From 586329bd4204c407e73e468dec9484d5de003e93 Mon Sep 17 00:00:00 2001
From: Hannah Russell <hannah.russell@studium.uni-hamburg.de>
Date: Wed, 12 Apr 2023 11:13:14 +0200
Subject: [PATCH] add ems data and update ipynb files

---
 data/input/ems/Readme.txt                     |   3 +
 data/input/ems/df_1/Readme.txt                |   3 +
 .../ems/df_1/SB_EFW_watersamples_2018.txt     |   3 +
 .../ems/df_1/SB_EFW_watersamples_2019.txt     |   3 +
 .../ems/df_1/SB_EMD_watersamples_2018.txt     |   3 +
 .../ems/df_1/SB_EMD_watersamples_2019.txt     |   3 +
 .../ems/df_1/SB_POG_watersamples_2018.txt     |   3 +
 .../ems/df_1/SB_POG_watersamples_2019.txt     |   3 +
 data/input/ems/df_1/ems_EFW.csv               |   3 +
 data/input/ems/df_1/ems_EMD.csv               |   3 +
 data/input/ems/report-EDoM_v2.0.pdf           |   3 +
 ...{Nieuwe Maas.csv => Nieuwe Maas depth.csv} |   0
 .../chlorophyll/df_1/Schelde_chlorophyll.csv  |   3 +
 .../chlorophyll/df_1/Schelde_chlorophyll.txt  |   3 +
 ...21_1_S_WISE6_SpatialObject_DerivedData.csv |   3 -
 ...terbase_v2021_1_T_WISE6_AggregatedData.csv |   3 -
 ...21_1_T_WISE6_AggregatedDataByWaterBody.csv |   3 -
 .../Elbe Chlorophyll-checkpoint.ipynb         | 495 +++++++++
 .../Elbe Turbidity-checkpoint.ipynb}          |  19 +-
 .../Ems_SSC-checkpoint.ipynb                  | 383 +++++++
 .../Schelde Turbidity-checkpoint.ipynb        | 750 ++++++++++++++
 .../Schelde_Turbidity-checkpoint.ipynb        | 751 ++++++++++++++
 .../Schelde_chlorophyll-checkpoint.ipynb      | 290 ++++++
 .../Waterbase-checkpoint.ipynb                | 942 ++++++++++++++++++
 ipynb/Elbe Chlorophyll.ipynb                  | 585 -----------
 ipynb/Ems_SSC.ipynb                           | 383 +++++++
 ipynb/Schelde_Turbidity.ipynb                 |   1 +
 ipynb/Schelde_chlorophyll.ipynb               | 290 ++++++
 28 files changed, 4342 insertions(+), 595 deletions(-)
 create mode 100644 data/input/ems/Readme.txt
 create mode 100644 data/input/ems/df_1/Readme.txt
 create mode 100644 data/input/ems/df_1/SB_EFW_watersamples_2018.txt
 create mode 100644 data/input/ems/df_1/SB_EFW_watersamples_2019.txt
 create mode 100644 data/input/ems/df_1/SB_EMD_watersamples_2018.txt
 create mode 100644 data/input/ems/df_1/SB_EMD_watersamples_2019.txt
 create mode 100644 data/input/ems/df_1/SB_POG_watersamples_2018.txt
 create mode 100644 data/input/ems/df_1/SB_POG_watersamples_2019.txt
 create mode 100644 data/input/ems/df_1/ems_EFW.csv
 create mode 100644 data/input/ems/df_1/ems_EMD.csv
 create mode 100644 data/input/ems/report-EDoM_v2.0.pdf
 rename data/input/nieuwe_maas/{Nieuwe Maas.csv => Nieuwe Maas depth.csv} (100%)
 create mode 100644 data/input/schelde/chlorophyll/df_1/Schelde_chlorophyll.csv
 create mode 100644 data/input/schelde/chlorophyll/df_1/Schelde_chlorophyll.txt
 delete mode 100644 data/input/schelde/mixed/df_1/Waterbase_v2021_1_S_WISE6_SpatialObject_DerivedData.csv
 delete mode 100644 data/input/schelde/mixed/df_1/Waterbase_v2021_1_T_WISE6_AggregatedData.csv
 delete mode 100644 data/input/schelde/mixed/df_1/Waterbase_v2021_1_T_WISE6_AggregatedDataByWaterBody.csv
 create mode 100644 ipynb/.ipynb_checkpoints/Elbe Chlorophyll-checkpoint.ipynb
 rename ipynb/{Elbe Turbidity.ipynb => .ipynb_checkpoints/Elbe Turbidity-checkpoint.ipynb} (99%)
 create mode 100644 ipynb/.ipynb_checkpoints/Ems_SSC-checkpoint.ipynb
 create mode 100644 ipynb/.ipynb_checkpoints/Schelde Turbidity-checkpoint.ipynb
 create mode 100644 ipynb/.ipynb_checkpoints/Schelde_Turbidity-checkpoint.ipynb
 create mode 100644 ipynb/.ipynb_checkpoints/Schelde_chlorophyll-checkpoint.ipynb
 create mode 100644 ipynb/.ipynb_checkpoints/Waterbase-checkpoint.ipynb
 delete mode 100644 ipynb/Elbe Chlorophyll.ipynb
 create mode 100644 ipynb/Ems_SSC.ipynb
 create mode 100644 ipynb/Schelde_chlorophyll.ipynb

diff --git a/data/input/ems/Readme.txt b/data/input/ems/Readme.txt
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diff --git a/data/input/ems/df_1/Readme.txt b/data/input/ems/df_1/Readme.txt
new file mode 100644
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diff --git a/data/input/ems/df_1/SB_EFW_watersamples_2018.txt b/data/input/ems/df_1/SB_EFW_watersamples_2018.txt
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diff --git a/data/input/ems/df_1/SB_EFW_watersamples_2019.txt b/data/input/ems/df_1/SB_EFW_watersamples_2019.txt
new file mode 100644
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diff --git a/data/input/ems/df_1/SB_EMD_watersamples_2018.txt b/data/input/ems/df_1/SB_EMD_watersamples_2018.txt
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diff --git a/data/input/ems/df_1/SB_EMD_watersamples_2019.txt b/data/input/ems/df_1/SB_EMD_watersamples_2019.txt
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diff --git a/data/input/ems/df_1/SB_POG_watersamples_2018.txt b/data/input/ems/df_1/SB_POG_watersamples_2018.txt
new file mode 100644
index 0000000..9c2bd09
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diff --git a/data/input/ems/df_1/SB_POG_watersamples_2019.txt b/data/input/ems/df_1/SB_POG_watersamples_2019.txt
new file mode 100644
index 0000000..4582fbb
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diff --git a/data/input/ems/df_1/ems_EFW.csv b/data/input/ems/df_1/ems_EFW.csv
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index 0000000..f4a0394
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diff --git a/data/input/ems/df_1/ems_EMD.csv b/data/input/ems/df_1/ems_EMD.csv
new file mode 100644
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diff --git a/data/input/ems/report-EDoM_v2.0.pdf b/data/input/ems/report-EDoM_v2.0.pdf
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diff --git a/data/input/nieuwe_maas/Nieuwe Maas.csv b/data/input/nieuwe_maas/Nieuwe Maas depth.csv
similarity index 100%
rename from data/input/nieuwe_maas/Nieuwe Maas.csv
rename to data/input/nieuwe_maas/Nieuwe Maas depth.csv
diff --git a/data/input/schelde/chlorophyll/df_1/Schelde_chlorophyll.csv b/data/input/schelde/chlorophyll/df_1/Schelde_chlorophyll.csv
new file mode 100644
index 0000000..61e2c95
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diff --git a/data/input/schelde/chlorophyll/df_1/Schelde_chlorophyll.txt b/data/input/schelde/chlorophyll/df_1/Schelde_chlorophyll.txt
new file mode 100644
index 0000000..79e1ed3
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diff --git a/data/input/schelde/mixed/df_1/Waterbase_v2021_1_S_WISE6_SpatialObject_DerivedData.csv b/data/input/schelde/mixed/df_1/Waterbase_v2021_1_S_WISE6_SpatialObject_DerivedData.csv
deleted file mode 100644
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diff --git a/data/input/schelde/mixed/df_1/Waterbase_v2021_1_T_WISE6_AggregatedData.csv b/data/input/schelde/mixed/df_1/Waterbase_v2021_1_T_WISE6_AggregatedData.csv
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diff --git a/ipynb/.ipynb_checkpoints/Elbe Chlorophyll-checkpoint.ipynb b/ipynb/.ipynb_checkpoints/Elbe Chlorophyll-checkpoint.ipynb
new file mode 100644
index 0000000..91ec595
--- /dev/null
+++ b/ipynb/.ipynb_checkpoints/Elbe Chlorophyll-checkpoint.ipynb	
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3d380a50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "#os.getcwd()\n",
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c79ca53",
+   "metadata": {},
+   "source": [
+    "## pre-processing elbe Chlorophyll data\n",
+    "The general aim is to create concateable (non-2d i guess) data frames of all estuaries with unified column names "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bdd39076",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cwd = os.path.abspath(os.curdir)\n",
+    "elbe_clorophyll_df_1 = glob.glob(os.path.join(cwd, 'data', 'input', 'elbe', 'chlorophyll','df_1', '*.csv'))\n",
+    "elbe_clorophyll_df_1 = [pd.read_csv(file, sep = ';', encoding= 'unicode_escape') for file in elbe_clorophyll_df_1]\n",
+    "elbe_clorophyll_df_1 = pd.concat(elbe_clorophyll_df_1, ignore_index=True)\n",
+    "#elbe_clorophyll_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c68f4427",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HANNAH~1\\AppData\\Local\\Temp/ipykernel_18444/3904582180.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  elbe_clorophyll_df_1.columns = elbe_clorophyll_df_1.columns.str.replace(\"['']\", \"\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "elbe_clorophyll_df_1.columns = elbe_clorophyll_df_1.columns.str.replace(\"['']\", \"\")\n",
+    "elbe_clorophyll_df_1.drop(elbe_clorophyll_df_1[elbe_clorophyll_df_1.Messwert.str.contains('[<]', na=True)].index, inplace=True) # removed < from columns with <2.0 string\n",
+    "elbe_clorophyll_df_1['Stromkilometer'] = elbe_clorophyll_df_1['Stromkilometer'].str.replace(\",\", \".\")\n",
+    "elbe_clorophyll_df_1['Messwert'] = elbe_clorophyll_df_1['Messwert'].str.replace(\",\", \".\")\n",
+    "#elbe_clorophyll_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f98cec41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Chlorophyll ug/L')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "Stromkilometer = elbe_clorophyll_df_1['Stromkilometer'].astype(float)\n",
+    "Messwert = elbe_clorophyll_df_1['Messwert'].astype(float)\n",
+    "\n",
+    "# plot of all cholorphyll values from all years on one plot\n",
+    "plt.scatter(Stromkilometer, Messwert)\n",
+    "plt.gca().invert_xaxis()\n",
+    "plt.title('Elbe-- All Years')\n",
+    "plt.xlabel('Kilometer')\n",
+    "plt.ylabel('Chlorophyll ug/L')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6b548829",
+   "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>Stromkilometer</th>\n",
+       "      <th>Messwert</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>585.9</td>\n",
+       "      <td>78.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>589.0</td>\n",
+       "      <td>45.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>598.7</td>\n",
+       "      <td>74.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>609.0</td>\n",
+       "      <td>40.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>615.3</td>\n",
+       "      <td>44.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>623.5</td>\n",
+       "      <td>37.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>628.8</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>628.9</td>\n",
+       "      <td>27.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>636.0</td>\n",
+       "      <td>70.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>639.4</td>\n",
+       "      <td>23.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>641.0</td>\n",
+       "      <td>42.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>645.5</td>\n",
+       "      <td>12.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>649.4</td>\n",
+       "      <td>14.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>650.0</td>\n",
+       "      <td>23.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>653.0</td>\n",
+       "      <td>22.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>655.0</td>\n",
+       "      <td>24.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>660.5</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>660.6</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>662.2</td>\n",
+       "      <td>14.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>662.7</td>\n",
+       "      <td>11.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>665.0</td>\n",
+       "      <td>22.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>670.0</td>\n",
+       "      <td>9.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>674.2</td>\n",
+       "      <td>11.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>675.5</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>681.3</td>\n",
+       "      <td>7.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>689.0</td>\n",
+       "      <td>11.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>693.0</td>\n",
+       "      <td>10.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>704.0</td>\n",
+       "      <td>14.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>710.0</td>\n",
+       "      <td>7.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>721.6</td>\n",
+       "      <td>8.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>725.2</td>\n",
+       "      <td>3.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>727.0</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Stromkilometer  Messwert\n",
+       "0           585.9     78.50\n",
+       "1           589.0     45.90\n",
+       "2           598.7     74.00\n",
+       "3           609.0     40.70\n",
+       "4           615.3     44.40\n",
+       "5           623.5     37.75\n",
+       "6           628.8     24.00\n",
+       "7           628.9     27.50\n",
+       "8           636.0     70.30\n",
+       "9           639.4     23.70\n",
+       "10          641.0     42.90\n",
+       "11          645.5     12.00\n",
+       "12          649.4     14.80\n",
+       "13          650.0     23.70\n",
+       "14          653.0     22.20\n",
+       "15          655.0     24.10\n",
+       "16          660.5     13.30\n",
+       "17          660.6     11.00\n",
+       "18          662.2     14.80\n",
+       "19          662.7     11.10\n",
+       "20          665.0     22.20\n",
+       "21          670.0      9.30\n",
+       "22          674.2     11.80\n",
+       "23          675.5     13.30\n",
+       "24          681.3      7.40\n",
+       "25          689.0     11.80\n",
+       "26          693.0     10.40\n",
+       "27          704.0     14.80\n",
+       "28          710.0      7.45\n",
+       "29          721.6      8.90\n",
+       "30          725.2      3.00\n",
+       "31          727.0      9.00"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "elbe_chlor_df_1_med = elbe_clorophyll_df_1[['Stromkilometer', 'Messwert']]\n",
+    "elbe_chlor_df_1_med.dropna()\n",
+    "\n",
+    "elbe_chlor_df_1_med = elbe_chlor_df_1_med.groupby('Stromkilometer', as_index=False).median() \n",
+    "\n",
+    "stromkilometer_med = elbe_chlor_df_1_med['Stromkilometer']\n",
+    "messwert_med = elbe_chlor_df_1_med['Messwert']\n",
+    "\n",
+    "#chlor_avg.plot()\n",
+    "\n",
+    "elbe_chlor_df_1_med"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0dbaa7",
+   "metadata": {},
+   "source": [
+    "## elbe depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "09dc619e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Depth')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "elbe_depth_df_1 = pd.read_csv(cwd + \"/data/input/elbe/depth/df_1/Elbe Depth.csv\")\n",
+    "#elbe_clorophyll_depth_df_1 = glob.glob(os.path.join(cwd, 'data', 'input', 'elbe', 'depth','df_1', '*.csv'))\n",
+    "\n",
+    "elbe_depth_df_1['Stromkilometer'] = elbe_depth_df_1['Stromkilometer'].astype(float).round(1)\n",
+    "Stromkilometer_d = elbe_depth_df_1['Stromkilometer']\n",
+    "Depth = elbe_depth_df_1['Depth']\n",
+    "elbe_depth_df_1\n",
+    "\n",
+    "# Depth plot\n",
+    "plt.plot(Stromkilometer_d, Depth)\n",
+    "plt.gca().invert_xaxis()\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.title('Elbe-- All Years')\n",
+    "plt.xlabel('Kilometer')\n",
+    "plt.ylabel('Depth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b313440",
+   "metadata": {},
+   "source": [
+    "## Elbe dephth and chlorophyll "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "20161543",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create figure and axis objects with subplots()\n",
+    "fig,ax1 = plt.subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "\n",
+    "ax1.plot(stromkilometer_med, messwert_med, color=\"red\") # this line won't show up when there is a limit on the x axis\n",
+    "ax2.plot(Stromkilometer_d, Depth, color=\"blue\")\n",
+    "\n",
+    "# x-axis\n",
+    "ax1.set_xlabel(\"Stromkilometer\", fontsize = 14)\n",
+    "ax2.set_xlim(586,830) # red line is apparently outside these limits even though the data is in that range\n",
+    "plt.xticks(np.arange(550, 850, step=50))\n",
+    "plt.gca().invert_xaxis()\n",
+    "\n",
+    "# y-axes\n",
+    "ax1.set_ylabel(\"Messwert\", color=\"red\", fontsize=14)\n",
+    "ax2.set_ylabel(\"Depth\",color=\"blue\",fontsize=14)\n",
+    "ax2.invert_yaxis()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "436ccec4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "ae321efca05d5287feb4e18c73c84aa717d56d176335c74bbc73c515f0d20084"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/Elbe Turbidity.ipynb b/ipynb/.ipynb_checkpoints/Elbe Turbidity-checkpoint.ipynb
similarity index 99%
rename from ipynb/Elbe Turbidity.ipynb
rename to ipynb/.ipynb_checkpoints/Elbe Turbidity-checkpoint.ipynb
index c135a57..bf7df77 100644
--- a/ipynb/Elbe Turbidity.ipynb	
+++ b/ipynb/.ipynb_checkpoints/Elbe Turbidity-checkpoint.ipynb	
@@ -401,7 +401,24 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1acc33ee",
+   "metadata": {},
+   "source": [
+    "## pre-processing elbe Chlorophyll data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdee84e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Get xls files list from a folder\n",
     "path = 'Elbe'\n",
     "xls_files = glob.glob(path + \"/*.xls\")\n",
diff --git a/ipynb/.ipynb_checkpoints/Ems_SSC-checkpoint.ipynb b/ipynb/.ipynb_checkpoints/Ems_SSC-checkpoint.ipynb
new file mode 100644
index 0000000..f186f8d
--- /dev/null
+++ b/ipynb/.ipynb_checkpoints/Ems_SSC-checkpoint.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0973acfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb524102",
+   "metadata": {},
+   "source": [
+    "## pre-processing ems data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d50e6e4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#os.getcwd()\n",
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n",
+    "\n",
+    "cwd = os.path.abspath(os.curdir)\n",
+    "ems_EFW_df_1 = pd.read_csv(cwd + '\\data\\input\\ems\\df_1\\ems_EFW.csv') #includes SSC and ignition loss\n",
+    "ems_EMD_df_1 = pd.read_csv(cwd + '\\data\\input\\ems\\df_1\\ems_EMD.csv') #includes SSC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "dbdc2f3a",
+   "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>sample#</th>\n",
+       "      <th>CET</th>\n",
+       "      <th>UTM East</th>\n",
+       "      <th>UTM North</th>\n",
+       "      <th>waterdepth [m]</th>\n",
+       "      <th>SSC [mg/l]</th>\n",
+       "      <th>ignition loss [%]</th>\n",
+       "      <th>vert. Profile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>E001</td>\n",
+       "      <td>28-8-2018 07:19</td>\n",
+       "      <td>372825</td>\n",
+       "      <td>5910331</td>\n",
+       "      <td>298</td>\n",
+       "      <td>1202</td>\n",
+       "      <td>143</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>E002</td>\n",
+       "      <td>28-8-2018 07:23</td>\n",
+       "      <td>372826</td>\n",
+       "      <td>5910330</td>\n",
+       "      <td>590</td>\n",
+       "      <td>3183</td>\n",
+       "      <td>145</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>E003</td>\n",
+       "      <td>28-8-2018 07:31</td>\n",
+       "      <td>372826</td>\n",
+       "      <td>5910329</td>\n",
+       "      <td>297</td>\n",
+       "      <td>1603</td>\n",
+       "      <td>166</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>E004</td>\n",
+       "      <td>28-8-2018 07:34</td>\n",
+       "      <td>372829</td>\n",
+       "      <td>5910330</td>\n",
+       "      <td>595</td>\n",
+       "      <td>2843</td>\n",
+       "      <td>139</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>E005</td>\n",
+       "      <td>28-8-2018 07:45</td>\n",
+       "      <td>372829</td>\n",
+       "      <td>5910330</td>\n",
+       "      <td>302</td>\n",
+       "      <td>921</td>\n",
+       "      <td>186</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>SB_EFW_097</td>\n",
+       "      <td>24-01-2019 21:17:25</td>\n",
+       "      <td>372898</td>\n",
+       "      <td>5910337</td>\n",
+       "      <td>554</td>\n",
+       "      <td>5447</td>\n",
+       "      <td>123</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>SB_EFW_098</td>\n",
+       "      <td>24-01-2019 21:32:19</td>\n",
+       "      <td>372902</td>\n",
+       "      <td>5910332</td>\n",
+       "      <td>296</td>\n",
+       "      <td>5044</td>\n",
+       "      <td>127</td>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>SB_EFW_099</td>\n",
+       "      <td>24-01-2019 21:33:21</td>\n",
+       "      <td>372904</td>\n",
+       "      <td>5910331</td>\n",
+       "      <td>579</td>\n",
+       "      <td>3759</td>\n",
+       "      <td>131</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>SB_EFW_100</td>\n",
+       "      <td>24-01-2019 21:46:59</td>\n",
+       "      <td>372949</td>\n",
+       "      <td>5910340</td>\n",
+       "      <td>297</td>\n",
+       "      <td>4791</td>\n",
+       "      <td>123</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>SB_EFW_101</td>\n",
+       "      <td>24-01-2019 21:48:00</td>\n",
+       "      <td>372956</td>\n",
+       "      <td>5910343</td>\n",
+       "      <td>644</td>\n",
+       "      <td>6399</td>\n",
+       "      <td>121</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>205 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  sample#                  CET  UTM East  UTM North  \\\n",
+       "0                    E001      28-8-2018 07:19    372825    5910331   \n",
+       "1                    E002      28-8-2018 07:23    372826    5910330   \n",
+       "2                    E003      28-8-2018 07:31    372826    5910329   \n",
+       "3                    E004      28-8-2018 07:34    372829    5910330   \n",
+       "4                    E005      28-8-2018 07:45    372829    5910330   \n",
+       "..                    ...                  ...       ...        ...   \n",
+       "200            SB_EFW_097  24-01-2019 21:17:25    372898    5910337   \n",
+       "201            SB_EFW_098  24-01-2019 21:32:19    372902    5910332   \n",
+       "202            SB_EFW_099  24-01-2019 21:33:21    372904    5910331   \n",
+       "203            SB_EFW_100  24-01-2019 21:46:59    372949    5910340   \n",
+       "204            SB_EFW_101  24-01-2019 21:48:00    372956    5910343   \n",
+       "\n",
+       "     waterdepth [m]  SSC [mg/l]  ignition loss [%]  vert. Profile  \n",
+       "0               298        1202                143            1.0  \n",
+       "1               590        3183                145            NaN  \n",
+       "2               297        1603                166            2.0  \n",
+       "3               595        2843                139            NaN  \n",
+       "4               302         921                186            3.0  \n",
+       "..              ...         ...                ...            ...  \n",
+       "200             554        5447                123            NaN  \n",
+       "201             296        5044                127           37.0  \n",
+       "202             579        3759                131            NaN  \n",
+       "203             297        4791                123           38.0  \n",
+       "204             644        6399                121            NaN  \n",
+       "\n",
+       "[205 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ems_EFW_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fa8847a7",
+   "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>sample#</th>\n",
+       "      <th>CET</th>\n",
+       "      <th>UTM East (avg)</th>\n",
+       "      <th>UTM North (avg)</th>\n",
+       "      <th>waterdepth [m]</th>\n",
+       "      <th>SSC [mg/l]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>28-08-2018 07:26:58</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>60</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>28-08-2018 07:29:00</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>25</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>28-08-2018 07:39:59</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>64</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>28-08-2018 07:42:00</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>25</td>\n",
+       "      <td>250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>28-08-2018 07:55:01</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>60</td>\n",
+       "      <td>240</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sample#                  CET  UTM East (avg)  UTM North (avg)  \\\n",
+       "0        1  28-08-2018 07:26:58          376996          5910984   \n",
+       "1        2  28-08-2018 07:29:00          376996          5910984   \n",
+       "2        3  28-08-2018 07:39:59          376996          5910984   \n",
+       "3        4  28-08-2018 07:42:00          376996          5910984   \n",
+       "4        5  28-08-2018 07:55:01          376996          5910984   \n",
+       "\n",
+       "   waterdepth [m]  SSC [mg/l]  \n",
+       "0              60         320  \n",
+       "1              25         230  \n",
+       "2              64         300  \n",
+       "3              25         250  \n",
+       "4              60         240  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ems_EMD_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9ee6be7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/.ipynb_checkpoints/Schelde Turbidity-checkpoint.ipynb b/ipynb/.ipynb_checkpoints/Schelde Turbidity-checkpoint.ipynb
new file mode 100644
index 0000000..3d7c1ca
--- /dev/null
+++ b/ipynb/.ipynb_checkpoints/Schelde Turbidity-checkpoint.ipynb	
@@ -0,0 +1,750 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ca1a2222",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c53b7ab1",
+   "metadata": {},
+   "source": [
+    "## pre-processing Schelde turbidity data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f1a9115b",
+   "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>id</th>\n",
+       "      <th>aphiaid</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>depth</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>value</th>\n",
+       "      <th>lod</th>\n",
+       "      <th>loq</th>\n",
+       "      <th>standardparameterid</th>\n",
+       "      <th>...</th>\n",
+       "      <th>parametername</th>\n",
+       "      <th>parameterunit</th>\n",
+       "      <th>dataprovider</th>\n",
+       "      <th>datasettitle</th>\n",
+       "      <th>datafichetitle</th>\n",
+       "      <th>stationname</th>\n",
+       "      <th>category</th>\n",
+       "      <th>valuesign</th>\n",
+       "      <th>dateprecision</th>\n",
+       "      <th>scientificname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8975471</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:00:00</td>\n",
+       "      <td>111</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8975472</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:10:00</td>\n",
+       "      <td>102</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8975473</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:20:00</td>\n",
+       "      <td>94</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8975474</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:30:00</td>\n",
+       "      <td>97</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8975475</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:40:00</td>\n",
+       "      <td>91</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  aphiaid   latitude  longitude  depth             datetime  value  \\\n",
+       "0  8975471      NaN  51.320855   4.276312    NaN  2005-09-21T10:00:00    111   \n",
+       "1  8975472      NaN  51.320855   4.276312    NaN  2005-09-21T10:10:00    102   \n",
+       "2  8975473      NaN  51.320855   4.276312    NaN  2005-09-21T10:20:00     94   \n",
+       "3  8975474      NaN  51.320855   4.276312    NaN  2005-09-21T10:30:00     97   \n",
+       "4  8975475      NaN  51.320855   4.276312    NaN  2005-09-21T10:40:00     91   \n",
+       "\n",
+       "   lod  loq  standardparameterid  ...                     parametername  \\\n",
+       "0  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "1  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "2  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "3  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "4  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "\n",
+       "   parameterunit                           dataprovider  \\\n",
+       "0            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "1            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "2            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "3            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "4            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "\n",
+       "                                        datasettitle  \\\n",
+       "0  Flanders Hydraulics Research: Continuous monit...   \n",
+       "1  Flanders Hydraulics Research: Continuous monit...   \n",
+       "2  Flanders Hydraulics Research: Continuous monit...   \n",
+       "3  Flanders Hydraulics Research: Continuous monit...   \n",
+       "4  Flanders Hydraulics Research: Continuous monit...   \n",
+       "\n",
+       "                       datafichetitle                 stationname  \\\n",
+       "0  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "1  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "2  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "3  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "4  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "\n",
+       "       category valuesign dateprecision scientificname  \n",
+       "0  lichtklimaat         =           NaN            NaN  \n",
+       "1  lichtklimaat         =           NaN            NaN  \n",
+       "2  lichtklimaat         =           NaN            NaN  \n",
+       "3  lichtklimaat         =           NaN            NaN  \n",
+       "4  lichtklimaat         =           NaN            NaN  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#os.getcwd()\n",
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n",
+    "\n",
+    "cwd = os.path.abspath(os.curdir)\n",
+    "Schelde_turbidity_df_1 = pd.read_csv(cwd + \"/data/input/schelde/turbidity/df_1/Turbidity Scheldt.csv\")\n",
+    "\n",
+    "Schelde_turbidity_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c023efd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "latitude = Schelde_turbidity_df_1['latitude']\n",
+    "longitude = Schelde_turbidity_df_1['longitude']\n",
+    "turbidity = Schelde_turbidity_df_1['value']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f59e83a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'turbidity')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "plt.scatter(latitude, turbidity)\n",
+    "#plt.xlim(586,700) \n",
+    "plt.title('Schelde Turbidity')\n",
+    "plt.xlabel('latitude')\n",
+    "plt.ylabel('turbidity')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "02398e90",
+   "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>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>51.004325</td>\n",
+       "      <td>3.805347</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>51.092564</td>\n",
+       "      <td>4.171004</td>\n",
+       "      <td>106.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>51.236968</td>\n",
+       "      <td>4.370562</td>\n",
+       "      <td>118.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>51.319507</td>\n",
+       "      <td>4.275884</td>\n",
+       "      <td>85.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>107.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    latitude  longitude  value\n",
+       "0  51.004325   3.805347   46.0\n",
+       "1  51.092564   4.171004  106.0\n",
+       "2  51.236968   4.370562  118.0\n",
+       "3  51.319507   4.275884   85.0\n",
+       "4  51.320855   4.276312  107.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Schelde_turbidity_df_1_med = Schelde_turbidity_df_1[['latitude', 'longitude', 'value']]\n",
+    "Schelde_turbidity_df_1_med.dropna()\n",
+    "\n",
+    "Schelde_turbidity_df_1_med = Schelde_turbidity_df_1_med.groupby('latitude', as_index=False).median() \n",
+    "\n",
+    "latitude_med = Schelde_turbidity_df_1_med['latitude']\n",
+    "turbidity_med = Schelde_turbidity_df_1_med['value']\n",
+    "\n",
+    "Schelde_turbidity_df_1_med"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "24f2c8c1",
+   "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>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>value</th>\n",
+       "      <th>km from North Sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>51.004325</td>\n",
+       "      <td>3.805347</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>51.092564</td>\n",
+       "      <td>4.171004</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>51.236968</td>\n",
+       "      <td>4.370562</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>28.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>51.319507</td>\n",
+       "      <td>4.275884</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>42.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>42.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    latitude  longitude  value km from North Sea\n",
+       "0  51.004325   3.805347   46.0               150\n",
+       "1  51.092564   4.171004  106.0               104\n",
+       "2  51.236968   4.370562  118.0              28.5\n",
+       "3  51.319507   4.275884   85.0              42.5\n",
+       "4  51.320855   4.276312  107.0              42.7"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km_from_North_Sea = ['150', '104', '28.5', '42.5', '42.7'] #values measured on google maps\n",
+    "Schelde_turbidity_df_1_med['km from North Sea'] = km_from_North_Sea\n",
+    "km = Schelde_turbidity_df_1_med['km from North Sea']\n",
+    "value = Schelde_turbidity_df_1_med['value']\n",
+    "\n",
+    "Schelde_turbidity_df_1_med"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3aab2ffc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Turbidity')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "# turbidity values from all years\n",
+    "plt.plot(km, value)\n",
+    "plt.gca().invert_xaxis()\n",
+    "plt.title('Schelde-- Median at km')\n",
+    "plt.xlabel('Kilometer from North Sea')\n",
+    "plt.ylabel('Turbidity')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8417f561",
+   "metadata": {},
+   "source": [
+    "## Schelde depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "85caf2c4",
+   "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>km</th>\n",
+       "      <th>depth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.400824</td>\n",
+       "      <td>8.621514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.912927</td>\n",
+       "      <td>9.181122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16.427276</td>\n",
+       "      <td>10.118064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>18.937997</td>\n",
+       "      <td>10.445467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21.450963</td>\n",
+       "      <td>11.150203</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          km      depth\n",
+       "0  11.400824   8.621514\n",
+       "1  13.912927   9.181122\n",
+       "2  16.427276  10.118064\n",
+       "3  18.937997  10.445467\n",
+       "4  21.450963  11.150203"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "schelde_depth_df_1 = pd.read_csv(cwd + \"/data/input/schelde/depth/df_1/Schelde depth.csv\")\n",
+    "\n",
+    "schelde_depth_df_1['km'] = schelde_depth_df_1['km'].astype(float)\n",
+    "schelde_depth_df_1.sort_values(by = 'km', inplace = True)\n",
+    "\n",
+    "schelde_depth_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4a493afb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Depth')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "Stromkilometer_d = schelde_depth_df_1['km']\n",
+    "Depth = schelde_depth_df_1['depth']\n",
+    "schelde_depth_df_1\n",
+    "\n",
+    "# Depth plot\n",
+    "plt.plot(Stromkilometer_d, Depth)\n",
+    "#plt.gca().invert_xaxis()\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.title('Schelde Depth')\n",
+    "plt.xlabel('Kilometer')\n",
+    "plt.ylabel('Depth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ad29fb63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create figure and axis objects with subplots()\n",
+    "fig,ax1 = plt.subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "\n",
+    "# make a plot\n",
+    "ax1.plot(km, value, color=\"red\") # I can't figure out why the axes won't line up\n",
+    "ax2.plot(Stromkilometer_d, Depth, color=\"blue\")\n",
+    "\n",
+    "# x-axis\n",
+    "ax1.set_xlabel(\"Stromkilometer\", fontsize = 14)\n",
+    "#ax2.set_xlim(586,830) \n",
+    "#plt.xticks(np.arange(550, 850, step=50))\n",
+    "plt.gca().invert_xaxis()\n",
+    "\n",
+    "# y-axis labels\n",
+    "ax1.set_ylabel(\"Messwert\", color=\"red\", fontsize=14)\n",
+    "ax2.set_ylabel(\"Depth\",color=\"blue\",fontsize=14)\n",
+    "ax2.invert_yaxis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16991969",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dacced9d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/.ipynb_checkpoints/Schelde_Turbidity-checkpoint.ipynb b/ipynb/.ipynb_checkpoints/Schelde_Turbidity-checkpoint.ipynb
new file mode 100644
index 0000000..f92357c
--- /dev/null
+++ b/ipynb/.ipynb_checkpoints/Schelde_Turbidity-checkpoint.ipynb
@@ -0,0 +1,751 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ca1a2222",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c53b7ab1",
+   "metadata": {},
+   "source": [
+    "## pre-processing Schelde turbidity data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f1a9115b",
+   "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>id</th>\n",
+       "      <th>aphiaid</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>depth</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>value</th>\n",
+       "      <th>lod</th>\n",
+       "      <th>loq</th>\n",
+       "      <th>standardparameterid</th>\n",
+       "      <th>...</th>\n",
+       "      <th>parametername</th>\n",
+       "      <th>parameterunit</th>\n",
+       "      <th>dataprovider</th>\n",
+       "      <th>datasettitle</th>\n",
+       "      <th>datafichetitle</th>\n",
+       "      <th>stationname</th>\n",
+       "      <th>category</th>\n",
+       "      <th>valuesign</th>\n",
+       "      <th>dateprecision</th>\n",
+       "      <th>scientificname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8975471</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:00:00</td>\n",
+       "      <td>111</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8975472</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:10:00</td>\n",
+       "      <td>102</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8975473</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:20:00</td>\n",
+       "      <td>94</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8975474</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:30:00</td>\n",
+       "      <td>97</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8975475</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005-09-21T10:40:00</td>\n",
+       "      <td>91</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Nefelometrisch troebelheid (NTU)</td>\n",
+       "      <td>NTU</td>\n",
+       "      <td>MOW WL - Waterbouwkundig Laboratorium</td>\n",
+       "      <td>Flanders Hydraulics Research: Continuous monit...</td>\n",
+       "      <td>S-FC-V-005 - Turbiditeit - Continu</td>\n",
+       "      <td>Boei84-Boven SF/Zeeschelde</td>\n",
+       "      <td>lichtklimaat</td>\n",
+       "      <td>=</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  aphiaid   latitude  longitude  depth             datetime  value  \\\n",
+       "0  8975471      NaN  51.320855   4.276312    NaN  2005-09-21T10:00:00    111   \n",
+       "1  8975472      NaN  51.320855   4.276312    NaN  2005-09-21T10:10:00    102   \n",
+       "2  8975473      NaN  51.320855   4.276312    NaN  2005-09-21T10:20:00     94   \n",
+       "3  8975474      NaN  51.320855   4.276312    NaN  2005-09-21T10:30:00     97   \n",
+       "4  8975475      NaN  51.320855   4.276312    NaN  2005-09-21T10:40:00     91   \n",
+       "\n",
+       "   lod  loq  standardparameterid  ...                     parametername  \\\n",
+       "0  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "1  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "2  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "3  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "4  NaN  NaN                 5384  ...  Nefelometrisch troebelheid (NTU)   \n",
+       "\n",
+       "   parameterunit                           dataprovider  \\\n",
+       "0            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "1            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "2            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "3            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "4            NTU  MOW WL - Waterbouwkundig Laboratorium   \n",
+       "\n",
+       "                                        datasettitle  \\\n",
+       "0  Flanders Hydraulics Research: Continuous monit...   \n",
+       "1  Flanders Hydraulics Research: Continuous monit...   \n",
+       "2  Flanders Hydraulics Research: Continuous monit...   \n",
+       "3  Flanders Hydraulics Research: Continuous monit...   \n",
+       "4  Flanders Hydraulics Research: Continuous monit...   \n",
+       "\n",
+       "                       datafichetitle                 stationname  \\\n",
+       "0  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "1  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "2  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "3  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "4  S-FC-V-005 - Turbiditeit - Continu  Boei84-Boven SF/Zeeschelde   \n",
+       "\n",
+       "       category valuesign dateprecision scientificname  \n",
+       "0  lichtklimaat         =           NaN            NaN  \n",
+       "1  lichtklimaat         =           NaN            NaN  \n",
+       "2  lichtklimaat         =           NaN            NaN  \n",
+       "3  lichtklimaat         =           NaN            NaN  \n",
+       "4  lichtklimaat         =           NaN            NaN  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#os.getcwd()\n",
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n",
+    "\n",
+    "cwd = os.path.abspath(os.curdir)\n",
+    "Schelde_turbidity_df_1 = pd.read_csv(cwd + \"/data/input/schelde/turbidity/df_1/Turbidity Scheldt.csv\")\n",
+    "\n",
+    "\n",
+    "Schelde_turbidity_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c023efd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "latitude = Schelde_turbidity_df_1['latitude']\n",
+    "longitude = Schelde_turbidity_df_1['longitude']\n",
+    "turbidity = Schelde_turbidity_df_1['value']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f59e83a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'turbidity')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "plt.scatter(latitude, turbidity)\n",
+    "#plt.xlim(586,700) \n",
+    "plt.title('Schelde Turbidity')\n",
+    "plt.xlabel('latitude')\n",
+    "plt.ylabel('turbidity')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "02398e90",
+   "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>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>51.004325</td>\n",
+       "      <td>3.805347</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>51.092564</td>\n",
+       "      <td>4.171004</td>\n",
+       "      <td>106.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>51.236968</td>\n",
+       "      <td>4.370562</td>\n",
+       "      <td>118.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>51.319507</td>\n",
+       "      <td>4.275884</td>\n",
+       "      <td>85.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>107.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    latitude  longitude  value\n",
+       "0  51.004325   3.805347   46.0\n",
+       "1  51.092564   4.171004  106.0\n",
+       "2  51.236968   4.370562  118.0\n",
+       "3  51.319507   4.275884   85.0\n",
+       "4  51.320855   4.276312  107.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Schelde_turbidity_df_1_med = Schelde_turbidity_df_1[['latitude', 'longitude', 'value']]\n",
+    "Schelde_turbidity_df_1_med.dropna()\n",
+    "\n",
+    "Schelde_turbidity_df_1_med = Schelde_turbidity_df_1_med.groupby('latitude', as_index=False).median() \n",
+    "\n",
+    "latitude_med = Schelde_turbidity_df_1_med['latitude']\n",
+    "turbidity_med = Schelde_turbidity_df_1_med['value']\n",
+    "\n",
+    "Schelde_turbidity_df_1_med"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "24f2c8c1",
+   "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>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>value</th>\n",
+       "      <th>km from North Sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>51.004325</td>\n",
+       "      <td>3.805347</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>51.092564</td>\n",
+       "      <td>4.171004</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>51.236968</td>\n",
+       "      <td>4.370562</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>28.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>51.319507</td>\n",
+       "      <td>4.275884</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>42.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>51.320855</td>\n",
+       "      <td>4.276312</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>42.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    latitude  longitude  value km from North Sea\n",
+       "0  51.004325   3.805347   46.0               150\n",
+       "1  51.092564   4.171004  106.0               104\n",
+       "2  51.236968   4.370562  118.0              28.5\n",
+       "3  51.319507   4.275884   85.0              42.5\n",
+       "4  51.320855   4.276312  107.0              42.7"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km_from_North_Sea = ['150', '104', '28.5', '42.5', '42.7'] #values measured on google maps\n",
+    "Schelde_turbidity_df_1_med['km from North Sea'] = km_from_North_Sea\n",
+    "km = Schelde_turbidity_df_1_med['km from North Sea']\n",
+    "value = Schelde_turbidity_df_1_med['value']\n",
+    "\n",
+    "Schelde_turbidity_df_1_med"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3aab2ffc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Turbidity')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "# turbidity values from all years\n",
+    "plt.plot(km, value)\n",
+    "plt.gca().invert_xaxis()\n",
+    "plt.title('Schelde-- Median at km')\n",
+    "plt.xlabel('Kilometer from North Sea')\n",
+    "plt.ylabel('Turbidity')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8417f561",
+   "metadata": {},
+   "source": [
+    "## Schelde depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "85caf2c4",
+   "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>km</th>\n",
+       "      <th>depth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.400824</td>\n",
+       "      <td>8.621514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.912927</td>\n",
+       "      <td>9.181122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16.427276</td>\n",
+       "      <td>10.118064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>18.937997</td>\n",
+       "      <td>10.445467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21.450963</td>\n",
+       "      <td>11.150203</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          km      depth\n",
+       "0  11.400824   8.621514\n",
+       "1  13.912927   9.181122\n",
+       "2  16.427276  10.118064\n",
+       "3  18.937997  10.445467\n",
+       "4  21.450963  11.150203"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "schelde_depth_df_1 = pd.read_csv(cwd + \"/data/input/schelde/depth/df_1/Schelde depth.csv\")\n",
+    "\n",
+    "schelde_depth_df_1['km'] = schelde_depth_df_1['km'].astype(float)\n",
+    "schelde_depth_df_1.sort_values(by = 'km', inplace = True)\n",
+    "\n",
+    "schelde_depth_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4a493afb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Depth')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "Stromkilometer_d = schelde_depth_df_1['km']\n",
+    "Depth = schelde_depth_df_1['depth']\n",
+    "schelde_depth_df_1\n",
+    "\n",
+    "# Depth plot\n",
+    "plt.plot(Stromkilometer_d, Depth)\n",
+    "#plt.gca().invert_xaxis()\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.title('Schelde Depth')\n",
+    "plt.xlabel('Kilometer')\n",
+    "plt.ylabel('Depth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ad29fb63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create figure and axis objects with subplots()\n",
+    "fig,ax1 = plt.subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "\n",
+    "# make a plot\n",
+    "ax1.plot(km, value, color=\"red\") # I can't figure out why the axes won't line up\n",
+    "ax2.plot(Stromkilometer_d, Depth, color=\"blue\")\n",
+    "\n",
+    "# x-axis\n",
+    "ax1.set_xlabel(\"Stromkilometer\", fontsize = 14)\n",
+    "#ax2.set_xlim(586,830) \n",
+    "#plt.xticks(np.arange(550, 850, step=50))\n",
+    "plt.gca().invert_xaxis()\n",
+    "\n",
+    "# y-axis labels\n",
+    "ax1.set_ylabel(\"Messwert\", color=\"red\", fontsize=14)\n",
+    "ax2.set_ylabel(\"Depth\",color=\"blue\",fontsize=14)\n",
+    "ax2.invert_yaxis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16991969",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dacced9d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/.ipynb_checkpoints/Schelde_chlorophyll-checkpoint.ipynb b/ipynb/.ipynb_checkpoints/Schelde_chlorophyll-checkpoint.ipynb
new file mode 100644
index 0000000..8f26ca0
--- /dev/null
+++ b/ipynb/.ipynb_checkpoints/Schelde_chlorophyll-checkpoint.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e2b1d348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69472657",
+   "metadata": {},
+   "source": [
+    "## pre-processing schelde chlorophyll data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "527f9be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HANNAH~1\\AppData\\Local\\Temp/ipykernel_27732/1206153600.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  Schelde_chlorophyll_df_1.columns = Schelde_chlorophyll_df_1.columns.str.replace(\"[;]\", \"\")\n"
+     ]
+    },
+    {
+     "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>Rid</th>\n",
+       "      <th>Compartment</th>\n",
+       "      <th>Parameter</th>\n",
+       "      <th>Substance</th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Datetime</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Sign</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Unit</th>\n",
+       "      <th>Column2</th>\n",
+       "      <th>Column3</th>\n",
+       "      <th>Column1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4190</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27/04/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>5.015475</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4200</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30/04/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>9.780414</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4214</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>26/02/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>0.1960663</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4228</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12/07/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>1.652203</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4239</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>02/06/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>0.6613134</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Rid   Compartment      Parameter      Substance  Taxon    Datetime  \\\n",
+       "0  4190  water column  Concentration  chlorophyll-a    NaN  27/04/1999   \n",
+       "1  4200  water column  Concentration  chlorophyll-a    NaN  30/04/1999   \n",
+       "2  4214  water column  Concentration  chlorophyll-a    NaN  26/02/1999   \n",
+       "3  4228  water column  Concentration  chlorophyll-a    NaN  12/07/1999   \n",
+       "4  4239  water column  Concentration  chlorophyll-a    NaN  02/06/1999   \n",
+       "\n",
+       "   Latitude  Longitude Sign      Value  Unit  Column2  Column3  Column1  \n",
+       "0  51.43333    2.80833    =   5.015475  ug/l      NaN      NaN      NaN  \n",
+       "1  51.43333    2.80833    =   9.780414  ug/l      NaN      NaN      NaN  \n",
+       "2  51.43333    2.80833    =  0.1960663  ug/l      NaN      NaN      NaN  \n",
+       "3  51.43333    2.80833    =   1.652203  ug/l      NaN      NaN      NaN  \n",
+       "4  51.43333    2.80833    =  0.6613134  ug/l      NaN      NaN      NaN  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#os.getcwd()\n",
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n",
+    "cwd = os.path.abspath(os.curdir)\n",
+    "\n",
+    "Schelde_chlorophyll_df_1 = pd.read_csv(cwd + \"\\data\\input\\schelde\\chlorophyll\\df_1\\Schelde_chlorophyll.csv\")\n",
+    "# the txt file in the folder is the original dataset; \n",
+    "# I manually removed the last two columns that contained websites because they were messing with the import\n",
+    "Schelde_chlorophyll_df_1.columns = Schelde_chlorophyll_df_1.columns.str.replace(\"[;]\", \"\")\n",
+    "Schelde_chlorophyll_df_1.drop(Schelde_chlorophyll_df_1[Schelde_chlorophyll_df_1['Value'] == .00000].index, inplace = True)\n",
+    "Schelde_chlorophyll_df_1['Value'] = Schelde_chlorophyll_df_1['Value'].str.replace(\",\", \".\")\n",
+    "\n",
+    "Schelde_chlorophyll_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "90e624ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "latitude = Schelde_chlorophyll_df_1['Latitude']\n",
+    "longitude = Schelde_chlorophyll_df_1['Longitude']\n",
+    "chlor = Schelde_chlorophyll_df_1['Value']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4c51345c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'chlorophyll')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "plt.scatter(latitude, chlor)\n",
+    "plt.xlim(51,52) \n",
+    "plt.yticks(np.arange(0, 500, step=50)) # I have no idea what's going wrong here\n",
+    "plt.title('Schelde chlorophyll')\n",
+    "plt.xlabel('latitude')\n",
+    "plt.ylabel('chlorophyll')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df413057",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5aada43c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/.ipynb_checkpoints/Waterbase-checkpoint.ipynb b/ipynb/.ipynb_checkpoints/Waterbase-checkpoint.ipynb
new file mode 100644
index 0000000..a767bb5
--- /dev/null
+++ b/ipynb/.ipynb_checkpoints/Waterbase-checkpoint.ipynb
@@ -0,0 +1,942 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "acec8070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29ab3c2f",
+   "metadata": {},
+   "source": [
+    "## pre-processing waterbase dataset\n",
+    "includes data about many rivers throughout Europe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "982023de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Hannah Russell\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: DtypeWarning: Columns (21,23,24,29) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+     ]
+    },
+    {
+     "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>countryCode</th>\n",
+       "      <th>thematicIdIdentifier</th>\n",
+       "      <th>thematicIdIdentifierScheme</th>\n",
+       "      <th>monitoringSiteIdentifier</th>\n",
+       "      <th>monitoringSiteIdentifierScheme</th>\n",
+       "      <th>monitoringSiteName</th>\n",
+       "      <th>waterBodyIdentifier</th>\n",
+       "      <th>waterBodyIdentifierScheme</th>\n",
+       "      <th>waterBodyName</th>\n",
+       "      <th>specialisedZoneType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>surfaceWaterBodyTypeCode</th>\n",
+       "      <th>subUnitIdentifier</th>\n",
+       "      <th>subUnitIdentifierScheme</th>\n",
+       "      <th>subUnitName</th>\n",
+       "      <th>rbdIdentifier</th>\n",
+       "      <th>rbdIdentifierScheme</th>\n",
+       "      <th>rbdName</th>\n",
+       "      <th>confidentialityStatus</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>ALGW_011</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ALGW_011</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>1 -DOBRAC</td>\n",
+       "      <td>groundWaterBody</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>inapplicable</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>ALGW_021</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ALGW_021</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>50 BARBULLONJE</td>\n",
+       "      <td>groundWaterBody</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>inapplicable</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>ALGW_022</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ALGW_022</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>26 FUSHE KUQE</td>\n",
+       "      <td>groundWaterBody</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>inapplicable</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>ALGW_031</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ALGW_031</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>5 KRASTE -ELBASAN</td>\n",
+       "      <td>groundWaterBody</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>inapplicable</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>ALGW_034</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ALGW_034</td>\n",
+       "      <td>eionetGroundWaterBodyCode</td>\n",
+       "      <td>17 A VIDHAS</td>\n",
+       "      <td>groundWaterBody</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>inapplicable</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  countryCode thematicIdIdentifier thematicIdIdentifierScheme  \\\n",
+       "0          AL             ALGW_011  eionetGroundWaterBodyCode   \n",
+       "1          AL             ALGW_021  eionetGroundWaterBodyCode   \n",
+       "2          AL             ALGW_022  eionetGroundWaterBodyCode   \n",
+       "3          AL             ALGW_031  eionetGroundWaterBodyCode   \n",
+       "4          AL             ALGW_034  eionetGroundWaterBodyCode   \n",
+       "\n",
+       "  monitoringSiteIdentifier monitoringSiteIdentifierScheme monitoringSiteName  \\\n",
+       "0                      NaN                            NaN                NaN   \n",
+       "1                      NaN                            NaN                NaN   \n",
+       "2                      NaN                            NaN                NaN   \n",
+       "3                      NaN                            NaN                NaN   \n",
+       "4                      NaN                            NaN                NaN   \n",
+       "\n",
+       "  waterBodyIdentifier  waterBodyIdentifierScheme      waterBodyName  \\\n",
+       "0            ALGW_011  eionetGroundWaterBodyCode          1 -DOBRAC   \n",
+       "1            ALGW_021  eionetGroundWaterBodyCode     50 BARBULLONJE   \n",
+       "2            ALGW_022  eionetGroundWaterBodyCode      26 FUSHE KUQE   \n",
+       "3            ALGW_031  eionetGroundWaterBodyCode  5 KRASTE -ELBASAN   \n",
+       "4            ALGW_034  eionetGroundWaterBodyCode        17 A VIDHAS   \n",
+       "\n",
+       "  specialisedZoneType  ... surfaceWaterBodyTypeCode subUnitIdentifier  \\\n",
+       "0     groundWaterBody  ...                      NaN               NaN   \n",
+       "1     groundWaterBody  ...                      NaN               NaN   \n",
+       "2     groundWaterBody  ...                      NaN               NaN   \n",
+       "3     groundWaterBody  ...                      NaN               NaN   \n",
+       "4     groundWaterBody  ...                      NaN               NaN   \n",
+       "\n",
+       "  subUnitIdentifierScheme subUnitName rbdIdentifier rbdIdentifierScheme  \\\n",
+       "0                     NaN         NaN           NaN                 NaN   \n",
+       "1                     NaN         NaN           NaN                 NaN   \n",
+       "2                     NaN         NaN           NaN                 NaN   \n",
+       "3                     NaN         NaN           NaN                 NaN   \n",
+       "4                     NaN         NaN           NaN                 NaN   \n",
+       "\n",
+       "  rbdName confidentialityStatus lat lon  \n",
+       "0     NaN          inapplicable NaN NaN  \n",
+       "1     NaN          inapplicable NaN NaN  \n",
+       "2     NaN          inapplicable NaN NaN  \n",
+       "3     NaN          inapplicable NaN NaN  \n",
+       "4     NaN          inapplicable NaN NaN  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\\\\\")\n",
+    "cwd = os.path.abspath(os.curdir)\n",
+    "\n",
+    "waterbase_sites = pd.read_csv(cwd + '/data/input/mixed/df_1/Waterbase_v2021_1_S_WISE6_SpatialObject_DerivedData.csv')\n",
+    "waterbase_agg = pd.read_csv(cwd + '/data/input/mixed/df_1/Waterbase_v2021_1_T_WISE6_AggregatedData.csv')\n",
+    "#waterbase_agg_by_wat = pd.read_csv('Waterbase_v2021_1_T_WISE6_AggregatedDataByWaterBody.csv')\n",
+    "\n",
+    "#waterbase_agg\n",
+    "waterbase_sites.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "afbd57d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Hannah Russell\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:311: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  return func(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "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>monitoringSiteIdentifier</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>AL011</td>\n",
+       "      <td>41.6856</td>\n",
+       "      <td>20.3489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>AL013</td>\n",
+       "      <td>42.0420</td>\n",
+       "      <td>19.4910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>AL014</td>\n",
+       "      <td>42.0990</td>\n",
+       "      <td>19.5530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>AL015</td>\n",
+       "      <td>42.0540</td>\n",
+       "      <td>19.5290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>AL017</td>\n",
+       "      <td>41.3500</td>\n",
+       "      <td>19.4000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   monitoringSiteIdentifier      lat      lon\n",
+       "10                    AL011  41.6856  20.3489\n",
+       "11                    AL013  42.0420  19.4910\n",
+       "12                    AL014  42.0990  19.5530\n",
+       "13                    AL015  42.0540  19.5290\n",
+       "14                    AL017  41.3500  19.4000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "waterbase_sites_coord = waterbase_sites[['monitoringSiteIdentifier', 'lat', 'lon']] #these are the relevant columns\n",
+    "waterbase_sites_coord.dropna(inplace = True)\n",
+    "waterbase_sites_coord.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "df424c99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge coordinates with df\n",
+    "#waterbase_agg.observedPropertyDeterminandLabel.unique()\n",
+    "waterbase_coord = waterbase_agg.merge(waterbase_sites_coord, how = 'left', left_on = 'monitoringSiteIdentifier', right_on = 'monitoringSiteIdentifier')\n",
+    "\n",
+    "# separate df for \n",
+    "chlor = waterbase_coord[waterbase_coord['observedPropertyDeterminandLabel'].str.contains('Chlorophyll a') == True]\n",
+    "#chlor\n",
+    "\n",
+    "turbidity = waterbase_coord[waterbase_coord['observedPropertyDeterminandLabel'].str.contains('Turbidity') == True]\n",
+    "#turbidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2d41c3c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All Elbe sites 250+ km upstream\n",
+    "Elbe_sites = waterbase_sites[waterbase_sites['waterBodyName'].str.contains('elbe ', case = False) == True]\n",
+    "Elbe_sites = Elbe_sites[Elbe_sites['countryCode'].str.contains('DE') == True]\n",
+    "Elbe_sites = Elbe_sites[['monitoringSiteIdentifier', 'lat', 'lon']]\n",
+    "#Elbe_sites\n",
+    "\n",
+    "\n",
+    "\n",
+    "Maas_sites = waterbase_sites[waterbase_sites['waterBodyName'].str.contains('maas', case = False) == True]\n",
+    "Maas_sites = Maas_sites[Maas_sites['countryCode'].str.contains('NL') == True]\n",
+    "Maas_sites = Maas_sites[['monitoringSiteIdentifier', 'lat', 'lon']]\n",
+    "Maas_sites_list = Maas_sites['monitoringSiteIdentifier'].dropna().values.tolist()\n",
+    "#Maas_sites\n",
+    "\n",
+    "Ems_sites = waterbase_sites[waterbase_sites['waterBodyName'].str.contains('ems ', case = False) == True]\n",
+    "Ems_sites = Ems_sites[['monitoringSiteIdentifier', 'lat', 'lon']]\n",
+    "Ems_sites_list = Ems_sites['monitoringSiteIdentifier'].dropna().values.tolist()\n",
+    "#Ems_sites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "d382e12f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "7795cd08",
+   "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>monitoringSiteIdentifier</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>km_from_sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3881</th>\n",
+       "      <td>BEVL_VMM_154100</td>\n",
+       "      <td>51.35300</td>\n",
+       "      <td>4.24068</td>\n",
+       "      <td>54.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3882</th>\n",
+       "      <td>BEVL_VMM_162000</td>\n",
+       "      <td>51.14311</td>\n",
+       "      <td>4.33057</td>\n",
+       "      <td>86.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3883</th>\n",
+       "      <td>BEVL_VMM_164000</td>\n",
+       "      <td>51.04082</td>\n",
+       "      <td>4.12334</td>\n",
+       "      <td>116.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3884</th>\n",
+       "      <td>BEVL_VMM_168900</td>\n",
+       "      <td>51.00578</td>\n",
+       "      <td>3.80358</td>\n",
+       "      <td>147.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3885</th>\n",
+       "      <td>BEVL_VMM_172100</td>\n",
+       "      <td>51.00160</td>\n",
+       "      <td>3.72403</td>\n",
+       "      <td>162.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3886</th>\n",
+       "      <td>BEVL_VMM_173000</td>\n",
+       "      <td>50.89344</td>\n",
+       "      <td>3.68000</td>\n",
+       "      <td>178.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3887</th>\n",
+       "      <td>BEVL_VMM_174000</td>\n",
+       "      <td>50.87044</td>\n",
+       "      <td>3.62801</td>\n",
+       "      <td>183.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3888</th>\n",
+       "      <td>BEVL_VMM_179000</td>\n",
+       "      <td>50.70959</td>\n",
+       "      <td>3.36074</td>\n",
+       "      <td>212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3908</th>\n",
+       "      <td>BEVL_VMM_351000</td>\n",
+       "      <td>51.06586</td>\n",
+       "      <td>4.36510</td>\n",
+       "      <td>Bosbeek</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3961</th>\n",
+       "      <td>BEVL_VMM_C05-181</td>\n",
+       "      <td>51.02646</td>\n",
+       "      <td>4.35861</td>\n",
+       "      <td>Zeekanal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3965</th>\n",
+       "      <td>BEVL_VMM_C05-42</td>\n",
+       "      <td>51.14007</td>\n",
+       "      <td>4.32743</td>\n",
+       "      <td>86.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3966</th>\n",
+       "      <td>BEVL_VMM_C05-58</td>\n",
+       "      <td>50.95630</td>\n",
+       "      <td>3.65863</td>\n",
+       "      <td>170.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3977</th>\n",
+       "      <td>BEVL_VMM_C08-43</td>\n",
+       "      <td>51.29667</td>\n",
+       "      <td>4.29795</td>\n",
+       "      <td>62.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3979</th>\n",
+       "      <td>BEVL_VMM_C08-55</td>\n",
+       "      <td>50.71769</td>\n",
+       "      <td>3.36461</td>\n",
+       "      <td>211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49218</th>\n",
+       "      <td>NL25_830002</td>\n",
+       "      <td>51.48858</td>\n",
+       "      <td>4.26269</td>\n",
+       "      <td>binnenschelde</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49531</th>\n",
+       "      <td>NL89_SCHAARVODDL</td>\n",
+       "      <td>51.35029</td>\n",
+       "      <td>4.25066</td>\n",
+       "      <td>55.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49536</th>\n",
+       "      <td>NL89_VLISSGBISSVH</td>\n",
+       "      <td>51.41199</td>\n",
+       "      <td>3.56562</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49537</th>\n",
+       "      <td>NL89_WISSKKE</td>\n",
+       "      <td>51.60158</td>\n",
+       "      <td>3.72057</td>\n",
+       "      <td>Eastern Schelde</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      monitoringSiteIdentifier       lat      lon      km_from_sea\n",
+       "3881           BEVL_VMM_154100  51.35300  4.24068             54.5\n",
+       "3882           BEVL_VMM_162000  51.14311  4.33057             86.5\n",
+       "3883           BEVL_VMM_164000  51.04082  4.12334            116.8\n",
+       "3884           BEVL_VMM_168900  51.00578  3.80358            147.6\n",
+       "3885           BEVL_VMM_172100  51.00160  3.72403            162.5\n",
+       "3886           BEVL_VMM_173000  50.89344  3.68000            178.9\n",
+       "3887           BEVL_VMM_174000  50.87044  3.62801            183.8\n",
+       "3888           BEVL_VMM_179000  50.70959  3.36074              212\n",
+       "3908           BEVL_VMM_351000  51.06586  4.36510          Bosbeek\n",
+       "3961          BEVL_VMM_C05-181  51.02646  4.35861         Zeekanal\n",
+       "3965           BEVL_VMM_C05-42  51.14007  4.32743             86.8\n",
+       "3966           BEVL_VMM_C05-58  50.95630  3.65863            170.3\n",
+       "3977           BEVL_VMM_C08-43  51.29667  4.29795             62.7\n",
+       "3979           BEVL_VMM_C08-55  50.71769  3.36461              211\n",
+       "49218              NL25_830002  51.48858  4.26269    binnenschelde\n",
+       "49531         NL89_SCHAARVODDL  51.35029  4.25066             55.2\n",
+       "49536        NL89_VLISSGBISSVH  51.41199  3.56562              0.5\n",
+       "49537             NL89_WISSKKE  51.60158  3.72057  Eastern Schelde"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Schelde_sites = waterbase_sites[waterbase_sites['waterBodyName'].str.contains('scheld', case = False) == True]\n",
+    "Schelde_sites = Schelde_sites[['monitoringSiteIdentifier', 'lat', 'lon']]\n",
+    "Schelde_sites = Schelde_sites.dropna()\n",
+    "Schelde_sites_list = Schelde_sites['monitoringSiteIdentifier'].values.tolist()\n",
+    "#Schelde_sites\n",
+    "\n",
+    "Schelde_sites_km_from_North_Sea = ['54.5', '86.5', '116.8', '147.6', '162.5', '178.9', '183.8', '212', 'Bosbeek', 'Zeekanal', '86.8', '170.3', '62.7', '211', 'binnenschelde', '55.2', '0.5', 'Eastern Schelde']\n",
+    "\n",
+    "Schelde_sites['km_from_sea'] = Schelde_sites_km_from_North_Sea\n",
+    "Schelde_sites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e3f206da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Chlorophyll ug/L')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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 Schelde chlorophyll values\n",
+    "Schelde_chlor = chlor[chlor['monitoringSiteIdentifier'].str.contains(\"|\".join(Schelde_sites_list))]\n",
+    "#Schelde_chlor\n",
+    "\n",
+    "sch_lat = Schelde_chlor['lat']\n",
+    "sch_value = Schelde_chlor['resultMeanValue']\n",
+    "\n",
+    "# plot of all cholorphyll values from all years on one plot\n",
+    "plt.scatter(sch_lat, sch_value)\n",
+    "plt.title('Schelde-- All Years')\n",
+    "plt.xlabel('lat')\n",
+    "plt.ylabel('Chlorophyll ug/L')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "89c75c78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Turbidity')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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 Schelde turbidity values (apparently there are none)\n",
+    "Schelde_turb = turbidity[turbidity['monitoringSiteIdentifier'].str.contains(\"|\".join(Schelde_sites_list))]\n",
+    "Schelde_turb\n",
+    "\n",
+    "sch_t_lat = Schelde_turb['lat']\n",
+    "sch_t_value = Schelde_turb['resultMeanValue']\n",
+    "\n",
+    "# plot of all turbidity values from all years on one plot\n",
+    "plt.scatter(sch_t_lat, sch_t_value)\n",
+    "plt.title('Schelde-- All Years')\n",
+    "plt.xlabel('lat')\n",
+    "plt.ylabel('Turbidity')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "01069ee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Chlorophyll ug/L')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "Schelde_c_avg = Schelde_chlor.groupby('monitoringSiteIdentifier').median()\n",
+    "Schelde_c_avg\n",
+    "# NL89_SCHAARVODDL is 55.43 km from the Sea\n",
+    "\n",
+    "# Chlorophyll values from all years\n",
+    "lat_a = Schelde_c_avg['lat']\n",
+    "Schelde_chlor_a = Schelde_c_avg['resultMeanValue']\n",
+    "plt.plot(lat_a, Schelde_chlor_a)\n",
+    "plt.title('Schelde')\n",
+    "plt.xlabel('lat')\n",
+    "plt.ylabel('Chlorophyll ug/L')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cf6b830",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract Maas sites from chlor\n",
+    "Maas_chlor = chlor_cd[chlor_cd['monitoringSiteIdentifier'].str.contains(\"|\".join(Maas_sites_list))]\n",
+    "Maas_chlor\n",
+    "\n",
+    "Maas_avg = Maas_chlor.groupby('monitoringSiteIdentifier').median()\n",
+    "Maas_avg\n",
+    "# NL89_SCHAARVODDL is 55.43 km from the Sea\n",
+    "\n",
+    "maas_lat = Maas_chlor['lat']\n",
+    "maas_value = Maas_chlor['resultMeanValue']\n",
+    "\n",
+    "# plot of all cholorphyll values from all years on one plot\n",
+    "plt.scatter(maas_lat, maas_value)\n",
+    "plt.title('Maas-- All Years')\n",
+    "plt.xlabel('lat')\n",
+    "plt.ylabel('Chlorophyll ug/L')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "189866c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Maas_c_avg = Maas_chlor.groupby('monitoringSiteIdentifier').median()\n",
+    "Maas_c_avg\n",
+    "\n",
+    "# Chlorophyll values from all years\n",
+    "Maas_lat_a = Maas_c_avg['lat']\n",
+    "Maas_chlor_a = Maas_c_avg['resultMeanValue']\n",
+    "plt.plot(Maas_lat_a, Maas_chlor_a)\n",
+    "plt.title('Maas')\n",
+    "plt.xlabel('lat')\n",
+    "plt.ylabel('Chlorophyll ug/L')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b5b014b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01bd2618",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1771f99",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42404b8c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fdbd01e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ba09097",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74b79b27",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef7140eb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a83d37d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a038a72c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9523149",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61571384",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract Ems sites from chlor\n",
+    "Ems_chlor = chlor_cd[chlor_cd['monitoringSiteIdentifier'].str.contains(\"|\".join(Ems_sites_list))]\n",
+    "Ems_chlor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78ad3173",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3f59cbb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/Elbe Chlorophyll.ipynb b/ipynb/Elbe Chlorophyll.ipynb
deleted file mode 100644
index 5bfe04a..0000000
--- a/ipynb/Elbe Chlorophyll.ipynb	
+++ /dev/null
@@ -1,585 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3d380a50",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import os\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c79ca53",
-   "metadata": {},
-   "source": [
-    "## pre-processing elbe Chlorophyll data\n",
-    "The general aim is to create concateable (non-2d i guess) data frames of all estuaries with unified column names "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "bdd39076",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>'Gew?sser'</th>\n",
-       "      <th>'Wasserk?rper'</th>\n",
-       "      <th>'Messstelle'</th>\n",
-       "      <th>'Stromkilometer'</th>\n",
-       "      <th>'Parameter'</th>\n",
-       "      <th>'Messwert'</th>\n",
-       "      <th>'Einheit'</th>\n",
-       "      <th>'Messwerttyp'</th>\n",
-       "      <th>'Erfassungsart'</th>\n",
-       "      <th>'Messwertart'</th>\n",
-       "      <th>'Messvorgang'</th>\n",
-       "      <th>'Datum'</th>\n",
-       "      <th>'Bezugsjahr'</th>\n",
-       "      <th>'Zeit'</th>\n",
-       "      <th>'Datum bis'</th>\n",
-       "      <th>'Zeit bis'</th>\n",
-       "      <th>'Status'</th>\n",
-       "      <th>'Analysemethode'</th>\n",
-       "      <th>'Bemerkung (Datenausgabe)'</th>\n",
-       "      <th>'zus?tzliche Informationen'</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589,0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>1,5</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>15.01.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:16</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589,0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>&lt; 2,0</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'unter Bestimmungsgrenze'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>11.02.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>13:09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589,0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>11,8</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>17.03.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:40</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589,0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>36,3</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>14.04.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:41</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589,0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>76,6</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>27.05.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:58</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  'Gew?sser' 'Wasserk?rper'                          'Messstelle'  \\\n",
-       "0     'Elbe'   'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'   \n",
-       "1     'Elbe'   'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'   \n",
-       "2     'Elbe'   'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'   \n",
-       "3     'Elbe'   'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'   \n",
-       "4     'Elbe'   'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'   \n",
-       "\n",
-       "  'Stromkilometer'      'Parameter' 'Messwert' 'Einheit'  \\\n",
-       "0            589,0  'Chlorophyll-A'        1,5    'µg/l'   \n",
-       "1            589,0  'Chlorophyll-A'      < 2,0    'µg/l'   \n",
-       "2            589,0  'Chlorophyll-A'       11,8    'µg/l'   \n",
-       "3            589,0  'Chlorophyll-A'       36,3    'µg/l'   \n",
-       "4            589,0  'Chlorophyll-A'       76,6    'µg/l'   \n",
-       "\n",
-       "                'Messwerttyp'         'Erfassungsart'  'Messwertart'  \\\n",
-       "0  'quantitativ nachgewiesen'  'Wasser - Gesamtprobe'  'Einzelprobe'   \n",
-       "1   'unter Bestimmungsgrenze'  'Wasser - Gesamtprobe'  'Einzelprobe'   \n",
-       "2  'quantitativ nachgewiesen'  'Wasser - Gesamtprobe'  'Einzelprobe'   \n",
-       "3  'quantitativ nachgewiesen'  'Wasser - Gesamtprobe'  'Einzelprobe'   \n",
-       "4  'quantitativ nachgewiesen'  'Wasser - Gesamtprobe'  'Einzelprobe'   \n",
-       "\n",
-       "               'Messvorgang'     'Datum'  'Bezugsjahr' 'Zeit'  'Datum bis'  \\\n",
-       "0  'Längsprofile (Tideelbe)'  15.01.1982           NaN  14:16          NaN   \n",
-       "1  'Längsprofile (Tideelbe)'  11.02.1982           NaN  13:09          NaN   \n",
-       "2  'Längsprofile (Tideelbe)'  17.03.1982           NaN  14:40          NaN   \n",
-       "3  'Längsprofile (Tideelbe)'  14.04.1982           NaN  14:41          NaN   \n",
-       "4  'Längsprofile (Tideelbe)'  27.05.1982           NaN  14:58          NaN   \n",
-       "\n",
-       "   'Zeit bis'       'Status' 'Analysemethode'  'Bemerkung (Datenausgabe)'  \\\n",
-       "0         NaN  'freigegeben'              '-'                         NaN   \n",
-       "1         NaN  'freigegeben'              '-'                         NaN   \n",
-       "2         NaN  'freigegeben'              '-'                         NaN   \n",
-       "3         NaN  'freigegeben'              '-'                         NaN   \n",
-       "4         NaN  'freigegeben'              '-'                         NaN   \n",
-       "\n",
-       "   'zus?tzliche Informationen'  \n",
-       "0                          NaN  \n",
-       "1                          NaN  \n",
-       "2                          NaN  \n",
-       "3                          NaN  \n",
-       "4                          NaN  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cwd = os.path.abspath(os.curdir)\n",
-    "elbe_clorophyll_df_1 = glob.glob(os.path.join(cwd, 'data', 'input', 'elbe', 'chlorophyll','df_1', '*.csv'))\n",
-    "elbe_clorophyll_df_1 = [pd.read_csv(file, sep = ';', encoding= 'unicode_escape') for file in elbe_clorophyll_df_1]\n",
-    "elbe_clorophyll_df_1 = pd.concat(elbe_clorophyll_df_1, ignore_index=True)\n",
-    "#elbe_clorophyll_df_1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "c68f4427",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\laurins\\AppData\\Local\\Temp\\ipykernel_14088\\3108878876.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
-      "  elbe_clorophyll_df_1.columns = elbe_clorophyll_df_1.columns.str.replace(\"['']\", \"\")\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th>Gew?sser</th>\n",
-       "      <th>Wasserk?rper</th>\n",
-       "      <th>Messstelle</th>\n",
-       "      <th>Stromkilometer</th>\n",
-       "      <th>Parameter</th>\n",
-       "      <th>Messwert</th>\n",
-       "      <th>Einheit</th>\n",
-       "      <th>Messwerttyp</th>\n",
-       "      <th>Erfassungsart</th>\n",
-       "      <th>Messwertart</th>\n",
-       "      <th>Messvorgang</th>\n",
-       "      <th>Datum</th>\n",
-       "      <th>Bezugsjahr</th>\n",
-       "      <th>Zeit</th>\n",
-       "      <th>Datum bis</th>\n",
-       "      <th>Zeit bis</th>\n",
-       "      <th>Status</th>\n",
-       "      <th>Analysemethode</th>\n",
-       "      <th>Bemerkung (Datenausgabe)</th>\n",
-       "      <th>zus?tzliche Informationen</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589.0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>15.01.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:16</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589.0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>11.8</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>17.03.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:40</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589.0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>36.3</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>14.04.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:41</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589.0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>76.6</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>27.05.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14:58</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>'Elbe'</td>\n",
-       "      <td>'Elbe (Ost)'</td>\n",
-       "      <td>'oberhalb Elbstorf - Strom-km 589,0'</td>\n",
-       "      <td>589.0</td>\n",
-       "      <td>'Chlorophyll-A'</td>\n",
-       "      <td>185.7</td>\n",
-       "      <td>'µg/l'</td>\n",
-       "      <td>'quantitativ nachgewiesen'</td>\n",
-       "      <td>'Wasser - Gesamtprobe'</td>\n",
-       "      <td>'Einzelprobe'</td>\n",
-       "      <td>'Längsprofile (Tideelbe)'</td>\n",
-       "      <td>24.06.1982</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>13:24</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>'freigegeben'</td>\n",
-       "      <td>'-'</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Gew?sser  Wasserk?rper                            Messstelle Stromkilometer  \\\n",
-       "0   'Elbe'  'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'          589.0   \n",
-       "2   'Elbe'  'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'          589.0   \n",
-       "3   'Elbe'  'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'          589.0   \n",
-       "4   'Elbe'  'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'          589.0   \n",
-       "5   'Elbe'  'Elbe (Ost)'  'oberhalb Elbstorf - Strom-km 589,0'          589.0   \n",
-       "\n",
-       "         Parameter Messwert Einheit                 Messwerttyp  \\\n",
-       "0  'Chlorophyll-A'      1.5  'µg/l'  'quantitativ nachgewiesen'   \n",
-       "2  'Chlorophyll-A'     11.8  'µg/l'  'quantitativ nachgewiesen'   \n",
-       "3  'Chlorophyll-A'     36.3  'µg/l'  'quantitativ nachgewiesen'   \n",
-       "4  'Chlorophyll-A'     76.6  'µg/l'  'quantitativ nachgewiesen'   \n",
-       "5  'Chlorophyll-A'    185.7  'µg/l'  'quantitativ nachgewiesen'   \n",
-       "\n",
-       "            Erfassungsart    Messwertart                Messvorgang  \\\n",
-       "0  'Wasser - Gesamtprobe'  'Einzelprobe'  'Längsprofile (Tideelbe)'   \n",
-       "2  'Wasser - Gesamtprobe'  'Einzelprobe'  'Längsprofile (Tideelbe)'   \n",
-       "3  'Wasser - Gesamtprobe'  'Einzelprobe'  'Längsprofile (Tideelbe)'   \n",
-       "4  'Wasser - Gesamtprobe'  'Einzelprobe'  'Längsprofile (Tideelbe)'   \n",
-       "5  'Wasser - Gesamtprobe'  'Einzelprobe'  'Längsprofile (Tideelbe)'   \n",
-       "\n",
-       "        Datum  Bezugsjahr   Zeit  Datum bis  Zeit bis         Status  \\\n",
-       "0  15.01.1982         NaN  14:16        NaN       NaN  'freigegeben'   \n",
-       "2  17.03.1982         NaN  14:40        NaN       NaN  'freigegeben'   \n",
-       "3  14.04.1982         NaN  14:41        NaN       NaN  'freigegeben'   \n",
-       "4  27.05.1982         NaN  14:58        NaN       NaN  'freigegeben'   \n",
-       "5  24.06.1982         NaN  13:24        NaN       NaN  'freigegeben'   \n",
-       "\n",
-       "  Analysemethode  Bemerkung (Datenausgabe)  zus?tzliche Informationen  \n",
-       "0            '-'                       NaN                        NaN  \n",
-       "2            '-'                       NaN                        NaN  \n",
-       "3            '-'                       NaN                        NaN  \n",
-       "4            '-'                       NaN                        NaN  \n",
-       "5            '-'                       NaN                        NaN  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "elbe_clorophyll_df_1.columns = elbe_clorophyll_df_1.columns.str.replace(\"['']\", \"\")\n",
-    "elbe_clorophyll_df_1.drop(elbe_clorophyll_df_1[elbe_clorophyll_df_1.Messwert.str.contains('[<]', na=True)].index, inplace=True) #some columns contained string <2.0, so I dropped them for now but probably not an ideal solution\n",
-    "elbe_clorophyll_df_1['Stromkilometer'] = elbe_clorophyll_df_1['Stromkilometer'].str.replace(\",\", \".\")\n",
-    "elbe_clorophyll_df_1['Messwert'] = elbe_clorophyll_df_1['Messwert'].str.replace(\",\", \".\")\n",
-    "#elbe_clorophyll_df_1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "f98cec41",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Chlorophyll ug/L')"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Stromkilometer = elbe_clorophyll_df_1['Stromkilometer'].astype(float)\n",
-    "Messwert = elbe_clorophyll_df_1['Messwert'].astype(float)\n",
-    "\n",
-    "# plot of all cholorphyll values from all years on one plot\n",
-    "plt.scatter(Stromkilometer, Messwert)\n",
-    "plt.gca().invert_xaxis()\n",
-    "plt.title('Elbe-- All Years')\n",
-    "plt.xlabel('Kilometer')\n",
-    "plt.ylabel('Chlorophyll ug/L')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "6b548829",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Stromkilometer'>"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "chlor_avg = elbe_clorophyll_df_1[['Stromkilometer', 'Messwert']]\n",
-    "chlor_avg = chlor_avg.groupby('Stromkilometer').median() #for some reason this is invalid even though median() works... not sure yet what's wrong here\n",
-    "chlor_avg.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5b7a017",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.8.8"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "ae321efca05d5287feb4e18c73c84aa717d56d176335c74bbc73c515f0d20084"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/ipynb/Ems_SSC.ipynb b/ipynb/Ems_SSC.ipynb
new file mode 100644
index 0000000..0b66cbd
--- /dev/null
+++ b/ipynb/Ems_SSC.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d4cd2d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f8f169",
+   "metadata": {},
+   "source": [
+    "## pre-processing ems data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0e92e1d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#os.getcwd()\n",
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n",
+    "\n",
+    "cwd = os.path.abspath(os.curdir)\n",
+    "ems_EFW_df_1 = pd.read_csv(cwd + '\\data\\input\\ems\\df_1\\ems_EFW.csv') #includes SSC and ignition loss, UTM not coordinates\n",
+    "ems_EMD_df_1 = pd.read_csv(cwd + '\\data\\input\\ems\\df_1\\ems_EMD.csv') #includes SSC, UTM not coordinates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "994d56f1",
+   "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>sample#</th>\n",
+       "      <th>CET</th>\n",
+       "      <th>UTM East</th>\n",
+       "      <th>UTM North</th>\n",
+       "      <th>waterdepth [m]</th>\n",
+       "      <th>SSC [mg/l]</th>\n",
+       "      <th>ignition loss [%]</th>\n",
+       "      <th>vert. Profile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>E001</td>\n",
+       "      <td>28-8-2018 07:19</td>\n",
+       "      <td>372825</td>\n",
+       "      <td>5910331</td>\n",
+       "      <td>298</td>\n",
+       "      <td>1202</td>\n",
+       "      <td>143</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>E002</td>\n",
+       "      <td>28-8-2018 07:23</td>\n",
+       "      <td>372826</td>\n",
+       "      <td>5910330</td>\n",
+       "      <td>590</td>\n",
+       "      <td>3183</td>\n",
+       "      <td>145</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>E003</td>\n",
+       "      <td>28-8-2018 07:31</td>\n",
+       "      <td>372826</td>\n",
+       "      <td>5910329</td>\n",
+       "      <td>297</td>\n",
+       "      <td>1603</td>\n",
+       "      <td>166</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>E004</td>\n",
+       "      <td>28-8-2018 07:34</td>\n",
+       "      <td>372829</td>\n",
+       "      <td>5910330</td>\n",
+       "      <td>595</td>\n",
+       "      <td>2843</td>\n",
+       "      <td>139</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>E005</td>\n",
+       "      <td>28-8-2018 07:45</td>\n",
+       "      <td>372829</td>\n",
+       "      <td>5910330</td>\n",
+       "      <td>302</td>\n",
+       "      <td>921</td>\n",
+       "      <td>186</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>SB_EFW_097</td>\n",
+       "      <td>24-01-2019 21:17:25</td>\n",
+       "      <td>372898</td>\n",
+       "      <td>5910337</td>\n",
+       "      <td>554</td>\n",
+       "      <td>5447</td>\n",
+       "      <td>123</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>SB_EFW_098</td>\n",
+       "      <td>24-01-2019 21:32:19</td>\n",
+       "      <td>372902</td>\n",
+       "      <td>5910332</td>\n",
+       "      <td>296</td>\n",
+       "      <td>5044</td>\n",
+       "      <td>127</td>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>SB_EFW_099</td>\n",
+       "      <td>24-01-2019 21:33:21</td>\n",
+       "      <td>372904</td>\n",
+       "      <td>5910331</td>\n",
+       "      <td>579</td>\n",
+       "      <td>3759</td>\n",
+       "      <td>131</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>SB_EFW_100</td>\n",
+       "      <td>24-01-2019 21:46:59</td>\n",
+       "      <td>372949</td>\n",
+       "      <td>5910340</td>\n",
+       "      <td>297</td>\n",
+       "      <td>4791</td>\n",
+       "      <td>123</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>SB_EFW_101</td>\n",
+       "      <td>24-01-2019 21:48:00</td>\n",
+       "      <td>372956</td>\n",
+       "      <td>5910343</td>\n",
+       "      <td>644</td>\n",
+       "      <td>6399</td>\n",
+       "      <td>121</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>205 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  sample#                  CET  UTM East  UTM North  \\\n",
+       "0                    E001      28-8-2018 07:19    372825    5910331   \n",
+       "1                    E002      28-8-2018 07:23    372826    5910330   \n",
+       "2                    E003      28-8-2018 07:31    372826    5910329   \n",
+       "3                    E004      28-8-2018 07:34    372829    5910330   \n",
+       "4                    E005      28-8-2018 07:45    372829    5910330   \n",
+       "..                    ...                  ...       ...        ...   \n",
+       "200            SB_EFW_097  24-01-2019 21:17:25    372898    5910337   \n",
+       "201            SB_EFW_098  24-01-2019 21:32:19    372902    5910332   \n",
+       "202            SB_EFW_099  24-01-2019 21:33:21    372904    5910331   \n",
+       "203            SB_EFW_100  24-01-2019 21:46:59    372949    5910340   \n",
+       "204            SB_EFW_101  24-01-2019 21:48:00    372956    5910343   \n",
+       "\n",
+       "     waterdepth [m]  SSC [mg/l]  ignition loss [%]  vert. Profile  \n",
+       "0               298        1202                143            1.0  \n",
+       "1               590        3183                145            NaN  \n",
+       "2               297        1603                166            2.0  \n",
+       "3               595        2843                139            NaN  \n",
+       "4               302         921                186            3.0  \n",
+       "..              ...         ...                ...            ...  \n",
+       "200             554        5447                123            NaN  \n",
+       "201             296        5044                127           37.0  \n",
+       "202             579        3759                131            NaN  \n",
+       "203             297        4791                123           38.0  \n",
+       "204             644        6399                121            NaN  \n",
+       "\n",
+       "[205 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ems_EFW_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "23a48970",
+   "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>sample#</th>\n",
+       "      <th>CET</th>\n",
+       "      <th>UTM East (avg)</th>\n",
+       "      <th>UTM North (avg)</th>\n",
+       "      <th>waterdepth [m]</th>\n",
+       "      <th>SSC [mg/l]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>28-08-2018 07:26:58</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>60</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>28-08-2018 07:29:00</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>25</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>28-08-2018 07:39:59</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>64</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>28-08-2018 07:42:00</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>25</td>\n",
+       "      <td>250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>28-08-2018 07:55:01</td>\n",
+       "      <td>376996</td>\n",
+       "      <td>5910984</td>\n",
+       "      <td>60</td>\n",
+       "      <td>240</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sample#                  CET  UTM East (avg)  UTM North (avg)  \\\n",
+       "0        1  28-08-2018 07:26:58          376996          5910984   \n",
+       "1        2  28-08-2018 07:29:00          376996          5910984   \n",
+       "2        3  28-08-2018 07:39:59          376996          5910984   \n",
+       "3        4  28-08-2018 07:42:00          376996          5910984   \n",
+       "4        5  28-08-2018 07:55:01          376996          5910984   \n",
+       "\n",
+       "   waterdepth [m]  SSC [mg/l]  \n",
+       "0              60         320  \n",
+       "1              25         230  \n",
+       "2              64         300  \n",
+       "3              25         250  \n",
+       "4              60         240  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ems_EMD_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fd082c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ipynb/Schelde_Turbidity.ipynb b/ipynb/Schelde_Turbidity.ipynb
index 3d7c1ca..f92357c 100644
--- a/ipynb/Schelde_Turbidity.ipynb
+++ b/ipynb/Schelde_Turbidity.ipynb
@@ -255,6 +255,7 @@
     "cwd = os.path.abspath(os.curdir)\n",
     "Schelde_turbidity_df_1 = pd.read_csv(cwd + \"/data/input/schelde/turbidity/df_1/Turbidity Scheldt.csv\")\n",
     "\n",
+    "\n",
     "Schelde_turbidity_df_1.head()"
    ]
   },
diff --git a/ipynb/Schelde_chlorophyll.ipynb b/ipynb/Schelde_chlorophyll.ipynb
new file mode 100644
index 0000000..8f26ca0
--- /dev/null
+++ b/ipynb/Schelde_chlorophyll.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e2b1d348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69472657",
+   "metadata": {},
+   "source": [
+    "## pre-processing schelde chlorophyll data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "527f9be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HANNAH~1\\AppData\\Local\\Temp/ipykernel_27732/1206153600.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  Schelde_chlorophyll_df_1.columns = Schelde_chlorophyll_df_1.columns.str.replace(\"[;]\", \"\")\n"
+     ]
+    },
+    {
+     "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>Rid</th>\n",
+       "      <th>Compartment</th>\n",
+       "      <th>Parameter</th>\n",
+       "      <th>Substance</th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Datetime</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Sign</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Unit</th>\n",
+       "      <th>Column2</th>\n",
+       "      <th>Column3</th>\n",
+       "      <th>Column1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4190</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27/04/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>5.015475</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4200</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30/04/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>9.780414</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4214</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>26/02/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>0.1960663</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4228</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12/07/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>1.652203</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4239</td>\n",
+       "      <td>water column</td>\n",
+       "      <td>Concentration</td>\n",
+       "      <td>chlorophyll-a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>02/06/1999</td>\n",
+       "      <td>51.43333</td>\n",
+       "      <td>2.80833</td>\n",
+       "      <td>=</td>\n",
+       "      <td>0.6613134</td>\n",
+       "      <td>ug/l</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Rid   Compartment      Parameter      Substance  Taxon    Datetime  \\\n",
+       "0  4190  water column  Concentration  chlorophyll-a    NaN  27/04/1999   \n",
+       "1  4200  water column  Concentration  chlorophyll-a    NaN  30/04/1999   \n",
+       "2  4214  water column  Concentration  chlorophyll-a    NaN  26/02/1999   \n",
+       "3  4228  water column  Concentration  chlorophyll-a    NaN  12/07/1999   \n",
+       "4  4239  water column  Concentration  chlorophyll-a    NaN  02/06/1999   \n",
+       "\n",
+       "   Latitude  Longitude Sign      Value  Unit  Column2  Column3  Column1  \n",
+       "0  51.43333    2.80833    =   5.015475  ug/l      NaN      NaN      NaN  \n",
+       "1  51.43333    2.80833    =   9.780414  ug/l      NaN      NaN      NaN  \n",
+       "2  51.43333    2.80833    =  0.1960663  ug/l      NaN      NaN      NaN  \n",
+       "3  51.43333    2.80833    =   1.652203  ug/l      NaN      NaN      NaN  \n",
+       "4  51.43333    2.80833    =  0.6613134  ug/l      NaN      NaN      NaN  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#os.getcwd()\n",
+    "os.chdir(\"C:\\\\Users\\\\Hannah Russell\\\\north_sea_estuaries_visualisations\")\n",
+    "cwd = os.path.abspath(os.curdir)\n",
+    "\n",
+    "Schelde_chlorophyll_df_1 = pd.read_csv(cwd + \"\\data\\input\\schelde\\chlorophyll\\df_1\\Schelde_chlorophyll.csv\")\n",
+    "# the txt file in the folder is the original dataset; \n",
+    "# I manually removed the last two columns that contained websites because they were messing with the import\n",
+    "Schelde_chlorophyll_df_1.columns = Schelde_chlorophyll_df_1.columns.str.replace(\"[;]\", \"\")\n",
+    "Schelde_chlorophyll_df_1.drop(Schelde_chlorophyll_df_1[Schelde_chlorophyll_df_1['Value'] == .00000].index, inplace = True)\n",
+    "Schelde_chlorophyll_df_1['Value'] = Schelde_chlorophyll_df_1['Value'].str.replace(\",\", \".\")\n",
+    "\n",
+    "Schelde_chlorophyll_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "90e624ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "latitude = Schelde_chlorophyll_df_1['Latitude']\n",
+    "longitude = Schelde_chlorophyll_df_1['Longitude']\n",
+    "chlor = Schelde_chlorophyll_df_1['Value']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4c51345c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'chlorophyll')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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W4CoMWAAPPbWVc7/9cFMErrgViQtV63q67E0LlrXyTjTep9UkqjWTRWHAKrU9a5ImlMRdz4tuX8W8a+8vOa1VT18/8669n0MuvSfR/s0qa9OCZa28E5EHrSaxKab2ELd9PJrhDzfpE3Ox69Y/MEj3natjr4ff+HbL2rRgWSvvRORBq0nsHzOOKm57oVzNoJgs33jzaz5JlLpuQ8PGVT9+LPI9v/HtVo+HqWrKWnknoqJBS9LLkl6K+HpZ0oSd6qiR4pqdosZXtUwSmwYGmXnpPRx22b1c0bM29py5mkExWb3xFtZ8koi6noVe2B69DJvf+Har9GGq3tqnxozPi9nu6q9oIoaZ7VWvgrjSkiQH5DqQp05pYdvO3U/7w2bcsnw9AFd3zR513qiaQZw03Xiv6FnLbSs2MGxGi8Q5Jxw45rNBeZ8vp2tuB73PbR05fzn2j0n2SOuNupa6588a9TsL6R6sHvdf7Umn6VGqpjW92Fe9CplFteiIL9Xs1DW3g4cuPYVnrj2NPw9FL3Z424oNY7aVM4N7Wm68V/Ss5Zbl60cCSi4oR9UmxxNoe/r6uWtlf9GAFTc7hs8qslvWBqu/OBhde47b7uqvVMr7SoIl6qOm+Dbg0KqXqAnUKl26VLNTfqpu3K026iY8SbArwZNkmm68UcE3t72wthVX8yk0OW8GjFK1s2KzY/jA5NGyNFjda8npV7SmZWaHmNmh4b+FXx6wYtSqI75Y/8B4+m1yigWstD4hlzMb/czXJrvhvGaP3c9wxWpnHe1tLDrrmHFdC0+FTzevJadfosHFku4EbgZ+ZmbR7U4NEi70eBNwFEHt72Nm9rCkzwCfBl4F7jGzz0ccezNwOrDZzI6qVplq1RFfrH9gPP02STx06SlVP2c1lLPu1/KnX0h0zvwmoGK1s9+/+Gd6n9saG7Tiatq9z23lrpX9PmA5xQr7MlskzjwuOzXFiSBpyvsNwLnAbyVdK+mIGpapXN8gCKZHAMcAT0g6GXg/cLSZHQl8LebY7wHvqXaBapUxVax/IGlAjLqpZ3FG83NOODDx9qSJFPn/P8WyB4v1n0F8Tfu2FRsmZCp8lmqXPX393P7IhlF9pbc/siHVZZ5oEgUtM/s3MzsXOBZ4Fvi5pF9J+qikht3ZJO0NvAP4TljOnWY2AHwSuNbMdoTbN0cdb2a/AKo+1UMtmxjyky0euvSUkSfApAEx6qaexRnNr+6azXknHjQShFskzjvxoMjswaSrLuc3I+Y/IMSJ61eLe4CIC55pysistqwNtL7qx48xNDz6/6nYmDxXf4kHF0t6LXA+8HGgj6CGcyzw85qULJlDgS3AdyX1SbpJ0jTgTcDbJa2Q9O+S3lLJD5F0gaReSb1btmwpuX8tM6bKGaeVVNfcDhaddcyo8o63z6aeru6azVPXnMqz157GU9ecGhmwIL5WVqiwGTH3gBAnLgjFPUDEBc9m7uTP2kDruLF3cdtd/SXt01oCHAH8K/A+M3s+fOt2Sb21KlwCkwkC52fMbIWkbwCXhtv3BU4E3gLcIelQs/GNtjCzG4EbATo7OxOdoxYZU+WM04or5A9XrI+8uUeVN+0ThyYtX+7zlhpzVfhe7vxx4oJQXN/jmcd1jOrTym1v5k5+H2jtqi3pLO/fNLPIOX7MrLOK5SnXRmCjma0IX99JELQ2AkvCIPVrSbuA/QhqZZkV99S68O7HWLRsHf0Dg7RIRbMH4zIFCwfqnnjovvxm/YupTRood1jB1V2zubprNjMTTuNUeP4ocTW4YinvnQdPT/WDQLVlLYW8va2VgYgxWWnu351oEgUtM7tf0tuAmfnHmNkPalSuRMzs95I2SJplZuuAdwKPA08BpwAPSnoTMAX4YwOLWhVxT6cDg0Mjf2jjWS8qN1A3Z9gsclb3XLNOGm6yxZqdospXqtaU5Pw5xWbfyImraWdpzFI1dM+fxSWLVzOc97TUMkmprV0uXHAk3YtXM5RX3rT37040SZsH/xU4DFgF5P6SDWho0Ap9BrhV0hTgaeCjwDbgZkmPAjuBj5iZSdofuMnMTgWQdBtwErCfpI3AlWb2nUZ8iCSSDpItpjWiFzMuoSBKWpp1yml26unrp/vO1WM62MdzfgFPXXNqyePT3rRaL73PbR0VsACGd1nRIQON5APD0y9p82An8Obx9gnVkpmtIihfofMi9t0EnJr3+pzalaz6ovpKyjVtj7HNHOXUztLSrFNOs1NURlg1z1+oMEjmljLJmUg3xHJmLkmLiVYbzpqk2YOPAm+oZUFcaVFZifuWOft01BxqSVPC05Q00D1/Fq0tBWn6LdHNTuPJ/Kpk2EJc2vTlS9fSfefqUenfxdblagblzFziXBJFa1qSfkzQDLgX8LikXwM7cu+b2YLaFs8VKnwKLLfpK6qmcM4JB47q08qZd9h0nv3TYHprBYUfucL7YFte22klzURxQTJ/1v2c3BigVF3XKipn5hLnkijVPBg3k4RrkKgsv6ib9eRJ4tWIVMGoefgKU8KTJBo02qJl60Z1lgMM7bLIRIy4jLBCZx53wKjX9WomauYxQHEPREnHzjlXqNR6Wv+e+17SG4DjCW6Rj5jZ72tcNlcgaZYfEBmwAB5+Onr/zoOn88CTW9g0MMgb9tmTzoPTvfJMOYkYURlhUR54cvSIiPxkivaprZgFzaulal1Jg+REkMUHIpduSbMHPw58CbifIIHq/0j6spndXMvCZVktssfKyfKLE3Xfjhrz9Le3r+Ki21fRkcZmQYKVZKNqKFErzOY39RXLvsx/r/Ca5P+sUmPC4tKmJ7eIwYh1zpp9DFBujJxz1ZA0EaMbmGtm55vZR4DjgC/UrljZVqv51mrVeR01Jin3k9I6V1ytV5gtNWt+samIuuZ2cPbxB46aF/Hs4w/kmjOOztwcj86lTdKgtRF4Oe/1y0Dlj/1NKmvzrZUae5XGspezwmz+Q0RSSfaNu25xM4UDmZzj0bk0STpOqx9YIen/ETyEv59geqSLAczsuhqVL5MaMd9aJf0o+yQ4Ni2DinPKGUdVq7XGopoiofhM4X1fercHKecqkLSm9RTQw+5Wo/8HPE+QCr9X9YuVbbVaTytumYyO9jYqySBOcqxB7PpRjXDyETMSb69VwN0REwh9pnDnaifpelpXmdlVwHXA13Ov87a7PLVaT6vYeZPeEOcdNjYrcCDhscUWPqy3e9Y8n3j7eB4Wkowj2h6RVOGcq61EQUvSUZL6CGbGeEzSSkneexyjVutpVXre1+81hVs/8dYx26dOSb4OVzUyGKuhnNpM0rXGpuVdh0rGEWVxJWjnsiJpn9aNwMVm9gCApJOAbwNvq02xsq9WA1PjztvWOikynTrfH17eyRU9a8ekH2+PmKkhThan30mS8i7gKx/YfV2SrMEVF4R8pvBs88mO0y1pn9a0XMACMLMHgWk1KZEblz0Trlr8wxVjZycoJwylZfqdcmszuVWIn732NK4/e86oWpWAc088aMyNKbcy8vVnzykrVT2rK0G7IGBdsnj0/JCXLG7u+SGzJmlN62lJ/4tg5WIIZlB/pjZFcuORtF8qanBx3PxwUdIy/U6ltZn862DAXSv76Tx4+ph5HcczIwb4TOH5CqceS/OMGJcvXRu5lMrlS9f6/2dKJA1aHwOuApaEr39BsG5VqklqB24CjiK4N33MzB7Oe/8kgkzIXABeYmZfrm8pq6OStbbi5ocrdN6JB6XmZtM1t4Pe57aOuhmeffyBsTeW/AAEY2uXhQtI9vT1c9Htq0ber1bm37nffnjU1FvzDpse2c/YLKKmHsu9TsvvUr6oSY2LbXf1VzJoSWoBFpvZu+pQnmr7BvAzM/tguEjk1Ih9fmlmp9e5XFWXdK2ttohVIJP03wCpmo+wp6+fu1b2jxrAG1Vbyu2bZCb8/KB/8R2riu5XbBoniK5dPLPllTFzRT701FbO/fbDTRu4srielku3kn1aZjYMbJe0Tx3KUzWS9gbeAXwHwMx2mtlAQwtVQ11zOzjzuI6RPqdJEV1Pk4Brzjg68vjOg6fzhn32LPoz0jSdUzmzjoxnEcgSc+sWnSUkV7vID6i3LF8fO7lx3PZmkLX1tOJ6bNPRk+sgeSLGn4G1kr4j6Z9yX7UsWBUcCmwBviupT9JNkqKSR94qabWkn8al8Uu6QFKvpN4tW7ZE7dJwhTWPXRYsitje1jqSDHDd2XMiawZJpzlK03RO5cw6UqtBvXHXKy3DAtIgLnEnLQk9hc498aCytrv6S9qndU/4lSWTgWOBz5jZCknfAC4F/lfePr8BDjazVySdSjDrx+GFJzKzGwnS/uns7EzlI2JUzWNo2Ji2x2RWXfnuso+NM95+s2orZxqnWom776a1FtEIWVtPy5dSSb+kM2J8H7gNWBl+/TDclmYbgY1mtiJ8fSdBEBthZi+Z2Svh9/cCrZL2q28xq6OS+Q7TEojK0T1/Fq0tBWnoLapo1pEpLeU9/cfFpnJrEVGzlDSLq7tmc/jrRjdwHP66aakOArmmckEm1pabaJLOiHES8Fvgn4F/Af5T0jtqV6zKhYtUbpCUu4u9E3g8fx9Jb5CCO4yk4wmux5/qWtAqiathTJI45NJ7mHft/anpj6qWqElpq3m+8Tp0RlS+TzAjSWFf4yTBWZ3N2/R0Rc9afrt526htv928LTXTgRWq1bJCrnqS9ml9HXi3mf1XM3sHMB/4x9oVq2o+A9wqaQ0wB/h7SRdKujB8/4PAo5JWA/8EfNgsm2073fNnRSZfDJuN/PF139k8gyQvW7KmrO1JlNu0GHW9AZ7asi1y+x9e3jkmwWOXkZp+wloolj2YRllbVmgiStqn1WpmI/9rZvafklI/kZqZrQI6CzbfkPf+N4Fv1rNMtdL73NaSGW+55TGaYZBk3JRVpaayKiZu5vg4cde71P9DoSw2zyaVtezBuP+LZv4/ypqkQatX0nfYPSPGuQR9Wy4lkj65VppJl9Kkr6p44Ml0Zoa6+pmk6IeOuFq1q7+kzYOfBB4DPgt8jqBv6MKiR7i6quTJNW6drigpfUAepbC/ZFrCWeyrte5W1ABulw3Vqj272kmaPbjDzK4zszPM7ANm9o9mtqPWhXPJJc1Yi9pr5muTB61yAlyjFK779Zo9kgWt/D6tqRUEnrgB3M65yhX9y5S0VtKauK96FdKVlnTcS9QD4/KnX0h0bDUWsqyX/ObSP7y8M9Ex+Z9tyuTSga5YYIvKEnTOVa5Un1bm5+SbKJLOHxil2P4d7W2ZXFdoPM2lvc9tHfl8A4PF+/4mCf4+pkZ11Y8fi8wSdM5VrmjQMrPn6lUQV7mru2aPBK85V90XeeMtd/Xchy49pSplq7fxTBN0y/L1XN01u+SwgI4SAbxW00Y555IPLj5D0m8lvSjpJUkvS3qp1oVz47dwwZFlLVwYZ96192dycHJ+c2nhjAzF9PT187dFZniHIJBnpcbpyhPXZ5uFvtyJImlv81eBBWa2j5ntbWZ7mdnetSyYq9y0PXZXpPed2hq7em6xOkk5MwP09PXXLcgVy9ArXPfr5xeflDhwdd+5uuIMSc8e3C1uZqwyZ8yqm+75s2grWAU8S325E0HSv64/mNkTNS2Jq5rcVDT5zYN/LjLodmrClPBiMwPUe/qba844eswv7yTg+rPnRM5r9/OLT+LZa08r2WxYjamc9mxNdj0ngq9/aE5Z2xuta24H15wxm472tpHVEa45Y7bXrFOkaJ+WpDPCb3sl3U4wC/pIqruZLYk6zjVWsaloov74tpexKmvcWKZyf2alcufMrUacNFEk6SrNlSi3T6uZm57G+//USF1zO1JdvomuVPbg+8J/DdgO5K9xYYAHrRQqd8b3uGU+4vaN0ojpb8Zzc7m6azZ3rdw47umekgSYFikye1FEDzkod/qorPEg4KqpaPOgmX3UzD4KtAB/m/f64rqUzo1LXGCJ2x7Vjt/aojGJHMXa9uPGIaVxfFKxptJS+gcGS/bXxaXbxzU8+vRRziWXtE/r6Pyl6s3sBWBuTUrkKlZuZ3JUO/6iDx7DorOOSdy2n6XpbypdKLJUf1256fbVmj7KuYkg6YS5kyTtGwYrJE0v41hXZ11zO+h9buuo1VfPPK78JppmbdaZOqXy7L5i/XXlDmyu52rLzmVd0sDzdeBXku4kaOX4EPCVmpWqSiS1AzcBRxGU+2Nm9nDe+wK+AZxK0Gd3vpn9pgFFraqevn5uf2T3zBjDZiPJB1GZdbnMv1wiRa4m0fvcVh54cktmOtCTKlyUcLziakj7Tm2NTMaYNqWFXcaohBVPp3auPImClpn9QFIvcApBf/IZZvZ4icPS4BvAz8zsg5KmAIVLyr4XODz8OgH4VvhvJvX09bNo2brY5Idblq+n8+DpYwJPXObfrcvXj/TD5AIZEBm4prSInRHp4uUuYZ8lcTWkuIpWa8skFi44MlOZdM6lTeImvjBIZSFQASBpb+AdwPkAZrYTKJw59f3AD8LVipdLapf0RjN7vq6FrYLC2lKcqCatuCBXeO8t1iQWFbCKbc+6YjWkF2PmLXxxcKhpm1ydq5dmHrp/KLAF+K6kPkk3SSqcFqEDyF89cWO4bRRJF0jqldS7ZUs6M72iaktRopq0ykkc8BVcSyellJu96ZxLrpmD1mTgWOBbZjYX2AZcWrBP1N16TNXAzG40s04z65wxI51japJmoBXeOHv6+stKHBjPRLTNptTcg93zZ9Fa0Cza2iLvu3KuCpo5aG0ENprZivD1nQRBrHCf/IWoDgA21aFsVdc+Ndns7fk3zp6+fi4uMTlsoUpWSJ5QCi+TXzbnqqJp09bN7PeSNkiaZWbrgHcytk/ubuDTkn5EkIDxYhb7syC+879Qfg3hC3etiR1HFTd7Q9anHKrHTPWLlq1jqODCDu2ykXkbPRHDufFr2qAV+gxwa5g5+DTwUUkXApjZDcC9BOnuvyNIef9oowpaqVKLFsLYgLPj1eKT6G4rmJOwWPJBR8xUUGkLcnET/ua0tbYk6hssJq6pNpeBWTi0AKIzMp1zYzVz8yBmtirsizrazLrM7AUzuyEMWFjgU2Z2mJnNNrPeRpd5vEr1NZU7HqgwYLW3tRZNPsjKkg6l+v5yM4NUIi7hokWKnVTYOZdMUwetiaRYX1M1lleQitcGsrKkQ7EMPhF8jkpXa44L4HH/Rz6Nk3PJNXvz4IRRrHmu0pswJFtuIwtjkLrnz+LiO1ZF9uWde+JBVfkZcctxxA389lR455LzoNUkuufPGjO4OI3Nc42WCyiX3LGK/HHPh79u2qgprtrbWiP7CdvbkmVpxgVw/z9yrjIetJpErRfba6Yl5Bf3rqdwoo7fbt7GFT1r6Tx4OouWrYtNbDn9mDeO++dmcUFE59JG5uNuytLZ2Wm9vZnN1xhl5qX3JN53auskHv/f761haeqjp6+fi25fFft+qezB9rZWVl357tj3nXPRJK00s85Kz+M1LZfI9goWTkyDUpMJ55RKd08ytMA5VzsetFzq5QJOkia1qH1hbF+Scy6bPGi5RPZNME1UpcElat+4tb5gbAp+3L57tk6qWsBKch2cc7XTPL3rLpGevn7mXXs/h5TRnwXBNFHFpkDKBYz+gUGM4kvSl7Nv3FpfUQNy4/ZNkq6fRGuLuPJ9R1blXM658fGaVkrF1UTKqc1EnXO8zWQDg0NFpxwqFlySLjoZtW/cwNuo7dUYpNve1sq0PSazaWCQfdpakWBg+5Bn+jmXEh60Uiiumav3ua3ctbI/8dx1hQFu+85XK2omK7YIZDWCS9T2/WMGTUcNyI3bt72tlR2v7hr12ScBhaklrS1i4YIjPTA5l2LePJhCcTWR21ZsSNxUFtUEV41msriAU87Ch+XsW86chnH7Llxw5Jgppv77iQeNWUzt1SZdZdm5ZuI1rRSKCwzlzF2XdCVjCCZyTbpOVlzAKWdGjnL2LWdAbql984858ks/i1zy6vKla72m5VyKedBKobhmrrjgEhVIkvbvtLW2cOZxHaOaHQFaJwkEQ3m1j2JTDlUzuETtnzSQJN23cBb7Utudc+mQ6aAlaU/gF8AeBJ/lTjO7smCfI4DvEqxafLmZfa3g/RagF+g3s9PrUvAS4moiUcElLpAU69/JJRrkB4vc9EWF45vKSfqoRXBxzrl8mQ5awA7gFDN7RVIr8B+Sfmpmy/P22Qp8FuiKOcfngCeAvWta0jIUq4lEBZeom39c4ItLNIgLImkOLJVkUjrnsinTQcuCiRNfCV+2hl9WsM9mYLOk0wqPl3QAcBrwFeDi2pa2PMWCSJIbc7NPztrT188li1czHK4x0j8wyCWLVwPpDrTOucpkOmjBSPPeSuAvgH82sxVlHH498HlgrxI/4wLgAoCDDqrOmkv10MxNcJcvXTsSsHKGd5knUjjX5DKf8m5mw2Y2BzgAOF7SUUmOk3Q6sNnMVib4GTeaWaeZdc6YMaOyAruq8EQK5yamzAetHDMbAB4E3pPwkHnAAknPAj8CTpF0S00K51Inbn2wZlo3zLlmlOm/UEkzJLWH37cB7wKeTHKsmV1mZgeY2Uzgw8D9ZnZercrq0uWaM44e88s/KdzunEuvrPdpvRH4ftivNQm4w8x+IulCADO7QdIbCFLa9wZ2SboIeLOZvdSoQqfRFT1ruW3FBobNaJE454QDRy0/32yaPVHFuWaV6aBlZmuAuRHbb8j7/vcE/V3FzvMgQdPihHRFz1puWb5+5PWw2cjrZg9cHqScy5ZMNw+66rg1L2Al2e6cc43iQcuNmYOv1HbnnGsUD1ouk847MXq8XNx251xzyHSflpu4cn1tEyl5xDnnQaupNfvcfFd3zfYg5dwE40GrSfncfM65ZuR9Wk2q2Nx8zjmXVR60mpTPzeeca0YetJzPw+ecywy/Kzmfh885lxmeiOF8Hj7nXGZ40HLAxJuHL244QP7EwQKmTmlh+87hsgN53PmbfRiCc7XmQctNCPnBYp+2VrbtfJWh4d3DAS5bspbFvet56KmtI8cYuxNX+gcGuej2VUDpIQM9ff1ctmQtg0O7j71syVp6n9vKXSv7x2xPck7nXMCDVhPJvzFX6zzl1AbSurxJYRAZGBwas8/g0PCogBWne/Gqktdi0bJ1Iz8r//y5a1O4fdGydR60nEuoqRMxJD0raa2kVZJ6i+z3FknDkj5Yz/JVU09fP913rqZ/YLCiiW5zN/jceXK1gZ6+/qLH5ZY3yd2Uc8ubXNHT+HFhUUFkvIZ2wbxr7y96PeIeGgoDVk5/hQ8Zzk0kE6GmdbKZ/THuzXAByX8AltWvSNV31Y8fG2nuKiZuQtlc7SrqBpqkNnDbig2x2xtd26p2UCjVrNc+tZUXto+tzU1UWevHy1p5J5qJELRK+QxwF/CWRhekEklukvMOmx4ZQAqbz6KUanKMq0XEba+GpDeXFqnq5SgWyHdUqVbXDHr6+ulevJqhvOnEulM8nVhPXz8X376KXeHr/oFBLk7Yl+nqo6mbBwn60u+TtFLSBYVvSuoAPgDcMObI0ftdIKlXUu+WLVtqVNTa+836FyObtZI0n+3f3lb0/RaprO2VKqcZs1aBM64Gt31oV+T2iWjh3Y+NBKycoV3Gwrsfa1CJirtsyRoK//d2hdtdOjR70JpnZscC7wU+JekdBe9fD3zBzIresc3sRjPrNLPOGTNm1KiolWlvay25T652UChJ4sa2Ha8W7cc554QDy9peqbhkh6jP11Ei4I5XtQLy4a+bVpXzpFFU0kux7Y02GPPAEbfd1V9TBy0z2xT+uxlYChxfsEsn8CNJzwIfBP5FUlc9y1gtCxccSeuk0jfRqNpBqVoUBDeZYgkZV3fNHnPzPfx102rWnxUXaKO2d8+fRVtrS9XLUK0a3AmHvrYq53FuImjaoCVpmqS9ct8D7wYezd/HzA4xs5lmNhO4E/ifZtZT77JWQ9fcDhaddQwd7W0UC11RtYPu+bNobSkd8OJqMhBkD/5287ZR2367eVvNsgfjAm3U9q65HVxzxuyq17iqVdO6Zfn6qpwnjabGzF8Zt73R4p77EjwPujpJ529Odbwe+A9Jq4FfA/eY2c8kXSjpwgaXrSa65nbw0KWn8My1p8XuE1s7SFhpiOvHuXVF9I03bnulompPba0tdM+fFbl/7tpU895TyySTZrFHTA03bnuj/fcTorNr47a7+mva7EEzexo4JmJ7ZNKFmZ1f6zLVU0d7W2SAiaptLFq2bkxneZy42kVsLKzRfX288yXuH3NdxqNWfWXNJC6rNa1DAnLN2WkcJO8CTRu0JrqTj5gR2ex08hFjE0nKmUEjTbWL8cyXOPO11QlarS2KrdW53eKGG9Qqq7QaOg+ezgNPbmHTwCBv2GdPOg+e3ugiuTzN3Dw4of1k9fOJtydJxMhJ880mieVPv1CdExWJ3UkyOSeKRozfq8R4Z4Rx9eNBq0mVk2pcTnZd3M0m7kZdyQ28p6+fOVfdx8xL72Hmpfcw98v3VXzzqNbNcmiXxSalRGVyJsnsbEZxTahpbVotZyiFawwPWm5Udp0IbihxwSbuZhN3o1644MhxlSk3k0J+kH1h+xDdd66uKHCVqinuvUfyBIG4ZtXCTM6O9jYWnXUMcQlzhdt7+vqZd+39HHLpPSXnOUy7qAei1haxbcerqfx85QylcI3hfVpNat+Y+e/2nRodjAr7h6KmdiqVnQfVW0gyLjlkaNgqmhX9nBMOLJpivuaq94xM/ltKsWbVuP623PIm+RadNWfk+7hlTXLnzJrC34v2qa288udXRx5G0vb54hJ1ymlCd7XlQauJFK4Z1TJJDOfd+FtbxJXvS1bzGU8QquZCksWebHPvjWdi06u7ZnN3Xz8v7Rg7CUpucPTVXbN5ZssrRZcqKRbAK1GseSoNN/XxyP+9mHft/WMeptL0+brnz6L7ztWjJp/2pJt08aDVJKLWjGqdJPae2srA9qFx1XwauZpxsdT0/dvbKqqRrLnqPfy36x4cNRj68NdN4+cXnzTy+tZPvJVDLr0nNt/imjNml31tvhgzf90Xl6wZOVezN09l4vMV/qenM2dkwvI+rRqpd79E1BP60C7jpcFXEx0fV95G9a9Epebnv1dJh3lPXz9/fGXnqG1bXt455rMVu1eNJ5jHTaSbv72cmT6yKO2fL6pZuljSjas/D1o10Ii02bhaybBZyTLElfeKnrUNS/994Mn42fRzY2iilHpizy2WWdhENTA4RPfi0UkejZjSJy5YFwviWVLuTCb1loma4ATnQasGGpE2m2T8VFwZii0P36j031J9WuN9Yl+0bF3sYpmFT9Rx0zEmmKZxjKSBPi5YFwviWRKVqTqeptZaSXtN0HmfVk004mkt6fijqDKUuzx8PZ46S/Vpdc+fVVZ2Y06psue/H7caxXhWqbh8abKJgyfCk34j+0pLGe/vlasfr2nVQCOe1pIO1owqQ1y54mpv9Xjq7J4/K3JAbi6Ta7xP7KXKXqvPtm1n/JJt+f93/qTfWGmvCTqvadVEI57Won5mobgyxJX3zOM6uGtlf0OeOnM3iYV3PzYypmffqa1c+b4jR94bzxN7VEpzTuuk0anN7W2tkTOIVHuapvyf6U/6jZfmmqDzoFUT1R5oO96fefIRM0aSFoqVoVh5Ow+eXtfPUViuav+s3Pmu+vFjo5Ix2ttaWbjgyFE/b+GCI+levHpUNlkls3yUKlP+94265s6lnayGE1eGKwK/DAwDr5pZZ8H7Ar4BnApsB843s9+E790MnA5sNrOj8o5ZCHwCyPVMf9HM7s17/yDgcWChmX0tXAjyl3k/9gDgFjO7SNL5wCIg10v+TTO7qdhn6uzstN7e3sTXwGXbeAYwR5l56T2x7z1bZP0z55qFpJWFMWA86lHTOtnM/hjz3nuBw8OvE4Bvhf8CfA/4JvCDiOP+0cy+FnPOfwR+mnthZi8Dc3KvJa0EluTtf7uZfbrkp3ATkjcVOZcujU7EeD/wAwssB9olvRHAzH4BxM+jE0FSF/A08FjM+4cDr2N0zcs551xG1DpoGXCfpJWSLoh4vwPYkPd6Y7itlE9LWiPpZkn7AkiaBnwBuKrIcecQ1Kzy20TPDM91p6QDow6SdIGkXkm9W7Y0x3gZ55zLoloHrXlmdixBM+CnJL2j4P2onOpSnWzfAg4jaPJ7Hvh6uP0qgmbDV4oc+2HgtrzXPwZmmtnRwL8B3486yMxuNLNOM+ucMaM5ZiZw9ZV0WRLnXHE1/ZMxs03hv5uBpcDxBbtsBPJrNwcAm0qc8w9mNmxmu4Bv553zBOCrYfLHRcAXJY30VUk6BphsZivzzvUnM9sRvvw2cFxZH9C5hPKXH0my3TkXrWZBS9K0MHMv13T3buDRgt3uBv5KgROBF80sep343ed9Y97LD+TOaWZvN7OZZjYTuB74ezP7Zt6+5zC6llV4rgXAEwk/nnNl6ZrbwfVnzxk1aPX6s+d4kodzZapl9uDrgaVBVjuTgR+a2c8kXQhgZjcA9xKku/+OIOX9o7mDJd0GnATsJ2kjcKWZfYegNjWHoBnxWeBvEpbnQ+HPyvdZSQuAVwmSPs4v90M6l5RnIjpXuZqO02pGPk7LOefKV61xWt4N7JxzLjM8aDnnnMsMD1rOOecyw4OWc865zPBEjDJJehmo/dK92bAfEDev5ETj12I3vxa7+bXYbZaZ7VXpSXxpkvKtq0YGTDOQ1OvXIuDXYje/Frv5tdhNUlXSrr150DnnXGZ40HLOOZcZHrTKd2OjC5Aifi1282uxm1+L3fxa7FaVa+GJGM455zLDa1rOOecyw4OWc865zPCglUfSs5LWSlqVS8+UdJakxyTtkhSbuirpPZLWSfqdpEvrV+raGO+1kHSgpAckPRHu+7n6lrz6Kvm9CPdtkdQn6Sf1KXHtVPg30h6uEP5k+Pvx1vqVvPoqvBZ/G+73qKTbJO1Zv5JXX8y1WBT+X6+RtFRSe8yx5d07zcy/wi+CpU72K9j2X4BZwINAZ8xxLcBTwKHAFGA18OZGf54GXYs3AseG3+8F/OdEvRZ5+14M/BD4SaM/SyOvBcHK4B8Pv58CtDf68zTiWgAdwDNAW/j6DuD8Rn+eGlyLdxMsvAvwD8A/RBxX9r3Ta1olmNkTZlZqBozjgd+Z2dNmthP4EfD+2peuvpJcCzN73sx+E37/MsHCmk23iFTC3wskHQCcBtxU+1I1RpJrIWlv4B3Ad8JjdprZQB2KV1dJfy8IJnZokzQZmEqJFduzyMzuM7NXw5fLCVamL1T2vdOD1mgG3CdppaQLyjiuA9iQ93oj2b9Rj/dajJA0E5gLrKhmwRqgkmtxPfB5YFfVS9UY470WhwJbgO+GTaU3hSuaZ9m4roWZ9QNfA9YDzxOs2H5fjcpYL6WuxceAn0ZsL/ve6UFrtHlmdizwXuBTkt6R8DhFbMv6WILxXgsAJL0GuAu4yMxeqkUB62hc10LS6cBmM1tZ09LV13h/LyYDxwLfMrO5wDYg632/4/292JegNnEIsD8wTdJ5tStmXcReC0mXE6wOf2vEcWXfOz1o5TGzTeG/m4GlBFXXJDYCB+a9PoCMV/cruBZIaiUIWLea2ZLalLB+KrgW84AFkp4laPY4RdItNSlknVT4N7LRzHK17jsJglhmVXAt3gU8Y2ZbzGwIWAK8rTalrI+4ayHpI8DpwLkWdmIVKPve6UErJGmapL1y3xN0Ij6a8PBHgMMlHSJpCvBh4O7alLT2KrkWkkTQb/GEmV1Xu1LWRyXXwswuM7MDzGwmwe/E/WaW2SfqCq/F74ENkmaFm94JPF6TgtZBhfeL9cCJkqaGfy/vJOj7zaS4ayHpPcAXgAVmtj3m8PLvnY3OOknLF0Gb++rw6zHg8nD7BwieBnYAfwCWhdv3B+7NO/5Ugky5p3LHZvWrkmsB/CVB9X4NsCr8OrXRn6lRvxd55zmJjGcPVuFvZA7QG/5u9AD7NvozNfBaXAU8SRDo/hXYo9GfqQbX4ncE/VW5+8ANMdeirHunT+PknHMuM7x50DnnXGZ40HLOOZcZHrScc85lhgct55xzmeFByznnXGZ40HKuwSS9UuL9dkn/M+/1/pLuDL+fI+nUcfzMhZL+rvzSOtdYHrScS792YCRomdkmM/tg+HIOwTgX5yYED1rOpYSk10j6/yT9JlybKDfb9bXAYeFaRYskzQzXYZoCfBk4O3zv7MIaVLjfzPD7y8N1i/6NYPmM3D6HSfpZONnpLyUdUb9P7Vx5Jje6AM65EX8GPmBmL0naD1gu6W6CiWWPMrM5MDJ7Pma2U9KXCNZt+nT43sKoE0s6jmCKnLkEf/e/AXIT+d4IXGhmv5V0AvAvwCk1+YTOVciDlnPpIeDvwxmydxEs0fD6Kp377cBSC+eAC4Nhbjb+twGLg2nwANijSj/TuarzoOVcepwLzACOM7OhcHb4cpdhf5XRzf75x0fN2TYJGMjV4pxLO+/Tci499iFYf2tI0snAweH2l4G9Yo4pfO9ZwiU/JB1LsGYTwC+AD0hqC2fkfh+ABWudPSPprPAYSTqmeh/JueryoOVcetwKdErqJah1PQlgZn8CHgqTKhYVHPMA8OZcIgbBOmbTJa0CPkkwezZm9hvgdoLZtu8Cfpl3jnOBv5aUm6W76HLnzjWSz/LunHMuM7ym5ZxzLjM8aDnnnMsMD1rOOecyw4OWc865zPCg5ZxzLjM8aDnnnMsMD1rOOecy4/8HEJIXXaEwNhYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(latitude, chlor)\n",
+    "plt.xlim(51,52) \n",
+    "plt.yticks(np.arange(0, 500, step=50)) # I have no idea what's going wrong here\n",
+    "plt.title('Schelde chlorophyll')\n",
+    "plt.xlabel('latitude')\n",
+    "plt.ylabel('chlorophyll')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df413057",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5aada43c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
GitLab