{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data for the Entire Season\n",
    "\n",
    "This section explores how much data would be needed to analyze the entire area of Brandenburg, Germany, over an entire year. This was explored to verify the feasibility of comparing data for an entire year with official numbers as provided by the Waldbrandstatistik published by the responsible ministry, the Landesbetrieb Forst Brandenburg.\n",
    "\n",
    "Data in the year 2018 is looked at to provide an estimate of the required resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import date\n",
    "import os\n",
    "from sentinel_helpers import search_osm\n",
    "from sentinelsat import SentinelAPI\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "api = SentinelAPI(os.getenv('SCIHUB_USERNAME'), os.getenv('SCIHUB_PASSWORD'))\n",
    "api._tqdm = tqdm\n",
    "\n",
    "start_date = date(2018, 1, 1)\n",
    "end_date = date(2018, 12, 31)\n",
    "cloud_coverage = (0, 30)\n",
    "\n",
    "brandenburg = search_osm('Brandenburg, Germany')\n",
    "brandenburg = brandenburg[brandenburg['type'] == 'administrative'][:1]\n",
    "brandenburg.plot(figsize=(9,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02eac3e4be3d4ab98ab6432228a081e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Querying products'), FloatProgress(value=0.0, max=638.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Found 638 results\n"
     ]
    }
   ],
   "source": [
    "footprint = brandenburg.iloc[0]['geometry'].convex_hull.wkt\n",
    "results = api.query(footprint,\n",
    "                    platformname='Sentinel-2',\n",
    "                    processinglevel='Level-2A',\n",
    "                    date=(start_date, end_date),\n",
    "                    cloudcoverpercentage=cloud_coverage)\n",
    "print(f'Found {len(results)} results')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is previously mentioned, `results` is an ordered dict of `UUID` → `product`, where each `product` contains information about download size. This download size is given as a string which ends with a unit of either Megabyte (MB) or Gigabyte (GB).\n",
    "\n",
    "These sizes are all converted to floats and normalized to be given in Gigabytes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def human_readable_size_to_bytes(size):\n",
    "    return float(size.split(' ')[0]) * 1024 * 1024 * (1024 if 'GB' in size else 1)\n",
    "\n",
    "gdf = SentinelAPI.to_geodataframe(results)\n",
    "size_in_bytes = gdf['size'].apply(human_readable_size_to_bytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entire size of all products covering Brandenburg with a cloud coverage less than 30% can thus be calculated to be just over 500GB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size in GB: 517.64GB\n"
     ]
    }
   ],
   "source": [
    "total_size_in_gb = size_in_bytes.sum() / 1024 ** 3\n",
    "print(f'Total size in GB: {total_size_in_gb:.2f}GB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is more than the disk space available on this virtual machine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filesystem      Size  Used Avail Use% Mounted on\n",
      "/dev/xvda1      247G  184G   50G  79% /home/jovyan\n"
     ]
    }
   ],
   "source": [
    "! df -h | grep -E '(Filesystem|/home/jovyan)'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloading these with an average download speed of 2 MB/s means obtaining this data, not counting the time it takes to restore it from the Long-Term-Archive takes…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.62 hours\n"
     ]
    }
   ],
   "source": [
    "duration_in_hours = size_in_bytes.sum() / (1024 ** 2) / 2 / 60 / 60\n",
    "print(f'{duration_in_hours:.2f} hours')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The official forest fire season in Brandenburg is from March 1 until October 31 ([source](https://mluk.brandenburg.de/mluk/de/landwirtschaft/forst/waldschutz/waldbrandgefahr-in-brandenburg/)). The above calculations can be repeated for this restricted time frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd4ad51e5b844d8f9e4cfb2a7bab7766",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Querying products'), FloatProgress(value=0.0, max=579.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Found 579 results\n"
     ]
    }
   ],
   "source": [
    "season_start = date(2018, 3, 1)\n",
    "season_end = date(2018, 10, 31)\n",
    "results = api.query(footprint,\n",
    "                    platformname='Sentinel-2',\n",
    "                    processinglevel='Level-2A',\n",
    "                    date=(season_start, season_end),\n",
    "                    cloudcoverpercentage=cloud_coverage)\n",
    "\n",
    "print(f'Found {len(results)} results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size in GB: 468.67GB\n"
     ]
    }
   ],
   "source": [
    "gdf = SentinelAPI.to_geodataframe(results)\n",
    "size_in_bytes = gdf['size'].apply(human_readable_size_to_bytes)\n",
    "\n",
    "total_size_in_gb = size_in_bytes.sum() / 1024 ** 3\n",
    "print(f'Total size in GB: {total_size_in_gb:.2f}GB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is a part of approximately 91% of the products for the entire year and therefore a download duration of roughly 67 hours.\n",
    "This number represents a fraction of the actual processing time needed due to problems with [data corruption](02c Corrupted Zip File.ipynb) and manual verification steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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