check_processed_files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This little script that checks sorted & reprojected files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "from eumetsat import get_filesize_megabytes, eumetsat_filename_to_datetime\n",
    "from consts import PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/storage/data/eumetsat/native/'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The directory containing the tar files downloaded from EUMETSAT\n",
    "SORTED_PATH = os.path.join(PATH, 'sorted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "walk_iterator = os.walk(SORTED_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.75 s, sys: 28.2 s, total: 35 s\n",
      "Wall time: 43min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "filenames = []\n",
    "for filename in (fnames for _, _, fnames in walk_iterator):\n",
    "    filenames.extend(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetimes = [eumetsat_filename_to_datetime(file) for file in filenames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetimes = pd.DatetimeIndex(np.sort(datetimes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94645"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datetimes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = pd.Series(1, index=datetimes).resample('5T').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113700"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-06-01 02:00:00    1\n",
       "2018-06-01 02:05:00    1\n",
       "2018-06-01 02:10:00    1\n",
       "2018-06-01 02:15:00    1\n",
       "2018-06-01 02:20:00    1\n",
       "2018-06-01 02:25:00    1\n",
       "2018-06-01 02:30:00    1\n",
       "2018-06-01 02:35:00    1\n",
       "2018-06-01 02:40:00    1\n",
       "2018-06-01 02:45:00    1\n",
       "2018-06-01 02:50:00    1\n",
       "2018-06-01 02:55:00    1\n",
       "2018-06-01 03:00:00    1\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-07-01 03:25:00    2\n",
       "2018-07-01 03:30:00    2\n",
       "2018-07-01 03:35:00    2\n",
       "2018-07-01 03:40:00    2\n",
       "2018-07-01 03:45:00    2\n",
       "                      ..\n",
       "2018-07-31 20:40:00    2\n",
       "2018-07-31 20:45:00    2\n",
       "2018-07-31 20:50:00    2\n",
       "2018-07-31 20:55:00    2\n",
       "2019-01-01 12:00:00    2\n",
       "Length: 7038, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Look into these duplicates!\n",
    "# If they really are duplicates then remove the duplicates.\n",
    "# And modify sort_and_compress code so it doesn't create duplicates (maybe have an option\n",
    "# to either overwrite the old copy (if the user believes the old copy has bad data),\n",
    "# or don't bother creating a new bz2 file if one already exists!\n",
    "series[series > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "START_HOUR = 3\n",
    "END_HOUR = 21\n",
    "series = series[(series.index.hour >= START_HOUR) & (series.index.hour < END_HOUR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-06-13 14:30:00', '2018-06-13 14:35:00',\n",
       "               '2018-06-18 08:25:00', '2018-06-20 09:20:00',\n",
       "               '2018-06-20 09:25:00', '2018-06-20 09:30:00',\n",
       "               '2018-06-20 09:35:00', '2018-06-20 09:40:00',\n",
       "               '2018-07-01 03:00:00', '2018-07-01 03:05:00',\n",
       "               ...\n",
       "               '2019-05-29 05:20:00', '2019-05-29 05:25:00',\n",
       "               '2019-05-29 05:30:00', '2019-05-29 05:35:00',\n",
       "               '2019-05-29 05:40:00', '2019-05-29 05:45:00',\n",
       "               '2019-05-29 05:50:00', '2019-05-29 05:55:00',\n",
       "               '2019-05-29 06:00:00', '2019-05-29 06:05:00'],\n",
       "              dtype='datetime64[ns]', length=269, freq=None)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = series[series == 0].index\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series(\n",
    "    series[series == 0].index).to_csv(\n",
    "    '/home/jack/dev/python/openclimatefix/solar/eumetsat/missing_eumetsat_images.csv', \n",
    "    header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.date(2018, 6, 13), datetime.date(2018, 6, 18),\n",
       "       datetime.date(2018, 6, 20), datetime.date(2018, 7, 1),\n",
       "       datetime.date(2018, 7, 2), datetime.date(2018, 7, 17),\n",
       "       datetime.date(2018, 8, 24), datetime.date(2018, 9, 3),\n",
       "       datetime.date(2018, 9, 4), datetime.date(2018, 9, 5),\n",
       "       datetime.date(2018, 10, 11), datetime.date(2018, 11, 11),\n",
       "       datetime.date(2018, 11, 22), datetime.date(2018, 11, 30),\n",
       "       datetime.date(2019, 3, 3), datetime.date(2019, 3, 11),\n",
       "       datetime.date(2019, 3, 12), datetime.date(2019, 4, 12),\n",
       "       datetime.date(2019, 5, 6), datetime.date(2019, 5, 18),\n",
       "       datetime.date(2019, 5, 20), datetime.date(2019, 5, 22),\n",
       "       datetime.date(2019, 5, 24), datetime.date(2019, 5, 27),\n",
       "       datetime.date(2019, 5, 28), datetime.date(2019, 5, 29)],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_days = np.unique(missing_data.date)\n",
    "missing_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-05-29 03:00:00', '2019-05-29 03:05:00',\n",
       "               '2019-05-29 03:10:00', '2019-05-29 03:15:00',\n",
       "               '2019-05-29 03:20:00', '2019-05-29 03:25:00',\n",
       "               '2019-05-29 03:30:00', '2019-05-29 03:35:00',\n",
       "               '2019-05-29 03:40:00', '2019-05-29 03:45:00',\n",
       "               '2019-05-29 03:50:00', '2019-05-29 03:55:00',\n",
       "               '2019-05-29 04:00:00', '2019-05-29 04:05:00',\n",
       "               '2019-05-29 04:10:00', '2019-05-29 04:15:00',\n",
       "               '2019-05-29 04:20:00', '2019-05-29 04:25:00',\n",
       "               '2019-05-29 04:30:00', '2019-05-29 04:35:00',\n",
       "               '2019-05-29 04:40:00', '2019-05-29 04:45:00',\n",
       "               '2019-05-29 04:50:00', '2019-05-29 04:55:00',\n",
       "               '2019-05-29 05:00:00', '2019-05-29 05:05:00',\n",
       "               '2019-05-29 05:10:00', '2019-05-29 05:15:00',\n",
       "               '2019-05-29 05:20:00', '2019-05-29 05:25:00',\n",
       "               '2019-05-29 05:30:00', '2019-05-29 05:35:00',\n",
       "               '2019-05-29 05:40:00', '2019-05-29 05:45:00',\n",
       "               '2019-05-29 05:50:00', '2019-05-29 05:55:00',\n",
       "               '2019-05-29 06:00:00', '2019-05-29 06:05:00'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[series == 0][\"2019-05-29\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_hours = pd.Series(1, index=missing_data).resample('H').sum()\n",
    "# missing_hours[missing_hours > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = series.clip(upper=1).plot(figsize=(25, 10))\n",
    "ax.set_title('0 means missing data.  1 means good.')  #   > 1 means duplicates.\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MISSING_FROM_EUMETSAT = [\n",
    "    \"2018-06-13 14:30\",\n",
    "    \"2018-06-13 14:35\",\n",
    "    \"2018-06-18 08:25\",\n",
    "    '2018-06-20 09:20:00',\n",
    "    '2018-06-20 09:25:00', \n",
    "    '2018-06-20 09:30:00',\n",
    "    '2018-06-20 09:35:00', \n",
    "    '2018-06-20 09:40:00',\n",
    "    '2018-07-01 03:00:00', '2018-07-01 03:05:00',\n",
    "               '2018-07-01 03:10:00', '2018-07-01 03:15:00',\n",
    "               '2018-07-01 03:20:00', '2018-07-01 04:10:00',\n",
    "               '2018-07-01 04:20:00', '2018-07-01 04:25:00',\n",
    "               '2018-07-01 04:30:00', '2018-07-01 04:35:00',\n",
    "               '2018-07-01 04:40:00', '2018-07-01 04:45:00',\n",
    "               '2018-07-01 05:05:00', '2018-07-01 05:10:00',\n",
    "               '2018-07-01 05:15:00',\n",
    "    '2019-05-29 03:00:00', '2019-05-29 03:05:00',\n",
    "               '2019-05-29 03:10:00', '2019-05-29 03:15:00',\n",
    "               '2019-05-29 03:20:00', '2019-05-29 03:25:00',\n",
    "               '2019-05-29 03:30:00', '2019-05-29 03:35:00',\n",
    "               '2019-05-29 03:40:00', '2019-05-29 03:45:00',\n",
    "               '2019-05-29 03:50:00', '2019-05-29 03:55:00',\n",
    "               '2019-05-29 04:00:00', '2019-05-29 04:05:00',\n",
    "               '2019-05-29 04:10:00', '2019-05-29 04:15:00',\n",
    "               '2019-05-29 04:20:00', '2019-05-29 04:25:00',\n",
    "               '2019-05-29 04:30:00', '2019-05-29 04:35:00',\n",
    "               '2019-05-29 04:40:00', '2019-05-29 04:45:00',\n",
    "               '2019-05-29 04:50:00', '2019-05-29 04:55:00',\n",
    "               '2019-05-29 05:00:00', '2019-05-29 05:05:00',\n",
    "               '2019-05-29 05:10:00', '2019-05-29 05:15:00',\n",
    "               '2019-05-29 05:20:00', '2019-05-29 05:25:00',\n",
    "               '2019-05-29 05:30:00', '2019-05-29 05:35:00',\n",
    "               '2019-05-29 05:40:00', '2019-05-29 05:45:00',\n",
    "               '2019-05-29 05:50:00', '2019-05-29 05:55:00',\n",
    "               '2019-05-29 06:00:00', '2019-05-29 06:05:00'\n",
    "]"
   ]
  }
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