1 - Raw Data Visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 - Raw Data Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"grideye_camera_view.png\" alt=\"grideye camera view\" width=\"250\" height=\"250\" align=\"left\">\n",
    "<img src=\"layout.png\" alt=\"layout\" width=\"200\" height=\"200\" align=\"right\"> \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; For some some context here is our setup.   \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The left image is our [Panasonic Grid-EYE Evaluation Kit](https://eu.industrial.panasonic.com/grideye-evalkit) connected to a   \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Raspberry Pi (for storing data locally)<sup>1</sup>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Grid-EYE is an 8×8 thermal camera. Just to make that clear, that’s 64 pixels, \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; not megapixels! This super low resolution allows us to stick in sensitive places  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; (say a bathroom) without privacy fears  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The right image is the range of view of our Grid-EYE, which is pointing right at   \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; the back of a urinal user.  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; It's placed for wall-to-wall, floor-to-ceiling viewing.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "___\n",
    "<sup>1</sup> <sub> See our [Medium post](http://www.something.com/) for more info</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "text": [
      "\n",
      "0it [00:00, ?it/s]\u001b[A"
     ]
    },
    {
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       "<IPython.core.display.Javascript object>"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import csv\n",
    "from tqdm import tqdm_notebook,tnrange,tqdm_pandas,tqdm\n",
    "tqdm.pandas(tqdm())\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import cPickle as pickle\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "\n",
    "import jupyternotify\n",
    "ip = get_ipython()\n",
    "ip.register_magics(jupyternotify.JupyterNotifyMagics)\n",
    "#notify if cell is finished for cells taking longer than 30 seconds\n",
    "%autonotify -a 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Path to your urinal-data-28-nov_clean _pickle_ file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_full = \"Data Files/urinal-data-28-nov_clean.p\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Path to your urinal-data-28-nov_clean [_CSV_](https://drive.google.com/uc?export=download&confirm=AC5G&id=1RaE3ckLvBRfzKpnq61KSp-kZp98Ia7yz) file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_full = \"../Data Files/urinal-data-28-nov_clean.csv\"\n",
    "if not os.path.isfile(csv_full):\n",
    "    print(\"Please Download the csv file \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Up Our Raw data for analyis\n",
    "read in a previously created pickle or read csv, cleanup, and create pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating pickle...\n",
      "this will take some time...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-310cf55f2b55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;31m# drop indexes where camera had fallen down/face was in camera\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m97710\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m14234\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#drop row\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"time\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"time\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"%a %b %d %H:%M:%S %Y\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tqdm/_tqdm.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(df, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    628\u001b[0m                 \u001b[0;31m# Apply the provided function (in *args and **kwargs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m                 \u001b[0;31m# on the df using our wrapper (which provides bar updating)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m                 \u001b[0;31m# Close bar and return pandas calculation result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   2508\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2509\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2510\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2512\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   2495\u001b[0m         \u001b[0;31m# handle ufuncs and lambdas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2496\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkwds\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mufunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2497\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2498\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2499\u001b[0m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tqdm/_tqdm.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0;31m# Define bar updating wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m                 \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tqdm/_tqdm.pyc\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m   1064\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1066\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoveto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m                     \u001b[0;31m# Print bar update\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tqdm/_tqdm.pyc\u001b[0m in \u001b[0;36mmoveto\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m   1218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmoveto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_unicode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_term_move_up\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1220\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1222\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnolock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flush\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0mevt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthreading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36mschedule\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m    184\u001b[0m         \"\"\"\n\u001b[1;32m    185\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[0mevent_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mevent_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_events\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m                 \u001b[0mevent_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "application/javascript": [
       "$(document).ready(\n",
       "    function() {\n",
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       "            // append a div with our uuid so we can check that it's already\n",
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       "            var notifiedDiv = document.createElement(\"div\")\n",
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       "                var notificationPayload = {\"body\": \"Cell Execution Has Finished!!\", \"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"autonotify_after\": \"30\", \"autonotify_output\": false};\n",
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      "text/plain": [
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     "metadata": {},
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   ],
   "source": [
    "     \n",
    "#---------- read in pick or read csv, cleanup, and create pickle----------   \n",
    "if os.path.isfile(pickle_full):\n",
    "    print(\"loading pickle\")\n",
    "    df = pd.read_pickle(pickle_full)\n",
    "   \n",
    "else:\n",
    "    print(\"creating pickle...\")\n",
    "    print(\"this will take some time...\")\n",
    "    df = pd.read_csv(csv_full)\n",
    "    df.columns = [\"time\"]+[\"P\"+str(x) for x in range(64)]\n",
    "    \n",
    "    # remove dirty data\n",
    "    df = df[[(x[:2] != \"TT\") for x in df[\"time\"]]]\n",
    "    df = df[[x[16:18] != \":S\" for x in df[\"time\"]]]\n",
    "        \n",
    "    # drop indexes where camera had fallen down/face was in camera\n",
    "    df = df.drop(range(0,97710+14234), axis=0) #drop row\n",
    "    df[\"time\"] = df[\"time\"].progress_apply(pd.to_datetime,format=\"%a %b %d %H:%M:%S %Y\") + dt.timedelta(hours=10)\n",
    "    df = df.set_index('time')\n",
    "    \n",
    "    #flip image\n",
    "    cols = []\n",
    "    for y in range(7,-1,-1):\n",
    "            for x in range(8):\n",
    "                cols.append(x*8+y)\n",
    "            \n",
    "    cols = [\"P\"+str(x) for x in cols] \n",
    "    df = df.reindex(columns=cols)\n",
    "   \n",
    "    # reset column names\n",
    "    df.columns = [\"P\"+str(x) for x in range(64)]\n",
    "        \n",
    "    df.to_pickle(pickle_full)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raw Data\n",
    "Our dataframe is a composed of rows containing the 64 (8x8) thermal sensor Pixels of the Grid-EYE.    \n",
    "with column P0 - P7 the top line of pixels, and P56 - P63 the bottom line.  \n",
    "Each value therefore corresponds to a temperature in <sup>o</sup>C  \n",
    "Each row is timestamped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P0</th>\n",
       "      <th>P1</th>\n",
       "      <th>P2</th>\n",
       "      <th>P3</th>\n",
       "      <th>P4</th>\n",
       "      <th>P5</th>\n",
       "      <th>P6</th>\n",
       "      <th>P7</th>\n",
       "      <th>P8</th>\n",
       "      <th>P9</th>\n",
       "      <th>...</th>\n",
       "      <th>P54</th>\n",
       "      <th>P55</th>\n",
       "      <th>P56</th>\n",
       "      <th>P57</th>\n",
       "      <th>P58</th>\n",
       "      <th>P59</th>\n",
       "      <th>P60</th>\n",
       "      <th>P61</th>\n",
       "      <th>P62</th>\n",
       "      <th>P63</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-26 09:06:46</th>\n",
       "      <td>22.5</td>\n",
       "      <td>23.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>24.75</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.50</td>\n",
       "      <td>21.50</td>\n",
       "      <td>23.25</td>\n",
       "      <td>...</td>\n",
       "      <td>23.00</td>\n",
       "      <td>21.25</td>\n",
       "      <td>21.50</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.50</td>\n",
       "      <td>22.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.25</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-25 13:16:38</th>\n",
       "      <td>23.5</td>\n",
       "      <td>24.25</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.50</td>\n",
       "      <td>24.25</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.25</td>\n",
       "      <td>...</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.25</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.25</td>\n",
       "      <td>24.50</td>\n",
       "      <td>22.75</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.50</td>\n",
       "      <td>22.50</td>\n",
       "      <td>23.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-25 15:18:19</th>\n",
       "      <td>22.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>20.75</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.50</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>23.25</td>\n",
       "      <td>...</td>\n",
       "      <td>22.00</td>\n",
       "      <td>21.75</td>\n",
       "      <td>23.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.50</td>\n",
       "      <td>22.50</td>\n",
       "      <td>24.25</td>\n",
       "      <td>23.75</td>\n",
       "      <td>21.75</td>\n",
       "      <td>23.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 02:34:07</th>\n",
       "      <td>22.0</td>\n",
       "      <td>22.75</td>\n",
       "      <td>21.75</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.25</td>\n",
       "      <td>22.75</td>\n",
       "      <td>...</td>\n",
       "      <td>22.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-25 20:25:01</th>\n",
       "      <td>22.5</td>\n",
       "      <td>22.25</td>\n",
       "      <td>23.00</td>\n",
       "      <td>21.50</td>\n",
       "      <td>22.50</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.75</td>\n",
       "      <td>22.25</td>\n",
       "      <td>23.50</td>\n",
       "      <td>...</td>\n",
       "      <td>22.25</td>\n",
       "      <td>22.00</td>\n",
       "      <td>21.50</td>\n",
       "      <td>23.00</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.25</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.00</td>\n",
       "      <td>21.75</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       P0     P1     P2     P3     P4     P5     P6     P7  \\\n",
       "time                                                                         \n",
       "2017-11-26 09:06:46  22.5  23.00  22.75  22.00  22.75  24.75  23.25  22.50   \n",
       "2017-11-25 13:16:38  23.5  24.25  22.75  23.25  23.50  24.25  23.50  23.50   \n",
       "2017-11-25 15:18:19  22.0  23.75  20.75  23.00  23.00  23.50  22.75  23.00   \n",
       "2017-11-24 02:34:07  22.0  22.75  21.75  23.00  23.00  23.25  22.50  22.00   \n",
       "2017-11-25 20:25:01  22.5  22.25  23.00  21.50  22.50  23.25  23.25  23.75   \n",
       "\n",
       "                        P8     P9  ...      P54    P55    P56    P57    P58  \\\n",
       "time                               ...                                        \n",
       "2017-11-26 09:06:46  21.50  23.25  ...    23.00  21.25  21.50  23.50  23.50   \n",
       "2017-11-25 13:16:38  23.75  23.25  ...    23.25  22.25  23.75  23.25  24.50   \n",
       "2017-11-25 15:18:19  22.00  23.25  ...    22.00  21.75  23.00  22.75  23.50   \n",
       "2017-11-24 02:34:07  22.25  22.75  ...    22.00  21.00  22.00  23.00  23.25   \n",
       "2017-11-25 20:25:01  22.25  23.50  ...    22.25  22.00  21.50  23.00  22.50   \n",
       "\n",
       "                       P59    P60    P61    P62    P63  \n",
       "time                                                    \n",
       "2017-11-26 09:06:46  22.00  24.00  23.25  22.25  21.00  \n",
       "2017-11-25 13:16:38  22.75  24.00  24.50  22.50  23.25  \n",
       "2017-11-25 15:18:19  22.50  24.25  23.75  21.75  23.25  \n",
       "2017-11-24 02:34:07  22.75  23.00  24.00  22.75  23.50  \n",
       "2017-11-25 20:25:01  22.25  23.50  23.00  21.75  23.50  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Urinal Section Visualisation\n",
    "Below you can see the the Urinal positions (_Left_, _Middle_, and _Right_) with respect to the camera.  \n",
    "The back of a head sits around row 3 or 4 (depending on height).    \n",
    "Also annotated is the _Empty Space_,   \n",
    "since no-one in our office is taller than 7 foot we'll never have a visitor in this region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc87c929dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "frame = np.array(df.iloc[0]).reshape(8,8).astype('float')\n",
    "\n",
    "ax = sns.heatmap(frame, vmin=22, vmax=25,square=True)\n",
    "ax.vlines([2, 5, 8], *ax.get_xlim(),color='white', linewidth=4)\n",
    "ax.hlines([2], *ax.get_xlim(),color='white', linewidth=4)\n",
    "ax.text(0.125, 0.5,'Left', horizontalalignment='center',\n",
    "        verticalalignment='center', transform=ax.transAxes,\n",
    "       color='white',size=16);\n",
    "ax.text(0.45, 0.5,'Middle', horizontalalignment='center',\n",
    "        verticalalignment='center', transform=ax.transAxes,\n",
    "       color='white',size=16);\n",
    "\n",
    "ax.text(0.82, 0.5,'Right', horizontalalignment='center',\n",
    "        verticalalignment='center', transform=ax.transAxes,\n",
    "       color='white',size=16);\n",
    "\n",
    "ax.text(0.45, 0.89,'Empty\\nSpace', horizontalalignment='center',\n",
    "        verticalalignment='center', transform=ax.transAxes,\n",
    "       color='white',size=16);\n",
    "ax.arrow(2.5, 0.89, -2, 0, head_width=0.2, head_length=0.2, fc='w', ec='w',linewidth=3)\n",
    "ax.arrow(4.5, 0.89, 2, 0, head_width=0.2, head_length=0.2, fc='w', ec='w',linewidth=3)\n",
    "\n",
    "plt.savefig('layout_sections.png', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise Our Data!\n",
    "We have an 8x8 grid of thermal pixels: Seaborns heatmap is perfect here.\n",
    "\n",
    "\n",
    "Below we'll look at a typical instance of a person walking in and out of our area of interest.  \n",
    "This particular person visited the left urinal at 4 pm on a Friday.  \n",
    "### Animation of a Walk in\n",
    "Here you can see a person walking in to use the left Urinal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "startIn = df.index.searchsorted(dt.datetime(2017, 11, 24, 15, 59,37,0))\n",
    "endIn = df.index.searchsorted(dt.datetime(2017, 11, 24, 15,59,41,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "framesIn = []\n",
    "for row in df.iloc[startIn:endIn].iterrows():\n",
    "        framesIn.append(np.array(row[1]).reshape(8,8).astype('float'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8c1d6b490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$(document).ready(\n",
       "    function() {\n",
       "        function appendUniqueDiv(){\n",
       "            // append a div with our uuid so we can check that it's already\n",
       "            // been sent and avoid duplicates on page reload\n",
       "            var notifiedDiv = document.createElement(\"div\")\n",
       "            notifiedDiv.id = \"2091f91c-a746-42fa-aab0-d834fa5e655e\"\n",
       "            element.append(notifiedDiv)\n",
       "        }\n",
       "\n",
       "        // only send notifications if the pageload is complete; this will\n",
       "        // help stop extra notifications when a saved notebook is loaded,\n",
       "        // which during testing gives us state \"interactive\", not \"complete\"\n",
       "        if (document.readyState === 'complete') {\n",
       "            // check for the div that signifies that the notification\n",
       "            // was already sent\n",
       "            if (document.getElementById(\"2091f91c-a746-42fa-aab0-d834fa5e655e\") === null) {\n",
       "                var notificationPayload = {\"body\": \"Cell Execution Has Finished!!\", \"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"autonotify_after\": \"30\", \"autonotify_output\": false};\n",
       "                if (Notification.permission !== 'denied') {\n",
       "                    if (Notification.permission !== 'granted') { \n",
       "                        Notification.requestPermission(function (permission) {\n",
       "                            if(!('permission' in Notification)) {\n",
       "                                Notification.permission = permission\n",
       "                            }\n",
       "                        })\n",
       "                    }\n",
       "                    if (Notification.permission === 'granted') {\n",
       "                    var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n",
       "                    appendUniqueDiv()\n",
       "                    notification.onclick = function () {\n",
       "                        window.focus();\n",
       "                        this.close();\n",
       "                        };\n",
       "                    } \n",
       "                }     \n",
       "            }\n",
       "        }\n",
       "    }\n",
       ")\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Low res animation\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['animation.writer'] = 'ffmpeg'\n",
    "# print(matplotlib.animation.writers.list())\n",
    "from matplotlib import animation, rc\n",
    "from IPython.display import HTML\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# fig.set_tight_layout(True)\n",
    "fig = plt.figure()\n",
    "\n",
    "def init():\n",
    "    plt.clf()\n",
    "    sns.heatmap(np.zeros((8, 8)),vmin=22,vmax=25,square=True)\n",
    "    plt.clf()\n",
    "#     return (sns,)\n",
    "\n",
    "def animate(i):\n",
    "    plt.clf()\n",
    "    data = framesIn[i]\n",
    "    sns.heatmap(data, vmin=22,vmax=25,square=True)\n",
    "#     return (sns,)\n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(framesIn),interval=100,repeat=True)#,blit = True)\n",
    "HTML(anim.to_html5_video())\n",
    "\n",
    "#need to return a seaborn object with an \"artist object\"\n",
    "#in order to use blit (and therefore speed up rendering)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Animation of a Walk Out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "startOut = df.index.searchsorted(dt.datetime(2017, 11, 24, 16,0,3,0))\n",
    "endOut = df.index.searchsorted(dt.datetime(2017, 11, 24, 16,0,7,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "framesOut = []\n",
    "for row in df.iloc[startOut:endOut].iterrows():\n",
    "        framesOut.append(np.array(row[1]).reshape(8,8).astype('float'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc879c9b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAEuCAYAAADx63eqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmdwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8b85458d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Low res animation\n",
    "plt.rcParams['animation.writer'] = 'ffmpeg'\n",
    "from matplotlib import animation, rc\n",
    "from IPython.display import HTML\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig = plt.figure()\n",
    "\n",
    "def init():\n",
    "    plt.clf()\n",
    "    sns.heatmap(np.zeros((8, 8)),vmin=22,vmax=25,square=True)\n",
    "    plt.clf()\n",
    "#     return (sns,)\n",
    "\n",
    "def animate(i):\n",
    "    plt.clf()\n",
    "    data = framesOut[i]\n",
    "    sns.heatmap(data, vmin=22,vmax=25,square=True)\n",
    "#     return (sns,)\n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(framesOut),interval=100,repeat=True)#,blit = True)\n",
    "HTML(anim.to_html5_video())\n",
    "\n",
    "#need to return a seaborn object with an \"artist object\"\n",
    "#in order to use blit (and therefore speed up rendering)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might have noticed how noisy the data is.\n",
    "There are two main reasons for this.\n",
    "1. The accuracy and precision of the Grideye is low (&pm;2.5&deg;C, and &pm;0.25&deg;C respectively) <sup>[1](https://industrial.panasonic.com/cdbs/www-data/pdf/ADI8000/ADI8000C53.pdf)</sup>\n",
    "2. Temperature of air varies in a ventilated (HVAC) system, often more than the Grideye's precision <sup>[2](https://www.sciencedirect.com/science/article/pii/S0360132396000340)</sup>\n",
    " \n",
    "Next we'll figure out how to find a person in this mess of pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Go to 2 - __Algorithm Design__"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}