL95eL1HcGqUXuQQp3dehi1fOPc/ISmlbALVtit/QjHFhvuxgFEtB2XJ8Qmu97qWvn8tNqj8t3aqVefbZuO7pZbrZMn5p6iR3HHdylvkd7t/RjvlFTe/4zOXMd4ZZ1rEu6ERYUSC4WzIfDwSiEituv+W7tnIy36osnJefOAob7xvqGlJstszFrzNvmDFL9wr3dqu9+upb4nPzhfvMu5rLVSoO6OHwHv1vWGa3pMwk1t3wGqINbjiCgfUj0nr0Nwq+u/auKFp6v2zYcplpmfVl9x/qac5yfX1sV7pMoFdUfM84ldq7RGs60/u83pUs7SY8PCO103XoPUmv4TJzitu0cquHDAXCmuvavKlt4p8stG+9F35DOjZYW9G2Z9WX1edzZu+G2LonIlcJ9D1L1AqmlL7GroEBq1f4rwdC7FxvqHe3s0sL+X/jxGbhYe67c0fJaeicbTVPXzmVLfHTh/nLPuvysv6GcS6UEr9EyZ5Cq/X62+VV/PdxnTFBTfWeQEF5clnoay2X48T1ILtbeVxBoWdRHrUNdn1OLFEjVfnfTPUhV91y1yD1IjXjfvapdstq5nT+R60zhXtdUL1hdtCi3e94IQzclmjpRIfWLCXdvwssty6Vb+hEOFEguFsyCGe4NDdqO9v5Y1dYiXJfYufkeJKnln2J3ttkZ1b7IlqsBagr3dis17vTv1T/TlujXkA7SzpNnb9dePTWoff6x1LoHKSy9QHUUSC7GKVa0pKXJ4e4BguGGM0huXDe1zBmk00UPmi5cl/P+a5j0eqF0W9LpYS29LI/pQoGE2s78k3DLOtYl3QgLCiSgnft4pLT1uHRdz3D3BMEI1w3uNe5BcuEpkZbeoXBfAq1HSZhuyhrV2dCozjWHtfTnyHKDutQqkMLSC1RHgeRirEjREgZ1MDSoQ7h7geZw0z1B4dLSOxSNqRH5eGo6eCrcPahbSxx7cOGxBbjAmYuFW5aT9ny+nCLVxdrzggmgbnVtOFtiRe729VH1L8jtzKE/17gp0frp63Z6mJsedV39z6klzs66ZL8XLvNw/5r/ZzkJPwokF+OxywCC4fS3urc2Twx0fh7B7Eynxlkb2cs6n7WpozqG8QEsvx4oPfct6e/fOj3MTY89b+nHsU/rYf3s7mmBmaHVuKanoS8vlfpGS9+MkzxUSGHHZrWVC/cX7gEITkpsaKZT13ZzbBfprwWhmX4VryGVVVu/tKZVTd8WeERwMHlsHip95Ze+ERPevZ13hkkXvh+eecdFGprh4vsbYyOkK7tLRRVScgssN+O6GvpguNki80Lr0ifG0L5LTBlGzTPi4XRBO7783vEzSJs3b9a0adM0depUTZ8+Xdu2bZMkvfbaa/L5fEpLS9ODDz6oioqKwDirV6/WxIkTlZaWpqefftrpLrqWm46yAWiafwyV5p0rze8bmunVtdl0YlO6a6TUO7rmkf/WoiUOHCV4z96mp9fQtzqEf0fngnhDF8aHuxfuZBiGXhxi6I2hRovtlA7taKgLpwhQh6gIQ5EuKI52jZReGSJd1DH8fQkXx88g3X///frTn/6klJQUvfnmm/r5z3+u//3f/9XSpUv1t7/9Tb1799add96pDRs2aMaMGdqyZYs2b96sV155RSUlJbr66qv17W9/W337hmjvohWJ53tpgFZvbBdDY7uEbnp1ba5CdXaqxjTjDO2/7PT/z3NgHk75poNHPV8ZIh3ySwPi2u+OQ3Nx5QPgbt/sYDi6Hm0NWuQepBMnTgR+JiQk6K233tKwYcPUp08fGYahmTNnKiMjQ5KUkZGh6dOny+v1qnPnzvL5fMrMzGyJbrrKQ8lS3zBfltHaXd4l3D0AWsb8vtKP+kibLnJuHv1jDb06RHp/uHPzaK7CMVLeaKmrg0fnJ3U3dGMi6+bm+C/7wQ3fauc7YADcy/EzSKtWrdIdd9yhmJgYVVZW6q9//atee+019erVK9AmKSlJ+fn5kqT8/HyNHTu2xmtffPFFk+dvmqaKi4ub/gZCpKSkpMbP0+o+LDunR4mC6bZpxqjqmLIb3me41MzXynRIjF/FxeVh7FXbUf/yi+ZqbLY/STL06pFo3ZZQXmP5/p/e1k8nVwNjYp2fR3N47H/V/+xZdi2VldGqOiYaym1FU/K9sYuUfH6ELupQ6dplyQ1Ydp1Fvs5ya76maQZ1Oa2jBVJFRYV+97vf6Y9//KMGDx6sN954Q3PmzNFVV11V7zhmHefem3NdsN/v1+7du5s8fqjl5OScMWRYne0+/fRTeYyzX4fgLx8syboY3k3vM1xycnLUy+igXDNaFx3/XLt385XloVR7+UWoNCbbV2Il44TEn3zw2vuyW1p6vqQ4Sc5sKxqbb09JufY/NKy9L7tOI19nuTFfr/fsN5E6WiB98sknOnr0qAYPHixJmjBhghYuXKhevXpp586dgXZ5eXlKTLTOuSclJSkvL6/GawkJCU3ug8fjUUpKSpPHD5WSkhLl5OSoX79+io2tdtZoa93tB51/vjxBXAAZtd0j+e1xBg1qfkdbqer5/ttbqVx/qVJi2t99a06pd/lFs5Gts8jXEvNRtGSfrQnltoJ8nUO2ziJfZ7k133379gXVztECKTExUfv379eBAwfUp08fbd++XV6vV2PGjNGyZct08OBB9erVS88995x8Pp8kyefzaeXKlbrmmmtUWlqqzMxMrV27tsl9MAxDcXFxIXpHzRcbG3tGf+o+SxQXFytPxNnPnBnVzjK56X2GS1W+PcLdkTaq9vKLUCFbZ7X3fCMinN1WtPd8nUS2ziJfZ7kt32CvSnO0QDrnnHO0aNEi3X777YqIiFBUVJRWrVqljh076oEHHtDNN9+siooKjRo1StOmTZMkXXrppRo9erQmT54sSbrlllva5RPsAAAAALQ8xx/SMG3atEDxU92kSZM0adKkOseZN2+e5s2b53DPAAAAAKCmFnnMNxov2MdS8HUSAAAAQOhQIAEAAACAjQIJAAAAAGwUSAAAAABgo0ByqWZ8Ny4AAACAJqJAAgAAAAAbBVIrx1PsAABnw0UJABA8CiQAAAAAsFEguRRH+wAAAICWR4EEAAAAADYKJAAAAACwUSC1ciZPaQAAAABChgLJpbgHCQAAAGh5FEgAAAAAYKNAAgCgjeNqbAAIHgUSAAAAANgokFzKMLgLCQAAAGhpFEitHJdNAAAAAKFDgQQAAAAANgokAAAAALBRIAEAAACAjQIJAAAAAGwUSK0cD2kAAAAAQocCCQAAAABsFEguxDcgAQAAAOFBgQQAAAAANgokAAAAALBRILVyPKQBAAAACB0KJBfiHiQAAAAgPCiQAAAAAMBGgQQAAAAANgokAAAAALBRILmQwU1IAAAAQFhQILVyJo+xAwAAAEKGAgkAAAAAbBRIAAAAAGBzvEAqLS3VAw88IJ/Pp+9+97tavXq1JOm1116Tz+dTWlqaHnzwQVVUVATGWb16tSZOnKi0tDQ9/fTTTnfRdbgFCQAAAAiPKKdn8Mgjj6hHjx7KzMyUJH311VcqKirS0qVL9be//U29e/fWnXfeqQ0bNmjGjBnasmWLNm/erFdeeUUlJSW6+uqr9e1vf1t9+/Z1uqsAAAAA2jlHzyCdPHlSr776qu68887AsHPOOUdvvfWWhg0bpj59+sgwDM2cOVMZGRmSpIyMDE2fPl1er1edO3eWz+cLFFeojWc0AAAAAKHj6Bmk/fv3q1u3bnrsscf0wQcfqHPnzlqwYIHy8/PVq1evQLukpCTl5+dLkvLz8zV27Ngar33xxRdN7oNpmiouLm76mwiRkpKSGj9Pi62jtani4jPb1cOMUdVFeW54n+FSf74IBfJ1Dtk6i3wtlZXRqjomGsptBfk6h2ydRb7Ocmu+pmnKCOL7dBwtkMrLy5Wdna158+Zp0aJFevPNN/Xf//3fuv766+sdx6zjudXBvJH6+P1+7d69u8njh1pOTs4ZQ4bVbmSaQfe5omKIqj5GN73PcKmdL0KJfJ1Dts5q7/mWlp4vKU6SM9uK9p6vk8jWWeTrLDfm6/V6z9rG0QIpKSlJsbGxSktLkySNHz9eCxcuVFJSkj788MNAu7y8PCUmJgbGycvLq/FaQkJCk/vg8XiUkpLS5PFDpaSkRDk5OerXr59iY6udNdpau61hGBo0aFBQ043cFinZz7cIdpy2qN58ERLk6xyydRb5WmI+ipbsE0eh3FaQr3PI1lnk6yy35rtv376g2jlaIHXv3l0XXHCBPvjgA1188cXauXOnYmJiNGbMGC1btkwHDx5Ur1699Nxzz8nn80mSfD6fVq5cqWuuuUalpaXKzMzU2rVrm9wHwzAUFxcXonfUfLGxsWf0p667iBrRZ+P0+G56n+FSO1+EEvk6h2yd1d7zjYhwdlvR3vN1Etk6i3yd5bZ8g70qzfGn2C1dulSLFi1SUVGRYmJi9Mtf/lIdO3bUAw88oJtvvlkVFRUaNWqUpk2bJkm69NJLNXr0aE2ePFmSdMstt/AEuwbwkAYAwNnw9REAEDzHC6T+/fvrmWeeqTV80qRJmjRpUp3jzJs3T/PmzXO6a67FhgwAAAAID8e/KBYAAAAAWgsKJAAAAACwUSABAAAAgI0CyYWa8bVPAAAAAJqBAqmV4yl2AAAAQOhQIAEAAACAjQIJAAAAAGwUSC7ELUgAAABAeFAgAQAAAICNAqmVM3lKAwAAABAyFEgAAAAAYKNAciHuQQIAAADCgwIJAAAAAGwUSAAAAABgo0ACAAAAABsFkgs15h4kHmIHAAAAhA4FEgAAbRwH0wAgeBRIAAAAAGCjQAIAAAAAGwWSCxl8ERIAAAAQFhRIrRzXlQMAAAChQ4EEAAAAADYKJAAAAACwUSC5ELcgAQAAAOFBgQQAAAAANgqkVo6HNAAAAAChQ4EEAAAAADYKJBfiHiQAAAAgPCiQAAAAAMBGgQQAAAAANgokAAAAALBRILVyJo+xAwAAAEKGAsmFeEgDAAAAEB4USAAAAABgo0ACAAAAABsFEgAAAADYWqxAWrRokVJTU1VeXi5Jeu211+Tz+ZSWlqYHH3xQFRUVgbarV6/WxIkTlZaWpqeffrqluugaRiNuQuIZDQCAs+HeVgAIXosUSP/6179qFEBFRUVaunSp1qxZo6ysLB0+fFgbNmyQJG3ZskWbN2/WK6+8onXr1umPf/yjvvzyy5boJgAAAIB2zvECqbi4WI8//rgWLlwYGPbWW29p2LBh6tOnjwzD0MyZM5WRkSFJysjI0PTp0+X1etW5c2f5fD5lZmY63U0AAAAAUJTTM3j88cd14403qmvXroFh+fn56tWrV+D/SUlJys/PD7w2duzYGq998cUXTZ6/aZoqLi5u8vihUlJSUuPnabG1G5umiovPbFefGFVdPOGG9xku9eeLUCBf55Cts8jXUlkZrapjoqHcVpCvc8jWWeTrLLfma5qmjCDuZXG0QNq+fbu+/PJLPfDAAzWGmw18u2ldrwXzRurj9/u1e/fuJo8fajk5OWcMGVarTWVFRdB9rqy8SFUFkpveZ7jUzhehRL7OIVtntfd8S0vPlxQnyZltRXvP10lk6yzydZYb8/V6vWdt42iBtG3bNn3yySeaMGFCYFh6errmzp2rDz/8MDAsLy9PiYmJkqwzRnl5eTVeS0hIaHIfPB6PUlJSmjx+qJSUlCgnJ0f9+vVTbGy1s0Zba7eNjIzUoEGDgppuxPuGVGn9Huw4bVG9+SIkyNc5ZOss8rXEfBQt2SeOQrmtIF/nkK2zyNdZbs133759QbVztECaPXu2Zs+eHfh/amqqsrKyVFpaqhUrVujgwYPq1auXnnvuOfl8PkmSz+fTypUrdc0116i0tFSZmZlau3Ztk/tgGIbi4uKa+1ZCJjY29oz+1HE2rRF9NquN76b3GS6180Uoka9zyNZZ7T3fiAhntxXtPV8nka2zyNdZbss32KvSHL8HqS7x8fF64IEHdPPNN6uiokKjRo3StGnTJEmXXnqpRo8ercmTJ0uSbrnlFvXt2zcc3Qybi+LD3QMAAACgfWrRAmnPnj2B3ydNmqRJkybV2W7evHmaN29eS3UrrMrHSbtOSpWmFBtp3U3UPybcvQIAAADap7CcQcJpEYahIZwxAgAAAFyhRb4oFgAAAABaAwqkVq7+B6YDAAAAaCwKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAauVMntIAAAAAhAwFEgAAAADYKJAAAAAAwEaBBAAAAAA2CiQAAAAAsFEgAQAAAICNAqmV4yF2AAAAQOhQIAEAAACAjQIJAAAAAGwUSAAAAABgo0ACAAAAABsFUivHQxoAAGfDtgIAgkeBBAAAAAA2CiQAAAAAsFEgAQAAAICNAgkAAAAAbBRIrVwsnyAAAAAQMuxet3KvXij1i5E2XBDungAAAACtX1S4O4Dmuayzoc8vDXcvAAAAgLaBM0gAAAAAYKNAAgAAAAAbBRIAAG2cEe4OAEArQoEEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2BwtkAoLC3Xrrbdq0qRJuuqqq7Ro0SKVlZVJkl577TX5fD6lpaXpwQcfVEVFRWC81atXa+LEiUpLS9PTTz/tZBcBAAAAIMDRAskwDP3whz9URkaGNm7cqJKSEv35z39WUVGRli5dqjVr1igrK0uHDx/Whg0bJElbtmzR5s2b9corr2jdunX64x//qC+//NLJbgIAAACAJCnKyYl36dJFI0aMkCRFRERo8ODBOnDggN566y0NGzZMffr0kSTNnDlTTz/9tGbMmKGMjAxNnz5dXq9XXq9XPp9PmZmZuu2225rUB9M0VVxcHLL31FQlJSU1fiK0yNdZ5OscsnUW+VoqK6NVdUw0lNtE8nUO2TqLfJ3l1nxN05RhGGdt52iBVN2pU6e0fv16zZ8/X/v27VOvXr0CryUlJSk/P1+SlJ+fr7Fjx9Z47YsvvmjyfP1+v3bv3t30jodYTk5OuLvQppGvs8jXOWTrrPaeb2np+ZLiJMmRbWJ7z9dJZOss8nWWG/P1er1nbdMiBVJlZaUWLlyoSy65RGPHjtXevXvrbWuaZq1hwVR69fF4PEpJSWny+KFSUlKinJwc9evXT7GxseHuTptDvs4iX+eQrbPI1xLzUbRknzgaNGhQyKZLvs4hW2eRr7Pcmu++ffuCatciBdLSpUsVFRWl+++/X5J1VujDDz8MvJ6Xl6fExMTAa3l5eTVeS0hIaPK8DcNQXFxck8cPtdjYWFf1p60hX2eRr3PI1lntPd+IiNMHH53Iob3n6ySydRb5Ostt+QZ70sXxx3w/+uijKigo0PLlywOdGjt2rLZv366DBw/KNE0999xz8vl8kiSfz6f169errKxMx48fV2ZmZuA1AAAAAHCSo2eQ9u7dqzVr1qh///6aMWOGJOmyyy7TwoUL9cADD+jmm29WRUWFRo0apWnTpkmSLr30Uo0ePVqTJ0+WJN1yyy3q27evk90EAAAAAEkOF0gDBgzQnj176nxt0qRJmjRpUp2vzZs3T/PmzXOyawAAAABQi+OX2AEAAABAa0GBBAAAAAA2CiQAAAAAsFEgAQAAAICNAgkAAAAAbBRIAAAAAGCjQAIAAAAAGwUSAAAAANgokAAAAADARoEEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2CiQAAAAAMBGgQQAAAAANgokAAAAALBRIAEAAACAjQIJAIA27vbe1s/Lu4S1GwDQKkSFuwMAAMBZtyVJIzpK3+wQ7p4AgPtRIAEA0MYZhqGhHcPdCwBoHbjEDgAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2CiQAAAAAMBGgQQAAAAANsM0TTPcnXDK9u3bZZqmvF5vuLsi0zTl9/vl8XhkGEa4u9PmkK+zyNc5ZOss8nUW+TqHbJ1Fvs5ya75lZWUyDEPDhg1rsF2b/h4kN30ghmG4olBrq8jXWeTrHLJ1Fvk6i3ydQ7bOIl9nuTVfwzCCqg/a9BkkAAAAAGgM7kECAAAAABsFEgAAAADYKJAAAAAAwEaBBAAAAAA2CiQAAAAAsFEgAQAAAICNAgkAAAAAbBRIAAAAAGCjQAIAAAAAGwUSAAAAANgokAAAAADARoEEAAAAADYKpBaQnZ2tGTNmyOfz6cYbb9ShQ4fC3SVXKiws1K233qpJkybpqquu0qJFi1RWVqYDBw5o8ODBmjp1qqZOnaprrrmmxnirV6/WxIkTlZaWpqeffjow/OTJk5ozZ47S09N15ZVX6oMPPgi81h4/kwkTJmjy5MmBHPfs2SNJeu211+Tz+ZSWlqYHH3xQFRUVgXHINjgFBQWBXKdOnaqxY8dq2rRpLLvNcP/992vMmDFKTU1VeXl5YHhLLq8Nzau1qyvf7du3a+bMmbriiit05ZVX6s9//nOg/QsvvKCRI0cGluWlS5cGXiPfmurKtqXXBW01W6nufF9//fUa6+Dhw4dr2bJlklh2G6O+/TCpHa57TTju+uuvNzMyMkzTNM01a9aYCxYsCHOP3KmwsNB87733TNM0zYqKCnPevHnmk08+ae7fv98cM2ZMneP861//Mq+++mrz1KlT5tGjR83x48ebX3zxhWmaprl69WrzoYceMk3TNHfu3Gl+5zvfMSsqKkzTbJ+fyfjx482cnJwaw06cOGGOHj3a3L9/v1lZWWnefvvt5rp160zTJNvmuOeee8zf//73LLvNsHXrVvOrr74yBw4caPr9ftM0W3Z5bWhebUFd+e7ZsyeQ2YkTJ8z09HTzww8/NE3TNJ9//nnz3nvvrXNa5FtTXdm25LqgLWdrmnXnW11FRYV5+eWXm//+979N02TZbYz69sPa47qXM0gOO3z4sLKzs5Weni5JmjlzprKyssLcK3fq0qWLRowYIUmKiIjQ4MGDlZub2+A4GRkZmj59urxerzp37iyfz6fMzMzAa7NmzZIkXXDBBerUqZN27drFZ1LNW2+9pWHDhqlPnz4yDEMzZ85URkaGJLJtqqKiIr3xxhuaOnVqg+3It2EjR45Ujx49agxryeW1oXm1BXXlO3DgQPXt21eSFB8fr+Tk5LOugyXyPVNd2TaEZbdxzpbvli1bFBsbqwsvvPCs0yLfmurbD2uP614KJIfl5+crKSlJhmFIsjY6Ho9HhYWFYe6Zu506dUrr16/XuHHjJFmnfadPn67vfe972rBhQ6Bdfn6+evXqFfh/UlKS8vPzJUl5eXk1XuvVq5fy8vLa9Wcyd+5cTZkyRStXrpTf728wP7JtmoyMDA0bNkwJCQmSWHZDqSWX14am1x785z//0c6dOzVy5MjAsH/+85+aMmWKbrrpJn344YeB4eQbnJZaF7THbKvbsGGDrr766hrDWHYbr/p+WHtc90a1+BzbGdM06xxetVCgtsrKSi1cuFCXXHKJxo4dq7KyMm3evFndunXTgQMH9IMf/EC9e/fWiBEj6sy3oWwNw2i3n8nTTz+txMREFRUVaf78+frDH/6gmJiYetuTbdNs2LBB1157rSSpZ8+eLLshVN/7r++15uTZ0LzausLCQs2ZM0eLFy9Wt27dJEnjx4/XFVdcoejoaL377ru68847lZGRoY4dO9Y5DfKtqSXXBe0t2+pOnjypTZs26Z577gkMY9ltvDP3w/bu3Vtv27a6/HIGyWFVlW/VB15UVCS/368uXbqEt2MutnTpUkVFRen++++XJHm93sBGuk+fPpo4caJ27Nghyco3Ly8vMG5eXl7gyP2Zr+Xm5ioxMbHdfiaJiYmSrCM1M2fO1I4dO5SUlFTjEpq8vLxAO7JtvAMHDmjPnj2aOHGiJJbdUGvJ5bWhebVlRUVFuu2223TDDTfI5/MFhnft2lXR0dGSpNGjRyshIUGff/65JPINRkuuC9pbttVlZmbqoosuqvF+WXYb78z9sPa47qVAcliPHj2UnJysTZs2SZLWrVuntLS0MPfKvR599FEVFBRo+fLlgSMQR44cCTyp5tixY3rnnXeUmpoqSfL5fFq/fr3Kysp0/PhxZWZmBjbqPp9Pf//73yVJH3/8sY4dO6bBgwe3y8+kuLhYRUVFkqTy8nJlZWUpNTVVY8eO1fbt23Xw4EGZpqnnnnuuRn5k2zgbNmzQpEmTAmfmWHZDqyWX14bm1VaVlpbq9ttvV3p6euAsaJWCgoLA759++qlyc3MD9yuR79m15LqgvWVbXV2X17HsNk5d+2Htcd1rmG45l9WG7d27VwsXLtTJkyfVs2dPrVixIlBd47S9e/fqyiuvVP/+/eX1eiVJl112mYYOHapf/vKXioiIUEVFhaZNm6bbbrstMN6qVav08ssvS5J+8IMf6Prrr5dkHZFYsGCB9u3bJ6/XqyVLlmj48OGBebWnz2T//v2aM2eOKisrVVFRoQsvvFAPPPCAOnTooIyMDK1atUoVFRUaNWpU4MiRRLaNlZaWpuXLl+viiy+WJGVlZbHsNtH8+fO1detWFRQUKCEhQcOHD9fjjz/eostrQ/Nq7erK9+KLL9ayZcuUkpISaPfDH/5QkydP1sqVK/XGG28oMjJSUVFRuuuuu3T55ZdLIt8z1ZXtpEmTWnRd0FazlepfN+Tm5mrKlCl65513alw+zrIbvPr2wxYuXNju1r0USAAAAABg4xI7AAAAALBRIAEAAACAjQIJANBsb775psaPH6+hQ4fqlVdeafJ0tm7dWuPBExMmTNC2bdvqbf+73/1ODz30UJPnJ0kvvviibr/99npfP1sfgpWamtomvy8FANqa1n9HGQAg7P7617/q+9//vm655ZYWnW9DhU2wpkyZoilTpoSgNwCAtoAzSACAZrn11lv17rvv6he/+IWGDh2qwsJCrVu3Tj6fT0OHDtWUKVP0/vvvB9rfcMMN2rhxY+D/v/3tb3XffffVmu6iRYuUm5urW265RUOHDtX69etrtfnVr36lRYsWSTp99umJJ57QqFGjNH78eG3evDnQtrCwUPPnz9dll10WeDKSJL3wwgu66aabAu02b96stLQ0jRw5Ur/+9a9rzO/UqVN6+OGHNXbsWH3729/W448/roqKikBf7r33Xt19990aOnSoZsyYoS+//FKSdPPNN0uSJk2apKFDh2rr1q3697//rWnTpmno0KEaPXq0/t//+3+Nyh0A4AwKJABAs/zxj3/U8OHD9fOf/1w7duxQ165ddc4552jt2rXatm2brrvuOs2bN09lZWWNmu7DDz+sXr16ac2aNdqxY0et7zepS25urjwej959913NmTNHDz74YOC1+fPnKyoqSllZWXr77bd15ZVX1hr/66+/1t133637779f77zzjoqLi2tcFvfYY4/pq6++0iuvvKIXX3xRW7du1XPPPRd4/fXXX9d//dd/6f3339fAgQO1evVqSdJTTz0lyXp87Y4dOzRq1CgtW7ZMN998s3bs2KFXX31Vo0aNalQ+AABnUCABAELu8ssvV1JSkiIjI3XdddepvLxcOTk5js83JiZGt9xyi6KionTVVVepoKBAX3/9tQoKCrRlyxY98MADio+Pl9frDXxfVXWbN2/W4MGDNX78eHm9Xv3oRz9SRIS1qaz60sKf/OQnio+PV7du3XTTTTcpIyMjMP4ll1yikSNHKioqSldccYX27NlTb1+joqL05Zdf6ujRo+rUqZO++c1vhj4QAECjcQ8SACDkNm3apN/85jfav3+/JOnkyZM6evSo4/Pt1q1b4NvfvV6vIiMjVVxcrCNHjqhbt27q0KFDg+N/9dVXSkxMDPw/NjZWXbp0kWSdXSotLdUVV1wReL2yslK9e/cO/L979+6B32NiYlRcXFzvvH7+859r9erVSk9P13nnnacFCxZo6NChjXq/AIDQo0ACAIRUWVmZ7rnnHv3qV7/St7/9bUVGRurSSy9V1feSx8bGqrS0NND+yJEjjvcpMTFRX3/9tYqLixUXF1dvu3POOUdvv/124P+lpaWBwq5r166Kjo7Wa6+9pq5duza7T/3799fq1atVXl6up59+Wvfee6/eeOONZk8XANA8XGIHAAipsrIy+f3+wNmUtWvX1jh7dP755yszM1NlZWX69NNP9eqrr9Y7re7du+vAgQPN7lNCQoJGjhyphx9+WEVFRSorK9P27dtrtbv88su1a9cubd68WWVlZfr1r3+tyspKSVJERISuvvpqLVu2TIWFhTJNU19++WXQjwDv3r174IyaZD1evLCwUFFRUerYsWPgUj4AQHixNgYAhFR8fLwWLFigW265RaNHj9bx48fVp0+fwOs33XSTKioqNGrUKC1fvrzGJWtnuu222/T4449r+PDh2rBhQ7P6tWLFCpWUlGjixIkaM2aMXn755VptunXrppUrV+pnP/uZRo8erZiYmBqX3P3kJz9Rjx49NH36dA0fPlw/+tGPdOjQoaDm/9///d+66667NHz4cL333nvavHlz4Kl2f/nLX/TII4806/0BAELDMKuueQAAAACAdo4zSAAAAABgo0ACAAAAABsFEgAAAADYKJAAAAAAwEaBBAAAAAA2CiQAAAAAsEWFuwNO2rFjh0zTlMfjCXdXAAAAAISR3++XYRgaOnRog+3a9Bkk0zTllq95Mk1TZWVlrulPW0O+ziJf55Cts8jXWeTrHLJ1Fvk6y635BlsbtOkzSFVnji644IIw90QqLi7W7t27lZKSori4uHB3p80hX2eRr3PI1lnk6yzydQ7ZOot8neXWfD/66KOg2rXpM0gAAAAA0BgUSAAAAABga9OX2MH9ispNXbdT2nxCMiSN7Sw9M1iKjzKaOD3pvz40tbNY6h4lPZQspXeXIo2mTa8lVJim3j4q5ZVJSV5pTBerv0Xlpm74RMoulc6Lkf7yzabnUqWs0tRvD5ye5g+SpIX7pNePSIfLpagIKT5S6uuR9p2STpRLhiH1iPLK6x+oIZ95dF68qW4e6Wi5ZErq7pESvFK3KGn9V9Kb9rQiDesITHmldEJSRbV+REn6SV/pvn7S1uPSwVNSXqm0o0j6/KT0n2Lpa0mVkuIjpP6x0iWdpZUpUmykez9LAADQ+lEgIWxGvm9qW1HNYS8XSp3elobHm3pvRON2hP+/ooH65IOYwP9zJF3xkeQxpGe+ZWr6Oe7bsX7hK1N375UOnDo9rE+0FG2Yyi49PWzXyabnUmXBPlOr9tcsVO7JPqNRhXSkQvqirNowUzrhj5QUrz2FkgqDnGED90CWS/rZl9a/szleKf37pPXvd7nSlO6mNgxx32cJAADahpBfYnf//fdrzJgxSk1NVXl5eZ1tDh06pBtvvFE+n08zZsxQdvbpvbRt27bpyiuvVHp6uubMmaOTJ0+GuotwgbqKo+q2FVltgnX5R159Utmhztf8pvS9XVYx4iYvfGXqml01iyPJ+n/14qi6xuZSZcE+UyvOKI5aqxePSNN2uuuzBAAAbUfIC6Rp06Zp/fr1DbZZuXKlxo0bp8zMTM2ePVuLFy+WJFVWVuq+++7TsmXLlJWVpYSEBK1ZsybUXUSYFZU3XBxV2VZktQ1meh8UVy3K9Z9ZuGuvdTmbG1SY1pmjpvQm2FyqlFVaZ47akhePSCUV7vgsAQBA2xLyS+xGjhx51jZZWVl68MEHJUnp6elasmSJjhw5ooMHD6pTp04aMmSIJGnWrFm6++67NXfu3Cb3xzRNFRcXN3n8UCkpKanxsz27bo9HwS56131Urr+n+kMyvYOnpNcKTmlsp8qg5u2kt45H6MCp6CaPH0wuVX6dF6kKeZs8L7e6+1O/ViXXfZa6NWHd4CzydRb5OodsnUW+znJrvqZpygjivvQWvwepsLBQXq9X8fHxkiTDMJSYmKi8vDzl5eWpd+/egba9evVSXl5es+bn9/u1e/fuZk0jlHJycsLdhbD79MT5CnbR+/RYmXbv/jRk09uek6tzPMHeROOc7f6ukpKbPH4wuVT5oKSPpJ5Nnpdb7TxSrN2lZ95E1XqxbnAW+TqLfJ1Dts4iX2e5MV+v9+wHjVu8QKrv22sNw6jztWCqvIZ4PB6lpKQ0axqhUFJSopycHPXr10+xsbHh7k5Ynb/Ho+yjQbbt7NWg1EEhm96wfr00qFNicI0d9NXxCKkZdXswuVS5OC9SzwXxMITWZkj3OA1KDi4DN2Pd4CzydRb5OodsnUW+znJrvvv27QuqXYsXSN26dVNZWZmKiooUHx8v0zRVUFCgxMREVVRUKDc3N9A2NzdXiYnN25k1DMNV3+AbGxvrqv6EwzMXmOr0drBtoxQX5QlielXFdf0Fde9oKS0h2hWP/E6LNdXnc+uyv6bcSRNMLlXuTja16Mu28YCG6n5xvkexkW3n0kHWDc4iX2eRr3PI1lnk6yy35RvsiZewfFFsWlqa1q1bJ0natGmTkpOT1b17dw0ePFhHjx7Vrl27JEnPPvusfD5fOLoIB8VHGRoef/Z2w+OD+96f+ChDF8dV3VdUf7mxeoB7vg8p0jD0iwHW743tUbC5VPFGGJp3biNn4nJTuvN9SAAAwBkhL5Dmz5+vsWPHSpImTJige+65RwUFBZo6dWqgzb333qvXX39dPp9PTzzxhJYuXWp1JiJCy5cv18KFC5Wenq68vDzdcsstoe4iXOC9EQ0XScPj1ajv+9l8QZm+GVH3I+E9hrRusFz3PUjTzzH03GDrzFZ150ZbX+Jal8bmUuXRFEM/PleKbEI/3WZKd/E9SAAAwDEhv8Tuscceq3P4xo0bA78nJCToL3/5S53tRowYoZdffjnU3YILvTfCUFG5qet2SptPWGdSxnaWnhncuDMkVf4U/5nOHTBIs/8Tq53FUvco6aFkKb27e84cnWn6OYam9jD19lEpr0xK8kpjulj9LSo3dcMn1ncinRcj/eWbTculyqMphn7e39RvD5ye5g+SpIX7pNePSIfLpagIKT5S6uuR9p2STpRLhiH1iKqQ11+iIV1jdF58lLp5pKPl1vm67h4pwSt1i5LWfyW9aU8r0rCOwJRXSidU8xK/KEk/6Svd10/aety61DCvVNpRJH1+UvpPsfS1pEpJ8RFS/1jpks7SyhTOHAEAAGe1+D1IQHXxUYZeGhbK6UkbLmxdO9CRhqFxXWsPj48ytH5IaOfljTB0d9+aw34XxHMOiovLtHv3Zxo0cJDi4uq/9+m7PRrfp7reOwAAQLiE5R4kAAAAAHAjCiQAAAAAsFEgAQAAAICNAgkAAAAAbBRIAAAAAGCjQAIAAAAAGwUSAAAAANgokAAAAADARoEEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAABblBMTzc7O1oIFC1RUVKSEhAStWLFCPXv2rNFm6tSpgd+Liop0/Phxvf/++5Kk1NRUnX/++YHX165dq65duzrRVQAAAAAIcKRAWrx4sWbPni2fz6ennnpKK1eu1COPPFKjzcaNGwO/r1q1SkePHq3x+vPPP6+oKEe6BwAAAAB1CnkFcvjwYWVnZys9PV2SNHPmTI0ZM6ZWgVTFNE29+OKLWrVqVai7Eph+cXGxI9NujJKSkho/EVrk6yzydQ7ZOot8nUW+ziFbZ5Gvs9yar2maMgzjrO1CXiDl5+crKSkpMPP4+Hh5PB4VFhbWeZncli1bFB0drYsuuqjG8FmzZqmyslJXXHGFbr311ib3x+/3a/fu3U0eP9RycnLC3YU2jXydRb7OIVtnka+zyNc5ZOss8nWWG/P1er1nbRPyAsk0zTqH11etbdiwQVdffXWNYZs3b1ZiYqKOHDmiO+64Qz169NC0adOa1B+Px6OUlJQmjRtKJSUlysnJUb9+/RQbGxvu7rQ55Oss8nUO2TqLfJ1Fvs4hW2eRr7Pcmu++ffuCahfyAikpKUn5+fmBU1hFRUXy+/3q0qVLrbbFxcXatGmTXn755RrDExMTJUndu3fXlClTtGPHjiYXSIZhKC4urknjOiE2NtZV/WlryNdZ5OscsnUW+TqLfJ1Dts4iX2e5Ld9gLq+THHjMd48ePZScnKxNmzZJktatW6e0tLQ622ZlZenCCy8MFESSdOzYMZ06dUqSVFpaqjfeeEOpqamh7iYAAAAA1OLI9yAtWbJETzzxhHw+n15//XXde++9KigoqPFob0lav359rTNDn3/+ub73ve9pypQpmj59ulJTUzVr1iwnugkAAAAANTjyHO0BAwbohRdeqDW8+qO9JelPf/pTrTZDhw7VSy+95ES3AAAAAKBBjpxBAgAAAIDWiAIJAAAAAGwUSAAAAABgo0ACAAAAABsFEgAAAADYKJAAAAAAwEaBBAAAAAA2CiQAAAAAsFEgAQAAAICNAgkAAAAAbBRIAAAAAGCjQAIAAAAAGwUSAAAAANgokAAAAADARoEEAAAAALYoJyaanZ2tBQsWqKioSAkJCVqxYoV69uxZo80NN9yg/Px8xcXFSZJ+/OMfa8yYMZKkbdu2acmSJSorK9PAgQP1yCOPqEOHDk50FQAAAAACHDmDtHjxYs2ePVuZmZkaN26cVq5cWWe7pUuXauPGjdq4cWOgOKqsrNR9992nZcuWKSsrSwkJCVqzZo0T3QQAAACAGkJ+Bunw4cPKzs5Wenq6JGnmzJkaM2aMHnnkkaDG37Vrlzp16qQhQ4ZIkmbNmqW7775bc+fObVJ/TNNUcXFxk8YNpZKSkho/EVrk6yzydQ7ZOot8nUW+ziFbZ5Gvs9yar2maMgzjrO1CXiDl5+crKSkpMPP4+Hh5PB4VFhaqa9euNdo+/PDDioiI0LBhwzR//nzFx8crLy9PvXv3DrTp1auX8vLymtwfv9+v3bt3N3n8UMvJyQl3F9o08nUW+TqHbJ1Fvs4iX+eQrbPI11luzNfr9Z61TcgLJNM06xx+ZrX22GOPKTExUWVlZXr44Ye1bNkyLVu2rM7xg6n06uPxeJSSktLk8UOlpKREOTk56tevn2JjY8PdnTaHfJ1Fvs4hW2eRr7PI1zlk6yzydZZb8923b19Q7UJeICUlJSk/Pz9wCquoqEh+v19dunSp0S4xMVGSVcVdf/31uuuuuyRZZ4xyc3MD7XJzcwNtm8IwjMCDINwgNjbWVf1pa8jXWeTrHLJ1Fvk6i3ydQ7bOIl9nuS3fYE+6hPwhDT169FBycrI2bdokSVq3bp3S0tJqtCkvL9eRI0cC/3/11VeVmpoqSRo8eLCOHj2qXbt2SZKeffZZ+Xy+UHcTAAAAAGpx5DHfS5Ys0cKFCwOP916xYoUKCgo0e/Zsbdy4UWVlZbrtttvk9/tlmqaSk5P105/+VJIUERGh5cuXa+HChfL7/RowYIDuvvtuJ7oJAAAAADU4UiANGDBAL7zwQq3hGzdulCTFxcXV+XqVESNG6OWXX3aiawAAAABQL0e+BwkAAAAAWiMKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2CiQAAAAAMBGgQQAAAAANgokAAAAALBRIAEAAACAjQIJAAAAAGwUSAAAAABgo0ACAAAAAFuUExPNzs7WggULVFRUpISEBK1YsUI9e/YMvF5ZWam5c+dq79698nq9Sk5O1kMPPaQuXbpIklJTU3X++ecH2q9du1Zdu3Z1oqsAAAAAEODIGaTFixdr9uzZyszM1Lhx47Ry5cpabaZPn66MjAy99NJLOvfcc7V69eoarz///PPauHGjNm7cSHEEAAAAoEWEvEA6fPiwsrOzlZ6eLkmaOXOmsrKyas40IkITJkyQYRiSpCFDhig3NzfUXQEAAACARgn5JXb5+flKSkoKFD/x8fHyeDwqLCys80yQaZr6+9//rrS0tBrDZ82apcrKSl1xxRW69dZbm9wf0zRVXFzc5PFDpaSkpMZPhBb5Oot8nUO2ziJfZ5Gvc8jWWeTrLLfma5pmoEZpSMgLJNM06xxeX2ceffRRxcXF6dprrw0M27x5sxITE3XkyBHdcccd6tGjh6ZNm9ak/vj9fu3evbtJ4zohJycn3F1o08jXWeTrHLJ1Fvk6i3ydQ7bOIl9nuTFfr9d71jYhL5CSkpKUn58fqNCKiork9/sDD2Co7g9/+IM+/vhjPfnkkzUKqMTERElS9+7dNWXKFO3YsaPJBZLH41FKSkqTxg2lkpIS5eTkqF+/foqNjQ13d9oc8nUW+TqHbJ1Fvs4iX+eQrbPI11luzXffvn1BtQt5gdSjRw8lJydr06ZNSktL07p162pdPidJf/3rX5WVlaW1a9cqOjo6MPzYsWOKiYlRdHS0SktL9cYbb2jixIlN7o9hGIqLi2vy+KEWGxvrqv60NeTrLPJ1Dtk6i3ydRb7OIVtnka+z3JZvMJfXSQ495nvJkiVauHBh4PHeK1asUEFBgWbPnq2NGzeqqKhIP/vZz9SnTx9df/31kqSUlBStXLlSn3/+uX7605/KMAyVl5fr8ssv16xZs5zoJgAAAADU4EiBNGDAAL3wwgu1hm/cuFGS9eCGTz/9tM5xhw4dqpdeesmJbgEAAABAgxz5HiQAAAAAaI0okAAAAADARoEEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2CiQAAAAAMBGgQQAAAAANgokAAAAALBRIAEAAACAjQIJAAAAAGxR4e5Ae9D7TVN58kq6SNoqSWZ4O9QmRYt8neTOfCvHG+HuAuB6ReWmOr3t1NTduW4IxiRJr4RwHXLJm6beC9nUpNacbetAvs6qO9/nB0pX93b/ttuRM0jZ2dmaMWOGfD6fbrzxRh06dKhWm0OHDunGG2+Uz+fTjBkzlJ2dHXht27ZtuvLKK5Wenq45c+bo5MmTTnSzRUS8aSpPkhQpK25O2jkjQuTrJHfmG/EmGzWgISPfd7I4kty6bghGhkK3DokIeXEkteZsWwfydVbd+c74rHVsux1ZKhYvXqzZs2crMzNT48aN08qVK2u1WblypcaNG6fMzEzNnj1bixcvliRVVlbqvvvu07Jly5SVlaWEhAStWbPGiW46rjUsAEBrx98ZULeR75vaVhTuXrhfc9chrIOAxnP7303IC6TDhw8rOztb6enpkqSZM2cqKyurVrusrCzNnDlTkpSenq7s7GwdOXJEu3btUqdOnTRkyBBJ0qxZs5SRkRHqbjqut8s/eKAtcfuKFmhpReUUR40xuYnrkEtY9wBNtv6ge/9+Qn4PUn5+vpKSkmQY1vWF8fHx8ng8KiwsVNeuXSVJhYWF8nq9io+PlyQZhqHExETl5eUpLy9PvXv3DkyvV69eysvLa3J/TNNUcXFxM95R01j3HEW2+HyB9qlCxcVl4e5Ek5SUlNT4idBqr/let8cjbjMOXkYT1yHvsa0HmmzGZxUq6tqy227TNAM1SkNCvvY0zbqrweqdaahNXa8F80bq4/f7tXv37iaP33QXhWGeQHtlhOnvPHRycnLC3YU2rb3l++mJ80WB1BhNXYdcFOqOAO1IeLbdXq/3rG1CvvZMSkpSfn5+oEIrKiqS3+9Xly5dAm26deumsrIyFRUVKT4+XqZpqqCgQImJiaqoqFBubm6gbW5urhITE5vcH4/Ho5SUlOa8pabZ6t7ThkDbY2rQoEHh7kSTlJSUKCcnR/369VNsbGy4u9PmtNd8z9/jUfbRcPeiNWniOoRtPdAMLb/t3rdvX1DtQl4g9ejRQ8nJydq0aZPS0tK0bt06paWl1WpX9dpNN92kTZs2KTk5Wd27d1fXrl119OhR7dq1S4MHD9azzz4rn8/X5P4YhqG4uLjmvKUmSVLV0+sAOC8yLH/noRQbG9vq34Obtbd8n7nA6afXtS2TmrgOGSknnl4HtA/PD2z5bXewV6U58hS7JUuW6IknnpDP59Prr7+ue++9VwUFBZo6dWqgzb333qvXX39dPp9PTzzxhJYuXWp1KCJCy5cv18KFC5Wenq68vDzdcsstTnTTUQf5fhagxfB9SEBN8VGGhseHuxetR1O/D2kL6x6gydz8fUiGWd8NQW3ARx99JEm64IILwtYHnq4FOKu1F0fFxcXavXu3Bg0a1K7OcLSU9p4vj/o+u1CsQ9jWA40Trm13sLUB347lsMrxhpIkSRWSKu1/CL1Kka+T3Jlvay+OAKe9N8LQ8TFOzsGd64ZgTFLo1iGV4w2NDMmUakxVrTXb1oF8nVV3vs8PbB3bbh5x0wIOjjdUXFzWro9iOq24+BT5Ooh8gdYrPspQ5Xhnps264bRQX25Hts4iX2e19nw5gwQAAAAANgokAAAAALBRIAEAAACAjQIJAAAAAGwUSAAAAABgo0ACAAAAABsFEgAAAADYKJAAAAAAwEaBBAAAAAA2CiQAAAAAsFEgAQAAAICNAgkAAAAAbBRIAAAAAGCLCuXEsrOztWDBAhUVFSkhIUErVqxQz549a7SprKzU3LlztXfvXnm9XiUnJ+uhhx5Sly5dJEmpqak6//zzA+3Xrl2rrl27hrKbAAAAAFCnkJ5BWrx4sWbPnq3MzEyNGzdOK1eurLPd9OnTlZGRoZdeeknnnnuuVq9eXeP1559/Xhs3btTGjRspjgAAAAC0mJAVSIcPH1Z2drbS09MlSTNnzlRWVlbtGUZEaMKECTIMQ5I0ZMgQ5ebmhqobAAAAANBkIbvELj8/X0lJSYHCJz4+Xh6PR4WFhfWeBTJNU3//+9+VlpZWY/isWbNUWVmpK664Qrfeemuz+mWapoqLi5s1jVAoKSmp8ROhRb7OIl/nkK2zyNdZ5OscsnUW+TrLrfmaphmoVRrSqALpuuuuU0FBQa3h5513nubOnVvnOA114tFHH1VcXJyuvfbawLDNmzcrMTFRR44c0R133KEePXpo2rRpjelmDX6/X7t3727y+KGWk5MT7i60aeTrLPJ1Dtk6i3ydRb7OIVtnka+z3Jiv1+s9a5tGFUjPPPNMva8dPnxY+fn5gcqsqKhIfr8/8PCFM/3hD3/Qxx9/rCeffLJGEZWYmChJ6t69u6ZMmaIdO3Y0q0DyeDxKSUlp8vihUlJSopycHPXr10+xsbHh7k6bQ77OIl/nkK2zyNdZ5OscsnUW+TrLrfnu27cvqHYhu8SuR48eSk5O1qZNm5SWlqZ169bVunSuyl//+ldlZWVp7dq1io6ODgw/duyYYmJiFB0drdLSUr3xxhuaOHFis/plGIbi4uKaNY1Qio2NdVV/2hrydRb5OodsnUW+ziJf55Cts8jXWW7LN5jL66QQP+Z7yZIlWrhwYeDx3itWrJAkFRQUaPbs2dq4caOKior0s5/9TH369NH1118vSUpJSdHKlSv1+eef66c//akMw1B5ebkuv/xyzZo1K5RdBAAAAIB6hbRAGjBggF544YVawxMSErRx40ZJ1sMbPv300zrHHzp0qF566aVQdgkAAAAAghbS70ECAAAAgNaMAgkAAAAAbBRIAAAAAGCjQAIAAAAAGwUSAAAAANgokAAAAADARoEEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2CiQAAAAAMBGgQQAAAAAtqhQTiw7O1sLFixQUVGREhIStGLFCvXs2bNWuxtuuEH5+fmKi4uTJP34xz/WmDFjJEnbtm3TkiVLVFZWpoEDB+qRRx5Rhw4dQtlNAAAAAKhTSM8gLV68WLNnz1ZmZqbGjRunlStX1tt26dKl2rhxozZu3BgojiorK3Xfffdp2bJlysrKUkJCgtasWRPKLgIAAABAvUJWIB0+fFjZ2dlKT0+XJM2cOVNZWVmNmsauXbvUqVMnDRkyRJI0a9YsZWRkhKqLAAAAANCgkF1il5+fr6SkJBmGIUmKj4+Xx+NRYWGhunbtWqv9ww8/rIiICA0bNkzz589XfHy88vLy1Lt370CbXr16KS8vr1n9Mk1TxcXFzZpGKJSUlNT4idAiX2eRr3PI1lnk6yzydQ7ZOot8neXWfE3TDNQqDWlUgXTdddepoKCg1vDzzjtPc+fOrXOcujrx2GOPKTExUWVlZXr44Ye1bNkyLVu2TKZpBjV+Y/j9fu3evbtZ0wilnJyccHehTSNfZ5Gvc8jWWeTrLPJ1Dtk6i3yd5cZ8vV7vWds0qkB65pln6n3t8OHDys/PD1RmRUVF8vv96tKlS622iYmJgQ5ef/31uuuuuyRZZ4xyc3MD7XJzcwNtm8rj8SglJaVZ0wiFkpIS5eTkqF+/foqNjQ13d9oc8nUW+TqHbJ1Fvs4iX+eQrbPI11luzXffvn1BtQvZJXY9evRQcnKyNm3apLS0NK1bt05paWm12pWXl+vYsWPq3r27JOnVV19VamqqJGnw4ME6evSodu3apcGDB+vZZ5+Vz+drVr8Mwwg8Lc8NYmNjXdWftoZ8nUW+ziFbZ5Gvs8jXOWTrLPJ1ltvyDfbKtJA+5nvJkiVauHBh4PHeK1askCQVFBRo9uzZ2rhxo8rKynTbbbfJ7/fLNE0lJyfrpz/9qSQpIiJCy5cv18KFC+X3+zVgwADdfffdoewiAAAAANQrpAXSgAED9MILL9QanpCQoI0bN0qS4uLi6mxTZcSIEXr55ZdD2S0AAAAACEpIvwcJAAAAAFozCiQAAAAAsFEgAQAAAICNAgkAAAAAbBRIAAAAAGCjQAIAAAAAGwUSAAAAANgokAAAAADARoEEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2KJCObHs7GwtWLBARUVFSkhI0IoVK9SzZ89a7aZOnRr4vaioSMePH9f7778vSUpNTdX5558feH3t2rXq2rVrKLsJAAAAAHUKaYG0ePFizZ49Wz6fT0899ZRWrlypRx55pFa7jRs3Bn5ftWqVjh49WuP1559/XlFRIe0aAAAAAJxVyKqQw4cPKzs7W+np6ZKkmTNnasyYMXUWSFVM09SLL76oVatWhaobdc6juLjYsekHq6SkpMZPhBb5Oot8nUO2ziJfZ5Gvc8jWWeTrLLfma5qmDMM4a7uQFUj5+flKSkoKzDQ+Pl4ej0eFhYX1XiK3ZcsWRUdH66KLLqoxfNasWaqsrNQVV1yhW2+9tVn98vv92r17d7OmEUo5OTnh7kKbRr7OIl/nkK2zyNdZ5OscsnUW+TrLjfl6vd6ztmlUgXTdddepoKCg1vDzzjtPc+fOrXOchqq0DRs26Oqrr64xbPPmzUpMTNSRI0d0xx13qEePHpo2bVpjulmDx+NRSkpKk8cPlZKSEuXk5Khfv36KjY0Nd3faHPJ1Fvk6h2ydRb7OIl/nkK2zyNdZbs133759QbVrVIH0zDPP1Pva4cOHlZ+fHzh1VVRUJL/fry5dutTZvri4WJs2bdLLL79cY3hiYqIkqXv37poyZYp27NjRrALJMAzFxcU1efxQi42NdVV/2hrydRb5OodsnUW+ziJf55Cts8jXWW7LN5jL66QQPua7R48eSk5O1qZNmyRJ69atU1paWr3ts7KydOGFFwYKIkk6duyYTp06JUkqLS3VG2+8odTU1FB1EQAAAAAaFNLvQVqyZImeeOIJ+Xw+vf7667r33nslSQUFBTUe7S1J69evr3Vm6PPPP9f3vvc9TZkyRdOnT1dqaqpmzZoVyi4CAAAAQL1C+iztAQMG6IUXXqg1PCEhocajvSXpT3/6U612Q4cO1UsvvRTKLgEAAABA0EJ6BgkAAAAAWjMKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2CiQAAAAAMBGgQQAAAAANgokAAAAALBRIAEAAACAjQIJAAAAAGwUSAAAAABgiwp3B9A8x/ymrtwpfXFK+ka09H9DpM4eI9zdAprtu2+aygx3J1pEtKSLpK2SZIa3K20S+dblF4nS3EHta1sR8WbLf/6V462MyypN/faAlF0qnRcj3dlH8ka0r/yB1oQCqRUb8C9T2aWn/3/glNT1Hem8GFN7L2XFi9YrHDsy4cOJfGeRb13uzpfuzjcDO/BtXbjWKRFvmvrxudKq/VJFteHzs6V555p6NKV95A+0NiHdctx///0aM2aMUlNTVV5eXm+7Q4cO6cYbb5TP59OMGTOUnZ0deG3btm268sorlZ6erjlz5ujkyZOh7GKbcWZxVF12qfU60Bq1r+IICK/28PcW7ve44oziSLL+v2K/tGBf288faI1CWiBNmzZN69evP2u7lStXaty4ccrMzNTs2bO1ePFiSVJlZaXuu+8+LVu2TFlZWUpISNCaNWtC2cU24Zi//uKoSnap1Q5oTb7bDnbWALf55e62+3cX7uLobFbtty6/A+AuIb3EbuTIkUG1y8rK0oMPPihJSk9P15IlS3TkyBEdPHhQnTp10pAhQyRJs2bN0t133625c+c2uU+maaq4uLjJ44dKSUlJjZ/N8d1dXkmRZ2+3o0KbBpc1e36tQSjzRW0tlW+mglu2AYTO3fkVuvUbTdtWuH/d6+51SoWkX/ynTHOSzjzH1Bqybd3I11luzdc0TRnG2S9tbfF7kAoLC+X1ehUfHy9JMgxDiYmJysvLU15ennr37h1o26tXL+Xl5TVrfn6/X7t3727WNEIpJyen2dP4/OS3FMwK//OT5a567y0hFPmifs7ne5HD0wdQm9HsbYV7170XhbsDZ/VB3lHtPnqg3tfdm23bQL7OcmO+Xq/3rG0aVSBdd911KigoqDX8vPPO05NPPhnUNEyz7lPJhmHU+VowVV5DPB6PUlJSmjWNUCgpKVFOTo769eun2NjYZk2r/64oHQri1qz+HaI0aNCgZs2rtQhlvqitxfLdyqUmQMszm7ytcP26txWsUy5O6qJBSR1rDXd9tq0c+TrLrfnu27cvqHaNKpCeeeaZJnWmum7duqmsrExFRUWKj4+XaZoqKChQYmKiKioqlJubG2ibm5urxMTEZs3PMAzFxcU1t9shExsb2+z+vDrUVNd3gmkXqTiPe957SwhFvqif0/n61F4e7Q24xy8SI5v9d+3eda+7C6RISXcnext85Ld7s20byNdZbss32BMvYXn+aVpamtatWydJ2rRpk5KTk9W9e3cNHjxYR48e1a5duyRJzz77rHw+Xzi66GqdPYbOi2m4zXkxfB8SWp9X28kjhwE3acvfh+T2x5jPO5fvQwLcKKQF0vz58zV27FhJ0oQJE3TPPfdIkgoKCjR16tRAu3vvvVevv/66fD6fnnjiCS1dutTqTESEli9froULFyo9PV15eXm65ZZbQtnFNmPvpfUXSefFiO9BQqvl9h0aoC1pD39v4X6PPz639l3DkfZwvgcJcKeQPqThscceq3N4QkKCNm7cWOP/f/nLX+psO2LECL388suh7FabtfdSQ8f8pq7cKX1xSvpGtPR/QzhzhNavcryh777ZXi63q6z2O19qGnrkW5dfJLbtM0dnqhxvhOWR31XF2c/7m/rtAesrOM6Lke7sw5kjwM1a/Cl2CK3OHkNvXxzuXgCh114utysuPqXdu3dr0KBBrrpOu60gX1QJ55kkb4Shu/uGbfYAGonDaQAAAABgo0ACAAAAAJth1vfFRG3A9u3bZZpmUF8I5TTTNOX3++XxeJr93U6ojXydRb7OIVtnka+zyNc5ZOss8nWWW/MtKyuTYRgaNmxYg+3a9D1IbvpADMNwRaHWVpGvs8jXOWTrLPJ1Fvk6h2ydRb7Ocmu+hmEEVR+06TNIAAAAANAY3IMEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAAAbBRIAAAAA2CiQAAAAAMBGgQQAAAAANgokAAAAALBRIAEAAACAjQIJAAAAAGwUSC0gOztbM2bMkM/n04033qhDhw6Fu0uuVFhYqFtvvVWTJk3SVVddpUWLFqmsrEwHDhzQ4MGDNXXqVE2dOlXXXHNNjfFWr16tiRMnKi0tTU8//XRg+MmTJzVnzhylp6fryiuv1AcffBB4rT1+JhMmTNDkyZMDOe7Zs0eS9Nprr8nn8yktLU0PPvigKioqAuOQbXAKCgoCuU6dOlVjx47VtGnTWHab4f7779eYMWOUmpqq8vLywPCWXF4bmldrV1e+27dv18yZM3XFFVfoyiuv1J///OdA+xdeeEEjR44MLMtLly4NvEa+NdWVbUuvC9pqtlLd+b7++us11sHDhw/XsmXLJLHsNkZ9+2FSO1z3mnDc9ddfb2ZkZJimaZpr1qwxFyxYEOYeuVNhYaH53nvvmaZpmhUVFea8efPMJ5980ty/f785ZsyYOsf517/+ZV599dXmqVOnzKNHj5rjx483v/jiC9M0TXP16tXmQw89ZJqmae7cudP8zne+Y1ZUVJim2T4/k/Hjx5s5OTk1hp04ccIcPXq0uX//frOystK8/fbbzXXr1pmmSbbNcc8995i///3vWXabYevWreZXX31lDhw40PT7/aZptuzy2tC82oK68t2zZ08gsxMnTpjp6enmhx9+aJqmaT7//PPmvffeW+e0yLemurJtyXVBW87WNOvOt7qKigrz8ssvN//973+bpsmy2xj17Ye1x3UvZ5AcdvjwYWVnZys9PV2SNHPmTGVlZYW5V+7UpUsXjRgxQpIUERGhwYMHKzc3t8FxMjIyNH36dHm9XnXu3Fk+n0+ZmZmB12bNmiVJuuCCC9SpUyft2rWLz6Sat956S8OGDVOfPn1kGIZmzpypjIwMSWTbVEVFRXrjjTc0derUBtuRb8NGjhypHj161BjWkstrQ/NqC+rKd+DAgerbt68kKT4+XsnJyWddB0vke6a6sm0Iy27jnC3fLVu2KDY2VhdeeOFZp0W+NdW3H9Ye170USA7Lz89XUlKSDMOQZG10PB6PCgsLw9wzdzt16pTWr1+vcePGSbJO+06fPl3f+973tGHDhkC7/Px89erVK/D/pKQk5efnS5Ly8vJqvNarVy/l5eW1689k7ty5mjJlilauXCm/399gfmTbNBkZGRo2bJgSEhIkseyGUksurw1Nrz34z3/+o507d2rkyJGBYf/85z81ZcoU3XTTTfrwww8Dw8k3OC21LmiP2Va3YcMGXX311TWGsew2XvX9sPa47o1q8Tm2M6Zp1jm8aqFAbZWVlVq4cKEuueQSjR07VmVlZdq8ebO6deumAwcO6Ac/+IF69+6tESNG1JlvQ9kahtFuP5Onn35aiYmJKioq0vz58/WHP/xBMTEx9bYn26bZsGGDrr32WklSz549WXZDqL73X99rzcmzoXm1dYWFhZozZ44WL16sbt26SZLGjx+vK664QtHR0Xr33Xd15513KiMjQx07dqxzGuRbU0uuC9pbttWdPHlSmzZt0j333BMYxrLbeGfuh+3du7fetm11+eUMksOqKt+qD7yoqEh+v19dunQJb8dcbOnSpYqKitL9998vSfJ6vYGNdJ8+fTRx4kTt2LFDkpVvXl5eYNy8vLzAkfszX8vNzVViYmK7/UwSExMlWUdqZs6cqR07digpKanGJTR5eXmBdmTbeAcOHNCePXs0ceJESSy7odaSy2tD82rLioqKdNttt+mGG26Qz+cLDO/atauio6MlSaNHj1ZCQoI+//xzSeQbjJZcF7S3bKvLzMzURRddVOP9suw23pn7Ye1x3UuB5LAePXooOTlZmzZtkiStW7dOaWlpYe6Vez366KMqKCjQ8uXLA0cgjhw5EnhSzbFjx/TOO+8oNTVVkuTz+bR+/XqVlZXp+PHjyszMDGzUfT6f/v73v0uSPv74Yx07dkyDBw9ul59JcXGxioqKJEnl5eXKyspSamqqxo4dq+3bt+vgwYMyTVPPPfdcjfzItnE2bNigSZMmBc7MseyGVksurw3Nq60qLS3V7bffrvT09MBZ0CoFBQWB3z/99FPl5uYG7lci37NryXVBe8u2urour2PZbZy69sPa47rXMN1yLqsN27t3rxYuXKiTJ0+qZ8+eWrFiRaC6xml79+7VlVdeqf79+8vr9UqSLrvsMg0dOlS//OUvFRERoYqKCk2bNk233XZbYLxVq1bp5ZdfliT94Ac/0PXXXy/JOiKxYMEC7du3T16vV0uWLNHw4cMD82pPn8n+/fs1Z84cVVZWqqKiQhdeeKEeeOABdejQQRkZGVq1apUqKio0atSowJEjiWwbKy0tTcuXL9fFF18sScrKymLZbaL58+dr69atKigoUEJCgoYPH67HH3+8RZfXhubV2tWV78UXX6xly5YpJSUl0O6HP/yhJk+erJUrV+qNN95QZGSkoqKidNddd+nyyy+XRL5nqivbSZMmtei6oK1mK9W/bsjNzdWUKVP0zjvv1Lh8nGU3ePXthy1cuLDdrXspkAAAAADAxiV2AAAAAGCjQAIAAAAAGwUSAAAAANgokAAAAADARoEEAAAAADYKJAAAAACwUSABAAAAgI0CCQAAAABsFEgAAAAAYKNAAgAAAADb/w/WQq8yeJk5EAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10,3 * 2))\n",
+    "\n",
+    "axs[0].plot(voltage_data)\n",
+    "axs[0].set_title(\"voltage\")\n",
+    "axs[1].scatter(range(len(fault_data)), fault_data)\n",
+    "axs[1].set_title(\"fault incidents\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_std = input_data.select(\n",
+    "    ((pl.col('PACK1_CRIDATA_AVG_CELL_TEMP') - pl.col('PACK1_CRIDATA_AVG_CELL_TEMP').mean())/ pl.col('PACK1_CRIDATA_AVG_CELL_TEMP').std())\n",
+    "    .alias('PACK1_CRIDATA_AVG_CELL_TEMP'),\n",
+    "    ((pl.col('PACK1_CRIDATA_AVG_CELL_VOL') - pl.col('PACK1_CRIDATA_AVG_CELL_VOL').mean())/ pl.col('PACK1_CRIDATA_AVG_CELL_VOL').std())\n",
+    "    .alias('PACK1_CRIDATA_AVG_CELL_VOL'),\n",
+    "    ((pl.col('PACK1_CRIDATA_BATT_VOL') - pl.col('PACK1_CRIDATA_BATT_VOL').mean())/ pl.col('PACK1_CRIDATA_BATT_VOL').std())\n",
+    "    .alias('PACK1_CRIDATA_BATT_VOL'),\n",
+    "    ((pl.col('PACK1_CRIDATA_SOC') - pl.col('PACK1_CRIDATA_SOC').mean())/ pl.col('PACK1_CRIDATA_SOC').std())\n",
+    "    .alias('PACK1_CRIDATA_SOC'),\n",
+    "    ((pl.col('PACK1_CRIDATA_CURR') - pl.col('PACK1_CRIDATA_CURR').mean())/ pl.col('PACK1_CRIDATA_CURR').std())\n",
+    "    .alias('PACK1_CRIDATA_CURR'),\n",
+    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = df_std[100000:]\n",
+    "test_data = df_std[60000:85000]\n",
+    "val_data = df_std[85000:100000]\n",
+    "anomaly_data = df_std[0:60000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100774, 5)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.to_numpy().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25000, 5)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.to_numpy().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_dataset(df, segment_size):\n",
+    "    # normalize the data\n",
+    "    data = df.to_numpy()\n",
+    "    # sliding window\n",
+    "    segments = [ torch.tensor(data[i:i + segment_size]).float() for i in range(0, len(data) - segment_size + 1, segment_size) ]\n",
+    "    # reject the last segment if it doesn't fit the shape\n",
+    "    if (segments[-1].shape[0] != segment_size):\n",
+    "        segments.pop()\n",
+    "    n_seq, seq_len, n_features = torch.stack(segments).shape\n",
+    "\n",
+    "    return segments, seq_len, n_features\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segment_size = 60\n",
+    "train_dataset, seq_len, n_features = create_dataset(train_data, segment_size)\n",
+    "val_dataset, _, _ = create_dataset(val_data, segment_size)\n",
+    "test_normal_dataset, _, _ = create_dataset(test_data, segment_size)\n",
+    "test_anomaly_dataset, _, _ = create_dataset(anomaly_data, segment_size)\n",
+    "whole_dataset, _, _ = create_dataset(df_std, segment_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Encoder Decoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([60, 5])\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = val_dataset[0]\n",
+    "print(x.shape)\n",
+    "x = x.reshape((1, 60, 5))\n",
+    "x.shape\n",
+    "\n",
+    "rnn_test_1 = nn.LSTM(  # 4\n",
+    "      input_size=5,\n",
+    "      # 64\n",
+    "      hidden_size=64 * 2,\n",
+    "      num_layers=1,\n",
+    "      batch_first=True\n",
+    ")\n",
+    "\n",
+    "rnn_test_2 = nn.LSTM(  # 4\n",
+    "      input_size=64 * 2,\n",
+    "      # 64\n",
+    "      hidden_size=64,\n",
+    "      num_layers=1,\n",
+    "      batch_first=True\n",
+    ")\n",
+    "\n",
+    "output, (_, _) = rnn_test_1(x)\n",
+    "output, (hidden, _) = rnn_test_2(output)\n",
+    "\n",
+    "# output is [1, 60, 128]\n",
+    "# hidden is [1, 1, 128]\n",
+    "# we first expand [1, 60, 4] to [1, 60, 128]\n",
+    "# then squeeze to [1, 1, 128]\n",
+    "# effectively compressed time of 60 to 1 vector of size 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 1, 64])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hidden.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 60, 64])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = hidden\n",
+    "x = x.repeat((1,60,1))\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Encoder(nn.Module):\n",
+    "\n",
+    "  def __init__(self, seq_len, n_features, embedding_dim=64, num_layers=1):\n",
+    "    super(Encoder, self).__init__()\n",
+    "\n",
+    "    self.seq_len, self.n_features = seq_len, n_features\n",
+    "    self.embedding_dim, self.hidden_dim = embedding_dim, 2 * embedding_dim\n",
+    "    self.num_layers = num_layers\n",
+    "\n",
+    "    self.rnn1 = nn.LSTM(\n",
+    "      # 4\n",
+    "      input_size=n_features,\n",
+    "      # 64\n",
+    "      hidden_size=self.hidden_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "    \n",
+    "    self.rnn2 = nn.LSTM(\n",
+    "      # 64\n",
+    "      input_size=self.hidden_dim,\n",
+    "      # 128\n",
+    "      hidden_size=embedding_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "\n",
+    "  def forward(self, x):\n",
+    "    x = x.reshape((1, self.seq_len, self.n_features))\n",
+    "\n",
+    "    x, (_, _) = self.rnn1(x)\n",
+    "    x, (hidden_n, _) = self.rnn2(x)\n",
+    "\n",
+    "    # hidden_n is 128 here\n",
+    "    # but we only have 128 values\n",
+    "\n",
+    "    # return hidden_n.reshape((self.n_features, self.embedding_dim))\n",
+    "    # hidden_n has same size as embedding_dim\n",
+    "    return hidden_n.reshape(self.num_layers * self.embedding_dim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Decoder(nn.Module):\n",
+    "\n",
+    "  def __init__(self, seq_len, input_dim=64, n_features=1, num_layers=1):\n",
+    "    super(Decoder, self).__init__()\n",
+    "\n",
+    "    self.seq_len, self.input_dim = seq_len, input_dim\n",
+    "    self.hidden_dim, self.n_features = 2 * input_dim, n_features\n",
+    "    self.num_layers = num_layers\n",
+    "\n",
+    "    self.rnn1 = nn.LSTM(\n",
+    "      # embedding_dim = 64\n",
+    "      # input_dim = 64\n",
+    "      input_size=num_layers * input_dim,\n",
+    "      hidden_size=input_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "\n",
+    "    self.rnn2 = nn.LSTM(\n",
+    "      # input_dim = 64\n",
+    "      input_size=input_dim,\n",
+    "      # hidden_size = 64 * 2\n",
+    "      hidden_size=self.hidden_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "\n",
+    "    # input: hidden_dim = 2 * 64\n",
+    "    # output: n_features = 4\n",
+    "    self.output_layer = nn.Linear(self.hidden_dim, n_features)\n",
+    "\n",
+    "  def forward(self, x):\n",
+    "    \n",
+    "    # x = x.repeat(self.n_features, self.seq_len, 1)\n",
+    "    # x = x.reshape((self.n_features, self.seq_len, self.input_dim))\n",
+    "    x = x.repeat(1, self.seq_len, 1)\n",
+    "\n",
+    "    x, (hidden_n, cell_n) = self.rnn1(x)\n",
+    "    x, (hidden_n, cell_n) = self.rnn2(x)\n",
+    "    x = x.reshape((self.seq_len, self.hidden_dim))\n",
+    "\n",
+    "    return self.output_layer(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RecurrentAutoencoder(nn.Module):\n",
+    "\n",
+    "  def __init__(self, seq_len, n_features, embedding_dim=64, num_layers=1):\n",
+    "    super(RecurrentAutoencoder, self).__init__()\n",
+    "\n",
+    "    self.encoder = Encoder(seq_len, n_features, embedding_dim, num_layers).to(device)\n",
+    "    self.decoder = Decoder(seq_len, embedding_dim, n_features, num_layers).to(device)\n",
+    "\n",
+    "  def forward(self, x):\n",
+    "    x = self.encoder(x)\n",
+    "    x = self.decoder(x)\n",
+    "\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RecurrentAutoencoder(seq_len, n_features, embedding_dim=64, num_layers=1)\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_model(model, train_dataset, val_dataset, n_epochs):\n",
+    "  optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n",
+    "  criterion = nn.L1Loss(reduction='sum').to(device)\n",
+    "  history = dict(train=[], val=[])\n",
+    "\n",
+    "  best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "  best_loss = 10000.0\n",
+    "  \n",
+    "  for epoch in range(1, n_epochs + 1):\n",
+    "    model = model.train()\n",
+    "\n",
+    "    train_losses = []\n",
+    "    for seq_true in train_dataset:\n",
+    "      optimizer.zero_grad()\n",
+    "\n",
+    "      seq_true = seq_true.to(device)\n",
+    "      seq_pred = model(seq_true)\n",
+    "\n",
+    "      loss = criterion(seq_pred, seq_true)\n",
+    "\n",
+    "      loss.backward()\n",
+    "      optimizer.step()\n",
+    "\n",
+    "      train_losses.append(loss.item())\n",
+    "\n",
+    "    val_losses = []\n",
+    "    model = model.eval()\n",
+    "    with torch.no_grad():\n",
+    "      for seq_true in val_dataset:\n",
+    "\n",
+    "        seq_true = seq_true.to(device)\n",
+    "        seq_pred = model(seq_true)\n",
+    "\n",
+    "        loss = criterion(seq_pred, seq_true)\n",
+    "        val_losses.append(loss.item())\n",
+    "\n",
+    "    train_loss = np.mean(train_losses)\n",
+    "    val_loss = np.mean(val_losses)\n",
+    "\n",
+    "    history['train'].append(train_loss)\n",
+    "    history['val'].append(val_loss)\n",
+    "\n",
+    "    if val_loss < best_loss:\n",
+    "      best_loss = val_loss\n",
+    "      best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "\n",
+    "    print(f'Epoch {epoch}: train loss {train_loss} val loss {val_loss}')\n",
+    "\n",
+    "  model.load_state_dict(best_model_wts)\n",
+    "  return model.eval(), history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: train loss 38.86843705290907 val loss 31.42728550720215\n",
+      "Epoch 2: train loss 33.15285356688031 val loss 30.997470954895018\n",
+      "Epoch 3: train loss 30.248853377602938 val loss 26.995422161102294\n",
+      "Epoch 4: train loss 20.282794476549423 val loss 23.825604535102844\n",
+      "Epoch 5: train loss 16.377526552212814 val loss 24.040671655654908\n",
+      "Epoch 6: train loss 15.591873613832393 val loss 23.611765365600586\n",
+      "Epoch 7: train loss 15.221078222887085 val loss 23.626889554977417\n",
+      "Epoch 8: train loss 14.928746445538247 val loss 23.297098026275634\n",
+      "Epoch 9: train loss 14.680893610814556 val loss 22.969089159965517\n",
+      "Epoch 10: train loss 14.512633638058198 val loss 23.19877135181427\n",
+      "Epoch 11: train loss 14.2985059658355 val loss 23.137717478752137\n",
+      "Epoch 12: train loss 14.14401307029338 val loss 22.674271161079407\n",
+      "Epoch 13: train loss 13.994837189089903 val loss 23.112126132965088\n",
+      "Epoch 14: train loss 13.851284402903522 val loss 23.070360283851624\n",
+      "Epoch 15: train loss 13.70330542975625 val loss 22.495113295555115\n",
+      "Epoch 16: train loss 13.540243803283301 val loss 22.37432998752594\n",
+      "Epoch 17: train loss 13.400565888854704 val loss 21.95571859359741\n",
+      "Epoch 18: train loss 13.243236444761811 val loss 21.879435116767883\n",
+      "Epoch 19: train loss 13.11524921701805 val loss 21.32238283443451\n",
+      "Epoch 20: train loss 13.023473753114056 val loss 21.33163222026825\n",
+      "Epoch 21: train loss 12.914216863249937 val loss 20.949963761806487\n",
+      "Epoch 22: train loss 12.810761129451409 val loss 20.877476187705994\n",
+      "Epoch 23: train loss 12.69247521517744 val loss 20.65351935005188\n",
+      "Epoch 24: train loss 12.58193458560821 val loss 20.758116281032564\n",
+      "Epoch 25: train loss 12.343759736253649 val loss 20.297971497535706\n",
+      "Epoch 26: train loss 12.229980894467033 val loss 20.63231620645523\n",
+      "Epoch 27: train loss 12.055519421871677 val loss 20.78471545791626\n",
+      "Epoch 28: train loss 11.89597285738156 val loss 19.9953883562088\n",
+      "Epoch 29: train loss 11.808162051556435 val loss 20.159356714725494\n",
+      "Epoch 30: train loss 11.71486991803656 val loss 20.361471618175507\n",
+      "Epoch 31: train loss 11.573541547941124 val loss 20.27868254184723\n",
+      "Epoch 32: train loss 11.512939579647307 val loss 19.748725774765013\n",
+      "Epoch 33: train loss 11.440065460165318 val loss 19.699584414958952\n",
+      "Epoch 34: train loss 11.357310095807497 val loss 19.62766425085068\n",
+      "Epoch 35: train loss 11.249971907290766 val loss 19.75169884300232\n",
+      "Epoch 36: train loss 11.168876630554177 val loss 19.306912044525145\n",
+      "Epoch 37: train loss 11.077471166227884 val loss 19.248602478027344\n",
+      "Epoch 38: train loss 11.043370378876528 val loss 19.437982461452485\n",
+      "Epoch 39: train loss 10.979938003298354 val loss 19.31774957513809\n",
+      "Epoch 40: train loss 10.902421442932711 val loss 18.986604588508605\n",
+      "Epoch 41: train loss 10.825572672097579 val loss 19.14624274110794\n",
+      "Epoch 42: train loss 10.748964093638858 val loss 19.890348776817323\n",
+      "Epoch 43: train loss 10.690735791005004 val loss 19.505379387378692\n",
+      "Epoch 44: train loss 10.635939868345652 val loss 19.15189327764511\n",
+      "Epoch 45: train loss 10.575765307883978 val loss 19.27545291376114\n",
+      "Epoch 46: train loss 10.517353279062366 val loss 19.12798267364502\n",
+      "Epoch 47: train loss 10.47650368398964 val loss 19.262065041542055\n",
+      "Epoch 48: train loss 10.401294536857536 val loss 19.28793490743637\n",
+      "Epoch 49: train loss 10.354887735176257 val loss 19.479482122421263\n",
+      "Epoch 50: train loss 10.335227646523817 val loss 19.279777328014372\n",
+      "Epoch 51: train loss 10.315433880344184 val loss 19.148805601596834\n",
+      "Epoch 52: train loss 10.240681151253188 val loss 18.902668324947356\n",
+      "Epoch 53: train loss 10.180605205664541 val loss 19.09464505434036\n",
+      "Epoch 54: train loss 10.128474695935287 val loss 18.842494136333464\n",
+      "Epoch 55: train loss 10.08075198671376 val loss 19.11138597726822\n",
+      "Epoch 56: train loss 10.096781223426623 val loss 18.495564115524292\n",
+      "Epoch 57: train loss 10.036804260136899 val loss 18.887975926399232\n",
+      "Epoch 58: train loss 10.004227935948352 val loss 18.73034571170807\n",
+      "Epoch 59: train loss 9.976196754007129 val loss 18.601321640491484\n",
+      "Epoch 60: train loss 9.906925555635876 val loss 18.60232736825943\n",
+      "Epoch 61: train loss 9.865915280463371 val loss 18.44057076215744\n",
+      "Epoch 62: train loss 9.854688118226901 val loss 18.460704574584963\n",
+      "Epoch 63: train loss 9.881754001277482 val loss 18.654272445201872\n",
+      "Epoch 64: train loss 9.819119888199731 val loss 18.61281745147705\n",
+      "Epoch 65: train loss 9.791684808760898 val loss 18.031773747444152\n",
+      "Epoch 66: train loss 9.74261095851855 val loss 18.42790675544739\n",
+      "Epoch 67: train loss 9.691091270834438 val loss 18.06329113674164\n",
+      "Epoch 68: train loss 9.692825360515418 val loss 18.32570272397995\n",
+      "Epoch 69: train loss 9.695701065661298 val loss 17.89037527036667\n",
+      "Epoch 70: train loss 9.687506776930961 val loss 17.886078037738802\n",
+      "Epoch 71: train loss 9.564859582101255 val loss 17.74414353466034\n",
+      "Epoch 72: train loss 9.540258792626709 val loss 16.97416575050354\n",
+      "Epoch 73: train loss 9.526702670237363 val loss 16.969112629413605\n",
+      "Epoch 74: train loss 9.524115598081051 val loss 16.92461084794998\n",
+      "Epoch 75: train loss 9.438481383269709 val loss 17.156900886058807\n",
+      "Epoch 76: train loss 9.431714758552065 val loss 16.5725666513443\n",
+      "Epoch 77: train loss 9.314281430146465 val loss 16.252663085460664\n",
+      "Epoch 78: train loss 9.346432527095665 val loss 16.173862711429596\n",
+      "Epoch 79: train loss 9.199182813830713 val loss 15.894011188983917\n",
+      "Epoch 80: train loss 9.25145694366731 val loss 15.820341364383697\n",
+      "Epoch 81: train loss 9.116767721171888 val loss 15.960952501773834\n",
+      "Epoch 82: train loss 9.072696581527262 val loss 15.735157964229584\n",
+      "Epoch 83: train loss 9.114809783993199 val loss 15.55242994260788\n",
+      "Epoch 84: train loss 9.054173212451833 val loss 15.36225093603134\n",
+      "Epoch 85: train loss 9.119249103962488 val loss 15.256216056346894\n",
+      "Epoch 86: train loss 8.962191241812748 val loss 15.580801096916199\n",
+      "Epoch 87: train loss 8.980852625673906 val loss 15.217423192977906\n",
+      "Epoch 88: train loss 8.91950746595043 val loss 15.25197039270401\n",
+      "Epoch 89: train loss 8.826644572315647 val loss 15.15044539308548\n",
+      "Epoch 90: train loss 8.858168052341911 val loss 15.222327210903167\n",
+      "Epoch 91: train loss 8.76176962023906 val loss 14.995795167922974\n",
+      "Epoch 92: train loss 8.720511651876926 val loss 15.157920863628387\n",
+      "Epoch 93: train loss 8.682562481192623 val loss 14.835923408985138\n",
+      "Epoch 94: train loss 8.701011724561505 val loss 14.898563205718995\n",
+      "Epoch 95: train loss 8.657228771705865 val loss 14.796268848896027\n",
+      "Epoch 96: train loss 8.535928185955976 val loss 14.77586179304123\n",
+      "Epoch 97: train loss 8.601159562732294 val loss 14.683023731231689\n",
+      "Epoch 98: train loss 8.605023592373245 val loss 14.646459495544434\n",
+      "Epoch 99: train loss 8.547713996861363 val loss 14.5896998462677\n",
+      "Epoch 100: train loss 8.467137120535432 val loss 14.808863736629487\n"
+     ]
+    }
+   ],
+   "source": [
+    "model, history = train_model(\n",
+    "  model, \n",
+    "  train_dataset, \n",
+    "  val_dataset, \n",
+    "  n_epochs=100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.figure().gca()\n",
+    "\n",
+    "ax.plot(history['train'])\n",
+    "ax.plot(history['val'])\n",
+    "plt.ylabel('Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.legend(['train', 'test'])\n",
+    "plt.title('Loss over training epochs')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "date = datetime.date.today().strftime('%y-%m-%d')\n",
+    "MODEL_PATH = f'model_save/multivar_model_{date}_01.pth'\n",
+    "\n",
+    "torch.save(model, MODEL_PATH)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# reload the model\n",
+    "model = torch.load('model_save/model_.pth')\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check reconstruction error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(model, dataset):\n",
+    "  predictions, losses = [], []\n",
+    "  criterion = nn.L1Loss(reduction='sum').to(device)\n",
+    "  with torch.no_grad():\n",
+    "    model = model.eval()\n",
+    "    for seq_true in dataset:\n",
+    "      seq_true = seq_true.to(device)\n",
+    "      seq_pred = model(seq_true)\n",
+    "\n",
+    "      loss = criterion(seq_pred, seq_true)\n",
+    "\n",
+    "      predictions.append(seq_pred.cpu().numpy().flatten())\n",
+    "      losses.append(loss.item())\n",
+    "  return predictions, losses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, test_normal_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, losses = predict(model, train_dataset)\n",
+    "plt.xlim(0, 100)\n",
+    "sns.kdeplot(losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# _, losses = predict(model, train_dataset)\n",
+    "_, losses = predict(model, test_normal_dataset)\n",
+    "plt.xlim(0, 100)\n",
+    "sns.kdeplot(losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# _, losses = predict(model, train_dataset)\n",
+    "_, losses = predict(model, test_anomaly_dataset)\n",
+    "plt.xlim(0, 1000)\n",
+    "sns.kdeplot(losses)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute THRESHOLD with training set data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions, losses = predict(model, train_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_array = np.array(losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stdev = np.std(loss_array)\n",
+    "mean = np.mean(loss_array)\n",
+    "THRESHOLD = mean + stdev * 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check on test_normal_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of intervals exceeding std dev loss: 28/416\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, losses = predict(model, test_normal_dataset)\n",
+    "exceed_count = sum(l > THRESHOLD for l in losses)\n",
+    "print(f'number of intervals exceeding std dev loss: {exceed_count}/{len(test_normal_dataset)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check on test_anomaly_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of intervals exceeding std dev loss: 214/1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, losses = predict(model, test_anomaly_dataset)\n",
+    "exceed_count = sum(l > THRESHOLD for l in losses)\n",
+    "print(f'number of intervals exceeding std dev loss: {exceed_count}/{len(test_anomaly_dataset)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot construction error vs original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_prediction(data, model, title, ax, picker):\n",
+    "    predictions, pred_losses = predict(model, [data])\n",
+    "\n",
+    "    ax.scatter(range(60), data[:,picker], label='true')\n",
+    "    ax.scatter(range(60), predictions[0].reshape(60,5)[:,picker], label='reconstructed')\n",
+    "    ax.set_title(f'{title} (loss: {np.around(pred_losses[0], 2)})')\n",
+    "    ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0ZcsWmaap5OTkeA8FAByltrZWhmEoNzc36G3IZACIDTIZAKyDTAYA6yCTAcA6Qs1k8hgAYifYTHb0jCSmaco0zbC3rampCXt7q3JiXdRkD9RkD8HWFE6+ksnNObEmyZl1UZM9JHJNZHLkqMkenFiT5My6ErkmMjly1GQP1GQfTqyLTO481GQP1GQPTqxJil0mR5LHoYzLTqjJHqjJHhK9pmAz1tEzkjR0Ko4aNSrkbSsrK/XJJ59o2LBh6tKlS7SHFjdOrIua7IGa7CHYmrZu3Rryvsnk5pxYk+TMuqjJHhK5JjI5ctRkD06sSXJmXYlcE5kcOWqyB2qyDyfWRSZ3HmqyB2qyByfWJMUukyPJ41DGZSfUZA/UZA+JXlOwmezoGUkAAAAAAAAAAAAAAAAQPBpJAAAAAAAAAAAAAAAAIMnhl7YBYB1er1eSVF1dLZfLGT1s1dXV/q9Orik5OVlutzuewwIQRaZpqq6uTpLz88vuyGTA+chk+yCTAedz4nkLKTEy2e12KykpSYZhxHlkAKLFiZnsxDyWyGQgEZDJ9hCL8xY0kgCIuYqKCu3bt09ut1uHDh1yTCh7vV4lJSU5vibDMJSTk6PMzMw4jw5ApGpqalRcXKyKigoy2QbIZMDZyGR7IZMBZ3PqeQspcTK5S5cu6t+/v1JSUuI8OgCRcmomOzGPJTIZcDoy2T5icd6CRhIAMeXxeHTgwAFlZGSob9++SktLc8yn9jwej6qrq5WamurYmkzTVGlpqQ4cOKDhw4c7pk4gEXm9Xu3evVtut1sDBgyQaZqOzi8nIJMB5yKT7YdMBpzLyectJOdnssvlUk1NjUpLS7V7924NHz7cMW8GAInIyZnsxDyWyGTAychke4nFeQsaSQDEVG1trUzTVK9evWQYhuNeaCQ5vqasrCzt2bNHtbW1jqkTSEQ1NTXyer0aOHCgUlNTdfLkScfnl92RyYBzkcn2QyYDzuXk8xZSYmRyenq6kpOTtXfvXtXU1CgtLS3OIwQQLidnshPzWCKTAScjk+0lFuctaAUE0Cm4JqJ98bMDnIVPgtgbmQw4C5lsb2Qy4Cw8p+2N11TAWchkeyOTAWchk+0r0p8dM5IAsCSPaWpjuVRcI/VPkfK6S25erACg05HHAGAdZDIAWAeZDADWQSYDgHWQyc5BWyAAyyksNTX0PWncP6Xr/+37OvQ93/JIPfLII/J6vRHvBwASQSzzWCKTASAUZDIAWAeZDADWQSYDgHWQyc5CIwkASyksNTVtm3Sguvnyg9XStG2Rv9g8+uijAV9k6urqItovADhNrPNYIpMBIFhkMgBYB5kMANZBJgOAdZDJzsOlbQBYhsc0dfcOKdBLiSnJkDRnh1TQ2wxrGqzFixdLkqZOnSq3262MjAzl5uZqy5YtGjBggMaMGaN3331XS5culeTrbKyrq9OcOXNUXV2tJUuW6F//+pdqamp06aWX6nvf+174xQKAhcU6jyUyGQCCRSYDgHWQyQBgHXbL5PHjx+vWW28Nr1gAsDgy2ZmYkQSAZWwsb92p2JQpaX+1b71w/PjHP5YkvfTSSyosLJQk7dmzR88995yWLFnS7rZPPvmkhgwZopdeekkvv/yy/vnPf+qdd94JbyAAYHGxzmOJTAaAYJHJAGAdZDIAWIcdM/ndd98NfzAAYGFksjMxIwkAyyiuie56wZg8ebJcro576t566y1VVlbqpZdekiRVVlZq9+7dOuecc6I3GACwiHjksUQmA0AgZDIAWAeZDADWYbdMPnHihPbu3RvdwQCARZDJzkQjCQDL6J8S3fWC0aVLF///3W53s2urVVdXy+12S5JM09SDDz6o3Nxc/+0ej0cnT56M3mAAwCLikccSmQwAgZDJAGAdZDIAWIfdMpk8BuBkZLIzcWkbAJaR113KSfVdKy0QQ9LAVN964crIyNCJEycC3jZw4EB9+umnqq2tVWVlpTZs2OC/7ZJLLtHvfvc71dbWSpJKSkpUWloa/kAAwMI6I48lMhkAgkEmA4B1kMkAYB12zOSysrLIBgMAFkUmOxMzkgCwDLdh6OHhpqZt872omE1ua3jxWT7ct164brrpJs2cOVMZGRlKTU1tdtvZZ5+tr33ta7riiis0ePBgnXHGGf7bbrvtNi1btkxTpkyR5OtyXLx4sbp27Rr2WADAqjojjyUyGQCCQSYDgHWQyQBgHXbL5PT0dC1YsCCisQCAVZHJzkQjCQBLmZJlaNVIU3fvkA5UNy7PSfW9yEzJiuxF5q677tJdd93V5u0PPvhgwOWpqan60Y9+1GwZU18BcLJY57FEJgNAsMhkALAOMhkArMNOmUweA3A6Mtl5aCQBYDlTsgwV9Da1sVwqrvFdMy2ve+SdigCA0JDHAGAdZDIAWAeZDADWQSYDgHWQyc5CIwkAS3IbhvJ7xHsUAADyGACsg0wGAOsgkwHAOshkALAOMtk5XPEeAAAAAAAAAAAAAAAAAKyBRhIAAAAAAAAAAAAAAABIopEEAAAAAAAAFuAxTW34wqWi2h7a8IVLHtOM95AAAAAAAEhISfEeAAAAAAAAABJbYampu3dIB6pTJQ2VPpFyPpMeHm5qSpYR7+EBAAAAAJBQmJEEgDWZHqnqLalipe+r6Yn3iKLuk08+0WuvvRb1/T7yyCPyer0hb/fuu+/qpptuivp4ANhcAuSxRCYDsAkyOSJksnUVlpqatk06UN18+cFqado23+2A5ZDJESGTAURVAmSyFfP4hhtuiPp4ADgAmRw2qx0j00gCwHpOFEr7hkjFY6UjM31f9w3xLY+iurq6qO4vVB290IQ7vkcffTSsFxoAaKWT8lgikwGgQ2SyH5nsLB7TNxNJoFaRhmVzdojL3MBayGQ/MhlA3HEuWRJ5DMAiyGRJzslkLm0DwFpOFEol16jVaUTPQd/yvquljClh737EiBG655579Oabb+ob3/iGLr30Uv3kJz9RaWmp6urq9L3vfU8TJkyQJBUVFenxxx+X1+tVcnKynnrqKfXs2VOPPvqoXn31VZmmqXPPPVfz58+X2+3WvHnzlJ6erp07d6q4uFiXX3655syZI6/Xq0WLFumjjz6Sy+XSiBEjtGDBAv3qV79SZWWlCgoKNGHCBF111VWaMWOGCgoKtGnTJt1yyy1asWKFbr/9dl1wwQWSpHHjxumZZ57R4MGDtX37di1evFjl5eUyDEPz58/X+vXrJUlTp06V2+1WYWGhiouL26zx2Wef1Ysvvqhu3brpnHPOCftxBeBAMc5jiUwmkwEEjUwmkx1sY3nrmUiaMiXtr/atl9+jkwYFtIdMJpMBWAfnksljANZBJjsuk2kkAWAdpkcqu0ttfxbNkMrulroUSIY77LtJTU3Viy++KEm66aabdO+99+orX/mKjh49qmuuuUajR49WWVmZFi9erN///vfKzs5WRUWFUlJS9Oabb2rdunX64x//KLfbrdtvv12rVq3St771LUnSnj179PTTT6u6uloTJkzQ9OnTdfz4ce3du1evvPKKJOn48ePq1q2bZs+erXfffVdLly6VJB04cEBHjhxRbm6u7rnnHknSihUrAtZQW1urO++8U/fff78uueQSeTwenThxQueff75WrFihl156SUlJvoifN29ewBqLi4u1YsUKFRYWqmvXrrrzzjvDfkwBOEwn5bFEJpPJADpEJpPJDldcE931gJgik8lkANbBuWTyGIB1kMmOzGQaSQBYx8mNkudAOyuYkme/b730/LDv5qqrrpIkVVZW6qOPPtL8+fP9t3m9Xu3bt0//+te/NH78eGVnZ0uSMjMzJUnvv/++rrzySnXp0kUej0cFBQV6/fXX/S80l156qZKTk5WcnKyhQ4fqwIEDOvPMM3X48GEtWrRIF154ofLy8tocW9euXTV+/PgOa9i9e7dSUlJ0ySWXSJLcbre6devWar32aty8ebPGjh2rU045xf+4tPXCBiDBdFIeS2QymQygQ2SyJDLZyfqnRHc9IKbIZElkMgCL4Fxy3PP4+eef7/C+ASQIMjnumRyLY2QaSQBYh6c4uuu1oUuXLpJ8get2u/Xyyy/L5XI1W+ef//ynDMNota1pms2Wmy2u052S0nh20+12y+PxqFu3blqzZo3ee+89vfHGG3rooYe0du3adsfWch8NqqurA46jLe3VuHnz5la1AYCkTstjiUxuQCYDaBOZ3GxsLffRgEy2r7zuUk6qdLA68GfXDPluz+veueMCAiKTm42t5T4akMkAOgXnksljANZBJjsyk10drwIAncTdP7rrdSAzM1MjR45s1jn98ccfyzRNXXDBBVq/fr0OHjwoSTpx4oRqamp0/vnn6y9/+YuqqqpUV1entWvX6rzzzmv3fo4ePaqamhqNHTtW9913n0pLS1VRUaHMzEydOHGi3W0HDhyobdu2SZI+/PBDlZWVSZJOPfVU1dTU6O2335YkeTweffHFF5KkjIwM/37bq3HMmDF68803dfz4cXm9Xq1ZsyaUhw+Ak3VyHktkMpkMoE1kckBksnO4DUMPD/f9v+WptIbvlw/3rQfEHZkcEJkMIC44l9wKeQwgbsjkVpyQyTSSALCOtDzJnaPWpw8bGJJ7oG+9KFm6dKl/OqvLL79cy5cvl2maOu2003Tffffpjjvu0OTJk3XzzTeroqJCY8eO1bhx43TNNddoypQpGjRokKZNm9bufRQXF+umm27S5MmTdc011+i2225Tjx49dN555+no0aOaMmWKfv3rXwfc9rvf/a6Kioo0bdo0vf766xowYIAkKTk5WY888oieeuopXXnllZoyZYo+/vhjSb7rws2cOVPTp09vt8YzzjhDN9xwg6ZNm6Ybb7xRp59+etQeVwA2F4c8lshkMhlAQGRywG3JZGeZkmVo1UgpO7X58pxUadVI3+2AJZDJAbclkwHEBeeSWyGPAcQNmdyKEzLZMKM418muXbv0wx/+UBUVFerbt6+WLl2qPn36tFrvyJEjuueee1RSUqLMzEz9/Oc/12mnnSbJ19mzaNEibd++XYZh6Nvf/rauvfbasMazdetWSdKoUaNC3rayslKffPKJzjzzzFZT0diZE+uiJms7efKkdu/erUGDBkmS0tLS5Ha7297gRKFUck39N03jqf7Fp+9qKWNKTMYaKo/Ho5MnT3Zck40EqqnhZzh06FClpaVJCi9fyeTmnFiT5My6nFJT0+dycnJyx/llozyWyGSJTI4ENdmDk2oik+3HkplsenSy/HUd2v+RBgz8utK6T5AMez/eHtPU6yXV2rznkM4eMkAT+qY6YiYSJ+VXAyfV5OTzFhKZLHGcHAlqsgcn1eTkTHZiHkuxy+RI8lhy1vOiATXZg5NqIpPtJdg8loLP2KjOSLJw4ULNmjVLRUVFys/P17JlywKut2zZMuXn56uoqEizZs3SwoUL/bfNmzdPeXl5Kioq0t/+9jeNHz8+mkMEYHUZU3wvJu7s5svdOZZ6kQEAxyOPAcA6yGS0dKJQ2jdEace+oVMzf6y0Y9+Q9g3xLbcxt2Ho4m5eTUo+pou7eR3RRAIHIpMBwDrIZACwDjLZcZKitaOysjLt2rVLEydOlCRNnz5deXl5WrJkSat1X3vtNd1///2SpIkTJ2rRokX6/PPP9cUXX2jfvn3+aWQMw1Dv3r0jGpdpmqqsrAx5u6qqqmZfncKJdVGTtVVXV8vr9co0TRmGIdM05fF42t8orUDKvkKq3ijDc1imu5+Umuf7dF1H23aihgmdgqrJJgLV5PF45PV6VVVVJa/X67/dCOOkLpncyIk1Sc6syyk1NeSxx+NRUpLvELDD/LJJHktkMpkcGWqyByfVRCbbj5Uy2X1yjVLKr5dkNps02PQclEquUU33F+RJKwh5DFYR6LnuMaW/f+nS4RpD/VJMXdjVK7eNekyclF8NnFSTk89bSGQyx8mRoSZ7cFJNTs5kJ+axFNtMDjePJWc9LxpQkz04qSYy2V6CzeOGdYLJ5Khd2mbbtm1asGCBCgsbP/0yevRoFRUVqUePHv5lx44d02WXXaYPPvjAv+zqq6/WT3/6Ux0+fFi/+93v1L9/f23fvl3Z2dmaP3++cnJywhrT1q1bVVNTE35RAKIiKSlJAwcOVGpqascrw3Kqq6u1f/9+1dXVNVuekpIS8vSwZDIQX+Sx/ZHJgHOQyfYXn0z2aNQpVyrZOKJA53xMU6o1+2rr8bWSnDE97xu13bXsZI6OmCn+ZX2MGs1NO6BxyeXxGxgchUy2P46TAecgk+0vGpkc3zz2KDNpi5KNMtWavVVRlyunHFsDoSKT7a2tPJaCy+SQZiSZMWOGSkpKWi0/7bTTNHv27IDbtOxmaatvxTAMeTwebd68WS+88IK++tWv6oUXXtD8+fO1YsWKUIbZTHJysoYNGxbydlVVVdqzZ4+GDBmi9PT0sO/fapxYFzVZW3V1tQ4dOqTk5GRJUmpqalifBrEi0zRVXV3t+JpM01RSUpIGDRrkP1jYuXNnWPsnkxs5sSbJmXU5paaamhodPHhQycnJSk1NTYj8sjsyufNQkz04qSYy2X6sksmu6g1KOXakzdsNQ0oxSvRfpx6VN/XisMYSb02f669VZWjejhS1PJNUaiZrXtVQPZ9To4Ke3oD7sRIn5VcDJ9Xk5PMWUuJkstfrVVJSkgYPHqyUFF/jGcfJkaMme3BSTU7OZCfmsRTbTA43j6Xwnxfuk2uU/MUP5PIe9C/zurJV2+0XcZ/1z0nP9QbUZG1ksr0Ee95CCj6TQ2okWblyZZu3lZWV6fDhw/6pUCoqKlRbW6vu3bs3W69nz56qqalRRUWFMjMzZZqmSkpK1K9fP3k8Hg0ePFhf/epXJUkFBQVaunRpKENsxTAMdenSJezt09PTI9reqpxYFzVZU2pqqkpKSnT06FFlZmZKktxuZ3TvejweVVdXS3JuTaZp6ujRo3K73erWrZu/znBfWMnk1pxYk+TMuuxeU1pamkpKSlRSUqJevXr5m3udml9OQCZ3PmqyByfURCbbj2Uy2XssqP2lJR+TbP48SUlL173bU1s1kUi+i/oYkubtS9X0bMltkxN/TsivlpxQk5PPW0jOz2SXy6WamhqVlpYqKSlJ3bp1k8vlksRxcjRRkz04oSYnZ7IT81iKbSZHmsdSiM+LE4VS/SUkm3J5Dym1fKbU4ydS8nDJ3V9Kq79URxw44bneEjVZE5lsL8Get5CCz+SQGkna07t3bw0dOlTr1q3ThAkTtHr1ak2YMCHgug2333zzzVq3bp2GDh2qXr16qUePHjIMQ3v37tXgwYO1YcMGjRgxIlpDBBAHbrdbOTk52rdvn44dO6bk5GT/waPdeb1e1dXVKSkpydE1GYahnJwc+76Ymh7p5EbJUxz3g3wgnlwul4YOHari4mIdOnRItbW1ZLLFkcmAc5HJ9mOZTHb3j+56Fvb3L106UN327aak/dXSxnIpv0fb6wEdcfJ5CylxMrlLly4aNGiQY2oEEpWTM9mJeSw5KJNNj1R2l1o2kdTf6PtybGHjIneO1PuXUsaUzhhd5AKdj5ECn6MJdt0obO+q3qAeyR/JVV0qpU+I/P6dWFMcHxN3Wp5ycgZo395P9UX5IbmTUmW40iQZkkzJrPbtx3BLRv1sF0Eti3T7yPdpek/KU1cjd1KKRWqKfPtYnLeIWiOJJC1atEj33nuvli5dqj59+vhnEykpKdGsWbO0Zs0aSdLcuXN1zz33aOXKlcrIyNDPf/5zSb6TaT/96U81Z84c1dXV6ZRTTtGDDz4YzSF2Dk6QA81kZmZq0KBB2r59uwYPHmz76bwaVFVV6bPPPtOgQYMcXVNycrJ937A8Uej7A8BzoHGZ3Q7ygShKSUnRoEGD9OWXX2rHjh1kssWRyYCzkcn2YplMTsvzZafnoAKf5DZ8tzecBLSxwzXBfUKquCbGA0FCcOp5CykxMtntdispKcne05JzPhnwc2omOzGPJQdl8smNzc9XdMRzUCq5Ruq7On7nNNp67Wi53FMmfT6neX2uXr6v3s8bl7lzpMwZUsXKjteN0vZp3s91aqakY5K+iPD+nVhTpOOPwj4zJY1Iq1StN0uSIbn6SV0ulypfkbyHm6xb393fdBbNgMsi3T6K+0yJ1pgssH2PH6nKyIv6eYuoNpIMHz5chYWFrZb37dvX30TS8P2KFSsC7iM3NzfgPmyDE+RAQA3db6mpqUpLS4vzaKLD6/Vdi5uaLOpEoe9gvuUJ9oaD/D5/kNxZnKRBwjEMQ0lJvkNARzzX6zkqv+o5qiYyGQiITLYPy9RkuH3nF0qukf8TS403+r70ftgRGdovJVCjTGv9UzpeBwiGE89bSBbKryhyXE2cTwZacWImOy676tm+roamixMvhbqhJEMqvUsyTpG8R6J3PqONmSKazXThrWjdHNJWg0AgTZsFGngOSMd/Edy6VtzeimOK9/ZR2qfbkNzuffUL90pVH/j+/Gz2q77X9yWYZZFuH4t92nr7fVL5VTK6vyDp9KjmcVQbSRJS00Cv2SGVL1KbJ8jj2ZkIAIkkmKkIj8yQ5Glc7ISTNPGcIq9++1ZT9wW7fQzHRE1RrMnC00vGraZYPSZO4qRMtuhUsLaYSpWaeJ2x8+uM1WRM8Z1fCPim48PWy84wXdjVq5xU6WB1m3OvKCdVyuveyQMDgEhxPhkArCFQI19ITMl7QDp8aeMid47U66HWH5aRIp49pNlMF4G01SAAwOF8jW3JX/xQ0ktR3TONJJEI+kWmvjOx7G6pS4E1T0QBgBM0HHhXrQ8imz0tvj0olUyVevxESh4enzcPInmTo3JN69ekOEzxl+Y50PgHzfEgt7fItIPUFOP7d2JNsXpMrNhAEQ67ZHKgN71tNhWs5adSpaZ2t+d1Jkr3H6uarJrJGVOkLgU6Wf66Du3/SAMGfl1p3Sc46nyD25AeHi5N29bm3CtaPlxy223adACJjfPJAGANbc2cGinPAenI9ObLQvlbJZBAM0UAQDOmXN4DykzaImlk1PZKI0m4Qn6RMSXPft/J6PT8GA4MABJUNDrIJenYwsZF0XjzIJipCNMnRNYI4upl7yn+rDgmaor+9lYcU7y3D7jPJp88NC5rfbtdWCGTg2nOq28Oafamd1sNCoHY5vfKic8Vm9fEYxL97WMyJotnsuGWN/ViHavNUr/UMx35JuOULEOrRpq6e4d0oLpxeU6qr4lkShZNJABsJNzzyccWSenjrTtTFgDYTbszp8ZAKH+rAEAEko2yqO6PRpJwRPIiU7Xe+tPkAoDdxKyDvJ1PxIfwBmWHUxEej7ARhK50wEGafPKw98fxHkxrbc3e0VRnZ7IU/ixNAe+HkzkAGlg8kxPElCxDBb1NbSyXimuk/im+y9kwEwkAW4nkfHL5Yt8/K8+UBQB2cnJjBB98AQDrqjV7R3V/NJKEI5IXmfLFjf/n4B8AOtbRm5Yx7SBv4xPxwU6RHgiNIAA65Pvkoavm75Ky4j2YRvWzjLSavaPp8WxnZ3KkszQBQIcsmskJxm0Yyu8R71EAQASi8aZl05myOJ8MAOHzFAe3XrfvSa4sqXxR/YJOmsEEAEJmyOvKVkVdblT3SiNJOIJ9kelwPxz8A0C7gnnTsrM7yEOZIh0AImB4D8syb1q2NctIy+PZzs5kGkYAdBJLZTIAwH6icj65fqas0rsk4xTJe4RZrwEgkI4+mOjuH9x+MqZK6flS6sgIL98LALHkm62zttvPpc+je0zoiureEkWwLzIdMn3/Sm+TvnxBqnrL9ylOAEDjm5YtD9Ab3rQ8UVj/fagnYzi5AsAeTFe/eA/Bp91ZRuqXld3tW49MBuBQlslkAIA9RfN8sveAdPhS6chMqXistG9I4zkSAEh0JwqlfUOUduwbOjXzx0o79o3WOZmW55t1Wm1dKtGQ3AMbL6ebMUUatEfq/6bU50Wp37oOtrcBV6/GWV4buAdKp/ygvrYO1rXi9lYcU7y3t+KYqCkG2+dIfVfLk1agaGNGknA0vMh4DioqU1l5S6XSb/n+z+VuACCINy3rr1XfpSD4kzHdfyylj5c8ZdKR6U32BQBWY0juHHlTLpT0n3gPJohZRnyXfdDJjWQyAAeyWCYDAOwp2ueTm/IclEqmSj1+IiUPZ5YSAIkr2NlUDbfvfbiSa+RrBmm6fn1zSO+Hm+eo4fbNTtKgze3jyD1Qyryu9SXZ3QOlXsskd5bvA0ANrxNS/Tmf4uavHT1/1np5oHWjsP3J8td1aP9HGjDw60rrPiHy+3diTRZ4TKjJer9nAbevrFS00UgSjg5fZMzGA/eaf0vli4PfNwf+ABDam5YdnozxnfxXj0WNWWqsZjpCABbVxgmLeAp2lhFPsZQxnUwG4CAWzGQAgD0Fez7ZrA3tXLLUuK9jCxsXuXOkXg+1ftPQcPs+vBPkmxQBLw0RwvZBv/ERyT7jXZMFHhNqst7vWULUZLXj41A+mGi4fQ0lfQOcj3Dn+I7/O/qwd1vbx0LD7ANNL+XbVnNIW29Gt/Xzatoc06Bl00x760a4vTf1Yh2rzVK/1DObnCeK8P6dWFOcHxNqiv79x2T7GKCRJFzBvshUvRXiwX8bB/69f+l7gYv2i3UsDjSsdgABwH5CedMynA7yjCnNM7Vmh1S+qP5Gi3SQh6S+blev5n9QBPojo60/PAJ1q3fW9lYcEzVFf3srjine2wfcZ5NjyRh0kYcl2FlG3P0TL5Nt83vlxOeKzWviMYn+9jEZkwUzGQBgX8GcTzY90pfPttOYHSTPgSYz/zW5n8wZIb0upnk/16mZko5J+iL07YN6rY1kn/GuKdLxU5M9aorSY+Komqw4s34oH0xseAO25fmIUN/jCrS9p0z6fE7Hj2sYs4cEnEEhkLbejAaAENFIEolgXmSiMW1hwywlLd8gbKuzXGqz6aNZ16u3IrgXtFAONMIYU6SNLK06eYPdPoZjiltNFugWt1xNMXxMHFeTlYTypqUUXgd5ywPq1JFx/ER8pI0g9XUGek2SOnXqtVZ/0AS7fQzHRE1RrMkC0w5arqZYPSZW0uHxbP0sIw312D2TbTgVbKBljpui04k18Tpjn9cZAACipaPzye02ZkfIc0A6/ovWy5ueX2hvWaTbx2Kfdt/eimOipuhvb8UxRbx9k0vFGJe1vj0ePMXhrRdp00Wg7TOujuxvlTb+Bgk4gwIAxBCNJJHq6EUmKgf/9du0fMEO1FneQdNHmudAY9drIJEeaIQxpkgbWZrVdDzI7a3axRtJTRbpFrdUTTF+TBxVk9U6yEN901KKfgd5rD4RH8tGECnuU68F/IPGztPBUVP079+JNcXiMbGSaMwy0qmZHEJzXn1zyMnarsE1KFh0KthEmaLTiTXxOhOD+7fJ9LAAgATW0fnkzrxcAgCEpcmlYnp/HO/B+IT6wcRYCuXvD2YPAWBhNJJ0hs48+A+l6aOzWLFj1opjivf2VhxTvLe34phiUpMFO8jDedOyYbtodpAH+kR8KFOkhzMVIW9yALCiaMwyEqpgMjkazXmGW97KyuAbFAAAAICWAs1cG8mnxVs2Vrv6SKU3t/OBGwDobL5Lxbhq/i4pK96DCe+DiQCAdtFIEki0D/yl5gf/dQd9l5TxlokDfwDxZ8EOcim8Ny1jMYa2PlEf7BTpTEUIwCnqMzHoa/LG6P5jMksTAAAAEK4ThVLZXc1nrv0iCrO/tmxsjtUlbwAgAob3sCzRSBLuBxMBAG2ikaSlWB34S80P/l3pHPgDsBCLdZA3iPebllLbn0gPZYpCAHCKti5ZYXrCv4xNiPdP9gIAAMAyThTWn+NtcX636eyv0fogDJe8AWBBpqtfvIfQqL0PJvZaJrl6+maZjuV5CwBwEBpJmuLAH0CCs0wHeVPxftMSANC++kbs1rNHRaERGwAQHYGOnSWOpwEgEqbHdxwc8EOCTWZ/7VIQvXxtOUtfzQ6pfFGT+wSAzuK7VIw35UJJ/4n3YBoF+mBicoXvKgGctwCAkNBI0iCYA//SuyTjFMl7JDonWTjwB2Axluogbw9vWgKANXRmIzYAIDyBjp1dvXxfvZ83LuN4GgBCc3JjBx8Q9M3+qmOLpPTx0WvYazlLX+pIPqwIoJNZ/FIxTT6YmG3+RzpyvThvAQChc8V7AJYRzIG/94B0+FLpyEypeKy0b4jvhEwkGg78M2dIPRf4XrTc2c3XaTjB0/DiDABRZ0jugfUd5BbX8KZly8xuOPiPNJcBAMHpsBFbvk9gmp5OHBQAQKZHqnrLN2330f8OfOzs/bx5E4nE8TQAhMpTHNx65Yujdy45kIwp0qA9Uv83pT4v1n9d5WsQbMo9UDrlB62Xu3o1Of/czrJIt4/FPu2+vRXHRE08JkFtn2OTBgyPkr/4gThvAQDhYUaSBsEe+DfbJkaXvGk6S0nDzCeVazqnszzQp5ICflJpoJR5ne/EFN3ugM1ZvIO8qXhMGwsACMhV8/fgPoF5cmPzT0wCAGIn0OwjQeN4GgBC4u4f2vqx/PR7y1lKJCnj6sCXMOv5s+AudyY1vzRE9wkhbx/0ZdUi2We8a7LAY0JN1vs9S4iabHCsmJm0RS7vwXbW4LwFALSHRpIGoR74S4rZSZaAB/4BGkw8Za2v6xZK04d7oNRrmeTOit6BRqRjirSRJdjtrTimeG9vxTHFe3srjikmNeX4mkgypkiVlbIy3rQEAOswvIeDWzGchm0AQOjautxYSDieBoCgpeX5zql4Diq47O3khr1A55jbWx5gWcOlIfqlntk43hC2D3pZpPuMd01xfkyoKfr3H4vtnViT1SUbZcGtyHkLAAiIRpIGIR/4N+jEkyzBdpZLbTZ9BOx6DSSSA40QxxRpx2yrmoLdPoZjiltNFugWt1xNMXxMHFeTDfCmJQBYh+nqF9yKYTVsAwBC0u7MfWE48ZLvq43+VgCATme4pd6/rG/iMxR0MwkNewDQKWrN3sGtyHkLAAiIRpIGTQ78TRkyQjz58u7RQ6rpYiqvu+/7jeVScY3UP0XK6y65DUMe02y1PNC6oW3v0saTlzQuS6vfvsUytySPXHqrOl+bK0/X2dUDNEEuyQJjCmbdkGoKcvtYjileNUV6/06sKZaPidNqsiqPaWrDFy5tru0hz8kBGh3ENuu/6Cd3bTwyObjtJflrKv3CpQnpZtzHFK3tW9YVymNCTdavKRq/+06rKVaPiVU1e6xPnqFJ7hwZbTRimzJUbeToT8cvUr+TscvkaOzTaZlMTfaoidcZa+aBXTPZU/6eRkfzsrNfPCp98ahMd46M3r+Up8vVnfIzCOXxDmWf8f69clp+UZM9anLq64zlZEyR+q6WWXaXjBBy+N9fHNK/vjAt/zOI9vPCiTVZ4TGhJuv9niVCTZbM5HoNr39bqsbp7m7ZSjMPtfGenyHTnaO3qy5SsQ0z2Qpjstpz3Yk1WeExoSbr/Z51VibTSNJUxhS9n7ZKg07crQHu0E7A3Le3v96ulnrVP6Kf1zXelpMqXdfH1O+PSAeqG5cHWjf226dKGip9IvXaEdz21GSPmiK9fyfWFPvHxDk1PTzc1JQs6x38F5aauntH42O94D+DtC8nR/1dB2UYrQ/+vaahA54cTdqeJ6+s/jNo/P3J+cxK+RPp9mSyU2vidSb629s9k/Wp9N1uD+v/dZ8mU4ZcTXLZa/rGf/2R5Xq5yteuaO3fK+dkMjXZoyZeZ6J//7GqyS6ZvO3A+3oxK/L9mqbU9PyTWXdQOnyNbi9fpd98McW/PBY/g4bH+7KMjsfZWH/07p/XmeC2pyZ71OTU1xnLZnLl1fq/BydrqDZqXNp63d/9gQ63uXOX71yyZPWfQfSeF06sideZxKiJ15nA21s2k5sdJw/TppJfalVW4PMWhqT/U7Zcv/ms8WOW1v4ZtP+aboXfC15nrPVcpyZ71BStTA7mb+lQGaZpRmneU+vZunWrJGnUqFFBrV9YamraNsmQR3mpG9XfXawSTx892/vbynYfbPYi06DhTctTD34mr6U/0w8AgTUc7q8aKV2WUaVPPvlEZ555prp06dLmNqHmazjbNGRyy+S9Or1Qq7KmSVLANy2nla7Sy1VTBAB2ZLdMlny5/HDPuzUw6YB/2b66gZpzdDl5DMDW7JTJl6S+pTf7jQv6PhvOBDVrGmnRRNKgs857NNz188OrdXrJx20+1u29JgFwLrtkskse7c4eyrlkAI7WGZkcToa3dz6Z8xYAnCrYv6WbCjZjXdEYoBN4TF+XoinJK7fers7X7ytn6M3q8br76MOSGt+kbNDw/ZyjyznwB2BbDQfWc3ZIHoucjW2ayS29XDVF00pX6aAnu9nyA54cmkgA2J7dMlny5fLQg7s19vAbmln6gsYefkOnHvyMPAZge3bK5I3Vedpfl9PqvEWDlh8hKvP20ufeXs2WtTUTrsswNShpv/JSN4Y56uA0DPHevcltPt4dvSYBcC67ZLJXbs4lA3A8u2RyA85bAHCyYP6WDldSdHdnXxvLm08X01TDm5YtOxYPeHLoWATgCKak/dXS3790KQozYkesvUyWfLm85mCBf/aoYk9/bazO40QMAEewWyZLjY3YAOA0dsnkhjcuV2VNk9cMdLkxUwuPLdLOuuH+Y2dJykvdqCldXtL3uz3W4X2PS1sf82NvU9KBGpf+mZSpkQFuD+Y1CYBz2SWTOZcMIBHYJZMbcN4CgJN19Ld0uGgkqVdc0/7tvGkJIBEcrjEsceDfUSZLHPwDcD47ZTIAOJ0dMjmcNy4bjqeDaSS5v/sD/v/vr8vR3UcfjtmboWVmcsDlvCYBkOyTyZxLBpAI7JDJAJAo2vpbOlw0ktTrn9LxOrxpCcDp+qVYYy7CYDIZAJyOTAYA67BLJofzxmXDZXGy3QebzWTSoOGyOE0vfZPtPqhVWdNidmnJ3kZtwOW8JgGQ7JPJnEsGkAjskskAkAja+ls6XK6o7s3G8rpLOalSG5cEBgBHMyQNTJUu7OqN91AkkckAEhuZDADWYcdMbnjj8veVM/R2dX6Hn35vuCy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+      "text/plain": [
+       "<Figure size 2200x800 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(\n",
+    "    nrows=2,\n",
+    "    ncols=6,\n",
+    "    sharey=True,\n",
+    "    sharex=True,\n",
+    "    figsize=(22, 8)\n",
+    ")\n",
+    "\n",
+    "picker = 4\n",
+    "sample_size = 6\n",
+    "sample_indices = random.sample(range(0,len(test_normal_dataset)), sample_size)\n",
+    "\n",
+    "sampled_test_normal_dataset = [test_normal_dataset[i] for i in sample_indices]\n",
+    "sampled_test_anomaly_dataset = [test_anomaly_dataset[i] for i in sample_indices]\n",
+    "\n",
+    "for i, data in enumerate(sampled_test_normal_dataset):\n",
+    "    plot_prediction(data, model, title='Normal', ax=axs[0, i], picker=picker)\n",
+    "\n",
+    "for i, data in enumerate(sampled_test_anomaly_dataset):\n",
+    "    plot_prediction(data, model, title='Anomaly', ax=axs[1, i], picker=picker)\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assess quality of prediction of flagged intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def convert_values(values):\n",
+    "    numerical_values = []\n",
+    "    for value in values:\n",
+    "        if value == 'False':\n",
+    "            numerical_values.append(0)\n",
+    "        elif value == 'True':\n",
+    "            numerical_values.append(1)\n",
+    "        else:\n",
+    "            # numerical_values.append(np.nan)\n",
+    "            # numerical_values.append(-1)\n",
+    "            numerical_values.append(-1)\n",
+    "    return numerical_values\n",
+    "\n",
+    "\n",
+    "fault_data = convert_values(df['BATT_PACK_1_FAULT']\n",
+    "                            .filter(filter_condition))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we want to identify the count of anomalies in each interval in the \"test_anomaly_dataset\"\n",
+    "fault_segments = [ fault_data[i:i + seq_len] for i in range(0, len(anomaly_data) - seq_len + 1 ,seq_len) ]\n",
+    "# len(test_anomaly_dataset)\n",
+    "# count all occurances of 1 in fault_segments[i]\n",
+    "anomaly_count_list = [ fault_segments[i].count(1) for i in range(len(fault_segments))]\n",
+    "anomaly_flag_actual = [ count > 0 for count in anomaly_count_list ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, test_anomaly_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anomaly_flag_prediction = [ l > THRESHOLD for l in losses ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "214"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.array(anomaly_flag_prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_mat = confusion_matrix(anomaly_flag_actual, anomaly_flag_prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[759 172]\n",
+      " [ 27  42]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result_mat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "42 27 172 759\n",
+      "786\n",
+      "214\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn, fp, fn, tp = result_mat.ravel()\n",
+    "print(tp, fn, fp, tn)\n",
+    "print(tn + fn )\n",
+    "print(fp + tp)\n",
+    "\n",
+    "#                     actual negative actual positive \n",
+    "# predicted negative: tn fp\n",
+    "# predicted positive: fn tp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8152524167561761\n",
+      "0.6086956521739131\n",
+      "0.801\n"
+     ]
+    }
+   ],
+   "source": [
+    "# accuracy of negatives\n",
+    "specificity = tn / (tn + fp)\n",
+    "print(specificity)\n",
+    "\n",
+    "# accuracy of positives\n",
+    "sensitivity = tp / (tp + fn)\n",
+    "print(sensitivity)\n",
+    "\n",
+    "\n",
+    "overall_accuracy = (tn + tp) / (tn + fp + fn + tp)\n",
+    "print(overall_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only 19.626168224299064 of flagged intervals are actual\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"only {tp / (fp + tp) * 100} of flagged intervals are actual\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6086956521739131"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tp / (fn + tp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot regions of predicted anomalies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### juxtapose over anomalous region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, test_anomaly_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anomaly_flag_prediction = [ l > THRESHOLD for l in losses ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_segments = []\n",
+    "count = 0\n",
+    "for i in anomaly_flag_prediction:\n",
+    "    count = count + 1\n",
+    "    x_segments.append([count*60, count*60 + 60])\n",
+    "y_segments = [ [0,0] for i in x_segments]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fault region\n",
+    "# fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10,3 * 2))\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "fault_incidents = fault_data[0:60000]\n",
+    "ax.scatter(range(len(fault_incidents)), fault_incidents)\n",
+    "true_color =\"green\"\n",
+    "false_color = \"red\"\n",
+    "\n",
+    "labels = anomaly_flag_prediction\n",
+    "sequences = x_segments\n",
+    "\n",
+    "y_level = 0.5\n",
+    "for i, (sequence, label) in enumerate(zip(sequences, labels)):\n",
+    "    if label:\n",
+    "        ax.plot((sequence[0],sequence[1]), (y_level,y_level), color=\"red\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### juxtapose over whole dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, whole_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anomaly_flag_prediction = [ l > THRESHOLD for l in losses ]\n",
+    "x_segments = []\n",
+    "count = 0\n",
+    "for i in anomaly_flag_prediction:\n",
+    "    count = count + 1\n",
+    "    x_segments.append([count*60, count*60 + 60])\n",
+    "y_segments = [ [0,0] for i in x_segments]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fault region\n",
+    "# fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10,3 * 2))\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "fault_incidents = fault_data\n",
+    "ax.scatter(range(len(fault_incidents)), fault_incidents)\n",
+    "true_color =\"green\"\n",
+    "false_color = \"red\"\n",
+    "\n",
+    "labels = anomaly_flag_prediction\n",
+    "sequences = x_segments\n",
+    "\n",
+    "y_level = 0.5\n",
+    "for i, (sequence, label) in enumerate(zip(sequences, labels)):\n",
+    "    if label:\n",
+    "        ax.plot((sequence[0],sequence[1]), (y_level,y_level), color=\"red\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/multivar_anomaly_detection_overlap_window.ipynb b/notebooks/multivar_anomaly_detection_overlap_window.ipynb
new file mode 100644
index 0000000..a1fdb66
--- /dev/null
+++ b/notebooks/multivar_anomaly_detection_overlap_window.ipynb
@@ -0,0 +1,1517 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Univariate Time Series Anomaly Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## boilerplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "import copy\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from pylab import rcParams\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rc\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "\n",
+    "from torch import nn, optim\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "import random\n",
+    "import datetime\n",
+    "# from arff2pandas import a2p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x7fab3bf04c10>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# %matplotlib inline\n",
+    "# %config InlineBackend.figure_format='retina'\n",
+    "\n",
+    "sns.set(style='whitegrid', palette='muted', font_scale=0.7)\n",
+    "\n",
+    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
+    "\n",
+    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
+    "\n",
+    "# rcParams['figure.figsize'] = 12, 8\n",
+    "\n",
+    "RANDOM_SEED = 42\n",
+    "np.random.seed(RANDOM_SEED)\n",
+    "torch.manual_seed(RANDOM_SEED) \n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "from io import StringIO\n",
+    "import math\n",
+    "df = pl.read_csv('../data/battery_1.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We only need 'PACK1_CRIDATA_BATT_VOL'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## visualize fault and non-fault regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "input_data = df.select(['PACK1_CRIDATA_AVG_CELL_TEMP', 'PACK1_CRIDATA_AVG_CELL_VOL', 'PACK1_CRIDATA_BATT_VOL', 'PACK1_CRIDATA_SOC'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# process explanatory variables\n",
+    "filter_condition =  df['PACK1_CRIDATA_BATT_VOL'].cast(pl.Float32) != 0\n",
+    "voltage_data = (df['PACK1_CRIDATA_BATT_VOL']\n",
+    "                .filter(filter_condition)\n",
+    "                .cast(pl.Float32))\n",
+    "\n",
+    "input_data = (df.select(\n",
+    "    pl.col('PACK1_CRIDATA_AVG_CELL_TEMP').cast(pl.Float32), \n",
+    "    pl.col('PACK1_CRIDATA_AVG_CELL_VOL').cast(pl.Float32),\n",
+    "    pl.col('PACK1_CRIDATA_BATT_VOL').cast(pl.Float32),\n",
+    "    pl.col('PACK1_CRIDATA_SOC').cast(pl.Float32),\n",
+    "    pl.col('PACK1_CRIDATA_CURR').cast(pl.Float32))\n",
+    ".filter(filter_condition))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def convert_values(values):\n",
+    "    numerical_values = []\n",
+    "    for value in values:\n",
+    "        if value == 'False':\n",
+    "            numerical_values.append(0)\n",
+    "        elif value == 'True':\n",
+    "            numerical_values.append(1)\n",
+    "        else:\n",
+    "            # numerical_values.append(np.nan)\n",
+    "            # numerical_values.append(-1)\n",
+    "            numerical_values.append(-1)\n",
+    "    return numerical_values\n",
+    "\n",
+    "\n",
+    "fault_data = convert_values(df['BATT_PACK_1_FAULT']\n",
+    "                            .filter(filter_condition))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'fault incidents')"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10,3 * 2))\n",
+    "\n",
+    "axs[0].plot(voltage_data)\n",
+    "axs[0].set_title(\"voltage\")\n",
+    "axs[1].scatter(range(len(fault_data)), fault_data)\n",
+    "axs[1].set_title(\"fault incidents\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_std = input_data.select(\n",
+    "    ((pl.col('PACK1_CRIDATA_AVG_CELL_TEMP') - pl.col('PACK1_CRIDATA_AVG_CELL_TEMP').mean())/ pl.col('PACK1_CRIDATA_AVG_CELL_TEMP').std())\n",
+    "    .alias('PACK1_CRIDATA_AVG_CELL_TEMP'),\n",
+    "    ((pl.col('PACK1_CRIDATA_AVG_CELL_VOL') - pl.col('PACK1_CRIDATA_AVG_CELL_VOL').mean())/ pl.col('PACK1_CRIDATA_AVG_CELL_VOL').std())\n",
+    "    .alias('PACK1_CRIDATA_AVG_CELL_VOL'),\n",
+    "    ((pl.col('PACK1_CRIDATA_BATT_VOL') - pl.col('PACK1_CRIDATA_BATT_VOL').mean())/ pl.col('PACK1_CRIDATA_BATT_VOL').std())\n",
+    "    .alias('PACK1_CRIDATA_BATT_VOL'),\n",
+    "    ((pl.col('PACK1_CRIDATA_SOC') - pl.col('PACK1_CRIDATA_SOC').mean())/ pl.col('PACK1_CRIDATA_SOC').std())\n",
+    "    .alias('PACK1_CRIDATA_SOC'),\n",
+    "    ((pl.col('PACK1_CRIDATA_CURR') - pl.col('PACK1_CRIDATA_CURR').mean())/ pl.col('PACK1_CRIDATA_CURR').std())\n",
+    "    .alias('PACK1_CRIDATA_CURR'),\n",
+    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = df_std[100000:]\n",
+    "test_data = df_std[60000:85000]\n",
+    "val_data = df_std[85000:100000]\n",
+    "anomaly_data = df_std[0:60000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100774, 5)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.to_numpy().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25000, 5)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.to_numpy().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_dataset(df, segment_size):\n",
+    "    # normalize the data\n",
+    "    data = df.to_numpy()\n",
+    "    # sliding window\n",
+    "    segments = [ torch.tensor(data[i:i + segment_size]).float() for i in range(0, len(data) - segment_size + 1, 10) ]\n",
+    "    # reject the last segment if it doesn't fit the shape\n",
+    "    if (segments[-1].shape[0] != segment_size):\n",
+    "        segments.pop()\n",
+    "    n_seq, seq_len, n_features = torch.stack(segments).shape\n",
+    "\n",
+    "    return segments, seq_len, n_features\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segment_size = 60\n",
+    "train_dataset, seq_len, n_features = create_dataset(train_data, segment_size)\n",
+    "val_dataset, _, _ = create_dataset(val_data, segment_size)\n",
+    "test_normal_dataset, _, _ = create_dataset(test_data, segment_size)\n",
+    "test_anomaly_dataset, _, _ = create_dataset(anomaly_data, segment_size)\n",
+    "whole_dataset, _, _ = create_dataset(df_std, segment_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Encoder Decoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([60, 5])\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = val_dataset[0]\n",
+    "print(x.shape)\n",
+    "x = x.reshape((1, 60, 5))\n",
+    "x.shape\n",
+    "\n",
+    "rnn_test_1 = nn.LSTM(  # 4\n",
+    "      input_size=5,\n",
+    "      # 64\n",
+    "      hidden_size=64 * 2,\n",
+    "      num_layers=1,\n",
+    "      batch_first=True\n",
+    ")\n",
+    "\n",
+    "rnn_test_2 = nn.LSTM(  # 4\n",
+    "      input_size=64 * 2,\n",
+    "      # 64\n",
+    "      hidden_size=64,\n",
+    "      num_layers=1,\n",
+    "      batch_first=True\n",
+    ")\n",
+    "\n",
+    "output, (_, _) = rnn_test_1(x)\n",
+    "output, (hidden, _) = rnn_test_2(output)\n",
+    "\n",
+    "# output is [1, 60, 128]\n",
+    "# hidden is [1, 1, 128]\n",
+    "# we first expand [1, 60, 4] to [1, 60, 128]\n",
+    "# then squeeze to [1, 1, 128]\n",
+    "# effectively compressed time of 60 to 1 vector of size 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 1, 64])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hidden.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 60, 64])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = hidden\n",
+    "x = x.repeat((1,60,1))\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Encoder(nn.Module):\n",
+    "\n",
+    "  def __init__(self, seq_len, n_features, embedding_dim=64, num_layers=1):\n",
+    "    super(Encoder, self).__init__()\n",
+    "\n",
+    "    self.seq_len, self.n_features = seq_len, n_features\n",
+    "    self.embedding_dim, self.hidden_dim = embedding_dim, 2 * embedding_dim\n",
+    "    self.num_layers = num_layers\n",
+    "\n",
+    "    self.rnn1 = nn.LSTM(\n",
+    "      # 4\n",
+    "      input_size=n_features,\n",
+    "      # 64\n",
+    "      hidden_size=self.hidden_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "    \n",
+    "    self.rnn2 = nn.LSTM(\n",
+    "      # 64\n",
+    "      input_size=self.hidden_dim,\n",
+    "      # 128\n",
+    "      hidden_size=embedding_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "\n",
+    "  def forward(self, x):\n",
+    "    x = x.reshape((1, self.seq_len, self.n_features))\n",
+    "\n",
+    "    x, (_, _) = self.rnn1(x)\n",
+    "    x, (hidden_n, _) = self.rnn2(x)\n",
+    "\n",
+    "    # hidden_n is 128 here\n",
+    "    # but we only have 128 values\n",
+    "\n",
+    "    # return hidden_n.reshape((self.n_features, self.embedding_dim))\n",
+    "    # hidden_n has same size as embedding_dim\n",
+    "    return hidden_n.reshape(self.num_layers * self.embedding_dim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Decoder(nn.Module):\n",
+    "\n",
+    "  def __init__(self, seq_len, input_dim=64, n_features=1, num_layers=1):\n",
+    "    super(Decoder, self).__init__()\n",
+    "\n",
+    "    self.seq_len, self.input_dim = seq_len, input_dim\n",
+    "    self.hidden_dim, self.n_features = 2 * input_dim, n_features\n",
+    "    self.num_layers = num_layers\n",
+    "\n",
+    "    self.rnn1 = nn.LSTM(\n",
+    "      # embedding_dim = 64\n",
+    "      # input_dim = 64\n",
+    "      input_size=num_layers * input_dim,\n",
+    "      hidden_size=input_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "\n",
+    "    self.rnn2 = nn.LSTM(\n",
+    "      # input_dim = 64\n",
+    "      input_size=input_dim,\n",
+    "      # hidden_size = 64 * 2\n",
+    "      hidden_size=self.hidden_dim,\n",
+    "      num_layers=self.num_layers,\n",
+    "      batch_first=True\n",
+    "    )\n",
+    "\n",
+    "    # input: hidden_dim = 2 * 64\n",
+    "    # output: n_features = 4\n",
+    "    self.output_layer = nn.Linear(self.hidden_dim, n_features)\n",
+    "\n",
+    "  def forward(self, x):\n",
+    "    \n",
+    "    # x = x.repeat(self.n_features, self.seq_len, 1)\n",
+    "    # x = x.reshape((self.n_features, self.seq_len, self.input_dim))\n",
+    "    x = x.repeat(1, self.seq_len, 1)\n",
+    "\n",
+    "    x, (hidden_n, cell_n) = self.rnn1(x)\n",
+    "    x, (hidden_n, cell_n) = self.rnn2(x)\n",
+    "    x = x.reshape((self.seq_len, self.hidden_dim))\n",
+    "\n",
+    "    return self.output_layer(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RecurrentAutoencoder(nn.Module):\n",
+    "\n",
+    "  def __init__(self, seq_len, n_features, embedding_dim=64, num_layers=1):\n",
+    "    super(RecurrentAutoencoder, self).__init__()\n",
+    "\n",
+    "    self.encoder = Encoder(seq_len, n_features, embedding_dim, num_layers).to(device)\n",
+    "    self.decoder = Decoder(seq_len, embedding_dim, n_features, num_layers).to(device)\n",
+    "\n",
+    "  def forward(self, x):\n",
+    "    x = self.encoder(x)\n",
+    "    x = self.decoder(x)\n",
+    "\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RecurrentAutoencoder(seq_len, n_features, embedding_dim=64, num_layers=1)\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_model(model, train_dataset, val_dataset, n_epochs):\n",
+    "  optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
+    "  criterion = nn.L1Loss(reduction='sum').to(device)\n",
+    "  history = dict(train=[], val=[])\n",
+    "\n",
+    "  best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "  best_loss = 10000.0\n",
+    "  \n",
+    "  for epoch in range(1, n_epochs + 1):\n",
+    "    model = model.train()\n",
+    "\n",
+    "    train_losses = []\n",
+    "    for seq_true in train_dataset:\n",
+    "      optimizer.zero_grad()\n",
+    "\n",
+    "      seq_true = seq_true.to(device)\n",
+    "      seq_pred = model(seq_true)\n",
+    "\n",
+    "      loss = criterion(seq_pred, seq_true)\n",
+    "\n",
+    "      loss.backward()\n",
+    "      optimizer.step()\n",
+    "\n",
+    "      train_losses.append(loss.item())\n",
+    "\n",
+    "    val_losses = []\n",
+    "    model = model.eval()\n",
+    "    with torch.no_grad():\n",
+    "      for seq_true in val_dataset:\n",
+    "\n",
+    "        seq_true = seq_true.to(device)\n",
+    "        seq_pred = model(seq_true)\n",
+    "\n",
+    "        loss = criterion(seq_pred, seq_true)\n",
+    "        val_losses.append(loss.item())\n",
+    "\n",
+    "    train_loss = np.mean(train_losses)\n",
+    "    val_loss = np.mean(val_losses)\n",
+    "\n",
+    "    history['train'].append(train_loss)\n",
+    "    history['val'].append(val_loss)\n",
+    "\n",
+    "    if val_loss < best_loss:\n",
+    "      best_loss = val_loss\n",
+    "      best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "\n",
+    "    print(f'Epoch {epoch}: train loss {train_loss} val loss {val_loss}')\n",
+    "\n",
+    "  model.load_state_dict(best_model_wts)\n",
+    "  return model.eval(), history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: train loss 14.991484802913119 val loss 47.635426118142625\n",
+      "Epoch 2: train loss 14.673848953528582 val loss 43.243409028579556\n",
+      "Epoch 3: train loss 14.333927784033705 val loss 44.07000463096593\n",
+      "Epoch 4: train loss 13.915726091935372 val loss 44.34826395232542\n",
+      "Epoch 5: train loss 14.657572755171392 val loss 43.69780085126692\n",
+      "Epoch 6: train loss 14.143000129734576 val loss 44.81571371340034\n",
+      "Epoch 7: train loss 14.038070801698778 val loss 44.12152769414079\n",
+      "Epoch 8: train loss 13.851427001248368 val loss 44.09310012995997\n",
+      "Epoch 9: train loss 13.729247768691389 val loss 43.32384617272827\n",
+      "Epoch 10: train loss 13.631058291626045 val loss 45.44009980039054\n",
+      "Epoch 11: train loss 13.418263957398553 val loss 41.21652204711302\n",
+      "Epoch 12: train loss 13.862140987962011 val loss 42.51774741463039\n",
+      "Epoch 13: train loss 12.650342613859568 val loss 40.25015159753653\n",
+      "Epoch 14: train loss 12.36933911645029 val loss 37.92169579183776\n",
+      "Epoch 15: train loss 11.98887928614705 val loss 36.10202267464985\n",
+      "Epoch 16: train loss 12.21290189172034 val loss 38.01268916975296\n",
+      "Epoch 17: train loss 11.711113982606824 val loss 28.44262305868908\n",
+      "Epoch 18: train loss 11.011346816528379 val loss 27.90213075880223\n",
+      "Epoch 19: train loss 10.898415433791799 val loss 29.32146102943548\n",
+      "Epoch 20: train loss 11.491236296296712 val loss 26.51003688107366\n",
+      "Epoch 21: train loss 10.863789315553673 val loss 24.472279914565707\n",
+      "Epoch 22: train loss 10.610268099098876 val loss 22.609055767250698\n",
+      "Epoch 23: train loss 10.373339420433043 val loss 23.78191721941715\n",
+      "Epoch 24: train loss 10.940695203234153 val loss 22.05775741749384\n",
+      "Epoch 25: train loss 10.885960484635177 val loss 23.156156875776208\n",
+      "Epoch 26: train loss 10.36299374574134 val loss 19.75051664094064\n",
+      "Epoch 27: train loss 10.089229504392144 val loss 18.16691262698094\n",
+      "Epoch 28: train loss 10.152652916431167 val loss 16.388314533233643\n",
+      "Epoch 29: train loss 10.094028000273047 val loss 16.266330868583857\n",
+      "Epoch 30: train loss 9.990942514965324 val loss 18.1360527489895\n",
+      "Epoch 31: train loss 9.790299692248142 val loss 16.15177443210895\n",
+      "Epoch 32: train loss 10.039502827225755 val loss 16.864113875775033\n",
+      "Epoch 33: train loss 9.889029200513558 val loss 14.908555627188155\n",
+      "Epoch 34: train loss 9.616837129562004 val loss 13.120580163049857\n",
+      "Epoch 35: train loss 9.42455815551085 val loss 14.049857989760945\n",
+      "Epoch 36: train loss 9.393890358449195 val loss 16.31934666091383\n",
+      "Epoch 37: train loss 9.2998268636161 val loss 14.642723051042461\n",
+      "Epoch 38: train loss 9.296024454156909 val loss 15.312676857945114\n",
+      "Epoch 39: train loss 9.201674998250388 val loss 14.407547143470483\n",
+      "Epoch 40: train loss 9.197074455388092 val loss 12.695137181808319\n",
+      "Epoch 41: train loss 9.022051593624617 val loss 15.047900228436575\n",
+      "Epoch 42: train loss 9.137689992548 val loss 14.91150041096984\n",
+      "Epoch 43: train loss 8.931943488525517 val loss 12.387148332755302\n",
+      "Epoch 44: train loss 8.874451617623743 val loss 14.627675122162172\n",
+      "Epoch 45: train loss 9.200509475629904 val loss 13.392843184662503\n",
+      "Epoch 46: train loss 9.126006494861354 val loss 13.105388391456476\n",
+      "Epoch 47: train loss 8.900100591822547 val loss 13.178430976756042\n",
+      "Epoch 48: train loss 9.019521129990737 val loss 12.107054019572344\n",
+      "Epoch 49: train loss 8.804207691363546 val loss 13.318513815777756\n",
+      "Epoch 50: train loss 8.858408954121746 val loss 12.733705319449255\n",
+      "Epoch 51: train loss 8.688436110203641 val loss 15.280156064352463\n",
+      "Epoch 52: train loss 8.092340259285344 val loss 18.562882003895815\n",
+      "Epoch 53: train loss 7.582048114958774 val loss 15.711433462634135\n",
+      "Epoch 54: train loss 7.539406633721346 val loss 17.022368440659946\n",
+      "Epoch 55: train loss 7.081485559127491 val loss 12.375487418637228\n",
+      "Epoch 56: train loss 7.130740252084515 val loss 13.577870465919725\n",
+      "Epoch 57: train loss 6.972742149814952 val loss 13.01242988739524\n",
+      "Epoch 58: train loss 6.974390369714212 val loss 12.156829171914321\n",
+      "Epoch 59: train loss 6.755032019958942 val loss 13.100684676361722\n",
+      "Epoch 60: train loss 6.97479202756636 val loss 12.936651795604158\n",
+      "Epoch 61: train loss 6.606993020248079 val loss 13.80874253260252\n",
+      "Epoch 62: train loss 6.520448666380317 val loss 13.02389475701246\n",
+      "Epoch 63: train loss 6.444385555646042 val loss 11.342601695427527\n",
+      "Epoch 64: train loss 6.519072658063532 val loss 12.230810649099956\n",
+      "Epoch 65: train loss 6.548562949943498 val loss 12.799046216920068\n",
+      "Epoch 66: train loss 6.350596522029143 val loss 12.202243250429031\n",
+      "Epoch 67: train loss 6.298089423301209 val loss 12.522010730979435\n",
+      "Epoch 68: train loss 6.234351575359069 val loss 12.915401603625371\n",
+      "Epoch 69: train loss 6.223840727439007 val loss 11.906952536305456\n",
+      "Epoch 70: train loss 6.2498281353994365 val loss 10.763029688337575\n",
+      "Epoch 71: train loss 6.094569119391778 val loss 11.903022861400974\n",
+      "Epoch 72: train loss 6.136690409408736 val loss 11.027987039766982\n",
+      "Epoch 73: train loss 6.112186732917959 val loss 11.157776807941321\n",
+      "Epoch 74: train loss 6.073190037388209 val loss 11.29262953538161\n",
+      "Epoch 75: train loss 6.087775080543931 val loss 11.137414576138143\n",
+      "Epoch 76: train loss 5.954031884364008 val loss 11.085648946219862\n",
+      "Epoch 77: train loss 5.843934960049341 val loss 10.795274752358528\n",
+      "Epoch 78: train loss 5.872989448278586 val loss 10.801579055339596\n",
+      "Epoch 79: train loss 5.7880489398711275 val loss 11.505093461374774\n",
+      "Epoch 80: train loss 5.737008810143939 val loss 10.853074759783155\n",
+      "Epoch 81: train loss 5.613592992576669 val loss 10.640041053813437\n",
+      "Epoch 82: train loss 5.395545467803132 val loss 10.255325305422014\n",
+      "Epoch 83: train loss 5.473923093295339 val loss 9.33503757141107\n",
+      "Epoch 84: train loss 5.4141714567417685 val loss 9.633902329744702\n",
+      "Epoch 85: train loss 5.473207396916702 val loss 8.909583377040748\n",
+      "Epoch 86: train loss 5.162455617662481 val loss 10.458845855320577\n",
+      "Epoch 87: train loss 5.1817696539313784 val loss 9.489331444131093\n",
+      "Epoch 88: train loss 5.0629925271562755 val loss 8.981164223135115\n",
+      "Epoch 89: train loss 4.949556847366285 val loss 9.014035170612527\n",
+      "Epoch 90: train loss 4.937979031550958 val loss 8.644254712197295\n",
+      "Epoch 91: train loss 4.885893268939744 val loss 8.747803532120376\n",
+      "Epoch 92: train loss 4.874019369166389 val loss 8.936452906586254\n",
+      "Epoch 93: train loss 4.725279761567813 val loss 9.418706700873614\n",
+      "Epoch 94: train loss 4.856170468588639 val loss 9.053455802110525\n",
+      "Epoch 95: train loss 4.62928881468673 val loss 9.051609508568626\n",
+      "Epoch 96: train loss 4.58303886756295 val loss 9.099835369818187\n",
+      "Epoch 97: train loss 4.5736183714217 val loss 8.80084493558941\n",
+      "Epoch 98: train loss 4.396421173492016 val loss 9.569508837537223\n",
+      "Epoch 99: train loss 4.527437411227232 val loss 8.769417099569953\n",
+      "Epoch 100: train loss 4.47397932656201 val loss 8.73031808185737\n"
+     ]
+    }
+   ],
+   "source": [
+    "model, history = train_model(\n",
+    "  model, \n",
+    "  train_dataset, \n",
+    "  val_dataset, \n",
+    "  n_epochs=100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.figure().gca()\n",
+    "\n",
+    "ax.plot(history['train'])\n",
+    "ax.plot(history['val'])\n",
+    "plt.ylabel('Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.legend(['train', 'test'])\n",
+    "plt.title('Loss over training epochs')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "date = datetime.date.today().strftime('%y-%m-%d')\n",
+    "MODEL_PATH = f'model_save/model_{date}.pth'\n",
+    "\n",
+    "torch.save(model, MODEL_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reload the model\n",
+    "model = torch.load('model_save/model_23-09-08.pth')\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check reconstruction error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(model, dataset):\n",
+    "  predictions, losses = [], []\n",
+    "  criterion = nn.L1Loss(reduction='sum').to(device)\n",
+    "  with torch.no_grad():\n",
+    "    model = model.eval()\n",
+    "    for seq_true in dataset:\n",
+    "      seq_true = seq_true.to(device)\n",
+    "      seq_pred = model(seq_true)\n",
+    "\n",
+    "      loss = criterion(seq_pred, seq_true)\n",
+    "\n",
+    "      predictions.append(seq_pred.cpu().numpy().flatten())\n",
+    "      losses.append(loss.item())\n",
+    "  return predictions, losses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, test_normal_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, losses = predict(model, train_dataset)\n",
+    "plt.xlim(0, 100)\n",
+    "sns.kdeplot(losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# _, losses = predict(model, train_dataset)\n",
+    "_, losses = predict(model, test_normal_dataset)\n",
+    "plt.xlim(0, 100)\n",
+    "sns.kdeplot(losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# _, losses = predict(model, train_dataset)\n",
+    "_, losses = predict(model, test_anomaly_dataset)\n",
+    "plt.xlim(0, 1000)\n",
+    "sns.kdeplot(losses)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute THRESHOLD with training set data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions, losses = predict(model, train_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_array = np.array(losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stdev = np.std(loss_array)\n",
+    "mean = np.mean(loss_array)\n",
+    "THRESHOLD = mean + stdev * 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check on test_normal_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of intervals exceeding std dev loss: 127/2495\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, losses = predict(model, test_normal_dataset)\n",
+    "exceed_count = sum(l > THRESHOLD for l in losses)\n",
+    "print(f'number of intervals exceeding std dev loss: {exceed_count}/{len(test_normal_dataset)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check on test_anomaly_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of intervals exceeding std dev loss: 772/5995\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, losses = predict(model, test_anomaly_dataset)\n",
+    "exceed_count = sum(l > THRESHOLD for l in losses)\n",
+    "print(f'number of intervals exceeding std dev loss: {exceed_count}/{len(test_anomaly_dataset)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot construction error vs original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_prediction(data, model, title, ax):\n",
+    "    predictions, pred_losses = predict(model, [data])\n",
+    "\n",
+    "    ax.plot(data[:,2], label='true')\n",
+    "    ax.plot(predictions[0].reshape(60,5)[:,2], label='reconstructed')\n",
+    "    ax.set_title(f'{title} (loss: {np.around(pred_losses[0], 2)})')\n",
+    "    ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2200x800 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(\n",
+    "    nrows=2,\n",
+    "    ncols=6,\n",
+    "    sharey=True,\n",
+    "    sharex=True,\n",
+    "    figsize=(22, 8)\n",
+    ")\n",
+    "\n",
+    "sample_size = 6\n",
+    "sample_indices = random.sample(range(0,len(test_normal_dataset)), sample_size)\n",
+    "\n",
+    "sampled_test_normal_dataset = [test_normal_dataset[i] for i in sample_indices]\n",
+    "sampled_test_anomaly_dataset = [test_anomaly_dataset[i] for i in sample_indices]\n",
+    "\n",
+    "for i, data in enumerate(sampled_test_normal_dataset):\n",
+    "    plot_prediction(data, model, title='Normal', ax=axs[0, i])\n",
+    "\n",
+    "for i, data in enumerate(sampled_test_anomaly_dataset):\n",
+    "    plot_prediction(data, model, title='Anomaly', ax=axs[1, i])\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assess quality of prediction of flagged intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def convert_values(values):\n",
+    "    numerical_values = []\n",
+    "    for value in values:\n",
+    "        if value == 'False':\n",
+    "            numerical_values.append(0)\n",
+    "        elif value == 'True':\n",
+    "            numerical_values.append(1)\n",
+    "        else:\n",
+    "            # numerical_values.append(np.nan)\n",
+    "            # numerical_values.append(-1)\n",
+    "            numerical_values.append(-1)\n",
+    "    return numerical_values\n",
+    "\n",
+    "\n",
+    "fault_data = convert_values(df['BATT_PACK_1_FAULT']\n",
+    "                            .filter(filter_condition))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we want to identify the count of anomalies in each interval in the \"test_anomaly_dataset\"\n",
+    "fault_segments = [ fault_data[i:i + seq_len] for i in range(0, len(anomaly_data) - seq_len + 1 ,10) ]\n",
+    "# len(test_anomaly_dataset)\n",
+    "# count all occurances of 1 in fault_segments[i]\n",
+    "anomaly_count_list = [ fault_segments[i].count(1) for i in range(len(fault_segments))]\n",
+    "anomaly_flag_actual = [ count > 0 for count in anomaly_count_list ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5995"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(fault_segments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "428"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.array(anomaly_flag_actual) > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "594"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# there are 50 - 100 minute segments where there is an anomaly\n",
+    "sum(np.array(anomaly_count_list))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, test_anomaly_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anomaly_flag_prediction = [ l > THRESHOLD for l in losses ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "772"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.array(anomaly_flag_prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_mat = confusion_matrix(anomaly_flag_actual, anomaly_flag_prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4872  695]\n",
+      " [ 351   77]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result_mat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# actual\n",
+    "# negative: 714 vs positive: 286\n",
+    "\n",
+    "# predict all as positive\n",
+    "# 0  0 \n",
+    "# 714 286\n",
+    "\n",
+    "# predict all as negative\n",
+    "# 714 286\n",
+    "# 0    0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "77 351 695 4872\n",
+      "5223\n",
+      "772\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn, fp, fn, tp = result_mat.ravel()\n",
+    "print(tp, fn, fp, tn)\n",
+    "print(tn + fn )\n",
+    "print(fp + tp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.875157176216993\n",
+      "0.17990654205607476\n",
+      "0.8255212677231026\n"
+     ]
+    }
+   ],
+   "source": [
+    "# accuracy of negatives\n",
+    "specificity = tn / (tn + fp)\n",
+    "print(specificity)\n",
+    "\n",
+    "# accuracy of positives\n",
+    "sensitivity = tp / (tp + fn)\n",
+    "print(sensitivity)\n",
+    "\n",
+    "\n",
+    "overall_accuracy = (tn + tp) / (tn + fp + fn + tp)\n",
+    "print(overall_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only 11.07913669064748 of flagged intervals are actual\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"only {tp / fp * 100} of flagged intervals are actual\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot regions of predicted anomalies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### juxtapose over anomalous region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, test_anomaly_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anomaly_flag_prediction = [ l > THRESHOLD for l in losses ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_segments = []\n",
+    "count = 0\n",
+    "for i in anomaly_flag_prediction:\n",
+    "    x_segments.append([count*10, count*10 + 60])\n",
+    "    count = count + 1\n",
+    "y_segments = [ [0,0] for i in x_segments]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fault region\n",
+    "# fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10,3 * 2))\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "fault_incidents = fault_data[0:60000]\n",
+    "ax.scatter(range(len(fault_incidents)), fault_incidents)\n",
+    "true_color =\"green\"\n",
+    "false_color = \"red\"\n",
+    "\n",
+    "labels = anomaly_flag_prediction\n",
+    "sequences = x_segments\n",
+    "\n",
+    "y_level = 0.5\n",
+    "for i, (sequence, label) in enumerate(zip(sequences, labels)):\n",
+    "    if label:\n",
+    "        ax.plot((sequence[0],sequence[1]), (y_level,y_level), color=\"red\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### juxtapose over whole dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, losses = predict(model, whole_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anomaly_flag_prediction = [ l > THRESHOLD for l in losses ]\n",
+    "x_segments = []\n",
+    "count = 0\n",
+    "for i in anomaly_flag_prediction:\n",
+    "    x_segments.append([count*10, count*10 + 60])\n",
+    "    count = count + 1\n",
+    "y_segments = [ [0,0] for i in x_segments]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fault region\n",
+    "# fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(10,3 * 2))\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "fault_incidents = fault_data\n",
+    "ax.scatter(range(len(fault_incidents)), fault_incidents)\n",
+    "true_color =\"green\"\n",
+    "false_color = \"red\"\n",
+    "\n",
+    "labels = anomaly_flag_prediction\n",
+    "sequences = x_segments\n",
+    "\n",
+    "y_level = 0.5\n",
+    "for i, (sequence, label) in enumerate(zip(sequences, labels)):\n",
+    "    if label:\n",
+    "        ax.plot((sequence[0],sequence[1]), (y_level,y_level), color=\"red\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/simple_anomaly_detection.ipynb b/notebooks/simple_anomaly_detection.ipynb
index 360cc77..4adeff7 100644
--- a/notebooks/simple_anomaly_detection.ipynb
+++ b/notebooks/simple_anomaly_detection.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,16 +42,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<torch._C.Generator at 0x7f968690c5f0>"
+       "<torch._C.Generator at 0x7f16083d1dd0>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -139,7 +139,7 @@
        "Text(0.5, 1.0, 'fault incidents')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -206,7 +206,7 @@
        "Text(0.5, 1.0, 'fault incidents')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -234,16 +234,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f95b02b9dd0>]"
+       "[<matplotlib.lines.Line2D at 0x7f151091b6d0>]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -265,16 +265,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f95b13107d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f15116719d0>]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -296,16 +296,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f95b12a0290>]"
+       "[<matplotlib.lines.Line2D at 0x7f1511629cd0>]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -345,7 +345,7 @@
        "(100774,)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -365,7 +365,7 @@
        "(25000,)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -406,7 +406,7 @@
        "torch.Size([1007, 100, 1])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -823,10 +823,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 107,
+   "cell_type": "raw",
    "metadata": {},
-   "outputs": [],
    "source": [
     "date = datetime.date.today().strftime('%y-%m-%d')\n",
     "MODEL_PATH = f'model_save/model_{date}.pth'\n",
@@ -836,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -854,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -894,7 +892,7 @@
        "<Axes: ylabel='Density'>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -917,7 +915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -926,7 +924,7 @@
        "<Axes: ylabel='Density'>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -950,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -959,7 +957,7 @@
        "<Axes: ylabel='Density'>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -997,7 +995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1006,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,7 +1013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1033,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1059,7 +1057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1085,27 +1083,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_prediction(data, model, title, ax):\n",
     "    predictions, pred_losses = predict(model, [data])\n",
     "\n",
-    "    ax.plot(data, label='true')\n",
-    "    ax.plot(predictions[0], label='reconstructed')\n",
+    "    ax.scatter(range(60), data, label='true')\n",
+    "    ax.scatter(range(60), predictions[0], label='reconstructed')\n",
     "    ax.set_title(f'{title} (loss: {np.around(pred_losses[0], 2)})')\n",
     "    ax.legend()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2200x800 with 12 Axes>"
       ]
@@ -1147,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1163,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1172,7 +1170,7 @@
        "69"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1184,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1193,7 +1191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1202,7 +1200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1211,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1229,16 +1227,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 58,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "642 289 25 44\n"
+     ]
+    }
+   ],
    "source": [
-    "tn, fp, fn, tp = result_mat.ravel()"
+    "tn, fp, fn, tp = result_mat.ravel()\n",
+    "\n",
+    "print(tn, fp, fn, tp)\n",
+    "\n",
+    "#                     actual negative actual positive \n",
+    "# predicted negative: tn fp\n",
+    "# predicted positive: fn tp"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1265,6 +1277,43 @@
     "print(overall_accuracy)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only 13.213213213213212 of flagged intervals are actual\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"only {tp / (fp + tp) * 100} of flagged intervals are actual\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6376811594202898"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tp / (fn + tp)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},