sealevelmonitor.ipynb

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    "Sealevel monitor\n",
    "========\n",
    "\n",
    "This document is used to monitor the current sea level along the Dutch coast. The sea level is measured using a number of tide gauges. Six long running tide gauges are considered \"main stations\". The mean of these stations is used to estimate the \"current sea-level rise\". The measurements since 1890 are taken into account. Measurements before that are considered less valid because the Amsterdam Ordnance Datum was not yet normalized. "
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    "# this is a list of packages that are used in this notebook\n",
    "# these come with python\n",
    "import io\n",
    "import zipfile\n",
    "import functools\n",
    "\n",
    "# you can install these packages using pip or anaconda\n",
    "# (requests numpy pandas bokeh pyproj statsmodels)\n",
    "\n",
    "# for downloading\n",
    "import requests\n",
    "\n",
    "# computation libraries\n",
    "import numpy as np\n",
    "import pandas\n",
    "\n",
    "# coordinate systems\n",
    "import pyproj \n",
    "\n",
    "# statistics\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# plotting\n",
    "import bokeh.charts\n",
    "import bokeh.io\n",
    "import bokeh.plotting\n",
    "import bokeh.tile_providers\n",
    "import bokeh.palettes\n",
    "\n",
    "# displaying things\n",
    "from ipywidgets import Image\n",
    "import IPython.display"
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   "source": [
    "# Some coordinate systems\n",
    "WEBMERCATOR = pyproj.Proj(init='epsg:3857')\n",
    "WGS84 = pyproj.Proj(init='epsg:4326')\n",
    "\n",
    "# If this notebook is not showing up with figures, you can use the following url:\n",
    "# https://nbviewer.ipython.org/github/openearth/notebooks/blob/master/sealevelmonitor.ipynb\n",
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   "source": [
    "The global collection of tide gauge records at the PSMSL is used to access the data. The other way to access the data is to ask the service desk data at Rijkswaterstaat. There are two types of datasets the \"Revised Local Reference\" and \"Metric\". For the Netherlands the difference is that the \"Revised Local Reference\" undoes the corrections from the  NAP correction in 2014, to get a consistent dataset. "
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    "urls = {\n",
    "    'metric_monthly': 'http://www.psmsl.org/data/obtaining/met.monthly.data/met_monthly.zip',\n",
    "    'rlr_monthly': 'http://www.psmsl.org/data/obtaining/rlr.annual.data/rlr_monthly.zip',\n",
    "    'rlr_annual': 'http://www.psmsl.org/data/obtaining/rlr.annual.data/rlr_annual.zip'\n",
    "}\n",
    "dataset_name = 'rlr_annual'"
   ]
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   "execution_count": 33,
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    "# these compute the rlr back to NAP (ignoring the undoing of the NAP correction)\n",
    "main_stations = {\n",
    "    20: {\n",
    "        'name': 'Vlissingen', \n",
    "        'rlr2nap': lambda x: x - (6976-46)\n",
    "    },\n",
    "    22: {\n",
    "        'name': 'Hoek van Holland', \n",
    "        'rlr2nap': lambda x:x - (6994 - 121)\n",
    "    },\n",
    "    23: {\n",
    "        'name': 'Den Helder', \n",
    "        'rlr2nap': lambda x: x - (6988-42)\n",
    "    },\n",
    "    24: {\n",
    "        'name': 'Delfzijl', \n",
    "        'rlr2nap': lambda x: x - (6978-155)\n",
    "    },\n",
    "    25: {\n",
    "        'name': 'Harlingen', \n",
    "        'rlr2nap': lambda x: x - (7036-122)\n",
    "    },\n",
    "    32: {\n",
    "        'name': 'IJmuiden', \n",
    "        'rlr2nap': lambda x: x - (7033-83)\n",
    "    }\n",
    "}"
   ]
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  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "[32, 20, 22, 23, 24, 25]"
      ]
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   "source": [
    "# the main stations are defined by their ids\n",
    "main_stations_idx = list(main_stations.keys())\n",
    "main_stations_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
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    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>coastline_code</th>\n",
       "      <th>station_code</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>52.462222</td>\n",
       "      <td>4.554722</td>\n",
       "      <td>IJMUIDEN</td>\n",
       "      <td>150</td>\n",
       "      <td>41</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51.442222</td>\n",
       "      <td>3.596111</td>\n",
       "      <td>VLISSINGEN</td>\n",
       "      <td>150</td>\n",
       "      <td>101</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>51.977500</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>HOEK VAN HOLLAND</td>\n",
       "      <td>150</td>\n",
       "      <td>51</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>52.964444</td>\n",
       "      <td>4.745000</td>\n",
       "      <td>DEN HELDER</td>\n",
       "      <td>150</td>\n",
       "      <td>31</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>53.326389</td>\n",
       "      <td>6.933056</td>\n",
       "      <td>DELFZIJL</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>53.175556</td>\n",
       "      <td>5.409444</td>\n",
       "      <td>HARLINGEN</td>\n",
       "      <td>150</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "          lat       lon              name  coastline_code  station_code  \\\n",
       "id                                                                        \n",
       "32  52.462222  4.554722          IJMUIDEN             150            41   \n",
       "20  51.442222  3.596111        VLISSINGEN             150           101   \n",
       "22  51.977500  4.120000  HOEK VAN HOLLAND             150            51   \n",
       "23  52.964444  4.745000        DEN HELDER             150            31   \n",
       "24  53.326389  6.933056          DELFZIJL             150             1   \n",
       "25  53.175556  5.409444         HARLINGEN             150            21   \n",
       "\n",
       "   quality  \n",
       "id          \n",
       "32       N  \n",
       "20       N  \n",
       "22       N  \n",
       "23       N  \n",
       "24       N  \n",
       "25       N  "
      ]
     },
     "execution_count": 35,
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   ],
   "source": [
    "# download the zipfile\n",
    "resp = requests.get(urls[dataset_name])\n",
    "\n",
    "# we can read the zipfile\n",
    "stream = io.BytesIO(resp.content)\n",
    "zf = zipfile.ZipFile(stream)\n",
    "\n",
    "# this list contains a table of \n",
    "# station ID, latitude, longitude, station name, coastline code, station code, and quality flag\n",
    "csvtext = zf.read('{}/filelist.txt'.format(dataset_name))\n",
    "\n",
    "stations = pandas.read_csv(\n",
    "    io.BytesIO(csvtext), \n",
    "    sep=';',\n",
    "    names=('id', 'lat', 'lon', 'name', 'coastline_code', 'station_code', 'quality'),\n",
    "    converters={\n",
    "        'name': str.strip,\n",
    "        'quality': str.strip\n",
    "    }\n",
    ")\n",
    "stations = stations.set_index('id')\n",
    "\n",
    "# the dutch stations in the PSMSL database, make a copy\n",
    "# or use stations.coastline_code == 150 for all dutch stations\n",
    "selected_stations = stations.ix[main_stations_idx].copy()\n",
    "# set the main stations, this should be a list of 6 stations\n",
    "selected_stations"
   ]
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    "# show all the stations on a map\n",
    "\n",
    "# compute the bounds of the plot\n",
    "sw = (50, -5)\n",
    "ne = (55, 10)\n",
    "# transform to web mercator\n",
    "sw_wm = pyproj.transform(WGS84, WEBMERCATOR, sw[1], sw[0])\n",
    "ne_wm = pyproj.transform(WGS84, WEBMERCATOR, ne[1], ne[0])\n",
    "# create a plot\n",
    "fig = bokeh.plotting.figure(tools='pan, wheel_zoom', plot_width=600, plot_height=200, x_range=(sw_wm[0], ne_wm[0]), y_range=(sw_wm[1], ne_wm[1]))\n",
    "fig.axis.visible = False\n",
    "# add some background tiles\n",
    "fig.add_tile(bokeh.tile_providers.STAMEN_TERRAIN)\n",
    "# add the stations\n",
    "x, y = pyproj.transform(WGS84, WEBMERCATOR, np.array(stations.lon), np.array(stations.lat))\n",
    "fig.circle(x, y)\n",
    "x, y = pyproj.transform(WGS84, WEBMERCATOR, np.array(selected_stations.lon), np.array(selected_stations.lat))\n",
    "_ = fig.circle(x, y, color='red')\n"
   ]
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       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"4b90aac1-0197-4484-8142-08eb300284e4\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '4b90aac1-0197-4484-8142-08eb300284e4' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
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\",\"dtype\":\"float64\",\"shape\":[1500]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1500]}}},\"id\":\"3462920f-4fc9-4024-a5d1-6993cf8226cf\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"formatter\":{\"id\":\"b535d765-8393-4e40-8187-8caaf5129cb2\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"44e78221-379b-4a00-bc57-26a55eb43936\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"13e56c3b-9d18-4203-b725-5bdeb965785c\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"9e341aec-4797-4966-8941-fe9b93d97ac8\",\"type\":\"LinearAxis\"},{\"attributes\":{\"plot\":{\"id\":\"44e78221-379b-4a00-bc57-26a55eb43936\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"549adb36-8c95-4b08-b82e-f9cd1c60855e\",\"type\":\"WheelZoomTool\"}],\"root_ids\":[\"44e78221-379b-4a00-bc57-26a55eb43936\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.5\"}};\n",
       "            var render_items = [{\"docid\":\"0b127e6a-88b6-4f96-b92c-f97be3c944c3\",\"elementid\":\"4b90aac1-0197-4484-8142-08eb300284e4\",\"modelid\":\"44e78221-379b-4a00-bc57-26a55eb43936\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"4b90aac1-0197-4484-8142-08eb300284e4\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the plot\n",
    "bokeh.io.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined which tide gauges we are monitoring we can start downloading the relevant data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# each station has a number of files that you can look at.\n",
    "# here we define a template for each filename\n",
    "\n",
    "# stations that we are using for our computation\n",
    "# define the name formats for the relevant files\n",
    "names = {\n",
    "    'datum': '{dataset}/RLR_info/{id}.txt',\n",
    "    'diagram': '{dataset}/RLR_info/{id}.png',\n",
    "    'url': 'http://www.psmsl.org/data/obtaining/rlr.diagrams/{id}.php',\n",
    "    'data': '{dataset}/data/{id}.rlrdata',\n",
    "    'doc': '{dataset}/docu/{id}.txt',\n",
    "    'contact': '{dataset}/docu/{id}_auth.txt'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>coastline_code</th>\n",
       "      <th>station_code</th>\n",
       "      <th>quality</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></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>32</th>\n",
       "      <td>52.462222</td>\n",
       "      <td>4.554722</td>\n",
       "      <td>IJMUIDEN</td>\n",
       "      <td>150</td>\n",
       "      <td>41</td>\n",
       "      <td>N</td>\n",
       "      <td>http://www.psmsl.org/data/obtaining/rlr.diagra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51.442222</td>\n",
       "      <td>3.596111</td>\n",
       "      <td>VLISSINGEN</td>\n",
       "      <td>150</td>\n",
       "      <td>101</td>\n",
       "      <td>N</td>\n",
       "      <td>http://www.psmsl.org/data/obtaining/rlr.diagra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>51.977500</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>HOEK VAN HOLLAND</td>\n",
       "      <td>150</td>\n",
       "      <td>51</td>\n",
       "      <td>N</td>\n",
       "      <td>http://www.psmsl.org/data/obtaining/rlr.diagra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>52.964444</td>\n",
       "      <td>4.745000</td>\n",
       "      <td>DEN HELDER</td>\n",
       "      <td>150</td>\n",
       "      <td>31</td>\n",
       "      <td>N</td>\n",
       "      <td>http://www.psmsl.org/data/obtaining/rlr.diagra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>53.326389</td>\n",
       "      <td>6.933056</td>\n",
       "      <td>DELFZIJL</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>http://www.psmsl.org/data/obtaining/rlr.diagra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>53.175556</td>\n",
       "      <td>5.409444</td>\n",
       "      <td>HARLINGEN</td>\n",
       "      <td>150</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "      <td>http://www.psmsl.org/data/obtaining/rlr.diagra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lat       lon              name  coastline_code  station_code  \\\n",
       "id                                                                        \n",
       "32  52.462222  4.554722          IJMUIDEN             150            41   \n",
       "20  51.442222  3.596111        VLISSINGEN             150           101   \n",
       "22  51.977500  4.120000  HOEK VAN HOLLAND             150            51   \n",
       "23  52.964444  4.745000        DEN HELDER             150            31   \n",
       "24  53.326389  6.933056          DELFZIJL             150             1   \n",
       "25  53.175556  5.409444         HARLINGEN             150            21   \n",
       "\n",
       "   quality                                                url  \n",
       "id                                                             \n",
       "32       N  http://www.psmsl.org/data/obtaining/rlr.diagra...  \n",
       "20       N  http://www.psmsl.org/data/obtaining/rlr.diagra...  \n",
       "22       N  http://www.psmsl.org/data/obtaining/rlr.diagra...  \n",
       "23       N  http://www.psmsl.org/data/obtaining/rlr.diagra...  \n",
       "24       N  http://www.psmsl.org/data/obtaining/rlr.diagra...  \n",
       "25       N  http://www.psmsl.org/data/obtaining/rlr.diagra...  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_url(station, dataset):\n",
    "    \"\"\"return the url of the station information (diagram and datum)\"\"\"\n",
    "    info = dict(\n",
    "        dataset=dataset,\n",
    "        id=station.name\n",
    "    )\n",
    "    url = names['url'].format(**info)\n",
    "    return url\n",
    "# fill in the dataset parameter using the global dataset_name\n",
    "f = functools.partial(get_url, dataset=dataset_name)\n",
    "# compute the url for each station\n",
    "selected_stations['url'] = selected_stations.apply(f, axis=1)\n",
    "selected_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def missing2nan(value, missing=-99999):\n",
    "    \"\"\"convert the value to nan if the float of value equals the missing value\"\"\"\n",
    "    value = float(value)\n",
    "    if value == missing:\n",
    "        return np.nan\n",
    "    return value\n",
    "\n",
    "def get_data(station, dataset):\n",
    "    \"\"\"get data for the station (pandas record) from the dataset (url)\"\"\"\n",
    "    info = dict(\n",
    "        dataset=dataset,\n",
    "        id=station.name\n",
    "    )\n",
    "    bytes = zf.read(names['data'].format(**info))\n",
    "    df = pandas.read_csv(\n",
    "        io.BytesIO(bytes), \n",
    "        sep=';', \n",
    "        names=('year', 'height', 'interpolated', 'flags'),\n",
    "        converters={\n",
    "            \"height\": lambda x: main_stations[station.name]['rlr2nap'](missing2nan(x)),\n",
    "            \"interpolated\": str.strip,\n",
    "        }\n",
    "    )\n",
    "    df['station'] = station.name\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get data for all stations\n",
    "f = functools.partial(get_data, dataset=dataset_name)\n",
    "# look up the data for each station\n",
    "selected_stations['data'] = [f(station) for _, station in selected_stations.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>IJMUIDEN</td>\n",
       "      <td>year  height interpolated  flags  station...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>VLISSINGEN</td>\n",
       "      <td>year  height interpolated  flags  station...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HOEK VAN HOLLAND</td>\n",
       "      <td>year  height interpolated  flags  station...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DEN HELDER</td>\n",
       "      <td>year  height interpolated  flags  station...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DELFZIJL</td>\n",
       "      <td>year  height interpolated  flags  station...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>HARLINGEN</td>\n",
       "      <td>year  height interpolated  flags  station...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name                                               data\n",
       "id                                                                     \n",
       "32          IJMUIDEN       year  height interpolated  flags  station...\n",
       "20        VLISSINGEN       year  height interpolated  flags  station...\n",
       "22  HOEK VAN HOLLAND       year  height interpolated  flags  station...\n",
       "23        DEN HELDER       year  height interpolated  flags  station...\n",
       "24          DELFZIJL       year  height interpolated  flags  station...\n",
       "25         HARLINGEN       year  height interpolated  flags  station..."
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we now have data for each station\n",
    "selected_stations[['name', 'data']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all data downloaded we can compute the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compute the mean\n",
    "grouped = pandas.concat(selected_stations['data'].tolist())[['year', 'height']].groupby('year')\n",
    "mean_df = grouped.mean().reset_index()\n",
    "# filter out non-trusted part (before NAP)\n",
    "mean_df = mean_df[mean_df['year'] >= 1890].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2011</td>\n",
       "      <td>60.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2012</td>\n",
       "      <td>50.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2013</td>\n",
       "      <td>25.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2014</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2015</td>\n",
       "      <td>92.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year     height\n",
       "149  2011  60.166667\n",
       "150  2012  50.666667\n",
       "151  2013  25.666667\n",
       "152  2014  66.666667\n",
       "153  2015  92.500000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# these are the mean waterlevels \n",
    "mean_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# show all the stations, including the mean\n",
    "title = 'Sea-surface height for Dutch tide gauges [{year_min} - {year_max}]'.format(\n",
    "    year_min=mean_df.year.min(),\n",
    "    year_max=mean_df.year.max() \n",
    ")\n",
    "fig = bokeh.plotting.figure(title=title, x_range=(1860, 2020), plot_width=900, plot_height=400)\n",
    "colors = bokeh.palettes.Accent6\n",
    "for color, (id_, station) in zip(colors, selected_stations.iterrows()):\n",
    "    data = station['data']\n",
    "    fig.circle(data.year, data.height, color=color, legend=station['name'], alpha=0.5)\n",
    "fig.line(mean_df.year, mean_df.height, line_width=3, alpha=0.7, color='black', legend='Mean')\n",
    "fig.legend.location = \"bottom_right\"\n",
    "fig.yaxis.axis_label = 'waterlevel [mm] above NAP'\n",
    "fig.xaxis.axis_label = 'year'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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    "Methods\n",
    "=====\n",
    "Now we can define the statistical model. The \"current sea-level rise\" is defined by the following formula. Please note that the selected epoch of 1970 is arbitrary. \n",
    "$\n",
    "H(t) = a + b_{trend}(t-1970) + b_u\\cos(2\\pi\\frac{t - 1970}{18.613}) + b_v\\sin(2\\pi\\frac{t - 1970}{18.613})\n",
    "$\n",
    "\n",
    "The terms are refered to as Constant ($a$), Trend ($b_{trend}$), Nodal U ($b_u$) and Nodal V ($b_v$). \n",
    "\n",
    "\n",
    "Alternative models are used to detect if sea-level rise is increasing. These models include the broken linear model, defined by a possible change in trend starting at 1993. This timespan is the start of the \"satellite era\" (start of TOPEX/Poseidon measurements), it is also often referred to as the start of acceleration because the satellite measurements tend to show a higher rate of sea level than the \"tide-gauge era\" (1900-2000). If this model fits better than the linear model, one could say that there is a \"increase in sea-level rise\". \n",
    "\n",
    "$\n",
    "H(t) = a + b_{trend}(t-1970) + b_{broken}(t > 1993)*(t-1993) + b_{u}\\cos(2\\pi\\frac{t - 1970}{18.613}) + b_{v}\\sin(2\\pi\\frac{t - 1970}{18.613})\n",
    "$\n",
    "\n",
    "Another way to look at increased sea-level rise is to look at sea-level acceleration. To detect sea-level acceleration one can use a quadratic model. \n",
    "\n",
    "$\n",
    "H(t) = a + b_{trend}(t-1970) + b_{quadratic}(t - 1970)*(t-1970) + b_{u}\\cos(2\\pi\\frac{t - 1970}{18.613}) + b_{v}\\sin(2\\pi\\frac{t - 1970}{18.613})\n",
    "$\n"
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    "# define the statistical model\n",
    "y = mean_df['height']\n",
    "X = np.c_[\n",
    "    mean_df['year']-1970, \n",
    "    np.cos(2*np.pi*(mean_df['year']-1970)/18.613),\n",
    "    np.sin(2*np.pi*(mean_df['year']-1970)/18.613)\n",
    "]\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(y, X)\n",
    "fit = model.fit()"
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       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Sea-surface height</td> <th>  R-squared:         </th> <td>   0.853</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   236.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Tue, 25 Apr 2017</td>  <th>  Prob (F-statistic):</th> <td>1.27e-50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>13:27:07</td>      <th>  Log-Likelihood:    </th> <td> -601.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>   126</td>       <th>  AIC:               </th> <td>   1211.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>   122</td>       <th>  BIC:               </th> <td>   1222.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     3</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Constant</th> <td>  -25.0320</td> <td>    2.877</td> <td>   -8.700</td> <td> 0.000</td> <td>  -30.728</td> <td>  -19.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend</th>    <td>    1.8991</td> <td>    0.071</td> <td>   26.578</td> <td> 0.000</td> <td>    1.758</td> <td>    2.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nodal U</th>  <td>    6.2529</td> <td>    3.699</td> <td>    1.691</td> <td> 0.093</td> <td>   -1.069</td> <td>   13.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nodal V</th>  <td>  -12.1237</td> <td>    3.643</td> <td>   -3.328</td> <td> 0.001</td> <td>  -19.335</td> <td>   -4.912</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.045</td> <th>  Durbin-Watson:     </th> <td>   1.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.218</td> <th>  Jarque-Bera (JB):  </th> <td>   2.768</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.363</td> <th>  Prob(JB):          </th> <td>   0.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.035</td> <th>  Cond. No.          </th> <td>    57.9</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     Sea-surface height   R-squared:                       0.853\n",
       "Model:                            OLS   Adj. R-squared:                  0.849\n",
       "Method:                 Least Squares   F-statistic:                     236.1\n",
       "Date:                Tue, 25 Apr 2017   Prob (F-statistic):           1.27e-50\n",
       "Time:                        13:27:07   Log-Likelihood:                -601.28\n",
       "No. Observations:                 126   AIC:                             1211.\n",
       "Df Residuals:                     122   BIC:                             1222.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Constant     -25.0320      2.877     -8.700      0.000     -30.728     -19.336\n",
       "Trend          1.8991      0.071     26.578      0.000       1.758       2.041\n",
       "Nodal U        6.2529      3.699      1.691      0.093      -1.069      13.575\n",
       "Nodal V      -12.1237      3.643     -3.328      0.001     -19.335      -4.912\n",
       "==============================================================================\n",
       "Omnibus:                        3.045   Durbin-Watson:                   1.706\n",
       "Prob(Omnibus):                  0.218   Jarque-Bera (JB):                2.768\n",
       "Skew:                          -0.363   Prob(JB):                        0.251\n",
       "Kurtosis:                       3.035   Cond. No.                         57.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
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    "fit.summary(yname='Sea-surface height', xname=['Constant', 'Trend', 'Nodal U', 'Nodal V'])\n",
    "# things to check:\n",
    "# Durbin Watson should be >1 for no worries, >2 for no autocorrelation\n",
    "# JB should be non-significant for normal residuals\n",
    "# abs(x2.t) + abs(x3.t) should be > 3, otherwise adding nodal is not useful"
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       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"647967c4-a8c2-4387-8292-cc089da56189\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"647967c4-a8c2-4387-8292-cc089da56189\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '647967c4-a8c2-4387-8292-cc089da56189' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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     "metadata": {},
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    }
   ],
   "source": [
    "fig = bokeh.plotting.figure(x_range=(1860, 2020), plot_width=900, plot_height=400)\n",
    "for color, (id_, station) in zip(colors, selected_stations.iterrows()):\n",
    "    data = station['data']\n",
    "    fig.circle(data.year, data.height, color=color, legend=station['name'], alpha=0.8)\n",
    "fig.circle(mean_df.year, mean_df.height, line_width=3, legend='Mean', color='black', alpha=0.5)\n",
    "fig.line(mean_df.year, fit.predict(), line_width=3, legend='Current')\n",
    "fig.legend.location = \"bottom_right\"\n",
    "fig.yaxis.axis_label = 'waterlevel [mm] above N.A.P.'\n",
    "fig.xaxis.axis_label = 'year'\n",
    "bokeh.io.show(fig)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a sea-level acceleration?\n",
    "==================\n",
    "\n",
    "The following section computes two common models to detect sea-level acceleration.  The broken linear model expects that sea level has been rising faster since 1990. The quadratic model assumes that the sea-level is accelerating continuously. Both models are compared to the linear model. The extra terms are tested for significance and the AIC is computed to see which model is \"better\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the statistical model\n",
    "y = mean_df['height']\n",
    "X = np.c_[\n",
    "    mean_df['year']-1970, \n",
    "    (mean_df['year'] > 1993) * (mean_df['year'] - 1993),\n",
    "    np.cos(2*np.pi*(mean_df['year']-1970)/18.613),\n",
    "    np.sin(2*np.pi*(mean_df['year']-1970)/18.613)\n",
    "]\n",
    "X = sm.add_constant(X)\n",
    "model_broken_linear = sm.OLS(y, X)\n",
    "fit_broken_linear = model_broken_linear.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the statistical model\n",
    "y = mean_df['height']\n",
    "X = np.c_[\n",
    "    mean_df['year']-1970, \n",
    "    (mean_df['year'] - 1970) * (mean_df['year'] - 1970),\n",
    "    np.cos(2*np.pi*(mean_df['year']-1970)/18.613),\n",
    "    np.sin(2*np.pi*(mean_df['year']-1970)/18.613)\n",
    "]\n",
    "X = sm.add_constant(X)\n",
    "model_quadratic = sm.OLS(y, X)\n",
    "fit_quadratic = model_quadratic.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Sea-surface height</td> <th>  R-squared:         </th> <td>   0.855</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   178.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Tue, 25 Apr 2017</td>  <th>  Prob (F-statistic):</th> <td>8.20e-50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>13:27:07</td>      <th>  Log-Likelihood:    </th> <td> -600.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>   126</td>       <th>  AIC:               </th> <td>   1211.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>   121</td>       <th>  BIC:               </th> <td>   1225.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     4</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Constant</th>           <td>  -28.0783</td> <td>    3.608</td> <td>   -7.781</td> <td> 0.000</td> <td>  -35.222</td> <td>  -20.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend</th>              <td>    1.8255</td> <td>    0.089</td> <td>   20.569</td> <td> 0.000</td> <td>    1.650</td> <td>    2.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend(year > 1990)</th> <td>    0.8810</td> <td>    0.634</td> <td>    1.390</td> <td> 0.167</td> <td>   -0.374</td> <td>    2.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nodal U</th>            <td>    6.0248</td> <td>    3.688</td> <td>    1.634</td> <td> 0.105</td> <td>   -1.277</td> <td>   13.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nodal V</th>            <td>  -12.5599</td> <td>    3.643</td> <td>   -3.448</td> <td> 0.001</td> <td>  -19.771</td> <td>   -5.348</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.828</td> <th>  Durbin-Watson:     </th> <td>   1.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.401</td> <th>  Jarque-Bera (JB):  </th> <td>   1.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.283</td> <th>  Prob(JB):          </th> <td>   0.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.952</td> <th>  Cond. No.          </th> <td>    58.2</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     Sea-surface height   R-squared:                       0.855\n",
       "Model:                            OLS   Adj. R-squared:                  0.851\n",
       "Method:                 Least Squares   F-statistic:                     178.9\n",
       "Date:                Tue, 25 Apr 2017   Prob (F-statistic):           8.20e-50\n",
       "Time:                        13:27:07   Log-Likelihood:                -600.28\n",
       "No. Observations:                 126   AIC:                             1211.\n",
       "Df Residuals:                     121   BIC:                             1225.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Constant             -28.0783      3.608     -7.781      0.000     -35.222     -20.935\n",
       "Trend                  1.8255      0.089     20.569      0.000       1.650       2.001\n",
       "Trend(year > 1990)     0.8810      0.634      1.390      0.167      -0.374       2.136\n",
       "Nodal U                6.0248      3.688      1.634      0.105      -1.277      13.327\n",
       "Nodal V              -12.5599      3.643     -3.448      0.001     -19.771      -5.348\n",
       "==============================================================================\n",
       "Omnibus:                        1.828   Durbin-Watson:                   1.733\n",
       "Prob(Omnibus):                  0.401   Jarque-Bera (JB):                1.688\n",
       "Skew:                          -0.283   Prob(JB):                        0.430\n",
       "Kurtosis:                       2.952   Cond. No.                         58.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_broken_linear.summary(yname='Sea-surface height', xname=['Constant', 'Trend', 'Trend(year > 1990)', 'Nodal U', 'Nodal V'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Sea-surface height</td> <th>  R-squared:         </th> <td>   0.853</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   176.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Tue, 25 Apr 2017</td>  <th>  Prob (F-statistic):</th> <td>1.82e-49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>13:27:07</td>      <th>  Log-Likelihood:    </th> <td> -601.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>   126</td>       <th>  AIC:               </th> <td>   1212.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>   121</td>       <th>  BIC:               </th> <td>   1226.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     4</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Constant</th> <td>  -26.2982</td> <td>    3.646</td> <td>   -7.214</td> <td> 0.000</td> <td>  -33.516</td> <td>  -19.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend</th>    <td>    1.9429</td> <td>    0.105</td> <td>   18.464</td> <td> 0.000</td> <td>    1.735</td> <td>    2.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend**2</th> <td>    0.0013</td> <td>    0.002</td> <td>    0.568</td> <td> 0.571</td> <td>   -0.003</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nodal U</th>  <td>    6.0876</td> <td>    3.720</td> <td>    1.636</td> <td> 0.104</td> <td>   -1.278</td> <td>   13.453</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nodal V</th>  <td>  -12.1889</td> <td>    3.655</td> <td>   -3.335</td> <td> 0.001</td> <td>  -19.425</td> <td>   -4.953</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.826</td> <th>  Durbin-Watson:     </th> <td>   1.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.243</td> <th>  Jarque-Bera (JB):  </th> <td>   2.578</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.350</td> <th>  Prob(JB):          </th> <td>   0.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.015</td> <th>  Cond. No.          </th> <td>3.44e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     Sea-surface height   R-squared:                       0.853\n",
       "Model:                            OLS   Adj. R-squared:                  0.849\n",
       "Method:                 Least Squares   F-statistic:                     176.2\n",
       "Date:                Tue, 25 Apr 2017   Prob (F-statistic):           1.82e-49\n",
       "Time:                        13:27:07   Log-Likelihood:                -601.11\n",
       "No. Observations:                 126   AIC:                             1212.\n",
       "Df Residuals:                     121   BIC:                             1226.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Constant     -26.2982      3.646     -7.214      0.000     -33.516     -19.081\n",
       "Trend          1.9429      0.105     18.464      0.000       1.735       2.151\n",
       "Trend**2       0.0013      0.002      0.568      0.571      -0.003       0.006\n",
       "Nodal U        6.0876      3.720      1.636      0.104      -1.278      13.453\n",
       "Nodal V      -12.1889      3.655     -3.335      0.001     -19.425      -4.953\n",
       "==============================================================================\n",
       "Omnibus:                        2.826   Durbin-Watson:                   1.711\n",
       "Prob(Omnibus):                  0.243   Jarque-Bera (JB):                2.578\n",
       "Skew:                          -0.350   Prob(JB):                        0.275\n",
       "Kurtosis:                       3.015   Cond. No.                     3.44e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.44e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_quadratic.summary(yname='Sea-surface height', xname=['Constant', 'Trend', 'Trend**2', 'Nodal U', 'Nodal V'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
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       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"0aaf770c-3f80-4b9d-9571-f1f672a8b1b4\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0aaf770c-3f80-4b9d-9571-f1f672a8b1b4' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = bokeh.plotting.figure(x_range=(1860, 2020), plot_width=900, plot_height=400)\n",
    "for color, (id_, station) in zip(colors, selected_stations.iterrows()):\n",
    "    data = station['data']\n",
    "    fig.circle(data.year, data.height, color=color, legend=station['name'], alpha=0.8)\n",
    "fig.circle(mean_df.year, mean_df.height, line_width=3, legend='Mean', color='black', alpha=0.5)\n",
    "fig.line(mean_df.year, fit.predict(), line_width=3, legend='Current')\n",
    "fig.line(mean_df.year, fit_broken_linear.predict(), line_width=3, color='#33bb33', legend='Broken')\n",
    "fig.line(mean_df.year, fit_quadratic.predict(), line_width=3, color='#3333bb', legend='Quadratic')\n",
    "\n",
    "fig.legend.location = \"top_left\"\n",
    "fig.yaxis.axis_label = 'waterlevel [mm] above N.A.P.'\n",
    "fig.xaxis.axis_label = 'year'\n",
    "bokeh.io.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusions\n",
    "======\n",
    "Below are some statements that depend on the output calculated above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current average waterlevel above NAP (in mm), \n",
      "based on the 6 main tide gauges for the year 2015 is 4.9 cm.\n",
      "The current sea-level rise is 19 cm/century\n"
     ]
    }
   ],
   "source": [
    "msg = '''The current average waterlevel above NAP (in mm), \n",
    "based on the 6 main tide gauges for the year {year} is {height:.1f} cm.\n",
    "The current sea-level rise is {rate:.0f} cm/century'''\n",
    "print(msg.format(year=mean_df['year'].iloc[-1], height=fit.predict()[-1]/10.0, rate=fit.params.x1*100.0/10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The linear model is a higher quality model (smaller AIC) than the broken linear model.\n",
      "Under the assumption that there is no trend break, we would have expected a trend break as big as we have seen.\n"
     ]
    }
   ],
   "source": [
    "if (fit.aic < fit_broken_linear.aic):\n",
    "    print('The linear model is a higher quality model (smaller AIC) than the broken linear model.')\n",
    "else:\n",
    "    print('The broken linear model is a higher quality model (smaller AIC) than the linear model.')\n",
    "if (fit_broken_linear.pvalues['x2'] < 0.05):\n",
    "    print('The trend break is bigger than we would have expected under the assumption that there was no trend break.')\n",
    "else:\n",
    "    print('Under the assumption that there is no trend break, we would have expected a trend break as big as we have seen.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The linear model is a higher quality model (smaller AIC) than the quadratic model.\n",
      "Under the assumption that there is no quadraticness, we would have expected a quadratic term as big as we have seen.\n"
     ]
    }
   ],
   "source": [
    "if (fit.aic < fit_quadratic.aic):\n",
    "    print('The linear model is a higher quality model (smaller AIC) than the quadratic model.')\n",
    "else:\n",
    "    print('The quadratic model is a higher quality model (smaller AIC) than the linear model.')\n",
    "if (fit_quadratic.pvalues['x2'] < 0.05):\n",
    "    print('The quadratic term is bigger than we would have expected under the assumption that there was no quadraticness.')\n",
    "else:\n",
    "    print('Under the assumption that there is no quadraticness, we would have expected a quadratic term as big as we have seen.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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