cvs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "turned-diploma",
   "metadata": {},
   "source": [
    "$$\\frac{dSV(t)}{dt} = I_{\\rm external}$$\n",
    "\n",
    "$$\\frac{dP_a(t)}{dt} = \\frac{1}{C_a}\\left(\\frac{P_a(t) - P_v(t)}{R_{\\rm TPR}(S)} - SV \\cdot f_{\\rm HR}(S)\\right)$$\n",
    "\n",
    "$$\\frac{dP_v(t)}{dt} = \\frac{1}{C_v} \\left( -C_a \\frac{dP_a(t)}{dt} + I_{\\rm external} \\right)$$\n",
    "\n",
    "$$\\frac{dS(t)}{dt} = \\frac{1}{\\tau_{\\rm Baro}} \\left( 1- \\frac{1}{1 + e^{-k_{\\rm width} \\left(P_a(t) - P_{a_{\\rm set}} \\right)}} -S \\right)$$\n",
    "\n",
    "where\n",
    "\n",
    "$$R_{\\rm TPR} (S) = S(t) \\left( R_{\\rm TPR_{\\rm Max}} - R_{\\rm TPR_{\\rm Min}}\\right) + R_{\\rm TPR_{\\rm Min}} +R_{\\rm TPR_{\\rm Mod}} $$\n",
    "\n",
    "$$ f_{\\rm HR}(S) = S(t) \\left(f_{\\rm HR_{\\rm Max}} - f_{\\rm HR_{\\rm Min}} \\right) + f_{\\rm HR_{\\rm Min}}$$\n",
    "\n",
    "\n",
    "* SV: cardiac stroke volume (amount of blood ejected by the heart)\n",
    "* Pa: arterial blood pressure\n",
    "* Pv: venous blood pressure\n",
    "* S: autonomic barorelfex tone (reflex responsible for adapting pertubations in blood pressure, keeping homeostasis)\n",
    "\n",
    "The ODE state: $x_t = \\left( SV(t), P_a(t), P_v(t), S(t) \\right)$ and observations $y_t = \\left( P_a(t), P_v(t), f_{\\rm HR} (t)\\right) + \\epsilon$ \n",
    "\n",
    "We wish gain a mechanistic understanding for system inputs:\n",
    "$u = \\left( I_{\\rm external}, R_{\\rm TPR_{\\rm Mod}}\\right)$. Note, $ I_{\\rm external}  < 0$ implies a patient is loosing blood, while $R_{\\rm TPR_{\\rm Mod}} \\ge 0$, implies spectic shoke (i.e., total peripheral resistance is getting low) resulting in 4 classes:\n",
    " * Healthy (both non-negative)\n",
    " * Hemorrhagic shock ($I_{\\rm external}  < 0,R_{\\rm TPR_{\\rm Mod}} \\ge 0 $)\n",
    " * Distributive shock ($I_{\\rm external}  \\ge 0, R_{\\rm TPR_{\\rm Mod}} < 0$)\n",
    " * Combined shock ($I_{\\rm external}  < 0, R_{\\rm TPR_{\\rm Mod}} < 0$)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c6c9d3e7-6d1d-4e1b-b8b5-9b9244f8977c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "from data.cvs.cvs_data import load_data_config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3577db-c142-4cda-a7f1-d8edb6c637e4",
   "metadata": {},
   "source": [
    "### Generate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f20965d7-d99f-4530-bf53-1d0915aec569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Munch({'output_dir': '/Users/paidamoyochapfuwa/Personal/structured_latent_ODEs/data/cvs/', 'seq_len': 86, 'data_size': 1000, 'delta_t': 1.0, 'noise_std': 0.05, 'seed': 12, 'num_epochs': 400, 'norm': 'zero_to_one', 'mini_batch_size': 128})\n",
      "raw_data:  (1000, 86, 3)\n",
      "latent_data:  (1000, 86, 4)\n",
      "params_data:  1000\n",
      "find_norm_params:  (900, 86, 3)\n",
      "max_val:  [3.5212814  1.28347305 3.14691054] min_val:  [ 0.55656853 -0.02966171  0.74440984]\n"
     ]
    }
   ],
   "source": [
    "args = load_data_config()\n",
    "print(args)\n",
    "set_seed(args.seed)\n",
    "\n",
    "make_dataset(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amber-juvenile",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "constant-engineering",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_dict = torch.load(args.output_dir + 'processed_data.pkl')\n",
    "train_params = torch.load(args.output_dir + 'train_params_data.pkl')\n",
    "data_norm_params = torch.load(args.output_dir + 'data_norm_params.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "gothic-department",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(900, 86, 3) (900,) (900,)\n",
      "(900, 86, 3)\n",
      "{'mean': array([1.26053264, 0.86790564, 1.41845876]), 'std': array([0.77864926, 0.2724141 , 0.49734277]), 'max': array([3.5212814 , 1.28347305, 3.14691054]), 'min': array([ 0.55656853, -0.02966171,  0.74440984])}\n"
     ]
    }
   ],
   "source": [
    "print(obs_dict['train'].shape, train_params['i_ext'].shape, train_params['r_tpr_mod'].shape)\n",
    "\n",
    "iext = train_params['i_ext']\n",
    "rtpr = train_params['r_tpr_mod']\n",
    "\n",
    "\n",
    "train_data = obs_dict['train']\n",
    "#train_data = np.swapaxes(train_data, 2, 1)\n",
    "print(train_data.shape)\n",
    "\n",
    "print(data_norm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "unable-technology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find_norm_params:  (900, 86, 3)\n",
      "max_val:  [3.5212814  1.28347305 3.14691054] min_val:  [ 0.55656853 -0.02966171  0.74440984]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean': array([1.26053264, 0.86790564, 1.41845876]),\n",
       " 'std': array([0.77864926, 0.2724141 , 0.49734277]),\n",
       " 'max': array([3.5212814 , 1.28347305, 3.14691054]),\n",
       " 'min': array([ 0.55656853, -0.02966171,  0.74440984])}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from data.cvs import cvs_data\n",
    "cvs_data.find_norm_params(obs_dict['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "married-arrangement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig max_min:  3.5212813985371514 0.5565685265929045\n",
      "0 max_val:  3.5212813985371514 min_val:  0.5565685265929045\n",
      "orig max_min:  1.2834730515820645 -0.029661711469034735\n",
      "1 max_val:  1.2834730515820645 min_val:  -0.029661711469034735\n",
      "orig max_min:  3.146910538990956 0.7444098434146311\n",
      "2 max_val:  3.146910538990956 min_val:  0.7444098434146311\n",
      "0.0 1.0\n"
     ]
    }
   ],
   "source": [
    "for idx in range(train_data.shape[2]):\n",
    "    print(\"orig max_min: \",data_norm_params['max'][idx], data_norm_params['min'][idx])\n",
    "    max_val = train_data[:,:, idx].max()\n",
    "    min_val = train_data[:,:, idx].min()\n",
    "    print(idx, \"max_val: \", max_val, \"min_val: \", min_val)\n",
    "    train_data[:,:, idx] = (train_data[:,:, idx]  - min_val)/ (max_val - min_val)\n",
    "\n",
    "print(train_data.min(), train_data.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "narrative-guyana",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.swapaxes(train_data, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "micro-trauma",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IR=-2,0', 'IR=-2,0', 'IR=-2,0', 'IR=0,0', 'IR=0,0', 'IR=-2,0', 'IR=0,0', 'IR=-2,0']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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L9gviAwAAAF7EuBYA3OfZmV1//etfdf/995+20CVJF154ocaOHat33303hi1DNFhnfnGaIwAAAPzIblwLAIg+zxa7kpKSdNZZZ9neLyEhQSUlJTFoEWKJ0xwBAPCnaB+g4gAY/MZueRAAQOR5tth1zTXXaNKkSdq2bdtp7/Pll19q0qRJuvrqq2PYsmDy2sDRbo0vAAiKHTt2qLBwntvNAKrNOmaw3nZ6gMru+aw4ABZ9XhsX+p11XEt8ASD6PFvsmjBhgs4++2z17t1bvXr10ujRozV+/HiNHz9ed911l26++WbdfPPNqlmzph5++GG3m+t7Xh84Ol0QHwD8oqSkRPn5091uBlBt1jGD9bbTA1R2z2flhwNgdsUM02JHtB/PchLR5fVxNwAEgWeLXb/4xS+0cOFCPfPMM2rfvr327t2rzz77TOvXr9fu3bvVpk0bPfHEE1q6dKkaNWrkdnN9xzpo8cPA8ecYdAEIiuTkZI0YMdLtZiBAot1HWscM1tt2s1jsxiCRviK03f+vTrycxtSumGFa7DCdPee0oOhGcSbIYz2/jbsBwI88W+ySTq7blZWVpSeffFJvvPGGVqxYoZUrV5YXwfr27asaNWq43Uxfsg5a/DZziiNiAIIiPT1d2dm3u90MeFQ4P/jtZuU4fU7r/a1jBrsxhN3MLafPZ9c+u8KO05lk1b3Pz9kVM+z2271HTh/vdPad3XISTmeueeFz7CWc1ggA0efpYheix+9HlKpqPwMFAEDQVKfIYjdTyq64U1g4T507dz7t2nGmB5icztxyyulpleG0x67YY42hXTHDaYHQyunjnc6+s/t/TmeuVeczZI2h08+xn3HaKABEXkIoFAq53QgTmzdv1pgxY3T22Werc+fOmjBhghIT46eGt3//YZWUlLndDE/o2bOHdu3aqSZNmmrFijW2909OTlS9einE0AAxNEP8zMUihmlpKUpKim6/wmcgfEH/Hi1YMF8FBTOUm5t32sKGXf9nfQ7rbevj7e7vNZFubzgxt3sPnI5RYvEaotk+u/ekqsebfo7teDmX230n40HQc3ksEENzxNBctGPoJJf7vtj15Zdf6ve//70KCgqUn5+v6667Tv/yL//idrNihi/i/3E66CGZmSOGZoifOYpd4HtkXhhZtKhQs2bN1LBhv1a/ftlx+UP756rz+q0xt8bQ7v5O99vx2+OrirFdDE35KZd7vcAcDeRyc8TQHDE0R7ELEcMXMXwkM3PE0AzxM0exC0H7HrnxI9caw3j8of1z4bx+08+h01lNTh8fbZGYGWYaQ7uZX37O5fHwnQxaLncDMTRHDM15qdgVP+f7Ie6w3gEAwG/CWaMr0rx20ZpY9+duvH67dcOcXj0x1pz+/2jEmDW9AAA/5+li14cffqhRo0apd+/euvfee/U///M/le6zefNmZWZmutA6f4nHTpGBAQAg2kyvbGhVnaJBkH7EV0c8vF674o/TqyfGmtv/X4r+hRDcZLdYPwCgMs8Wu4qKijRkyBDt2LFDLVu2VFFRkXJycjRvXsUrBf3000/auXOnS630j3jsFBkYAACiza5vsRa37A7EVKdoEKQf8dURb6+3Kl4oJnmdNUZBipn1tfCdAAB7ni12TZkyRdddd52WLl2qqVOnavXq1erRo4f+8Ic/6PXXX3e7eb4Tj52i3cDA7lLrAADYcXr6WSQOxATpR3x1xNvrBexU9Z3gDAYAqMizxa5//OMfGjBggBITTzaxbt26ev7559WrVy89/vjjWrVqlcst9BcGipVjkJ8/XcXFxcrPn+5yywAAfuX09DNmaACIBs5gAICKkt1uwOnUrl1bhw8frrAtISFBTz/9tPbs2aMHHnhA9evXV1JSkksthN+NGDGy/BLXAABEQ//+OWc80GS3HwCqIzc3r/yKjQAAD8/s6tixo1588UXt2bOnwvbk5GRNmzZNTZs21ciRI/Xuu++61EL4XXb27Vq3bp2ys2+XxPRvAO4jD8WeXcyt+53eHwBiwTprlFwEIN55ttg1btw47du3Tz169NDkyZMr7KtTp45mzZqltLQ0TZkyxaUWImiY/g3AbeSh2LOLuXW/0/sDgBu4KjmAeOfZYleLFi305ptvaty4cbr44osr7W/cuLEWLVqkIUOGqHHjxi60EEHDuikA3EYeij7rDz5rzO32271HvIcAvMDuYhgUvwAEXUIoFAq53QiEb//+wyopKXO7Gb6UnJyoevVSiKEBYmiG+JmLRQzT0lKUlBTdY0N8BsLn9DPQs2cP7dq1U02aNNWKFWsc7w8icpE5Ymgu2jGM91y+YMH88jW9+vfPqZTrrPtjje+QOWJojhia81Iu9+zMrtMpLS3VxRdfrI0bN7rdFE/jaI05YggAwcPMLADxyO5KsJyCDSBofFfskiQmo9mjwzJHDAEgeKw/+JzuB4AgsCt+Ob04BwB4jS+LXbDHkWlzdp0+AEQbeQcAEAvW4pfdxTnonwB4HcWugOLItDm7Th8Aoo284xw/wADAnN3FOeifAHid74pdiYmJGjNmjBo2bOh2UxBnmOkFINaYpes81/IDDADMWQ/62p32CABe44ti186dO3XixAlJUkJCgm699VbVq1fP5VYh3jDTC0CsMUvXea7lBxgARJ+1f+IgMACv8UWxKzMzU5s3b5Z08mqMmZmZ2rJli8utQryz+0FFpw8g0uIxrzgtXlEgBIDY4yAwAK/xRbHLevVFrsYIL7A7okWnDyDS4jGv2BWvCgvnqXPnziosnFfl/ngsEAJArDld7oPcDCDafFHsAvzA+iOUU2kARBp5pbL8/OkqLi5Wfv70KvfHY4EQAGLN6XIfdvsphgEwRbErIOgQ3Gf9EWo384v3DIBTnKJX2YgRI9WsWTONGDGyyv0UCAEg9uxmeln3W2fpcqACgKmEkA/OCWzTpo3eeOMNZWRkqLS0VJdeeqkWLVqkSy+91O2muW7//sMqKSlTz549tGvXTjVp0lQrVqxxu1m+kJycqHr1UspjGG3W98h6e8GC+SoomKHc3Dzf/JCNdQyDhviZi0UM09JSlJQU3WNDfAaqz5or+R6ZI4bmiKG5aMeQXO4uu98qQRgXu408ZI4YmvNSLmdmV0Bw5Nr7rO+R9bb1CBYzvwDEI7vcx9F+APAfu98q1lm6zGQGYIpiV0DQIXif9T2y3rYrfgFAPLDLfRzcAQD/sfutkp19u9atW6fs7Nur3M9BYABO+aLYdcUVVyglJUWSlJCQUOE2EBR2xS8AsAri4N8u93FwBwDiD2dAAHDKF8Wu1157Ta1atZIkJSYm6rXXXtP5559fvv/48eMutQyIHrsF7gEgCDNArbmNYhYAwMp0+Q/G0UD88Xyx68svv9S2bdtOu/+tt95Sz549Y9giwB0c0QJgZXe1Kz8IQsEOABBdTpf/sPaHpn2NH/tXIN55tti1d+9eZWdnKysrS71791ZOTo72799fvn/r1q0aOnSo7rvvPh04cMDFlgKxwREtAFbWwb8fiuJ2l58HAMCO0+KX3cEhLo4CBI9ni13PPPOMNm3apBEjRui+++7TV199pWeffVaSlJ+fr1tuuUXr1q1Tnz59tGLFCpdbC0Sf20e0AHifFy504fQHA6ctAgBM2Y2T7Q4OcXEUIHg8W+x67733NHLkSI0bN055eXmaOHGi3n77bU2dOlXPPvusWrVqpXnz5unpp59WgwYN3G4uEHORPqIFwP+8cKELfjAAANxmdyDF2hfZjZOrc2CGsTXgLQmhUCjkdiOq0q5dO82YMUNdunSRJP3444/q3LmzkpOTNWrUKN15551KSkpyuZXu27//sEpKytxuhi8lJyeqXr2UwMZwwYL5KiiYodzcvCo75p49e2jXrp1q0qSpVqxYE9b/CHoMo434mYtFDNPSUpSUFN1jQ0H6DNjlnkjje2SOGJojhuaiHUNyubd57TtUnXGytb+zPsauP4x0f+m1GPoRMTTnpVzu2ZldJ06cUEpKSvnt1NRUSdLw4cM1ZswYCl2SduzYocLCeW43Ax7l9IhWOEejCgvnqXPnznwOAZ+KxlFoTksEAPhddWYh251F4XSJEWaGAZHl2WLX6WRmZrrdBM8oKSlRfv50t5sBnwpnYWvrtvz86SouLuZz+L8YpMBvInGhCy6OAQAImuocuLFbF8zpEiN2ywA4XVQ/iExfczzGzCm3YxTJ/++7YpdfZ3Tt379fEydOVPfu3ZWRkaE+ffpo4cKFRs+ZnJysESNGRqiFiHfV6XCt20aMGKlmzZqd9nMY9E7Z9CIAXpgZF7T3xKl4f/2RuNCF3eeei2MAAILIriDmdNF8u7MunC6qbxXpg1NujKFMxxR2jzcdm0c7hpEu9lX1fJH+XDltQyTHjZ4udm3atEkffPBB+V9CQkKlbaf+vOzIkSPKzc1VYWGhrr/+ek2YMEH16tXTww8/rJdffjns501PT1d29u0RbCniWXUWtrZuy86+XevWrSv/HDrtlL3WITi9v90ROrvnj8TMONPX6PQootPnd1rwjPTjTV9/0NgtuOv0KPTptv0cC9IDAOC8OGbXB1tvWws1Tk+bdDomMh1DVmeMZ93mdKxtZVdQtI7NTX8bmBZ2Ij1urU7B1C5GkX4NTn9POeHpYtejjz6qIUOGaMiQIRo2bJhCoZAeeeSR8m1DhgzR4MGDNWTIELebekZz5szRpk2b9OSTT2rChAnKycnRrFmzdPXVV2vq1KnatWuX200EKqmqQ7brpJ12yqYdgl0HFO0Ox+4Ind3zW2fGVadDdRoTu/vbdSiR7sCcDrTsblsHdk6fP94KMXbvp9Oj0KfbdqbnBAAA9uz6YOtta6HG6WmTToscTmeiWVWn8GLd5vS3iN1BPruxudNxtl2Mncbc9MC6Xfuqej67GNm1yW5sbtcGu99TTni22DV79my9+uqrFf5mz55dafup2162ZMkSNWjQQFlZWeXbEhMTNXz4cJ04cUJvvvmmi60DIsdpp2zaIdh1QNHucEyLf9aZcVV18k47Paev0a5DMe2EnRY8nT7eOrBz+vxVvYcmnarXOS3uUagCAMAdTvtga6HG6WmTToscTmeihVN4cVrccboOmt3Y3Ok42y7GTmNuemDdrn3hrE1n1ya7sbnTNpjMZksIhUIhx49CtR08eFBXXHGFevTooRdffLHCvkOHDqlTp066/vrrNXXq1LCen8uiho9Ly5qLdAwXLKh4CWbrbbv7210m2unzO72EtN3/s7LGr6r7m17G2mmb7WJoGhOnMbKzaFGhZs2aqWHDfq1+/bIj8vzW18jl6r2NXG6OGJojhua8dLn6cPH+h4/vkDmvjcut7MaY4XA69rdjF0PTcbYd03F7tNtXHXZjc6esr8FJLvdssatNmzZKSEio1n1PreXlRVu3blXv3r01aNAgPfLII5X2d+zYUS1atNCSJUvCen46hPDRqZrzWgzd7nCcqk78Yt1JmQ5kYi0an0GTTjVcXvkO+ZHX8pAfEUNzxNAcxa74xnfInNdjGI0xZKSf0+sxtPLauFzyVi73bLFrypQp1S52SdKYMWOi2JrwffLJJ8rJyVFeXp7GjRtXaf/VV1+t1NRUrVy5MqznP3DgqEpLvf9F9KKkpETVrVubGBoghmaIn7lYxLBu3dpR/4HEZyB8fI/MEUNzxNBctGNILvc2vkPmiKE5YmjOS7k8OeL/PULuvvtut5sQEXa1xFAo5KioZ1W3bu2wH4uTiKE5YmiG+Jnzewz93n4vIIbmiKE5YmjOzzH0c9u9ghiaI4bmiKE5L8TQswvUB0VKSook6ejRo1XuP3r0qOrWrRvLJgEAAAAAAAQWxa4oS09PV0JCgnbv3l1p38GDB3XkyBE1btzYhZYBAAAAAAAED8WuKEtNTVWrVq302WefVdq3fv16SScXqQcAAAAAAIA5il0x0KdPH+3atUvLli0r31ZWVqaCggLVqFFDvXv3drF1AAAAAAAAweHZqzEGybFjx9SvXz9t375dgwcPVsuWLbV8+XIVFRVp/PjxGj58uNtNBAAAAAAACASKXTGyb98+TZ48WWvWrNHhw4fVsmVLDR06VH379nW7aQAAAAAAAIFBsQsAAAAAAACBwZpdAAAAAAAACAyKXQAAAAAAAAgMil0AAAAAAAAIDIpdAAAAAAAACAyKXQAAAAAAAAgMil0AAAAAAAAIDIpdPrN//35NnDhR3bt3V0ZGhvr06aOFCxe63SxP2rJli/7f//t/uuqqq9S2bVt1795djz32mA4cOFDhfl988YVGjx6tLl266LLLLtOvfvUrffTRRy612rtKS0s1cOBAXXTRRSopKamwb+fOnRo/fryuueYatW/fXgMGDNCf//xnl1rqLWVlZZozZ4769OmjjIwMdevWTQ899JB2795d4X7E8PS++uor3XPPPercubPatm2rnj176o9//KPKysoq3M9PMSSXO0M+jxxyefjI52aCmMsl8rkT5PLIIZeHj1xuzi/5PCEUCoVi9t9g5MiRI7rjjjv0xRdfaODAgbrgggu0YsUKvffee7rvvvs0atQot5voGdu2bVO/fv2UnJysgQMHqkmTJvr000+1dOlStWrVSoWFhUpJSdGXX36pnJwc1axZUwMHDlRKSormzp2rnTt3qqCgQFdeeaXbL8Uzpk2bphdeeEGStHHjRiUnJ0uS9uzZo+zsbP3www8aPHiwGjVqpIULF2rjxo169tlndfPNN7vZbNeNHz9eS5cuVWZmpq699lp99dVXmjt3rpo2baqFCxeqbt26xPAMiouL1a9fPx05ckSDBg1S8+bNtXr1aq1du1bZ2dn6wx/+IMlfn0NyuTPk88gil4ePfB6+IOZyiXzuBLk8ssjl4SOXm/FVPg/BN6ZPnx5q3bp16E9/+lP5ttLS0tCwYcNCl156aWjnzp0uts5bTsVk69atFba/+uqrodatW4defvnlUCgUCg0fPjzUrl270DfffFN+n++//z7UtWvXUK9evWLaZi9bv3596JJLLgm1bds21Lp169CJEyfK9/3ud78LtW7dOvThhx+Wbzt69GgoKysr1Llz59Dhw4fdaLInrFq1KtS6devQ73//+wrbFy9eHGrdunVo+vTpoVCIGJ7Jo48+GmrdunVo6dKl5dvKyspCgwYNCrVu3br8O+6nGJLLnSGfRw65PHzkczNBzOWhEPncCXJ55JDLw0cuN+enfM5pjD6yZMkSNWjQQFlZWeXbEhMTNXz4cJ04cUJvvvmmi63zjuPHj+uDDz7Q5ZdfrlatWlXY17dvX0nS+++/r7179+pvf/ubMjMz1bx58/L7pKWl6bbbbtPWrVu1fv36WDbdkw4fPqz7779fv/zlL9WhQ4cK+0pLS/WnP/1J7du3V6dOncq316pVS4MHD9b+/fv1zjvvxLbBHjJv3jylpKRo3LhxFbb37t1beXl5Ov/884mhje3bt0uSunfvXr4tISFBmZmZkqTPP//cdzEkl1cf+TxyyOVmyOdmgpjLJfJ5dZHLI4dcboZcbs5P+Zxil08cPHhQ27ZtU0ZGhhISEirsa9++vSRpw4YNbjTNc8466ywtX75cjz76aKV9e/fulSQlJSWVd5an4vdzGRkZkhT3HaokPf744zp48KAee+yxSvu++OILHTlyhBhWobS0VB9++KE6deqk1NRUSdKxY8f0008/qUaNGho3bpxuuOEGYmjjggsukHTys/ZzX3/9tSSpUaNGvoohudwZ8nnkkMvDRz43F7RcLpHPnSCXRw65PHzk8sjwUz6n2OUTu3fvVigUUpMmTSrtS01NVUpKioqLi11omfckJiaqefPmOu+88yrtmzlzpiSpc+fO+u677ySpypg2btxYkuI+pqtWrdKiRYs0ceJE1a9fv9L+Uws5EsPKiouLdfz4cTVr1kyrVq3SzTffrPbt26tDhw4aPny4tm3bJokY2snLy1OrVq00YcIEFRUVqbi4WHPmzNGCBQvUtWtXderUyVcxJJc7Qz6PDHK5GfK5uaDlcol87gS5PDLI5WbI5ZHhp3xOscsnDh48KEk6++yzq9xfu3ZtHT16NJZN8p3Fixdr8eLFatKkibKzs3Xo0CFJVce0Vq1akhTXMd29e7ceeeQR3XbbbbruuuuqvM+ZPpe1a9eWFL8x/PHHHyVJRUVFuv/++9WjRw9NmzZNo0eP1kcffaTbb79d3377LTG00aBBA91zzz3as2ePhg4dqszMTE2cOFEZGRmaOnWqEhISfBVDcnlkkM+rj1xujnxuLmi5XCKfRwK5vPrI5ebI5ZHhp3yeHPX/gIgI2Vw0MxQKVZpCjf+zcOFC/e53v9PZZ5+tF154QampqbYxlRS3MQ2FQnrwwQdVp04dTZgw4Yz3s9sXrzH86aefJJ28NO+0adPKBybXXXedLr30Uo0aNUrPP/+8unXrdtrniPcYStKMGTM0adIkNWvWTGPHjlWjRo20YcMGvfrqq8rJydErr7ziq88hudwc+bz6yOWRQT43F7RcLpHPTZHLq49cHhnk8sjwUz6n2OUTKSkpkk5fAT169KiaNWsWyyb5xgsvvKBp06YpNTVV06dPLz9P+FRMjx07Vukxp+Jct27d2DXUQ2bNmqX33ntP06ZN0/Hjx3X8+HFJ0okTJyRJP/zwg8466yxieAanjmQ0atSo0hG47t27q2HDhlq7dq169eoliRhW5dChQ5o2bZrq16+vBQsWKC0tTdLJQclVV12l3NxcPfXUU+ULA/shhuRyM+RzZ8jlkUE+NxPEXC6Rz02Qy50hl0cGudyc3/I5xS6fSE9PV0JCQvn5rz938OBBHTlypPz8V5x04sQJ/fa3v9WSJUvUsGFDzZw5U23atCnff2oAcmp9gJ8705oB8eAvf/mLQqGQRo8eXeX+q6++Wunp6Xr55ZclVR3DU5/VeP1cnvrsVLWmwqntW7duPePnMN5j+NVXX+nYsWP6t3/7t/LO9JSuXbuqRYsWevfddzVq1ChJ/oghuTw85PPwkMsjg3xuJoi5XCKfh4NcHh5yeWSQy835LZ9T7PKJ1NRUtWrVSp999lmlfaeuZNCxY8dYN8uzSktLNXbsWK1atUqtW7fWjBkzKnWO7dq1U2JiYpVXyjm17bLLLotJe73mwQcf1IEDByptf+qpp7Rlyxa98sorql27ti644ALVqVOnyhjG++eyXr16atGihb7++msdP35cNWvWLN9XWlqq4uJiNWvWjBiewamYlZWVVbm/tLRUZWVlvoohudw58nn4yOWRQT43E8RcLpHPnSKXh49cHhnkcnN+y+csUO8jffr00a5du7Rs2bLybWVlZSooKFCNGjXUu3dvF1vnLc8995xWrVqljIwMzZ07t8qjQPXr11fXrl21atUqffvtt+Xb9+3bp0WLFqlNmza65JJLYtlsz2jbtq26du1a6e+cc86RJF111VXq1KmTkpOT1atXL3388cf6+OOPyx9/7NgxzZkzR/Xr19e1117r1stwXb9+/XT48GHl5+dX2D5//nwdOHBAWVlZxPAMLrzwQqWnp2vlypXasWNHhX1//vOfVVxcrGuuucZ3MSSXO0M+Dx+5PHLI5+ELai6XyOdOkMvDRy6PHHK5Gb/l84RQdVYChCccO3ZM/fr10/bt2zV48GC1bNlSy5cvV1FRkcaPH6/hw4e73URP+Pbbb3XTTTeVH0Fq1KhRpfv84he/0DXXXKN//OMfys7OVkpKioYOHaoaNWpo7ty52rlzp2bNmqXLL7/chVfgXYMHD9b777+vjRs3Kjn55MTQPXv26JZbbtHRo0c1bNgwpaWlaeHChdq0aZMmT55cft57PPrpp580ZMgQffLJJ8rKytKVV16pjRs3asGCBWrdurUKCwtVq1YtYngGRUVFysvLU2pqqrKzs9WkSRNt3LhRixYtUlpamgoLC9W0aVNfxZBcXn3k8+gglztHPjcTxFwukc+ri1weHeRy58jl5vyUzyl2+cy+ffs0efJkrVmzRocPH1bLli01dOhQ9e3b1+2mecbrr7+uRx999Iz36dixo+bNmydJ2rx5syZPnqyPPvpIiYmJatu2re6991516NAhBq31l6o6VenkIGbSpElau3atTpw4oYsuukh33nnnGa9mEi+OHj2qmTNn6s0339SuXbtUv3593XTTTRozZoxSU1PL70cMT+/zzz/Xiy++qPfff18HDx5UgwYN1K1bN40ZM0YNGjQov5+fYkgurx7yeXSQy8NDPjcTxFwukc+rg1weHeTy8JDLzfkln1PsAgAAAAAAQGCwZhcAAAAAAAACg2IXAAAAAAAAAoNiFwAAAAAAAAIj2f4uAAAAQHBMmTJFU6dOrdZ9b7nlFqWnp2vq1KmaNWuWunbtGuXWOTdy5Ei1aNFCDz/8cLUfs3nzZg0aNEhLly5Vs2bNotg6AABijwXqAQAAEFc+//xzbdmypcK2J598Uvv379d//Md/VNjeokUL1a5dW1u2bFHXrl0rXGnKC5YsWaInn3xSq1at0jnnnOPosQ8//LCKi4v1xz/+UQkJCVFqIQAAsUexCwAAAHGvR48e2rFjR6UimJcdOnRImZmZGjZsmEaNGuX48d99952uu+46PfbYY+rbt2/kGwgAgEtYswsAAADwofnz5+vw4cMaMGBAWI9v3LixMjMzNXPmTHH8GwAQJBS7AAAAgDOYMmWKLrroIq1du1aSVFxcrIsuukj5+fl67bXXdOONN6pdu3a64YYbNH/+fEnSvHnzdNNNN6l9+/bq1auXlixZUul5t23bprFjx6pLly5q27atbrjhBj333HM6duyYbZtKS0s1e/ZsXXPNNUpLS6uwb9OmTRo1apR++ctfqm3btsrMzNRjjz2mH374odLzZGVlaevWrfrrX//qPDAAAHgUC9QDAAAAYZgzZ47Kyso0aNAg1apVSwUFBfr3f/93vfPOO/r88881cOBA1apVS7NmzdJvfvMbnX/++erQoYMkacOGDRo6dKhSU1N1xx13KC0tTZ9++qlefvllFRUVafbs2apZs+Zp//eGDRu0e/du/frXv66w/dtvv9WvfvUrNWjQQMOGDVOdOnW0fv16zZkzRxs2bFBhYWGF9bm6dOmixMRErV69Wv/6r/8ajTABABBzFLsAAACAMHz//fdavny5mjdvLklq0qSJxowZo3Xr1umtt95So0aNJEnNmzfXqFGj9M4776hDhw4KhUKaMGGC6tatqyVLlujcc8+VJA0cOFBXXHGFfvvb32r27NmVClk/995770mS2rRpU2H7qlWrdODAAb3yyivKyMiQJPXv318pKSn64IMP9M9//rO8XZKUmpqq5s2bq6ioKGJxAQDAbZzGCAAAAIShffv25YUuSbrgggskSR07dqxQUDrvvPMkSbt375YkbdmyRV988YW6deumsrIy7du3r/yve/fuqlmzplavXn3G/719+3ZJJ68W+XNNmjSRJP3nf/6n1q5dq59++kmS9NBDD2nx4sUV2nVKixYttHPnTpWUlDh6/QAAeBUzuwAAAIAw1K9fv8Lt5OTkKrcnJSVJUvki8Nu2bZN0coH5U2t8We3YseOM/3vfvn2SpDp16lTYfuONN6pfv35avHix1q5dq1q1aqlTp07q1q2b+vbtq3POOafSc9WtW1ehUEj79u1Tw4YNz/h/AQDwA4pdAAAAQBhOFbesfr4mVlVOFb1ycnJ04403OnruUxITT56gUVZWVmF7UlKSnnjiCY0ePVp/+ctftHbtWn3wwQf6+9//runTp2v+/PmVZoOVlpaWPxYAgCCg2AUAAADEULNmzSSdLHp17dq1wr6ysjKtXLmywumRVWnQoIEkaf/+/UpNTS3fvmPHDn3zzTfq0qWLBg8erMGDB6ukpEQzZ87Uc889p3nz5unBBx+s8Fz79u1TcnJypas6AgDgV6zZBQAAAMRQ27ZtlZ6erqVLl+qrr76qsK+wsFD33nuvFi1adMbnOFUws57u+NJLL2no0KFav359+bbk5OTyxeqrmr21c+dONW3a1HZGGgAAfsHMLgAAACCGkpKS9Nhjj2nkyJG67bbblJOTo/POO0+fffaZFi1apBYtWmj06NFnfI5rr71WkydP1scff6yrrrqqfPvQoUO1fPly5eXlKScnR82aNdPu3bv1+uuvq06dOhowYECF59m9e7eKi4s1aNCgqLxWAADcQLELAAAAiLGuXbvqjTfe0EsvvaTFixfr4MGDaty4sQYOHKiRI0eWn6Z4OhdffLHS09P13nvvVSiMXXjhhZo7d65eeuklLVmyRN9//73OPfdcdenSRXfddVel9brWrVsnScrMzIz8iwQAwCUJoVMrZAIAAADwjZkzZ2rSpEl6++23y09rdGrYsGHavXu3li1bVr7oPQAAfkePBgAAAPjQHXfcoXPPPVcLFiwI6/HffPONioqKNHr0aApdAIBAoVcDAAAAfOjss8/WuHHjNGfOHO3bt8/x41944QVddtll6tWrVxRaBwCAeyh2AQAAAD7Vv39/dezYUVOmTHH0uI0bN2rNmjV6+umnmdUFAAgc1uwCAAAAAABAYHAYBwAAAAAAAIFBsQsAAAAAAACBQbELAAAAAAAAgUGxCwAAAAAAAIFBsQsAAAAAAACBQbELAAAAAAAAgUGxCwAAAAAAAIFBsQsAAAAAAACBQbELAAAAAAAAgfH/AS2l70sXlnSYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = np.arange(train_data.shape[1])\n",
    "rows = np.arange(train_data.shape[0])\n",
    "t = np.arange(0.0, stop=(args.seq_len) * args.delta_t, step=args.delta_t) \n",
    "\n",
    "colors = ['tab:gray', 'r', 'y', 'c']\n",
    "fs = 14\n",
    "# print(rows)\n",
    "# selected_rows = random.sample(rows, subset)\n",
    "plt.clf()\n",
    "nplots = 8\n",
    "rows = rows[0:nplots]\n",
    "\n",
    "fig, axs = plt.subplots(len(rows), len(columns), sharex=True, sharey=True, figsize=(12, 8))\n",
    "mesh = np.array(np.meshgrid(rows, columns))\n",
    "combinations = mesh.T.reshape(-1, 2)\n",
    "\n",
    "for r, c in combinations:\n",
    "        # print(\"r: \", r, \"c\", c)\n",
    "    loc = r\n",
    "    empirical = train_data[loc, c, :]\n",
    "\n",
    "    axs[r, c].plot(t, empirical, 'k.', markersize=2)\n",
    "    axs[r, c].set_xlim(0.0, len(t) + 0.01)\n",
    "    axs[r, c].set_ylim(-0.01, 1.01)\n",
    "    axs[r, c].set_xticks([0, 20, 40, 60, 80])\n",
    "    axs[r, c].tick_params(axis='both', which='major', labelsize=fs)\n",
    "\n",
    "cols = ['Pa', 'Pv', 'fHR']\n",
    "for ax, col in zip(axs[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "rows = [\"IR={},{}\".format(int(row[0]), int(row[1])) for row in zip(iext[0:nplots], rtpr[0:nplots])]\n",
    "print(rows)\n",
    "for ax, row in zip(axs[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation=90, labelpad=25, fontsize=fs - 2)\n",
    "\n",
    "fig.text(0, 0.5, \"Normalized output\", ha=\"center\", va=\"center\", rotation=90, fontsize=fs)\n",
    "fig.text(0.6, 0, \"Time (s)\", ha=\"center\", va=\"bottom\", fontsize=fs)\n",
    "#plt.ylabel(\"Normalized output\", fontsize=fs)\n",
    "#plt.xlabel(\"Time (h)\", fontsize=fs)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "resident-slovenia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.   0. ]\n",
      " [-2.   0.5]\n",
      " [ 0.   0. ]\n",
      " [ 0.   0.5]]\n",
      "-2.0 0.0\n",
      "-2.0 0.5\n",
      "0.0 0.0\n",
      "0.0 0.5\n"
     ]
    }
   ],
   "source": [
    "mesh = np.array(np.meshgrid(np.unique(iext), np.unique(rtpr)))\n",
    "combinations = mesh.T.reshape(-1, 2)\n",
    "print(combinations)\n",
    "for c in combinations:\n",
    "    print(c[0], c[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ranging-strengthening",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  3   4   8   9  10  15  26  32  33  35  41  43  45  47  56  58  59  61\n",
      "  65  71  77  83  85  95  97 104 106 108 111 113 114 115 116 117 122 126\n",
      " 130 136 140 143 144 150 158 167 180 184 186 187 189 191 194 199 204 205\n",
      " 212 213 218 224 227 235 237 245 248 252 255 256 269 273 277 278 280 285\n",
      " 286 291 292 294 296 297 299 303 304 306 307 311 321 326 349 356 362 363\n",
      " 365 370 376 379 380 384 386 394 395 398 399 411 421 422 427 428 436 437\n",
      " 438 441 443 445 448 449 450 454 455 456 457 461 466 467 469 470 472 475\n",
      " 476 480 485 494 499 501 502 510 513 518 521 522 523 525 528 536 537 538\n",
      " 541 543 545 546 547 548 554 557 561 565 567 568 571 578 584 587 595 606\n",
      " 608 612 617 620 626 627 628 629 632 634 637 639 640 644 650 663 670 673\n",
      " 676 677 679 688 689 692 697 700 701 703 705 717 723 726 728 730 732 747\n",
      " 752 765 767 771 777 784 787 788 798 804 811 812 813 818 821 822 823 824\n",
      " 828 829 833 841 843 845 851 853 854 857 858 862 865 874 879 888 893 896\n",
      " 899]\n",
      "235\n"
     ]
    }
   ],
   "source": [
    "idx = np.where((np.logical_and(iext == 0, rtpr==0.5)))[0]\n",
    "print(idx)\n",
    "print(len(idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "express-contact",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IR=0.0,0.5', 'IR=0.0,0.5', 'IR=0.0,0.5', 'IR=0.0,0.5', 'IR=0.0,0.5']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pnj01fPhw5eXlSZL69++vyZMnZ3TQFSROAwEAhEFNzSIVFhaqpmaRq9/nNH0AQbNuh/g7GwDMRSbsOpqDBw9q27ZtQZeRcTgNBACQDnan81RVVaqhocH1FS3ZqQQQNOt2iL+zAcBc5MOudevWaejQoUGXkfGYWwAA4Ae7kVfl5ROVn5/v+oqW7FQCCBrbIQDwXuTDri5dumjy5MlBlxE5XodTnAYCAPCD3cirkpIxqq+vP+oVLTkYAwAAkHkiH3adfvrpmjJlStBlRI7X4RSngQAA/GA64oGDMQAAAJknEmHXgQMH9NZbb+m1117TmjVrtHHjRh08eDDosiLNGk6ZHvlm+DUAIIw4GAMAAJB5soMu4Fj+/ve/61e/+pXq6+t16NChI+477rjjNHDgQE2ZMkX9+vULpsAIKy4uPSKYOvzIN4EVACAurN93ABB1tbWLVV09T2VlFWzfAOAoQjuy6/nnn9e4ceO0d+9eTZs2TXPmzFF1dbWqq6t133336aabbtKePXt07bXX6sUXXwy63Mjz+8g3c6YAAAAA5jg9GwDshXZk1+zZszVs2DDNnj37qI8pLy/XTTfdpHvvvVeDBw9OY3Xx4/eRb0aOAQAAAM5ZR3KVlVW03AYAtC20I7s2bNigq6++2vZxV199tTZs2JCGimCCOVMAwB6jYAEAVtaRXMyVCwD2Qht2nX766Vq7dq3t41599VXl5eWloSKY4EsZAOxxaoo9AkEAmYaDxgDgXGjDrmuuuUZz5szRrFmztGbNGn344Ydqbm5Wc3Oz9uzZo3/84x+65557VFlZqdGjRwddLizYGQEA59ihsUcgCCDTcNAYAJwL7Zxd119/vZqbm/XAAw9o/vz5bT7m+OOP14033qhJkyaluTrYYY4uAHCOKwfaY64aAAAA2Alt2CVJEyZM0NixY7V69Wpt2LBBe/bsUTKZVG5urs466yydd955Ov7444MuE21gZwQA4AcCQQBozTqJPQBkulCHXZLUoUMHDRw4UAMHDgy6FDjAzggAAACQHtazKgi/AGS60M7ZlarGxka98sorWrNmjfbv3x90OUgRc3oBAAAA3rDO+cj8hgAyXeTDrg0bNmjcuHFauHChRo0apR07dgRdElLAFzAAAADgDesk9lzwBECmi3zY1aNHD/385z/XXXfdpblz56pDhw5Bl4QU8AUMAAAA+MMafnFWBYBME/mwKy8vT6NGjZIkFRQU6DOf+UywBSElXEIZANAWdsgAwHucVQEg00Qi7Nq8ebOef/55Pfnkk1q+fLleeOEFbdu2LeiyIoWdBwBAFLBDBgDe46wKAJkm1FdjXL58uWbPnq3NmzcrmUwecV8ikdAZZ5yhm2++WZdeemlAFUaH9QotAACEUVlZRcsVxAAA3uBK6QAyTWjDrmXLlmnGjBkqKirStGnTdMYZZygnJ0fSJ1dg3LRpk55++mlNmzZNBw8e1OWXXx5wxeHGzgMAIArYIQMAAICp0J7GOG/ePI0ZM0b33nuvLr30Un3hC19Qz5491bNnT/Xp00dFRUX65S9/qdGjR6uysjLockMv7HNkcZolAGQmtv8AkH5sewHEXWjDrq1bt2rYsGG2jxs2bJi2bNmShorgJ+ZoAYDMxPYfANKPbS+AuAtt2NWjRw+9+OKLto9bsWKFunTpkoaK4CcmzQSA1uJ45N36mtj+A0D6Wbe9cfy+AZDZEknrzO8h8cQTT+h73/ueLrvsMg0dOlS9evVSbm6uJGnv3r0tc3bV1dXppz/9qUaPHh1wxcHYvXuvmpqagy7Dc7W1i1vmGPPr1Mvs7Hbq3Dkntj1MB3pohv6ZS0cP8/JylJXl77Gho9VfVDRE27dvU9eu3VRXt8LXGvxi3Z57/ZpYj8zRQ3P00JzfPQxyWx4FQX/fsA6Zo4fm6KG5MG3LQzuya+TIkfrlL3+ptWvX6pZbbtG3vvUtfeMb39A3vvENjRo1St/5zne0Zs0a/exnP8vYoOtYon50hqHVABCPUU/W7XkcXhMAxA3bZgBxE9qRXYfbunWrNmzYoMbGRjU3N6tjx47q1auXevbsGXRpgYvraABGdkUDPTRD/8zFfWRXHPi9PWc9MkcPzdFDc2EaDeBWnN7/dPwtfjjWIXP00Bw9NBembXm258/ug+7du6t79+5BlxEpZWUVLV9QUcSl5wEgmqw7SGzPASB6Dh+VyzYcQBSF9jTGVO3atUvLli0LuozQKS4uVV3dCr6cAABpxWnoABB9nNYIIOoiH3Zt2bJFP/jBD4IuAwAAiB0kAIgDDpwDiLpInMZ4LAUFBXr44YeDLgMAAIjT0AEAABC8yI/sys3N1cCBA4MuAz6L+tUlAQAAAABAeoR+ZNeBAwf0t7/9TRs3btTevXuVSCTUsWNHFRQUqG/fvurQoUPQJSINmCQTAAAAAACkItRh1/z581VZWanGxsY27+/UqZMmTZqksrKyNFeGdIv61SUBIA7SfSl6AEA48X0AIOxCexpjdXW1Zs2apdLSUtXW1mrVqlVau3at1q5dq/r6ei1ZskTFxcWaOXOmFixYEHS58BmTZAJA8LjSIgBkJuuUInwfAAi70IZdCxcu1KRJk/Td735X5557rjp16qSsrCxlZWXppJNOUt++ffW9731PFRUVWrhwYdDlAgAQe1xpEQAykzXc4vsAQNiFNuzauXOnLrjgAtvHnX/++dqxY0caKkKYMGE9gEwU9LaPUbYAkJms4RbfBwDCLrRh12c/+1k98cQTto9bunSpevXqlYaKECYMnQaQidK97UslXAs6gAMA+M9puGX9buC7AkC6hTbsuvnmm/XEE0+opKRE8+fP13PPPaeVK1dq5cqVeu6551RdXa3S0lI9++yzmjp1atDlIs0YOg0gE6V725dKuMbBBwCA3ZxefFcASLfQXo1x8ODBevTRRzVnzhzdd999Onjw4BH3Z2VlqX///qqurtaXv/zlgKpEUIqLSxk2DSDjpHvbl8qVcLlaLgDg8DCruLi01XcD3xUA0i2RTCaTQRdh58CBA9qyZYsaGxvV3Nysjh07qmfPnmrfvn3QpQVu9+69ampqDrqMUHB6CeTs7Hbq3DmHHhqgh2bon7l09DAvL0dZWf4OhA7LZyCKl5JnPTJHD83RQ3N+9zCTtuVBMP3+YB0yRw/N0UNzYdqWh/Y0xsO999576tmzp770pS/pvPPO04knnqh27SJROtKI4dEAYIbtKADADSasBxA2kUiMhg4dqvXr10uSDh06pKFDh+qNN94IuKpwYdJH5vECkHm83vazHQUApAP7LgD8Fomwy3qmZQTOvEw7jsZzRAlA5vF62892FACQDuy7APBbJMIu2ONofGscMQIQd2z7AQBRZP3+qqlZpMLCQtXULAq4MgBxQdgVExyNb40jRgDizrrtJ+QHAESB9furqqpSDQ0NqqqqDLgyAHFB2IXYYsQDgExDyA8AiKLy8onKz89XefnEoEsBEBOEXYgtRjwAyDSE/ACAKCopGaP6+nqVlIyRxN/tmYj3HF4j7ELGYMQDgLhzeko7f1gCAMLI7u92vr/ih301eC0SYdeAAQOUk5MjSUokEkfcBlLFiAcAOBJ/WAIAwsju7/Yofn9lWkDn9PX6sa/md88z7T1tS5h7EImw65FHHlFBQYEkqV27dnrkkUd05plnttz/8ccfB1QZooRJ/AHgSBwEAACEkd10JFH8/kp3QOd1COF0eU5frx/7an73POjQNQxBk9c98PI1hT7s2rBhgzZu3HjU+5966ikVFRWlsSIAAKLJ+gcEBwEAAFFg3aF2OjevH6GA3yOXTF+T1yGEdXl2z+9FIGl9jjCMFjNZvtefwzCc7mvXA79D0mMJbdj13nvvqaSkRCNHjtSIESNUWlqq3bt3t9z/1ltvafz48Zo2bZo+/PDDACsFACAagj4CCQCAG6anNfrx/ef3yCWnr6mmZpEKCwtVU7NIkvdBj3V5dvV5cUDN+hxe99w0DHK6fK8/h2E43deuB6Yhqcl7FNqw6+6779a6detUXl6uadOm6e2339bMmTMlSVVVVRo1apTq6+t1xRVXqK6uLuBqAQAIvyie9gEAgN0Otd33mx8jcIIeNWS9v6qqUg0NDaqqqpTk/eg36/JMQwk3PfYyCJH8HxllXb7Xo6BM1wsvarBjGpKaBHaJZDKZdF+6fy6++GIVFxdrypQpkqRnnnlGt912m8aNG6e5c+eqd+/euv3229WvX79gCw3Y7t171dTUHHQZkZSd3U6dO+fQQwP00Az9M5eOHubl5Sgry99jQ3wG3GM9MkcPzdFDc373kG15uHn9/tfWLlZ19TyVlVW0GQRY77feLioaou3bt6lr126qq1thXI8X7F7T0qU1euih+Zow4du66qqSVvdbX5Pda7Z7Prvl29XvRY+dLsPufbd+Dr1evmlPnL4nbpi+L3afQ6evwfp4J9vy0I7s2rVrly644IKW24WFhfrggw/04IMPasqUKVq6dGnGB10wYx3qG4YJ/gDAS2zXAACZyOkpgHYjcFL5PjWdX8r0NZWUjFF9fb1KSsa0eb/dCBunI3Dslm9Xvxcj45wuw27uN7tTQZ3Ok+b0FD+/T0tM5TNq95rtemAdYWjl9PRWk9NhQxt2HTx4UDk5OS23c3NzJUk33HCDpkyZoqysrKBKQ0xYV0TmsgEQN2zXAACZyOkpgNbbbk6lcjq/lNMQwTQcsjsN0e5+p8u3q9+LOb2cnqpp95rsTgW1e0+d9syuJ06vQuo0jGvr9di9ZrselJdPVH5+vsrLJ6bUAz+F9jTG3r17a8mSJerbt68k6dChQzrnnHOO+BkYLm3COsQyHcNC44bTNszQP3OcxnhsmbBdYz0yRw/N0UNznMaY2cK+DqXyfer0FDbr6WKmp4+FvYfpELZT8Ew5fT1OT4P043Pt9HPotKdOtuWRC7uWLl2qc845J+DqwiOTN2am+EIwRw/N0D9zhF1gPTJHD83RQ3OEXZktE9chN8HDsWR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AAAAIGzS7AAAAAAAAEDb+H0+bHw82X3CeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = np.arange(train_data.shape[1])\n",
    "rows = np.arange(len(idx))\n",
    "t = np.arange(0.0, stop=(args.seq_len) * args.delta_t, step=args.delta_t) \n",
    "\n",
    "colors = ['tab:gray', 'r', 'y', 'c']\n",
    "fs = 14\n",
    "nplots = 5\n",
    "rows = rows[0:nplots]\n",
    "idx = idx[0:nplots]\n",
    "plt.clf()\n",
    "fig, axs = plt.subplots(len(rows), len(columns), sharex=True, sharey=True, figsize=(12, 8))\n",
    "mesh = np.array(np.meshgrid(rows, columns))\n",
    "combinations = mesh.T.reshape(-1, 2)\n",
    "\n",
    "for r, c in combinations:\n",
    "    loc = idx[r]\n",
    "    empirical = train_data[loc, c, :]\n",
    "\n",
    "    axs[r, c].plot(t, empirical, 'k.', markersize=2)\n",
    "    axs[r, c].set_xlim(0.0, len(t) + 0.01)\n",
    "    axs[r, c].set_ylim(-0.01, 1.01)\n",
    "    axs[r, c].set_xticks([0, 20, 40, 60, 80])\n",
    "    axs[r, c].tick_params(axis='both', which='major', labelsize=fs)\n",
    "\n",
    "cols = ['Pa', 'Pv', 'fHR']\n",
    "for ax, col in zip(axs[0], cols):\n",
    "    ax.set_title(col)\n",
    "\n",
    "rows = [\"IR={},{}\".format(row[0], row[1]) for row in zip(iext[idx], rtpr[idx])]\n",
    "print(rows)\n",
    "for ax, row in zip(axs[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation=90, labelpad=25, fontsize=fs - 2)\n",
    "\n",
    "fig.text(0, 0.5, \"Normalized output\", ha=\"center\", va=\"center\", rotation=90, fontsize=fs)\n",
    "fig.text(0.6, 0, \"Time (s)\", ha=\"center\", va=\"bottom\", fontsize=fs)\n",
    "#plt.ylabel(\"Normalized output\", fontsize=fs)\n",
    "#plt.xlabel(\"Time (h)\", fontsize=fs)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "spectacular-prevention",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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