duartenina/Nitain Alerts.ipynb

## Nitain Alerts.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import csv\n",
    "import datetime as d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def time_to_secs(t):\n",
    "    return t.hour * 60 * 60 + t.minute * 60 + t.second\n",
    "\n",
    "def time_to_mins(t):\n",
    "    return t.hour * 60 + t.minute\n",
    "\n",
    "def secs_to_time(secs):\n",
    "    t = secs\n",
    "    hours = t//(60*60)\n",
    "    t -= hours * 60 * 60\n",
    "    mins = t//60\n",
    "    t -= mins * 60\n",
    "    return d.time(hours, mins, t)\n",
    "\n",
    "def mins_to_time(mins):\n",
    "    t = mins\n",
    "    hours = t//60\n",
    "    t -= hours * 60\n",
    "    return d.time(hours, t, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "alerts_raw = []\n",
    "\n",
    "with open('nitain_with_length.csv', 'r') as csvfile:\n",
    "    table = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
    "    for line in table:\n",
    "        if line[0][0] != '2':\n",
    "            continue\n",
    "        alerts_raw.append(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "alerts = []\n",
    "\n",
    "for alert in alerts_raw:\n",
    "    date_list = alert[0].split('-')\n",
    "    year = int(date_list[0])\n",
    "    month = int(date_list[1])\n",
    "    day = int(date_list[2])\n",
    "    date = d.date(year, month, day)\n",
    "\n",
    "    time_list = alert[1].split(':')\n",
    "    hour = int(time_list[0])\n",
    "    minute = int(time_list[1])\n",
    "    second = int(time_list[2])\n",
    "    time = d.time(hour, minute, second)\n",
    "\n",
    "    duration = d.timedelta(minutes=int(alert[2][:-1]))\n",
    "    \n",
    "    alerts.append([date, time, duration])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "occupied = {}\n",
    "\n",
    "for alert in alerts:\n",
    "    date = alert[0]\n",
    "    time = alert[1]\n",
    "    duration = alert[2]\n",
    "    \n",
    "    if date not in occupied:\n",
    "        occupied[date] = []\n",
    "    \n",
    "    start_time = d.datetime.combine(date, time)\n",
    "    end_time = start_time + duration\n",
    "    \n",
    "    if start_time.day == end_time.day:\n",
    "        occupied[date].append( (start_time.time(), end_time.time()) )\n",
    "    else:\n",
    "        next_day = start_time.date() + d.timedelta(days=1)\n",
    "        midnight = d.datetime.combine(next_day, d.time.min)\n",
    "        occupied[date].append( (start_time.time(), d.time.max) )\n",
    "        occupied[next_day].append( (d.time.min, end_time.time()) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mAAAA4Hrz3hJ4ZpJHLBv27CRv6e67J3lrkufMOQMAAACDuZbA7n57ki8tG/yYJGcN189K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\nf3BbS+cxRZkQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab504a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Earliest start: 00:04:01\n",
      "Latest start: 23:59:01\n"
     ]
    }
   ],
   "source": [
    "start_times = np.array([time_to_secs(alert[1]) for alert in alerts])\n",
    "    \n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "n, bins, patches = plt.hist(start_times/(60*60), bins=24)\n",
    "\n",
    "for i in range(len(patches)):\n",
    "    if i%2 == 1:\n",
    "        patches[i].set_facecolor('c')\n",
    "\n",
    "plt.xlabel('t (h)')\n",
    "plt.xlim(0,24)\n",
    "plt.locator_params(axis='x',nbins=24)\n",
    "plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d:00'))\n",
    "\n",
    "plt.ylabel('# instances')\n",
    "\n",
    "plt.title('Histogram of start times')\n",
    "\n",
    "plt.gcf().savefig('starts.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('Earliest start:', secs_to_time(min(start_times)))\n",
    "print('Latest start:', secs_to_time(max(start_times)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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77LdfrpvteljmZNSf3Q477JqNG28dXXs775yNP//5yNrbEqMIeTV4TPhIkmOTnJjkBUk+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\nAHpEyAMAAOiR/x//bCzcQ8aq/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaea7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration is (50.25 +/- 8.59) min .\n",
      "Minimum duration is 35.0 min and maximum is 65.0 min.\n"
     ]
    }
   ],
   "source": [
    "durations = []\n",
    "\n",
    "for alert in alerts:\n",
    "    durations.append(alert[2].seconds / 60)\n",
    "\n",
    "avg = np.mean(durations)\n",
    "deviation = np.std(durations)    \n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "n, bins, patches = plt.hist(durations, bins=30)\n",
    "\n",
    "plt.plot([avg, avg], [0, np.max(n)+1], \"r-\", lw=3)\n",
    "plt.text(avg, max(n)*0.9, \"avg = %.2f\"%avg,\n",
    "         color='r', fontsize=14,\n",
    "         bbox=dict(facecolor='w', alpha=1, edgecolor=None))\n",
    "\n",
    "for i in range(len(patches)):\n",
    "    if i%2 == 1:\n",
    "        patches[i].set_facecolor('c')\n",
    "\n",
    "plt.xlabel('duration (min)')\n",
    "\n",
    "plt.ylabel('# instances')\n",
    "plt.ylim(0, np.max(n)+1)\n",
    "\n",
    "plt.title('Histogram of durations')\n",
    "\n",
    "plt.gcf().savefig('durations.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('Duration is (%.2f +/- %.2f) min .'%(avg, deviation))\n",
    "print('Minimum duration is', np.min(durations), 'min and maximum is', np.max(durations), 'min.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X4Pjjgy4lhZmoatBlSEpENMzlIyIiIiIqtJ12spB31FHA8OHAK68A228PbLmlvd+zJzB9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\nbW3SI48AJ51kj5XzmczlgBUrBrZrDz4IHHdcbevNolqEwJd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zlLQUhkAKSnsIfP31xt4O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\nDhenAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8094710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average chance was 3.49% +/- 1.17%.\n",
      "Maximum chance was 7.96% at 20:30:00.\n",
      "Minimum chance was 0.88% at 17:04:00.\n"
     ]
    }
   ],
   "source": [
    "alert_available = np.zeros((24*60, 2), dtype='int')\n",
    "alert_available[:, 0] = np.linspace(0, 24*60-1, 24*60, dtype='int')\n",
    "\n",
    "for date in occupied:\n",
    "    for start_and_end in occupied[date]:\n",
    "        start_min = time_to_mins(start_and_end[0])\n",
    "        end_min = time_to_mins(start_and_end[1])\n",
    "        \n",
    "        alert_available[start_min:end_min, 1] += 1\n",
    "        \n",
    "        if end_min == 60*24 - 1:\n",
    "            alert_available[end_min, 1] += 1\n",
    "\n",
    "chance_hour = np.array(alert_available, dtype='float')\n",
    "chance_hour[:, 0] /= 60\n",
    "chance_hour[:, 1] *= 100/len(alerts)\n",
    "\n",
    "max_chance_idx = np.argmax(chance_hour[:,1])\n",
    "min_chance_idx = np.argmin(chance_hour[:,1])\n",
    "avg_chance = np.mean(chance_hour[:,1])\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "plt.plot(chance_hour[:, 0], chance_hour[:, 1])\n",
    "\n",
    "plt.plot([0, 24], [avg_chance, avg_chance], \"r-\", lw=1)\n",
    "plt.text(12, avg_chance, \"avg = %.2f\"%avg_chance,\n",
    "         color='r', fontsize=14,\n",
    "         bbox=dict(facecolor='w', alpha=1, edgecolor=None))\n",
    "\n",
    "plt.xlabel('time (h)')\n",
    "plt.xlim(0, 24)\n",
    "plt.locator_params(axis='x',nbins=24)\n",
    "plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d:00'))\n",
    "\n",
    "plt.ylim(0, 10)\n",
    "plt.ylabel('chance of having alert (%)')\n",
    "\n",
    "plt.title('Chance of having alert at a certain time')\n",
    "\n",
    "plt.gcf().savefig('chance.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('Average chance was %.2f%% +/- %.2f%%.'%(avg_chance, np.std(chance_hour[:,1])))\n",
    "print('Maximum chance was %.2f%% at '%chance_hour[max_chance_idx, 1] + str(mins_to_time(max_chance_idx)) + '.')\n",
    "print('Minimum chance was %.2f%% at '%chance_hour[min_chance_idx, 1] + str(mins_to_time(min_chance_idx)) + '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ae9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of alerts per day: 5.04\n",
      "Average of alerts per 30min: 12.62\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 30))\n",
    "\n",
    "gs1 = matplotlib.gridspec.GridSpec(2, 2)\n",
    "gs1.update(wspace=0.01, hspace=0.01)\n",
    "\n",
    "sorted_occupied_dates = sorted(occupied)\n",
    "first_date, last_date = sorted_occupied_dates[0], sorted_occupied_dates[-1]\n",
    "\n",
    "time_blocks = np.zeros(24*2)\n",
    "days = OrderedDict()\n",
    "\n",
    "plt.subplot(gs1[0])\n",
    "\n",
    "# PLOT OF ALERTS IN DAY\n",
    "\n",
    "for i in range(24*2):\n",
    "    plt.bar(left = i*0.5, bottom = 0,\n",
    "            height = 10**6, width = 0.5,\n",
    "            color = 'k', alpha = .1 if i%2==0 else .05)\n",
    "\n",
    "for i, date in zip(range(len(occupied)), sorted(occupied)):\n",
    "    if date not in days:\n",
    "        days[date] = 0\n",
    "        \n",
    "    for alert in sorted(occupied[date], key=lambda t:t[0]):\n",
    "        start = time_to_mins(alert[0])\n",
    "        end   = time_to_mins(alert[1])\n",
    "        \n",
    "        plt.bar(left = start/60, bottom = date,\n",
    "                height = 1, width = (end-start)/60,\n",
    "                color = ('c' if i%2==1 else 'b'),\n",
    "                orientation = 'horizontal')\n",
    "        \n",
    "        time_blocks[start//30 : (end//30 + 1)] += 1\n",
    "        days[date] += 1\n",
    "        \n",
    "        \n",
    "#x axis\n",
    "#plt.xlabel('Alerts (hour)')\n",
    "plt.xlim(0, 24)\n",
    "plt.gca().set_xticklabels([])\n",
    "#plt.locator_params(axis='x',nbins=24)\n",
    "#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d:00'))\n",
    "\n",
    "#y axis\n",
    "plt.ylim(first_date, last_date)\n",
    "dloc = matplotlib.dates.DayLocator(interval=3)\n",
    "plt.gca().yaxis.set_major_locator(dloc)\n",
    "plt.gca().yaxis.set_major_formatter(matplotlib.dates.DateFormatter('%a, %b %d, %Y'))\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "plt.title('Alerts in days')\n",
    "\n",
    "# PLOT OF FREQUENCY (30MIN)\n",
    "\n",
    "plt.subplot(gs1[2])\n",
    "\n",
    "for i in range(24*2):\n",
    "    plt.bar(left = i*0.5, bottom = 0,\n",
    "        height = 10**2, width = 0.5,\n",
    "        color = 'k', alpha = .1 if i%2==0 else .05)\n",
    "    \n",
    "    \n",
    "    plt.bar(left = i*0.5, bottom = 0,\n",
    "            height = time_blocks[i], width = 0.5,\n",
    "            color = ('c' if i%2==0 else 'b'))\n",
    "    \n",
    "avg_in_half_hour = np.mean(time_blocks)\n",
    "\n",
    "plt.plot([0,24], (avg_in_half_hour, avg_in_half_hour), \"r-\", lw=3)\n",
    "plt.text(12, avg_in_half_hour, \"avg = %.2f\"%avg_in_half_hour,\n",
    "         color='r', fontsize=14,\n",
    "         bbox=dict(facecolor='w', alpha=1, edgecolor=None))\n",
    "    \n",
    "#x axis\n",
    "plt.xlabel('Hour')\n",
    "plt.xlim(0, 24)\n",
    "plt.locator_params(axis='x',nbins=24)\n",
    "plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d:00'))\n",
    "\n",
    "#y axis\n",
    "plt.ylabel('# Alerts')\n",
    "plt.ylim(0, np.max(time_blocks)+1)\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "# Plot of # alerts in day\n",
    "\n",
    "plt.subplot(gs1[1])\n",
    "\n",
    "i = 0\n",
    "for date, num in days.items():\n",
    "    plt.bar(left = 0, bottom = date,\n",
    "            height = 1, width = num,\n",
    "            color = ('c' if i%2==1 else 'b'),\n",
    "            orientation = 'horizontal')\n",
    "    i += 1\n",
    "    \n",
    "avg_in_day = np.mean([i for i in days.values()])\n",
    "\n",
    "d2n = matplotlib.dates.date2num\n",
    "\n",
    "plt.plot([avg_in_day, avg_in_day], d2n([first_date, last_date]), \"r-\", lw=3)\n",
    "plt.text(avg_in_day, np.mean(d2n([first_date, last_date])), \"avg = %.2f\"%avg_in_day,\n",
    "         color='r', fontsize=14,\n",
    "         bbox=dict(facecolor='w', alpha=1, edgecolor=None))\n",
    "\n",
    "plt.ylim(first_date, last_date)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.gca().set_yticklabels([])\n",
    "\n",
    "plt.xlabel('# occurrences')\n",
    "\n",
    "#########################################\n",
    "\n",
    "plt.gcf().savefig('distr30.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"Average of alerts per day: %.2f\"%avg_in_day)\n",
    "print(\"Average of alerts per 30min: %.2f\"%avg_in_half_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rq9r5sa/vNMvPfjZ58IOHMPeylw2PCNjq4IOHnz/3c9f8zJlnDgHrZ39227qTThpG5P76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\n6aGgCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f980f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average interval is (242.58 +/- 84.34) min .\n",
      "Minimum interval is 99.00 min and interval is 808.02 min.\n"
     ]
    }
   ],
   "source": [
    "last_alert = None\n",
    "intervals = []\n",
    "\n",
    "for date in sorted(occupied):\n",
    "    for start, end in sorted(occupied[date]):\n",
    "        if last_alert:\n",
    "            start_date = d.datetime.combine(date, start)\n",
    "            intervals.append((start_date-last_alert).total_seconds())\n",
    "        \n",
    "        if end != d.time.max:\n",
    "            last_alert = d.datetime.combine(date, end)\n",
    "        else:\n",
    "            last_alert = None\n",
    "            \n",
    "intervals = np.array(intervals)/60\n",
    "\n",
    "avg = np.mean(intervals)\n",
    "deviation = np.std(intervals)\n",
    "min_interval = np.min(intervals)\n",
    "max_interval = np.max(intervals)\n",
    "\n",
    "n_bins = 5*int(np.round((max_interval - min_interval)/deviation))\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "n, bins, patches = plt.hist(intervals, bins = n_bins, color = 'b')\n",
    "\n",
    "for i in range(len(patches)):\n",
    "    if i%2 == 1:\n",
    "        patches[i].set_facecolor('c')\n",
    "\n",
    "plt.plot([avg, avg], [0, max_interval+1], \"r-\", lw=3)\n",
    "plt.text(avg, max(n)*0.9, \"avg = %.2f\"%avg,\n",
    "         color='r', fontsize=14,\n",
    "         bbox=dict(facecolor='w', alpha=1, edgecolor=None))\n",
    "\n",
    "plt.xlabel('interval (min)')\n",
    "plt.xlim(min_interval, max_interval)\n",
    "\n",
    "plt.ylabel('# instances')\n",
    "plt.ylim(0, max(n)+1)\n",
    "\n",
    "plt.title('Histogram of intervals')\n",
    "\n",
    "plt.gcf().savefig('intervals.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('Average interval is (%.2f +/- %.2f) min .'%(avg, deviation))\n",
    "print('Minimum interval is %.2f min and interval is %.2f min.'%(min_interval, max_interval))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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