Predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "57edcc47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f6118df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "1\n",
      "11.1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.is_available()) # 查看CUDA是否可用\n",
    "print(torch.cuda.device_count()) # 查看可用的CUDA数量\n",
    "print(torch.version.cuda) # 查看CUDA的版本号\n",
    "print(torch.cuda.device_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38113945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F:\\code\\Informer2020-main\\Informer2020-main_large_diff_dil\\2.15_effinformer-2\n"
     ]
    }
   ],
   "source": [
    "%cd F:\\code\\Informer2020-main\\Informer2020-main_large_diff_dil\\2.15_effinformer-2   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "efb04917",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 驱动器 F 中的卷是 phd\n",
      " 卷的序列号是 F4E4-EFA1\n",
      "\n",
      " F:\\code\\Informer2020-main\\Informer2020-main_large_diff_dil\\2.15_effinformer-2 的目录\n",
      "\n",
      "2023/02/22  08:53    <DIR>          .\n",
      "2023/02/22  08:53    <DIR>          ..\n",
      "2023/02/21  20:21    <DIR>          .idea\n",
      "2023/02/22  09:00             9,416 A_array.mat\n",
      "2023/02/21  21:17             9,416 A_array1.mat\n",
      "2023/02/21  20:14    <DIR>          checkpoints\n",
      "2023/02/19  22:43    <DIR>          data\n",
      "2023/02/21  17:16    <DIR>          data - 副本\n",
      "2023/02/19  22:36             2,200 Effinformer.sh\n",
      "2023/02/21  20:13    <DIR>          EffInformer-main\n",
      "2023/02/21  20:12         3,758,150 EffInformer-main.zip\n",
      "2023/02/19  22:43    <DIR>          exp\n",
      "2023/02/22  09:00         1,233,440 heatmap.eps\n",
      "2023/02/22  09:00            29,019 heatmap.pdf\n",
      "2023/02/22  09:00            76,897 heatmap.svg\n",
      "2022/12/05  19:47             7,610 main_informer.py\n",
      "2023/02/19  22:43    <DIR>          models\n",
      "2023/02/21  20:16    <DIR>          results\n",
      "2023/02/19  22:43    <DIR>          summary\n",
      "2023/02/21  20:16    <DIR>          test_results\n",
      "2023/02/19  22:43    <DIR>          utils\n",
      "               8 个文件      5,126,148 字节\n",
      "              13 个目录 81,476,558,848 可用字节\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7709a11e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "if not 'EffInformer' in sys.path:\n",
    "    sys.path += ['EffInformer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "77181763",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.tools import dotdict\n",
    "from exp.exp_informer import Exp_Informer\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b182aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "args = dotdict()\n",
    "\n",
    "args.model = 'informer' # model of experiment, options: [informer, informerstack, informerlight(TBD)]\n",
    "\n",
    "args.data = 'DAB' # data\n",
    "args.root_path = './data/DAB' # root path of data file\n",
    "args.data_path = 'DAB.csv' # data file\n",
    "args.features = 'M' # forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate\n",
    "args.target = 'OT' # target feature in S or MS task\n",
    "args.freq = 'h' # freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h\n",
    "args.checkpoints = './checkpoints/' # location of model checkpoints\n",
    "\n",
    "args.seq_len = 12 # input sequence length of Informer encoder\n",
    "args.label_len = 6 # start token length of Informer decoder\n",
    "args.pred_len = 1 # prediction sequence length\n",
    "# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]\n",
    "\n",
    "args.enc_in = 2 # encoder input size\n",
    "args.dec_in = 2 # decoder input size\n",
    "args.c_out = 2 # output size\n",
    "args.factor = 5 # probsparse attn factor\n",
    "args.d_model = 512 # dimension of model\n",
    "args.n_heads = 8 # num of heads\n",
    "args.e_layers = 3 # num of encoder layers\n",
    "args.d_layers = 2 # num of decoder layers\n",
    "args.d_ff = 2048 # dimension of fcn in model\n",
    "args.dropout = 0.05 # dropout\n",
    "args.attn = 'prob' # attention used in encoder, options:[prob, full]\n",
    "args.embed = 'timeF' # time features encoding, options:[timeF, fixed, learned]\n",
    "args.activation = 'ReGLU' # activation\n",
    "args.distil = True # whether to use distilling in encoder\n",
    "args.output_attention = False # whether to output attention in ecoder\n",
    "args.mix = False\n",
    "args.padding = 0\n",
    "args.freq = 'h'\n",
    "\n",
    "args.batch_size = 32 \n",
    "args.learning_rate = 0.0001\n",
    "args.loss = 'mse'\n",
    "args.lradj = 'type1'\n",
    "args.use_amp = False # whether to use automatic mixed precision training\n",
    "\n",
    "args.num_workers = 0\n",
    "args.itr = 1\n",
    "args.train_epochs = 6\n",
    "args.patience = 3\n",
    "args.des = 'exp'\n",
    "\n",
    "args.use_gpu = True if torch.cuda.is_available() else False\n",
    "args.gpu = 0\n",
    "\n",
    "args.use_multi_gpu = False\n",
    "args.devices = '0,1,2,3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e0987724",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False\n",
    "\n",
    "if args.use_gpu and args.use_multi_gpu:\n",
    "    args.devices = args.devices.replace(' ','')\n",
    "    device_ids = args.devices.split(',')\n",
    "    args.device_ids = [int(id_) for id_ in device_ids]\n",
    "    args.gpu = args.device_ids[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a4d35b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set augments by using data name\n",
    "data_parser = {\n",
    "    'DAB':{'data':'DAB.csv','T':'OT','M':[2,2,2],'S':[1,1,1],'MS':[2,2,1]},\n",
    "    'ETTh1':{'data':'ETTh1.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},\n",
    "    'ETTh2':{'data':'ETTh2.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},\n",
    "    'ETTm1':{'data':'ETTm1.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},\n",
    "    'ETTm2':{'data':'ETTm2.csv','T':'OT','M':[7,7,7],'S':[1,1,1],'MS':[7,7,1]},\n",
    "}\n",
    "if args.data in data_parser.keys():\n",
    "    data_info = data_parser[args.data]\n",
    "    args.data_path = data_info['data']\n",
    "    args.target = data_info['T']\n",
    "    args.enc_in, args.dec_in, args.c_out = data_info[args.features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a1e84480",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.detail_freq = args.freq\n",
    "args.freq = args.freq[-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a5e95b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Args in experiment:\n",
      "{'model': 'informer', 'data': 'DAB', 'root_path': './data/DAB', 'data_path': 'DAB.csv', 'features': 'M', 'target': 'OT', 'freq': 'h', 'checkpoints': './checkpoints/', 'seq_len': 12, 'label_len': 6, 'pred_len': 1, 'enc_in': 2, 'dec_in': 2, 'c_out': 2, 'factor': 5, 'd_model': 512, 'n_heads': 8, 'e_layers': 3, 'd_layers': 2, 'd_ff': 2048, 'dropout': 0.05, 'attn': 'prob', 'embed': 'timeF', 'activation': 'ReGLU', 'distil': True, 'output_attention': False, 'mix': False, 'padding': 0, 'batch_size': 32, 'learning_rate': 0.0001, 'loss': 'mse', 'lradj': 'type1', 'use_amp': False, 'num_workers': 0, 'itr': 1, 'train_epochs': 6, 'patience': 3, 'des': 'exp', 'use_gpu': True, 'gpu': 0, 'use_multi_gpu': False, 'devices': '0,1,2,3', 'detail_freq': 'h'}\n"
     ]
    }
   ],
   "source": [
    "print('Args in experiment:')\n",
    "print(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "153467fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "Exp = Exp_Informer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee24d7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use GPU: cuda:0\n",
      ">>>>>>>start training : informer_DAB_ftM_sl12_ll6_pl1_dm512_nh8_el3_dl2_df2048_atprob_fc5_ebtimeF_dtTrue_mxFalse_exp_0>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
      "train 14630\n",
      "val 4146\n",
      "test 2211\n",
      "Epoch: 1 cost time: 20.2248752117157\n",
      "Epoch: 1, Steps: 457 | Train Loss: 0.0480813 Vali Loss: 0.0032527 Test Loss: 0.0017034\n",
      "Validation loss decreased (inf --> 0.003253).  Saving model ...\n",
      "Updating learning rate to 0.0001\n",
      "Epoch: 2 cost time: 18.795799255371094\n",
      "Epoch: 2, Steps: 457 | Train Loss: 0.0146728 Vali Loss: 0.0019425 Test Loss: 0.0008593\n",
      "Validation loss decreased (0.003253 --> 0.001943).  Saving model ...\n",
      "Updating learning rate to 5e-05\n",
      "Epoch: 3 cost time: 18.404831886291504\n",
      "Epoch: 3, Steps: 457 | Train Loss: 0.0090740 Vali Loss: 0.0017872 Test Loss: 0.0012242\n",
      "Validation loss decreased (0.001943 --> 0.001787).  Saving model ...\n",
      "Updating learning rate to 2.5e-05\n",
      "Epoch: 4 cost time: 18.57537817955017\n",
      "Epoch: 4, Steps: 457 | Train Loss: 0.0059953 Vali Loss: 0.0011631 Test Loss: 0.0006613\n",
      "Validation loss decreased (0.001787 --> 0.001163).  Saving model ...\n",
      "Updating learning rate to 1.25e-05\n",
      "Epoch: 5 cost time: 18.200379848480225\n",
      "Epoch: 5, Steps: 457 | Train Loss: 0.0052607 Vali Loss: 0.0011183 Test Loss: 0.0005896\n",
      "Validation loss decreased (0.001163 --> 0.001118).  Saving model ...\n",
      "Updating learning rate to 6.25e-06\n",
      "Epoch: 6 cost time: 18.06773066520691\n",
      "Epoch: 6, Steps: 457 | Train Loss: 0.0048182 Vali Loss: 0.0011246 Test Loss: 0.0005905\n",
      "EarlyStopping counter: 1 out of 3\n",
      "Updating learning rate to 3.125e-06\n",
      ">>>>>>>testing : informer_DAB_ftM_sl12_ll6_pl1_dm512_nh8_el3_dl2_df2048_atprob_fc5_ebtimeF_dtTrue_mxFalse_exp_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n",
      "test 2211\n",
      "test shape: (69, 32, 1, 2) (69, 32, 1, 2)\n",
      "test shape: (2208, 1, 2) (2208, 1, 2)\n",
      "mse:0.0005895508220419288, mae:0.015320359729230404, rmse:0.024280667304992676, mape:0.1399742066860199, mspe:0.09304000437259674\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kYHx5E+sEwMweNLMiMyuKhq+cs7g4Rky+l1ULNWmciPgVTAE0dmy+4bc/n3eMmY2k9rDQz5qxztqFzr3knMtzzuWlpKQEETf8db2gJ70vymXD1x/5jiIiMSyYAigHBta7ngrsPtcYM0sAugMHA9dTgfnA/c65LfXG1//m4sbWGdXShuZwuvI4e3Zu9h1FRGJUMAWwAsg0swwzSwTuAhrOb7CA2jd5AW4DFjnnnJn1AN4DfumcW3ZmsHPuO+CYmY0PnP1zP/DXVm5LxMnKv5ny4o81aZyIeNFkAQSO6T8GLATWA28759aa2bNmdmNg2CtAspmVAb8Azpwq+hgwBHjazIoDPxcGbnsYeBkoA7YAH4RqoyJJVsFMVi+c7TuGiMQgqz0JJzLk5eW5oqIi3zFCbs/OzVRsX0dWfsOTq0REWs/MVjrn8hou1yeBw0DfQZl06NiJnZuKfUcRkRiiAggTwy4toKJspSaNE5F2owIIIzkFMzVpnIi0GxVAGIlPSGDIhFsp+WSu7ygiEgNUAGGmZ0o/ul6YRlnJsqYHi4i0ggogDA3JnsDxip0c3Pet7ygiEsVUAGEqe9KdbPliPtVVVb6jiEiUUgGEqTOTxhUXzml6sIhIC6gAwliXbj1IGTKWDcsLfUcRkSikAghzgy7O4fTJHzRpnIiEnAogAmTlT6e8+BNOVn7vO4qIRBEVQIQYPXUWa/R+gIiEkAogQiR2TCI1dwprPnvXdxQRiRIqgAjSd+AQOnTqxo4N3/iOIiJRQAUQYYaNm8L+rSUcP3rIdxQRiXAqgAiUO/V+1i96Q5PGiUirqAAiUFx8PJkTb6P44zd9RxGRCKYCiFA9evflgn5DKCtZ6juKiEQoFUAEuyhrPMcrdnFgb7nvKCISgVQAES570p1s/fIvmjRORJpNBRDhLC6OkdfcR/HC2b6jiEiEUQFEgc5du3PhxZewfvlC31FEJIKoAKLEwMxsqk9Vsnv7Rt9RRCRCqACiyKgrprN79WIqfzjhO4qIRAAVQJTRpHEiEiwVQJRJ7JhEWt61rP70Hd9RRCTMqQCi0IUDMkjs2oMd61f6jiIiYUwFEKWG5U3mwPbVHDty0HcUEQlTKoAollOgSeNE5NxUAFEsLj6eofl3aNI4EWmUCiDKdU/uQ/f+mWxetcR3FBEJMyqAGDB41KWcOLhbk8aJyFlUADEi++o7NGmciJxFBRAjLC6OUVNmsmrhq76jiEiYUAHEkE5dutFn6HjWffmB7ygiEgZUADFm4JAsaqpPs3vbBt9RRMQzFUAMGjXxRnaXfqZJ40RiXFAFYGbTzGyjmZWZ2VON3N7RzN4K3L7czNIDy5PNbLGZHTez/2pwn08D6ywO/FwYig2S4GRr0jiRmNdkAZhZPPA8cC0wAphhZiMaDHsAOOScGwL8HngusLwSeBr4l3Os/h7nXE7gZ19LNkBapkNiR9Iv+QmrF8/zHUVEPAlmD2AcUOac2+qcOwXMBaY3GDMdOPNych4w2czMOXfCObeU2iKQMJPSP52O3XqxfX2R7ygi4kEwBTAA2FXvenlgWaNjnHNVwBEgOYh1vxo4/PO0mVkQ4yXEhuZN4uCOUo4ePuA7ioi0s2AKoLEnZteCMQ3d45zLAq4I/NzX6IObPWhmRWZWVFFR0WRYab6cKfex8dM/adI4kRgTTAGUAwPrXU8Fdp9rjJklAN2B885D7Jz7NvD7GPAmtYeaGhv3knMuzzmXl5KSEkRcaa64+Hguzr+TVR+94TuKiLSjYApgBZBpZhlmlgjcBSxoMGYBMDNw+TZgkXPunHsAZpZgZr0DlzsA1wOlzQ0vodO9Vwo9Uy9m0zef+Y4iIu2kyQIIHNN/DFgIrAfeds6tNbNnzezGwLBXgGQzKwN+AdSdKmpm24HfAbPMrDxwBlFHYKGZrQaKgW+B/w7dZklLZIy8lB8Of8f+PTt9RxGRdmDneaEedvLy8lxRkc5YaUuupoaiBS+Qe92DJHRI9B1HRELAzFY65/IaLtcngeUsZyaNK9aHxESingpAfqRTl270G345a79433cUEWlDKgBp1IDBI8E5vt263ncUEWkjKgA5p5ETruO7tUuo/P647ygi0gZUAHJeOdN+ypqPXvMdQ0TagApAziuhQyIZl1xHyeI/+44iIiGmApAm9e6fRqfuKWxbt8J3FBEJIRWABOXiMVdxaOdajhza7zuKiISICkCClltwvyaNE4kiKgAJmsXFMfSqGRR/9LrvKCISAioAaZbuPXvTY+AINn3zqe8oItJKKgBptowRl/DDkQr2797hO4qItIIKQFok++rb2fb136k6fcp3FBFpIRWAtFhWwUyKF872HUNEWkgFIC2W1Lkr/UZcwdpl7/mOIiItoAKQVhkweDiY8e3Wtb6jiESfvetg/d/g4NY2Wb0KQFpt5OU/4bt1yzRpnEgoHNxW+6S//m8QnwjDb4Beg9vkoRLaZK0Sc3KmzmLVe//NJTc96juKSOQ5tgfKiwAHPdJg2PVg1uYPqwKQkEjokEjG+BsoWfQ22ZPu8B1HJPz9cAh2fAmuGrr2gaE/gbj2PSijApCQ6d13EAd3b2Xb2uVkjLzUdxyR8HPqBGxfBtUnIakHZBZAvL+nYRWAhNTFY65i1cI5HOk3mO69UnzHEfGv6hTsWFr75N+hM2TkQ4ck36kAFYC0gZwp97Hir8+Td+MjxMXH+44j0v5qqmHnV/DDwdo3cgddBkkX+E71IyoACTmLi2PY1XdT/NHrjJk2y3cckfbhHHz7DRzbDRYPAy+FLsm+U52XCkDaxAU9kumVNoqNRYsYmjfJdxyRtrN3HRwoqz1rp/8YSB3rO1HQVADSZtKH57F68Twqdm8npX+67zgioXNwG+wtrb2cMhxG3Og3TwupAKRNjb76Nlb89QV6XPsAHRI7+o4j0nKeztVvSyoAaXNZBTMpKZxD3vUP+o4i0jxnztWvqYJufb2cq9+WVADS5pI6daH/yHxKl/2NURNu8B1H5PzC7Fz9thSdWyVhp3/GMA7v2UZ5WSmpQ0b5jiNytqqTsGNZ4Fz9TmF1rn5bUgFIuxlx2bUU/f0lkvul0alLN99xJNZFyLn6bUkFIO2qdtK4l8i78REsio6lSoQ461z9OBg4PuzP1W9LKgBpV7WTxk1n9advkz3pLt9xJFZE8Ln6bUkFIO2ud9+BHPquP1tLlzN4lCaNk7Zx+LutnNq5EoDTyUOp6jO59oYq4MAJf8FaoEfnRLp36hDy9aoAxIvM3HxWFf6RI/3S6Z7cx3cciTIVu7ezfc1X9Miud65+VY3fUK1QU+PaZL0qAPEm55q7KVrwAmNveFiTxknInD51ku0rPuCS6Q/7jhL29C6ceFM3aVzha76jSBQpKZxDVsH9vmNEBBWAeNWtey+S00ezoegT31EkCpQu+xv9R11JUqcuvqNEBBWAeJc2fCynjh9m37fbfEeRCFZeVkpcXAL904f6jhIxgioAM5tmZhvNrMzMnmrk9o5m9lbg9uVmlh5Ynmxmi83suJn9V4P7jDWzNYH7/KdZhM+qJK0y+qpb2VH0AadPnfQdRSLQDyeOsXfjV4y47FrfUSJKkwVgZvHA88C1wAhghpmNaDDsAeCQc24I8HvgucDySuBp4F8aWfUfgAeBzMDPtJZsgESPrIKZlCyc7TuGRBhXU0PpR6+RM3WW7ygRJ5g9gHFAmXNuq3PuFDAXmN5gzHRgTuDyPGCymZlz7oRzbim1RVDHzPoBFzjnvnTOOeA14KbWbIhEvqROXeifdRWlSxf4jiIRZPWn8xh82XTiE3RSY3MFUwADgF31rpcHljU6xjlXBRwBzvf56gGB9ZxvnQCY2YNmVmRmRRUVFUHElUjWP30ocQmJ7Cpb4zuKRICtpcvp3KsfyX1SfUeJSMEUQGPH5ht+KiGYMS0a75x7yTmX55zLS0lJOc8qJVqMGD+NfRuX8/3xI76jSBg7cmAvR3ZvJjPnCt9RIlYwBVAODKx3PRXYfa4xZpYAdAcONrHO+pXd2DolhuVMncXaj1/H1UTupzel7dRUV7Pp8z+Tc83dvqNEtGAKYAWQaWYZZpYI3AU0PEi7AJgZuHwbsChwbL9RzrnvgGNmNj5w9s/9wF+bnV6iVnxCAoMvu4mSxW/7jiJhqLjwNYZdfbdmlG2lJv/1Asf0HwMWAuuBt51za83sWTM7803IrwDJZlYG/AKoO1XUzLYDvwNmmVl5vTOIHgZeBsqALcAHodkkiRbJfVLp0qs/W9Z85TuKhJENRZ+QnD6abt17+Y4S8ew8L9TDTl5enisqKvIdQ9rZNwtfZ/DYKfTo3dd3FPFs37fb2LP5G0ZfdavvKBHFzFY65/IaLtf+k4S93Cn3sOnzt6mprvYdRTw6feokO4o+1JN/CKkAJOxZXBzDJ92rSeNiXO0kbzObHihBUwFIROjWvRfJGdlsWPGx7yjiQenSBYFJ3jr7jhJVVAASMdKGjeH090fZW77FdxRpR7vK1hCX0FGTvLUBFYBElKwrb2HnyoWcOlnZ9GCJeN8fP8LejV8zYvxU31GikgpAIs7oqbNYXTin6YES0VxNDaUf/5HcqTru31ZUABJxOiZ1ZsDoqyj9XJ8djGYli9/mostv0iRvbUgFIBGpX9pQ4hOT2LW5xHcUaQNb1nxFl+RUki9sdI5ICREVgESs4ZdOZd+mFZo0Lsoc3r+HI9+VkZkz0XeUqKcCkIimSeOiS011NZuXziNXk7y1CxWARLT4hAQuuvxmTRoXJYoLX2P4pHs0yVs70b+yRLxeFw6ga+9Utqz+wncUaYUNRZ+QPDiXrhf09B0lZqgAJCoMyZ7I0T1bObx/j+8o0gJ7y7dw+vujpA3N8R0lpqgAJGrkXHM3mz7/syaNizCnTlay65tCsvJv9h0l5qgAJGpYXBwjJt/LqoWzfUeRZli9cLYmefNEBSBRpesFPek9OJcNX3/kO4oEofTzvzIg+2o6JmmSNx9UABJ10oaN4fQPx9izq8x3FDmPXZtLiE9Mol+aJnnzRQUgUSnrylvY9U2hJo0LUyeOHWbfphUMv1STvPmkApCoNXrqLFYvfNV3DGnA1dSw7pM/kjN1lu8oMU8FIFGrY1JnBmRPYs0STRoXTkoWvcVFl9+sSd7CgApAolq/tKEkdExi56Zi31EE2LL6C7qmDKSXJnkLCyoAiXrDL51KxeYiThw77DtKTDu8fw9H925jSLYmeQsXKgCJCZo0zq+a6mo2LZ1HzuQZvqNIPSoAiQnxCQlkTryNkkVv+Y4Sk1YVvsaIyfdqkrcwo7+GxIyeKf3omjKIspJlvqPElA1ff0Tvwbl07dbDdxRpQAUgMWVI9gSO79vBoYrvfEeJCXt2lXG68oQmeQtTKgCJOdmT76Js2TtUV1X5jhLVTp2sZNeqj8nKv8l3FDkHFYDEnDOTxhUXvu47SlRbXTiH0QX3+44h56ECkJjUpVsPel+kSePaSunnfyU1e5ImeQtzKgCJWWlDczhdeZw9Ozf7jhJVdm4qJiGxE30HZfqOIk1QAUhMy8q/mfLijzVpXIicOHaYirJVDLu0wHcUCYIKQGJeVsFMTRoXAnWTvBXc5zuKBEkFIDGvY1JnUnOuYc2S+b6jRLSST+YyZMKtmuQtgqgARIC+gzLp0LELOzZq0riWKCtZRtcL0+iZ0s93FGkGFYBIwLBLC9i/ZSXHjx7yHSWiHKr4juP7djAke4LvKNJMKgCRenIKZrLukz9q0rggVVdVUbbsHbIn3+U7irSACkCknjOTxhV//KbvKBGhuHCOJnmLYEH91cxsmpltNLMyM3uqkds7mtlbgduXm1l6vdt+GVi+0cym1lu+3czWmFmxmRWFYmNEQqFnSj+69cmgrGSp7yhhbcPyQlKGjKWLJnmLWE0WgJnFA88D1wIjgBlmNqLBsAeAQ865IcDvgecC9x0B3AWMBKYBLwTWd8bVzrkc51xeq7dEJIRqJ43bycF93/qOEpb27NzM6ZM/MOhiTfIWyYLZAxgHlDnntjrnTgFzgekNxkwH5gQuzwMmm5kFls91zp10zm0DygLrEwl7tZPGvatJ4xo4Wfk95cWfkJXf8GlAIk0wBTAA2FXvenlgWaNjnHNVwBEguZbuHZUAAAYsSURBVIn7OqDQzFaa2YPnenAze9DMisysqKKiIoi4IqFhcXGMvOY+ihfO9h0lrKwpnMPoqbN8x5AQCKYArJFlLsgx57vvBOfcGGoPLT1qZvmNPbhz7iXnXJ5zLi8lJSWIuCKh06VbD1Iy81i/fKHvKGFhzZL5pOZOIbFjku8oEgLBFEA5MLDe9VRg97nGmFkC0B04eL77OufO/N4HzEeHhiRMDbo4h6qTlTE/adyOjcV0SOpK34FDfEeREAmmAFYAmWaWYWaJ1L6pu6DBmAXAzMDl24BFzjkXWH5X4CyhDCAT+NrMuphZNwAz6wIUAKWt3xyRtpGVP53y4k84Wfm97yheHD96iP1bVzFs3BTfUSSEmiyAwDH9x4CFwHrgbefcWjN71sxuDAx7BUg2szLgF8BTgfuuBd4G1gEfAo8656qBPsBSMysBvgbec859GNpNEwmt0VNnsaZwTtMDo4yrqWH9ojfJ1Ze7RB2rfaEeGfLy8lxRkT4yIP7s2VVGxbY1ZOXf7DtKuyn+6E3ScyfRo3df31GkhcxsZWOn2+vjeyLN0HfgEDokdY2ZSePKSpbRrU+6nvyjlApApJmGjZvC/i2ron7SuIP7vuX4/l1cNPpy31GkjagARFogd+r9UT1pXHVVFVu+mE/21Xf4jiJtSAUg0gJx8fFcfMXtUTtpXHHhHEZec58meYty+uuKtFCP3n25oO9gNhd/7jtKSK1fvpALM/Po3LW77yjSxvTdbSKtcNHoyyn+6E1Kly4gvkNH33FararyBBYXz8DMbN9RpB2oAERaKXvyXRw9fMB3jNAwo3vP3r5TSDtRAYi0ksXF0b2X5qmSyKP3AEREYpQKQEQkRqkARERilApARCRGqQBERGKUCkBEJEapAEREYpQKQEQkRkXUF8KYWQWwo4V37w3sD2Ecn6JlW6JlO0DbEq6iZVtaux1pzrkffVoxogqgNcysqLFvxIlE0bIt0bIdoG0JV9GyLW21HToEJCISo1QAIiIxKpYK4CXfAUIoWrYlWrYDtC3hKlq2pU22I2beAxARkbPF0h6AiIjUowIQEYlRUV8AZjbNzDaaWZmZPeU7T2uY2f+Y2T4zK/WdpTXMbKCZLTaz9Wa21sz+2XemljKzJDP72sxKAtvya9+ZWsPM4s1slZn93XeW1jCz7Wa2xsyKzazId57WMLMeZjbPzDYE/s9cFrJ1R/N7AGYWD2wCpgDlwApghnNunddgLWRm+cBx4DXn3CjfeVrKzPoB/Zxz35hZN2AlcFMk/l3MzIAuzrnjZtYBWAr8s3PuK8/RWsTMfgHkARc45673naelzGw7kOeci/gPgZnZHOBz59zLZpYIdHbOHQ7FuqN9D2AcUOac2+qcOwXMBaZ7ztRizrklwEHfOVrLOfedc+6bwOVjwHpggN9ULeNqHQ9c7RD4ichXVWaWClwHvOw7i9QyswuAfOAVAOfcqVA9+UP0F8AAYFe96+VE6BNNtDKzdCAXWO43ScsFDpsUA/uAj5xzkbot/xd4EqjxHSQEHFBoZivN7EHfYVphMFABvBo4NPeymXUJ1cqjvQCskWUR+eosGplZV+Ad4H855476ztNSzrlq51wOkAqMM7OIOzxnZtcD+5xzK31nCZEJzrkxwLXAo4HDp5EoARgD/ME5lwucAEL2Xma0F0A5MLDe9VRgt6csUk/gePk7wBvOuXd95wmFwK75p8A0z1FaYgJwY+DY+Vxgkpn90W+klnPO7Q783gfMp/ZwcCQqB8rr7VXOo7YQQiLaC2AFkGlmGYE3T+4CFnjOFPMCb5y+Aqx3zv3Od57WMLMUM+sRuNwJuAbY4DdV8znnfumcS3XOpVP7/2SRc+5ez7FaxMy6BE4uIHC4pACIyDPnnHN7gF1mNjSwaDIQspMlEkK1onDknKsys8eAhUA88D/OubWeY7WYmf0JuArobWblwDPOuVf8pmqRCcB9wJrAsXOA/+2ce99jppbqB8wJnHEWB7ztnIvoUyijQB9gfu3rDBKAN51zH/qN1CqPA28EXsRuBX4aqhVH9WmgIiJybtF+CEhERM5BBSAiEqNUACIiMUoFICISo1QAIiIxSgUgIhKjVAAiIjHq/wGDgg9W7ANPuQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RqQBmOq8REY+IPO+0mQNMBh7yuqS1uJV+n3Aumd0KPAE8FGBOY0KqYt2rFH12jtsxOt3IafMoX/2y2zEi04F1kDvZ7RStkmi6Y5vH49HS0lK3Y5gYt2fzapJT0xh0S2t/C0WHyq3vk5iUTM6IsW5HiRxNjbBrGdx6t9tJABCRTarq8bXNLnQ2Jogu1ddx7tj+mCkQAMNGTaB632YaGxrcjhI5DqyDwZPcTtEmViSMCaKylX9g5PR5bscIufxJD7Bt5WK3Y0SOi7XQNTIui7YiYUyQHNy9hR798kjqkux2lJBL7dGLpNR0jh2qdDtK+DuxE7Ly3U7RZlYkjAmCpsZGqndvJG90+J+I7CwFn/k8VZ8sR5ua3I4S3qp3Q28rEsbElG2r/sgtk2e7HcN1uSWfp/z9P7kdI3zVnYKuvqaqC19WJIwJ0InD+0nomkqPtPCdWiFUMvsOovHKRc6csilyfDqwHnIj44T1VVYkjAnQp5veoXDCF9yOETaKps6hYsMSt2OEn0bn6q+4eHdztJMVCWMCULZ+KYPGznI7RliRuDj65I9nz+bVbkcJLwfWwuDIO2dlRcKYDjpbW0PDxfP0HjDY7ShhZ+CwkZw/vp/6ixfcjhI+6s9G3PkIsCJhTIftWfcqRdNuuNGicRROm0f5qj+6HSM8HCuDPgVup+gQKxLGdEDFJ2vJuuV24uIja3w5lJK6JNOj/zAO7t7idhT31VSG/ZTg/liRMKadLtXXcfZoBTnDY2fqjY7KK55EdcVHNDU2uh3FPRdqoFu62yk6zIqEMe1UtvJlCj77JbdjRIzhkx5gWywPOx18H3Imup2iw6xIGNMOhyq20r3vYJK7prgdJWJ075lOYtcenDi83+0oodd4BSQO4iL3V23kJjcmxJoaGzm+8wNuGTPV7SgRp2DCnXy66S9uxwi9/Wsi8rJXb1YkjGmjbatfIW/SA27HiFg5nlmUxdqUHZfOR8Td527GioQxbXDyyEHiE7vSs1em21EiVlb/XBrqL3C2tqb1xtHg6DboO9LtFAGzImFMGxwofYvCiTb1RqCKps5mz/rX3I4RGqf2QcZQt1MEzIqEMa0of38Z2aNnIhF88jFcxMXH0/uWEio+Wet2lM51vhpSouOo0/6vN+Ymzp89zeULp+k7cJjbUaLGoFuKOXt0L5fq69yO0nk+3QA5E9xOERRWJIy5iV1rX6Fomn0nItgKp82lfNXLbsfoHI1XIC4BRNxOEhRWJIzxo3LrerKGjiU+IcHtKFGnS3I3UvsM4VDFVrejBN++1TB4itspgsaKhDE+XLl8iTNVu8gZMdbtKFHrljFTOL77w+ibsuPyBeiS6naKoAmoSIhIuoi8JyIVznMvH22KReQDESkXkW0iMtdr20IR2S8iW5xHsbNeROTnIlLpvGdMIDmNaa/tK35PwbR5bseIenkT7mfb6lfcjhE8Rz6BfqPcThFUgR5JPAWsUNU8YIXzuqU64EFVLQBmAT8TEe9J1Z9U1WLncXW6yM8Bec7jUeC5AHMa02ZVlWV0yxpkU2+EQM9emSQkdeXkkYNuRwmO0wchPbruLxJokbgHWOQsLwLubdlAVfeoaoWzfAQ4AWS1od8XtdlGIE1E+gWY1ZhWaVMTx3asJ98z3e0oMaNgwhc4sOkdt2ME7twxSO3jdoqgC7RI9FHVowDOc++bNRaREiAJ2Ou1+sfOkNJPRaSLs24AcMirTZWzzlefj4pIqYiUVlfbzddNYLat/iN5E+5zO0ZMkbg4Bo6eQfmGt9yOEphPN8KgcW6nCLpWi4SILBeRMh+Pe9qzI+dI4CXgYVVtclb/C5AP3AakA/98tbmPLtRXv6q6QFU9qurJymrtAMUY/04eO0RcfBI9M6Lvr8Fw1yd7KFcu1HLuzCm3o3RMwyWIT4qay169tVokVHWGqhb6eLwJHL86DOQ8n/DVh4j0AJYB33WGj672fdQZUroEvACUOJuqgIFeXWQDRzryAY1pqwMf/ZnCSTeMmJoQKZo2l93rXnU7RsfsWwNDprqdolMEOty0FJjvLM8H3mzZQESSgCU0n2N4pcW2qwVGaD6fUebV74POVU7jgDNXh7WM6Qw7PnibAaOm2dQbLoqLjydrmIfKrevdjtI+qnClDpK6uZ2kUwT6E/E0MFNEKoCZzmtExCMizztt5gCTgYdaXuoK/E5EtgPbgUzgR876t4B9QCXwa+CxAHMa49eFc7VcOldDv5zhbkeJeTn5YzhzuILLl+rdjtJ2hzfDgOi9Sl9UfQ71RySPx6OlpaVuxzARpvTPCxg96//YN6vDRP3FC+xY8wpjZj3kdpS2KX8DCiJ7mFJENqmqx9c2O7Y2MW3vtg1kDBltBSKMJHdNIbV3LlWVZa03dtvZI9A9uq/OtyJhYtaVy5c4faicwbfe5nYU08ItY6ZybOf7aFNT643ddOgjGFjSersIZkXCxKztK/9AwVSb4TVcNU/ZEcZXO12ph4QuUXnZqzcrEiYmHd63k27pA+ia0t3tKMaPnulZxCV04eSxQ603dsO+1VF72as3KxIm5mhTE0fLVpNfMtPtKKYVhRO/wIGPw/Cb2KrQUA+JXd1O0umsSJiYs23NawwdH9lXo8QKiYsje9Q0dmwMs7mdqkoh2+fFQFHHioSJKadOHEYkjl5Z0X1FSjTpOyiPS2dPcuFcrdtR/ursYeiZ7XaKkLAiYWLKvo1/YuRkm8Av0hRN+xI714bJfSdqD8VMgQArEiaG7PzwL/Qv+qxNvRGB4hMSyBwymr3bNrgdBQ5vggGxc8dC+2kxMaHu/Bnqa4/RP9em3ohUuSM81Fbt5MrlS+6FuHKx+WR1lF/26s2KhIkJO9b8kZF2O9KIVzB1LmWrFrsXIEYue/VmRcJEvX1lH5KeU0RCYpLbUUyAkrul0jU9m8P7doR+56rN941I6NJ62yhiRcJEtYYrlzl1YCtDCm93O4oJkvzbpnO0fF3op+w49CEMjL3/j6xImKi2beXL3Dp1rtsxTJANG38P29e8HtqdnjsGPWLv0mkrEiZqHdm/i65pvemW2tPtKCbI0jL7InFxnDpxODQ7PH0Q0gaFZl9hxoqEiUra1MSR7asYcfvfuB3FdJLCSfey78M/h2ZnRz6J6hsL3YwVCROVtq9dwpBxd7sdw3QiiYtjQNFUdn74bufu6PIFSIzOW5O2hRUJE3VqTx5DtYn03gPcjmI6Wb+c4VysPUHd+TOdt5N9a2LusldvViRM1Knc8AZFU77odgwTIkXT5rBzTSdN2aEKTVcgIXYvn7YiYaLKro/eo1/hFJt6I4YkJCaRnjuSfWUfBr/zgxtg0Pjg9xtB7CfJRI2LF85Rd+owA4aMcDuKCbHBBbdz+uB2Gq5cDm7HF6ohtXdw+4wwViRM1Chfvdim3ohht06dy7aVLwevw1P7oFdu8PqLUFYkTFTYv+Njeg0sIDEptqZMMH/VNaU7XXv15cj+XcHp8Og26F8cnL4iWEBFQkTSReQ9Ealwnnv5aFMsIh+ISLmIbBORuV7bForIfhHZ4jyKnfVTReSM1/rvBZLTRLfGhgZq9n3C0KLPuB3FuGxEyUyOlK0JfMqOS+cgKTU4oSJcoEcSTwErVDUPWOG8bqkOeFBVC4BZwM9EJM1r+5OqWuw8tnitX+e1/ocB5jRRbNvKxYyYMsftGCZMDB13N9vXvRFYJ/vWwJApwQkU4QItEvcAi5zlRcANNw5W1T2qWuEsHwFOAFkB7tcYAI4e3E2X7hmkdE9rvbGJCb2y+kFTE6erj3asg6Ym0EaITwxusAgVaJHoo6pHAZznm14GICIlQBKw12v1j51hqJ+KiPeA8ngR2Soib4tIwU36fFRESkWktLq6OoCPYiKNNjVxeOtKbh3/ObejmDAzcsr97N24tGNvPvg+5EwIbqAI1mqREJHlIlLm43FPe3YkIv2Al4CHVfXqgOG/APnAbUA68M/O+s1AjqqOAn4B+D12VNUFqupRVU9Wlh2gxJKy9W+SW3KX2zFMGJK4OPoXTmHXx8vb/+a6GkjJDH6oCJXQWgNVneFvm4gcF5F+qnrUKQIn/LTrASwDvquqG736vno8eElEXgC+7aw/69XmLRF5VkQyVfVkmz6ViXpnao7T1HCFzL4D3Y5iwlT/wfkc2/MxFy+co2tK97a96WQlZAzt3GARJtDhpqXAfGd5PvBmywYikgQsAV5U1VdabOvnPAvN5zPKnNd9nXVXh6jigJoAs5ooUvH+EoqmPuB2DBPmiqbNpXz1H9v+huNl0Hdk5wWKQIEWiaeBmSJSAcx0XiMiHhF53mkzB5gMPNTyUlfgdyKyHdgOZAI/ctY/AJSJyFbg58CXVFUDzGqixK7SFfS9daJNvWFalZCYRK9BBezf8XHrjevPQpc2HnHEEImm370ej0dLS0vdjmE6UX3deXaseYUxn3vY7SgmgpQu+zWj/+Zh4hNuMsK+889wyyyIb3UUPuqIyCZV9fjaZn+KmYhSvmoxhTb1hmmnEZNns23lYv8Nrl32GnsFojVWJEzEOLhzEz2z80nqkux2FBNhUrqnkdwzi6MHd/tucGAd5E4KbagIYUXCRITGhgaqK0sZNmqi21FMhBpx+99weNtq31N2XDwN3dJDHyoCWJEwEWHbysXk29QbJkCDS+6kbH2LizCrd0PmLe4EigBWJEzYO3aokqSUXqT2uGH+SGPaJaNPNk2NDZypOf7XlSd2Qp9b3QsV5qxImLCmTU1UffIeBRPudDuKiRJFU75IxQZnEoeLtZDcw91AYc5O5ZuwVv7+n8j1fN7tGCaKSFwc/Qomsqt0JfkpF2C4/f91M1YkHKerj3Llcr3bMYyXSxfP03D5Ipn9c9yOYqLMgCEFbN5TyuUuPUmKi3c7TlizIuE4f+Ykly6ccTuGuY5QNHW22yFMlBo5fR7vlVVhxxE3Z0XCMXCYzddiTCxJTExiUO8MKo6fI6+PTcfhj524NsbErMIBPdl57ByNTdEzPVGwWZEwxsS0afm9WbHzeOsNY5QVCWNMTEvtkkCProkcqb3odpSwZEXCGBPzxg3J4KP9p4imWbGDxYqEMcYAnxmWwYa9dm+zlqxIGGMM0Lt7MpcaGjlTd8XtKGHFioQxxjg+O7w3q/eccDtGWLEiYYwxDhGhKDuNLYdq3Y4SNqxIGGOMl8GZKRypvUj9lUa3o4QFKxLGGNPCjBF9WLHThp3AioQxxtwgKSGOQendqDxx3u0orrMiYYwxPozM7kn5kTM0xfiUHVYkjDHGj2n5vVm5K7aHnQIqEiKSLiLviUiF83zD/SVFpFhEPhCRchHZJiJzvbaJiPxYRPaIyE4RecJr/c9FpNJ5z5hAchpjTEd0T04kpUsCx87E7r1mAj2SeApYoap5wArndUt1wIOqWgDMAn4mImnOtoeAgUC+qo4AFjvrPwfkOY9HgecCzGmMMR0yfmgGG/fF7jexAy0S9wCLnOVFwL0tG6jqHlWtcJaPACeALGfzN4AfqmqTs/3qcd09wIvabCOQJiL9AsxqjDEdMn5oBhv2nnQ7hisCLRJ9VPUogPPc+2aNRaQESAL2OquGAnNFpFRE3haRPGf9AOCQ11urnHXGGBNyfXokU3epkbP1sTdlR6tFQkSWi0iZj8c97dmRcyTwEvDw1SMHoAtQr6oe4NfAb68299GFz0sMRORRp8iUVldXtyeSMca02bT83qyKwZPYrd6+VFVn+NsmIsdFpJ+qHnWKgM//giLSA1gGfNcZPrqqCnjNWV4CvOC1fqBXu2zgiJ98C4AFAB6PJ7avVTPGdJq4OKFwQE+2VdVSlJ3W+huiRKDDTUuB+c7yfODNlg1EJInmAvCiqr7SYvMbwDRneQqwx6vfB52rnMYBZ64OaxljjFuGZqVy6NRFLjXEzpQdgRaJp4GZIlIBzHReIyIeEXneaTMHmAw8JCJbnEex1/u/KCLbgf8AHnHWvwXsAyppHoZ6LMCcxhgTFDNu7R1TU3ZINN2JyePxaGlpqdsxjDFRbuuhWlKTExialep2lKAQkU3OueEb2DeujTGmnUYNTKPscGxM2WFFwhhjOmDq8N6s2h39w05WJIwxpgN6dk0kOTGeE2eje8oOKxLGGNNBnxmawYa90cCDA/gAAApSSURBVD1lhxUJY4zpIBHh9iHpUT23kxUJY4wJQL+eXTlX38C5KJ2yw4qEMcYEKJrvO2FFwhhjAhQfJxT070HZ4TNuRwk6KxLGGBMEw3p350DNBS43NLXeOIJYkTDGmCCZMaIPK3YedztGUFmRMMaYIElOjKdvz2T2n7zgdpSgsSJhjDFBNHpQL7YeqiVa5sWzImGMMUH22eG9Wb07Om6CZkXCGGOCrGe3RBLihepzl9yOEjArEsYY0wkmDsvk/cqTbscImBUJY4zpBCLCbYPT+TDCp+ywImGMMZ1kQFpXai9e4cKlBrejdJgVCWOM6UQzRvRheQR/d8KKhDHGdKL4OCG/bw/Kj0TmlB1WJIwxppMN79udfdUXuNIYeVN2WJEwxpgQmHlrZE7ZYUXCGGNCIDkxnqzuyRysiawpO6xIGGNMiIzN6cUnn0bWlB1WJIwxJoSm3JLF6j2RM2VHQEVCRNJF5D0RqXCee/loUywiH4hIuYhsE5G5XttERH4sIntEZKeIPOGsnyoiZ0Rki/P4XiA5jTEmXPRKSSJOhJrzkTFlR6BHEk8BK1Q1D1jhvG6pDnhQVQuAWcDPRCTN2fYQMBDIV9URwGKv961T1WLn8cMAcxpjTNiYnJfJuorImLIj0CJxD7DIWV4E3NuygaruUdUKZ/kIcALIcjZ/A/ihqjY526PzJrHGGONFRBib04uPD5xyO0qrAi0SfVT1KIDz3PtmjUWkBEgC9jqrhgJzRaRURN4WkTyv5uNFZKuzvuAmfT7qvL+0ujpyxvmMMbFtYHo3as5fpu5yeE/Z0WqREJHlIlLm43FPe3YkIv2Al4CHrx45AF2AelX1AL8Gfuus3wzkqOoo4BfAG/76VdUFqupRVU9WVpa/ZsYYE3ZmjOjN8p3hPYCS0FoDVZ3hb5uIHBeRfqp61CkCPj+tiPQAlgHfVdWNXpuqgNec5SXAC84+z3rt/y0ReVZEMlU1MgbxjDGmDRLi48jrncrOo2cZ0a+H23F8CnS4aSkw31meD7zZsoGIJNFcAF5U1VdabH4DmOYsTwH2OO/pKyLiLJc4OSN7vl1jjPFhRL8eVJw4T0OYTtkRaJF4GpgpIhXATOc1IuIRkeedNnOAycBDXpe0Fnu9/4sish34D+ARZ/0DQJmIbAV+DnxJI+nbJ8YY0w7Nw07hOWWHRNPvXo/Ho6WlpW7HMMaYdvv4wCn69khmYHq3kO9bRDY554ZvYN+4NsaYMHBbbjqbDp4Ouyk7rEgYY0yYmJSXydow+5KdFQljjAkTGaldaFLl1IXLbke5xoqEMcaEkam3ZLE2jCYAtCJhjDFhREQYM6gXmw6Gx5QdViSMMSbMDMroxomzl7h4udHtKFYkjDEmHM24tQ/vhcF3J6xIGGNMGEqMj2NoVgq7j51zNYcVCWOMCVMF/Xuy69hZGpvc++6EFQljjAljM0b0cXXKDisSxhgTxlK6JJDWNZGq03Wu7N+KhDHGhLnbh2Tw8YFTrkzZYUXCGGMiwMRhWa7cF9uKhDHGRICs7l1oaGqiti60U3ZYkTDGmAjx2eG9Wb07tFN2WJEwxpgIISIUD0xj86enQ7ZPKxLGGBNBcjNTOHamnvoroZmyw4qEMcZEmJm39uG9HaH57oQVCWOMiTCJ8XEMzkyh4njnT9lhRcIYYyJQ4YCe7Dja+VN2WJEwxpgINT0EU3ZYkTDGmAiV2iWBHsmJHKm92Gn7sCJhjDERbPzQDD7cX9Np/QdUJEQkXUTeE5EK57mXjzbFIvKBiJSLyDYRmeu1bZ2IbHEeR0TkDWe9iMjPRaTSec+YQHIaY0w0u6uof6f1HeiRxFPAClXNA1Y4r1uqAx5U1QJgFvAzEUkDUNVJqlqsqsXAB8Drzns+B+Q5j0eB5wLMaYwxUSsxvvMGhQLt+R5gkbO8CLi3ZQNV3aOqFc7yEeAEkOXdRkS6A9OAN7z6fVGbbQTSRKRfgFmNMca0U6BFoo+qHgVwnnvfrLGIlABJwN4Wm+6j+YjkrPN6AHDIa3uVs85Xn4+KSKmIlFZXh3ZOE2OMiXYJrTUQkeVAXx+b/rU9O3KOBF4C5qtqU4vN84DnvZv76MLnxcCqugBYAODxeNy7x58xxkShVouEqs7wt01EjotIP1U96hSBE37a9QCWAd91ho+8t2UAJTQfTVxVBQz0ep0NHGktqzHGmOAKdLhpKTDfWZ4PvNmygYgkAUtoPsfwio8+ZgN/VtX6Fv0+6FzlNA44c3VYyxhjTOgEWiSeBmaKSAUw03mNiHhE5Orw0RxgMvCQ1+WuxV59fAn4Q4t+3wL2AZXAr4HHAsxpjDGmA8SNe6Z2Fo/Ho6WlpW7HMMaYiCIim1TV42ubfePaGGOMX1F1JCEi1cDBDr49Ewj9XcY7h32W8BQtnyVaPgfYZ7kqR1WzfG2IqiIRCBEp9Xe4FWnss4SnaPks0fI5wD5LW9hwkzHGGL+sSBhjjPHLisRfLXA7QBDZZwlP0fJZouVzgH2WVtk5CWOMMX7ZkYQxxhi/rEgYY4zxy4oEICKzRGS3cyc8XzdOiggi8lsROSEiZW5nCYSIDBSRVSKy07mj4T+4namjRCRZRD4Ska3OZ/mB25kCJSLxIvKJiPzZ7SyBEJEDIrLdmSooYqdqEJE0EXlVRHY5PzPjg9p/rJ+TEJF4YA/Nc09VAR8D81R1h6vBOkBEJgPnaZ5MsdDtPB3lzCjcT1U3Ozek2gTcG6H/JgKkqOp5EUkE1gP/0HI25EgiIv8EeIAeqnqX23k6SkQOAB5Vjegv04nIImCdqj7vTKjaTVVrg9W/HUk0T1Neqar7VPUysJjmO+NFHFVdC5xyO0egVPWoqm52ls8BO/Fz06lw59xd8bzzMtF5ROxfZiKSDdzJ9fd/MS5xbsMwGfgNgKpeDmaBACsS0I674JnQE5FcYDTwobtJOs4ZntlC8/1W3lPViP0swM+A7wAtbxwWiRR4V0Q2icijbofpoCFANfCCMwT4vIikBHMHViTacRc8E1oikgq8Bvxfr1vbRhxVbVTVYppvnlUiIhE5FCgidwEnVHWT21mCZIKqjgE+B3zTGa6NNAnAGOA5VR0NXACCel7VioTdBS8sOeP3rwG/U9XX3c4TDM4wwGpglstROmoCcLczlr8YmCYi/+tupI5T1SPO8wmab4xW4m6iDqkCqryOTl+luWgEjRWJ5hPVeSIy2Dnp8yWa74xnXOKc7P0NsFNVf+J2nkCISJaIpDnLXYEZwC53U3WMqv6Lqmarai7NPycrVfUrLsfqEBFJcS6KwBmeuQOIuKsCVfUYcEhEhjurpgNBvcCj1XtcRztVbRCRx4G/APHAb1W13OVYHSIifwCmApkiUgV8X1V/426qDpkAfBXY7ozlA/w/VX3LxUwd1Q9Y5FxFFwf8UVUj+tLRKNEHWNL89wgJwO9V9R13I3XYt4DfOX/k7gMeDmbnMX8JrDHGGP9suMkYY4xfViSMMcb4ZUXCGGOMX1YkjDHG+GVFwhhjjF9WJIwxxvhlRcIYY4xf/x9jV1+meBPD7QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ii in range(args.itr):\n",
    "    # setting record of experiments\n",
    "    setting = '{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_at{}_fc{}_eb{}_dt{}_mx{}_{}_{}'.format(args.model, args.data, args.features, \n",
    "                args.seq_len, args.label_len, args.pred_len,\n",
    "                args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.attn, args.factor, args.embed, args.distil, args.mix, args.des, ii)\n",
    "\n",
    "    # set experiments\n",
    "    exp = Exp(args)\n",
    "    \n",
    "    # train\n",
    "    print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))\n",
    "    exp.train(setting)\n",
    "    \n",
    "    # test\n",
    "    print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))\n",
    "    exp.test(setting)\n",
    "\n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cc3e5937",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# the prediction will be saved in ./results/{setting}/real_prediction.npy\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "# set saved model path\n",
    "setting = 'informer_DAB_ftM_sl12_ll6_pl1_dm512_nh8_el3_dl2_df2048_atprob_fc5_ebtimeF_dtTrue_mxFalse_exp_0'\n",
    "# path = os.path.join(args.checkpoints,setting,'checkpoint.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "26397b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from data.data_loader import Dataset_Custom\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "Data = Dataset_Custom\n",
    "timeenc = 0 if args.embed!='timeF' else 1\n",
    "flag = 'test'; shuffle_flag = False; drop_last = True; batch_size = 1\n",
    "\n",
    "data_set = Data(\n",
    "    root_path=args.root_path,\n",
    "    data_path=args.data_path,\n",
    "    flag=flag,\n",
    "    size=[args.seq_len, args.label_len, args.pred_len],\n",
    "    features=args.features,\n",
    "    timeenc=timeenc,\n",
    "    freq=args.freq\n",
    ")\n",
    "data_loader = DataLoader(\n",
    "    data_set,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=shuffle_flag,\n",
    "    num_workers=args.num_workers,\n",
    "    drop_last=drop_last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2c185dba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use GPU: cuda:0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "args.output_attention = True\n",
    "\n",
    "exp = Exp(args)\n",
    "\n",
    "model = exp.model\n",
    "\n",
    "setting = 'informer_DAB_ftM_sl12_ll6_pl1_dm512_nh8_el3_dl2_df2048_atprob_fc5_ebtimeF_dtTrue_mxFalse_exp_0'\n",
    "path = os.path.join(args.checkpoints,setting,'checkpoint.pth')          \n",
    "#os.path.join(args.checkpoints,setting,'checkpoint.pth')\n",
    "model.load_state_dict(torch.load(path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "499d40f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2208, 2), (2208, 2))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = np.load('./results/'+setting+'/pred.npy')\n",
    "trues = np.load('./results/'+setting+'/true.npy')\n",
    "preds = np.squeeze(preds)\n",
    "trues = np.squeeze(trues)\n",
    "preds.shape, trues.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a4c97e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9ee673f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw HUFL prediction\n",
    "plt.figure()\n",
    "plt.plot(trues[1000:1900,0], label='GroundTruth')\n",
    "plt.plot(preds[1000:1900,0], label='Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "879d791a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from data.data_loader import Dataset_Custom\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "Data = Dataset_Custom\n",
    "timeenc = 0 if args.embed!='timeF' else 1\n",
    "flag = 'test'; shuffle_flag = False; drop_last = True; batch_size = 1\n",
    "\n",
    "data_set = Data(\n",
    "    root_path=args.root_path,\n",
    "    data_path=args.data_path,\n",
    "    flag=flag,\n",
    "    size=[args.seq_len, args.label_len, args.pred_len],\n",
    "    features=args.features,\n",
    "    timeenc=timeenc,\n",
    "    freq=args.freq\n",
    ")\n",
    "data_loader = DataLoader(\n",
    "    data_set,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=shuffle_flag,\n",
    "    num_workers=args.num_workers,\n",
    "    drop_last=drop_last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2090097e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use GPU: cuda:0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "args.output_attention = True\n",
    "\n",
    "exp = Exp(args)\n",
    "\n",
    "model = exp.model\n",
    "\n",
    "setting = 'informer_DAB_ftM_sl12_ll6_pl1_dm512_nh8_el3_dl2_df2048_atprob_fc5_ebtimeF_dtTrue_mxFalse_exp_0'\n",
    "path = os.path.join(args.checkpoints,setting,'checkpoint.pth')       \n",
    "model.load_state_dict(torch.load(path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "72f5f5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# attention visualization\n",
    "idx = 0\n",
    "for i, (batch_x,batch_y,batch_x_mark,batch_y_mark) in enumerate(data_loader):\n",
    "    if i!=idx:\n",
    "        continue\n",
    "    batch_x = batch_x.float().to(exp.device)\n",
    "    batch_y = batch_y.float()\n",
    "\n",
    "    batch_x_mark = batch_x_mark.float().to(exp.device)\n",
    "    batch_y_mark = batch_y_mark.float().to(exp.device)\n",
    "    \n",
    "    dec_inp = torch.zeros_like(batch_y[:,-args.pred_len:,:]).float()\n",
    "    dec_inp = torch.cat([batch_y[:,:args.label_len,:], dec_inp], dim=1).float().to(exp.device)\n",
    "    \n",
    "    outputs,attn = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1b342e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1, 8, 12, 12]), torch.Size([1, 8, 6, 6]))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attn[0].shape, attn[1].shape #, attn[2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8937332d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 12, 12)\n"
     ]
    }
   ],
   "source": [
    "from scipy import io\n",
    "# 假设循环生成的矩阵大小为 (3, 3)，需要生成 5 个矩阵\n",
    "A_size = (12, 12)\n",
    "num_matrices = 8\n",
    "\n",
    "# 创建一个空的数组，用于保存生成的矩阵\n",
    "A_array = np.empty((num_matrices,) + A_size)\n",
    "\n",
    "layer = 0\n",
    "distil = 'Distil' if args.distil else 'NoDistil'\n",
    "for h in range(0,8):\n",
    "    plt.figure(figsize=[5,4])\n",
    "    A = attn[layer][0,h].detach().cpu().numpy()\n",
    "    A_array[h] = A\n",
    "    print(A.shape)\n",
    "    ax = sns.heatmap(A, vmin=0, vmax=A.max()+0.01, cmap=\"RdBu_r\")\n",
    "    # 设置横轴和纵轴标题以及字体大小\n",
    "    ax.set_xlabel(\"Time steps\", fontsize=10)\n",
    "    ax.set_ylabel(\"The softmax score\", fontsize=10)\n",
    "    plt.xticks(fontsize=8)\n",
    "    plt.yticks(fontsize=8)\n",
    "    plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "    plt.tight_layout() # 自动调整子图布局\n",
    "    if h==0:\n",
    "        plt.savefig(\"heatmap.pdf\", format=\"pdf\", dpi=1200)\n",
    "        plt.savefig(\"heatmap.svg\", format=\"svg\", dpi=600)\n",
    "        plt.savefig(\"heatmap.eps\", format=\"eps\", dpi=600)\n",
    "    plt.show()\n",
    "\n",
    "print(A_array.shape)\n",
    "\n",
    "# 将矩阵保存为 mat 文件\n",
    "io.savemat('A_array.mat', {' A_array':  A_array})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0b454281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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16sqRmAAAK0NiAgCjW6MJ1hQmADA6XTkAAIunMAGA0W1sLP6xC1V1dlVdWVX7q+q8Q2y/fVX9eVX9bVW9qaoeuNMxFSYAwGGrqqOTPDvJA5KcluThVXXapt1+IslLuvvuSR6W5P/udFxjTABgdHszwdoZSfZ391VJUlUXJDknyeXzLUty8+n5LZJcs9NBFSYAMLjeWPxVOVV1bpJz51bt6+59c8snJrl6bvlAknttOszTkvxJVf1Qki9KctZO51WYAACfZSpC9m2zSx3qZZuWH57kud3981V17yQvqKq7dW8d8ShMAGB0e3O58IEkJ88tn5TP7qp5TJKzk6S7X1dVxyU5Icl7tjqowa8AwJG4JMmpVXXHqjo2s8GtF27a5x1J7pckVXXXJMclee92B5WYAMDo9mDwa3dfX1WPS/KqJEcneU53X1ZVT09yaXdfmOSJSX69qn44s26eR3dvP02twgQAOCLdfVGSizatO3/u+eVJ7nM4x1SYAMDolnBVzl5RmADA6NwrBwBg8SQmADA6iQkAwOJJTABgdNtfgTsUhQkAjE5XDgDA4klMAGB0azSPicQEAFgZEhMAGN0e3CtnWRQmADA6XTkAAIsnMQGAwbXLhQEAFk9iAgCjM8YEAGDxJCYAMDqXCwMAK0NXDgDA4klMAGB0LhcGAFg8iQkAjG6NxpgoTABgdGt0VY6uHABgZUhMAGB0a9SVIzEBAFaGxAQABrdOdxdWmADA6HTlAAAsnsQEAEYnMQEAWDyJCQCMzgRrAACLJzEBgNGt0RgThQkADK7XqDDRlQMArAyJCQCMTmICALB4EhMAGJ175QAAK0NXDgDA4klMAGB0EhMAgMWTmADA4LrXJzFRmADA6HTlAAAsnsQEAEYnMQEAWDyJCQAMzt2FAQCWQGICAKNbo8REYQIAo1ufe/jpygEAVofEBAAGZ/ArAMASSEwAYHRrlJgoTABgdAa/AgAsnsQEAAZn8CsAwBJITABgdGs0xkRhAgCD05UDALAEEhMAGN0adeVITACAlSExAYDB9RolJgoTABjdGhUmunIAgJUhMQGAwa1TV47EBABYGRITABidxAQAYPEkJgAwuHUaY6IwAYDBrVNhoisHAFgZEhMAGJzEBAC40auqs6vqyqraX1XnbbHPd1bV5VV1WVW9cKdjSkwAYHRdn/dTVtXRSZ6d5P5JDiS5pKou7O7L5/Y5NclTktynuz9YVbfa6bgKEwAY3B515ZyRZH93X5UkVXVBknOSXD63z/cneXZ3fzBJuvs9Ox1UVw4A8Fmq6tyqunTuce6mXU5McvXc8oFp3bw7J7lzVb22ql5fVWfvdF6JCQAMrjcW35XT3fuS7Ntml0OdtDct3yTJqUnOTHJSkr+oqrt194e2OqjEBAA4EgeSnDy3fFKSaw6xz8u7+1Pd/bYkV2ZWqGxJYQIAg+uNxT924ZIkp1bVHavq2CQPS3Lhpn3+IMm/T5KqOiGzrp2rtjuorhwAGFzvwVU53X19VT0uyauSHJ3kOd19WVU9Pcml3X3htO2bq+ryJJ9O8mPd/f7tjqswAQCOSHdflOSiTevOn3veSX5keuyKwgQABmfmVwCAJZCYAMDglnG58F6RmAAAK0NiAgCD683Tmg1MYQIAg9OVAwCwBBITABicxAQAYAkkJgAwOINfAYCVoSsHAGAJJCYAMLi9uLvwskhMAICVITEBgMGt092FFSYAMLgNXTkAAIsnMQGAwRn8CgCwBBITABicCdYAAJZAYgIAg3OvHABgZejKAQBYAokJAAzOBGsAAEsgMQGAwa3TBGsKEwAY3DpdlaMrBwBYGRITABicwa8AAEsgMQGAwRn8CgCsDINfAQCWQGICAIMz+DVJVX3PNtvOrapLq+rSjY1/PtJTAAA3Mp9LV85PbrWhu/d19+ndffpRR33R53AKAGAn3bXwx17Ztiunqt601aYkt158cwCAG7OdxpjcOsm3JPngpvWV5K+W0iIA4LCs0xiTnQqTVyS5WXf/3eYNVXXxUloEAByWNbpaePvCpLsfs82271p8cwCAGzOXCwPA4NapK8cEawDAypCYAMDg3CsHAFgZG3vdgAXSlQMArAyJCQAMrrM+XTkSEwBgZUhMAGBwG2s0w5rCBAAGt6ErBwBg8SQmADA4g18BAJZAYgIAgzPBGgDAEkhMAGBw6zTGRGECAIPTlQMAsAQSEwAYnMQEAGAJJCYAMDiDXwGAlbGxPnWJrhwAYHVITABgcO4uDACwBBITABhc73UDFkhhAgCDM48JAMASSEwAYHAbZfArAMDCSUwAYHDrNPhVYgIArAyJCQAMbp2uylGYAMDg3CsHAGAJJCYAMDj3ygEAWAKFCQAMrpfw2I2qOruqrqyq/VV13jb7fXtVdVWdvtMxdeUAwOD2YvBrVR2d5NlJ7p/kQJJLqurC7r58037HJ3l8kr/ezXElJgDAkTgjyf7uvqq7P5nkgiTnHGK//5HkmUk+sZuDKkwAYHAbS3jswolJrp5bPjCt+4yqunuSk7v7Fbt9LwoTAOCzVNW5VXXp3OPczbsc4mWfGZ5SVUcl+cUkTzyc8xpjAgCDW8a9crp7X5J92+xyIMnJc8snJblmbvn4JHdLcnHN7n58myQXVtWDu/vSrQ6qMAGAwe3RzK+XJDm1qu6Y5J1JHpbkuw5u7O4PJznh4HJVXZzkR7crShJdOQDAEeju65M8LsmrklyR5CXdfVlVPb2qHnykx5WYAMDg9uomft19UZKLNq07f4t9z9zNMSUmAMDKkJgAwOD2KjFZBokJALAyJCYAMLhen5sLK0wAYHS6cgAAlkBiAgCDk5gAACyBxAQABreMe+XsFYUJAAxuj+6VsxS6cgCAlSExAYDBGfwKALAEEhMAGNw6JSYKEwAY3DpdlaMrBwBYGRITABicy4UBAJZAYgIAg1unwa8SEwBgZUhMAGBw63RVjsIEAAa3sUalia4cAGBlSEwAYHAGvwIALIHEBAAGtz4jTBQmADA8XTkAAEsgMQGAwblXDgDAEkhMAGBw6zTBmsIEAAa3PmWJrhwAYIVITABgcC4XBgBYAokJAAzO4FcAYGWsT1miKwcAWCESEwAYnMGvAABLIDEBgMGt0+BXiQkAsDIkJgAwuPXJSxQmADA8g18BAJZAYgIAg+s16syRmAAAK0NiAgCDW6cxJgoTABiceUwAAJZAYgIAg1ufvERiAgCsEIkJAAxuncaYKEwAYHDrdFWOrhwAYGVITABgcGZ+BQBYAokJAAzOGBMAgCWQmADA4NZpjInCBAAGpysHAGAJJCYAMLiNXp+uHIkJALAyJCYAMLj1yUsUJgAwvHW6iZ+uHABgZUhMAGBw6zSPicQEAFgZEhMAGNw6TbCmMAGAwRn8CgCwBBITABicwa8AAEsgMQGAwa3T4FeJCQCwMiQmADC4dndhAGBVbKQX/tiNqjq7qq6sqv1Vdd4htv9IVV1eVW+qqj+rqlN2OqbCBAA4bFV1dJJnJ3lAktOSPLyqTtu0298mOb27vyrJy5I8c6fjKkwAYHAbS3jswhlJ9nf3Vd39ySQXJDlnfofu/vPu/ti0+PokJ+10UIUJAHAkTkxy9dzygWndVh6T5JU7HdTgVwAY3DImWKuqc5OcO7dqX3fvm9/lkE059LEekeT0JPfd6bwKEwAY3DLulTMVIfu22eVAkpPnlk9Kcs3mnarqrCQ/nuS+3f0vO51XVw4AcCQuSXJqVd2xqo5N8rAkF87vUFV3T/JrSR7c3e/ZzUElJgAwuL2Yx6S7r6+qxyV5VZKjkzynuy+rqqcnubS7L0zyP5PcLMlLqypJ3tHdD97uuAoTAOCIdPdFSS7atO78uednHe4xFSYAMLh1uleOwgQABreMq3L2isGvAMDKkJgAwOCWcbnwXpGYAAArQ2ICAIPbi8uFl0ViAgCsDIkJAAxuncaYKEwAYHAuFwYAWAKJCQAMbsPgVwCAxZOYAMDg1icvUZgAwPDW6aocXTkAwMqQmADA4CQmAABLIDEBgMGt071yFCYAMDhdOQAASyAxAYDBuVcOAMASSEwAYHDrNPhVYgIArAyJCQAMbp2uylGYAMDgdOUAACyBxAQABrdOXTkSEwBgZUhMAGBw6zTBmsIEAAa3YfArAMDiSUwAYHDr1JUjMQEAVobEBAAGt05jTBQmADA4XTkAAEsgMQGAwa1TV47EBABYGRITABicMSYAAEsgMQGAwa3TGBOFCQAMTlcOAMASSEwAYHDdG3vdhIWRmAAAK0NiAgCD21ijMSYKEwAYXK/RVTm6cgCAlSExAYDBrVNXjsQEAFgZEhMAGNw6jTFRmADA4NZpSnpdOQDAypCYAMDg3CsHAGAJJCYAMLh1GvwqMQEAVobEBAAGt04TrClMAGBwunIAAJZAYgIAgzPBGgDAEkhMAGBw6zTGRGECAINbp6tydOUAACtDYgIAg1unrhyJCQCwMiQmADC4dbpcWGECAINrg18BABZPYgIAg1unrhyJCQCwMiQmADA4lwsDACyBxAQABrdOV+UoTABgcLpyAIAbvao6u6qurKr9VXXeIbbftKpePG3/66q6w07HVJgAwOC6e+GPnVTV0UmeneQBSU5L8vCqOm3Tbo9J8sHu/vIkv5jk53Y67o6FSVV9RVXdr6putmn92Tu2GgBYV2ck2d/dV3X3J5NckOScTfuck+R50/OXJblfVdV2B922MKmqxyd5eZIfSvLmqpo/4c8cRuMBgCXpJTx24cQkV88tH5jWHXKf7r4+yYeTfMl2B91p8Ov3J7lnd1839Qu9rKru0N3PSrJlxVNV5yY5d1r8ge7et8N5yOxz81mxKH5PLJrf1Oq6/pPv3DaFOBKb/pYnyb5N3/+hzrm5ptnNPjewU2FydHdflyTd/faqOjOz4uSULU6Wad99Sfx4D9+58bmxOH5PLJrf1I3ILv6WH0hy8tzySUmu2WKfA1V1kyS3SPKB7c670xiTa6vqa+YaeV2Sb01yQpJ/u8NrAYD1dUmSU6vqjlV1bJKHJblw0z4XJnnU9Pzbk7y6dxhZu1Ni8sgk18+vmPqIHllVv7bblgMA66W7r6+qxyV5VZKjkzynuy+rqqcnubS7L0zym0leUFX7M0tKHrbTcWudJmUZnf5bFsnviUXzm+LzQWECAKwME6wBACtDYbICdprSFw5HVT2nqt5TVW/e67awHqrq5Kr686q6oqouq6r/utdtYn3pytlj05S+b0ly/8wuq7okycO7+/I9bRjDqqpvTHJdkud39932uj2Mr6pum+S23f3Gqjo+yRuSPMT/p1gGicne282UvrBr3f2a7DBPAByO7n5Xd79xev7RJFfks2f4hIVQmOy93UzpC7ASplnA757kr/e2JawrhcneO+zpegH2wnQz199N8oTu/shet4f1pDDZe7uZ0hdgT1XVMZkVJb/T3b+31+1hfSlM9t5upvQF2DPTbep/M8kV3f0Le90e1pvCZI9NU/wfnNL3iiQv6e7L9rZVjKyqXpTkdUnuUlUHquoxe90mhnefJN+d5Juq6u+mxwP3ulGsJ5cLAwArQ2ICAKwMhQkAsDIUJgDAylCYAAArQ2ECAKwMhQkAsDIUJgDAylCYAAAr4/8DKJtE5bz0MOgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiYAAAHiCAYAAADcXwd5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAehUlEQVR4nO3de/StdV0n8PfHI4gNqFOUF25qoUlOZRrmmMkoFjAJTKtSGm9FnZqRHMtKtIYxp7RsqnEmupzyXoJoF0+G0SpjWaYFWjkBYUc0OSDiFWWpAf6+88d+frb58budH3uz9/c5r9dae7Gfy36e774sfp/z/n6f71OttQAALIO7LboBAACrFCYAwNJQmAAAS0NhAgAsDYUJALA0FCYAwNJQmLClqrpvVb29qj5TVb+46PbMQ1U9rqqu3uFr31pVzxyeP6uq/nK2rVteVfXBqjp5m/t2+dlU1Yuq6rfvonNdWlXff1ecC5aVwuQgdSB/UJLsTvKxJPdqrT1vjs2ai+EPy61DYfWZqnpfVf1KVd1/dZ/W2l+01h66zWPd7o9Ua+3U1tpr5tDu231HVfXAqmpVdfdZn+tgUlVfUVUXVNX1VXVTVb2jqh696HZtpapeWFU3Tz0+V1UrVXXkotsGs6QwYTuOS3Jl28FsfPP+I3oAx39Da+2IJF+a5D8luV+Sd08XJ9zeGAqgDd7D4UkuS/LITH4Pr0nyR1V1+F3ZtgPVWntJa+3w1UeSn09yaWvtY4tuG8ySwoQvRuxV9b+q6pNV9YGqOnXY9uokz0zyE8O/0k6uqntU1f8e/sV5/fD8HsP+J1XV/qp6flXdkORVU+t+oqpurKoPV9WZVXXakF58oqpeONWeu1XVuVX1/qr6eFVdVFVfOmxbTQ3OrqoPJXnbgbzX1tqtrbUrkjwlyUeTPG+63VNteH5VXTckLFdX1ROr6pQkL0zylOGz+Pth3x3F71X1lVX1tuE9fqyqfqeq7jNse12SY5P84XCun0jy9uGlnxrWPWaz726Dc15aVS+tqr8Z0oI3b/XZVtXpVXVFVX1qeP3D1hz2G6vqyuH8r6qqw7b5/l9eVddW1aer6t1V9bhh/f2q6rNV9WVT+z6yqj5aVYcMy99XVVcN57ykqo6b2rdV1bOr6p+S/NPa87bWrmmt/VJr7cOttS+01vYkOTTJZonZoVX12uH3cEVVPWrqfA+oqt8d2veBqnrO1LYTq+qdw2f34ZokdYdObX9SVf3j8F38SpLa5mdXSZ6eSVEFo6IwYdWjk1yd5MgkL0vyiqqq1tqzkvxOkpcN/1L70yQ/meSbknx9kq9LcmKSn5o61v0y+ZfocZl0A62uOyzJUUnOS/KbSZ6Wyb9aH5fkvKp68LDvc5KcmeTxSR6Q5JNJzl/T3scneViSb9vJm22tfSHJm4dz305VPTTJOUm+cUhZvi3JB1trf5zkJZmkL4e31r5uJ+eePlWSl2byHh+W5JgkLxra9/QkH0ry5OFcL0vyLcPr7jOse+ewvO53N7yXc6vqLWvO+4wk3zec97Yk/2fN9i9+tlX1kCQXJHluki9PcnEmxdKhU/v/50w+o69M8pDc/rewmcsy+Q19aZLXJ3ljVR3WWrshyaVJvntq36clubC1dmtVnZlJgfgdQ5v+YmjjtDMz+VxOGD6Ht1TVues1oqq+PpPCZN8mbT09yYVJ7pNkb5JfGV57tyR/mOTvM/ltPzHJc6tq9Xf5hSQ/ksl385hh+38dXntkkt/N5PM6Msn7kzx2ql3HDgXNseu053FJ7ju8HsalteZxED6SfDDJycPzZyXZN7XtS5K0JPcbll+d5Gemtr8/yWlTy6t/uJPkpCS3JDlsavtJST6XZNewfMRw/EdP7fPuJGcOz69K8sSpbfdPcmuSuyd54PDaBx/Ae31Rkt9eZ/0PJfmnqTbuH55/VZIbk5yc5JCtjpXJH9Hvn/os/3KH38mZSf52ve9oWF5973efWrfpd7fOOS5N8nNTyycM39eu9T7bJP89yUVTy3dLcl2Sk6ba+ENT209L8v4Nzr3pZ5NJAfp1w/OnJHnH8HxXkhuSnDgsvzXJ2Wva9Nkkxw3LLckTtvmZ3yvJ/0vygi1+P3+65jP73PD80Uk+tGb/FyR51QbHem6S3x+ePyPJu6a2VZL9q7+lLdr9iiSv3snvzMNj2R8SE1bdsPqktfbZ4elGfe4PSPLPU8v/PKxb9dHW2ufXvObjbZJSJJMiJUk+MrX9c1PnOy7J7w//WvxUJoXKFzL5F+Kqazd5L9t1VJJPrF3ZWtuXyR+QFyW5saourKoHrN3vzqrJIMwLhy6jTyf57Uz+5XygDuS7S27/2f1zkkPWnHd6++2+69bayrD9qE2Ot63PqqqeN3TH3DR8z/eeasebk5wwpGhPSnJTa+1vhm3HJXn51O/jE5n8Ud+oTRud/56ZpB3vaq29dIvdb5h6/tkkh9Vk/MpxSR6w2pahPS/M8FutqocMac0Nw3f8kqn3+IDpdrbW2gG0+7uiG4eRUpiwE9dn8j/kVccO61bd2VtWX5vk1NbafaYeh7XWrpvVOYYI/smZdAPcQWvt9a21b87kfbZMBhre6fOu8dLheF/bWrtXJt0V02MM1p5rVuc+Zur5sZmkUdMDKKfPc7vveugiOiaT1GSj403/FtY1jCd5fibdNf+2tXafJDdleP9DYXtRJt1ET0/yuqmXX5vkB9f8Pu7ZWvurDd7Deue/R5I/GN7HD27V3k1cm+QDa9pyRGvttGH7ryX5xyTHD9/xC/Ov3/GHM/XZTX22W/mOTIqxS+9Eu2FpKUzYiQuS/FRVffnQT35eJv/an5VfT/KzqwMah/OcsdkLanJp7bO2OnBVHTIM3rwgk3Evv7TOPg+tqicMf7w+n0mas5r2fCTJA4fCZkvDYNEXbbD5iCQ3ZzKY9agkP75m+0eSPHhq+aNJVtas24mnVdUJVfUlSV6c5E1TadZaFyX5jzUZ/HtIJoOF/yXJdBHw7Ko6ehhE+8Ikb9hGG47IZHzLR5PcvarOy6RbZdprM+n+OT23/339epIXVNXXJElV3buqvmsb58yw/yFJ3pTJ9/qMIQXaqb9J8umaDJa+Z1XtqqqHV9U3DtuPSPLpJDdX1Vcn+S9Tr/2jJF9TVd8xpC/PyeQ3uZVnJnntkLDA6ChM2ImfSXJ5kvdm0j//nmHdrLw8kwGGf1JVn0nyrkz68tc1DMT8smG/jTylqm5O8qnh2B9P8sjW2nr/ur9Hkp/LJEW4IclXZPIHN0neOPz341X1nm28l2OSvGODbT+d5BsySQr+KMnvrdn+0kwKwE9V1Y8N3TQ/m+Qdw7pv2urkNZn74q1rVr8uk3FDN2QyIPk5a1+3qrV2dSZJzv/N5PN4ciYDcm+Z2u31Sf4kyTXDYzu/hUsyGSvyvky6fz6fNd0YrbV3ZFKIvae19sGp9b+fSYJ14dA98g9JNrwSKfniJHir3+G/T/LtSb41/3qF081DinNAhoLuyZkM4v1AJp/Rb2XSLZUkP5bke5J8JpMB32+Yeu3HMumS+blMfo/HZ+q3Mgx+vXl68OtQwD4hk6INRqkU3fSuqr45ybNba2ctui3TquroJG9srT1m0W1ZVVWXZjJ497cW3ZbtqKq3JXl9L+0F7rzuJ1CC1tpfJlm6qc5ba/szuUSUHRi6Q74hyabdeMC46MoBlk5VvSbJnyZ5bmvtM4tuD3DX0ZUDACyNLbtyhpHkZ2QyR0DL5FLAva21q+bcNgDgILNpV05VPT+TaZgrk8viLhueX7DR9M4AADu1aVdOVb0vyde01m5ds/7QJFe01o7f4HW7M9wj5Vd/4acf+f1Pf8rsWsxB757HPGHRTQDY1G23XLetGzLOyq0fu2bm4zIOOfLBd+l7WLVVV85K7jj9eDK5d8mGkxK1yd069yTJrR+52iAWABiZqnplJnMC3dhae/g62yuTealOy+RWDs9qrW05/9NWhclzk/xZTW4fvjr50bGZ3OTsnO03HwCYm5WNJm+eq1dncqftjSb8OzWTiQOPz2SSzF/LJpNlrtq0MGmt/fFw2/MTMxn8unr3y8s2mcIaABi51trbq+qBm+xyRv719gnvqqr7VNX9W2sf3uy4W16VM9xHYrOpvgGARbpTt3xa3/R40cGeYajGdh2V299qYv+w7s4VJgDAwWd6vOgOrTd4dstxpwoTAOjdyuwTkxnYn8mNTFcdnclcaJsyJT0AdK61lZk/ZmBvkmfUxDcluWmr8SWJxAQA2IGquiDJSUmOrKr9Sf5HkkOSpLX260kuzuRS4X2ZXC78vds5rsIEAHq3gK6c1tpZW2xvSZ59oMfVlQMALA2JCQD0bg6XCy+KwgQAereYmV/nQlcOALA0JCYA0LsRdeVITACApSExAYDeLefMrzuiMAGAzs1optaloCsHAFgaEhMA6N2IunIkJgDA0pCYAEDvjDEBAJg9iQkA9G5EU9IrTACgd7pyAABmT2ICAL1zuTAAwOxJTACgdyMaY6IwAYDe6coBAJg9iQkAdK618cxjIjEBAJaGxAQAemfwKwCwNAx+BQCYPYkJAPRuRF05EhMAYGlITACgdyvjuVxYYQIAvdOVAwAwexITAOidy4UBAGZPYgIAvTPGBABg9iQmANC7EY0xUZgAQO9GVJjoygEAlobEBAA619p4Zn6VmAAAS0NiAgC9G9EYE4UJAPTOPCYAALMnMQGA3o2oK0diAgAsDYkJAPRuRGNMFCYA0DtdOQAAsycxAYDejagrR2ICACwNiQkA9M4YEwCA2ZOYAEDvRpSYKEwAoHcGvwIAzJ7EBAB6N6KuHIkJALA0JCYA0LsRjTFRmABA73TlAADMnsQEAHo3oq4ciQkAsDQkJgDQuxGNMVGYAEDvRlSY6MoBAJaGxAQAetfaolswMxITAGBpSEwAoHfGmAAAzJ7EBAB6N6LERGECAL0z8ysAwOxJTACgdyPqypGYAABLQ2ICAL0b0QRrChMA6J2uHACA2VOYAEDvVlZm/9iGqjqlqq6uqn1Vde4624+tqj+vqr+tqvdW1WlbHVNhAgAcsKraleT8JKcmOSHJWVV1wprdfirJRa21RyR5apJf3eq4xpgAQO8WM8HaiUn2tdauSZKqujDJGUmunG5ZknsNz++d5PqtDqowAYDOtZXZX5VTVbuT7J5atae1tmdq+agk104t70/y6DWHeVGSP6mqH07yb5KcvNV5FSYAwB0MRcieTXap9V62ZvmsJK9urf1iVT0myeuq6uGtbRzxKEwAoHeLuVx4f5JjppaPzh27as5OckqStNbeWVWHJTkyyY0bHdTgVwBgJy5LcnxVPaiqDs1kcOveNft8KMkTk6SqHpbksCQf3eygEhMA6N0CBr+21m6rqnOSXJJkV5JXttauqKoXJ7m8tbY3yfOS/GZV/Ugm3TzPam3zaWoVJgDAjrTWLk5y8Zp15009vzLJYw/kmAoTAOjdHK7KWRSFCQD0zr1yAABmT2ICAL2TmAAAzJ7EBAB6t/kVuF1RmABA73TlAADMnsQEAHo3onlMJCYAwNKQmABA7xZwr5x5UZgAQO905QAAzJ7EBAA611wuDAAwexITAOidMSYAALMnMQGA3rlcGABYGrpyAABmT2ICAL1zuTAAwOxJTACgdyMaY6IwAYDejeiqHF05AMDSkJgAQO9G1JUjMQEAlobEBAA6N6a7CytMAKB3unIAAGZPYgIAvZOYAADMnsQEAHpngjUAgNmTmABA70Y0xkRhAgCdayMqTHTlAABLQ2ICAL2TmAAAzJ7EBAB65145AMDS0JUDADB7EhMA6J3EBABg9iQmANC51saTmChMAKB3unIAAGZPYgIAvZOYAADMnsQEADrn7sIAAHMgMQGA3o0oMVGYAEDvxnMPP105AMDykJgAQOcMfgUAmAOJCQD0bkSJicIEAHpn8CsAwOxJTACgcwa/AgDMgcQEAHo3ojEmChMA6JyuHACAOZCYAEDvRtSVIzEBAJaGxAQAOtdGlJgoTACgdyMqTHTlAABLQ2ICAJ0bU1eOxAQAWBoSEwDoncQEAGD2JCYA0LkxjTFRmABA58ZUmOjKAQCWhsQEADonMQEADnpVdUpVXV1V+6rq3A32+e6qurKqrqiq1291TIkJAPSu1V1+yqraleT8JE9Ksj/JZVW1t7V25dQ+xyd5QZLHttY+WVVfsdVxFSYA0LkFdeWcmGRfa+2aJKmqC5OckeTKqX1+IMn5rbVPJklr7catDqorBwC4g6raXVWXTz12r9nlqCTXTi3vH9ZNe0iSh1TVO6rqXVV1ylbnlZgAQOfayuy7clpre5Ls2WSX9U7a1izfPcnxSU5KcnSSv6iqh7fWPrXRQSUmAMBO7E9yzNTy0UmuX2efN7fWbm2tfSDJ1ZkUKhtSmABA59rK7B/bcFmS46vqQVV1aJKnJtm7Zp8/SPIfkqSqjsyka+eazQ6qKwcAOtcWcFVOa+22qjonySVJdiV5ZWvtiqp6cZLLW2t7h23fWlVXJvlCkh9vrX18s+MqTACAHWmtXZzk4jXrzpt63pL86PDYFoUJAHTOzK8AAHMgMQGAzs3jcuFFkZgAAEtDYgIAnWtrpzXrmMIEADqnKwcAYA4kJgDQOYkJAMAcSEwAoHMGvwIAS0NXDgDAHEhMAKBzi7i78LxITACApSExAYDOjenuwgoTAOjciq4cAIDZk5gAQOcMfgUAmAOJCQB0zgRrAABzIDEBgM65Vw4AsDR05QAAzIHEBAA6Z4I1AIA5kJgAQOfGNMGawgQAOjemq3J05QAAS0NiAgCdM/gVAGAOJCYA0DmDXwGApWHwKwDAHEhMAKBzBr8mqarv3WTb7qq6vKou/63XvWGnpwAADjJ3JjH56SSvWm9Da21Pkj1JcutHrh5RzxcALJ+DZvBrVb13o01J7jv75gAAB7OtEpP7Jvm2JJ9cs76S/NVcWgQAHJAxjTHZqjB5S5LDW2t/t3ZDVV06lxYBAAdkTGMmNi1MWmtnb7Lte2bfHADgYOZyYQDo3Ji6ckywBgAsDYkJAHTuoLlcGABYfiuLbsAM6coBAJaGxAQAOtcynq4ciQkAsDQkJgDQuZURzbCmMAGAzq3oygEAmD2JCQB0zuBXAIA5kJgAQOdMsAYAMAcSEwDo3JjGmChMAKBzunIAAOZAYgIAnZOYAADMgcQEADpn8CsAsDRWxlOX6MoBAJaHxAQAOufuwgAAcyAxAYDOtUU3YIYUJgDQOfOYAADMgcQEADq3Uga/AgDMnMQEADo3psGvEhMAYGlITACgc2O6KkdhAgCdc68cAIA5kJgAQOfcKwcAYA4UJgDQuTaHx3ZU1SlVdXVV7auqczfZ7zurqlXVo7Y6pq4cAOjcIga/VtWuJOcneVKS/Ukuq6q9rbUr1+x3RJLnJPnr7RxXYgIA7MSJSfa11q5prd2S5MIkZ6yz3/9M8rIkn9/OQRUmANC5lTk8tuGoJNdOLe8f1n1RVT0iyTGttbds970oTACAO6iq3VV1+dRj99pd1nnZF4enVNXdkvxykucdyHmNMQGAzs3jXjmttT1J9myyy/4kx0wtH53k+qnlI5I8PMmlNbn78f2S7K2q01trl290UIUJAHRuQTO/Xpbk+Kp6UJLrkjw1yfesbmyt3ZTkyNXlqro0yY9tVpQkunIAgB1ord2W5JwklyS5KslFrbUrqurFVXX6To8rMQGAzi3qJn6ttYuTXLxm3Xkb7HvSdo4pMQEAlobEBAA6t6jEZB4kJgDA0pCYAEDn2nhuLqwwAYDe6coBAJgDiQkAdE5iAgAwBxITAOjcPO6VsygKEwDo3ILulTMXunIAgKUhMQGAzhn8CgAwBxITAOjcmBIThQkAdG5MV+XoygEAlobEBAA653JhAIA5kJgAQOfGNPhVYgIALA2JCQB0bkxX5ShMAKBzKyMqTXTlAABLQ2ICAJ0z+BUAYA4kJgDQufGMMFGYAED3dOUAAMyBxAQAOudeOQAAcyAxAYDOjWmCNYUJAHRuPGWJrhwAYIlITACgcy4XBgCYA4kJAHTO4FcAYGmMpyzRlQMALBGJCQB0zuBXAIA5kJgAQOfGNPhVYgIALA2JCQB0bjx5icIEALpn8CsAwBxITACgc21EnTkSEwBgaUhMAKBzYxpjojABgM6ZxwQAYA4kJgDQufHkJRITAGCJSEwAoHNjGmOiMAGAzo3pqhxdOQDA0pCYAEDnzPwKADAHEhMA6JwxJgAAcyAxAYDOjWmMicIEADqnKwcAYA4kJgDQuZU2nq4ciQkAsDQkJgDQufHkJQoTAOjemG7ipysHAFgaEhMA6NyY5jGRmAAAS0NiAgCdG9MEawoTAOicwa8AAHMgMQGAzhn8CgAwBxITAOjcmAa/SkwAgKUhMQGAzjV3FwYAlsVK2swf21FVp1TV1VW1r6rOXWf7j1bVlVX13qr6s6o6bqtjKkwAgANWVbuSnJ/k1CQnJDmrqk5Ys9vfJnlUa+1rk7wpycu2Oq7CBAA6tzKHxzacmGRfa+2a1totSS5Mcsb0Dq21P2+tfXZYfFeSo7c6qMIEANiJo5JcO7W8f1i3kbOTvHWrgxr8CgCdm8cEa1W1O8nuqVV7Wmt7pndZtynrH+tpSR6V5PFbnVdhAgCdm8e9coYiZM8mu+xPcszU8tFJrl+7U1WdnOQnkzy+tfYvW51XVw4AsBOXJTm+qh5UVYcmeWqSvdM7VNUjkvxGktNbazdu56ASEwDo3CLmMWmt3VZV5yS5JMmuJK9srV1RVS9OcnlrbW+SX0hyeJI3VlWSfKi1dvpmx1WYAAA70lq7OMnFa9adN/X85AM9psIEADo3pnvlKEwAoHPzuCpnUQx+BQCWhsQEADo3j8uFF0ViAgAsDYkJAHRuEZcLz4vEBABYGhITAOjcmMaYKEwAoHMuFwYAmAOJCQB0bsXgVwCA2ZOYAEDnxpOXKEwAoHtjuipHVw4AsDQkJgDQOYkJAMAcSEwAoHNjuleOwgQAOqcrBwBgDiQmANA598oBAJgDiQkAdG5Mg18lJgDA0pCYAEDnxnRVjsIEADqnKwcAYA4kJgDQuTF15UhMAIClITEBgM6NaYI1hQkAdG7F4FcAgNmTmABA58bUlSMxAQCWhsQEADo3pjEmChMA6JyuHACAOZCYAEDnxtSVIzEBAJaGxAQAOmeMCQDAHEhMAKBzYxpjojABgM7pygEAmAOJCQB0rrWVRTdhZiQmAMDSkJgAQOdWRjTGRGECAJ1rI7oqR1cOALA0JCYA0LkxdeVITACApSExAYDOjWmMicIEADo3pinpdeUAAEtDYgIAnXOvHACAOZCYAEDnxjT4VWICACwNiQkAdG5ME6wpTACgc7pyAADmQGICAJ0zwRoAwBxITACgc2MaY6IwAYDOjemqHF05AMDSkJgAQOfG1JUjMQEAlobEBAA6N6bLhRUmANC5ZvArAMDsSUwAoHNj6sqRmAAAS0NiAgCdc7kwAMAcSEwAoHNjuipHYQIAndOVAwAc9KrqlKq6uqr2VdW562y/R1W9Ydj+11X1wK2OqTABgM611mb+2EpV7UpyfpJTk5yQ5KyqOmHNbmcn+WRr7auS/HKSn9/quFsWJlX11VX1xKo6fM36U7ZsNQAwVicm2ddau6a1dkuSC5OcsWafM5K8Znj+piRPrKra7KCbFiZV9Zwkb07yw0n+oaqmT/iSA2g8ADAnbQ6PbTgqybVTy/uHdevu01q7LclNSb5ss4NuNfj1B5I8srV289Av9KaqemBr7eVJNqx4qmp3kt3D4g+21vZscR4y+dx8Vlu77ZbrFt2ELvg9MWt+U8vrtluu2zSF2Ik1f8uTZM+a73+9c66tabazz+1sVZjsaq3dnCSttQ9W1UmZFCfHbXCyDPvuSeLHe+B2x+fG7Pg9MWt+UweRbfwt35/kmKnlo5Ncv8E++6vq7knuneQTm513qzEmN1TV10818uYk357kyCT/bovXAgDjdVmS46vqQVV1aJKnJtm7Zp+9SZ45PP/OJG9rW4ys3SoxeUaS26ZXDH1Ez6iq39huywGAcWmt3VZV5yS5JMmuJK9srV1RVS9OcnlrbW+SVyR5XVXtyyQpeepWx60xTcrSO/23zJLfE7PmN8VdQWECACwNE6wBAEtDYbIEtprSFw5EVb2yqm6sqn9YdFsYh6o6pqr+vKquqqorquq/LbpNjJeunAUbpvR9X5InZXJZ1WVJzmqtXbnQhtGtqvqWJDcneW1r7eGLbg/9q6r7J7l/a+09VXVEkncnOdP/p5gHicnibWdKX9i21trbs8U8AXAgWmsfbq29Z3j+mSRX5Y4zfMJMKEwWbztT+gIshWEW8Eck+evFtoSxUpgs3gFP1wuwCMPNXH83yXNba59edHsYJ4XJ4m1nSl+AhaqqQzIpSn6ntfZ7i24P46UwWbztTOkLsDDDbepfkeSq1tovLbo9jJvCZMGGKf5Xp/S9KslFrbUrFtsqelZVFyR5Z5KHVtX+qjp70W2ie49N8vQkT6iqvxsepy26UYyTy4UBgKUhMQEAlobCBABYGgoTAGBpKEwAgKWhMAEAlobCBABYGgoTAGBpKEwAgKXx/wHENSctdwILtQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer = 2\n",
    "distil = 'Distil' if args.distil else 'NoDistil'\n",
    "for h in range(0,8):\n",
    "    plt.figure(figsize=[10,8])\n",
    "    plt.title('Informer, {}, attn:{} layer:{} head:{}'.format(distil, args.attn, layer, h))\n",
    "    A = attn[layer][0,h].detach().cpu().numpy()\n",
    "    ax = sns.heatmap(A, vmin=0, vmax=A.max()+0.01)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f91a22a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Pytorch",
   "language": "python",
   "name": "py1.9.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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