maximum_waterlevels_in_west_terschelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import netCDF4\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the URL for West Terschelling\n",
    "url = 'http://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/waterbase/27_Waterhoogte_in_cm_t.o.v._normaal_amsterdams_peil_in_oppervlaktewater/nc/id1-WESTTSLG.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the dataset\n",
    "ds = netCDF4.Dataset(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup relevant variables\n",
    "t = ds.variables['time']\n",
    "ssh = ds.variables['sea_surface_height']\n",
    "                 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to table\n",
    "# get all times and convert to date objects (so we can group by year)\n",
    "dates = netCDF4.num2date(t[:], ds.variables['time'].units)\n",
    "# query history of sea surface height and squeeze out empty dimension (1 location x time)\n",
    "ssh_values = np.squeeze(ssh[:])\n",
    "df = pd.DataFrame(data=dict(dates=dates, ssh=ssh_values))\n",
    "# use date as an index\n",
    "df = df.set_index('dates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JGjvDJAw/bm+3a29zkmRbmhaEV1fV1LEP7wdeX1W3JJnpHAcCBwIsX758riFo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad64d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a new figure\n",
    "fig, ax = plt.subplots(figsize=(13, 8))\n",
    "# group data per year (max over 2000 is stored as 2000-12-31)\n",
    "grouped = df.groupby(pd.Grouper(freq=\"A\", label='right'))\n",
    "grouped_max = grouped.max()\n",
    "grouped_min = grouped.min()\n",
    "ax.plot(\n",
    "    grouped_max.index.to_pydatetime(), \n",
    "    grouped_max.ssh, \n",
    "    marker='_', \n",
    "    linewidth=0, \n",
    "    markeredgewidth=5, \n",
    "    markersize=12\n",
    ")\n",
    "\n",
    "y_label = \"{name}, maximum per jaar [{units}]\".format(\n",
    "    name=ds.variables['sea_surface_height'].Aquo_Grootheid_omschrijving,\n",
    "    units=ds.variables['sea_surface_height'].Aquo_Eenheid_code\n",
    ")\n",
    "ax.set_ylabel(y_label)\n",
    "ax.set_xlabel('Tijd')\n",
    "title = \"station: {station}, donar id: {donar_id}\".format(station=ds.stationname, donar_id=ds.donar_code)\n",
    "ax.set_title(title)\n",
    "ticks = list(\n",
    "    datetime.datetime(1920 + i, 1, 1).toordinal() \n",
    "    for i \n",
    "    in range(0, 110, 20)\n",
    ")\n",
    "ax.xaxis.set_ticks(ticks);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}