diff --git a/.ipynb_checkpoints/CALCE_ed-checkpoint.ipynb b/.ipynb_checkpoints/CALCE_ed-checkpoint.ipynb
new file mode 100644
index 0000000..7c99f25
--- /dev/null
+++ b/.ipynb_checkpoints/CALCE_ed-checkpoint.ipynb
@@ -0,0 +1,597 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9e944851",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ab2343b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ec93854",
+   "metadata": {},
+   "source": [
+    "### Extract File From Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb625c73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Battary_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'dataset/'\n",
+    "Battery = {}\n",
+    "for name in Battary_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    print('path: ', path)\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheet_name=1)\n",
+    "#         print(df)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "    print(idx)\n",
+    "    path_sorted = np.array(path)[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "938cddd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load Dataset CS2_35 ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load Dataset CS2_36 ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_37 ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_38 ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'dataset/'\n",
+    "Battery = {}\n",
+    "for name in Battery_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "#     print(idx)\n",
+    "    path_sorted = np.array(path)[idx]\n",
+    "    \n",
+    "    count = 0\n",
+    "    discharge_capacities = []\n",
+    "    health_indicator = []\n",
+    "    internal_resistance = []\n",
+    "    CCCT = []\n",
+    "    CVCT = []\n",
+    "    for p in path_sorted:\n",
+    "        df = pd.read_excel(p,sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        cycles = list(set(df['Cycle_Index']))\n",
+    "        for c in cycles:\n",
+    "            df_lim = df[df['Cycle_Index'] == c]\n",
+    "            #Charging\n",
+    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "            c_v = df_c['Voltage(V)']\n",
+    "            c_c = df_c['Current(A)']\n",
+    "            c_t = df_c['Test_Time(s)']\n",
+    "            #CC or CV\n",
+    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "            #Discharging\n",
+    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "            d_v = df_d['Voltage(V)']\n",
+    "            d_c = df_d['Current(A)']\n",
+    "            d_t = df_d['Test_Time(s)']\n",
+    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "            if(len(list(d_c)) != 0):\n",
+    "                time_diff = np.diff(list(d_t))\n",
+    "                d_c = np.array(list(d_c))[1:]\n",
+    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
+    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "                count += 1\n",
+    "\n",
+    "    discharge_capacities = np.array(discharge_capacities)\n",
+    "    health_indicator = np.array(health_indicator)\n",
+    "    internal_resistance = np.array(internal_resistance)\n",
+    "    CCCT = np.array(CCCT)\n",
+    "    CVCT = np.array(CVCT)\n",
+    "    \n",
+    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "#     print(idx)\n",
+    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "                              'capacity':discharge_capacities[idx],\n",
+    "                              'SoH':health_indicator[idx],\n",
+    "                              'resistance':internal_resistance[idx],\n",
+    "                              'CCCT':CCCT[idx],\n",
+    "                              'CVCT':CVCT[idx]})\n",
+    "    Battery[name] = df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "83dacf57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def load_data(Battery_list, dir_path):\n",
+    "#     Battery = {}\n",
+    "#     for name in Battery_list:\n",
+    "#         print('Load Dataset ' + name + ' ...')\n",
+    "#         path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "#         dates = []\n",
+    "#         for p in path:\n",
+    "#             df = pd.read_excel(p, sheetname=1)\n",
+    "#             print('Load ' + str(p) + ' ...')\n",
+    "#             dates.append(df['Date_Time'][0])\n",
+    "#         idx = np.argsort(dates)\n",
+    "#         path_sorted = np.array(path)[idx]\n",
+    "\n",
+    "#         count = 0\n",
+    "#         discharge_capacities = []\n",
+    "#         health_indicator = []\n",
+    "#         internal_resistance = []\n",
+    "#         CCCT = []\n",
+    "#         CVCT = []\n",
+    "#         for p in path_sorted:\n",
+    "#             df = pd.read_excel(p,sheetname=1)\n",
+    "#             print('Load ' + str(p) + ' ...')\n",
+    "#             cycles = list(set(df['Cycle_Index']))\n",
+    "#             for c in cycles:\n",
+    "#                 df_lim = df[df['Cycle_Index'] == c]\n",
+    "#                 #Charging\n",
+    "#                 df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "#                 c_v = df_c['Voltage(V)']\n",
+    "#                 c_c = df_c['Current(A)']\n",
+    "#                 c_t = df_c['Test_Time(s)']\n",
+    "#                 #CC or CV\n",
+    "#                 df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "#                 df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "#                 CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "#                 CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "#                 #Discharging\n",
+    "#                 df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "#                 d_v = df_d['Voltage(V)']\n",
+    "#                 d_c = df_d['Current(A)']\n",
+    "#                 d_t = df_d['Test_Time(s)']\n",
+    "#                 d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "#                 if(len(list(d_c)) != 0):\n",
+    "#                     time_diff = np.diff(list(d_t))\n",
+    "#                     d_c = np.array(list(d_c))[1:]\n",
+    "#                     discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "#                     discharge_capacity = [np.sum(discharge_capacity[:n]) \n",
+    "#                                           for n in range(discharge_capacity.shape[0])]\n",
+    "#                     discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "#                     dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "#                     start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "#                     dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "#                     end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "#                     health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "#                     internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "#                     count += 1\n",
+    "\n",
+    "#         discharge_capacities = np.array(discharge_capacities)\n",
+    "#         health_indicator = np.array(health_indicator)\n",
+    "#         internal_resistance = np.array(internal_resistance)\n",
+    "#         CCCT = np.array(CCCT)\n",
+    "#         CVCT = np.array(CVCT)\n",
+    "\n",
+    "#         idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "#         df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "#                                   'capacity':discharge_capacities[idx],\n",
+    "#                                   'SoH':health_indicator[idx],\n",
+    "#                                   'resistance':internal_resistance[idx],\n",
+    "#                                   'CCCT':CCCT[idx],\n",
+    "#                                   'CVCT':CVCT[idx]})\n",
+    "#         Battery[name] = df_result\n",
+    "#     return Battery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a498ac9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e77737a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff974c7ea60>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Rated_Capacity = 1.1\n",
+    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "color_list = ['b:', 'g--', 'r-.', 'c:']\n",
+    "for name,color in zip(Battary_list, color_list):\n",
+    "    battery = Battery[name]\n",
+    "    ax.plot(battery['cycle'], battery['capacity'], color, label='Battery_'+name)\n",
+    "#plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "341d2784",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'State of Health')"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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2pzu3X3xqeZ/bXPzyOvxAw3SDrOWDg45fXhwIIuB6mfSFTP6C25JEnJ0BcTrl89TDS1izfDNpkTBHwRUkUILoznZTaZ+nn1vZ/w9hkLS1Jtm6rRkcwfH8zrxiIm5n39WV8G+9Q+dntXLdNoIg91/0Hn9+BYlUOLkvnfZZuHTtIDyNMcYYY/rLAtwhNnf+TDoSZ+OlMY44aQ7xkmhmO8pR/VisYc7cyThCOGEta0ReOhJxA3ACCFwQXzvv71fE0WDnghHRWIQjjp/NhMmjiQSZWWa+oo7geDvbjkVdjjpkRp/7OdjKyuOMGV0BgRJE3DAvORYNF9bI9F18zeRAE+5TZeqEmj5ViDhy7jRKMhMDo1GXg2bZ6K0xxpiBp+Q3wcwmmZkhM332BK77/ZU8+bcXmXHARN5ywZGcfO5hPP/k6xx09D4cdeqBfW5z5r7j+O4N7+Heu55l1ao6KmvKef8lx/PKK+t5duEKtmxtQtoC0qOixBJKpDWNtqbBcYiOqWL8mHLGTqjhTWcfwmkXHMlJZxzM3Tc/Rl19C6+v2ELd+gb8yhhOU0C0MQltaX74qbv4cdTl4stO4v0fPWWX/qgqd/7+aR55bCkHzpnMp696G/GsShGPPrqU3/72acaPq+Kaz53J6NEVACSTaX74i4dZ/NpGTj1hfy577wmI5P5/zpdeWUfltBrS6uO1JtGUj1NRgpNM4Xs+CT8AzyeaTOFoJRJzcZrT1NXXceIFP0CSAU6bj1/qQqlLaVmMRHMKcYVYRZT2lEesyaM0EuHo+dNZtngDNdFSqmPxPv/MjDHGmFxYHdz+sRzcEW57QwsXffQWkkkPxxGmjqlm28sbd34lL0IsHuEr33s3Rx4/e5dr16ys4+PvuhHP8/FHleG2JnFaU2g80pkOISJcf8uHmHfY9M7rnvj3a3zn+r+RSKSJxVwuOG8+H730ZADWrdvORy+/jWTSw3WFgw+exvU/fA8AN93xOPfe/xyplE9JPMoXP3UGp5yQW55vc0uCd73vFyQSaRxHmDJ5FHfe8pHO4+88/H9oTvlIazIsnVZTGpZHUyUxLoabUtwWj6DExS91wmfzFALwygUCiDd5uMnwc8tO2aiqKuUvd3+iT8G4McaYvdvekIM7Y16FfuVPh+bVxmX7P1Xw5ygE+7VghNu2vQWnIyc3ULZuadyZxZDZ7/sBGzc07HZt/bZmVIPwtOySZLLzWlVlW13TLtdt3Lyjc+ngVMpnbVYN3bq6JlzXydxX2ZB13/UbG0ilwus8z2dzXWPOz1nf0NrZvyBQNm/ZtU+tbSnws36Zi7qI6i4l1SQAdXeeIpo5lKmX5nja7cLLLS0J0mlbKtkYY4zZW1iAO8LtM30s42qrKC2JUlIS5YyzDqW8IvOVeibSLSmJcuxJu4+UHnjINMaMrwYFSftoRNCIEwaKquD7lMQjTJxUw8a1W1m/uo41yzez34xaYrEIpSVRYq7wtjfNob09ReOONvbddxxVVaWUlESJRV3OfvuhJFMeO3a0ct4ZhxKPRygrjVESj/Km4/a8eksQKHVbm2huTjCutpKpU0ZRUhKltCTKGW89aJdz3/b2Q9GSaFhJAYg0JyHq4KQDJJ35OGIObjLIfDRK0BHsKogPqQoHFQgEohGXkpIo8bjLsUfvS0trO1u3NrF50w42bWqgvT3VeW9VJZlK09LcThAEpNIerW0J/Kz851Ta22WiW9rz6fh2JQjC6wdS9r2NMcbsrYQgz1exshSFItCeSPHsc6upqijh8IOn0daa5KEFL9JQ38yUabXMP24WY3qouZtKpnn0wVdIticpG13BujVb2ba2nleeXUnduu2ERXcVUimIRfHLS9DyGIEqbspHUh6pseW4iQCNCEHUQTQg0uzhl0XxSsLavR0rp6mARB2+/MVzOPXkOT0+0/btLVz64VtobEmGI6sOuKNKSCU89ps1nht/8B6i0Z15v6rKc0+8ytNPvEZpPMpbzj2caEWcX/3vozzzxOsk4sLYydXsO3k0Da1JXt62DfUCoiqUlcZobk0inuKkA/wyF1SJtoaz1dyE4qT9cM6aC24qHOm9+APHEx1Xxk2/eZyS7R6+A22T4zjBzkHwb3/ybJ59dhUPPbaE0pIY3/t/53Prgv/w38VrGTuqgtNOPZA7//ws4sO4MZX85jvvp7qi/4tubG1o4ePfuof1mxs44sBp/PCa83fJjzbGGBPaG1IUps+r1C/98fC82rjigCcK/hyFYAGu6bP1q7bykXN/jLYlIR6FRAqiLhqAN6EKJxkGgbG6FpJjyxEyywlHhCDmEG1OoYHg1cRQFDe5+1f/lZUl3LfgMz324Wc//Qd//NvzOJlg0itx0EiYF1taEuXznzidN594QK/PctF5P2H7thYA4vEIN956GZd9+y6aksnOPFwnAEkrjq/4LhCVMKj1NdyfCsJnjguRxM7fl8UVWifFiTZ4RFp9WifFCCIOOwu1QdxxiKWFRCIcoR0zoZKGdIpEMo2IkI4pbnLn+R845yg+fuGJOf6kdvet/32Qv/97KX6gxGMRPvP+Uzjv1IP73Z4xxoxUe0uA+4U/5teFKw94rODPUQg2dGP6TPpQUis7dzd7CeDevjXx/T1/hR6W9cpqXHY2qoDmWNd2t9/vVPf89X1n/nHv7XdWZ9Eu213un707TFPQzDFFsld0pvfPpTcpz6fjo1FVUmkvr/aMMcYMHkUIirjUVz4swDV9NnlGLWe8Yz4P3PUMeAG4LiRTSDSK05pES2OQCvDLY0R3JEmNLcNt9RBx0EDxyyJEGtM4ST8ceXVBkj5EHFBwkmlmTh/D/1x1J+qFpb06ljEORPCDIKzQAKTcMD/WbfUIamKI47D/vuM56bjZvT4HwDVfPJuvf/lePC/gtLMOYfrMsVx76Vv4ys0PogpOVKhworSTQlWJpBQvovgxwWntmKQm4IKTDve7KUUFps2opa0E1qUacZM+0XqPxIQYrg+BryDKnCOmsGNtM4k3GiDqsN+ciSxduZl0wiPiOIyZXMOGNdvBg1IR6lbX843v/IVAw/rE4vnUVJZx+ukHs//+E3nmpdUsW7WZdJtHu+dx2klzGD+6kn88/Rq1o8r50HnH8J+X19DenGRcZTlnnjCXDXU7eHXNFvwgYEt9C5u2NVEdj3HacXOYPmUMLy1Zz6uvb6KurolZ+4xj7v6TWL+unjlzJrFhWyMPPrqElh1t7DOllrefeSglJVFeeHU9E2qrmDVtbF5/11SVJcs2kEx6HHLwNBwRnvjvcp57+Q3Kq0qQiginHjqL2VP6fp/XNmylvqWNw/eZTDxq/yk0xpiRxFIUTL8lk2na25I4OJlqC4ITcUglPdraUpRVxEkmPbxkmpt+8ACvLFpDIMKcQ6fS1pbmjTVbaSl1ibancVq9sFhBKo2WxJCkB0GApAPQANJemA6BoKk0VJejUZdUTRy3NY0TKCoCjvCRK07lwvcdl/NzeJ5POuVTWhbr3NfanmRjXSP/e/1DvLZsI77AOecfwYXvPY4gUHY0tzGhtoqW1iSe5+MgRFyHhu0tfPKqO0lqgHgBydp4OKlNgWDnGhPpaodUiYMoRFt9xFe8UicM1pMBqJCqccCFWKNPpD3AbQlQJ7MEs0C0NRzN7RhUnnb0FJa+sQWnXfEzE+JEIOLROWo7Z5/xTImX859FqxCEufOn8p83NpLy/PDzDwKibeG5jsChsyez9JX1+OkgbEyVSDqgrDRGW1RJaoCT1M6KE5GoS8m0CtoSafwg4AuXvoUzT+x7becOP/nFw/z9H68gwNwDJpGKwvOvrMWPCckxbqZUHXzng2dw+pG9p6R0+M1jz/Pzvz0VlpQbU83vP/seohG39wuNMUVjb0hRmDavSq++96i82vj0nEcK/hyFYMMWpt/i8SjxeHT3A5UwOmszmUjzwlMrOqsELPnvaiJlcdqdcNtpTofL6ioEkQjih8GUpANwBRI+4jio44Tf6UdccB38uBuW70qkIR4N69Aq/N8fnu1TgBuJuES6BDflpXFKxeXVJeHoIcD99y7iio+/BYAxo8rD88p2XeThycdeJR2E0Z5XEdYLFkAzucIiIF5AqjRzP4VYo0f72CiOBxqBaBLaR4fBOihuCtxkZnXhtE86GsHJZCp0fHEVKCxdWxdO1nMy6RCOgKdkZ2u8tmoLa5sCPC9s4OnX1hJEMq044CZ2tquB8vLi9eHPIZMR4qTDKL2tLUVidAS8rHJqQFICki3tpDPt/+b+hXkFuAv+9gJ+przby0vX01YOkYTSXut29kmBmx54pk8B7u2PLCSRSc9Yv72RJeu2cOjMSf3upzHGDAYFArWCV/1hn5oZdNGYS1nFzkCwY9a+ZII0dTsK0RIGtx25tY7sTJLN/qYhk4cqQWbZ3eycYFXGT6gekH5XVZfukmlbU1Pe6zUd91Y6lgXe/RuSzj2ZbmtEOmvtAgQO4He2Ep6TOSWQndtddVSh0Ky2uuY6iyOddYgB3OwTFLL/O6qwy7koaKbMWmfTXdv3d9484jpMHpffz2JU1mfuCOG3BQ442anDCpPH9O0+42sqOutD+4FSW1mWVz+NMWZwCH6er2JlI7hm0DmOw3f+90P89Bv3EQTKVf9zDq2tKW7+8UM0+B7pihKoayWd8qgdX0VzS5K2pnY0FkFSmdSEVDp8RSNIxIWWdlwtobSqBG9CJf7WNnBg6vRavn7duwek31XVZXz56+/gpp/+g7LyOF/4f+f2es1xJ+7HO847gvsWPI+f8AiiDsRdTjxuFkuXbWJHYxvTZtRSXVvOU5s2oQKJMVFiOzzSlS54kK4QSloC2iIuRCFV41AWcQga0zgeuEmfIObgx8BNgYriuA4xhKQLQVTDNIUywY0I0YhDOhUgEaFidCnOKCValyAVVUqrSpBUimRmxNWLCeIrjg9OzKHEjRKIkGj3UAe0RIh6DkFEqC4toSWdIvB9nJSSrnBwKyJhybY0lEqEFa9v4W0f+hmBH0AqwFEoi0fRdECyJQUpH3WEoMylvLKEfWeMY3R1GRuWbWHT1iba8HGdsE8lZTHcKDRWKKVb07SNjyKOUtIGKx5dw/sW/S+XXnIip54yd7efy9rtO/jbi69SGY3Rsq6VI8dMwGvz2b6hkZMO2JcJo6pIpT3uffgldjS3cd4pBzNpXDXPvbqOf7+0inn7TOTNR+65LrMxxgw0G8HtP8vBNWYY++Z3/sKjDy6mdXIcje1cPliFMD0hHubrSjrcF5SAeBBrCvDKHBDFSYZBNU74y8gEKWH7tmai7SCeEmn3QSBV4eIkFaKCuuB1LGmsSmk8RlOQRgAnpTheOI4bOEAQplngKJImLK/mhzWFAwG/zEVjTpheoRBpTBG4QlDqIl6YFuFHHfxyhyAuOCmltC5F29hw4Y5om99Zak4Evv3td3H0MbM6P6PtLW2c9cPbaU2kqFjj43phFY4gUFTDbxTe/paDqGtu46kXV+F5ARVlcb71ibP47M/+QjLlURKL8Ln3nco5J84b+h+yMaYg9oYc3CnzqvWT9+SectedLxz4YMGfoxDs1wJjhrEXX1yLRp2d1Rwygo5KZpmoTxTIpP06vu5MC+k4xwnPC1TZvr0F8XaeCxC4gmStRhxkpYUo0Oylw3sIOP7OzAV1w22csMoEmbxr6ZhtFxGIyC7l0NyUj0adzsU/JHNeR53jcBJeJsAmrKLReT+Fl15au8tntGT9lnARDj8MsMNF+LQzeySZ8nj2hdUsXLKWVNonyJSKe/T5FZ1LTidSHk+8uDKnn4kxxgwkS1HoHwtwjRnGjj9+NuIFmXzknVFiR7AZlm3QMG82E6CGwaruDG5153kClFfG0cjOc6EjKN55X6dLneEy1w1HjRUCd2eesfjhNgEE0Z1BsjqZe3sKnu6SU+zHXSQdhCkR7ByNFi98Rj/ugEAkEXT2sfN+AocfMXOXvs2aMAZfNWwvmqnq5krn7wPxWIRjDpvJoftPJhZ1M78TCCccMrOzskJJLMKx82b09uMwxpgBpSoE6uT1KlaWomDMMOb7AXfd/Sx/feAFtkc8KsdUsO/0sdSOKidA+eezr5MKfMbGSmlobqXNDYiURZk5upqtm5tpSqeIRBzGVpUTq4gxd98JfPTMY7j7r8/x9yeWQtJn/8ljmTymiglTanj0P8up27QDdYSWVJpYRZQLz5rP2089iG//6iFeXrmJIAgo8xySzSk8J8zlFSAShDPlUp6PkwooFZeID7iQjki4elurh9OSwEn6UFuBO6qE8pIYmvBoaE+h5Q4Tp4zinBPm0bCxkRWtTby2cgupbe34bWn8QPFKM9U10hqunucI6SohXeMyvrKc5k1ttEfC0mZuBNKipKuECTWV7JeuZMmKTTRVZCp8JAOiKSE5WvDcMN+5pAlGl5fx40+cy9x9Jhb052+MGTx7Q4rC5ANr9KN3n5RXG1896P6CP0chDGmAKyKnAz8h/LL0V6p6XZfjnwPem9mMAHOAsapa31ObFuAas2eLXlzDl759H4lkmmjU5ey3HsRnrnhrv9v7+yOL+dFND5NIesRjES658Fje965jOo/XbW3i/ZfdQjLlIQKz9h3PLT//IM2tCc778E0kU2H+w9jRFfz5livw/IC3v/MnJJoSuE0pItubO79UO+TYWXzoK+fymU/9lmTSw3Udjpg/g+9ed2Hn/VSVs97+Q9rb03hlLhoRnGRYYi5wBT+eyRkuEdSVMF0hCiBIWmmfADiC2xZQuU5pHxUuPuKmhWhrQLJG8EvAj0BZXWeWBfF4hMd+eRWuU7wjJMaMZBbgDm9D9l9mEXGBG4EzgLnAxSKyy3RnVf2Bqh6qqocCXwQe31Nwa4zp3YZNO+j4RTad9lmzbnte7W3cvKMzSE2mPNZu3PX/otvrW3AyqQ2qsGVLEwDNLYldzmtoCleUSCXTpFIeBOFCE9kpY3UbG9i6tRknE0T6fsCWzU27tJNO+ySSXmdd3K40k1+sroSl6Rw6T9SsaxwvTHVQFySzM6zNDAQSLpyRNR6QSnnhfY0xZpCEawRJXq9iNZRDD0cBK1R1laqmgLuAPdVduhj4w5D0zJgR7Lij9iUWi1BWGiMej/DOtx/Rp+vTaX+X7be8aQ6lJTHKS2OUxKOcd/qhuxyftc94Jk2oobQ0Skk8yjvPD+83cVw1B8+ZTGlJlJJ4hHefFe4vK4tzykkH4MYdNB4BJ1yFLRJzuejKt3D44dOpri6lrCzs/8XvOXaX+8ViEY47ZlaYh5sOI1B1BALCcsqpMHfXTQb4UTIT6DKRqoCTBALFK4V41CGSBHXDvGA/CpEWRV3F8QWvNAyKfYFD9ptIJCK0tidobGmnpT1JezJFMp2mvqmNLfVNbG9opr6pla7flAWBkvZ2/VyNMWZ3gq9OXq9iNWQpCiLyTuB0Vf1wZvv9wNGqelU355YB64FZvY3gWoqCMb2rb2jlpSXrmTZlNPvOGJvTNTt2tPHJz/2edeu3s9+sCfzoexdRllm5bev2Zpa9volZM8cxaULNbtcmUx7Pv/AG1dWlzD1g5wphvh/w8qsbKIlHmDNrZ/6qqvLKkvUsW7aBloZ2Jo0qZ+6h05g+ewIA7e0pXnllHePHVzN9eu1u91NVnnzydZ5+ZjkJL40fdcJUgkDBdWgKkjy5dSOpdIDrKyftP5Pa0RUcPnsK/331DeqTCTZsaeSNN7YRbwI/LlROKGX/aePZtmw76+p34I+JMH2fWtaua6BNfdBw5TcnFeCXhfnFGhHcRDgS7SZ3jviWl0T57XcvYdK4ap5Z8gbX/HIBKc/nktPmc9X5J+T4UzTGDKW9IUVh4oGj9NI/nJJXG9855M8Ff45CGMqFHrobJ+8pun478FRPwa2IXA5cDjBt2rSB6Z0xI9joUeWccsL+fbrmt3c/w4aNDajCqjVbue+vL/Ced4e5tmPHVDL22Moer43HIhx79L677Xddh8MOnLrbfhHh4HlTOXje7scASktjHHXU7u1lX3/SSftz0kndP+MlP7ub1KYAEQgiwnMbN/HvT34cgLOOnsOfn1rM95c+SrQ1U4s3obA5zRFHjOfuf68lkoDoBo8gnqItU47C8cLV9DSWqRYRl3A0WMM6wB0VKgRoS6T5ye8e43ufOZev3v4giUyKx+//+QLnHj+PqeNqenw2Y0xx8wf5y/Yc5kcdANwOHA58WVWvzzp2G3A2UKeq87L2fw34CLA1s+tLqvrAYD5HV0M5dr0eyP7XawqwsYdzL2IP6QmqerOqzlfV+WPH5jYaZYzpG98LOr9aV1X8zBLJw1GqSzqAH+z6LGnf725VZYKsnarslmqwC8n6Yw+nBV3a6NoXY4wZKrnMjwLqgU8C17O7XwOn99D8jzrmVQ11cAtDO4K7EJgtIjOBDYRB7Hu6niQi1cCbgPcNYd+MMV2858JjeOrZ5TTsaGPCuCrOPfuwQnep3758wal84Cd343vhKO5X3v2WXY4fO3sq1aVxGqp93HofV4QjD5rOBy44hsVLN/Dy0vWUxKN89fLTuebOv7G5rY0gAuKFK6v5ZSBpDWv9BkIQVdxk2LYGSkSECMIv7n6CWRPH8FzLejSA8WMquOPRhaRVSSd8or5QUhVj+qRRuJ4we2It8/efisjuX4Ct397I5h3NzJs2gZKorbpuzEikCIEO6kSxzvlRACLSMT9qaWcfVOuAOhE5a7f+qT4hIjMGs4P9NWT/VVRVT0SuAh4iHAa/TVWXiMgVmeM3ZU49H/iHqrYOVd+MMbsbW1vJH359Bc3NCaqqSnGc4Tsbd960CTz7vStZu20Hk2qqKCuJdR5bs3Yb7/2f39Ce2XXo/KnccOU5VJSXAPCjb7yb5pYEZWVxIq7DQ9+5nE31TWxrbEUciLlR4lGXlkSCz//qAXa0tJP2fD7z3pPYsLaBP/31BYKI8sii10mVOuF//dLgl8DKHQ0sX7iDSFu4yppXTTjZ7THpXBHu9KP259sfPnOX53n45eV86Q8P4joOtZVl3POZ91EWjw7Rp2mMGUrB4H7ZPhlYl7W9Hjh6gNq+SkQ+ACwCrlbVhgFqNydD+mt/Zoj6gS77buqy/WvCIW9jTIG5rkNNTVmhuzEgYpEIsybsPkHt/n+8RCKmnaOkL6zYSMLzqcgcFxGqKks7zxcRJo2pZtKY6l3aeeKVVTS1JmjP5Nfe8+TLNK1u6ixF5kUkTApTwUFJx8IRYBxw0+DHwSsNJ6llL3f80MLX+MolbyOeNUr7i388QyId3kdRnln+Bm+eN2tAPidjzN5DFfz8R3BrRSR7Nv7Nqnpz5n1f5kf1xS+Bb2ba+ibwQ+DSAWg3Z/a9ljGmqI2rrUJUOpcLdhyhPGuEN1e11eX4mfxaR4TxoyqJ7vBpa0kBmYoKCmSWIO6sqdtRizfITFrrko4QjbhEXXeXfeOrK1i9pR5flSBQxlSMjF9CjDG7G4AUhW17qKLQl/lROVPVLR3vReQW4K/5ttlXFuAaY4raBWcfznOvreeJ5W8Qi0f4zkfPpLQfX/fPnTaeT5xzPHf88zkm11bzjQ+chpfyuerrd7OprpGIHy46EcQFXIi0gVcW1uFNVQjRZiXaBKlK8Eog1qq4rrDfzLF85tf3U9fYwtatLUipcMi+k9hvQi1r1mwj6gvfu/tffOys4zhp3j679CkIlPteWsrmxmbOOWQOU0ZV99B7Y0yRyml+VF+JyERV3ZTZPB9YnG+bfe7DUC7VOxisDq4xZm93z18W8avf/ZtEMk08FuHn372Y/WdN2O28ZNrj9OtvY8fWViLbA5LVQhANa+xG2yBZk1lmOIDSbUrggkYABBH45cfewbEHTO9s71sPPMofn19M0vMpj8d46JMfZHS5jfYak4u9oQ7uuLlj9MLf9lSkIDc/P+L3e3wOETkT+DE750d9O3t+lIhMIMyjrSJcQqcFmKuqTSLyB+BkoBbYAnxVVW8Vkd8AhxJ+YbUG+GhWwDskbATXGGMG2bPPrSKRTAOZRS2Wbeg2wH1j+w5aEiloD8LAtaP0WLiuRBjcCkgQrsgWbmeWHQb+vXT1LgHuI8tW0t6Rq6vKkk11nDhrxqA9pzFm4PmDvNxub/OjVHUzYepCd9de3MP+9+fTJxG5PZfzVPVDPR0r3jXcjDFmiBx9+ExK4uF4gohw0JzJ3Z43eVQVruMQlEi4pHDmCzZ1wpxd8cN96oC6ZJYjDk8S4IhZu/4bdPj0ScQjYf6uHyizx44Z+IczxgyacEFGyes1TI3J8dUjG8E1xphB9u5z51NVVcprKzZz8vH7dzt6C1Aej/G7j13IbY8vom5jI0s3bKU1mUJdCMqgtBnSFaAeaBlUBBHSQUCQClBfufZ7fyGIQuBAxIN0ueCUC+Vt4MXSvPN7d5IKAtKej7gO86aM52eXnUt1WckQfyLGGNMzVT0n3zZsBNcYYwaZiHDGqfP49OVv4dBulirOtu+4MXz7XaexobWF1vYUgYIGmRSFUocgAElDgNAsPnjhcTcR5uMGLkRTkKoUghhIo5IqVQIHGpMp2hMenipp3+eFNzZy9Z1DPrnZGJMzIVAnr1exshFcY4zZC21qaA5TELJycQPYpUKlQOcSygIEQiYnVzsnn3VcIEpnTd5sq+vqB+sRjDEDIBjkHNy9UW85uHvKve1QvKG9Mcbsxd4+fw5uzEECQMO4tDwSoaMkrgIIzJg8GghHbh0fnEDxXXDbAQ0rLYST1HTX8u2ZurwXH38InueTSnv4QdB5OAiUZGaCmjGmMDoWesjnNUx1zbWdAJxKWHJs9xV7umEjuMYYsxf6+rvfylsOmsXiNZvRQBk/upJTDppF0vO4/fFFNDS1ccHRB3H07Gk88OxSHnthJcn2NK6Gwa4GytKmera2t1IpURLik/SCjgHeztdPFjzNz+57miAGCFx4zMEcOnUiX7v1QQigprKU33/lfYwbVVnYD8SYIlWMaQbd5eCKiEO4QtqSXNqwOrjGGDMC/WvxSr7wh7/TnkrvtvCmZA3MOj4EUTq/z3OAWBsEKe38YvRtR+3Pdy4/awh6bczeY2+og1s7p1bPuuPcvNq48+jbCv4cA0VE9gceUdVuy5ZlsxFcY4wZgRLpdO8n9fDtZddxj7Tn598hY0yfKcO61NdgmArktFqNBbjGGDMCnTpvFrc/tojVWxvCyWiBksoEqhKBwA8nngUOOCnw4+FA7+QxlUydWs0Li9ehCtESl1OP3I9fLniK+m0tRHA4fO5UTjlyPyJu8X11asxQK9JJZl/tuguYBLwL+HMubViAa4wxI1BJNMJdn3oPGxuaGFNRRsR1WL5pO2Mqy3AdIR5xWbm5nlTaIxUEfPHuB9nRmmDjpmY2+81hFYaYIBH40s0PEEkqTlrRmPDHx19mv78t5M6vvw/HKb5/fI0ZKh0LPRShC7psB0AdcB3wk1wasADXGGNGKNdxmDqmpnP7wKnjdzl+2D7himr/WrySHYkEBGFOLoC4ggLpVEA0DY4HGpPOpYFXrN/G6k3b2XdyThOajTEmZ6p6cL5t2PdLxhhT5EZXlHbm43am33a8kaz6ucrOpYEFRlXmlApnjMmDLfSwk4hU9VYjt4ON4BpjTJE7dMYkLjthPrc+voggCm4aJICaeAnNySRapvgKbkpRR1BXiUcivOsLt6EORMShuqyEhPokEmkmjalm9j7jEIE3Vm8jisNV730T+0yt5Z6HX6ChuY0LTj2EyeNqCv3oxuzdtDgnmYnIbOAaYAYQyzoUA44VkRkAqnpKT21YgGuMMYbPnHUinznrxM7tL97yNx5+8XUCBUTwy4Xj5s/gPy+sRlNKSyINLkggiK9sb090pjfUtydYvH4LkfYANxkO/n50ye855PBpvPj6BtKez/2PL+HPN1xGRWm8IM9rzHCgFOckM+DXhMHsf4HsMi5lwDHAK701YAGuMcaY3Ty1eA2+skspseeWrsMHnAA0CtIxstT139/MtpPeeSgIAl5evpFUOvy3yg8C1m5qYO4+EwbxKYwZ/opxBBc4FJirqm9k7xSRscClqvrJ3hoYWckZxhhjBsTc6ePD4DSrJu7s6WNxFdQBAlDJHOy6XlBmlbQguvOQ4zjMmzWRWNRFBBxHmDZh1GA/hjFmeCoBWrrZ3zEboFcW4BpjjNnN9R97OxefdCjTxtYwvqaCtx89h19+5p3ceM0FzJw6mtKYS4nrEou7xBwhlgJxwY0JGheCqJCuEIIyIVIVpa0Mnn19He2Bj+dAYzrJJd/+PXUNzYV+VGP2Wh1lwvJ5DVMzVXV7N/u3AjNzacCW6jXGGNNvzy1ey+e/+2cSyTTRqEvTWCHwdi7z67YHdCQqqAviEw6tZMZhZk+t5Q9f+0BhOm/MHuwNS/XWHDBOT7zlwrza+OtJPy/4c/SHiEwErgQOJ6yD+xzwS1XdnMv1NoJrjDGm3zZs2kHHQEnK9wmCXQdNpGN6SEfEm31YYNN2G8E1picdS/UW2wiuiOwLvEC4clkCeBtwHPCKiMzNpQ0LcI0xxvTbsUfsQyzqUlYSJR51mTa2Zpc5Z0E8sxVkdnTU21XQQHnXyXnXczdmRAuQvF7D1HeBp4C5wNVAUlXfCtwCfD+XBqyKgjHGmH4bO7qC3/34Ul5Yso5pk0czc9oYFjy7lNc3bCUqwsrldby4ZD1Ehf2mjmfmtDEQwD8fXYo0Bdxz57PsM2oUZ5w6r9CPYozZe7wZOENVfRHJjtJ/DeSUl2ojuMYYY/IyZlQ5bznhAPabOY6o63LB8QfxxXefygXHHsyy1zeHaQkpZcfWFr72odNpaGlHmvwwDVfh+hv/UehHMGbvpEU7yayEcEJZVzGgPZcGbATXGGPMoAi6TGLu2Pa9oLvTjTFddFRRKEJrgX2B1R07RGQU8G3gkVwasBFcY4wxg2KfKWM488S5uK5DPBbhSx85DYCr33My7ugYvgOJKph+7CS++usHueexF2lPpgvca2P2LkU6gvsPwglmHcqA7cAE4DO5NGBlwowxxgyq1rYksViEaMTt3Pfy8g1c8vN78APFTQEugDCmuoz7v34ppbFoobprDLB3lAmr2n+8HnnTe/Nq41+n/qjgz9FXIhIB4qraKiLlwNnASlXNOeCzEVxjjDGDqrwsvktwC/CXxxeTLFWibYTBrQgI1De18fzyDQXppzF7m2ItE6aqnqq2Zt63qurdfQluwXJwjTHGFMD0CaOQ1wUc3VkbV0BEmDCqoqB9M2ZvosM0SM2HiNy+p+Oq+qHe2rAA1xhjzJB7z+lH8NIbm/iHriTSCEEUtFx462H7MWFMFbc/9RwtySQXHnkw4yot4DXFaxjXss3HmC7b5cA8oBR4PJcGhjTAFZHTgZ8QfiH1K1W9rptzTgZ+DESBbar6piHsojHGmCEQibj88MpzAWhoa+e0n95OSzLFP1at5D8/30BDWzt+EHDP84v556cuJR618RhTfFSLs4qCqp7TdV8mL/d24JVc2hiyHFwRcYEbgTMIV6a4uOtyayJSA/wCOEdVD2TXGXTGGGNGoGWb6ghUCVRJpD22NLWQ8nz8QGlNptiwo6nQXTTGFJiqeoQrnH0il/OHcpLZUcAKVV2lqingLuDcLue8B/iTqq4FUNW6IeyfMcaYAth37BiCIEzEjUdcRpWVEHUdHAm3J1ZXFriHxhSOquT1GmEEiIpIr2VWhjLAnQysy9pen9mXbT9glIg8JiLPicgHhqx3xhhjCmJ8VQW3vP98Ro8ro7kqzZZ4Kyk3YEptNb+77EIrGWaKWHFWURCR6d29gBbgaFXttWD2UCY1dfcpdy3CGwGOIFyDuBR4RkSeVdXXd2lI5HLgcoBp06YNQleNMcYMpXteXcymRAuUBjhNLn4EVrU18uDyFXy89qhCd8+YghmBo7C5WEX3cWOHXgdohzLAXQ9MzdqeAmzs5pxtmdpnrSLyBHAIsEuAq6o3AzdDuNDDoPXYGGPMkFhaVxf+c+Zn7VR4ra675eiNKQ5FvFTvkV22o8BBwGeB/8mlgaFMUVgIzBaRmSISAy4CFnQ55y/AiSISEZEy4Ghg2RD20RhjTAFcdvgROCpoREFARRGB9x1+CEA4CS0ICIb56pvG7G1E5HQReU1EVojItd0cP0BEnhGRpIhc0+XYbSJSJyKLu+wfLSIPi8jyzJ+j+tInVX2+y+s/qvor4BrgilzaGLIRXFX1ROQq4CHCMmG3qeoSEbkic/wmVV0mIg8CLwMBYSmxxT23aowxZiQ4b+5cJlVV8ZsXX6Qu1URlNM4Tm9byrr/dRVQcNA20Cq4IXzjlRC498ohCd9mYwadhqbDBklXh6q2E36IvFJEFqro067R64JPAed008Wvg58CdXfZfCzyiqtdlguZrgS8MQJdfA47P5cQhLSyoqg8AD3TZd1OX7R8APxjKfhljjCm8o6ZM4agpUwA44Xc3kwrCfIW0BritTpjBoMoPHn+Kcw+cw5iysgL21pihMcgLPXRWuAIQkY4KV50BbqaiVZ2InNX1YlV9QkRmdNPuucDJmfd3AI+RZ4ArIpOA/wesyeX8nANcETmacPLXOLqkNqjqJ3PvojHGGLNnKd/v8ZgAfhAMXWeMKRBlQCaZ1YrIoqztmzNzmaD7CldH53tDYLyqbgJQ1U0iMq4vF4uIT/eTzLYDF+fSRk4Bbibn4vvACsKJYdkD5pYQZYwxZkB97bhTufKf9xOg4b9yJQoJcMThA0ccyrgKW77XmBxtU9X5PRzLpcJVIXRd6CsA6oAXVbUtlwZyHcH9FPBJVf15HzpnjDHG9NmOtnZ+cO8TlBOhuTaFBqA+ROMOUdfl/IPm9t6IMSPCoNeyzaXCVX9sEZGJmdHbiYTBac5U9U/5diDXKgpVdMmdNcYYYwbDI0tX0tSeoKU8DQJOOvynKh0EtKfT/ObFFwvbQWOGkGp+r17kUuGqPxYAl2TeX0JYJStnIhIVkatE5KcicmHW/oiI5BS75hrg/gE4vS+dM8YYY/pjbGU5iCBeuK3Ozn+lY67LREtPMEVkMJfqVVUP6KhwtQy4p6PCVUeVKxGZICLrydSgFZH1IlKVOfYH4Blg/8z+yzJNXwe8VUSWE1ZouK6Pj/0L4NuEtW9/3dEXwhq4N/d4VZYeUxRE5LNZm+uAr4vI8YQlvHZZIk1Vb+hDp40xxpgenbjfDN5/7KH83wuL2ea3k4ykcWJCKRGm1FSzb+3oQnfRmCERjsIO7kIPvVW4UtXNhKkL3V3b7YQvVd1OWJigv94BXKiqD4rIx4DLgJsIR4bvzaWBPeXgfqLLdgtwXOaVTQELcI0xxgwIEeHTp53Ap087gb+tfJVrHv87yahPSyrN6zu289mH/05DIsHF8w4udFeNMYNDCAsbQDhC/PXM+23AhFwa6DHAVdWZeXXNGGOMydOCla/S7nkQhP/iBQrtnsf9y1+1ANcUhSJdqvePhOXAvgk0AaWZ/UcRTozrVU45uCLylczSuV33l4rIV3LrqzHGGNM3R02cQmkkkilmFP5DXxqJcPSkbr8xNWbEGeRJZnurN4CrRWQBYY5wVES+S5h/e0suDeRaJuyrhLkPXWuPlWWOfSPHdowxxpicfWjeEagq/920nvElFSx4YxlNyQQ/ff4ZHlu7mpvPOo8SN8KVf7mfl9duZuqoaiIVLm807GBiRSUXzZ3Hvf9dzNp19bSXKdXlJfzk/LM4ZvrU3m9uzF5gsHNw91KXEi4RPC/zWgMcAVytqrfn0kCuAa7QfeHfwzIdMMYYYwacI8KHDz6SDx98JGcvuJMd6QSScAiAFzdv4gN/uZfJJVU8+/o6FGVxS134hSawoy3BN9c8RrQRUqNAXdieaudD//cn/nPVR6kqKSnosxnTG6X3Sggjkaruk28bewxwRaSZzEpxwCoRyQ5yXaCEcGTXGGOMGVRrGuvD9Yw6CW/s2EGLm0I7xmCk4w/ZZVhG3Z3HgkCpa221ANeYEay3EdyrCP+TcBvwZaAx61gKWKOqzwxS34wxxphO75h1IHcsfQEEVBVBOGf/A5hZOZofPvzvcCKap+CEwa0jQjTqIJEANwF+PGxnyqgqptfUFPRZjMnV8E2j7T8R+Wqu56rq17vbv8cAV1XvyNxoNfC0qqb3dL4xxhgzWL5+zFs4bOwkfr/sBV5q3EAyCPjDmhfBAakVnCYHjYB4IL6goiQCH6dcIAnRuJD2lTd2NDLnhz/l7AP254a3n4EjxfcVsBkmhqAO7l7qghzPE3aWENvFnhZ6yK6k/QpQKT38R0BVLQ/XGGPMoBIRzp91IE/UrWBhfQC+E/7zJkBaUCfzPtj5p2jmfSl4vobJdRn/XLGSR1as4q2z9x36hzEmV0U4hKuqedcA3NMI7jZ6/1g7Jp+5vZxnjDHGDIiWdDITuXYhPbzv4TwFUp43gD0zxuwt9hTgnjJkvTDGGGNydM0hJ/PYutWk3MworgKlAdIeIYgp4gA+4IA6IL4iKjgRwfPDFSNUYExFCfGoS317O4l0mqdWvcHBEyew//ixBX5CY3Yq0hSFvO1pJbPHh7IjxhhjTC72rxnHyxd9lkV16/nbmmXcu2oxrjgcOm8inzvoTUQcoTJeQiKdpr6tnVGlpVSXlFBTWsI59/6GtVuaCFxlfUsTH12wgJjjoM0KHojA1W89kQ8fP7/Qj2kMMKwXayioXOvgdhKRCUAse5+qrh2wHhljjDG9KI1EOXHSTD717wV4GuBpwCsNmygpdZk3uvul6p9ct4bNLS0EokhWubFUOiDihaXFVOEXjz1rAa7ZKyg2gttfOQW4IlIN/BR4N12C2wzLwTXGGDPkqmIl1CfbAfBVqYr2XNt2dGkZARrOTHd2pvF2nT9dVRofrO4a0zdKmE9j+szJ8bzrgUOA84AE8B7gc8B64MJB6ZkxxhjTi1+cdB7TK2qojpXw5SNOYVplTY/nHlg7js8dcyLlJRGcACQIA4fSWJR4ZQS/JCAYqxxz2DQSnlXFNGY4yzVF4QzgYlV9UkR84DlVvVtENgEfBe4dtB4aY4wxPZg7ejyPn39Fzud/+JD5XHbwEZxz829Y1rQNVWjz0vhugFaHE9D+sPIlFjdu5q/nXTKIPTcmN8WYg5uJNXsdulbVHgdqcw1wa4A3Mu8bgTHACuAZ4Fc5tmGMMcYU3I72BKu3N6Bxdv4T2rGUb2b7le1b8IMA18n1i05jBkkRBrjAu7psjyfMJnh/ZrsauHVPDeQa4K4E9gHWAsuAi0Tkv8A7AFvkwRhjzLBRVRJndFkZG9LNYaJex+IQWYHEpIpKC27NXkCKcpKZqv4pe1tE9gG+37FfRMb11kauAe6vgYOBx4DrgL8CVxH+p+FTOffYGGOMKTDXcbjrQxdy/b/+zdOb1rIjmSQeuIyNlLGVFtrcJJtTjcz73Q1ENPxn8qpDjuXDBx1Z4J6bolScI7hdTQVKRMRR1QAoB1r3dEFOAa6q/ijr/b9E5ABgPrBcVV/Jo8PGGGPMkJtUXcUN55/Jiu3bOe+O35PwPDY1tZAe3Q4qBCo0J9JAuNLZtxc+xgFjxnHCpOmF7bgxRUZE9gVuIEwkeidwD2GBg5V7uq5f37+o6lpV/ZMFt8YYY4az1fU7iGRSEdJ4PQ6WBaosb9g2dB0zBsKSdip5vYYrEZknIrcDzwM/ALYCd4nINuA7wI/2dH3OAa6IfFxElohIWyYXAhG5VkTe3f/uG2OMMYUzf8okYq5LaSRCqcaQoOOfxV1D3ajjcOrUfYa+g8Zonq9hSEQeBF4CRgNHqupdwLGEabK3A6ep6h17aiPXhR4+DXwe+F6m8Q4bCHNx7+lr540xxphCG1VaygOXvp9/rljFlOoqxlSW8ouXn+LfG9dRH7Qjrk8kplRE46xr28H0qlGF7rIpOsN3FDZPR6vqoo4NVV0FfCnXi3Mdwb0C+Iiq/oSOhKTQ88CBud7MGGOM2dvUlpdz0SEHccKM6cwZM46L9jucxnQCcRQnqijQnE5y1VN/6rUtY0z+VPV0VV0kIm8Rkc+LyDUicmpf2si1isJ0YHE3+9NAaV9uaIwxxuzNWtOpcHXULgNnCd9DVZGua/saM5iGaZpBPkSkHPg7cBywBZgAtIrIy8CZqtrUWxu5juCuAg7vZv+ZwNIc2zDGGGP2em+aOpN9ykeDCuoDChGED8w+3IJbM/SKMAcX+BbhYg6zgBOANmAs4doL1+fSQK4B7vXAz0XkvYS/0x4rIl8Fvk04sy0nInK6iLwmIitE5Npujp8sIo0i8mLm9ZVc2zbGGGMGQtyN8PAFl3H/WZfwyxPfSdxxcONJfrvmKT630NIUzBBSQCW/1/B0AfAFVV1D5rsUVU0CXwfOzaWBXOvg3i4iEcKyDGXAbwgnmH1SVe/OpQ0RcYEbgbcC64GFIrJAVbuOAD+pqmfn0qYxxhgzGBwRDqqdwN2vv4zGEnQM3P59wyv8zyFnUB2z7DwzNHT4jsLmYxzwajf7G8gxNTbnMmGqeouqTs/cdIKqTlXVPa4D3MVRwApVXaWqKeAucozCjTHGmEKoLSnbZVtEiLu5Tl8xxvTTFmBiN/svAZ7LpYE+/79UVftb6XoysC5rez1wdDfnHSsiLwEbgWtUdUk/72eMMcbk5fKDjuKxLct5tXUtfuBQFa3k7Ad/xayqWuaNmsCoeBlV8TgbWhrxW10mlVfx9gMO6Fw8wpi8FecI7lPAacAzme24iLwAzATenEsDewxwReRRcvhoVTWX0g3dJYJ0bft5YLqqtojImcB9wOxu+nU5cDnAtGnTcri1McYY03dl0RjfPfYMznv4V3hpYau2sDXZyurmeh7esJyIq6go/qYS1BdK3CiPr1nDj888s9BdNyPF8M2jzceXgUmZ903ArYRL8/5GVbfk0kBvI7hdS4NdDtwNNPahkx3WA1OztqcQjtJ2yi77oKoPiMgvRKS266ixqt4M3Awwf/784vzdxhhjzJB4cstKPB8cF7rWDwskgLSgfjihJ+F5/GPlioL11Yw8UoRRjqquBlZn3m8DPtbXNvYY4KrqJ7K3ReSDwNcyq0n01UJgtojMJJygdhHwni7tTwC2qKqKyFGEOcLb+3EvY4wxZkDMrZmA44DngeNq5rvHTJCrQKQjAlFcx2H/2trCdNSMPMO71Fe/iciXgLiqfjWzfTphkLsW+PJA1sHNm6p6hMv6PgQsA+5R1SUicoWIXJE57Z3A4kwO7k+Bi1SLdP6gMcaYvcIJ4/fl60eczpiSEhx1cN2AaNTDdX1cR3EcJTI+AVUewag0rWVtvNHcUOhuGzOcXUyYtoqIjAbuBbYBxxNW5OrVkGbBq+oDqrqfqu6rqt/O7LtJVW/KvP+5qh6oqoeo6jGq+vRQ9s8YY4zpznv2nc+id1zNi++8mpKoEKC4ro/nC6CkFYKo4hPweuM2zvnbHdj4jMlfnjVwc8jfzWGNggNE5BkRSYrINblcKyJfE5ENWesa9DUpfSbQUWTgDMIqXJcBHwHelksDVuvEGGOMydHWRDO+BoCgCiKKImiwayDRlEqQ9D1KItHCdNSMHIP4e1KOaxTUA58EzuvjtT9S1ZxWHetGAuj4P8+bgYcz77cCVbk00FsVhXd02eUAp4nILjPYVNWWdjHGGDPiTSkbxbjSSja0NiMSEAQRIm6AGwnwUzu/FD24dqIFt2ZgDO4XAZ1rFACISMcaBZ0BrqrWAXUiclZfr83Ds8DXROSPwLuAjnvPIJzH1aveRnDv7WZf19wHBdxcbmaMMcYMZxHH5a9v/ji3vP5vljduJeGnSQUBFdE4nqcsqttEc9DCitQqZt/1LbxElDEl5dz65gs4ZGx3deuNGXS1IrIoa/vmTDUqyH2Ngu70du1VIvIBYBFwtar2JTH9amAB4ZK9d6rqE5n9YwnnaPWqtyoKVqnaGGOMyVIejfPpA3evNX/7a//lX5tWURL3CQIh3R4FhG3tbXz80b/w1Luv2L0xY3qT/wjuNlWd38OxXNYo6Mmerv0l8M3M9jeBHwKX5tguqvoasL+IRFU1nbX/j7m2YTm4xhhjzABoSid3bnQJEVq91NB2xowMymAv9NDrGgX9uTZ7MQYRuQX4a386lx3c9pWN0BpjjDED4KJ9D6XcjZFOC+IqTtQHlKjj8KX5Jxe6e2aYEs3v1YvONQpEJEa4RsGCHLvW47Uikp2Pcz67Lxy252cWWSUiq3t6Zc4Z0/G+OzaCa4wxxgyA8aWVPHLWFZz/r1upT7UQKfWIlsKUikrevs+cQnfPDFeDOMlMVT0R6VijwAVu61ijIHP8pswiXIsIqxcEIvJpYK6qNnV3babp74vIoZnerwE+2seu/TyHc1qBn/V00AJcY4wxZoA8tnkFzekEXhAgAh4edYkmHtu8nDOmzC1094zZjao+ADzQZd9NWe83E6Yf5HRtZv/78+zTDTmckwB6PK/HFAUR+YCIxPvZN2OMMaboVEbju828UZTqWElB+mPMcCQix2eXJRORChE5X0R6miy3mz3l4N5OppiuiPgiMq7/XTXGGGNGvrdNnsPpU+ZS4kSJOxGiEmF8SSUv168nFXiF7p4ZhgY5B3dv9U1gXwARcYB/A7cBz4hITukOewpwtwLHZt6HaxEaY4wxpkeOCNfNP4eXz/8i3z38PALSrG3bxk+X/YtLnvx1obtnhqNBXqp3L3UQ0FH79iTCmrtTgPcDn82lgT3l4N4E3CciShjcbhbp/oNSVVvowRhjjMlyz6oXUJSOfzpfaliPF/hEHPsn0+SoIwIrPmWESwRDuBTwQ6raKiJPAdNyaaDHAFdVvyYi/wfMBv4EfATYkVd3jTHGmCIxf+w0FjYsRzX8GrS2pMKCW2NysxI4VUR+R1hm7LrM/hqgOZcGelvJbAmwRES+DvxBVdv631djjDGmeHx8zvHUJ1u4f8PzuI7wrhmHoar09G2oMd0qzhHc64FbgR8RlgP7U2b/qcBzuTSQ00IPqvp1VW0TkX1E5GwROUtE9ulPj40xxphi4DoOR4ybjDhKm5/kN6ue4b61LxS6W2aYKcZJZqp6J/Am4HPAfFVtyRz6GXBWjxdmySnAFZHKTLrCCuA+4C/AchG5R0Qq+9pxY4wxphgs2bGBhB+uNtrup1m8Y0OBe2SGHc3zNUyp6tOq+qtMHd4OpcD/y+X6XBd6+ClwMHAK8HRm3/GEE9F+DFyWYzvGGGNM0XjbxAO5943nCFRxRDh98rxCd8mYvZ6I1BLO/ZoBxLIOlQLvFpEZAKr6oZ7ayDXAPQc4T1WfzNr3mIhcDvwZC3CNMcaY3Rw2Zhp3HH8pL9Sv5dDRU3l2+zI+9t9fEQAxJ8JP53+Ao2r3LXQ3zd5sGI/C5uG3wBxgMeBn7Y8TfiJjemsg1wC3FNjezf56wJZnMcYYY3owt2YSc2smsa51Gzcvf5QAACEV+Hz6ud/y9GlfLXAPzd5qOOfR5uk4wtzb17N3ishYYIuqntNbAznl4AJPAd8UkbKsm5QDX2dnyoIxxhhjetDiJXYbjEv5HqrFGcGYHBXnQg/ldD+wmvPCY7mO4H4GeBDYICIvZxo/hLB0w2k5tmGMMcYUpXUtDaxuqmNaWTWrWhozQa3wgX1O6LVsWNJP82LDKrYn6wkU0ipMLBnFYaP3Ie5Gh+YBTOEU5+8/pwCN3eyvzxzrVU4BrqouFpHZwPuAAwgj6N8Cv1PV9tz6aowxxhSfG5c9zq/X/J3SqEd7OgIKGsSIOVH+vO4l3j3jGCaUVnd77bZkExf/+/uI00SbFyPlu0Qy371WRku5+/jPURMrH8KnMWbwqeoTmbK0+wFPqep/Mvs9di7hu0e5piigqu2qeouqXq2qn82UbrDg1hhjjOmBqvK/yx+lNOqR9CKAQ6ARApREkGZHspW71/y3x+sXrP8PnrTjabgCWsdorwi0pNt5cNPzQ/EYpoCKsQ6uiHybcHGHTwD/FpF3ZvZ/WUS+m0sbOQe4xhhjjOm7qLgEgGQKk0rWd86u41ARifd4bUW0lI4UXemafShQGbF53iNecdbB/TDwQVWdCXwV+FRm/yOES/f2ygJcY4wxZpCICD8+6mKSqVKiEQ9HlIijjI2XUx71mV1dxWmTDuDG1//M51/8Bfevf5JAg87rz5x4OONiY3FFcVAcCQgygcsRo2Zx2sTDC/dwZvDlOXo7XEdwCat3PZN5/xAwK/N+IzAtlwZynWRmjDHGmH44cfwsFp75zV32/b9XfsWLDQ20BJu49L/X4Tph8Lu0aRkvNC7jKwdejqryzaX/S+BspSbmsl/lDL598CcK9BTGDKl/AKcDvwR2ABWZ/XOBLbk0YAGuMcYYM4S8wOc/25eiKA4B7V6UqrjiZIopPFe/BIBWv53Xmt/A17DO/eLGFST8FCVurKemzUg0fEdh8/EwYXnaOcAawBWRjwJfBO7OpYE+BbgiMh/YF/irqrZmauEmM7PajDHGGNMLVxxq49VsSzYSIDiyMyVBFcaU1ABQ6pZQESmlKd0KQHWsgrhjZcGKTnEGuL/I/Hll5s80cDVwB/DtXBrIKcAVkfHAAuBIwo96NrAKuAFIsDP51xhjjDF7ICJcf+iVfGPxr1jbuoVoxCeRjlIS8SmLRDlr4olAGAh/9+BP8evVf0EQLt3nvF5r5pqRZxjn0fabaqZsSB5yHcH9EbCZcO3ftVn7/w/4Wb6dMMYYY4pJXWIbK1vqcB0fXx2iEQ8RJREk+cPavzOpdCzH1h7C1LIJ/L8DP1ro7hoz7OQa4L4ZeLOqNnT57XElOc5mM8YYY0xoZct6lIBAHUAQMmXAgGSQYnnLWo6tPaSQXTSmYETkq3s6rqpf762NXAPcUiDVzf6xhCkKxhhjjMnR4aMOwOUBHEnjqUuggqoSEZeI43DU6HmF7qLZWxRhigJwQZftKDAd8AknnfUa4OZaB/cJ4INZ2yoiLvAFwqK7ORGR00XkNRFZISLX7uG8I0XE71i5whhjjBlJZlZM4RdHfobDa+YxvayWU8Ydzuf3/yD7VE4g6sBda/9Km2eLhRa9Iq2Dq6oHd3nNAcYRlg/7Vi5t5DqC+3ngcRE5EogDPwQOBKqB43NpIBMQ3wi8FVgPLBSRBaq6tJvzvkdY2NcYY4wZkfatmMq3D9mZX/vw5qfY2L6ZZJBmSdMK7ln3dz448x0F7KHZKwzTIHWgqWqLiHwF+As5lArLaQQ3E4QeBDxNGD2XEE4wO0xVV+bYt6OAFaq6SlVTwF3Aud2c9wngj0Bdju0aY4wxw16T14qXWcXMU48d6aYC98jsFYpzqd6eVBCO5PYq1zJh04B1qrpb0q+ITFPVtd1c1tVkYF3W9nrg6C5tTSZcY/hUwpJkxhhjTFE4ZexRLNjwT5J+ChU4fcKbCt0lYwpCRC7puguYBFxOjqmxuaYorAYm0mVUVUTGZI7lUq+su+J9XX+3+DHwBVX191TrT0QuJ3xIpk2zIg7GGGOGv39seZpWr4U238VXh6tf/BnXH/JJ5lTPKHTXTIEIwzePNk9dS9AGhDHo34Ev59JArgGu0P1AdwW5V1FYD0zN2p4CbOxyznzgrkxwWwucKSKeqt6XfZKq3gzcDDB//vzi/NEbY4wZMdJBmnvWPYgQ4GsUEDwNuGnln/jJ4Z8tdPdMIRVhlKOqVfm2sccAV0R+2nEv4Lsi0pZ12CXMq30xx3stBGaLyExgA3AR8J7sE1R1Zta9f024JPB9ObZvjDHGDEuOODg4XWIZpcwtKVCPzF5hGFdCGCgiMg5QVd3al+t6m2R2UOYlwJys7YOAWcDz7Fo+rEeq6gFXEVZHWAbco6pLROQKEbmiL502xhhjRhJXXK7e/4NEpYSY4xFz0oyNJymJNPJo3RMEmclnAEk/xUObFvLPzc/hBX4Be23M4MnEh+sIV9LdIiLrRCTnZf32OIKrqqdkbnI78ClVzWtKp6o+ADzQZd9NPZz7wXzuZYwxxgwnx9YeyrG1P+bedX/m/o0L8HB5vWUlr7esYmnja1w5+yOoKp994Resad0MwD+3PMd1h1xe4J6bQVWEI7gi8iHgBsK5WR2Tyk4FfiwiCVW9o7c2ci0T9qF8g1tjjDHG9O6xrY8RdM7LDv/8T8NzADSkW1jZsoFEkCIRpFhU/5qN4o50g1wmrLdFuETkABF5RkSSInJNLteKyGgReVhElmf+HNXHp74a+JKqfklVH8m8vgxcmznWq1xXMkNEThGRm0XkQRH5V/arj502xhhjTA8mlUzerexQbWw0AFWRMkrcOAI4CGPj1UScXAoZmeFqMFcyy1qE6wxgLnCxiMztclo98Eng+j5cey3wiKrOJhyB7XH12h7MAv7Wzf4HgP1yaSCnAFdEPkhYmqESOBnYCowCDgeW9nihMcYYY/rkU/tdyQGV+xEFouJTE4lw5oSTAYg4Lj867EpmV45lQmmMt08+HNUi/A67mAzuCG6vi3Cpap2qLgTSfbj2XKAjjeAO4LycnzdUD5R3s78C2JFLA7mO4F4DXKWqFxM+4BdV9TDgt0BLjm0YY4wxphflkXK+NPdaPjDjQioiLmlt476N/8crjS8BsL59Aw3pOlr8JhZsfIR/1T1b4B6bYay7RbgmD8C141V1E0Dmz5xWH8vyAGHObVen0mUuV09yDXD3Af6ZeZ8kjKABfk6OVRSMMcYYk7sVLa+TClJAWCf3jdY1mf1vkMzsTwYpXm9eU6AemkGX7+htOIJbKyKLsl7ZsxJzWYSrJ/lcu0eq+mFVvaGb/T9U1UtzaSPXhR62E6YnQFjDdh7wMjAGKM2xDWOMMcbkaP6oo3m58UW8wMOVCPOqDwLg6NGH8ODmJwk0wBWH42sPK3BPzWAagDq421R1fg/HclmEqyd7unaLiExU1U0isttKuLkSkbcQpsMGwPOqmvO8r1wD3CeBtwGvAPcAPxWRtwJvBh7uW3eNMcYY05vDR8+nLFLG6tZVzKk6kBnl4VpIc6tnccU+7+CPG+5CBBJBY4F7agbV4KZY97oIVz+vXQBcAlyX+fMvfemUiJQTpiIcD2wBJgCtIvIycGYulb1yDXCvAjqWU/ku4GVueg/wrb502hhjjDG5OaBqLgdUdZ3UDn/eeDeJIFxc9PbVv2Je1UGURcqGuntmCAzmSmaq6olIxyJcLnBbxyJcmeM3icgEYBFQBQQi8mlgrqo2dXdtpunrgHtE5DJgLfCuPnbtW0ANYTUFIcwaGAv8H2E1h16LP+cU4Kpqfdb7APheHztqjDHGmAGgqiT9xM5tlFSQpAwLcE3f9bYIl6puJkw/yOnazP7thN/y99cFwOWqukZE9sm0mRSRr2fu12uAm2uZMD+zFnDX/WNExCpMG2OMMUNERDh70jlEiOIQ49DqI6iJ9bWOvhk2Bnmhh73UOODVbvY3kOPcr1yrKHQ3Uw4gDqRybMMYY4wxA+DN496GIxNo9kp5avty1rSuL3SXzGAYmCoKw9EWYGI3+y8BnsulgT2mKIjIZzNvFbhCRLJr3rrAiXQfYRtjjDFmkPx72yIa0k2kgjQp0ty19q9cO+eKQnfLDDCh5xHGEe4p4DTgmcx2XEReAGaSY+pDbzm4n8j8KcCHgex0hBSwBrD/RxljjDFDKO7GkEzo4+JQ6pb0coUxw8qXgUmZ903ArcBK4DequiWXBvYY4KrqTAAReRR4h6o29L+vxhhjjBkIJ9TOZ+G2l3i95RnGxiu4cOrbCt0lM1iGb5pBv6nqamB15v024GN9bSOnHFxVPSU7uBWRiIhU7OkaY4wxxgwOV1zmj4YDK+sZG1vLgg3Xkg4SvV9ohh3R/F7Fao8Broi8WUTe3WXftUALsENEHhSRmkHsnzHGGGO6saTxYdKawNMECb+ZrYlVhe6SGQzFOcksb72N4F5LVu0zETkK+A7wG+DzwCGEeRLGGGOMGUKjY9NwcAEI1KcqOr7APTKDwgLcfuktwD0IeDxr+13A06r6EVW9AfgkcM5gdc4YY4wx3Tt3yv9jTPxgtqdqqU9P5OZVN3Hnml/RmN6xy3mL6h/n1lXX8WjdAgINCtNZY4ZYb1UUaoC6rO3j2XXFioXA5AHukzHGGGN60eK18XLTG/ga0OC1UpdaxoqW11ndupyvHhguOLqs6QX+uP5W0ppkRctSXHE5aexZBe65yVmR59Hmo7cR3E3AvgAiEgcOY2dNMoBKIDk4XTPGGGNMTzYn1mW+he6olCoEBGxObEI1jIo2tK/G03A9prQmWdP6emE6a/qvCFMURMQRkbNF5Gci8m8ReVVEXhCR+0TkahGZ2lsbvQW4fwe+LyKnAt8DWoEns44fDKzo9xMYY4wxpl9mlO9HRJysxQCUqEQ5oPJARMI9B1QeSkSiuLhEJcZhNccXsMemP4qpikImsP0oYYmw24DpwL+B24H7gM3AxcBKEblHRGb31FZvKQpfAf4E/JOwcsIlqpq9NO+lwMP9fA5jjDHG9FNVdBSf3/8Gbl/9Y1rTi9nulVPmtFOffJpvLH4Hb5vwQY6pPYeLpl3OQxtvQvHwtanQ3TZ9NcyC1Dx9nnClsiuAh1S7TxoXkSnABwkHXSd0d05vCz1sA04SkWqgRVX9Lqe8izDwNcYYY8wQUxFavFdJaQRHlBLXQwQCfB7afDsHVB3Do1tuJxE0A/DAxl+yb8XhVEZHF7jnxnTrRlW9rreTVHU98C0R+UlP5/Q2gtvRUGMP++tzud4YY4wxA6/Na8WVgIBItzmHCb+VhN/auS04JIM2KrEAd7gYbmkG+VDV5oE6P6eVzIwxxhiz95laNp2K6Byq3ASpwCVQyMwvY2LJPrR725lbdTwOESLEmFZ2IGNiVvxo2Mh3gtkwD45F5N2ZiWVLM6/7ui5A1pOcRnCNMcYYs/dxxOHT+32HFS2LWdG4gLUtj5LUCJPLDmVj2wvctnoFrigICC5vtC9jQ/vrTCnbv9BdN7ka5kFqf0g4S/Ju4AJgObCUcC7lHOAuEXkHcLF2lAvpho3gGmOMMcOYIw6zKg5kfeu/EPEocRJsaHuOJj9CRJRMQQWUgFSQ4OHNvy5of43JwVXAW4DzVfUAVX2Hqp6vqgcA5wJvy5zTIwtwjTHGmGFOcHBl55eyCki3Q39C3C0bsn6Z/AjFVSYsy6XAF1V1QdcDqno/cC3woT01YAGuMcYYM8yJCG+e+FViTjmuxJlRtj/VER+/MydXKBGP8dEU46PQktpS6C6bXBVnDu5+wEN7OP4PYI95NhbgGmOMMSPAjMrjuXDmb3HxaUktYpTbyP6lW3j7hDdz1rhzGB1pIkIzq5r/wT1rLqY1XVfoLpsciGper2HKA0r3cLw0c06PLMA1xhhjRohNbc+jJMOvtiV8rWq+n6WNC3bZF5BmfdvCQnfX9KZ4qygsAi7Zw/FLMuf0yKooGGOMMSNEdWwq2WNXqlAZnUZJpIpN7fWgZCadOdTEphWqm8b05nvA30SkFPieqm4EEJFJwOeATwBn76mBIR3BFZHTReQ1EVkhItd2c/xcEXlZRF4UkUUicsJQ9s8YY4wZzmpL9uf4cZ9HpRolypiSgzh50g952+RvMqHkMERcBJcJJQdRHZte6O6aHBTjJDNV/QdhlYTLgfUiskNEdgDrCZfxvVJVH9xTG0M2gisiLnAj8FbCDi4UkQWqujTrtEeABaqqInIwcA9wwFD10RhjjBnu9q85m/1rdh/cOmPqdfxh1QWkgla2JJbw0IbPce60/y1AD02fDNMgNV+q+r8icj9hLdz9CItKvAb8sWNEd0+GMkXhKGCFqq4CEJG7CGuZdQa4qtqSdX45RftjNcYYYwZWS3oLgfqAonjUJ1cUuksmB8N1FHYgZALZn/Xn2qEMcCcD67K21wNHdz1JRM4HvguMA87qriERuZxw2Jpp0yyHyBhjjOlNVXQycbeKwPMQcZhSdlShu2RyUYQBrojsaYLZLlT1ju72D2WAK93s2+3Hpqp/Bv4sIicB3yRcyaLrOTcDNwPMnz+/CH/0xhhjTN+4Tozzp/+K1xsfJOaUsV/1mYXukjE96TpqGwHigA8ksvYLUPAAdz0wNWt7CtBjDoWqPiEi+4pIrapuG/TeGWOMMSNciVvDwaMvKnQ3TK6G8USxfKhqVdd9IjIL+AVwnar+q7c2hrKKwkJgtojMFJEYcBGwyxJsIjJLJCxgIiKHAzFg+xD20RhjjDFm71GcdXB3o6orgC8CP8nl/CEbwVVVT0SuIlx6zQVuU9UlInJF5vhNhDPlPiAiaaAduFB1+C7DYYwxxuxtAk3Rnt5Au99OxB1NTWxCobtkeiAU5wjuHiSBmbmcOKQLPajqA8ADXfbdlPX+e4TFfY0xxhgzwNrTa3lu47tYmXBZmx4DwMHVJ3Hu1M8VuGfG7CQib+q6C5gEXAP8N5c2bCUzY4wxpkisbbyJVr+Zdel90Mzc71ca/82bxl9CTWxcgXtnulWcX2T/izCozX74NPAo4UIPvbIA1xhjjCkSjpTiiCBdkjMjEi1Qj0xvijRFYWyX7UBVd/SlgSFdqtcYY4wxhTOj5kqqIzPZJ15HiSSYFt3KcTUTiTsWDuyV8p1glkNwLCKni8hrIrJCRK7t5riIyE8zx1/OFAHoOPYpEVksIktE5NNZ+78mIhtE5MXMq0816VS1vstrR1+uBxvBNcYYY4pGxBmFRiYwOrKJUZH1CJBOP8GT60/npKn/JOKUF7qLpgsJBrFtERe4EXgrYTnXhSKyQFWXZp12BjA78zoa+CVwtIjMAz5CuFJtCnhQRP6mqssz1/1IVa/vZ7++uqfjqvr13tqwANcYY4wpEp62Up9YBKR3WX0pHTTTmFzMmNLdFhg1I9tRwApVXQUgIncB5wLZAe65wJ2ZqlbPikiNiEwE5gDPqmpb5trHgfOB7w9Avy7osh0FpgEBsAboNcC17ySMMcaYIhGRUiJORecEsw6CUBaZUqBemT3KP0WhVkQWZb0uz2p9MrAua3t9Zh85nLMYOElExohIGXAmuy7odVUmpeE2ERnVp0dWPbjLaw4wnrDU7A9yacMCXGOMMaZIiLgcM/HXjCk5llJ3BmmtpN4fQyx+NlG3ttDdM90Qze8FbFPV+Vmvm7Ob7+aWXTN3uz1HVZcRlnZ9GHgQeAnwMsd/CewLHApsAn7Y3+fPumEL8BVyGL0FC3CNMcaYolIZm8XRE2/h8Im3ss2vojWA1a3/5cktVoZ+r6OEZcLyee3ZenYddZ0CbMz1HFW9VVUPV9WTgHpgeWb/FlX1VTUAbiFMhRgIk4GcfhOzHFxjjDGmCDUkVyG4APiaoq59cYF7ZLozyGXCFgKzRWQmsAG4CHhPl3MWEKYb3EU4yaxRVTcBiMg4Va0TkWnAO4BjM/sndpxDmJfbp79cIvKzrrsIUxROB+7JpQ0LcI0xxpgiNLZkLiKCqIsrUWZUnlLoLpkhpqqeiFxFmNvqArep6hIRuSJz/CbCFWjPBFYAbcCHspr4o4iMIVyE4UpVbcjs/76IHEo4Br0G+Ggfu3Zgl+0osB/wJLbQgzHGGGN6UhoZxVlTfsB9b3yRF1sqeL39eSaVvcHU8umF7prJNsgLPajqA4RBbPa+m7LeK3BlD9ee2MP+9+fZp1O77hORCPC/hKXJftFbG5aDa4wxxhSppfW/4LmWKhIaoT7dzi9XDkSFJzNQhAGZZDYiqKpHWIJst8UoumMjuMYYY0yRaknXI51jXUKr317Q/pgucpsoVkwOAMpyOdECXGOMMaZIHVF7JU/vuIF6rxRwOHPCOYXukjGIyB+67iKcZHY80HUCWrcswDXGGGOK1LjyE/ni3FmsbH6RMSVzmVRm+bd7m5GUZtAHbpftAHgF+Kmq/jmXBizANcYYY4pYWXQiB42eWOhumJ4UYYCrqu/Otw0LcI0xxhhj9lJFOoKbN6uiYIwxxhizN1Ig0Pxew4iIXC0i3xeRyb2c54jI2SLyn57OsRFcY4wxxhizN7gT+CawUkSeIVzYYTHhMsClwCTCZX/PBJqBz/fUkAW4xhhjjDF7q+E1CJsXVd0KXCEi3yBcMe104GqghPCT2AA8Dlyqqn/fU1sW4BpjjDHG7KWKMQdXVTcC3868EJEyIKGqQa5tWIBrjDHGFDFVZemOP7K5/RX2qTyVmZVvGvB7bG5/mWU7/kxVdAqHjn4/rhMb8HuMWLbQA6ra1tdrLMA1xhhjitiSHfeyaNvNeJpgXevTxN0KJpUdMWDtN6U28OD6q/E0gStx2rxtnDjhCwPW/khXjCO4A8GqKBhjjDFFbFPbC3iaAMDXNFsTywa0/frkSkScTPtJNrW/NKDtG9MdC3CNMcaYIjaz8mQiUgIIjkSYVDZ/QNsfWzoXEASHiJQws2LgUyBGLB2AV5GyANcYY4wpYrOq3sZBoy6i3PEodxp5ZfsN+EFywNovj9Ry2qTvU+7GKZUGNrbcS1Nq1YC1P5IJIKp5vYqVBbjGGGNMEfOCJMsabscliRCwrf05XtvxmwG9xwv1t+DodhxRkn49T2z8xIC2P6IFeb6KlAW4xhhjTBHzNAn4u+xr9+sG9B7t3tbO9yKQCpoGtH1jurIA1xhjjCliJW4VE8tOBgRVcCTK7OqLBvQeh4+5Ep8IqqAIc0d9ZEDbH8ksRaF/rEyYMcYYU+ROnfQdNre/kzZvI1PKTyTu1gxo+zMqT2R0/M9sbv8PY0vmMSo+e0DbH7GKfKJYPizANcYYY4qciDCx7Ahg4OrfdlUVm0RV7PxBa39kUlvooZ8swDXGGGOM2UvZQg/9M6Q5uCJyuoi8JiIrROTabo6/V0RezryeFpFDhrJ/xhhjTLFSDdjU+gTrWx7G19Sg3GNH8jXWNv+VNm/zoLRvTIchG8EVERe4EXgrsB5YKCILVHVp1mmrgTepaoOInAHcDBw9VH00xhhjitWiuq+wsfVfgFAV24eTJ9/RuQLZQNjU+iT/3fJ5OhZ9OHXqH6iITh2w9kcsS1Hol6EcwT0KWKGqq1Q1BdwFnJt9gqo+raoNmc1ngSlD2D9jjDGmKKkq61v+jq/t+NpGU2o57QM8yrq66f/wNYGv7QSaYlPrYwPa/oikIEF+r2I1lAHuZGBd1vb6zL6eXAb8vbsDInK5iCwSkUVbt27t7hRjjDHG5EhEKI1MoCMsEFxi7qgBvUdVbDaOxDP3i1ARnTGg7Y9Yqvm9itRQBrjSzb5uP3kROYUwwP1Cd8dV9WZVna+q88eOHTuAXTTGGGOK0wmTfsn4+EzGR2McWH0mbiYYHShzRn+U6eVvZlwkxvTS6dSWzB3Q9o3JNpQB7nogO9lmCrCx60kicjDwK+BcVd0+RH0zxhhjipoG24kFS4hoHfWtv2Nj000D2r4gaOphomwjmVrEsi3vGdD2RyzN81WkhjLAXQjMFpGZIhIDLgIWZJ8gItOAPwHvV9XXh7BvxhhjTFFrTb6EZr7SDrSdpvanBrR9z9+Op01AAHi0p5ejWsRJojmylcz6Z8iqKKiqJyJXAQ8BLnCbqi4RkSsyx28CvgKMAX4hIgCeqs4fqj4aY4wxxaqq5BhEHFQFR0oYXX7GgLYfdccScyeS8jYADuXxgwa0SsOIVcRBaj6GdKEHVX0AeKDLvpuy3n8Y+PBQ9skYY4wxUBY7gP1rb6Bxx7UIKcqdgQ0RRFwOHH83m7e9D4LVlEbHoJoi/FLXdEsJB7xNn9mvTsYYY4wBoK35etA6VBvYseOLeN4bA9p+e/ufwF8O2kJ74iGaW24b0PaN6WBL9RpjjDEGAD+oo3Nmkrj4wTYiTB+49v1NQDKzlcT3NwxY2yORUNx5tPmwEVxjjDHGAFBV+WlEShApJxrZj2hkLml/M6regLRfUf4+RCpQLcfTMsrKwkoKnt+AHzQPyD1GHKuD2y82gmuMMcYYACorLqUkfgJBUI+403h985vw/O1E3Fr2Hf9Xom5+teej0dlEyz/O6h0/IQC2bL6AyeVn0dh2LyBMGvUdRldcNCDPMmIUcZCaDxvBNcYYY0ynaHQ/4vFj2N5yO2l/C0qStL+Fbc235t22asCGxp9k5k0Jqm3saLsbJY2SYmPDl/O+x4jSMcksn1cvROR0EXlNRFaIyLXdHBcR+Wnm+MsicnjWsU+JyGIRWSIin87aP1pEHhaR5Zk/B3ZZvBxYgGuMMcaY3TgSRzqX7hWcAal2IOwaeuy6yKlIdADuYXIlIi5wI3AGMBe4WES6LjF3BjA787oc+GXm2nnAR4CjgEOAs0Vkduaaa4FHVHU28Ehme0hZgGuMMcaYTml/B+sabqC17T8IcXwFpJqy+HF5ty0izBxzA44KgUIMh5gzmUAFXyNUlb4D1XT+DzGCDPJCD0cBK1R1laqmgLuAc7uccy5wp4aeBWpEZCIwB3hWVds0TNJ+HDg/65o7Mu/vAM7L+4PoIwtwjTHGGANA2m/kuQ0n0dz8A8T/N17QggMEwTbWbH03Te3/yvseo8rPZVTFZdQ4ScqkHTd4gwBw8GhovZMVW97RuaKaYSAmmdWKyKKs1+VZrU8G1mVtr8/sI4dzFgMnicgYESkDzgSmZs4Zr6qbwu7rJmDcQH0cubJJZsYYY4wBoCHxX8poQEVwVPFwiBIQLi4asK35FqpKT837Pttb76JGlLijtAYuLpq5ByTSL+EHDUTc0XnfZ/gbkEoI2/awKqx0s6/rDbs9R1WXicj3gIeBFuAlYGDKbQwAG8E1xhhjDAClkal4GkGVzoCzgyqURg8ckPtE3HCQ0GdnINIRx4nEcJ3KAbmP6dV6do66AkwBNuZ6jqreqqqHq+pJQD2wPHPOlkwaA5k/6wah73tkAa4xxhhjAKiMH8D4musIpIJWdah0fAIixBCqIpMYXX7egNzn4Am/Ju0cREod4hIgRIiIQ4nEmVBxKfYFc4Yy2HVwFwKzRWSmhGsmXwQs6HLOAuADmWoKxwCNHekHIjIu8+c04B3AH7KuuSTz/hLgL3l+En1mf4OMMcYY02ly9YVMrr6wc3vz1veQSD4FwXo2b72AKRP/g+vU5HWPksgEDpnyUOd2e+Jp6ra/D9U2Wlt/RSwykaqKD+V1jxEjh1Jf/aWqnohcBTwEuMBtqrpERK7IHL8JeIAwv3YF0AZk/2D+KCJjgDRwpao2ZPZfB9wjIpcBa4F3Dd5TdM8CXGOMMcb0KJl6DkhlthTPW4MbO3SA77GIcBI/qLbTnnjSAtyMwV6qV1UfIAxis/fdlPVegSt7uPbEHvZvB948gN3sM0tRMMYYY0yPSktORqQEcBGJEY3MGoR7nIhk6uyKlFJWeuaA32PYsqV6+8UCXGOMMcb0aOzon6ORE6iQOFFtZNPmw9jR+G3S6XVs2nI26zYeRlPzbXndIx47jOqaH+IymnI8mnZ8ls11p5FOr6Wh8Xus23gom7deiO/XD9BTmZHOUhSMMcYY06OW5ItUeE+jJPAIQFtoafkVre3/IO2tAAIamr5FSfwYYrGui2Dlrm7H95goLewgXOghSL/M1u3vIe1vQrUNP1lP/Y7/x9gxNw7Qkw0DCgTFOwqbDxvBNcYYY0yPUn4dUQlLenVQfPxgGztnQDn4QX6VoJygkaBLCVY/2J5VlTWN52/I6x7DT57pCZaiYIwxxhizu+rSk9iuNZRmvvRVFYQKqio+g1BCoKWIjCMWOzqv+5RVfATBxe1YV0AjVJR/DMepRLUMX+NUVX4838cZfizA7RcLcI0xxhjTo4hTyfSJTyLVP6I8ejRpoC3YwcbGb9CkaVpJ0eivZeWWMwk00e/7TBr1WSK1f6K87GOgMRL4bGn6Ho3+NlpI0obHqm0fobn9qYF7uOHAAtx+sQDXGGOMMXvkOmVUlB5PW+o5AjLrD+CRvapr0ltBY9sDPTWRk7L4YeC9TKIjDxdFd0lb8NnQcHVe9zDFwSaZGWOMMSYHbq9niETzv00vbQzIPYYLm2TWbzaCa4wxxpheOW4to6o+g4sQBZzOEEIpBWojk6iMHpz3fSpqvkO5VOEoxBAkk5MbQakRlynl70WL5qt3BQ3yexUpC3CNMcYYk5Pyqs8ypuQU0oBDQJSAMU6EKW6MGt1KettbCdIr8rqHG5nBuNo7ERx8lAgBJfhMdWPUOi5u6/dJN/7PwDzQcGA5uP1iAa4xxhhjcqKapDXxeOe2A5ShiIAjgKYJEvnl4QIkWu8gnSlBJkCpCC7hPQQI2v+Y9z3MyGYBrjHGGGNyFCPilHduKeChBB0jhSJIZHred4lE5uyy7aEdxcNQBXHH5X2PYaEjBzefV5GyANcYY4wxORERRtf+mQp3AuDgSDVuxTW0U8kO38cnht/6e4L02rzuE6/8KOPLzschghAhEj+Ttuh8dvhKQgXVNEHL7wfmofZ2lqLQL1ZFwRhjjDE5i8TmMHnS853bqsq61l8zwY3g4kP6eXT7Oej4ZxAp7dc9RKLUjLmRmqxleRuafk1J6r9UOBFEt6MtXyNwKnDKzsn7mfZqRRyk5sNGcI0xxhjTb4G24ATbcHEQEUQESIK/cUDv05b8F1FxMu2Hubik/j2g99j72FK9/WUBrjHGGGP6zZEK1JmMR4CqhiW8pBzcKQN6n/LSs0lp0FkiTAEpecuA3sOMHJaiYIwxxph+ExEmTXiIhvqv4qYeppQkrd4WKjfNxRUXBIQYxI5Fam5AnMp+3aem4t004dDSegtxiRCrvAopedsAP81eRoGgeGvZ5sMCXGOMMcbkxXWqqaw4Fxr+RkuQpEwiuOIgHaUPSEHqSbT5+0j1N/t9n6qKd0LFOwekz8NGEacZ5MMCXGOMMcbkzfc3EQUCFAfpzJXdKQB/fSG6NrxZgNsvQ5qDKyKni8hrIrJCRK7t5vgBIvKMiCRF5Jqh7Jsxxhhj9swPdhBosttjJSWn0UaMMomSUK8zH1c1rJOrxJDyy3u9h2oK328Y6K4PU3nWwC3iOrhDNoIrIi5wI/BWYD2wUEQWqOrSrNPqgU8C5w1Vv4wxxhizZ6rK5vpP09R2HyIRJtfeRnnJm3Y5x3HHUDn+GdJtf8ZJv0oCH8d7Az/9X+LioCSh/U84sWO6Gd0NtSX/w/qt70M1RUXpW5k05mZEbD686buh/FtzFLBCVVepagq4Czg3+wRVrVPVhUB6CPtljDHGmD1Ipl+huf1vQBrVdjbXf67b8xyninjFJZSO+i7lo75POv0CsUxpL0HRxN/BW9LjfbY0fBHVViBNa+Jx2pPPDs4DDRcKqkFer2I1lAHuZGBd1vb6zL4+E5HLRWSRiCzaunXrgHTOGGOMMT1xCKf0hwQ3t8uku/N6/vJ413a1h+uLjKUo9MtQBrjdfR/Rr09eVW9W1fmqOn/s2LF5dssYY4wxexKPHkh1+cWAiyMVTBjz45yuK6m5gZRKmIuLIGUXItEDejx//Kgf4Eg14FBZ+nZKY0cNSP+HNVvooV+GsorCemBq1vYUYGCXOTHGGGPMgBMRxo/6FuNqvgJEcs6LjZWeQbTkdVR9RMIlePekNH4osyYvBdKIxPLvuClaQxngLgRmi8hMYANwEfCeIby/McYYY/LQn6BTxOnTRLFwApoFt0A4AmsLPfTLkAW4quqJyFXAQ4AL3KaqS0Tkiszxm0RkArAIqAICEfk0MFdVm4aqn8YYY4wxe40iTjPIx5Au9KCqDwAPdNl3U9b7zYSpC8YYY4wxRU9tBLdfbCUzY4wxxpi9UnFPFMuHVU82xhhjjDEjio3gGmOMMcbsjZSirmWbDwtwjTHGGGP2VkW8Glk+LMA1xhhjjNkLKaA2gtsvFuAaY4wxxuyNVG0Et59skpkxxhhjjBlRbATXGGOMMWYvZSkK/WMBrjHGGGPM3spSFPpFdJgXEBaRrcAbg3iLWmDbILZvBo/97IYn+7kNX/azG57s59a96ao6tpAdEJEHCX8++dimqqcPRH+Gk2Ef4A42EVmkqvML3Q/Td/azG57s5zZ82c9ueLKfmxmJbJKZMcYYY4wZUSzANcYYY/5/e3cfbFVVxnH8+wsUBVMps1BMXqIEYoQk30ixJMLBpLJmsFAGTLM0NSt8GW2sqdSRcUrDTEGhNJFBS8QGU3DSMQITRQQ0riCCojjjK74g6NMfa91pezoH7kXknLvv7zNzZ5+99tprP+c8cw8P+669t5mVigvcrbu23gHYNnPu2ibnre1y7tom581Kx3NwzczMzKxUfAbXzMzMzErFBW4NkkZIekJSk6Tz6h2PvZek/STdK2m5pKWSzsrtH5F0t6QVedm1sM/5OZ9PSPpK/aI3SR0kPSxpdl533toASXtKminp8fy7d5hz1/gk/Sh/Tz4m6WZJuzhvVnYucKuQ1AGYBBwD9ANOkNSvvlFZhc3AjyOiL3AocHrO0XnA3IjoA8zN6+Rto4H+wAjg6pxnq4+zgOWFdeetbfgtMCciDgAOJOXQuWtgkvYFzgQGR8RngQ6kvDhvVmoucKs7GGiKiJUR8TYwHRhV55isICLWRcSi/Po10j+0+5LyNC13mwZ8Lb8eBUyPiI0RsQpoIuXZdjBJ3YGRwORCs/PW4CTtDhwJTAGIiLcj4mWcu7agI7CrpI5AZ+BZnDcrORe41e0LrCmsr81t1oAk9QAGAQuAj0fEOkhFMLB37uacNo7fABOA4vMnnbfG1wt4AbghTy+ZLKkLzl1Di4hngInA08A64JWI+DvOm5WcC9zqVKXNt5toQJJ2A24Fzo6IV7fUtUqbc7qDSToWWB8RD7V0lyptzlt9dAQ+B/w+IgYBr5P/rF2Dc9cA8tzaUUBPYB+gi6QxW9qlSpvzZm2OC9zq1gL7Fda7k/6kYw1E0k6k4vamiLgtNz8vqVve3g1Yn9ud08YwBDhO0lOkqT9fknQjzltbsBZYGxEL8vpMUsHr3DW2YcCqiHghIjYBtwGH47xZybnAre5BoI+knpJ2Jk24n1XnmKxAkkhzAZdHxBWFTbOAsfn1WOD2QvtoSZ0k9QT6AAt3VLyWRMT5EdE9InqQfq/mRcQYnLeGFxHPAWskfSY3HQ0sw7lrdE8Dh0rqnL83jyZds+C8Wal1rHcAjSgiNks6A7iLdMXp9RGxtM5h2XsNAU4Elkh6JLddAFwKzJB0MumL/VsAEbFU0gzSP8ibgdMj4p0dHrXV4ry1DT8Ebsr/8V8JjCOdKHHuGlRELJA0E1hEysPDpCeX7YbzZiXmJ5mZmZmZWal4ioKZmZmZlYoLXDMzMzMrFRe4ZmZmZlYqLnDNzMzMrFRc4JqZmZlZqbjANbO6kTRV0ux6x1EkaZSkFZI2S5paxzh6SApJg+sVg5lZW+UC16ydysVlSLqwov2o3L5XvWKrs8mkJ+TtD5xVq5Ok3pKmSFojaaOkpyTNlHT4DovUzMyqcoFr1r69BUyQ9LF6B7I95cc4b8t+ewJ7AXdFxDMR8UqNfoNJN87vD/wA6AccBzwEXLUtxzYzs+3HBa5Z+3Yv8BRwUa0O1c7oVv75vNDnGEkPSXpT0v2SuksaKmmxpA2SZkv6aJVjXCjp+dznBkm7FrZJ0gRJT+Zxl0gaUyWWEyTNk/Qm8L0a76WrpGmSXspj3SOpf/N7AF7KXeflMY+qMoaAqaQneQ2JiDsi4smIeDQiLiE9CpUcy+8q9t1d0huSvpHXd5b0a0mr81nglZLO3EIu+km6U9JrktZLulnSJwrbB0iaK+nV3GexpC/WGs/MrKxc4Jq1b+8C5wGnSeq9Hcb7OXA2cAjQFbgF+BlwKnAU6YznxRX7DAUOJBWGxwPDgcsK238JnAycTjpTegnwB0kjK8a5BLg69/lrjfim5thGAQcDbwBzckH9zxwfOY5uua3SwNzv8mqPMI2Il/PL64BvS+pU2HwCsAG4I69PA04CzgH65vf5MlVI6gbcBzyWYx9GetzqLEnN3+V/Btbl7YNIn/Vb1cYzMyuzjvUOwMzqKyL+JukB4FfA6Pc53EURcT+ApGtIf64/KCIW5bZpwDcr9nkHGBcRG4DHJJ0LTJF0ft5+DjC8eVxglaSDSQXvnYVxroqImbUCk9SHNI1gaETcl9tOBJ4GvhMRkyWtz91fjIjnagzVJy+X1/4YALiN9P6/DkzPbeOBP0bEphzPaOCYiJiTt6/cwnjfBxZHxLmF93QS8CIwGFhImjc8MSIez12athKjmVkpucA1M4AJwL8kTXyf4zxaeP18Xi6paNu7cp9c3DabD+wM9AY6AbuQzrJGoc9OpKkVRf/eSmx9SWes5zc3RMQrkpaQzvq2lFrSKSI2SvoTqaidLqkf6czquNxlUI7n3hYe9yDgSEkbqmzrTSpwrwAmSxoLzAVuLRS7ZmbthqcomBkR8SDpzgGXVdn8bl4WC7taF3FtKg6bx65sa833TnPfr5KmBjT/9CdNZSh6fStjbakwjS1sq/SfvOzbgr6TgaMlfZI0/WB+RCxrQTzVfIh0xnpgxU8fYDZARFzM/6ZoHA48Kml8K49jZtbmucA1s2YXAEcAIyraX8jLboW2gdvxuAMkdSmsHwq8DTwJLAM2AvtHRFPFz+pWHmcZ6TvvsOYGSbsDA/K2lnok9/+ppA6VG/OdGACIiKXAAuAUYAxwfaHrohxPSy8Ca75rw+oqn8VrhWOuiIgrI2IkMAX4bivem5lZKbjANTMAIqIJuJb/v/drE7AGuFjSpyUNBy6s3P996AhcL6m/pC8DlwLXRcTruXCbCEyUNF7SpyQNlHSapFNbc5CIWAHcTrpA7QhJA4AbgVdJF2e1dJwgTTPoDTwg6Vile+IOkDQBuKdil+tIU0C6kC66K8YzgzSl4HhJPXNcJ9Y49CRgD+AWSYdI6iVpmKRrJX1Y0q6SJuU7WvSQdAjwBVpXvJuZlYILXDMr+gWwudiQpxiMBnoBi0l3SrhgOx7zH8BS0lzUvwDzSAVhs4tIdwP4Se53N+kuB6u24VjjSHNVZ+VlZ2BERLzZmkEiYiFpTuxy4Jq8vJM0x/aMiu63kM5Izyieac1OIhXXVwKPk+7ysEeNYz4LDCFNGZlD+iwmkc5wbyRdrNeVdGeGJ0if5XzSRXpmZu2K0skIMzP7IEjah3SnhqER8UC94zEzaw9c4JqZfQCUnqbWjXT7tQMi4vN1DsnMrN3wFAUzsw/GEGA16cESp9Q5FjOzdsVncM3MzMysVHwG18zMzMxKxQWumZmZmZWKC1wzMzMzKxUXuGZmZmZWKi5wzczMzKxUXOCamZmZWan8F5FAnIk66C/NAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "battery = Battery['CS2_35']\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.scatter(battery['cycle'], battery['SoH'], c=battery['resistance'], s=10)\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label('Internal Resistance (Ohm)', fontsize=14, rotation=-90, labelpad=20)\n",
+    "plt.xlabel('Number of Cycles', fontsize=14)\n",
+    "plt.ylabel('State of Health', fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37f542a4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c798b24",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/MLP-checkpoint.ipynb b/.ipynb_checkpoints/MLP-checkpoint.ipynb
new file mode 100644
index 0000000..bc12538
--- /dev/null
+++ b/.ipynb_checkpoints/MLP-checkpoint.ipynb
@@ -0,0 +1,1126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cycled at constant current of 1°C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load Dataset CS2_35 ...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:188: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
+      "  return func(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
+      "Load Dataset CS2_36 ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_2_3_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_37 ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_38 ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "Battary_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'datasets/CALCE/'\n",
+    "Battery = {}\n",
+    "for name in Battary_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheetname=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "    path_sorted = np.array(path)[idx]\n",
+    "    \n",
+    "    count = 0\n",
+    "    discharge_capacities = []\n",
+    "    health_indicator = []\n",
+    "    internal_resistance = []\n",
+    "    CCCT = []\n",
+    "    CVCT = []\n",
+    "    for p in path_sorted:\n",
+    "        df = pd.read_excel(p,sheetname=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        cycles = list(set(df['Cycle_Index']))\n",
+    "        for c in cycles:\n",
+    "            df_lim = df[df['Cycle_Index'] == c]\n",
+    "            #Charging\n",
+    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "            c_v = df_c['Voltage(V)']\n",
+    "            c_c = df_c['Current(A)']\n",
+    "            c_t = df_c['Test_Time(s)']\n",
+    "            #CC or CV\n",
+    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "            #Discharging\n",
+    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "            d_v = df_d['Voltage(V)']\n",
+    "            d_c = df_d['Current(A)']\n",
+    "            d_t = df_d['Test_Time(s)']\n",
+    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "            if(len(list(d_c)) != 0):\n",
+    "                time_diff = np.diff(list(d_t))\n",
+    "                d_c = np.array(list(d_c))[1:]\n",
+    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
+    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "                count += 1\n",
+    "\n",
+    "    discharge_capacities = np.array(discharge_capacities)\n",
+    "    health_indicator = np.array(health_indicator)\n",
+    "    internal_resistance = np.array(internal_resistance)\n",
+    "    CCCT = np.array(CCCT)\n",
+    "    CVCT = np.array(CVCT)\n",
+    "    \n",
+    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "                              'capacity':discharge_capacities[idx],\n",
+    "                              'SoH':health_indicator[idx],\n",
+    "                              'resistance':internal_resistance[idx],\n",
+    "                              'CCCT':CCCT[idx],\n",
+    "                              'CVCT':CVCT[idx]})\n",
+    "    Battery[name] = df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2442b50bf98>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Txo0bEx8fT0JCAp9++ikmkwkHBwc++OADkpKS+OWXX5g7dy6JiYlV5jFu3Dj2799PXFwcAwYMKBc433333cTFxREXF8frr79+WWWvDQmohRBCCCGukV69YNEi/X1xsb78n//oy/n5+vKXX+rL2dn68rff6suZmfryd9/py2lpl5//+fPnqVevHgC5ubn07t2boKAgAgICWLVqFQDjx4/n2LFjmM1mxo0bx/jx49m2bRtms5mPPvqI0tJSxo0bR5cuXQgMDOTjjz8G9J7ekJAQwsPDCQgIYNKkSeV6jSdOnMisWbMqLdfSpUsJDg5m4MCBtnUhISH4+/uTmppKs2bNbOvbtm2Lk5MTTZs2JSgoCAA3Nzd8fX1JSUmpsu7u7u6293l5eTd0bLtMmyeEEEIIcQsrKCjAbDZTWFhIamoqmzZtAvS5lVesWIG7uzuZmZl0796dQYMG8e6775KQkEBcXBygB8rTp09nzZo1ACxcuBAPDw927drFhQsX6NGjB6GhoQDExMSQkJBA69atSU5OJiwsjMjISCwWC8uWLSMmJqbSMiYkJNCpU6dKt40cOZLQ0FCWL19O7969GT58OHfccUe5fZKTk4mNjaVbt27VtsXEiRNZsmQJHh4ebN682bY+OjqaDh064O3tzfTp0/Hz86tFy9aeBNRCCCGEENdIVNTF9yZT+eW6dcsve3iUX27QoPxykya1y7NsyAfogeOwYcNISEhA0zRee+01tm7disFgICUlhfT09BrT27BhA/v372f58uUAZGdnc+TIERwdHenatattrmYfHx+8vLyIjY0lPT2djh074uXlVbtC2zGbzRw/fpwNGzawceNGunTpQnR0NL6+voDe0z5kyBBmzJhRrhe6MlOmTGHKlClMmzaNOXPmMHnyZIKCgjhx4gSurq6sW7eOhx9+mCNHjlx2OasjAbUQQgghxJ9EcHAwmZmZZGRksG7dOjIyMtizZw8mkwkfH59azbWsaRqzZ8+mT58+5dZHRUXh4uJSbt2oUaNYtGgRaWlpjBw5sso0/fz82LJlS5XbXV1dCQsLIywsDIPBwLp16/D19aW4uJghQ4YQERFBWFhYjWUvEx4eTv/+/Zk8eXK5ILxfv36MGTOGzMxMGjRoUOv0aiJjqIUQQggh/iQOHjxIaWkpXl5eZGdn06hRI0wmE5s3b+bEiROAPh45JyfHdkzF5T59+jB//nyKi4sBfXaOvLy8SvMbPHgw69evZ9euXZcE4PbCw8PZsWMHa9euta1bv3498fHxbN++nXPnzgFQVFREYmIirVq1QtM0nnzySXx9fXnxxRdrrLt9r/Pq1atp164dAGlpaWiaBuhDViwWyxX1pFdHeqiFEEIIIW5hZWOoQe9dXrx4MUajkYiICAYOHEjnzp0xm822ANPLy4sePXrg7+9P3759mTp1Kg4ODnTo0IERI0YQGRlJcnIyQUFBaJpGw4YNWblyZaV5Ozo6EhISgqenJ0ajscoyOjs7s2bNGsaOHcvYsWMxmUwEBgYyc+ZMYmNjGT16NJqmYbFY6N+/P0OGDGH79u18/vnnBAQE2Oo3depU+vXrV2ke48eP59ChQxgMBlq1asWCBQsAWL58OfPnz8fBwQFnZ2eWLVt2zW9YVGUR+62ic+fO2u7du294vpqmyZOQhBBCCFFOUlKSbazv/yKLxUJQUBBff/31JTcS3moqO5dKqT2apnWu6VgZ8lFLs2Nm0/VfXTlw5sDNLooQQgghxE2XmJhImzZt6N279y0fTF8tGfJRS1mOzdjn6M8rMYtZO+D9m10cIYQQQoibqn379hw/frzcuvj4eIYOHVpunZOTEzt37rxm+T777LNs37693LrIyEieeOKJa5bH5ZKAuhais7OZcr4BRS0fZ52mEXkwgZnt/G92sYQQQggh/lACAgJsU/hdL3Pnzr2u6V8JGfJRC0vS0iiyaKAMoAzMSs3g5SOHb3axhBBCCCHEH4AE1LVmvXnTemPiRymnic7OvonlEUIIIYQQfwQSUNfCsCZNMCoFmqa/rEF1VFbWTS6ZEEIIIYS42SSgroVgDw+2dezIXSlpKE0DTW84L5PpZhdNCCGEEELcZBJQ11KwhwdrS5xZ8OGHGC0llGgWxh49KsM+hBBCCHFTGY1GzGYzHTp0ICgoiB07dlS7f1ZWFvPmzbMtJycns3Tp0utdTA4fPky/fv1o06YNvr6+PProo6Snp5Ofn09ERAQBAQH4+/vTs2dPcnNzOXnyJCEhIfj6+uLn58fMmTOrTX/SpEkEBgZiNpsJDQ3l9OnTtm1RUVGYzWb8/Py49957r3nd5MEul2na1KlM6taNUqMRIzDMo5SjcZNxcXShrVdbQm8Ppd8dlT/BRwghhBB/Ln+EB7u4urqSm5sLwA8//MDUqVPZsmVLlfsnJyczYMAAEhISAD3YnD59OmvWrKl1nqWlpdU+GbGiwsJCAgIC+PDDDxk4cCAAmzdvpmHDhnz33XdkZGTw4YcfAnDo0CF8fHw4e/YsqampBAUFkZOTQ6dOnVi5ciXt27evNI/z58/j7u4OwKxZs0hMTGTBggVkZWVx1113sX79elq2bMmZM2do1KjRJcdfzYNdZNq8y9SrSRMci4spUBpGg5EOThrR+RnkFuWy9cRWZu6cScLoBPwa+d3sogohhBDiBuu1qNcl6x71e5QxXcaQX5xPv/9e2uk2wjyCEeYRZOZn8pev/lJuW9SIqMvK//z589SrVw+A3NxcHnroIc6dO0dxcTHvvPMODz30EOPHj+fYsWOYzWYeeOABtm3bRlJSEmazmeHDh/P8888zfvx4oqKiuHDhAs8++yxPP/00UVFRTJ48maZNmxIXF8eQIUNo0KABkZGRAEycOJHGjRvz/PPPX1KupUuXEhwcbAumAUJCQgBYuHAhrVq1sq1v27YtAE2bNqVp06YAuLm54evrS0pKSpUBdVkwDZCXl2d7wvXSpUsJCwujZcuWAJUG01dLAurL1K29Pz89+QRPDe1MsXcuI7p/RmT7JAAy8jLw/tCblJwU/Br5ca7gHK6OrpiMMtZaCCGEENdHQUEBZrOZwsJCUlNT2bRpEwB16tRhxYoVuLu7k5mZSffu3Rk0aBDvvvsuCQkJtvmiK/ZQL1y4EA8PD3bt2sWFCxfo0aMHoaGhAMTExJCQkEDr1q1JTk4mLCyMyMhILBYLy5YtIyYmptIyJiQk0KlTp0q3jRw5ktDQUJYvX07v3r0ZPnz4JU9eTE5OJjY2lm7dulXbFhMnTmTJkiV4eHiwefNmQB9qUlxcTK9evcjJySEyMpJhw4bVsnVrRwLqy2Ro347gxEQ+2NeFCONW9qXv46CpDd9kZDCkYUMWDljIA7c9AMDLG15macJSgpoGMfW+qdzrc2VjdpKzkvnXnn+RX5xPZ+/ORARGXMsqCSGEEOIaqa5Hua6pbrXbG9RtcNk90gDOzs624Dg6Opphw4aRkJCApmm89tprbN26FYPBQEpKCunp6TWmt2HDBvbv38/y5csByM7O5siRIzg6OtK1a1dat24NgI+PD15eXsTGxpKenk7Hjh3x8vK67PKbzWaOHz/Ohg0b2LhxI126dCE6Oto2/CI3N5chQ4YwY8aMcr3QlZkyZQpTpkxh2rRpzJkzh8mTJ1NSUsKePXv46aefKCgoIDg4mO7du3PnnXdedlmrIgH15XJ3J/r119nTowf/1/xx5mUW82WO/pCXDefO8fGdfWxfMTzi9wgedTz4ZO8nfBL7yRUH1C9veJlvkr6xLf/V/68YDbUftySEEEKI/w3BwcFkZmaSkZHBunXryMjIYM+ePZhMJnx8fCgsLKwxDU3TmD17Nn369Cm3PioqChcXl3LrRo0axaJFi0hLS2PkyJFVpunn51ftuG5XV1fCwsIICwvDYDCwbt06fH19KS4uZsiQIURERBAWFlZj2cuEh4fTv39/Jk+eTPPmzWnQoAEuLi64uLhwzz33sG/fvmsaUMssH5cpOjub3r17M9HRkRfOOPBlTvnA9oWjR+m4axftY2L4uLA5j3R/g+AWwSRmJF5xno1dGgPQN+hlaBHOV6cOXFUdhBBCCPHndPDgQUpLS/Hy8iI7O5tGjRphMpnYvHkzJ06cAPTxyDk5ObZjKi736dOH+fPnU1xcDOhDJvLy8irNb/Dgwaxfv55du3ZdEoDbCw8PZ8eOHaxdu9a2bv369cTHx7N9+3bOnTsHQFFREYmJibRq1QpN03jyySfx9fXlxRdfrLHuR44csb1fvXo17dq1A+Chhx5i27ZtlJSUkJ+fz86dO6/5jaTSQ32ZorKyKCixVPlRJN9iIc560SXl57MyMxO3ps+Tl7WP7VlZ9PD0vOw83ZzccPDswE/u/cHVwrDjv+PjkU2wh8fVVEUIIYQQfwJlY6hB711evHgxRqORiIgIBg4cSOfOnTGbzbYA08vLix49euDv70/fvn2ZOnUqDg4OdOjQgREjRhAZGUlycjJBQUFomkbDhg1ZuXJlpXk7OjoSEhKCp6dntbN+ODs7s2bNGsaOHcvYsWMxmUwEBgYyc+ZMYmNjGT16NJqmYbFY6N+/P0OGDGH79u18/vnnBAQE2Oo3depU+vWrfDa18ePHc+jQIQwGA61atWLBggUA+Pr68uCDDxIYGIjBYGDUqFH4+/tfcXtXRqbNu0zR2dn03BuLBesTE7UKP6thUootZvNlB8LPrXuOhTkuFDV+UF+haTzj7c18612wQgghhLg5/gjT5t1MFouFoKAgvv7660tuJLzVXM20eTLk4zIFe3jwcssW+kLZhxGL5eKy/auCYk0jKiuL3KJcdqXsqnWeg7v9H61aXpxmpqbAXQghhBDiektMTKRNmzb07t37lg+mr5YM+bgC791+OxcKYXb6b1jQcDAYeLFFC5LO5xF3MhMTBRx3d6lwlIYRjYLMaNwW/Q2A9JfTaeRS/VyI0dnZ9E04SJFdgG4AfJ3l1AkhhBDi5mnfvj3Hjx8vty4+Pp6hQ4eWW+fk5MTOnTuvWb7PPvss27dvL7cuMjKSJ5544prlcbkkKrtCRVkOKM0CRiMa4OngwOqOgdBR3/7twWxeXhyLd6vDnG3fjnP5h8g4OJu3s+NtaSRmJNLIpRFbT2wlKSOJpzo9ZZshpExUVhbFpaVguPhlggWNF44ep4tHAxlHLYQQQog/jICAANsUftfL3Llzr2v6V0KGfFyhof6emDQwWjQcDQZ6VbjZ0E95MHSnkR8+X06i2cz+ux8mrJl+M0DXZl0BiE+PJ784n3sX3csza5/hQMals3f0MpkwFRdbh5Ggv1BYUKzNOH3J/kIIIYQQ4saSHuorFOzhwaYunYnKyqKXp+clPcVt28LkTXcDG5g9G1JS4Kt3v2LHyR1kGhsxdPMHrE47Rpf0/bZjEs4k4N+o/F2nwUYjUS++yKcP9mGzeTgnmp2jVOnDP+bvmMY7bZZc97oKIYQQQoiqSQ/1VQj28GBCq1bVD7soLaVk07vsKVlLdHY2CUYf/nIkhfPej7DJrR8rTusPhXmh+wt0alrJIzkLCghOTKR02xb++clUDJYSQIEycrb5CPy3byQ6O/v6VFAIIYQQQtRIeqivs+jcXCY+05Eik4lecXGUahqlAMqApkx4NL6XXyN/pZVHK7IKs2y91Jt+3YSj0ZGehQ0AaONXl+juQygpm+PROtb6QJGRe+Ni2WLuKOOphRBCCCFuAumhvs6isrIoMpkoNRgoKgumrRyUgRBPT3w8fVBKse7IOrp90o3G0xvTe0lv/rnjn1BQAICXtwthf+2DVmywjqNGD6qVsk3HJ4QQQoj/PUajEbPZTIcOHQgKCmLHjh3V7p+VlcW8efNsy8nJySxduvR6F5PDhw/Tr18/2rRpg6+vL48++ijp6enk5+cTERFBQEAA/v7+9OzZk9zcXE6ePElISAi+vr74+fkxc+bMatOfNGkSgYGBmM1mQkNDOX1av9fsn//8J2azGbPZjL+/P0ajkbNnz17TuklAfZ318vTEaLFcvKkQUICDUsy54w6CPTxYePo0ffbtI8bSiPzifM7knQFgX9o+SvP1py66pYwh2N2dWemN6GSpczEDTQOtlNtUDkIIIYT43+Ps7ExcXBzRD699AAAgAElEQVT79u1j2rRpTJgwodr9r0VAXVpaWvNOdgoLC+nfvz+jR4/m6NGjJCUlMXr0aDIyMpg5cyaNGzcmPj6ehIQEPv30U0wmEw4ODnzwwQckJSXxyy+/MHfuXBITE6vMY9y4cezfv5+4uDgGDBjAW2+9ZVsfFxdHXFwc06ZN495776V+/fqXVf6aSEB9nQV7ePDE2bPWJykCmoZmsfBYUhK/Fxfz6rFjPH34MBvOnWNWlgmXlo8B8HSnpzmRfYKhS/8CwLqNbpCby3MjA9k9bgyveHvrJ08BmoUnVo0gMz/zZlVTCCGEEAC9etX8mj69/P6LFunvMzMv3fcynT9/nnr16gGQm5tL7969CQoKIiAggFWrVgH6I7qPHTuG2Wxm3LhxjB8/nm3btmE2m/noo48oLS1l3LhxdOnShcDAQD7++GMAoqKiCAkJITw8nICAACZNmlSu13jixInMmjWr0nItXbqU4OBgBg68+KC6kJAQ/P39SU1NpVmzZrb1bdu2xcnJiaZNmxIUFASAm5sbvr6+pKSkVFl3d3d32/u8vLxLpiIG+OKLL/jb3/5WYzteLhlDfQMMb96cT/LzKbUO0QD4b9u2GH799ZJ9u5tf4d0+L3OhbhtyGg8kffdi5ob9woTXLoCbG0yaBG+8wZ5lGah7ABRGgyN/7fkeDeo2IOV8Co1dG+NgkFMrhBBC/C8oKCjAbDZTWFhIamoqmzZtAqBOnTqsWLECd3d3MjMz6d69O4MGDeLdd98lISHBNl90VFQU06dPZ82aNQAsXLgQDw8Pdu3axYULF+jRowehoaEAxMTEkJCQQOvWrUlOTiYsLIzIyEgsFgvLli0jJiam0jImJCTQqVMlky8AI0eOJDQ0lOXLl9O7d2+GDx9+yZMXk5OTiY2NpVu3btW2xcSJE1myZAkeHh5s3ry53Lb8/HzWr1/PnDlzamjRyydR1w0QfNddDPz8c1Y2b66vUAo0DQt6BzNg7cFWPNq4CcUuLjywbx9FFhcc/f7B0wPe4E5v64X12GPwxhs03ngSx17eXLA+9vycqTHv7V/D+BUDiRkVQ5dmXW50NYUQQggRFXXl+zdocPnHc3HIB0B0dDTDhg0jISEBTdN47bXX2Lp1KwaDgZSUFNLT02tMb8OGDezfv5/ly5cDkJ2dzZEjR3B0dKRr1660bt0aAB8fH7y8vIiNjSU9PZ2OHTvi5eV12eU3m80cP36cDRs2sHHjRrp06UJ0dDS+vr6A3tM+ZMgQZsyYUa4XujJTpkxhypQpTJs2jTlz5jB58mTbtu+++44ePXpc8+EeIAH1DfN4vgdrS0oodjBx8a5CXZPSUuqkp2P29ibAxYUlaWkUWixoQJHFwlc/FnEmx8CzkcBttwHwnz1PMsdlJy/kZFIKrMzMZDWu0HEu/TbP59F6iwB4teertPRoeSOrKoQQQoibJDg4mMzMTDIyMli3bh0ZGRns2bMHk8mEj48PhYWFNaahaRqzZ8+mT58+5dZHRUXh4uJSbt2oUaNYtGgRaWlpjBw5sso0/fz82LJlS5XbXV1dCQsLIywsDIPBwLp16/D19aW4uJghQ4YQERFBWFhYjWUvEx4eTv/+/csF1MuWLbsuwz1AxlDfMPc/eA9bxo7lmdWr6FPsgLG0VB9PrWmkGQwkN23KKk2jV1wcn6amoml60O1gsfDiB4+zc2WqnpDJBIDKz+fkhwsotYvNLQDuvmQ2G8rHp1OYt3se7k7unC04y9GzR29shYUQQghxwx08eJDS0lK8vLzIzs6mUaNGmEwmNm/ezIkTJwB9PHJOzsXJDCou9+nTh/nz51NcXAzos3Pk5eVVmt/gwYNZv349u3btuiQAtxceHs6OHTtYu3atbd369euJj49n+/btnDt3DoCioiISExNp1aoVmqbx5JNP4uvry4svvlhj3Y8cOWJ7v3r1atq1a2dbzs7OZsuWLTz00EM1pnMlpIf6BvFo5Um3ZV8RvDOaaW2asSE52TaeGtCDa+sUeGXDPxTwhKsrwa++SvAjDS7u+69/ob37HoPjNvOB9hcs5cbcK1DQM+g1PhzwOp51PJm0aRJTtk1h5oMzySrMwmQ00dS1KcPNw2td/gW7F3D/bffTpn6bq2wJIYQQQlxLZWOoQe9dXrx4MUajkYiICAYOHEjnzp0xm822ANPLy4sePXrg7+9P3759mTp1Kg4ODnTo0IERI0YQGRlJcnIyQUFBaJpGw4YNWblyZaV5Ozo6EhISgqenJ8ayZ2VUwtnZmTVr1jB27FjGjh2LyWQiMDCQmTNnEhsby+jRo9E0DYvFQv/+/RkyZAjbt2/n888/JyAgwFa/qVOn0q9fv0rzGD9+PIcOHcJgMNCqVSsWLFhg27ZixQpCQ0Mv6WG/VlRZT+itonPnztru3btvdjGuSnR2NnfH7KW07OOMdUx12XsD+qAQA4rZt9/B6BbelyaybRvcfz8LN2xgTIX5rQFC69WjrsHA6aIiGuXsZs2WZ+jbpi9n8s6QX5xPUmYSQU2D+Jv/33gp+CXbnbAnsk7w12/+Ssr5FF7p8Qr/6PoPDmYexHeuL71b92bjsI3XqVWEEEKIW09SUpJtrO//IovFQlBQEF9//fUlNxLeaio7l0qpPZqmda7pWOmhvgmCPTz4qpEfPy6axoE77qCwcxc8t+/gR3MgYB26oUGppvHsscNM2XmYOx2bMuXeJhefhhgcDLm5DM4yUVpwmshfj+i921YbrF+d6O6E7l+xuySTBtomjDkHAdibupe9qXv5195/seHxDRw9e5SP93zML6d+AeCbpG94pvMzfJP4DQAtPFrUqn6appGWm0bCmQSSs5I5f+E8nbw70cun19U0mxBCCCH+QBITExkwYACDBw++5YPpqyUB9U0S1qERYV5e8OyzdCGG4tnOFfbQwKDQNI0UL0ghlZC4NDabzXpQ7eDATz/B3+4/Q/8Xv8MysG31GTo2IMOxAZl176DNb3OABO70upMXu7/IM2ufYXnicv4b/19i02JpWLchY7qMYfKWyTi+7YhmvYly4t0TbcldKLlAqVZKXVPdctnkXMih7Zy2pOamllv/UvBLdG3WleWJy+nQuAMdmnS4pIgllhI6LezED4//QBPXJrVuSyGEEELceO3bt+f48ePl1sXHxzN06NBy65ycnNi5c+c1y/fZZ59l+/bt5dZFRkbyxBNPXLM8LpcE1DfT//0fK9qM48EDTjQJLCGqtNA2fhoo/x4osj5ivKyX+r77IGbYXE5vWcuXD31omxnkUso2P5+mGUn2Hg6FJThaTvB056eZsm0Kv6T8woGMAzza/U3cWv2FNOXA5EEBfL5xGkfz9/B6wJJy46cHfzmYjPwMdv19V7mc3JzcGGEeQVPXpvg38uf2+rfj4eSBq6MrRaVFjFw1kgk9J1QaUEefjGZ/+n7i0+MloBZCCCFuQQEBAbYp/K6XuXPnXtf0r4QE1DfZ4L86MRiAYH47dox/njxpHT9dNvTj4thqR6Xo5elpO1Yp8Hl/DD5OL7B0zymS6uZwwMODpWfOVBJYX3z0ebGDJ9z5EoeLMmgfE0OJ7+uk5u5iysMrmZjpTFFaBgBOqgEfDfqQ3WdzWGVx5PfY9QxyN1DfuT4HMg7gWediWZKzkvnl1C881PYhpvaeWmldnQ3OtG/Ynj2pe2zrdp/ejY+nD0ZlxN1Jn1syJUd/ClKppZQtJ7bQxbsLbk5uV9C6QgghhBDXnwTUfyAPn7md7xc0oP59WUwNc2XXiKdZ2u8+nIqKqOvblze6242hLtO4MUePaHj37c/DxTvg229xa9+ej1NTbYG5l8lERnGx3VNkAE2jyLEhSfn5UOd2ztRpQ/EFN4q5OG3OBU3jw731OdrEAmjsK3JgbswHkKZPefNb9m/M3zXfNs765R9f5tQLp2hmakZVgpsHszRhKTkXcvjx+I8M+WoIXs5eNKjbgEO/HwLgdM5pAF776TXe3/E+Mx+cyfPdnq+x/RLOJLA0filFpUUUlxZTbCkmqzCLWX1n0aBugxqPF0IIIYS4EjIP9R9IQAD8rYMHH3ZuxV0NvLir52i2TZvO/70WRZ/EtpcG01b1vRSvtfma0voNYPJkhjVpQh2DASPgZDAw3rshpooH2T0GHRSlQExOTvmebQ2OFhbogbh1X/fmgxje4eJ0e2PWjQFgX/o+vN28aeZedTAN8ETHJ8gtyuWLhC9YvG8xAHnFeRz6/RAvBb9EvTr1bAH1j8d/xK+hH891fc52/JJ9Sxi1epRt+btD37H+6Ho0TSM1J5X3t7/P/N3zWbxvMV8nfs2237YRl3Z9v3q6XCeyTvDjsR8pLKl5cn0hhBBC/PFJQP0H4uoKEyZAUJC+3GV8bxxPHic0fyUvvFD1cfXrw/f7vDkzcjzs20fjfTn81KEDb7duzU8dOlD3900Uxz7LnReSaEIe+tiPWkyXqMD9tgv6e+vQkwecvFi3eirGNkugSX99N6WIS4sjoFEA0dnZDI6Pp31MDB137cInOprW0dF03LWL9jExvHfOhZbNHyQ2NZbY1FjCA8L5qM9HADzf7Xm83bw5V3iOEksJBzIO0LdNX9uUfinnU4hPj+ffcf+moLgAgE9iP+GfO/4JwH2t76NoUhF5r+WRNT6LjHEZnHzhJF7OXizYvYDKHDhzgPBvwjl29ljN7XGNLE9cTuh/Qmk3px3fHfruhuUrhBBCiOtDhnz8SZhMUHh3KEyHpn+9h9seGUjwoUMwciQH2jjA+UR+3zeJHi17cFrVY3dJPZo2DsbT3YeGJhPR2dkUV5LueU2f4VoVncP591zSinuQMfQw0Bx4CaPBgUmbJhF/Jp5GbUbSIza28lD9gh6YJ+XnY2oznrD2d/DxkZ8526AvP2YcwtXRlRbuLdj79F4cjY6sSFpBUWkRqbmp3L/kfp7r+hwTN03kQMYBQB/eYW5iZtOvmxgaOBSlFEZV+YTya4+sZdLmSQwNHEpdU11m/DKD3KJcJt07idWHVvNFwhfUNbrjtnUePXpaONNiIaYSL6LmPMaYMdCjx6Vp/nT8J/ak7uHU+VMcO3cMB4MDnnU8Wfyw3us+b9c8ikqLGNt97CXH/pLyC95u3mTkZ7Dq0CoGth1Yw9kVQgghqmY0GgkICEDTNIxGI3PmzOGuu+6qcv+srCyWLl3KmDH6t8zJycns2LGD8PDw61rOw4cPM3bsWA4fPozJZCIgIIDZs2fj5ubG3//+d/bv34+maXh6erJ+/XrOnTvHsGHDSEtLw2Aw8NRTTxEZGVll+pMmTWLVqlUYDAYaNWrEokWL8Pb2Jjs7m8cff5zffvuNkpISXn755Ws+I4gE1H8irQf6w3PP4fzVVzBrFgDnHn4CozKy+FvwLsgiZnAGP/sVwq+LaVeyj033bwJg9KFDLEhNrTJtzbEe+U08iTactK7RH0ajNbib0V3+RuIFWGUy16bfm2JN49uzOdTpNJcfLRZKHRtCnW9RSuFodASgqLSIfnf0IyIggseWP8bDXz4MQLdm3YhJiSH0P6E0cmlEblEud7e8u9r8/Br6ARDxbQRBTYN4I+oNAPrf2Z+dKfo0Pp/u+5gmG58nuc2/WRk/HaMy0mTLYzz2VwurDn6H+/m7eGdCQ95/Hzp1gm+TvmXe7nk4GZ1o37A9Fs3C746/2/Kcv3s+CWcSeLLjk5fcULkvbR/BzYPxcvZi4d6FtKnfhvE9x9ei5YQQQvwZRGdnE5WVRS9PzyqHc14OZ2dn28waP/zwAxMmTGDLli1V7p+VlcW8efPKBdRLly69rIC6tLS02icjVlRYWEj//v358MMPGThQ70javHkzGRkZfPbZZzRu3Jj4+HgADh06hMlkwsHBgQ8++ICgoCBycnLo1KkTDzzwAO3bt680j3HjxvH2228DMGvWLN566y0WLFjA3Llzad++Pd999x0ZGRm0bduWiIgIHB0da13+msiQjz8TpWDWLAq27WapiuAODhPTPIx6J9IZth/uP1LKa+9HM2/iDhxLwGjQfxGis7MBLh1nbT8yRCkwGLBolnK7OJ79haau3hzPfJzSWj5002QdwnHBYqEUMBideG3AV+X2ecz/MdaGr6XvHX1JHptM1PAoxnQew/TQ6Sx6eBHtG7anqWtTAAIbB1ab34A7BzDSPJLVh1bbgmmAz/d9zr70fZib6I8zHTL1X6w7OwsXkwvPdH6G48nF3NHtCA9/+TAbT6whLT+FNw48wu7Tu3nvgffInZBLwcQC9j69l7hn4vh55M+2tKfcNwXA1qNexqJZOJF9gtvq3cabvd6kYd2GvLf9PUosJWw8vpFn1jzDrfb0UiGEELUXnZ1N7337mPTrr/Tet8/2P/haOX/+PPXq1QMgNzeX3r17ExQUREBAAKtWrQL0R3QfO3YMs9nMuHHjGD9+PNu2bcNsNvPRRx9RWlrKuHHj6NKlC4GBgXz88ccAREVFERISQnh4OAEBAUyaNImZM2fa8p44cSKzrB16FS1dupTg4GBbMA0QEhKCv78/qampNGt28R6stm3b4uTkRNOmTQmyjoN1c3PD19eXlJSUKuvu7u5ue5+Xl2cbMqqUIicnB03TyM3NpX79+jg4XNs+Zemh/hNyvqM520f/h4iG0KHJKZp0mQDAvM5QNyCIEf/eS+gxKG5rJDo7m15xcRRpGgbgnrp1aX/sGB23buXz/gPY7upysddZ0wDFHXXqkJqdRm5WEv8K/oBfzmcT1/TS3u076tShSNNQgKeDAxc0jbZ16/JKixbE5+VhsfaIW4BWLvWqrE995/rc63Mv9/rcC0DPlj0Z1mEYr/74KtGnornT685q28NkNPHpQ5/ydOen6fZJN+5pdQ+9W/emkUsjkncm80bPKcSlxTF3zwwAvo/4nvta3wfAvjOxAExNHEmXkV1Y++suJpSMxdXRtdo8/Rv5AxCfHk/35t2J/D6SDcc38Po9r1NUWkRrz9Y0dWvKvP7zGLpiKIkZiRz5/Qgf7/mYV3u8Sut6rQH9YTdfHfgKH08f7mpR9dd3Qgghbg1RWVkUWTuUiiyWcs+XuFIFBQWYzWYKCwtJTU1l0yb92+c6deqwYsUK3N3dyczMpHv37gwaNIh3332XhIQEW692VFQU06dPZ82aNQAsXLgQDw8Pdu3axYULF+jRowehoaEAxMTEkJCQQOvWrUlOTiYsLIzIyEgsFgvLli0jJiam0jImJCTQqVOnSreNHDmS0NBQli9fTu/evRk+fPglT15MTk4mNjaWbt26VdsWEydOZMmSJXh4eLB582YA/vGPfzBo0CC8vb3Jycnhyy+/xGC4tn3KElD/SdnmPC/w4qOQmbywORJXdy/2PNSNv3yVQO/jRWwv1FiydStFbvqQBAuwPTeXd995h+5Hj/KUnx/RQ4fyZnIyG86e1XupNY0jhYXg6AGNujLszHHc4l2gEbZp+drXrcsnbauelQRgSVqa7b0BiM3JYfQhfdq8YU0qmR6wEm/2epPh5uGYjJf0rVeqa7OuaG9c7P09fu44t9e7HdPJUFTacrQmsbRv2J6eLXtyMvsk9y66l/zifEwGE83dm7PrtP4QG0NGILSsPi8fTx9cHV2ZvGUyI8wjWHVoFSeyTzBz50xin4619a4PvHMgZ185y5rDa2wzpsSkxNgC6nEbxvFFwhdM7T21VgF1YUkhGXkZZORn0L5he+o41CHlfAoHMg7gYnKhR8tKBoQLIYS4YXp5euJoMFBkseBoMJR7vsSVsh/yER0dzbBhw0hISEDTNF577TW2bt2KwWAgJSWF9PT0GtPbsGED+/fvZ/ny5QBkZ2dz5MgRHB0d6dq1K61b6/+jfHx88PLyIjY2lvT0dDp27IiXl9dll99sNnP8+HE2bNjAxo0b6dKlC9HR0fj6+gJ6T/uQIUOYMWNGuV7oykyZMoUpU6Ywbdo05syZw+TJk/nhhx8wm81s2rSJY8eO8cADD3D33XfXmNblkID6z87Zmb8sfh7LHeMZFvQErTz/SYTz43gPeJ9HEgvYfGATDBpkmxbPAmwOCmKy5y/0KXaj3Q54M+8Imzw8KHFwKPfkRpQRDY3zjfLKZRlc14N9X3hwyEljxBMKCgsp+L8p1HngblSfUKKzs/nMLqA2Ap+mplJsHerw7zS7R6xXVzWT/qCYGqWlgdEIDRuWW31bvdtIfDaRgwccyTsezbOjLTRrpD8CXilFcItgCooL6NasGz6ePvx3/1LWLejJijw3giv/kG1jUAa+efQb9qbuJbcolxPZJwB9ekG/hn62DwFODk4AxJ+Jtx0bkxLDY/6PoWkaSxOW0sW7C0+YL948kXMhh2+TvqVb8260a9AOTdMI/zacn47/REZ+xsX9Juhzir+99W0+3vMxCsVvL/xGc/fmNbeZEEKI6yLYw4OfOnS4pmOoy6UfHExmZiYZGRmsW7eOjIwM9uzZg8lkwsfHh8LCmqds1TSN2bNn06dPn3Lro6KicHFxKbdu1KhRLFq0iLS0NEaOHFllmn5+ftWO63Z1dSUsLIywsDAMBgPr1q3D19eX4uJihgwZQkREBGFhYTWWvUx4eDj9+/dn8uTJ/Pvf/2b8+PEopWjTpg2tW7fm4MGDdO3atdbp1UTGUP/ZlZTgHr+Co5OeIS/8EVxcwCnkLt64PZI+K9MZtmEDplJ9Jg80DUeLBY8uT7H3iBsvv6Sx6ttSgufPZ+7MmSiL5eKTG63727qlFWABRxRhmQU89HoH3MfrPa44OmKZMYvx7ybyVNIhRh06ZAueARo7OlJisdjmxi57xPo1UVoKnTvDF19UutnR6EhgIEx9y8kWTAM0d2/Of8P+y7ePfcurPV/lMf/HWB2+iti543jjDbvqVyP09lDG9xzP3tS9AIw0j6SwpJAp26aU2+/o2aO8vVW/ieKuFnfZbpRMzU3lTN4ZerfuzeHfD5OWm8ap86f4LPYzRqwawfdHvgdgf/p+liUso2uzrrwd8jYLByxk9V9X42LS/+iN7DiSJQ8vQUMjKjnqsppPCCHEtRfs4cGEVq2ueTANcPDgQUpLS/Hy8iI7O5tGjRphMpnYvHkzJ07onTtubm7k5Fx8kFvF5T59+jB//nyKi/X5vw4fPkxeXvnOszKDBw9m/fr17Nq165IA3F54eDg7duxg7dq1tnXr168nPj6e7du3c+7cOQCKiopITEykVatWaJrGk08+ia+vLy+++GKNdT9y5Ijt/erVq2nXrh0ALVu25KeffgIgPT2dQ4cOcdttt9WY3uWQHuo/u/x8PPqHMTkU1nqs5bmuz3Hvy4qdz8/noV8PEAxEzZvH59OmwWef0SenLl3/7w2e6JGEU8/OlO4NhL2/0LxrPe77ATb3fUkfU20pRTMYbUG1EXDZ0pSpIU3ot+BptMJf6TflH3oZDAbe/iSZj1rtpzhNf2iLPnxEf3uqqMg2nASwfQW28PBhvjl3jiEtWvCUt/fl1VvTICuL6JISlsyYAc7ODMvOrvSPV3Gxnn1t7k8ICLj4/v4Zp9nbOJUWt1soURpOSnGupAQFtKxTh/Z16zKsSRPG/TgOgGc6P8NncZ+xZN8S3uz1pi0db7eLdQtuHszWE1vRNI2ff9NvcmxTvw3t5up/FPrf0Z+1R/Q/RokZiYB+U+b+Z/bTwqNFucfBl+narCudvTsTuT6SqOQoHg98vNo6FpcW82bUm8ScjuGTgZ/QyrNVzQ0jhBDipikbQw167/LixYsxGo1EREQwcOBAOnfujNlstgWYXl5e9OjRA39/f/r27cvUqVNxcHCgQ4cOjBgxgsjISJKTkwkKCkLTNBo2bMjKlSsrzdvR0ZGQkBA8PT2rnfXD2dmZNWvWMHbsWMaOHYvJZCIwMJCZM2cSGxvL6NGj0TQNi8VC//79GTJkCNu3b+fzzz8nICDAVr+pU6fSr1+/SvMYP348hw4dwmAw0KpVKxYs0J9BMWnSJEaMGGGbWvC9996jQYNr+wRldavNKNC5c2dt9+7dN7sYt468PHB15ecWMPIhuP/B0eQV5/FNzBI+3R3MYz9GE28KwnHXdtqanfky4B2eOjGRlcsKadivM/7os1S88URrYnq35fUBy4jKysI86RUWtvFj5d13g9ID6rcbNGDCRx/BZ59R8sw/cJg/21aMaSdOMPHXX8tPq2fB+hRG9ABY0zBqGvN8fSE7m6fLhoUoxcd33klA3bpE/fwzverVI9hucujokyd5//hxYgsKUAUFeJ4/zzlHRy44OZFev74tTyelLhlKsvD0aT74OYXDS7wpXd0MQ+55omNiiLrjDttXcdGHDhFlMtHLy4stu44x69RZLjSvy1ljETU9IMdJ0/iqXUvOZ/7C44GPs3DPQh647QHbGOkyn+z9BP9G/nRr1s12V/I7W99h/dH1/Dj0R0Z9N4rvj3zPucJzdGraibqmuuxP30/XZl15K+QtujfvXuOl8MDnD+BkdGJN+Jpq95u/az5j1o0hqGkQSx5egl8jP9u2pIwkluxbQmFJIU4OTjSs25CGLg3pc3sfGrs2rrEMQgjxZ5OUlGQb6/u/yGKxEBQUxNdff33JjYS3msrOpVJqj6ZpnWs6Vnqo/+ys3a49T8KjB+B8PwcclAN5TvDzkMY8ulFxe3sn6rjrNync97fGfNkRut1Th59/SKB56ww8/5+9M4+Lqtz/+Puc2ViEAQFlEcQ9FxTNJXIJzVJLU7N9MSuvZVrZbV+sX9Zt8XZLK+tqmWVleculzTUTTUNNBXfBDVABQZZhh5k55/fHMzOsbqVl8bxfL2TmLM95ZgDnM9/5PJ/v95+xznshpr276dhlP3Hd+kBwBAELF7L8sj44zBbMwBXjx8PmzbzFFIwtX+bBGtOIDwjApChUae7YPQVVFZrag6qC00me3U5CejpYLB7P9tysLHYVFVHl7Y25tJQ1rmpz4mefMaBZMxwmkzjeYoFTLPDwrKZWFPD3ZyA5nQoAACAASURBVE5mJvelpEAI8OgBPsxWiLnzTq58/HGqjhzBrKrMaNuWKenpVBmNKBkZOAw6tDSAUuV5HKejStfZYzfxdNc74IsvmLD/OLzYqt5x43uMr7ftuQHP8dyA5wD4/PrPsTvt7Dixgy7NurD60Gpmb5vNybKTZx2zt/z25RhV8fuwPWs7Kw6uwNvozV2xd9HUuykAs7bM4s1NbxLpH8m2CdvqjXG44DBvbnoTi8FChaMCuyY+Dtxw9wYpqCUSiaSRsXfvXoYPH87o0aP/8mL69yIF9d8dl6Be2gE+6g43KwYMqoGhB2DwnsMon32Gj8MBJ3MACO7cnKFDxakiIScEHnmEufnXkR3bloBXLoMrriB/+WKKEubywrz3SXjqRcZs3szlmzcTZ95GaYcefHtL7WnEWa0kxMYyf9ky9mZnU9GvH/FNmzLj2DGqdB0UBRUwm0wEmUz4FBWJRYSufeEWC1t1Hc1goEJRmJ+dTZzDwfzt23EMH157sWRD6DqqrhNvtZJ4/fUkXHcdSzt3FiLede6izEySunalwmxGRwjwuVlZVBiN6O54nTNdp841zbpOfECACPH//nvik5OJe/55sUjyHDEZTPQMF2+SR3QYcc4dFo2qEbvTjkNz8MWuL3gj8Q0AuoV2Y1CrQRSUFzB5ubDpdAzuiN1pJ688j9Amoew/uZ+C8gKGtRtG5XOVroenU1RZxMmyk4T7hfPKz69gtViZ1HvSOT82iUQikfz16NSpE4cPH661bdeuXdx55521tlksFjZv3nzerjtp0iQ2btxYa9vDDz983rsfngsXTFArivIRMBzI0XW9SwP7FWAmcA1QBozTdX37hZpPo8UlBHeEQpa/EFUGxUB8GozcuBNGK6xz9KXo4wP4TviSe27syYZD0KJOEESbpm246ypYuDGMiJ07WbB4GvvbteCju+6nqqCAn1u3Jubyy/lxVQ/qLAD2EGe1go8PV3bsSFVpKdsLSxmWXMqK3k1wuPKqOzgcTExJQQ8OxuBw0P7YMUKaNSPf3x/N5bPWFYW52dl0z8tj7rBhZyVyVU3jrWVr2KnG8vAzz1ClKKjuxZguQg4eZO4116C7rqMqCknFxZ77ta7jaXgD4UYzfgYj9jKFtJMOAgOhc4gXTY1GQk0mlp44wVuZmTjvuQeL3c6aPXvYFRzMopwcxvj4MKG9yNE+352z6nIw/yBxc+M4WXYSENF+aYVprE9fT1yLOG5ZVP0u6P6e93PT1zexdP9SZg6dyac7PyU1L5Wcx3I8ySSKomD1smL1EnP94cAPeBm9pKCWSCSSRkxMTIwnwu9CMcuTDXzxcCEr1B8D7wLzT7F/GNDO9dUHeN/1XXI+cYnAp3b683MvL5o3aY653IzTrQ1vu42AVqPoenQDqRtyuTkAGvLpL9m3hI1RkHUygoiUrUy+Yya9pt1GlckkwukVhYS4OOJOIabdJISFUVVcLDokqhrlSiqa3h0dcOqQ7BatqopTVUmJimKfqkJRUS1B69B13ilXsNeI8vNGRSky0aa5kSLNQVERFOz14q5BPtw383W6LVjKQO0SKkeKEGmnwUBsdjbJzcNQ0PgyOBinweBZINnObGZ/ebmoJrs83u5rBSsmzDYvHusYxqOXhNPyMnjnHZjzP/jXv+CASXTCqnQ4xBsBAIOBSmB6ZiZL8/NB11lVUAAVFcS0bMmVO3aIXFJgTffu4vk6jwI7yhrF/Zfez7asbWzI2MDn13/O8AXDeXHdi7y47kUA7uh6B/NHzUdRFPwt/mzI2MDDKx4G4N1h73rEdEOE+4WzO2e35/5Xe77iROkJJvee/LvnLpFIJBczuq571r9I/pr83jWFF0xQ67q+XlGU6NMcMhKYr4tHsElRlABFUcJ0Xa/fck/yu7EUFPFji2lwuRBHX7+/BNgH7dvTNW0ZBV8up08fOFUDone2vEOfo9AqOQ28vFjx8LVs9UnGjAYYMAPxTZuecR7x0dGYt2+nStcxG43cOHggG4uKqNA0UfStUwXW6lojXL/wqlNh9y9NYEClZ9eVzuYceKIDsz6A/v3h+HFIMUHfS8By3XVU/LKdHiSxRY/0XMfs5YWqgKaoOF2xfbjm0d7Hh9TKSjS3kHZd26IofBvbxSNy234j7DEWC7i6szI1JZtyTROfENQ832Ag1dcXKio82xZlZpJksXiegyqHg/kZGXxSUOAJ/l/TrZvwjNtsJOzYQfzEicR9+WXt2JEzYDaYeWmQiOfTdA1VUVl711rWpa9jbtJcdp7YyYj2IzwvCuNixzEudhxHbUepcFTQLuj0/riwJmGsPrTacz/xWCJvbXqLSb0myRcaiUTyt8XLy4u8vDyCgoLk/3V/UXRdJy8vDy8vr988xp/poY4Ajta4f8y1TQrqC0SRswx3T6AbYm6GRf8H776LUlZG05GDKCoS+07VOOiFdRCUcRJatGDPdXGwehEtj83i7vZPEv/OO8Q9/fQZ5xDXrBlrIiNJeOUV4jt3JuiGZ1gWbmNhQbZo7gL1gzP06o0qcN3PPzOpRz+WtXPwti4q2wYUnukZStz+6tMiIsRXos3G/I4d2fvRe+SmpGBwOnGqKqqmgepdvazQ9R+h4rpOSlUVrby9OVRRgeZ0euwzdac3YoSI3lu7Vti+P91u48OorBpzV8CVs60BeysqaoVYh+TmMtfLy/UwdYyaBqWlVGoaGlDpcDB/82bmt2zJ3OxsHJqG+a23WLtyJXExMczJzGRRbi5jAgOZEHWGFo4uVEU8lm6h3egW2o0BLQfwQsIL9AjrUe/YSGvkWY0Z7heOrdJGaVUpvmZfT0v4dFs60QHRZzWGRCKR/NVo0aIFx44dIzc398wHSy5avLy8aFHX73oO/JmCuqG3cQ3W2xVFmQBMABHOLfltPPHTM8QNCkdHx7jnS+4A2LUL+vUj+5OVhI0bQp8+sGlT/XO9jF7VNhGDwZMW4VuRztPNmkF6uojoOwviOncmLi2NfUnlDHz2OOsPRrDhBiuTp4RS3i+bb47YyLKUVf+GKIjfDPc7/9hYxltNZPgWous6Kgae8GrXoC0i0WYjPjlZLHwEiI4WQ7oWOG7xr/8noANOYF9ZWfVGtboHksPVeKbm9XbuhEGD4NprYVlgIdxbY+7uQT0X0Ksflq6zIDy8Ro8chWGbNtGi3eVoYUbQdTRFYa7RiCMry1PFrzSZmK8o7MrM5L7UVGEfyc+HjRuZcOutp33+G6LcuxVq55e5/YiNeysyzz33G2hpbUmHoA44NAe/Hv+VNUdEiP62zG1EB0Sz5fgWvk/9nn5R/bi6zdXnPL5EIpFcjJhMJk8rbknj5c8U1MeAmqWvFkBmQwfquj4HmAMih/rCT+3vxfFpjxPx/L9xqHCs6Bil9lK0fFcp99FH4ZJLCLXb2bHjID4+DY/hbfLG19sPKAajkUGtBgHgZ/aDNm2gzmrbM7JiBd7/28LLhBEeDt26wYbZVvJesbJou/AfV2hatdB0NYLRFFjq5w8O14JCVQV0/CLsDV4mobCwVldG9zi62sACw9NR42M8g6IQXyear3Nn+OADuOEGWHoggPvKFKo018AK1YLc/cZAB931uITrqbrrZGhBAcdzilDDmqIpCorTiUNR608zN5dF7oqI2z5SWCjeeZ4DiTYbA5KScLjub3F1yzpXUX19x+u5rMVlWL2sLD+4nK/3fg2IiL4hbYfQ76N+2DU742LHSUEtkUgkkr8Vf2br8W+BsYrgMsAm/dMXhtJhgwFwqNUpH86aP/n9+8FopGtXaNu24TF0XSff7mpLajAQ0zyGXuG9aGJu8tsm5edH9L1Xcve9Kt7e8Nln8OST0KMHdNStvO/Tja5pYShOpTqsuuZnGjVENlBP4Lpx51/XombVWK9x+wwogFFReLdd/Wq4lxeMHy8isMf1svJTt1iu8g4SOlyvM4jre12rCbqOUYHswEAywzVMrihBVdcxak4U18JIRdMwO52M/e47xlhqLxKM3bmTxMJCJqakMDElhUSbrd7jmJOZSZ9t2+j+669EJyZyzc6dHjHtZtFv+OjSYrTQKrAVX+35ihcSXqBVQCu6Nu/K9uztJGUlYdfsfHvLt8wbOe+cx5ZIJBKJ5GLmQsbmfQHEA8GKohwDXgBMALqu/xdYhojMO4iIzfvzwgP/5jTZLNIInSoYVJFD7aj7VuoMfbdfuOIFUpQlnmPzyvJIzk7mqjZXnbd5jhkD+/aJlJEPP7Sy/34rn24KZYN3NvOys3HoOmjUfjMAxOyNJC6+4RQMT/51djabbDZ2lpVV62gFDMCVeZH4hTrI9S6lQte5NywMEM1kvFSVTj4+dPfzI89uP+vEjY8ft7JtcQzvJmUy+cABdEWv9/bVUzh3vzlAvHdY2r+/2O50orrSTpyuWEG3oA5XVR546CEqjx+nXUUFh3x90YD/jBnDf5KTcQcCzsvOZm1sLLtKS5mblUVmZaVo9X4qXAsoY5s0YWJKCgBjQ0M9jznRZmN6RgaZVVXcGxZWr4qdaLMxLasY2k3BnxxmXP4PArwCPNXqXhG9alxKZ+zSsRzIO8Av9/7i8XWfDqfmJL88n9yyXAorCql0VBIfHf+bFwJlZsKaNVAnMlUikUgkknPiQqZ8nNbI6Ur3kIG1fwDhk8ViQXeF2qgaq/3Qbs4gqJt6N60Wsl268F3qd9g1O7OuOb9ZkEOHgp+fEDh33QWKYuV2rIwNDeXt9YX88K8Aps4rZV7xMRSgb3YLhoae3poQZ7XWEoTPH07jx/wCoaaBVYuNvNypDc8+W/u83+IjdjN5Mlx2GdwbFU53qy8JhYXs2mhirZbLiYgCYfeAeisJNKhlL9F03ZMUogOoKpqqkgbQvr041sfHI4Sd7og/1xhVus70jAyW5uVVK/jTiU9dZ0BKCjMUxeM7d4tyoLY1pKgIpk9nwqhREB9f7Vc3toawVuxVwBLYnVirlU3HNtEqoBVFlUWM+GIEr175KoNbD6ZnWE8+2/kZ6YXpWIwWHJqDwwWHiY+OB8DutGMymAB4fcPrPPPTM2h6rf6a5D+RT6B34Fn+ZGrz00/wxhvi903XYdYs8bOTSCQSieRckJ0SGwFVc97HPGEigb5BhDUJo6CigH9dCvcXt6fTxlRx0Bk69y3dv5QAFYojQvBbuBDDjk8BcGh1zQK/j549xVdd4qxWLhtuZUERXFJm5fHev03sxlmtTGsdzc82m4ikM6rc3jOA/pf+zonXoVs38eW+ZpzVykvz4cBBX/LusmHXNI+gPyU1ha/HFuK+X2NbDetLzXg/dB0VyHQvFm1ISNdpWGPRdTqlpvLzJZd4tlW5FmEC9a0h4eFM+OEHiI+v7VdXFBzgWbx5Q6cbGNF+BP4Wf5Kyklh7ZK0Q1K7OjyO/HMmunF0A+Fv8KXyyEEVRuPGrG7Frdn647Qf6tOjDM/2eIcQ3hBCfEAK9A7EYLFQ6K/k25Vv6RPQ55/bngYFw443gdMKxY/Dgg9C1KwwYcE7DSCQSiaSRIwV1I8Dc53IA3hs5B7pcj1NzsurQKnx25QEuQX2GCvXX+77mbkV0HARhHQGYtWUWbw1964LNvSaKArff/vvHibNaWdu9W3XTlAHnvythQ0ydClOxkmjrxsqcQppZTNh0O4UOB8klJcQ2acLMY8eoPF24/Nlm47iaDITk5pIRGCh+vnUq1BFmM0ZFQQGivLyEtWXPHpYHBHiCVQDMrkWYS0+erHeZMevXk9iuHdN37WJTUVHtMBNgXlYWn544gUVRqNR1OvgUcEnrMbyy4RW+2vsVbw0RvztuMQ1QVFlEhi2DSGsk69PXM/qS0QDER8d7Ktc12Zq5lZFfjuSbW77hug7Xnfq5q8Po0SJW8d13IStLPC1Ll0JVFbz3HkyceG6d5iUSiUTSeJGCuhFQtXsnu5+4E2u3KNogxPDits/CtzVKwWcQ1Lqu41TBNysPxo3D+Pg1AKzPWH8BZ37hqGkD+TOuvXmelUmPQFkZeHtX7xsVHExCYaFHZI8JCQFcfu4SCy/HRHLfa6WUXpGFf1ON0iodbx/YX1aGVuc6OpDtbnup60RnZREQFYXZ27ue/7mwEBbtt/FA0yyq+vfHHa8ysqiIJ+d9xNLnn2e6O6gciLbbeXrmTGKOHGHAlCk48vI816nJgfLyWvf3lZZiDJ8Aubs5XJDCJcGiEh7WJIyskiz8Lf4UVRYxZsEdxEXHUlBRwICWpy8XhzYJBSC7JBuA40XH+SjpI8b3GE+YX9gpzxsyBA4dErdvvllkif/yC/z3vzBpEgwfDjKlUyKRSCRngxTUjQB12jQOGQ5wk8+nLLl5CU7Nyb73n+a5mgedQVBHWiN5uw/cbuyBV0gIBuVMfgXJ6RgwQDSEqSmm4dRCX10WzmuvQck70OmwlcGtw8lLhhkz4JP9Nobs2SE6M9bM64ZaCSLtKytZ2a8fAHv3wsiJMHeuWAS6ejWMX1oI/6CWvSQsYS0cOMAbhYW1srjbN23KhD59eLVvXxw1tnssJ6cp7TpQefyaz5kS3oxwv3DynsjDx+TDl7u/pKV9CIOeepdtA15hW+4GACqOXIre7dRDNvNtBlQL6vHfjWfFwRX8stHAzeHPMG6cOO7DD6F5c2E9X7MGrr9epLJUVMAdd4hul4oCt9wCI0dCWA0tXlwsflZn+DORSCQSSSNFvjw0AtS8Am48CRE2sFXYOF58nKmRB7j+is50WrdHHHQGD3WQdxBHowPxen8bAL1sGQC0CpBh9r+FHj3g22/P/vjbbhOtzUND4WsRmEFyshDDCe9ZmRDajfTLMlh6Mq9mpLXAVTWOvbT6EwmHA44eFYvybrpJtGr/t28ATwFOjyAW5yVceqkrK7t6vFhg9KhRJGVk1KtKn8knYVJVRoe2ItzPyvIMGy99W4xzu4HNH40D4Mjcf2HxmsZbu55m5uaZTLqlA75zhehtCLPBTJB3EFnFInUzOTsZgIz1g1jVFI+gnj5d+PPj40UFesQIEXcIMKFGeHdgoPh6+mlRse7USVStk5OrffESiUQikdRECupGgOryvl5yUtg93OJo6f3xdGrWCb76Cl577bRjlNnLCE8voHjJ//AbfRNR1iia+TYjxCfkgs9fIoI8aka75eZCUBDcdx8MGwYhIVaW/EO0If/4SC4tDoYweDC8kpFORmUlAO8UFjDKZqO7xUrXrrB9e/V4oaHw2DVW/DPb88C+fWiKgsHhYG9UFMsHDvR4qlVd59Z163gzPl4sUPT19QjqUIOBphYLw4OCSC0rI6W83OOdtigKBQ4HvgYDD5eUwKRJXPfgw3xXUgodwdhR5fktbdl4opj27WFsYChdA/rzWB9f2s02YbNBzM02hk8t5LrIAPr4WWsWzAltEorZYGbz7lyyS7L5z9Vv8sjzl6EoUOWsYvy343n7u3FcETUIiwWuGlrFT3kL6WnpQedmnRt8zlUV1q8XX88+K55vN3l5kJEB3buflx+vRCKRSP7iSEHdCHDGdGFH7m42RMF41YiiKPQ8DkM3rINJLwmzaO/epx3j1i630vnVufi9fzPcvJiCee+TU5qDflZtBiXnkx07RLxg//7wv//B8uVi++rV0L9FOFVJ4Tz4ILydBblhdl5IO4ITqNI0Vp4o5MpYK7NnN5y9PCE8nJgVK5i/axdzRoxkfWx33JVqVdN4v3Vr8lq3xnHkSPVJror0qMWLGba7Ncn9H2TJ87XHTbSJ7pdVlZU8iII+bhz28lKRza2AU3fyUnEqNIGfsuDDY9m0e+dy9iwZgaLAB7/Y2NtuB3tPaszMV+k8txu/fGjFZBJZ0iMjx/P8VRNZsCwd5VgcW1e15dvAb1h+cDltAtuwYNcCXhr4El5eIsWjw9BfSI58kXRbOk9c/gT/uvJftea7PWs7Jy+bzc1flmOz6fzjppdpEdDSs3/cOJEKkpQk7j/2GISEiOZEUF3NlosaJRKJpHHwZ3ZKlPxReHuT6wuVJjze50gb9PhxNyQmCrNoYuJph7AYLbwZB5rZBDYb27KE9eOOrqf4HF5ywejaVQjpadNqb581C/r0gS5dRAU1NBTy1wTgrFRRNFCcKn28Arj/ftEt/lTE3X03UQ8+6MrERqhCRUFTFPJciR8GqN1pEqB7d1baLmPbtvpjJhQWUqVpQtijYDe43ssreJrVKAY8VhWn4qR7+2XscTmSTkYUopg1NKDCobHVUcjKlWJRZ2wsHP1qChajhduHtueVm8Zx1fBCRi0cxexts1l2cBlWLytR1igUBVq0gIiqeJbfvpzmvs15Z8s7ODVnrflOWzeNecnzSMxax4qsz1i693v27wdXsZ9HH4V33qk+Pi1NWGgAcnLguuto8HmQSCQSyd8TWaFuBKhp6QzJgYHGdjRv0pz88vzqTonTp4uvUaNgyZJTjrH8wHKOWaG0fTR+BgNGVfzqnO8casmZURRRna7Lhx+KpjgWi3BhFBXBNdFW0td144BvIa2KAhjY18qwN898gfigIIwZGdW50zqgKXz/uYmnn7GyPrY7j6UeYlOZiMqzKApjR44kbmzDySnxAQGYVZVKTUOr8zbe4HTy6MKFzLjjDtFMRtcxOxxMHtyc4kgbE1Oyya6qQlUUnLqOrsHQy02kp4sUjmXLYEupjdG7MkgqKUFpEoOPXYPusyBrGVXOAjqHdOa6L68jwCuAefM+xe60ExnUjtcHv84dS+5gd85uuoUKg7Su6yRlJ3FXt7v47/D/suLgCj54uRtTPgWTCX79Vfiwa+L2tYN43nv1Ej8HiUQikTQOpKBuBCg5OQD8dNVn0LI3nUM6U9rmf8BPcOWVIvJg+vTTjpGQnkDP4+C3+wC07uipdH+y45MGs4ElfzzuhDwQCx7vuQdmz4avp1kBKytXCsG9bx+0bXv6seKsVtZ37860AxlsPlmCzasSXYXEngdJyPElvpmVH9r1YPDDNtrdUMjDVwSw/D9WivuJtIyGxnurTVseOODKPXf5rtvm5TF440ZGtWzJqC5dmH/yJNmbNhG6cye7Jk9mclIS9rqDGWFtlwMM133p8mQpN1Slc1SphLwax+g6+HUEv46Y1i/g/cXL2BYRxcc9/Olru47D6d8x97q5DGo1CKCWoFYUhUMPHaK4shiDauDa9tfiNx66tRKJIO3anfp5S7TZSDAV8thHAcT8SbGMEolEIvnjkYK6EbE3P4VO9CbIJ4h/9n8C+EmYQR9/HFq3PuP5I1JcN4xGT4V6d87uCzZfyW8nPBymTBFZy25atRKV7ejosxsjzmrlBUcMcZ+mo/7jCLoCBrNGYnkh74yxMnAg+GVYGVlqpb0Dbv5YCM6XXxbRcxMnwjffiKi6uDhY8IMdrS0u37SI1zsYFMTh4cP5xGRiDTD2yBGubN6cqmuugcOHRSt1N3Vaqv+o7mBdL020b6wb1Vfj9vr+t7IyG54ZdQNVRiOa4qSj0cLVba6mhX8LVt6xkqtaX8XxouNsyNjAyEtG4mX08rQz/zn9ZyoiynnxxQbeKdTA4xPXNMyqyppu3f60rHOJRCKR/LFIQd2IuPWbsbzTsSV2p52FyyYyB2DlSlGl/uknuOqqU57bM6wnTvUXcUdRPJ0SJRcnvXqJr5q0by9+zOdCz56w/NUArk9TPUIx9EQAixfD4sWiGUp2tkjASE4WFfAnnxSxfIoCDzwAgwfD8eOwfmYA6kyqLR8u0asZDJRrGretXEmzwkIqO3RAMxhAq9uqBk9l22y3k1lUVF2WP83qP0VVWX7/ZKry83EqCgYM3Nn/DVr4twDg6jZCKC/et5iHVjwEwEfXfcTd3e8G4JUNr5BXluc57lTU9ImX2zUe+7yQjQ+cX0GdaLMxP1vkbY8NDZWCXSKRSC4SpKBuDEydCi+9hNMVIfbr8V9JtblSGj77THzddttpBXW4XzhFNbyvrQNFRbtPRJ8LOXPJn4zRCEOjrKyxVrdqb1NpZf58GDgQtmyBvn3hhx/gGtE8kzddHu1t22DmTDhyBObMgVu7WbkxqjszSg6xwdWm3LOmUddJCwsjrWY3FXcuXo2W6YrTSUtdJ+DkSTJ8fevtb2ow4G80YlIUDlZUiMegKIwJCWFdQQGapmF0Layck5nJ3MxMwr28eCIykr5RfcG/EzS7msXOllxisxFntRLaJJRfbDYuW7eQEmMQmsmfEJMJdJ1ch4MOPj4Ma9qULTVarysKdAg11RPAQIOCeE5mJnOzsqjSNAocDhQgwGisdTu7qopse7UBZl52Nm+3a0dScXG98SQSiUTyxyIFdWOgUycAHKpI+dARbcRrUadFdF1ySnMw1SgC+pn9AIjwizifM5VcpNTt4OiO3MvKgshIkS5Sk+xs0Q2yQ4fqSvmCBQBWRtNDeI0LC/nsxAn2lpXVrjDXaSJT086hGwykQXUbQ9f+6NJSnr70Uk879USbjYHJyVTpuqfHje4ZUmfpyZNMP3pUnF9czA95ecxs2xql6wx01cj3JQqrk5NZGxuLwdqVoia3sFkzgx2wl7Gvxvz2lZWy1JX17tmswsdNU/k4yf1wdOZmZaFpeP723IL48+xs1tdo6+7BHSlS97Z7k67zQGoq7nySD7KyeK99eyaEh3ue3/iAACmyJRKJ5A9ACurGgOtzfqeKx6rhqCuozxCY2z2sOztqnFNUKQRAueP0Qlzy96ZXL9HgpC6hoXDokGjAGRJSv5miW6AHmUzcl5pae2cDXmgTLqt0Q8cg8rPdYhqE/cKhi5R0h66zKDcXJ6CrKk5NY3Fubq1x7LrO0pP5oBpr+bQTCgsJj7wGMo43/DdSo7V73f3VcxX/2PXaLSwrnU4eSEnB+RvDqg1ATVOME5h84AAAUw4elF5uiUQi+QOROdSNgQ8+AKor1AD23/CT94jwrl3JKxeRCh2DO56PGUr+hoSGCjENp36/NiE8nNnt29Pbz48BViujgoKo6843qSrvdujAfWFhJXyi8QAAIABJREFUmBoYw6SqxNeJLXHH9KkIYbuzpER0ZNc00DSauBc7uircJmBMSAgG12JJdB0VCMrKYtimrZgU9y+/ftoKuud+Q8doGop7QrqOQdNowCV+VoSaTLR2902vgVMXlfAKd+a3ppFQWAiIqv2r6ekk2my/6ZpzMjMZsmMHczIzf+OsJRKJ5O+LrFA3Bl56CaZOpWtEd4J8ggjwCmBbBJyMbkZwWs5ZDbEubR3eblE0aRJGpQAQHe4kkt9D3epyos3G9IwMUsrL6eDjwxORkZ4K69jQUOZnZ7O3tNTjX665302c1cqMtm2ZmJqKBtXeY0XBaTCQXFICuk6o00no4cNUtm3LG4cP46whhJ3AfWVltHM6cTqdLk+3BpX5KF7BQjN7xLTuEcqKqqCjuErUNYS1qnruBTjNhJxI40CNxw3QzsvLY1PxeKh1nQCTSdwuLeWoqpJtt1c/phqCXtN1trg81e6rbyku5o69e/kiJwcd8FJVZrRtS1Jxsed5dLeGb8i7XeBwUOxwkO8Uf+urCsTffoyvr7SVSCQSiQspqBsDrjLhN7d/D8HhWAwWJvWahN+nP1Qfc4aPnY8XH6eVu0jndFLkFJaPpfuXym6JkvNKnNXKkpiYU+47W/GWZ7fXrwDX+T3PNpnIbt9e3Kkhuj0VZlXlQGSkZ7uqKWhlR9AtTUExoOiaOFRRUTQnbfMzORgcUS2y3WPVuq5OodFOYQ0xrWjw1oqfePj1F0FRKC2Fj0d/wwP7JqMcOwY2G/j78+qHH/JsQxGXdSviNQT20tzcWtev0LRa3ut6nMG7DTA3K4tdpaXSViKRSCQupOWjMRAcLJI+mjcHoFVgK97t9jSWQ2lnPcT80fO5vsuN4s7TT2NSxYfveq3e0xLJxUN8QMDpKwZ1vdrur7qG7xrbDZrG6zHD8FKNqOiYVAPgQEXHy+HgsU8WYnA6UF2Rf4rm8k3XHNN9Xfe4gK7o5J04we4dQuauXQtRGz7HWeoK9naNF3/jjZgMdUwxdcdr6HHVeUy/1WriJtxiqW4lX8NWIpFIJI0VWaFuDCxbBqtWwbRp1dtckWJnS7BPMFc+Ngs258DAgcQ0j+Gz0Z8xvP3w8zxZieT84O726LaPhJhMJBYVuRYH1qCm/9m9SdPQVbWWOFWBd2fMYML119M/IICEgADiY7uhqyoJtkL63TeBAStXc8DrCG9cE8vbg19m1UaVTXmbOdmpqRjB7cWuYwcxKgovJU9j0OOvwwg/+t32ACa/X1H6xsOzz9Z6TAmxsczPzmaTzcbOsrLq+MG6lfC6j9NFkNHISYejwX1nQ6yvL/lVVeJ5cm1bkZ9PRkWFjO6TSCSNFkU/xX+6Fys9e/bUt27d+mdP469Fly6wZ48Q0RZL9fYWLaCwEEpL4YYb4Kuv/rw5SiR/AO44uUKHg+SSEsa47FBzs7KoOnkSs8nEvX5+xIwbx/RbbyVp0CCOVlWhA2ZFYe2DDxK3a1f1gFVVYDKRlJVE1oAeXHMQpo/vyJMt9vHjnT9S6awk3C+c7l/czt3xM7mseSeSiotZuHcDTWwlNNH96XD0KAN/Xsh/hlZy5Pso1IS1lPuY8C6z81bryfz4TD7Zzv3899r/0iuidreeDQU21hcVEjx3LsuNRlIGDiQkNZWmoaF85+1dO0Gk7hsH1/1oi6VB33Td21FeXiTabPVbwdfAoiisjY2VoloikfxtUBRlm67rPc90nKxQNwb27BHf7fbagjolBZYsEaHC99135nEOHIDVq0UTmICACzNXieQCcioP9oQ6iwP58kuWWK28qutMPXIEDRG/lzBvHnE2G+zaBSaT+AK8jF7kzHmTZRn5DB40Aj7qw+BPBwOQPiWdige3YzFW/+1dYU/i10PJBKwpJ3WtH14P3cJLvVui+pSSuz2DQrWAzJB8Flyxjb1ZuyhzlvDTkZ/oFdELp1PEEWoa3HuZlZUrrUQ/8wwTTp4ULSvHjuXVJUv4pq4Nw13BbiBy8OmWLc/43L2ans76MySEuKMGpaCWSCSNDemhbgxMngyBgeDuLAeQmwujRgmRvXJl/c4cDfHrrzBpEkyYcOHmKpFcDMTEQFSUJ37PAJjd8XyDBsHDD4u+6i46hnRk3MBHuOaul+gWcSkWg4XOIZ0B+HD7h9gqbSQeTaTSUYndaeeWLrcwquk7PP/Oh3y2+y1Gj5rK2G5jYeJELrUepH1hHjfc25roYRGUPleMj9GH6e+dQFHglVfENXNzRSfL1asBLy/xiZO3Nxw8SHyrVpjqRvnVvO26b3I6ib/7bli/HtLTT/uUxAcE1Is0rIsKBJkaCjeUSCSSvzeyQt0YMBrB6axdmXI44Mcfxe0RI0QSSPfupx9n1Cjx3Z2KIJH8zYmzWlnTrds5xcMZVAPtgtrRrmk79uTu4aX1L2ExWHhu7XPcFnMbC3YtYP+k/fTo0YGPP4arr67O6wbRqv3AAXhya08S0n7izVklBJqbowflcOml0KMHZJdk4231ZudOK2lpcPKkWHssJmCgl6+V6fZYtgVlU1yeR+iRI3T39SVp3Tr2xseTm59Ph5AQnnA6ievdG664At54Ax59VIyh6/DFF9CunejeY7MR99JLvBcby8SICLGoUVFQgUiDgcqyMnIsFpzAxNRU1hcW4udaPFnXV+2ORUwqKfHYSip1HYuiYFZV7g0Lq/+JgUQikVzkSA91Y6B9e/EKrWnVojonx5P6AcAzz8C//nX6cZxOIc5ffBGef/7CzVci+YvzxOon6B3RmxcSXiA2NJahbYYydulYz/6KZytqWUAa4vvU7xnxxQi8ln/M/Y8UcFnnMG7ucjPr0tYzaN4QFvTdw5U9WhMSAjNnwkMPVZ974oRorDNrliikHz8Od/Xcw1LncJpsWYveqTN2u445aQt07gxNmsD998N//lM9SJMm4tOoN98U6yyCg6GigsROnZh/9dVkBwbyQ79+2GumjLip4dOu6atOtNkYkJTEmZZEzna1UJdIJJI/G+mhllTjakdc6wXP6PrRX3ONSAEZNOjM47i92Dln1wxGImmsTL9qOgA3dLoBALvTjsVo4UTJCdo2bXtGMQ0wvP1wjj1yjOPXBdIr1sfz57s+fR2aWsGs10O59hv4+GOIixP7/vEPcfv22+Hnn6FVK7FdUaD5oM5kTNlNp2hf1t34LpXzv6RXs0toqigQFgZZWbUnsGNHddnb11eI6rw84kpKiCst5dUvv+Qb9+ANUaeFe5zVKlrCn8Xztyg3VwpqiUTyl0IK6sbAiy/CCy/U3ubOsu3dG157Ddq0OfM4+/eL7ydOnN/5SSR/c0wGEzd1vumcz4vwjyCiO1Q4KsgtzSXSGkl2STaBXoE8Oms1B0tactddsYD4AGrPHoiKEmuP+/WrHic8HD7/HMCX7Gzo8c7dZDx+N4EhNQ5wtxSPixOpP277hxtVFd4Ulz8lPiwMk91OldkMioKrlU1tv7aiYFYU4l2LmAvPMq4vxGRiyI4djAkJkcJaIpH8JZCLEhsDzz9fP5PWXaFesEC80rqrzxKJ5KLj5fUvEzUjipu/vpn3tr5HmF8Ydy65k0+SPwHAt/M6mrzUlFH/ns7UqaceJylJFKO//hrMZhg+XKw1JjxcLExUFNi0ydNI5nTEtWpFwj//yf2HDzOqqAiz04kCqLrO1du20dvPj1HBwcLusW0bT86bx/SjRz3nNzUYiLZYiPX1paOPD7G+vvT28+PqwEA+z8lhVUEB96WmMsct9CUSieQiRgrqxoq7Qp2aCq+/Dt9//+fORyKRnJLbYm5jcOvBrDm8BoBmvs0IbRJKdmk2AK1GfUw5Baw8tPK048TGivfXN90kgkHS06GkBLRHHmVjjwfhwQdFhXrEiDNPauhQ4iZP5v2bbqL3/v04cFWoNY21PXqwraSElfn5sGkTiQ8+yBtRUbXe2Pf09+dIXBxJvXqxt3dvknr1YkbbtvxYUFDrMotyc8/lqZJIJJI/BSmoGyt12xe7fdYSieSio1NIJ1bfuZpXr3wVgAFRA4SgLhGCukmrfQCM6zbutOMoinCANWkirCG7d8PAgXCoaS/6bZvJsqFvc/izX+CSS848KVWFiRPBz4/4e+4R8YK6jqqqOFVVtCV3Okk4coSE2Fghtmv4rcfUjDZxkVBYSN1l8g0dJ5FIJBcbUlA3Vkwm8fnv+++L+6daWCSRSC4aeob3pHtod67rcB3NmzQnIS0Bp+bkUMEhJvWaxJ3d7jznMfftEw6PrVth1SqxnKKk5NzGiAsOZk2PHrzUujX/bNkSg6KgahqKpvFpUBArBg7EqOugaSjAE5GRDXqjC11dGUG8OLmPS7TZmJiSwsSUFBLP0FxGIpFI/gzkosTGTGysKFGdLe7GMGdTvZJIJOed7mHd2X7fdgBimsXw9d6v0XSNzH9mUlJVQmpeKk29mxLsE3yGkQQ//gh33CEsIG+/LXzVsbG/7f21O2v6yh07cOo6qCoOYF9wMPtcaSGKrmNSFEYFB8P06fDRR0LFR0UxJzOzlsf6Mbud1w8dIjEoiPgdO6hy2UXmZWfL9uYSieSiQ1aoGzs+PuJ706ZnPtZ9TN++F24+EonkrJg6YCpZj2ZhVI2YDCaKq4rp8G4HFu1d5Dnm+9TvSTyaeMoxBg4UqZlPPy3ux8TAuHHivXNBgQj++OEH+PLLs5tTQmEhVZqGBniWNdZod66rKk5dJyE/H558EtLSPPtqeaV1neSkJBgyhISdO7HX8F67Y/gkEonkYkIK6sbOqFGiccNrr535WLfv+iyjryQSyYVDURRCm4SiuARppH8kfmY/dueIT50W7l7IiC9G8HzCqZswGQyi82JYWPW27GzYvl00SoyIgGefhVdfPbs51WzVXu/jT1dnRbOqEu8Wzx98AJGRQH2vdGxBAa+uWkVhaGitF6qaMXyJNhuvpqdLG4hEIvnTkZaPxo6qwiOPnN2xaWni+yefiLwtiURy0aAoCp1COvH5rs/Jr8inmU8zADS9dgTewt0L2XJ8C/vz9hPXIo7nBjwHwBUfX4Gu6+zZrRJY3pNlU95g1iwYMwYCA8W5lZUi0j4yEiZNqj+Huq3al548yb+PHvUsNFSAGfPnE/eJiPsjIEC0cYyIEJ7qtDQWpacT+9NPzLjlFqoMBjh+HAUwKAojgoN5IjKSOKuVOZmZTD5wAKeuY1FV1nTr5unGeC6t4iUSieR8ICvUkrNnwADRnviJJ/7smUgkkgZ4uM/DhPmFsWDXArZlbQMgvzzfs3//yf3csugW3tv6HseLjuNr8vXsMxvMGFUjhqA0jjSfQdt2Gg88AM2bi7S7Hj3grbfEOuY+fWpft7xc9IiaN0+I6onWllzdwsreX2vXbDQgb+BAkXvt5QU331zd7vztt5nQty8rx4+nqFUrIaZd6K4qfG9fX+KMRhJtNiYdOIBd19GASk0jobCQRJuNK3fsYOqRI1y5Y4esXEskkj8MWaGWnD2hodWpIBKJ5KLj1phbGdp2KCVVJdyy6BYAckpzPPu3Z4kFjZvHb6Zr8661zl1952oAZmyawSMrH6GwopCm3k2x28WixaQkeO45yMurf12zGYKCqpdk+PjAhAnQJjSAVYpClSZq1GZVIX74cOEnOXoU/u//qru0Ll0KnTvDL7+INujuVuiuLoxmVSX+zjuhSxcS+vZFa9HC4782uGwgbg+3E6hyiWxZpZZIJH8EUlBLJBLJ34hA70ACvQPJKxPKN6c0B03XUBWVcns5La0t6Rjc8ZTnD2kzhPmj5mMxWNB0jSq9gsREH267Da6+WjRZXbwYkpNh2jQ4eBBatIDly6vHMJvdhWcr3W2xvLErG3R4rGsocX5+YqXj0KEwerQ4Qddh507R8tzfn7G6zrzsbCp1HQXo6OPD8KAg5g8ZwvSwMNIUBXQdxeXLvjYoCIAgkwlVUdB1XQjwGl5raQORSCQXEkWv25L6Iqdnz5761q1b/+xp/OWRLzASyd+TgvIC3vv1PRbuWchVra/inu730DGkI6pybg6/e7+5l4+SPwKg5HEHO3cYuPlm0WkxJQUWLRJi+pJLRNTe//53lgNv2iS6MXbvDp9+CidOwKBBUFoKZWXgWpw4JzOTB1JTcdY8t+7rlaJgVBQ0l/BWFQWnrqMqCrPatWNCePgpvdYSiURyNiiKsk3X9Z5nOk5WqBshbp9hlaZhli8wEsnfiipnFc+tfY53h73LpN4NrBw8A5WOSrZmbmXFoRXV2xQb0dFNuf56kQhy113w73+LfVOnQvv2px8zN1ckh/TvD3u1HgT0vIW2HzwJM2cKZX7ypMjq8632dOfZ7Wh1B2ogINtRQ2Q7XbcVXSfPbvd4rd3HVEobiEQiuUBIQd0IkT5DieTvS5BPEAoKOaU5nCg5wVd7v+LadtfSKrAVg+cPZljbYTx6+aOnPD+vPI9+8/p57htVI029m4I3zJhR//g7z6I547p1cOONwoe9dJmZ15O/oLQTmLt3F9F5X30Fv/4KTz0lzNiICD6TongaugCiQu2yewAef3XNurU7ESSjooL52dloNc53e60TbTbmZ4u27WNDQxv8/y/RZmN6RgYp5eVYFAWzqnKvK19wUW4uY0JCGuz2KJFIGidSUDdC3Fmx7gq122cokUj++hhVIzo609ZPw8/ix+OrHye0SSjRAdGsS19Hr/Bepz2/bpdFg2I4xZGwL3cfHYI7cLLsJEv2LeGmzjcR6B1Y77j4eNi4EaZMAZNJFKTNZuDSS8UBt94qNjz+uOecOKuVhNhYj/D1Nxr5T3q6qEJrGoqqolOdAOIW281NJnLtdv6blYWKENG4bCDvtmsn5pOcXK/zIsD0jAySSkqo1DSy7fZ6j2NLcbHn9qqCAgApqiUSCSAFdaOkblbs761OSz+2RHJxcWWrK0lIS6BnuLD95ZTmUFhRiENz0My32WnPNRvMeBm9qHBUAFDprGR9+noGtBwAQGlVKSdKT1BcWUzs7FheGfQKP6X9xI+Hf8ShORq0mQQHi6+nnoKVK8FqhQ8/hMzDXXkeQNP49/gUHq/T3CXOaq31f8qolBQSPv+cjGbN+GD4cJxqDV+4SzjXFMLu6vSEsDBPJfrV9PR6nRfnZ2fzYVYW59qyalFurhTUEokEkIK60VL3heq3Iv3YEsnFx+o7V6PpGjq6x/6RWya6E55JUIOoUh8rOua5vztnt0dQv7juRd745Q1PzJ7JYOJk2Uk6hXTivp73nXbcq68WPmqAJUvAz88LfvwRBg/GtmEXKFGnPT9u2DDiTCYSNY1P7HYqVRWtjgWkFq7EjygvL8//S3WtJGbXOb+l/2vd7o4SiaTxIhu7SH4XDfmxJRLJn4uiKBhUA0bVSJBPkBDUpUJQh/ieWQQObTMUgAAvYQdzZ1nbnXb+/cu/0dE9DWN6hPUgtzSX3hG9Maqnr9GoavW6wx9+gC+/RHSMAV7eM9pzXFIStGzZQHKIosBVVxHXty9rHn2Ul48fZ3bLloz6+WdUTaufAqLroOusOHCA0bt2ccX27dy6dy/hZjOxvr509PGho48Py/PzOR0tzGbqLoccYLUS43owczIzGbJjB3MyM087jkQi+fsiK9SS34X0Y0skFzfNfJsRZY3CqBrpH9WfSP/IM57zSNwj/HjkR9IK04BqQX0w/6DnGPe+zOJMckpzWLxvMXEt4phw6YRzm6C7r7ndzssvi+aJ5eWQkQGTJ4umMm7mzYOBAyG6pS9xKSnEvfEGOBxMeOEFFvcdw+pekeKE79Yxe3AfdFVBB9YbDPU70lRWNjidUJMJL1XFmO9F3xY+3Bcd6ml1XjPGb4Pr07kHIyKYfvQoIH3VEkljRlaoJb8Ltx/7pVatpN1DIrkIearvU9zZ9U76tOjD+rvX0zHk1E1d3HQK6cT47uM9991Cek/uHs82t6B+es3TPBr3KEWVRXy287PfNsnZs9kw5WumToWCArj8cvj5Z1cF24Wuw7PPwnvvgY7Cmq6PoBcXQ1kZNu/m+Jt78L6vL+/v2kVU+i482R+K0mDc3qkYFRzMjs5xHBzenRZfdfD8n5ZXZ5GihvhUbnFubq3ti+rcl0gkjQNZoZb8bs6XH1sikZx/7ux2Frl2DfDsgGc5UniEuUlzPVaOPTnVgrp/y/5sz96OQ3Pw6uBXOVRwiJ0ndv62SU6YQJwTiqZBkyZiU79+tQ9RFJg9WzSSOXYMBif9m48++jd3j9OxPvAAg90Hvvoq8cnJmOx2qszmmiO4vtcJ2WtAa/v7C/F+1VXV29yfxlVqGhqiGmVWVa4PCfFUqEH6qiWSxooU1BKJRCJpEKfuJNI/kmW3L2Nf7j525uykmW8znu3/LD3CetAmsA3LDy7nUP4hQnxCPAsfAfLK8jhefJyYZjEoZ1EhNhjAz6/6vs0Gu3eLhoqvvAJbt8KK6l4zHD8O3t6IMnZyMrz2GuTnw/jxxIWFkfDPfzL/6qtJviSe0BM72OXbB3NMCGU+ZfgeOED3/cV8XjWEyKE2jlnKADApCmNDQykuFkl+rVtXX69mOlKQycTyvDySSkr4X04Osb6+VOo6ISYTScXFJNps7CotrZdXLRORJJK/L7L1uEQikUjq8X8J/8eL614kOiCaQw8dovXM1rTwb8FtMbfxQK8HWHVoFUM+G+I5/vaY2/l81+eUP1uOl9GLLu91YU/uHlbfuZrBrQef5koN8+23MHKk6FS+ZQvs2QPTpsHEiXD77XD99a4Db71ViOr8fNG2/NFHRX90d6k7NVW0cvT2hpwcaNIEx5Br2LvqGP8Zu5N582BzXi5rd+4iM+VSqpKtxMXBPfeIJJJRo+rPbU5mJvelpp5y7gao1TJ9dvv2xPj6ykQkieQviGw9LpFIJJLfjHshYt/IvqiKSrotnYGtBvJArwc4ajvK25vfBmDF7SuwellJL0znq71fkVaYRpvANuzJ3UOgVyDdmnf7TdcfMkRYPHr3hj59xLbNm4Xdo5adefZsEXAdGAhffy06Lfr6is6L5eXQrp1QxUuXoq9aDYcOYfQ2E2NJ5d2ZTlTVQNzLLxP39tt8839JbAmJJToaunaF0aPF2sVazhHO7JN21rm/KDeXPLtddqiVSP7GSEEtkUgkknoYFAOBXoF8dr1YaNgrvBff7P8G5wgnU1ZO4YcDP+Bt9GZIW1Gl7hHWg9JOpRhVI/ty9wEwY+iMs4rpawiLBSbUCQzp00eI6lr4+0M3l2ivWU7uWaOg9NFHsHQpX9/8FTc6voBhw1AqKzmSkM5X21ozLTERBRgZlcTIu0XXxA8+gMTE+kl8IHzS7kSPs2FMSAiHysvRqfZeuxOR3C3Ok0pKUIAAo5EChwMFiPLyopOPzynbo0skkosHmfIhkUgkknoYVANOvbrWmmHLoKCigJ0ndtI5pDNArdxps8GMUTXy8vqX2Za1DQBN11i4e6HnmE+SP+H5tc9jd9Zv631BCQykMqwlraJ1ju/K533LFACyVu3k9dfBkWcDoHjNFrKzYdUq8Ck7yYM+c7GkpdQbbkJ4OLPbt6ejjw/RFgujgoN5IjKyofWNhJpMvH/8ONOPHkVDpIM8GBFBnNXKk4cOcXlSEkvz8kivrCStspLk0lLP7fU2G//NyuKKpCQmpqSQaLNdsKdIIpH8PqSglkgkEkk9DIqBosoirl1wLQBzr5tL55DOdAzp6BHUxVXFtc45XnScqWunsjVzK8PbD+fn9J+57/vq7onjvhnHS+tf8gjuPxLLmBH0fHkUEV0COegvmslc/cGNlBwrxHTkAABHliTz2GMwekgpQQNj0F55jaqsvAbHmxAezt5PP+XI5MksueceAr79loZWJGXb7SSXltballxSwpzMzFrpIKfDDszOyuLKHTukqJZILlKkoJZIJBJJPTo3E6L5eNFxAK5tfy27H9iNl9HLs68uTcxiIWBok1C+u/U7WgW2wlZpo8pZRaWjupGKu2vjH8o774iuMcDrc4PRvvsBpaQEc2Ya6DqV/sG0b3Kchx6COU8dIYxsnk0bz0YtrvY4hw7B4cPw/fcwaxZUVYHTSfyHH561h3JMSMg551XryG60EsnFjPRQSyQSiaQe93S/h8X7FpNVklVvX/ug9hhVI49f/nit7VYvKy38W7A1UyQxhfgI/3RuaS4R/hGkPZxG9Mxoz4LHPwujERh+jbjTpQvs24clIACaN6e3Ar2LT8BrcMWES+hUsBG0y0XfdICHHoJly8TtNm1g5kwICSGua1fW5+QwvWNHUsrLsShKvcp0tMVCX6uVuVlZZNbp1NjUYMDfaKzloQ4wGqnUdQ6Ul+NwmbmDTCYSbTbmZ2eTXVVFqNksPdYSyUWAFNQSiUQiaRCn7sSgGOptNxvMfHPLN3QK6VRvX+eQzizat4jXNrxGh6AOAOSWCUEd2iSUd4a9w+WRl9c6Z/rG6VQ6Kpl6xVQAFuxawKRlk9B0zXNM6uRUmjdpzr/W/4uZm2ey8o6VJGUnsWT/Et4a8hZtm7Y99wf4xBPwv//BgQNgMsG6ddjffJudWgyXAtf4JMANMyAtDQICRF7fsmWi//lTT8GAAeDlBU4n+PoSd999LOnXD4KDYcsW5syfzytVVaS7xHNaZSVpOfXfTNzerBmfdar9XCa6Wpu7G8mASA+Z7Irrq+lCn5edzdrY2PMuqmVutkRy9kjLh0QikUjq8Wbim6w4uAJVafhl4pp21xAdEF1v+/NXPM+UPlO4qfNNnoSP3NJcNmZsZPgX/8/encdVXaUPHP8cVgERZFPAPXEBFTI1cYuy0sxcW+dXtli2aVlNjpPtme3TYtuYraZji62a5pKoJWqaoIILbiQqgiiogGz3/P443MuOF2RTn/e87uve7/d7vl/OdZrp4fic5xnO0I5Dy7U//yLuC5btXWY7XrhrIQ7Kgbsi7rK93JzdAIhoGUFadhq/7fuNIM8gFu5ayJLdS6iRzp3h9ttNMA3pE9sdAAAgAElEQVTg7IxKiGfNLycAODLsTk5+vdgEyGvWwKJFptTIa6/B1VebYBpMV5pXX4Urr4QjR0w3moQEJqxdS2d39zNOIy2//CbN6IwM8koE01b5lA6mAfK0rvVUEGtA/9S+fZK7LYQdZIVaCCFEOVl5Jl1hQJsBZxhZWr/W/Wwr0C08WrD53s2E+ITwbcK3LN+7nN3HdpNfmE9X/64kZSTx2trX0OhSXRavD72eQW0HcV+v+8o9/9pO19LCowXxafFM7jsZhap5Csn48WUm3w+nxB1Menwq+h0XWl7dneef78FTNwBbt5oxv/4KFa3WPvCAeYGptffMMxAfz9ixY89YYq+iduVlW51bFYX+pYJqF6VsZfhqizWgl7rZQthHAmohhBDlODqYVI8Xr3ixxs/wcPGgs29nViWt4o8DfwDw6K+P4uHiwQuXv8CGgxt478/3CPUPLbVRcUzXMZU9EoBQ/1Di0+JZuGshGl3rOdmOr78CwOzZikEOv8P0aFi7Ftq0qTiYLmnLFpg3z6xqx8fb2o5b25ADfHz4sK1j4vjAQNuYkqytzq250sfy80krKMBVKVubcx8nJ1sONcD9O3eWy6uuKG3DmoOdUFSir6L612iNBhTgWBSwW+8DKszblhQRcSGTgFoIIUQ51tzpkrWoayI1K5Vr5l4DgI+bDz1a9OCr+K9s58DkXSekJVBgKSArL4vkE8l08u2Es6Nzhc8M8w/jg40fMOor08jlSNaRs5pjOQ4OYLEwfjycHPE+/Pw/c3706DPfu2QJvPIKDB8Of/wBWjMhKKhU0FxRAF2Zz48cKbdKDbAd09J8QlAQMZmZRMXGkleiC82nKSm8ExLC5N27S7U7B8qNBUxLyCL7y2yY1MDWrCwmJSba7iubt21NEZHW6uJCJTnUQgghyrGuUI/9euxZPadl05bEjI9h7V1rib03lo+u+4h149fx662/2sb0aNGDFh4tyDydyYp9K+j2QTfijsRV+syH+z7Mdzd9B0CQZxCXBl96VnMsJy0Njhzh8GF4oPBdhjsvYcsri02b8zN59FGzyfGqq+D4cUhJMcHqvHkwfTrs3m33NCrLo7aylt6Lzsggv0yAnKc1C9LSyqVtVDT2TAqKnlXyvrJ52xWliAhxIZEVaiGEEOVYK3hYc6lrytXJlb6t+pY6d2mrS9Fa8+TAJ3lxzYv8e8C/eXLQkwDEp8YD0NWva7lnWXX06cjxHJOXPGv4LK7tdO1ZzbEcHx8A/u8KyM31YVnGEOzYW2g4OUHHjqYcH0B8PPj6mgohAO3amet2qCyP2srf2ZnRW7ey+dSpctdclCKiaVNWlMjf9nV2pruHB06U39hYFQWctlhwwFQasT6/ZN62da7WFerazukWorGTgFoIIUQ5w0KGcUngJXi6etbJ85VS5BTk0KJpCxwdHNlxdAcvrH6B9cnrae/dHg8Xj0rv1VrzeszrAAR4BKC1RinT+DuvMI+3171NR5+OjO5qR4pGFaZNM+/u7qZ/i5NTcTnqMworan4zfTq88Qb8+COMHAkly+b9/DMcOAD33w+qfONyax51dEYGvs7ObD55knWZmWzJzsYCzK2gBJ8CRvr5cY2PD5MSE20BcCHwUGIi74SEmD8rrVFAC2dnmjg42HKocy0WUvPzsRQ9S2Papa8uUeXDEXgnJKRUSkfJnG8hLkSS8iGEEKJCldWhri2vX/06hx49BMDJ3JNsOLgBpRS3h99e5X1KKbTW9Anuw8r9K3Gd7sqxnGMAbEvdxpTlUxjzddUbG+0xeDDMmgW33ALNmsHBg9W4OSDApH3s3Alt28J114Gra3FAnZlpVq1PnQJLZUkdJlD9d9u2TAgK4oPOnbmxRQvKh96l9fH0JD0/v9I0kMKi8w7AQ61asS8yks29e7M/MpKHWrWyPb+qxJD0Ckr9gcn5/kjapIsLkKxQCyGEKOfTzZ8SmxKLm5Nbnf4c68py7+DeJE5KtPu+r2/4GoBfEn8h35JPfGo8A9sOpGdgT54c+CQzfp9BoaXQlgteE0eOmL4v48bBpEkmHrabUrB0aelzAQHmoQCenhAbC25upo61nc6UBlIyFcNZqVKbD12UYqy/P2syMytNzSiZugHFKR5WDlBpSoeU2hMXMgmohRBClFNgKQCqX4e6voX5m9SKbanbGNBmAEopAjwCsGgLx3KO2ZrL1MSiReb9uedM6vNZCwgoXqF2cIAOHUwqSPPmpuuiHcqmgSxOT2dnTg7+zs6Eurtzsacnr/79Nztzcgh1d7eV2ENrknJzeSkpid6enoS6u1dY+q7s8x9KTCS3KD2krasrEZ6eXOPjY9t0aC3NZy3v51SUTiJ51OJCIwG1EEKIcpwczL8eHuz9YAPPpGptvNrg6eLJA788wJI9prNjQloCYEr2+Xv4s/PoTh745QEW3LgA7yb2B3lXXWXSnP38TK+WwkKTR11jLVrA4sVmdbpdO3joIVNi7+KLTUCdmwsjRsDvv8NLL5nrJW3bBn/8QWReHpF5eZCXx4QhQ6BPH8CUrhu0eTMFZX7sLkqvNO/PzSXmxAlb/eqyIr28bIF2dw8PXv37b35ITzet03NzWZSejqUoaH6rY8dS5fScgXsCAysM1oU4n0lALYQQohxrqoR1pbqxUkoxb+w8vtv+HXdE3MHQL4dyWbvLGNNlDF5NTED3aeyn/LbvN/IK86r17NatzSsz03QZf+klUxWvxp55xlT/yMuDw4ehaVNzHG8qm7B7d3GayO+/lw+ob70V4sqUE/T2hp49AZNyUdF/WxVVEs8vKnt3pqA30suL7DI53tbc7DyLpVw5vQJMYxgJpsWFRgJqIYQQ5Vg3I971012sumNVA8+masM7DWd4p+HsObaHnIIcbgi9gbsuvst2/UjWEVo2bUmAR0CNnt+smQmke/c+y4n26WNbTbbZts3kluTmmi6M//43zJ5duhoIQHa2GfvYY2aMi4t5ORc3v4ny9sYJygXVjpQPqp2r0a58rL9/qfbp1ucVAvtycmzVQMAEFZLqIS5EdRpQK6WGAm9j/vc3W2v9cpnrbYDPAe+iMVO11r/U5ZyEEEKcWYhvCADuzvYWYG5YOfk5PPHbE4DJq844nYFC4dXEi22p20g5lcKcuDncFn4bYOprx6bE0r9Nf8CU4vtx54+2aiEAIT4hDGw7EKXgiruXMfCiq2p/4t26QUEBfPIJRETAjBmwaxckJBSP2bYN/vc/k3MyYICpa2315Zfwr3/Brl1Eenmx+uKLbTnU1rzqcS1bsjUri7eSk8kpLCTC05MprVvbvYpcsn16RNOmvHHggO1a4unTpcZaMF0VZYVaXGjqLKBWSjkC7wFXAcnAn0qpn7TWJf5fgieBr7XWHyilQoFfgHZ1NSchhBD26RPch8CmgbTybNXQU7FLgaWABQkLcHJwoqt/V/xe9eORvo/w0pUv2ZrFvLPhHVtA/cHGD3h82eMcffwovu6+bE3dyuivStetviPiDga2Hciu9F0M+XII+x5Ipa2/X+1OvFcv8/7AA+Z97Vrw9zfdGsF0WnzoIVi50iRwX1qmK2SrVnD11ab83jffELl7N99Pn17ux0R6eVWr5XlZ1vbpLyUlVZhCYlUITExMpLuHhwTV4oJSlyvUfYDdWuu9AEqp+cBIoGRArYFmRZ+9gEN1OB8hhBDVUKjPruxcffJ09STt8TSUUjRzbUZnv85sP7qdpIwkcgpyAEjLSrOND/YMNuey0/B19yU1K5UgzyC+HP0lF/lcBICHs2kuE5sSi0Yz4h8pxC2r5YA6JAT274eNG+H666FfP3jySUhPNyvXLVvCggWmnXlgoHmVFBVlXgDffWcSvutQZWklJRXamZ8txPmkLgPqYOBAieNkoMyv1jwLLFVKTQI8gCvrcD5CCCHs9PPOn0nNSiXuSNyZBzcSzd2a2z6H+Yfx56E/ad+8PcemHGPq8qnM2TLHdt2aypKalUoXvy5c2eFKDj5acecWa+710LHlOxPWirZtTeAMpqRIy5amrEh6uim117x5+dzrkrSGPXvgv/8tfk4dqSytpJmTE28mJ1OoNa5SMk9cgOoyoK6omVPZxku3AJ9prd9QSkUCc5RS3bTWpbYUK6UmABMA2rRpUyeTFUIIUcxBmUa6/Vr1a+CZ1EyYfxhfx3/Nw4sf5u1r3qZD8w7kFOTw9Mqn6R3Um1FfjQJKr1pXxt/d1LLuOeDMY2vM1RXmzYPwcLPKPHGiqVV98cUmz/rLLyu/9+qrYflyCA0trhhShyK9vPi+e/dy50f5+RGdkWELpl9KSiLK21tWqsUFoS4D6mSgdYnjVpRP6RgPDAXQWscopZoAfkCpZQCt9SxgFkCvXr2q6oYqhBCiFlhTPa4Pvb6BZ1IzY7qOITopmnyLaZFtXWV+YfULpcalZpl/3Qz5cghDLhrCo5Hl6+JZm8PsPZJKYddqNTasnltuKf4cGWneR4+G4OCq72vf3rw7OJh25nfcYYpo1zNr/eqYzEwGx8XZujGuCA+XoFqc9xzq8Nl/AiFKqfZKKRfgZuCnMmP+BgYDKKW6Ak2AOlwCEEIIYQ9r2bzGXoe6MmEBYawYt4L3r30fgJu63cSf9/wJwLUh1wLQ0acjHZp3IDs/m2V7lnEq71SFz/J186Wf2508cW9nDh2CrVthzhw4VB+7fp55Bu6+u+oxYaZbJM2awfz5sGZN3c+rChW1IBfifFdnAbXWugCYCPwKbMdU84hXSj2vlBpRNOwx4B6lVBzwP+AOrbWsQAshRAOzrlBPWT6lgWdSO9yd3UlMTwSwbVLcfO9megf3Zu2BtWi0rY15WY4Ojsy+7hPeefhqmjaFed9mMW4c3HhjvU2/at26mfdTp8wmx61bi6+dPm1yrOuRb4na2BUdC3E+qtM61EU1pX8pc+7pEp8TgP51OQchhBDV18arTan3c11Ofg7zts0D4NDJQ7Rq1oqmLk0ZNncYi3cvBqB7i/J5wVZdumgC22fi5uTGRx5teeyZeHp3bVEvcz+jrl3Ne7t2ptnLt9+ayiAHD8LDD8Pff5uWj7XpyBGYMgVmzjQr40ViMjOZlJhoK61XCDxUURm9tWvhH/+A2FjT7VGIc5x0ShRCCFFOR5+OKBSdfDo19FRqhZuzG3dffDcP9n6Qo9lH6epngtCJfSYysvNI/D386eRb+Xf9x3f/IGZfLF12fUR6cDqD/+8vrgm5pr6mX7WgIPjlF1Ojet8+E1D/8AM8/bQ5rov85c8/hy++MEF1WPHKfnRGRqlW5AB5FZXRe/llSEqCZcvghhtqf35C1DMJqIUQQpRTaClEF/3nfDGyy8hy54aFDLPr3g7eHZifNZ+0v1IhGFZsjSei6TX4+ppF4QZ3TVFw7+MDQ4eatI+QEHjzTdiyxWxYtKaG1IaJE83Gxy5dSp2O8vbGWSnySgTVLhW1OQ8Lg59/NvW1hTgP1OWmRCGEEOeoDQc3ALBi34oGnknjEBZgVmGzrxsL+W78FBNPUFDpdOWaKrAUsC11G9n52QAcPHGQ5XuXs2jXIn7a+ROpWanM2zqP9cnr7Xtg9+4QFwdPPQXJyaZSyD33wI4dplkMwKZNpnZ1Tbm7g8UCK1aYro5FAXSklxfRERHcFxjIKF9f7gsMZGVERPkqHzNmmFSRgxXX/hbiXCMr1EIIIcqxbkrsE1RFQ5ELSN9WfXFQDnT164q/W0vST8Xz/vvg6pPGnLglDO04FD93P5SqqAVD1Z767Sle/uNlNty9gd7BvVm8ezH3/HyP7fp1na7j510/A3D4scO0bHqG5i39+8Nrr8H06TBgAFxxBcyebXKtH3wQ3n3XrFynp8Nikz/O11+b9uczZsCECcXPio01rc/z8qBpU8jJMYG0i4vp7phtfgmgZUv4/Xe46CJb+bwqKQWdO8Pu3dX94xKiUZIVaiGEEOW4OJo8hqh2UQ07kUaiQ/MOZD2RRex9sQxs149gH1/uvx/25a1j3A/jGDF/BD0+7FGjZ8ckxxDmH2ZreT6803DW3LmGZy97FoDkE8m2sSU/V2rkSLNqnJYGQ4bAe++ZwPmSS0zOclaWOf/gg8X3tGxpyvPdc0/pZ82fDzExZjU5Oxvc3Exg7eQEnUrknD/+uFm1ttf8+fDnn6YyiRDnAVmhFkIIUY61U6I1DUFAE6cmADx/+fNobbIp1u8znQkvDb6Ud9a/Q05+Dm7ObnY/U2vNliNbuDHsRnzcfABo2bQlLZu2JMQnhGdXPcsdEXfwfvD7RH4caWtEc0Z+fsWfXVxMXvWKFfD66yYgTk4u3TBm0CDzKmvjRoiIgKVLy1/73/9MpY7gYHi0uCFOTGamrWNipSvVSUnmPSvLvu8jRCMnK9RCCCHKsTY5ee/P9xp4Jo3TnmN7aP3ixby57g1aNWuFb3Y/NJqd6TsBWJCwgMiPI8/4nEd/fZTjp4/To0X51e0AjwD2P7yfiX0m0sm3Ex9d9xHdAs5iY2GPEj/D1xf++AMOH4bcXJNT/fvvcOutsH+/CaS1NkF4r14VP89a3cPLy4zfudPWJfGpffsYHBtLTGolvwD861+mM6QE1OI8IQG1EEKIcqyrpd0DKq/NfCFzcXLmIr82RLbqx7SB0/jkZRNcxqeaFes5W+awLnldqXu01lwy6xKeXmlrx8DAtgO5vN3ljOoyimPHTHqzda+gUoo2Xm24ZNYlfB3/NXf3vLtcXfAFCQtYtGuRfZMeMgSGD4cXXjCB9IABptLG1q0maP7yS5g7F8LDoXdvyMiA8eNh3LiKnxcWBvfea9I3rr0W/vOf0l0SCwqI/uWXiu8Fs2ly/nz75i5EIycpH0IIIcpp3qQ5AOEtwxt4Jo1TG6827H7hR9vxwC/yCP/aieVb4rmlG8SnxTO6y2iSTyTj5eqFp6snSinyCvPYnLLZdt+YrmMY03UMYPb/TZoEL70EU6ea6z/t/InYlFhO5p4kNiWWJk5N6OJnStUlpCXwxG9P0N67Pdd2uvbMkw4IMAE0mI2F7u4QH2/yocGsTv/3v3DihDk+dsxsZqyMoyN8+KH5/NVXEBRElLc3Lg4O5FksuABRw4dXfO+LL8LOnaaWtRDnAVmhFkIIUU6+JR+A0wWnG3gmjdtNN8E//wlhXVwY5DKZz17qSfzfB9l7fC9uzm60frM1P+z4AYDle5ezLXUbC3ctZN/xfazYu4LVSavJK8wDTLW73r1NzxSrVUmrAFNab9T8UcxYM8N2bciXQ9iVvsv+vOqSHBwgNBQ2bzabDt3doV8/8Pc316+5xmxQ/O03+54XFQUhIUR6ebEiPJwX2rdnRa9e4OzMS0lJxGRmlh4fFwdz5sCnn9Z7a3Qh6oIE1EIIIco5kWtWKWNTYht4Jo3b8eNw+eUmNnyu/2v8MON65u36LxZt4cr2VwLYAt6SFTo6vNOBK+dcyWWfXcaRU0cAs+D7+usm7cOqf+v+gFkRb9G0BYdOHjI/N+e47XlHso7UbPLh4bBmjVmFDg83QbY1zzoiwlT0yM+371lamz+IlSuJ9PLi3xs3wqxZDF63zuRTx8WZoHrZMhPE5+aa++66y/6fIUQjJikfQgghygn1D2XRPxZJ2bwzmDnTFMwYNw6WLDEV6zqm3kT/dn0Y2nEo9y68l7TsNIByK8mzr5tNiG8Irb1aAyYDYuPG0inLY0PHsuHuDfQK6sWSPUtYuW8lANtStwEQ3iKc+LR4tNbVr4E9fToMHAiFhWZ1GkwKx++/w9ixsH27KbVnj5wcWLUK5s0zwfn11xM9eTJ5PXuafGqLxbQfv/pqM37IEOjY0QTYThKKiHOf/FMshBCiQva25b6Qde5sOns//LBJ11i/Htq0CSOsk9mkGOARQFqWCait71Z3XXxXqSB40yZ47DGTdTFqFHh6mvO9g3sDEOYfxpdbviTzdCZbU02LxsHtBxN3JI6M0xk0d2tevcm3bAm33176XMeO5gXQpxpNfdzdTQOZuDhb+8gob28cCwqwODriqBS+Tk7cP3kyAOPS0ojMzoZ27ao3ZyEaKUn5EEIIIc7CL7/AwoUmi6FvX/j+++Jr/h7+ZBeYWt5bU7fSxqsNS29dysxrZpZbUb7pJvjmG7NCHRdX/ueE+YfRxqsNiccSOXjiIEGeQUwbNI0DjxzAq0lxvef07HRmrJmBru/c5B49TDD9wQfmuE8f8x21RmvNxMREPhwxgg9HjODy228nxsnJdGZMS6v6uUKcAySgFkIIIc7CzJmwa5dZWf75ZxgxovjawDYDmXypWZUd03UMA9sM5KqLrmJin4nlnuPoaNKQFy40+wXLurTVpdwYeiM9A3sydcBU+rfuj4+bD62atbI14gFYl7yOab9NY1f6rlr/rlUaPNi0KP/qK+jYkei2bSlwdEQrRQGQD6bluFLkOToSHRgI06bBgQP1O08h6oCq999gz1KvXr30xo0bG3oaQgghRK1avdoU3Hj8cbM/sCoWbUGhKNSFvL72dSJaRjC041AOZB4gJjmGm769idV3rGZg24H1M3mr3FzTotzdnZicHAavX0+eszOOjo7oggLyi76Ya0EBK598ksgNG8wXH1jP8xTCTkqpTVrrSrobFZMcaiGEEKKW7Ntnsh5KrlLb69df4ZVXTGzp62vysytjXZF2Uk68ue5NhocMZ2jHoYz+arStEoh1M2S9cnU1LyDS1ZUVX3zBF927w+jRXJyfz+b162HbNsYtXUpkTo65J1va24tzn6R8CCGEELXk889NXeqCgvLX3n/fFMDYZgp08N57Zt+f9S+Kp083PVWGDTO51PbqHtCdT2I/YXHiYlKzUm3tyVOzUon6LIq31r11lt/qLHTowOcREXyUnMzkY8cY164dH7z1FpEJCdCpkxkzdix07QrPPttw8xTiLElALYQQQtSSm2+GKVNMJbhx4+CRR4qvXXIJtGlTXETD0xOCgkxa8ebN0K2b2Yz43HPFpfMsljP/zGcuewaAXxJ/IS07jVB/k4B95NQRViWt4pFfH6nq9joVfe215Lm4UOjoaErn+ZiW9nz0EcyaZT5nZcGOHaV3cwpxjpGAWgghhKglXbrA3Xebz82bg1dx8Q0uvdRsWmzSxByPGwc/mCaKBARAQoLp/P3wwybwvu46s3h7JgPbDqRvq76sP7ie0wWnCfYMZuXtKxnddXTtfrkaiOraFRcnJxwBFwcHotq3Nx0YO3Ys7spoZW15LsQ5SHKohRBCiDrw9ttmhXnvXpMTnZBQ3ICwrOBgGDMGYks0prziCvt7njw16CkOZB7gz0N/EuARQFS7KLanbQdg3ph5tfBtaibSy4u3Onbk48OHCXJ1BaWIee01ol97jajkZCKBmNBQovv1I2rrViIbbKZCnB2p8iGEEELUkWPHTDB9553w6afwxx/FTQkrUlhoyudZ5eebxjEtW5qgu8qflXOMr7Z9xZUdriQtO40tR7bQwqMFvYN706pZq9r5QtUUk5lJVGwseUWxhjPgoBQFhYW4aM1bc+Yw+dZbyXNwwCUvjxV9+xLp7d0gcxWiIvZW+ZCUDyGEEKKOuLmZDIe77oKffjK9T6pSMpgGyMyEXr1Maecz8XHz4f7e9xPiG8Knmz/lqZVPEeARwMj5I4lPja/5lzgL0RkZ5JdYuMsH8rSm0MGBPEdHFvTrR56Dgzl2ciJamryIc5SkfAghhBB1xM2teO9dTfj5mTzrXmdcHystLCCM2ZtnM3vzbP46/BfHco7VfBJnIcrbG2elbCvUDoACCouu+wcEQNE5Jycnonx9q/0zYjIzic7IIMrbm8iSSetC1CNZoRZCCCHqUHa2qS+dmFiz+0eONKkj06aZaiDLl5uGhFUJ8w8D4LPYzwA4XXC6Zj/8LEV6eREdEcEoX18cAU1xMF0IzPXyolApNGCB8kv0ZxCTmcnguDie2rePwXFxxGRm1ub0hbCbBNRCCCFEHfrmG3j6adNSvKZ274ZFi0zjmBEjYMGCqsf3Ce5D76DetPduD0BOQU7Nf/hZivTyok+zZoAJqEspakUOUKA10cnJ1Xp2dEYGeRYLhWDK8mVknP2EhagBCaiFEEKIOnT77aYj9yNnUQ569Gj46y/T9OXll01Zvap4NfFiwz0bWPgPE8Xn5DdcQA0m9cPFwaHKoMMlP5+ow4dr9FxbWT7Z0CgaiORQCyGEEOcABwc4dcpscnzmGZP+MXFi1fd4N/FmUNtBNHdrXj+TrESklxcrwsOJzsjA19mZxenp7MzJwd/ZGR8nJ1o6OjLOwYHI9u2r/dy3OnZkQVoaY/39JYdaNJgqA2qlVCvgZmAgEATkANuARcBirbUdPZyEEEIIcbbWroX+/U3qR2wsnLYjLTrIM4hVd6yq+8nZIdLLi0gvL2IyM5mUmEie1mwHXJViZUREjYLhmMxMJu/eTZ7FwprMTLp7eEhQLRpEpQG1UupTIBhYCLwCpAJNgE7AUGCaUmqq1np1fUxUCCGEuJD17g1Tp0LPnrB0aUPPpubKltLL05rojAwiP/8cWrUyHW6q8ayyOdQSUIuGUNUK9Rta620VnN8GfKeUcgHOkMUlhBBCiNrg7AwvvVS9ewosBXR7vxuT+kziwT4P1s3EqqlsKT0XpUzu84cfQkhItQJqaw51nsUiOdSiQVW6P6CSYLrk9Tyt9e7an5IQQgghqrJzJwwaBBs2VD3OUTmSeCyRvcf38uOOH/lxx49YGjhb01pK777AQEb5+nJny5Zszcpi9GOPcemIEcw6dKhaz1oRHs49gYEMad6cL1JSpHSeaBBn3JSolOoPPAu0LRqvAK217lC3UxNCCCFERZo1g61boVOnqscppWji1IQle5awZM8SEtISWHvXWiJbR9bPRCthTcsYHBdHrsWC5fBh6GDCig27dgEwISjI7ud9kpJiW/H+NCWlxjnZQtSUPWXzPgb+AwwAegO9it6FEEII0QACA2HTJvD2No1jquLm5EZU272SxNEAACAASURBVCjeuPoNAI6fPl4PMzwza/6zbb28RE3qBdVoQV5ZTrYQ9cmesnmZWuvFdT4TIYQQQtitQwd49ll47TU4edKU1auIm7MbOQU5tGrWCoDs/DNE4PXEmv+cWzKoLgqM/Z2dbeNiMjP5IiWFhKwsknJzUYC3kxMuDg6MDwwsl5OtgPisLC7dtIkgV1emtG4tq9WizlVV5aNn0ceVSqnXgO+AXOt1rfVfdTw3IYQQQlQhKgpcXU3jmNmzIT4eZswAH5/iMUMuGsLHmz9m2d5lAGTlZdXaz4/JzCQ6I4Mob+9qB61la1N/kJxMbJaZ29wjRxi0YgXdb7uNqLg4W7Bsk2vCkQ0nT/LfTp2YGRLC/bt2YcG0MJ+bmmrGnTzJovR0VkkKiKhjVVb5KHPcq8RnDVxR+9MRQgghhL2ioswLTKMXX19brGkze8RsDp48yP6M/bxy5Sv0DDTrZenpkJRkyvDVRExmJoPj4mwVNlaEh9coqLbesyAtDbKyTNqH1izIySE9OblUOkdFFqSlEeXtXb6teZF8a1k+CahFHao0oNZaX17ZNaVUi7qZjhBCCCFq4pNPKr92IvcEwZ7BTOk/xXbu7bfhhRdMgxhX1+r/vNquAT3W35+lx47Z0j7Grl5N98GDS6VzVHZfdw+PSsc5W8vyCVGH7G49rpTyAsYC/wC6Ypq+CCGEEKKROHYMUlIgNLT43A3f3MDaA2sZ03UMe47toalLU1o0bcHXX5vrRfsAq622a0BPCAqChQv5uKCAPEdH3ho7ls4ZGUxu184WvB8vKLDlUFs/Lz52jO4eHkRHRPDq33+zMycHV6Vs1yM8PW0/42xSVISoyplaj7sBIzBBdE/AExgFSHdEIYQQopF58EHTRTE9vficNWe6mWszun/QnYl9JvLqVa/ywgtw4gS4uNTsZ5XMga6tALX7mDH8tXUrBUXH25WCAwdwAFwdHFjxv/8RedllzBo0iHuLyuvtz81lUXo674aE8MuxY+VWqffn5rI4PZ13QkJsbcprmqIiRGUqLZunlJoL7AKuBt4F2gHHtdbRWjdwVXghhBBClPPkk/Dmm6XPtfduD8DANgNxd3a3BdjXXQfduoF1/15NRHp58e+2bWstMI3OyjLBdIkSemA2GuZZLESfOAG33FKurF6+1ixIS6s03zqv6HrZFBUhaktVdai7AceB7cAOrXUhVJrzL4QQQogGFhYG48aVORcQBsBVHa7Cw8WDrHwTUMfHQ9++8Ouv9T3LykV5e+NUWGjLo7ZSgAsQFRsLn3/OWH//UtedlWKsvz/OleSvaCAhK4uSq4G+JUrzCXG2qmo9Hg7cCDQDliul1gCeSqmW9TU5IYQQQlTPRx9Bu3ZQUJQ3kZdsAuqEtAQ8nIsD6lGjwMsLrryygSZagUgvL1bPmcOo1FRaHj9uC6w1MOnoUSITEmDQICYEBfHfTp3o4+nJKD8/VkVEMCEoyNbS/L7AQP7bqRODmjWzPTs5L8+2KlgIPJSYKG3KRa2pModaa70DeBp4WinVC7gF2KCUStZa96uPCQohhBDCft7eMHAgWCxw5Ag8dX8o3G8qfZRM+Xj2WQgKMl0XG5PIzz/ne2DId9+RUuJ8rKVofXnVKmjXjglBQeXak5cswwdVd1zMk3J6ohbZXeVDa70R2KiU+icwqO6mJIQQQoiauv56uPxys9kwNxduGeHPP2/RdOoETg5ONHFqAsD48bBjB6xYAYMHN/CkKzC2b1+WFm08BBhr/TxrFtx+u33P8Pdn6fGKW627SDk9UYuUriSBXyn1JPC+1vpYJdevANy11gvrcH7l9OrVS2/cuLE+f6QQQghxXtHarF5PmgTr1sGBAw09owpYLMzato0FhYWMbdGCCXfeaXZQbtpUeZ/1Csw6dIiPDx8mz2IhV2vT1lxrTmvN+MDAcqvcQpSklNqkte51pnFVrVBvBX5WSp0G/gLSgCZACBABLAdm1MJchRBCCFFHtDaVP1q3hj5XJXH89HG6No8gMBBuuw2WLGnoGVZi4UImjBzJhLFj4dtvTTDdqlW1gmmgXGrIrEOHbCX3Npw8aRsjxNmoalPij1rr/sB9QDzgCJwAvgT6aK0f0VpXnpwkhBBCiAb36acwdy6cOgXTV0/nmrnXoBS8/z48/LCpDGLdwNioDCrKLv3+e8jPh7Q0WLkSpkyp+r4zKJtXXVWetRD2OuOveVrrRK31Z1rrl7TWb2mtf9Va59TH5IQQQghxdu66y2RJ3HEH7N4SQNqpozg7a+6/Hy65BP7zH2jWDPLyGnqmZXh7w5w5ZndlYiKsXQtDh5oc6rNQtuRe2WMhaqJ6f28ihBBCiHOSUrB1nT+FFHA4I4NduyAnRxMctp9Jj5wmJweOH4fTpxt6piWEmZJ//PEHtGkDnTpBVlbNnqU1FBbaSu51dXennasrHxw8SLuYGNrHxDB62zYppSdqRAJqIYQQ4gLxxvMBACxbm0rnzjD92x+4eV17ErrdQG4u+PjAJ5808CRL6tLFvE+YAI8+CikpJj+lJsvp114LTsVbx7ZnZ7M/N5fYrCyScnPZn5vLD0ePcllsrATVotrOGFArpXzqYyJCCCGEqFtBXia9IaBdGh9+CLn+6wFIykiiaVN46y0YMKAhZ1iGmxu88IL5/OabxcvnNVmlXrrU9rGqvOn8ovrUQlSHPSvU65VS3yilhilVSU9PIYQQQjR6ES0j+Pr6r+nVrjP33gs7j8cD8M417+DubjYpdugAO3c28ERLuv9+8/7ss8XRfnZ29Z8zaBD07w9UnTetgIxGuUtTNGb2BNSdgFnAbcBupdQMpVSnup2WEEIIIWqbv4c/N4TdwLrkdQAEuAcwqc8kotpF2cZMmgRRURXf3yB8faFFC0hKMr3SoWYr1EeOmNfp06Val0d4eNDW1RUfR0fAtDl/9cABZh06VHvfQZz3Km3sUuFgpS7HlM3zAOKAqVrrmDqaW4WksYsQQghRc9vTtjNy/kh+vfVX2jdvz55je0hIS+C6ztcBptHL4cMwapTZyNgoxMWZGtQbNsCwYbBmTfVzU6xfZtMm6Nmz3OUhcXGluipe3bw5v4aHn82sxXnA3sYu9uRQ+yqlHlZKbQT+CUwC/IDHgHlnPVMhhBBC1Jvjp4+TeCyRbanbAJi/bT4j5o8gr9Bs9OvbF0aPbkTBNEB4uFmp7trVHCck1Ow5Hh6mWkgFpJyeOBtVdUq0igHmAKO01sklzm9USn1YN9MSQgghRF0I9Q8FYMH2Bfxz2T9txydyT+Dn7ofWsG2b2Q/YsWNDzrQCbdqApyccPFj9e11d4YEHwM+vwsvWbokfHz5MEwcHFqens/nkSca1bEmkNdVEiErYE1A/qbX+uuQJpdQNWutvtNav1NG8hBBCCFEHvJt4E+wZzPK9yzl48iD9W5uNetaAGqBfP4ia+jZtInbx3rXvNeR0S3NwMC3ImzSBmTOhaVO4884z35eba17r18OIEfDNNybALqO7hwexp06RVyId9tOUFFZGREhQLapkz6bEqRWc+3dtT0QIIYQQ9SMsIIyDJ80q70XNLwJMQA0m1ePJJ2FhwWTe3/h+g82xUk2amPcvv4TvvrPvnpMnzfvq1fDzz2YJvgLRGRnkl9lblidl9IQdKg2olVLXKKVmAsFKqXdKvD4DpJ6MEEIIcY66MfRGmjdpDkBHH5PXYQ2oAaZMsb9gQYNZtgx++sm+sW5u8NFHZmUaKs3BjvL2xrlM8riLUkR5e5/NTMUFoKoV6kPARuA0sKnE6ydgSN1PTQghhBB1YXzP8UzsMxGAKztcyW/jfqNHix626xZtwT23Xbn7atr1u040a1bxzslTp2DHDtNqHMzq9I4dcNNNMHIkODubQLyCKmeRXl5ER0QwyteXtq6utHN1pau7O5N37y5XRi8mM5OXkpKkq6IAqsih1lrHAXFKqblaa1mRFkIIIc4jd/e8m89iP8PX3ZfL219e6pqjgyNPe+3D2bn43JYtJh79/XcIDq7nyVZk/354+mnTjeaSS4rPjxwJv/0GCxbAmDGwcSNccQWsXGkKbF90EXz7remc6OEBrVtD27alHv3LsWPFedS5uQBsKEobmRAURExmJoPj4sizWHBxcGBFeLjkWF/gqkr5sG5E3KyU2lL2VU/zE0IIIUQdaN2sNdF3RAPw886fWbF3he3aZZ9dRvMrZ/Hoo8Xjs7NNDPvXX/U7z0o5OsKcOWajoZXWJoAG+PNP8969O/z4I3TrZo6/+sq8b9wIY8fC88+XemxFedRW1pbl0RkZ5FksFAJ5FovkWIsqq3w8XPQ+vD4mIoQQQoj6o5SiQ/MOAEz7bRptvNowuMNgThecZnXSak4XnGbGsvcZeeJX3p7RgvBwSE6GoupyDa9VK5P2ER9ffC4/36R2fPSRCah794Zp00yXGqsePcyKdHy8Ccg3b4bMTFsXRmsedV4FQbV/0ZJ9lLc3jkph0RpHybEWVLFCrbU+XGLMEa11ktY6CUjFtLoXQgghxHkgLCCMRYmL2HRoE2lZZhXWzcmNpNw4fl56Aq1Nc8GXXjpzw5f41Hjmbplb95NWCsLC4Jdf4NFHTS6KiwvMmgU33mhSPDZuhIIKslbDwuB//zOr3FOnwsSJsHUrUJxHfV9ODqM8PAgpUV5vbmqqLZfa+scgAZEA+8rmfQNYShwXFp0TQgghxHlgZOeRALwe8zqpWakAtPNuB8B3C7NQymRHpKTAa69V/az52+Zz1093oStJm6hVo0bB8ePwzjvwyCNwyy0msB42DNzdITAQIiPL3zd8uFndvuQSs1o9dy68+KLtcqSzMx8MG8b3d9xBe3f3UrcuSEsjOiODAq3RQIGU1RPYF1A7aa3zrAdFn13qbkpCCCGEqE83d7uZYSHDSEhLIC3brFBbA+rs/GwApk+Hdu3MKrXFUsmDgGauzcgrzONU3qk6njUwZQpkZJjmLhs3wrp1cPQo3H67qe5x6FDFOyjvv9+keXh7m8TwYcNKp44U5Uqza1eFLcmjvL1xcXDAEXBxcJCUD2FXp8Q0pdQIrfVPAEqpkcDRup2WEEIIIerT7eG3c/jkYVwdXbk0+FJCfEIAyMorrpX33HPwyiumYWFlNh42mwLTstPwdPWs0znbhIWZ93XroEWLmt2/dKnJwXZ2Nt0YAbQu15J888mTdPfwYEV4ONEZGUR5e0uFD2FXQH0fMFcp9S4mVegAMK5OZyWEEEKIenVj2I22z+vuXsfOozuJahdVKij28DDv110HN99s9gXOnGkKZzg6mmvfxJus0NSsVNumxzoXFgbt29e8UHa3biaY7tABPv8cTp8udblkS/LVmZm2duT/LlNuT1y4zpjyobXeo7XuC4QCoVrrflrr3XU/NSGEEELUp89iP+Oyzy5Da01nv86svH0lfVv1LTcuMdEs4n7yidkTWHLF2t3Z5BxbNzfWi0GD4IknTEBcE8OHw0MPmVSPRYtMZ0XAWoi7bCk9aUcuyrJnhRql1LVAGNBEFW3v1Vo/X+VNQgghhDinnMo7xeqk1Yz+ajQ/3PxDpeN27DDvhw7BhAmlK3/4uPmQlZ+Fn7tfHc+2BFdXuPvumt/fvDm8/Tbs22eW4S+/3BTczjb542VL6Uk7clHWGVeolVIfAjcBkzApHzcAdv0dh1JqqFJqp1Jqt1JqaiVjblRKJSil4pVS86oxdyGEEELUojB/k4uckJbAidwThMwMYfZfsysce+SIqUi3Zo3JkrBq6tKUsV3HEtk60jbu99/LZVE0Tj/9BM88YzothoebDY1paeVakge6uPDqgQPMOnRI2o8LwL4V6n5a6x5KqS1a6+eUUm8A353pJqWUI/AecBWQDPyplPpJa51QYkwI8G+gv9b6uFIqoGZfQwghhBBnKyzABNTjwsfh5uTGnmN7OJB5oNy4v/6Ca681r99/hwceKL6WnZ+Nh4sHuQW5uDq5smgRjB8PSUmmd8qcOaYDuLVxYaOTlweDB5sW5p06QUICLFwIwML0dKxVrffn5vLD0aM4AK7SfvyCZ0/ZvJyi92ylVBCQD7S3474+wG6t9d6iUnvzgZFlxtwDvKe1Pg6gtU61b9pCCCGEqG0BHgGkPZ7GEwOfwNnRmQ7NO7D96PZy4yIiTOnmRx4xOdTXX198bc2da9iWuo1r510LwJAhsGwZ+PtDYSFMmmSOGy1XV/NbwgcfmLJ6Q4YAJo+6ghYxWJD248K+FeqFSilv4DXgL0ADH9lxXzCmIohVMnBpmTGdAJRSfwCOwLNa6yV2PFsIIYQQdaBk7nNYQBjxafHlxjg4wBVXFB/v3w9xcSZLorVXa8JbhLN492Lee890/77ySjPOzc3kXbdsWcdf4mw4OED//ubzE0/YTkd5e+MEFQbVGsgoKCAmM1NK6V2g7Kny8YLWOkNrvQCTO91Fa/20Hc+uqBtn2bZJTkAIEAXcAswuCt5LP0ipCUqpjUqpjWlp9bhrWAghhLiAhfmHkZCWQIGlojCy2B13wOuvg0VbmLFmBifzTpJyKoWHpqazaFHpsYGBZ25f3qikpUFmJpFeXqy++GJG+frS1d2dUX5+/F+AyVS1AK8eOMBlsbE8tW8fg+PiJK/6AmPPpsQmSqlHlVLfAfOAu5RSTex4djLQusRxK+BQBWN+1Frna633ATsxAXYpWutZWuteWute/mU6FgkhhBCibozqMoqrOlyFk0PVf6E9apRJ5cjJz2Hab9NIz04HYNGGeCZPLj02Jgb++c+quy02GklJEBAA8+cDEOnlxffdu5PQpw/fd+tGWn5+qeH5WlOIpIBciOzJof4CUzJvJvAu0BWYY8d9fwIhSqn2SikX4GbgpzJjfgAuB1BK+WFSQPbaN3UhhBBC1KU+wX1YettSfv/7d0LfCyU+tXz6B8DkydCnD5wq6qrYJ7gPAPtOxdO8eemxW7fC++9DSkqdTr12tG5tyujFV/y9y7Yld1ZK2pFfoOzJoe6stQ4vcbxSKRV3ppu01gVKqYnAr5j86E+01vFKqeeBjUWtzH8FrlZKJQCFwONa6/Tqfw0hhBBC1BVPF0+2H93O5pTNtkogAAWWAgothbg6ufLMM/Dl4gy4Fjr7dmZcyGNsXd6DYxeBj0/xs+64w5SMrqp9eaPh4AChoZUG1Na25G8lJ5NTWIi3kxO5WtPZ3b0+ZykaAXv+cd6slLK1SVJKXQr8Yc/Dtda/aK07aa0v0lq/WHTu6aJgGm08qrUO1Vp311rPr8mXEEIIIUTd6ezXGYDbvr+NI6eOACa9I/g/wVw629QbCA2FrCG3AdDMtRkDsl7ngyf6W3uj2Li4nCPBtFW3bqYutZ+fKapdge3Z2ezPzSU2K4vt2dn8cPQol8fGSh71BcSef6QvBdYqpfYrpfYDMcBlSqmtSqktdTo7IYQQQjS4Jk5NmNJvCgCbDm8C4ODJg6RmpXJZ28sAuOkmeGXsA9zd6i2evXUo143KZcO+eIoWcUt57TV46616m/7ZmToV7r0X0tPhzz/LXV5QSbEEaU9+YbEnoB6KqTt9WdGrPTAMGA5cV3dTE0IIIURj8a8B/wJgx1HTdzw1y7SOuCbkGtuY2yNu5x8dHqZtkAfv/fk+fT7vRnpO+YBz1SqzOfGc0KkTvPyy+VxB6kfZPGorJ5A86guIPWXzkrTWSZgGL9r6KnFeCCGEEOc5HzcfDj56kEf6PgJAWpYJlP3dSweUl19uGgse3Gxyrd/d8C4AixMXM2vTLPYe38vPP8NXXxXfs/f4Xlug3ih5e5s25CV7rBeZEBTEfzt1oo+nJxEeHjhg6gY7nFO1AcXZsqds3gilVCKwD1gF7AcW1/G8hBBCCNHIBHkGoYoCResKdYBHQIVjLQcvBuD51c+z7/g+hs0bxr0L72Xm+pnl6lBf9M5FdH2va91NvDb07WtaPYJZYp8xw3ZpQlAQ6y+5hBsDAlCYlccCSfm4oNiT8vEC0BfYpbVuDwzGzk2JQgghhDh//LbvN8b/OB6LtnBrj1vZ89AegjwrSJIGpj/hz8wrPwHgm4RvAPjuxu94c+ibHDwIN94I0dFmbJ/gPgy5aEh9fIWamzsXli41n6OjYfp0yM0tNSTK2xsXBwcpnXcBsiegzi8qZeeglHLQWq8EIup4XkIIIYRoZPYc28MnsZ+QlJGEm7MbHZp3wNHBscKxrVrB1V36E9kqkr8O/wVAZOtIAJo2hb/+Mk0IAU7knsCrSSNv1e3qCm3bms+PPw4nT5pzJUR6ebEiPJx7AgMZ0rw5X6SkSKWPC4Q9AXWGUqopsBqYq5R6m4pb2QshhBDiPGatQT3m6zF0frczH236qMrxnXw7sXb8WkZ3GU3zJs1JOZXCgE8GsC8nlt274YYbzLgdR3fwdfzXdT392uPuDo6Opj1kBS0fP0lJ4Yf0dD48fFjK510g7AmoRwLZwCPAEmAPUt1DCCGEuOD0DOzJ9aHX4+XqRWDTQDJO25cjfEPYDUzpPwU3Jzf+OPAHsSmxFY7LL8yv8Hyj9Mcf8O67sGdPqdPRGRnka207lvJ5F4ZKOyUqpToCLbTW1nxpC/C5UmoQ4A1IR0MhhBDiAtLEqQnf3PBNte9zUA5MHTCVAksBLo4uxKfGM28ezJwJv/8OlwRewqbDmziZdxIfN58zP7AxcHY27/HxEBJiOx3l7Y2zUuQVBdUuSkku9QWgqhXqt4CTFZzPLromhBBCCGE3Jwcnuvh1YVXSKpydNV5ekJkJE/tMBCDz9DmUGhEaat7XrSt1OtLLi+iICEb5+tLW1ZWL3Nx47+BBhsTFMevQoQaYqKgPVQXU7bTW5Tohaq03Au3qbEZCCCGEOG/1aNGDPw/9yaHW77BkCTTxzGZb6jbAbE48ZzRtanZevvIKbNxY7vLC9HSScnNJyM5mbmoqS48f595duySoPk9VFVA3qeKaW21PRAghhBDnv5cHv8yc0XO4t9e97N0L017fwxsxbwDYalyfM941TWtYu7bU6eiMjEqrN1TWqlyc26oKqP9USt1T9qRSajywqe6mJIQQQojzVXCzYG7tcStNnJqwdy+89aFJ8/j11l/p0aJHA8+umkaMAB+fci3Jo7y9K92ktis7W1apz0NVBdSTgTuVUtFKqTeKXquAu4GH62d6QgghhDjfbDy0kSFfDiGaZ7ntjc8AWLnYi+XLG3Ze1aYUhIXBrFmQkgLffw+5uUR6ebH64osZ1KxZuUBrf26upH6chyoNqLXWR7TW/YDnMO3G9wPPaa0jtdYp9TM9IYQQQpxvjmYfZfne5bz4x3PMif8YgJcPXs3bqz+hRMW5c0NUlHm//noYMwaeeQYwmxOH+vpSWRKLpH6cX85Yh1prvVJrPbPo9Vt9TEoIIYQQ56+hHYdyetppPhv5me1ccy9n/Huu5VxLo+a55yA5GRYuNMcpxWuO1lbkFQVbY/3962d+ol5UWodaCCGEEKKuODs623Km/6/7/3Ho5CHij24jKws8PBp4ctWhFAQHm88XX1wqoLa2Io/OyMDX2ZnF6elsPnUKBSw+dozuHh5EejXyluvCLvZ0ShRCCCGEqHVhAWFsuW8Ln4z8hFD/MDYcXM/wpz5r6GlV39q10L+/Caajo+GKK0yBbSBy0SL+/cUXTAgK4hpfX5Jyc9mfm8sPR49ymbQlP29IQC2EEEKIBuHi6EL3Ft1xcXTh7p7jATjV9qty4+bPh4cequ/ZVUNAAAQGwsiR0K8frFxZ3PDl//4Pnn4aKJ83nS9tyc8bkvIhhBBCiAYX0TKCI/88Ajk+/PgjDBoEzZtDQQFs2AArVoDFAg6NcSmwY0f49lvz+ehR8Pc3pfSGDCkeozVj/f1Zevy47ZSztCU/b0hALYQQQohGIcAjgFUbYdQoWLLExKOxsfDmm/Dzz400mC7Lz8+sWO/YUfp8ZiYTgoIA+PjwYfIsFo4XFPCPhAS8nZxwcXBgfGCgbUxJsw4d4q3kZHIKC4nw9GRK69aSe93ISEAthBBCiEYhKSOJOcdfZs6y+xnYz2xYDAiA6dPN+4IFpjJdo68EEhEBDz5YfDx+PBQFwNaA+d5du4qv5+YCsOHkyVJjwATTJcfuz81lUXo6qyIiJKhuRM6F3/WEEEIIcQHQaD6O+5As7xi++QaCgsyq9LRp8NdfptTzgQMNPUs7zJ4NbdrAokXmOCSk1G8BVdWgLnutorGSe934SEAthBBCiEahjVcbPJw9+G1bPH/8AUOHQsuW5tro0bBpk9n71+i1bm0i/+HDzfF//mN+IyhSVQ3qI3l5pSp/RDRtWm6MA+Dr7Fxr0xVnT1I+hBBCCNEoOCgHQv1DWbJ3EbmJbcle8SgODmZlt0UL85o92+wBtDYobLS6dIGvvoL1601AnZAAPXsCxSkd1rzoAq1JzssDIC4ri8tiY1kVEQHAzIMHUYAGfBwdySgsBGDy7t1Sx7oRkRVqIYQQQjQao7qM4oTjXvqPW86p/JPlrj/wALzySgNMrLpcXODGG+H11015kltvLXV5QlAQCX36sC8yktAynWysKR3RGRnkWSxowBHo1awZCrAAeRaLpH00IrJCLYQQQohG44mBT/DEwCcqvZ6Tc45U+7CyYwdlVeX0XBwcyLNYcHFwYKy/P2syM23HUnKv8ZCAWgghhBCNUoGlACeH0qGKo2MDTeZsfPyx6aA4Z06Fl8uW08vVmql79xLq7s5bHTuSnp9PlLe3Lb1jQVoaY/39Jd2jETmXfscTQgghxAWi90e9cX7BGb9X/fB71Y9/LfsXAMeOmeyJJUsaeILVkZQE8+bB6dOVDpkQFMT4wEBis7LYnp3N6sxMPjx8mIcSE23BdExmJpN372bF8eNM3r1b2pY3IhJQCyGEEKLRmdp/Kg/2fpCbu92Mv4c/C7YvAKBpU1i7FlJSGniC1REWZvKod+6sclhFJfLySpTIs+ZUFyI51I2NpHwIIYQQotEZGzqWyXlGuwAAIABJREFUsaFjAfh++/fsSt+F1hoXF8XevQ08ueoKCzPv8fEQHl7psLK51AAuJfKpo7y9S+VUSw514yEBtRBCCCEatdFdRwOwfO9yLg2+FE9XzwaeUTV16gROTrB0KfTvD23bVjisotbkHo6ObM3KItLLi0gvL1aEh/PFObU8f2GQlA8hhBBCnBOumnMVz696np9+gn79TMWPc4KLC3TvDp9/DgMGVDl0QlAQb3XsyLasLJJyc0nIzubeXbuYdeiQbcznR47w0eHDDI6LkzzqRkICaiGEEEKcM/7+//buPD6q6vzj+OdkISskLIksArIvAUENKK4gLrihFkXrhhaXWtefpbV1a6u1Yq3aWq0rWrXuYhWtCoogKCiETQRklX0LJARIIAnJ+f1xZrIxSYgzk8nMfN+vV17Jvffce88wDj6cPPd5dq8nNhYSEtwDimHjo4/g1lth40aoo/U4uFzpAzX2efOrlUfdNCmgFhERkbBwYqcT2V64nXPOgWnToEOHUM+oAdq3h3POcT8vWVLn0KHp6Qfl5HrblXvzqGMAY4xakDcRCqhFREQkLGQkZ5Bb6FZqd+2Cm26Cr74K8aQa4sgj4Re/gObN4fe/hyee8DlsSFoaM446igtat2Zw8+Y827NnRX71kLQ0/t69O7HGUG6tyuc1EQqoRUREJCxkpmSyvXA74Eo6v/02rF7tjs3bPJ+n5jwVwtkdgrZtXZOXkhIYPx5uuw0KC30OHZKWxn/79+fbY46pCKa9dpaWUm6tWpA3IQqoRUREJCzcecKdfDHmC8DFprm5cMkl8OGHkH3pJ9z8yc0UlTTxJxXz890TlV4//NDgS3jTPmJB5fOaCAXUIiIiEha6tOxCv8x+1fYdOAAjRwLD7wFg3Y66H/gLudTU6t/ryaf2xZv20TM5mS6JiSyuZZVbGo8CahEREQkLG3dv5OaPb2b0O6Mr9qWmwty5lWN2l20PwcwaID4erHUr1TExMGYMbNnSoEvMLijgphUrWFZU5LOsnjQ+BdQiIiISFtIT03lt8Wt8tuYzrLUV+7OzK8fk7W/iK9RecXEwbpz7+ZtvGnRqXWX1JDQUUIuIiEhYSG2Wyv1D72fX/l1s2et7VXfllia+Ql3Vvfe67w1M+/BVVi9n9266zJ7NKQsWcOPy5ar80cgUUIuIiEjYyMrMAuC9Ze9V7JuzaQ7NYhJh+Xl0LRpd26lNT2oqzJrl6v81gLes3sktWlTsyysrY21xMTMKCnhmyxaGLVyooLoRKaAWERGRsDHgsAEkxCZUBNQbd2/k0ncv5YJeP+Pp665l+ClJIZ5hAw0ZAi1bNvy0tDRGtG5d6/ESa1VOrxEpoBYREZGw0Tq5NRvv2MirF74KwKq8Vfy460fGHjOGNoeV8MJ3/wzxDBto8WK4/34oLW3wqb5SP7yaGaNyeo2otvdBREREpElqk9ym4uesDJcCMmv9t0zOWc6a8unccuwtoZpawy1cCH/8I/z859CjR4NO9aZ+/HX9ehbs3YsB0uPiaBYTw9h27RiSlhaUKcvBTNWnZMNBdna2zcnJCfU0REREpIkwfzLuh83HQPt55N+ZT3pimKzOFhW5MnopKX5fanZBAcMXLaKkvJxmMTFMHTBAQbWfjDHzrLXZ9Y1TyoeIiIiEtQeGPcCpXU7lD+eNBWBp7tKg3s9al/p8//0BuFhyckCCaXDl9ErKyylDLckbmwJqERERCWv3nHwPU6+aypgTRgCwZHvDuw82hDHQvz8cfniALvjoo/Dww35fRi3JQ0c51CIiIhIRdq7pTDNSWV+wIaj32bsX0tOhd+8AXXDGDFi1Cu6806/LDElLY+qAAfx1/Xo2l5Tw1KZN3L5qFSXl5eQfOEBKbCy3HX4417dvH6CJi5cCahEREYkIc76NofQv2/nV2uCWzisqgiefhG7dXOqHMX5eMCsLPv4YSkqgWTO/LrW4sJD3d+4EYM6ePQcdv2HFCgAF1QGmlA8RERGJCGPGQFFBEu3aBfc+mZnwv/+5BeX58wNwwawsOHAAVq70+1KH0oJcbcoDTwG1iIiIRISUFJi7bSbnv3k++fvyg3qvLl3gsstcs0O/ZbnSfw1tQe7LqIyMesesKCriuc2b/b6XVFJALSIiIhHjjff2MGn5JP42628V++ZsmsOa/DUBu0d+Pvz9725FvFevAFywd2+IiYH//Me1IgdYuhSeeQa2bWvQpa5v357fduxYEeAZoEdiIm3j4yvGrC0u5oYVKxRUB5ACahEREYkY3316DAB/+eov7D+wH4BjXziWnv/sGbB77NkD//63y9A4cCAAF0xMhEGD4MMPYfRot+/rr+HGG39S9Y/0uDi8ad0xwDXt2nGrj5IkSv0IHAXUIiIiEjFmfnoYL1/wMgDLdyxnZ5F7QK/MlgXsHp06wa5d8MYbMHRogC46c6brmLhpE+TmwhVXwFFHudbkDeSrfJ6vNuWHkh4ih0ZVPkRERCRiGANHtzsagCW5SwIaSNd0ySWwb1+ALhYfD6ee6qL0zExX9ePII2Hy5AZfyls+b/quXQxNT6/olli1TXlKbGyAJi6ggFpEREQiyLRp8NobvZjymy8Y3PFo0hLTWH3ramJN4ALIrVvhT3+CsWMhu96m1A1w0knwyScud7p3b/ew4ssvQ14etGp16NfZto0ht9zCkOefhyqtx4ekpXFW69YVZfVUQi9wlPIhIiIiEWP1avjfh/EMTB/G7uLdvLPkHTKSM+ic3jlg99izByZOhA0boLAQyssDdmlXPuThh933n1r94+GH4Z134IUXDjpUM29aedSBoYBaREREIsa118KWLZCUtpdOf+/E6HdH8/ri13ls9mMBu0ePHrB9u0t1Tk11K9ZB0a+f+97QgNpby89HY5eaedPKow4MBdQiIiIScVKbVRaIXpO/hnFTxmGtDeg9jjsOxo93RTqComNHaN4cPvusYec1b+6++wior2/fnmd79mRw8+Zc0KYN/VNSqh2fXVDAQ+vWMbug4KfOOiopoBYREZGIsXOny22eNg2Mp3hcm+Q2WCyFpYUBucf69e4eZWWuW2JD0psbxBiXS/3ee+4hxUOV5Gm97iOgBuifksLiwkI+3LGD4YsWVQTPswsKGL5oEff++GO1/VI/BdQiIiISMVJT3XN9q1bBpjs2sebWNbRIaAHA7uLdAbnH7t0wZYpL+9i716V+BM0rr7ia1GeffejnXHWV+17Livz0XbsoKS+nDCgpL2f6rl117pf6qcqHiIiIRIyEBKhsANgOgLREV+miYH8B7Zv7X9GiXz/3QCK4Cnfnnw/PP+/3ZX3r3bvh57Ro4R5K7NTJ52Fvner95eWUA5/m5VXUqm4WE0NJeXlF/Wo5NFqhFhERkYjz2mtulRoI+Ap1VY88AldeGfDLVjdxIpxySuX2yy/Dffe52n3eyL6q6dPd/sGDfV5uSFoat3TogAUsMKOggFMWLgRg6oABPNClC1MHDKioXy310wq1iIiIRJxrrnFfzz4Lw44YxtZfb6V1cuuAXHvlShfL3nknjBkTkEvWLS8P1q2r3H79dfegorWus8z48dXHf/QRPPoo9OpVa6rIwr17q22XWsv0Xbv4fefOCqR/AgXUIiIiEnFmz4Z2LuODpPgk4mLiWLFzBZ3TOpPSLKXuk+uxe7e7vjfF+I47oKAAJkzwc9K1ue46Vw/Qy9s98cgj4fvvDx4/fjzExcE557gHE1NTDxoyKiODKfn5FdvxxijFww8KqEVERCTiHHNM9e0v133J6a+ezudXfs7wrsP9vvbq1ZXbyclQWurXJetnzMH7srLgm28O3h8XB7/6FVx0Ua01/bzdEf++cSP7ysrolJjIK1u3sriwkJ2lpdValjeG2QUFB7VKDycKqEVERCTizJ/vgt6LL3bbWRmu6+AZ/zmDoruKSIhLCNi9/vzngF2qYbKy4M03oU8fV1qvTx+3/9//hm3bXE5KHfqnpLB63z5KrGVtcTEzCgpgyxZigISYmEbLo/aW6/M+DBmO+dt6KFFEREQizssvV8+SaJvalkHtB1Fuy1lXsK72Ew/B4sUwejQsW+bnJP11+eVw9dXwww/Vm79MmuSeynzrrTrrV0/ftYtSH6X1ymncsnmRUK4vqAG1MWaEMWa5MWaVMeZ3dYy7yBhjjTHZwZyPiIiIRIff/Q48hSsAMMbwwLAHAMgt9K9w9N69LnW5qMht5+ZC375uYbhRdekCL77oOsssXVq5f98+19xl/Hj45z9rPX1oejrxPlJJYqBRy+Z5y/XFNvJ9AyloKR/GmFjgKeB0YCMw1xgzyVq7tMa45sCtwLfBmouIiIhEF+8DiVV1TOvICR1PIDYm1q9rDxlSGb8WlhRSllhI376ZZGT4ddmfxhhYsADat3dPSnbtCvv3u9zpHj1gxoxaTx2Slsb0gQN5ZetWlhYWknvgABnx8WAt+61lcWFho6ReDElL4+/duzNhyxbaJwQuFacxBTOHejCwylq7BsAY8yZwPrC0xrgHgL8C44I4FxEREYkiO3a4tOIzz4TOnd2+vhl9+eoXXwX0Pie+dCILty7Evuu7K2Gj8DZwOf10GDTIrVCnp7sc69dec2VJWrTweao3YB66cCEl1lI1i2WOp3W59wHGYJldUMAtK1dSYi3s2cMnO3cybeDAsMqjDmbKRwegarXxjZ59FYwxRwEdrbUfBXEeIiIiEmV27IAbbqhzgfYnmzvXdUdcuRIWb1tcsf/BB+HttwN/v0P25puuBvW+fW6FOss9iFktHcSH2nKpASYGta+67/uXeGpih5NgBtQ+6rtQ8adljIkBHgd+Xe+FjLneGJNjjMnJbYQ3VkRERMJb9+6wZo17bq+qE148gfum3efXtfftc31WSkrgwj4X0qeNq67x2mswbZpfl/bPuedCSopL+UhKqgyolyyp87TacqnB1asOtpr3bxaGNbGNreVfJH5f2JghwB+ttWd6tn8PYK19yLOdBqwGvK162gJ5wEhrbU5t183OzrY5ObUeFhEREanw9tvuAcL773fb3Z/ozur81ez4zQ5aJ7fm++3f8+ScJym35cTFxPHbE37LEelHHPL1z3vjPFbnrea7G7+D8jji4uCdd+C77+CBB4Lzmmq1fTvcey8895xrE/nCCy7AjomBrVuhefNaT51dUMArW7cC0CIujoV79zIqIyPo6R6+7n9V27ZNJt3DGDPPWltv0Yxg5lDPBXoYY7oAm4BLgcu8B621BUAb77YxZjowrq5gWkRERKQhZs1yleO8AfUJnU5gdf5q3v/hfcYePZZ/zf0Xz89/nlZJrTgi/Qg27d7UoID6ix+/oKi0iDmb5nB8x+MB92zgpEkhCKiTkyEnBzp2hJNPdoH0ySfDlCnwySeu1l8thqSlhTSIDfX9/RW0lA9r7QHgZmAysAx421q7xBhzvzFmZLDuKyIiIuL197/DihWwaJHLqR5/3IskxiWyJNelQSzJXcJxhx9H7m9ymXvdXE7odEK915w5E846C9auhZnXzARge+H2iuOPPQarVgXl5dQtNRXmzYP16119aoAPPnCBdT1pH+KfoNahttZ+bK3taa3tZq190LPvPmvtJB9jh2p1WkRERIJh5054913YuCGWPm36sGLnCgBaJ7XmxI4nAlBaVkrB/oJ6r1VSAnl5UF4OmSmZwMG1rffsgZEj3XOCIZWYCI8+6sqdNMDsggIeWreO2QX1/3kESijuGShqPS4iIiIRb+hQV/nDGJiSNYVWSa0AeO+S9yrGnPDiCbRObs0nl39S57WGD4dvPd0zfj35cQC2FW6rNiY11aUtFxYG7jX8ZLffDtbCiBEuwbuOXGoITSvwcG8/rtbjIiIiEvFiYlwwDdAmuQ0rd67k6GePZuqaqRVjMlIyqqVuHIpPVrng27vi7WUMzJkDY8f6N++AmTbNfS1eXO/QULQCD/f24wqoRUREJCo89RTcdpv7uU1yGxZsXcBpr57GnmLXwCQzJfOQ2pJPmQKnngqbN0NxWTEAI3s18cfDhgyBDRvg+OPrHeptBW5w9Y53HTgQ9Ol57+kNTOfs2RNWqR8KqEVERCQqrF1b+Wxe6+TWFftTm6UCkJGcwbbCbawvWF/ndcrLobTU/bz/wH7GHjWWi/pedNC4jz6CXr2gSbTQSEqC1q3hv/+t/Fq71ufQIWlp3NKhAxYoB/66YQPPbd4c1Ol5248boAx4f8cOhi1cGDZBtXKoRUREJCo88kj17cNbHE7xgWKMJxeke6vulJSVMGPdDLq17Maa/DW0SmpFXEwc3Vt1Z2XeSs7odgYjRrh0ZIDiA8VYa1mxcwWd0jqRGJdYcf0WLVxX8KSkxnqF9ThwAH72s8rt445zNf58WLh3b7Xtibm5Qa9JvbO0lPIq296OieGQS62AWkRERKLGihUut/mKK2DFzdXznq89+lpO6nQSbVPbctfUu3hryVv0y+xHuS1nUPtBPDf/Ofb8fg8xpvIX/OmJ6czbMo9eT/ZizrVzGNRhUMWxk06Cl15yDyg2CfHxsHCh+/mxx+CVV1yy9/jxcOed1YaOyshgSn5+te1g83ZMLPE0HQynjolK+RAREZGo8d57cOWVUFAASfFJJMVXLh/HmBj6ZPRhwdYFPDPvGfL35zNz/UyyMrLIysyiqLSItbvW8sEHcOKJrmrIqltX8ey5zwKQW1Q9t8MY9/XAA7B6daO+TN9iYmDAAPd10kmV++fNO2jo9e3b82zPnvRJTuaIhAQ+ycvjuc2buXH5cm5cvjwoqRhD0tKYPnAgF7RuTeeEBLolJbG4SZRJqZ9WqEVERCRqXHMNXHxx3ZXjHvrqoWrbWZlZZGVkAXD1+1fz24wZJCZWVg3x1qL2VSFk92647z7o0we6dQvMawgI72SGDHH92Wt65RVYt45lnsB7bXEx7+/YUXH4pa1bmTZwYFDSMT7auRPvY5A3rHC/RWisFug/lVaoRUREJGocdpiLJWPqiICuOvIqjm53NMOOGMaQw4cwovsIBrYdSGJcIjPXz+TYYbl8/jmkphUz8o2RfLX+K+Dg5i7gHkrcuxcuOviZxdAaMgTOPx+efdb38XHjmJiQ4OpX++DNbw606bt2UbOmyMQm8VRn3RRQi4iISNTYtQsmTKg7BePKAVcy7/p5fDHmC2aNnUXP1j1Jik/iy6u/5NqjrqWkrASAfQf28eGKD8ktyiUpLsnnCnVMDKSkBOvV+CExEd5/3/1BDB0KRUXVj0+YwKgZM2o9PVj5zUPT0w9Kn2iM/G1/KaAWERGRqLF1K1x7rXswsaZaFmMrDO4wmOdHPs/UD9sweDDsyHc1qBPjEnnq7Kd8ls4rL4e77oKpUw861DTk5cGXX8I2T6fH8nLYtw/OOovrd+3i2UmTGNy8OQNTUuickMARCQkMTElhQGpqUPKbh6SlMeOoo7igdWv6JCczMCWFCVu2BL1sn7+UQy0iIiJRo1s3WL/elWTeuhU2boTsbBdHfvCBG3PhhbWfX1pWyl0bBtDt8EmUWlciLyE2gWuOusbneGPgb39zC8LDhwf61QRA27bu+8iRrovijz9C9+4uhzori+tfeQXGjeOGlSsrzyl2/5CYs8c1xAl0fvOQtDR+26kTJy9YUJH+Eax7BYpWqEVERCRqxMdDx47uYcFbb4XzznP7jYE//tF1567LqrxVbCpeztg/fENcggssE+ISOFB+gOznsvnbrL9VG28MlJS4YH34cHj5ZSgshC++gEcfddshdeqprlj299+7ILpVK1dG76ijICsL8vKYWMfqcLDym8Mtl1oBtYiIiESNvXvhX/+Cdu1g/3544w3X9fChh1yAfffddZ/fvVV3msU2Y8l213KxW8tutExsSVxMHDv37WTeloNL0AFceimkp8PVV7tujTfeCOPGwZ/+FOAX2FCJie5fEuAm1LKlq0ndr58LqIFRu3fXevr+8vKglNALt1xqBdQiIiISNfbsgZtucqWY77nHPY+3dq0LpJs1c1VA6hIfG09GTC+e+HgKa3ZuZNWtqzin5zkAZGVkVQTaVd19t6v28dJLsHw5HHOMa0s+cyYsWxb419hgnsC5tv3XL1jAsz17Mrh5cy5o04bLMzPxVAxkRkFBUFqEV82lHty8Oc/27Nlk0z1AOdQiIiISRTIzXe50ixaQkAAzZrh86oIC91zehAmuxF1d5ZX7NT+RyaVP8/ay1zmz57CK/VkZWXy25jMOlB8gLqYyxHrxRSgrc2nKLVq4FOQePdxXk9Cnj/teo1sihx0GXbvC2Wdzffv2FQHtQ+vWVRsWrBbhQ9LS+G///gG9ZrBohVpERESiRmwsZGRAbq5L/xg2zMWRqamugty118KiRXVf4+Obn2TbuG08PuLRavuzMrMoKSthVd6qavu3bHGpJTfeCJMnuyyL++93xTT+/GeYPj3AL7Khmjd3eS933FF9vzEut/rwwyEnBw64rGZvi3CvcGoRHiwKqEVERCRqlJTAgw9C587w/PNw773wv/+5leqTToKVK+GEE+q+RoyJITMlkxYJLartP6bdMYzOGu3znF/+Es44w90TXOvyZs1c7nYd5Z4bT1xcZevHqpKSXL3qQYNglfuHQtUW4X2SkzmrdetGnmzTY2x9RRebmOzsbJuTkxPqaYiIiEgYKix0q9G9e7uO261awccfu3bkh7rI+s9/wtNPw9Klvo9v3L2RK967gifOeoKluUt59u01XNf7Li67DHbscAvCCQlu7L59LmZt0jZtgm+/dWVKPGkdswsKGLpwISWeODLBmKC1Ig8lY8w8a212feO0Qi0iIiJRIzkZ8vNh4ULo3x86dIDrrqsMpqdMqVxFrs1hh7lza1NaVsqX677kv8v+y88n/pzpsXfz9SyLtdCmTWUwDWEQTIP7Q/rZz1wCeHk54MralVZZlA1WK/JwoYBaREREooYxboV6wwZXi7qmt9+GP/yh7muMHg1vvVX78S4tu9C1ZVeW5C5hRPcRALz47kZ+85uDx86ZA9dc4x6KbNLmz3cl9T77DFAedU0KqEVERCSq/N//uQob77xz8LFHHnFl9PzVL7Mfi7Yt4pbBtwBwyc1LGDxsG3tL9lYbt20bfPqp69jYpHXs6KJ+T55LzTzqPsnJ3L5qFc9t3szsggIuXLyYY+fNa/ItwwNFZfNEREQkqjz5pHso8ZRTDj7WsqUra7dsWWU1uZrGj4f//McVwKjNgMMGMGn5JNbtciXmNiR8yiU5Z9Huh3Zs/nVlkHnuua4KSJOXkeG+llSvs/1xXl5FHjW4FuExQHmVbWi6LcMDRSvUIiIiElUKC13Biu7dfR9/+GHX06SoyPfxzp3h2GPrvscdQ+7gtZ+9xlk9zmLIjDxYfQYAmSmZlJWXVYwzxpXvmzwZtm//Ka+mEWVlVQuoa+ZRe5XX2G7KLcMDRQG1iIiIRJWYGNexsLaO2qNGweuv+64iB/Dzn7sGMHVJT0znsv6XcUT6EXz5aUu++NZFyxNHTyQ2Jrba2DVrYMQImDatoa+kkWVluZQPTxBdM4+6NsFqT96UKKAWERGRqHLDDdCvn6vo4UtWFlx6qasUFwiTV38CF1wDQKukVuwp3lPteO/eMH7ixyxM/yMPfPkAm3ZvCsyNAy0ry/0r5J574MEHGTJhAtP79OGX7drRo2rpkhqC1Z68KVFALSIiIlHl1Vehbdu60zZWr3YdFFeuPPjYPffUXTavpufeXg/A6KzRdPlHF+7/8v5qx+PjLX9bPYbx3/yJ+6bfx9M5Tx/6xRvTiSe6mn9/+Yv7Q8jJYci+fTzdqxddkpPrPDXSy+rpoUQRERGJKiUlrgV5XdkKRUWu3nRp6cHH+vSBPXsO3l+b/OVZ0BMu6zuGWRtmkVtUPae4qLSI3m16M2bAGB7/5nG+317H046h1L+/S/j25k3HxLg/SGBURgZT8vNrPTXSy+opoBYREZGosn+/W3nu3t11LfSlf3+YNMn3scsvd1+H6qWHs+gxAZ79aA6Z6ZkHBdQpzVKYec1MwKWEpCU04W6DcVVCx8JCV/eva9eKKh4TtmwhMSaGvsnJHNW8OQs8//K4qm3biOuiWJVaj4uIiEhUueAC+OAD16PktNPqHnveea76RlkZ3HILjBnT8PsVFUHKI245/MxuZ5K3L485183hsomXMWfTHAB+uPkH4mIqg9XLJl7GH4f+kZ6tezb8ho1l1Cj47jv44Qe3Wn0IDyiGG7UeFxEREfFh+nRo1QoGDKh/bHKyC8BPOqmyZfjtt8PAgYd+vyeegAvL3+DzKz8nMyWT7YXb2Vuylze+f4P0xHSOO/w4YkxlSLZt7zbe+P4NPl/zecNeWGP79a+ha1e3ar1+fahnE1JK+RAREZGo0pBn43y1GM/OdoH2oVq2DPLWXcoJv4c9JXvon9mfpbmu4+A9J9/DBb0vqDY+MyWTFgktmm4utdfxx7suNxkZ8Pzz7kHFxMRQzyokFFCLiIhIVMnPhxUrXBW41NS6x1oLb77pHkR87z2YNQs+b+DC8b33ulbn774LV1zhgueXFrwEQFZG1kHjjTFkZWQxd/Nclu9YTq82vRp2w8aydq1LSB85Ei65BC666KCl+9kFBbyydStLCwvZby1j27WLyK6JSvkQERGRqDJqFBx3nEv9rU95OVx2Gdx8s9s+++yG369NG9fufNAgsNayvmA9I7qP4Murv6Rry64+zzmq7VHkbM7h8vcuZ9f+Jlpu7qmn3Cq1918ly5dXOzy7oIChCxfyzJYtzNi9mzl79nDDihU8t3mzj4uFN61Qi4iISFRZswbS0tyqcX1iY12c2Late+Zuzx648EKYMwc2HWL/lRdegA0b4KabYPaGbzj+xeOZdOkkzut1Xq3n/GX4XxjZaySDOgwiPbGJlpvzVvw45xz3vUbrydpak0/MzY24VWoF1CIiIhJV1q5t2PieVQptpKTA++837Px162BcMYfMAAAVIklEQVT2bJcd0TejLwBjJ43lo8s+YnCHwT7PSUtM48zuZzbsRo0trkYYWVRUbdPbmrykRlC9raSEO1evZvquXRUl9sK9rJ5SPkRERCSqbN8OM2ceFP8dkpgYePllt0J9qK6+GubNc2X60hLTSEtII7col6lrptZ53o6iHQx+fjBvfe/jycimoGZAXVhYbXNIWhrTBw48qDX5osJC/rphA3P27GFGQQHPbNkS9q3JFVCLiIhIVDn3XDj55ENP2ajpqqtcPvSh6tTJFcHwlum7ZuA1dGjegdO7nV7neSnxKczdPJfV+at/2kSDrZ6AGlxQHQ2tyRVQi4iISFQpLXXP0XXo0Dj3e+MN+OorF1gDPD7icTbesZHs9nX3C0mKT6J5s+bkFubWOS5kqgbUzZv7DKi9RmVk1HmpcG9NrhxqERERiSoLFjTu/bZsqcyhbmiZ5oyUDLYXbQ/OxPxVNaBOSakzoK7amrykvJz8AwcwQHpcHMXW0qshhb2bIK1Qi4iISFTZsMHVki4paZz7nXaaq3s9d27Dz81MyWTb3m2Bn1Qg1Ayo60lKv759e8a2a8fCwkLWFReztriYxYWFLCsq4v0dO8I6j1or1CIiIhJVTj/dlcLLzXU1ooOtb1/3IGPVaiGHamjnoewt2Rv4SQVC1YB6ypT6u+TgSuZVVVblZ28edThW+1BALSIiIlGlXTu3St1YcdsHH8Dkye5hxoZ66LSHAj+hQBk+HNLTYd8+6Oq7QU1NozIymJKfX7EdS2VQHc551AqoRUREJKpMm9a499uxw6V7FBdDlepxhyx/Xz4z1s3AGANAh+YdOKb9MQGe5U/Qu7fry754sevLvmcPjBlT5yneXOqJubmMysigf0oKr2zdChDWtagVUIuIiEhUWbXKpXyMGOE6IQZbnz6uRN/y5XDkkQ07d2fRTo585kg276ls1x0XE8eO3+wgLTHEwWdurkvzuOkmuPRS18GmnoAaXFBdtVNiuAbRVSmgFhERkagybBhs3OgeSmyMgHrwYFc6z1s2ryFaJ7dm5jUz2bXf1Wj+ev3X3P3F3awvWE//xP4BnmkDTZ4MV14JM2a4F1izLnUUMdZHj/WmLDs72+bk5IR6GiIiIhKmRo2CTz5xVd48WRRB9e9/w0svwZdf+n+tsvIyYkxMRfpHSG3e7DrclJa69pMRyBgzz1pbd8FwtEItIiIiUWbixMa9X16eSzP+qTnUVcXGNMKS+qFq396tTG/ZAh9+6FaqH3kk1LMKCdWhFhERkaiyeDFMmtR490tPh/x82BagctK3fHwLD3/1cGAu5o/Nm92LOuMM+PZbeOwxCLPMh0DRCrWIiIhElVNOcQFuY8V+w4e7Ihj1dN8+ZF+u+5LurboH5mL+mD8fRo+GF1+EFi2gvNw1d0lJCfXMGp1WqEVERCSqXHhh48Z8s2bBww+7eDMQmsU2o6Sskdo81sX7EOIvfuECaoAw7XToLwXUIiIiElUmTIC9jdh8sFkz10QmUCviTS6ghsouObt3h2YuIaaAWkRERCSIRo1yFeYOoTP3IWmSAbV3hTpKA2rlUIuIiIiEkc7pnSk+UBzqafgOqKM05UMBtYiIiEgYefmCl0M9BUcpHxWU8iEiIiIiDVe1zWSUp3wooBYREREJI3+Z+ReueO+KUE9DKR9VKOVDREREJIws37mcrzd8HeppVA+oW7Z0NagTE0M3nxDSCrWIiIhIGGkW00SqfHTp4r4feywYA0lJcN99sGdPaOcVAlqhFhEREQkjTaZsXmoq/Pe/lfUAly6F55+HE0+EM88M7dwamVaoRURERMJIs9hmTaNsXmEhHDgA3bq57Y4d4ZlnIDs7tPMKAQXUIiIiImGkc3pn+mX2C/U0IC8PLr7YrUoDNG8OF1wQuJaQYUQBtYiIiEgYuf2425k1dlaopwFt27rv339fua+szK1Uv/VWaOYUIsqhFhEREZGGi4+H6dMhPb1ynzGwfz/MmgWXXBKyqTU2rVCLiIiIhJFXF73KMc8dw77SfaGdSHExLFoE5eWV+2JiXA71kiWhm1cIKKAWERERCSM7inYwf8t8istC/GBicTHcdhv84x/V9/frB1OnwvLloZlXCCigFhEREQkjCXEJAKEvnedt7PLyy9X3Dx3qvt91V6NOJ5QUUIuIiIiEkWaxzYAmFFDXNGYMnHNO9YcVI5wCahEREZEw0uQDaoCjj4ZVq9wDilFAAbWIiIhIGGnfvD0Ad00NcUpFjCeMjI09+FhWlntY8YorGndOIaKAWkRERCSMnNb1NGJMDG8taQK1nh95BObPP3j/GWe4pi+33db4cwqBoNahNsaMAP4BxAIvWGvH1zh+B3AtcADIBX5hrV0XzDmJiIiIhLvWSa05r+d5oZ4GjBvne3/LlvD22407lxAK2gq1MSYWeAo4C+gL/NwY07fGsAVAtrX2SOBd4K/Bmo+IiIhIJJi0fBK5Rbl8sPwD3lnyDntL9oZ6Sr7t2wevvw5z5oR6JkEXzJSPwcAqa+0aa20J8CZwftUB1tpp1toiz+Y3wOFBnI+IiIhI2IuPiQdg576djH53NFe/f3VoJ1Sb0lJX8SMnJ9QzCbpgpnx0ADZU2d4IHFvH+LHAJ74OGGOuB64H6NSpU6DmJyIiIhJ2zupxFot+uYiy8jLGfTaOnM1NNGBt0QJmz4b27UM9k6ALZkBtfOyzPgcacwWQDZzi67i19jngOYDs7Gyf1xARERGJFkcediQAw44Yxhc/fsHekr2kNksN8ax8yM4O9QwaRTBTPjYCHatsHw5srjnIGHMacDcw0lob4h6aIiIiIuEjKyOLWBPL6rzVoZ5KVAvmCvVcoIcxpguwCbgUuKzqAGPMUcCzwAhr7fYgzkVEREQk4pzd42z23rWXxLjEUE8lqgVthdpaewC4GZgMLAPettYuMcbcb4wZ6Rn2CJAKvGOMWWiMmRSs+YiIiIhEmoS4BMptOTPWzWBn0c5QTydqBbUOtbX2Y+DjGvvuq/LzacG8v4iIiEikW5a7jFP+fQrvXPwOF/W9KNTTiUrqlCgiIiISxvpk9MFguPr9q8l8JJPMRzJ5JueZWsevL1hPuS1vxBlGvqCuUIuIiIhIcCXHJ/Pk2U/y/fbvAUhtlsrIXiMPGpezOYcXF7zI0zlP8+gZj3LHkDsae6oRSwG1iIiISJj71aBf1Ttm6pqpPJ3zNAB92vQJ9pSiilI+RERERCLImvw1jJsyjg9++ICSshKW71jOlNVTmL5uesWYs18/m09WfsLS3KWhm2gEMdaGV5+U7OxsmxMFLSxFREREforP13zO6a+eDsBz5z7HDzt+4LFvHvM5Nikuibw781R2rxbGmHnW2nq70yjlQ0RERCSCDO8ynLnXzWXQ84PILcrllmNvYVTfUQB0a9kNgI27NzJ59WTu/uJulu9YzoC2A0I55bCngFpEREQkghhjOKbdMcSaWO7+4m46NO/AmIFjqo05LPUwkuKTeP+H99l3YF+IZho5FFCLiIiIRBhjDJ3SOvHjrh9ZtmOZzzF9M/oy57o55O/L58r/XgnAuT3O5ZJ+lzTmVCOCHkoUERERiUArblkBQEp8Sp3jisuKmbVhFrM2zGJdwbrGmFrE0Qq1iIiISAQqLCkEIKVZ3QF129S2rL51dWNMKWJphVpEREQkAt34vxsB1/hFgksBtYiIiEgEmrl+JgCtk1qHeCaRTykfIiIiIhGod5vedGzRkYuzLg71VCKeVqhFREREIlByfDJFpUWhnkZUUEAtIiIiEoGW5i5l0bZFai/eCBRQi4iIiESg+Jh4APYf2B/imUQ+BdQiIiIiEeiek+8B6q9DLf5TQC0iIiISgbx1qFU2L/gUUIuIiIhEoAkLJgAQHxsf4plEPgXUIiIiIhHoX+f8i+uPvp7MlMxQTyXiqQ61iIiISAQ6ut3RPHves6GeRlTQCrWIiIiIiB8UUIuIiIiI+EEBtYiIiIiIHxRQi4iIiIj4QQG1iIiIiIgfFFCLiIiIiPhBAbWIiIiIiB8UUIuIiIiI+EEBtYiIiIiIHxRQi4iIiIj4QQG1iIiIiIgfFFCLiIiIiPhBAbWIiIiIiB8UUIuIiIiI+EEBtYiIiIiIHxRQi4iIiIj4QQG1iIiIiIgfFFCLiIiIiPjBWGtDPYcGMcbkAutCcOs2wI4Q3Fcal97n6KH3OjrofY4Oep+jR2O/152ttRn1DQq7gDpUjDE51trsUM9Dgkvvc/TQex0d9D5HB73P0aOpvtdK+RARERER8YMCahERERERPyigPnTPhXoC0ij0PkcPvdfRQe9zdND7HD2a5HutHGoRERERET9ohVpERERExA8KqOthjBlhjFlujFlljPldqOcj/jHGdDTGTDPGLDPGLDHG3ObZ38oY85kxZqXne0vPfmOMecLz/n9njDk6tK9AGsIYE2uMWWCM+ciz3cUY863nfX7LGNPMsz/Bs73Kc/yIUM5bDp0xJt0Y864x5gfP53qIPs+RyRjzf56/t783xrxhjEnUZzr8GWNeNMZsN8Z8X2Vfgz/DxpgxnvErjTFjGvt1KKCugzEmFngKOAvoC/zcGNM3tLMSPx0Afm2t7QMcB9zkeU9/B0y11vYApnq2wb33PTxf1wNPN/6UxQ+3AcuqbD8MPO55n/OBsZ79Y4F8a2134HHPOAkP/wA+tdb2Bgbg3m99niOMMaYDcCuQba3tB8QCl6LPdCT4NzCixr4GfYaNMa2APwDHAoOBP3iD8MaigLpug4FV1to11toS4E3g/BDPSfxgrd1irZ3v+XkP7n++HXDv68ueYS8DF3h+Ph94xTrfAOnGmHaNPG35CYwxhwPnAC94tg1wKvCuZ0jN99n7/r8LDPeMlybMGNMCOBmYAGCtLbHW7kKf50gVByQZY+KAZGAL+kyHPWvtDCCvxu6GfobPBD6z1uZZa/OBzzg4SA8qBdR16wBsqLK90bNPIoDnV4BHAd8Ch1lrt4ALuoFMzzD9NxC+/g78Fij3bLcGdllrD3i2q76XFe+z53iBZ7w0bV2BXOAlT2rPC8aYFPR5jjjW2k3A34D1uEC6AJiHPtORqqGf4ZB/thVQ183Xv2ZVFiUCGGNSgYnA7dba3XUN9bFP/w00ccaYc4Ht1tp5VXf7GGoP4Zg0XXHA0cDT1tqjgEIqfzXsi97nMOX59f35QBegPZCC+/V/TfpMR7ba3teQv98KqOu2EehYZftwYHOI5iIBYoyJxwXTr1lr3/Ps3ub91a/n+3bPfv03EJ5OAEYaY9biUrVOxa1Yp3t+XQzV38uK99lzPI2DfwUpTc9GYKO19lvP9ru4AFuf58hzGvCjtTbXWlsKvAccjz7Tkaqhn+GQf7YVUNdtLtDD8xRxM9wDEJNCPCfxgyeHbgKwzFr7WJVDkwDvU8FjgA+q7L/K82TxcUCB99dQ0nRZa39vrT3cWnsE7nP7hbX2cmAacJFnWM332fv+X+QZr9WsJs5auxXYYIzp5dk1HFiKPs+RaD1wnDEm2fP3uPe91mc6MjX0MzwZOMMY09Lz24wzPPsajRq71MMYczZuZSsWeNFa+2CIpyR+MMacCMwEFlOZW3sXLo/6baAT7i/ui621eZ6/uJ/EPdxQBFxjrc1p9InLT2aMGQqMs9aea4zpiluxbgUsAK6w1hYbYxKBV3E59XnApdbaNaGasxw6Y8xA3IOnzYA1wDW4xSJ9niOMMeZPwCW4ak0LgGtxebL6TIcxY8wbwFCgDbANV63jfRr4GTbG/AL3/3OAB621LzXq61BALSIiIiLy0ynlQ0RERETEDwqoRURERET8oIBaRERERMQPCqhFRERERPyggFpERERExA8KqEVE/GCMKTPGLDTGLDHGLDLG3GGMifEcyzbGPPETrjndGJMd+Nk2HmPMH40x40I9DxGRxhBX/xAREanDPmvtQABjTCbwOq4r2x889VEbtc6xMSbWWlvWmPcUEYl2WqEWEQkQa+124HrgZk8nr6HGmI8AjDGneFayFxpjFhhjmnv2/9YYs9izuj2+yuUuNsbMMcasMMac5Bl7hDFmpjFmvufreM/+ocaYacaY13FNizDG3GuM+cEY85kx5g3varExppsx5lNjzDzPtXrXfB3GmFRjzEueeX1njBlljBlrjHm8ypjrjDGPeX6+yjNukTHmVR/X83lPY8zFxpjvPefNCMR7ICISClqhFhEJIGvtGk/KR2aNQ+OAm6y1XxtjUoH9xpizgAuAY621RcaYVlXGx1lrB3u6tf4BOA3YDpxurd1vjOkBvAF4U0MGA/2stT960kVG4brExQHzgXmecc8Bv7TWrjTGHAv8Czi1xlzvxbX07Q/gaeVbAnxnjPmttbYU15HwBmNMFnA3cIK1dkeN1+BV2z3vA8601m4yxqTX/6crItI0KaAWEQk842Pf18BjxpjXgPestRuNMacBL1lriwCstXlVxr/n+T4POMLzczzwpKfddhnQs8r4OdbaHz0/nwh8YK3dB2CM+dDzPRU4HnjHdfAFIMHHXE8DLvVuWGvzPed/AZxrjFkGxFtrFxtjbgHetdbu8PEa6rvn18C/jTFvV3m9IiJhRwG1iEgAGWO64oLd7UAf735r7XhjzP+As4FvPMG0AWwtlyr2fC+j8u/q/wO2AQNwKXv7q4wvrDqNWq4ZA+zy5nzX9TJqmdcLwF3AD8BL9Yyt957W2l96VqzPARYaYwZaa3fWMzcRkSZHOdQiIgFijMkAngGetNbaGse6WWsXW2sfxj2o2BuYAvzCGJPsGeMrXaKqNGCLtbYcuBKIrWXcV8B5xphEzwrxOQDW2t3Aj8aYiz33M8aYAT7OnwLcXGXuLT3nfwt0BC7DpZsATAVGG2Na+3oNdd3T82fyrbX2PmCH59oiImFHAbWIiH+SvGXzgM9xweiffIy73fsAHrAP+MRa+ykwCcgxxizE5VnX5V/AGGPMN7h0j0Jfg6y1cz3XXYRLpcgBCjyHLwfGeuaxBDjfxyX+DLSsMt9hVY69DXztTQOx1i4BHgS+9Ix9zMf1arvnI54HH78HZnjmKyISdkyNRRQREYkAxphUa+1ez+r3DOB6a+38AFz3I+Bxa+1UvycpIhIhtEItIhKZnvOses8HJvobTBtj0o0xK3B1txVMi4hUoRVqERERERE/aIVaRERERMQPCqhFRERERPyggFpERERExA8KqEVERERE/KCAWkRERETEDwqoRURERET88P+8QFpQB0HB7QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Rated_Capacity = 1.1\n",
+    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "color_list = ['b:', 'g--', 'r-.', 'c.']\n",
+    "for name,color in zip(Battary_list, color_list):\n",
+    "    df_result = Battery[name]\n",
+    "    ax.plot(df_result['cycle'], df_result['capacity'], color, label='Battery_'+name)\n",
+    "#plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_sequences(text, window_size):\n",
+    "    #text:list of capacity\n",
+    "    x, y = [],[]\n",
+    "    for i in range(len(text) - window_size):\n",
+    "        sequence = text[i:i+window_size]\n",
+    "        target = text[i+1:i+1+window_size]\n",
+    "\n",
+    "        x.append(sequence)\n",
+    "        y.append(target)\n",
+    "\n",
+    "    return np.array(x), np.array(y)\n",
+    "\n",
+    "\n",
+    "# 留一评估：一组数据为测试集，其他所有数据全部拿来训练\n",
+    "def get_train_test(data_dict, name, window_size=8, train_ratio=0.):\n",
+    "    data_sequence=data_dict[name]['capacity']\n",
+    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
+    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
+    "    for k, v in data_dict.items():\n",
+    "        if k != name:\n",
+    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
+    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
+    "            \n",
+    "    return train_x, train_y, list(train_data), list(test_data)\n",
+    "\n",
+    "\n",
+    "def evaluation(y_test, y_predict):\n",
+    "    mae = mean_absolute_error(y_test, y_predict)\n",
+    "    mse = mean_squared_error(y_test, y_predict)\n",
+    "    rmse = sqrt(mean_squared_error(y_test, y_predict))\n",
+    "    return mae, rmse\n",
+    "\n",
+    "\n",
+    "def relative_error(y_test, y_predict, threshold):\n",
+    "    true_re, pred_re = len(y_test), 0\n",
+    "    for i in range(len(y_test)-1):\n",
+    "        if y_test[i] <= threshold >= y_test[i+1]:\n",
+    "            true_re = i - 1\n",
+    "            break\n",
+    "    for i in range(len(y_predict)-1):\n",
+    "        if y_predict[i] <= threshold:\n",
+    "            pred_re = i - 1\n",
+    "            break\n",
+    "    return abs(true_re - pred_re)/true_re    \n",
+    "    \n",
+    "    \n",
+    "def setup_seed(seed):\n",
+    "    np.random.seed(seed)  # Numpy module.\n",
+    "    random.seed(seed)  # Python random module.\n",
+    "    os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现。\n",
+    "    torch.manual_seed(seed) # 为CPU设置随机种子\n",
+    "    if torch.cuda.is_available():\n",
+    "        torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子\n",
+    "        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU，为所有GPU设置随机种子\n",
+    "        torch.backends.cudnn.benchmark = False\n",
+    "        torch.backends.cudnn.deterministic = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Net(nn.Module):\n",
+    "    def __init__(self, feature_size=8, hidden_size=[16, 8]):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.feature_size, self.hidden_size = feature_size, hidden_size\n",
+    "        self.layer0 = nn.Linear(self.feature_size, self.hidden_size[0])\n",
+    "        self.layers = [nn.Sequential(nn.Linear(self.hidden_size[i], self.hidden_size[i+1]), nn.ReLU()) \n",
+    "                       for i in range(len(self.hidden_size) - 1)]\n",
+    "        self.linear = nn.Linear(self.hidden_size[-1], 1)\n",
+    " \n",
+    "    def forward(self, x):\n",
+    "        out = self.layer0(x)\n",
+    "        for layer in self.layers:\n",
+    "            out = layer(out)\n",
+    "        out = self.linear(out) \n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 留一评估"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tain(LR=0.01, feature_size=8, hidden_size=[16,8], weight_decay=0.0, window_size=8, EPOCH=1000, seed=0):\n",
+    "    mae_list, rmse_list, re_list = [], [], []\n",
+    "    result_list = []\n",
+    "    for i in range(4):\n",
+    "        name = Battary_list[i]\n",
+    "        train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size)\n",
+    "        train_size = len(train_x)\n",
+    "        print('sample size: {}'.format(train_size))\n",
+    "\n",
+    "        setup_seed(seed)\n",
+    "        model = Net(feature_size=feature_size, hidden_size=hidden_size)\n",
+    "        if torch.cuda.is_available():\n",
+    "            model = model.cuda()\n",
+    "\n",
+    "        optimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=weight_decay)\n",
+    "        criterion = nn.MSELoss()\n",
+    "\n",
+    "        test_x = train_data.copy()\n",
+    "        loss_list, y_ = [0], []\n",
+    "        for epoch in range(EPOCH):\n",
+    "            X = np.reshape(train_x/Rated_Capacity, (-1, feature_size)).astype(np.float32)\n",
+    "            y = np.reshape(train_y[:,-1]/Rated_Capacity,(-1,1)).astype(np.float32)\n",
+    "\n",
+    "            X, y = torch.from_numpy(X), torch.from_numpy(y)\n",
+    "            output= model(X)\n",
+    "            loss = criterion(output, y)\n",
+    "            optimizer.zero_grad()              # clear gradients for this training step\n",
+    "            loss.backward()                    # backpropagation, compute gradients\n",
+    "            optimizer.step()                   # apply gradients\n",
+    "\n",
+    "            if (epoch + 1)%100 == 0:\n",
+    "                test_x = train_data.copy() #每100次重新预测一次\n",
+    "                point_list = []\n",
+    "                while (len(test_x) - len(train_data)) < len(test_data):\n",
+    "                    x = np.reshape(np.array(test_x[-feature_size:])/Rated_Capacity, (-1, feature_size)).astype(np.float32)\n",
+    "                    x = torch.from_numpy(x)\n",
+    "                    pred = model(x) # 测试集 模型预测#pred shape为(batch_size=1, feature_size=1)\n",
+    "                    next_point = pred.data.numpy()[0,0] * Rated_Capacity\n",
+    "                    test_x.append(next_point)#测试值加入原来序列用来继续预测下一个点\n",
+    "                    point_list.append(next_point)#保存输出序列最后一个点的预测值\n",
+    "                y_.append(point_list)#保存本次预测所有的预测值\n",
+    "                loss_list.append(loss)\n",
+    "                mae, rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
+    "                re = relative_error(\n",
+    "                    y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
+    "                print('epoch:{:<2d} | loss:{:<6.4f} | MAE:{:<6.4f} | RMSE:{:<6.4f} | RE:{:<6.4f}'.format(epoch, loss, mae, rmse, re))\n",
+    "            if (len(loss_list) > 1) and (abs(loss_list[-2] - loss_list[-1]) < 1e-6):\n",
+    "                break\n",
+    "\n",
+    "        mae, rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
+    "        re = relative_error(y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
+    "        mae_list.append(mae)\n",
+    "        rmse_list.append(rmse)\n",
+    "        re_list.append(re)\n",
+    "        result_list.append(y_[-1])\n",
+    "    return re_list, mae_list, rmse_list, result_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 设置 10 个不同的随机种子，然后取均值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1863 | RMSE:0.2072 | RE:0.6413\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0550 | RMSE:0.0661 | RE:0.1032\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0537 | RMSE:0.0643 | RE:0.1000\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0535 | RMSE:0.0641 | RE:0.0984\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0533 | RMSE:0.0638 | RE:0.0984\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0530 | RMSE:0.0634 | RE:0.0984\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0509 | RMSE:0.0605 | RE:0.0921\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0478 | RMSE:0.0565 | RE:0.0841\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0476 | RMSE:0.0561 | RE:0.0825\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0490 | RMSE:0.0580 | RE:0.0873\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1854 | RMSE:0.2368 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0342 | RMSE:0.0416 | RE:0.0772\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0347 | RMSE:0.0417 | RE:0.0740\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0334 | RMSE:0.0404 | RE:0.0724\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0324 | RMSE:0.0395 | RE:0.0693\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0349 | RMSE:0.0428 | RE:0.0787\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0305 | RMSE:0.0376 | RE:0.0661\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0320 | RMSE:0.0396 | RE:0.0724\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0298 | RMSE:0.0370 | RE:0.0661\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0272 | RMSE:0.0338 | RE:0.0504\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1647 | RMSE:0.1857 | RE:0.5836\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1432 | RMSE:0.1825 | RE:0.2620\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1395 | RMSE:0.1779 | RE:0.2564\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1391 | RMSE:0.1777 | RE:0.2550\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1388 | RMSE:0.1776 | RE:0.2550\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1383 | RMSE:0.1774 | RE:0.2535\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1378 | RMSE:0.1770 | RE:0.2521\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1371 | RMSE:0.1764 | RE:0.2507\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1364 | RMSE:0.1759 | RE:0.2479\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1357 | RMSE:0.1753 | RE:0.2465\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1707 | RMSE:0.1884 | RE:0.6212\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1607 | RMSE:0.2183 | RE:0.2677\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1562 | RMSE:0.2126 | RE:0.2610\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1556 | RMSE:0.2120 | RE:0.2610\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1551 | RMSE:0.2115 | RE:0.2597\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1546 | RMSE:0.2108 | RE:0.2584\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1540 | RMSE:0.2102 | RE:0.2584\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1534 | RMSE:0.2096 | RE:0.2570\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1527 | RMSE:0.2089 | RE:0.2557\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1519 | RMSE:0.2080 | RE:0.2544\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1507 | RMSE:0.2060 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1481 | RMSE:0.1821 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0852 | RMSE:0.1033 | RE:0.2333\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0428 | RMSE:0.0519 | RE:0.0556\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0413 | RMSE:0.0539 | RE:0.0063\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0015 | MAE:0.1937 | RMSE:0.2646 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1836 | RMSE:0.2448 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1121 | RMSE:0.1313 | RE:0.2929\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0347 | RMSE:0.0426 | RE:0.0772\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0335 | RMSE:0.0502 | RE:0.0047\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1484 | RMSE:0.2078 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1427 | RMSE:0.1837 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1302 | RMSE:0.1536 | RE:0.3470\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1303 | RMSE:0.1631 | RE:0.2649\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1246 | RMSE:0.1582 | RE:0.2465\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1456 | RMSE:0.2023 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1468 | RMSE:0.1783 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1590 | RMSE:0.1935 | RE:0.3601\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1452 | RMSE:0.1909 | RE:0.2771\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1343 | RMSE:0.1790 | RE:0.2530\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1631 | RMSE:0.2028 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1510 | RMSE:0.1904 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1014 | RMSE:0.1225 | RE:0.2905\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0830 | RMSE:0.1050 | RE:0.1397\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1469 | RMSE:0.1845 | RE:0.3444\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0016 | MAE:0.1977 | RMSE:0.2570 | RE:1.0000\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1874 | RMSE:0.2511 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1416 | RMSE:0.1719 | RE:0.3150\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0457 | RMSE:0.0719 | RE:0.0126\n",
+      "epoch:499 | loss:0.0002 | MAE:0.2516 | RMSE:0.3310 | RE:1.0000\n",
+      "epoch:599 | loss:0.0001 | MAE:0.3389 | RMSE:0.4527 | RE:1.0000\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1565 | RMSE:0.2030 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1451 | RMSE:0.1917 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1305 | RMSE:0.1558 | RE:0.3938\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0797 | RMSE:0.0995 | RE:0.1530\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0368 | RMSE:0.0448 | RE:0.0255\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0798 | RMSE:0.0877 | RE:0.0878\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1277 | RMSE:0.1482 | RE:0.2040\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1556 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1458 | RMSE:0.1862 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1611 | RMSE:0.1940 | RE:0.4056\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0413 | RMSE:0.0490 | RE:0.0254\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1125 | RMSE:0.1281 | RE:0.1365\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1644 | RMSE:0.2012 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0506 | RMSE:0.0772 | RE:0.0698\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0426 | RMSE:0.0695 | RE:0.0540\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0408 | RMSE:0.0651 | RE:0.0429\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0384 | RMSE:0.0596 | RE:0.0286\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0365 | RMSE:0.0552 | RE:0.0159\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0350 | RMSE:0.0519 | RE:0.0063\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0337 | RMSE:0.0491 | RE:0.0032\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0324 | RMSE:0.0462 | RE:0.0159\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0320 | RMSE:0.0447 | RE:0.0254\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1783 | RMSE:0.2436 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.2161 | RMSE:0.2861 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1194 | RMSE:0.1615 | RE:0.2189\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1348 | RMSE:0.1803 | RE:0.2583\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1432 | RMSE:0.1911 | RE:0.2835\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1466 | RMSE:0.1969 | RE:0.2976\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0862 | RMSE:0.1166 | RE:0.1638\n",
+      "epoch:799 | loss:0.0001 | MAE:0.3506 | RMSE:0.4155 | RE:0.6866\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1363 | RMSE:0.1826 | RE:0.2646\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0919 | RMSE:0.1214 | RE:0.1528\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1570 | RMSE:0.2013 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1563 | RMSE:0.1897 | RE:0.3343\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1002 | RMSE:0.1224 | RE:0.2181\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1014 | RMSE:0.1243 | RE:0.2195\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1051 | RMSE:0.1295 | RE:0.2238\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1099 | RMSE:0.1360 | RE:0.2295\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1151 | RMSE:0.1434 | RE:0.2365\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1192 | RMSE:0.1490 | RE:0.2422\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1218 | RMSE:0.1526 | RE:0.2450\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1243 | RMSE:0.1561 | RE:0.2479\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1568 | RMSE:0.1974 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0491 | RMSE:0.0600 | RE:0.1218\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0835 | RMSE:0.1093 | RE:0.1727\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0968 | RMSE:0.1275 | RE:0.1914\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1042 | RMSE:0.1377 | RE:0.2021\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1128 | RMSE:0.1498 | RE:0.2155\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1165 | RMSE:0.1550 | RE:0.2195\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1209 | RMSE:0.1611 | RE:0.2262\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1257 | RMSE:0.1680 | RE:0.2343\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1295 | RMSE:0.1734 | RE:0.2396\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1618 | RMSE:0.2035 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1539 | RMSE:0.1967 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1250 | RMSE:0.1471 | RE:0.3619\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0472 | RMSE:0.0546 | RE:0.0810\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0796 | RMSE:0.1021 | RE:0.1317\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0021 | MAE:0.1976 | RMSE:0.2583 | RE:1.0000\n",
+      "epoch:199 | loss:0.0006 | MAE:0.1911 | RMSE:0.2552 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1682 | RMSE:0.2197 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0717 | RMSE:0.0847 | RE:0.1937\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1257 | RMSE:0.1662 | RE:0.2142\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1830 | RMSE:0.2394 | RE:0.4047\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1560 | RMSE:0.2038 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1484 | RMSE:0.1978 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1340 | RMSE:0.1546 | RE:0.4306\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1238 | RMSE:0.1530 | RE:0.2663\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0815 | RMSE:0.1011 | RE:0.1615\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1547 | RMSE:0.1996 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1477 | RMSE:0.1925 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1402 | RMSE:0.1594 | RE:0.4793\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1083 | RMSE:0.1391 | RE:0.2102\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0406 | RMSE:0.0483 | RE:0.0295\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1294 | RMSE:0.2011 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1291 | RMSE:0.1545 | RE:0.3254\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0965 | RMSE:0.1163 | RE:0.2746\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0579 | RMSE:0.0687 | RE:0.1254\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0396 | RMSE:0.0507 | RE:0.0127\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0531 | RMSE:0.0719 | RE:0.0603\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1759 | RMSE:0.2613 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1590 | RMSE:0.2042 | RE:0.2299\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1060 | RMSE:0.1244 | RE:0.2772\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0458 | RMSE:0.0580 | RE:0.0898\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0861 | RMSE:0.1192 | RE:0.1118\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1306 | RMSE:0.2041 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1346 | RMSE:0.1594 | RE:0.3909\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1245 | RMSE:0.1490 | RE:0.3796\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1304 | RMSE:0.1581 | RE:0.2890\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1110 | RMSE:0.1389 | RE:0.2309\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0980 | RMSE:0.1231 | RE:0.1997\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1271 | RMSE:0.1963 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1383 | RMSE:0.1580 | RE:0.4578\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1579 | RMSE:0.1879 | RE:0.4003\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1372 | RMSE:0.1769 | RE:0.2704\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0900 | RMSE:0.1127 | RE:0.1700\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0580 | RMSE:0.0671 | RE:0.1017\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1197 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0541 | RMSE:0.0680 | RE:0.1238\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0357 | RMSE:0.0496 | RE:0.0429\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0332 | RMSE:0.0510 | RE:0.0143\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0739 | RMSE:0.1258 | RE:0.2143\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0897 | RMSE:0.1487 | RE:0.2857\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0833 | RMSE:0.1371 | RE:0.2476\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0854 | RMSE:0.1396 | RE:0.2540\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0853 | RMSE:0.1385 | RE:0.2508\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0922 | RMSE:0.1486 | RE:0.2810\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1852 | RMSE:0.2481 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0731 | RMSE:0.1070 | RE:0.0819\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0976 | RMSE:0.1334 | RE:0.1417\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1093 | RMSE:0.1470 | RE:0.1717\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1161 | RMSE:0.1552 | RE:0.1906\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1087 | RMSE:0.1464 | RE:0.1717\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0754 | RMSE:0.1068 | RE:0.0961\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0645 | RMSE:0.0944 | RE:0.0740\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0619 | RMSE:0.0915 | RE:0.0693\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0592 | RMSE:0.0883 | RE:0.0646\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1224 | RMSE:0.2014 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1048 | RMSE:0.1253 | RE:0.2635\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1094 | RMSE:0.1366 | RE:0.2337\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1007 | RMSE:0.1289 | RE:0.2096\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0942 | RMSE:0.1230 | RE:0.1926\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0828 | RMSE:0.1111 | RE:0.1700\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0680 | RMSE:0.0948 | RE:0.1360\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0414 | RMSE:0.0576 | RE:0.0737\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0186 | RMSE:0.0237 | RE:0.0071\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0499 | RMSE:0.0621 | RE:0.0850\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1146 | RMSE:0.1920 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1503 | RMSE:0.1948 | RE:0.2918\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1301 | RMSE:0.1751 | RE:0.2423\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0996 | RMSE:0.1374 | RE:0.1928\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0848 | RMSE:0.1194 | RE:0.1660\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0711 | RMSE:0.1014 | RE:0.1419\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0660 | RMSE:0.0948 | RE:0.1312\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0632 | RMSE:0.0909 | RE:0.1245\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0584 | RMSE:0.0838 | RE:0.1138\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0558 | RMSE:0.0787 | RE:0.1044\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.0589 | RMSE:0.0706 | RE:0.0286\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1897 | RMSE:0.2791 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1708 | RMSE:0.2509 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1459 | RMSE:0.2134 | RE:1.0000\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0001 | MAE:0.3334 | RMSE:0.4240 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1883 | RMSE:0.2464 | RE:0.4268\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1746 | RMSE:0.2285 | RE:0.3717\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1723 | RMSE:0.2255 | RE:0.3638\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1710 | RMSE:0.2238 | RE:0.3591\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1685 | RMSE:0.2207 | RE:0.3496\n",
+      "epoch:699 | loss:0.0001 | MAE:0.1686 | RMSE:0.2208 | RE:0.3512\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1672 | RMSE:0.2191 | RE:0.3465\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1660 | RMSE:0.2176 | RE:0.3417\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1648 | RMSE:0.2162 | RE:0.3386\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3412 | RMSE:0.4112 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0355 | RMSE:0.0432 | RE:0.0326\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0552 | RMSE:0.0639 | RE:0.0935\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0641 | RMSE:0.0763 | RE:0.1161\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0682 | RMSE:0.0824 | RE:0.1275\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0701 | RMSE:0.0853 | RE:0.1317\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0715 | RMSE:0.0873 | RE:0.1360\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0723 | RMSE:0.0886 | RE:0.1374\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0731 | RMSE:0.0898 | RE:0.1388\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0740 | RMSE:0.0912 | RE:0.1416\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1478 | RMSE:0.1649 | RE:0.1914\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1590 | RMSE:0.2266 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1120 | RMSE:0.1553 | RE:0.2289\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0749 | RMSE:0.0995 | RE:0.1098\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0663 | RMSE:0.0876 | RE:0.0884\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0662 | RMSE:0.0877 | RE:0.0884\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0716 | RMSE:0.0952 | RE:0.1004\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0635 | RMSE:0.0838 | RE:0.0803\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0784 | RMSE:0.1054 | RE:0.1191\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0766 | RMSE:0.1027 | RE:0.1138\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3086 | RMSE:0.3816 | RE:1.0000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch:199 | loss:0.0001 | MAE:0.1248 | RMSE:0.2174 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0830 | RMSE:0.1367 | RE:0.2508\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0002 | MAE:0.7786 | RMSE:0.9941 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1441 | RMSE:0.2373 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1336 | RMSE:0.2207 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0755 | RMSE:0.1110 | RE:0.1795\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0590 | RMSE:0.0832 | RE:0.1276\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1912 | RMSE:0.2359 | RE:0.4490\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0209 | RMSE:0.0303 | RE:0.0127\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0617 | RMSE:0.0888 | RE:0.0963\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3071 | RMSE:0.3759 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.0814 | RMSE:0.1324 | RE:0.1928\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0224 | RMSE:0.0347 | RE:0.0241\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0011 | MAE:0.1557 | RMSE:0.2035 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0536 | RMSE:0.0610 | RE:0.0952\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1097 | RMSE:0.1695 | RE:0.3571\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0634 | RMSE:0.1037 | RE:0.1492\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0634 | RMSE:0.1035 | RE:0.1492\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0645 | RMSE:0.1046 | RE:0.1524\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0735 | RMSE:0.1168 | RE:0.1841\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0814 | RMSE:0.1273 | RE:0.2143\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0906 | RMSE:0.1396 | RE:0.2492\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0904 | RMSE:0.1381 | RE:0.2444\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1949 | RMSE:0.2679 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1490 | RMSE:0.2156 | RE:0.3858\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0657 | RMSE:0.0971 | RE:0.0646\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0727 | RMSE:0.1043 | RE:0.0803\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0819 | RMSE:0.1145 | RE:0.1024\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0909 | RMSE:0.1247 | RE:0.1244\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0994 | RMSE:0.1347 | RE:0.1465\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1074 | RMSE:0.1443 | RE:0.1669\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1149 | RMSE:0.1534 | RE:0.1858\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1221 | RMSE:0.1622 | RE:0.2063\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0008 | MAE:0.1472 | RMSE:0.2358 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1374 | RMSE:0.1718 | RE:0.2805\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1110 | RMSE:0.1396 | RE:0.2309\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1099 | RMSE:0.1385 | RE:0.2280\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1085 | RMSE:0.1370 | RE:0.2252\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1068 | RMSE:0.1351 | RE:0.2195\n",
+      "epoch:699 | loss:0.0001 | MAE:0.1045 | RMSE:0.1324 | RE:0.2139\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1015 | RMSE:0.1290 | RE:0.2068\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0981 | RMSE:0.1248 | RE:0.1983\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0949 | RMSE:0.1208 | RE:0.1898\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0010 | MAE:0.2005 | RMSE:0.2195 | RE:0.9076\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0685 | RMSE:0.0815 | RE:0.1392\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0858 | RMSE:0.1063 | RE:0.1620\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0833 | RMSE:0.1027 | RE:0.1566\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0796 | RMSE:0.0974 | RE:0.1486\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0742 | RMSE:0.0895 | RE:0.1365\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0649 | RMSE:0.0767 | RE:0.1151\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0541 | RMSE:0.0627 | RE:0.0910\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0436 | RMSE:0.0513 | RE:0.0643\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0382 | RMSE:0.0471 | RE:0.0415\n",
+      "------------------------------------------------------------------\n",
+      "RE: mean: 0.2003 | std: 0.1069\n",
+      "MAE: mean: 0.0943 | std: 0.0332\n",
+      "RMSE: mean: 0.1253 | std: 0.0391\n",
+      "------------------------------------------------------------------\n",
+      "------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "window_size = 8\n",
+    "EPOCH = 1000\n",
+    "LR = 0.01    # learning rate\n",
+    "feature_size = window_size\n",
+    "hidden_size = [32,16]\n",
+    "weight_decay = 0.0\n",
+    "Rated_Capacity = 1.1\n",
+    "\n",
+    "MAE, RMSE, RE = [], [], []\n",
+    "for seed in range(10):\n",
+    "    re_list, mae_list, rmse_list, _ = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n",
+    "                                           window_size=window_size, EPOCH=EPOCH, seed=seed)\n",
+    "    RE.append(np.mean(np.array(re_list)))\n",
+    "    MAE.append(np.mean(np.array(mae_list)))\n",
+    "    RMSE.append(np.mean(np.array(rmse_list)))\n",
+    "    print('------------------------------------------------------------------')\n",
+    "\n",
+    "print('RE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(RE)), np.std(np.array(RE))))\n",
+    "print('MAE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(MAE)), np.std(np.array(MAE))))\n",
+    "print('RMSE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(RMSE)), np.std(np.array(RMSE))))\n",
+    "print('------------------------------------------------------------------')\n",
+    "print('------------------------------------------------------------------')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 查看每组电池的曲线拟合效果"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1197 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0541 | RMSE:0.0680 | RE:0.1238\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0357 | RMSE:0.0496 | RE:0.0429\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0332 | RMSE:0.0510 | RE:0.0143\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0739 | RMSE:0.1258 | RE:0.2143\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0897 | RMSE:0.1487 | RE:0.2857\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0833 | RMSE:0.1371 | RE:0.2476\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0854 | RMSE:0.1396 | RE:0.2540\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0853 | RMSE:0.1385 | RE:0.2508\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0922 | RMSE:0.1486 | RE:0.2810\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1852 | RMSE:0.2481 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0731 | RMSE:0.1070 | RE:0.0819\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0976 | RMSE:0.1334 | RE:0.1417\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1093 | RMSE:0.1470 | RE:0.1717\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1161 | RMSE:0.1552 | RE:0.1906\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1087 | RMSE:0.1464 | RE:0.1717\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0754 | RMSE:0.1068 | RE:0.0961\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0645 | RMSE:0.0944 | RE:0.0740\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0619 | RMSE:0.0915 | RE:0.0693\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0592 | RMSE:0.0883 | RE:0.0646\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1224 | RMSE:0.2014 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1048 | RMSE:0.1253 | RE:0.2635\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1094 | RMSE:0.1366 | RE:0.2337\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1007 | RMSE:0.1289 | RE:0.2096\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0942 | RMSE:0.1230 | RE:0.1926\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0828 | RMSE:0.1111 | RE:0.1700\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0680 | RMSE:0.0948 | RE:0.1360\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0414 | RMSE:0.0576 | RE:0.0737\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0186 | RMSE:0.0237 | RE:0.0071\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0499 | RMSE:0.0621 | RE:0.0850\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1146 | RMSE:0.1920 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1503 | RMSE:0.1948 | RE:0.2918\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1301 | RMSE:0.1751 | RE:0.2423\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0996 | RMSE:0.1374 | RE:0.1928\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0848 | RMSE:0.1194 | RE:0.1660\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0711 | RMSE:0.1014 | RE:0.1419\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0660 | RMSE:0.0948 | RE:0.1312\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0632 | RMSE:0.0909 | RE:0.1245\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0584 | RMSE:0.0838 | RE:0.1138\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0558 | RMSE:0.0787 | RE:0.1044\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seed = 6\n",
+    "_, _, _, result_list = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n",
+    "                            window_size=window_size, EPOCH=EPOCH, seed=seed)\n",
+    "for i in range(4):\n",
+    "    name = Battary_list[i]\n",
+    "    train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size)\n",
+    "\n",
+    "    aa = train_data[:window_size+1].copy() # 第一个输入序列\n",
+    "    [aa.append(a) for a in result_list[i]] # 测试集预测结果\n",
+    "\n",
+    "    battery = Battery[name]\n",
+    "    fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "    ax.plot(battery['cycle'], battery['capacity'], 'b.', label=name)\n",
+    "    ax.plot(battery['cycle'], aa, 'r.', label='Prediction')\n",
+    "    plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "    ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "    plt.legend()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/.ipynb_checkpoints/MLP_ed-checkpoint.ipynb b/.ipynb_checkpoints/MLP_ed-checkpoint.ipynb
new file mode 100644
index 0000000..51b6c37
--- /dev/null
+++ b/.ipynb_checkpoints/MLP_ed-checkpoint.ipynb
@@ -0,0 +1,1172 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0941abb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "562529b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install torch\n",
+    "# !pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3c74c5bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        idx = idx.astype(int)\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e35fd3",
+   "metadata": {},
+   "source": [
+    "#### Cycled at constant current of 1°C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "35c9e5e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load Dataset CS2_35 ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load Dataset CS2_36 ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_37 ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_38 ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "Battary_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'dataset/'\n",
+    "Battery = {}\n",
+    "for name in Battary_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "    path_sorted = np.array(path)[idx]\n",
+    "    \n",
+    "    count = 0\n",
+    "    discharge_capacities = []\n",
+    "    health_indicator = []\n",
+    "    internal_resistance = []\n",
+    "    CCCT = []\n",
+    "    CVCT = []\n",
+    "    for p in path_sorted:\n",
+    "        df = pd.read_excel(p,sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        cycles = list(set(df['Cycle_Index']))\n",
+    "        for c in cycles:\n",
+    "            df_lim = df[df['Cycle_Index'] == c]\n",
+    "            #Charging\n",
+    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "            c_v = df_c['Voltage(V)']\n",
+    "            c_c = df_c['Current(A)']\n",
+    "            c_t = df_c['Test_Time(s)']\n",
+    "            #CC or CV\n",
+    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "            #Discharging\n",
+    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "            d_v = df_d['Voltage(V)']\n",
+    "            d_c = df_d['Current(A)']\n",
+    "            d_t = df_d['Test_Time(s)']\n",
+    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "            if(len(list(d_c)) != 0):\n",
+    "                time_diff = np.diff(list(d_t))\n",
+    "                d_c = np.array(list(d_c))[1:]\n",
+    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
+    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "                count += 1\n",
+    "\n",
+    "    discharge_capacities = np.array(discharge_capacities)\n",
+    "    health_indicator = np.array(health_indicator)\n",
+    "    internal_resistance = np.array(internal_resistance)\n",
+    "    CCCT = np.array(CCCT)\n",
+    "    CVCT = np.array(CVCT)\n",
+    "    \n",
+    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "                              'capacity':discharge_capacities[idx],\n",
+    "                              'SoH':health_indicator[idx],\n",
+    "                              'resistance':internal_resistance[idx],\n",
+    "                              'CCCT':CCCT[idx],\n",
+    "                              'CVCT':CVCT[idx]})\n",
+    "    Battery[name] = df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "636d37c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fbaf40061f0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Rated_Capacity = 1.1\n",
+    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "color_list = ['b:', 'g--', 'r-.', 'c.']\n",
+    "for name,color in zip(Battary_list, color_list):\n",
+    "    df_result = Battery[name]\n",
+    "    ax.plot(df_result['cycle'], df_result['capacity'], color, label='Battery_'+name)\n",
+    "#plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90009950",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_sequences(text, window_size):\n",
+    "    #text:list of capacity\n",
+    "    x, y = [],[]\n",
+    "    for i in range(len(text) - window_size):\n",
+    "        sequence = text[i:i+window_size]\n",
+    "        target = text[i+1:i+1+window_size]\n",
+    "\n",
+    "        x.append(sequence)\n",
+    "        y.append(target)\n",
+    "\n",
+    "    return np.array(x), np.array(y)\n",
+    "\n",
+    "\n",
+    "# 留一评估：一组数据为测试集，其他所有数据全部拿来训练\n",
+    "def get_train_test(data_dict, name, window_size=8, train_ratio=0.):\n",
+    "    data_sequence=data_dict[name]['capacity']\n",
+    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
+    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
+    "    for k, v in data_dict.items():\n",
+    "        if k != name:\n",
+    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
+    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
+    "            \n",
+    "    return train_x, train_y, list(train_data), list(test_data)\n",
+    "\n",
+    "\n",
+    "def evaluation(y_test, y_predict):\n",
+    "    mae = mean_absolute_error(y_test, y_predict)\n",
+    "    mse = mean_squared_error(y_test, y_predict)\n",
+    "    rmse = sqrt(mean_squared_error(y_test, y_predict))\n",
+    "    return mae, rmse\n",
+    "\n",
+    "\n",
+    "def relative_error(y_test, y_predict, threshold):\n",
+    "    true_re, pred_re = len(y_test), 0\n",
+    "    for i in range(len(y_test)-1):\n",
+    "        if y_test[i] <= threshold >= y_test[i+1]:\n",
+    "            true_re = i - 1\n",
+    "            break\n",
+    "    for i in range(len(y_predict)-1):\n",
+    "        if y_predict[i] <= threshold:\n",
+    "            pred_re = i - 1\n",
+    "            break\n",
+    "    return abs(true_re - pred_re)/true_re    \n",
+    "    \n",
+    "    \n",
+    "def setup_seed(seed):\n",
+    "    np.random.seed(seed)  # Numpy module.\n",
+    "    random.seed(seed)  # Python random module.\n",
+    "    os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现。\n",
+    "    torch.manual_seed(seed) # 为CPU设置随机种子\n",
+    "    if torch.cuda.is_available():\n",
+    "        torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子\n",
+    "        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU，为所有GPU设置随机种子\n",
+    "        torch.backends.cudnn.benchmark = False\n",
+    "        torch.backends.cudnn.deterministic = True\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4cebcebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Net(nn.Module):\n",
+    "    def __init__(self, feature_size=8, hidden_size=[16, 8]):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.feature_size, self.hidden_size = feature_size, hidden_size\n",
+    "        self.layer0 = nn.Linear(self.feature_size, self.hidden_size[0])\n",
+    "        self.layers = [nn.Sequential(nn.Linear(self.hidden_size[i], self.hidden_size[i+1]), nn.ReLU()) \n",
+    "                       for i in range(len(self.hidden_size) - 1)]\n",
+    "        self.linear = nn.Linear(self.hidden_size[-1], 1)\n",
+    " \n",
+    "    def forward(self, x):\n",
+    "        out = self.layer0(x)\n",
+    "        for layer in self.layers:\n",
+    "            out = layer(out)\n",
+    "        out = self.linear(out) \n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8c0fd57",
+   "metadata": {},
+   "source": [
+    "#### 留一评估"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f79300c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tain(LR=0.01, feature_size=8, hidden_size=[16,8], weight_decay=0.0, window_size=8, EPOCH=1000, seed=0):\n",
+    "    mae_list, rmse_list, re_list = [], [], []\n",
+    "    result_list = []\n",
+    "    for i in range(4):\n",
+    "        name = Battary_list[i]\n",
+    "        train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size)\n",
+    "        train_size = len(train_x)\n",
+    "        print('sample size: {}'.format(train_size))\n",
+    "\n",
+    "        setup_seed(seed)\n",
+    "        model = Net(feature_size=feature_size, hidden_size=hidden_size)\n",
+    "        if torch.cuda.is_available():\n",
+    "            model = model.cuda()\n",
+    "\n",
+    "        optimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=weight_decay)\n",
+    "        criterion = nn.MSELoss()\n",
+    "\n",
+    "        test_x = train_data.copy()\n",
+    "        loss_list, y_ = [0], []\n",
+    "        for epoch in range(EPOCH):\n",
+    "            X = np.reshape(train_x/Rated_Capacity, (-1, feature_size)).astype(np.float32)\n",
+    "            y = np.reshape(train_y[:,-1]/Rated_Capacity,(-1,1)).astype(np.float32)\n",
+    "\n",
+    "            X, y = torch.from_numpy(X), torch.from_numpy(y)\n",
+    "            output= model(X)\n",
+    "            loss = criterion(output, y)\n",
+    "            optimizer.zero_grad()              # clear gradients for this training step\n",
+    "            loss.backward()                    # backpropagation, compute gradients\n",
+    "            optimizer.step()                   # apply gradients\n",
+    "\n",
+    "            if (epoch + 1)%100 == 0:\n",
+    "                test_x = train_data.copy() #每100次重新预测一次\n",
+    "                point_list = []\n",
+    "                while (len(test_x) - len(train_data)) < len(test_data):\n",
+    "                    x = np.reshape(np.array(test_x[-feature_size:])/Rated_Capacity, (-1, feature_size)).astype(np.float32)\n",
+    "                    x = torch.from_numpy(x)\n",
+    "                    pred = model(x) # 测试集 模型预测#pred shape为(batch_size=1, feature_size=1)\n",
+    "                    next_point = pred.data.numpy()[0,0] * Rated_Capacity\n",
+    "                    test_x.append(next_point)#测试值加入原来序列用来继续预测下一个点\n",
+    "                    point_list.append(next_point)#保存输出序列最后一个点的预测值\n",
+    "                y_.append(point_list)#保存本次预测所有的预测值\n",
+    "                loss_list.append(loss)\n",
+    "                mae, rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
+    "                re = relative_error(\n",
+    "                    y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
+    "                print('epoch:{:<2d} | loss:{:<6.4f} | MAE:{:<6.4f} | RMSE:{:<6.4f} | RE:{:<6.4f}'.format(epoch, loss, mae, rmse, re))\n",
+    "            if (len(loss_list) > 1) and (abs(loss_list[-2] - loss_list[-1]) < 1e-6):\n",
+    "                break\n",
+    "\n",
+    "        mae, rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
+    "        re = relative_error(y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
+    "        mae_list.append(mae)\n",
+    "        rmse_list.append(rmse)\n",
+    "        re_list.append(re)\n",
+    "        result_list.append(y_[-1])\n",
+    "    return re_list, mae_list, rmse_list, result_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c9c71a9",
+   "metadata": {},
+   "source": [
+    "### 设置 10 个不同的随机种子，然后取均值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "21309988",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1863 | RMSE:0.2071 | RE:0.6413\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0550 | RMSE:0.0660 | RE:0.1032\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0537 | RMSE:0.0643 | RE:0.1000\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0535 | RMSE:0.0641 | RE:0.0984\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0532 | RMSE:0.0637 | RE:0.0984\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0530 | RMSE:0.0634 | RE:0.0984\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0510 | RMSE:0.0605 | RE:0.0921\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0469 | RMSE:0.0553 | RE:0.0810\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0489 | RMSE:0.0579 | RE:0.0857\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0493 | RMSE:0.0584 | RE:0.0873\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1854 | RMSE:0.2368 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0342 | RMSE:0.0417 | RE:0.0772\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0344 | RMSE:0.0413 | RE:0.0724\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0334 | RMSE:0.0404 | RE:0.0724\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0324 | RMSE:0.0395 | RE:0.0693\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0343 | RMSE:0.0421 | RE:0.0772\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0305 | RMSE:0.0376 | RE:0.0661\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0304 | RMSE:0.0376 | RE:0.0677\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0294 | RMSE:0.0364 | RE:0.0646\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0271 | RMSE:0.0338 | RE:0.0504\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1647 | RMSE:0.1857 | RE:0.5836\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1432 | RMSE:0.1825 | RE:0.2620\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1395 | RMSE:0.1779 | RE:0.2564\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1392 | RMSE:0.1779 | RE:0.2564\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1387 | RMSE:0.1776 | RE:0.2550\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1383 | RMSE:0.1774 | RE:0.2535\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1377 | RMSE:0.1769 | RE:0.2521\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1372 | RMSE:0.1765 | RE:0.2507\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1364 | RMSE:0.1759 | RE:0.2479\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1357 | RMSE:0.1753 | RE:0.2465\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1707 | RMSE:0.1884 | RE:0.6212\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1607 | RMSE:0.2183 | RE:0.2677\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1562 | RMSE:0.2126 | RE:0.2610\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1556 | RMSE:0.2120 | RE:0.2610\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1552 | RMSE:0.2115 | RE:0.2597\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1546 | RMSE:0.2109 | RE:0.2584\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1539 | RMSE:0.2102 | RE:0.2584\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1533 | RMSE:0.2095 | RE:0.2570\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1526 | RMSE:0.2086 | RE:0.2557\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1520 | RMSE:0.2081 | RE:0.2544\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1507 | RMSE:0.2060 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1481 | RMSE:0.1821 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0852 | RMSE:0.1033 | RE:0.2333\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0428 | RMSE:0.0519 | RE:0.0556\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0413 | RMSE:0.0539 | RE:0.0063\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0015 | MAE:0.1937 | RMSE:0.2646 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1836 | RMSE:0.2448 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1121 | RMSE:0.1313 | RE:0.2929\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0347 | RMSE:0.0426 | RE:0.0772\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0335 | RMSE:0.0502 | RE:0.0047\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1484 | RMSE:0.2078 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1427 | RMSE:0.1837 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1302 | RMSE:0.1536 | RE:0.3470\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1303 | RMSE:0.1631 | RE:0.2649\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1245 | RMSE:0.1582 | RE:0.2465\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1456 | RMSE:0.2023 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1468 | RMSE:0.1783 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1590 | RMSE:0.1935 | RE:0.3601\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1452 | RMSE:0.1909 | RE:0.2771\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1343 | RMSE:0.1790 | RE:0.2530\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1631 | RMSE:0.2028 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1510 | RMSE:0.1904 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1014 | RMSE:0.1225 | RE:0.2905\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0830 | RMSE:0.1050 | RE:0.1397\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1468 | RMSE:0.1845 | RE:0.3444\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0016 | MAE:0.1977 | RMSE:0.2570 | RE:1.0000\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1874 | RMSE:0.2511 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1416 | RMSE:0.1719 | RE:0.3150\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0457 | RMSE:0.0719 | RE:0.0126\n",
+      "epoch:499 | loss:0.0002 | MAE:0.2515 | RMSE:0.3310 | RE:1.0000\n",
+      "epoch:599 | loss:0.0001 | MAE:0.3389 | RMSE:0.4527 | RE:1.0000\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1565 | RMSE:0.2030 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1451 | RMSE:0.1917 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1305 | RMSE:0.1558 | RE:0.3938\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0797 | RMSE:0.0995 | RE:0.1530\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0368 | RMSE:0.0448 | RE:0.0255\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0798 | RMSE:0.0877 | RE:0.0878\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1277 | RMSE:0.1482 | RE:0.2040\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1556 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1458 | RMSE:0.1862 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1611 | RMSE:0.1940 | RE:0.4056\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0413 | RMSE:0.0490 | RE:0.0254\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1125 | RMSE:0.1281 | RE:0.1365\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1644 | RMSE:0.2012 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0506 | RMSE:0.0772 | RE:0.0698\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0426 | RMSE:0.0695 | RE:0.0540\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0408 | RMSE:0.0652 | RE:0.0429\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0384 | RMSE:0.0596 | RE:0.0286\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0365 | RMSE:0.0552 | RE:0.0159\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0350 | RMSE:0.0519 | RE:0.0063\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0338 | RMSE:0.0492 | RE:0.0032\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0325 | RMSE:0.0463 | RE:0.0143\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0320 | RMSE:0.0446 | RE:0.0254\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1783 | RMSE:0.2436 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.2161 | RMSE:0.2861 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1194 | RMSE:0.1614 | RE:0.2189\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1348 | RMSE:0.1803 | RE:0.2583\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1431 | RMSE:0.1910 | RE:0.2819\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1514 | RMSE:0.2036 | RE:0.3134\n",
+      "epoch:699 | loss:0.0001 | MAE:0.5429 | RMSE:0.7141 | RE:1.0000\n",
+      "epoch:799 | loss:0.0001 | MAE:0.3657 | RMSE:0.4295 | RE:0.7165\n",
+      "epoch:899 | loss:0.0002 | MAE:0.4312 | RMSE:0.4909 | RE:0.8346\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0995 | RMSE:0.1321 | RE:0.1669\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1570 | RMSE:0.2013 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1563 | RMSE:0.1897 | RE:0.3343\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1002 | RMSE:0.1224 | RE:0.2181\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1014 | RMSE:0.1243 | RE:0.2195\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1052 | RMSE:0.1296 | RE:0.2238\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1097 | RMSE:0.1359 | RE:0.2295\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1152 | RMSE:0.1434 | RE:0.2365\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1190 | RMSE:0.1487 | RE:0.2422\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1216 | RMSE:0.1524 | RE:0.2450\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1242 | RMSE:0.1560 | RE:0.2479\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1568 | RMSE:0.1974 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0491 | RMSE:0.0600 | RE:0.1218\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0835 | RMSE:0.1093 | RE:0.1727\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0968 | RMSE:0.1275 | RE:0.1914\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1042 | RMSE:0.1377 | RE:0.2021\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1128 | RMSE:0.1497 | RE:0.2155\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1162 | RMSE:0.1546 | RE:0.2195\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1207 | RMSE:0.1609 | RE:0.2262\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1257 | RMSE:0.1680 | RE:0.2343\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1295 | RMSE:0.1733 | RE:0.2396\n",
+      "------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1618 | RMSE:0.2035 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1539 | RMSE:0.1967 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1250 | RMSE:0.1471 | RE:0.3619\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0472 | RMSE:0.0546 | RE:0.0810\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0797 | RMSE:0.1021 | RE:0.1317\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0021 | MAE:0.1976 | RMSE:0.2583 | RE:1.0000\n",
+      "epoch:199 | loss:0.0006 | MAE:0.1911 | RMSE:0.2552 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1682 | RMSE:0.2197 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0717 | RMSE:0.0847 | RE:0.1937\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1257 | RMSE:0.1662 | RE:0.2142\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1830 | RMSE:0.2394 | RE:0.4047\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1560 | RMSE:0.2038 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1484 | RMSE:0.1978 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1340 | RMSE:0.1546 | RE:0.4306\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1238 | RMSE:0.1530 | RE:0.2663\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0815 | RMSE:0.1011 | RE:0.1615\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1547 | RMSE:0.1996 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1477 | RMSE:0.1925 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1402 | RMSE:0.1594 | RE:0.4793\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1083 | RMSE:0.1391 | RE:0.2102\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0406 | RMSE:0.0483 | RE:0.0295\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1294 | RMSE:0.2011 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1291 | RMSE:0.1545 | RE:0.3254\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0965 | RMSE:0.1163 | RE:0.2746\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0579 | RMSE:0.0687 | RE:0.1254\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0396 | RMSE:0.0508 | RE:0.0127\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0531 | RMSE:0.0719 | RE:0.0603\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1759 | RMSE:0.2613 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1590 | RMSE:0.2042 | RE:0.2299\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1060 | RMSE:0.1244 | RE:0.2772\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0458 | RMSE:0.0580 | RE:0.0898\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0861 | RMSE:0.1192 | RE:0.1118\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1306 | RMSE:0.2041 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1346 | RMSE:0.1594 | RE:0.3909\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1245 | RMSE:0.1490 | RE:0.3796\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1304 | RMSE:0.1581 | RE:0.2890\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1110 | RMSE:0.1388 | RE:0.2309\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0980 | RMSE:0.1231 | RE:0.1997\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1271 | RMSE:0.1963 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1383 | RMSE:0.1580 | RE:0.4578\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1579 | RMSE:0.1879 | RE:0.4003\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1372 | RMSE:0.1769 | RE:0.2704\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0900 | RMSE:0.1127 | RE:0.1700\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0580 | RMSE:0.0671 | RE:0.1017\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1197 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0541 | RMSE:0.0680 | RE:0.1238\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0357 | RMSE:0.0496 | RE:0.0429\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0335 | RMSE:0.0515 | RE:0.0159\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0747 | RMSE:0.1269 | RE:0.2175\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0902 | RMSE:0.1495 | RE:0.2873\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0832 | RMSE:0.1369 | RE:0.2460\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0850 | RMSE:0.1390 | RE:0.2524\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0850 | RMSE:0.1380 | RE:0.2492\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0922 | RMSE:0.1486 | RE:0.2810\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1852 | RMSE:0.2481 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0732 | RMSE:0.1070 | RE:0.0819\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0976 | RMSE:0.1334 | RE:0.1417\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1093 | RMSE:0.1470 | RE:0.1717\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1161 | RMSE:0.1552 | RE:0.1906\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1087 | RMSE:0.1464 | RE:0.1717\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0754 | RMSE:0.1068 | RE:0.0961\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0644 | RMSE:0.0944 | RE:0.0740\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0619 | RMSE:0.0915 | RE:0.0693\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0592 | RMSE:0.0883 | RE:0.0646\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1224 | RMSE:0.2014 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1283 | RMSE:0.1545 | RE:0.2932\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1103 | RMSE:0.1379 | RE:0.2351\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1006 | RMSE:0.1287 | RE:0.2096\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0937 | RMSE:0.1224 | RE:0.1926\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0827 | RMSE:0.1110 | RE:0.1700\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0681 | RMSE:0.0949 | RE:0.1360\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0404 | RMSE:0.0560 | RE:0.0708\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0189 | RMSE:0.0240 | RE:0.0099\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0498 | RMSE:0.0620 | RE:0.0850\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1146 | RMSE:0.1920 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1503 | RMSE:0.1948 | RE:0.2918\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1301 | RMSE:0.1751 | RE:0.2423\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0996 | RMSE:0.1374 | RE:0.1928\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0855 | RMSE:0.1203 | RE:0.1673\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0713 | RMSE:0.1018 | RE:0.1419\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0660 | RMSE:0.0948 | RE:0.1312\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0618 | RMSE:0.0887 | RE:0.1218\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0586 | RMSE:0.0841 | RE:0.1138\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0513 | RMSE:0.0719 | RE:0.0964\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.0589 | RMSE:0.0706 | RE:0.0286\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1897 | RMSE:0.2791 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1707 | RMSE:0.2509 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1459 | RMSE:0.2134 | RE:1.0000\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0001 | MAE:0.3334 | RMSE:0.4240 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1883 | RMSE:0.2464 | RE:0.4268\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1746 | RMSE:0.2284 | RE:0.3717\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1723 | RMSE:0.2255 | RE:0.3638\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1709 | RMSE:0.2238 | RE:0.3591\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1685 | RMSE:0.2207 | RE:0.3512\n",
+      "epoch:699 | loss:0.0001 | MAE:0.1685 | RMSE:0.2208 | RE:0.3512\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1672 | RMSE:0.2191 | RE:0.3465\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1660 | RMSE:0.2176 | RE:0.3417\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1648 | RMSE:0.2161 | RE:0.3386\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3412 | RMSE:0.4113 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0355 | RMSE:0.0432 | RE:0.0326\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0552 | RMSE:0.0639 | RE:0.0935\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0641 | RMSE:0.0763 | RE:0.1161\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0682 | RMSE:0.0824 | RE:0.1275\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0701 | RMSE:0.0852 | RE:0.1317\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0714 | RMSE:0.0872 | RE:0.1360\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0723 | RMSE:0.0886 | RE:0.1374\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0731 | RMSE:0.0898 | RE:0.1388\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0740 | RMSE:0.0911 | RE:0.1416\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1479 | RMSE:0.1650 | RE:0.1914\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1590 | RMSE:0.2266 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1120 | RMSE:0.1553 | RE:0.2289\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0749 | RMSE:0.0995 | RE:0.1098\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0674 | RMSE:0.0892 | RE:0.0910\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0662 | RMSE:0.0877 | RE:0.0884\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0681 | RMSE:0.0902 | RE:0.0924\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0791 | RMSE:0.1063 | RE:0.1218\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0692 | RMSE:0.0921 | RE:0.0950\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0767 | RMSE:0.1028 | RE:0.1151\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3086 | RMSE:0.3816 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1248 | RMSE:0.2174 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0830 | RMSE:0.1366 | RE:0.2508\n",
+      "sample size: 2827\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch:99 | loss:0.0002 | MAE:0.7786 | RMSE:0.9940 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1441 | RMSE:0.2373 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1336 | RMSE:0.2207 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0755 | RMSE:0.1110 | RE:0.1795\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0590 | RMSE:0.0832 | RE:0.1276\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1912 | RMSE:0.2359 | RE:0.4490\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0210 | RMSE:0.0305 | RE:0.0142\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0618 | RMSE:0.0890 | RE:0.0963\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3070 | RMSE:0.3759 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.0814 | RMSE:0.1324 | RE:0.1928\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0224 | RMSE:0.0347 | RE:0.0241\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0011 | MAE:0.1557 | RMSE:0.2035 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0536 | RMSE:0.0610 | RE:0.0952\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1080 | RMSE:0.1671 | RE:0.3476\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0635 | RMSE:0.1038 | RE:0.1492\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0634 | RMSE:0.1035 | RE:0.1492\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0646 | RMSE:0.1048 | RE:0.1524\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0734 | RMSE:0.1168 | RE:0.1841\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0815 | RMSE:0.1274 | RE:0.2143\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0913 | RMSE:0.1405 | RE:0.2524\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0903 | RMSE:0.1381 | RE:0.2444\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1949 | RMSE:0.2679 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1489 | RMSE:0.2155 | RE:0.3843\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0657 | RMSE:0.0971 | RE:0.0646\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0727 | RMSE:0.1043 | RE:0.0803\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0819 | RMSE:0.1145 | RE:0.1024\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0909 | RMSE:0.1247 | RE:0.1244\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0994 | RMSE:0.1347 | RE:0.1465\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1074 | RMSE:0.1443 | RE:0.1669\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1150 | RMSE:0.1535 | RE:0.1858\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1221 | RMSE:0.1623 | RE:0.2063\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0008 | MAE:0.1472 | RMSE:0.2358 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1374 | RMSE:0.1718 | RE:0.2805\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1110 | RMSE:0.1396 | RE:0.2309\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1099 | RMSE:0.1385 | RE:0.2280\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1085 | RMSE:0.1370 | RE:0.2238\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1067 | RMSE:0.1350 | RE:0.2195\n",
+      "epoch:699 | loss:0.0001 | MAE:0.1045 | RMSE:0.1325 | RE:0.2139\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1015 | RMSE:0.1290 | RE:0.2068\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0981 | RMSE:0.1248 | RE:0.1983\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0949 | RMSE:0.1208 | RE:0.1898\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0010 | MAE:0.2005 | RMSE:0.2195 | RE:0.9076\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0685 | RMSE:0.0814 | RE:0.1392\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0858 | RMSE:0.1063 | RE:0.1620\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0833 | RMSE:0.1027 | RE:0.1566\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0796 | RMSE:0.0974 | RE:0.1486\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0742 | RMSE:0.0896 | RE:0.1365\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0649 | RMSE:0.0766 | RE:0.1151\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0540 | RMSE:0.0626 | RE:0.0910\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0436 | RMSE:0.0513 | RE:0.0643\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0382 | RMSE:0.0471 | RE:0.0415\n",
+      "------------------------------------------------------------------\n",
+      "RE: mean: 0.2004 | std: 0.1070\n",
+      "MAE: mean: 0.0944 | std: 0.0333\n",
+      "RMSE: mean: 0.1254 | std: 0.0393\n",
+      "------------------------------------------------------------------\n",
+      "------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "window_size = 8\n",
+    "EPOCH = 1000\n",
+    "LR = 0.01    # learning rate\n",
+    "feature_size = window_size\n",
+    "hidden_size = [32,16]\n",
+    "weight_decay = 0.0\n",
+    "Rated_Capacity = 1.1\n",
+    "\n",
+    "MAE, RMSE, RE = [], [], []\n",
+    "for seed in range(10):\n",
+    "    re_list, mae_list, rmse_list, _ = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n",
+    "                                           window_size=window_size, EPOCH=EPOCH, seed=seed)\n",
+    "    RE.append(np.mean(np.array(re_list)))\n",
+    "    MAE.append(np.mean(np.array(mae_list)))\n",
+    "    RMSE.append(np.mean(np.array(rmse_list)))\n",
+    "    print('------------------------------------------------------------------')\n",
+    "\n",
+    "print('RE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(RE)), np.std(np.array(RE))))\n",
+    "print('MAE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(MAE)), np.std(np.array(MAE))))\n",
+    "print('RMSE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(RMSE)), np.std(np.array(RMSE))))\n",
+    "print('------------------------------------------------------------------')\n",
+    "print('------------------------------------------------------------------')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7496047b",
+   "metadata": {},
+   "source": [
+    "#### 查看每组电池的曲线拟合效果"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "aaf87962",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1197 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0541 | RMSE:0.0680 | RE:0.1238\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0357 | RMSE:0.0496 | RE:0.0429\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0335 | RMSE:0.0515 | RE:0.0159\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0747 | RMSE:0.1269 | RE:0.2175\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0902 | RMSE:0.1495 | RE:0.2873\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0832 | RMSE:0.1369 | RE:0.2460\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0850 | RMSE:0.1390 | RE:0.2524\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0850 | RMSE:0.1380 | RE:0.2492\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0922 | RMSE:0.1486 | RE:0.2810\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1852 | RMSE:0.2481 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0732 | RMSE:0.1070 | RE:0.0819\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0976 | RMSE:0.1334 | RE:0.1417\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1093 | RMSE:0.1470 | RE:0.1717\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1161 | RMSE:0.1552 | RE:0.1906\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1087 | RMSE:0.1464 | RE:0.1717\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0754 | RMSE:0.1068 | RE:0.0961\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0644 | RMSE:0.0944 | RE:0.0740\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0619 | RMSE:0.0915 | RE:0.0693\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0592 | RMSE:0.0883 | RE:0.0646\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1224 | RMSE:0.2014 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1283 | RMSE:0.1545 | RE:0.2932\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1103 | RMSE:0.1379 | RE:0.2351\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1006 | RMSE:0.1287 | RE:0.2096\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0937 | RMSE:0.1224 | RE:0.1926\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0827 | RMSE:0.1110 | RE:0.1700\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0681 | RMSE:0.0949 | RE:0.1360\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0404 | RMSE:0.0560 | RE:0.0708\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0189 | RMSE:0.0240 | RE:0.0099\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0498 | RMSE:0.0620 | RE:0.0850\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1146 | RMSE:0.1920 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1503 | RMSE:0.1948 | RE:0.2918\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1301 | RMSE:0.1751 | RE:0.2423\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0996 | RMSE:0.1374 | RE:0.1928\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0855 | RMSE:0.1203 | RE:0.1673\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0713 | RMSE:0.1018 | RE:0.1419\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0660 | RMSE:0.0948 | RE:0.1312\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0618 | RMSE:0.0887 | RE:0.1218\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0586 | RMSE:0.0841 | RE:0.1138\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0513 | RMSE:0.0719 | RE:0.0964\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seed = 6\n",
+    "_, _, _, result_list = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n",
+    "                            window_size=window_size, EPOCH=EPOCH, seed=seed)\n",
+    "for i in range(4):\n",
+    "    name = Battary_list[i]\n",
+    "    train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size)\n",
+    "\n",
+    "    aa = train_data[:window_size+1].copy() # 第一个输入序列\n",
+    "    [aa.append(a) for a in result_list[i]] # 测试集预测结果\n",
+    "\n",
+    "    battery = Battery[name]\n",
+    "    fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "    ax.plot(battery['cycle'], battery['capacity'], 'b.', label=name)\n",
+    "    ax.plot(battery['cycle'], aa, 'r.', label='Prediction')\n",
+    "    plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "    ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1a8cec1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34eb40dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d37eac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/RNN & LSTM-checkpoint.ipynb b/.ipynb_checkpoints/RNN & LSTM-checkpoint.ipynb
new file mode 100644
index 0000000..d15020a
--- /dev/null
+++ b/.ipynb_checkpoints/RNN & LSTM-checkpoint.ipynb	
@@ -0,0 +1,842 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)\n",
+    "\n",
+    "\n",
+    "def build_sequences(text, window_size):\n",
+    "    #text:list of capacity\n",
+    "    x, y = [],[]\n",
+    "    for i in range(len(text) - window_size):\n",
+    "        sequence = text[i:i+window_size]\n",
+    "        target = text[i+1:i+1+window_size]\n",
+    "\n",
+    "        x.append(sequence)\n",
+    "        y.append(target)\n",
+    "\n",
+    "    return np.array(x), np.array(y)\n",
+    "\n",
+    "\n",
+    "# 留一评估：一组数据为测试集，其他所有数据全部拿来训练\n",
+    "def get_train_test(data_dict, name, window_size=8):\n",
+    "    data_sequence=data_dict[name]['capacity']\n",
+    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
+    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
+    "    for k, v in data_dict.items():\n",
+    "        if k != name:\n",
+    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
+    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
+    "            \n",
+    "    return train_x, train_y, list(train_data), list(test_data)\n",
+    "\n",
+    "\n",
+    "def relative_error(y_test, y_predict, threshold):\n",
+    "    true_re, pred_re = len(y_test), 0\n",
+    "    for i in range(len(y_test)-1):\n",
+    "        if y_test[i] <= threshold >= y_test[i+1]:\n",
+    "            true_re = i - 1\n",
+    "            break\n",
+    "    for i in range(len(y_predict)-1):\n",
+    "        if y_predict[i] <= threshold:\n",
+    "            pred_re = i - 1\n",
+    "            break\n",
+    "    return abs(true_re - pred_re)/true_re if abs(true_re - pred_re)/true_re<=1 else 1\n",
+    "\n",
+    "\n",
+    "def evaluation(y_test, y_predict):\n",
+    "    mae = mean_absolute_error(y_test, y_predict)\n",
+    "    mse = mean_squared_error(y_test, y_predict)\n",
+    "    rmse = sqrt(mean_squared_error(y_test, y_predict))\n",
+    "    return mae, rmse\n",
+    "    \n",
+    "    \n",
+    "def setup_seed(seed):\n",
+    "    np.random.seed(seed)  # Numpy module.\n",
+    "    random.seed(seed)  # Python random module.\n",
+    "    os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现。\n",
+    "    torch.manual_seed(seed) # 为CPU设置随机种子\n",
+    "    if torch.cuda.is_available():\n",
+    "        torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子\n",
+    "        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU，为所有GPU设置随机种子\n",
+    "        torch.backends.cudnn.benchmark = False\n",
+    "        torch.backends.cudnn.deterministic = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load Dataset CS2_35 ...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:188: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
+      "  return func(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
+      "Load Dataset CS2_36 ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_2_3_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_9_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_36\\CS2_36_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_37 ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_38 ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_17_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_18_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
+      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "Battary_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'datasets/CALCE/'\n",
+    "Battery = {}\n",
+    "for name in Battary_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheetname=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "    path_sorted = np.array(path)[idx]\n",
+    "    \n",
+    "    count = 0\n",
+    "    discharge_capacities = []\n",
+    "    health_indicator = []\n",
+    "    internal_resistance = []\n",
+    "    CCCT = []\n",
+    "    CVCT = []\n",
+    "    for p in path_sorted:\n",
+    "        df = pd.read_excel(p,sheetname=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        cycles = list(set(df['Cycle_Index']))\n",
+    "        for c in cycles:\n",
+    "            df_lim = df[df['Cycle_Index'] == c]\n",
+    "            #Charging\n",
+    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "            c_v = df_c['Voltage(V)']\n",
+    "            c_c = df_c['Current(A)']\n",
+    "            c_t = df_c['Test_Time(s)']\n",
+    "            #CC or CV\n",
+    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "            #Discharging\n",
+    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "            d_v = df_d['Voltage(V)']\n",
+    "            d_c = df_d['Current(A)']\n",
+    "            d_t = df_d['Test_Time(s)']\n",
+    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "            if(len(list(d_c)) != 0):\n",
+    "                time_diff = np.diff(list(d_t))\n",
+    "                d_c = np.array(list(d_c))[1:]\n",
+    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
+    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "                count += 1\n",
+    "\n",
+    "    discharge_capacities = np.array(discharge_capacities)\n",
+    "    health_indicator = np.array(health_indicator)\n",
+    "    internal_resistance = np.array(internal_resistance)\n",
+    "    CCCT = np.array(CCCT)\n",
+    "    CVCT = np.array(CVCT)\n",
+    "    \n",
+    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "                              'capacity':discharge_capacities[idx],\n",
+    "                              'SoH':health_indicator[idx],\n",
+    "                              'resistance':internal_resistance[idx],\n",
+    "                              'CCCT':CCCT[idx],\n",
+    "                              'CVCT':CVCT[idx]})\n",
+    "    Battery[name] = df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x190f43874a8>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Rated_Capacity = 1.1\n",
+    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "color_list = ['b:', 'g--', 'r-.', 'c.']\n",
+    "for name,color in zip(Battary_list, color_list):\n",
+    "    df_result = Battery[name]\n",
+    "    ax.plot(df_result['cycle'], df_result['capacity'], color, label='Battery_'+name)\n",
+    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Net(nn.Module):\n",
+    "    def __init__(self, input_size, hidden_dim, num_layers, n_class=1, mode='LSTM'):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.hidden_dim = hidden_dim\n",
+    "        self.cell = nn.LSTM(input_size=input_size, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)\n",
+    "        if mode == 'GRU':\n",
+    "            self.cell = nn.GRU(input_size=input_size, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)\n",
+    "        elif mode == 'RNN':\n",
+    "            self.cell = nn.RNN(input_size=input_size, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)\n",
+    "        self.linear = nn.Linear(hidden_dim, n_class)\n",
+    " \n",
+    "    def forward(self, x):           # x shape: (batch_size, seq_len, input_size)\n",
+    "        out, _ = self.cell(x) \n",
+    "        out = out.reshape(-1, self.hidden_dim)\n",
+    "        out = self.linear(out)      # out shape: (batch_size, n_class=1)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tain(lr=0.001, feature_size=16, hidden_dim=128, num_layers=2, weight_decay=0.0, mode = 'LSTM', EPOCH=1000, seed=0):\n",
+    "    score_list, result_list = [], []\n",
+    "    for i in range(4):\n",
+    "        name = Battary_list[i]\n",
+    "        train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size=feature_size)\n",
+    "        train_size = len(train_x)\n",
+    "        print('sample size: {}'.format(train_size))\n",
+    "\n",
+    "        setup_seed(seed)\n",
+    "        model = Net(input_size=feature_size, hidden_dim=hidden_dim, num_layers=num_layers, mode=mode)\n",
+    "\n",
+    "        optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
+    "        criterion = nn.MSELoss()\n",
+    "\n",
+    "        test_x = train_data.copy()\n",
+    "        loss_list, y_ = [0], []\n",
+    "        mae, rmse, re = 1, 1, 1\n",
+    "        score_, score = 1,1\n",
+    "        for epoch in range(EPOCH):\n",
+    "            X = np.reshape(train_x/Rated_Capacity,(-1, 1, feature_size)).astype(np.float32)#(batch_size, seq_len, input_size)\n",
+    "            y = np.reshape(train_y[:,-1]/Rated_Capacity,(-1,1)).astype(np.float32)# shape 为 (batch_size, 1)\n",
+    "\n",
+    "            X, y = torch.from_numpy(X), torch.from_numpy(y)\n",
+    "            output= model(X)\n",
+    "            output = output.reshape(-1, 1)\n",
+    "            loss = criterion(output, y)\n",
+    "            optimizer.zero_grad()              # clear gradients for this training step\n",
+    "            loss.backward()                    # backpropagation, compute gradients\n",
+    "            optimizer.step()                   # apply gradients\n",
+    "\n",
+    "            if (epoch + 1)%100 == 0:\n",
+    "                test_x = train_data.copy()    #每100次重新预测一次\n",
+    "                point_list = []\n",
+    "                while (len(test_x) - len(train_data)) < len(test_data):\n",
+    "                    x = np.reshape(np.array(test_x[-feature_size:])/Rated_Capacity,(-1, 1, feature_size)).astype(np.float32)\n",
+    "                    x = torch.from_numpy(x)                           # shape: (batch_size, 1, input_size)\n",
+    "                    pred = model(x)\n",
+    "                    next_point = pred.data.numpy()[0,0] * Rated_Capacity\n",
+    "                    test_x.append(next_point)                          #测试值加入原来序列用来继续预测下一个点\n",
+    "                    point_list.append(next_point)                     #保存输出序列最后一个点的预测值\n",
+    "                y_.append(point_list)                                 #保存本次预测所有的预测值\n",
+    "                loss_list.append(loss)\n",
+    "                mae, rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
+    "                re = relative_error(y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
+    "                print('epoch:{:<2d} | loss:{:<6.4f} | MAE:{:<6.4f} | RMSE:{:<6.4f} | RE:{:<6.4f}'.format(epoch, loss, mae, rmse, re))\n",
+    "            score = [re, mae, rmse]\n",
+    "            if (loss < 1e-3) and (score_[0] < score[0]):\n",
+    "                break\n",
+    "            score_ = score.copy()\n",
+    "            \n",
+    "        score_list.append(score_)\n",
+    "        result_list.append(y_[-1])\n",
+    "    return score_list, result_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seed:  0\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1486 | RMSE:0.2104 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0917 | RMSE:0.1292 | RE:0.2510\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0713 | RMSE:0.1027 | RE:0.1882\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0572 | RMSE:0.0845 | RE:0.1490\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0471 | RMSE:0.0715 | RE:0.1216\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0405 | RMSE:0.0627 | RE:0.1039\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0365 | RMSE:0.0569 | RE:0.0922\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0328 | RMSE:0.0511 | RE:0.0804\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0832 | RMSE:0.1049 | RE:0.1294\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0005 | MAE:0.1123 | RMSE:0.1620 | RE:0.2466\n",
+      "epoch:199 | loss:0.0004 | MAE:0.3690 | RMSE:0.5049 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0004 | MAE:0.0378 | RMSE:0.0528 | RE:0.0751\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1081 | RMSE:0.1502 | RE:0.1928\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1570 | RMSE:0.2108 | RE:1.0000\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0717 | RMSE:0.0856 | RE:0.1053\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0456 | RMSE:0.0514 | RE:0.0478\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0286 | RMSE:0.0317 | RE:0.0112\n",
+      "epoch:499 | loss:0.0003 | MAE:0.0224 | RMSE:0.0287 | RE:0.0223\n",
+      "------------------------------------------------------------------\n",
+      "seed:  1\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1184 | RMSE:0.1447 | RE:0.2235\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0742 | RMSE:0.1064 | RE:0.1980\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0549 | RMSE:0.0815 | RE:0.1431\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0501 | RMSE:0.0758 | RE:0.1333\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0458 | RMSE:0.0705 | RE:0.1235\n",
+      "epoch:599 | loss:0.0002 | MAE:0.2396 | RMSE:0.3321 | RE:1.0000\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0005 | MAE:0.0402 | RMSE:0.0479 | RE:0.0990\n",
+      "epoch:199 | loss:0.0003 | MAE:0.3038 | RMSE:0.4188 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0005 | MAE:0.2277 | RMSE:0.2799 | RE:0.4130\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1137 | RMSE:0.1565 | RE:0.2014\n",
+      "epoch:299 | loss:0.0003 | MAE:0.1156 | RMSE:0.1587 | RE:0.2031\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1690 | RMSE:0.2212 | RE:0.3190\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0382 | RMSE:0.0420 | RE:0.0319\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0219 | RMSE:0.0280 | RE:0.0191\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0298 | RMSE:0.0330 | RE:0.0175\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1121 | RMSE:0.1500 | RE:0.2376\n",
+      "------------------------------------------------------------------\n",
+      "seed:  2\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1365 | RMSE:0.1949 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0785 | RMSE:0.1120 | RE:0.2098\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0608 | RMSE:0.0892 | RE:0.1588\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0486 | RMSE:0.0735 | RE:0.1255\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0416 | RMSE:0.0642 | RE:0.1078\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0372 | RMSE:0.0580 | RE:0.0941\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0326 | RMSE:0.0506 | RE:0.0784\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0256 | RMSE:0.0374 | RE:0.0490\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0231 | RMSE:0.0304 | RE:0.0098\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1551 | RMSE:0.1926 | RE:0.2549\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0005 | MAE:0.0444 | RMSE:0.0705 | RE:0.0485\n",
+      "epoch:199 | loss:0.0003 | MAE:0.3285 | RMSE:0.4515 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0004 | MAE:0.0350 | RMSE:0.0485 | RE:0.0700\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1124 | RMSE:0.1552 | RE:0.1997\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1430 | RMSE:0.1916 | RE:0.3557\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0461 | RMSE:0.0520 | RE:0.0494\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0275 | RMSE:0.0308 | RE:0.0080\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0221 | RMSE:0.0283 | RE:0.0207\n",
+      "------------------------------------------------------------------\n",
+      "seed:  3\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1369 | RMSE:0.1633 | RE:0.2706\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0758 | RMSE:0.1084 | RE:0.2020\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0648 | RMSE:0.0944 | RE:0.1706\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0541 | RMSE:0.0806 | RE:0.1412\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0457 | RMSE:0.0697 | RE:0.1176\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0398 | RMSE:0.0618 | RE:0.1020\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0352 | RMSE:0.0551 | RE:0.0882\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0318 | RMSE:0.0496 | RE:0.0784\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0380 | RMSE:0.0605 | RE:0.1020\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0006 | MAE:0.0489 | RMSE:0.0564 | RE:0.1282\n",
+      "epoch:199 | loss:0.0003 | MAE:0.3241 | RMSE:0.4462 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0005 | MAE:0.2404 | RMSE:0.2893 | RE:0.4420\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1182 | RMSE:0.1620 | RE:0.2065\n",
+      "epoch:299 | loss:0.0004 | MAE:0.1187 | RMSE:0.1625 | RE:0.2065\n",
+      "epoch:399 | loss:0.0003 | MAE:0.1203 | RMSE:0.1643 | RE:0.2099\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0005 | MAE:0.2087 | RMSE:0.2620 | RE:0.3907\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0414 | RMSE:0.0460 | RE:0.0383\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0316 | RMSE:0.0346 | RE:0.0175\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0224 | RMSE:0.0274 | RE:0.0064\n",
+      "epoch:499 | loss:0.0003 | MAE:0.0239 | RMSE:0.0306 | RE:0.0303\n",
+      "------------------------------------------------------------------\n",
+      "seed:  4\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0004 | MAE:0.0380 | RMSE:0.0536 | RE:0.0118\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0329 | RMSE:0.0502 | RE:0.0686\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0006 | MAE:0.0496 | RMSE:0.0687 | RE:0.0136\n",
+      "epoch:199 | loss:0.0004 | MAE:0.2103 | RMSE:0.2938 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0005 | MAE:0.1166 | RMSE:0.1425 | RE:0.2338\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1441 | RMSE:0.1920 | RE:0.2355\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0005 | MAE:0.0698 | RMSE:0.0899 | RE:0.1675\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0575 | RMSE:0.0797 | RE:0.1069\n",
+      "epoch:299 | loss:0.0004 | MAE:0.0498 | RMSE:0.0685 | RE:0.0925\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0433 | RMSE:0.0590 | RE:0.0797\n",
+      "epoch:499 | loss:0.0003 | MAE:0.0382 | RMSE:0.0514 | RE:0.0686\n",
+      "epoch:599 | loss:0.0003 | MAE:0.0347 | RMSE:0.0462 | RE:0.0606\n",
+      "epoch:699 | loss:0.0003 | MAE:0.0328 | RMSE:0.0436 | RE:0.0558\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0327 | RMSE:0.0436 | RE:0.0558\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0342 | RMSE:0.0461 | RE:0.0590\n",
+      "------------------------------------------------------------------\n",
+      "seed:  5\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0003 | MAE:0.0866 | RMSE:0.1284 | RE:0.2451\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0674 | RMSE:0.0975 | RE:0.1765\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0541 | RMSE:0.0805 | RE:0.1392\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0459 | RMSE:0.0699 | RE:0.1176\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0393 | RMSE:0.0610 | RE:0.1000\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0355 | RMSE:0.0553 | RE:0.0882\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0324 | RMSE:0.0506 | RE:0.0804\n",
+      "epoch:799 | loss:0.0002 | MAE:0.2214 | RMSE:0.3121 | RE:1.0000\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0005 | MAE:0.0293 | RMSE:0.0371 | RE:0.0641\n",
+      "epoch:199 | loss:0.0003 | MAE:0.3055 | RMSE:0.4215 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0004 | MAE:0.0701 | RMSE:0.0944 | RE:0.1416\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1206 | RMSE:0.1647 | RE:0.2099\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1039 | RMSE:0.1371 | RE:0.2041\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0294 | RMSE:0.0324 | RE:0.0096\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0216 | RMSE:0.0275 | RE:0.0144\n",
+      "------------------------------------------------------------------\n",
+      "seed:  6\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0004 | MAE:0.0376 | RMSE:0.0507 | RE:0.0255\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0305 | RMSE:0.0458 | RE:0.0569\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0007 | MAE:0.0508 | RMSE:0.0568 | RE:0.0990\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1866 | RMSE:0.2625 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0006 | MAE:0.1238 | RMSE:0.1489 | RE:0.2526\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1482 | RMSE:0.1965 | RE:0.2423\n",
+      "epoch:299 | loss:0.0004 | MAE:0.1424 | RMSE:0.1898 | RE:0.2355\n",
+      "epoch:399 | loss:0.0003 | MAE:0.1375 | RMSE:0.1841 | RE:0.2304\n",
+      "epoch:499 | loss:0.0003 | MAE:0.1576 | RMSE:0.2079 | RE:0.2628\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0005 | MAE:0.0783 | RMSE:0.0992 | RE:0.1866\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0659 | RMSE:0.0921 | RE:0.1228\n",
+      "epoch:299 | loss:0.0004 | MAE:0.0569 | RMSE:0.0789 | RE:0.1069\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0512 | RMSE:0.0708 | RE:0.0957\n",
+      "epoch:499 | loss:0.0003 | MAE:0.1802 | RMSE:0.2426 | RE:0.2982\n",
+      "------------------------------------------------------------------\n",
+      "seed:  7\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0004 | MAE:0.0373 | RMSE:0.0445 | RE:0.0569\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0369 | RMSE:0.0567 | RE:0.0843\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0006 | MAE:0.0702 | RMSE:0.1120 | RE:0.1049\n",
+      "epoch:199 | loss:0.0004 | MAE:0.2280 | RMSE:0.3176 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0005 | MAE:0.1637 | RMSE:0.2042 | RE:0.2969\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1383 | RMSE:0.1849 | RE:0.2304\n",
+      "epoch:299 | loss:0.0004 | MAE:0.1333 | RMSE:0.1791 | RE:0.2253\n",
+      "epoch:399 | loss:0.0003 | MAE:0.1308 | RMSE:0.1761 | RE:0.2218\n",
+      "epoch:499 | loss:0.0003 | MAE:0.1236 | RMSE:0.1680 | RE:0.2116\n",
+      "epoch:599 | loss:0.0003 | MAE:0.1066 | RMSE:0.1472 | RE:0.1843\n",
+      "epoch:699 | loss:0.0002 | MAE:0.2554 | RMSE:0.3166 | RE:0.4266\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1353 | RMSE:0.1781 | RE:0.2600\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0462 | RMSE:0.0633 | RE:0.0861\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0392 | RMSE:0.0529 | RE:0.0718\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0367 | RMSE:0.0491 | RE:0.0654\n",
+      "epoch:499 | loss:0.0003 | MAE:0.0351 | RMSE:0.0469 | RE:0.0622\n",
+      "epoch:599 | loss:0.0003 | MAE:0.1610 | RMSE:0.2186 | RE:0.2759\n",
+      "------------------------------------------------------------------\n",
+      "seed:  8\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1123 | RMSE:0.1364 | RE:0.2098\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0562 | RMSE:0.0830 | RE:0.1431\n",
+      "epoch:299 | loss:0.0003 | MAE:0.0491 | RMSE:0.0738 | RE:0.1255\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0430 | RMSE:0.0658 | RE:0.1078\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0419 | RMSE:0.0646 | RE:0.1059\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0725 | RMSE:0.1090 | RE:0.2039\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0005 | MAE:0.0362 | RMSE:0.0501 | RE:0.0214\n",
+      "epoch:199 | loss:0.0004 | MAE:0.2799 | RMSE:0.3869 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0005 | MAE:0.2318 | RMSE:0.2846 | RE:0.4044\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1256 | RMSE:0.1703 | RE:0.2167\n",
+      "epoch:299 | loss:0.0004 | MAE:0.1247 | RMSE:0.1692 | RE:0.2150\n",
+      "epoch:399 | loss:0.0004 | MAE:0.1244 | RMSE:0.1688 | RE:0.2150\n",
+      "epoch:499 | loss:0.0003 | MAE:0.1183 | RMSE:0.1619 | RE:0.2048\n",
+      "epoch:599 | loss:0.0003 | MAE:0.2577 | RMSE:0.3194 | RE:0.4317\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1949 | RMSE:0.2510 | RE:0.3493\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0238 | RMSE:0.0304 | RE:0.0303\n",
+      "epoch:299 | loss:0.0004 | MAE:0.0261 | RMSE:0.0333 | RE:0.0383\n",
+      "------------------------------------------------------------------\n",
+      "seed:  9\n",
+      "sample size: 2521\n",
+      "epoch:99 | loss:0.0004 | MAE:0.0350 | RMSE:0.0465 | RE:0.0412\n",
+      "epoch:199 | loss:0.0003 | MAE:0.0364 | RMSE:0.0561 | RE:0.0824\n",
+      "sample size: 2467\n",
+      "epoch:99 | loss:0.0006 | MAE:0.0617 | RMSE:0.0967 | RE:0.0427\n",
+      "epoch:199 | loss:0.0004 | MAE:0.2189 | RMSE:0.3057 | RE:1.0000\n",
+      "sample size: 2431\n",
+      "epoch:99 | loss:0.0005 | MAE:0.1396 | RMSE:0.1699 | RE:0.2730\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1415 | RMSE:0.1886 | RE:0.2338\n",
+      "epoch:299 | loss:0.0004 | MAE:0.1387 | RMSE:0.1854 | RE:0.2321\n",
+      "epoch:399 | loss:0.0004 | MAE:0.1372 | RMSE:0.1835 | RE:0.2304\n",
+      "epoch:499 | loss:0.0003 | MAE:0.1290 | RMSE:0.1742 | RE:0.2184\n",
+      "epoch:599 | loss:0.0003 | MAE:0.1089 | RMSE:0.1496 | RE:0.1877\n",
+      "epoch:699 | loss:0.0002 | MAE:0.3630 | RMSE:0.4617 | RE:1.0000\n",
+      "sample size: 2407\n",
+      "epoch:99 | loss:0.0005 | MAE:0.1152 | RMSE:0.1477 | RE:0.2424\n",
+      "epoch:199 | loss:0.0004 | MAE:0.0535 | RMSE:0.0740 | RE:0.1005\n",
+      "epoch:299 | loss:0.0004 | MAE:0.0490 | RMSE:0.0675 | RE:0.0925\n",
+      "epoch:399 | loss:0.0003 | MAE:0.0500 | RMSE:0.0690 | RE:0.0941\n",
+      "------------------------------------------------------------------\n",
+      "re mean: 0.0902\n",
+      "mae mean: 0.0548\n",
+      "rmse mean: 0.0746\n",
+      "------------------------------------------------------------------\n",
+      "------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "window_size = 128\n",
+    "EPOCH = 1000\n",
+    "lr = 0.001    # learning rate  0.01 epoch 10\n",
+    "hidden_dim = 256\n",
+    "num_layers = 2\n",
+    "weight_decay = 0.0\n",
+    "mode = 'LSTM'# RNN, LSTM, GRU\n",
+    "Rated_Capacity = 1.1\n",
+    "\n",
+    "SCORE = []\n",
+    "for seed in range(10):\n",
+    "    print('seed: ', seed)\n",
+    "    score_list, _ = tain(lr=lr, feature_size=window_size, hidden_dim=hidden_dim, num_layers=num_layers, \n",
+    "                         weight_decay=weight_decay, mode=mode, EPOCH=EPOCH, seed=seed)\n",
+    "    print('------------------------------------------------------------------')\n",
+    "    for s in score_list:\n",
+    "        SCORE.append(s)\n",
+    "\n",
+    "mlist = ['re', 'mae', 'rmse']\n",
+    "for i in range(3):\n",
+    "    s = [line[i] for line in SCORE]\n",
+    "    print(mlist[i] + ' mean: {:<6.4f}'.format(np.mean(np.array(s))))\n",
+    "print('------------------------------------------------------------------')\n",
+    "print('------------------------------------------------------------------')"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/.ipynb_checkpoints/RNN & LSTM_ed-checkpoint.ipynb b/.ipynb_checkpoints/RNN & LSTM_ed-checkpoint.ipynb
new file mode 100644
index 0000000..7b23f1d
--- /dev/null
+++ b/.ipynb_checkpoints/RNN & LSTM_ed-checkpoint.ipynb	
@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1100232a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e76422e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)\n",
+    "\n",
+    "def build_sequences(text, window_size):\n",
+    "    #text:list of capacity\n",
+    "    x, y = [],[]\n",
+    "    for i in range(len(text) - window_size):\n",
+    "        sequence = text[i:i+window_size]\n",
+    "        target = text[i+1:i+1+window_size]\n",
+    "\n",
+    "        x.append(sequence)\n",
+    "        y.append(target)\n",
+    "\n",
+    "    return np.array(x), np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "02188eb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 留一评估：一组数据为测试集，其他所有数据全部拿来训练\n",
+    "def get_train_test(data_dict, name, window_size=8):\n",
+    "    data_sequence=data_dict[name]['capacity']\n",
+    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
+    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
+    "    for k, v in data_dict.items():\n",
+    "        if k != name:\n",
+    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
+    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
+    "            \n",
+    "    return train_x, train_y, list(train_data), list(test_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd3cafbb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/CALCE_ed.ipynb b/CALCE_ed.ipynb
new file mode 100644
index 0000000..f3f8a6f
--- /dev/null
+++ b/CALCE_ed.ipynb
@@ -0,0 +1,627 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "1ad2f026",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "edcfa8ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b47ca0c",
+   "metadata": {},
+   "source": [
+    "### Extract File From Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec08042f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Battary_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'dataset/'\n",
+    "Battery = {}\n",
+    "for name in Battary_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    print('path: ', path)\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheet_name=1)\n",
+    "#         print(df)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "    print(idx)\n",
+    "    path_sorted = np.array(path)[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "fbe660d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load Dataset CS2_35 ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load Dataset CS2_36 ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_37 ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_38 ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'dataset/'\n",
+    "Battery = {}\n",
+    "for name in Battery_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "#     print(idx)\n",
+    "    path_sorted = np.array(path)[idx]\n",
+    "    \n",
+    "    count = 0\n",
+    "    discharge_capacities = []\n",
+    "    health_indicator = []\n",
+    "    internal_resistance = []\n",
+    "    CCCT = []\n",
+    "    CVCT = []\n",
+    "    for p in path_sorted:\n",
+    "        df = pd.read_excel(p,sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        cycles = list(set(df['Cycle_Index']))\n",
+    "        for c in cycles:\n",
+    "            df_lim = df[df['Cycle_Index'] == c]\n",
+    "            #Charging\n",
+    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "            c_v = df_c['Voltage(V)']\n",
+    "            c_c = df_c['Current(A)']\n",
+    "            c_t = df_c['Test_Time(s)']\n",
+    "            #CC or CV\n",
+    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "            #Discharging\n",
+    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "            d_v = df_d['Voltage(V)']\n",
+    "            d_c = df_d['Current(A)']\n",
+    "            d_t = df_d['Test_Time(s)']\n",
+    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "            if(len(list(d_c)) != 0):\n",
+    "                time_diff = np.diff(list(d_t))\n",
+    "                d_c = np.array(list(d_c))[1:]\n",
+    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
+    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "                count += 1\n",
+    "\n",
+    "    discharge_capacities = np.array(discharge_capacities)\n",
+    "    health_indicator = np.array(health_indicator)\n",
+    "    internal_resistance = np.array(internal_resistance)\n",
+    "    CCCT = np.array(CCCT)\n",
+    "    CVCT = np.array(CVCT)\n",
+    "    \n",
+    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "#     print(idx)\n",
+    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "                              'capacity':discharge_capacities[idx],\n",
+    "                              'SoH':health_indicator[idx],\n",
+    "                              'resistance':internal_resistance[idx],\n",
+    "                              'CCCT':CCCT[idx],\n",
+    "                              'CVCT':CVCT[idx]})\n",
+    "    Battery[name] = df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "fbc843cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def load_data(Battery_list, dir_path):\n",
+    "#     Battery = {}\n",
+    "#     for name in Battery_list:\n",
+    "#         print('Load Dataset ' + name + ' ...')\n",
+    "#         path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "#         dates = []\n",
+    "#         for p in path:\n",
+    "#             df = pd.read_excel(p, sheetname=1)\n",
+    "#             print('Load ' + str(p) + ' ...')\n",
+    "#             dates.append(df['Date_Time'][0])\n",
+    "#         idx = np.argsort(dates)\n",
+    "#         path_sorted = np.array(path)[idx]\n",
+    "\n",
+    "#         count = 0\n",
+    "#         discharge_capacities = []\n",
+    "#         health_indicator = []\n",
+    "#         internal_resistance = []\n",
+    "#         CCCT = []\n",
+    "#         CVCT = []\n",
+    "#         for p in path_sorted:\n",
+    "#             df = pd.read_excel(p,sheetname=1)\n",
+    "#             print('Load ' + str(p) + ' ...')\n",
+    "#             cycles = list(set(df['Cycle_Index']))\n",
+    "#             for c in cycles:\n",
+    "#                 df_lim = df[df['Cycle_Index'] == c]\n",
+    "#                 #Charging\n",
+    "#                 df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "#                 c_v = df_c['Voltage(V)']\n",
+    "#                 c_c = df_c['Current(A)']\n",
+    "#                 c_t = df_c['Test_Time(s)']\n",
+    "#                 #CC or CV\n",
+    "#                 df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "#                 df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "#                 CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "#                 CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "#                 #Discharging\n",
+    "#                 df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "#                 d_v = df_d['Voltage(V)']\n",
+    "#                 d_c = df_d['Current(A)']\n",
+    "#                 d_t = df_d['Test_Time(s)']\n",
+    "#                 d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "#                 if(len(list(d_c)) != 0):\n",
+    "#                     time_diff = np.diff(list(d_t))\n",
+    "#                     d_c = np.array(list(d_c))[1:]\n",
+    "#                     discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "#                     discharge_capacity = [np.sum(discharge_capacity[:n]) \n",
+    "#                                           for n in range(discharge_capacity.shape[0])]\n",
+    "#                     discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "#                     dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "#                     start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "#                     dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "#                     end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "#                     health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "#                     internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "#                     count += 1\n",
+    "\n",
+    "#         discharge_capacities = np.array(discharge_capacities)\n",
+    "#         health_indicator = np.array(health_indicator)\n",
+    "#         internal_resistance = np.array(internal_resistance)\n",
+    "#         CCCT = np.array(CCCT)\n",
+    "#         CVCT = np.array(CVCT)\n",
+    "\n",
+    "#         idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "#         df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "#                                   'capacity':discharge_capacities[idx],\n",
+    "#                                   'SoH':health_indicator[idx],\n",
+    "#                                   'resistance':internal_resistance[idx],\n",
+    "#                                   'CCCT':CCCT[idx],\n",
+    "#                                   'CVCT':CVCT[idx]})\n",
+    "#         Battery[name] = df_result\n",
+    "#     return Battery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d836bcc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "d8d85efc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff974c7ea60>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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eXtSrV4+WLVuSnp7O7du38ff3x8PDA09PT+bMmVNq+RMnTsTb2xsfHx86depEVFRU4fWZmpri4+ODj48Pb7/99l++FhlAS5IkSZIkPcVMTU0JDAzk/PnzTJs2jY8//rjU/f+OALqgoOCR9s/OzqZbt24MHz6ca9eucenSJYYPH05cXBxz5szB2dmZoKAggoOD+fHHHzE0NMTAwICZM2dy6dIlTpw4wYIFCwgJCXnoOcaOHcuFCxcIDAyke/fufPHFF4XbatSoQWBgIIGBgSxatOiR6l4SGUBLkiRJkiT9Tdq2hRUr1Nd5eery6tXqcmamuvzLL+pySoq6vHGjuhwfry5v3aoux8Q8+vlTU1OxtbUFID09nfbt2+Pr64uXlxe///47AOPHj+f69ev4+PgwduxYxo8fz+HDh/Hx8WHWrFkUFBQwduxYGjdujLe3N4sXLwbgwIED+Pv7M2DAALy8vJg4cWKxVuEJEyYwd+7cEuu1Zs0a/Pz86NGjR+E6f39/6tWrR3R0NJUqVSpcX7t2bYyNjXFxccHX1xcAS0tLPDw8iIyMfOi1W1lZFb7OyMj4R/umyzR2kiRJkiRJT7GsrCx8fHzIzs4mOjqaffv2AWpu402bNmFlZUV8fDzNmjWjZ8+eTJ8+neDgYAIDAwE1MJ4xYwbbtm0DYMmSJVhbW3P69GlycnJo0aIFnTp1AuDUqVMEBwdTrVo1wsPD6dOnD6NGjUKn07Fu3TpOnTpVYh2Dg4Np2LBhiduGDh1Kp06d2LBhA+3bt2fQoEHUrFmz2D7h4eEEBATQtGnTUu/FhAkTWLlyJdbW1uzfv79w/Y0bN2jQoAFWVlZ89dVXtGrVquwbWwoZQEuSJEmSJP1NDhy499rQsPiymVnxZWvr4ssODsWXK1Qo3znvduEAOH78OK+99hrBwcEIIfjkk084dOgQGo2GyMhIYmNjyyxv9+7dXLhwgQ0bNgCQkpJCaGgoRkZGNGnSpDBXspubG/b29gQEBBAbG0uDBg2wt7cvX6WL8PHxISwsjN27d7N3714aN27M8ePH8fDwANSW9L59+zJ79uxircwlmTJlClOmTGHatGnMnz+fyZMn4+Liwq1bt7C3t+fs2bM8//zzXLx4scyySiMDaEmSJEmSpP8Tfn5+xMfHExcXx44dO4iLi+Ps2bMYGhri5uZWrlzHQgjmzZtH586di60/cOAA5ubmxdYNGzaMFStWEBMTw9ChQx9apqenJwcPHnzodgsLC/r06UOfPn3QaDTs2LEDDw8P8vLy6Nu3LwMHDqRPnz5l1v2uAQMG0K1bNyZPnoyxsTHGxsYANGzYkBo1anD16lUaNWpU7vLuJ/tAS5IkSZIk/Z+4fPkyBQUF2Nvbk5KSgpOTE4aGhuzfv5+bN28Can/itLS0wmPuX+7cuTMLFy4kLy8PULNnZGRklHi+3r17s2vXLk6fPv1AwF3UgAEDOHbsGNu3by9ct2vXLoKCgjh69ChJSUkA5ObmEhISgqurK0IIXn/9dTw8PBgzZkyZ1x4aGlr4esuWLdSpUweAuLi4wkGPYWFhhIaGUr169TLLK41sgZYkSZIkSXqK3e0DDWrr8U8//YRWq2XgwIH06NGDRo0a4ePjUxhQ2tvb06JFC+rVq0eXLl2YOnUqBgYG1K9fn8GDBzNq1CjCw8Px9fVFCIGjoyObN28u8dxGRkb4+/tjY2ODVqt9aB1NTU3Ztm0bo0ePZvTo0RgaGuLt7c2cOXMICAhg+PDhCCHQ6XR069aNvn37cvToUVatWoWXl1fh9U2dOpWuXbuWeI7x48dz5coVNBoNrq6uhdk2Dh06xGeffYaBgQFarZZFixZhZ2f3eDdbTxFC/KUC/m2NGjUSZ86c+dfPK4SQMw1JkiRJklTMpUuXCvvqPot0Oh2+vr6sX7/+gYF/T5uSnqWiKGeFEA/09ZBdOMppy5Ut1Pu+HgfCDzzpqkiSJEmSJD1xISEhuLu70759+6c+eH5UsgtHOZkZmpGYlcjkg5Np69b2SVdHkiRJkiTpiapbty5hYWHF1gUFBfHqq68WW2dsbMzJkyf/tvOOHDmSo0ePFls3atQohgwZ8redoywygC6nus5t6FzvLVacmMS1xGu427k/6SpJkiRJkiT9p3h5eRWm1PunLFiw4B8tvzxkF45y+mpHBCuM24BNI/x+9CMpK+lJV0mSJEmSJEl6AmQAXU6v3zjGng8+IPR6HbrV7EZYUljZB0mSJEmSJEn/d2QAXU6NXvCjg6LgeuQiH3SYj4+L75OukiRJkiRJkvQEyAC6vGrUIKVNT9Y6OuJ95gzHEyKedI0kSZIkSZKkJ0AG0I/AqE832p07x5RF39Ppx0bohO5JV0mSJEmSpGecVqvFx8eH+vXr4+vry7Fjx0rdPzk5me+//75wOTw8nDVr1vzT1eTq1at07doVd3d3PDw86N+/P7GxsWRmZjJw4EC8vLyoV68eLVu2JD09ndu3b+Pv74+Hhweenp7MmTOn1PInTpyIt7c3Pj4+dOrUiaioqMJtFy5cwM/PD09PT7y8vMo1pXlp5EQqj2rMGHRzZmM5XnByVBAFugJG7RqFkdaImnY1aVm1JS/Ve0lOuiJJkiRJz4D/wkQqFhYWpKenA/DHH38wdepUDh48+ND9w8PD6d69O8HBwQAcOHCAGTNmsG3btnKfs6CgoNSZB++XnZ2Nl5cX3333HT169ABg//79ODo6snXrVuLi4vjuu+8AuHLlCm5ubiQmJhIdHY2vry9paWk0bNiQzZs3U7du3RLPkZqaipWVFQBz584lJCSERYsWkZ+fj6+vL6tWraJ+/fokJCSUOHPio0ykItPYPar27QndsIE6mQr7b+zHr4ofqTmpaBQNq4NW8/2Z77E2saZrzZKnmZQkSZIk6f9X2xVtH1jX37M/IxqPIDMvk64/PxgfDPYZzGCfwcRnxvPCry8U23Zg8IFHOn9qaiq2trYApKen06tXL5KSksjLy+Orr76iV69ejB8/nuvXr+Pj40PHjh05fPgwly5dwsfHh0GDBvHee+8xfvx4Dhw4QE5ODiNHjuStt97iwIEDTJ48GRcXFwIDA+nbty8ODg6MGjUKgAkTJuDs7Mx77733QL3WrFmDn59fYfAM4O/vD8CSJUtwdXUtXF+7dm0AXFxccHFxAcDS0hIPDw8iIyMfGkDfDZ4BMjIyChszd+/ejbe3N/Xr1wfUqcz/KhlAP6rGjXnxs8/I0CUx68Qsnq/zPOfeOgdAbkEulb+rTESq2j86NScVI60RJgYmT7LGkiRJkiT9H8vKysLHx4fs7Gyio6PZt28fACYmJmzatAkrKyvi4+Np1qwZPXv2ZPr06QQHBxfma76/BXrJkiVYW1tz+vRpcnJyaNGiBZ06dQLg1KlTBAcHU61aNcLDw+nTpw+jRo1Cp9Oxbt06Tp06VWIdg4ODadiwYYnbhg4dSqdOndiwYQPt27dn0KBBD8xsGB4eTkBAAE2bNi31XkyYMIGVK1dibW3N/v37AbXriKIodO7cmbi4OF566SU++uij8t3ch5AB9KNycuLbJSuIreHJGN90TkScoIp1FQCMtEYs6LqA59yfA2DOiTl8eehLvJ29Gd9yPC/UfaG0kh8qOTuZr498Tb4un6rWVXm36bt/2+VIkiRJkvT3Ka3F2MzQrNTtDmYOj9ziDGBqaloYDB8/fpzXXnuN4OBghBB88sknHDp0CI1GQ2RkJLGxsWWWt3v3bi5cuMCGDRsASElJITQ0FCMjI5o0aUK1atUAcHNzw97enoCAAGJjY2nQoMFjte76+PgQFhbG7t272bt3L40bN+b48eOF3SnS09Pp27cvs2fPLtbKXJIpU6YwZcoUpk2bxvz585k8eTL5+fkcOXKE06dPY2ZmRvv27WnYsCHt27d/5LreJQPox9CxS3uoWZM67d7gZvLNYtv6efYrfN2+ensy8zJZcX4Fc07OeewAeunZpUw/Or1wuW/dvlS0rPh4lZckSZIk6f+Wn58f8fHxxMXFsWPHDuLi4jh79iyGhoa4ubmVa/CcEIJ58+bRuXPnYusPHDiAubl5sXXDhg1jxYoVxMTEMHTo0IeW6enpWWq/bAsLC/r06UOfPn3QaDTs2LEDDw8P8vLy6Nu3LwMHDqRPnz5l1v2uAQMG0K1bNyZPnkzlypVp06YNDg4OAHTt2pVz5879pQBaZuF4DDGffMJvbbvg4dyATrV6kaPTsSI6mqk3iwfTzas0Z1qHafSs1ZOLdy7yuAM27c3UT3N9PNQ3zoXYC3/tAiRJkiRJ+r90+fJlCgoKsLe3JyUlBScnJwwNDdm/fz839XGKpaUlaWlphcfcv9y5c2cWLlxIXl4eoHaByMjIKPF8vXv3ZteuXZw+ffqBgLuoAQMGcOzYMbZv3164bteuXQQFBXH06FGSktQZnnNzcwkJCcHV1RUhBK+//joeHh6MGTOmzGsPDQ0tfL1lyxbq1KlTeD0XLlwgMzOT/Px8Dh48+NB+1OUlW6Afw5SfM5nvFsSXYRXZYZFOWFYWzayscDYyAtRPbitiYmhva0tVExPqOtYlKTuJOxl3cLZwfuTz5RbkAvCl/5dsvLSRC7EXCruJSJIkSZL0bLvbBxrUGOSnn35Cq9UycOBAevToQaNGjfDx8SkMKO3t7WnRogX16tWjS5cuTJ06FQMDA+rXr8/gwYMZNWoU4eHh+Pr6IoTA0dGRzZs3l3huIyMj/P39S8xqUZSpqSnbtm1j9OjRjB49GkNDQ7y9vZkzZw4BAQEMHz4cIQQ6nY5u3brRt29fjh49yqpVq/Dy8iq8vqlTp9K1a8mJGsaPH8+VK1fQaDS4urqyaNEiAGxtbRkzZgyNGzdGURS6du1Kt27dHu9m68k0do8h7koi1/q0o2GzZiwvqEfV4F9pnRZLVNOmWHu1J/mVLtS+EsL3NWsyvFIlbqbFE5ieTi8Xt8c638xjM/lwz4ckj0vGa1EDmlVuyq8vrP17L0qSJEmSpEf2X0hj9yTpdDp8fX1Zv379AwP/njaPksZOduF4DI617fCbOxPNW2/y1k/v4n07DfPGjXE/dAinjwbjVrkKob//Tn9HRwBWJaSzKC618Pjs/GwCogPIzi9fEu8RjUfw+5vX+CIijkifZbzScnrZB0mSJEmSJP2DQkJCcHd3p3379k998PyoZBeOx5Tp155me/7k6h9/kG1kBG3bIgoE+74+Re2rW3Ef/hrCUO3SYWtoiFPWVSYd2Eizys0YtHkQdzLucHLYSZpUalLmuUwNTQnI0TInIpyxVapQx8qR1JxUrIxLH4kqSZIkSZL0T6lbty5hYWHF1gUFBfHqq68WW2dsbMzJkyf/tvOOHDmSo0ePFls3atQohgwZ8redoywygH5MCQnQ6qc/8fTUwrhxAGi0Cu0/aQo0JSsL2le8wU7RmZE/L+Rq7mEmn5pbrIyQuBCaVGpCQHQAB28eZETjERhpjR44V9D4obS5HkbTD+ZhE1eDK/lb6b9/IoFvB/4LVypJkiRJklQ+Xl5ehSn1/ikLFiz4R8svDxlAP6YqVeBDj2q4Jl0AC4sHtqemQtumWeTF1oZKlZjk9hU5BbksPruIWva1uJVyi6DYIAAaLmmIQOBu5073Wt0fKMvr6+UA/OmdRosvBMO3X+J87Hku3rmIp5PnP3uhkiRJkiRJUjGyD/RfUG3KG2jmzytxm7MzTN1cF4fjW3nzsjV2J87yecfZBL4VyNaXt9LQpSHHIo4Rkx6DQB3IGXwnuMSy1vn747l8Ocsu1eC51ddYbNQYFEPqLaz32KnxJEmSJEmSpMcjA+h/QaPNv+G/IR6NTkP9CvVxsqpGPdfOJGYlciLiBAD96vajc40S8icKgU16OiI1lv6HxvLl6jHMcKsEigZjYzt+ufjLv3w1kiRJkiRJzzbZheNf8ObVNbxpbAymL5CWn4/t0aM4G7fiK7+K9Krdi8gxkdib2qNRNJyIOEGzys04GXGS5OxkOlduw3OnTxPqHIfba9NxM7uDRW4tqr6wluFROl4O3EdNu5o0rFjy/PKSJEmSJEnS30u2QP8batcm9/p1onNyMNNqudKkCX82aMzrvkNRFIWKlhUxNjDmVOQpWixrgctMF5r92IyP9n4EWVkAGNmYUH/qizy3/V3eeAN6e/Smf2UPNBnhbAjZ8IQvUJIkSZKkJ0Wr1eLj40P9+vXx9fXl2LFjpe6fnJzM999/X7gcHh7OmjVr/ulqcvXqVbp27Yq7uzseHh7079+f2NhYMjMzGThwIF5eXtSrV4+WLVuSnp7O7du38ff3x8PDA09PT+bMmVNq+RMnTsTb2xsfHx86depEVFQUAD///DM+Pj6FPxqN5i8PdJQTqfwbpk2jsa0t6RWacOl5X3VdZiaYmRXbLTQhlFrzaxUuG2oMSRl0mRmfTmJZt5e59kIXjm+IxEybg+8L1QFos6INt1Nus77fetkKLUmSJEn/sv/CRCoWFhakp6cD8McffzB16lQOHjz40P3Dw8Pp3r07wcHq2KsDBw4wY8YMtm3bVu5zFhQUlDrz4P2ys7Px8vLiu+++o0ePHgDs378fR0dHtm7dSlxcHN999x0AV65cwc3NjcTERKKjo/H19SUtLY2GDRuyefPmh07DnZqaipWVmuJ37ty5hISEFM5GeFdQUBC9evV6IP0eyIlU/ntq1WLc2rV0uJDLniVL+LFbNwZ9/z3bExKK7VbTviYtq7YE4K2Gb5Gny6PNwia4xcRgfCYXjSJoOboRvmPaIhLVOePfa/YRCbk5NF7amJC4kH/90iRJkiRJKqJt27J/Zswovv+KFerr+PgH931Eqamp2NraApCenk779u3x9fXFy8uL33//HVCnvL5+/To+Pj6MHTuW8ePHc/jwYXx8fJg1axYFBQWMHTuWxo0b4+3tzeLFiwE10Pb392fAgAF4eXkxceLEYq3CEyZMYO7cuQ9WClizZg1+fn6FwTOAv78/9erVIzo6mkqVKhWur127NsbGxri4uODrqzY8Wlpa4uHhQWRk5EOv/W7wDJCRkYGiKA/ss3btWl5++eUy72NZZB/of0Pjxrxw8iQvMI4x/fpx6rnniHRwoFFiBtjbk5yXx9zISLrY2bHvtX2cjT5L00pNeaHuCyz7cwZxlwJY0S4ORaPAN9/Aa68xo/Me3jnRl5djLfiw9580yb9MXce6xGXEYWZohrmR+ZO+akmSJEmS/gVZWVn4+PiQnZ1NdHQ0+/btA8DExIRNmzZhZWVFfHw8zZo1o2fPnkyfPp3g4ODCbgz3t0AvWbIEa2trTp8+TU5ODi1atKBTp04AnDp1iuDgYKpVq0Z4eDh9+vRh1KhR6HQ61q1bx6lTp0qsY3BwMA0blvxN+dChQ+nUqRMbNmygffv2DBo06IGZDcPDwwkICKBp06al3osJEyawcuVKrK2t2b9//wPbf/nll8IPEn+FDKD/DVWrwqRJ8PHHtJ28glu/n+fIe705tzKYjOoFfHvpElMTE7GLj6dxo0Y0q9wMgA7VO9Chegdu9ruJvZm9WlbfvohBg8gLuozI0TKjRg2aWVnRxKoOgTGBNFjcgF9f+JV+nv2e3PVKkiRJ0rPqwIHH39/B4dGPB0xNTQuD4ePHj/Paa68RHByMEIJPPvmEQ4cOodFoiIyMJDY2tszydu/ezYULF9iwQR1jlZKSQmhoKEZGRjRp0oRq1aoB4Obmhr29PQEBAcTGxtKgQQPs7e0fuf4+Pj6EhYWxe/du9u7dS+PGjTl+/Hhhd4r09HT69u3L7Nmzi7Uyl2TKlClMmTKFadOmMX/+fCZPnly47eTJk5iZmVGvXr1HruP9ZAD9L4lv2BkHPub4ywe5EeMFgKfBFS7dsWNqYiKrp0xh4GuvQaNGZBQUMPP2bay0Wkbb2zN4XyzRBUlc7uej9puuWpVPbn5OevALvNdE7QeUUVCAp6MnpgamjP5jNHvC9mCsNWZIgyH4uvg+wSuXJEmSJOnf4ufnR3x8PHFxcezYsYO4uDjOnj2LoaEhbm5uZGdnl1mGEIJ58+bRuXPx9LoHDhzA3Lz4N9zDhg1jxYoVxMTEMHTo0IeW6enpWWq/bAsLC/r06UOfPn3QaDTs2LEDDw8P8vLy6Nu3LwMHDqRPnz5l1v2uAQMG0K1bt2IB9Lp16/6W7hsg+0D/a+z86wMwLWYIv+0w45C3N+525lzcvZvk7t3pc+gQcReiAfjk+nU+Dw8nIC4Ojh6l15pvMD2UUliWotMBYNLUm/XL0ghKT6fmyZNE5uazsvdKGro0ZG3wWuafno+CQmpOKqEJoeQV5P37Fy5JkiRJ0r/m8uXLFBQUYG9vT0pKCk5OThgaGrJ//35u3rwJqP2J09LSCo+5f7lz584sXLiQvDw1brh69SoZGRklnq93797s2rWL06dPPxBwFzVgwACOHTvG9u3bC9ft2rWLoKAgjh49SlKSOrYrNzeXkJAQXF1dEULw+uuv4+HhwZgxY8q89tDQ0MLXW7ZsoU6dOoXLOp2O9evX89JLL5VZTnnIFuh/icZAgy78FpqNG3DrXJu8L+xoeDaArg0bENj2czLubGZzXScWC0HrAwdwOnSICd26QZs2jO7Vi9HPFRlxOn8+4rPPMDh/HvuI8yj40NTKit8TEvgtpwZb+22kIC+Vy/GXaeDSgB/P/ciwrcOY1n4a0WnReDp5kpGbwft+75e7/uuC1+Fu506jig8MRJUkSZIk6Qm62wca1Nbjn376Ca1Wy8CBA+nRoweNGjXCx8enMKC0t7enRYsW1KtXjy5dujB16lQMDAyoX78+gwcPZtSoUYSHh+Pr64sQAkdHRzZv3lziuY2MjPD398fGxqbUrBympqZs27aN0aNHM3r0aAwNDfH29mbOnDkEBAQwfPhwhBDodDq6detG3759OXr0KKtWrcLLy6vw+qZOnUrXrl1LPMf48eO5cuUKGo0GV1fXYhk4Dh06ROXKlalevfqj3+ASyDR2T8iMF0/z4a9N0C1cjObtN1k0bBhf9u5NYMeOOBoakn05nCs5btRwV7CwKKGA27ehWjX46ScYOJCNcXHMvH0bHbDaw4MapqaFu64IXMGQ34fQtWZXTkacpEmlJuy8tpPa9rV5xfsVxrccj4FG/SyVk59D25/aEpUWxaver/JVu69IyU7B5msbLI0sSf049V+5P5IkSZL0NPgvpLF7knQ6Hb6+vqxfv/6BgX9PG5nG7inw8sxG5LtWRzP8LXKjE3glKJSj73xLTrwhAenpfB9li8+2YP7YfYvqGw/Q6pdLxQuoXBnS00ntMZD0dDiWkkJsbi7HfX2xMzAgJiencNdsDEFjTE5+DlqNFq1G/YR4JeEKE/dPxHW2K+eiz3E26izv7nyXExEnuJVyi3XB68jJz2HbVXVUrqO5Y7mvLz4znoPhB1kWsIxZx2ex5cqWv37TJEmSJEn6zwgJCcHd3Z327ds/9cHzo5IB9BNSqbKCwdQvAXi54gESDVxwCT/O4Td643v2LCc3rKJ69WCqRuzhlW0r4fR9BSgKobdNcLbO5mCdt5jh7s7lJk0A2JKQgMvx4+zU55memuYEdT8npyAHA41BYWuziYEJv77wK8nZySwPWM6Uw1NYem4pJgYmzOw0k+tJ1zGZYsIrm14BYNOLmwpPn1uQS0bug/2hhBB4L/TG8VtH2v7Ulte3vM6Y3WNYeX4lQgjWBa/j8M3DJd6TfF0+nVd3JjAm8C/cWUmSJEmS/g1169YlLCyMmTNnFq4LCgoqNuufj49PmannHtXIkSMfOMfy5cv/1nOURfaBfpIGDOCIc18c1xtT4V1v+M6Gvit/ZLGpFXnXGrN66asYjhhB4+XLIWHGA4fXrAkXPl5LzWlLIOpzDCpWBNSMHACTw8PpbGdHF9Msltz6mVxLc7SKFq2itkAbaAzo59mPeafmcTb6LLEZsfSu05uVvVdiYWRBHYc6fLV3Nsfv7OFFu2/wdvYuPPfoXaNZH7KeuLFxxeqkKAqver+KRtHg5exFTbua2JjYYGlsiaIofLD7A9pVa0cr11YPXE90WjS7r+/mBY8X8Kng83fdZUmSJEmS/iVeXl5/eZrssixYsOAfLb88ZAD9hLVsb0zL9gA14cdF5Hw8gTesTVESE6BOAXm/bsQQoEif5qJqjn0eRnfj1rVcqt44Cs2bM6JSJQZXqEBcXh4aReGj6p74iJFsv3Waa3ZdyRfXAQpbohtVbMTBmwdZ2G0hlkaWWBipna671uxKZavKnIsIwdLIgq1XtmKkNcLR3JFz0efIzr+XCiclO4UNIRvoW7cvY1uMfej1NnRpyNmos4XLwXeCsTa2xtbUFiOtEQoKkWnqLENCCI7ePkpNu5o4Wzg/9j2WJEmSJEn6O8kA+j/k8mUYM6YK1tawdq0D143qUCP2MgDLfjZm6LASDrK1JTkZTrcfSNXsNTBrFowejZlWi6t+NGwNuxq8bvMWR83b4JKezm+NGjHdsTbrw08z7vp1+noPpo9HH1pWbYlOCHJ1OrYlJOCrs2PhZC8qDXJiyuouZNsEPnD6b45+w4fNP2Rv2F6GbR1GHYc6tKja4qHX6FfZj61XtxKWFMadjDu0Wt4KA40BHg4eBMQEABCVFgXAojOLGLFjBGOajWFm55kPLfOu2ym3WXx2Mdn52eTr8skryCM9L52Pmn+Ep5NnmcdLkiRJkiSVhwyg/0Pc3aFbN6hRQ11OWfAzWd8O4XaEhouXHt5d3cQEFjZYStfbpzD96isYORIMDQu3p+akcj3xOovdaxVO8f1Jq0/A9RZf37rFpObNMdFoCM3M5I0rVziYouacHpnkwaItltD/MmZONSA3EDcbN3rU6sG8U/MAGLd3HK94v8L52PNoFE2Zk7a8Wv9VJu6fyA/nfiAjN4N8XT6OZo4ExATQ16Mv15OuFwbQe8L2YGVsxdcdvy48fkfoDn4M+JFfX/gVrUbLmagzBMUGMdhnMNn52Uw7Mg0TAxMMNYYYag0x0hrRzq3dfyqATsxK5NjtY7RxbYOlseWTro4kSZIkSY9IDiL8DzEwUGPf555Tl32H+WJ65Ty1MgKYWUoDrIkJ7DpkRubE6ZCQQMDi4vPQH7t9DN8lvgzaPIixu8fic2QHVgd20dPenrgWLTDVaskVAu8zZwhMT+dLNzf21a/P1C4OhJww5Ieadajlkg7AOw3f58iEuZgHjC8sv0BXQGBMIDXtamJqWHJXk2uZmdzOzqayVWXaV29PQEwAATEBNK/SnGW9lgHwXtP3qGRZieTsZAAuxF6gU41OhV1NYtNjuZZ4jY2XNnI9Se2Gsur8Kr499i25Bbm427mT+2kuGZ9kkDw+mbixcUSOiaR5lebMOTGH3ILcB+qVmJVI7196cybq30uNuP/Gfnqs7YHjt458f/r7f+28kiRJkiT9PWQA/X/CwACMu7SjAA2e47vDBx9A586wYEFhAHog/AC7w3ZjmxpAen4+PqdPkq3TcTo1lQvp6fSwt+dkw4Z86uaGv60t5sYaLhknM7CCI9F27bGxHkn6n+9RoQJkBLXDXKj9kpcHLmfr1a3UsFObzoUQtA8MZPCle6n3egYHM+a6GvRu6LeBHQN2cCn+Ej7OPgTFBgHg5eTFxhc3cmToEW4k3eB60nXyCvLoubYnS88uZeDGgYzaNQqA8zHnAfjj+h+42bhhbGCMoiiFKfqKOhl5ktF/jOZ6onr+1RdW8/6u99EJHX9c+4PNlzczaf9kPv4ikYULBT9f+JlFZxbx8suwbVvJ9/t05Gm+PfotH+7+kG5rutFzbU/6/tqXzLxMAH69+Cuf7f8MndA9cOyJiBMYa41xMndi46WNj/agJUmSJOk+Wq0WHx8f6tevj6+vL8eOHSt1/+TkZL7//l4DTnh4OGvWrPmnq8nVq1fp2rUr7u7ueHh40L9/f2JjY8nMzGTgwIF4eXlRr149WrZsSXp6Ordv38bf3x8PDw88PT2ZM2dOqeVPnDgRb29vfHx86NSpE1FR6jfaeXl5DBo0CC8vLzw8PJg2bdpfvhYZQP8fsahii/bLyRg528F338Hu3URdz0KraJm6F7Z8n0S3M6l45oRD4Hs4hH/P1vh4mpw7x3cREWyIi6OGiUlheTMPJvLa+SucSksj2aQGKWjx75LMjh2QG9qeyV0+A2Cg10A+9PuQoU3GIoRAURS62dvjXWQGmO9q1GBM5coAhRk5Qt67SaeG4/j9yu8AhQMJAcKTw3nO/TlGNB7B6ajTvLntTf688Sc1bGtgbmjO4N8H03JZS64kXKFV1QczehTl6ah233h7+9vMOj6LVze9yuyTs/n98u+cjDwJwPZr21h7+AT70r/nlU2vMHz7cI6fzSA8XO02cv5GBK1bwx9/qGUeCD/AR3s/YvaJ2USkRnA79TZX4q8UnnN9yHq+PPQlIXEhD9TnfOx56jnVY3ij4fx540/e2vrWIzxlSZIkSSrO1NSUwMBAzp8/z7Rp0/j4449L3f/vCKAL9Bm/yis7O5tu3boxfPhwrl27xqVLlxg+fDhxcXHMmTMHZ2dngoKCCA4O5scff8TQ0BADAwNmzpzJpUuXOHHiBAsWLCAk5MG/q3eNHTuWCxcuEBgYSPfu3fniiy8AWL9+PTk5OQQFBXH27FkWL15MeHj4I9X/fjKA/n/z6aeIcwFssR1EU06wwe1DLG5E8vYZaH5Tx9Qfw5n01i84xt3GKv0CbW1s2FKvHt/VqMGVJk0w0Nx7S0RsskP3fQ3i8/I4Xb8mLroNjA/uwVtXrmB06BC/pavBrr2ZPc5B3zD0hhFvXb0KwJgqVRhTpUphWc/Z2+NoaMjW+PjCdV/eiuT5y2Gs6b+Zm6NvFrsM/2r+7By4k041OhH6bijHhh7jQ78P+bbjt6zvt55GFRthZ2oHUCy9Xkl8KvjwccuPORFxgjG7xxSuX3lhZWEwC+A1eCknDKajUTS83uB1TgdkMviNTHqs7cEPAUvJzstjWlgfdl/fzTtN3iF1fCq5E3M5//Z5At4KIHhEMGaGZgBMbD0RULOM3C88OZzqttV5p8k71HWsy9JzS4nLiONs1FmGbRlGWk5aGQ9ZkiRJ+q9qGxDAiuhoAPJ0OtoGBLA6JgaAzIIC2gYE8MudOwCk5OfTNiCAjXFqStj43FzaBgQU/q0sOilaeaWmpmJrawtAeno67du3x9fXFy8vL37/XW2wGj9+PNevX8fHx4exY8cyfvx4Dh8+jI+PD7NmzaKgoICxY8fSuHFjvL29Wbx4MQAHDhzA39+fAQMG4OXlxcSJE4u1Ck+YMIG5c+eWWK81a9bg5+dHjx49Ctf5+/tTr149oqOjqVSpUuH62rVrY2xsjIuLC76+6tgqS0tLPDw8iIyMfOi1W1lZFb7OyMhAURRATbGbkZFBfn4+WVlZGBkZFdv3cchBhP+HFGsrzr67gtaZ0LUrVK89GI0OttaCox1qcd3xZZKbN8Xs1ndUNjGhsokJBULgoiig08HXX8OWLczq0JlJP0zCzg7AkRoOdcnUWvPLnTvY52dyPHg+2/wjsTK24o89glrJ1RgwxuSh9VoVG8uXN2+S16YNWkVheMWKNLeywtHUFlOtw0OPszCywK+KH35V/ArXdanZhYWnF7L16tYyA2hFUZjafiqjm43GeYYzNWxrMMx3GDYmNozbO45+Hi8RfCeYbdc2A7C4+2LebPgmoHa30Akd84O+oNmw3RyMOMEbGX0f2tf7rtr2tdEqWoJig3ip3kt8c/Qb5pycw8JuC7mZcpPedXpjaWzJT8//hN+PfpyJOoNA8GPAjwz0Goh/NX9A7Q6z+fJmzAzN6OzeudRzSpIkSc+mrKwsfHx8yM7OJjo6mn379gFgYmLCpk2bsLKyIj4+nmbNmtGzZ0+mT59OcHBwYb7mAwcOMGPGDLbp+y0uWbIEa2trTp8+TU5ODi1atKBTp04AnDp1iuDgYKpVq0Z4eDh9+vRh1KhR6HQ61q1bx6lTp0qsY3BwMA0bNixx29ChQ+nUqRMbNmygffv2DBo06IGZDcPDwwkICChzUpYJEyawcuVKrK2t2b9/PwAvvPACv//+Oy4uLmRmZjJr1izs1ODmsckA+v/U5Mn3Xv/cZyEDN7yJtYU9l1t50GP9aX5v0ZyIau/D6tUkN26MbXQ0Oyws6PLbbzB9OgDa9u25+/46vnMpYfltuFPJl9tNmzJv/2ympFwgKcOc4W9raNoIvvrSpdQ6DXNx4XkHBxTUT+F1zMzwMDd/7Gt83fd1Wrm2orJV5XLt72TuhPhcFC4nZCaw+Oxi3HkO5WIqwnMdVa2r8nyd59EJHe5z3cnX5QNQx6EOJyJOACCiG0DpMTvGBsbUcajDvFPzGNVsFDtCdxCVFsWI7SM4++ZZLI3U7Bu+Lr4kfJTAlfgrNF/WHIBTkacKA+i5J+fy6f5P+az1Z3Ss0RGNUvqXRrkFucRnxnMn4w7VbKphbWJNfGY852POIxC0r9a+8BO5JEmS9Pc70KBB4WtDjabYsplWW2zZ2sCg2LKDkVGx5QrGxuU6590uHADHjx/ntddeIzg4GCEEn3zyCYcOHUKj0RAZGUlsbGyZ5e3evZsLFy6wYcMGAFJSUggNDcXIyIgmTZpQrVo1ANzc3LC3tycgIIDY2FgaNGiAvb19uepclI+PD2FhYezevZu9e/fSuHFjjh8/joeHB6C2pPft25fZs2eX2XI8ZcoUpkyZwrRp05g/fz6TJ0/m1KlTaLVaoqKiSEpKolWrVnTo0IHq1as/cl3vkgH0M6DrkjfIOD2L1tU9qdpsPQPfvsigBovJt8yC0T+Q5u7OoC1bSF63rjB47lz1Ei9Wr4PZOnjJ5SB+Xd9kXb16fP9KIM7tu2JnZQAmFRhkcobeBn5EOqVyIsWUZtbW5OfD6tWQkiwYNVoN1rI/n0al2tWoMuAlAEZcvcqN7Gzm16yJs5ERuZcuseeHHxji5qYOgCwHI61RYfeLUiUlQUYGVC4eaNub2XNy2EmiI4xIP9ed/i8uxbuO2m87Ky+LllVbkp6bTr+6/ehasyvzTs3j4G+1+f1iHV4pR2Pwsl7L2HRpE45mjoXTk0emRWJjYlMY9GsUDVbGVgTdCSoM1k9F3fv0vjZ4LTVsazC62ejC4Dlfl8/6i+upYVeDJpXU6ds/+OMD1l1cV5gCECBoeBDWJtb8cO4HPv5T7Q93dOhRmldpXnblJUmSpKeSn58f8fHxxMXFsWPHDuLi4jh79iyGhoa4ubmRnZ1dZhlCCObNm0fnzsX/2B04cADz+xq+hg0bxooVK4iJiWHo0KEPLdPT05ODBw8+dLuFhQV9+vShT58+aDQaduzYgYeHB3l5efTt25eBAwfSp0+fMut+14ABA+jWrRuTJ09mzZo1PPfccxgaGuLk5ESLFi04c+bMXwqgZR/oZ4BJwG4i3+pK0ntvkpcHVXt6MKbua0xbpvY7rnLtGis8PHj5+HGoW5eQ2bsJyqvDW2/BT8sKYOVKAFoGB9Pysjqxi0EBrP7dmorXj2DvIjjkE8qglXfYvx8yr0XRYXJLPMff6+eUMO9nfvrwEMuu3yEiO5sW1tY0sbLC+8wZ1sfFseLMGUZ37kz++vV//w3o1k0dVFkCI60Rrq7w1WTDwuAZwNTQlJW9V7LxxY3M7DyT9tXbs/mlzRz/8mt+WKr+txGixCILNanUhGkdphGeHE5KTgpDfdRfLO/seKfYfqk5qby+5XUAXvR8kZMR6sDGfF0+52PP4+/mT0RqBDeTb5Kem86KwBUM2DiAny/8DKip+GafnE112+pMajOJhd0W8lv/36hpp3791a1mN3YO3ImCwr4b+x7x5kmSJElPk8uXL1NQUIC9vT0pKSk4OTlhaGjI/v37uXlT/btvaWlJWtq98Tb3L3fu3JmFCxeSl5cHqNkzMjIySjxf79692bVrF6dPn34g4C5qwIABHDt2jO3btxeu27VrF0FBQRw9epSkpCQAcnNzCQkJwdXVFSEEr7/+Oh4eHowZM+ZhRRcKDQ0tfL1lyxbq1KkDQNWqVdm3bx9CCDIyMjhx4kThtsclW6CfAYZ9+7GrViozTH5lUP1BRA1vTt0cU07l3KJC0R1jY0m0d8eoW0fCB6RAndpwxQL2XOdsA2fWOsTSqcdgAFwDb9Dr+GU6B06iYv7nbDjYmJGLtMR8ClZ/zMXy1nEcJnxZWPSxhedJM81m1O3T7N8TQa8+r/Dim4I337LAqFYBQ3bvpmF4OAb6gRQApKXBnTv3ZpZ5FEJAcjLY2MDQoVDKQIz8fHX3InPPPFTR/28zZ8Jvv8HWreDw8C7cTDuipst5sd6LLAtcVph15K673TkAmldpzrnoc6TmpBKeHE52fja+Lr7UWVCH3IJcXvR8kV8u/gJASLw6EtnO1I7LIy9jbmRORcuKD5zfy9kLL2cvvJ29ORB+gE9bf1rqNQohmHViFjtCdzC9w3QaVWxU6v6SJEnSk3W3DzSov8N/+ukntFotAwcOpEePHjRq1AgfH5/CoNHe3p4WLVpQr149unTpwtSpUzEwMKB+/foMHjyYUaNGER4ejq+vL0IIHB0d2bx5c4nnNjIywt/fHxsbG7TaB1PJ3mVqasq2bdsYPXo0o0ePxtDQEG9vb+bMmUNAQADDhw9HCIFOp6Nbt2707duXo0ePsmrVKry8vAqvb+rUqXTt2rXEc4wfP54rV66g0WhwdXVl0aJFAIwcOZIhQ4ZQr149hBAMGTIEb+8y+mKWQRFlNaP9xzRq1EicOfPvTXrx/yDfwY4bIokX+kOlll2wqdiZtUp9rDJSSOn+PAAHDwhav1iB4/Y9aH1lKSdOQGrjdrRD7YD/a7+6fN5Gx6WRam7nuKEv4bj8Fw5XhTpB+ThaadWuEl9/DV9/Td5z3THcubVYPQqEYPrUqQycsopRnS+RlqLQ95U0fqh4lKmTJpFoackGf382ffwxZGXB3a+JsrPhbj+wXbtAUdQc13elp8PFixAQADt2QFgY3LxJRkEBJCRgbmqqzpNuaFhiML5mfiJD3jXncpgx1SrmwIED0K7dvYg6JgbMzMDKirizt1g35jjJHV7EyUXHinM/0/f2Lj64egYlL48RAwbgGR7OyK1boaAA3n2XtEmf8HPQz7zZ8E02hGygpl1NGrg0KFaHjZc2YmJgQhf3LoV9lH8K/EkNZAfuYNbxWay6sIrYjFhcrV1pWLEhO0J30KJKC95r+h49a/cs830wbMswTkScIHjEg5lBitoZupOua7piY2LD1pe30rJqy8Jt0WnRzDs1j6y8LAy1hjiaOeJg5kAbtzZUt338r8IkSZKeVpcuXSrsq/ss0ul0+Pr6sn79+gcG/j1tSnqWiqKcFUI80JIkW6CfBVoDat6Bd0/CltYGeGhS4dRg6tg0IcO5GkaZybT0K0CJi8P7ZWd++QIaNoSd2/cRWycD5y1L2WtzGF3QVhJO7se+qT+OtRsAv7DDvx/+5w4zw8mJ5iNG0OTgQdbwMsFV5jL1/mooChPy8thVz4otow6yoGZNXnFyYsbv6fzUuTP1gagKFchLT8cwOJjNLVow7s03OXb5Mvb168PJk9Cli1rY3Q9+W7fCkCGkZmXxzqhR9DUyope7O3e6dsW5a1eWxMYyqGpVkl94AaeGDeGnnyA1FSwtEcCoAwcYPXUgPwz5GQcHf+jbl42pqfyWn8/P3bqp5/jgA9BoYNUqRp/7k4RucUxrnUODZsas/dmSU+EtEaYFKMaGhHp6YluxotokvXMn7NiB5bRpvN3obdi/n/6/7IOF/R54RH08HuzXNchnEIN8BgHwbadv+brj15yPOU8NuxpcvHORAl0BcZlx5BXklettsKj7osJJdcKSwvj14q9oFS0v1H2BarbqgJCV51cy/9R8DDQGRH8QjYlB8awqiVmJzDg2AxMDE3IKcgpnd1zde7UMoCVJkp4xISEhdO/end69ez/1wfOjkgH0s8BAy6mK8FVraKDRUqAY0vBGIiNvHMV87iyIjYWURNDpMK/mTN++6mHqNyTmMHo0n6b0oc13G7H/rh34+pK2awu7Dy+hy9lgLCa58X54OBNq1ODnsFGsy36eve8+JNND7dr8lq5OC97AwgIrQ0OmeHgw0MGBcQ0b8rGl2p0h9fJlMkxNqXvzJpkmJtjXr4/Ys4eVnTvjkJJCt/x80Grh1VfB2RmL6dM56eBA85EjoVIlrAoKmBIRQWM7O4ZcvsyJyZO5rs9jSa9eYGZG0sSJLM/Px/yrr5jWrhqY6+DgQdZ+8AEbzM1ZJQQaReEPZ2eu3bnDSKB9t86cu3kT74aGCAH7B/TStxgPB2CP/jI/DQvDzseHMUW/8mrXTv3388/BpfSMJSXRKJrClmu/Kn5sfmlz6Qfcx0BjQIGugOz8bHaG7iwcWGhvZk8122rohI73dr5HSk4KAIYaQ6LSoqhoWZHI1EiuJFyhZdWW5E5Ug2YhBBl5GcRnxmNnasfiM4uJSY/h87afP/K1SZIkSU+funXrEhYWVmxdUFAQr776arF1xsbGnDx58m8778iRIzl69GixdaNGjWLIkCF/2znKJIT4R36AZcAdIPgh2xVgLnANuAD4lqfchg0bCunR5FWpJFbURzAJ8cKvL4imB9YJ9u8Xxz08hJg1S5xdc1n8PHSvCJqwVngYXBXHjpVcTvcPKooA30pCgNi67gsxsgtCgBDXr4uYGTNEqqmpSI9NL70yZ86IOCsrkbJxo/hiwCWxtPVKUZCaKs6lporcPJ3ID7kkMhcsED/PmiXYv19cqlJFiI8/FkII8dP77wv27xcvfP65EFFRQsTFifPVq4vAhQuFTqd74FTx8UIcPCjEpogEsWL8eJFbu57YvVuI2OXLxdk33hDCy0sIRRFpd9JEQlyGEKtWqdcDQnToIIQQ4lZgoOi6YYOosGGD0IWFFZb9zTfqbidPCpGVJURSkhCBgUK4ugoxebIQ3S9cEG9evixEfr66Q3r6vbJ37rxXybi4R3iSf01iZqKou6CuYJL6XnCZ4SKMvjQSQzYPEek56WL4tuGF297d8a4YvXO0YBLi6yNfi64/dxWayRpxO+X2Q8sf8NsAUX1O9X/teiRJkv4LQkJCnnQVpL9JSc8SOCNKiEf/ySwcK4DnStneBaip/3kTWPgP1uWZpjEyZtB5GJhencqWlfEzVgfUvfrJJ/D++1h9NZYByzoguveg14c1cXV9sIx9N/axzTKK0HrqsMPuL31GgQbyTYxJ++MPbG/exNLICHOnMvI616yJQ2oqVsOH0zTiN4Ydeg1Nfj4NLC3p/3o+DQ+H0Cs7m2YdOrDU1JSqdetyeNs2Nh04xFd9+9IqJYVfJ08m91YMZ4IMGTp/Pn516hCTm8vVq/DyyxARoZ7q2DFo0wYqRdkxKCkJ5XooQT3fo2G12vxhY4MICgJ3d2ocv0TXGevh1VfZ0bQp2X5+YG0NQrBk8WJ22NsT+sorRDRtStI33wAwYAC8/z74+ECFCtCggdo1u1MnGDwYtnp5sbh2bVi4EKytmbdiBd2n6ju1XLhAQl4emydNIqNKFbWid+Xnw59/Fi4m5pWve0Z52JjYMKj+IF6o+wKmBqbM7zofT0dPlgcux2KaBQvPLKRV1VYUfFbA3C5z6VKzCxUtKzJu7zh2hO7gA78PSs25XdGiIlFpUXc/IHMg/ACf75et0ZIk/f+7+3tPeno96jP8x7pwCCEOKYriVsouvYCV+uj+hKIoNoqiuAghov+pOj2rNFr1Ma/O7wnPzQLA5dMXafvbBfDwwD1kK1HLduHV2IRpzUou49eLv+IVA/XPqlNonpswlBUsw7yhPzM9PKjv5sYSQ0OalFUZKyuwtYXYWDp97wFLLqvLgEmuIfWTffHr4E7ILU/Srii89eWXrM7IAHQkerdEF3wSBdiyOJp+yxuwZUsXWvsVYG2sJVXA6dNqPNq/P7RoAXv2QPVaOm698grWgZcYcnE52ZF+NFYU9jRqxI5XXqGnmQ1umcmwbh1vOTpSS6vlmqIwNy6OAd264aUoWGRnM3TcOM42bMh1oFKle5nxfv0VmjVTL23JkuKXe6j2IMIaaMHCAiUlhYI1a9B2786JlBR6t23LoY0baXXgADRvTlhWFq6ffIJ29mw4c4atrq70DA4mtEkT3M3M7hUaHg7VqsH27Xf72ZSLoih81OIjAHRCh0bR4O3szZ7re9gWuo0doTvoXqt7Yb7pTjU6ETkmktj0WOIz46nrWLfU8l0sXcjOzyYlJwUbExsuxF7gi0NfMMZvDNYm1uWupyRJ0tPExMSEhIQE7O3t5URVTykhBAkJCZiYPHw25fs9yT7QlYDbRZYj9OtkAP030xlo0QDJ+RnYAKn5+bxapwcul3+FNWugoICKr3QmK0tNflHS7JYGGgOGn4FaF2MAuPFiZ7I3LmOm9hBT7L5mQmIie597ruwAGuDmTXWQ3bp13Pz2V6yT1Wxza9cCuDF0KAy3u4JpniEz+lfEOjKP2JfOo8GX1A3L+PqttxjUJIGdzW7hZ5NMzUr1eOc9+OwzuHbt3mns7KBDB9idmEzn/HwOb1lPS2dnPhk4gLgJE5jZoAErPOqyNNWdfnNHEZuby/L0dHTA6thYHI2N8ejWDQ9g6sCB2KalMcPN7YHL0c9uyr59amB97Bjc8rvFpJgwMASj15rRwuAK+yZMgPx8bgQFceb2bTZ//TWNL1+Gs2fRCYHfuXP06NiRH2bPhpgYMqtUoZapKZUmTgQvLxg8mMS8PK7u3k0zgFmzigfQeXnly8UHhUGyu5077nbudK/VnTG7x+BX2e+BfZ0tnHG2cC6zzLsp9KLSorAxsaGWfS0AAmICaOvWtlz1kiRJetpUrlyZiIgI4oqmYZWeOiYmJlSuXL6ZjeHJBtAlfUwrsf1cUZQ3Ubt5ULVq1X+yTv+XdBoFDbAoYCkcqc4pk4ZsqlyZI/Xq0eLgQRg2jIxVG7F4rQ/W1mr65PtpFS0FRZ6YgYGR+qIgk4/d3RnRpQvG5ZxBEEtL6N+fmNO36eIWwvITdfn8c7UbxLRp8Nxz8LtlEhXttYyo1BgqwR+Dt/Nxkj8etx2Z078/rezsuBm4j20bAnjjze9oUkrk7m1uzg+1a1PD2poYe3sM8vKo0q4d76xfz5zPrtH/ZEdmXo7ix5QIzjZsiIlWSyc7O2Jzcxlz7RoTXF3Z1Ls39S9epPdDclInJED79tCxo9rq/c0fVmAEHhm29G1rg9HGa6Q1aMALZ84w9LNJTPp4HBf8/TFISSNn8xb2TjhI34GOLMzL46PKlVEORvJit270W7UazXffkdJnAAv9/allZkbfWrUId3bGVZ8Q//f4eHYeOsTCl15CGTsWpkwp33Mooop1Fdb3+2uT2Lhau+Js7oy5oTnXEq+xM3QnAGejztLWrS0hcSFsvLSRGrY1eNnr5b90LkmSpP8KQ0PDwqmtpWfHkwygI4AqRZYrA1El7SiEWAIsATUP9D9ftf8vOVO+wKDXC+Rr4GbidWpWqAhUZcTo0ZwfNgxOncL8wgVCQ/PJzCy5DAONARoDQyAPtFrqu/gAYG1iS6KhIWb79mFaSgL1B/zwA9rNR3k3shoNGqjpmQ8dUltwz5+HGLsmaBSFDz5Q+xkH9e7KwoSb7DZy4YujlZmwSqHjuHTCmjux48XSz1vB2JjX9VkvLH/aTLp5Potd3blxsxmf3R6It18+n/8Zz8p+1TApcg2nU1OZFRFBcysrTvTpg/ZuepIS2NjAihVqg/CNG9CkiQ1V1rXl5Zeh33nwfu455nXpwu60NGqfaE+/72qRvdoXw/btscjMJK2bPxV+q0XYshXkabQknY2kJvCyqSnN+/aFLHM+vnGD0c7OTF+7FsvMTDV7Smoqt7OzWWxnxxfm5jjNnw9ffaV2yP6XNavcjIODD+Jq48rK8yuZe2ouAOdiziGEoOOqjkSlRdHatbUMoCVJkqSn2pMMoLcA7yiKsg5oCqTI/s//EP2kI/kaNRCuZZBH559f5Zt9+lbkgAAwNsbd/eFFaDVastAPaNNqcbNxo1/dfgQm3MDh6FGqGBuz0dOTRlZW5auTsTGOL7bTJ3+DBQvg8GGYP19tzU1P17Bokdol4/BhOHrClbdyKlDD1ICEW9CoEexe1RBNvDEFL6gZ7cpjXcO6JKVA36rWtPmgAa+lptHMyJbUVG96Oxbft7uDA1mtWhULqh96f7QwSE3ZjKOjmm6jSRM1mP7lF1hyuxHfzdXRJTubo4frY20v2BAbD0B9RWH+88/T99o1dllZIHJzuOidhvfp09z28sI9Jo0vV8xnIAMoaNEC55QUdQTjzJlw+TIjGzfmncqVYfJkGDVKDawrVCiltg9af+cOOsBaq8XVxAQP8zIGg5ZAURRqO9Rmb9heBm0ehKHGkI41OnIu+hwRqRFEpUUx97m5vNv03UcuW5IkSZL+S/6xLByKoqwFjgO1FUWJUBTldUVR3lYU5W39LjuAMNQ0dkuBEf9UXZ51BmcDACjQqIFwDoboyMO7aO7GMoLE8S3H3+vCYWBAWk4agTGBFOSnM8nNjds5ORxNTf1L9WzVCnr0UKfLPnJEHaQ3fDicOgWGGg3VTUxZuRJ694Zly+DXWabERWm4O99JeXSrYMfAWnbsDsghyiAT35b53Lnz8LTM5QmeSzJ1Kri7qxMYJibCrVtgpNHgbmbGB6kXeefmZU6Nq0yHeS34sU0bvvrwQ64ePoyoV48jrVohft+M6/HjHLG0YIouFU1UFItv36bCxo2EVKlCsD5aPxsayp8LFyJCQgrnGc++fJkPrl3jkxJm7Lw7yjgpL4/3r17lxNWrkJvLgshIFoSF0ePCBVYFBj7WNd8VFBsEgImBCZ+3+Zwfe/7InzfUzCKNKzUutu/Hez+mzvw6pGSnlKtsndCRmJXIlfgrnIg4wcHwg2TlZT12XTMzYd68xz5ckiRJelaVlNvuv/wj80A/Ol2jhkKAGN8eMWrnKNHr+O+C/fvFQW/ve7mJzc3LLGdaS/2+rVqJ/Tf2CyYh9t/YL3Q6ncjIzxc5BQV/ua6XLgkxbZoQublC3F/ciRNCGBoKcf78vXUHDwqxYMGjn6eOd75oMeuWyMgpEFZWQgwb9tfqfb9bt9Rc0UXt3i1Eu3ZCfL01WbT+KF589pkQS5eq21bHxIjsggLR7fx5YbJ3r7hWp44QWq0QffqIgGVbRKVffxXN5s8XfvPni/pHT4pKR48K8f33Yu6ZM8Jlyxbx+euvi6EnTwoB4mbdusLtyBGxoU8fIa5fF0IIEZOTI+rt3y/mTp8uRFSUiMrOFlZ794oVnTsLERMj8nU6ETNihDhVu7aIqVBBfQAl2J+YKFbHxKgLR48K0bq1EOvWFW6Pzs4WSyJuile3vivmnphbuH5n6E5hNsVMRKdFC78f/MTygOVCCCG2XtkqmITYF7ZPRKZGipi0GLErdFfhcbn59+qx5sIaoZ2sLcxVfffnXNS5x31MYssWIerUESIzU13+/nsh8vIeuzhJkiTp/wwPyQMtZyJ8FsyaDa1aYWVqg5O1K24WBvyeDcM+eoerr7xZriIO3zxMngaEAsqhQ2hvHgYgryCfmNxcLLRazB6ztbaoOnVg/PiStzVtCps3w50799a1bq3+PKrtm7QsX14FpUAduPiIPR7KVKUKjB1bfF1UFMTHg3uuNVEb4aUt4OGhbhvorGa5+LFOHSKqVaN6SIi6IScHbTI4rTjCqLCLzFnakcZfezBmpAaaN6dLYg4fzrbmQnc3NmUmUOujjxj3zTdcu30bbW4urF8PL7+MU5Uq+J08SaU//wR/f1xcXEhxdoY+fcDREa2i4PzmmzhbWMA337Dt8mVq1qhB7aLp84Al0dEcTklhgJMTyvffqx3XnZzgxRcB2J2UxJuhYXzfYBzDK1UqPK5FlRZcePsCzubO3Ei+wZ6wPQz2GUyDCurMiqN2jSLoTlDh/gkfJWBnaseHuz/kzxt/EjQ8CE8nTz5q8RGOZo44mjtib2qPkdaIipYV2X51Ox6OHo88nbijI/TrBzk56pcwo0eDuTm89tojFSNJkiQ9a0qKqv/LP7IF+jGkpKgtxzNnFq7qt2W0WP/+wHK3QH+27zPxeRv9vjqdOHrrqGASovnytoL9+4XZwYPibGrqP30l/zcyM++1epaXTidEZKQQd+7cW5edLYSZmRCz5uhE/+Bg8VFwsIi3thZZH30kdkdEiLAKFYSYOlV9D2g0QkycWOo5zh46JNa3bi0s9+0TbQMCRGhGRrHt+TqdOJKcLCodPSp+6dNHfT80b15snwR96/XNrCyRVEJL9ksbXhJMQlSYUUH8FvLbAy3KTEIcuHFACCFE/YX1Rbuf2pVa57iMOMEkxJwTc0rd734jRwrx/PP6MuLUbw127BDijz+EWLhQvWWSJEnSs40nMBOh9F9x6hTB775IcAdvQJ3d7quab9F31s/39ikja4NAUHD33dK+PQYa9cuLYzcP8amrK5k6HefS0v6J2v9f2rpV7R999Wr5jzlxQm3sHTMGxo1TW01Pn4b0dBj9nsJqDw/2ZWfjsHkzEUZGdAoNJcjXl1tBQby3fj2XKldWp2YsQUYGnDkD3xtbMn3AAA4FXuNAcjKfHTgCXl7knzlD9wsXmHX7Nl7m5nTOzaXS3cpHRbEwMpIaJ06Qu3Mndh9+CAMG4LdnDx/OmaPmJezbl9qbNzP/m28Yf8UJgJj0GOxM7bA2tsbM8F5Lt4LCm5veY+K+iZyPPU/rqqV/xWBnaoeBxoCYdDVHeXJ2MtOPTOdy/OVSj+veHQz038GNGaN+k9G2LRQUqH3v/2JXcEmSJOn/mOzC8Sz47jtyLuyikf0vLMhZwAWjeixO0rGrcWM6nz6t7lNGAG1iYMIKT/hA+GFTqRJa5W53DR1fuLkxyc2txMTeUslatwY/PyhhXpYS7d2rTh8+e7aaRtvMDDZsgDffVIPwSpXUgZZ9r9cma3ke1ea78bWhhp4ZGRy5dYsVAwfSz9e3MICOj4fXX1e7r9StCyEhataQyduqMOXL7hT49YWf+/JeflcIDiZo+nTSJkygiZUVVgYG/Ojnp0732KYNLF2Kh5kZ7bRa7owaReWwMHBz4zsLCyrl5KijKCMiqBQdTYVjx6jv4kLUN1HkFOTgZuPGrfdvoaCwN2wvLoZ18Ruwl9DnPuCrw18BYBjfkJwcMDYu+d5oFA3O5s6FAfTEfROZf3o+O05c53nNUsaMUfdbv1790NGhg/r6+efVwahCgL8/fPQRmJqql3TjBsWmtM/KAo3m4XWQJEmSni0ygH4W5OTQMFJQJw7iMuLwMksHzBjzzlAuDjpdriJMDEwIdQDNrl1gbEW1rEQA6jjU4VZODpZaLXblnAVPUvtcHztW/v3btlVT/Dk7w/ffq+tCQ9VsHxs3QlwcfPEF1DOzwN0YNNVt+UgBVq5k5NZQnPdk4zVxxr0mV9TMIHv2qAF07drw++/QtJE9ztWqwYa5ZLYMwfTISTA0pMHmzRxYvRolPV3tIDx6NOLTT1HmzqUgP5+2Oh1tGzeGy5fVGRGNjXnxvmvYB2rTLnA36Ul8PPzwgxWrV0NwcG8A4nbUJjllOH8kLOSdne8w+2NvdJfh009LuZ8WFYhOV7NgnolWs48oIf347QqFAfTSpWprvZMTvPeeml+8VSt125Ah98oyM1M/2MyeDatWQc+eMGmS2v++V6/yPjFJkiTp/5nswvEs0KeXa3FLTWNXw1gDJ1/GIX2jOmoK4IcfSi1CJ3RUS4TkX1ZCXh52pnY0qdQENxs33E6cwP7oUc6np//DF/LsMjC41+AL6iNVFDXt84ULato/ULslzJoFc+boH3vVqnTo6Mbg0fWw8VVnUczOVmdSDwhQjwewslIDRWdnCtPhJVwOZL+PD7z0EhQUoFy+rEbde/fSLSUF37NnSWjfHos9e1iRlqaO7iyjmTY9P5/8oCBo3x5x5izNmqlTsDdvrn4gmD9frZt7DQ1Vjb0Z1eR95n5ZBScn9UPEqVNqOTpd8XIrWFTASGtEVLSOgMgg3m38Hgd+7MTRo+o4jw93f8jL01axd686W2RUlCDS6jeO3X74pxitFs6dU4PnAQOgVq1721JS1O4zkiRJ0rNJBtDPgipVSDSFtV7qRCoZOi0VM0z58OfzamqL5cvVyKsUfT360uMqVB30LtSvT1ZeFgHRAeTk5/CJfnr1ixkZ/8bVPPNiY8HXtzDxBUuXwr59ah/m48fh4kV1npXLl9X+vDNq1mCCm9ofQQioVk3tuvFQL7wAwMR5v9Nl+mx077zDqx9/TJ9jx9UIOyqKXrVq4XapAnccajGyalU87ezA2ZkA//fvNjI/YHNcHJZHjrD9pkJOciZKTjZTpkBgxw9ZYjeeMWNg7Vr9vkvjeP85X6b7f8dLLyl06aJ2v7g7U2bnzoWfC0lMhKZWffix549EJCSRc60F5/bU5sitwwz9fSg/BvzIzOMz8XCuiZmZ+lZ/9YOLfHtyKi2WtWDQ5kEP1DUsKYzL1Ucy4NdBdFw0gDHfBBZmTAG1NbxTp3vL33wDRWeyDwqC/PxS7rEkSZL0VJMB9LPAyIh4c4VMI9AqWvZnGRDVbhn5mlrw009Qs6YaeZXCUGvIunqQUsUJ8vKITIskT5fH0AZDmVK9OqJtWwboU7FJ/yxnZ1i3Tg2ci1q7Vu1bnZ0N0dFqn+adO8HWVu2iMGqUGtQNGgSenqWcoHNnyM7GKd4H8/U1yKrti22WoGrANfId1Hx/nfIqsfm1yqxZYMQMd3caW1rC9Okct+jEgQNqoH6/BpaWDMiqxvtfuvPmyzt52daWKdVPM6mxBXz9NYsWwcqVgBA8/6YTP9mOLnxbVqkCR44JWrVSW6n37lX31enUluk93wzFwcyBRnXtmTNkEGOHVaf1itYsD1zOmqA1ANR1rAuo/cWtsuuxfcB2vJy8WHl+JXcy7hSr69yTc1l0dhFHIw+yN2Yda4J/5sqVewH8oEH3gn2AyEi13zRAWprav3zz5jIfpSRJkvSUkn2gnwXJydSKFzyXVRlXG1dqWliwICmDRT170vujj9Tvoh0d4dKlhxZx/PZx7ljAnRoVsI4rKMzCka/L51pmJlYGBjgZGf1bV/TMa9TowXUTJsBXX6kD4UAN5Fxc1O4H+flqgFegz3tdxphRMDZm2gj4WmPGH4mJzOvVmYpTGxAzSMvy5eoAu+RkKOz2rigwbhwjxj18SlFXExN+7uJKQAU4ok1lZkoKA62tqXj+PAC7M0MZGhlJm1gjtpmY0GJUI/C/d/yIq1cJSE8n5puGDBwIFSuqb9t16yA3V91Ho4H3/AeQW5BbeJylsSVVrKowbs84LidcZuPX+0lIzqOCRQUWdltIy+UtOX77OL3q3OvgHBATQLtq7djz6h52X9/Nxh+rU+c5ddvWrQ9+YTNnzr3XQqhBuoNDGfdYkiRJemrJAPpZkKgO+NvpOxM8+wMwYscrfPLNH2r6gf379U1/DxcYE0jtOKh54AJUr16YhePbY98yJNkNgODGjfE0N//nrkMqlZ3dvdcHD8LQoeqYvbuDDi9cUIO6zZvVTBRl0WjUFtfTP1vwpld1fN42ZsZYiIlRu4FYWalTr/v7q90XvvsOKldW+2qXRKeDPHTssLtNlQULCF+xQq3I5s3QowdWRkYoQFxSEjF2dri3bQvAJ2FhOOTk0Dw1lSVZWYzeeoOZXtW4ckWd1t3JLZ9EhzTAFoC5x48zKSMDU0MLsvLSqblsCxOibLAXv7PHNJrnshtzKuYMk9pMYlzLcRhrjQm+E1wsgN4/aD9JWUkAdKrRCecXoaqF+qHB27v0+2ZlBZs2lX1/JUmSpKeX7MLxDAmOV1uY43NzGdpqPI7JydCtG+za9dD8wEU1v61/kZJS2AIdEhfCBH0f6LCsrH+i2tJjqFBB7UbQr9+9dY6Oast148blLyc1FSa+Z0Tb6KoM72PC9etq8Pzuu+rgOiMjdYBjbq76GezAATUmvpsxY+9e2L1bfb14MTj1SuDX6DiM6tRR012sXUuOmRk5ixbRt3JldLa2XOzZE/eoqMLpANNTUti2Zw8DmzdnyM6dWAQeQnPpIh5Rf/LDD3Cl4Brtzp/ncFQUAB5HjvDK9u1cfS+MgLcCaGpUjWqKHU75xrx9FiziU/By8uL5Os9jYmDC9gHbGddyHAmZCfx68VeSs5PRKBrszewBOBd9jjCjTXzyidrXWf92lyRJkp5lJc2u8l/+kTMRPobGjYUA0fMlxJoLa8Tky2cE+/eLVR06CFG3rhAbNgjx22+lFrHo9CLx2vP6mQjt7UVsemzhrHHS/6+CAiHi44XIz7+3Ljz83gSWOp26LIQQGRlCpKUJ8c03Qowbp67z8xOiVSshDh9W92/ZUoiCAp3Q6XRC6HQi5ORJYXzggPg1KEgIQ8N7Bb/7rhAvvCBEfr7IXbtWJJubi+xXXrm33clJiM6dhRBCRC9bJnY1bizyz5xRTxoTI0RioriWmSnWxsQU1vvDfftEx2++Ebo//yzxWrdc3lL4np60f1Lh+re3vi0cv3Es1/0KzcgQr4SEiDaD00SfPuW8yZIkSdJ/FnImwmeYPlVdgQIZeRnUM8wGYH3btuoMGi+8oCYRLoWNiQ35muLLAP5u/lzOyCAiO/sfqLj0pGk0YG+vpnS7y8UFlixR085FRak5kxcuVBuULSxg7FiYPl3d/vbb8Morasttjx7qAECNRkFRFFAU3Bs14t3Klak5axYtvvuOj956i69ee42X3niDbQsX8trVq2Rcvsyybt0wf/11Er281Dx9zz3HSktLWp47h8mpU3S+fh2tr69aQWdnsLVl/Z07vHzpEin6dBiuDg543LqFcu3avYspkg+veZXmha8rW1UufF3BogJxmXHMOzmPnwJ/KvV+GSgKe5OSqNE8h9alT6AoSZIkPcVkAP0sqF4dgHyNmoWjgqGCyx99+eHbb+/tc+tWqUWk5aZRUGTgmZHWiAoWFahpVxOP06epcuIEl2Uau2eCkRG88Qa4u6uDEqtUebAHUGYmtG+vTmISFQXXrsGWLVC/fvH9DDUavq1RA5927eh84wYmubloa9RAo9USnZnJsZAQLFevxtzamjpmZkQdO4Zto0bsa90a44QEzM+dw3rlSjXlyH0jIwdVqMCVJk2w1Ef/L9WqRdWEBC5FqxOuEB+vdgpv3x4AezN7XnL0Z+wRaF7hXj8XNxs3AN7b9R4X4y4yLyKCDoGBnEyOp+WBn3jh1HYAwrOy+PjGDbbWq8cPw+zo+1Y2BUIQdCKDVd1/QYTfZOUaHavD4gnPynogVcmR5GQWRkYWLk8OD6fFuXOFy2tjY9mRkFDsGCEEOxMSOJqSUo4nJ0mSJP1d5CDCZ8HJkwAUaNSJVJILBDkWLlhkhd7bp4y0DB4OHvxR5ONWga6AmPQYUnNTmejqypc3bxKVm0sdOYjwmVK1asmfvczMIDBQnQK7UiX4/PMyCho4kHf69cPqzh0MKlZUm77Dwnhj9myIi+NNa2vebNKElPx8XI2NqdqiBe2WLuXFiRPVVCAlTFPoYmxcOONhvk5Hmk7Hh2++id2OHXgA0ZaWnH71VT719uZAUhK2NjY4N/6a7ouH4XHuFlRURwu+7PUylxPDqGThzDtNRvD1rVu0srGhggHcysrkZPJVRGAlCrp1Y+fPP/PtypXs8fLiNTc3rA0MeGP5SdqGLiBzuBeDDeYiKl5l+u7bjBv+mtqU/8YbAPyRlMT0W7d4rUIFzLVaKhoZFRuUuzgqioT8fHJ0Oq5kZjLe1ZWTqal0DQri+5o1aWFt/cjPT5IkSXo8sgX6WbB6NXCvBfpkpo7EFvNZVXQmiDIIxL0uHE2bkqfLA8DbyZsvqlVDtG1LO1vbv7vm0lPMzk4NnqEcafMAOyMjDCpXVoNnUL85OXxYnfZP/22JtYEBgY0b416nDpw4oSZgjo+HFi1KLHNXQgJrY2NZHB2N79mzXNuyhcFLl5KXn4/biRN806MHrrGxpF+7RnxeHj9kZTF50CA65+Wp3ZJu3MBoyY9UcO7PxFxv5p39iX2H3uBzNzdcLRz4yL6A/MTTfH/2BI7JyQSdPEnI0aN8rDUhfnlFrmZlMfZlb8aMGIFBwCmuzqpIn01NOPuOGQB/3LrF80FBROfkMK5KFdJatsRc32L+RsWKLKldu/Batnl5ccLXl/3JyXwfFYUQgmbW1txq1oxX78vBviw6Gs9TpygoKSG3JEmS9JfJAPpZoJ8izd2pNlWsq9DcUk0UvKFd89KOKiYoNuheF44vvijMwlEgCghOT+e6zMAhPQkajZoG5CGWREfz5c2beJqZ8ZqzM9U7dODU++9z8ORJ5mzZwndJSWydMAGnU6ewz8piW0IC+3x92W1rS0xurpriccQIfAYO5I20NPLTotl9fTft9i9DOXCAKptP42DanM0e1bDKzMQuI5nIrz7AtFJFBrk4sDvJjN8++4xZy9YyNnU0oVnpfPKaGd2W9aGgYSOS09MJzcrC1sAAC60Wk6Kdze/KyoKCAiwMDDDft49vf/2Vm97e3M7JYdSxY9j+/jsxly6xPjYWdu3i7JkzLI6KoqKxMamPMB1iVkEBuffPkV7EpYwMMgsKyl2eJEnS/zMZQD8L9C3DS59fRmvX1jRzrMnQzC0s2bnz3j5lNBEmZiVScPfdUlBQmAd68dnFeJ05g/vJkzKIlv5zFtaqRUCjRrS1tWVOzZrEd+hAB39/IkxMePvqVZrUqQNAv5QU6mzZgueQIbyxaxcpb79NIysrdvXqRZW9e8m0suKbrl0Z9NVWdTbPkBUoyRdw27SDeVPepVXeWbC353rwITZfmcOhoa/y41dWdOzTlOcPH+EHr2xOfmNO14QzBM4cwPhqR1jZqSMv/vknwY2bMPfNa6T6tC7sF63TwfTeJ9FVdwdzc3WwL8DNmxh/+ilKQQFhWVmsSk9nz/z5/DxrFpP+/JO87t35Y9MmwrOz2ejpie2BA2q+wcOHi92XAiH4Mjyc3foc8fG5uZgdPszciAgAEvPysD1yhB/0qQF/iIqi7unThMvBwpIkSYAMoJ8NRkbqPM4NGwKQpzHl1UbjcTh8utxFfNj8Qwb5DlUXXnlFzaIA6ISOz1xdAUjIy/t76y1Jf5GzkRHGmnu/5sy1Wua5u9PT21tNEeLlBatW8Yq9PVN+/BHHZs1YYmCAlX6WlipXrtC3alWsf/kFRo3C7s9jxA4M5MbAlfzuUQ3bwyf4aoQn6bnJUKkS3puOUfHgOTLfGIyoVYsLfSbhYHyNRTXPcyrlI76wsqKtiwtvR6eSY2gNkZHcuQNnV18i71IoZzerydZDQsBs528QcRs++wxsbNQLGDYM8vLA0pK2trYc8vSk2bx5DDx2jKGbN2PYrRufTJ5MiH7w5Ps7d9KgUiUaX7/OyyEhtA4IAOBmdja/3LnDlvh4AOwMDZlSrVphNyytovCKszO1zNSuJgOcnZnr7k5VY+Ni9/duOidJkqRnjfK0/fJr1KiROHPmzJOuxtPlyy/VP8L5+aDVsikujj4XLzJr/nxG//abuo+9vdqXtDQpKer0ds2awdixbLmyBV8X32IpvyTpqdWqlRqcfvUVdOyorvvtN0526ECzc+fYYmJCDz8/mDIF6tZVBy+2a4cwMQEgq5M/ZnsPstMdur4CISNCCDnkwZQNWwjQz3IoPld/31avDlPMpvDyxU+5tWU1BbXr4OVXk4V11/CqfxSZH00i288fS4NMDAPK8UG3Rg0IC4Off1bnbgeGnTnDj+npNL5yBdPcXNp068a17GxWeXigLU+n9BLsSkjAWKOhlbU130VE8O3t2xxp0IDa+kBbkiTp/42iKGeFEI3uXy9boJ8FV66o/+pTZLXUj9YPaNFCTdwL5RvlZW0Nv/2mJvoFetbuSWWrylxITydEprCTnnYffqiOF7ibTxqgdWsqGxszq0YNGnl7qyMjJ0xQ5xDv3h0SElAUhaTsJJamHQQgrl41AAJjAsHjN35f1ACAlc+vLCy2/rjR7Kr3FQBVe75CbvNG5L5ry8vVTsKXX5Jdyx674EP8Hu9Mz4Xv4rPIh21Xtz1QZZ1OTSXIwIHqCl9fNafg1Km4xcXx3MmTnNq3j4PvvccXtrasEYKTb7/N8GXLSM/NfeRbNC4sjL1JSZxJS2NcWBgp+fmyBVqSpGeSTGP3LLh6Vf03JgaqVsXRyIjkli0xaNRI7dbRpg1Mnlx2OTExsHGjOiNGlSqFqxucOYMOuNmsGVX1rXGS9NTp1eve67v/ZxwcqASMvvt+P3dObem9dg1iYwvTjOTk52D6zXdsvx5H266D0Sz2YMBGtSV4+4DtZE/IxtjgXveHkR27s7VSPjPcszn2myWdB1vwnb8zBjFVSd5+iDsZGVytrLC05S2CUi4RnRPG9qvb6V6rOwUF9ya26dxZjefbTpoEgwerTds5OdC8Oe9XrswLN2/Cc8/Br79C69YQHExe/fpsb9cO74AAhjdooLakl7NFekyVKgxwckKrKCyoWZNXnZ0pEIKonBxcjIwKu3ZJkiT9v5NdOJ4FM2eqrWspKWBlRXxuLufS02kyZAg27dpBnTrQoAE4OZVezrFjarowR0e4c6dw9eTwcCaFhxPcuHGxvLWS9KyqOa8mFkYWBMYE0tejL0t6LOFqwlW8nb3RKBpMDEy4fVvNow1qXN5AbaimVy910pmqn7bHqWoqp984jef3nkQH1SZp0UaGDoUff1R7ZLVoofY2+eqrIie/O+H53b7fR46o3VPu8vQk/9IllFWr0EZFqQf/+qtaGf2gykexMDKSEaGhjK1ShVGVK1Ppvn7SkiRJTzPZheNZdjfNlz4F1dn0dDpfuMBCCwuYNg1u3oRLl8oup1EjMDYunLntrs/d3BBt28rgWZL06jrWxUhrBMBvl35jR+gO/H70461tb2E6xZS9YXupXBmWLYPr1+8FzwCbN6sN3F0bNOJs1Fm+WxqFpeKMYnEHT0/w94eEzASSc+M5cUIdV3h3ckVAbU3WaNi/X20sp1YtdaKZPXvU7S++iIGioA0JUbt8vPkmvPgiLFhQ/CK2bFHT+N310UfqNOrLlsEvv8D+/Xx47RojQkN5IyqKb2/fljMiSpL0zJAB9LNAPxMhsbEANLW0BOCms7O67q231D+MZTEyUlufTU2LrQ5ISyMgLe1vrbIkPc06Vu9I/7r96Vm7J00qNcHVWs1Us/qCOqlRDdsaKAoMGaL2uihKUdQxga8364dAMPG3FTTQvMaUF18hOBiadgmlwtdVWbhB/dBbr17hPDPFdO0KixYBTk7oJn9Jg8H1SXSoCR07onOviW7qNER+AXzzDVSsqM65XtQnn8C8efeW161T52Z//XV46SVo144269cz2caG+YMGER4bi7lWy6dhYSRu365+81VC3uj0/PzCfNOROTksiowk7jH6Y0uSJD1Jsg/0syBUP2V3aioANoaGZLRqhbZPH+jWDbZvV//aliUmBiIi7vUP1Wt67hx5QhDl54eL/PpWkninyTsAfNBcncRICMHWl7dyI+kGzhbOVLOtVmYZjSo24s6Hd7h5w5B67jbcHV5wIuIE+ZpM1i6z5c3OaoNwzZrqtk8+Ucf6jhsHf/4JdycozM0Fn46OnO8bgH8zcwIHziDzs2lUsGuMO4CLy33N2Ki/F+4OMgb1m6rEREhPh4wM+PJLenz5JT3efx9u3cK1QgWuJSczKyKCsBMnyDE05MeTJ7FpXnzCpnFhYbS2seFFJyeOp6QwPDSUxlZWaBSF4IwMmllZFUs9KEmS9F8kf0s9C8aMUf/V/5VNyMvjz6QkUi0t1UFQ589D//5llxMXp/57N6uH3gR9HujsUmYxk6RnmaIodK/VnXebvkt/z3L8X9NzNHekUT0bNIa53E65TYGugJj0GACmLAzlWs5R+ve/1wXk6lV9tw2geXO1JRvAxASWLwf/7uYkJoLbyG44Xz1C5Xo26g5FW6D79FHHTLi6qukt712Euuzqqqbxe/55dZbEt98GFxeOpaZyKDmZDI2GzqdPs7F1a0ZlZj6QpSNLp2OzPmXm8w4ORPn54WVuzq7ERNoGBnJDTsgkSdJTQAbQz4KXX1YHFeknSQjJyKBncDDz2reHJUvUr2ZPnSq7nIdMmXy3D3S1+7p2SJL091h5fiVVZ1el0+pOfLT3I8wMzfjswGfMOD4DgFrNL2P+WUUqDH2HxYsfXk5CAjg4wOzZaka+l19WuzpTsaLawmxqCps2QWZm2ZXy81P/vXoV1q3jwE8/MePWLVL27GHQH38w18WFg+bmZOl0cOYMus8+Q1y7xrI6dVjj4QGAgUaDi7ExRhoN7Wxs2OPtTWX9t1j5Oh26p2yQuyRJzw4ZQD+DfPRfy6bfDXinTdN3lizD3dxZ9zmXlsZxOXhIkv4x3Wp2o1ftXoTEqVN6G2oMcbFwKWyNrtLlFzK10WwI2VBqOfb2atfkN94AKyv1y6TMTOD11znSeDS6t4arGTvK841U1aqwciWsXw8XLjDs88/5aNkyvr92jRlffMG7tWsT7ueHWUgING3KmuPH+XnbNgqEKDHdnYuxMR3s7LAwMGD9nTsYHTrE1fIE8pIkSU+A7AP9DLLQaslv06Z4No0TJ8o+8CEBdIuAALJ1OuJbtMDe0PBvqqUkSXe5WLqw+aXN7Lm+h06rO9HGrQ3WxtaE3lLHNzh6XIKL8EmrT8os6/33770OUeNxMjNr0vbcLOYPhedGQ+XK5fzj8Oqr6r9ffolT+/Z8HhhISzc3jtrb4/3113QyMlIHH+t0nHd3Z3nt2gx8SFE6ITiUnMyhlBSqm5gw0dUV64d86yVJkvSkyRboZ1Byfj6b4+O5c+GCmoAWyjeRwkP+mH2sT2ZbIL9ulaR/VAOXBng6evJWw7dwNncmPDmc5Oxkriddp1vNbrzX9L1HLvPmTTVtXkAAJCVBtWoPjBMum1arfiD/4AOWdenCc3Z25ERGMtDenkNRUWBnx7cxMVz/5JOHTraiAM9duMDn4eEcS01lcrVqclCyJEn/WfLj/TPoSmYmL1y8yNgqVfhGP613udxtgW7cuNjqz9zc+MzN7e+roCRJJXIwcyB4RDAA8ZnqQLzY9FhOvH6C1JxUbiTdwEhrRCWrSuUqLyhIHTPo7g47d6oZPBwcyp5TqTS1zMzY6e1N0syZDDp5kpF37oCXF9SujfXmzZCXp85+uGqVOonLqlXQpAmKovCnjw8mGg0VFIW8PXvIz8nBtHNndX9JkqT/ENkC/Qxy1/d9NlKUe63KFSqUfeDdALpnz2Krz6alsS8p6e+soiRJZXit/mvc+fAONexqoNVosTCyoPb82sw/Nb9wnwPhB9gbtvehZdSrp44ZvDuHStWqav9oBwc1U114OBw6BEuXPnr9bA0NCWvalOaNGhE5fjwjOnTgkrv7vQGKH3+sNnUXSZXXwtqahpaWOK1ejbFWy7fr18Ovv5Kv0zH79m2OJCc/ekUkSZL+AbIF+hnkYGSEaNv23oqlS8uXB/puAJ2fX2x1m4AAMnQ6Ulu2xFL2WZSkf42juWPha0OtIbUdahMcp7ZQH7t9DP+f/LEytiJlfMmDfBVFDaKLSkxU49rERDVNfP36aiD9+uv3ZgcvLxtDQzZ5evL6lSsUCMGg/fvVnHpxcRAZCV98oabEu4/hvn1MCwigxfPPk9epEx3Pn+dgSgrjq1alpY3No1VCkiTpHyBboJ9BKfn5rImN5WZ2trpi2DA1jVV5TZ5cbHFc1apoAdkDWpKeLE9HT/bd2Ee3Nd04dPMQADpRPD/7rmu7GL93PM+ve56R20cWrn9xw4u0Xt4ar5lt6bp0EHU9C1i0SJ1aPCpKDZ6FUJP23PcroFQp+flUNzEhrFkzGleqpOaPvvv7xskJbty4t/Ply+osMOvXMy49nZa9e5N46RIF16+z1MWFKdWKT0CzKyGBNfoZViVJkv5NMoB+Bl3LymLgpUvMvH370Q60tVUnTVi1qtjqiW5u5Ldti5VsfZakJ+oN3zeo61iXHaE72BG6AwCtci97TlpOGt3XdOe7498RmhiKhdG97hNGWiMMNAYY2MSQVHUlWpso3noL3NzAzAw6dVJn/Z4+XW2Vvt9zz8HUqfeWq1RR52MZ7OLC6YaN+PQ9QzSKAv36gT4PNBMmwAfqbI38/ru6ftYsqF8fMWQIv8XFcTMigsNvvMEwjQaNTgdpaQDE5+bSLSiI2RERf+s9lCRJKg8Z8TyDXIyMAKjyqCPcDQ1h4cIHVp9NSyM6J4fuDg5/R/UkSXpM7au3Z3/l/cRnxjN+73gAUnJSyC3IxUhrRNCdIApEARtf3EjP2sXHMqzqrX4w3nx5M71/6U1cZhxVrKsA8OmnsGePGiQnJT3QiwsAR0ewtLy3PGQI+Pqqr2fNgt9+0//6GDpUzf5z8yb8/LM6chHUANreXs2t5+REVkEBA44c4VMfH5okJbEpLo7bH37Ie5cuQb9+OCQmUjB2LHn3zYC64c4dGlla4iYndpIk6R8kA+hnUEVj4+J9oP+itoGBpBcUkNu6NYaP2klSkqS/lYWRBRZGFiRnJxeuu5Nxh8pWlUnOTsbV2pUGFRo89PhGFRuxotcKqlhVQQhBRl4Gu3ZZ4O8PgwapXTmOHoWNG2HuXDUOdnB44Ispvvji3uvOne8F0wgBu3erwXLnzvd2unABGjUqTAFiptVywteXqbduMeLqVTILCjjcpg3vHTyI2LMH5fx5GDUKQ32DAEByXh79Q0IYX7UqU6tXf9xbKEmSVCYZ7TyD0vPzWREdTejfNMvXuCpVsNRqKUcmaUmS/mEFugKmH5lOQlYCQ32Gcv7t8ziZq0Fp15pdCR8dXtiyXJLKVpUZ5DOIRWcWoflCg+U0S1Zvu8H330ObNjBxotpIvHatOjX4m29Cu3al18nTEwo/sycmQmio2vc5LAx2qF1NOHoUli8vdlwDS0vcTExwMjRkce3aXNuyhUG9e9PyjTdYWKsWLU6coNbJk8y4dQsAawMDBlWowLRbt8gt0jJ9IyuLpLy8R7qPkiRJpZEB9DMoOjeXIVeu8PPfNPjmUzc3Ulu1wkC2PkvSE6dRNEw+OJm2rm35sdePeDt7Y6Q1KvtAPSEEJyJOsOnypsJ111IvUqkSdO+u5ox+6y01kYaDg9qF+bPPSi8zNRX27lWPuXTHnsAWI9Dt2AXr1qmpPlJSwNgYXFweOPbbGjWYVK0axhoNirMzLc6epYGBAUb5+Vjm59Pa2ppq+u4aiqLwhn6wYb5+YqeNcXH4nTvHloSEct8DSZKkssguHM+gmmZmLK9dm+f/pj7LZ9PSCMvKot9fmX1BkqS/haIoOJk7cSfzDhm5Gaw8v5LmVZpTv0J9Xt30Ks7mzszoNKPUMlovb02e7l6LbYMKDbC0VAcQ3q9oL4yHuXoVOnZUc07Hx8MbRxcQVgeqoW8l3rABLl6EESPUCP1hnJx4c/p0NRrPyGDooEEo7dqxJT6e54OCGOriggJ84upaeEhtMzNGVKpEf0fHh5crSZL0iGQA/YwaXEJLz+NqFxhIakEBQgbQkvSfYKw1ZuX5lTSo0ID3/3ifbzt+S/0K9TkRcYJGFRuVeuzdADwyLbJwnVajLXHf64nXqWxVmZyCHNZfXE/HGh2pal31gf08PNQJWRYsgODge63XmDdUdxg2TE318dJLpV+Y/ndMflYWvj/8QH+Nhu5pafQKDkYrBJfj43GysaGdrS1CCCwMDPA0N8fT3Lz0cu+TkJeHsaJgYWBAnk7HL3fuYGtoSFc7O+7k5eFsVP4WfUmS/j/J79ylv+yjqlWpJP+gSNJ/Rlu3toCaF9rEwIQ7GXcAdTChk1nZH3QrWhbPC7/1ytbC19n52YQlhZGem477PHcGbR7EZ/s/Y9jWYcw7Oa/E8szNoVUr+PxzqFVLDZ43boR3vnAqDIpndtgJjRuXXjEvLwAMdDoSray4rdNxWT+W4/rLL3Pu+edZ4O6O7ZEjTAoPh2PHuB4fz7jr16ly/DhC3MtWH5+b+9BxIBWOHWOqvl91jk7Hq5cvk5yfz/WsLBqcOVOsHEmSnk0ygJb+sgmurkQ0b/6kqyFJkt7i7ovJm5hHxxod1e4cGXfIyc8hNSe1cEBhaWra1yx8baw1JjQxtHB5WcAyasytwaW4S/9j767jq66/B46/3utmHcBggxFjIDVaBAQURMRuQUUxALu7v2InKiqKXeAPA0GlROmGkRtd6+54//54b2NjHXd5no/HHvfeT91zucbZm/M5x+y3sedoylGcbZ15cfSLlV43NBS++MI8/+sv082OtWsByFy/g7z8Km5FPussWL0aVq9m/5QpvLd9O1f5+pL4yCN0jI7GKSWFXsDCnj05x90dXnmFK/75h1eOHuVCLy9yCxPfPenpjN2+nZv27Cm+9JtHj/J/sbEAvB0SwkQvL8B0A9k3cCAXe3uzNzOTro6OxMoNiUK0epJACyFEC6OUwsbKVOgVJdBxGXFA6fHfFTnL96zi50XnF5m+yEwvPJR0CIARHUcQnRbNgHYDqnWzokvh7JYPPjC5MEFBADxxagZFs5ji4swgljffLOcCQ4bAkCE4Ojlhm5iIUgr3gwfNTYgAMTGM9/LiIm9vciZP5qXdu1mWlMQHXbtiV3ij85ht24jJyeGFEpMN3zt+nEUJCQDc2a4dQwr7U1spRRcnJ5ILV6C/DwvjlSNHeFcGuAjRqkkCLYQQLZivsy+BboHkFuQyMmgknT06V3nOVT2vYljgMACOphzlcPJhANJz0ouPKUqgDyUdIiY9hhWHVjDr31k1D1CdXnV+913YsMF0ujt2DO67r/ShCxbAtm2FL9q0gcWL4ccf0TGxRDoVJv3HjnF08BW88OwP2Ht60js2llE338zOtDRu2L0brTWzO3Xip7AwRri7819yMnvS04kcNIi3StzA+O23EBV1+r2PZmdzd2Qk61NS2JmezoGsrFKxvXfsGHlnDHURQrRckkALIUQLNmPADO4YcAdB7kEsn7Kc0Z1GV3lOkHsQN/W5qfh1VILJJHfH7S7eVpRAP//P89zY50YcbBx4f8P7tQty/nyiHvqQu+4y7aG7djXlHb/8YuauFHntNXixsErktw53km9rD6dOkeoawP6OY+CRRyA6msB1PzHl/RmMBjzb+EJiIp9ERrIqKQkOHuSigACGLFlCbG4ul+7cydcxMSilcLI+fbPktdeaSeNF+rm4cHzIECZ6e7O4d2/eLJFs70pP553jx9lQOGZcCNHySRcOIYRowcZ3GV+r86b2m8rJtJM8ufxJ3OzdyC/IJyImonh/YJtArul5DYsjF/PY8MdIykrinXXvoLVGqRqOVbr0UjpdAkmPgZub2dS3r/kp6bnnzD2Hublw2er7eeSR+3l2hsZt5kyKP+W335r4YmP5e9QofrOZxIVARN8Ybnx2LCrIjEzM/+hj/i/3Ut7u0Yvhbe3LhPTKK9Ct2+nXtlZWtLUvexyAh40Nk/38CJCbqYVoNWQFWgghRLnyC/IB2H7Hdg4kHmDzyc3YWdvx+nmvM7bTWDp5dCIxK5Ht0dvxdvImOz+btJw0AFKzU9l8cnPxNaqilKnKKMq9MzJg3TpTzvHhhzBwIAwdau4jtLWF48dN22j+/BNuvdUsVSclwejRsHJl8XXdLxsDwAUdImjrr+DiiwGIPpjJbdfbsGehG+3OSIyzs2HcuBLTE88QlZnJmK1b+ScpiZ9iYrhh925ub9uWoMKBLkKIlk8SaCGEEGV8vOljnln5DGCmG97y6y0sPbiUp0c8zX1D7kOjeXvd2wD0/rA326O3A3Ai9QQANy28if5z+vPZ1s/KvX5VIiNh8GD47Tdwdzc3Febmwr33wqxZphWenx+wZg188omZK96lC8ycCeecQ9EdiWffEw4+Ptx75D6mjT9qlrjvuAOXQzs4Z7jmiSeAggJYvJjP5mpuvBH++MMk6vPmQV5e2dg8bGxIzMujnb09/VxdiczMxNnamoOZmbX6rEKI5kcSaCGEEGUkZiUCMKT9EAAOJx3Gx9mHx4Y/Rkx6DHM2zSEtJ41PJn7CiikrmD5gOjZWNhxJNv2TN5/cDMD5nasxqrAcPXvCxx/DVVeZ+Srz50Niounc4eRU4sDHHzet8Ly9zUHXXmu2b99ubjIcNMjMHgf074vQr78BOTm46RR++eikybM//xzGj6fHxi/w9QVXV7NQXVSTfSZPW1tmd+1KZ0dHOjk6srpfP2bs38+QzeYzx+Tk8Ed8PJ+cOEFsTk7xeVtSU3lPuncI0SJIDbQQQogyrJW5oW7x9YsBGN5xOF9t/4rc/Fxm/TuLjzd/DMDEbhPxdfYlvyCf9MfSsbO2Iz0nnYNJB3l25LMEtgms1ftbWZkBhSUFBZmyjlJsbU2SDGbluUhoqPkBeO458t56l1/uXsGlOd/BeFMxnbJ+D29815ZH4yNwAAY572TQK+aU9u3N5Tw8yo9vUFGxNtDW3p6bAwKYe+oUAzZtYrS7O7OOHgXAz86Oid7eAPyVmMjDBw5wo78/Ljbyv18hmjNZgRZCCFFG0fjuohrm3HwzPOTzrZ8T5htWfJyzrXPx8XbWdry19i1WHVkFgIONA59t+ax4ct9v+37jkb8fISkrqaE+hqEU+T370Nv9ELvWpvBxt9cASFm1lRdegMxNuwBIX7eT+Hj4+2/IiktjhtsXeB/cUK23GOrmxkBXVzamppKnNV+FhrJzwADO9/REa83KpCQWxsXx51ln4VzY7SO3oIDkwhoRrTVzT55kY0oKAPla8+Lhw0RJWYgQTZIk0EIIIcooWoHu9YEZn/3o2Y8S5B7ExG4TCfM5nUA72p6+cS49J517l9zLT7t+4tLQSzmcdJibf7mZ5OxkAG78vxuZ9d8sFkcubsBPYthfdiGd77+EHoNcifcNJU25EPbp/aRFReNxcAsAqf9u5c03YexYUGcPhQcfIOtITMUX/d//IDwcBgxAXXEF6wqT5pc7deI6Pz/CnJ35NzmZs7dswVYpFGBnZVXcpWTYli08d+gQYIbf3LZvHwvizMAbBTxx8CCfnTxpwT8VIURtyd8hCSGEKKOTRycAjqceB6C3f28O3m0KgotWncHcYFjEydYJDwcPbKxsmH/lfL7a/hWzN84mNj0Wdwd3PBw9iM+MJzY9tgE/SaEHHih++tDDCj3wV+jfB0etITqanDbe+GQmce2kdDp4as66fwezE+/AMfkCbip5naNHIScHTpwwCbSPD7Rta+qvp00j7LzzSr3tmpQUkvLyCHd15fuwMH6IicHH1pZQJyeu8fUlv0Sj68hBg/AoLO2wUoqsc87BvnB6YmxODi7W1jiW6FUthGg8sgIthBCijAldJzB9wHQ8HT3L7HO1d6VDmw5c0/OaUtuVUoT5hrHxxEYAfJzM2PCiUeB7Z+zFSlmVGg3eGKyswHr0SNPeo00biIzEbvsmrDPT6THAmWmTogHocUVPRjqtg7S00ye//DKEhJgC6fx8ePpp0yoEYP36Mu/1eMeOrOrbl42pqaTn53NfVBT/JSejlOLewEAe6NCh+NiODg64laiNLkqer4iIwHf1apYnJdX3H4UQopYkgRZCCFGu/IL84lKOM3026TOeGvFUme1hPmFsOrmJO367A19nXwBiM8yKs5WyYvYFs7mo20Wlzvl2x7dM+3Va8esVh1bg86oPbV5uU/yz4bipRf5y25d4zvLk932/8/u+37ng6wtYe2xt7T7ga69Bu3amtV2HDrBrF/mXXcmWJ+YDMLLdfoKvGQKbNpl+dtddB7NnQ69e8OuvcPIkTJ5skvDu3U1/vREj4O67zRSWFSsA2J+ZyfAtW0jOyyNx2DCu8PHhxcOHSSuvR14JG1NSuDIigp9izZ9f91LtR4QQjUlKOIQQQpTxY8SPfLjpwwr3nxt8brnb7xp0Fw42DkzpPQUfZ7MCHZseS1RCFNN+m8bzo55nQLsBpc75Zuc3bDu1rfj1n1F/kpSVxIwBM4q3FV2rq1dXsvOzWRy5mGn9p/FH5B/08OnB4PaDa/4hu3aFSy81ZRgA7u5Ybd3M6uhg+gIJwyZi1e9c3Hv1gj174JtvzOrz66+bQumSnnvOlHFERpp+1O+8Y5LskSMZ5ObGTf7+9HZxwc7KisTcXJ4+eJADmZl82r17heFlFRSwKTWV9f36EerkVKZzR1xODgWArwUmIMbn5vJXQgJjPT3xsrWt9+sL0dxJAi2EEKKMrLwsAIZ3GF6j83r49OCtcW8BkFeQx8ZbN9LZszPbTm1j2cFlXNfrOpxsnejj34f0nHQe+PMB4jLiiM2ILR4DPjp4NG3s2/Dw2Q+Xuf6g9oPo5duLiNgIevn1omObjrUvCbnoIvNTpH17VFQkd8z+EKbD2Jnd6XNBWz69AViyxBwzf76ZsnKmK64wP0U+/RQiTo8+/6REouxha0v2iBFYVzHy/Gx3d6IGm18M0vLy2JqaSh9XV36Ni+PGPXtIyMtjip8fnxe166tHq5OTuWb3bv7r25ehbdrU+/WFaO4kgRZCCFFGURu7jyd+XOtr2FjZ0L9tfxZHLua/I/8B8Nrq10jLSWPOxDkcTznOh5s+pIdPD7LyskjPTcfFzoXRnUYzutPoCq/bw6cHv+//nT+j/iQuI67ea6qtpt8BwMOv+dA1dys8/TMcO2amG1ayYgyYmwvffNMcWyKBPlNVyXORL0+dIiozkxytefXoUdKHD6edvT1X+/oS6uTEWS4uxcdmFxSQVVBAm3roMX2+pydbw8PxldVnIcolNdBCCCHKKKp9ztf5db7W+K/H88KqF7CxsmF4h+EcTTnK+K/Hc8uvZlJKUVu8mPQYsvOy2XJyS/EKeHnCfMKISY9h/NfjSc9NJzo9us4xltKnDwBXXmdLyOYfTHnG3Lmm9rmqcomNG01ttaenSbqTk+sUyqcnT/L84cNc5+fHgjDz59TL2Zl3u3RhRvv2nOPuDpgph97//cebhQNcqrI/I4OBmzaxNNFMnDyQmcnorVuL+1DbWVnx6cmT9NqwobiPd1Z+Pk8cOMC30fX85y1EMyQJtBBCiDKKVqAHfjywztdaM3UNq29eza47d/H2+LdZd8s61kxdU7y/l28vvJ28Sc1OJSI2gn5z+vHbvt8qvN7VPa9m3S3rKNAFtLFvw7lB5ddj19qqVXD0KHFx8Fj644zlT/59YjEsXFj1uRMnmnrp6dPN611mSAsLF8Izz5gbEmvgq9BQ9g8aRJizMxO9vVmamIjTqlXsTE8nMTeXXenpAHRxdOS7Hj04t5zRicl5eUSXGCkOJkH2srXFobDTR0Z+PlkFBWQUFPBDTAxh69fTzt6ep4KCilvt7c3M5Mvo6AqHu8Tk5LAmOZnM/Lr/0iVEUyclHEIIIcro2KYjUD8r0Gfe4DewnUnKP5n4Cbf8egs39L6BJ0c8CZguG0CpYS1naufWDjtrsxL84rkvMn3g9DrHWIqLC7i48NDNsGSJM/vTx1LtBhhKmQ4cRaUPERHm9cUXm9c2NtC/f7VDae/gUOp1qJMTeVozets2pvr788axY2Sfcw4uNjZM8PIiNS+P+Nzc4hv/vo+J4epdu7i9bVs+6NoVMBMQOzo48EeJWu6eLi78168fABN37GBXRgY5BQU8XKLNXm8XFw4PGVJhrH8kJHDjnj282qlTqfZ8QrREkkALIYQoY0C7AVwWehm743Zb7D0ycjOwsbLBx8mH+Ix47lp8FztjdmJrZUuIZ0il536w8QMAfJ19i0sMlFJorflg4wc42Dhwc9+b6xTfbbeZvNfJybR8Lig4nRdXKSjInPjBB9CpE/z3HwwbBjEl6rVXroQNG0zbu2peOMjRkS+6dyc+N5dRHh6Eu7qSWVDAzP37uat9e66IiKCviwvfF5Z7/FtYQnKDn1/xNcZs24a3rS3ze/Ys9z2+69GDAq3J15qk3Fw2pKYy1tP0A8/XmticHDxtbbGzKv2X2OcVrn4fzKq4/EaIlkJKOIQQQpQrX1fcB7o+zBw0k5wncnC2cyYnP4f1x9eTkZvBlN5TsLWuPKFMzEykt19vYtJjcHzRkS2nzDjuU2mnmL5oOlN/mUpGbkad4hs0CP78E2680cxcWVuTdtNWVnD11eamQk9PGDrUrEQXJdBaw1VXQWys6TFdAzf4+3NPYCC9XVy43NeXXK2Ze+oUfycm8nRQEDcHBBQf+26XLuSec06pThqX+fhwgZdXhdd3trbG1cYGd1tbJu7cybW7zS9R523bxrDNmwlYs4aIwtKRkgLs7ckbMYL3C1e6hWjJZAVaCCFEGUsPLOX/9vyfxd9HFXajCHANYP/M/dU+781xbwKwK3YX2fnZRMRE0C+gHwGuAcWlIbHpsXR071jr2NLT4YsvYORImDYNSiziVs+nn5Z+7esLJW/A27TJjAV3dKx1jABu1tbkldMW73BWFj62tlgrxfbUVEIcHXGzseGu9u2rfe2/e/dmc2oqBVrjZWtLPxcXbvD3J6Ccmyl3p6eTkJfHMGl7J1oBWYEWQghRRoEuAGBk0MjGDaQKXTy7YGtly86YncWlHEUTEOva3m7dOkhNhfvuM7NT6ryw6ut7egVaKTMF8eBB+P33Ol1WKVWcPKfl5RXf5HfZzp1csnMnm1JT6b9pEyuTkkjNyyOrBjf52VtZ0dfFhdXJybwVEsLLnTszvV07/O3tyxz71rFjnL1lC/dGRtbp8wjRHEgCLYQQogwbK/MXlM+MeKZxA6mCrbUt3by78crqV+jzUR/u/P1OHl/2OHA6gY7LiGPUvFEcTDxYo2v36WOaZ/TsaSoualhpUZafn+nK4eJiaqRffRVefNF05ygyebLZ/+CDZc8/dAjmzIH33oM33oCXX4bly0sdMuvoUULWrUNrzdNBQdxXWOrxY48eDHJzY9aRIziuWkVqDT7Mkexshm/dyt4MUxJzIjubmDO6egA81KED57q783t8fLWvLURzJSUcQgghyihqY5dXUNes0fJmXzCbuVvnckWPK7hvyX14O3nz4NAHi8s3ftv3GysOrSA6PZpgj+BqX9fT8/SgwoAAM2jwnXfqEOhdd4GbmynbOHHClG706gUff2zuUMzMhC9NFxJWrix7/j33lG2l99BDMGpU8ctrfH1xtLJCKcVEb+/i7Zf7mlX5id7eOBbWOFdXF0dHHu3QgYGurgD02biRi7y8iqcrnszOZkVSEkEODiwt7KEtREsnCbQQQogyim4eHPPlGPTTupGjqdzwjsMZ3nE4WXlZRCZEcmXYlTw36rni/YeTDqNQnOVXzgjuarr7brMSXSfdusH//ld628cfQ0YGHD4M3t7wxBPw7belu3UU2bDBZPGzZ5uBLkU/JfRwdqaHs3OFIQxyc2OQm1uNwlZK8VKnTsWv3+vShY4l2utdu3s3K5KS8LKx4c/evelXmGgL0ZJZtIRDKTVOKbVXKRWplHqknP1tlFK/KqW2KaUilFI3WTIeIYQQ1ePv4g+Aj5NPI0dSPQW6gFn/ziJf59PTtycp2SnEZ5hSgp2xO9Fo5m6ZW3x8fkE+Sw8sLXWNpQeWMnfL3OKfRfsXFe8Lv/JvJkywwC8SRVn5vHlmiuGzz5reeSUT6MhIc0PiiROmm4e3t1nJdnAwJRwdO8Juy7UbPNOVvr4EOziwLDGRtLw8Hg4M5JVOnbC1smLstm1M3r2bpNzcBotHiMZgsQRaKWUNvA+MB3oA1yilepxx2HRgl9a6NzASeF0pVcWcVCGEEJbW2bMzQ9oPobd/78YOpVoUitkbZwMwoO0A+n3UjzsX3QnAzpidADy78tni43/d9ytjvhzDumPrANMW77yvzmPqL1OLf15d/SoAR5KPcMPPN/DfgS31H3hYmCnlePZZOPdc+OYbkyBnZpo2IFlZpnTjFjP2nLPPLn1+27Zmm1KwbJmpoW4AK5OSGL1tG4ezsxnn5cWDHTqwtHdv7m7fnlXJyaTJNELRwlmyhGMgEKm1PgCglPoOmATsKnGMBlyV6WPkAiQATb/gTgghWgFL94GuT0opImdGkp2fjbeTN6E+oeyO3U1Ofg6RCaYrRHxGPPkF+VhbWePnbHrSJWYlAnAy7SSBboE8P+p5RgSNAMDe2nSaOJV2ilNpp5h47QkS1/Wr38Dd3CAqytwgOHQo3HDD6ZsKY2IgOBh+/BG2bQMfH+jcufT5oaHw9dfm+UcfmSS6AYx0d2dlnz4EOThwOCsLG6Xo4ezMU87OPBUU1CAxCNGYLJlAtwOOlnh9DBh0xjHvAb8AJwBX4CqtC3snCSGEaDTbo7ez/vj6xg6jRlztXXHF1N+G+YTxZ9SfWCkrEh9O5M01b/LUiqeIz4zH19kXJ1szm7uoU0cPnx4cuudQudctaos3/vK6tcWrUECA+SnSq5d5jIkx3TocHWHw4HJPBUyLkEOH4P77TXeOBuBjZ4ePnR237NnDp6dOcYGnJ7+fVfsacyGaG0vWQKtytp1ZQHY+sBVoC/QB3lNKlbm7QSk1TSm1USm1MTY2tr7jFEIIcYailecRHUc0ciS1E+YTRk5+Dlf8eAUudi509TJNnGf9O4t5W+dx/lfnAxCbXvX/U4rqwHsPsfD/fxYuNDcK9uoFd9wBHh4waRKcc07l502bZsaFd+tmSjka0L7MTIa4ufFgYKB5nZHB1RERbEtLa9A4hGhollyBPgYElnjdHrPSXNJNwMvadL+PVEodBLoDpZY9tNZzgDkA4eHhTft2cCGEaAGK2tjd1v+2Ro6kdsZ2Hsv5nc9HFa7lFK0iv7HWrNAWbS9agb7x/27E19mXV8a+UuZaznbOONo4ciQ+hpycMo0v6k9RzzwwnTYALryw9PTC8gQXtuazt4ebboLRo+H66y0T4xn+6du31OvsggK2pKWRKDcRihbOkivQG4AuSqngwhsDr8aUa5R0BBgNoJTyA7oBBywYkxBCiGooWoFuDn2gy+Pv4s/i6xez4KoFAAwNHMqJ+8wazviQ8Wg0Xo5exa3tFkcuJi4jrsLrjfO5jdlPhLNunek499lnpjmGxU2bBk8+WfkxYWHmMTkZ/voL/vzT8nGVsDcjg6NZWQD0cnFh76BB5GrNC4cOkV0gVZmiZbLYCrTWOk8pNQNYAlgDc7XWEUqp2wv3fwg8D3yulNqBKfl4WGtd8X/BhBBCNIiiFejJ/zeZG3rf0MjR1J29jT1HU8xtOUW/FKy8cSXt3dqzJ24P0enRhPmEVXj+exe+yYhMU5L8+5/p3HGL6bWsm8LfiRa1wisoMM937Di9LzsbbGzA2jI3g96+dy8fnTzJVT4+fBd2+s9vRVISLx05QmRmJp+HhlrkvYVoTBbtA621XqS17qq17qy1frFw24eFyTNa6xNa6/O01r201j211l9ZMh4hhBDV4+7gDkCIZ0jjBlKPPtj4AQC743Zjpazo4tWFR5c+Suj7JsHr5derwnPbtoUptyXhG5DN+9mDuf+1Lcye3UQS6KISjnbtTP301q1mZOL//Z/pFf3vv/X/nhkZcNNNnExOpr+LC/cW1kBn5Odz2c6dhLu68lBgIN2dnMo//9Ah84e6f3/9xyZEA5BJhEIIIcrwdPQk2D2Ywe0r6f7QzFzf63ou6noR+Tofbydv7KztuKbnNfT2642znTOjg0dXeO6jfz/K22vfYeLRXexsu5M7L1jLHQP6Vnh8g7KyMqO/g4PNmPDXXjOt7779Fh555HSCXZ+WLYPPP2fhpElm8Esheysr9mRkEJ+by6wzW+6VtGABnDwJb70F779f//EJYWGSQAshhChDa01ydjL5BS1nIMboTmUT5KIx4FXp5NGJzPwMFu1eivK3YdnOCC7tAC4uUMnk7IZTslPH7beb5LldOzM6fN8+M+UwPLz+3u+880ypSHAw29LScLexoaODA9ZKsXPAAG7ft4/AhATO9/Qs//yi0eANUkguRP2zaAmHEEKI5ik2I5aEzAS+3fltY4fSJIT5mvretNFT0Xl2/LsvAn9/WLy47tfWWrM7djdJWUkAJGQm8PeBv1kcuZhvdnxDSnYK3+/8nmUHl6GrUzPSq5e5ofC220yJxB13wKWXwq5dZrIhmKR669baB21nB9bWzPz3X/ps3MgLhw4V70rPz+eX+HgeiIrC97//SM0r50bUiy+Gs84y9dlCNEOSQAshhCijqAvH8A5Vr862Br18e+Fq54qnoyfX9L2UXPcI3nsPevTKYe6WuRxOOly95LYcczbNocfsHvwV9RcAm09uZuyXYxn/9XiuW3Adk3+ezNXzr2b0F6OrN9xmyBDTD/rjj2H5cjMi/OhR063j8svNMV98Addcc/qcf/8Ff/+yHT+OHTNt8QYNgjFjzNjwIUNg4EAYO5aUFSvoFRXFjGuugf/+A+DVo0e5xNubt0NCuMzHp8wAiGLduskKtGi25Fc/IYQQZRR14bik+yWNHEnT4GrvSuyDsSil+GzLZxxI2s9NUzKISohi6rdTGdB2AFGJUcQ8EFP8Z1dda46twcfJhyGBQwAIbxvOqptWEZkQyU0LbyoeRQ5wNOUog8oM9T1D374QH2/qof38ID/fjAl/5RWTUCclwYAB0KPH6XNcXODKK8sm0L/9ZuqdR482Nw46OpqOHnl50LEj815+2Rz35JPmvYCMggLS8/M518ODcz08yo9x6VJTp13RTYZCNHGyAi2EEKKMohXotByZKFfE3sYeO2s7bgu/jbW3rCUl3olVeyMAGBY4jITMBKISo2p83e3R2+kX0I/2bu0B0wHl7A5nM7n3ZJxsnTiv83mcvP8kcHrwS5U8PIoTWqytYdQoM5QlI8Ps8/GBa689fXyfPqZzx5lTYjZuBG9v01969WqT+P75p0mqX3sNgHWhoRx69FEIMR1bLvP2xlopTmRnVxzfSfN5yMio3ucRoomRBFoIIUQZRauoT614qpEjaZoyczMJerkvD/x1P9bKmn5OlwKwK3YXAP8e+Zf+c/qTkJlQ6XXmbJrDllNbige6lGSlrNg9fTevjn0VbydvPpzwIaOCRtU+6LNKvEdICKxfD1GFCf+GDbB9O9x8s3ksan33xx/m5sPyRoT37Mm9d97J4NmzeXfbNnMecDwnhyUJCWw4dAiPVav4K6GcP4Prr4dHH5UaaNFsSQIthBCiDDtrsxI5sN3ARo6kaVJKEeLdgX7+4Tw14inmvWxa2u2MMSvSP+36ic0nNxePDC8y6btJ3LTwpuLXnTw6MabTGCb3nkxeHrz7bul7+zq06cBF313Ecyuf47bw2wj1KT2U5K+ov/h2RzVv9BwwAK64AqZPB3d3GDYMPv0U4uJMTfPs2WbE4oUXwvDhsHYtTJliunqUx9UVPXAgPbTm1hdegAcfBOAyHx+Ou7nRb9Ikbtizh4CKZp/feqtpv9ckmmkLUTPyq58QQogybKxscLBxYETHEY0dSpPkYOPAzicWFr8+HARDvu/If/siyAiHiNgI+gf0Jysvi4TMBDwdTTu3Al3AxhMbi88b02kMYzqNAUxp8l13mUXgTz81+zef3Myi/YvwcPBgT9wesvOy6e3fG4D98fv537//40TqCa7pVeKGwAqDdoAffjj9uksXiIgoXjnmkkvgyy/NDYdgJhu+9FKll3xryhTzxNGxdD2znx+Bd97JOxdeaOqrz/TppyaWJUuqjluIJkhWoIUQQpQrKy9LaqCrcO+9ppS4Y0cY4347iz8cydptieyM2UlXr660faMt7683g0L2xO3ht32/sTNmJ4eSDrH66Gr+2P8HGbmmDtjNzdzH9+WXpxdljyabZDY2I5bbf7udmX/MLH7vm3+5meWHlle/LvpMYWGml/OqVeZ1375mG5hOG88+C99/X+VlViQmcqh3b9M+r4iPj/ltICCg/O4ke/eaWurPP4fU1NrFL0QjkgRaCCFEhZYdXNbYITRp6ekwbpzJA28NfYQFj08jIv9nTqWdIrxtOO4O7sUJ7qm0U8XnBb8dzLC5w7jgmwvYcnILYAYKPvywGdJXlHMWlWz09uuNn4sfJ1JPAKZ39I7oHQAkZiWSk59T8+B794aDB+GZZyAgAHx9TRINJpH29zfL4pV4OCqKUdu28fmpU3DddfDVV2bHokUUvPUWbosW8cyGDadPWLcOVqyAohsMb7rp9A2FQjQjUsIhhBCiXCumrKCHT4+qD2zFnnrKdIzr0wc+/NDMLjmYOIpA7585r/N5fLjxQ2IzYoGyHTReOvcl+gb0ZWjgUABOnTLNLS67zCTTAF29urLlti308OnB8yuf56ddP5GVl0VseizJ2cn09uvNtuhtxKbH0s6tXc2CnznTLJ3n5Z1ePX72WRg8GC64AKKjobJx3ICDlRXdnZyY4udngo+NNYn0tddi1bYt04cNY2jJftODC0fD33abKfuIiID27WsWtxBNgCTQQgghyjUiSOqfq9K+valGuPlmc3/exo3g5RXMxd2DAfB19i1OoGPTY0udO63/NLycvIpfHzhg7sPLyTH31/n4mO19/PsAZhpigS5gX/w+jqUcA2B08Gi2RW8jJj2m5gl0mzZwww2lt/n7m1VhON0GrxLPBgfzbLD5rEyYAL/+CocPm0mIU6bwv3feMYNcigQEmBXnrCzTHq/oXCGaGSnhEEIIIerg33/hu+/MPXTDhsEHH5ze5+PsU1zjvDtuNwD/3fwfL577YqnkGczi7MaN8Pjj5d9bF+YThp+zH4eTDhOZEImnoyf3DrmXQ3cfoqdvz+LjtNY8sewJMnMz6//DniFfaxbHx3MoM9O0yYuNheeeMzvPOw+AxNhY8otqUm691bTEy8iAxET43//MuHEhmhlZgRZCCCHq4MEH4YknzLySX34pXfUwLHAYHdp0AGBi14lsi97G0MChxWUbJVlZmeGACxea1stn6ubdjYldJzKh6wTyCvJYenBp8fCVkqISo3hx1YsM7zCc80POr7fPWZ4HoqJ469gxXuvcmftHjDCDWD77zLTJO+ccvj//fK4+6yx2ZWQQeuoUK9PSODp6NNcfOwZpafDYY2akd5cuFo1TiPomCbQQQghRByW7t51/Rr76wNAHTu8LOb/ShHbbNli0CO64w+SfZ7KztuPjiz4ufv7zVT8DMHvDbNwd3Lm217WcTD1ZXGtd6+4cNRBgZ0cPJyeu9vWFwEDTUSMjwyTSTk4MLCjglaVLcR8yhPQvv+TtNm1YN20a1997rykhSU42d2IK0cxICYcQQghRT06ehJ9+Mvfl1dT69WZB9t9/TTJdFStl/hc+b9s8Pt1iGkfP/GMml/9wOUBx7bUlPdShAxEDB9LO3t5ssLMz2X/hbxXBAQE8OHcuAfb2fHTllfx8zjn8e9ddcPw4eJre2DLOWzRHkkALIYQQ9WTJEtP+OC6u7L6ffzad4/766/Tr8HDT7ALglltMLvnEE/DOO9V/z16+vVh2cBmfbP6EmPQYunh1wdbKlpj0GCb/PJn7ltxX9w9Wgaz8fH6Ji+NwVlb5B4SFkZ6QwIHBgxlpb8+b7u50LPrAPQo7vNx1F4SGwrRpFotTiPomCbQQQghRT8aPh3vuMc0sHn4Yrrrq9L4uXaB7d5Mrguni5udnFm1PnDAtmH/5BR56CO680xxTUFD1e94z+B4AFu5dSGxGLH7Ofvg4+xCbHsuX27/kzbVv1utnLOmBqCgm7dzJkoSE8g+4+mqunTOHiQ8/TL82bbgoOJg3r7yS2GeeMR/W2tq0Hdmzp1pDW4RoKqQGWgghhKgnfn4mAQZT4ltUpQDQs2fpHHHcOPMDZtp1RAS8+64p4QCYPh3WroVNmyp/z56+Pbm659WsO7aO5OxkfJx8+OmKn/Bz8WPu1rn19+HKEersTJiTE5d4e5d/QIcO3H3hhaTl5xPr5saaxEQeuP12Bqek4GNlZVrZFa1Ip6aa3xisZG1PNH2SQAshhBAW8Nhj5jEqyvSLXrfODFxxcyt7rK2tqWT49NPT2wYPPt0Luip3ht/Jpd0v5cqfrsTX2ZchgUOK29i9dO5LdfsglZjerh3T21Xef/pcDw+y8vO5aOdOojIzSTr7bNyuuw527jR/GNHR5oPGxprOHOX9AQnRxKhyZ9Q3YeHh4Xrjxo2NHYYQQghRLY6OcM01prvb11/DtddWfGx+vqlqKKI17Nhh7skLCan8fXLyc/h86+f0C+iHnbUdqw6vor1be7p7d6ebd7f6+TBnSM3LY3FCAoPc3Ojg4FDhcf8kJTFi61a+79GDK3194cUX4dgxM4UmJwcmTzbTCY8elcmEoklRSm3SWpdpLCl/TyKEEEJY0E03wfXXwx9/lB7KV56SyXORYcOqd1OhnbUd0/pPI7xtOL/u/ZUZf8wg2COY6xZcx19Rf9Uu+Co8EBXFlbt28V9ycqXH7c/MZJKXFxd4evLRiRN8f/nl8NJL4OtLYloaz7Vty5aQENPWTohmQEo4hBBCCAuaPbv25ypl2uKVHM5SHWG+YQC8tvo1Np3cxKm0U7UPohJD27RhXUoK40sWe5djakAAo9zdydGaT06eJNDenqs8PODaa3l0/34+cnEh4LPP6CujvUUzIQm0EEIIYUHZ2fDRR3D22dCvX83PP/980zb50UfNcycnCA6uvD46zMck0F9u/xKArLwK2szV0RR/f6b4+1fr2OFbtjDe05MVffrgXLTUbmNDv927uXHECG4tb/xiNfyZkEAnBwdCSk60EcLCpIRDCCGEsKBVq+CRR+CLL2p/jVOnTAePI0fghhvgk08qP76TRyfGdhpbnEhn5mXW/s0rkZCby9fR0RzPzq7y2O5OToQ6O+NsbU1cTg4709LggguY9sknfNatG3z+OezfX+MYroiI4MMTJ2oRvRC1JyvQQgghhAWNGVP3YXv9+0NkpOnwlpUFVTS+wNrKmj9v+JOM3AycX3Iu7shR3x6MimLuqVPMDwvj0ipahizt06f4+bR9+4hIT+f1zp3Zl5lJdmIiXT/7jMvy8kzD7Bq4xNubns7OtQlfiFqTBFoIIYRoBqysICkJrr7alISsXw/PPlv5OQ42Dpzd4Wzaura1SEzjPT3ZlZHBSHf3Gp33QGAgGfn5/BAbyx8JCdgC573+OpcVTSesgTdCQtiUmkq+1lgrVePzhaiNStvYKaXaA1cDw4G2QCawE/gd+ENrXY0ZSfVL2tgJIYRojY4fNx3eXnvNtFBOToYFCxo7qrrLys/H3soKVcvk9/OTJ7lp7172DhxIV6mDFvWsxm3slFKfAXOBHGAWcA1wJ/A3MA74Vyl1jmXCFUIIIURJbduazm/nn296SjeF5PlkdjafnTzJqWrUQJ/p/ePH6bx2LXZFyfOXX5aeJFMNsTk53L5vH9MCAmhvb1/jGISorcpKOF7XWu8sZ/tOYIFSyg7oYJmwhBBCCFGSUqYTR02N/HwkQwOH8tLo+p9I+MiBA3wRHV2tGugzBdrbcyAri+9iYsgpKODUgQM8smABTJ1a7WtYKcWVvr5c4+uLU3lNtIWwkApXoCtInkvuz9FaR9Z/SEIIIYSoTHw8jBtnekRX5WjKUQ4kHmDR/kXM3zW/XlvaXePry3keHgxr06bG517k7Y2frS2bUlNZnpTEz2edBXv2QF5eta/hZWvLF6GhOFtb83V0NM1turJovqq8iVApNQx4BuhYeLwCtNa6k2VDE0IIIUR53NxgzRp4//2qj3WwcWDtsbUcST7CmmNr+ObSb7im1zX1Esc4Ly/GeXnV+vxTw4ZRoDVWSsG6dWasd1QUdKvZ6PGXjhwhOieHvi4u9JCOHKIBVKcP9KfAG8DZwAAgvPBRCCGEEI3A1ha2b4eOHU2LvMoWXh1tHAnzDWPupLkAJGYl1lschzIz+ejECWJzcmp9DavCmwejevSgz8cfs2jfvmqfuyopiXarV3NLQADf9eghybNoMNVJoJO11n9orWO01vFFPxaPTAghhBAV6tjR3Hfn4gLR0RUf52jrSGZuJu3d2gOQkVvHptQlPHHwILfv28c/ycl1vpZP9+4ExMdjf+hQtc/xsLFhvKcnoU5OdHBwkBIO0WAq68LRTynVD1iulHpVKTWkaFvhdiGEEEI0ovBwePppsLaGb781998dPFj6mHM6nMPyQ8vpObsnAOk56fX2/re1bcul3t4MdnOr87Xc3Nz446OPGP3ff/DGG3DHHZCVRXp+Pg9FRZFTYDrn/h4fz21791KgNT1dXPike3c6OTry4uHDePz7ryTRokFU2oXjjNcle+Bp4Nz6D0cIIYQQ1dWrl/kBM6kwLw/y80sf8+LoF0nITGD+7vm8OvZVhrQfAkBKiik37tEDatsBbri7O8NrOESlMrm9evGGvz/9v/6aMZs3ww038GeXLrx69CjTAgIIcXJiX0YGv8TH835R7XShPi4uXOztTXxuLvdHRTGzXTvC6yGxF6I8FSbQWutRFe1TSvlZJhwhhBBC1MaTT1a8LyUnBTd7Nx4Y+kDxtp9+MivWe/bU+J49i7EJDWXW0KHckpVlEujYWC4aMoT9AwcS6OAAwL2BgdwbGAjAh8eP89ShQ+wdOJAJXl5M8PJiW1oafycmEp+by/7MTPYMHFjrIS1CVKTao7yVUm2Ay4BrgVCgnaWCEkIIIUTNpabCgQPQu/fpbfctuY9vdnxDv4B+HE46jLWVNe3d2vP772Z/QEDjxFoeFRbG0auuwjmrsNVeTAzWShFSzoTBC7ZvJyM/n8t8fIp7QK9LSeGD48fZPXAgfycm8nt8PJkFBdIjWtS7Sm8iVEo5KqWuUkotxAxQeQN4AQhsiOCEEEIIUX0vvQR9+pTuylHU99nN3o3xX4/nviX3AXDbbfD666YlXpNx1VU4L1tGVNu2uP7+O1ODg3nr6FFWJiWZ/XfeaeqjAV9bWy729uaDrl2xtzLpTExODidzckjJy+NSHx8+7d69VPI8cssWXj58uKE/lWiBKlyBVkp9DZwD/Am8BywDIrXWKxomNCGEEELUxJ13Qrt2ZmphkWD3YAAGtxvMXwf+Ij3X3EQ4ejR4esKRI9ChqcwVtrOD+Hjc0tO5282NhwYPJmD1ama2a8eINm3ggw/Mcffdx+ehoWVOH+PhgbVStC8s99iZlsZX0dE81rEjjlZWrExOrpcbHoWobAW6J5AI7Ab2aK3zMTcPCiGEEKIJCgyEGTNKbwvzDQPggi4X4GznXNyFIzkZBgyAefMaOsoqfPEFPsnJvNCtG27W1iSefTaPdewIJ0+a/ZUUeztaW3NBicEuh7KyeOPYMaIyM7G1sqJgxAheCA629CcQrUBlo7x7A1cCbsDfSqlVgKtSyr+hghNCCCFEzfz2m1lRPn7cvE4/aNrX7YrdhbOtc/EK9JVXmv3XXdcYUVbC0REmTyb+1lt58qWXWJOSgpuNDezYYfafW/0mYOd7epI+fDgdHRxYGBfH7owM7omMrNPgFyGgihporfUerfVTWutuwL3AF8B6pdTqBolOCCGEEDXi5wchIeDhYVrVPTbD3LYUnxmPk61T8Qr07beb3tGdOjVmtOWYNw/mzUOfdx6zhg4lJS/PbC+aUPj339W+lK2VFbZWVuzPzOS2vXu5fd8+3j9xguzCntJC1JaqacNxZXrBnKO1XmmZkCoXHh6uN27c2BhvLYQQQjQLcXHg7Q2ZmaYu+o47YOBA+CvqLxKzErkyzCw/Hz4MO3fChAmNHHAFDmdl4Wljg6uNjSndeOEF0zbkxIkaXSc9P5/IzEz87exIyM2li6MjNlbVGcYsWjul1CatdfiZ2yu7ifAJYLbWOqHkdm0y7pVKqXMBJ631b/UerRBCCCFqzdvbPDo6wmefnd4+tvPY4ucxMfD22/DOO5CeXvthKhajNR3j402bEFdXE7CHBxw7VuNLOVtb09vFBQAfW1uOZGXhYWtLG5tqd/MVopTK/snZAfyqlMoCNgOxgAPQBegD/A28ZOkAhRBCCFE3X31lbhq8dPJJjqYcZWC7gfTuDWedBRs3QpPMI3fsMA2tQ0MhIsIk0O3aQR1Xjnelp9Nr40aW9+7NSA+PegpWtDaV3US4UGs9DLgdiACsgRTgK2Cg1vperXVsw4QphBBCiNpYtAjeeguSkmDulrkM+mQQ2XnZvPQSPPro6b7RNazotLwePaBtW9i92/Tai4019Sa33w65ubW+bDcnJy729pbkWdRJjWugG5vUQAshhBC1c8NbH/NV8jSO3nuU9m7tAfjxR7j+ejPSu8l1ePv3Xxg+HH7/Hfr2hZdfNjUniYng7t7Y0YlWoKIaaKmgF0IIIVqJbat9AIhOjWXPHlP77BV8nNvvSsPW1qxSZ2Q0boylhJke1qxcaW4eLHqdnl7za2kN+fkA5BQUMGzzZt4trKfOKSiQzhyiRiSBFkIIIVqJd/7nC8ChuBhCQ+G5j7cw+vf2/Bk0gPbtTcXEM880boyleHiAjw+88gpcccXpVna1SaAffNAUe2uNnZUVAXZ2pr80cOOePfTasKEeAxctXZUJtFLKsyECEUIIIYRltXM3K9DpOpZ33wW/fusA2BO3BzB56kUXNVp45fvoI/P400+n29fVJoFevtw8FvaV/qlnT6b4m9lwV/v6cn9gYF0jFa1IdVag1ymlflRKXVDYA1oIIYQQzVB7t/Z8e9m3jAgazowZcDAtAoDfrjEdaWfMgPBw2LWrMaM8w8UXmzZ2U6fCNdeYbbWpM+nd23TxsLUts+sib2+m+vuzIy2NaJlSKKqhOgl0V2AOcAMQqZR6SSnV1bJhCSGEEKK+Odo6cnXPq4mIjSCvIA93B3cuC72MCV1PT1KZNQt69mxCtdBKmY4cBw6AZ+FfitdmBTouznTySEkpd3dMbi5nbdzIDzExdQhWtBZVdn4sHJzyF/CXUmoUpo3dnUqpbcAjWus1Fo5RCCGEEPXkSPIRXlz1Io42jjx/7vOcSD3Br3t/ZXSn0TjZOnH55dC9u8lbm4y5c009dFKSeV2bJHfNGsjJMaPAL720zO4AOzu+DQ1laJs2dYtVtArVqYH2UkrdrZTaCDwAzAS8gfuBbywcnxBCCCHqkY2VDauPrmZnzE4Alh9czkXfXcTxlOOAaXRx1VVmimGT0aOH6cLRubO5EbA2NSZF4xnDy3QkA0ApxdV+fnRwcKhDoKK1qE4JxxrADbhYaz1Ba71Aa52ntd4IfGjZ8IQQQghRnwJcAnB3cGfd8XWEvBPCL/t+ASA5O7n4mH37zMySJsfODrp2PX0zYU1obTp5dOhQySGaQZs28dTBg3UIUrQG1Umgn9BaP6+1Lh4+r5S6AkBrPctikQkhhBCi3imlCPMJY8WhFUQlRuFs6wxASvbp2uDLLoP7PvmJSd9NaqwwK7ZpE3z+OcyfDy++WP3zUlLMVMOLLjK10OVQSjHA1ZVgWYUWVahOAv1IOdsere9AhBBCCNEwwnzCOJ5qSjY6e3QGSifQ998Pf3lcwS97fyGvIK9RYqxQUXL7ww/w3XfVPy8lxSyr//or/PNPhYe917UrNwUEkJmfT0Ezm9YsGk6FCbRSarxS6l2gnVLqnRI/nwNN7N8mIYQQQlTXBV0uKE6cQzxDgNIJ9I03nj42PacWHS8awiefwLZt1T/+7bfNDYRQZQ31h8eP4/nff1ivXMlXp07VIUjRUlW2An0C2AhkAZtK/PwCnG/50IQQQghhCZO6T+Kx4Y8B0Me/D0snL+X8zqX/194hx7wuuQJdm+5xFuPqClblpDHZ2RARUTy2m+xsU/YxaRKMHg3BwWYVupLR3YsSEsgq3L+jgg+dV1AgK9StWIUJtNZ6m9Z6HtBZaz2vxM8CrXViA8YohBBCiHp2fufz6ezRmcA2gZwbfC5+Ln6l9j/deTGPZmu8nLwAiI6Gs84yuWiTkJxshqv88Ufp7ffcYxpZv/22eX3ihOm88ZsZFsNZZ8GGDfDxx7Bx4+nx4CUsCAtDjxxJwYgRzOrcudy3t/3nH67bvbseP5BoTior4fih8OkWpdT2M38aKD4hhBBCWEA7t3YsuX4JTrZOrDi0goV7Fhbvu+qnqzjW+Tleeun08VqbWSarVzdCsOVxdoavvjo9orvIhg2lH/38YOFCOPdc8/qDD8zjxo0wfTrccUeZS9sUrmwrpViTnEy71atZm5xc6pgLPD0Z5Opabx9HNC+VDVK5u/DxwoYIRAghhBANq7OnWV19Y80bHEg8wKTupuvGykMr2Ru3ly82/Eyfw5/y4zv98PGBgwcr7QLXsGxszMSXiIjS20eNMsvkEREmab7ySrj99tP7AwJgxAiz/513TF308eNmzHc52tvbM8bDAydr61Lbfz/rrPr+RKIZqayE42SJY6K11oe11oeBGKApzScSQgghRB2E+YQRERvBH/v/oEAXEJcRh7uDO1HpW1m2Np6kJLjgArj77vLLjks6nnKc99e/3yBxExYGa9fCvfeaumaAV1+Fhx4yCfLy5ZCWVv55a9aYJPyJJ0yCvWpV6WP+/BP27CHQwYF5oaGc5eJSvCszP5+VSUlsSEkhq6jWWrQq1Wlj9yNQstI+v3CbEEIIIVqAcSHjAHjun+dIzEwkX+cT5B4EwNwv0/HwML2h09NNvlnZvXN/RP7BjD9mcCqtAbpXXHihCeb992HmTLjpJvjlFzjvPHOToaenWZE+07hxZr+PD/TpA4sWwYMPlj7m/PMhNBSAxNxc7tm/n5WFo8SPZGczcutWBm7ezM4mdWelaCjVSaBttNY5RS8Kn9tZLiQhhBBCNKQRQSO4M/xOdsXuIjbDDBkpSqAzcjMAmDYNLr4YXnmlwjkkALSxbwNAbHolB9WXa6+FhAR4+mk4fBiWLIFDh0y3jaQkiI+H/v3LnjdxoukL3aEDbNli6qB37Tr9m8EZq8qOVlbMPXWKXYXJcnt7e+Z1787zQUEEytCVVqmyGugisUqpi7TWvwAopSYBcZYNSwghhBAN6ZLQS2jn1o7svGyGBg4lzCcMKN0HeupUuO02sLWt+Dp74/cCFCfiDSLMxMrPP8OgQbU7PzUVjh41SXVCQqndDtbWJJ99NkqZClZna2sm+/vXNWrRjFUngb4d+Fop9R6m9vkoMNmiUQkhhBCiQY3pNIYxncYA8N/N/5GSncKIjiPwcfYpPsbR0TzedhuEhJip2DNnwoIFUFQivOzgMgBi0mMaLviwMGjT5vSUwprq2dM8DhgAr78O/fqVOUQpxfcxMdgrxUA3N3ZnZBBgZ4enjQ3+9vZ1CF40R1WWcGito7TWg4EeQA+t9VCtdaTlQxNCCCFEQ/pj/x8M/mQwSVlJuNm7seLGFVzc/eIyx+3ZA3FxZpr2X3+ZWSVFnGydgAYq4SgSEgIvvQS9e9fu/MGDzY2H+flmFbukEgXf7x0/zv/FxbEkIYEx27YRtmEDbxw7VofARXNVnRVolFITgDDAoeivL7TWz1kwLiGEEEI0sHydz7rj6wh5J4S4hyqu1ly50jzGxcHZZ4OX1+l99jZmNbaohrpBKAV33ln7821tYdYsM5wlPx969DC/JRw/bhLowtzn9c6daW9vj41SrOzTh8NZWaW6c4jWo8oVaKXUh8BVwExMCccVQMfqXFwpNU4ptVcpFamUeqSCY0YqpbYqpSKUUitrELsQQggh6lFR3XN8ZjwA4XPCeWbFM+Uem5QEiYkQGQlvvHF6e2ZuJv0D+jOx28Ti4/791+SmTd6HH5oJhStXQseOpjTk+PHi3QPd3MjVmj0ZGQxv04Yb/P3pLQl0q1SdLhxDtdaTgUSt9bPAECCwqpOUUtbA+8B4TPnHNUqpHmcc4w7MBi7SWodhknMhhBBCNIKO7mZ97Jqe1wCmjvlg0sEyx504AQMHwq23wvz5ULKKISM3A2c7Z7LzTF3H5s0wfDhs2wa5uTBnjkmom7QJE0zQP/0E48eX2vXFqVOM2LqVAiAuJ4f1KSmNE6NoVNUp4cgsfMxQSrUF4oHgapw3EIjUWh8AUEp9B0wCdpU45lpggdb6CIDWugHvOBBCCCFESVbKioSHEnCxM6uqXb26sjt2d5nj2raFzz4De3to3970hy7y3eXfceuvt9Lzg57sn7mfPn1MnXSvXmZuyYMPwo03mtKPJmvZMvD1NcNULr+8VBnH9X5+DGvTBivgxSNHmHPiBKnDh2OlZMZca1KdBPq3wpXiV4HNgAY+rsZ57TAdO4ocA87sLdMVsFVKrQBcgbe11l9U49pCCCGEsAAPR4/i52E+YXy65VMKdAFWqvRfWg8bdvr5iROwfr1ZlfZ38WdA2wH8vu93Pv48g06BTowZc/rYvXtNbtqkDRxoHqdNK7Mr2NGR4MJ2JLcEBHChlxeVzJURLVR1unA8r7VO0lrPx9Q+d9daP1WNa5f3q9iZ/4zZAP2BCcD5wJNKqa5lLqTUNKXURqXUxtjKurcLIYQQot6E+YaRnpvO0eSjlR732GNw//3m+bvr3uVYyjE0miff3s0XZyyL+ftXPQ68SUlKMndLliPM2ZnRHh78Fm9qxk9lZzP7+HF0ZaMaRYtQnZsIHZRS9ymlFgDfADcrparTaPEYpWul2wMnyjlmsdY6XWsdB/wDlOlBo7Weo7UO11qH+/j4nLlbCCGEEBYwttNYBrYbSIBrQKXHXXgh3Huvef7cP88RmWC63b4wJ4LXXit97K5dpmNcYc7ZtOXmmuXy11+v8JDNqak8euAAsTk5vH7sGNP37ye7oKABgxSNoTq/A36BaWH3LvAeEAp8WY3zNgBdlFLBSik74GrglzOOWQgMV0rZKKWcMCUeZYuthBBCCNHggj2CWXfLOo4kH6HPh31YHLm43OMuvxwuvdSUCqfnpNPbrze2VrbsT4rgzHWvY8fg7bdN944mz9YWunSBiIgKD8nVmqFubuRozXhPT54NCsLB2roBgxSNoTo10N201iVXhZcrpbZVdZLWOk8pNQNYAlgDc7XWEUqp2wv3f6i13q2UWgxsBwqAT7TWO2v+MYQQQghhKd5O3myL3saG4xsYFzKueHt+QT65Bbk42DgwZw48+kQ2mdMzaePQhrv6PEn0+v4c6gJBQaevde655qZDm2pNomgCwsJg06YKdw9yc2OQmxsA7ezt8bezY096Ot2dnRsqQtEIqrMCvUUpNbjohVJqEPBfdS6utV6kte6qte6stX6xcNuHWusPSxzzqta6h9a6p9b6rRrGL4QQQggLc3dwJ8AlgKdWPMW2U6fX0PrN6Yf/a/6AGQboMXUyAK52rpzv8CTznriAMwf12dg0o+QZzJjvAwfA2Rm2b6/wsB7r13NvZCTjtm/n+cOHGzBA0Riqk0APAlYrpQ4ppQ4Ba4ARSqkdSqmK/0kSQgghRIvxythXAPjvqFlDy8nPYXv0dkYEjQDMyvJrU65hZrdX+eKByXj75rH5yB769M8uc625c+HJJxsu9jq59VZzl2RGBvzzT4WHXertzUBXVz7r3p0nOlZr3pxoxqqTQI/D9H0eUfgTDFwAXAhMtFxoQgghhGgqrut1HW72buyJ2wNAXIbpTDE+5PSgkYu7X8z0vg/g5+LD0qO/0m9uKLsTyq61bdwIf//dMHHXWUAAvPACuLtXWgv9QqdOXOPnx2gPD0KlfKPFq04bu8Na68OYgSq66KfEdiGEEEK0cEop9kzfw1vj3gIgNt20lfVxKn2XYLduZv5I1lEzFvzV1a8CsOrwKuZsmsO2U9t4/31Ys+b0OafSTrHl5BbLf4jaUsrUQs+bV+WhyXl5/BoXR2xOTgMEJhpLddrYXaSU2g8cBFYCh4A/LByXEEIIIZqYANeA4oEqMelmeLCvc/lTUayTOwPw464f2Ru3l/Ffj+e2327jhVUvcObQvrPnnk2/Of0sF3h9GDwY8vPN8y1b4PHHIS+vzGH7MjK4aOdO/k1ObuAARUOqTgnH88BgYJ/WOhgYTTVvIhRCCCFEy7Hl5BZu+eUW4jLiGBE0gkN3HyK8bXi5x0692ZqfL1oJwMK9C0nPTeeN897gh8t/IDMTrrsOvv/eHNu/bX+6eXVrqI9RO7Nmwe7CTrvbt8Mrr8CRI2UO6+XszOq+fTnP07OBAxQNqToJdK7WOh6wUkpZaa2XA30sG5YQQgghmpq4jDg+3fIpO6J3YGdtR0f3jjjaOpZ7rK8vjA3rz4C2A4rrpocEDkEphYMD7NwJ0dHm2JTsFNo4tGmoj1E71tbQqZN5ftVVphdf0esSHKytGdKmDc7SC7pFq04CnaSUcsFMCfxaKfU2UPbvLIQQQgjRooX5mrrmOxfdSed3OvPSqpcqPd7Zzpn1t67nyrAr8XDwwN3BnVHzRrFo/+9s2wZ33WWOWxy5mPXH1xOf0RzGEwIODmBnZ0YqpqWV2b0lNZWrIyLYmJLSCMGJhlCdBHoSkAHcCywGopDuG0IIIUSrE+ASwK39bsXP2Y9At8BqJ7zjQsbxwNAH6NCmAysPrWTDiQ3lHpeYlVif4VrWrl0we3bpuyEL7c7I4EBWFuky0rvFqrCVuVIqBPDTWhfVOxcA85RS5wDuQDP5NVEIIYQQ9UEpxZyJc2p17mPDHwOgk0cnImIjWLrU3If3888wpfcU5m2bR0p2M1qx9fY2ZRwRETB2bKld1/r5ca2fXyMFJhpCZSvQbwGp5WzPKNwnhBBCCFEjYb5hrDq8CnuHApycICUFbuxzIwDJWc2oc4Wvr0miy1mBLnIyOxutNVrrBgxMNITKEuggrXWZ7uda641AkMUiEkIIIUSLdZbvWUSnRzM/9X6WLYMuXQvYHm3SjWa1Ag3Qowf88AMsWFBm18cnTtB2zRoOZmVxeUQE+ZJEtyiVTaN3qGRf+bfcCiGEEEJU4r4h9xHkHsRVPa8iJgY+/Sqdx1LvBqiwo0eT9f770KsX/PsvXHppqV1X+PiQmJeHnVIsSUjg05Mnmda2bSMFKupbZSvQG5RSt565USk1FdhkuZCEEEII0VJ5OHowtd9UXOxcSEmBx541ZRsfXfgR53U+r5Gjq6GePaFfv3JHfLvb2vJQhw4cyc7mpU6dyNeaFYnN6CZJUanKVqDvAX5WSl3H6YQ5HLADLrFwXEIIIYRoofbF7+PZlc/ibu/BnV84MnsrbFvfhp+PwCXNLcMIC4Mvv4TISDhwAAYMAA+P4t2zjhwhMjOTHK3p4+LCyBL7RPNVYQKttY4GhiqlRgE9Czf/rrVe1iCRCSGEEKJFysnP4addP5GTn1O8bXbM1Ww6tI+xY5/ExaURg6upESNMAj1zJixebDpy/Pln8e43Q0JwsrIis6AAfzu7RgxU1Kcq+0BrrZdrrd8t/JHkWQghhBB10tO3J+mPpbNiyoribW1dAnEKXdm8kmeAqVPh+HHTjw/gjDKNTo6O+NvbE+zoyPHsbK6KiGBranlNzkRzUp1BKkIIIYQQ9crGyoaz/M4CoH9Af84PGcOuuJ00y9yybVsznfCSS6jsA9goxcbUVKJzcxswOGEJkkALIYQQolF4OHqw9bat/HPTP4T5hBGdHk2f+55o7LBqbvduGDMGNm2Cfftg6FA4eNDsW7oUZswAIMjRkajBgznf05MCaWvXrEkCLYQQQohG09u/N062Tlze43IAMoPnlznmzz/hhhugyU7G9vUFHx847zyYONEMVymqgx4zxrS7yzld7/2/w4fx+e8/SaKbscq6cAghhBBCNIiO7h2JfTAW6zw3Fi0y7ZUDA82+HTtMq+W4OJOrNjleXvDtt+a51uDmVra1XVycKfUA+rq4cGtAANkFBThaWzdwsKI+yAq0EEIIIZoEbydvUhLtmDABFi0y2+Li4IEHzE+TTJ7PpJSZULhnT+ntMTHFT8d5efFy587E5+YSX4N66LyCAjLy8+srUlEHkkALIYQQoklIzkrm9V138+bCpVx9tVnMtbKCWbNMTrpwYaX36DUdffrA9OnmeVAQjBoFZ51V5rABmzfz2IEDlV4qr6CAY1lZaK05mJVFrw0b0FL60egkgRZCCCFEk+Bo68gHG2cT7fQ3W7eaFefISHjoIcjOhosvhi1bGjvKanjxRRgyBFavhkOHoFs385vAGd7s3Jkp/v4czcriwu3bWVbOpMLdGRkErl3LD7Gx/JmYiIu1NemyCt3opAZaCCGEEE2CnbUdXb26svZABKnbTVe4Dh3MvqFDYe3achdymx5vb0hOhmHDzOsPP4TLLjM3FJZwtZ8fAMl5eRzJzia1nMTY186O97t0YaibG4EODkxv187i4YuqyQq0EEIIIZqMMJ8w1p9czfsRz/DcC7n4+5vtbm4waBD89psp5Wjy2rQxwc6aZV6vX1/xoTY2bB8wgPGenvxQolYawM/OjjvbtSPQwcGS0YoakgRaCCGEEE3GhV0vJIN4+l/1Jxk2x8rsf+IJePhhUx/d5E2YYOpPCgrgsceqPHxrWhpX7dpVqkTjYGYmJ7Kzi1/ftGcPzxT1mBaNRko4hBBCCNFkTO49mcm9J1e4f9cuU06sVAMGVVfVDFYB/wsOJr/Ebwe37t1LZkEB//XrB0CB1jSH3x1aOkmghRBCCNEk5RXkYWNVOlVplm2Tf/kFPvrIPFbyAQa4uTHAza3Utic6diSnREI9LzTUYmGK6pMSDiGEEEI0OZf/cDm2z9vi/Yo33q94c+38awFTDXHzzfDFF40cYE3ExZnG1tUovTiYmUm3dev4LS4OgJEeHpzn6WnpCEUNyQq0EEIIIZqc6QOm4+9i7iDccGID83fPL16R3rkTQkIaOcCaCAszjxERVQbuYGVFP1dX3G1sKNCajampdHZ0xMvW1lwiPZ2rd+3inZAQRnl4WDpyUQFJoIUQQgjR5IwKHsWo4FEArDy0kpWHV5Kdl42NnU1lDS2aph49zGNEBEyaVOmhAfb2fFt4/NGsLAZt3sxbISHc3b49AL62tgQ5OGBXTl9p0XBUc5tmEx4erjdu3NjYYQghhBCiga06vIpQn1C8nbwbO5Sa69gRQkPh7bfNYJUq3LFvHz/HxvJJt26EOjvT2dGxAYIUZ1JKbdJah5+5XX59EUIIIUSzMPbLsdzx+x2sXQvnnANRUY0dUQ307w9LlkDv3pCWVuXhEzw9eSAwkAleXuUmz81tAbSlkQRaCCGEEM2Cn4sfR5KP4OBgbiZMSWnsiGrgk0/gpZfMTPLdu6s8/EJvbx7o0AFVTgu8lw8fpv2aNZJENyJJoIUQQgjRLJzT8Rxi0mPo0wf+/Rf69m3siGrA09OM8wZTC10HYc7OXOHjQ3ZBQT0EJmpDEmghhBBCNAs+Tj7EpscWv77rLtNaudno3BkmTzb10LNmwTPP1Gqk4kRvb97q0gWHZtkUu2WQBFoIIYQQzYKvsy/puemk56QD8NNPsH272bc3bh/vr3+ftJyq64sbjbU1zJsH3bvDI4/As8/C3r21upTWmqwSI79Fw5IEWgghhBDNwuTek9ly2xbsbewBOHECHn/c3JsXNnYDM/6YwZ4TRxs5yipoDUOHnn5dy3KO0PXrmb5/fz0FJWpKEmghhBBCNAttXdvSx79PqfHeSsH110P+OU8BsPNgbEWnNw1KmTsgfXzM61om0NPatuUCL696DEzUhCTQQgghhGgWkrKSeGr5Uwz+ZHCp7X/9BSHBZlXaziOmMUKrmcOHISYGOnWCp5+GLVtqfIn7AgO51Nubxw8c4Hh2tgWCFJWRBFoIIYQQzYKrnSvzd89n3fF1JGUlFW/v0wdcHE0CnZzbxFegS3r2WfO4YkWtTt+dkcHX0dH8m5xcfzGJapEEWgghhBDNgrWVNS+PfhmAXbG7Su2zwQ6APUebwQp0keuvN6UctSzj6OzoyNzu3Rnn6VnPgYmq2FR9iBBCCCFE0xDmGwbAgt0LGBpobsbbcHwDqdkZEBNGSOx9jRlezS1aBIGBtTrV3sqKcz082JCSQr7WDG7Tpp6DExWRFWghhBBCNBtB7kH4OfuxYPcCAGLSY7hnyT109e7ESxPv56brXBs5whoKDwc/vzpd4omDB7m3cK55vtYk5ubWR2SiEpJACyGEEKLZsFJW7J2xl2VTlgGQkZvB6qOrubDbBMK7tOfNTc83coQ1dOgQ/O9/EB1d60u8FRLCx127AtB/40ZurmVvaVF9UsIhhBBCiGaljUMb2jiYcoUObTrgbOvM2mNr+WP1EX5J+B8PDnsQBxuHRo6ymo4ehcceg4EDa70SHersXPz8vsBAXGVCocVJAi2EEEKIZstKWdHRvSOfbf0M29j+FPgUsDduL739ezd2aNUzaBAkJUE91S9P9vevl+uIykkJhxBCCCGatfsG38fwDsN56aK7AYiIrV1Xi5q4/HKYNq0eLmRnV2/JM5gR30eyskjJy6u3a4qyJIEWQgghRLM2td9U/rnpH+4afRU2VjZExFg+ge7aFTp3rqeLzZsHDz1UL5falZFBx7Vr+TU+vl6uJ8onCbQQQgghWoTDB+zwKOjCgfhjFn8vpaBnz3q62ObNMHu2GfFdR10cHfmgSxeGuLkBZkW6QGu01nW+tjhNEmghhBBCtAj790PsC5u4p+M8i7/Xhx/C6tVQL3lpWBikp8ORI3W+lJ2VFbe3a0cnR0cAIjMzsV65km9imtGAmWZAEmghhBBCtAijR0NqoiODBln+vdasgTlzYMGCerhYmBkOU9uJhGfKLShg6ObNvH3sGJ62tjwbFESvwk4d+bISXS8kgRZCCCFEi2BvD0cyd3H5D5eXGfVd39q3h0mTzGOd1XMCbWtlxYHMTO6JjORIVhZPFSbQndau5bEDB+rlPVo7SaCFEEII0WL89hvM3z2fJ5c/WbwtIiaC7dHb6/V9HnsMLruM+lntdneHtm3hl19gyRKz7dgxUxddy4T31169uMLHB387OwCUUlzj68ugwtpoUTeSQAshhBCixdi5sgsAC3Yv4ECiST57ftCT3h/Wb1/ozz+HLVug3rrFDR8O//0HF14ImZmwezdMnw4PPFCryw1wc+OHsDAC7O2Lt73YqRPne3qyKimJVGlzVyeSQAshhBCixZj3mS2rbloFwI7oHRbrPpGUBMePQ1BQPV3w669h7lyTkW/fDiNGwEUXwY4d9fQGxtqUFM7ZupX1qan1et3WRhJoIYQQQrQYSkFvP7PaHBEbQWxGrMXea9w4mDmzni5mbQ1nnw2jRsHgwWaJu29fiIqCjIx6ehMY4OrKol696O/iUm/XbI0kgRZCCCFEi7FtGzx0jys/TljOrf1uxdfZl6P3HmXvjL319h65uXDHHWaI4MMP19tloUsX+PFHeOIJ6NfP3FyoNezZU7PrZGbC1VfD4cNldrnZ2DDeywt3W1vpDV0HkkALIYQQosWIjob58yHEZiR21nZ8ue1LHGwc6OrVtd7eIz/ftK/bu9fkqvVaTuzlBc8/D+Hhte/O8ddf8P33MGNGubtPZGfTbvVqPj91qo7Btl42jR2AEEIIIUR9Oe88KJoZop51B+Clc1/CSlkxc9BMnGyd6vweDg4mUf/zT3ByMvf+DR1a58uW1aUL2NrWPIEu7PlMBXXOfnZ2nO/pSZCDQx0DbL1kBVoIIYQQLVpGbgaPLH2EuIy4er1uaCi89BK0a1evlz3N1ha6dYO//67ZyEOrwvSuggTaWinmdu/OKA+PMvvyCgqktKMaJIEWQgghRItRUAC33QY//AB9/PsA0NO3JwAp2Sn18h4ZGTB1qilNfvRR6NixXi5bvt69YdMm+OCD6p+TlWUeq+i0kZmfX+p1fG4utv/8w3vHj9c0ylZHEmghhBBCtBhWVrB2LezaBcsmL2PXnbtwd3AH6i+Bzskx5RtHjphc9eTJerls+V55Bf74w9y1WF0jRkCbNmY4SwXeP34cl1WrSClRwK21ZqCrKx2ktKNKUgMthBBCiBZl27aiZx54OHqQnJ0MQHJWcr1c390djh41zwcONLnqX3/Vy6XLatu20kS4XE5O8O23pli7AoNcXXk6KIj8EuUa3nZ2rOvfv7aRtiqSQAshhBCixfn1V1Ob3K8fuNmb8dX1tQJd0iOPmHZ2FrV8Odx3H2zYADY2ZuT3xo1m31VXne7WUWTLFnPj4e23V3jJcDc3wt3c+DshAS9bW/q6upJdUMCm1FQcrazo6+pqwQ/U/EkCLYQQQogW5/77ITgYliyBLp5dOHHfCbycvOrl2omJZoDKzTfDpZfWyyUrl50NO3eevjnw99/h44/NjYX79sF335U+/r//4MEHTUu86683NyNW4LnDh8nVmjX9+rEoPp5LIyLwsrEh7uyzLfiBmj+pgRZCCCFEizN/Pnz0kXlua22Lr7Mvh5MOk5CZUOdr5+SYOuvoaPP61VdhwoQ6X7Zi48aZNy1KoD/6yNwtOXGiSazPdMcd5qbDm282CXYlXu/cmS+6dwcgzNmZK318+PHMFW1RhqxACyGEEKLF6dWr9OuDSQfp+l5XPpn4CVP7Ta3Ttf38IDLy9GtHR9N6WWszStwiyrtwWBgsXmxGI5ZcZba2hssug7POgs6dK73sADe34uddnZz4XpLnapEVaCGEEEK0OHv3wrx5p9snB7sHY2dtxy2/3sKptPqdwDdjhmmbZ7HkuSJhYSZ57tbN1EkX+eUXeO01M92lio4a6fn5zD5+nIk7dpCcl8fW1FR+i4ujQHpBV0oSaCGEEEK0OIsWwY03QlKSeW1tZc11va4DYHv09jpdOzoarrwS/vmnbjHW2YQJ5kbBEyfMXZNFVqwwJRx//FG2PvoMNkqxKTWVEEdHXjp8mL6bNjFx506OFPWSbgB5BQX03rCBlw8fbrD3rCuLJtBKqXFKqb1KqUil1COVHDdAKZWvlLrckvEIIYQQonW44QZTZlGiQoGHhz0MQGx6bJ2uXXRPX1FyXlAA4eHw/PN1umzNeXiYRDkszDS+LpKZaepK5syBZ5+t9BL2VlZ82r07b4aEcLG3N88FBbG+Xz/a2ttbOPjTbKysGODqSsdm1H/aYjXQSilr4H1gLHAM2KCU+kVrvauc42YBSywVixBCCCFaF29v81OSn4sfg9sPxsXOpU7X7tDhdL6ak59DXEYc/fq1pUOHOl229n75xXzYLVvA09NMd3FwMIn1r7+ajL+KhHhpYiKvHT3Kz2Fh2FlZEZmZSVcnpwYJ/2BmJquSkznP05PsggLsrZp+gYQlIxwIRGqtD2itc4DvgEnlHDcTmA/EWDAWIYQQQrQiGRnw+eewvUS1hruDO2umrmFS9/LSkdqZ9us02r3RjndnZzNlSr1dtmbatTMJ8g03wMiRp1egw8IgP7/KThwASXl5pOTlkac1MTk5dFu/ni1VjAKvL3ZWVvR1cWFDaiqPHDjQIO9ZV5ZMoNsBR0u8Pla4rZhSqh1wCfChBeMQQgghRCt0882wcGH9X/fIEZg0CVavhj1xewDILcjlgw/gw8bMaN54AzZvNgl00Qo0mKEqVZjk5cV7XbrgYmNDVkEB1/v54WJtbeGAjXb29nwXFsY/SUnE5+Y2yHvWlSXb2JV3L+qZt3S+BTystc5Xldy6qpSaBkwD6NBofz8ihBBCiObCycnUQJ+ZNlz545W42rny6aRPa33tnBw4fBjS02F4h+Fsj96Oi50LCxeaDnKVDAC0rPPOM49ZWWYFuls3E1A1EmibEtMHgxwd+TI01JKRlmttv35Ulg82JZZcgT4GBJZ43R44ccYx4cB3SqlDwOXAbKXUxWdeSGs9R2sdrrUO9/HxsVC4QgghhGhJOnUyDSnuvRfy8sy2xKxE5m6dS1RCFADHUo4x/ffpTPt1Grf9ehtbT22t8rohIbB1K4wdC1l5WVgpK3Lyc/jtNzMkcOlS8545ORb7aOXLyjITCP/80yTQ9vYm2BdegOPHa3QprTXZBQUWCrS0HWlpBK9dy7KiuzKbAUsm0BuALkqpYKWUHXA18EvJA7TWwVrrIK11EPATcKfW+v8sGJMQQgghWpHt22Hu3NMJ9Pmdzwfg862fA/DNjm+YvXE2v+37jWWHlnEs5ViNrh+ZGEl6bjo/7foJm8K/19+xw7xnfn59fYpqsrc3NxK2bw+jR5ttF11kHr/+ukaXCli9mgeiouo5wPK5WFszvE0b1iQnM6Ma9dpNgcUSaK11HjAD011jN/CD1jpCKXW7Uqqx/nJDCCGEEK3IffdBcrLp3TxzJoxv8wBdvboSEWvKGiJiI2jr2pYT959g/8z9XNj1wiqvuW8fjB8P69fD15eaxDQm/XQvhHvuMe/p6GiRj1QxpeDvv+HoUXj8cbPtlVegbdtqlXGU9GCHDoz39LRAkGUFOzryRWgotlZWLIiLI7eBVr7rwqKjvLXWi4BFZ2wrt7xea32jJWMRQgghROuVn28mE44eDWE+YeyLNyudrnaujAoaBUBeQR5pOWm4O7hXeq28PEhIMEMA3R3csbGyKdNbWmu47joYMMCUczSq554znTpq4P7AwKoPqmcPd+jAw83kXjeLJtBCCCGEEE1BcLBZFVYKzsn8BDd7M2HlvQveKz7m2vnXsiNmB7un7670Wj16wLp15vkba94iryCP6PToUscoBWlppp1eo5s69XRG/+KLEBRU5Slaa2Jzc/G1s7N4eGuSk7k8IoIfw8IY2qaNxd+vPjT9TtVCCCGEEHWklPkB8HT0JC0njUGfDGLe1nnFx/g4+ZQqxaiOvw/8DVC8ol3SL7+crqRodHv3moBWr67W4Y8eOEDHtWvJ12c2UKt/HjY2XODlBcDUPXv4OyHB4u9ZV5JACyGEEKJV+P57uPZa87yNfRvWH1/PjQtvZH/8fgB8nX1JyEwgryCv0uts3w7nnmtaLmfnZwMwufdki8ZeZyEhsGcPXHNNtQ6f5O3N6507N0g9cndnZz7u1o1+Li4sTkjgSHa2xd+zriSBFkIIIUSrcOqUGcGdlUWpfsN+Ln4A+DibVrmbTmyq9DoFBab+WWvTxm508Ghu6XdLmeM2bICePU2i3ehsbEwd9OLF8PPP5qdoHnk5hrRpw53t2nG4AZNZB2trjg8dys0BAQ32nrUlCbQQQgghWoW77zb9mx0czOuxncYCFNdDh3iGAPDXgb/YFbuLz7d+zl9Rf7Fg9wJOpp7kl72/oLWmTx9YtQr694fsvGyslBVRCVGkZKeUej8fH5Nkd+7cUJ+wGm6+GS691PyMHm0CrEBsTg7jt28n1sINrf9MSMDnv//YkpqK1pq/EhLY1EBjxGtLEmghhBBCtBrHjsEXX0B2Nvx27W+kPno6URvbaSz7Z+5nWv9p/Lr3V25aeBPvb3ifu/64ix8ifmDSd5PK1Ei72bsRnxlPyLshLIlcUmpfUBAsWQJN6r64v/82v0U8+qhZkreyMsNXyrEnIwM/OzsS8iovaamrADs7rvDxwcvWFqUUU/bs4d1jNevH3dAkgRZCCCFEq/HvvzBlirmnzs7aDhc7l+J9SilCPEM4mHiQp1Y8BcDCvQsJ8w0jzDcMMH2j16+Hs882A1OWTVnGomtNx97YjNgy7+fmBrNmmXKOJiEsDHr3NkXcRf77r9xDh7u7s6ZfP7o5OVk0pF4uLszu2pUOhX818HuvXrzapJbty5IEWgghhBCtxrhxsHu3aUVXkU82f0JO/umyhTCfMMJ8TAJ9/YLryde5ODiAtbXZ7+XkhUKV28HD2tos9v7zT71+jLoLCzv9vLzOHH/8AVdfDTt2cOPu3bzdgCvCfV1dySwo4La9e0nMzW2w960JSaCFEEII0Wq4u0P37hSP3S7PlWFX0i+gH6ODRzOg7QAuC70Mfxd/Qr1DOZl2Epv2W/n7b5OE3/DzDXy6+VM8HT3LDFMBcHaGpCS4/36LfaTa8fc3S/F//ln+/tdfN21LvviCFUlJ2CnFzrQ0bt6zhwOZmfUayvzYWNxWrWJfiabZUZmZzDl5ko1NtBZaBqkIIYQQotXIyzMTCcPDTSVDecZ2HsvYzmPLbF903SKeXvE0jranZ3T/uvdXPB088XX2JSaj/B7Sbm71Enr9Ugo+/9ysPo8eDXPmlL7b8YknYOlS2LmTQ6++CsCi+Hi+jYmhi6Mj1/n5FZdc1FWwgwNTAwJwL/FbzUh3d/TIkfVyfUuQFWghhBBCtBp5eXDLLbBoUdl9Vc0MCXIPYt7F8zgZ0YWBA00ddXZ+NvY29jw78llu7397uee9+ir89FM9BG8JWVmwbBkcPmxeaw2ZmTBsmJlcGBEBQHxuLoPc3NgRHs5jBw/yd2JivYXQz9WVN0NCSk09LNlmsCmSBFoIIYQQrYa9PRw5AjNmQEJC6fLfjRvhnXcqPz+vII/Hdk5AdVqGtbUmOy8be2t7rgi7gtGdRpd7zkcfmZLiJsnf3zwWtbQrKAAnJ3j5ZVMnffQomUlJeP/3H48fOECwoyNb+vfnWl9fi4f22cmT2KxYwfqUlKoPbmCSQAshhBCi1VAKAgPNwuuLL5puGkWlt88/b7q8VSY7L5uNCUuZcMe/dAzOQ6Oxt7EH4MJvLuSexfeUOScyEhIToVcv+OYbOHkS1qyBt9+Gd9+t5w9YU6GhZrUZ4MknzR/Qyy+bLh2FNxo67tnDt6Gh3BwQgLVS9HF1xVop0uqpvd1nJ0/isHIlx7KySm0f5ObGEx070rbEynRTIQm0EEIIIVqVDz8EX1+z+rxkibmh8M03Yfx4eO+9ys91tnOmk0cnImIjyC3IJcQzBB8nM8EwPTeddcfXlXveBRfAgAGmKmLVKnjmGbjnHnNzYaM2mlDK/OYA5jcKKyt4+GGTVBd16oiI4Go/PwYWFnNnFxTgumoVrx09Wi8hhDk7c3f79riecWdnD2dnngkOpn091VrXJ0mghRBCCNGq3H03tG8PL7wAY8dCfr6ZJRIXBx06VH2+jw7j5w1r+XrN3+yfuZ/bwm8DTLu7XbG70GcUU7/+uknSP/zQ9I6++GL4+GNYscJ06LC1rfePWDMlW9qVFBwMjo6mtqUEeysrng0O5lwPj3p5+4Fubszq3Jk2lbVGaWIkgRZCCCFEq3LkCOzZY8p+16+HnTshJQVuuAG+/BKqWljt63U2uU5H+HJv6fqLMJ8wUrJTOJZSumfyDz+YbnF2dtCzpyk17tABRoww5caNrqie+ZZbSm+3soIhQ2DixDKnPNyhA+e4u1s+tiZKEmghhBBCtCp+fqYmOTERbrwRrr/eJLdZWTB5MixfXvn5s697kNgHY1l4/Q+ltpecVljSunVmhPgFF8DateDgAFOnmn2vvw4LFtTXJ6uDvDzTyu5Mf/wBw4fDtm1wRk/mGfv28eyhQ3V+67eOHsVqxQqSmujQlPJIAi2EEEKIVuWjj8yNhE89Ze6b27cPvvoKQkJg1y645prKz1dK4e3kjYdj6RKGMJ8wLul+Ca52rmXOufZauPxy+O0383rMGPP48cfw++/18anqyNra1EOfyc4OtmyBPn3KjPxOzs8ntR5uJBxYeLOgY9Fox2ZAnVmn09SFh4frjWfU4gghhBBCVJePD+TkwOLF0K0b/PorjBwJHTtW7/wFC+Dxx82ckbZty+7PK8hj1LxRPDPiGZRSPPPdL1zp+hYzZphVbzs7M6EQTMtlR8ey12hSUlLMSvSoUafLPVoJpdQmrXX4mdubT7W2EEIIIUQ92L/f9IMuSlynTDm9b+1as9Ba2ehtDw846yyTCJdHofj3yL98teMrPt/6OQDOy55i+nRPzrzvrsknz2BGKV51lXmen29Wqwv9k5TEsexsrvXzq/Xl8woK0ICNUk1+gEoRKeEQQgghRKvi7g7Hj5tBKmf6+2/Txa2oN3R5Ro2C778Hb+/y91tbWXNu8LlExETw4NAHAdgdH8EVV5Q9dv9+mDbNlJE0adHRZun+k09KbZ578iQPREXV6dL/O3IEu3/+Ib8ZVUVIAi2EEEKIVuWjj6BLF9P7+Ux33WXulatrd4yePj3ZFbuL63pdB8CQiyKYcHkCSVlJpY7LyoL586Ee7sWzLB8fU2+ya1epzS936sSegQNJycsjPT+/eHu+1qVeV2akuzvPBwVh3UxWn0ESaCGEEEK0Mq+8Yh4vu6zsPjc305d5zx7TmKI8X34JnTtDfHzF79HbvzfpueksiVqCPS5EZWzl5r1eeM7yJCvv9MS9nj1N/+nzzqvDB2oIVlbQowdElO4w4m9vj5uNDXfu24f/6tUk5ebyV0IC47Zvr/agleHu7jwRFNRsyjdAEmghhBBCtDI7d0J2tmksUZ5vvjETriMjy98fEABDh1Y+AOXaXtfy3WXfMaHLBCZGHUGtmwmY8o70nPTi45QykwiXL4eDB2v5gRpKWFiZBLrIBC8vFvXqRUJeHudt387q5GSic3KqddnsgoJqr1Y3FZJACyGEEKJVsbc3tccVrSCPGgXz5lXccGLMGLMKXTjZulwONg5c1fMqwnzD+PELD6JOxQKw+LrFeDl5lTo2MxPOPdcMXGnSwsLg1Klyi8ev8fNjuLs7AXZ2LOvdm83h4bzXpQsx1Uiinzp4EK9//7VExBYjCbQQQgghWpW33jKlE599Vv7+wEAzUGXXrorLOGpi3bF1JE40jZ+9nbxJzkoutb9NG3j3/1YT3+sZnl3xLHvi9tT9TS2haOT3k0/Ciy+aWpi4uOLdSxISeP7wYTalptJj/XquiIhg3PbtVV72Ai8v/tepk6WitghpYyeEEEKIVmXuXPN44YUVH3PyJMyaZW4qHDu29L6PPoLnnzd10i4uVb/fVz8lUUA+g9oN4qLvLmJgu4H8eMWPpY6ZF3M3G0+YORf7E/bz1aVf1eQjNYzwcJPtz55tXo8eDePGFbcjee/4cU5mZ/NscDA2SjHA1ZVxnp5VXnaEuzsjmtlYcEmghRBCCNGqbN1a8eC9Ira2ZvXZwaHsvqAgkzfaVDOLSjsYBp5wbdhkftzzLbHpsWWO8Xfx55kRz7D+xHp2xuys3oUbmo+PqXspKDCvlSr1h7CwZ08yCwpwtrYme8QIAAqq0ZouLS+PPK1xr6yovImRSYRCCCGEaFVycsy9cB06gJdX1cfXldYaq+esCM29ntCzMtgTt4eIO8u/Ge+vqL9Iz03n4u4XWz6wusrOhiNHTE/ACrxw6BDHc3L4oGvXCo+Zvm8fP8TGEjtsmCWirJOKJhFKDbQQQgghWpX//Q/69YOff6762NtugwEDYOBAc15tFLVn2237FT5OPsUr0A/99RAh74QQ8k4Ih5MOAzC281gu7n4x9yy+h1WHV9XuDRvKY49Br17mN5IKFmRT8/NJqqKQ/AofH14MDrZEhBYjJRxCCCGEaFUWLzaPY8ZUfaytLUyaZBZbiyZYv/66qY8+caJ6ZRyffgoTEpYwZVoyO2J2EJcRR35BPp9t/QxvJ2/6B/THweZ0rUh+QT4fb/4YO2s7hnccXotP2ECmTDE3Edrbw4oVUFi2UdKszp2rvMxIDw9GnjnjvImTBFoIIYQQrcqaNdU/9r33ym7r0QMuv9zMFqmOQ4dg76LzmPA/8HX2xcbKhhOpJ4jLiOPx4Y9zz+B7Sh1vbWVNiGdI062FLnLWWfDMM+DoCP/3f9C1q2mSXUNJubnkao2PnV29h2gpUsIhhBBCiFYlIwPWri23nXG5fv0VVq2CDz4wyfO4caYRRXUT6KefNkNZ3ngDRgSN4KkRTxGZYKa0hPmElXtOmE8Y26O3sz16O032frVTp8wy/A03mN6A69aVOeTvhAQGb9rE8ezsCi9zd2QkAzdvtmCg9U8SaCGEEEK0Kk89BUOGwLJl1Tv+ootgwgRTqTBhgin5rQmlzEr2uHHmhsJTaafo6tWV1TevZlD7QeWe09e/L8dTj3PN/GvYn7C/Zm/YUObPh7PPPt0se+/eMocUAD52djhU8tvGFH9/XpAaaCGEEEKIpmtnYWXE2WdX7/hDh8DJyUwePHkSHn/c1EFXd2H466/hv/9g+nQ4mXqKtm+05Z1x7zBz0MwKz5k5aCZ9/PvQw6cH7dzaVe+NGlpRAfjIkeYxJaXMIaM9PAi0tychN5d7IiN5ODCQnmc0zz63mdU/g6xACyGEEKKVWbzYJL/+/tU7vmNH0wLZ3t70gH799Zq9X3Q0LF8O6emm37OHgwf3LLmHpQeWVniOg40DYzuPbbrJM5S9gzIjo8wh1koR6uwMwIqkJI6Xs3wfk5PDyUpKPJoiSaCFEEII0aokJ8M//0BiYu3Onz+/+uUfADffbMqF5841Le3CfMMo0AX8EPFDpedprTl77tm8seaN2gVqaWcm0OnpFR7axcmJo0OGcL6nJ/NjY1kcH8/9kZG8f/w4fqtXM2bbNgsHW78kgRZCCCFEq/Lww6bjWm3nsl16KYwaVf3jnZzg44/h3HMLz+9+KX7OfkzoOqHS85RSRCZEsiduT+0CtbQaJNBF1iYnc+vevbhYW7MzPZ2PT5wA4MKGmGhTj6QGWgghhBCtStHKc//+DfN+CxeaLm8332xe3zvkXu4dcm+1zvV19iU2o+zo7yahZALt5latBDouN5eb/f0Z4ObGkt690VqTWVCAY3VbmjQRzStaIYQQQog6+v57UwPt6dkw75eYaG4iLOceuyr5OPsQkx5T/0HVh5IJdJs21UqgL/T25pEOHdiWlkZ6fj7rUlLYlJrKMamBFkIIIYRoumJj4e+/TS10QzjvPEhKMv2ka8rX2ZfotOh6j6lenJlAl3MTYXl2pKczaPNmdqenM2TLFs7ZupUOa9daKEjLkBIOIYQQQrQq99wD33wDGzZAeLjl38/HB+bNg2HDan7u4HaDyS/IR2uNUqr+g6uLkgn0F1+Ar2+1Tuvv6sozQUH0d3Xl8+7dScvPp6ujo4WCtAzVZKfbVCA8PFxvrG3VvxBCCCFavVtvhU8+MSUVrq6Wf7+ffzaTC3/+Gc5ogdy8HTsGEyfC1q1mib1Nm8aOqN4ppTZprcv8miUr0EIIIYRoVT7+2Pw0lNRU0/EjObl2CXRWXhZLIpcUr0B7OnpydodqToGxpPbtzXzzv/4yRd6RkXDXXY0dVYOQBFoIIYQQrcrx42bRdMSIhlkRDgszC7SbNkG7Gs5FyS/Ip/+c/uyK3VVq+647dxHqE1p/QdZGSgoUFMDMmfD006ZOpZUk0HIToRBCCCFalRkz4MILzVjuhhAcDN99V7u2edZW1vx5/Z9smraJTdM28dMVP+Fo48ihpEP1HmeNbd9uCrt//hleftlMi2klZAVaCCGEEK1KSIh5DAxsmPdbv97UQI8YUbvz27m1Kx7p3ce/D2mhaVipJrAGGhYGQ4aYBtc33ghN7SZHC2oCf/pCCCGEEA3n1VdNH2gHh4Z5v7Q02LHDPNaVlbJqGskzgIcHvPeeKShfvdqUb9THh2wGmsg3IIQQQgjRMA4cMFUHWVkN836enmaYSuHU6jp7fuXz3L/k/vq5WF0kJ5s/zHHjYPduePddSEho7KgahCTQQgghhGhV7rgDLr309EhvS+vZExYsgB496ud6G05sYPmh5fVzsbo4ehSuuMKsQru5mW2pqY0bUwORBFoIIYQQrcrw4eaxKOeztF27YNYs04mjPthZ25GTn1M/F6uLokEqs2ad/sNsqPGOjUwSaCGEEEK0Kk88YWqgnZ0b5v1sbc2MEat6yrqaXAINp4eopKQ0TiwNTLpwCCGEEEJY0LBhsGRJ/V2vSSbQRSvQrSSBlhVoIYQQQohmpK1rW9q7tW/sMMpPoFtJCYesQAshhBBCNCMvjX6psUMwWnEJh6xACyGEEEKImrO2Pv28aCa6JNBCCCGEEKKp+XTzp4z5Ykxjh1F6BdrKClxdpYRDCCGEEEI0PUeSj7D04FK01qjGHJ9tc0YaeeoUODo2TiwNTFaghRBCCCGaETtrOwDyCvIaNxAnJ9MLsKj+2ckJ3nwTjhxp3LgagKxACyGEEEI0I0UJdE5+DrbWto0XiLU1/N//nZ4+GBdnphI6O8NttzVeXA1AEmghhBBCiGakKIHOzs/GmQaaBlOR5GTo1s089/IyUwmHDm3cmBqAlHAIIYQQQjQjAa4B9Avoh9a6sUOByy+Hp582z5WCK64Ae3sz6rEFkwRaCCGEEKIZuTLsSjZN24SXk1djhwLe3rBrV+ltQ4fCK680TjwNREo4hBBCCCFE7fz2W9nV5gMHYPXqxomngcgKtBBCCCFEM/L3gb/pP6c/UQlRjR0KbN0K2dmlt11yCURENEo4DUUSaCGEEEKIZiQ1O5XNJzeTmpPa2KHA7bfDgw+W3tazJ0RFwT//NE5MDUASaCGEEEKIZsTexh4wbeyahA0bSr8+5xzzeNddDR9LA5EEWgghhBCiGSnZB7pJGjUKpk+HvXshP7+xo7EISaCFEEIIIZqRJp9AA/TrB1lZ5obCFkgSaCGEEEKIZsTL0bSvu/P3O5tGL+jyhIWZx4kTW2RPaGljJ4QQQgjRjIT5htHbrzfbordRoAuwVtaNF8zcuRAUVHZ7v35wzTVw7bUmgVaqwUOzJIsm0EqpccDbgDXwidb65TP2Xwc8XPgyDbhDa73NkjEJIYQQQjR3dtZ2nBt8LtZWjZg8A9x0U/nbbW3hm28aNpYGZLESDqWUNfA+MB7oAVyjlOpxxmEHgRFa67OA54E5lopHCCGEEKIlOJR0iA0nNrDs4DK+2PYFCZkJjR1S+QoKYOFCWLy4sSOpd5asgR4IRGqtD2itc4DvgEklD9Bar9ZaJxa+XAu0t2A8QgghhBDNnpU6nb5N+b8pnPfleY0YTSWUgmnTYOnSxo6k3lmyhKMdcLTE62PAoEqOnwr8Ud4OpdQ0YBpAhw4d6is+IYQQQohmp0ObDuy4YwfWyprn/nmOn3b9RE5+TnF3jiZDKVi+HOyaWFz1wJIr0OVVi5d7G6ZSahQmgX64vP1a6zla63CtdbiPj089hiiEEEII0fz09O1JqE8oE7pMIK8gj/3x+xs7pPL16AEhIY0dRb2zZAJ9DAgs8bo9cOLMg5RSZwGfAJO01vEWjEcIIYQQokUJ8zHt4g4ktsx+y02VJUs4NgBdlFLBwHHgauDakgcopToAC4AbtNb7LBiLEEIIIUSL08uvF2mPpuFs59zYobQqFkugtdZ5SqkZwBJMG7u5WusIpdTthfs/BJ4CvIDZyvQHzNNah1sqJiGEEEKIlsTGyoYC6wLWHltLO9d2BLYJrPokUWeqyU6wqUB4eLjeuHFjY4chhBBCCNEkxGfE4/2qN6+OfZUHhj7Q2OG0KEqpTeUt7soobyGEEEKIZszLyQs/Zz+eWv4Uvq/64vOqD08se6LC448mHyW/IL8BI2x5ZJS3EEIIIUQz9/a4t1l5eCUACsXUvlPLHHMw8SAfbPyAV1e/ysyBM3ln/DsNHWaLIQm0EEIIIUQzd1XPq7iq51WVHrP++HpeXf0qAKHeoWitKbwHTdSQJNBCCCGEEC1IdFo0H236iPZu7bky7EoSMhPYE7eHPyJPz6u7c9GduNm70d27O/3b9m/EaJsnSaCFEEIIIVqQlOwUnl7xNABRCVH4u/hz1+K7yhx3/c/XA3Do7kN0dO/YoDE2d5JACyGEEEK0IF28urDrzl0M/2w4sRmxzBg4o3iVOcg9CK01CZkJbDixgam/TGXrqa2SQNeQJNBCCCGEEC1MqE8o7g7ufLz5Y7p6dS3T3q6dWzs6unfknXVyI2FtSAIthBBCCNECtXNrR1RiFBtPlD8/w83eja23bwXgxv+7kXydz+B2g5k+cHoDRtk8SQIthBBCCNECrbxxJe3faI+zbdVjvtccW0NeQR4eDh4NEFnzJwm0EEIIIUQLlZ6bjrNd1Qn03hl7GyCalkMmEQohhBBCtECz/p1FUlYSTrZOjR1KiyMJtBBCCCFEC7Tl1BYA/Jz9GjmSlkcSaCGEEEKIFsjVzpUAlwDuHXJvY4fS4kgCLYQQQgjRAjnZOpGRm9HYYbRIkkALIYQQQrRAiVmJJGcns/zg8sYOpcWRBFoIIYQQogWyt7YHIDYjtpEjaXkkgRZCCCGEaIFu6nsTAG3s2zRyJC2PJNBCCCGEEC1Qek46gLSxswBJoIUQQgghWqDf9/8OQFxGXCNH0vLIJEIhhBBCiBbogaEPEJMew7iQcY0dSosjCbQQQgghRAvU3q0931z2TWOH0SJJCYcQQgghhBA1IAm0EEIIIYQQNSAJtBBCCCGEEDUgCbQQQgghhBA1IAm0EEIIIYQQNSAJtBBCCCGEEDUgCbQQQgghhBA1IAm0EEIIIYQQNSAJtBBCCCGEEDUgCbQQQgghhBA1IAm0EEIIIYQQNSAJtBBCCCGEEDUgCbQQQgghhBA1IAm0EEIIIYQQNSAJtBBCCCGEEDUgCbQQQgghhBA1IAm0EEIIIYQQNSAJtBBCCCGEEDWgtNaNHUONKKVigcON8NbeQFwjvK9oWPI9tx7yXbcO8j23DvI9tx4N/V131Fr7nLmx2SXQjUUptVFrHd7YcQjLku+59ZDvunWQ77l1kO+59Wgq37WUcAghhBBCCFEDkkALIYQQQghRA5JAV9+cxg5ANAj5nlsP+a5bB/meWwf5nluPJvFdSw20EEIIIYQQNSAr0EIIIYQQQtSAJNBVUEqNU0rtVUpFKqUeaex4RN0opQKVUsuVUruVUhFKqbsLt3sqpf5SSu0vfPQocc6jhd//XqXU+Y0XvagppZS1UmqLUuq3wtfyPbcwSil3pdRPSqk9hf9eD5HvuWVSSt1b+N/tnUqpb5VSDvJdN39KqblKqRil1M4S22r8vSql+iuldhTue0cppSwZtyTQlVBKWQPvA+OBHsA1SqkejRuVqKM84H6tdSgwGJhe+J0+AizVWncBlha+pnDf1UAYMA6YXfjPhWge7gZ2l3gt33PL8zawWGvdHeiN+b7le25hlFLtgLuAcK11T8Aa813Kd938fY75jkqqzff6ATAN6FL4c+Y165Uk0JUbCERqrQ9orXOA74BJjRyTqAOt9Umt9ebC56mY/9m2w3yv8woPmwdcXPh8EvCd1jpba30QiMT8cyGaOKVUe2AC8EmJzfI9tyBKKTfgHOBTAK11jtY6CfmeWyobwFEpZQM4ASeQ77rZ01r/AyScsblG36tSKgBw01qv0ebmvi9KnGMRkkBXrh1wtMTrY4XbRAuglAoC+gLrAD+t9UkwSTbgW3iY/DPQfL0FPAQUlNgm33PL0gmIBT4rLNX5RCnljHzPLY7W+jjwGnAEOAkka63/RL7rlqqm32u7wudnbrcYSaArV179jLQtaQGUUi7AfOAerXVKZYeWs03+GWjilFIXAjFa603VPaWcbfI9N302QD/gA611XyCdwr/qrYB8z81UYQ3sJCAYaAs4K6Wur+yUcrbJd938VfS9Nvj3LQl05Y4BgSVet8f8lZFoxpRStpjk+Wut9YLCzdGFfwVE4WNM4Xb5Z6B5GgZcpJQ6hCm9Olcp9RXyPbc0x4BjWut1ha9/wiTU8j23PGOAg1rrWK11LrAAGIp81y1VTb/XY4XPz9xuMZJAV24D0EUpFayUssMUrv/SyDGJOii8K/dTYLfW+o0Su34BphQ+nwIsLLH9aqWUvVIqGHNjwvqGilfUjtb6Ua11e611EObf22Va6+uR77lF0VqfAo4qpboVbhoN7EK+55boCDBYKeVU+N/x0Zh7WOS7bplq9L0WlnmkKqUGF/7zMbnEORZhY8mLN3da6zyl1AxgCeaO37la64hGDkvUzTDgBmCHUmpr4bbHgJeBH5RSUzH/ob4CQGsdoZT6AfM/5TxgutY6v8GjFvVFvueWZybwdeEixwHgJszikHzPLYjWep1S6idgM+a724KZSOeCfNfNmlLqW2Ak4K2UOgY8Te3+W30HpqOHI/BH4Y/l4pZJhEIIIYQQQlSflHAIIYQQQghRA5JACyGEEEIIUQOSQAshhBBCCFEDkkALIYQQQghRA5JACyGEEEIIUQOSQAshRB0ppfKVUluVUhFKqW1KqfuUUlaF+8KVUu/U4porlFLh9R9tw1FKfa6Uuryx4xBCiPomfaCFEKLuMrXWfQCUUr7AN0Ab4Gmt9UZgY0MGo5Sylp63QghhObICLYQQ9UhrHQNMA2YoY6RS6jcApdSIwpXqrUqpLUop18LtDymldhSuXr9c4nJXKKXWK6X2KaWGFx4bpJRapZTaXPgztHD7SKXUcqXUN5hBQVZKqdmFq+K/KaUWFa0GK6X6K6VWKqU2KaWWFI3MLUkp5aeU+rkwpm1KqaFKqeeVUneXOOZFpdRdVXyGomPLfU+l1F1KqV1Kqe1Kqe/q51sQQgjLkhVoIYSoZ1rrA4UlHL5n7HoAMznrP6WUC5CllBoPXAwM0lpnKKU8Sxxvo7UeqJS6ADOdawwQA4zVWmcppboA3wJFpR4DgZ5a64OFyXIQ0Kswjt3AXKWULfAuMElrHauUugp4Ebj5jFjfAVZqrS9RSlljJr6dABYAbxd+vquBgVV8Bqp4z0eAYK11tlLKvbp/xkII0ZgkgRZCCMtQ5Wz7D3hDKfU1sEBrfUwpNQb4TGudAaC1Tihx/ILCx02YZBjAFnhPKdUHyAe6ljh+vdb6YOHzs4EftdYFwCml1PLC7d2AnsBfSikAa+BkObGeC0wujCkfSAaSlVLxSqm+gB+wRWsdX8VnqOo9t2NGcf8f8H/lxCGEEE2OJNBCCFHPlFKdMMltDBBatF1r/bJS6nfgAmBtYeKpAF3BpbILH/M5/d/re4FooDemDC+rxPHpJcOoKDwgQms9pNofqLRPgBsBf2BuiWtW9Bmqes8JwDnARcCTSqkwrXVeLWMTQogGITXQQghRj5RSPsCHwHtaa33Gvs5a6x1a61mYGwu7xnHXwgAAAY5JREFUA38CNyulnAqP8TzzmmdoA5wsXFm+AbOaW55/gcsKa6H9gJGF2/cCPkqpIYXvZ6uUCivn/KXAHYXHWCul3Aq3/wyMAwYASwq3VfUZyn3PwjKQQK318v9v5w5VIgqiOIx/xyAIgoJvYbKbxGfYJzCZNBmsdhWDwap1sSxq2yiKiEEN6wsYVzAtwjHcK+giF4bdsK7fL17u3JlJ8+dw7gA7wCJVq4gkTTQr0JI0urmIeKBqr/gAToH9X97bjog1qoryM3BZ9/6uAHcRMQAugN2GuY6BdkS0gC4/q87ftYF14BHoATfAW2YO6v7oo4hYoDoHDoGnofFbwElEbNTr3QSu6/FdoP9100dmXjXtoWHOHnBWPwvgIDP7DXuXpIkQQwUSSdKUiIj5zHyPiCXgFljNzNcRvzkD3AOtzHwZxzol6a+xAi1J06tT32wxC+yNITwvAx3g3PAs6T+zAi1JkiQV8CdCSZIkqYABWpIkSSpggJYkSZIKGKAlSZKkAgZoSZIkqYABWpIkSSrwCfhlX+THFU2qAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Rated_Capacity = 1.1\n",
+    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "color_list = ['b:', 'g--', 'r-.', 'c:']\n",
+    "for name,color in zip(Battary_list, color_list):\n",
+    "    battery = Battery[name]\n",
+    "    ax.plot(battery['cycle'], battery['capacity'], color, label='Battery_'+name)\n",
+    "#plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "4164abd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'State of Health')"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "battery = Battery['CS2_35']\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.scatter(battery['cycle'], battery['SoH'], c=battery['resistance'], s=10)\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label('Internal Resistance (Ohm)', fontsize=14, rotation=-90, labelpad=20)\n",
+    "plt.xlabel('Number of Cycles', fontsize=14)\n",
+    "plt.ylabel('State of Health', fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a08b18fe",
+   "metadata": {},
+   "source": [
+    "### 各项指标 v.s. 充放电周期"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "394016c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x648 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "battery = Battery['CS2_35']\n",
+    "plt.figure(figsize=(12,9))\n",
+    "names = ['capacity', 'resistance', 'CCCT', 'CVCT']\n",
+    "for i in range(4):\n",
+    "    plt.subplot(2, 2, i+1)\n",
+    "    plt.scatter(battery['cycle'], battery[names[i]], s=10)\n",
+    "    plt.xlabel('Number of Cycles', fontsize=14)\n",
+    "    plt.ylabel(names[i], fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "356ad021",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/MLP.ipynb b/MLP.ipynb
index 712d2f8..3d20306 100644
--- a/MLP.ipynb
+++ b/MLP.ipynb
@@ -234,13 +234,7 @@
       "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
       "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
       "Load Dataset CS2_38 ...\n",
-      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
       "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
       "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
       "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
@@ -584,375 +578,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sample size: 2881\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1863 | RMSE:0.2072 | RE:0.6413\n",
-      "epoch:199 | loss:0.0002 | MAE:0.0550 | RMSE:0.0661 | RE:0.1032\n",
-      "epoch:299 | loss:0.0002 | MAE:0.0537 | RMSE:0.0643 | RE:0.1000\n",
-      "epoch:399 | loss:0.0002 | MAE:0.0535 | RMSE:0.0641 | RE:0.0984\n",
-      "epoch:499 | loss:0.0002 | MAE:0.0533 | RMSE:0.0638 | RE:0.0984\n",
-      "epoch:599 | loss:0.0002 | MAE:0.0530 | RMSE:0.0634 | RE:0.0984\n",
-      "epoch:699 | loss:0.0002 | MAE:0.0509 | RMSE:0.0605 | RE:0.0921\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0478 | RMSE:0.0565 | RE:0.0841\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0476 | RMSE:0.0561 | RE:0.0825\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0490 | RMSE:0.0580 | RE:0.0873\n",
-      "sample size: 2827\n",
-      "epoch:99 | loss:0.0003 | MAE:0.1854 | RMSE:0.2368 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.0342 | RMSE:0.0416 | RE:0.0772\n",
-      "epoch:299 | loss:0.0002 | MAE:0.0347 | RMSE:0.0417 | RE:0.0740\n",
-      "epoch:399 | loss:0.0002 | MAE:0.0334 | RMSE:0.0404 | RE:0.0724\n",
-      "epoch:499 | loss:0.0002 | MAE:0.0324 | RMSE:0.0395 | RE:0.0693\n",
-      "epoch:599 | loss:0.0002 | MAE:0.0349 | RMSE:0.0428 | RE:0.0787\n",
-      "epoch:699 | loss:0.0001 | MAE:0.0305 | RMSE:0.0376 | RE:0.0661\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0320 | RMSE:0.0396 | RE:0.0724\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0298 | RMSE:0.0370 | RE:0.0661\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0272 | RMSE:0.0338 | RE:0.0504\n",
-      "sample size: 2791\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1647 | RMSE:0.1857 | RE:0.5836\n",
-      "epoch:199 | loss:0.0002 | MAE:0.1432 | RMSE:0.1825 | RE:0.2620\n",
-      "epoch:299 | loss:0.0002 | MAE:0.1395 | RMSE:0.1779 | RE:0.2564\n",
-      "epoch:399 | loss:0.0002 | MAE:0.1391 | RMSE:0.1777 | RE:0.2550\n",
-      "epoch:499 | loss:0.0002 | MAE:0.1388 | RMSE:0.1776 | RE:0.2550\n",
-      "epoch:599 | loss:0.0002 | MAE:0.1383 | RMSE:0.1774 | RE:0.2535\n",
-      "epoch:699 | loss:0.0002 | MAE:0.1378 | RMSE:0.1770 | RE:0.2521\n",
-      "epoch:799 | loss:0.0002 | MAE:0.1371 | RMSE:0.1764 | RE:0.2507\n",
-      "epoch:899 | loss:0.0002 | MAE:0.1364 | RMSE:0.1759 | RE:0.2479\n",
-      "epoch:999 | loss:0.0002 | MAE:0.1357 | RMSE:0.1753 | RE:0.2465\n",
-      "sample size: 2767\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1707 | RMSE:0.1884 | RE:0.6212\n",
-      "epoch:199 | loss:0.0002 | MAE:0.1607 | RMSE:0.2183 | RE:0.2677\n",
-      "epoch:299 | loss:0.0002 | MAE:0.1562 | RMSE:0.2126 | RE:0.2610\n",
-      "epoch:399 | loss:0.0002 | MAE:0.1556 | RMSE:0.2120 | RE:0.2610\n",
-      "epoch:499 | loss:0.0002 | MAE:0.1551 | RMSE:0.2115 | RE:0.2597\n",
-      "epoch:599 | loss:0.0002 | MAE:0.1546 | RMSE:0.2108 | RE:0.2584\n",
-      "epoch:699 | loss:0.0002 | MAE:0.1540 | RMSE:0.2102 | RE:0.2584\n",
-      "epoch:799 | loss:0.0002 | MAE:0.1534 | RMSE:0.2096 | RE:0.2570\n",
-      "epoch:899 | loss:0.0001 | MAE:0.1527 | RMSE:0.2089 | RE:0.2557\n",
-      "epoch:999 | loss:0.0001 | MAE:0.1519 | RMSE:0.2080 | RE:0.2544\n",
-      "------------------------------------------------------------------\n",
-      "sample size: 2881\n",
-      "epoch:99 | loss:0.0014 | MAE:0.1507 | RMSE:0.2060 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.1481 | RMSE:0.1821 | RE:1.0000\n",
-      "epoch:299 | loss:0.0002 | MAE:0.0852 | RMSE:0.1033 | RE:0.2333\n",
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-     "text": [
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-      "epoch:999 | loss:0.0001 | MAE:0.0740 | RMSE:0.0912 | RE:0.1416\n",
-      "sample size: 2767\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1478 | RMSE:0.1649 | RE:0.1914\n",
-      "epoch:199 | loss:0.0001 | MAE:0.1590 | RMSE:0.2266 | RE:1.0000\n",
-      "epoch:299 | loss:0.0001 | MAE:0.1120 | RMSE:0.1553 | RE:0.2289\n",
-      "epoch:399 | loss:0.0001 | MAE:0.0749 | RMSE:0.0995 | RE:0.1098\n",
-      "epoch:499 | loss:0.0001 | MAE:0.0663 | RMSE:0.0876 | RE:0.0884\n",
-      "epoch:599 | loss:0.0001 | MAE:0.0662 | RMSE:0.0877 | RE:0.0884\n",
-      "epoch:699 | loss:0.0001 | MAE:0.0716 | RMSE:0.0952 | RE:0.1004\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0635 | RMSE:0.0838 | RE:0.0803\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0784 | RMSE:0.1054 | RE:0.1191\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0766 | RMSE:0.1027 | RE:0.1138\n",
-      "------------------------------------------------------------------\n",
-      "sample size: 2881\n",
-      "epoch:99 | loss:0.0002 | MAE:0.3086 | RMSE:0.3816 | RE:1.0000\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch:199 | loss:0.0001 | MAE:0.1248 | RMSE:0.2174 | RE:1.0000\n",
-      "epoch:299 | loss:0.0001 | MAE:0.0830 | RMSE:0.1367 | RE:0.2508\n",
-      "sample size: 2827\n",
-      "epoch:99 | loss:0.0002 | MAE:0.7786 | RMSE:0.9941 | RE:1.0000\n",
-      "epoch:199 | loss:0.0001 | MAE:0.1441 | RMSE:0.2373 | RE:1.0000\n",
-      "epoch:299 | loss:0.0001 | MAE:0.1336 | RMSE:0.2207 | RE:1.0000\n",
-      "epoch:399 | loss:0.0001 | MAE:0.0755 | RMSE:0.1110 | RE:0.1795\n",
-      "epoch:499 | loss:0.0001 | MAE:0.0590 | RMSE:0.0832 | RE:0.1276\n",
-      "sample size: 2791\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1912 | RMSE:0.2359 | RE:0.4490\n",
-      "epoch:199 | loss:0.0002 | MAE:0.0209 | RMSE:0.0303 | RE:0.0127\n",
-      "epoch:299 | loss:0.0002 | MAE:0.0617 | RMSE:0.0888 | RE:0.0963\n",
-      "sample size: 2767\n",
-      "epoch:99 | loss:0.0002 | MAE:0.3071 | RMSE:0.3759 | RE:1.0000\n",
-      "epoch:199 | loss:0.0001 | MAE:0.0814 | RMSE:0.1324 | RE:0.1928\n",
-      "epoch:299 | loss:0.0001 | MAE:0.0224 | RMSE:0.0347 | RE:0.0241\n",
-      "------------------------------------------------------------------\n",
-      "sample size: 2881\n",
-      "epoch:99 | loss:0.0011 | MAE:0.1557 | RMSE:0.2035 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.0536 | RMSE:0.0610 | RE:0.0952\n",
-      "epoch:299 | loss:0.0001 | MAE:0.1097 | RMSE:0.1695 | RE:0.3571\n",
-      "epoch:399 | loss:0.0001 | MAE:0.0634 | RMSE:0.1037 | RE:0.1492\n",
-      "epoch:499 | loss:0.0001 | MAE:0.0634 | RMSE:0.1035 | RE:0.1492\n",
-      "epoch:599 | loss:0.0001 | MAE:0.0645 | RMSE:0.1046 | RE:0.1524\n",
-      "epoch:699 | loss:0.0001 | MAE:0.0735 | RMSE:0.1168 | RE:0.1841\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0814 | RMSE:0.1273 | RE:0.2143\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0906 | RMSE:0.1396 | RE:0.2492\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0904 | RMSE:0.1381 | RE:0.2444\n",
-      "sample size: 2827\n",
-      "epoch:99 | loss:0.0017 | MAE:0.1949 | RMSE:0.2679 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.1490 | RMSE:0.2156 | RE:0.3858\n",
-      "epoch:299 | loss:0.0001 | MAE:0.0657 | RMSE:0.0971 | RE:0.0646\n",
-      "epoch:399 | loss:0.0001 | MAE:0.0727 | RMSE:0.1043 | RE:0.0803\n",
-      "epoch:499 | loss:0.0001 | MAE:0.0819 | RMSE:0.1145 | RE:0.1024\n",
-      "epoch:599 | loss:0.0001 | MAE:0.0909 | RMSE:0.1247 | RE:0.1244\n",
-      "epoch:699 | loss:0.0001 | MAE:0.0994 | RMSE:0.1347 | RE:0.1465\n",
-      "epoch:799 | loss:0.0001 | MAE:0.1074 | RMSE:0.1443 | RE:0.1669\n",
-      "epoch:899 | loss:0.0001 | MAE:0.1149 | RMSE:0.1534 | RE:0.1858\n",
-      "epoch:999 | loss:0.0001 | MAE:0.1221 | RMSE:0.1622 | RE:0.2063\n",
-      "sample size: 2791\n",
-      "epoch:99 | loss:0.0008 | MAE:0.1472 | RMSE:0.2358 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.1374 | RMSE:0.1718 | RE:0.2805\n",
-      "epoch:299 | loss:0.0002 | MAE:0.1110 | RMSE:0.1396 | RE:0.2309\n",
-      "epoch:399 | loss:0.0002 | MAE:0.1099 | RMSE:0.1385 | RE:0.2280\n",
-      "epoch:499 | loss:0.0002 | MAE:0.1085 | RMSE:0.1370 | RE:0.2252\n",
-      "epoch:599 | loss:0.0001 | MAE:0.1068 | RMSE:0.1351 | RE:0.2195\n",
-      "epoch:699 | loss:0.0001 | MAE:0.1045 | RMSE:0.1324 | RE:0.2139\n",
-      "epoch:799 | loss:0.0001 | MAE:0.1015 | RMSE:0.1290 | RE:0.2068\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0981 | RMSE:0.1248 | RE:0.1983\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0949 | RMSE:0.1208 | RE:0.1898\n",
-      "sample size: 2767\n",
-      "epoch:99 | loss:0.0010 | MAE:0.2005 | RMSE:0.2195 | RE:0.9076\n",
-      "epoch:199 | loss:0.0002 | MAE:0.0685 | RMSE:0.0815 | RE:0.1392\n",
-      "epoch:299 | loss:0.0002 | MAE:0.0858 | RMSE:0.1063 | RE:0.1620\n",
-      "epoch:399 | loss:0.0001 | MAE:0.0833 | RMSE:0.1027 | RE:0.1566\n",
-      "epoch:499 | loss:0.0001 | MAE:0.0796 | RMSE:0.0974 | RE:0.1486\n",
-      "epoch:599 | loss:0.0001 | MAE:0.0742 | RMSE:0.0895 | RE:0.1365\n",
-      "epoch:699 | loss:0.0001 | MAE:0.0649 | RMSE:0.0767 | RE:0.1151\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0541 | RMSE:0.0627 | RE:0.0910\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0436 | RMSE:0.0513 | RE:0.0643\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0382 | RMSE:0.0471 | RE:0.0415\n",
-      "------------------------------------------------------------------\n",
-      "RE: mean: 0.2003 | std: 0.1069\n",
-      "MAE: mean: 0.0943 | std: 0.0332\n",
-      "RMSE: mean: 0.1253 | std: 0.0391\n",
-      "------------------------------------------------------------------\n",
-      "------------------------------------------------------------------\n"
+     "ename": "NameError",
+     "evalue": "name 'tain' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-1fc6f80d91bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mMAE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRMSE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     re_list, mae_list, rmse_list, _ = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n\u001b[0m\u001b[1;32m     12\u001b[0m                                            window_size=window_size, EPOCH=EPOCH, seed=seed)\n\u001b[1;32m     13\u001b[0m     \u001b[0mRE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'tain' is not defined"
      ]
     }
    ],
@@ -990,106 +627,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sample size: 2881\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1197 | RMSE:0.1990 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.0541 | RMSE:0.0680 | RE:0.1238\n",
-      "epoch:299 | loss:0.0002 | MAE:0.0357 | RMSE:0.0496 | RE:0.0429\n",
-      "epoch:399 | loss:0.0002 | MAE:0.0332 | RMSE:0.0510 | RE:0.0143\n",
-      "epoch:499 | loss:0.0002 | MAE:0.0739 | RMSE:0.1258 | RE:0.2143\n",
-      "epoch:599 | loss:0.0002 | MAE:0.0897 | RMSE:0.1487 | RE:0.2857\n",
-      "epoch:699 | loss:0.0001 | MAE:0.0833 | RMSE:0.1371 | RE:0.2476\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0854 | RMSE:0.1396 | RE:0.2540\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0853 | RMSE:0.1385 | RE:0.2508\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0922 | RMSE:0.1486 | RE:0.2810\n",
-      "sample size: 2827\n",
-      "epoch:99 | loss:0.0004 | MAE:0.1852 | RMSE:0.2481 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.0731 | RMSE:0.1070 | RE:0.0819\n",
-      "epoch:299 | loss:0.0002 | MAE:0.0976 | RMSE:0.1334 | RE:0.1417\n",
-      "epoch:399 | loss:0.0002 | MAE:0.1093 | RMSE:0.1470 | RE:0.1717\n",
-      "epoch:499 | loss:0.0002 | MAE:0.1161 | RMSE:0.1552 | RE:0.1906\n",
-      "epoch:599 | loss:0.0002 | MAE:0.1087 | RMSE:0.1464 | RE:0.1717\n",
-      "epoch:699 | loss:0.0001 | MAE:0.0754 | RMSE:0.1068 | RE:0.0961\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0645 | RMSE:0.0944 | RE:0.0740\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0619 | RMSE:0.0915 | RE:0.0693\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0592 | RMSE:0.0883 | RE:0.0646\n",
-      "sample size: 2791\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1224 | RMSE:0.2014 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.1048 | RMSE:0.1253 | RE:0.2635\n",
-      "epoch:299 | loss:0.0002 | MAE:0.1094 | RMSE:0.1366 | RE:0.2337\n",
-      "epoch:399 | loss:0.0002 | MAE:0.1007 | RMSE:0.1289 | RE:0.2096\n",
-      "epoch:499 | loss:0.0002 | MAE:0.0942 | RMSE:0.1230 | RE:0.1926\n",
-      "epoch:599 | loss:0.0002 | MAE:0.0828 | RMSE:0.1111 | RE:0.1700\n",
-      "epoch:699 | loss:0.0002 | MAE:0.0680 | RMSE:0.0948 | RE:0.1360\n",
-      "epoch:799 | loss:0.0002 | MAE:0.0414 | RMSE:0.0576 | RE:0.0737\n",
-      "epoch:899 | loss:0.0002 | MAE:0.0186 | RMSE:0.0237 | RE:0.0071\n",
-      "epoch:999 | loss:0.0002 | MAE:0.0499 | RMSE:0.0621 | RE:0.0850\n",
-      "sample size: 2767\n",
-      "epoch:99 | loss:0.0002 | MAE:0.1146 | RMSE:0.1920 | RE:1.0000\n",
-      "epoch:199 | loss:0.0002 | MAE:0.1503 | RMSE:0.1948 | RE:0.2918\n",
-      "epoch:299 | loss:0.0002 | MAE:0.1301 | RMSE:0.1751 | RE:0.2423\n",
-      "epoch:399 | loss:0.0002 | MAE:0.0996 | RMSE:0.1374 | RE:0.1928\n",
-      "epoch:499 | loss:0.0002 | MAE:0.0848 | RMSE:0.1194 | RE:0.1660\n",
-      "epoch:599 | loss:0.0002 | MAE:0.0711 | RMSE:0.1014 | RE:0.1419\n",
-      "epoch:699 | loss:0.0002 | MAE:0.0660 | RMSE:0.0948 | RE:0.1312\n",
-      "epoch:799 | loss:0.0001 | MAE:0.0632 | RMSE:0.0909 | RE:0.1245\n",
-      "epoch:899 | loss:0.0001 | MAE:0.0584 | RMSE:0.0838 | RE:0.1138\n",
-      "epoch:999 | loss:0.0001 | MAE:0.0558 | RMSE:0.0787 | RE:0.1044\n"
+     "ename": "NameError",
+     "evalue": "name 'tain' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-aedeb223caea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m _, _, _, result_list = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n\u001b[0m\u001b[1;32m      3\u001b[0m                             window_size=window_size, EPOCH=EPOCH, seed=seed)\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBattary_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'tain' is not defined"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1111,6 +661,13 @@
     "    ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
     "    plt.legend()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1130,7 +687,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/MLP_ed.ipynb b/MLP_ed.ipynb
new file mode 100644
index 0000000..51b6c37
--- /dev/null
+++ b/MLP_ed.ipynb
@@ -0,0 +1,1172 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0941abb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "562529b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install torch\n",
+    "# !pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3c74c5bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        idx = idx.astype(int)\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e35fd3",
+   "metadata": {},
+   "source": [
+    "#### Cycled at constant current of 1°C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "35c9e5e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load Dataset CS2_35 ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_8_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_15_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_22_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_10_29_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_35/CS2_35_2_4_11.xlsx ...\n",
+      "Load Dataset CS2_36 ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_9_30_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_36/CS2_36_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_37 ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_37/CS2_37_2_3_11.xlsx ...\n",
+      "Load Dataset CS2_38 ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_17_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_18_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_19_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_8_30_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_7_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_9_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_04_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_05_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_14_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_21_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_10_28_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_01_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_08_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_15_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_22_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_11_24_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_06_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_13_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_20_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_12_23_10.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_18_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_24_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_1_28_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_10_11.xlsx ...\n",
+      "Load dataset/CS2_38/CS2_38_2_4_11.xlsx ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "Battary_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
+    "\n",
+    "dir_path = 'dataset/'\n",
+    "Battery = {}\n",
+    "for name in Battary_list:\n",
+    "    print('Load Dataset ' + name + ' ...')\n",
+    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
+    "    dates = []\n",
+    "    for p in path:\n",
+    "        df = pd.read_excel(p, sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        dates.append(df['Date_Time'][0])\n",
+    "    idx = np.argsort(dates)\n",
+    "    path_sorted = np.array(path)[idx]\n",
+    "    \n",
+    "    count = 0\n",
+    "    discharge_capacities = []\n",
+    "    health_indicator = []\n",
+    "    internal_resistance = []\n",
+    "    CCCT = []\n",
+    "    CVCT = []\n",
+    "    for p in path_sorted:\n",
+    "        df = pd.read_excel(p,sheet_name=1)\n",
+    "        print('Load ' + str(p) + ' ...')\n",
+    "        cycles = list(set(df['Cycle_Index']))\n",
+    "        for c in cycles:\n",
+    "            df_lim = df[df['Cycle_Index'] == c]\n",
+    "            #Charging\n",
+    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
+    "            c_v = df_c['Voltage(V)']\n",
+    "            c_c = df_c['Current(A)']\n",
+    "            c_t = df_c['Test_Time(s)']\n",
+    "            #CC or CV\n",
+    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
+    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
+    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
+    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
+    "\n",
+    "            #Discharging\n",
+    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
+    "            d_v = df_d['Voltage(V)']\n",
+    "            d_c = df_d['Current(A)']\n",
+    "            d_t = df_d['Test_Time(s)']\n",
+    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
+    "\n",
+    "            if(len(list(d_c)) != 0):\n",
+    "                time_diff = np.diff(list(d_t))\n",
+    "                d_c = np.array(list(d_c))[1:]\n",
+    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
+    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
+    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
+    "\n",
+    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
+    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
+    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
+    "                health_indicator.append(-1 * (end - start))\n",
+    "\n",
+    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
+    "                count += 1\n",
+    "\n",
+    "    discharge_capacities = np.array(discharge_capacities)\n",
+    "    health_indicator = np.array(health_indicator)\n",
+    "    internal_resistance = np.array(internal_resistance)\n",
+    "    CCCT = np.array(CCCT)\n",
+    "    CVCT = np.array(CVCT)\n",
+    "    \n",
+    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
+    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
+    "                              'capacity':discharge_capacities[idx],\n",
+    "                              'SoH':health_indicator[idx],\n",
+    "                              'resistance':internal_resistance[idx],\n",
+    "                              'CCCT':CCCT[idx],\n",
+    "                              'CVCT':CVCT[idx]})\n",
+    "    Battery[name] = df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "636d37c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fbaf40061f0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Rated_Capacity = 1.1\n",
+    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "color_list = ['b:', 'g--', 'r-.', 'c.']\n",
+    "for name,color in zip(Battary_list, color_list):\n",
+    "    df_result = Battery[name]\n",
+    "    ax.plot(df_result['cycle'], df_result['capacity'], color, label='Battery_'+name)\n",
+    "#plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90009950",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_sequences(text, window_size):\n",
+    "    #text:list of capacity\n",
+    "    x, y = [],[]\n",
+    "    for i in range(len(text) - window_size):\n",
+    "        sequence = text[i:i+window_size]\n",
+    "        target = text[i+1:i+1+window_size]\n",
+    "\n",
+    "        x.append(sequence)\n",
+    "        y.append(target)\n",
+    "\n",
+    "    return np.array(x), np.array(y)\n",
+    "\n",
+    "\n",
+    "# 留一评估：一组数据为测试集，其他所有数据全部拿来训练\n",
+    "def get_train_test(data_dict, name, window_size=8, train_ratio=0.):\n",
+    "    data_sequence=data_dict[name]['capacity']\n",
+    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
+    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
+    "    for k, v in data_dict.items():\n",
+    "        if k != name:\n",
+    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
+    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
+    "            \n",
+    "    return train_x, train_y, list(train_data), list(test_data)\n",
+    "\n",
+    "\n",
+    "def evaluation(y_test, y_predict):\n",
+    "    mae = mean_absolute_error(y_test, y_predict)\n",
+    "    mse = mean_squared_error(y_test, y_predict)\n",
+    "    rmse = sqrt(mean_squared_error(y_test, y_predict))\n",
+    "    return mae, rmse\n",
+    "\n",
+    "\n",
+    "def relative_error(y_test, y_predict, threshold):\n",
+    "    true_re, pred_re = len(y_test), 0\n",
+    "    for i in range(len(y_test)-1):\n",
+    "        if y_test[i] <= threshold >= y_test[i+1]:\n",
+    "            true_re = i - 1\n",
+    "            break\n",
+    "    for i in range(len(y_predict)-1):\n",
+    "        if y_predict[i] <= threshold:\n",
+    "            pred_re = i - 1\n",
+    "            break\n",
+    "    return abs(true_re - pred_re)/true_re    \n",
+    "    \n",
+    "    \n",
+    "def setup_seed(seed):\n",
+    "    np.random.seed(seed)  # Numpy module.\n",
+    "    random.seed(seed)  # Python random module.\n",
+    "    os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现。\n",
+    "    torch.manual_seed(seed) # 为CPU设置随机种子\n",
+    "    if torch.cuda.is_available():\n",
+    "        torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子\n",
+    "        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU，为所有GPU设置随机种子\n",
+    "        torch.backends.cudnn.benchmark = False\n",
+    "        torch.backends.cudnn.deterministic = True\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4cebcebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Net(nn.Module):\n",
+    "    def __init__(self, feature_size=8, hidden_size=[16, 8]):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.feature_size, self.hidden_size = feature_size, hidden_size\n",
+    "        self.layer0 = nn.Linear(self.feature_size, self.hidden_size[0])\n",
+    "        self.layers = [nn.Sequential(nn.Linear(self.hidden_size[i], self.hidden_size[i+1]), nn.ReLU()) \n",
+    "                       for i in range(len(self.hidden_size) - 1)]\n",
+    "        self.linear = nn.Linear(self.hidden_size[-1], 1)\n",
+    " \n",
+    "    def forward(self, x):\n",
+    "        out = self.layer0(x)\n",
+    "        for layer in self.layers:\n",
+    "            out = layer(out)\n",
+    "        out = self.linear(out) \n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8c0fd57",
+   "metadata": {},
+   "source": [
+    "#### 留一评估"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f79300c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tain(LR=0.01, feature_size=8, hidden_size=[16,8], weight_decay=0.0, window_size=8, EPOCH=1000, seed=0):\n",
+    "    mae_list, rmse_list, re_list = [], [], []\n",
+    "    result_list = []\n",
+    "    for i in range(4):\n",
+    "        name = Battary_list[i]\n",
+    "        train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size)\n",
+    "        train_size = len(train_x)\n",
+    "        print('sample size: {}'.format(train_size))\n",
+    "\n",
+    "        setup_seed(seed)\n",
+    "        model = Net(feature_size=feature_size, hidden_size=hidden_size)\n",
+    "        if torch.cuda.is_available():\n",
+    "            model = model.cuda()\n",
+    "\n",
+    "        optimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=weight_decay)\n",
+    "        criterion = nn.MSELoss()\n",
+    "\n",
+    "        test_x = train_data.copy()\n",
+    "        loss_list, y_ = [0], []\n",
+    "        for epoch in range(EPOCH):\n",
+    "            X = np.reshape(train_x/Rated_Capacity, (-1, feature_size)).astype(np.float32)\n",
+    "            y = np.reshape(train_y[:,-1]/Rated_Capacity,(-1,1)).astype(np.float32)\n",
+    "\n",
+    "            X, y = torch.from_numpy(X), torch.from_numpy(y)\n",
+    "            output= model(X)\n",
+    "            loss = criterion(output, y)\n",
+    "            optimizer.zero_grad()              # clear gradients for this training step\n",
+    "            loss.backward()                    # backpropagation, compute gradients\n",
+    "            optimizer.step()                   # apply gradients\n",
+    "\n",
+    "            if (epoch + 1)%100 == 0:\n",
+    "                test_x = train_data.copy() #每100次重新预测一次\n",
+    "                point_list = []\n",
+    "                while (len(test_x) - len(train_data)) < len(test_data):\n",
+    "                    x = np.reshape(np.array(test_x[-feature_size:])/Rated_Capacity, (-1, feature_size)).astype(np.float32)\n",
+    "                    x = torch.from_numpy(x)\n",
+    "                    pred = model(x) # 测试集 模型预测#pred shape为(batch_size=1, feature_size=1)\n",
+    "                    next_point = pred.data.numpy()[0,0] * Rated_Capacity\n",
+    "                    test_x.append(next_point)#测试值加入原来序列用来继续预测下一个点\n",
+    "                    point_list.append(next_point)#保存输出序列最后一个点的预测值\n",
+    "                y_.append(point_list)#保存本次预测所有的预测值\n",
+    "                loss_list.append(loss)\n",
+    "                mae, rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
+    "                re = relative_error(\n",
+    "                    y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
+    "                print('epoch:{:<2d} | loss:{:<6.4f} | MAE:{:<6.4f} | RMSE:{:<6.4f} | RE:{:<6.4f}'.format(epoch, loss, mae, rmse, re))\n",
+    "            if (len(loss_list) > 1) and (abs(loss_list[-2] - loss_list[-1]) < 1e-6):\n",
+    "                break\n",
+    "\n",
+    "        mae, rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
+    "        re = relative_error(y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
+    "        mae_list.append(mae)\n",
+    "        rmse_list.append(rmse)\n",
+    "        re_list.append(re)\n",
+    "        result_list.append(y_[-1])\n",
+    "    return re_list, mae_list, rmse_list, result_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c9c71a9",
+   "metadata": {},
+   "source": [
+    "### 设置 10 个不同的随机种子，然后取均值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "21309988",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1863 | RMSE:0.2071 | RE:0.6413\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0550 | RMSE:0.0660 | RE:0.1032\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0537 | RMSE:0.0643 | RE:0.1000\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0535 | RMSE:0.0641 | RE:0.0984\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0532 | RMSE:0.0637 | RE:0.0984\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0530 | RMSE:0.0634 | RE:0.0984\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0510 | RMSE:0.0605 | RE:0.0921\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0469 | RMSE:0.0553 | RE:0.0810\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0489 | RMSE:0.0579 | RE:0.0857\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0493 | RMSE:0.0584 | RE:0.0873\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1854 | RMSE:0.2368 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0342 | RMSE:0.0417 | RE:0.0772\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0344 | RMSE:0.0413 | RE:0.0724\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0334 | RMSE:0.0404 | RE:0.0724\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0324 | RMSE:0.0395 | RE:0.0693\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0343 | RMSE:0.0421 | RE:0.0772\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0305 | RMSE:0.0376 | RE:0.0661\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0304 | RMSE:0.0376 | RE:0.0677\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0294 | RMSE:0.0364 | RE:0.0646\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0271 | RMSE:0.0338 | RE:0.0504\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1647 | RMSE:0.1857 | RE:0.5836\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1432 | RMSE:0.1825 | RE:0.2620\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1395 | RMSE:0.1779 | RE:0.2564\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1392 | RMSE:0.1779 | RE:0.2564\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1387 | RMSE:0.1776 | RE:0.2550\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1383 | RMSE:0.1774 | RE:0.2535\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1377 | RMSE:0.1769 | RE:0.2521\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1372 | RMSE:0.1765 | RE:0.2507\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1364 | RMSE:0.1759 | RE:0.2479\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1357 | RMSE:0.1753 | RE:0.2465\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1707 | RMSE:0.1884 | RE:0.6212\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1607 | RMSE:0.2183 | RE:0.2677\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1562 | RMSE:0.2126 | RE:0.2610\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1556 | RMSE:0.2120 | RE:0.2610\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1552 | RMSE:0.2115 | RE:0.2597\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1546 | RMSE:0.2109 | RE:0.2584\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1539 | RMSE:0.2102 | RE:0.2584\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1533 | RMSE:0.2095 | RE:0.2570\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1526 | RMSE:0.2086 | RE:0.2557\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1520 | RMSE:0.2081 | RE:0.2544\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1507 | RMSE:0.2060 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1481 | RMSE:0.1821 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0852 | RMSE:0.1033 | RE:0.2333\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0428 | RMSE:0.0519 | RE:0.0556\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0413 | RMSE:0.0539 | RE:0.0063\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0015 | MAE:0.1937 | RMSE:0.2646 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1836 | RMSE:0.2448 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1121 | RMSE:0.1313 | RE:0.2929\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0347 | RMSE:0.0426 | RE:0.0772\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0335 | RMSE:0.0502 | RE:0.0047\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1484 | RMSE:0.2078 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1427 | RMSE:0.1837 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1302 | RMSE:0.1536 | RE:0.3470\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1303 | RMSE:0.1631 | RE:0.2649\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1245 | RMSE:0.1582 | RE:0.2465\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0014 | MAE:0.1456 | RMSE:0.2023 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1468 | RMSE:0.1783 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1590 | RMSE:0.1935 | RE:0.3601\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1452 | RMSE:0.1909 | RE:0.2771\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1343 | RMSE:0.1790 | RE:0.2530\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1631 | RMSE:0.2028 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1510 | RMSE:0.1904 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1014 | RMSE:0.1225 | RE:0.2905\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0830 | RMSE:0.1050 | RE:0.1397\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1468 | RMSE:0.1845 | RE:0.3444\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0016 | MAE:0.1977 | RMSE:0.2570 | RE:1.0000\n",
+      "epoch:199 | loss:0.0004 | MAE:0.1874 | RMSE:0.2511 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1416 | RMSE:0.1719 | RE:0.3150\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0457 | RMSE:0.0719 | RE:0.0126\n",
+      "epoch:499 | loss:0.0002 | MAE:0.2515 | RMSE:0.3310 | RE:1.0000\n",
+      "epoch:599 | loss:0.0001 | MAE:0.3389 | RMSE:0.4527 | RE:1.0000\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1565 | RMSE:0.2030 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1451 | RMSE:0.1917 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1305 | RMSE:0.1558 | RE:0.3938\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0797 | RMSE:0.0995 | RE:0.1530\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0368 | RMSE:0.0448 | RE:0.0255\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0798 | RMSE:0.0877 | RE:0.0878\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1277 | RMSE:0.1482 | RE:0.2040\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1556 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0003 | MAE:0.1458 | RMSE:0.1862 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1611 | RMSE:0.1940 | RE:0.4056\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0413 | RMSE:0.0490 | RE:0.0254\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1125 | RMSE:0.1281 | RE:0.1365\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1644 | RMSE:0.2012 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0506 | RMSE:0.0772 | RE:0.0698\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0426 | RMSE:0.0695 | RE:0.0540\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0408 | RMSE:0.0652 | RE:0.0429\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0384 | RMSE:0.0596 | RE:0.0286\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0365 | RMSE:0.0552 | RE:0.0159\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0350 | RMSE:0.0519 | RE:0.0063\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0338 | RMSE:0.0492 | RE:0.0032\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0325 | RMSE:0.0463 | RE:0.0143\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0320 | RMSE:0.0446 | RE:0.0254\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1783 | RMSE:0.2436 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.2161 | RMSE:0.2861 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1194 | RMSE:0.1614 | RE:0.2189\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1348 | RMSE:0.1803 | RE:0.2583\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1431 | RMSE:0.1910 | RE:0.2819\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1514 | RMSE:0.2036 | RE:0.3134\n",
+      "epoch:699 | loss:0.0001 | MAE:0.5429 | RMSE:0.7141 | RE:1.0000\n",
+      "epoch:799 | loss:0.0001 | MAE:0.3657 | RMSE:0.4295 | RE:0.7165\n",
+      "epoch:899 | loss:0.0002 | MAE:0.4312 | RMSE:0.4909 | RE:0.8346\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0995 | RMSE:0.1321 | RE:0.1669\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1570 | RMSE:0.2013 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1563 | RMSE:0.1897 | RE:0.3343\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1002 | RMSE:0.1224 | RE:0.2181\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1014 | RMSE:0.1243 | RE:0.2195\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1052 | RMSE:0.1296 | RE:0.2238\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1097 | RMSE:0.1359 | RE:0.2295\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1152 | RMSE:0.1434 | RE:0.2365\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1190 | RMSE:0.1487 | RE:0.2422\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1216 | RMSE:0.1524 | RE:0.2450\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1242 | RMSE:0.1560 | RE:0.2479\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0013 | MAE:0.1568 | RMSE:0.1974 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0491 | RMSE:0.0600 | RE:0.1218\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0835 | RMSE:0.1093 | RE:0.1727\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0968 | RMSE:0.1275 | RE:0.1914\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1042 | RMSE:0.1377 | RE:0.2021\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1128 | RMSE:0.1497 | RE:0.2155\n",
+      "epoch:699 | loss:0.0002 | MAE:0.1162 | RMSE:0.1546 | RE:0.2195\n",
+      "epoch:799 | loss:0.0002 | MAE:0.1207 | RMSE:0.1609 | RE:0.2262\n",
+      "epoch:899 | loss:0.0002 | MAE:0.1257 | RMSE:0.1680 | RE:0.2343\n",
+      "epoch:999 | loss:0.0002 | MAE:0.1295 | RMSE:0.1733 | RE:0.2396\n",
+      "------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1618 | RMSE:0.2035 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1539 | RMSE:0.1967 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1250 | RMSE:0.1471 | RE:0.3619\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0472 | RMSE:0.0546 | RE:0.0810\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0797 | RMSE:0.1021 | RE:0.1317\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0021 | MAE:0.1976 | RMSE:0.2583 | RE:1.0000\n",
+      "epoch:199 | loss:0.0006 | MAE:0.1911 | RMSE:0.2552 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1682 | RMSE:0.2197 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0717 | RMSE:0.0847 | RE:0.1937\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1257 | RMSE:0.1662 | RE:0.2142\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1830 | RMSE:0.2394 | RE:0.4047\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1560 | RMSE:0.2038 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1484 | RMSE:0.1978 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1340 | RMSE:0.1546 | RE:0.4306\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1238 | RMSE:0.1530 | RE:0.2663\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0815 | RMSE:0.1011 | RE:0.1615\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0022 | MAE:0.1547 | RMSE:0.1996 | RE:1.0000\n",
+      "epoch:199 | loss:0.0005 | MAE:0.1477 | RMSE:0.1925 | RE:1.0000\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1402 | RMSE:0.1594 | RE:0.4793\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1083 | RMSE:0.1391 | RE:0.2102\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0406 | RMSE:0.0483 | RE:0.0295\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1294 | RMSE:0.2011 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1291 | RMSE:0.1545 | RE:0.3254\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0965 | RMSE:0.1163 | RE:0.2746\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0579 | RMSE:0.0687 | RE:0.1254\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0396 | RMSE:0.0508 | RE:0.0127\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0531 | RMSE:0.0719 | RE:0.0603\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1759 | RMSE:0.2613 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1590 | RMSE:0.2042 | RE:0.2299\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1060 | RMSE:0.1244 | RE:0.2772\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0458 | RMSE:0.0580 | RE:0.0898\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0861 | RMSE:0.1192 | RE:0.1118\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1306 | RMSE:0.2041 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1346 | RMSE:0.1594 | RE:0.3909\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1245 | RMSE:0.1490 | RE:0.3796\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1304 | RMSE:0.1581 | RE:0.2890\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1110 | RMSE:0.1388 | RE:0.2309\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0980 | RMSE:0.1231 | RE:0.1997\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0003 | MAE:0.1271 | RMSE:0.1963 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1383 | RMSE:0.1580 | RE:0.4578\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1579 | RMSE:0.1879 | RE:0.4003\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1372 | RMSE:0.1769 | RE:0.2704\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0900 | RMSE:0.1127 | RE:0.1700\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0580 | RMSE:0.0671 | RE:0.1017\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1197 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0541 | RMSE:0.0680 | RE:0.1238\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0357 | RMSE:0.0496 | RE:0.0429\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0335 | RMSE:0.0515 | RE:0.0159\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0747 | RMSE:0.1269 | RE:0.2175\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0902 | RMSE:0.1495 | RE:0.2873\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0832 | RMSE:0.1369 | RE:0.2460\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0850 | RMSE:0.1390 | RE:0.2524\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0850 | RMSE:0.1380 | RE:0.2492\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0922 | RMSE:0.1486 | RE:0.2810\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1852 | RMSE:0.2481 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0732 | RMSE:0.1070 | RE:0.0819\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0976 | RMSE:0.1334 | RE:0.1417\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1093 | RMSE:0.1470 | RE:0.1717\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1161 | RMSE:0.1552 | RE:0.1906\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1087 | RMSE:0.1464 | RE:0.1717\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0754 | RMSE:0.1068 | RE:0.0961\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0644 | RMSE:0.0944 | RE:0.0740\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0619 | RMSE:0.0915 | RE:0.0693\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0592 | RMSE:0.0883 | RE:0.0646\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1224 | RMSE:0.2014 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1283 | RMSE:0.1545 | RE:0.2932\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1103 | RMSE:0.1379 | RE:0.2351\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1006 | RMSE:0.1287 | RE:0.2096\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0937 | RMSE:0.1224 | RE:0.1926\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0827 | RMSE:0.1110 | RE:0.1700\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0681 | RMSE:0.0949 | RE:0.1360\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0404 | RMSE:0.0560 | RE:0.0708\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0189 | RMSE:0.0240 | RE:0.0099\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0498 | RMSE:0.0620 | RE:0.0850\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1146 | RMSE:0.1920 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1503 | RMSE:0.1948 | RE:0.2918\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1301 | RMSE:0.1751 | RE:0.2423\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0996 | RMSE:0.1374 | RE:0.1928\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0855 | RMSE:0.1203 | RE:0.1673\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0713 | RMSE:0.1018 | RE:0.1419\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0660 | RMSE:0.0948 | RE:0.1312\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0618 | RMSE:0.0887 | RE:0.1218\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0586 | RMSE:0.0841 | RE:0.1138\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0513 | RMSE:0.0719 | RE:0.0964\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.0589 | RMSE:0.0706 | RE:0.0286\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1897 | RMSE:0.2791 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1707 | RMSE:0.2509 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1459 | RMSE:0.2134 | RE:1.0000\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0001 | MAE:0.3334 | RMSE:0.4240 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1883 | RMSE:0.2464 | RE:0.4268\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1746 | RMSE:0.2284 | RE:0.3717\n",
+      "epoch:399 | loss:0.0001 | MAE:0.1723 | RMSE:0.2255 | RE:0.3638\n",
+      "epoch:499 | loss:0.0001 | MAE:0.1709 | RMSE:0.2238 | RE:0.3591\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1685 | RMSE:0.2207 | RE:0.3512\n",
+      "epoch:699 | loss:0.0001 | MAE:0.1685 | RMSE:0.2208 | RE:0.3512\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1672 | RMSE:0.2191 | RE:0.3465\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1660 | RMSE:0.2176 | RE:0.3417\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1648 | RMSE:0.2161 | RE:0.3386\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3412 | RMSE:0.4113 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0355 | RMSE:0.0432 | RE:0.0326\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0552 | RMSE:0.0639 | RE:0.0935\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0641 | RMSE:0.0763 | RE:0.1161\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0682 | RMSE:0.0824 | RE:0.1275\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0701 | RMSE:0.0852 | RE:0.1317\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0714 | RMSE:0.0872 | RE:0.1360\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0723 | RMSE:0.0886 | RE:0.1374\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0731 | RMSE:0.0898 | RE:0.1388\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0740 | RMSE:0.0911 | RE:0.1416\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1479 | RMSE:0.1650 | RE:0.1914\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1590 | RMSE:0.2266 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1120 | RMSE:0.1553 | RE:0.2289\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0749 | RMSE:0.0995 | RE:0.1098\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0674 | RMSE:0.0892 | RE:0.0910\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0662 | RMSE:0.0877 | RE:0.0884\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0681 | RMSE:0.0902 | RE:0.0924\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0791 | RMSE:0.1063 | RE:0.1218\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0692 | RMSE:0.0921 | RE:0.0950\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0767 | RMSE:0.1028 | RE:0.1151\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3086 | RMSE:0.3816 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1248 | RMSE:0.2174 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0830 | RMSE:0.1366 | RE:0.2508\n",
+      "sample size: 2827\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch:99 | loss:0.0002 | MAE:0.7786 | RMSE:0.9940 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.1441 | RMSE:0.2373 | RE:1.0000\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1336 | RMSE:0.2207 | RE:1.0000\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0755 | RMSE:0.1110 | RE:0.1795\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0590 | RMSE:0.0832 | RE:0.1276\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1912 | RMSE:0.2359 | RE:0.4490\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0210 | RMSE:0.0305 | RE:0.0142\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0618 | RMSE:0.0890 | RE:0.0963\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.3070 | RMSE:0.3759 | RE:1.0000\n",
+      "epoch:199 | loss:0.0001 | MAE:0.0814 | RMSE:0.1324 | RE:0.1928\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0224 | RMSE:0.0347 | RE:0.0241\n",
+      "------------------------------------------------------------------\n",
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0011 | MAE:0.1557 | RMSE:0.2035 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0536 | RMSE:0.0610 | RE:0.0952\n",
+      "epoch:299 | loss:0.0001 | MAE:0.1080 | RMSE:0.1671 | RE:0.3476\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0635 | RMSE:0.1038 | RE:0.1492\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0634 | RMSE:0.1035 | RE:0.1492\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0646 | RMSE:0.1048 | RE:0.1524\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0734 | RMSE:0.1168 | RE:0.1841\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0815 | RMSE:0.1274 | RE:0.2143\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0913 | RMSE:0.1405 | RE:0.2524\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0903 | RMSE:0.1381 | RE:0.2444\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0017 | MAE:0.1949 | RMSE:0.2679 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1489 | RMSE:0.2155 | RE:0.3843\n",
+      "epoch:299 | loss:0.0001 | MAE:0.0657 | RMSE:0.0971 | RE:0.0646\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0727 | RMSE:0.1043 | RE:0.0803\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0819 | RMSE:0.1145 | RE:0.1024\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0909 | RMSE:0.1247 | RE:0.1244\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0994 | RMSE:0.1347 | RE:0.1465\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1074 | RMSE:0.1443 | RE:0.1669\n",
+      "epoch:899 | loss:0.0001 | MAE:0.1150 | RMSE:0.1535 | RE:0.1858\n",
+      "epoch:999 | loss:0.0001 | MAE:0.1221 | RMSE:0.1623 | RE:0.2063\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0008 | MAE:0.1472 | RMSE:0.2358 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1374 | RMSE:0.1718 | RE:0.2805\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1110 | RMSE:0.1396 | RE:0.2309\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1099 | RMSE:0.1385 | RE:0.2280\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1085 | RMSE:0.1370 | RE:0.2238\n",
+      "epoch:599 | loss:0.0001 | MAE:0.1067 | RMSE:0.1350 | RE:0.2195\n",
+      "epoch:699 | loss:0.0001 | MAE:0.1045 | RMSE:0.1325 | RE:0.2139\n",
+      "epoch:799 | loss:0.0001 | MAE:0.1015 | RMSE:0.1290 | RE:0.2068\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0981 | RMSE:0.1248 | RE:0.1983\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0949 | RMSE:0.1208 | RE:0.1898\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0010 | MAE:0.2005 | RMSE:0.2195 | RE:0.9076\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0685 | RMSE:0.0814 | RE:0.1392\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0858 | RMSE:0.1063 | RE:0.1620\n",
+      "epoch:399 | loss:0.0001 | MAE:0.0833 | RMSE:0.1027 | RE:0.1566\n",
+      "epoch:499 | loss:0.0001 | MAE:0.0796 | RMSE:0.0974 | RE:0.1486\n",
+      "epoch:599 | loss:0.0001 | MAE:0.0742 | RMSE:0.0896 | RE:0.1365\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0649 | RMSE:0.0766 | RE:0.1151\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0540 | RMSE:0.0626 | RE:0.0910\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0436 | RMSE:0.0513 | RE:0.0643\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0382 | RMSE:0.0471 | RE:0.0415\n",
+      "------------------------------------------------------------------\n",
+      "RE: mean: 0.2004 | std: 0.1070\n",
+      "MAE: mean: 0.0944 | std: 0.0333\n",
+      "RMSE: mean: 0.1254 | std: 0.0393\n",
+      "------------------------------------------------------------------\n",
+      "------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "window_size = 8\n",
+    "EPOCH = 1000\n",
+    "LR = 0.01    # learning rate\n",
+    "feature_size = window_size\n",
+    "hidden_size = [32,16]\n",
+    "weight_decay = 0.0\n",
+    "Rated_Capacity = 1.1\n",
+    "\n",
+    "MAE, RMSE, RE = [], [], []\n",
+    "for seed in range(10):\n",
+    "    re_list, mae_list, rmse_list, _ = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n",
+    "                                           window_size=window_size, EPOCH=EPOCH, seed=seed)\n",
+    "    RE.append(np.mean(np.array(re_list)))\n",
+    "    MAE.append(np.mean(np.array(mae_list)))\n",
+    "    RMSE.append(np.mean(np.array(rmse_list)))\n",
+    "    print('------------------------------------------------------------------')\n",
+    "\n",
+    "print('RE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(RE)), np.std(np.array(RE))))\n",
+    "print('MAE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(MAE)), np.std(np.array(MAE))))\n",
+    "print('RMSE: mean: {:<6.4f} | std: {:<6.4f}'.format(np.mean(np.array(RMSE)), np.std(np.array(RMSE))))\n",
+    "print('------------------------------------------------------------------')\n",
+    "print('------------------------------------------------------------------')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7496047b",
+   "metadata": {},
+   "source": [
+    "#### 查看每组电池的曲线拟合效果"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "aaf87962",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample size: 2881\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1197 | RMSE:0.1990 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0541 | RMSE:0.0680 | RE:0.1238\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0357 | RMSE:0.0496 | RE:0.0429\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0335 | RMSE:0.0515 | RE:0.0159\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0747 | RMSE:0.1269 | RE:0.2175\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0902 | RMSE:0.1495 | RE:0.2873\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0832 | RMSE:0.1369 | RE:0.2460\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0850 | RMSE:0.1390 | RE:0.2524\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0850 | RMSE:0.1380 | RE:0.2492\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0922 | RMSE:0.1486 | RE:0.2810\n",
+      "sample size: 2827\n",
+      "epoch:99 | loss:0.0004 | MAE:0.1852 | RMSE:0.2481 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.0732 | RMSE:0.1070 | RE:0.0819\n",
+      "epoch:299 | loss:0.0002 | MAE:0.0976 | RMSE:0.1334 | RE:0.1417\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1093 | RMSE:0.1470 | RE:0.1717\n",
+      "epoch:499 | loss:0.0002 | MAE:0.1161 | RMSE:0.1552 | RE:0.1906\n",
+      "epoch:599 | loss:0.0002 | MAE:0.1087 | RMSE:0.1464 | RE:0.1717\n",
+      "epoch:699 | loss:0.0001 | MAE:0.0754 | RMSE:0.1068 | RE:0.0961\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0644 | RMSE:0.0944 | RE:0.0740\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0619 | RMSE:0.0915 | RE:0.0693\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0592 | RMSE:0.0883 | RE:0.0646\n",
+      "sample size: 2791\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1224 | RMSE:0.2014 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1283 | RMSE:0.1545 | RE:0.2932\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1103 | RMSE:0.1379 | RE:0.2351\n",
+      "epoch:399 | loss:0.0002 | MAE:0.1006 | RMSE:0.1287 | RE:0.2096\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0937 | RMSE:0.1224 | RE:0.1926\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0827 | RMSE:0.1110 | RE:0.1700\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0681 | RMSE:0.0949 | RE:0.1360\n",
+      "epoch:799 | loss:0.0002 | MAE:0.0404 | RMSE:0.0560 | RE:0.0708\n",
+      "epoch:899 | loss:0.0002 | MAE:0.0189 | RMSE:0.0240 | RE:0.0099\n",
+      "epoch:999 | loss:0.0002 | MAE:0.0498 | RMSE:0.0620 | RE:0.0850\n",
+      "sample size: 2767\n",
+      "epoch:99 | loss:0.0002 | MAE:0.1146 | RMSE:0.1920 | RE:1.0000\n",
+      "epoch:199 | loss:0.0002 | MAE:0.1503 | RMSE:0.1948 | RE:0.2918\n",
+      "epoch:299 | loss:0.0002 | MAE:0.1301 | RMSE:0.1751 | RE:0.2423\n",
+      "epoch:399 | loss:0.0002 | MAE:0.0996 | RMSE:0.1374 | RE:0.1928\n",
+      "epoch:499 | loss:0.0002 | MAE:0.0855 | RMSE:0.1203 | RE:0.1673\n",
+      "epoch:599 | loss:0.0002 | MAE:0.0713 | RMSE:0.1018 | RE:0.1419\n",
+      "epoch:699 | loss:0.0002 | MAE:0.0660 | RMSE:0.0948 | RE:0.1312\n",
+      "epoch:799 | loss:0.0001 | MAE:0.0618 | RMSE:0.0887 | RE:0.1218\n",
+      "epoch:899 | loss:0.0001 | MAE:0.0586 | RMSE:0.0841 | RE:0.1138\n",
+      "epoch:999 | loss:0.0001 | MAE:0.0513 | RMSE:0.0719 | RE:0.0964\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seed = 6\n",
+    "_, _, _, result_list = tain(LR=LR, feature_size=feature_size, hidden_size=hidden_size, weight_decay=weight_decay,\n",
+    "                            window_size=window_size, EPOCH=EPOCH, seed=seed)\n",
+    "for i in range(4):\n",
+    "    name = Battary_list[i]\n",
+    "    train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size)\n",
+    "\n",
+    "    aa = train_data[:window_size+1].copy() # 第一个输入序列\n",
+    "    [aa.append(a) for a in result_list[i]] # 测试集预测结果\n",
+    "\n",
+    "    battery = Battery[name]\n",
+    "    fig, ax = plt.subplots(1, figsize=(12, 8))\n",
+    "    ax.plot(battery['cycle'], battery['capacity'], 'b.', label=name)\n",
+    "    ax.plot(battery['cycle'], aa, 'r.', label='Prediction')\n",
+    "    plt.plot([-1,1000],[Rated_Capacity*0.7, Rated_Capacity*0.7], c='black', lw=1, ls='--')  # 临界点直线\n",
+    "    ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1a8cec1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34eb40dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d37eac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/RNN & LSTM.ipynb b/RNN & LSTM.ipynb
index a152023..d15020a 100644
--- a/RNN & LSTM.ipynb	
+++ b/RNN & LSTM.ipynb	
@@ -286,13 +286,7 @@
       "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
       "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
       "Load Dataset CS2_38 ...\n",
-      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
       "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
       "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
       "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
@@ -717,13 +711,7 @@
       "epoch:99 | loss:0.0005 | MAE:0.0783 | RMSE:0.0992 | RE:0.1866\n",
       "epoch:199 | loss:0.0004 | MAE:0.0659 | RMSE:0.0921 | RE:0.1228\n",
       "epoch:299 | loss:0.0004 | MAE:0.0569 | RMSE:0.0789 | RE:0.1069\n",
-      "epoch:399 | loss:0.0003 | MAE:0.0512 | RMSE:0.0708 | RE:0.0957\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch:399 | loss:0.0003 | MAE:0.0512 | RMSE:0.0708 | RE:0.0957\n",
       "epoch:499 | loss:0.0003 | MAE:0.1802 | RMSE:0.2426 | RE:0.2982\n",
       "------------------------------------------------------------------\n",
       "seed:  7\n",
@@ -846,7 +834,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/RNN & LSTM_ed.ipynb b/RNN & LSTM_ed.ipynb
new file mode 100644
index 0000000..7b23f1d
--- /dev/null
+++ b/RNN & LSTM_ed.ipynb	
@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1100232a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from math import sqrt\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e76422e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_outlier(array,count,bins):\n",
+    "    index = []\n",
+    "    range_ = np.arange(1,count,bins)\n",
+    "    for i in range_[:-1]:\n",
+    "        array_lim = array[i:i+bins]\n",
+    "        sigma = np.std(array_lim)\n",
+    "        mean = np.mean(array_lim)\n",
+    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
+    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
+    "        idx = idx[0] + i\n",
+    "        index.extend(list(idx))\n",
+    "    return np.array(index)\n",
+    "\n",
+    "def build_sequences(text, window_size):\n",
+    "    #text:list of capacity\n",
+    "    x, y = [],[]\n",
+    "    for i in range(len(text) - window_size):\n",
+    "        sequence = text[i:i+window_size]\n",
+    "        target = text[i+1:i+1+window_size]\n",
+    "\n",
+    "        x.append(sequence)\n",
+    "        y.append(target)\n",
+    "\n",
+    "    return np.array(x), np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "02188eb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 留一评估：一组数据为测试集，其他所有数据全部拿来训练\n",
+    "def get_train_test(data_dict, name, window_size=8):\n",
+    "    data_sequence=data_dict[name]['capacity']\n",
+    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
+    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
+    "    for k, v in data_dict.items():\n",
+    "        if k != name:\n",
+    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
+    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
+    "            \n",
+    "    return train_x, train_y, list(train_data), list(test_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd3cafbb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}