component_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.human import Human\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pmv': [-1.14, -1.07, -1.01, -0.94, -0.88, -0.81, -0.75, -0.69, -0.62, -0.56, -0.49, -0.43, -0.37, -0.3, -0.24, -0.18, -0.11, -0.05, 0.01, 0.07, 0.14, 0.2, 0.26, 0.32, 0.37, 0.43, 0.48, 0.54, 0.59, 0.65, 0.71, 0.76, 0.82, 0.88, 0.94, 0.99, 1.05, 1.11, 1.17, 1.23, 1.28, 1.34, 1.4, 1.46, 1.52, 1.58, 1.64, 1.71, 1.77, 1.83, 1.89, 2.05, 1.88, 2.06, 2.13, 2.19, 2.26, 2.32, 2.39, 2.45], 'tdb': [10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0, 22.5, 23.0, 23.5, 24.0, 24.5, 25.0, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0, 36.5, 37.0, 37.5, 38.0, 38.5, 39.0, 39.5]}\n"
     ]
    }
   ],
   "source": [
    "# Create a human model and sample the PMV\n",
    "human = Human()\n",
    "pmvs = human.temp2pmv()\n",
    "print(pmvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the PMV distribution as points where the y-axis is the PMV and the x-axis is the temperature\n",
    "plt.scatter(pmvs['tdb'], pmvs['pmv'])\n",
    "plt.ylabel('PMV')\n",
    "plt.xlabel('Temperature (C)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'probability': [0.9744192116889072, 0.9670736052036336, 0.957710228161391, 0.9458332995253901, 0.9308615796694732, 0.912136085195503, 0.8889440333365836, 0.860566127131203, 0.8263533531606466, 0.7858349833901475, 0.7388500067567614, 0.6856801152394258, 0.6271477688306937, 0.5646362966738656, 0.5000000094240575, 0.4353637264305658, 0.37285226896502444, 0.3143199543177744, 0.26115012597698356, 0.21416527246847125, 0.1736471413796697, 0.13943482944643318, 0.1110578173091799, 0.08786749534549175, 0.06914534787543922, 0.05418010373042838, 0.042315704020916625, 0.03297656696281981, 0.02567785647908712, 0.020028095439886477, 0.015730975878493526, 0.012593745666428567, 0.010552225317717007, 0.009728893981048967, 0.010552928232916717, 0.01399220526539495, 0.021975499515993594, 0.03809363673340659, 0.06855409656017761, 0.12282002014847633, 0.21197618039536067, 0.34163263883091755, 0.5006887109140795, 0.6597915038397357, 0.7895912736872434, 0.8789969891353435, 0.9336355009646428, 0.964616600433174, 0.9814382918217377, 0.9903549877160785, 0.9950194633526616, 0.9974412629539697, 0.9986926657073345, 0.9993368532230591, 0.9996671612436824, 0.9998356747578805, 0.9999210242420534, 0.9999637780738543, 0.9999848242090187, 0.9999948905011813], 'pmv': [-3.0, -2.9, -2.8, -2.6999999999999997, -2.5999999999999996, -2.4999999999999996, -2.3999999999999995, -2.2999999999999994, -2.1999999999999993, -2.099999999999999, -1.9999999999999991, -1.899999999999999, -1.799999999999999, -1.6999999999999988, -1.5999999999999988, -1.4999999999999987, -1.3999999999999986, -1.2999999999999985, -1.1999999999999984, -1.0999999999999983, -0.9999999999999982, -0.8999999999999981, -0.799999999999998, -0.699999999999998, -0.5999999999999979, -0.4999999999999978, -0.3999999999999977, -0.2999999999999976, -0.1999999999999975, -0.09999999999999742, 2.6645352591003757e-15, 0.10000000000000275, 0.20000000000000284, 0.30000000000000293, 0.400000000000003, 0.5000000000000031, 0.6000000000000032, 0.7000000000000033, 0.8000000000000034, 0.9000000000000035, 1.0000000000000036, 1.1000000000000032, 1.2000000000000037, 1.3000000000000043, 1.400000000000004, 1.5000000000000036, 1.600000000000004, 1.7000000000000046, 1.8000000000000043, 1.900000000000004, 2.0000000000000044, 2.100000000000005, 2.2000000000000046, 2.3000000000000043, 2.400000000000005, 2.5000000000000053, 2.600000000000005, 2.7000000000000046, 2.800000000000005, 2.9000000000000057]}\n"
     ]
    }
   ],
   "source": [
    "human.setProbabilityFunction('sigmoid')\n",
    "probabilities = human.pmv2prob()\n",
    "print(probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the probability of complaint distribution as points where the y-axis is the probability of complaint and the x-axis is the PMV\n",
    "plt.scatter(probabilities['pmv'], probabilities['probability'])\n",
    "plt.ylabel('Probability of Complaint')\n",
    "plt.xlabel('PMV')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'probability': [0.23218775466536218, 0.20133071205717773, 0.17740965446061602, 0.1523878412357064, 0.13331156688596701, 0.11365055948047541, 0.09885864729882755, 0.08580620617390625, 0.07256703297879741, 0.06274675792371791, 0.05286430584522942, 0.04558538971437016, 0.03927266591292921, 0.03297656696282001, 0.028379643773774214, 0.02442686326831359, 0.02052807969692549, 0.017725110788055408, 0.015367248174680203, 0.013419858734133433, 0.011643889905738542, 0.010552225317717046, 0.009893469913253754, 0.009740683159106965, 0.010089019724583037, 0.01124590775440713, 0.013021100576923302, 0.016485862933335163, 0.020887497808341475, 0.028702867680364466, 0.040363968411583136, 0.0541150723368874, 0.0771468838586457, 0.10951712643419421, 0.1537402107430382, 0.2012354524510806, 0.27192895639673886, 0.3565877235601904, 0.4514056243887987, 0.5499759810060912, 0.6295759712516625, 0.7163351114645201, 0.7895912736872389, 0.8479633230023333, 0.892363884357182, 0.9249572823534153, 0.9482633039199261, 0.9668078578673149, 0.9774470331596009, 0.9847359399521198, 0.9896991963363896, 0.9964278835669613, 0.9889995067221059, 0.9966581241381117, 0.9979068168983901, 0.9986014610549585, 0.999129093326399, 0.9994216844967384, 0.9996431876914094, 0.999765531591106], 'tdb': [10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0, 22.5, 23.0, 23.5, 24.0, 24.5, 25.0, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0, 36.5, 37.0, 37.5, 38.0, 38.5, 39.0, 39.5], 'pmv': [-1.14, -1.07, -1.01, -0.94, -0.88, -0.81, -0.75, -0.69, -0.62, -0.56, -0.49, -0.43, -0.37, -0.3, -0.24, -0.18, -0.11, -0.05, 0.01, 0.07, 0.14, 0.2, 0.26, 0.32, 0.37, 0.43, 0.48, 0.54, 0.59, 0.65, 0.71, 0.76, 0.82, 0.88, 0.94, 0.99, 1.05, 1.11, 1.17, 1.23, 1.28, 1.34, 1.4, 1.46, 1.52, 1.58, 1.64, 1.71, 1.77, 1.83, 1.89, 2.05, 1.88, 2.06, 2.13, 2.19, 2.26, 2.32, 2.39, 2.45]}\n"
     ]
    }
   ],
   "source": [
    "# sample the probability of complaint\n",
    "complaints = human.temp2prob()\n",
    "print(complaints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the probability of complaint distribution as points where the y-axis is the probability of complaint and the x-axis is the PMV\n",
    "plt.scatter(complaints['pmv'], complaints['probability'])\n",
    "plt.ylabel('Probability of Complaint')\n",
    "plt.xlabel('PMV')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PMV with dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pythermalcomfort.models import pmv\n",
    "obs_dict = {}\n",
    "obs_dict[\"air_tmp\"] = [19.884539631339198, 21.937755212848106, 23.35934389007046, 23.866064450491052, 23.999562126174766, 23.999933191588696, 23.999989912061043, 23.999998496100783, 23.99999977785304, 23.999999967732645, 23.999999995353, 23.99999999933561]\n",
    "obs_dict[\"rad_tmp\"] = [13.952785666969064, 15.607167114502788, 16.27508663669154, 16.847737844292606, 17.068079116410036, 17.274436459091955, 17.44241877076414, 17.586782745785893, 17.713808514167955, 17.925140557311927, 18.043112512987925, 18.14705458359187]\n",
    "obs_dict[\"air_hum\"] = [22.557631881357757, 22.639134025923678, 21.879037519214346, 21.995063750975103, 22.08333765714951, 22.271015691147184, 22.342709556866065, 22.342797634465892, 22.30468957381933, 22.24391763629504, 22.167392354025594, 22.101863792358007]\n",
    "\n",
    "vr = 0.1\n",
    "met = 1.1\n",
    "clo = 1.4\n",
    "\n",
    "pmv(tdb=obs_dict[\"air_tmp\"][-1], tr=obs_dict[\"rad_tmp\"][-1], vr=vr, rh=obs_dict[\"air_hum\"][-1], met=met, clo=clo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0 <class 'numpy.float64'>\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "from typing import Any, Dict, List, Optional, Tuple, Union\n",
    "\n",
    "import gymnasium as gym\n",
    "\n",
    "def post_process_action(action: Union[float, List[float]]) -> Union[float, List[float]]:\n",
    "    actual_range = (0, 40.0)\n",
    "    action_space = gym.spaces.Box(low=0, high=40.0, shape=(1,), dtype=float)\n",
    "    return _rescale(\n",
    "        n=float(action), range1=(action_space.low, action_space.high), range2=actual_range  # noqa  # noqa\n",
    "    ) [0]\n",
    "\n",
    "def _rescale(n: int, range1: Tuple[float, float], range2: Tuple[float, float]) -> float:\n",
    "    delta1 = range1[1] - range1[0]\n",
    "    delta2 = range2[1] - range2[0]\n",
    "    return (delta2 * (n - range1[0]) / delta1) + range2[0]\n",
    "post = post_process_action(10)\n",
    "print(post, type(post))\n",
    "assert(isinstance(post, float))\n",
    "print(isinstance(post, float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 0\n",
      "{'air_tmp': [20, 21, 22, 23, 24], 'rad_tmp': [15, 15, 15, 15, 15], 'air_hum': [23, 23, 23, 23, 23], 'action': [10, 10, 10, 10, 10], 'reward': [1.0, 2.0, 3.0, 4.0, 5.0]}\n",
      "simulation 1\n",
      "{'air_tmp': [20, 21, 22, 23, 24], 'rad_tmp': [15, 15, 15, 15, 15], 'air_hum': [23, 23, 23, 23, 23], 'action': [10, 10, 10, 10, 10], 'reward': [1.0, 2.0, 3.0, 4.0, 5.0]}\n",
      "simulation 2\n",
      "{'air_tmp': [20, 21, 22, 23, 24], 'rad_tmp': [15, 15, 15, 15, 15], 'air_hum': [23, 23, 23, 23, 23], 'action': [10, 10, 10, 10, 10], 'reward': [1.0, 2.0, 3.0, 4.0, 5.0]}\n",
      "[{'air_tmp': [20, 21, 22, 23, 24], 'rad_tmp': [15, 15, 15, 15, 15], 'air_hum': [23, 23, 23, 23, 23], 'action': [10, 10, 10, 10, 10], 'reward': [1.0, 2.0, 3.0, 4.0, 5.0]}, {'air_tmp': [20, 21, 22, 23, 24], 'rad_tmp': [15, 15, 15, 15, 15], 'air_hum': [23, 23, 23, 23, 23], 'action': [10, 10, 10, 10, 10], 'reward': [1.0, 2.0, 3.0, 4.0, 5.0]}, {'air_tmp': [20, 21, 22, 23, 24], 'rad_tmp': [15, 15, 15, 15, 15], 'air_hum': [23, 23, 23, 23, 23], 'action': [10, 10, 10, 10, 10], 'reward': [1.0, 2.0, 3.0, 4.0, 5.0]}]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "path = \"./tmp\"\n",
    "# 3 complete simulations\n",
    "for i in range(3):\n",
    "    obs_history = []\n",
    "    rewards_history = []\n",
    "    # 5 timesteps per simulation\n",
    "    for j in range(5):\n",
    "        obs = {\n",
    "            \"air_tmp\": 20+j,\n",
    "            \"rad_tmp\": 15,\n",
    "            \"air_hum\": 23,\n",
    "            \"action\": 10\n",
    "        }\n",
    "        obs_history.append(obs)\n",
    "        rewards_history.append(1.0+j)\n",
    "\n",
    "    # combine dicts in obs_history to single dict\n",
    "    obs_history = {key: [obs[key] for obs in obs_history] for key in obs_history[0]}\n",
    "    \n",
    "    print('simulation', i)\n",
    "    \n",
    "    # combine obs_history and rewards_history to a single dict\n",
    "    comb_history = {key: obs_history[key] for key in obs_history}\n",
    "    comb_history[\"reward\"] = rewards_history\n",
    "    \n",
    "    print(comb_history)\n",
    "\n",
    "    # save the combined history to a pickle file\n",
    "    with open(f\"{path}/simulation_{i}.pkl\", \"wb\") as f:\n",
    "        pickle.dump(comb_history, f)\n",
    "    \n",
    "    # save the combined history to a pickle file containing all simulations\n",
    "    with open(f\"{path}/all_simulations.pkl\", \"ab\") as f:\n",
    "        pickle.dump(comb_history, f)\n",
    "    \n",
    "# load combined history for all simulations from the pickle file\n",
    "with open(f\"{path}/all_simulations.pkl\", \"rb\") as f:\n",
    "    all_simulations = []\n",
    "    while True:\n",
    "        try:\n",
    "            all_simulations.append(pickle.load(f))\n",
    "        except EOFError:\n",
    "            break\n",
    "    print(all_simulations)\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
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