timeToChangeTemp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "restricted-defeat",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import datetime as dt\n",
    "import os\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "import statistics\n",
    "import scipy.stats as stats\n",
    "import math\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "plain-blogger",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Dataset\n",
    "file_names = glob.glob ('./EthernetData/dataset5.csv')\n",
    "k = 0\n",
    "\n",
    "# Change these two variables to get different results\n",
    "#degree change\n",
    "n = 1\n",
    "#operating state\n",
    "op_state = 'Heating'\n",
    "exclude_state = 'Cooling'\n",
    "# op_state = 'Cooling'\n",
    "# exclude_state = 'Heating'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "north-bosnia",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given millisecond utc timestamp return time\n",
    "def tz_from_utc_ms_ts(utc_ms_ts):\n",
    "    \n",
    "    # convert from time stamp to datetime\n",
    "    utc_datetime = dt.datetime.utcfromtimestamp(utc_ms_ts / 1000.)\n",
    "    \n",
    "    # getting time from datetime object\n",
    "    res = utc_datetime.hour + utc_datetime.minute / 60\n",
    "    if res - 5 < 0:\n",
    "        res = 24 + (res - 5)\n",
    "    else:\n",
    "        res = res - 5\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "photographic-choir",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualising the result as time of day vs cooling time of machine\n",
    "def plot (time_difference, y, temp_change):\n",
    "    fig = plt.figure (k)\n",
    "    ax = plt.subplot ()\n",
    "    plt.bar (y, time_difference, 0.05, align = 'center', alpha = 1)\n",
    "    plt.xlabel ('Time of day')\n",
    "    plt.ylabel ('Time taken for ' + op_state + ' ' + str (temp_change) + ' Degree (in Minutes)')\n",
    "    ax.set_xticks ([0,5,10,15,20,24])\n",
    "    ax.set_xticklabels (['00:00', '05:00', '10:00', '15:00', '20:00', '00:00'])\n",
    "    plt.title (os.path.basename (path))\n",
    "    plt.show ()\n",
    "    pp.savefig (fig)\n",
    "    plt.close (fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "seeing-coalition",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Finding time required for temparature change...\n",
    "import json\n",
    "def time_to_change_temperature (X, temp_change):\n",
    "    # Time to cool one degree in minutes\n",
    "    time_difference = []\n",
    "    # Factor represts temparature change between cooling/heating on and off.\n",
    "    factor = 1\n",
    "    y = []\n",
    "    curr_temp = 0\n",
    "    i = 0\n",
    "    avg_time_to_change = 0\n",
    "    while i < len(X):\n",
    "        content = json.loads (X.content[i])\n",
    "        if 'status' in content and \"room_temp\" in content[\"status\"]:\n",
    "            curr_temp = content[\"status\"][\"room_temp\"]\n",
    "        \n",
    "        if 'status' in content and \"operating_state\" in content[\"status\"] and content[\"status\"][\"operating_state\"] in [op_state] :\n",
    "            curr_index = i\n",
    "            \n",
    "            hh = tz_from_utc_ms_ts(X.created_date_time[i])\n",
    "            while i < len(X):\n",
    "                content = json.loads (X.content[i])\n",
    "                if 'status' in content and \"room_temp\" in content[\"status\"]:\n",
    "                    if(op_state == \"Cooling\"):\n",
    "                        factor = curr_temp - content[\"status\"][\"room_temp\"]\n",
    "                    else:\n",
    "                        factor =  content[\"status\"][\"room_temp\"] - curr_temp\n",
    "                    \n",
    "                    if( factor == temp_change):\n",
    "                      print(i+1, curr_temp, content[\"status\"][\"room_temp\"])\n",
    "                      diff = abs((X.created_date_time[i] - X.created_date_time[curr_index])/(60000))\n",
    "                      time_difference.append (diff)\n",
    "                      avg_time_to_change = avg_time_to_change + (diff)\n",
    "                      y.append(hh)\n",
    "                      break\n",
    "                if 'status' in content and \"operating_state\" in content[\"status\"] and content[\"status\"][\"operating_state\"] in [\"Off\", exclude_state, \"Fan_Only\"]:\n",
    "                    break\n",
    "                i=i+1\n",
    "\n",
    "        i=i+1       \n",
    "    # print((time_difference))\n",
    "    if (time_difference):\n",
    "        mean = (avg_time_to_change/ len(time_difference))\n",
    "        sd = statistics.stdev(time_difference)\n",
    "        print('average time to ' + op_state + ' ' + str(temp_change) +'F: ', mean)\n",
    "        print('Standard deviation ', sd)\n",
    "    plot (time_difference, y, temp_change) \n",
    "    # time_difference.sort()\n",
    "    # pdf1 = stats.norm.pdf(time_difference, (avg_time_to_change/ len(time_difference)), statistics.stdev(time_difference))\n",
    "    # plt.plot(time_difference, pdf1)\n",
    "    return (mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "adjacent-circulation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "average time to Heating 1F:  72.06266650868878\n",
      "Standard deviation  73.60150086894416\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124 62 64\n",
      "145 66 68\n",
      "311 68 70\n",
      "average time to Heating 2F:  63.73758888888889\n",
      "Standard deviation  44.76417640309397\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'mean' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-720f2ca7fd55>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mavg1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime_to_change_temperature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0mavg2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime_to_change_temperature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mavg3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime_to_change_temperature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0mavg4\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime_to_change_temperature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[0mavg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mavg1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mavg2\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mavg3\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m3\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mavg4\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-45-276dc4b45df2>\u001b[0m in \u001b[0;36mtime_to_change_temperature\u001b[1;34m(X, temp_change)\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[1;31m# pdf1 = stats.norm.pdf(time_difference, (avg_time_to_change/ len(time_difference)), statistics.stdev(time_difference))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;31m# plt.plot(time_difference, pdf1)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'mean' referenced before assignment"
     ]
    }
   ],
   "source": [
    "path = ''\n",
    "pp = PdfPages ('./Plot' + str (n) + '.pdf')\n",
    "\n",
    "for file_name in file_names:\n",
    "    try:\n",
    "      path = file_name\n",
    "      dataset = pd.read_csv ((path))\n",
    "    except:\n",
    "      continue\n",
    "    \n",
    "    X = dataset.iloc[:, :]\n",
    "\n",
    "    avg1 = time_to_change_temperature(X, 1)\n",
    "    avg2 = time_to_change_temperature(X, 2)\n",
    "    avg3 = time_to_change_temperature(X, 3)\n",
    "    avg4 = time_to_change_temperature(X, 4)\n",
    "    avg = (avg1 + avg2/2 + avg3/3 + avg4/4) / 4\n",
    "\n",
    "    print('Average time to heat 1F ', avg)\n",
    "    print(' standard deviation ', statistics.stdev([avg1, avg2/2, avg3/3, avg4/4]))\n",
    "\n",
    "pp.close ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "associate-perry",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "logical-husband",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}