{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HELEN Prepare Flickr API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "from os.path import join\n",
    "from glob import glob, iglob\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import h5py\n",
    "from scipy import misc\n",
    "from io import BytesIO\n",
    "from base64 import b64decode\n",
    "\n",
    "from PIL import Image, ImageDraw\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io as sio\n",
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('/work/megapixels_dev/megapixels/')\n",
    "from app.utils import file_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_in = '/data_store/datasets/people/helen/research/flickr_photo_ids.txt'\n",
    "fp_out = '/data_store/datasets/people/helen/research/helen_flickr_meta.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "photo_id_list = file_utils.load_text(fp_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "items = [{'photo_id': x.split('_')[0]} for x in photo_id_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(items)\n",
    "df.to_csv(fp_out, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Flickr API Dump\n",
    "\n",
    "- and create Flickr meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_api_dump = '/data_store/datasets/people/helen/research/helen_flickr_api_dump.csv'\n",
    "df = pd.read_csv(fp_api_dump)\n",
    "#records = df.to_dict('records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = df.groupby('nsid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for nsid, group in groups:\n",
    "  obj = {\n",
    "    'nsid': nsid,\n",
    "    'count': len(group)\n",
    "  }\n",
    "  results.append(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame.from_dict(results).to_csv(fp_out, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "megapixels",
   "language": "python",
   "name": "megapixels"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
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