circadian_rythm_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Dataset 1: CM_SAF SARAH2.1 (Sunshine Duration Unit Feature Only)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import pandas as pd\n",
    "import pytz\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Read in sunshine hours"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Read in NetCDF and convert to dataframes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/192 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "path_to_netCDF_sunhours = \"E:\\ORD49239\"\n",
    "df_parts = []\n",
    "for netcdf_file in tqdm(glob.glob(f\"{path_to_netCDF_sunhours}\\*\")):\n",
    "    ds_part = xr.open_dataset(netcdf_file, engine='netcdf4')\n",
    "    df_parts.append(ds_part.to_dataframe())\n",
    "    break # TODO: Remove after debugging"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Handle NaNs\n",
    "\n",
    "* Where do the NaNs come from: I assume they come from stations not recording values on a certain date. Since\n",
    "we are interested in averages over countries as a whole, it should not pose an issue to drop them. This will also\n",
    "reduce the number of rows."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "    lon        lat       time         SDU\n0 -65.0 -22.299999 2000-01-01  110.577332\n1 -65.0 -22.250000 2000-01-01  114.464058\n2 -65.0 -22.200001 2000-01-01  121.085159\n3 -65.0 -22.150000 2000-01-01  120.345100\n4 -65.0 -22.100000 2000-01-01  121.679955",
      "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>lon</th>\n      <th>lat</th>\n      <th>time</th>\n      <th>SDU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-65.0</td>\n      <td>-22.299999</td>\n      <td>2000-01-01</td>\n      <td>110.577332</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-65.0</td>\n      <td>-22.250000</td>\n      <td>2000-01-01</td>\n      <td>114.464058</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-65.0</td>\n      <td>-22.200001</td>\n      <td>2000-01-01</td>\n      <td>121.085159</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-65.0</td>\n      <td>-22.150000</td>\n      <td>2000-01-01</td>\n      <td>120.345100</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-65.0</td>\n      <td>-22.100000</td>\n      <td>2000-01-01</td>\n      <td>121.679955</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = df_parts[0].dropna().reset_index()\n",
    "test_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Reduce dataset size by excluding non-EU countries\n",
    "\n",
    "We can do this by loading the world map, filtering out the EU countries\n",
    "and then removing all coordinates outside the min-max square of the EU.\n",
    "\n",
    "For this, the following shape file will be used to define coordinate bounds\n",
    "between countries:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "europe = world[world.continent == 'Europe']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Ignored countries (Blacklist)\n",
    "In this section, we define countries to leave out of the processing by adding their country\n",
    "codes in ISO_A3 to the list below:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "ignored_countries = ['RUS']\n",
    "europe = europe[~europe['iso_a3'].isin(ignored_countries)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Map coordinates to country\n",
    "\n",
    "We use geopandas to merge the world borders with the dataset by long/lat"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame(test_df, geometry=gpd.points_from_xy(test_df.lon, test_df.lat))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Calculate median SDU (sunshine duration) per country in europe:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\phili\\AppData\\Local\\Temp\\ipykernel_17504\\2981836489.py:1: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n",
      "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n",
      "\n",
      "Left CRS: None\n",
      "Right CRS: EPSG:4326\n",
      "\n",
      "  gdf_country = gpd.sjoin(gdf, europe, how='inner')\n"
     ]
    },
    {
     "data": {
      "text/plain": "              lon   lat       time         SDU                   geometry  \\\n278892 -54.500000  2.35 2000-01-01  138.491837  POINT (-54.50000 2.35000)   \n280560 -54.450001  2.30 2000-01-01  140.067657  POINT (-54.45000 2.30000)   \n280561 -54.450001  2.35 2000-01-01  139.915192  POINT (-54.45000 2.35000)   \n280562 -54.450001  2.40 2000-01-01  135.425186  POINT (-54.45000 2.40000)   \n280607 -54.450001  4.65 2000-01-01  153.570526  POINT (-54.45000 4.65000)   \n\n        index_right     pop_est continent    name iso_a3  gdp_md_est  \n278892           43  67059887.0    Europe  France    FRA     2715518  \n280560           43  67059887.0    Europe  France    FRA     2715518  \n280561           43  67059887.0    Europe  France    FRA     2715518  \n280562           43  67059887.0    Europe  France    FRA     2715518  \n280607           43  67059887.0    Europe  France    FRA     2715518  ",
      "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>lon</th>\n      <th>lat</th>\n      <th>time</th>\n      <th>SDU</th>\n      <th>geometry</th>\n      <th>index_right</th>\n      <th>pop_est</th>\n      <th>continent</th>\n      <th>name</th>\n      <th>iso_a3</th>\n      <th>gdp_md_est</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>278892</th>\n      <td>-54.500000</td>\n      <td>2.35</td>\n      <td>2000-01-01</td>\n      <td>138.491837</td>\n      <td>POINT (-54.50000 2.35000)</td>\n      <td>43</td>\n      <td>67059887.0</td>\n      <td>Europe</td>\n      <td>France</td>\n      <td>FRA</td>\n      <td>2715518</td>\n    </tr>\n    <tr>\n      <th>280560</th>\n      <td>-54.450001</td>\n      <td>2.30</td>\n      <td>2000-01-01</td>\n      <td>140.067657</td>\n      <td>POINT (-54.45000 2.30000)</td>\n      <td>43</td>\n      <td>67059887.0</td>\n      <td>Europe</td>\n      <td>France</td>\n      <td>FRA</td>\n      <td>2715518</td>\n    </tr>\n    <tr>\n      <th>280561</th>\n      <td>-54.450001</td>\n      <td>2.35</td>\n      <td>2000-01-01</td>\n      <td>139.915192</td>\n      <td>POINT (-54.45000 2.35000)</td>\n      <td>43</td>\n      <td>67059887.0</td>\n      <td>Europe</td>\n      <td>France</td>\n      <td>FRA</td>\n      <td>2715518</td>\n    </tr>\n    <tr>\n      <th>280562</th>\n      <td>-54.450001</td>\n      <td>2.40</td>\n      <td>2000-01-01</td>\n      <td>135.425186</td>\n      <td>POINT (-54.45000 2.40000)</td>\n      <td>43</td>\n      <td>67059887.0</td>\n      <td>Europe</td>\n      <td>France</td>\n      <td>FRA</td>\n      <td>2715518</td>\n    </tr>\n    <tr>\n      <th>280607</th>\n      <td>-54.450001</td>\n      <td>4.65</td>\n      <td>2000-01-01</td>\n      <td>153.570526</td>\n      <td>POINT (-54.45000 4.65000)</td>\n      <td>43</td>\n      <td>67059887.0</td>\n      <td>Europe</td>\n      <td>France</td>\n      <td>FRA</td>\n      <td>2715518</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_country = gpd.sjoin(gdf, europe, how='inner')\n",
    "gdf_country.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "                         SDU\nname                        \nAlbania           166.964035\nAustria            68.798660\nBelarus            33.689892\nBelgium            58.523132\nBosnia and Herz.   87.071968",
      "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>SDU</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Albania</th>\n      <td>166.964035</td>\n    </tr>\n    <tr>\n      <th>Austria</th>\n      <td>68.798660</td>\n    </tr>\n    <tr>\n      <th>Belarus</th>\n      <td>33.689892</td>\n    </tr>\n    <tr>\n      <th>Belgium</th>\n      <td>58.523132</td>\n    </tr>\n    <tr>\n      <th>Bosnia and Herz.</th>\n      <td>87.071968</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_country_median = gdf_country.groupby('name').median().loc[:, ['SDU']]\n",
    "gdf_country_median.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create a cloropleth plot for SDU"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "            pop_est continent iso_a3  gdp_md_est  \\\nname                                               \nNorway    5347896.0    Europe    NOR      403336   \nFrance   67059887.0    Europe    FRA     2715518   \nSweden   10285453.0    Europe    SWE      530883   \nBelarus   9466856.0    Europe    BLR       63080   \nUkraine  44385155.0    Europe    UKR      153781   \n\n                                                  geometry        SDU  \nname                                                                   \nNorway   MULTIPOLYGON (((15.14282 79.67431, 15.52255 80...   8.169410  \nFrance   MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3...  87.718155  \nSweden   POLYGON ((11.02737 58.85615, 11.46827 59.43239...  27.591003  \nBelarus  POLYGON ((28.17671 56.16913, 29.22951 55.91834...  33.689892  \nUkraine  POLYGON ((32.15944 52.06125, 32.41206 52.28869...  42.825497  ",
      "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>pop_est</th>\n      <th>continent</th>\n      <th>iso_a3</th>\n      <th>gdp_md_est</th>\n      <th>geometry</th>\n      <th>SDU</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Norway</th>\n      <td>5347896.0</td>\n      <td>Europe</td>\n      <td>NOR</td>\n      <td>403336</td>\n      <td>MULTIPOLYGON (((15.14282 79.67431, 15.52255 80...</td>\n      <td>8.169410</td>\n    </tr>\n    <tr>\n      <th>France</th>\n      <td>67059887.0</td>\n      <td>Europe</td>\n      <td>FRA</td>\n      <td>2715518</td>\n      <td>MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3...</td>\n      <td>87.718155</td>\n    </tr>\n    <tr>\n      <th>Sweden</th>\n      <td>10285453.0</td>\n      <td>Europe</td>\n      <td>SWE</td>\n      <td>530883</td>\n      <td>POLYGON ((11.02737 58.85615, 11.46827 59.43239...</td>\n      <td>27.591003</td>\n    </tr>\n    <tr>\n      <th>Belarus</th>\n      <td>9466856.0</td>\n      <td>Europe</td>\n      <td>BLR</td>\n      <td>63080</td>\n      <td>POLYGON ((28.17671 56.16913, 29.22951 55.91834...</td>\n      <td>33.689892</td>\n    </tr>\n    <tr>\n      <th>Ukraine</th>\n      <td>44385155.0</td>\n      <td>Europe</td>\n      <td>UKR</td>\n      <td>153781</td>\n      <td>POLYGON ((32.15944 52.06125, 32.41206 52.28869...</td>\n      <td>42.825497</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = europe.set_index('name').join(gdf_country_median)\n",
    "merged.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x1080 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(20, 15))\n",
    "merged.plot(column='SDU', cmap='OrRd', linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)\n",
    "ax.axis('off')\n",
    "plt.title(\"Median Sunlight hours (SDU) for Europe\");"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Dataset 2: Sun phases (Sunrise-Sunset) creation via Astral package"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "from astral.geocoder import database, lookup\n",
    "from astral import sun\n",
    "from unidecode import unidecode\n",
    "from countryinfo import CountryInfo"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "First, get all capital cities for all EU countries, as the astral library works on capitals only:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "country = CountryInfo()\n",
    "iso_to_cap = {v['ISO']['alpha3']: v['capital'] for k,v in country.all().items() if 'capital' in v.keys()}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "iso_to_cap_EU = {k:v for k,v in iso_to_cap.items() if k in europe['iso_a3'].tolist()}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "# Normalize capital names\n",
    "iso_to_cap_EU_norm = {k:unidecode(v) for k,v in iso_to_cap_EU.items()}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Get sunset and sunrise data for all European countries.\n",
    "Times are all in UTC.\n",
    "We convert the results to a dataframe with separate columns for the year, month and day columns.\n",
    "The format is as follows:\n",
    "Country code (ISO_A3), City, Day, Month, Year, Sunrise Hour (24h UTC), Sunset Hour (24h UTC)\n",
    "\n",
    "This will allow the aggregation over days, months, years, etc. to build visualizations on top of."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dates = pd.date_range(start='2022-01-01', end='2022-12-31').to_pydatetime()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not process day 2022-06-16 00:00:00\n"
     ]
    }
   ],
   "source": [
    "sun_data = []\n",
    "for country_isoa3,capital in iso_to_cap_EU_norm.items():\n",
    "    city = lookup(capital, database())\n",
    "    for d in dates:\n",
    "        try:\n",
    "            sunrise = sun.sunrise(observer=city.observer, date=d)\n",
    "            sunset = sun.sunset(observer=city.observer, date=d)\n",
    "            sun_data.append([country_isoa3, capital, d.day, d.month, d.year, sunrise.hour, sunset.hour])\n",
    "        except ValueError:\n",
    "            print(f\"Could not process day {d}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "sun_info_df = pd.DataFrame(sun_data, columns=['iso_a3', 'capital', 'day', 'month', 'year', 'sunrise_UTC', 'sunset_UTC'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create a cloropleth plot for the sunrise/sunset"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "First we will have to merge the df with the europe geo dataframe again:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "europe_plot = europe.loc[:, ['iso_a3', 'geometry']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Index: 13139 entries, ALB to GBR\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   capital      13139 non-null  object  \n",
      " 1   day          13139 non-null  int64   \n",
      " 2   month        13139 non-null  int64   \n",
      " 3   year         13139 non-null  int64   \n",
      " 4   sunrise_UTC  13139 non-null  int64   \n",
      " 5   sunset_UTC   13139 non-null  int64   \n",
      " 6   geometry     13139 non-null  geometry\n",
      "dtypes: geometry(1), int64(5), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "sun_info_plot = gpd.GeoDataFrame(sun_info_df.merge(europe_plot, on='iso_a3', how='left').set_index('iso_a3'))\n",
    "sun_info_plot.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2160x720 with 4 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(30, 10))\n",
    "sun_info_plot.plot(column='sunrise_UTC', cmap='OrRd', linewidth=0.8, ax=axs[0], edgecolor='0.8', legend=True)\n",
    "sun_info_plot.plot(column='sunset_UTC', cmap='Blues', linewidth=0.8, ax=axs[1], edgecolor='0.8', legend=True)\n",
    "for a in axs:\n",
    "    a.axis('off')\n",
    "axs[0].set_title('Sunrise UTC')\n",
    "axs[1].set_title('Sunset UTC');"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Exploring distance to Timezones"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Since we have to get a measure for a whole country somehow, we take the largest 3 cities (based on population size)\n",
    "from each EU country using the Eurostat Urban Audit dataset. For these cities we can then calculate the distance\n",
    "to the time meridian to the east using the coordinates of both (~1 degree of lat ~ 111 km)."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Urban Audit City Population dataset"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "eu_cities_pop = pd.read_csv('datasets/Eurostat/urban_population/urb_cpop1_page_tabular.tsv', sep='\\t', header=0)\n",
    "city_codes = pd.read_excel('datasets/Eurostat/urban_population/urb_esms_an4.xlsx', dtype=str)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Split up the identifier into country (ISO_A2) and city code.\n",
    "We also need to handle to conversion of weird population numbers with d,e,... at the end to integers.\n",
    "I assume those are identifiers on how the data was collected, which we can gladly ignore as we just\n",
    "need geographical points of reference for our visualization later on."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "eu_cities_pop.columns = ['code_info','population']\n",
    "eu_cities_pop['population'] = eu_cities_pop['population'].apply(lambda x: int(x.split(' ')[0]))\n",
    "eu_cities_pop['CODE'] = eu_cities_pop.iloc[:,0].apply(lambda x: x.split(',')[-1])\n",
    "eu_cities_pop['country_ISO_A2'] = eu_cities_pop['CODE'].apply(lambda x: x[0:2])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "eu_cities = eu_cities_pop.merge(city_codes, on='CODE')\n",
    "eu_cities = eu_cities.iloc[:,1:]\n",
    "eu_cities = eu_cities[eu_cities_pop['CODE'].apply(lambda x: len(x) > 2)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "   population    CODE country_ISO_A2               NAME\n0     1205492  BE001C             BE  Bruxelles/Brussel\n1      523591  BE002C             BE          Antwerpen\n2      383710  BE005C             BE              Liège\n3     1238438  BG001C             BG              Sofia\n4      345213  BG002C             BG            Plovdiv",
      "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>population</th>\n      <th>CODE</th>\n      <th>country_ISO_A2</th>\n      <th>NAME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1205492</td>\n      <td>BE001C</td>\n      <td>BE</td>\n      <td>Bruxelles/Brussel</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>523591</td>\n      <td>BE002C</td>\n      <td>BE</td>\n      <td>Antwerpen</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>383710</td>\n      <td>BE005C</td>\n      <td>BE</td>\n      <td>Liège</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1238438</td>\n      <td>BG001C</td>\n      <td>BG</td>\n      <td>Sofia</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>345213</td>\n      <td>BG002C</td>\n      <td>BG</td>\n      <td>Plovdiv</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TOP_N_COUNTRIES = 3\n",
    "top_3_cities_per_country = eu_cities.groupby('country_ISO_A2').apply(lambda x: x.nlargest(TOP_N_COUNTRIES, 'population')).reset_index(drop=True)\n",
    "top_3_cities_per_country.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Adding geographical information\n",
    "We will now use **geopy** to add geographical information (lat,lon) of\n",
    "each of the cities we got from the Urban Audit dataset."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [],
   "source": [
    "from geopy.geocoders import Nominatim\n",
    "from timezonefinder import TimezoneFinder\n",
    "from pytz import timezone\n",
    "from datetime import datetime\n",
    "\n",
    "geolocator = Nominatim(user_agent='CircadianRythmEU')\n",
    "tf = TimezoneFinder()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "def get_geo_location(x):\n",
    "    city_name = f\"{x['NAME']}, {x['country_ISO_A2']}\"\n",
    "    try:\n",
    "        geo_info = geolocator.geocode(city_name)\n",
    "        return geo_info.longitude, geo_info.latitude\n",
    "    except AttributeError:\n",
    "        return None, None"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "geo_city_df = top_3_cities_per_country.apply(lambda x: get_geo_location(x), axis='columns', result_type='expand')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "geo_city_df.columns = ['longitude','latitude']\n",
    "top_cities_geo = pd.concat([top_3_cities_per_country, geo_city_df], axis=1)\n",
    "top_cities_geo = top_cities_geo.dropna()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Adding timezone information and distance to nearest timezone border\n",
    "It is important to know, that we are interested only in the **physical sun-based** timezones for this!!"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "dt_now = datetime.now()\n",
    "\n",
    "ADJUST_LOCAL_SUMMERTIME = True\n",
    "LONGITUDE_DEGREE_KM_RATIO = 80\n",
    "\n",
    "def _add_tz_info(x):\n",
    "    long = x['longitude']\n",
    "    lat = x['latitude']\n",
    "    tz_name = tf.timezone_at(lng=long, lat=lat)\n",
    "    utc_offset = timezone(tz_name).utcoffset(dt_now, is_dst=True).total_seconds()/60/60\n",
    "\n",
    "    # Subtract one hour so the timezone of Europe/London is 0 --> All others are fine then too\n",
    "    # THis works since we are interested in the sun-based timezones only for this\n",
    "    if ADJUST_LOCAL_SUMMERTIME:\n",
    "        utc_offset -= 1\n",
    "\n",
    "    # Get longitudinal meridian values for each timezone --> 15 degrees per tz (360 deg / 24 hours)\n",
    "    # We then calculate the distance to it similar to [Trang Vophan et. al 2018]\n",
    "    meridian_east = 15 * utc_offset\n",
    "    long_diff = meridian_east - long\n",
    "    long_diff_km = LONGITUDE_DEGREE_KM_RATIO * long_diff\n",
    "\n",
    "    return tz_name, utc_offset, long_diff, long_diff_km\n",
    "\n",
    "def get_timezone_data(top_city_data: pd.DataFrame) -> pd.DataFrame:\n",
    "    # Load timezones and offsets\n",
    "    tz_geo_info = top_city_data.apply(lambda x: _add_tz_info(x), axis='columns', result_type='expand')\n",
    "    tz_geo_info.columns = ['social_timezone', 'utc_sun_timezone_offset', 'longitudinal_diff', 'longitudinal_diff_km']\n",
    "\n",
    "    # Check if adjustment was correct, if not, maybe look at if summertime is active in you current location\n",
    "    london_tz = tz_geo_info[tz_geo_info['social_timezone'] == 'Europe/London']\n",
    "    assert all(london_tz['utc_sun_timezone_offset'] == 0)\n",
    "\n",
    "    return tz_geo_info"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [],
   "source": [
    "tz_geo_data = get_timezone_data(top_cities_geo)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [
    {
     "data": {
      "text/plain": "   population    CODE country_ISO_A2               NAME  longitude   latitude  \\\n0     1205492  BE001C             BE  Bruxelles/Brussel   4.351697  50.846557   \n1      523591  BE002C             BE          Antwerpen   4.399708  51.221110   \n2      383710  BE005C             BE              Liège   5.573611  50.645094   \n3     1238438  BG001C             BG              Sofia  23.321736  42.697703   \n4      345213  BG002C             BG            Plovdiv  24.749930  42.141854   \n\n   social_timezone  utc_sun_timezone_offset  longitudinal_diff  \\\n0  Europe/Brussels                      1.0          10.648303   \n1  Europe/Brussels                      1.0          10.600292   \n2  Europe/Brussels                      1.0           9.426389   \n3     Europe/Sofia                      2.0           6.678264   \n4     Europe/Sofia                      2.0           5.250070   \n\n   longitudinal_diff_km  \n0            851.864240  \n1            848.023352  \n2            754.111104  \n3            534.261128  \n4            420.005624  ",
      "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>population</th>\n      <th>CODE</th>\n      <th>country_ISO_A2</th>\n      <th>NAME</th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>social_timezone</th>\n      <th>utc_sun_timezone_offset</th>\n      <th>longitudinal_diff</th>\n      <th>longitudinal_diff_km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1205492</td>\n      <td>BE001C</td>\n      <td>BE</td>\n      <td>Bruxelles/Brussel</td>\n      <td>4.351697</td>\n      <td>50.846557</td>\n      <td>Europe/Brussels</td>\n      <td>1.0</td>\n      <td>10.648303</td>\n      <td>851.864240</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>523591</td>\n      <td>BE002C</td>\n      <td>BE</td>\n      <td>Antwerpen</td>\n      <td>4.399708</td>\n      <td>51.221110</td>\n      <td>Europe/Brussels</td>\n      <td>1.0</td>\n      <td>10.600292</td>\n      <td>848.023352</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>383710</td>\n      <td>BE005C</td>\n      <td>BE</td>\n      <td>Liège</td>\n      <td>5.573611</td>\n      <td>50.645094</td>\n      <td>Europe/Brussels</td>\n      <td>1.0</td>\n      <td>9.426389</td>\n      <td>754.111104</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1238438</td>\n      <td>BG001C</td>\n      <td>BG</td>\n      <td>Sofia</td>\n      <td>23.321736</td>\n      <td>42.697703</td>\n      <td>Europe/Sofia</td>\n      <td>2.0</td>\n      <td>6.678264</td>\n      <td>534.261128</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>345213</td>\n      <td>BG002C</td>\n      <td>BG</td>\n      <td>Plovdiv</td>\n      <td>24.749930</td>\n      <td>42.141854</td>\n      <td>Europe/Sofia</td>\n      <td>2.0</td>\n      <td>5.250070</td>\n      <td>420.005624</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_cities_geo_tz = pd.concat([top_cities_geo, tz_geo_data], axis=1)\n",
    "top_cities_geo_tz.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}