path: root/megapixels/notebooks/datasets/uccs/uccs_exif.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UCCS Exif\n",
    "\n",
    "- read in all images and extract metadata\n",
    "- export to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from os.path import join\n",
    "from glob import glob\n",
    "from pprint import pprint\n",
    "\n",
    "import collections\n",
    "import cv2 as cv\n",
    "import pandas as pd\n",
    "from PIL import Image, ImageDraw, ExifTags\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.gridspec as gridspec\n",
    "import math\n",
    "\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_dir_uccs = '/data_store/datasets/people/uccs/dataset'\n",
    "fp_dir_ims = '/data_store/datasets/people/uccs/dataset/media/original'\n",
    "fp_out_exif = '/data_store/datasets/people/uccs/processed/exif/uccs_camera_exif.csv'\n",
    "fp_out_exif_test = '/data_store/datasets/people/uccs/processed/exif/uccs_camera_exif_test.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10917\n"
     ]
    }
   ],
   "source": [
    "fp_ims = glob(join(fp_dir_ims, '*.jpg'))\n",
    "print(len(fp_ims))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_exif(fp_im, raw=False):\n",
    "  im = Image.open(fp_im)\n",
    "  exif_raw = im._getexif()  \n",
    "  exif_data = {}\n",
    "  for tag, value in exif_raw.items():\n",
    "    decoded = ExifTags.TAGS.get(tag, tag)\n",
    "    exif_data[decoded] = value\n",
    "  if raw:\n",
    "    return exif_data\n",
    "  else:\n",
    "    im_w = exif_data['ExifImageWidth']\n",
    "    im_h = exif_data['ExifImageHeight']\n",
    "    #date_time_digitized = exif_data['DateTimeDigitized']\n",
    "    #date_time_original = exif_data['DateTimeOriginal']\n",
    "    date_times = exif_data['DateTime'].split(' ')\n",
    "    date_time = date_times[0].replace(':', '-') + ' ' + date_times[1]\n",
    "    copyright = exif_data['Copyright']\n",
    "    aperture_value = exif_data['ApertureValue'][0] / exif_data['ApertureValue'][0]\n",
    "    fnumber = exif_data['FNumber'][0] / exif_data['FNumber'][1]\n",
    "    focal_length = int(exif_data['FocalLength'][0] / exif_data['FocalLength'][1])\n",
    "    iso = int(exif_data['ISOSpeedRatings'])\n",
    "    exposure_mode = exif_data['ExposureMode']\n",
    "    exposure_program = exif_data['ExposureProgram']\n",
    "    #focal_plane_x = exif_data['FocalPlaneXResolution'][0] / exif_data['FocalPlaneXResolution'][1]\n",
    "    #focal_plane_y = exif_data['FocalPlaneYResolution'][0] / exif_data['FocalPlaneYResolution'][1]\n",
    "    result = {\n",
    "  #     'image_width': im_w,\n",
    "  #     'image_height': im_h,\n",
    "      'date_time': date_time,\n",
    "      #'date_time_digitized': date_time_digitized,\n",
    "      #'date_time_original': date_time_original,\n",
    "      #'copyright': copyright,\n",
    "      'aperture': aperture_value,\n",
    "      'fnumber': fnumber,\n",
    "      'focal_length': focal_length,\n",
    "      'exposure_program': exposure_program,\n",
    "      'exposure_mode': exposure_mode,\n",
    "      'iso': iso\n",
    "    }\n",
    "    return result\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "285797b2b773435aba4a89740ddd38e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10917), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "exif_data = []\n",
    "for fp_im in tqdm(fp_ims):\n",
    "  im_exif = get_exif(fp_im)\n",
    "  im_exif.update({'filename': Path(fp_im).name})\n",
    "  exif_data.append(im_exif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ex in exif_data:\n",
    "  #ex['focal_length'] = int(ex['focal_length'])\n",
    "  splits = ex['date_time'].split(' ')\n",
    "  ex['date'] = splits[0]\n",
    "  ex['time'] = splits[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exif = pd.DataFrame.from_dict(exif_data)\n",
    "df_exif['date_time'] = pd.to_datetime(df_exif['date_time'])\n",
    "#df_exif.to_csv(fp_out_exif, index=False)\n",
    "df_exif.to_csv(fp_out_exif, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aperture</th>\n",
       "      <th>date</th>\n",
       "      <th>date_time</th>\n",
       "      <th>exposure_mode</th>\n",
       "      <th>exposure_program</th>\n",
       "      <th>filename</th>\n",
       "      <th>fnumber</th>\n",
       "      <th>focal_length</th>\n",
       "      <th>iso</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-29</td>\n",
       "      <td>2013-01-29 12:07:45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>e87a43c32cc697d3e6b40be3e3594057.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>100</td>\n",
       "      <td>12:07:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-04-03</td>\n",
       "      <td>2012-04-03 11:07:53</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9d15290fdd811d5cbaeb44448a4b54d3.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>400</td>\n",
       "      <td>11:07:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-29</td>\n",
       "      <td>2013-01-29 12:08:01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d9cad73c2f47022195169e07f21dc567.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>100</td>\n",
       "      <td>12:08:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-02-20</td>\n",
       "      <td>2013-02-20 12:16:35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>decf44da0b963a33c88362e613878820.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>160</td>\n",
       "      <td>12:16:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-02-19</td>\n",
       "      <td>2013-02-19 16:30:51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4a59b6b9b50cf6fc87e45caa0fdb86df.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>400</td>\n",
       "      <td>16:30:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aperture        date           date_time  exposure_mode  exposure_program  \\\n",
       "0       1.0  2013-01-29 2013-01-29 12:07:45              1                 1   \n",
       "1       1.0  2012-04-03 2012-04-03 11:07:53              0                 3   \n",
       "2       1.0  2013-01-29 2013-01-29 12:08:01              1                 1   \n",
       "3       1.0  2013-02-20 2013-02-20 12:16:35              1                 1   \n",
       "4       1.0  2013-02-19 2013-02-19 16:30:51              1                 1   \n",
       "\n",
       "                               filename  fnumber  focal_length  iso      time  \n",
       "0  e87a43c32cc697d3e6b40be3e3594057.jpg      5.6           800  100  12:07:45  \n",
       "1  9d15290fdd811d5cbaeb44448a4b54d3.jpg      5.6           800  400  11:07:53  \n",
       "2  d9cad73c2f47022195169e07f21dc567.jpg      5.6           800  100  12:08:01  \n",
       "3  decf44da0b963a33c88362e613878820.jpg      5.6           800  160  12:16:35  \n",
       "4  4a59b6b9b50cf6fc87e45caa0fdb86df.jpg      5.6           800  400  16:30:51  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exif.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "exif_dates = df_exif.groupby('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n",
      "['2012-02-23',\n",
      " '2012-03-06',\n",
      " '2012-03-08',\n",
      " '2012-03-13',\n",
      " '2012-03-20',\n",
      " '2012-03-22',\n",
      " '2012-04-03',\n",
      " '2012-04-12',\n",
      " '2012-04-17',\n",
      " '2012-04-24',\n",
      " '2012-04-25',\n",
      " '2012-04-26',\n",
      " '2013-01-28',\n",
      " '2013-01-29',\n",
      " '2013-02-13',\n",
      " '2013-02-19',\n",
      " '2013-02-20',\n",
      " '2013-02-26']\n"
     ]
    }
   ],
   "source": [
    "dates = list(exif_dates.groups.keys())\n",
    "print(len(dates))\n",
    "pprint(dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse CSV to Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exif = pd.read_csv(fp_out_exif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aperture</th>\n",
       "      <th>date</th>\n",
       "      <th>date_time</th>\n",
       "      <th>exposure_mode</th>\n",
       "      <th>exposure_program</th>\n",
       "      <th>filename</th>\n",
       "      <th>fnumber</th>\n",
       "      <th>focal_length</th>\n",
       "      <th>iso</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-29</td>\n",
       "      <td>2013-01-29 12:07:45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>e87a43c32cc697d3e6b40be3e3594057.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>100</td>\n",
       "      <td>12:07:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-04-03</td>\n",
       "      <td>2012-04-03 11:07:53</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9d15290fdd811d5cbaeb44448a4b54d3.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>400</td>\n",
       "      <td>11:07:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-29</td>\n",
       "      <td>2013-01-29 12:08:01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>d9cad73c2f47022195169e07f21dc567.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>100</td>\n",
       "      <td>12:08:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-02-20</td>\n",
       "      <td>2013-02-20 12:16:35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>decf44da0b963a33c88362e613878820.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>160</td>\n",
       "      <td>12:16:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-02-19</td>\n",
       "      <td>2013-02-19 16:30:51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4a59b6b9b50cf6fc87e45caa0fdb86df.jpg</td>\n",
       "      <td>5.6</td>\n",
       "      <td>800</td>\n",
       "      <td>400</td>\n",
       "      <td>16:30:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aperture        date            date_time  exposure_mode  exposure_program  \\\n",
       "0       1.0  2013-01-29  2013-01-29 12:07:45              1                 1   \n",
       "1       1.0  2012-04-03  2012-04-03 11:07:53              0                 3   \n",
       "2       1.0  2013-01-29  2013-01-29 12:08:01              1                 1   \n",
       "3       1.0  2013-02-20  2013-02-20 12:16:35              1                 1   \n",
       "4       1.0  2013-02-19  2013-02-19 16:30:51              1                 1   \n",
       "\n",
       "                               filename  fnumber  focal_length  iso      time  \n",
       "0  e87a43c32cc697d3e6b40be3e3594057.jpg      5.6           800  100  12:07:45  \n",
       "1  9d15290fdd811d5cbaeb44448a4b54d3.jpg      5.6           800  400  11:07:53  \n",
       "2  d9cad73c2f47022195169e07f21dc567.jpg      5.6           800  100  12:08:01  \n",
       "3  decf44da0b963a33c88362e613878820.jpg      5.6           800  160  12:16:35  \n",
       "4  4a59b6b9b50cf6fc87e45caa0fdb86df.jpg      5.6           800  400  16:30:51  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exif.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_groups = df_exif.groupby('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monday 1056\n",
      "Tuesday 7029\n",
      "Wednesday 1715\n",
      "Thursday 1117\n",
      "Friday 0\n",
      "Saturday 0\n",
      "Sunday 0\n",
      "2012-02-23 132\n",
      "2012-03-06 288\n",
      "2012-03-08 506\n",
      "2012-03-13 160\n",
      "2012-03-20 1840\n",
      "2012-03-22 445\n",
      "2012-04-03 1639\n",
      "2012-04-12 14\n",
      "2012-04-17 19\n",
      "2012-04-24 63\n",
      "2012-04-25 11\n",
      "2012-04-26 20\n",
      "2013-01-28 1056\n",
      "2013-01-29 1561\n",
      "2013-02-13 739\n",
      "2013-02-19 723\n",
      "2013-02-20 965\n",
      "2013-02-26 736\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "date_times = {}\n",
    "weekday_times = {}\n",
    "cal_date_counts = {}\n",
    "\n",
    "for d in days:\n",
    "  date_times[d] = []\n",
    "  weekday_times = []\n",
    "  \n",
    "for date_str, date_group in date_groups:\n",
    "  \n",
    "  year, month, date = (int(x) for x in date_str.split('-'))    \n",
    "  day_idx = datetime.date(year, month, date).weekday()\n",
    "  day = days[day_idx]\n",
    "  for item in date_group.itertuples():\n",
    "    hour, minute, second = (int(x) for x in item.time.split(':'))\n",
    "    dt = datetime.datetime(year, month, date, hour, minute, second, 0)\n",
    "    date_times[day].append(dt)\n",
    "    # date count\n",
    "    if date_str not in cal_date_counts.keys():\n",
    "      cal_date_counts[date_str] = 0\n",
    "    cal_date_counts[date_str] += 1\n",
    "\n",
    "date_counts = {}\n",
    "for day, hours in date_times.items():\n",
    "  date_counts[day] = len(hours)\n",
    "  print(day, len(hours))\n",
    "  \n",
    "# print date counts\n",
    "for k,v in cal_date_counts.items():\n",
    "  print(k,v)\n",
    "\n",
    "print(len(cal_date_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(['dark_background'])\n",
    "bar_clr = '#9E0142'\n",
    "face_clr = '#181818'\n",
    "grid_clr = '#333333'\n",
    "frame_clr =  '#333333'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure(figsize=(20,8), dpi=72, facecolor=face_clr)\n",
    "ax = plt.subplot(111) # Upper Plot\n",
    "\n",
    "num_photos = list(date_counts.values())\n",
    "x_pos = [i for i, _ in enumerate(days)]\n",
    "plt.bar(x_pos, num_photos, color=bar_clr)\n",
    "plt.grid(linestyle='-', linewidth='1', color=grid_clr)\n",
    "plt.setp(ax.spines.values(), color=frame_clr)\n",
    "plt.ylabel(\"Photos\")\n",
    "plt.title('UCCS Dataset Photos Captured per Day')\n",
    "fig.set_facecolor(face_clr)\n",
    "plt.xticks(x_pos, days)\n",
    "plt.show()\n",
    "fig.savefig('/mnt/ahprojects/tmp/uccs_exif_plot_days.png', dpi=fig.dpi, transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bucket times into 30min intervals\n",
    "delta = 10\n",
    "time_buckets = {}\n",
    "for day_name, times in date_times.items():\n",
    "  time_buckets[day_name] = {}\n",
    "  for t in times:\n",
    "    # conver times\n",
    "    bucket_name = '{:02}:{:02}'.format(t.hour, (t.minute // delta) * delta)\n",
    "    if not bucket_name in time_buckets[day_name].keys():\n",
    "      time_buckets[day_name][bucket_name] = 0\n",
    "    time_buckets[day_name][bucket_name] += 1\n",
    "\n",
    "# add all keys to all days\n",
    "all_time_keys = []\n",
    "for k, v in time_buckets.items():\n",
    "  all_time_keys += list(v.keys())\n",
    "\n",
    "# reduce to unique\n",
    "all_time_keys = set(all_time_keys)\n",
    "\n",
    "for k, v in time_buckets.items():\n",
    "  for atk in all_time_keys:\n",
    "    if atk not in time_buckets[k].keys():\n",
    "      time_buckets[k][atk] = 0\n",
    "  \n",
    "for k, v in time_buckets.items():\n",
    "  time_buckets[k] = collections.OrderedDict(sorted(v.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('09:30', 0), ('09:40', 0), ('10:40', 0), ('10:50', 0), ('11:00', 0), ('11:10', 0), ('11:50', 0), ('12:00', 0), ('12:10', 0), ('12:20', 0), ('12:30', 662), ('12:40', 394), ('12:50', 0), ('13:00', 0), ('13:10', 0), ('13:20', 0), ('13:30', 0), ('13:50', 0), ('14:00', 0), ('14:10', 0), ('14:50', 0), ('15:00', 0), ('15:20', 0), ('16:10', 0), ('16:20', 0), ('16:30', 0)])\n",
      "OrderedDict([('09:30', 414), ('09:40', 664), ('10:40', 0), ('10:50', 0), ('11:00', 832), ('11:10', 376), ('11:50', 0), ('12:00', 361), ('12:10', 710), ('12:20', 306), ('12:30', 901), ('12:40', 48), ('12:50', 0), ('13:00', 0), ('13:10', 0), ('13:20', 167), ('13:30', 121), ('13:50', 365), ('14:00', 0), ('14:10', 0), ('14:50', 0), ('15:00', 0), ('15:20', 312), ('16:10', 183), ('16:20', 797), ('16:30', 472)])\n",
      "OrderedDict([('09:30', 0), ('09:40', 0), ('10:40', 0), ('10:50', 5), ('11:00', 6), ('11:10', 0), ('11:50', 5), ('12:00', 356), ('12:10', 380), ('12:20', 305), ('12:30', 133), ('12:40', 152), ('12:50', 74), ('13:00', 55), ('13:10', 116), ('13:20', 101), ('13:30', 20), ('13:50', 0), ('14:00', 0), ('14:10', 7), ('14:50', 0), ('15:00', 0), ('15:20', 0), ('16:10', 0), ('16:20', 0), ('16:30', 0)])\n",
      "OrderedDict([('09:30', 0), ('09:40', 0), ('10:40', 213), ('10:50', 0), ('11:00', 309), ('11:10', 62), ('11:50', 0), ('12:00', 2), ('12:10', 130), ('12:20', 0), ('12:30', 14), ('12:40', 0), ('12:50', 0), ('13:00', 0), ('13:10', 0), ('13:20', 0), ('13:30', 0), ('13:50', 51), ('14:00', 43), ('14:10', 0), ('14:50', 266), ('15:00', 27), ('15:20', 0), ('16:10', 0), ('16:20', 0), ('16:30', 0)])\n",
      "OrderedDict([('09:30', 0), ('09:40', 0), ('10:40', 0), ('10:50', 0), ('11:00', 0), ('11:10', 0), ('11:50', 0), ('12:00', 0), ('12:10', 0), ('12:20', 0), ('12:30', 0), ('12:40', 0), ('12:50', 0), ('13:00', 0), ('13:10', 0), ('13:20', 0), ('13:30', 0), ('13:50', 0), ('14:00', 0), ('14:10', 0), ('14:50', 0), ('15:00', 0), ('15:20', 0), ('16:10', 0), ('16:20', 0), ('16:30', 0)])\n",
      "OrderedDict([('09:30', 0), ('09:40', 0), ('10:40', 0), ('10:50', 0), ('11:00', 0), ('11:10', 0), ('11:50', 0), ('12:00', 0), ('12:10', 0), ('12:20', 0), ('12:30', 0), ('12:40', 0), ('12:50', 0), ('13:00', 0), ('13:10', 0), ('13:20', 0), ('13:30', 0), ('13:50', 0), ('14:00', 0), ('14:10', 0), ('14:50', 0), ('15:00', 0), ('15:20', 0), ('16:10', 0), ('16:20', 0), ('16:30', 0)])\n",
      "OrderedDict([('09:30', 0), ('09:40', 0), ('10:40', 0), ('10:50', 0), ('11:00', 0), ('11:10', 0), ('11:50', 0), ('12:00', 0), ('12:10', 0), ('12:20', 0), ('12:30', 0), ('12:40', 0), ('12:50', 0), ('13:00', 0), ('13:10', 0), ('13:20', 0), ('13:30', 0), ('13:50', 0), ('14:00', 0), ('14:10', 0), ('14:50', 0), ('15:00', 0), ('15:20', 0), ('16:10', 0), ('16:20', 0), ('16:30', 0)])\n"
     ]
    }
   ],
   "source": [
    "for k, v in time_buckets.items():\n",
    "  print(time_buckets[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "901\n"
     ]
    }
   ],
   "source": [
    "# find max photos per bucket\n",
    "max_photos = 0\n",
    "for display_day in display_days:\n",
    "  for k,v in time_buckets[display_day].items():\n",
    "    max_photos = max(max_photos, v)\n",
    "print(max_photos)\n",
    "max_photos = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monday\n",
      "Tuesday\n",
      "Wednesday\n",
      "Thursday\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1296 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure(figsize=(20,18), dpi=72, facecolor=face_clr)\n",
    "fig.subplots_adjust(hspace=0.5)\n",
    "\n",
    "display_days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday']\n",
    "for i, display_day in enumerate(display_days):\n",
    "  # Monday\n",
    "  print(display_day)\n",
    "  ax = plt.subplot('41{}'.format(i+1))\n",
    "  x = list(time_buckets[display_day].keys())\n",
    "  x_label_idxs = list(range(0,len(x)))\n",
    "  x_labels = x.copy()\n",
    "  y = [v for k,v in time_buckets[display_day].items()]\n",
    "  plt.ylim([0,max_photos])\n",
    "  plt.ylabel(display_day)\n",
    "  ax.set_facecolor(face_clr)\n",
    "  plt.bar(x, y, color=bar_clr)\n",
    "  ax.grid(linestyle='-', linewidth='1', color=grid_clr)\n",
    "  plt.setp(ax.spines.values(), color=frame_clr)\n",
    "\n",
    "ax = plt.subplot('411')\n",
    "plt.title('Unconstrained College Students Dataset: Photos Captured per Weekday')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('/mnt/ahprojects/tmp/uccs_exif_plot.png', dpi=fig.dpi, transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "megapixels",
   "language": "python",
   "name": "megapixels"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}