check_sweden_no3_lod.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import nivapy3 as nivapy\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection successful.\n"
     ]
    }
   ],
   "source": [
    "# Connect to db\n",
    "eng = nivapy.da.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check nitrate limit of detection values in ICPW database\n",
    "\n",
    "Filip has pointed out that annual nitrate concentrations for Swedish sites (as shown on the [Explore Data]() page of the ICPW website) seem too low - well below the limit of detection for the lab performing the analysis. The lowest reported $NO_3^-$ values from the lab should be 10 µgN/l, which is 0.71 µeq/l. \n",
    "\n",
    "This notebook performs a quick check to see whether the problem is with the raw data in our database, or with my data processing and analysis for the website.  \n",
    "\n",
    "# 1. Get Swedish stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "581 projects in the RESA database.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44fd729b12dd482caae1931e6e0ac4a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get projects table\n",
    "prj_grid = nivapy.da.select_resa_projects(eng)\n",
    "prj_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>project_id</th>\n",
       "      <th>project_number</th>\n",
       "      <th>project_name</th>\n",
       "      <th>contact_person</th>\n",
       "      <th>project_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2944</td>\n",
       "      <td>None</td>\n",
       "      <td>ICPWaters SE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     project_id project_number  project_name contact_person  \\\n",
       "469        2944           None  ICPWaters SE           None   \n",
       "\n",
       "    project_description  \n",
       "469                None  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select project\n",
    "prj_df = prj_grid.get_selected_df()\n",
    "prj_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>station_id</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29290</td>\n",
       "      <td>SE13</td>\n",
       "      <td>Allgjuttern</td>\n",
       "      <td>57.948283</td>\n",
       "      <td>16.099441</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29334</td>\n",
       "      <td>SE14</td>\n",
       "      <td>Remmarsjön</td>\n",
       "      <td>63.863089</td>\n",
       "      <td>18.276358</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29335</td>\n",
       "      <td>SE15</td>\n",
       "      <td>Rotehogstjärnen</td>\n",
       "      <td>58.815478</td>\n",
       "      <td>11.614963</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29341</td>\n",
       "      <td>SE17</td>\n",
       "      <td>Stora Skärsjön</td>\n",
       "      <td>56.671708</td>\n",
       "      <td>13.068004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29343</td>\n",
       "      <td>SE16</td>\n",
       "      <td>Stora Envättern</td>\n",
       "      <td>59.115419</td>\n",
       "      <td>17.356840</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29371</td>\n",
       "      <td>SE18</td>\n",
       "      <td>Övre Skärsjön</td>\n",
       "      <td>59.837666</td>\n",
       "      <td>15.553400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23597</td>\n",
       "      <td>SE01</td>\n",
       "      <td>Delångersån Iggersund</td>\n",
       "      <td>61.636500</td>\n",
       "      <td>17.087167</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23598</td>\n",
       "      <td>SE02</td>\n",
       "      <td>Alsterån Getebro</td>\n",
       "      <td>57.009000</td>\n",
       "      <td>16.159000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23600</td>\n",
       "      <td>SE05</td>\n",
       "      <td>Tväringen</td>\n",
       "      <td>62.240000</td>\n",
       "      <td>15.672500</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23601</td>\n",
       "      <td>SE06</td>\n",
       "      <td>Stensjön</td>\n",
       "      <td>61.639500</td>\n",
       "      <td>15.904500</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>23602</td>\n",
       "      <td>SE08</td>\n",
       "      <td>Brunnsjön</td>\n",
       "      <td>56.597378</td>\n",
       "      <td>15.731103</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23603</td>\n",
       "      <td>SE09</td>\n",
       "      <td>Fiolen</td>\n",
       "      <td>57.092239</td>\n",
       "      <td>14.532463</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23604</td>\n",
       "      <td>SE10</td>\n",
       "      <td>Storasjö</td>\n",
       "      <td>56.942000</td>\n",
       "      <td>15.271167</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23605</td>\n",
       "      <td>SE11</td>\n",
       "      <td>Fräcksjön</td>\n",
       "      <td>58.142500</td>\n",
       "      <td>12.183667</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23606</td>\n",
       "      <td>SE12</td>\n",
       "      <td>Härsvatten</td>\n",
       "      <td>58.020578</td>\n",
       "      <td>12.032941</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36458</td>\n",
       "      <td>SE19</td>\n",
       "      <td>Abiskojaure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>36459</td>\n",
       "      <td>SE20</td>\n",
       "      <td>Stormyrbäcken</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>36460</td>\n",
       "      <td>SE21</td>\n",
       "      <td>Pipbäcken Nedre</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36461</td>\n",
       "      <td>SE22</td>\n",
       "      <td>Muddusälven</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36462</td>\n",
       "      <td>SE23</td>\n",
       "      <td>Lommabäcken Nedre</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36463</td>\n",
       "      <td>SE24</td>\n",
       "      <td>Lill-Fämtan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>36464</td>\n",
       "      <td>SE25</td>\n",
       "      <td>Laxtjärnsbäcken</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id station_code           station_name   latitude  longitude  \\\n",
       "0        29290         SE13            Allgjuttern  57.948283  16.099441   \n",
       "1        29334         SE14             Remmarsjön  63.863089  18.276358   \n",
       "2        29335         SE15        Rotehogstjärnen  58.815478  11.614963   \n",
       "3        29341         SE17         Stora Skärsjön  56.671708  13.068004   \n",
       "4        29343         SE16        Stora Envättern  59.115419  17.356840   \n",
       "5        29371         SE18          Övre Skärsjön  59.837666  15.553400   \n",
       "6        23597         SE01  Delångersån Iggersund  61.636500  17.087167   \n",
       "7        23598         SE02       Alsterån Getebro  57.009000  16.159000   \n",
       "8        23600         SE05              Tväringen  62.240000  15.672500   \n",
       "9        23601         SE06               Stensjön  61.639500  15.904500   \n",
       "10       23602         SE08              Brunnsjön  56.597378  15.731103   \n",
       "11       23603         SE09                 Fiolen  57.092239  14.532463   \n",
       "12       23604         SE10               Storasjö  56.942000  15.271167   \n",
       "13       23605         SE11              Fräcksjön  58.142500  12.183667   \n",
       "14       23606         SE12             Härsvatten  58.020578  12.032941   \n",
       "15       36458         SE19            Abiskojaure        NaN        NaN   \n",
       "16       36459         SE20          Stormyrbäcken        NaN        NaN   \n",
       "17       36460         SE21        Pipbäcken Nedre        NaN        NaN   \n",
       "18       36461         SE22            Muddusälven        NaN        NaN   \n",
       "19       36462         SE23      Lommabäcken Nedre        NaN        NaN   \n",
       "20       36463         SE24            Lill-Fämtan        NaN        NaN   \n",
       "21       36464         SE25        Laxtjärnsbäcken        NaN        NaN   \n",
       "\n",
       "    altitude  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "5        NaN  \n",
       "6        0.0  \n",
       "7       60.0  \n",
       "8      308.0  \n",
       "9      268.0  \n",
       "10       0.0  \n",
       "11     226.0  \n",
       "12     252.0  \n",
       "13      58.0  \n",
       "14     129.0  \n",
       "15       NaN  \n",
       "16       NaN  \n",
       "17       NaN  \n",
       "18       NaN  \n",
       "19       NaN  \n",
       "20       NaN  \n",
       "21       NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get stations\n",
    "stn_df = nivapy.da.select_resa_project_stations(prj_df, eng)\n",
    "stn_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
    <style>#map_3bfbd6114a0e42a4927afe24a7091b53 {
        position: relative;
        width: 100.0%;
        height: 100.0%;
        left: 0.0%;
        top: 0.0%;
        }
    </style>
</head>
<body>    
    
    <div class="folium-map" id="map_3bfbd6114a0e42a4927afe24a7091b53" ></div>
</body>
<script>    
    
    
        var bounds = null;
    

    var map_3bfbd6114a0e42a4927afe24a7091b53 = L.map(
        'map_3bfbd6114a0e42a4927afe24a7091b53', {
        center: [59.03808920000001, 15.102900933333334],
        zoom: 4,
        maxBounds: bounds,
        layers: [],
        worldCopyJump: false,
        crs: L.CRS.EPSG3857,
        zoomControl: true,
        });

    
    
    var tile_layer_75fe3aaec94a46c194d1057d4cdd3291 = L.tileLayer(
        'https://stamen-tiles-{s}.a.ssl.fastly.net/terrain/{z}/{x}/{y}.jpg',
        {
        "attribution": null,
        "detectRetina": false,
        "maxNativeZoom": 18,
        "maxZoom": 18,
        "minZoom": 0,
        "noWrap": false,
        "subdomains": "abc"
}).addTo(map_3bfbd6114a0e42a4927afe24a7091b53);
    
            var feature_group_67449d9a1acb47a48eece20c1896d3d0 = L.featureGroup(
                ).addTo(map_3bfbd6114a0e42a4927afe24a7091b53);
        
    
            var circle_marker_0d3d783784ea416a80ef1c52b0271dad = L.circleMarker(
                [57.948283, 16.099441],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_131d4e3ccb9a40b5850c833e534c21b2 = L.popup({maxWidth: '300'
            
            });

            
                var html_4e9d614bcba44e4eb3443f7809d985fe = $('<div id="html_4e9d614bcba44e4eb3443f7809d985fe" style="width: 100.0%; height: 100.0%;">SE13</div>')[0];
                popup_131d4e3ccb9a40b5850c833e534c21b2.setContent(html_4e9d614bcba44e4eb3443f7809d985fe);
            

            circle_marker_0d3d783784ea416a80ef1c52b0271dad.bindPopup(popup_131d4e3ccb9a40b5850c833e534c21b2)
            ;

            
        
    
            var circle_marker_c854c744daa445adbb12ed6aaf3e2169 = L.circleMarker(
                [63.863089, 18.276358],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_97308fb8160f4246929ef681e537f86e = L.popup({maxWidth: '300'
            
            });

            
                var html_b7fa442d70a24ea99d8192937aeea3d5 = $('<div id="html_b7fa442d70a24ea99d8192937aeea3d5" style="width: 100.0%; height: 100.0%;">SE14</div>')[0];
                popup_97308fb8160f4246929ef681e537f86e.setContent(html_b7fa442d70a24ea99d8192937aeea3d5);
            

            circle_marker_c854c744daa445adbb12ed6aaf3e2169.bindPopup(popup_97308fb8160f4246929ef681e537f86e)
            ;

            
        
    
            var circle_marker_c7974a7c240544589ac59fdc9bc81656 = L.circleMarker(
                [58.815478, 11.614963],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_b4bebc3c0f1c457799e3df6ec0aa0f39 = L.popup({maxWidth: '300'
            
            });

            
                var html_c58f37d46fa545e0ad9f7e530fbcd990 = $('<div id="html_c58f37d46fa545e0ad9f7e530fbcd990" style="width: 100.0%; height: 100.0%;">SE15</div>')[0];
                popup_b4bebc3c0f1c457799e3df6ec0aa0f39.setContent(html_c58f37d46fa545e0ad9f7e530fbcd990);
            

            circle_marker_c7974a7c240544589ac59fdc9bc81656.bindPopup(popup_b4bebc3c0f1c457799e3df6ec0aa0f39)
            ;

            
        
    
            var circle_marker_f7733494f2cd44288c8371925b1deca8 = L.circleMarker(
                [56.671708, 13.068004],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_063b713d4d7943f28d86891f4c75572f = L.popup({maxWidth: '300'
            
            });

            
                var html_12995b5bd9c14fcea760ff7f9d692b07 = $('<div id="html_12995b5bd9c14fcea760ff7f9d692b07" style="width: 100.0%; height: 100.0%;">SE17</div>')[0];
                popup_063b713d4d7943f28d86891f4c75572f.setContent(html_12995b5bd9c14fcea760ff7f9d692b07);
            

            circle_marker_f7733494f2cd44288c8371925b1deca8.bindPopup(popup_063b713d4d7943f28d86891f4c75572f)
            ;

            
        
    
            var circle_marker_8dc63c34edfd4fc696a6d9b735d11ba6 = L.circleMarker(
                [59.115419, 17.35684],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_7bdc2689fad8458f9ddee63bf22de69b = L.popup({maxWidth: '300'
            
            });

            
                var html_96960f74131943b1ac036d14bdad7bc5 = $('<div id="html_96960f74131943b1ac036d14bdad7bc5" style="width: 100.0%; height: 100.0%;">SE16</div>')[0];
                popup_7bdc2689fad8458f9ddee63bf22de69b.setContent(html_96960f74131943b1ac036d14bdad7bc5);
            

            circle_marker_8dc63c34edfd4fc696a6d9b735d11ba6.bindPopup(popup_7bdc2689fad8458f9ddee63bf22de69b)
            ;

            
        
    
            var circle_marker_d0ae643d13d642cd8dda2bf296884997 = L.circleMarker(
                [59.837666, 15.5534],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_75a84f75c1dd49c29d9c4e6d358f4522 = L.popup({maxWidth: '300'
            
            });

            
                var html_490616b429764d748556e025481a4477 = $('<div id="html_490616b429764d748556e025481a4477" style="width: 100.0%; height: 100.0%;">SE18</div>')[0];
                popup_75a84f75c1dd49c29d9c4e6d358f4522.setContent(html_490616b429764d748556e025481a4477);
            

            circle_marker_d0ae643d13d642cd8dda2bf296884997.bindPopup(popup_75a84f75c1dd49c29d9c4e6d358f4522)
            ;

            
        
    
            var circle_marker_519655cd3bf2413c825e73f51a12d292 = L.circleMarker(
                [61.6365, 17.087167],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_dda0fac5296b497a9a32b6721d696da9 = L.popup({maxWidth: '300'
            
            });

            
                var html_a65da22e600049e2a548a90c6eca9bc0 = $('<div id="html_a65da22e600049e2a548a90c6eca9bc0" style="width: 100.0%; height: 100.0%;">SE01</div>')[0];
                popup_dda0fac5296b497a9a32b6721d696da9.setContent(html_a65da22e600049e2a548a90c6eca9bc0);
            

            circle_marker_519655cd3bf2413c825e73f51a12d292.bindPopup(popup_dda0fac5296b497a9a32b6721d696da9)
            ;

            
        
    
            var circle_marker_51778d7d6a44425c81ca12ef36ba09b3 = L.circleMarker(
                [57.009, 16.159],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_0785f90f2e524c0b831df8f6a1336f5c = L.popup({maxWidth: '300'
            
            });

            
                var html_9a1d748be38a4f3b88b024a349c6612a = $('<div id="html_9a1d748be38a4f3b88b024a349c6612a" style="width: 100.0%; height: 100.0%;">SE02</div>')[0];
                popup_0785f90f2e524c0b831df8f6a1336f5c.setContent(html_9a1d748be38a4f3b88b024a349c6612a);
            

            circle_marker_51778d7d6a44425c81ca12ef36ba09b3.bindPopup(popup_0785f90f2e524c0b831df8f6a1336f5c)
            ;

            
        
    
            var circle_marker_3f3dba5b22b042dbaad131845c2db89e = L.circleMarker(
                [62.24, 15.6725],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_56b20ffb57f24fbc88a00615fba79ce5 = L.popup({maxWidth: '300'
            
            });

            
                var html_fa9c4201036147b7802d99c3bc7b7401 = $('<div id="html_fa9c4201036147b7802d99c3bc7b7401" style="width: 100.0%; height: 100.0%;">SE05</div>')[0];
                popup_56b20ffb57f24fbc88a00615fba79ce5.setContent(html_fa9c4201036147b7802d99c3bc7b7401);
            

            circle_marker_3f3dba5b22b042dbaad131845c2db89e.bindPopup(popup_56b20ffb57f24fbc88a00615fba79ce5)
            ;

            
        
    
            var circle_marker_4bb004e3eb794524836cef59c2edb582 = L.circleMarker(
                [61.6395, 15.9045],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_c42da23e04a74d0e917d0593515a779c = L.popup({maxWidth: '300'
            
            });

            
                var html_5ed1112d6969469d84968caf3a0b3fc6 = $('<div id="html_5ed1112d6969469d84968caf3a0b3fc6" style="width: 100.0%; height: 100.0%;">SE06</div>')[0];
                popup_c42da23e04a74d0e917d0593515a779c.setContent(html_5ed1112d6969469d84968caf3a0b3fc6);
            

            circle_marker_4bb004e3eb794524836cef59c2edb582.bindPopup(popup_c42da23e04a74d0e917d0593515a779c)
            ;

            
        
    
            var circle_marker_c5c718cbab0e4c7d9b03be3d92030ff4 = L.circleMarker(
                [56.597378, 15.731103],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_76937ec1c0544902907c4fe13ca709e4 = L.popup({maxWidth: '300'
            
            });

            
                var html_b84c23cec97b4a669858d0d08e82e3d5 = $('<div id="html_b84c23cec97b4a669858d0d08e82e3d5" style="width: 100.0%; height: 100.0%;">SE08</div>')[0];
                popup_76937ec1c0544902907c4fe13ca709e4.setContent(html_b84c23cec97b4a669858d0d08e82e3d5);
            

            circle_marker_c5c718cbab0e4c7d9b03be3d92030ff4.bindPopup(popup_76937ec1c0544902907c4fe13ca709e4)
            ;

            
        
    
            var circle_marker_82bfcb1799064132bf8920c32fea0ed4 = L.circleMarker(
                [57.092239, 14.532463],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_a68bff356f4b49cb905c2a618f2f248b = L.popup({maxWidth: '300'
            
            });

            
                var html_8be8d88b48d64652af606ba279b8b60a = $('<div id="html_8be8d88b48d64652af606ba279b8b60a" style="width: 100.0%; height: 100.0%;">SE09</div>')[0];
                popup_a68bff356f4b49cb905c2a618f2f248b.setContent(html_8be8d88b48d64652af606ba279b8b60a);
            

            circle_marker_82bfcb1799064132bf8920c32fea0ed4.bindPopup(popup_a68bff356f4b49cb905c2a618f2f248b)
            ;

            
        
    
            var circle_marker_bfc498deb0244627adb1b61f14d3e5f6 = L.circleMarker(
                [56.942, 15.271167],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_d1ad2b46f04c42978eee0f50f98fd80b = L.popup({maxWidth: '300'
            
            });

            
                var html_d3e3e19736c2498ca83ff36d9c12637c = $('<div id="html_d3e3e19736c2498ca83ff36d9c12637c" style="width: 100.0%; height: 100.0%;">SE10</div>')[0];
                popup_d1ad2b46f04c42978eee0f50f98fd80b.setContent(html_d3e3e19736c2498ca83ff36d9c12637c);
            

            circle_marker_bfc498deb0244627adb1b61f14d3e5f6.bindPopup(popup_d1ad2b46f04c42978eee0f50f98fd80b)
            ;

            
        
    
            var circle_marker_3230e6bce7a84eb69d7840d4fafdd4d4 = L.circleMarker(
                [58.1425, 12.183667],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_54cad78e901e436caaf9e62d8fd2b5d0 = L.popup({maxWidth: '300'
            
            });

            
                var html_07414c5964b345bc9168b0672cdc5707 = $('<div id="html_07414c5964b345bc9168b0672cdc5707" style="width: 100.0%; height: 100.0%;">SE11</div>')[0];
                popup_54cad78e901e436caaf9e62d8fd2b5d0.setContent(html_07414c5964b345bc9168b0672cdc5707);
            

            circle_marker_3230e6bce7a84eb69d7840d4fafdd4d4.bindPopup(popup_54cad78e901e436caaf9e62d8fd2b5d0)
            ;

            
        
    
            var circle_marker_5dc71772bd08456caf35b702f7005802 = L.circleMarker(
                [58.020578, 12.032941],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 1,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 1
}
                )
                .addTo(feature_group_67449d9a1acb47a48eece20c1896d3d0);
            
    
            var popup_3c46ae959e9c43eebc9b4f0603840273 = L.popup({maxWidth: '300'
            
            });

            
                var html_b376c3161a8e4f26a95caca90954dfa6 = $('<div id="html_b376c3161a8e4f26a95caca90954dfa6" style="width: 100.0%; height: 100.0%;">SE12</div>')[0];
                popup_3c46ae959e9c43eebc9b4f0603840273.setContent(html_b376c3161a8e4f26a95caca90954dfa6);
            

            circle_marker_5dc71772bd08456caf35b702f7005802.bindPopup(popup_3c46ae959e9c43eebc9b4f0603840273)
            ;

            
        
    
            var layer_control_111f572eecbe4e9ca20953434f6fbefb = {
                base_layers : { "stamenterrain" : tile_layer_75fe3aaec94a46c194d1057d4cdd3291, },
                overlays : { "Stations" : feature_group_67449d9a1acb47a48eece20c1896d3d0, }
                };
            L.control.layers(
                layer_control_111f572eecbe4e9ca20953434f6fbefb.base_layers,
                layer_control_111f572eecbe4e9ca20953434f6fbefb.overlays,
                {position: 'topright',
                 collapsed: true,
                 autoZIndex: true
                }).addTo(map_3bfbd6114a0e42a4927afe24a7091b53);
            
        
    
                

                map_3bfbd6114a0e42a4927afe24a7091b53.fitBounds(
                    [[56.597378, 11.614963], [63.863089, 18.276358]],
                    {}
                    );
            
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fc6704eac50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map\n",
    "nivapy.spatial.quickmap(stn_df, popup='station_code')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Get NO3 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33 parameters available for the selected stations and dates.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1ca551cb7d44f24b293f8af6056893a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get available params\n",
    "st_dt = '1990-01-01'\n",
    "end_dt = '2012-12-31'\n",
    "par_grid = nivapy.da.select_resa_station_parameters(stn_df,\n",
    "                                                    st_dt,\n",
    "                                                    end_dt,\n",
    "                                                    eng)\n",
    "par_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>parameter_id</th>\n",
       "      <th>parameter_name</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>NO3-N</td>\n",
       "      <td>µg/l N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    parameter_id parameter_name    unit\n",
       "18             5          NO3-N  µg/l N"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select NO3\n",
    "par_df = par_grid.get_selected_df()\n",
    "par_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>station_id</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>sample_date</th>\n",
       "      <th>depth1</th>\n",
       "      <th>depth2</th>\n",
       "      <th>NO3-N_µg/l N</th>\n",
       "      <th>NO3-N_µeq/l</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></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>0</th>\n",
       "      <td>23597</td>\n",
       "      <td>SE01</td>\n",
       "      <td>Delångersån Iggersund</td>\n",
       "      <td>1990-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>10.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23597</td>\n",
       "      <td>SE01</td>\n",
       "      <td>Delångersån Iggersund</td>\n",
       "      <td>1990-02-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>15.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23597</td>\n",
       "      <td>SE01</td>\n",
       "      <td>Delångersån Iggersund</td>\n",
       "      <td>1990-03-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>16.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23597</td>\n",
       "      <td>SE01</td>\n",
       "      <td>Delångersån Iggersund</td>\n",
       "      <td>1990-04-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>13.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23597</td>\n",
       "      <td>SE01</td>\n",
       "      <td>Delångersån Iggersund</td>\n",
       "      <td>1990-05-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id station_code           station_name sample_date  depth1  depth2  \\\n",
       "                                                                                \n",
       "0       23597         SE01  Delångersån Iggersund  1990-01-23     0.0     0.0   \n",
       "1       23597         SE01  Delångersån Iggersund  1990-02-26     0.0     0.0   \n",
       "2       23597         SE01  Delångersån Iggersund  1990-03-26     0.0     0.0   \n",
       "3       23597         SE01  Delångersån Iggersund  1990-04-23     0.0     0.0   \n",
       "4       23597         SE01  Delångersån Iggersund  1990-05-30     0.0     0.0   \n",
       "\n",
       "   NO3-N_µg/l N  NO3-N_µeq/l  \n",
       "                              \n",
       "0         151.0    10.785714  \n",
       "1         222.0    15.857143  \n",
       "2         226.0    16.142857  \n",
       "3         186.0    13.285714  \n",
       "4          68.0     4.857143  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get chem data\n",
    "wc_df, dup_df = nivapy.da.select_resa_water_chemistry(stn_df,\n",
    "                                              par_df,\n",
    "                                              st_dt,\n",
    "                                              end_dt,\n",
    "                                              eng,\n",
    "                                              lod_flags=False)\n",
    "\n",
    "# Get just surface samples\n",
    "wc_df = wc_df.query('depth1 <= 1 and depth2 <= 1')\n",
    "\n",
    "# Convert units to ueq/l\n",
    "wc_df['NO3-N_µeq/l'] = wc_df['NO3-N_µg/l N']/14.\n",
    "\n",
    "wc_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Stora Envättern example\n",
    "\n",
    "Let's look at trends for one site (Stora Envättern, station code SE16), which has some fairly low concentrations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>NO3-N_µeq/l</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>0.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>0.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.607143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.607143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.821429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.607143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             NO3-N_µeq/l\n",
       "sample_date             \n",
       "1990            0.500000\n",
       "1991            0.785714\n",
       "1992            1.285714\n",
       "1993            0.392857\n",
       "1994            0.750000\n",
       "1995            1.857143\n",
       "1996            1.750000\n",
       "1997            0.428571\n",
       "1998            0.571429\n",
       "1999            0.750000\n",
       "2000            0.607143\n",
       "2001            0.785714\n",
       "2002            0.607143\n",
       "2003            1.892857\n",
       "2004            0.821429\n",
       "2005            0.892857\n",
       "2006            1.000000\n",
       "2007            0.714286\n",
       "2008            0.142857\n",
       "2009            0.214286\n",
       "2010            0.250000\n",
       "2011            0.214286\n",
       "2012            0.607143"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get just data for Stensjön\n",
    "sten_df = wc_df.query('station_code == \"SE16\"')\n",
    "sten_df = sten_df[['sample_date', 'NO3-N_µeq/l']]\n",
    "sten_df.set_index('sample_date', inplace=True)\n",
    "\n",
    "# Resample to annual means\n",
    "sten_df = sten_df.resample('A').median()\n",
    "sten_df.index = sten_df.index.year\n",
    "\n",
    "sten_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>description</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sslp</th>\n",
       "      <td>Median slope estimate</td>\n",
       "      <td>-0.0162338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icpt</th>\n",
       "      <td>Estimated intercept</td>\n",
       "      <td>33.1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lb</th>\n",
       "      <td>Lower bound on slope estimate at specified alpha</td>\n",
       "      <td>-0.0446429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ub</th>\n",
       "      <td>Upper bound on slope estimate at specified alpha</td>\n",
       "      <td>0.0119048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trend</th>\n",
       "      <td>Type of trend (if present)</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            description      value\n",
       "sslp                              Median slope estimate -0.0162338\n",
       "icpt                                Estimated intercept    33.1981\n",
       "lb     Lower bound on slope estimate at specified alpha -0.0446429\n",
       "ub     Upper bound on slope estimate at specified alpha  0.0119048\n",
       "trend                        Type of trend (if present)   no trend"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply Sen's slope\n",
    "res_df, sen_df = nivapy.stats.sens_slope(sten_df, \n",
    "                                         value_col='NO3-N_µeq/l',\n",
    "                                         index_col=sten_df.index)\n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Sen's slope results\n",
    "nivapy.plotting.plot_sens_slope(res_df, sen_df,\n",
    "                                ylabel='NO3-N_µeq/l',\n",
    "                                title='NO3-N at Stora Envättern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same as the plot on the website, so I think the code is working correctly. However, it is clear that our database has values well below 0.71 µeq/l. Looking at the raw data previously supplied to the ICPW programme, it looks as though the lowest values submitted are consistently 1 µeq/l (and not 10, which the the detection limit stated by Filip)."
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}