From bca611c861bb11ab25f71ae9e7089834bb457bc3 Mon Sep 17 00:00:00 2001
From: Martijn Visser <mgvisser@gmail.com>
Date: Tue, 13 Aug 2024 16:19:16 +0200
Subject: [PATCH] pixi run pre-commit

---
 .../01_parse_crossings.ipynb                  | 1494 +---------
 .../01_test_parse_crossings.ipynb             |   14 +-
 .../01b_ad_krw_to_peilgebieden.ipynb          |    8 +-
 .../02_crossings_to_ribasim_notebook.ipynb    |  295 +-
 src/peilbeheerst_model/03_test_outlets.ipynb  | 2391 +----------------
 .../AmstelGooienVecht_parametrize.ipynb       | 1768 +-----------
 .../sturing_AmstelGooienVecht.json            |   18 +-
 src/peilbeheerst_model/compute_voronoi.ipynb  |   20 +-
 .../postprocess_data/post-process_WSRL.ipynb  |  210 +-
 .../postprocess_data/post-process_agv.ipynb   |  171 +-
 .../post-process_delfland.ipynb               |  198 +-
 .../post-process_rijnland.ipynb               |  468 +---
 .../post-process_wetterskip.ipynb             |  139 +-
 .../post-process_zuiderzeeland.ipynb          |  493 +---
 .../postprocess_data/post-processing_HD.ipynb |  154 +-
 .../post-processing_HHNK.ipynb                |  198 +-
 .../post-processing_HHSK.ipynb                |  222 +-
 .../post-processing_scheldestromen.ipynb      |  336 +--
 .../preprocess_data/AmstelGooienVecht.ipynb   |   38 +-
 .../preprocess_data/Delfland.ipynb            |   32 +-
 .../preprocess_data/HHNK.ipynb                |   46 +-
 .../preprocess_data/HHSK.ipynb                |   78 +-
 .../preprocess_data/Hollandse_Delta.ipynb     |   32 +-
 .../preprocess_data/Rijnland.ipynb            |   78 +-
 .../preprocess_data/Rivierenland.ipynb        |   56 +-
 .../preprocess_data/Scheldestromen.ipynb      |   50 +-
 .../preprocess_data/Wetterskip.ipynb          |   48 +-
 .../preprocess_data/Zuiderzeeland.ipynb       |   38 +-
 src/ribasim_lumping/README.md                 |    6 +-
 29 files changed, 1001 insertions(+), 8098 deletions(-)

diff --git a/src/peilbeheerst_model/01_parse_crossings.ipynb b/src/peilbeheerst_model/01_parse_crossings.ipynb
index 61a978fa..026f9761 100644
--- a/src/peilbeheerst_model/01_parse_crossings.ipynb
+++ b/src/peilbeheerst_model/01_parse_crossings.ipynb
@@ -2,8 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "bd2ec4f5-5df5-4c1e-a010-00e47c676d17",
+   "execution_count": null,
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,366 +20,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "9340d65c-0872-43ae-ada8-4a4e3ab21a9a",
+   "execution_count": null,
+   "id": "1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<h2>Function init:</h2>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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>gpkg_path</th>\n",
-       "      <th>output_path</th>\n",
-       "      <th>search_radius_structure</th>\n",
-       "      <th>agg_peilgebieden_layer</th>\n",
-       "      <th>agg_peilgebieden_column</th>\n",
-       "      <th>krw_path</th>\n",
-       "      <th>krw_column_id</th>\n",
-       "      <th>krw_column_name</th>\n",
-       "      <th>krw_min_overlap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HHNK</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/H...</td>\n",
-       "      <td>../../../../Data_crossings/HHNK/hhnk_crossings...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Delfland</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/D...</td>\n",
-       "      <td>../../../../Data_crossings/Delfland/delfland_c...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Hollandse Delta</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/H...</td>\n",
-       "      <td>../../../../Data_crossings/Hollandse_Delta/hd_...</td>\n",
-       "      <td>300</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AmstelGooienVecht</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/A...</td>\n",
-       "      <td>../../../../Data_crossings/AmstelGooienVecht/a...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HHSK</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/H...</td>\n",
-       "      <td>../../../../Data_crossings/HHSK/hhsk_crossings...</td>\n",
-       "      <td>300</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rijnland</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/R...</td>\n",
-       "      <td>../../../../Data_crossings/Rijnland/rijnland_c...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Scheldestromen</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/S...</td>\n",
-       "      <td>../../../../Data_crossings/Scheldestromen/sche...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wetterskip</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/W...</td>\n",
-       "      <td>../../../../Data_crossings/Wetterskip/wettersk...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WSRL</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/W...</td>\n",
-       "      <td>../../../../Data_crossings/WSRL/wsrl_crossings...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Zuiderzeeland</th>\n",
-       "      <td>../../../../Data_postprocessed/Waterschappen/Z...</td>\n",
-       "      <td>../../../../Data_crossings/Zuiderzeeland/zzl_c...</td>\n",
-       "      <td>60</td>\n",
-       "      <td>aggregation_area</td>\n",
-       "      <td>code</td>\n",
-       "      <td>../../../../Data_overig/KRW/KRW_lichamen_per_w...</td>\n",
-       "      <td>owmident</td>\n",
-       "      <td>owmnaam</td>\n",
-       "      <td>0.025</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                           gpkg_path  \\\n",
-       "HHNK               ../../../../Data_postprocessed/Waterschappen/H...   \n",
-       "Delfland           ../../../../Data_postprocessed/Waterschappen/D...   \n",
-       "Hollandse Delta    ../../../../Data_postprocessed/Waterschappen/H...   \n",
-       "AmstelGooienVecht  ../../../../Data_postprocessed/Waterschappen/A...   \n",
-       "HHSK               ../../../../Data_postprocessed/Waterschappen/H...   \n",
-       "Rijnland           ../../../../Data_postprocessed/Waterschappen/R...   \n",
-       "Scheldestromen     ../../../../Data_postprocessed/Waterschappen/S...   \n",
-       "Wetterskip         ../../../../Data_postprocessed/Waterschappen/W...   \n",
-       "WSRL               ../../../../Data_postprocessed/Waterschappen/W...   \n",
-       "Zuiderzeeland      ../../../../Data_postprocessed/Waterschappen/Z...   \n",
-       "\n",
-       "                                                         output_path  \\\n",
-       "HHNK               ../../../../Data_crossings/HHNK/hhnk_crossings...   \n",
-       "Delfland           ../../../../Data_crossings/Delfland/delfland_c...   \n",
-       "Hollandse Delta    ../../../../Data_crossings/Hollandse_Delta/hd_...   \n",
-       "AmstelGooienVecht  ../../../../Data_crossings/AmstelGooienVecht/a...   \n",
-       "HHSK               ../../../../Data_crossings/HHSK/hhsk_crossings...   \n",
-       "Rijnland           ../../../../Data_crossings/Rijnland/rijnland_c...   \n",
-       "Scheldestromen     ../../../../Data_crossings/Scheldestromen/sche...   \n",
-       "Wetterskip         ../../../../Data_crossings/Wetterskip/wettersk...   \n",
-       "WSRL               ../../../../Data_crossings/WSRL/wsrl_crossings...   \n",
-       "Zuiderzeeland      ../../../../Data_crossings/Zuiderzeeland/zzl_c...   \n",
-       "\n",
-       "                   search_radius_structure agg_peilgebieden_layer  \\\n",
-       "HHNK                                    60       aggregation_area   \n",
-       "Delfland                                60       aggregation_area   \n",
-       "Hollandse Delta                        300       aggregation_area   \n",
-       "AmstelGooienVecht                       60       aggregation_area   \n",
-       "HHSK                                   300       aggregation_area   \n",
-       "Rijnland                                60       aggregation_area   \n",
-       "Scheldestromen                          60       aggregation_area   \n",
-       "Wetterskip                              60       aggregation_area   \n",
-       "WSRL                                    60       aggregation_area   \n",
-       "Zuiderzeeland                           60       aggregation_area   \n",
-       "\n",
-       "                  agg_peilgebieden_column  \\\n",
-       "HHNK                                 code   \n",
-       "Delfland                             code   \n",
-       "Hollandse Delta                      code   \n",
-       "AmstelGooienVecht                    code   \n",
-       "HHSK                                 code   \n",
-       "Rijnland                             code   \n",
-       "Scheldestromen                       code   \n",
-       "Wetterskip                           code   \n",
-       "WSRL                                 code   \n",
-       "Zuiderzeeland                        code   \n",
-       "\n",
-       "                                                            krw_path  \\\n",
-       "HHNK               ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "Delfland           ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "Hollandse Delta    ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "AmstelGooienVecht  ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "HHSK               ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "Rijnland           ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "Scheldestromen     ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "Wetterskip         ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "WSRL               ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "Zuiderzeeland      ../../../../Data_overig/KRW/KRW_lichamen_per_w...   \n",
-       "\n",
-       "                  krw_column_id krw_column_name  krw_min_overlap  \n",
-       "HHNK                   owmident         owmnaam            0.025  \n",
-       "Delfland               owmident         owmnaam            0.025  \n",
-       "Hollandse Delta        owmident         owmnaam            0.025  \n",
-       "AmstelGooienVecht      owmident         owmnaam            0.025  \n",
-       "HHSK                   owmident         owmnaam            0.025  \n",
-       "Rijnland               owmident         owmnaam            0.025  \n",
-       "Scheldestromen         owmident         owmnaam            0.025  \n",
-       "Wetterskip             owmident         owmnaam            0.025  \n",
-       "WSRL                   owmident         owmnaam            0.025  \n",
-       "Zuiderzeeland          owmident         owmnaam            0.025  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<h2>Function find_crossings_with_peilgebieden:</h2>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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>filterlayer</th>\n",
-       "      <th>group_stacked</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HHNK</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Delfland</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Hollandse Delta</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AmstelGooienVecht</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HHSK</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rijnland</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Scheldestromen</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wetterskip</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WSRL</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Zuiderzeeland</th>\n",
-       "      <td>duikersifonhevel</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        filterlayer  group_stacked\n",
-       "HHNK               duikersifonhevel           True\n",
-       "Delfland           duikersifonhevel           True\n",
-       "Hollandse Delta    duikersifonhevel           True\n",
-       "AmstelGooienVecht  duikersifonhevel           True\n",
-       "HHSK               duikersifonhevel           True\n",
-       "Rijnland           duikersifonhevel           True\n",
-       "Scheldestromen     duikersifonhevel           True\n",
-       "Wetterskip         duikersifonhevel           True\n",
-       "WSRL               duikersifonhevel           True\n",
-       "Zuiderzeeland      duikersifonhevel           True"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(\"waterschappen.json\") as f:\n",
     "    waterschap_data = json.load(f)\n",
@@ -398,883 +42,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "816b0266-4eb7-4465-8439-316d44efd5db",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "HHNK...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "02183bcd33bd4c9185d763aeb620c302",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Snap geometries in 'hydroobject':   0%|          | 0/190044 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d349ef09af6a44a89626ffc7a87f0f0c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Find which endpoints need to be extended:   0%|          | 0/380046 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aa68ade915ec40a195cac377791ddca8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Move groups of endpoints away from boundary:   0%|          | 0/7623 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93d8b713536946408121e53f3308df61",
-       "version_major": 2,
-       "version_minor": 0
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-      "text/plain": [
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d6e3bfb6f1f849c8ae5a99ca7878c9af",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Assign aggregate areas:   0%|          | 0/2693 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "69fbe46670c64a86add7faaae419a4c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Disable crossings in aggregation areas:   0%|          | 0/817 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6c7c4f513cdb49059953c460e633513d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Aggregate links between aggregate areas:   0%|          | 0/881 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "75a16e6807444c51a0e6403e76010596",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  - Add structures of 'stuw' to crossings:   0%|          | 0/3468 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "02446905d91f47a7aa16aede84276428",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  - Add structures of 'stuw' to crossings:   0%|          | 0/23 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae12d6431589479dad3dbb978e400b34",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  - Add structures of 'gemaal' to crossings:   0%|          | 0/306 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "53adcac5a9c94b45b14ebf4506b7b63b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  - Correct free water flow:   0%|          | 0/13306 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4150eec2fce84c50a444f4923624e818",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  - Correct pumped water flow:   0%|          | 0/13306 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45d777fc688947929975d4af9820c396",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Add double links for crossings with 'stuw' and 'gemaal':   0%|          | 0/1035 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Delfland...\n",
-      "\n",
-      "Hollandse Delta...\n",
-      "\n",
-      "AmstelGooienVecht...\n",
-      "\n",
-      "HHSK...\n",
-      "\n",
-      "Rijnland...\n",
-      "\n",
-      "Scheldestromen...\n",
-      "\n",
-      "Wetterskip...\n",
-      "\n",
-      "WSRL...\n",
-      "\n",
-      "Zuiderzeeland...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for waterschap, waterschap_struct in waterschap_data.items():\n",
     "    print(f\"\\n{waterschap}...\")\n",
@@ -1303,254 +74,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ace4708f-3744-4dc2-9c57-2b86a3276efd",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "50eb7223-cb3e-4a59-a0e5-bf56e9326fa5",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>in_use</th>\n",
-       "      <th>agg_links_in_use</th>\n",
-       "      <th>agg_areas_in_use</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HHNK</th>\n",
-       "      <td>8546</td>\n",
-       "      <td>5326</td>\n",
-       "      <td>5326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Delfland</th>\n",
-       "      <td>4080</td>\n",
-       "      <td>2916</td>\n",
-       "      <td>410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Hollandse Delta</th>\n",
-       "      <td>3139</td>\n",
-       "      <td>1789</td>\n",
-       "      <td>1789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AmstelGooienVecht</th>\n",
-       "      <td>1476</td>\n",
-       "      <td>676</td>\n",
-       "      <td>670</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HHSK</th>\n",
-       "      <td>1247</td>\n",
-       "      <td>492</td>\n",
-       "      <td>453</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rijnland</th>\n",
-       "      <td>5405</td>\n",
-       "      <td>2833</td>\n",
-       "      <td>1005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Scheldestromen</th>\n",
-       "      <td>1713</td>\n",
-       "      <td>1172</td>\n",
-       "      <td>372</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wetterskip</th>\n",
-       "      <td>12912</td>\n",
-       "      <td>10061</td>\n",
-       "      <td>2671</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WSRL</th>\n",
-       "      <td>2121</td>\n",
-       "      <td>1611</td>\n",
-       "      <td>657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Zuiderzeeland</th>\n",
-       "      <td>1892</td>\n",
-       "      <td>554</td>\n",
-       "      <td>527</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   in_use  agg_links_in_use  agg_areas_in_use\n",
-       "HHNK                 8546              5326              5326\n",
-       "Delfland             4080              2916               410\n",
-       "Hollandse Delta      3139              1789              1789\n",
-       "AmstelGooienVecht    1476               676               670\n",
-       "HHSK                 1247               492               453\n",
-       "Rijnland             5405              2833              1005\n",
-       "Scheldestromen       1713              1172               372\n",
-       "Wetterskip          12912             10061              2671\n",
-       "WSRL                 2121              1611               657\n",
-       "Zuiderzeeland        1892               554               527"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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>Basins</th>\n",
-       "      <th>Edges</th>\n",
-       "      <th>Peilgebieden</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HHNK</th>\n",
-       "      <td>223</td>\n",
-       "      <td>10652</td>\n",
-       "      <td>2649</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Delfland</th>\n",
-       "      <td>105</td>\n",
-       "      <td>820</td>\n",
-       "      <td>283</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Hollandse Delta</th>\n",
-       "      <td>783</td>\n",
-       "      <td>3578</td>\n",
-       "      <td>785</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AmstelGooienVecht</th>\n",
-       "      <td>228</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>228</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HHSK</th>\n",
-       "      <td>156</td>\n",
-       "      <td>906</td>\n",
-       "      <td>157</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rijnland</th>\n",
-       "      <td>285</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>542</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Scheldestromen</th>\n",
-       "      <td>190</td>\n",
-       "      <td>744</td>\n",
-       "      <td>294</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wetterskip</th>\n",
-       "      <td>948</td>\n",
-       "      <td>5342</td>\n",
-       "      <td>2158</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WSRL</th>\n",
-       "      <td>129</td>\n",
-       "      <td>1314</td>\n",
-       "      <td>446</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Zuiderzeeland</th>\n",
-       "      <td>289</td>\n",
-       "      <td>1054</td>\n",
-       "      <td>289</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   Basins  Edges  Peilgebieden\n",
-       "HHNK                  223  10652          2649\n",
-       "Delfland              105    820           283\n",
-       "Hollandse Delta       783   3578           785\n",
-       "AmstelGooienVecht     228   1340           228\n",
-       "HHSK                  156    906           157\n",
-       "Rijnland              285   2010           542\n",
-       "Scheldestromen        190    744           294\n",
-       "Wetterskip            948   5342          2158\n",
-       "WSRL                  129   1314           446\n",
-       "Zuiderzeeland         289   1054           289"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x740 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x740 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.close(\"all\")\n",
     "fig1, ax1 = plt.subplots(figsize=(12, 7.4), dpi=100)\n",
@@ -1637,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8980f137-8176-4156-8319-5039e61a11c5",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1645,7 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ba8a41fb-5e6d-41e0-b0c1-2ba7bf113908",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/01_test_parse_crossings.ipynb b/src/peilbeheerst_model/01_test_parse_crossings.ipynb
index 10736ea3..4df0fd6a 100644
--- a/src/peilbeheerst_model/01_test_parse_crossings.ipynb
+++ b/src/peilbeheerst_model/01_test_parse_crossings.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4bf1ab0f-3ade-41c7-ac67-5ccd41930481",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2122bf39-a6e1-4f80-a9dd-4782abe7d351",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +340,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8a965c64-38f9-4955-ba7e-680d52c151de",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +399,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "633f4754-9354-4f3b-8bb5-087d9de1d5a5",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,7 +470,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7833bb4e-0c62-40df-a820-e5621bd924cd",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,7 +523,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b2630053-7e0b-4040-abbf-c1993f1b1732",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +569,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4f834db9-ddfd-4ebd-8e6e-7232dd99a7cf",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/01b_ad_krw_to_peilgebieden.ipynb b/src/peilbeheerst_model/01b_ad_krw_to_peilgebieden.ipynb
index 8c4ef7de..edb465e8 100644
--- a/src/peilbeheerst_model/01b_ad_krw_to_peilgebieden.ipynb
+++ b/src/peilbeheerst_model/01b_ad_krw_to_peilgebieden.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8e3aee81-bf33-4516-8850-26b5765669e2",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d037c21b-381a-4910-a801-f1d83924c555",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a797396c-2f6a-4c59-983f-2f195de9bf87",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5225ced2-77c6-4cfb-b7c5-50a4e86fb95b",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/02_crossings_to_ribasim_notebook.ipynb b/src/peilbeheerst_model/02_crossings_to_ribasim_notebook.ipynb
index e6b75c54..92082cb3 100644
--- a/src/peilbeheerst_model/02_crossings_to_ribasim_notebook.ipynb
+++ b/src/peilbeheerst_model/02_crossings_to_ribasim_notebook.ipynb
@@ -2,8 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "698d0fb7-f655-4ef4-8f2c-4007711a6f97",
+   "execution_count": null,
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1727b840-0c96-4801-976f-44638063e2ef",
+   "id": "1",
    "metadata": {},
    "source": [
     "# Amstel, Gooi en Vecht"
@@ -43,20 +43,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "894a203e-0681-407e-83c9-f30cff83287c",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model_characteristics = {\n",
     "    # model description\n",
@@ -153,7 +143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00b919b6-92ba-4d8e-b6aa-6818ae84a9e2",
+   "id": "3",
    "metadata": {},
    "source": [
     "# Delfland"
@@ -162,10 +152,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5fc9b8a4-0ce0-46dd-9103-a10123e5bb3c",
-   "metadata": {
-    "scrolled": true
-   },
+   "id": "4",
+   "metadata": {},
    "outputs": [],
    "source": [
     "model_characteristics = {\n",
@@ -265,14 +253,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5b309773-f18f-4d93-9986-f8a88d4c118f",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "3d1ef23c-590a-467d-a855-7c122d7b2950",
+   "id": "6",
    "metadata": {},
    "source": [
     "# Hollandse Delta"
@@ -281,10 +269,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b093ce38-a5e7-4c89-a5c8-0715a0262f32",
-   "metadata": {
-    "scrolled": true
-   },
+   "id": "7",
+   "metadata": {},
    "outputs": [],
    "source": [
     "model_characteristics = {\n",
@@ -383,7 +369,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "164a32d0-1bfa-458e-96fe-ce3e39b4f896",
+   "id": "8",
    "metadata": {},
    "source": [
     "# Hollands Noorderkwartier"
@@ -391,18 +377,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "dacfbfbf-c8de-486f-a1e3-3ae0ab5084c2",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_characteristics = {\n",
     "    # model description\n",
@@ -498,7 +476,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1584b072-7343-45f9-931f-c47b5e017919",
+   "id": "10",
    "metadata": {},
    "source": [
     "# Rijnland"
@@ -506,18 +484,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "4d849e83-6a82-4e0e-ba71-455a3ac70f1b",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_characteristics = {\n",
     "    # model description\n",
@@ -617,7 +587,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bf50e62-98ed-415c-a79e-7819d010c8d5",
+   "id": "12",
    "metadata": {},
    "source": [
     "# Rivierenland"
@@ -626,7 +596,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d1a952bc-f7d4-41da-98c1-8600f6c5b11c",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,7 +714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c079f11-7f01-44e6-8794-0eb8209b7110",
+   "id": "14",
    "metadata": {},
    "source": [
     "# Scheldestromen"
@@ -752,18 +722,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "5ac210c0-151c-4490-9cd0-e708659bbe4c",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_characteristics = {\n",
     "    # model description\n",
@@ -863,7 +825,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e9a9726-a95c-4f59-9d2c-cf1d1a7d557b",
+   "id": "16",
    "metadata": {},
    "source": [
     "# Schieland en de Krimpenerwaard"
@@ -871,18 +833,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "44f18817-06c8-40c0-8918-750587a6f599",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_characteristics = {\n",
     "    # model description\n",
@@ -981,174 +935,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "f96e0c9e-cf29-4eb2-abf0-6ab07873d2bb",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>TabulatedRatingCurve / static</div><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>node_id</th>\n",
-       "      <th>active</th>\n",
-       "      <th>control_state</th>\n",
-       "      <th>level</th>\n",
-       "      <th>flow_rate</th>\n",
-       "      <th>meta_type_verbinding</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>161</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Inlaat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>161</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Inlaat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>162</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>162</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>163</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Uitlaat</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>529</th>\n",
-       "      <td>607</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>530</th>\n",
-       "      <td>608</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Inlaat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>531</th>\n",
-       "      <td>608</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Inlaat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>532</th>\n",
-       "      <td>612</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>533</th>\n",
-       "      <td>612</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>534 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "TabulatedRatingCurve / static\n",
-       "     node_id  active control_state  level  flow_rate meta_type_verbinding\n",
-       "0        161    <NA>           NaN    0.0        0.0               Inlaat\n",
-       "1        161    <NA>           NaN    1.0        1.0               Inlaat\n",
-       "2        162    <NA>           NaN    0.0        0.0                  NaN\n",
-       "3        162    <NA>           NaN    1.0        1.0                  NaN\n",
-       "4        163    <NA>           NaN    0.0        0.0              Uitlaat\n",
-       "..       ...     ...           ...    ...        ...                  ...\n",
-       "529      607    <NA>           NaN    1.0        1.0                  NaN\n",
-       "530      608    <NA>           NaN    0.0        0.0               Inlaat\n",
-       "531      608    <NA>           NaN    1.0        1.0               Inlaat\n",
-       "532      612    <NA>           NaN    0.0        0.0                  NaN\n",
-       "533      612    <NA>           NaN    1.0        1.0                  NaN\n",
-       "\n",
-       "[534 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.tabulated_rating_curve.static"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f8176275-f42d-4ab4-923d-3b739613ea2f",
+   "id": "19",
    "metadata": {},
    "source": [
     "# Wetterskip"
@@ -1157,7 +954,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2ad4796a-a184-4be9-8106-cd9bd62fa369",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1258,7 +1055,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5455f6bc-88e3-43cd-922b-467b5c58a917",
+   "id": "21",
    "metadata": {},
    "source": [
     "# Zuiderzeeland"
@@ -1266,18 +1063,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "c261b5e9-8c1f-44c8-9076-28003b203611",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_characteristics = {\n",
     "    # model description\n",
@@ -1376,7 +1165,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "03982b8e-1288-48b1-9279-eadb47d9952a",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/03_test_outlets.ipynb b/src/peilbeheerst_model/03_test_outlets.ipynb
index 523f9a30..d05157ba 100644
--- a/src/peilbeheerst_model/03_test_outlets.ipynb
+++ b/src/peilbeheerst_model/03_test_outlets.ipynb
@@ -2,19 +2,10 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "90f0b71e-aba0-4772-8931-1a22240566d8",
+   "execution_count": null,
+   "id": "0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import sys\n",
     "\n",
@@ -28,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0eb85747-970f-4c98-a335-25e500d54863",
+   "id": "1",
    "metadata": {},
    "source": [
     "# Case 1"
@@ -36,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7315c3e5-66a9-4b4d-a7e1-e396ec8084cf",
+   "id": "2",
    "metadata": {},
    "source": [
     "### Example 1: boundary and basin levels on target "
@@ -44,295 +35,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "7c117602-860f-4b79-85f0-4baeb2b571e2",
+   "execution_count": null,
+   "id": "3",
    "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>time</th>\n",
-       "      <th>node_id</th>\n",
-       "      <th>storage</th>\n",
-       "      <th>level</th>\n",
-       "      <th>inflow_rate</th>\n",
-       "      <th>outflow_rate</th>\n",
-       "      <th>storage_rate</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>evaporation</th>\n",
-       "      <th>drainage</th>\n",
-       "      <th>infiltration</th>\n",
-       "      <th>balance_error</th>\n",
-       "      <th>relative_error</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.008750</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.005550</td>\n",
-       "      <td>0.004450</td>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000966</td>\n",
-       "      <td>0.190740</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000.009000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.004450</td>\n",
-       "      <td>0.005304</td>\n",
-       "      <td>-0.000580</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000390</td>\n",
-       "      <td>-0.083897</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.008069</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.004738</td>\n",
-       "      <td>0.005262</td>\n",
-       "      <td>-0.000367</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000302</td>\n",
-       "      <td>-0.061862</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.974196</td>\n",
-       "      <td>0.999983</td>\n",
-       "      <td>0.005262</td>\n",
-       "      <td>0.003804</td>\n",
-       "      <td>0.000948</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000394</td>\n",
-       "      <td>0.095823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2024-01-01 00:02:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1249.986076</td>\n",
-       "      <td>1.999991</td>\n",
-       "      <td>0.005517</td>\n",
-       "      <td>0.004483</td>\n",
-       "      <td>0.001021</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000131</td>\n",
-       "      <td>-0.028820</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89275</th>\n",
-       "      <td>2024-01-31 23:57:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.992810</td>\n",
-       "      <td>0.999992</td>\n",
-       "      <td>0.004696</td>\n",
-       "      <td>0.004146</td>\n",
-       "      <td>0.000412</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000022</td>\n",
-       "      <td>0.005092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89276</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.005225</td>\n",
-       "      <td>1.999999</td>\n",
-       "      <td>0.005070</td>\n",
-       "      <td>0.004930</td>\n",
-       "      <td>-0.000139</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000135</td>\n",
-       "      <td>0.026956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89277</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000.017538</td>\n",
-       "      <td>1.000004</td>\n",
-       "      <td>0.004930</td>\n",
-       "      <td>0.005271</td>\n",
-       "      <td>-0.000269</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000189</td>\n",
-       "      <td>-0.037547</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89278</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1249.996861</td>\n",
-       "      <td>1.999995</td>\n",
-       "      <td>0.005407</td>\n",
-       "      <td>0.004593</td>\n",
-       "      <td>0.000083</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000586</td>\n",
-       "      <td>0.116452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89279</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000.001428</td>\n",
-       "      <td>0.999996</td>\n",
-       "      <td>0.004593</td>\n",
-       "      <td>0.004688</td>\n",
-       "      <td>-0.000424</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000213</td>\n",
-       "      <td>0.047562</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>89280 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     time  node_id      storage     level  inflow_rate  \\\n",
-       "0     2024-01-01 00:00:00        3  1250.008750  2.000000     0.005550   \n",
-       "1     2024-01-01 00:00:00        4  1000.009000  1.000000     0.004450   \n",
-       "2     2024-01-01 00:01:00        3  1250.008069  2.000000     0.004738   \n",
-       "3     2024-01-01 00:01:00        4   999.974196  0.999983     0.005262   \n",
-       "4     2024-01-01 00:02:00        3  1249.986076  1.999991     0.005517   \n",
-       "...                   ...      ...          ...       ...          ...   \n",
-       "89275 2024-01-31 23:57:00        4   999.992810  0.999992     0.004696   \n",
-       "89276 2024-01-31 23:58:00        3  1250.005225  1.999999     0.005070   \n",
-       "89277 2024-01-31 23:58:00        4  1000.017538  1.000004     0.004930   \n",
-       "89278 2024-01-31 23:59:00        3  1249.996861  1.999995     0.005407   \n",
-       "89279 2024-01-31 23:59:00        4  1000.001428  0.999996     0.004593   \n",
-       "\n",
-       "       outflow_rate  storage_rate  precipitation  evaporation  drainage  \\\n",
-       "0          0.004450     -0.000011            0.0     0.000145       0.0   \n",
-       "1          0.005304     -0.000580            0.0     0.000116       0.0   \n",
-       "2          0.005262     -0.000367            0.0     0.000145       0.0   \n",
-       "3          0.003804      0.000948            0.0     0.000116       0.0   \n",
-       "4          0.004483      0.001021            0.0     0.000145       0.0   \n",
-       "...             ...           ...            ...          ...       ...   \n",
-       "89275      0.004146      0.000412            0.0     0.000116       0.0   \n",
-       "89276      0.004930     -0.000139            0.0     0.000145       0.0   \n",
-       "89277      0.005271     -0.000269            0.0     0.000116       0.0   \n",
-       "89278      0.004593      0.000083            0.0     0.000145       0.0   \n",
-       "89279      0.004688     -0.000424            0.0     0.000116       0.0   \n",
-       "\n",
-       "       infiltration  balance_error  relative_error  \n",
-       "0               0.0       0.000966        0.190740  \n",
-       "1               0.0      -0.000390       -0.083897  \n",
-       "2               0.0      -0.000302       -0.061862  \n",
-       "3               0.0       0.000394        0.095823  \n",
-       "4               0.0      -0.000131       -0.028820  \n",
-       "...             ...            ...             ...  \n",
-       "89275           0.0       0.000022        0.005092  \n",
-       "89276           0.0       0.000135        0.026956  \n",
-       "89277           0.0      -0.000189       -0.037547  \n",
-       "89278           0.0       0.000586        0.116452  \n",
-       "89279           0.0       0.000213        0.047562  \n",
-       "\n",
-       "[89280 rows x 13 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "case1_example1 = case1(\"case1_example1\")\n",
     "case1_example1.create_model()"
@@ -340,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e86db4e-2425-483d-9a64-380c6b69a023",
+   "id": "4",
    "metadata": {},
    "source": [
     "### Example 2: boundary levels below target"
@@ -348,297 +54,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "a49607e1-2214-4b0b-953a-552a600d0f3a",
-   "metadata": {
-    "scrolled": true
-   },
-   "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>time</th>\n",
-       "      <th>node_id</th>\n",
-       "      <th>storage</th>\n",
-       "      <th>level</th>\n",
-       "      <th>inflow_rate</th>\n",
-       "      <th>outflow_rate</th>\n",
-       "      <th>storage_rate</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>evaporation</th>\n",
-       "      <th>drainage</th>\n",
-       "      <th>infiltration</th>\n",
-       "      <th>balance_error</th>\n",
-       "      <th>relative_error</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.008750</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.268499e-08</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000.009000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000014</td>\n",
-       "      <td>-0.000194</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.448805e-05</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.000070</td>\n",
-       "      <td>1.999997</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.257827e-10</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.997338</td>\n",
-       "      <td>0.999994</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000117</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.985815e-07</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2024-01-01 00:02:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1249.991390</td>\n",
-       "      <td>1.999993</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.843947e-10</td>\n",
-       "      <td>-2.0</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89275</th>\n",
-       "      <td>2024-01-31 23:57:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>714.046605</td>\n",
-       "      <td>0.845008</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000098</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000098</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.265895e-10</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89276</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>892.553867</td>\n",
-       "      <td>1.845007</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000122</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000122</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.582412e-10</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89277</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>714.040737</td>\n",
-       "      <td>0.845005</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000098</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000098</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.265901e-10</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89278</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>892.546532</td>\n",
-       "      <td>1.845004</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000122</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.582377e-10</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89279</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>714.034869</td>\n",
-       "      <td>0.845001</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000098</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000098</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.265908e-10</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>89280 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     time  node_id      storage     level  inflow_rate  \\\n",
-       "0     2024-01-01 00:00:00        3  1250.008750  2.000000          0.0   \n",
-       "1     2024-01-01 00:00:00        4  1000.009000  1.000000          0.0   \n",
-       "2     2024-01-01 00:01:00        3  1250.000070  1.999997          0.0   \n",
-       "3     2024-01-01 00:01:00        4   999.997338  0.999994          0.0   \n",
-       "4     2024-01-01 00:02:00        3  1249.991390  1.999993          0.0   \n",
-       "...                   ...      ...          ...       ...          ...   \n",
-       "89275 2024-01-31 23:57:00        4   714.046605  0.845008          0.0   \n",
-       "89276 2024-01-31 23:58:00        3   892.553867  1.845007          0.0   \n",
-       "89277 2024-01-31 23:58:00        4   714.040737  0.845005          0.0   \n",
-       "89278 2024-01-31 23:59:00        3   892.546532  1.845004          0.0   \n",
-       "89279 2024-01-31 23:59:00        4   714.034869  0.845001          0.0   \n",
-       "\n",
-       "       outflow_rate  storage_rate  precipitation  evaporation  drainage  \\\n",
-       "0          0.000000     -0.000145            0.0     0.000145       0.0   \n",
-       "1          0.000014     -0.000194            0.0     0.000116       0.0   \n",
-       "2          0.000000     -0.000145            0.0     0.000145       0.0   \n",
-       "3          0.000000     -0.000117            0.0     0.000116       0.0   \n",
-       "4          0.000000     -0.000145            0.0     0.000145       0.0   \n",
-       "...             ...           ...            ...          ...       ...   \n",
-       "89275      0.000000     -0.000098            0.0     0.000098       0.0   \n",
-       "89276      0.000000     -0.000122            0.0     0.000122       0.0   \n",
-       "89277      0.000000     -0.000098            0.0     0.000098       0.0   \n",
-       "89278      0.000000     -0.000122            0.0     0.000122       0.0   \n",
-       "89279      0.000000     -0.000098            0.0     0.000098       0.0   \n",
-       "\n",
-       "       infiltration  balance_error  relative_error  \n",
-       "0               0.0  -1.268499e-08            -2.0  \n",
-       "1               0.0   6.448805e-05            -2.0  \n",
-       "2               0.0  -3.257827e-10            -2.0  \n",
-       "3               0.0   9.985815e-07            -2.0  \n",
-       "4               0.0  -1.843947e-10            -2.0  \n",
-       "...             ...            ...             ...  \n",
-       "89275           0.0  -1.265895e-10            -2.0  \n",
-       "89276           0.0  -1.582412e-10            -2.0  \n",
-       "89277           0.0  -1.265901e-10            -2.0  \n",
-       "89278           0.0  -1.582377e-10            -2.0  \n",
-       "89279           0.0  -1.265908e-10            -2.0  \n",
-       "\n",
-       "[89280 rows x 13 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "case1_example2 = case1(\"case1_example2\")\n",
     "case1_example2.create_model()"
@@ -646,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f65cb535-55c1-4a83-b97c-224b8b1aceb7",
+   "id": "6",
    "metadata": {},
    "source": [
     "### Example 3: boundary levels on target, initial state below target"
@@ -654,295 +73,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "88d99768-cab2-4bdf-943a-d6ce6975cde0",
+   "execution_count": null,
+   "id": "7",
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-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>node_id</th>\n",
-       "      <th>storage</th>\n",
-       "      <th>level</th>\n",
-       "      <th>inflow_rate</th>\n",
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-       "      <th>0</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>4</td>\n",
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-       "      <td>2.925180e-09</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.634707</td>\n",
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-       "      <th>3</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
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-       "      <td>0.000579</td>\n",
-       "      <td>1.274586e-08</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.839159e-09</td>\n",
-       "      <td>0.200429</td>\n",
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-       "      <th>4</th>\n",
-       "      <td>2024-01-01 00:02:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.269374</td>\n",
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-       "      <td>1.507221e-06</td>\n",
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-       "      <th>89275</th>\n",
-       "      <td>2024-01-31 23:57:00</td>\n",
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-       "      <th>89277</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
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-       "      <th>89279</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
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-       "                     time  node_id      storage     level  inflow_rate  \\\n",
-       "0     2024-01-01 00:00:00        3     0.000000  1.000000     0.010000   \n",
-       "1     2024-01-01 00:00:00        4     0.000000  0.000000     0.000000   \n",
-       "2     2024-01-01 00:01:00        3     0.634707  1.022530     0.010000   \n",
-       "3     2024-01-01 00:01:00        4     0.034722  0.005888     0.000000   \n",
-       "4     2024-01-01 00:02:00        3     1.269374  1.031863     0.010000   \n",
-       "...                   ...      ...          ...       ...          ...   \n",
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-       "89276 2024-01-31 23:58:00        3  1249.987428  1.999991     0.005519   \n",
-       "89277 2024-01-31 23:58:00        4   999.988640  0.999990     0.004481   \n",
-       "89278 2024-01-31 23:59:00        3  1250.001290  1.999997     0.005336   \n",
-       "89279 2024-01-31 23:59:00        4  1000.002481  0.999997     0.004664   \n",
-       "\n",
-       "       outflow_rate  storage_rate  precipitation   evaporation  drainage  \\\n",
-       "0          0.000000      0.010578       0.000579  1.840109e-07       0.0   \n",
-       "1          0.000000      0.000579       0.000579  2.925180e-09       0.0   \n",
-       "2          0.000000      0.010578       0.000579  7.583603e-07       0.0   \n",
-       "3          0.000000      0.000579       0.000579  1.274586e-08       0.0   \n",
-       "4          0.000000      0.010577       0.000579  1.507221e-06       0.0   \n",
-       "...             ...           ...            ...           ...       ...   \n",
-       "89275      0.006290     -0.000351       0.000579  1.157395e-04       0.0   \n",
-       "89276      0.004481      0.000231       0.000579  1.446765e-04       0.0   \n",
-       "89277      0.005203      0.000231       0.000579  1.157402e-04       0.0   \n",
-       "89278      0.004664      0.000207       0.000579  1.446766e-04       0.0   \n",
-       "89279      0.005471     -0.000063       0.000579  1.157402e-04       0.0   \n",
-       "\n",
-       "       infiltration  balance_error  relative_error  \n",
-       "0               0.0   6.906490e-08        0.316024  \n",
-       "1               0.0   1.169242e-09        0.333136  \n",
-       "2               0.0   1.609514e-07        0.191875  \n",
-       "3               0.0   2.839159e-09        0.200429  \n",
-       "4               0.0   2.753056e-07        0.167372  \n",
-       "...             ...            ...             ...  \n",
-       "89275           0.0  -2.707639e-04       -0.045742  \n",
-       "89276           0.0   1.241401e-03        0.236630  \n",
-       "89277           0.0  -4.899401e-04       -0.096563  \n",
-       "89278           0.0   8.995734e-04        0.171079  \n",
-       "89279           0.0  -2.811831e-04       -0.052235  \n",
-       "\n",
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "case1_example3 = case1(\"case1_example3\")\n",
     "case1_example3.create_model()"
@@ -950,7 +84,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67bf3197-b08b-43b2-977c-c49b16b4741d",
+   "id": "8",
    "metadata": {},
    "source": [
     "### Example 4: boundary levels on target, initial state above target"
@@ -958,295 +92,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "3fbb8789-ac5e-492a-be94-add01dcf779a",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "      <th>node_id</th>\n",
-       "      <th>storage</th>\n",
-       "      <th>level</th>\n",
-       "      <th>inflow_rate</th>\n",
-       "      <th>outflow_rate</th>\n",
-       "      <th>storage_rate</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>evaporation</th>\n",
-       "      <th>drainage</th>\n",
-       "      <th>infiltration</th>\n",
-       "      <th>balance_error</th>\n",
-       "      <th>relative_error</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>101249.988750</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>3.209287e-07</td>\n",
-       "      <td>-0.000723</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.001302</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-9.478442e-07</td>\n",
-       "      <td>-1.636535e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>100000.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>3.209287e-07</td>\n",
-       "      <td>1.666667e-01</td>\n",
-       "      <td>-0.166977</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.001157</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2.679622e-04</td>\n",
-       "      <td>-3.758205e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>101249.945385</td>\n",
-       "      <td>9.999998</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.856004e-06</td>\n",
-       "      <td>-0.000725</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.001302</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.486129e-07</td>\n",
-       "      <td>2.568361e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>99989.981377</td>\n",
-       "      <td>9.999499</td>\n",
-       "      <td>1.856004e-06</td>\n",
-       "      <td>1.666667e-01</td>\n",
-       "      <td>-0.167208</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.001157</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.493134e-05</td>\n",
-       "      <td>-5.841113e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2024-01-01 00:02:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>101249.901862</td>\n",
-       "      <td>9.999996</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>4.820334e-06</td>\n",
-       "      <td>-0.000728</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.001302</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.231118e-07</td>\n",
-       "      <td>3.856115e-04</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89275</th>\n",
-       "      <td>2024-01-31 23:57:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.989840</td>\n",
-       "      <td>0.999990</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>9.348269e-03</td>\n",
-       "      <td>-0.000008</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.123059e-03</td>\n",
-       "      <td>1.121133e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89276</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>72849.147047</td>\n",
-       "      <td>8.634089</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>-0.010526</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.001104</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-8.461456e-11</td>\n",
-       "      <td>-1.462140e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89277</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.989338</td>\n",
-       "      <td>0.999990</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>8.271165e-03</td>\n",
-       "      <td>0.000854</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.338075e-03</td>\n",
-       "      <td>1.477566e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89278</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>72848.515502</td>\n",
-       "      <td>8.634056</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>-0.010526</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.001104</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-7.554613e-11</td>\n",
-       "      <td>-1.305437e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89279</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000.040562</td>\n",
-       "      <td>1.000016</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>1.033537e-02</td>\n",
-       "      <td>-0.000686</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.132637e-04</td>\n",
-       "      <td>7.995064e-02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>89280 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     time  node_id        storage      level   inflow_rate  \\\n",
-       "0     2024-01-01 00:00:00        3  101249.988750  10.000000  0.000000e+00   \n",
-       "1     2024-01-01 00:00:00        4  100000.000000  10.000000  3.209287e-07   \n",
-       "2     2024-01-01 00:01:00        3  101249.945385   9.999998  0.000000e+00   \n",
-       "3     2024-01-01 00:01:00        4   99989.981377   9.999499  1.856004e-06   \n",
-       "4     2024-01-01 00:02:00        3  101249.901862   9.999996  0.000000e+00   \n",
-       "...                   ...      ...            ...        ...           ...   \n",
-       "89275 2024-01-31 23:57:00        4     999.989840   0.999990  1.000000e-02   \n",
-       "89276 2024-01-31 23:58:00        3   72849.147047   8.634089  0.000000e+00   \n",
-       "89277 2024-01-31 23:58:00        4     999.989338   0.999990  1.000000e-02   \n",
-       "89278 2024-01-31 23:59:00        3   72848.515502   8.634056  0.000000e+00   \n",
-       "89279 2024-01-31 23:59:00        4    1000.040562   1.000016  1.000000e-02   \n",
-       "\n",
-       "       outflow_rate  storage_rate  precipitation  evaporation  drainage  \\\n",
-       "0      3.209287e-07     -0.000723       0.000579     0.001302       0.0   \n",
-       "1      1.666667e-01     -0.166977       0.000579     0.001157       0.0   \n",
-       "2      1.856004e-06     -0.000725       0.000579     0.001302       0.0   \n",
-       "3      1.666667e-01     -0.167208       0.000579     0.001157       0.0   \n",
-       "4      4.820334e-06     -0.000728       0.000579     0.001302       0.0   \n",
-       "...             ...           ...            ...          ...       ...   \n",
-       "89275  9.348269e-03     -0.000008       0.000579     0.000116       0.0   \n",
-       "89276  1.000000e-02     -0.010526       0.000579     0.001104       0.0   \n",
-       "89277  8.271165e-03      0.000854       0.000579     0.000116       0.0   \n",
-       "89278  1.000000e-02     -0.010526       0.000579     0.001104       0.0   \n",
-       "89279  1.033537e-02     -0.000686       0.000579     0.000116       0.0   \n",
-       "\n",
-       "       infiltration  balance_error  relative_error  \n",
-       "0               0.0  -9.478442e-07   -1.636535e-03  \n",
-       "1               0.0  -2.679622e-04   -3.758205e-01  \n",
-       "2               0.0   1.486129e-07    2.568361e-04  \n",
-       "3               0.0  -3.493134e-05   -5.841113e-02  \n",
-       "4               0.0   2.231118e-07    3.856115e-04  \n",
-       "...             ...            ...             ...  \n",
-       "89275           0.0   1.123059e-03    1.121133e-01  \n",
-       "89276           0.0  -8.461456e-11   -1.462140e-07  \n",
-       "89277           0.0   1.338075e-03    1.477566e-01  \n",
-       "89278           0.0  -7.554613e-11   -1.305437e-07  \n",
-       "89279           0.0   8.132637e-04    7.995064e-02  \n",
-       "\n",
-       "[89280 rows x 13 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "case1_example1 = case1(\"case1_example4\")\n",
     "case1_example1.create_model()"
@@ -1254,7 +103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e907351e-2d44-4031-8201-5effcb6a232b",
+   "id": "10",
    "metadata": {},
    "source": [
     "# Case 2"
@@ -1262,7 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64862d6c-7da0-41a4-9285-105749b91ef6",
+   "id": "11",
    "metadata": {},
    "source": [
     "### Example 1: boundary and basin levels on target "
@@ -1270,297 +119,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "029f65b0-0158-46b1-be23-79564bc49f20",
-   "metadata": {
-    "scrolled": true
-   },
-   "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>time</th>\n",
-       "      <th>node_id</th>\n",
-       "      <th>storage</th>\n",
-       "      <th>level</th>\n",
-       "      <th>inflow_rate</th>\n",
-       "      <th>outflow_rate</th>\n",
-       "      <th>storage_rate</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>evaporation</th>\n",
-       "      <th>drainage</th>\n",
-       "      <th>infiltration</th>\n",
-       "      <th>balance_error</th>\n",
-       "      <th>relative_error</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.008750</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.005550</td>\n",
-       "      <td>0.004450</td>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000966</td>\n",
-       "      <td>0.190740</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000.009000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.004450</td>\n",
-       "      <td>0.005304</td>\n",
-       "      <td>-0.000580</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000390</td>\n",
-       "      <td>-0.083897</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.008069</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.004738</td>\n",
-       "      <td>0.005262</td>\n",
-       "      <td>-0.000367</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000302</td>\n",
-       "      <td>-0.061862</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.974196</td>\n",
-       "      <td>0.999983</td>\n",
-       "      <td>0.005262</td>\n",
-       "      <td>0.003804</td>\n",
-       "      <td>0.000948</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000394</td>\n",
-       "      <td>0.095823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2024-01-01 00:02:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1249.986076</td>\n",
-       "      <td>1.999991</td>\n",
-       "      <td>0.005517</td>\n",
-       "      <td>0.004483</td>\n",
-       "      <td>0.001021</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000131</td>\n",
-       "      <td>-0.028820</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2875</th>\n",
-       "      <td>2024-01-01 23:57:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000.007736</td>\n",
-       "      <td>0.999999</td>\n",
-       "      <td>0.005627</td>\n",
-       "      <td>0.005870</td>\n",
-       "      <td>-0.000150</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000209</td>\n",
-       "      <td>-0.036459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2876</th>\n",
-       "      <td>2024-01-01 23:58:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1249.996935</td>\n",
-       "      <td>1.999995</td>\n",
-       "      <td>0.005094</td>\n",
-       "      <td>0.004906</td>\n",
-       "      <td>0.000299</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000256</td>\n",
-       "      <td>-0.052041</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2877</th>\n",
-       "      <td>2024-01-01 23:58:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.998753</td>\n",
-       "      <td>0.999995</td>\n",
-       "      <td>0.004906</td>\n",
-       "      <td>0.004242</td>\n",
-       "      <td>-0.000002</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000550</td>\n",
-       "      <td>0.118730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2878</th>\n",
-       "      <td>2024-01-01 23:59:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1250.014871</td>\n",
-       "      <td>2.000002</td>\n",
-       "      <td>0.005783</td>\n",
-       "      <td>0.004217</td>\n",
-       "      <td>0.000006</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000145</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.001416</td>\n",
-       "      <td>0.279322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2879</th>\n",
-       "      <td>2024-01-01 23:59:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>999.998659</td>\n",
-       "      <td>0.999995</td>\n",
-       "      <td>0.004217</td>\n",
-       "      <td>0.004927</td>\n",
-       "      <td>-0.000476</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.000350</td>\n",
-       "      <td>-0.079689</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2880 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    time  node_id      storage     level  inflow_rate  \\\n",
-       "0    2024-01-01 00:00:00        3  1250.008750  2.000000     0.005550   \n",
-       "1    2024-01-01 00:00:00        4  1000.009000  1.000000     0.004450   \n",
-       "2    2024-01-01 00:01:00        3  1250.008069  2.000000     0.004738   \n",
-       "3    2024-01-01 00:01:00        4   999.974196  0.999983     0.005262   \n",
-       "4    2024-01-01 00:02:00        3  1249.986076  1.999991     0.005517   \n",
-       "...                  ...      ...          ...       ...          ...   \n",
-       "2875 2024-01-01 23:57:00        4  1000.007736  0.999999     0.005627   \n",
-       "2876 2024-01-01 23:58:00        3  1249.996935  1.999995     0.005094   \n",
-       "2877 2024-01-01 23:58:00        4   999.998753  0.999995     0.004906   \n",
-       "2878 2024-01-01 23:59:00        3  1250.014871  2.000002     0.005783   \n",
-       "2879 2024-01-01 23:59:00        4   999.998659  0.999995     0.004217   \n",
-       "\n",
-       "      outflow_rate  storage_rate  precipitation  evaporation  drainage  \\\n",
-       "0         0.004450     -0.000011            0.0     0.000145       0.0   \n",
-       "1         0.005304     -0.000580            0.0     0.000116       0.0   \n",
-       "2         0.005262     -0.000367            0.0     0.000145       0.0   \n",
-       "3         0.003804      0.000948            0.0     0.000116       0.0   \n",
-       "4         0.004483      0.001021            0.0     0.000145       0.0   \n",
-       "...            ...           ...            ...          ...       ...   \n",
-       "2875      0.005870     -0.000150            0.0     0.000116       0.0   \n",
-       "2876      0.004906      0.000299            0.0     0.000145       0.0   \n",
-       "2877      0.004242     -0.000002            0.0     0.000116       0.0   \n",
-       "2878      0.004217      0.000006            0.0     0.000145       0.0   \n",
-       "2879      0.004927     -0.000476            0.0     0.000116       0.0   \n",
-       "\n",
-       "      infiltration  balance_error  relative_error  \n",
-       "0              0.0       0.000966        0.190740  \n",
-       "1              0.0      -0.000390       -0.083897  \n",
-       "2              0.0      -0.000302       -0.061862  \n",
-       "3              0.0       0.000394        0.095823  \n",
-       "4              0.0      -0.000131       -0.028820  \n",
-       "...            ...            ...             ...  \n",
-       "2875           0.0      -0.000209       -0.036459  \n",
-       "2876           0.0      -0.000256       -0.052041  \n",
-       "2877           0.0       0.000550        0.118730  \n",
-       "2878           0.0       0.001416        0.279322  \n",
-       "2879           0.0      -0.000350       -0.079689  \n",
-       "\n",
-       "[2880 rows x 13 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# first, load in the simple model of case 1. Copy it.\n",
     "case2_example1 = case1(\"case2_example1\")\n",
@@ -1573,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c22f340-c381-4992-91db-f6186c4e3c80",
+   "id": "13",
    "metadata": {},
    "source": [
     "### Example 2: boundary and basins below target, third basin above\n",
@@ -1582,297 +144,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "a37e627d-2f8a-4d19-9ee6-f535420d277c",
-   "metadata": {
-    "scrolled": true
-   },
-   "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>time</th>\n",
-       "      <th>node_id</th>\n",
-       "      <th>storage</th>\n",
-       "      <th>level</th>\n",
-       "      <th>inflow_rate</th>\n",
-       "      <th>outflow_rate</th>\n",
-       "      <th>storage_rate</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>evaporation</th>\n",
-       "      <th>drainage</th>\n",
-       "      <th>infiltration</th>\n",
-       "      <th>balance_error</th>\n",
-       "      <th>relative_error</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.679887e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.045863e-08</td>\n",
-       "      <td>0.305011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.845589e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.611968e-08</td>\n",
-       "      <td>0.303833</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>8</td>\n",
-       "      <td>100000.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.186667</td>\n",
-       "      <td>-0.187824</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.157374e-03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.483442e-08</td>\n",
-       "      <td>-2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.599986</td>\n",
-       "      <td>1.021905</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.009999</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.009689e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.399768e-07</td>\n",
-       "      <td>0.181562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.599985</td>\n",
-       "      <td>0.024490</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.009999</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.624346e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.508812e-07</td>\n",
-       "      <td>0.180076</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133915</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>763.804815</td>\n",
-       "      <td>0.873955</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000101</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.011524e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.265898e-10</td>\n",
-       "      <td>-2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133916</th>\n",
-       "      <td>2024-01-31 23:58:00</td>\n",
-       "      <td>8</td>\n",
-       "      <td>5635.436365</td>\n",
-       "      <td>2.373904</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000275</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.747575e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.265873e-10</td>\n",
-       "      <td>-2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133917</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>953.253234</td>\n",
-       "      <td>1.873268</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000126</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.263410e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.582402e-10</td>\n",
-       "      <td>-2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133918</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>763.798745</td>\n",
-       "      <td>0.873951</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000101</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.011520e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.265904e-10</td>\n",
-       "      <td>-2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133919</th>\n",
-       "      <td>2024-01-31 23:59:00</td>\n",
-       "      <td>8</td>\n",
-       "      <td>5635.419879</td>\n",
-       "      <td>2.373900</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000275</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.747571e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.265705e-10</td>\n",
-       "      <td>-2.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>133920 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      time  node_id        storage      level  inflow_rate  \\\n",
-       "0      2024-01-01 00:00:00        3       0.000000   1.000000         0.01   \n",
-       "1      2024-01-01 00:00:00        4       0.000000   0.000000         0.01   \n",
-       "2      2024-01-01 00:00:00        8  100000.000000  10.000000         0.00   \n",
-       "3      2024-01-01 00:01:00        3       0.599986   1.021905         0.01   \n",
-       "4      2024-01-01 00:01:00        4       0.599985   0.024490         0.01   \n",
-       "...                    ...      ...            ...        ...          ...   \n",
-       "133915 2024-01-31 23:58:00        4     763.804815   0.873955         0.00   \n",
-       "133916 2024-01-31 23:58:00        8    5635.436365   2.373904         0.00   \n",
-       "133917 2024-01-31 23:59:00        3     953.253234   1.873268         0.00   \n",
-       "133918 2024-01-31 23:59:00        4     763.798745   0.873951         0.00   \n",
-       "133919 2024-01-31 23:59:00        8    5635.419879   2.373900         0.00   \n",
-       "\n",
-       "        outflow_rate  storage_rate  precipitation   evaporation  drainage  \\\n",
-       "0           0.000000      0.010000            0.0  1.679887e-07       0.0   \n",
-       "1           0.000000      0.010000            0.0  1.845589e-07       0.0   \n",
-       "2           0.186667     -0.187824            0.0  1.157374e-03       0.0   \n",
-       "3           0.000000      0.009999            0.0  7.009689e-07       0.0   \n",
-       "4           0.000000      0.009999            0.0  7.624346e-07       0.0   \n",
-       "...              ...           ...            ...           ...       ...   \n",
-       "133915      0.000000     -0.000101            0.0  1.011524e-04       0.0   \n",
-       "133916      0.000000     -0.000275            0.0  2.747575e-04       0.0   \n",
-       "133917      0.000000     -0.000126            0.0  1.263410e-04       0.0   \n",
-       "133918      0.000000     -0.000101            0.0  1.011520e-04       0.0   \n",
-       "133919      0.000000     -0.000275            0.0  2.747571e-04       0.0   \n",
-       "\n",
-       "        infiltration  balance_error  relative_error  \n",
-       "0                0.0   6.045863e-08        0.305011  \n",
-       "1                0.0   6.611968e-08        0.303833  \n",
-       "2                0.0  -3.483442e-08       -2.000000  \n",
-       "3                0.0   1.399768e-07        0.181562  \n",
-       "4                0.0   1.508812e-07        0.180076  \n",
-       "...              ...            ...             ...  \n",
-       "133915           0.0  -1.265898e-10       -2.000000  \n",
-       "133916           0.0  -1.265873e-10       -2.000000  \n",
-       "133917           0.0  -1.582402e-10       -2.000000  \n",
-       "133918           0.0  -1.265904e-10       -2.000000  \n",
-       "133919           0.0  -1.265705e-10       -2.000000  \n",
-       "\n",
-       "[133920 rows x 13 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# first, load in the simple model of case 1. Copy it.\n",
     "case2_example1 = case1(\"case2_example2\")\n",
@@ -1884,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8239f609-2cc3-473c-8608-65837df994e0",
+   "id": "15",
    "metadata": {},
    "source": [
     "### Example 3: boundary and basins below target, third basin above, pump rate of third peilgebied set to 0. \n",
@@ -1893,295 +168,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "4b4404cb-233a-485d-a9b0-ec1d841f3a7a",
+   "execution_count": null,
+   "id": "16",
    "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>time</th>\n",
-       "      <th>node_id</th>\n",
-       "      <th>storage</th>\n",
-       "      <th>level</th>\n",
-       "      <th>inflow_rate</th>\n",
-       "      <th>outflow_rate</th>\n",
-       "      <th>storage_rate</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>evaporation</th>\n",
-       "      <th>drainage</th>\n",
-       "      <th>infiltration</th>\n",
-       "      <th>balance_error</th>\n",
-       "      <th>relative_error</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.679887e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.045863e-08</td>\n",
-       "      <td>3.050107e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.845589e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.611968e-08</td>\n",
-       "      <td>3.038326e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>8</td>\n",
-       "      <td>100000.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.020000</td>\n",
-       "      <td>-0.021157</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.157403e-03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.912639e-09</td>\n",
-       "      <td>-2.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.599986</td>\n",
-       "      <td>1.021905</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.009999</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.009689e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.399768e-07</td>\n",
-       "      <td>1.815624e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2024-01-01 00:01:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.599985</td>\n",
-       "      <td>0.024490</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.009999</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.624346e-07</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.508812e-07</td>\n",
-       "      <td>1.800760e-01</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>259195</th>\n",
-       "      <td>2024-02-29 23:58:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>31693.700440</td>\n",
-       "      <td>5.629714</td>\n",
-       "      <td>1.743486e-07</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000651</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.515870e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.002416e-13</td>\n",
-       "      <td>-5.749492e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>259196</th>\n",
-       "      <td>2024-02-29 23:58:00</td>\n",
-       "      <td>8</td>\n",
-       "      <td>31696.417105</td>\n",
-       "      <td>5.629955</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>-0.000654</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.516149e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-8.542787e-14</td>\n",
-       "      <td>-2.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>259197</th>\n",
-       "      <td>2024-02-29 23:59:00</td>\n",
-       "      <td>3</td>\n",
-       "      <td>26785.514296</td>\n",
-       "      <td>5.629082</td>\n",
-       "      <td>2.257008e-06</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000667</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.697163e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2.457064e-14</td>\n",
-       "      <td>-1.088638e-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>259198</th>\n",
-       "      <td>2024-02-29 23:59:00</td>\n",
-       "      <td>4</td>\n",
-       "      <td>31693.661355</td>\n",
-       "      <td>5.629710</td>\n",
-       "      <td>1.743285e-07</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000651</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.515866e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.875781e-13</td>\n",
-       "      <td>-1.076003e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>259199</th>\n",
-       "      <td>2024-02-29 23:59:00</td>\n",
-       "      <td>8</td>\n",
-       "      <td>31696.377862</td>\n",
-       "      <td>5.629951</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>-0.000654</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.516145e-04</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.678335e-13</td>\n",
-       "      <td>-2.000000e+00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>259200 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      time  node_id        storage      level   inflow_rate  \\\n",
-       "0      2024-01-01 00:00:00        3       0.000000   1.000000  1.000000e-02   \n",
-       "1      2024-01-01 00:00:00        4       0.000000   0.000000  1.000000e-02   \n",
-       "2      2024-01-01 00:00:00        8  100000.000000  10.000000  0.000000e+00   \n",
-       "3      2024-01-01 00:01:00        3       0.599986   1.021905  1.000000e-02   \n",
-       "4      2024-01-01 00:01:00        4       0.599985   0.024490  1.000000e-02   \n",
-       "...                    ...      ...            ...        ...           ...   \n",
-       "259195 2024-02-29 23:58:00        4   31693.700440   5.629714  1.743486e-07   \n",
-       "259196 2024-02-29 23:58:00        8   31696.417105   5.629955  0.000000e+00   \n",
-       "259197 2024-02-29 23:59:00        3   26785.514296   5.629082  2.257008e-06   \n",
-       "259198 2024-02-29 23:59:00        4   31693.661355   5.629710  1.743285e-07   \n",
-       "259199 2024-02-29 23:59:00        8   31696.377862   5.629951  0.000000e+00   \n",
-       "\n",
-       "        outflow_rate  storage_rate  precipitation   evaporation  drainage  \\\n",
-       "0           0.000000      0.010000            0.0  1.679887e-07       0.0   \n",
-       "1           0.000000      0.010000            0.0  1.845589e-07       0.0   \n",
-       "2           0.020000     -0.021157            0.0  1.157403e-03       0.0   \n",
-       "3           0.000000      0.009999            0.0  7.009689e-07       0.0   \n",
-       "4           0.000000      0.009999            0.0  7.624346e-07       0.0   \n",
-       "...              ...           ...            ...           ...       ...   \n",
-       "259195      0.000000     -0.000651            0.0  6.515870e-04       0.0   \n",
-       "259196      0.000002     -0.000654            0.0  6.516149e-04       0.0   \n",
-       "259197      0.000000     -0.000667            0.0  6.697163e-04       0.0   \n",
-       "259198      0.000000     -0.000651            0.0  6.515866e-04       0.0   \n",
-       "259199      0.000002     -0.000654            0.0  6.516145e-04       0.0   \n",
-       "\n",
-       "        infiltration  balance_error  relative_error  \n",
-       "0                0.0   6.045863e-08    3.050107e-01  \n",
-       "1                0.0   6.611968e-08    3.038326e-01  \n",
-       "2                0.0  -3.912639e-09   -2.000000e+00  \n",
-       "3                0.0   1.399768e-07    1.815624e-01  \n",
-       "4                0.0   1.508812e-07    1.800760e-01  \n",
-       "...              ...            ...             ...  \n",
-       "259195           0.0  -1.002416e-13   -5.749492e-07  \n",
-       "259196           0.0  -8.542787e-14   -2.000000e+00  \n",
-       "259197           0.0  -2.457064e-14   -1.088638e-08  \n",
-       "259198           0.0  -1.875781e-13   -1.076003e-06  \n",
-       "259199           0.0  -1.678335e-13   -2.000000e+00  \n",
-       "\n",
-       "[259200 rows x 13 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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iwYIFKCwsxMiRIzF79mycPHmS28+CBQuQnp6OyZMnQyQS4X//+x8GDBgAkUjEbfP111/Dzs4OK1euxKNHj2BpaYkOHTrg//7v/2p1flSJYct3TCCEEEJIg1BYWIjExES4u7vD0NCQ73CIikgkEvj4+GDMmDH4+uuv+Q5H4XZHFVRCCCGEEC3x+PFjnDp1Cv7+/igqKsKGDRuQmJiICRMm8B1ardAgKUIIIYQQLaGjo4PQ0FB06tQJ3bp1Q0xMDM6cOVOrwWFCQBVUQgghhBAt4ezsjMuXL/MdRr1RBZUQQgghhAgKJaiEEEIIIURQKEElhBBCCCGCQgkqIYQQQggRFEpQCSGEEEKIoFCCSgghhBBCBIUSVEIIIYRoDekyp9ogNDQUlpaWfIfBC0pQCSGEEKIxgoKCMGzYsCqfT0tLw8CBAxXalzYls9qGJuonhBBCiNZwcHDgOwSiBFRBJYQQQojWKF8VLS4uxocffghHR0cYGhrCzc0NK1euBAC4ubkBAIYPHw6GYbjfAeDvv/9Gx44dYWhoiKZNm2Lp0qUoLS2VOcbvv/+O4cOHw9jYGJ6enjh8+HC1cRUXF+Ozzz5DkyZNYGJigi5duiA8PFxmm9DQULi4uMDY2BjDhw/Hy5cvK+3nm2++gZ2dHczMzDBjxgx8/vnnaNeuncw2ISEh8PHxgaGhIby9vbFx40bFTp6AUIJKCCGEEBnFxcVV/pRP1GratqSkpMZtVenHH3/E4cOHsXv3bsTHx2Pbtm1cIhoZGQmgLJlLS0vjfj958iQmTZqEOXPmIDY2Fr/++itCQ0OxfPlymX0vXboUY8aMwe3btzFo0CBMnDgRr169qjKWqVOn4vLlywgLC8Pt27cxevRoBAYG4sGDBwCAf//9F9OmTcP777+P6Oho9OrVC998843MPrZv347ly5dj9erVuHHjBlxcXLBp0yaZbTZv3owvvvgCy5cvR1xcHFasWIFFixZhy5Yt9TqX6sawLMvyHQQhhBBC1KuwsBCJiYlwd3eHoaGhzHNLly6t8nWenp6YMGEC9/uKFSsqJaJSrq6uCAoK4n5fs2YNCgoKZLZZvHhxreIOCgpCVlZWlX1HGYbBgQMHMGzYMMyZMwd3797FmTNnwDBMtdtK9ezZEwMHDsTChQu5x7Zt24bPPvsMqamp3Ou+/PJLfP311wCA/Px8mJmZ4dixYwgMDKx0nIcPH8LT0xNPnjxB48aNucf79u2Lzp07Y8WKFZgwYQIyMzNx/Phx7vlx48bhxIkTyMrKAgC89dZb8PX1xYYNG7htunfvjry8PERHRwMAXFxcsHr1aowfP57b5ptvvsGxY8dw5cqVKs6q+lTX7sqjCqqGu3DhAoYMGYLGjRvL7ezNsiyWLFmCxo0bw8jICAEBAbh7967MNkVFRfjoo49gY2MDExMTvPPOO3jy5Ika3wURGmW0q99++w0BAQEwNzcHwzDcByxp2Orbtl69eoWPPvoIXl5eMDY2houLC+bMmYPs7Gw1vxOiCYKCghAdHQ0vLy/MmTMHp06dqvE1N27cwLJly2Bqasr9zJw5E2lpaTLJdZs2bbi/m5iYwMzMDBkZGXL3efPmTbAsi+bNm8vsNyIiAg8fPgQAxMXFoWvXrjKvq/h7fHw8OnfuLPNY+d+fP3+OlJQUTJ8+XeY433zzDXccTUGDpDRcfn4+2rZti6lTp2LkyJGVnv/222+xdu1ahIaGonnz5vjmm2/Qr18/xMfHw8zMDADw8ccf4++//0ZYWBisra0xd+5cDB48GDdu3IBIJFL3WyICoIx2VVBQgMDAQAQGBspUIkjDVt+2lZqaitTUVHz33Xdo0aIFHj9+jPfeew+pqanYu3cvD+9IO1X3f1ZHR7a2NW/evCq3rVi1DA4Orl9gtdShQwckJibi+PHjOHPmDMaMGYO+fftW21YkEgmWLl2KESNGVHqufMVPT09P5jmGYSCRSKrcp0gkkntdNTU1BVD25UwRFc9p+ddJj79582Z06dJFZjuNu56zRGsAYA8cOMD9LpFIWAcHB3bVqlXcY4WFhayFhQX7yy+/sCzLsllZWayenh4bFhbGbfP06VNWR0eHPXHihNpiJ8JVl3ZV3vnz51kAbGZmphqiJZqkvm1Lavfu3ay+vj5bUlKiynC1zuvXr9nY2Fj29evXfIdSK1OmTGGHDh1a5fMV21V5J06cYAGwL1++ZFmWZfX09Ni9e/fKbOPn58dOmzat2hjkHcPCwoINCQmRu318fDwLgL1w4UKV+xw/fjw7cOBAmcfGjRvHWlhYcL936dKF/fDDD2W26d69O9u2bVvu9yZNmrDLli2rNn4+KdruqIKqxRITE5Geno7+/ftzjxkYGMDf3x9XrlzBrFmzcOPGDZSUlMhs07hxY7Rq1QpXrlzBgAED+AidCJgi7YqQuqhr28rOzoa5uTl0demS1lBkZ2dzfS6lrKys4OLiIvPYDz/8AEdHR7Rr1w46OjrYs2cPHBwcuMnv3dzccPbsWXTr1g0GBgZo1KgRvvrqKwwePBjOzs4YPXo0dHR0cPv2bcTExFQatKSo5s2bY+LEiZg8eTK+//57tG/fHi9evMC5c+fQunVrDBo0CHPmzIGfnx++/fZbDBs2DKdOncKJEydk9vPRRx9h5syZ8PX1hZ+fH3bt2oXbt2+jadOm3DZLlizBnDlzYG5ujoEDB6KoqAjXr19HZmYmPv300zrFzwfqg6rF0tPTAQD29vYyj9vb23PPpaenQ19fH40aNapyG0LKU6RdEVIXdWlbL1++xNdff01fjBqY8PBwtG/fXubnq6++qrSdqakpVq9eDV9fX3Tq1AlJSUk4duwY103h+++/x+nTp+Hs7Iz27dsDAAYMGIAjR47g9OnT6NSpE9566y2sXbsWrq6u9Yo5JCQEkydPxty5c+Hl5YV33nkH//77L5ydnQGUDYD6/fff8dNPP6Fdu3Y4deoUvvzyS5l9TJw4EQsXLsS8efO47gtBQUEyXQ9mzJiB33//HaGhoWjdujX8/f0RGhoKd3f3esWvbvR1swGQ119F3mjG2m5DGra6tCtCFKFo28rJycHbb7+NFi1a1HokONFcoaGhCA0NrfJ5tlyfzJkzZ2LmzJlVbjtkyBAMGTKk0uMDBgyo9g4iK6e/aE0DQfX09LB06dJqZ0iYNm0apk2bJvPY3LlzZX5ftGgRFi1axP3er18/eHh4yGwzYcIEmZkWNBFVULWYdDWNipWHjIwMrkLh4OCA4uJiZGZmVrkNIeUp0q4IqYvatK3c3FwEBgbC1NQUBw4cqDRghRBtVFBQgLVr1+Lu3bu4d+8eFi9ejDNnzmDKlCl8h6Z0lKBqEJZlER4ervBIP3d3dzg4OOD06dPcY8XFxYiIiICfnx8AoGPHjtDT05PZJi0tDXfu3OG2IdpNFe2KkNq2K0DxtpWTk4P+/ftDX18fhw8frnYuRUK0CcMwOHbsGHr06IGOHTvi77//xr59+9C3b1++Q1M6usWvQU6cOIFBgwbh+PHj3ETAeXl5SEhI4LZJTExEdHQ011n8448/xooVK+Dp6QlPT0+sWLECxsbGXOnfwsIC06dPx9y5c2FtbQ0rKyvMmzcPrVu31soGTypTRbsCyqpg6enp3H5iYmJgZmYGFxcXWFlZqfdNErWT166A+ret3Nxc9O/fHwUFBdi2bRtycnKQk5MDALC1tdW8qXQIqQUjIyOcOXOG7zDUQ4UzCRAlmzZtGguAnT59OveYdAqfij9TpkxhWbZs2pbFixezDg4OrIGBAduzZ082JiZGZr+vX79mP/zwQ9bKyoo1MjJiBw8ezCYnJ6vzrREeqapdLV68WO4+qpqGhWgXee2KZevftqp6PQA2MTFRje9Q82nqNFNEsyna7mipUwGTSCTYtGkT1/H622+/RU5ODiwsLDB//nwAgKWlJWbPnl1p4mRCqkLtiqgCtSvNo+iSk4Qok6LtjhJUAcvNzYWbmxtevXoFhmGgo6MDsVgMkUgEiUQClmVhZWWFpKQkbvUeQmpC7YqoArUrzUMJKuGDou2OvsYKmJmZGaKiotC5c2ewLAuxWAwA3J9+fn6Ijo6mD3tSK9J21bVrV2pXRGmk7crPz4/aFSGk3ihBFTgzMzOMGjWq0hQqhoaGCA8P5yb4JaQ2mjRpgvfff79Su9LX18fZs2epXZE6cXFxwZo1ayq1K2NjY/q8IoTUCiWoApaZmYnQ0FDExcWhpKRE5rnXr19jy5YtPEVGNJlYLMaePXtw7ty5Su2qqKgIq1ev5qpehNRGZGQkfv/990rtKj8/H9euXeMpKkKIJqIEVaCkyWlOTg5SUlIAAN7e3pgzZw66du0KoGw1jcjISD7DJBpGmpzGx8dzU/1I21WfPn0AAKdPn8aePXsoSSW1EhkZiWPHjuH+/fsA3rQr6So9hw8f5jM8QmolNDQUlpaWfIfRoFGCKlBnzpxBTk4ObGxs8Nlnn2HLli0YO3YsrKyscO7cOXz++efw8vLCqVOnkJuby3e4REPcuXMH8fHx0NXVxYcffijTro4ePYq1a9fCx8cH8fHxuHPnDt/hEg2Rm5uLU6dOAQBGjRqF0NBQrl3t2bMH27dvxzvvvMNzlESbBAUFgWEY7sfa2hqBgYG4ffu2UvY/duxY7ssW4QdN1C9QQ4YMga6uLvr27QszMzMUFxdj5cqVAAAdHR2sWLEC58+fh5ubGw06IApr06YNXr58CVdXVzRr1kymXTEMg08++QQPHz7E48eP0aZNG56jJZrCzMwM48ePR1JSEnr16oWSkhKZdqXpa4KT6onFwMWLQFoa4OgI9OgBqGO9hMDAQISEhAAoWxjkyy+/xODBg5GcnFzvfRsZGcHIyKje+yF1RxVUASkqKuL+bmhoiOHDh1eZfDIMg969e6Np06ZyX0+IlFgs5voESttNs2bNqty+WbNm6N27NxiGAQCUlJTQ7X4iV/nPnKZNm8q0G9Iw7N8PuLkBvXoBEyaU/enmVva4qhkYGMDBwQEODg5o164dFixYgJSUFDx//hwAsGDBAjRv3hzGxsZo2rQpFi1aJNM/+tatW+jVqxfMzMxgbm6Ojh074vr16wAq3+JfsmQJ2rVrh61bt8LNzQ0WFhYYN24c3cFUIUpQBSIzMxMbN27ElStX6vT6Fy9eYMOGDdQnlciQ9jndtWtXpYEriigpKcGuXbuoTyqpJDIyEhs2bMCLFy/4DoXwZP9+YNQo4MkT2cefPi17XB1JqlReXh62b98ODw8PWFtbAyir7IeGhiI2Nhbr16/H5s2b8cMPP3CvmThxIpycnBAZGYkbN27g888/rzQDRXkPHz7EwYMHceTIERw5cgQRERFYtWqVyt9bQ0UJqgCUHxAVFRVVp0QiNjYWeXl5OHbsGCWpBIDsgKikpCQ8e/as1vt49uwZkpKSEB8fT0kq4UgHROXl5SE2NpbvcAgPxGIgOBiQt9SP9LGPPy7bTlWOHDkCU1NTmJqawszMDIcPH8auXbu4lcq+/PJL+Pn5wc3NDUOGDMHcuXOxe/du7vXJycno27cvvL294enpidGjR6Nt27ZVHk8ikSA0NBStWrVCjx498O677+Ls2bOqe4MNHCWoPCufnNrY2GDy5MnVfoOrSo8ePeDn5wcAlKQSmeRUJBJh/PjxcHJyqvV+nJycMH78eIhEIkpSCYA3ySlQNvl+jx49eI6I8OHixcqV0/JYFkhJKdtOVXr16oXo6GhER0fj33//Rf/+/TFw4EA8fvwYALB37150794dDg4OMDU1xaJFi2T6p3766aeYMWMG+vbti1WrVuHhw4fVHq/imA9HR0dkZGSo5s0RSlD5JC85reuAJ4Zh0LdvX0pSidzktLo+pzVp1qwZJakEQOXktG/fvtTntIFKS1PudnVhYmICDw8PeHh4oHPnzvjjjz+Qn5+PzZs3459//sG4ceMwcOBAHDlyBFFRUfjiiy9QXFzMvX7JkiW4e/cu3n77bZw7dw4tWrTAgQMHqjxexeIRwzCQSCQqe38NHSWoPFFmcipFSSpRdnIqRUkqoeSUlOfoqNztlIFhGOjo6OD169e4fPkyXF1d8cUXX8DX1xeenp5cZbW85s2b45NPPsGpU6cwYsQIblYAwj9KUHny8OFDpSanUhWT1Fu3btE3vAbk1atXSEpKUmpyKlU+SU1KSsKrV6+Utm8ibBKJBLdu3QJAySkp06MH4OQEVNUMGAZwdi7bTlWKioqQnp6O9PR0xMXF4aOPPkJeXh6GDBkCDw8PJCcnIywsDA8fPsSPP/4oUx19/fo1PvzwQ4SHh+Px48e4fPkyIiMj4ePjo7qASa3QPKg88fX1hY6ODjw9PZU+j6k0SbWwsECbNm24DuNE+9na2uLdd99FYWGhUpNTKWmSamhoCFtbW6XvnwiTjo4OJk2ahNu3b6NTp06UnBKIRMD69WWj9RlGdrCUtHmsW6fa+VBPnDgBx/9KtGZmZvD29saePXsQEBAAAPjkk0/w4YcfoqioCG+//TYWLVqEJUuW/Be/CC9fvsTkyZPx7Nkz2NjYYMSIEVi6dKnqAia1wrCsvDF4RBWysrJgaGgIQ0PDWr+2/ITqCxcuhL6+fq338ezZM9jb29f6dUTYxGIxXr16VaeEURnt6vnz57CysoJIHTNzE7Wq62eGMtoVUb3CwkIkJibC3d29TtcloGwqqeBg2QFTzs5lyemIEcqJk2gXRdsdldbUJDMzEyEhIdi+fTsKCwvVfvyrV6/il19+oT6pWkba5/SPP/5Aamqq2o+fmpqKP/74g/qkaqHIyEj88ssvuHr1Kt+hEAEbMQJISgLOnwd27Cj7MzGRklNSf3SLXw3KD4jS19dHaWmp2mOQrnYhHeTQqVMntcdAlKvigKjXr1+rPYbXr1+jtLSUGzg1evRoqqRqgfIDovLy8niOhgidSAT8d1edEKWhCqqKyRutb2pqqvY4+vXrh65duwKg0f3aQFWj9WuLRvdrH3mj9QkhRN0oQVUhVUwlVVcMw1CSqiWEkpxKUZKqPWgqKUKIUFCCqiJCSk6lKEnVfEJLTqUoSdV8lJwSQoSEElQVKS0thVgsFkxyKlUxSS2/qgYRPolEgpKSEkElp1Llk9SSkhKaf1fDFBUVAaDklBAiDDRISkVsbW0RFBQEAwMDwSSnUtIktXnz5nBzc+M7HFILenp6GDduHJ49ewYnJye+w6mkWbNmCAoKgr29faVlAYmwde/eHU5OTnB1daXklBDCO6qgKlFmZiaSkpK4321sbASXnEoxDCOTnBYVFSE2Npa/gEiVxGIxbt++DemUxXp6eoJMTqWcnJy45JRlWdy+fZtu9wtUbGwsVzkFADc3N0pOCSGCQAmqkkj7nG7fvl3uer9CVlJSgm3btmHPnj3UJ1VgpH1ODxw4gPDwcL7DqbXw8HAcOHCA+qQKUGRkJPbs2YNt27ahpKSE73AIIUQGJahKUH5AlKWlJaysrPgOqVZ0dXXh7OwMgAZOCUn5AVG6urpwcXHhO6Rac3FxoYFTAlR+QJSLiwt0dam3F9FODMPg4MGDfIfRoLi5uWHdunX13g8lqPUkxNH6tUWj+4WnYnI6btw4QQ2IUhSN7hceGq1PtEFQUBAYhgHDMNDT04O9vT369euHP//8U2aAZlpaGgYOHMhbnKGhobC0tKzTa1mWxW+//YYuXbrA1NQUlpaW8PX1xbp161BQUCCIGFWJEtR60IbkVIqSVOHQluRUipJU4aDklKgKy7IIDw/n+sqrQ2BgINLS0pCUlITjx4+jV69eCA4OxuDBg7kVGx0cHGBgYKD0Y4vFYpXPVPLuu+/i448/xtChQ3H+/HlER0dj0aJFOHToEE6dOqXSY8uj7ll/KEGto5ycHK1JTqXkJalRUVE8R9WwsCyLvXv3ak1yKlUxSd27d69aL2QEiIqKouSUqMyJEyfQq1cvnDx5Um3HNDAwgIODA5o0aYIOHTrg//7v/3Do0CEcP34coaGhAGRv8RcXF+PDDz+Eo6MjDA0N4ebmhpUrV3L7y8rKwv/+9z/Y29vD0NAQrVq1wpEjRwC8qTIeOXIELVq0gIGBAR4/fozi4mJ89tlnaNKkCUxMTNClSxduvEB4eDimTp2K7Oxsrtq7ZMkSLpaqXgcAu3fvxvbt27Fz50783//9Hzp16gQ3NzcMHToU586dQ69evQCUTT24bNkyODk5wcDAAO3atcOJEye4/SQlJYFhGOzfvx+9evWCsbEx2rZti6tXr9YYo5ubG7755hsEBQXBwsICM2fOBADs27cPLVu2hIGBAdzc3PD9998r85+VQwlqHZmYmMDJyUlrklOp8kmqiYkJmjRpwndIDQrDMPD09NSq5FRKmqTq6urC09OTkiM1k14IKTklqrB3716ZP/nSu3dvtG3bFvv376/03I8//ojDhw9j9+7diI+Px7Zt27jZbCQSCQYOHIgrV65g27ZtiI2NxapVqyASibjXFxQUYOXKlfj9999x9+5d2NnZYerUqbh8+TLCwsJw+/ZtjB49GoGBgXjw4AH8/Pywbt06mJubIy0tDWlpaZg3bx4AVPs6ANi+fTu8vLwwdOjQSu+DYRhYWFgAANavX4/vv/8e3333HW7fvo0BAwbgnXfe4fYj9cUXX2DevHmIjo5G8+bNMX78eJSWllYbIwCsWbMGrVq1wo0bN7Bo0SLcuHEDY8aMwbhx4xATE4MlS5Zg0aJF3BcCZaKe8XUkEokwYsQIFBYWwsTEhO9wlKp8kqotibcm6dChAzw9PbXy3Ddr1gxz5szRyvcmdHZ2dpg1axZMTU0pOSX1JpFIsGnTJmRlZQGQTVDd3d0BAJaWlpg9ezZ0dNRbC/P29sbt27crPZ6cnAxPT090794dDMPA1dWVe+7MmTO4du0a4uLi0Lx5cwBA06ZNZV5fUlKCjRs3om3btgCAhw8fYufOnXjy5AkaN24MAJg3bx5OnDiBkJAQrFixAhYWFmAYBg4ODtx+FHndgwcP4OXlVeN7/e6777BgwQKMGzcOALB69WqcP38e69atw88//8xtN2/ePLz99tsAgKVLl6Jly5ZISEiAt7e33BilevfuLZOwTpw4EX369MGiRYsAAM2bN0dsbCzWrFmDoKCgGuOtDUpQayEzMxM3b95E7969wTAMRCKR1iWnUgzDyCQRiYmJePnyJXx9fXmMSjuJxWKcOXMG3bt359qTNidw5d9bfn4+Ll26hL59+8pUKohyXL9+HdbW1lzCoM3tiqhXfn4+vvrqK7x69QoMw3BJaF5eHhYtWgSWZWFlZcXLHUaWZeV+CQsKCkK/fv3g5eWFwMBADB48GP379wcAREdHw8nJiUtO5dHX10ebNm2432/evAmWZSu9pqioCNbW1lXuR5HXVfUeysvJyUFqaiq6desm83i3bt1w69YtmcfKx+3o6AgAyMjIgLe3d7XHqHjNj4uLq1TV7datG9atWwexWKzUz3FKUBVUfkCUSCRCQEAA3yGpzatXr7Bjxw6UlpaCZVl06tSJ75C0RvkBUcnJyZgxY0aDqW6xLIsdO3YgNTUVmZmZGD16NCWpSiQdEKWrq4vZs2dr3PR3RNjMzMwQFRWF8ePH4+rVq9zAR7FYDIZh4Ofnh7CwMF6+FMXFxXFfysrr0KEDEhMTcfz4cZw5cwZjxoxB3759sXfvXhgZGdW4XyMjI5nPZ4lEApFIhBs3blT67DI1Na1yP4q8rnnz5oiLi6sxJgCVrhnyktvyK/tJn1NkkFfFIpy8fatqPAH1QVVAxdH6HTt25DsktWrUqBGXlNLofuUpn5yKRCKuMt9QMAyD3r170+h+FSg/Wr9z585o1KgRzxERbeTi4oLz58/D2NhY5nFjY2OEh4dz82ur07lz5xATE4ORI0fKfd7c3Bxjx47F5s2bsWvXLuzbtw+vXr1CmzZt8OTJE9y/f1/hY7Vv3x5isRgZGRnw8PCQ+ZHeLtfX16/0uabI6yZMmID79+/j0KFDlY7Lsiyys7Nhbm6Oxo0b49KlSzLPX7lyBT4+Pgq/D3kxVqVFixZyj9e8eXOlFxgoQa2BNk0lVVc0BZXyVUxOx48fr1UDohRFU1ApH00lRdTp2rVryM/Pl3ksPz8f165dU/mxi4qKkJ6ejqdPn+LmzZtYsWIFhg4disGDB2Py5MmVtv/hhx8QFhaGe/fu4f79+9izZw8cHBxgaWkJf39/9OzZEyNHjsTp06e5Smv5EfEVNW/eHBMnTsTkyZOxf/9+JCYmIjIyEqtXr+b+D7q5uSEvLw9nz57FixcvUFBQoNDrxowZg7Fjx2L8+PFYuXIlrl+/jsePH+PIkSPo27cvzp8/DwCYP38+Vq9ejV27diE+Ph6ff/45oqOjERwcrPB5lBdjVebOnYuzZ8/i66+/xv3797FlyxZs2LBBpp+qslCCWg1KTt+gJFV5KDmVRUmq8lByStTt77//BgAMGzYMCQkJXP/Ew4cPq/zYJ06cgKOjI9zc3BAYGIjz58/jxx9/xKFDh+RW80xNTbF69Wr4+vqiU6dOSEpKwrFjx7j+s/v27UOnTp0wfvx4tGjRAp999lmNn0UhISGYPHky5s6dCy8vL7zzzjv4999/ueqxn58f3nvvPYwdOxa2trb49ttvFXodwzDYsWMH1q5diwMHDsDf3x9t2rTBkiVLMHToUAwYMAAAMGfOHMydOxdz585F69atceLECRw+fBienp4Kn8eqYpSnQ4cO2L17N8LCwtCqVSt89dVXWLZsmdIHSAEAw9JkhHKVlpZi48aNyMzMFERyWlxczM3XtnDhQujr6/MSB8uyOH36NDeH2sSJE+Hh4cFLLJrq6NGjuH79uiCSU6G0K+DNyFaxWAxfX19uxClRzIMHD7Bjxw4A/CenQmpXpGqFhYVITEyEu7s7DA0N67SPy5cv4/Hjxxg/fjwYhgHLsti5cydcXV0rDd4hBFC83VGCWo24uDiEh4dj0qRJvCWnLMtiy5YtSEtL41ZxkH7YOzo6YsqUKWq/CEmT1KysLIwcOZIGttRSVlYWtm/fjsDAQN6SUyG2K6AsST1x4gQmTpwoyKX3hEwsFmPfvn1o1KgRb8mpUNsVkU8ZCSohtaVou6NR/NXw8fGBl5eX2udwK08ikSArK0tmiTHp37OysrjRgOokvd3Psiyv50ZT8TU3YHlCbFdA2e1+vs+NphKJRBg1ahS3GgwfhNquCCGah64C5Uj7nEonHgbA+4VSJBIhMDBQ7nMDBw7k7cO+/Lx3LMviyJEj1Ce1CtI+p+WnDKF2VbXy5yYuLo76pFYjMjISR48e5aZ50dHR4bVCKeR2RQjRLJSg/keanD5+/BhHjx7lOxwZXl5eMite6OjowN3dvdoJhdXp3r17uHHjBg2ckkOanMbGxuLgwYPVjo5UN6G3q4KCAhw8eBCxsbGUpMohHRB1/fp13Lt3j+9wOEJvV4QQzUAJKiqP1n/nnXf4DkmG9Ja6FMuyCAwMFExfLm9vbxrdL0fF0fpjxoypNF8gn4TeroyNjTFmzBga3S9HxdH6Na0Go05Cb1eEEM3Q4BNUTZlKytbWlvt7hw4dYGdnx2M0smgKqso0ZSopIbcrgKagkkcTppISersihAhfg05QNSU5rahnz558h1AJJalvaEpyWpEQ2xVASWp5mpCcViTUdkUIEbYGnaAeO3ZM45JTAIKdDqRiknrixAlkZmbyHJX6RUVFaVxyCgi3XQGVk9SoqCi+Q1K7zMxMblUbTUlOAWG3K0KIcDXoaaaGDRuGo0ePYuDAgRqTnAqdNEnV0dFB48aNG+Qa4B07dkRGRga8vLw0JjnVBNIkNT4+Hh07duQ7HLVr1KgRRo4cidTUVPTp00cjklNCCKmrBldBLSkp4f5uYmKCMWPGUHKqZAzDoG/fvmjRogX3WPnzro3EYjEkEgmAsvc/aNAgSk5VoFmzZhg0aBCXnEkkEq2/3V/+/06LFi00pnJKiDZjGAYHDx7kOwyFLVmyBO3ateM7jFppUAlqZmYmfv755wZ5e5BP2dnZ2LRpk9b2SZX2Od23bx+XpBLVk0gk2Ldvn1b3SY2MjMSmTZuQnZ3NdyiECEpQUBCGDRvGdxgc6QIZDMNAV1cXLi4u+PTTT1FUVMR3aBqrwSSo0gFR2dnZuHr1KkpLS/kOqcGIiYlBZmamVg6cKj8gKj4+Hs+ePeM7pAbj2bNn3HnXxiRVOiAqMzMTMTExfIdDSJXEEjHCk8KxM2YnwpPCIZZo1/9FRYWEhCAtLQ2JiYnYuHEjtm7dim+++YbvsJRG3XdCG0SCWnG0/rvvvgtd3Qbd/VatunXrppWj++WN1nd0dOQ7rAbD0dFRa0f3lx+t37VrV3Tr1o3niAiRb3/cfritd0OvLb0wYf8E9NrSC27r3bA/bj9vMcXGxmLQoEEwNTWFvb093n33Xbx48QIA8Ouvv6JJkyaV7na98847mDJlCvf733//jY4dO8LQ0BBNmzbF0qVLayxsWVpawsHBAc7Ozhg8eDDeeecd3Lx5U2abTZs2oVmzZtDX14eXlxe2bt3KPZeUlASGYRAdHc09lpWVBYZhEB4eDgAIDw8HwzA4e/YsfH19YWxsDD8/P8THx8scZ9WqVbC3t4eZmRmmT5+OwsJCmecjIyPRr18/2NjYwMLCAv7+/pViZRgGv/zyC4YOHQoTExN888038PDwwHfffSez3Z07d6Cjo4OHDx9We35qS+sTVE2dSkqbaOMUVJo6lZS20cYpqComp/369aM+p0SQ9sftx6jdo/Ak54nM409znmLU7lG8JKlpaWnw9/dHu3btcP36dZw4cQLPnj3DmDFjAACjR4/GixcvcP78ee41mZmZOHnyJCZOnAgAOHnyJCZNmoQ5c+YgNjYWv/76K0JDQ7F8+XKF47h//z7Onz+PLl26cI8dOHAAwcHBmDt3Lu7cuYNZs2Zh6tSpMrEo6osvvsD333+P69evQ1dXF9OmTeOe2717NxYvXozly5fj+vXrcHR0xMaNG2Ven5ubiylTpuDixYv4559/4OnpiUGDBiE3N1dmu8WLF2Po0KGIiYnBtGnTMG3aNISEhMhs8+eff6JHjx5KvwZqdYJKyalwaFOSSsmpsGhTkkrJKdEUYokYwSeCwYKt9Jz0sY9PfKz22/2bNm1Chw4dsGLFCnh7e6N9+/b4888/cf78edy/fx9WVlYIDAzEjh07uNfs2bMHVlZW6NOnDwBg+fLl+PzzzzFlyhQ0bdoU/fr1w9dff41ff/212mOPHz8epqamMDQ0hJeXF1q2bImFCxdyz3/33XcICgrC+++/j+bNm+PTTz/FiBEjKlUkFbF8+XL4+/ujRYsW+Pzzz3HlyhWuSrpu3TpMmzYNM2bMgJeXF7755huZQcsA0Lt3b0yaNAk+Pj7w8fHBr7/+ioKCAkRERMhsN2HCBEybNg1NmzaFq6srpk6divj4eFy7dg1A2W3/bdu2ySTIyqLVCWpsbCwlpwJSMUmNjIzUyL7AGRkZePjwISWnAlI+SX348CEyMjL4DqnWSktLuS9tlJwSobuYfLFS5bQ8FixSclJwMfmiGqMCbty4gfPnz8PU1JT7kS4FLL0FPXHiROzbt48bwLR9+3aMGzcOIpGI28eyZctk9jFz5kykpaWhoKCgymP/8MMPiI6Oxq1bt3DkyBHcv38f7777Lvd8XFxcpe463bp1Q1xcXK3fZ5s2bbi/S7uWST/34uLiuOusVMXfMzIy8N5776F58+awsLCAhYUF8vLykJycLLOdr6+vzO+Ojo54++238eeffwIAjhw5gsLCQowePbrW76EmWt0R08/PDzo6OmjVqhUlpwIhTVLNzMzQunVrjewLLO37yLIsJacCIk1SGYbRyL7Aurq6mDx5MmJiYvDWW29RckoELS03TanbKYtEIsGQIUOwevXqSs9JPxeGDBkCiUSCo0ePolOnTrh48SLWrl0rs4+lS5dixIgRlfZR3cITDg4O8PDwAAB4eXkhNzcX48eP5/puAqj0/5plWe4xHR0d7jGpqgYm6enpcX8vP+2eooKCgvD8+XOsW7cOrq6uMDAwQNeuXVFcXCyznYmJSaXXzpgxA++++y5++OEHhISEYOzYsTA2Nlb42IrSvOygBtnZ2TA2Noaenh4Yhqn0rYHwT96/y8uXL2Ftbc1TRDUTi8XIycnhFh5o2rQpzxEReSp+YcjMzIS5uTlXGRGi8m3f1NSUPrOIRnA0U+xLoKLbKUuHDh2wb98+uLm5VVkAMTIywogRI7B9+3YkJCSgefPmMot/dOjQAfHx8VxSWVfSz53Xr18DAHx8fHDp0iVMnjyZ2+bKlSvw8fEBANja2gIo60fbvn17AJAZMKUoHx8f/PPPPzLH+eeff2S2uXjxIjZu3IhBgwYBAFJSUriBZDUZNGgQTExMsGnTJhw/fhwXLlyodYyK0KoENTMzE1u2bIG1tTXGjRsn8w2DCFd0dDQOHz6MgQMHolOnTnyHU4m0z+mTJ08QFBQEGxsbvkMiCnj+/Dm2bNkCJycnjB49WpBJamRkJI4fP4533nlH4ybRJg1bD5cecDJ3wtOcp3L7oTJg4GTuhB4uPVQWQ3Z2dqUEbtasWdi8eTPGjx+P+fPnw8bGBgkJCQgLC8PmzZu5z4GJEydiyJAhuHv3LiZNmiSzj6+++gqDBw+Gs7MzRo8eDR0dHdy+fRsxMTHVThuVlZWF9PR0SCQSPHjwAMuWLUPz5s25BHT+/PkYM2YMOnTogD59+uDvv//G/v37cebMGQBlifNbb72FVatWwc3NDS9evMCXX35Z6/MSHByMKVOmwNfXF927d8f27dtx9+5dmcKKh4cHtm7dCl9fX+Tk5GD+/PkwMjJSaP8ikQhBQUFYuHAhPDw8VPalWmv6oEqT0+zsbGRnZ9PkuBrk+fPnYFlWkAOnyg+IKiwspAnTNUhOTg4KCwsFO3BKOiCKZVk8f/6c73AIqRWRjgjrA9cDKEtGy5P+vi5wHUQ6qvtiGB4ejvbt28v8fPXVV7h8+TLEYjEGDBiAVq1aITg4GBYWFtwtdKBskJCVlRXi4+MxYcIEmf0OGDAAR44cwenTp9GpUye89dZbWLt2LVxdXauNZ+rUqXB0dISTkxPGjx+Pli1b4vjx41wld9iwYVi/fj3WrFmDli1b4tdff0VISAgCAgK4ffz5558oKSmBr68vgoOD6zSP6tixY/HVV19hwYIF6NixIx4/fozZs2fLbPPnn38iMzMT7du3x7vvvos5c+bAzs5O4WNMnz4dxcXFKhkcJcWw5Ts7aKjyyam1tTWmTJmidX1Oi4uLsXLlSgDAwoULoa+vz3NEysOyLE6fPo2rV68CKLt9IIRKakMYra/N7QoAVzkRi8Xw8vISTCVV20fra3u70haFhYVITEyEu7t7tX0rq7M/bj+CTwTLDJhyNnfGusB1GOFTuQ8n0Q6XL19GQEAAnjx5Ant7+1q9VtF2p/G3+BtCcqrtpAOnAODq1avchZvPJLUhJKcNgYeHB8aNG4ewsDCuksp3kqrtySlpWEb4jMBQr6G4mHwRablpcDRzRA+XHiqtnBL+FBUVISUlBYsWLcKYMWNqnZzWhkbf4qfkVHsIaZ5USk61izRJFcI8qZScEm0k0hEhwC0A41uPR4BbACWnWmznzp3w8vJCdnY2vv32W5UeS6MT1NevX6OwsJCSUy1RMUnlq79naWkp8vLyKDnVIuWT1Ly8PN7m35W2aUpOCSGaKCgoCGKxGDdu3ECTJk1UeiyNvsXfuHFjTJkyBaamppScaglpkuru7l7vKT7qysDAAJMmTUJGRgZcXFx4iYEon4eHByZPngw7OzsYGBjwEkOfPn3g6uoKDw8PSk4JIaQaGldBzczMxNOnT7nfHR0dKTnVMgzDwNPTk7uAl5aWIj4+XqXHFIvFMqt5GBoaUnKqhVxcXGQ65cfFxan8dn98fDxXsa3YtgkhhMinUQlqZmYmQkNDsXXrVqSmpvIdDlEDsViMsLAwhIWFqaxPqrTP6e7du7mZBIj2u3r1Knbv3q3SPqmRkZFc+xXaNFeEECJkgkxQL1y4gCFDhqBx48ZgGAYHDx7kktOcnByYmZlR1fQ/S5YsAcMwMj8ODg58h6U0Ojo63Nxs9R04tWnTJrRp0wbm5uYwNzdH165dceTIEZkBUbWZB07bPX36FJMmTYK1tTWMjY3Rrl073Lhxg++wlMbOzk4lA6dWrlwJhmEwfvx4bkCUvb29zByMDZWbm1ulzyuGYfDBBx/wHZrgrFy5Ep06dYKZmRns7OwwbNgwld9JIkRIBPmJmZ+fj7Zt22LDhg0AgLy8PC45tbGxweTJkylBLadly5ZIS0vjfmJiYvgOSWmUObrfyckJq1atwvXr13H9+nUEBARg2LBhuHjxIg2IqiAzMxPdunWDnp4ejh8/jtjYWHz//fewtLTkOzSladasGcaPH6/UJDUyMhK//fYbPDw8kJiYCADw8/ND37596bY+ys5P+c+q06dPAwBGjx7Nc2TCExERgQ8++AD//PMPTp8+jdLSUvTv3x/5+fl8h0aIWghykNTAgQMxcOBA7vdz587B1dWVktMq6OrqalXVtCJlzZM6ZMgQ7u9isRht27aFnp4eUlNT8emnn1JyWs7q1avh7OyMkJAQ7jE3Nzf+AlIRaZK6c+fOes+TmpeXh4kTJ2LevHlYs2YNAEpOK5KuNS61atUqNGvWDP7+/jxFJFwnTpyQ+T0kJAR2dna4ceMGevbsyVNUhKiPICuoUtIpWQoKCig5rcaDBw/QuHFjuLu7Y9y4cXj06BHfISmdvEpqXW83syyLXbt2Ye/evSgpKcGsWbMoOa3g8OHD8PX1xejRo2FnZ4f27dtj8+bNfIelEvIqqXVZYO+DDz6Ar68vXrx4AaBslhFKTqtWXFyMbdu2Ydq0aXSOFCC9HlpZWfEcScMQFBSEYcOG8R1GgyboBNXY2BgAYG5uTslpFbp06YK//voLJ0+exObNm5Geng4/Pz+8fPmS79CUrnySamBgUKcVLGJiYmBmZobJkyfj6NGj+PXXXxEYGKiCaDXbo0ePsGnTJnh6euLkyZN47733MGfOHPz11198h6YS0iRVV1cXzs7OtU6YwsLCcPPmTSxbtgwGBgawsLCo034akoMHDyIrKwtBQUF8hyJ4LMvi008/Rffu3dGqVSu+wxGMlJQUTJ8+HY0bN4a+vj5cXV0RHBxcq+tfUlISGIZBdHR0vWIJDw8HwzDIysqq137IG4K8xS+lp6cHAOjVqxclp1Uo3xWidevW6Nq1K5o1a4YtW7bg008/5TEy1ZAmqZ07d65Tf0gvLy9ER0cjKysL27dvx+eff46uXbuiRYsWyg9Wg0kkEvj6+mLFihUAgPbt2+Pu3bvYtGkTJk+ezHN0qtGsWTN88MEHtW5XKSkpCA4OxqlTp+Dh4YH33nsPJ06coOS0Bn/88QcGDhyIxo0b8x2K4H344Ye4ffs2Ll26xHcoVSosLcSVlCvwc/aDoW7V66sry6NHj9C1a1c0b94cO3fuhLu7O+7evYv58+fj+PHj+Oeff6jarOEEV0HNzMzE5cuXZW6xGRkZ8RiRZjExMUHr1q3x4MEDvkNRGYZhZJKI1NRU3Lx5s9rXiMVinDt3DhKJBB4eHvD19cUPP/yAtm3bYv369SqOWPM4OjpWStp9fHyQnJzMU0TqUb5dFRYW4ty5czUOnNq9ezcyMjLQsWNH6OrqwsbGBhEREfjxxx+hq6tL00vJ8fjxY5w5cwYzZszgOxTB++ijj3D48GGcP38eTk5OfIdTCcuyOBB3AM1/ao4+f/VB85+a40DcgTp1k6mNDz74APr6+jh16hT8/f3h4uKCgQMH4syZM3j69Cm++OILAOBmAirP0tISoaGhAAB3d3cAZV/CGYZBQECA3OOxLItvv/0WTZs2hZGREdq2bYu9e/cCKKvC9urVCwDQqFEjMAxDdwaUgNcKqlgixsXki0jLTYOjmSNambXC1r+2IicnBzo6Olx/Q1KmBCVIQQoKSwuhr68vd5uioiLExcWhR48eao6OH9nZ2fjrr79QVFQEsViMTp06QSwGLl4E0tIAR0fAz0+M/fvLppJKSUnB5MmTueoWy7IoKiri+V3wS1676tatW6Upbe7fvw9XV1c+QlQ7aT/lpKQkZGRk/DfKXCTTrnr0AG7ejMTLly8RHByMsWPHcnd6pk6dCm9vbyxYsKBOA660SWlpaaXPK+mAn7fffpunqISj4nWwh0sPiHREYFkWH330EQ4cOIDw8HAukRKSey/u4b0j7yHicQR0mLJ619PcpxixewT8Xf3xy+Bf4G3jrfTjvnr1CidPnsTy5csrFbAcHBwwceJE7Nq1Cxs3bqxxX9euXUPnzp1x5swZtGzZsspr65dffon9+/dzXZ8uXLiASZMmwdbWFt27d8e+ffswcuRIxMfHw9zcnAprSsBbgro/bj+CTwTjSc4T7jELxgID2AHobNoZEomE6xOSmJiI6OhoWFlZqXx1H5ZlIZaw0BUJp7jMsiwOxR/CT/gJOcjBuV/P4cfAHzHMexjmz5+PIUOGwMXFBRkZGfjmm2+Qk5ODKVOmqCUuvs+Vubk5OnTowI3uP3++EX76yQNP3jQrWFkVoW9fBunp5zB79mw8fvwYubm5CAsLQ3h4eKXRsqoghHNVUXXt6pNPPoGfnx9WrFiBMWPG4Nq1a/jtt9/w22+/qS02Ps8XwzDo1q0bUlJSEB8fj/nzr2LPnm548uTNbXs7uyIEBDxCixYGCAgIwFtvvcV98TExMYG1tbVa+gvyfa4qYllW5suNtJ+3t7c3GIaBRCJBSEgIpkyZAl1d9V6ChHau5F0HncydsD5wPc78dAY7duzAoUOHYGZmhvT0dACAhYWFYJKfaYem4drTawAACSuR+fNS8iVMOzQNV6ZfUfpxHzx4AJZl4ePjI/d5Hx8fZGZm4vnz5zXuSzqzhLW1dZWz4eTn52Pt2rU4d+4cVzhr2rQpLl26hF9//RX+/v5cdwI7Ozutmo6PT7wkqPvj9mPU7lFgIXsLIJvNxm7shoeZB7p37849Lu1LOWXKFK4sryqz991CWk4hPvX3QM+m1rz3Iyv/DZVBWSypuancN1Tjh8bYMX4HXrx4AVtbW7z11lv4559/1FLpEsK5Kj8F1R9/ZGL37mYAWABvYnn1ygi7d49B166H8PnnnyMtLQ0WFhZo06YNTpw4wb1elYRwrsqrqV39MvgXHDhwAAsXLsSyZcvg7u6OdevWYeLEiWqJTwjny8PDA+PGjcOiRVEIC+tW6fmMDH3s3j0GixbdQr9+bXn7NxXCuZJ68eIFjhw5gsePH3OP5ebmYvfu3XB1dcXgwYNx8+ZNJCcnY9q0aWqPT0jnqqrr4NOcp2WPbyp7vOIt55CQEMHcPs4tzoWYld+FRcyKkVucq+aIyki7Fyjr3zc2NhaFhYWVrhXFxcVo3769Uo5BKlN7giqWiBF8IrjSf0opBgz+yv8LuYVFEOlUvjX2ukTF62Zn5KGgRIzPjtxF+yYW+Kh7UzS1NlHpMasTdHAqrqeWTUwvPWflv6H6vt0JD3enVHqdqs8TIKxz1bVHb0ycWPrfbxU/lBgwDIuUlO2IeyBBxTuuDe1cATW3q6CDU3F+yiVcGzBQ5nXqOFeAcM6Xg5M7zodLv+xVblcAi5CQtvjs/2Tb1fHTZwE0vLZ14OBBpFVYhlqaLCQnJ+PAwYOYNGUqCorL/q+qqz1JCeVciSVizDku/zrIggUDBs5rnZEYnCj3OtjQeXh4gGEYxMbGyp0K6t69e2jUqBFsbGzAMEyl/rAlJSW1Op5EUvbZePToUTRp0kTmOQMDg9oFTxSm9gT1YvJFmdsZFbFg8STnCTr9tBHmevx+M4l6mo1pu6J4jSEm51m131Bvpz1DwEb+R3byfa5yHljgxYt2VT7PsgyePAE6zYuBuWe2+gKTg+9zBWhOuwL4PV85DyzwLL1dNVsIp10B/LetHjmZMKticAzLsohPy6R2BSCnJApP86u/DqbkpOBi8kUEuAWoLzANYW1tjX79+mHjxo345JNPZLo8pKenY/v27dxYA1tbW6SlpXHPP3jwAAUFBdzv0j6n1Q1mbNGiBQwMDJCcnFzlohKK7IfUjto74qTlptW8EYAS9pWKIyHapCRHfsf2um5HCEDtiqiGotc3Ra+XfDHTN4OIkV/hFTEimOmrbnrIDRs2oKioCAMGDMCFCxeQkpLCddlq0qQJli9fDgDo3bs3NmzYgJs3b+L69et47733uCksgbI+o0ZGRjhx4gSePXvGLYhQnpmZGebNm4dPPvkEW7ZswcOHDxEVFYWff/4ZW7ZsAQC4urqCYRgcOXIEz58/R15ensree0Oh9gqqo5mjQtv9PNwfPV2717yhkg3afBUF/912EjEMJGAxrJUjpnV2hZmB+rvs+m42Rmw1/bzdrY0RPlP95wkQ1rm6EAEEbq15u58ne6Gnv5fqA6pASOcKEHa7AoRzvoTergDhnCsA+HPzXbx4XlDl8+7WJvia2hUuPC5F4Pava9xO0eslX/4c+qfMKH4JK+H+7O7SHb8M/kVlx/b09MT169exZMkSjB07Fi9fvoSDgwOGDRuGxYsXc4OWvv/+e0ydOhU9e/ZE48aNsX79eplVCHV1dfHjjz9i2bJl+Oqrr9CjRw+Eh4dXOt7XX38NOzs7rFy5Eo8ePYKlpSU6dOiA//u//wMANGnSBEuXLsXnn3+OqVOnYvLkySofM6Pt1H5l7OHSA07mTnia87TKfqiWBo7o4dITRnr89r1p18QC8wI8eO0raGFgDhEjkns7VsSIYGFgzvt5Avg/V317AU5OwNOngLw7jAxT9nzfXqJKfVDVje9zBWhOuwL4PV9v2hULlq084EJI7Qrgv20ZGhjI7fMHlA1YMTQwaPDtqqhUgvg0F+gztihm5X9LZMDAydwJPVyEPV2gt403zk85j0PxhzDn+Byk5KRwsxAM9Rqq8kForq6uCAkJqXabxo0b4+TJkzKPVVztacaMGZXm5K2YXDIMgzlz5mDOnDlVHmvRokVYtGhRzYEThaj9Fr9IR4T1gWUTozNyBx0ANqLZmLorGnfSc9QcHeBlZwoHMwOsGdISP49ow2sSAZR9Q+3uUlZxkM4zJ/2zu0t3/Dn0T95iE9K5EokA6Xz7DFPx4lj2+7p14C2JENK5AoTdrgDhnC9puyrLt2TblfTay2e7AoRzrgBg6NCh3FSA0uRE+qeLiwuGDh3KW2wA/+fqTnoOJu+4ge03U+Fi9BHKroKy10Hp7+sC12nEACmGYTDMexjuf3QfZyefRfyH8RjmPYz3mUqI5mNYVS/3UIX9cfsx5/gcPM19yj3mbO6Mme2W4UpCc7wqKIEOA0zs4IyZb7nBQFc9ubTQ5skD3sxXKf2G6mLhorZvqDXFJbRztX8/MGcOi6dP35wXA8tC/PqzCFMm6FXzStUS4rkSaruSxiaU8/Uyvxg9ghPwcG9TFGe/WcLR2bksOR0xgr/YAGGdK+DNPKjHjx9HTk4OLCwsEBgYCC8vrwbbropKJdj8TxK230yBhAWsjfXxeR9PvCi6IPc6uC5wHUb4qL5hFRYWIjExEe7u7jA0VP3ypIQAirc73hJUAHhd+BqzV89GHvIwa8Is9G7WGyIdEbILS7A2PAEn4jMAAG5WxljUzwutHMz5ClUQ1L3WsaZ6/boYs2fvQF6eGV77tMBzqwxM7OiE4J7N+A5NkKhdVW/dhQTsjHqKVrbGEJ+6i7w8M8ya9Q5699YTxG19oSotLUVKSgqcnZ3VPiG/kNxJz8HXp+KRlFnWNzfQ2w6f+nvAwrDsC3NV10F1oASV8EHRdsfrp4ZIRwR3lC3f5u/qz/2ntDDUw9JAH/T2tMWqc/eR9KoAM3dHqb2aKjSGuobo7d6b7zAETyQC3N3LJgrvM7E75h7NwL6YVEzq6AxrExptXRG1q6q9zC/G/ttlI6mndnbB2QfHAAD+/kMoOa2Brq6uIJfnVJeqqqY9m9rIbFfVdZCQho7XBFVHRweenp7c3yvyb2aDdk0suGrq1hspuJj4kqqppFrl21UXVyu0cjDDnfRcbLuRQlVUUitbbySjSCxBa0dzdHG1QnI1n1eESNVUNS2vpusgIQ0Vr7f4ayPi4QusOneft76pRHP98/gVgg/GwEBXBweCulAVlSjkZX4xhof8iyKxBOuHtcZbrlZ8h0QETtGqqVDQLX7CB0XbncZkd/7NbBD2bicEetlBwgJbb6Rg8s4bvIz0J5qli0sjtHIwQ1GpBNtuVF4WlhB5ZKqnLo34DocInHSE/tYbZclpoLcddr7rK9jklBCh05gEFXjTN/XbwS1hZazH9U3dcOkRikolfIdHBIphGMx8yw0AsC8mFS/zi/kNiAhe+b6nM7q48j76nAhXUakEGy49wszdUUjKLIC1sT7WDGmJpQN85N7SJ4QohtcEtbi4GCtWrMCKFStQXKx40kDVVFIdee2KqqikNuRVT+v6eUW0lzKqptSuCJGP9wpqSUkJSkpKav06qqaS6lRsV1RFJYqqrnpa188rol2UXTWldkVIZbwnqPVF1VSiKKqiEkVQ31NSHeprKgxBQUFgGAYMw0BPTw9NmzbFvHnzkJ+fz3doREk0PkEFqJpKFENVVFIT6ntKqkJ9TatXWlqKxMRElJaWqu2YgYGBSEtLw6NHj/DNN99g48aNmDdvntqOT1RLKxJUKaqmkppQFZVUh6qnRB6qmlaNZVnExcXhp59+wl9//YWffvoJcXFxUMcMlgYGBnBwcICzszMmTJiAiRMn4uDBgwgKCsKwYcNktv34448REBDA/R4QEICPPvoIH3/8MRo1agR7e3v89ttvyM/Px9SpU2FmZoZmzZrh+PHj3GvCw8PBMAyOHj2Ktm3bwtDQEF26dEFMTIzK32tDpFUJKkDVVFI9qqKSqlD1lFREVdPqvXjxAlu2bMHu3buRm5sLAMjNzcXu3buxZcsWvHjxQq3xGBkZ1aov75YtW2BjY4Nr167ho48+wuzZszF69Gj4+fnh5s2bGDBgAN59910UFBTIvG7+/Pn47rvvEBkZCTs7O7zzzjvUh1gFtC5BlaJqKqkKVVGJPFQ9JeVR1bRmhw4dQnJyMgBwFVPpn8nJyTh06JDaYrl27Rp27NiBPn36KPyatm3b4ssvv4SnpycWLlwIIyMj2NjYYObMmfD09MRXX32Fly9f4vbt2zKvW7x4Mfr164fWrVtjy5YtePbsGQ4cOKDst9Tg8ZqgMgwDV1dXuLqqplpB1dSGqaZ2RVVUUpEi1VNVf14RYVB31VST21VxcXGVt/JZllX5tFlHjhyBqakpDA0N0bVrV/Ts2RM//fSTwq9v06YN93eRSARra2u0bt2ae8ze3h4AkJGRIfO6rl27cn+3srKCl5cX4uLi6vo2SBV0+Ty4np4egoKCVH4c/2Y2aNfEAmvDE3AiPgNbb6TgYuJLfNXPCy0dzFV+fKJeirQraRX1Tnoutt1IQXDPZuoJjgiSItVTdX1eEf7cSc/B16fjkfSq7JZuoLcdPvX3UOntfGpXdderVy9s2rQJenp6aNy4MfT0yv6ddHR0KiXO8m7BS7eXks4IUP53AJBIai5oadqXC02gtbf4K5JXTZ2xOwo/UzW1QaIqKpGivqdEpmr6ivqaagoTExN4eHjA1dVVJrG0tbVFWlqazLbR0dFKO+4///zD/T0zMxP379+Ht7e30vZPyjSYBFWqYt/Uv/7rm3qX+qY2ONQXlQDU97Shu5Oeg8k7qa9pXejr61f5hY5hGOjr66s5ojK9e/fG9evX8ddff+HBgwdYvHgx7ty5o7T9L1u2DGfPnsWdO3cQFBQEGxubSrMGkPrjfanTNWvWYM2aNWpd4o2qqdpN0XZFVVRSm+opX59XRDWEUjXV5HY1dOhQuLi4AHhzi1v6p4uLC4YOHcpLXAMGDMCiRYvw2WefoVOnTsjNzcXkyZOVtv9Vq1YhODgYHTt2RFpaGg4fPsxbMq7NeO2DCqDS9A3qVLFv6l83UnCB+qZqBUXbFfVFbdhqWz3l8/OKKA8ffU2ro6ntysbGBlOmTEF8fDyOHz+OnJwcmJubIzAwEF5eXirtLhMaGlrt80uXLsXSpUurfD48PLzSY0lJSZUekzcIrHv37kqtyBL5eE9Q+Satpvb2tMWqc/e5auqkDs6Y8ZYbDHQbXC+IBkVaRQ0+GIN9MamY1NEZ1ib0TbghoL6nDU9RqQSb/0nC9ptlt/OtjfXxeR9Pup1fDwzDwNvbGx4eHkhJSYGzszN0dRt8akGUgLKv/1Df1IaL+qI2TNT3tGGhvqaqpaurC3d3d0pOidJQgloO9U1tmKgvasND1dOGQyh9TYnmCwgIAMuysLS05DuUBoESVDmomtrwUBW1YaHqacNAVVNCNBclqFWgamrDQlXUhoOqp9qPqqa1U9VqUISogqLtjdfOIgzDoHHjxtzfhYhG+mueurYrGtHfMNS1eqoJn1dEeCP0a8Jnu5JObl9QUAAjIyO1Hps0XNJZKyqu5FURw9JXJ4VFPHyBVefu41VBCXQY0Eh/LfTP41cIPhgDA10dHAjqQiP6tczL/GIMD/kXRWIJ1g9rjbdcrfgOiSgJjdCvm7S0NGRlZcHOzg7Gxsb05YuoDMuyKCgoQEZGBiwtLeHo6Fjt9pSg1lJ2YQlXTQUANytjqqZqEZZlMWN3FO6k52JCeyeqomqZdRcSsDPqKVo7mmPz6HZ0MdYSmlY1FRKWZZGeno6srCy+QyENhKWlJRwcHGr8/KUEtY6omqq9qIqqnah6qn2oaqo8YrEYJSUlfIdBtJyenh5EIpFC2/KaoJaUlODnn38GAHzwwQc19kcQGqqmClN92xVVUbVTfaunmv55pW20pWpK7YoQ+Xgt97Esi+zsbGRnZ2vkKEJFRvpLWBbhCS+QllPIc7QNR33bVfkR/XtvP8XjV5q5DCF542V+MfbdSgVQ95H7mv55pS20bYQ+tStC5KP70UpQ3bype2+lYsHRu/j6dDzfYZJakI7oLxazGL/tOvbeegoJXTw0koRlsehELIolLCwMdWneUw12Oy0bE7dfp3lNCWkAaE0yJZFWU3t72mLVuftIelWA6buiIC3U3HiShdup2WjT2ILfQIlCpFXU4IMxELMs1oQn4PT951jUzwtOljQdi6Z4kvUaX52Iw91nuQAAQz0RDYzSECzLYvHJe7j/PA+5RaXIel2CUknZl0QRw2Dl2z7wb2bLc5SEEFWhCqqSSaupA5rbggXw3+cpdBhg879JfIZGaqmLSyPo6rxJZmLScjB+WyTCop5QNVXgJCyLsKgnGLctErH/JacAIJbQv5umyC0qxfmE50h8VYAX+cVccgoA7ZtYUHJKiJajBFUFLAz10MpRtlIqYYFryVmIepLFT1Ck1hiGgU65apuYZVEsZvHDhYe4+Oglj5GRmlx4+BI/XHiIEjGL8ilpQbGYt5hI7RjoitC2ijtOs7u5qzkaQoi6UYKqAjmFJfjp0kO5z318KAZ303PUHBGpCwnLokT8Zllbaara2aURvO1M+QmKKMTH3hSd5fQ1fV0ipuq3BriTnoPJO28gMiULwJv/eyKGQWcXS7SimVII0Xq8JqgMw8DW1ha2trZa1S/MzEAXvT1t0dTaGLam+jDWezPnV2GpBNN3yY70J8qlrHZVUCxGxVRm7Tut8NPwNrA3M6xfkESl7M0MsaS/N/TKddFgALCoexVVWz+vhETeCP0FvT2hJyo732KWxf/+m2FDW1C7IkQ+mqhfTSQsi7ScQmy8nIgzD54DoHlTha6wRIwP9t+Csb4IL/KK8ehVAc2LqkHKz3s6p3tTbP43CQXFYvw8oi0M9RSbKJqoT3Xzmu6OforvIxLQ0ckSG0e25TlSQog6UILKA1qFSnOwLAuGYWh1KQ1T1apR0n9PIhyKrAYlYVlcfPQSXramcDCnuxeENASUEfGgunlTibBIkxnpvKhFpRJsu5HCc1SkJltvJKNILEFrR3OZeU8pORUWaV/TmuY11WEY+DezoeSUkAaE96VON2/eDACYOXNmg1zijaqpyqeqdkVVVM1QVfW0vujzSnkUqZo2FNSuCJGP14n6WZbF8+fPub83RP7NbNCuiQXWhifgRHwG/rqRgguJL6lvaj2oql1Jq6h30nOx7UYK9UUVqKqqp/VFn1fKUV1f04aI2hUh8lGZTgCkq1B9O7glrIz1kPSqADN200h/oZGuLgUA+2JS8TK/mN+ASCUv84ux/3YaAGBGF1e6pS8g8kborxnSEksH+DTY5JQQUjVKUAWE+qYKH/VFFTZVVU9J/Sja15QQQqQoQRUYqqYKG1VRhYuqp8JDVVNCSF1RgipQVE0VLqqiChNVT4WFqqaEkPqgBFXAqJoqTFRFFR6qngoHVU0JIcrA+1KnFhYWsLCwoAtKNaiaWjvqaFdURRUWdVRP6fOqZlQ1rT1qV4TIRytJaRiaN1U4aF5UYVDVvKdEcTSvKSFE2Sir0TBUTRUOqqIKA/U95RdVTQkhqkAVVA1G1VT+URWVX1Q95Q9VTQkhqsRrJiNd4m3z5s0oKSnhMxSNRNVU+dTZrqiKyi91Vk/p8+qNu1Q1VRpqV4TIx2uCyrIsUlNTkZqaSku81VGVI/0vN9yR/upsVzSinz/qHrlPn1dlVdOfLz3CDBqhrzTUrgiRj+4Fa4lK1dTrKZhC1VS1oCoqP6jvqXpJq6Z/UdWUEKIGlKBqkYrV1ESqpqoFVVHVj+Y9VR+qmhJC+EAJqhaiaqr6URVVvah6qh5UNSWE8IUSVC1F1VT1oiqq+lD1VPWoakoI4RslqFqOqqnqQ1VU9aDqqWpR1ZQQIgS8J6jGxsYwNjbmOwyt1hCrqXy0K6qiqh7f1VNt/ryiqil/tLldEVJnLGlQsl4Xs18dj2U7rwtnO68LZ8f+dY29k5at9OPk5OSwwcHBrIuLC2toaMh27dqVvXbtmtKPUxsRERHs4MGDWUdHRxYAe+DAAZnnJRIJu3jxYtbR0ZE1NDRk/f392Tt37sjdl0QiYQMDAyvtRyKRsNPCbrCd14Wz6yIS1B6Xtvvwx+2sZcuurHEjG7nnat++fWz//v1Za2trFgAbFRWl8piU8e/n7+/PApD5GTt2rMriKi4uZj/77DO2VatWrLGxMWtr78C6dhvItlu6l+28Lpz96kQsm/W6uF7HJ4SQ+uC9gkrUS13V1BkzZuD06dPYunUrYmJi0L9/f/Tt2xdPnz5V2jFqKz8/H23btsWGDRvkPv/tt99i7dq12LBhAyIjI+Hg4IB+/fohNze30rbr1q2TW72rSxVVmXFps5f5xTgf+xTGjZth0crv5G6Tn5+Pbt26YdWqVWqLS1n/fjNnzkRaWhr38+uvv6osroKCAty8eROf/98XmLv5EOwmLMKLJ0l49OeXVDUlhAgD3xky4Y+qqqkFBQWsSCRijxw5IvN427Zt2S+++KLe+1cGyKl8Ojg4sKtWreIeKywsZC0sLNhffvlF5rXR0dGsk5MTm5aWVmXFrLZVVGXEpe1+iHjAdl4Xzk7fdZOVSCRyz71UYmKi2iqo5dX138/f358NDg5WW1wsy7J30rLZMX9d4/7/T1+/iwXAPn78WGVxEEKIonhf6jQ0NBShoaG0xBsPVFVNLS0thVgshqGhoczjRkZGuHTpUn3DrlFd2lViYiLS09PRv39/7jEDAwP4+/vjypUr3GMFBQUYP348NmzYAAcHB7n7UmZfVEXj0nZ89z0FVNuuAGD79u2wsbFBy5YtMW/ePJVVyKvqazquhRUYhoGlpaVKjkvko+sgIfLp8nlwlmXx+PFj7u+EH/7NbNCuiQXWhifgRHwG/rqegouPXmJRPy+0dDCv9f7MzMzQtWtXfP311/Dx8YG9vT127tyJf//9F56enip4B7Lq0q7S09MBAPb29jKP29vbc/sCgE8++QR+fn4YOnRotfuTjui/k56LbTdSENyzWW3eQq3j0nZCGLmvynY1ceJEuLu7w8HBAXfu3MHChQtx69YtnD59WknRl7mbnoNlp+OR9KoAQNkI/U/9PWAAMbqP+RwTJkyAuXnt/8+TuqPrICHy8ZqgEuGQVlN7e9pi1bn7XDV1UkdnzOjiBgPd2hXbt27dimnTpqFJkyYQiUTo0KEDJkyYgJs3b6roHShHxcocy7LcY4cPH8a5c+cQFRWl0H5mvuWG4IMx2BeTikkdnWFtoq+SuLSdEKqn9VXTv9/MmTO5v7dq1Qqenp7w9fXFzZs30aFDB6XEcCw2HTHJUZCwgLWxPj7v44meTW1QUlKC0aPHQSKRYOPGjUo5FiGE1BcNkiIylDVvarNmzRAREYG8vDykpKTg2rVrKCkpgbu7u4oirx/p7XppxUsqIyODq36dO3cODx8+hKWlJXR1daGrW/b9buTIkQgICKi0T2XMi6pIXNpOCNXTuqrrv1+HDh2gp6eHBw8e1DsG6f/d8IcvKs1rWlJSgjFjxiAxMRGnT5+m6ikhRDAoQSWVKLNvqomJCRwdHZGZmYmTJ0/WeGucL9Lbq+VvqRYXFyMiIgJ+fn4AgM8//xy3b99GdHQ09wMAP/zwA0JCQirtUxl9URWJS5tpevW0rv9+d+/eRUlJCRwdHet87PJ9TQHAzEBXZoS+NDl98OABzpw5A2tr6zofixBClI1u8ZMqKdI3tbC0EFdSrsDP2Q+Gum8GRZ08eRIsy8LLywsJCQmYP38+vLy8MHXqVLXEzrIskpKSZPp05eXlISEhgfs9MTER0dHRsLKygouLCz7++GOsWLECnp6e8PT0xIoVK2BsbIwJEyYAKKuGyRsY5eLiUmVluHxf1JBrCejgll7pXNU3Lm1VWFqIr87swetSe7RtbIsuLo1qPFevXr1CcnIyUlNTAQDx8fEAqv63qy1VtKuHDx9i+/btGDRoEGxsbBAbG4u5c+eiffv26Natm0JxlZaWIiUlBc7OztDV1cXd9Bws+jsK9++/qcCOdNOFec4TJCcXoHHjxhg1ahRu3ryJI0eOQCwWc1VeKysr6OvXvTsKIYQohfonDnijqKiIXbJkCbtkyRK2qKiIz1BIDcITnrOBv11mO68LZ99aH87+dDGB3RWzl3Ve68xiCVjntc7s/tj9rEQiYVmWZXft2sU2bdqU1dfXZx0cHNgPPviAzcrKUkusRUVF7MSJE1kA7OHDh7nHz58/X2kydADslClTWJZ9M6G6g4MDa2BgwPbs2ZO9ffs2W1paWuWxUM1UR1JXEl+wHt9+zRoss5N7ruoSV0xMTL3OkZBJJBJ2f+x+tsn3TiyWgNVfaseujNjCSiSSGs9VSEiI3OcXL17MlpaWcue8LpTZrsr/+yUnJ7M9e/ZkraysWH19fbZZs2bsnDlz2JcvXyp0rmJjY9m1a9eyS5YsYb9fu5b9ft959q1151nvD36oMi7pNFzyfs6fP1/nc0Rqj66DhMjHsCx/wwaLi4vx3XdlE27PmzePvrULXHZhCdaGJ+BAbCSSCtYiV3wLOtCBBBLoMDqQsBL4u/rjl8G/wNvGm7c4i4uL4efnhxs3bmDq1Kn4888/67yv0NBQZGVlITAwEF5eXrW+xXzvxT28d+Q9RDyOAMAAYAV1roSm/PlioAMWEkjPW13PF8uyiI+Px4kTJ2BpaYmgoKA6xabMdqUML168wJEjR/D48WMwDAOWZcGi7Gy9FFmgUauu+DSwA024L3B0HSREPl4TVKKZWv3cCXdf3ARQuT+qiBGhc5POuDJdvXN0SiQSbNq0CVlZWQDKVu/JycmBubk55s6dCwCwsLDArFmzoKOjeNfr77//HsXFZX1HXV1d0a9fP9ja2ir8ev+//BGZGgkxK670HF/nSsj8/vDDtafXqjxfnRp3QsTkCIX39/z5c5w6dQrJyckAAH19fa49KEIikeDXX39FdnY2AOC7775Dbm4uLCwsMH/+fACApaUlZs+eXat2pQx//PEHnj59Kn9qIoaBU5MmmD59ulpjIoQQZaE+qKTWGJ1CyEtOAUDMipFbrP4lOPPz8/HVV1/h1atXYBiGSxZyc3OxePFiAGULBTx79gwGBgZ1Osbjx4/x+++/1+o1j/AIYlROtgD+zpWQ5Rbnyk1OgbLz9ejpI6xcubLO+y8uLq7V64uKirBu3Tq8fv0awJvpovLy8rBo0SKwLAsrKytMnjwZZmZmdY6rLoqLi6ueN5NluS9WhBCiiWgUP6m1mlY7kfBQlDczM0NUVBQ3MlosLktypBdwZ2dnvPfee3VOTknDZGBggFmzZsHZ2RnAm/YkbV9+fn6Ijo5We3JaVCrBy/witR6TEELUidcKamlpKXbv3g0AGDNmDDevJBEmsViMixcvIjMzs9rtnmS9xt30nDqtQlUfLi4uOH/+PKysrJCfn889rqenhyNHjsDbu/Z9Pcvf4tfR0QHLsujQoQN69uxZaSlXefZs3oOMFxm1Pi6Rz9bGFgtnLqxxu8LCQly4cIFbGEKaWNb2Fj9QNsrewsICK1askPlyZmxsjPDwcOjpqbePp3Q1KOfXJaguLc7Ly4NYLIZIJFJbbKT26DpIiHy8/k+QSCTcRNQSSd3Xfieql56ejoMHD+LZs2fQhz43OKoyHUgkRvVahao+rl27JpOcAmUV359++gkLFiyAh4dHnfft4uKCgQMHws7OTuHXmBuaQ8SIquxTaaav3sqb0BnpmqLsxo78/s3mhuYKDSLR19fH4MGD0blzZxw/fhxJSUkyzykqISEB+/btQ3JycqU7B/n5+bh27ZrCU0HVV1GpBL//k4RtN1MgYQFnkR7AMkAVdywKCgqwefNmDBs2TClTbBHVoOsgIfLRLX5SLbFYjPDwcGzevBnPnj2DkZERfu73M3q49gAA6DA6Mn92d+6Od1uurtcqVPXx999/AwCGDRuGhIQEvPPOOwCAuLg4hIWFcfNjKsrR0REWFhYYN24cJk+eXKvkFAD+HPonurt0B/DmHJWNswbcLXzx51B+R4ILTR+nJTATtQYgp225dK/1+bKzs8PkyZMxduxYWFhY1Gri+9TUVISFhUEsFuPFixcA3rQr6YIThw8frlU8dXU3PQeTd97AXzfKktNALzt8MGUsXF1cALzpGyv908bGBgYGBnj27Bk2b96M8PBwrlsCIYRoAt6nmZIOWFi4cCFNryEw5aumAODj44O3334bJiYmYFkWh+IPYc7xOUjJSYGLhQvWB67HUK+hYBgGEQ9fYNW5+3hVUAIdBmqrpl6+fBmPHz/G+PHjual3tm/fjkePHsHb2xsjR46s1S1PlmUhkUjqdZu04rmyN3GCOTsL9kY9cXDqW7A2oXYPlK0aNTzkXxSWijGyQwp+vvGF3LZVV2KxGDo6OgrvQywWY9++fWBZFo6Ojnjy5IlMu9q5cydcXV1VWkGtWDW1MtbD572bw7+ZDYA3U2gdP34cOTk5sLCw4KZEy8/Px7FjxxAXFwcAsLe3p2qqANF1kBD5KEEllUj7ml68eBESiQRGRkYYNGgQWrZsWeniXtVKUgCQ/boE30ck4GR8WR9MdytjmVWo1ElaPeKzP570XHV16ooP9sfhbnouJrR3QnDPZrzFJCTrLiRgZ9RTtHIww+9j2qNIXFRl21IXPtuNtK9p0qsCAGVV008DPOTOa1pxJSkplmVx9+5dHDt2DK9fv4aOjg569OiBHj16UN9UgaDrICHy0S1+IiM9PR2bN29GREQEJBIJfHx88MEHH6BVq1ZyK0+Guobo7d5bbgJhYaSHZYE++HZwS1gZ6yHxVQFm7I7Cz5cfoahUvX2tRCIRd0FmWRbHjx+XWZ5SHaTnykjPCDO7uAEA9sWk4mU+TQf0Mr8Y+2+nAQBmvuUGhmGqbVuqkpCQgOPHj3ODqsq3G3UpKpXg50uPMGN3FJJeFcDKWA/fDm6JpYE+VU66r6urC3d390oDbBiGQatWrfD+++/Dx8cHEokEERER2Lx5M7e0KSGECBElqASA/L6mI0eOxOjRo2FiYlKvffs3s0HYpE4Y4GXHW9/U8m7evIlr164hLCxM7Umq1FuujdDSwQxFpRJsu5HCSwxCsvVGMorEErRyMEMXl0a8xJCQkICwsDBcu3aNG/2vbvL6moa924m7pV9XpqamGD16NEaOHMnNB0x9UwkhQkYJKql11bQuhFRNbdeuHby9vSEWi3lLUhmGoSrqf+RVT9VNmpyKxWJ4e3ujXbt2aj1+XaqmtUXVVEKIJqGlThuw2vQ1VSYh9E0Vi8XYu3cv7t27B5FIhHHjxtVrCqq6YFkW03dHNfi+qBX7nqo7Qa2YnI4aNUqtt/Vr09dUWahvKiFE6ChBbaCqG6GvLnyN9JcSQpJ6NekVPj4UAwNdHRwI6tLgRvRLR+4XiSVYP6w13nK1Uuvx+UxOaxqhrw55eXk00p8QIkiUoDYwfFVNq8J3NbV8kqqvr4/g4GAYGxur5dgAVVH5rJ4WFBRg3bp1KCkpUXtyykfVtCpUTSWECBGvCWppaSkOHDgAABg+fDgt8aZiQqiaVoXPaqpYLMaBAwfQokULtGjRQuXHq6ihVlH5rp4CQGxsLGJjYzF8+HC1JGNCqJpWhaqp/KDrICHy0TyoDYDQqqZV4buaWp5EIoGOjnq6GjTUKipf1VN1/tuWJ6SqaVWomqp+dB0kRD4axa/l1DFCX1mEMtI/OzsbmzZtUtvo/oY4op+vkfsJCQnYtGkTsrOz1XI8QD0j9JWFRvoTQoSCElQtpcp5TVWN73lTr1y5ghcvXqh1CqqGNi8qH/OeSgdEvXjxAleuXFHLMVU1r6mq0byphBC+UYKqhTSpaloVPqup/fv3V/s8qQ2pispH9bTiaP3+/fur9HiaVDWtClVTCSF8ogRVi2hy1bQqfFRTRSIRRo0apfYktaFUUdVdPVX3VFKaWjWtClVTCSF8oARVS2hD1bQqfFRT+UhSG0IVVd3VU3Ump9pQNa0KVVMJIepGCaqG08aqaVXUXU2tmKSeO3cOqp70QturqOqsnrIsi7Nnz6olOdW2qmlVqJpKCFEXXqeZYlkWJSUlAAA9PT2Nr/Spm5DnNVU1dc6bKhaLcfbsWfj5+cHU1FTp+69IW+dF5WPe07y8PFy5cgV9+vRRSXIq5HlNVY3mTVUOug4SIh+tJKWBNGVeU1Xjc97U3NxcmJmZqWTf2jovqrrmPVXlv015mjCvqarRvKmEEFWhBFXDNOSqaVXUvQpVVFQUjh07hrFjx8LDw0Mlx9C2Kqq6qqcJCQnYtWsXBg0ahPbt26vkGA25aloVqqYSQpSN96VOjxw5AgAYPHgwLfFWDaqaVk9d1VSWZbFnzx7ExcVBJBJh3LhxKklSta2Kqo7qafkBUS1atMCoUaOUfhyqmlaNqql1Q9dBQuTjdZCURCLBrVu3cOvWLUgk6lspSNNo8wh9ZVHXSH+GYTBy5EiVj+7XphH96hi5X3G0/ogRI5R6HG0eoa8sNNK/bug6SIh8NIpfwBrSCH1lUcdIf3VNQaUtI/pVPXJf1VNJNZQR+spCI/0JIcpACapAUdW07tRRTVVHkqoNVVRVV09VmZxS1bTuqJpKCKkvSlAFhqqmyqPqamrFJDU5OVkp+y1P06uoqq6eJicnqyQ5paqpclA1lRBSV9QbW0BohL7ySaupfTxtsercfa6aqqyR/tIk9c6dO2jTpo2Son5DWkX9+FAM9sWkYlJHZ40Z0a+Ovqe9evWCtbU1WrVqpZTklEboK5+0murm5saN9I+IiMC9e/dopD8hpEpUQRUAqpqqniqrqSKRCG3btuUSsNLSUjx58qTe+5XS1CqqqqqnT548QWlpKYCy5Kdt27ZKSU6paqpaVE0lhNQGJag8o76m6qOOvqmlpaUICwtDaGio0vqkamJfVFVVTxMSEhAaGoqwsDAuSa0v6muqPtQ3lRCiKN6XOi0oKJtP0NjYuEElZDSvKb9UNW+qWCzG3r17ce/ePaXOk6pp86KqYt5TVQyIonlN+UPzppZpyNdBQqpDK0nxgPqaCocqVqFSVZKqKatLqWLVKGUnp9TXVDhoFSpCiDyUoKoRVU2FqaZqqoRlceHhS3jZmcLR3FChfaoiSS1fRR3VujHm9/as1/5URVo9bWlvij/Gdqh321Z2ckpVU+GhaiohpCLelzo9efIkAGDAgAFavcRbWloaDhw4gOfPnwOgqqkQVVVNPXQnDd9HJKCjkyU2jmyr8P5UkaRKq6gA8L6fO971dYaOgL7cPM8rwrA//0Upy6JDEwtsGtWuXvtTZnL6ukSMTZcTsfvWU7CgqqkQVaym2tnZYdCgQXBxcdHaL/EN6TpISG3w+j9BIpHg+vXrAIB+/frxGYpSsSyLAwcOID09HYWFhcjPz+eWsGMYBsOHD6dBUALk38wG7RpbcNXUv66n4NyD53iWWwQAuPEkC7dTs9GmsYVC+5NOQbV3714kJibC0FCx6mt13nJtBH2RDorFEmy8kogrSa+wqJ8XnCyN6r3v+nqS9Rqz9kaj9L/vvK9el9R7n4aGhtDV1YWnp2etklOWZbH45D3cf56HvKJS5BSVygyEG9DcFnN7eVLVVGCkI/3v3r2Lo0ePIiMjA6GhoQAAPT09GBoawtDQEA4ODhg+fLhWfIZq63WQkPqir2oqUFhYiLi4OLmjjF1dXdG6dWseoiKKKD9v6sqz8XiSXcg9J2KAzf8m4afhildRpUnqq1evYGtrW+/4GIaBoW5ZggoAt9OyMX5bJD7o1hRj2jXhpZoqYVnsjn6KDZceoUTy5oZMXlH9R9k7OTlh+vTpsLKyqlXlNLeoFOcTnqNYXPkGUXNbEywb2KLesRHVKD9v6i+//IL8/HwAQElJCUpKSpCbm4vMzEwMHDgQRkb8fzEjhKgGTTOlAvr6+nBzc5P7XJ8+fdQbDKkT/2Y2GN/eWeYxMQtcS87CnVrOnSoSiWSS09TU1HpNQVUieVMJlLBAsZjFDxce4uKjl3XeZ31cePgSP1x4KJOcAkBBcd3mtkxISEBqair3u62tba1v65sb6mFUmyZyn1vQu3md4iLqZWpqirFjx8p9rk+fPpScEqLlKEFVMum8phUTEB0dHTRt2hROTk48RUZqI6ewBL//myT3uUXH4+o8b+qLFy/w119/ISwsrE5JqoRlUVhS+didXRrB2860TjHVl4+9Kdo3qdzt4XWJGJJadnGX9jn966+/8OLFizrFI53XdGeU7GIJIoZBZxdLtKrnVGJEfZydneHu7l7pVn5UVBTNm0qIlqMEVUnkrQY1YMAArvIjkUgQEBDAb5BEYWYGuujtaYum1sawNdWHkd6b/yqpOYWYvON6nVahatSoEdzd3SEWi+uUpBYUi8ECKH+57t/cFj8NbwN7s/r3ca0LezNDLjk20S9r7wwAFrWropYfEOXu7o5GjWq/+lT51aBYAD3craAnKjtbYpbF/95yq/U+Cb969eoF6VheHR0dGBgYICMjg1ahIkTLUR9UJZA3r+mgQYNgamoKhmFw4sQJuLm5wdnZuYY9EaFgGAZLB/jIPCZhWZyKz8C6CwlIynyNGbujaj1vavmBU/fu3UNYWFitRvfr6jBo5WAGY31d+LlaYd3Fh4h49BIv84t5mxe1/KpRKwa1gIm+CL/9k4SCYjF0dRTrE1vf0frVzWu6O/opNwtDa0fFBrgR4XB2doabmxuSkpIwYMAAtGjRAkePHsW9e/cQERGBe/fu0byphGghXqeZKi4uxsqVKwEACxcuhL6+MCcer4oi85qyLIv4+Hg4OjrCwoIujtpAGatQ1WcKKpZlwTCMYFaXqmrVKGmcNalvclpxXtMBXnaY6+8BC6OyEfoSlsXFRy/hZWsKBwXnsSXCkp2djbS0NHh5eXFtX1vmTdX06yAhqsL7UqfZ2dkAAAsLC42aMqS6qilpGOq7ClXFJHXKlCm1rrLzvbpUfVeNSklJwZYtW+qUnNJqUCQvL4+rpgKauQqVJl8HCVElWkmqlmg1KFJefaup0iS1uLgY48aNg55e7ebl5LuKWlX1VFElJSUICwuDvr5+rZLTmqqmpOHQpmoqIeQNSlBrgaqmpCr1qaaKxWJIJJJaJ6dSfFVR61s9lSopKYGOjo5CyQRVTUlVtKGaSgh5g9cEVSwW4+zZswDK5rUT6rddqpoSRSijbyrLsggPD4ezs3Ot+qTyUUWta/U0ISEBKSkpCAgIqNX/H6qakppoYjVVU66DhKgbr6P4xWIxrl69CgAICAgQ5H9MqpoSRZVfhWrVuftIfFVQ65H+d+7cwYULF2o1cIphGMzs4oaPD8VgX0wqJnV0VnkVtfzI/ZlvudUqOZUOiLKxsVFoVTWqmhJFlV+FSlNG+mvCdZAQPtA8qFWQN6/pyJEjMXr0aEpOSbX8m9kgbFInDPCyg4QF/rqegik7byg0b2qLFi3g7e1d63lS33JthJYOZigqlWDbjZT6voUabb2RjCKxBK0czNDFRbH5SiuO1m/RoublRsvPayphy6qmYZM6UXJKqmVqaooxY8Zg5MiRMDIywrNnz2jeVEI0DCWockhXg4qIiIBEIoGPjw/ef/99tGrVim7pE4VIq6nfDm4JK2M9rpr68+VH1a5CJZ0ntbZJqrSKCgD7YlLxMr9YWW+lkrpUT2s7lZR0NagZu6OQ9KoAVsZ6+HZwSywL9KFb+kQh0mrq+++/D29vb0gkEkRERGDz5s20ChUhGoAS1HKoakqUrS7V1Lomqeqqota2elrb5JSqpkSZqJpKiGaiBPU/VDUlqlKXamrFJHXXrl3Izc2t9jjqqKLWtnqam5uLXbt2KZScUtWUqApVUwnRPA0+QaWqKVGX2lZTpUmqj48PBg4cCDMzsxqPoeoqam2rp2ZmZhg4cCB8fHyqTU6pakrUgaqphGgOXkfx841G6BN1q+1If5FIhNGjR8tUKqtbQlSVI/prUz0tH2OHDh3Qvr38aahohD5RN00c6U9IQ8T7UqfPnz8HANja2qrtVjrNa0qEoC7zpubn52PHjh3o1atXlVNQqWpeVEXnPU1ISMD58+cxYcIEmJiYVLk/mteU8E0I86bydR0kROga3EpSVDUlQlObVahOnTqFq1ev1jhPqrJXl1J01ajyA6K6du2K/v37V9qGqqZEaGgVKkKEp8EkqFQ1JUKmaDVVLBZj7969uHfvXrVJqrKrqIpUTxUZrU9VUyJUQqimEkLe4H2p04sXLwKASj8EqGpKNIUi1VRFk1RlVVEVqZ7WlJxS1ZRoCnVXU9V1HSRE0/A6il8sFiMiIgIREREqGUFJI/SJplFkpL+i86Qqa0R/TSP3a0pOaYQ+0STqHumv6usgIZpKa6eZonlNiaZSZN7UiknqiRMnIJHIzqmqjHlRaxq5L5FIcOLECbnJKc1rSjQVzZtKCP+0LkGlqinRFjVVU6VJaqdOnTBp0iTo6FT+71zfKmpN1VMdHR1MmjQJnTp1kklOqWpKtAHNm0oIf7QqQaWqKdE2NVVTRSIRBg0aBEtLS+41BQUF3N/rU0Wtrnpa/hiWlpYYNGgQRCIRVU2J1qFqKiH80IoElaqmRNspugpVXFwc1q9fL9Mnta5V1KqqpwkJCVi/fj03iESKqqZEm1E1lRD10vgElaqmpKFQpG/q3bt3UVxcLDNwqi5V1Kqqp9IBUcXFxbhz5w4A6mtKGg6qphKiPoJMUC9cuIAhQ4agcePGYBgGBw8elHmeZVl89dVXsLGxgbOzM1atWoXs7GyZqumsWbPQrFkzGBkZwdbWFkOHDq1U8SFEE1VXTR0+fLjc0f21raKG/vsIDw//htjlE9DLuwmaNm2K4OBg7NixgxsQNXz4cKqakgZJXdVURa6FS5YsQePGjWFkZISAgADcvXu30n6uXr2K3r17w8TEBJaWlggICMDr16+VFichqsBrgqqrq4sZM2ZgxowZ0NXV5R7Pz89H27ZtsWHDBrmvW7RoEdasWYM+ffpg5syZaNy4MXbu3AlXV1eu0tOxY0eEhIQgLi4OJ0+eBMuy6N+/P92KIVqhqmrqL/88xpBhIyolqbWpor7ML8ZPP3yPjCuHsWT194iLi8Mnn3yCX375BVevXoW3tzeGDBuBX64+pqopabCUVU2t6joI1Hwt/Pbbb7F27Vps2LABkZGRcHBwQL9+/ZCbm8ttc/XqVQQGBqJ///64du0aIiMj8eGHH8odVEmIkAh+JSmGYXDgwAEMGzYMYrEYFy5cwDvvvIO33noL/fr1w6BBg+Dh4QEHBwesXr0as2bNkruf27dvo23btkhISECzZspZm5wQIZC3CtUXvT0Qe+mUzGT+zZo1U2h1qXUXErBk9ruws7NH/LkDePjwIcLCwrBjxw5YW1tjY9hhfHMugVaDIuQ/6liFqvy1UHrMxo0b4+OPP8aCBQsAAEVFRbC3t5e5FkqvlV9//bVS4iBEXTTmK5S0r+nBgweRl5eHvn37cn1NDQ0N4e/vjytXrsh9bX5+PkJCQuDu7g5nZ2c1R06Iasmrpv5v3208b9wBzb28IBaLER8fr1AVVdr31Kxpa+Q+uIkHDx4gPj4eT58+xdOnT9Gs1wj8b99tqpoSUg4ffVMTExORnp6O/v37c48ZGBjIXAszMjLw77//ws7ODn5+frC3t4e/vz8uXbqkkpgIUSbdmjdRHbFYjH/++QdA2be8qr5p3rlzBzExMZBIJCgpKQEATJo0SWaEvr29PR4/fizzuo0bN+Kzzz5Dfn4+vL29cfr0aejr1225R0KEzr+ZDdo1tuCqqVtvpsK9kTsm9GiCQb26A3jTF/Vuei623UipVEWVjtzvN3EW7FpawdvbGyKRCGKxGK1Gvoc46w4AS1VTQuSR9k2VVlOlfVOrq6Yqeh2sSJr42tvbyzxe/lr46NEjAMCSJUvw3XffoV27dvjrr7/Qp08f3LlzB56ennV+r4SoGu9LnZ45cwZnzpyR2zdU+h/wzp073Aj9ESNGAECl/jMsy1YatT9x4kRERUUhIiICnp6eGDNmDAoLC1X0bgjhX6VqamYhVt6RYOOVRBSVSsCyLMZ6GAOoXEUtP3Lf/lE4tm/fji1bt2H+74fRdOJCxB7bjtfRp6lqSkg1altNrek6qMjxyit/LZSuLjdr1ixMnToV7du3xw8//AAvLy/8+eefdXh3hKiPIG/xl5/XFCi7bSEdod+0aVMAqPQfPSMjo9I3SQsLC3h6eqJnz57Yu3cv7t27hwMHDqjnTRDCI7kj/XdEImR7GCKP70VH09eVRvRLq6e+pq+xcfUy9OzdB0fEzXA2yxjWvv3RY1QQCq/soRH6hChA1SP9HRwcAFR/LXR0dAQAtGjRQmYbHx8fJCcn1zsGQlSJ9wSVZVkkJiZCOlar4rymABAYGMjNa+ru7g4HBwecPn2a20dxcTEiIiLg5+dX47GKiopU92YIEZCK1dSkVwW4kZIJsVgMx9TrsCl5ib23UnHoeAl+CynFn/sKYF30Eg6p11FSUoKUrEI8LtfXtL+3AyDsMZWECIqi1dSK10FFKHItdHNzQ+PGjREfHy/z2vv378PV1bWe744Q1eK1DypQNvH39u3bMXz4cFhYWODixYt4/fo1CgoK0L17Wb+59PR0REdHw8rKCi4uLvj444+xYsUKeHp6wtPTEytWrICxsTEmTJgAoKzfza5du9C/f3/Y2tri6dOnWL16NYyMjDBo0CA+3y4hale+b+qpe0D7glg4lLyAyXUxzp3oiGHZ0lv1bfDY3BVmgSVw9m6DfyLOYmTHACwM7IOHty9i7dq1mDZtGq/vhRBNVF3f1C5dunDXwZEjR2LIkCHc6/Ly8mRWhUtMTKzVtZBhGMyfPx+LFy9G27Zt0a5dO2zZsgX37t3D3r171X4eCKkVlkdFRUVs+/btWQCsn58fu2TJEnbJkiXsV199xQKo9DNlyhSWZVlWIpGwixcvZh0cHFgDAwO2Z8+ebExMDLffp0+fsgMHDmTt7OxYPT091snJiZ0wYQJ77949nt4pIcIQnvCcHfjrRbb/hGMsIPnvhy33U/ZYm6mR7Mig/7EuLi6soaEh27RpU/aLL75gi4qK+H4LhGi03NxcNiwsjLvebdy4kbsOTp06VWbb8+fP1+taKLVy5UrWycmJNTY2Zrt27cpevHhRHW+VkHpR+zyoEokEmzZtQlZWFkpLS7Fy5UoUFRVx02N4enrC29sb77//PnR0dCAWi6vtr6Orq8sNmBLCthKJBKWlpVVuKxKJuFGaQtiWZVluZoT6bqujo8NNNK2qbYGy21jK2JZhGOjp6dVp25KSkipvx6lqWwAys1DUZtvS0lKuy0xmXgk8m+sh96UeAHnLAbNo0oTFgwelEInonFa1bflzWt9t9fT0uIEtqtq2ps+x2mxLn7v127a0tBSLFi3CzZs3UVpaisuXL6OoqAjm5ub47LPPAACWlpaYPXs2TahPGiy1J6i5ublwc3PDq1evuA9D9r9Rh9JQLC0tkZycDDMzM4SHhyMiIqLK/c2YMQNNmjQBAFy+fBlnzpypctspU6bAzc0NAHDt2jUcP368ym3Hjx+P5s2bAwCio6Nx6NChKrcdNWoUWrZsCaBsLfTqbp0MHToU7dq1A1DWD2jnzp1Vbjtw4EB07twZAJCUlIQtW7ZUuW3fvn3RrVs3AMDTp0/x+++/V7mtv78/AgICAJR1qN+0aVOV23bt2pWbZy8rKwvr16+vcltfX1+8/fbbAMrmnv3uu++q3LZt27bchNPFxcVYuXJlldu2aNECo0eP5n5funRpldt6enpyt7cAYMWKFVUmv66urggKCuJ+X7NmDQoKCuRu27hxY8ycOZP7fd26dcjOzpa7ra2tLd5//33u940bN+L58+dyt7WwsMDHH3/M/b5582akpqbK3dbY2Bjz58/nfg8NDa00tZqUnp4e/u///o/7fceOHXjw4AEAIDHRFVu2BMl9XXlTpoTC3f0xndP/VHdO5Vm8eDH39z179iA2NrbKbRcuXMgltAcPHsStW7eq3HbevHkwMTEBABw9ehTXr1+vctvg4GBYWloCAE6dOoWrV69Wue3s2bNhZ2cHAPS5q+LP3UePHqFVq1bccqPS659IJIJEUjbbhpWVFZKSkmBmZlblPgnRZmr/amZmZoaoqCiuE7c0KZX+6ezsjHPnztF/SkJUJC9Psf9bim5HCKkdU1NTzJo1i1s4Rnr9k1aM/fz8EB0dTddB0qDxttRpcXExrKyskJ+fzz1mYmKCZ8+ewdjYmG41qWlbusXf8G5HR0Qw6N+/5jlMT50qgb8/S+e0im3pFj997tb3c7e4uBgODg6VroOZmZkybZiQhoi3BPXSpUvo0aOH3Melt0wIIconFgNubsDTp/JnjWIYwMkJSEwElLSMOCFEDroOElI13npf//333wCAYcOGISEhAUOHDgUAHD58mK+QCGkQRCJA2pW4wiI03O/r1lFySoiq0XWQkKrxVkG9fPkyHj9+jPHjx3MdxHfu3AlXV1f65kiIGuzfDwQHA0+evHnM2bksOf1vRWFCiArRdZCQqvGWoBJC+CcWAxcvAmlpgKMj0KMHVU4JIYTwjxJUQgghhBAiKDQDMCGEEEIIERRKUAkhhBBCiKBQgkoIIYQQQgSFElRCCCGEECIolKASQgghhBBBoQSVEEIIIYQICiWohBBCCCFEUChBJYQQQgghgkIJKiGEEEIIERRKUAkhhBBCiKBQgkoIIYQQQgSFElRCCCGEECIolKASQgghhBBBoQSVEEIIIYQIyv8Dm6GYWLHOA9YAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# first, load in the simple model of case 1. Copy it.\n",
     "case2_example1 = case1(\"case2_example3\")\n",
@@ -2194,7 +184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2a05bd1-bb84-4221-9b79-87a02066de8e",
+   "id": "17",
    "metadata": {},
    "source": [
     "### Example 4: low target level in third basin, results in incorrect flow direction\n",
@@ -2204,295 +194,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "b7244d23-d1da-48a7-83c2-64d361a10470",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
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-       "                     time  node_id      storage     level  inflow_rate  \\\n",
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-       "...                   ...      ...          ...       ...          ...   \n",
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-       "38879 2024-01-09 23:59:00        8   202.661846  0.450175          0.0   \n",
-       "\n",
-       "       outflow_rate  storage_rate  precipitation  evaporation  drainage  \\\n",
-       "0             0.000      0.000000            0.0     0.000000       0.0   \n",
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-       "\n",
-       "       infiltration  balance_error  relative_error  \n",
-       "0               0.0   0.000000e+00             0.0  \n",
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-       "...             ...            ...             ...  \n",
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-       "38879           0.0  -1.265905e-10            -2.0  \n",
-       "\n",
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# first, load in the simple model of case 1. Copy it.\n",
     "case2_example4 = case1(\"case2_example4\")\n",
@@ -2505,7 +210,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "d65f9fa7-70e8-4206-81ec-71a64b742083",
+   "id": "19",
    "metadata": {},
    "source": [
     "Volgende case zou kunnen zijn dat er tussen twee peilgebieden (met wel of niet andere target levels) er outlets zijn, die van de een naar de ander gaan, en andersom. Maar in principe verwacht ik daar geen gekke situaties: de outlet laat alleen water stromen als dit onder vrij verval kan, en pompt geen water omhoog. Het enige wat wel gek zou kunnen worden, is als beide peilen rond hetzelfde niveau komen. Dan zou het water de ene tijdstap van links naar rechts kunnen stromen, en de andere momenten de andere kant op. Ik kan me voorstellen dat dit tot instabiliteiten leidt. Weet alleen niet zeker of dit gaat optreden bij simpele voorbeelden als hier."
@@ -2513,7 +218,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "7575a037-5d76-469f-a2c0-021248515678",
+   "id": "20",
    "metadata": {},
    "source": [
     "Wat wel interessant zou zijn is het toch wel toevoegen van ContinuousControls. Eerst leek dit niet een logische stap, omdat ik wilde dat de outlet zou luisteren naar boven- en benedenstroomse peil. Maar nu doet dat het eigenlijk alleen naar bovenstrooms. \n",
@@ -2523,7 +228,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "2776ed89-2c4a-4fe2-b50f-fb2a9eb707e2",
+   "id": "21",
    "metadata": {},
    "source": [
     "Conclusie(?): de discrete controls moeten OOK gaan luisteren naar benedenstroomse peil. Dit toch wel doen aan de hand van de vier verschillende opties, afhankelijk wat de streefpeil van peilgebied 1 en peilgebied 2 is. \n",
@@ -2533,7 +238,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "76c92c05-e7b1-4c9c-a763-b54dfbfb1c5d",
+   "id": "22",
    "metadata": {},
    "source": [
     "1) Hoe verhoudt dit zich tot de min_crest_level en een enkele listen_to_node?\n",
@@ -2554,7 +259,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "b43144a5-e2fc-486b-a2ea-6c81a295abf7",
+   "id": "23",
    "metadata": {},
    "source": [
     "Conclusie: vorige conclusie is correct. Luisteren naar zowel boven- als benedestrooms."
@@ -2562,7 +267,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "672b28d7-806a-42a3-a584-15ff3c59273f",
+   "id": "24",
    "metadata": {},
    "source": [
     "Stappenplan voor AGV:\n",
@@ -2573,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15e38a1d-5017-4432-9538-4a94f5c27937",
+   "id": "25",
    "metadata": {},
    "source": [
     "# Thrashbin"
@@ -2582,7 +287,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "042e3e2b-1fbe-4932-ba8b-ce878acc8af5",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/src/peilbeheerst_model/Parametrize/AmstelGooienVecht_parametrize.ipynb b/src/peilbeheerst_model/Parametrize/AmstelGooienVecht_parametrize.ipynb
index c8093382..4a3f38b7 100644
--- a/src/peilbeheerst_model/Parametrize/AmstelGooienVecht_parametrize.ipynb
+++ b/src/peilbeheerst_model/Parametrize/AmstelGooienVecht_parametrize.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,19 +135,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processed all actions\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "name = \"Ron Bruijns (HKV)\"\n",
     "versie = \"2024_8_8\"\n",
@@ -175,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,1530 +244,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Processing Basin Node ID: 1\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1038\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1039\n",
-      "INFO:root:Connected new Basin Node ID: 1039 to original Basin Node ID: 1 via Manning Resistance Node ID: 1038\n",
-      "INFO:root:Processing Basin Node ID: 2\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1040\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1041\n",
-      "INFO:root:Connected new Basin Node ID: 1041 to original Basin Node ID: 2 via Manning Resistance Node ID: 1040\n",
-      "INFO:root:Processing Basin Node ID: 3\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1042\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1043\n",
-      "INFO:root:Connected new Basin Node ID: 1043 to original Basin Node ID: 3 via Manning Resistance Node ID: 1042\n",
-      "INFO:root:Processing Basin Node ID: 4\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1044\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1045\n",
-      "INFO:root:Connected new Basin Node ID: 1045 to original Basin Node ID: 4 via Manning Resistance Node ID: 1044\n",
-      "INFO:root:Processing Basin Node ID: 5\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1046\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1047\n",
-      "INFO:root:Connected new Basin Node ID: 1047 to original Basin Node ID: 5 via Manning Resistance Node ID: 1046\n",
-      "INFO:root:Processing Basin Node ID: 6\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1048\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1049\n",
-      "INFO:root:Connected new Basin Node ID: 1049 to original Basin Node ID: 6 via Manning Resistance Node ID: 1048\n",
-      "INFO:root:Processing Basin Node ID: 7\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1050\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1051\n",
-      "INFO:root:Connected new Basin Node ID: 1051 to original Basin Node ID: 7 via Manning Resistance Node ID: 1050\n",
-      "INFO:root:Processing Basin Node ID: 8\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1052\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1053\n",
-      "INFO:root:Connected new Basin Node ID: 1053 to original Basin Node ID: 8 via Manning Resistance Node ID: 1052\n",
-      "INFO:root:Processing Basin Node ID: 9\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1054\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1055\n",
-      "INFO:root:Connected new Basin Node ID: 1055 to original Basin Node ID: 9 via Manning Resistance Node ID: 1054\n",
-      "INFO:root:Processing Basin Node ID: 10\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1056\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1057\n",
-      "INFO:root:Connected new Basin Node ID: 1057 to original Basin Node ID: 10 via Manning Resistance Node ID: 1056\n",
-      "INFO:root:Processing Basin Node ID: 11\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1058\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1059\n",
-      "INFO:root:Connected new Basin Node ID: 1059 to original Basin Node ID: 11 via Manning Resistance Node ID: 1058\n",
-      "INFO:root:Processing Basin Node ID: 12\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1060\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1061\n",
-      "INFO:root:Connected new Basin Node ID: 1061 to original Basin Node ID: 12 via Manning Resistance Node ID: 1060\n",
-      "INFO:root:Processing Basin Node ID: 13\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1062\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1063\n",
-      "INFO:root:Connected new Basin Node ID: 1063 to original Basin Node ID: 13 via Manning Resistance Node ID: 1062\n",
-      "INFO:root:Processing Basin Node ID: 14\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1064\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1065\n",
-      "INFO:root:Connected new Basin Node ID: 1065 to original Basin Node ID: 14 via Manning Resistance Node ID: 1064\n",
-      "INFO:root:Processing Basin Node ID: 15\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1066\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1067\n",
-      "INFO:root:Connected new Basin Node ID: 1067 to original Basin Node ID: 15 via Manning Resistance Node ID: 1066\n",
-      "INFO:root:Processing Basin Node ID: 17\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1068\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1069\n",
-      "INFO:root:Connected new Basin Node ID: 1069 to original Basin Node ID: 17 via Manning Resistance Node ID: 1068\n",
-      "INFO:root:Processing Basin Node ID: 18\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1070\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1071\n",
-      "INFO:root:Connected new Basin Node ID: 1071 to original Basin Node ID: 18 via Manning Resistance Node ID: 1070\n",
-      "INFO:root:Processing Basin Node ID: 24\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1072\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1073\n",
-      "INFO:root:Connected new Basin Node ID: 1073 to original Basin Node ID: 24 via Manning Resistance Node ID: 1072\n",
-      "INFO:root:Processing Basin Node ID: 25\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1074\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1075\n",
-      "INFO:root:Connected new Basin Node ID: 1075 to original Basin Node ID: 25 via Manning Resistance Node ID: 1074\n",
-      "INFO:root:Processing Basin Node ID: 26\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1076\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1077\n",
-      "INFO:root:Connected new Basin Node ID: 1077 to original Basin Node ID: 26 via Manning Resistance Node ID: 1076\n",
-      "INFO:root:Processing Basin Node ID: 27\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1078\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1079\n",
-      "INFO:root:Connected new Basin Node ID: 1079 to original Basin Node ID: 27 via Manning Resistance Node ID: 1078\n",
-      "INFO:root:Processing Basin Node ID: 28\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1080\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1081\n",
-      "INFO:root:Connected new Basin Node ID: 1081 to original Basin Node ID: 28 via Manning Resistance Node ID: 1080\n",
-      "INFO:root:Processing Basin Node ID: 29\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1082\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1083\n",
-      "INFO:root:Connected new Basin Node ID: 1083 to original Basin Node ID: 29 via Manning Resistance Node ID: 1082\n",
-      "INFO:root:Processing Basin Node ID: 30\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1084\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1085\n",
-      "INFO:root:Connected new Basin Node ID: 1085 to original Basin Node ID: 30 via Manning Resistance Node ID: 1084\n",
-      "INFO:root:Processing Basin Node ID: 31\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1086\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1087\n",
-      "INFO:root:Connected new Basin Node ID: 1087 to original Basin Node ID: 31 via Manning Resistance Node ID: 1086\n",
-      "INFO:root:Processing Basin Node ID: 32\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1088\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1089\n",
-      "INFO:root:Connected new Basin Node ID: 1089 to original Basin Node ID: 32 via Manning Resistance Node ID: 1088\n",
-      "INFO:root:Processing Basin Node ID: 33\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1090\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1091\n",
-      "INFO:root:Connected new Basin Node ID: 1091 to original Basin Node ID: 33 via Manning Resistance Node ID: 1090\n",
-      "INFO:root:Processing Basin Node ID: 34\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1092\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1093\n",
-      "INFO:root:Connected new Basin Node ID: 1093 to original Basin Node ID: 34 via Manning Resistance Node ID: 1092\n",
-      "INFO:root:Processing Basin Node ID: 35\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1094\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1095\n",
-      "INFO:root:Connected new Basin Node ID: 1095 to original Basin Node ID: 35 via Manning Resistance Node ID: 1094\n",
-      "INFO:root:Processing Basin Node ID: 36\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1096\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1097\n",
-      "INFO:root:Connected new Basin Node ID: 1097 to original Basin Node ID: 36 via Manning Resistance Node ID: 1096\n",
-      "INFO:root:Processing Basin Node ID: 37\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1098\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1099\n",
-      "INFO:root:Connected new Basin Node ID: 1099 to original Basin Node ID: 37 via Manning Resistance Node ID: 1098\n",
-      "INFO:root:Processing Basin Node ID: 38\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1100\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1101\n",
-      "INFO:root:Connected new Basin Node ID: 1101 to original Basin Node ID: 38 via Manning Resistance Node ID: 1100\n",
-      "INFO:root:Processing Basin Node ID: 39\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1102\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1103\n",
-      "INFO:root:Connected new Basin Node ID: 1103 to original Basin Node ID: 39 via Manning Resistance Node ID: 1102\n",
-      "INFO:root:Processing Basin Node ID: 40\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1104\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1105\n",
-      "INFO:root:Connected new Basin Node ID: 1105 to original Basin Node ID: 40 via Manning Resistance Node ID: 1104\n",
-      "INFO:root:Processing Basin Node ID: 41\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1106\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1107\n",
-      "INFO:root:Connected new Basin Node ID: 1107 to original Basin Node ID: 41 via Manning Resistance Node ID: 1106\n",
-      "INFO:root:Processing Basin Node ID: 42\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1108\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1109\n",
-      "INFO:root:Connected new Basin Node ID: 1109 to original Basin Node ID: 42 via Manning Resistance Node ID: 1108\n",
-      "INFO:root:Processing Basin Node ID: 43\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1110\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1111\n",
-      "INFO:root:Connected new Basin Node ID: 1111 to original Basin Node ID: 43 via Manning Resistance Node ID: 1110\n",
-      "INFO:root:Processing Basin Node ID: 44\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1112\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1113\n",
-      "INFO:root:Connected new Basin Node ID: 1113 to original Basin Node ID: 44 via Manning Resistance Node ID: 1112\n",
-      "INFO:root:Processing Basin Node ID: 45\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1114\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1115\n",
-      "INFO:root:Connected new Basin Node ID: 1115 to original Basin Node ID: 45 via Manning Resistance Node ID: 1114\n",
-      "INFO:root:Processing Basin Node ID: 46\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1116\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1117\n",
-      "INFO:root:Connected new Basin Node ID: 1117 to original Basin Node ID: 46 via Manning Resistance Node ID: 1116\n",
-      "INFO:root:Processing Basin Node ID: 47\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1118\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1119\n",
-      "INFO:root:Connected new Basin Node ID: 1119 to original Basin Node ID: 47 via Manning Resistance Node ID: 1118\n",
-      "INFO:root:Processing Basin Node ID: 48\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1120\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1121\n",
-      "INFO:root:Connected new Basin Node ID: 1121 to original Basin Node ID: 48 via Manning Resistance Node ID: 1120\n",
-      "INFO:root:Processing Basin Node ID: 49\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1122\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1123\n",
-      "INFO:root:Connected new Basin Node ID: 1123 to original Basin Node ID: 49 via Manning Resistance Node ID: 1122\n",
-      "INFO:root:Processing Basin Node ID: 50\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1124\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1125\n",
-      "INFO:root:Connected new Basin Node ID: 1125 to original Basin Node ID: 50 via Manning Resistance Node ID: 1124\n",
-      "INFO:root:Processing Basin Node ID: 51\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1126\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1127\n",
-      "INFO:root:Connected new Basin Node ID: 1127 to original Basin Node ID: 51 via Manning Resistance Node ID: 1126\n",
-      "INFO:root:Processing Basin Node ID: 52\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1128\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1129\n",
-      "INFO:root:Connected new Basin Node ID: 1129 to original Basin Node ID: 52 via Manning Resistance Node ID: 1128\n",
-      "INFO:root:Processing Basin Node ID: 53\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1130\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1131\n",
-      "INFO:root:Connected new Basin Node ID: 1131 to original Basin Node ID: 53 via Manning Resistance Node ID: 1130\n",
-      "INFO:root:Processing Basin Node ID: 54\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1132\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1133\n",
-      "INFO:root:Connected new Basin Node ID: 1133 to original Basin Node ID: 54 via Manning Resistance Node ID: 1132\n",
-      "INFO:root:Processing Basin Node ID: 55\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1134\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1135\n",
-      "INFO:root:Connected new Basin Node ID: 1135 to original Basin Node ID: 55 via Manning Resistance Node ID: 1134\n",
-      "INFO:root:Processing Basin Node ID: 56\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1136\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1137\n",
-      "INFO:root:Connected new Basin Node ID: 1137 to original Basin Node ID: 56 via Manning Resistance Node ID: 1136\n",
-      "INFO:root:Processing Basin Node ID: 57\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1138\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1139\n",
-      "INFO:root:Connected new Basin Node ID: 1139 to original Basin Node ID: 57 via Manning Resistance Node ID: 1138\n",
-      "INFO:root:Processing Basin Node ID: 58\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1140\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1141\n",
-      "INFO:root:Connected new Basin Node ID: 1141 to original Basin Node ID: 58 via Manning Resistance Node ID: 1140\n",
-      "INFO:root:Processing Basin Node ID: 59\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1142\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1143\n",
-      "INFO:root:Connected new Basin Node ID: 1143 to original Basin Node ID: 59 via Manning Resistance Node ID: 1142\n",
-      "INFO:root:Processing Basin Node ID: 60\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1144\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1145\n",
-      "INFO:root:Connected new Basin Node ID: 1145 to original Basin Node ID: 60 via Manning Resistance Node ID: 1144\n",
-      "INFO:root:Processing Basin Node ID: 61\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1146\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1147\n",
-      "INFO:root:Connected new Basin Node ID: 1147 to original Basin Node ID: 61 via Manning Resistance Node ID: 1146\n",
-      "INFO:root:Processing Basin Node ID: 62\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1148\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1149\n",
-      "INFO:root:Connected new Basin Node ID: 1149 to original Basin Node ID: 62 via Manning Resistance Node ID: 1148\n",
-      "INFO:root:Processing Basin Node ID: 63\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1150\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1151\n",
-      "INFO:root:Connected new Basin Node ID: 1151 to original Basin Node ID: 63 via Manning Resistance Node ID: 1150\n",
-      "INFO:root:Processing Basin Node ID: 64\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1152\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1153\n",
-      "INFO:root:Connected new Basin Node ID: 1153 to original Basin Node ID: 64 via Manning Resistance Node ID: 1152\n",
-      "INFO:root:Processing Basin Node ID: 65\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1154\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1155\n",
-      "INFO:root:Connected new Basin Node ID: 1155 to original Basin Node ID: 65 via Manning Resistance Node ID: 1154\n",
-      "INFO:root:Processing Basin Node ID: 66\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1156\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1157\n",
-      "INFO:root:Connected new Basin Node ID: 1157 to original Basin Node ID: 66 via Manning Resistance Node ID: 1156\n",
-      "INFO:root:Processing Basin Node ID: 67\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1158\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1159\n",
-      "INFO:root:Connected new Basin Node ID: 1159 to original Basin Node ID: 67 via Manning Resistance Node ID: 1158\n",
-      "INFO:root:Processing Basin Node ID: 68\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1160\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1161\n",
-      "INFO:root:Connected new Basin Node ID: 1161 to original Basin Node ID: 68 via Manning Resistance Node ID: 1160\n",
-      "INFO:root:Processing Basin Node ID: 69\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1162\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1163\n",
-      "INFO:root:Connected new Basin Node ID: 1163 to original Basin Node ID: 69 via Manning Resistance Node ID: 1162\n",
-      "INFO:root:Processing Basin Node ID: 70\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1164\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1165\n",
-      "INFO:root:Connected new Basin Node ID: 1165 to original Basin Node ID: 70 via Manning Resistance Node ID: 1164\n",
-      "INFO:root:Processing Basin Node ID: 71\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1166\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1167\n",
-      "INFO:root:Connected new Basin Node ID: 1167 to original Basin Node ID: 71 via Manning Resistance Node ID: 1166\n",
-      "INFO:root:Processing Basin Node ID: 72\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1168\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1169\n",
-      "INFO:root:Connected new Basin Node ID: 1169 to original Basin Node ID: 72 via Manning Resistance Node ID: 1168\n",
-      "INFO:root:Processing Basin Node ID: 73\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1170\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1171\n",
-      "INFO:root:Connected new Basin Node ID: 1171 to original Basin Node ID: 73 via Manning Resistance Node ID: 1170\n",
-      "INFO:root:Processing Basin Node ID: 74\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1172\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1173\n",
-      "INFO:root:Connected new Basin Node ID: 1173 to original Basin Node ID: 74 via Manning Resistance Node ID: 1172\n",
-      "INFO:root:Processing Basin Node ID: 75\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1174\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1175\n",
-      "INFO:root:Connected new Basin Node ID: 1175 to original Basin Node ID: 75 via Manning Resistance Node ID: 1174\n",
-      "INFO:root:Processing Basin Node ID: 76\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1176\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1177\n",
-      "INFO:root:Connected new Basin Node ID: 1177 to original Basin Node ID: 76 via Manning Resistance Node ID: 1176\n",
-      "INFO:root:Processing Basin Node ID: 77\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1178\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1179\n",
-      "INFO:root:Connected new Basin Node ID: 1179 to original Basin Node ID: 77 via Manning Resistance Node ID: 1178\n",
-      "INFO:root:Processing Basin Node ID: 78\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1180\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1181\n",
-      "INFO:root:Connected new Basin Node ID: 1181 to original Basin Node ID: 78 via Manning Resistance Node ID: 1180\n",
-      "INFO:root:Processing Basin Node ID: 79\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1182\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1183\n",
-      "INFO:root:Connected new Basin Node ID: 1183 to original Basin Node ID: 79 via Manning Resistance Node ID: 1182\n",
-      "INFO:root:Processing Basin Node ID: 80\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1184\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1185\n",
-      "INFO:root:Connected new Basin Node ID: 1185 to original Basin Node ID: 80 via Manning Resistance Node ID: 1184\n",
-      "INFO:root:Processing Basin Node ID: 81\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1186\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1187\n",
-      "INFO:root:Connected new Basin Node ID: 1187 to original Basin Node ID: 81 via Manning Resistance Node ID: 1186\n",
-      "INFO:root:Processing Basin Node ID: 82\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1188\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1189\n",
-      "INFO:root:Connected new Basin Node ID: 1189 to original Basin Node ID: 82 via Manning Resistance Node ID: 1188\n",
-      "INFO:root:Processing Basin Node ID: 83\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1190\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1191\n",
-      "INFO:root:Connected new Basin Node ID: 1191 to original Basin Node ID: 83 via Manning Resistance Node ID: 1190\n",
-      "INFO:root:Processing Basin Node ID: 87\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1192\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1193\n",
-      "INFO:root:Connected new Basin Node ID: 1193 to original Basin Node ID: 87 via Manning Resistance Node ID: 1192\n",
-      "INFO:root:Processing Basin Node ID: 88\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1194\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1195\n",
-      "INFO:root:Connected new Basin Node ID: 1195 to original Basin Node ID: 88 via Manning Resistance Node ID: 1194\n",
-      "INFO:root:Processing Basin Node ID: 89\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1196\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1197\n",
-      "INFO:root:Connected new Basin Node ID: 1197 to original Basin Node ID: 89 via Manning Resistance Node ID: 1196\n",
-      "INFO:root:Processing Basin Node ID: 90\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1198\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1199\n",
-      "INFO:root:Connected new Basin Node ID: 1199 to original Basin Node ID: 90 via Manning Resistance Node ID: 1198\n",
-      "INFO:root:Processing Basin Node ID: 91\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1200\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1201\n",
-      "INFO:root:Connected new Basin Node ID: 1201 to original Basin Node ID: 91 via Manning Resistance Node ID: 1200\n",
-      "INFO:root:Processing Basin Node ID: 92\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1202\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1203\n",
-      "INFO:root:Connected new Basin Node ID: 1203 to original Basin Node ID: 92 via Manning Resistance Node ID: 1202\n",
-      "INFO:root:Processing Basin Node ID: 93\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1204\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1205\n",
-      "INFO:root:Connected new Basin Node ID: 1205 to original Basin Node ID: 93 via Manning Resistance Node ID: 1204\n",
-      "INFO:root:Processing Basin Node ID: 94\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1206\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1207\n",
-      "INFO:root:Connected new Basin Node ID: 1207 to original Basin Node ID: 94 via Manning Resistance Node ID: 1206\n",
-      "INFO:root:Processing Basin Node ID: 95\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1208\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1209\n",
-      "INFO:root:Connected new Basin Node ID: 1209 to original Basin Node ID: 95 via Manning Resistance Node ID: 1208\n",
-      "INFO:root:Processing Basin Node ID: 96\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1210\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1211\n",
-      "INFO:root:Connected new Basin Node ID: 1211 to original Basin Node ID: 96 via Manning Resistance Node ID: 1210\n",
-      "INFO:root:Processing Basin Node ID: 98\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1212\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1213\n",
-      "INFO:root:Connected new Basin Node ID: 1213 to original Basin Node ID: 98 via Manning Resistance Node ID: 1212\n",
-      "INFO:root:Processing Basin Node ID: 99\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1214\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1215\n",
-      "INFO:root:Connected new Basin Node ID: 1215 to original Basin Node ID: 99 via Manning Resistance Node ID: 1214\n",
-      "INFO:root:Processing Basin Node ID: 100\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1216\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1217\n",
-      "INFO:root:Connected new Basin Node ID: 1217 to original Basin Node ID: 100 via Manning Resistance Node ID: 1216\n",
-      "INFO:root:Processing Basin Node ID: 101\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1218\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1219\n",
-      "INFO:root:Connected new Basin Node ID: 1219 to original Basin Node ID: 101 via Manning Resistance Node ID: 1218\n",
-      "INFO:root:Processing Basin Node ID: 102\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1220\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1221\n",
-      "INFO:root:Connected new Basin Node ID: 1221 to original Basin Node ID: 102 via Manning Resistance Node ID: 1220\n",
-      "INFO:root:Processing Basin Node ID: 103\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1222\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1223\n",
-      "INFO:root:Connected new Basin Node ID: 1223 to original Basin Node ID: 103 via Manning Resistance Node ID: 1222\n",
-      "INFO:root:Processing Basin Node ID: 104\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1224\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1225\n",
-      "INFO:root:Connected new Basin Node ID: 1225 to original Basin Node ID: 104 via Manning Resistance Node ID: 1224\n",
-      "INFO:root:Processing Basin Node ID: 105\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1226\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1227\n",
-      "INFO:root:Connected new Basin Node ID: 1227 to original Basin Node ID: 105 via Manning Resistance Node ID: 1226\n",
-      "INFO:root:Processing Basin Node ID: 106\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1228\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1229\n",
-      "INFO:root:Connected new Basin Node ID: 1229 to original Basin Node ID: 106 via Manning Resistance Node ID: 1228\n",
-      "INFO:root:Processing Basin Node ID: 107\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1230\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1231\n",
-      "INFO:root:Connected new Basin Node ID: 1231 to original Basin Node ID: 107 via Manning Resistance Node ID: 1230\n",
-      "INFO:root:Processing Basin Node ID: 108\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1232\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1233\n",
-      "INFO:root:Connected new Basin Node ID: 1233 to original Basin Node ID: 108 via Manning Resistance Node ID: 1232\n",
-      "INFO:root:Processing Basin Node ID: 109\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1234\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1235\n",
-      "INFO:root:Connected new Basin Node ID: 1235 to original Basin Node ID: 109 via Manning Resistance Node ID: 1234\n",
-      "INFO:root:Processing Basin Node ID: 110\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1236\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1237\n",
-      "INFO:root:Connected new Basin Node ID: 1237 to original Basin Node ID: 110 via Manning Resistance Node ID: 1236\n",
-      "INFO:root:Processing Basin Node ID: 111\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1238\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1239\n",
-      "INFO:root:Connected new Basin Node ID: 1239 to original Basin Node ID: 111 via Manning Resistance Node ID: 1238\n",
-      "INFO:root:Processing Basin Node ID: 112\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1240\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1241\n",
-      "INFO:root:Connected new Basin Node ID: 1241 to original Basin Node ID: 112 via Manning Resistance Node ID: 1240\n",
-      "INFO:root:Processing Basin Node ID: 113\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1242\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1243\n",
-      "INFO:root:Connected new Basin Node ID: 1243 to original Basin Node ID: 113 via Manning Resistance Node ID: 1242\n",
-      "INFO:root:Processing Basin Node ID: 114\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1244\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1245\n",
-      "INFO:root:Connected new Basin Node ID: 1245 to original Basin Node ID: 114 via Manning Resistance Node ID: 1244\n",
-      "INFO:root:Processing Basin Node ID: 116\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1246\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1247\n",
-      "INFO:root:Connected new Basin Node ID: 1247 to original Basin Node ID: 116 via Manning Resistance Node ID: 1246\n",
-      "INFO:root:Processing Basin Node ID: 117\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1248\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1249\n",
-      "INFO:root:Connected new Basin Node ID: 1249 to original Basin Node ID: 117 via Manning Resistance Node ID: 1248\n",
-      "INFO:root:Processing Basin Node ID: 118\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1250\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1251\n",
-      "INFO:root:Connected new Basin Node ID: 1251 to original Basin Node ID: 118 via Manning Resistance Node ID: 1250\n",
-      "INFO:root:Processing Basin Node ID: 119\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1252\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1253\n",
-      "INFO:root:Connected new Basin Node ID: 1253 to original Basin Node ID: 119 via Manning Resistance Node ID: 1252\n",
-      "INFO:root:Processing Basin Node ID: 120\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1254\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1255\n",
-      "INFO:root:Connected new Basin Node ID: 1255 to original Basin Node ID: 120 via Manning Resistance Node ID: 1254\n",
-      "INFO:root:Processing Basin Node ID: 121\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1256\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1257\n",
-      "INFO:root:Connected new Basin Node ID: 1257 to original Basin Node ID: 121 via Manning Resistance Node ID: 1256\n",
-      "INFO:root:Processing Basin Node ID: 122\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1258\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1259\n",
-      "INFO:root:Connected new Basin Node ID: 1259 to original Basin Node ID: 122 via Manning Resistance Node ID: 1258\n",
-      "INFO:root:Processing Basin Node ID: 123\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1260\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1261\n",
-      "INFO:root:Connected new Basin Node ID: 1261 to original Basin Node ID: 123 via Manning Resistance Node ID: 1260\n",
-      "INFO:root:Processing Basin Node ID: 124\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1262\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1263\n",
-      "INFO:root:Connected new Basin Node ID: 1263 to original Basin Node ID: 124 via Manning Resistance Node ID: 1262\n",
-      "INFO:root:Processing Basin Node ID: 125\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1264\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1265\n",
-      "INFO:root:Connected new Basin Node ID: 1265 to original Basin Node ID: 125 via Manning Resistance Node ID: 1264\n",
-      "INFO:root:Processing Basin Node ID: 126\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1266\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1267\n",
-      "INFO:root:Connected new Basin Node ID: 1267 to original Basin Node ID: 126 via Manning Resistance Node ID: 1266\n",
-      "INFO:root:Processing Basin Node ID: 127\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1268\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1269\n",
-      "INFO:root:Connected new Basin Node ID: 1269 to original Basin Node ID: 127 via Manning Resistance Node ID: 1268\n",
-      "INFO:root:Processing Basin Node ID: 128\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1270\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1271\n",
-      "INFO:root:Connected new Basin Node ID: 1271 to original Basin Node ID: 128 via Manning Resistance Node ID: 1270\n",
-      "INFO:root:Processing Basin Node ID: 129\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1272\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1273\n",
-      "INFO:root:Connected new Basin Node ID: 1273 to original Basin Node ID: 129 via Manning Resistance Node ID: 1272\n",
-      "INFO:root:Processing Basin Node ID: 130\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1274\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1275\n",
-      "INFO:root:Connected new Basin Node ID: 1275 to original Basin Node ID: 130 via Manning Resistance Node ID: 1274\n",
-      "INFO:root:Processing Basin Node ID: 131\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1276\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1277\n",
-      "INFO:root:Connected new Basin Node ID: 1277 to original Basin Node ID: 131 via Manning Resistance Node ID: 1276\n",
-      "INFO:root:Processing Basin Node ID: 132\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1278\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1279\n",
-      "INFO:root:Connected new Basin Node ID: 1279 to original Basin Node ID: 132 via Manning Resistance Node ID: 1278\n",
-      "INFO:root:Processing Basin Node ID: 133\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1280\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1281\n",
-      "INFO:root:Connected new Basin Node ID: 1281 to original Basin Node ID: 133 via Manning Resistance Node ID: 1280\n",
-      "INFO:root:Processing Basin Node ID: 134\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1282\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1283\n",
-      "INFO:root:Connected new Basin Node ID: 1283 to original Basin Node ID: 134 via Manning Resistance Node ID: 1282\n",
-      "INFO:root:Processing Basin Node ID: 135\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1284\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1285\n",
-      "INFO:root:Connected new Basin Node ID: 1285 to original Basin Node ID: 135 via Manning Resistance Node ID: 1284\n",
-      "INFO:root:Processing Basin Node ID: 136\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1286\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1287\n",
-      "INFO:root:Connected new Basin Node ID: 1287 to original Basin Node ID: 136 via Manning Resistance Node ID: 1286\n",
-      "INFO:root:Processing Basin Node ID: 137\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1288\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1289\n",
-      "INFO:root:Connected new Basin Node ID: 1289 to original Basin Node ID: 137 via Manning Resistance Node ID: 1288\n",
-      "INFO:root:Processing Basin Node ID: 138\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1290\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1291\n",
-      "INFO:root:Connected new Basin Node ID: 1291 to original Basin Node ID: 138 via Manning Resistance Node ID: 1290\n",
-      "INFO:root:Processing Basin Node ID: 139\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1292\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1293\n",
-      "INFO:root:Connected new Basin Node ID: 1293 to original Basin Node ID: 139 via Manning Resistance Node ID: 1292\n",
-      "INFO:root:Processing Basin Node ID: 140\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1294\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1295\n",
-      "INFO:root:Connected new Basin Node ID: 1295 to original Basin Node ID: 140 via Manning Resistance Node ID: 1294\n",
-      "INFO:root:Processing Basin Node ID: 141\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1296\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1297\n",
-      "INFO:root:Connected new Basin Node ID: 1297 to original Basin Node ID: 141 via Manning Resistance Node ID: 1296\n",
-      "INFO:root:Processing Basin Node ID: 142\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1298\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1299\n",
-      "INFO:root:Connected new Basin Node ID: 1299 to original Basin Node ID: 142 via Manning Resistance Node ID: 1298\n",
-      "INFO:root:Processing Basin Node ID: 143\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1300\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1301\n",
-      "INFO:root:Connected new Basin Node ID: 1301 to original Basin Node ID: 143 via Manning Resistance Node ID: 1300\n",
-      "INFO:root:Processing Basin Node ID: 144\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1302\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1303\n",
-      "INFO:root:Connected new Basin Node ID: 1303 to original Basin Node ID: 144 via Manning Resistance Node ID: 1302\n",
-      "INFO:root:Processing Basin Node ID: 145\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1304\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1305\n",
-      "INFO:root:Connected new Basin Node ID: 1305 to original Basin Node ID: 145 via Manning Resistance Node ID: 1304\n",
-      "INFO:root:Processing Basin Node ID: 146\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1306\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1307\n",
-      "INFO:root:Connected new Basin Node ID: 1307 to original Basin Node ID: 146 via Manning Resistance Node ID: 1306\n",
-      "INFO:root:Processing Basin Node ID: 147\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1308\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1309\n",
-      "INFO:root:Connected new Basin Node ID: 1309 to original Basin Node ID: 147 via Manning Resistance Node ID: 1308\n",
-      "INFO:root:Processing Basin Node ID: 148\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1310\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1311\n",
-      "INFO:root:Connected new Basin Node ID: 1311 to original Basin Node ID: 148 via Manning Resistance Node ID: 1310\n",
-      "INFO:root:Processing Basin Node ID: 149\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1312\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1313\n",
-      "INFO:root:Connected new Basin Node ID: 1313 to original Basin Node ID: 149 via Manning Resistance Node ID: 1312\n",
-      "INFO:root:Processing Basin Node ID: 150\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1314\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1315\n",
-      "INFO:root:Connected new Basin Node ID: 1315 to original Basin Node ID: 150 via Manning Resistance Node ID: 1314\n",
-      "INFO:root:Processing Basin Node ID: 151\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1316\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1317\n",
-      "INFO:root:Connected new Basin Node ID: 1317 to original Basin Node ID: 151 via Manning Resistance Node ID: 1316\n",
-      "INFO:root:Processing Basin Node ID: 152\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1318\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1319\n",
-      "INFO:root:Connected new Basin Node ID: 1319 to original Basin Node ID: 152 via Manning Resistance Node ID: 1318\n",
-      "INFO:root:Processing Basin Node ID: 153\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1320\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1321\n",
-      "INFO:root:Connected new Basin Node ID: 1321 to original Basin Node ID: 153 via Manning Resistance Node ID: 1320\n",
-      "INFO:root:Processing Basin Node ID: 154\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1322\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1323\n",
-      "INFO:root:Connected new Basin Node ID: 1323 to original Basin Node ID: 154 via Manning Resistance Node ID: 1322\n",
-      "INFO:root:Processing Basin Node ID: 155\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1324\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1325\n",
-      "INFO:root:Connected new Basin Node ID: 1325 to original Basin Node ID: 155 via Manning Resistance Node ID: 1324\n",
-      "INFO:root:Processing Basin Node ID: 156\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1326\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1327\n",
-      "INFO:root:Connected new Basin Node ID: 1327 to original Basin Node ID: 156 via Manning Resistance Node ID: 1326\n",
-      "INFO:root:Processing Basin Node ID: 157\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1328\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1329\n",
-      "INFO:root:Connected new Basin Node ID: 1329 to original Basin Node ID: 157 via Manning Resistance Node ID: 1328\n",
-      "INFO:root:Processing Basin Node ID: 158\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1330\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1331\n",
-      "INFO:root:Connected new Basin Node ID: 1331 to original Basin Node ID: 158 via Manning Resistance Node ID: 1330\n",
-      "INFO:root:Processing Basin Node ID: 159\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1332\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1333\n",
-      "INFO:root:Connected new Basin Node ID: 1333 to original Basin Node ID: 159 via Manning Resistance Node ID: 1332\n",
-      "INFO:root:Processing Basin Node ID: 160\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1334\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1335\n",
-      "INFO:root:Connected new Basin Node ID: 1335 to original Basin Node ID: 160 via Manning Resistance Node ID: 1334\n",
-      "INFO:root:Processing Basin Node ID: 161\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1336\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1337\n",
-      "INFO:root:Connected new Basin Node ID: 1337 to original Basin Node ID: 161 via Manning Resistance Node ID: 1336\n",
-      "INFO:root:Processing Basin Node ID: 162\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1338\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1339\n",
-      "INFO:root:Connected new Basin Node ID: 1339 to original Basin Node ID: 162 via Manning Resistance Node ID: 1338\n",
-      "INFO:root:Processing Basin Node ID: 164\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1340\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1341\n",
-      "INFO:root:Connected new Basin Node ID: 1341 to original Basin Node ID: 164 via Manning Resistance Node ID: 1340\n",
-      "INFO:root:Processing Basin Node ID: 165\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1342\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1343\n",
-      "INFO:root:Connected new Basin Node ID: 1343 to original Basin Node ID: 165 via Manning Resistance Node ID: 1342\n",
-      "INFO:root:Processing Basin Node ID: 166\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1344\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1345\n",
-      "INFO:root:Connected new Basin Node ID: 1345 to original Basin Node ID: 166 via Manning Resistance Node ID: 1344\n",
-      "INFO:root:Processing Basin Node ID: 167\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1346\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1347\n",
-      "INFO:root:Connected new Basin Node ID: 1347 to original Basin Node ID: 167 via Manning Resistance Node ID: 1346\n",
-      "INFO:root:Processing Basin Node ID: 168\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1348\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1349\n",
-      "INFO:root:Connected new Basin Node ID: 1349 to original Basin Node ID: 168 via Manning Resistance Node ID: 1348\n",
-      "INFO:root:Processing Basin Node ID: 169\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1350\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1351\n",
-      "INFO:root:Connected new Basin Node ID: 1351 to original Basin Node ID: 169 via Manning Resistance Node ID: 1350\n",
-      "INFO:root:Processing Basin Node ID: 170\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1352\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1353\n",
-      "INFO:root:Connected new Basin Node ID: 1353 to original Basin Node ID: 170 via Manning Resistance Node ID: 1352\n",
-      "INFO:root:Processing Basin Node ID: 171\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1354\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1355\n",
-      "INFO:root:Connected new Basin Node ID: 1355 to original Basin Node ID: 171 via Manning Resistance Node ID: 1354\n",
-      "INFO:root:Processing Basin Node ID: 172\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1356\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1357\n",
-      "INFO:root:Connected new Basin Node ID: 1357 to original Basin Node ID: 172 via Manning Resistance Node ID: 1356\n",
-      "INFO:root:Processing Basin Node ID: 173\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1358\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1359\n",
-      "INFO:root:Connected new Basin Node ID: 1359 to original Basin Node ID: 173 via Manning Resistance Node ID: 1358\n",
-      "INFO:root:Processing Basin Node ID: 174\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1360\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1361\n",
-      "INFO:root:Connected new Basin Node ID: 1361 to original Basin Node ID: 174 via Manning Resistance Node ID: 1360\n",
-      "INFO:root:Processing Basin Node ID: 175\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1362\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1363\n",
-      "INFO:root:Connected new Basin Node ID: 1363 to original Basin Node ID: 175 via Manning Resistance Node ID: 1362\n",
-      "INFO:root:Processing Basin Node ID: 176\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1364\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1365\n",
-      "INFO:root:Connected new Basin Node ID: 1365 to original Basin Node ID: 176 via Manning Resistance Node ID: 1364\n",
-      "INFO:root:Processing Basin Node ID: 177\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1366\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1367\n",
-      "INFO:root:Connected new Basin Node ID: 1367 to original Basin Node ID: 177 via Manning Resistance Node ID: 1366\n",
-      "INFO:root:Processing Basin Node ID: 178\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1368\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1369\n",
-      "INFO:root:Connected new Basin Node ID: 1369 to original Basin Node ID: 178 via Manning Resistance Node ID: 1368\n",
-      "INFO:root:Processing Basin Node ID: 179\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1370\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1371\n",
-      "INFO:root:Connected new Basin Node ID: 1371 to original Basin Node ID: 179 via Manning Resistance Node ID: 1370\n",
-      "INFO:root:Processing Basin Node ID: 180\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1372\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1373\n",
-      "INFO:root:Connected new Basin Node ID: 1373 to original Basin Node ID: 180 via Manning Resistance Node ID: 1372\n",
-      "INFO:root:Processing Basin Node ID: 181\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1374\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1375\n",
-      "INFO:root:Connected new Basin Node ID: 1375 to original Basin Node ID: 181 via Manning Resistance Node ID: 1374\n",
-      "INFO:root:Processing Basin Node ID: 182\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1376\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1377\n",
-      "INFO:root:Connected new Basin Node ID: 1377 to original Basin Node ID: 182 via Manning Resistance Node ID: 1376\n",
-      "INFO:root:Processing Basin Node ID: 183\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1378\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1379\n",
-      "INFO:root:Connected new Basin Node ID: 1379 to original Basin Node ID: 183 via Manning Resistance Node ID: 1378\n",
-      "INFO:root:Processing Basin Node ID: 184\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1380\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1381\n",
-      "INFO:root:Connected new Basin Node ID: 1381 to original Basin Node ID: 184 via Manning Resistance Node ID: 1380\n",
-      "INFO:root:Processing Basin Node ID: 185\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1382\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1383\n",
-      "INFO:root:Connected new Basin Node ID: 1383 to original Basin Node ID: 185 via Manning Resistance Node ID: 1382\n",
-      "INFO:root:Processing Basin Node ID: 186\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1384\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1385\n",
-      "INFO:root:Connected new Basin Node ID: 1385 to original Basin Node ID: 186 via Manning Resistance Node ID: 1384\n",
-      "INFO:root:Processing Basin Node ID: 188\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1386\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1387\n",
-      "INFO:root:Connected new Basin Node ID: 1387 to original Basin Node ID: 188 via Manning Resistance Node ID: 1386\n",
-      "INFO:root:Processing Basin Node ID: 189\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1388\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1389\n",
-      "INFO:root:Connected new Basin Node ID: 1389 to original Basin Node ID: 189 via Manning Resistance Node ID: 1388\n",
-      "INFO:root:Processing Basin Node ID: 190\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1390\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1391\n",
-      "INFO:root:Connected new Basin Node ID: 1391 to original Basin Node ID: 190 via Manning Resistance Node ID: 1390\n",
-      "INFO:root:Processing Basin Node ID: 191\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1392\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1393\n",
-      "INFO:root:Connected new Basin Node ID: 1393 to original Basin Node ID: 191 via Manning Resistance Node ID: 1392\n",
-      "INFO:root:Processing Basin Node ID: 192\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1394\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1395\n",
-      "INFO:root:Connected new Basin Node ID: 1395 to original Basin Node ID: 192 via Manning Resistance Node ID: 1394\n",
-      "INFO:root:Processing Basin Node ID: 193\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1396\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1397\n",
-      "INFO:root:Connected new Basin Node ID: 1397 to original Basin Node ID: 193 via Manning Resistance Node ID: 1396\n",
-      "INFO:root:Processing Basin Node ID: 194\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1398\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1399\n",
-      "INFO:root:Connected new Basin Node ID: 1399 to original Basin Node ID: 194 via Manning Resistance Node ID: 1398\n",
-      "INFO:root:Processing Basin Node ID: 195\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1400\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1401\n",
-      "INFO:root:Connected new Basin Node ID: 1401 to original Basin Node ID: 195 via Manning Resistance Node ID: 1400\n",
-      "INFO:root:Processing Basin Node ID: 196\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1402\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1403\n",
-      "INFO:root:Connected new Basin Node ID: 1403 to original Basin Node ID: 196 via Manning Resistance Node ID: 1402\n",
-      "INFO:root:Processing Basin Node ID: 197\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1404\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1405\n",
-      "INFO:root:Connected new Basin Node ID: 1405 to original Basin Node ID: 197 via Manning Resistance Node ID: 1404\n",
-      "INFO:root:Processing Basin Node ID: 198\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1406\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1407\n",
-      "INFO:root:Connected new Basin Node ID: 1407 to original Basin Node ID: 198 via Manning Resistance Node ID: 1406\n",
-      "INFO:root:Processing Basin Node ID: 199\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1408\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1409\n",
-      "INFO:root:Connected new Basin Node ID: 1409 to original Basin Node ID: 199 via Manning Resistance Node ID: 1408\n",
-      "INFO:root:Processing Basin Node ID: 200\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1410\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1411\n",
-      "INFO:root:Connected new Basin Node ID: 1411 to original Basin Node ID: 200 via Manning Resistance Node ID: 1410\n",
-      "INFO:root:Processing Basin Node ID: 201\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1412\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1413\n",
-      "INFO:root:Connected new Basin Node ID: 1413 to original Basin Node ID: 201 via Manning Resistance Node ID: 1412\n",
-      "INFO:root:Processing Basin Node ID: 202\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1414\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1415\n",
-      "INFO:root:Connected new Basin Node ID: 1415 to original Basin Node ID: 202 via Manning Resistance Node ID: 1414\n",
-      "INFO:root:Processing Basin Node ID: 203\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1416\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1417\n",
-      "INFO:root:Connected new Basin Node ID: 1417 to original Basin Node ID: 203 via Manning Resistance Node ID: 1416\n",
-      "INFO:root:Processing Basin Node ID: 204\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1418\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1419\n",
-      "INFO:root:Connected new Basin Node ID: 1419 to original Basin Node ID: 204 via Manning Resistance Node ID: 1418\n",
-      "INFO:root:Processing Basin Node ID: 205\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1420\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1421\n",
-      "INFO:root:Connected new Basin Node ID: 1421 to original Basin Node ID: 205 via Manning Resistance Node ID: 1420\n",
-      "INFO:root:Processing Basin Node ID: 207\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1422\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1423\n",
-      "INFO:root:Connected new Basin Node ID: 1423 to original Basin Node ID: 207 via Manning Resistance Node ID: 1422\n",
-      "INFO:root:Processing Basin Node ID: 208\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1424\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1425\n",
-      "INFO:root:Connected new Basin Node ID: 1425 to original Basin Node ID: 208 via Manning Resistance Node ID: 1424\n",
-      "INFO:root:Processing Basin Node ID: 209\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1426\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1427\n",
-      "INFO:root:Connected new Basin Node ID: 1427 to original Basin Node ID: 209 via Manning Resistance Node ID: 1426\n",
-      "INFO:root:Processing Basin Node ID: 210\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1428\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1429\n",
-      "INFO:root:Connected new Basin Node ID: 1429 to original Basin Node ID: 210 via Manning Resistance Node ID: 1428\n",
-      "INFO:root:Processing Basin Node ID: 211\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1430\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1431\n",
-      "INFO:root:Connected new Basin Node ID: 1431 to original Basin Node ID: 211 via Manning Resistance Node ID: 1430\n",
-      "INFO:root:Processing Basin Node ID: 212\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1432\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1433\n",
-      "INFO:root:Connected new Basin Node ID: 1433 to original Basin Node ID: 212 via Manning Resistance Node ID: 1432\n",
-      "INFO:root:Processing Basin Node ID: 213\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1434\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1435\n",
-      "INFO:root:Connected new Basin Node ID: 1435 to original Basin Node ID: 213 via Manning Resistance Node ID: 1434\n",
-      "INFO:root:Processing Basin Node ID: 214\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1436\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1437\n",
-      "INFO:root:Connected new Basin Node ID: 1437 to original Basin Node ID: 214 via Manning Resistance Node ID: 1436\n",
-      "INFO:root:Processing Basin Node ID: 215\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1438\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1439\n",
-      "INFO:root:Connected new Basin Node ID: 1439 to original Basin Node ID: 215 via Manning Resistance Node ID: 1438\n",
-      "INFO:root:Processing Basin Node ID: 216\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1440\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1441\n",
-      "INFO:root:Connected new Basin Node ID: 1441 to original Basin Node ID: 216 via Manning Resistance Node ID: 1440\n",
-      "INFO:root:Processing Basin Node ID: 217\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1442\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1443\n",
-      "INFO:root:Connected new Basin Node ID: 1443 to original Basin Node ID: 217 via Manning Resistance Node ID: 1442\n",
-      "INFO:root:Processing Basin Node ID: 218\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1444\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1445\n",
-      "INFO:root:Connected new Basin Node ID: 1445 to original Basin Node ID: 218 via Manning Resistance Node ID: 1444\n",
-      "INFO:root:Processing Basin Node ID: 219\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1446\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1447\n",
-      "INFO:root:Connected new Basin Node ID: 1447 to original Basin Node ID: 219 via Manning Resistance Node ID: 1446\n",
-      "INFO:root:Processing Basin Node ID: 220\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1448\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1449\n",
-      "INFO:root:Connected new Basin Node ID: 1449 to original Basin Node ID: 220 via Manning Resistance Node ID: 1448\n",
-      "INFO:root:Processing Basin Node ID: 221\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1450\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1451\n",
-      "INFO:root:Connected new Basin Node ID: 1451 to original Basin Node ID: 221 via Manning Resistance Node ID: 1450\n",
-      "INFO:root:Processing Basin Node ID: 222\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1452\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1453\n",
-      "INFO:root:Connected new Basin Node ID: 1453 to original Basin Node ID: 222 via Manning Resistance Node ID: 1452\n",
-      "INFO:root:Processing Basin Node ID: 223\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1454\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1455\n",
-      "INFO:root:Connected new Basin Node ID: 1455 to original Basin Node ID: 223 via Manning Resistance Node ID: 1454\n",
-      "INFO:root:Processing Basin Node ID: 224\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1456\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1457\n",
-      "INFO:root:Connected new Basin Node ID: 1457 to original Basin Node ID: 224 via Manning Resistance Node ID: 1456\n",
-      "INFO:root:Processing Basin Node ID: 225\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1458\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1459\n",
-      "INFO:root:Connected new Basin Node ID: 1459 to original Basin Node ID: 225 via Manning Resistance Node ID: 1458\n",
-      "INFO:root:Processing Basin Node ID: 226\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1460\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1461\n",
-      "INFO:root:Connected new Basin Node ID: 1461 to original Basin Node ID: 226 via Manning Resistance Node ID: 1460\n",
-      "INFO:root:Processing Basin Node ID: 227\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1462\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1463\n",
-      "INFO:root:Connected new Basin Node ID: 1463 to original Basin Node ID: 227 via Manning Resistance Node ID: 1462\n",
-      "INFO:root:Processing Basin Node ID: 228\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1464\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1465\n",
-      "INFO:root:Connected new Basin Node ID: 1465 to original Basin Node ID: 228 via Manning Resistance Node ID: 1464\n",
-      "INFO:root:Processing Basin Node ID: 229\n",
-      "INFO:root:Successfully added Manning Resistance node with Node ID: 1466\n",
-      "WARNING:root:Sub value for key 'concentration' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_external' is None or has no DataFrame\n",
-      "WARNING:root:Sub value for key 'concentration_state' is None or has no DataFrame\n",
-      "INFO:root:Successfully added new basin node with Node ID: 1467\n",
-      "INFO:root:Connected new Basin Node ID: 1467 to original Basin Node ID: 229 via Manning Resistance Node ID: 1466\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All basins are larger than 100 m²\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Add storage basins\n",
     "model_name = \"AmstelGooienVecht_StorageBasins\"\n",
@@ -1796,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1805,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1820,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1843,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1868,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1885,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1903,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1919,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1928,62 +397,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:pyproj:PROJ_ERROR: proj_create: unrecognized format / unknown name\n",
-      "DEBUG:pyproj:PROJ_ERROR: proj_create: unrecognized format / unknown name\n",
-      "DEBUG:pyproj:PROJ_ERROR: proj_create: unrecognized format / unknown name\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sturing has been added for the category Inlaat boezem, stuw\n",
-      "Sturing has been added for the category Uitlaat boezem, stuw\n",
-      "Sturing has been added for the category Reguliere stuw\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:pyproj:PROJ_ERROR: proj_create: unrecognized format / unknown name\n",
-      "DEBUG:pyproj:PROJ_ERROR: proj_create: unrecognized format / unknown name\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sturing has been added for the category Inlaat buitenwater peilgebied, stuw\n",
-      "Sturing has been added for the category Uitlaat buitenwater peilgebied, stuw\n",
-      "No stuwen are found in the category of Boezem boezem, stuw\n",
-      "Sturing has been added for the category Inlaat boezem, gemaal\n",
-      "Sturing has been added for the category Uitlaat boezem, gemaal\n",
-      "Sturing has been added for the category Regulier afvoer gemaal\n",
-      "Sturing has been added for the category Regulier aanvoer gemaal\n",
-      "Sturing has been added for the category Uitlaat buitenwater peilgebied, afvoer gemaal\n",
-      "Sturing has been added for the category Uitlaat buitenwater peilgebied, aanvoer gemaal\n",
-      "Sturing has been added for the category Inlaat buitenwater peilgebied, afvoer gemaal\n",
-      "Sturing has been added for the category Inlaat buitenwater peilgebied, aanvoer gemaal\n",
-      "No gemalen are found in the category of Boezem boezem, afvoer gemaal\n",
-      "No gemalen are found in the category of Boezem boezem, aanvoer gemaal\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ribasim_param.add_discrete_control(ribasim_model, waterschap, default_level)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2001,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2018,19 +441,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:pyogrio._io:Created 2,343 records\n",
-      "INFO:pyogrio._io:Created 2,088 records\n",
-      "INFO:pyogrio._io:Created 443 records\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Write model output\n",
     "ribasim_param.index_reset(ribasim_model)\n",
@@ -2063,38 +476,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f173194a7c04992b1d34f6959d6af62",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Simulating init:   0%|          | 0/100 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f47456acf2c44d368665847c90fd6bd1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "try:\n",
     "    ribasim_param.iterate_TRC(\n",
@@ -2128,73 +512,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:pyogrio._io:Created 2,343 records\n",
-      "INFO:pyogrio._io:Created 2,088 records\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized/results\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:pyogrio._io:Created 443 records\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"GET /remote.php/dav/files/nhi_api/Ribasim%20modeldata HTTP/1.1\" 200 None\n",
-      "INFO:ribasim_nl.cloud:valid credentials\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PROPFIND /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen HTTP/1.1\" 207 None\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"MKCOL /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24 HTTP/1.1\" 201 0\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PROPFIND /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24 HTTP/1.1\" 207 None\n",
-      "INFO:ribasim_nl.cloud:uploading file ../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/ribasim.toml\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PUT /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/ribasim.toml HTTP/1.1\" 201 0\n",
-      "INFO:ribasim_nl.cloud:uploading file ../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/database.gpkg\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PUT /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/database.gpkg HTTP/1.1\" 201 0\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"MKCOL /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results HTTP/1.1\" 201 0\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PROPFIND /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results HTTP/1.1\" 207 None\n",
-      "INFO:ribasim_nl.cloud:uploading file ../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/ribasim.log\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PUT /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/ribasim.log HTTP/1.1\" 201 0\n",
-      "INFO:ribasim_nl.cloud:uploading file ../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/basin_state.arrow\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PUT /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/basin_state.arrow HTTP/1.1\" 201 0\n",
-      "INFO:ribasim_nl.cloud:uploading file ../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/basin.arrow\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PUT /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/basin.arrow HTTP/1.1\" 201 0\n",
-      "INFO:ribasim_nl.cloud:uploading file ../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/control.arrow\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PUT /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/control.arrow HTTP/1.1\" 201 0\n",
-      "INFO:ribasim_nl.cloud:uploading file ../../../../../Ribasim_networks/Waterschappen/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/flow.arrow\n",
-      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): deltares.thegood.cloud:443\n",
-      "DEBUG:urllib3.connectionpool:https://deltares.thegood.cloud:443 \"PUT /remote.php/dav/files/nhi_api/Ribasim%20modeldata/AmstelGooienVecht/modellen/AmstelGooienVecht_parametrized_2024_8_24/results/flow.arrow HTTP/1.1\" 201 0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model of waterboard AmstelGooienVecht has been uploaded to the goodcloud in the directory of boezemmodel!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# control_dict = Control(work_dir = work_dir).run_all()\n",
     "ribasim_param.write_ribasim_model_GoodCloud(\n",
@@ -2208,23 +528,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'stop' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstop\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'stop' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "stop"
    ]
@@ -2278,9 +584,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Add discrete control nodes and control edges\n",
diff --git a/src/peilbeheerst_model/Parametrize/sturing_AmstelGooienVecht.json b/src/peilbeheerst_model/Parametrize/sturing_AmstelGooienVecht.json
index 9431e140..58e2e06c 100644
--- a/src/peilbeheerst_model/Parametrize/sturing_AmstelGooienVecht.json
+++ b/src/peilbeheerst_model/Parametrize/sturing_AmstelGooienVecht.json
@@ -52,7 +52,7 @@
     "flow_rate_pass": 10.0,
     "node_type": "outlet"
   },
-    
+
   "Inlaat boezem, gemaal": {
     "upstream_level_offset": 0.10,
     "truth_state": ["FF", "FT", "TF", "TT"],
@@ -61,7 +61,7 @@
     "flow_rate_pass": 0.2,
     "node_type": "pump"
   },
-    
+
   "Uitlaat boezem, gemaal": {
     "upstream_level_offset": 0.00,
     "truth_state": ["FF", "FT", "TF", "TT"],
@@ -70,7 +70,7 @@
     "flow_rate_pass": 0.2,
     "node_type": "pump"
   },
-    
+
   "Regulier afvoer gemaal": {
     "upstream_level_offset": 0.00,
     "truth_state": ["FF", "FT", "TF", "TT"],
@@ -107,7 +107,7 @@
     "node_type": "pump"
   },
 
-  "Inlaat buitenwater peilgebied, afvoer gemaal": { 
+  "Inlaat buitenwater peilgebied, afvoer gemaal": {
     "upstream_level_offset": 0.00,
     "truth_state": ["FF", "FT", "TF", "TT"],
     "control_state": ["block", "block", "pass", "pass"],
@@ -116,7 +116,7 @@
     "node_type": "pump"
   },
 
-  "Inlaat buitenwater peilgebied, aanvoer gemaal": { 
+  "Inlaat buitenwater peilgebied, aanvoer gemaal": {
     "upstream_level_offset": 0.15,
     "truth_state": ["FF", "FT", "TF", "TT"],
     "control_state": ["pass", "block", "pass", "block"],
@@ -134,11 +134,3 @@
     "node_type": "pump"
   }
 }
-
-
-
-
-
-
-
-
diff --git a/src/peilbeheerst_model/compute_voronoi.ipynb b/src/peilbeheerst_model/compute_voronoi.ipynb
index 16ab0102..1cc3562d 100644
--- a/src/peilbeheerst_model/compute_voronoi.ipynb
+++ b/src/peilbeheerst_model/compute_voronoi.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5c447874-07f5-4c13-9d5f-f2a3063ae446",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d811cd9a-bd64-4dc6-92f3-b302f7d4ebc9",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e0b86228-791d-475d-ac64-ce27e5e50f98",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2ea23b73-0db8-4bac-ba30-b36faa3e224b",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "429f7bf3-4c68-46b9-a7a8-caf8e1868b55",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a794fa1a-89b2-4bdf-899d-d19d5077fd1a",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9d2e1086-511c-477f-8173-6da9985e67d1",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "472c5702-5b9c-4599-907f-74465ae09b8c",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,7 +314,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0bb4e2be-66f5-4bfd-88f1-c9e0e7a1e78c",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fd42deff-1761-4489-bab6-b2f99fa37093",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_WSRL.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_WSRL.ipynb
index bcae967c..3fcfcff9 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_WSRL.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_WSRL.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# WSRL"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer (Daniel):\n",
@@ -21,8 +21,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## WSRL"
@@ -46,8 +46,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "### Load Files"
@@ -76,8 +76,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,21 +110,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "1c0d88a9-1141-4f86-8345-0eb4678eadc1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# check primary key\n",
     "WSRL[\"peilgebied\"][\"globalid\"].is_unique"
@@ -132,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed0d59f7-011b-4114-94ff-791d9c8ba514",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -140,8 +129,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "5f40630a-2a94-42f7-8ee6-7b74bcde912e",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,19 +143,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "06e7be9e-6154-457e-b0ee-3c1c83a5d9f4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 378\n",
-      "Number of overlapping shapes with filter: 54\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(WSRL[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -189,19 +169,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "a2c9f5ef-364a-47b6-ae0c-ffc4e7072108",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "yes\n",
-      "yes\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Add occurence to geodataframe\n",
     "peilgebieden_cat = []\n",
@@ -222,8 +193,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "528cd71d-848f-4ba7-a2af-965bdbd16888",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,8 +203,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "b2d4a069-9ff4-4996-9e04-fd05db9e2573",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad5df059-48f2-4a5d-a911-e924b6e44116",
+   "id": "14",
    "metadata": {},
    "source": [
     "## Adjust globalid, code, nen3610id ['streefpeil'], ['peilgebied']"
@@ -269,8 +240,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "9a9719a8-9003-42bf-88d8-dc0e14a6235c",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,28 +263,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "7099292c-fbe8-448b-bcb9-d512852168ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "WSRL[\"peilgebied\"][\"globalid\"].is_unique"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "517f54ec-74da-4247-9984-f3dbe770a508",
+   "id": "17",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied','streefpeil']"
@@ -321,8 +281,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "37606d1c-0408-4dc7-a4cc-72147b35aabd",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,8 +299,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "e56bed85-30fb-4aaa-bf2c-55a0432857f2",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,28 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "e0480ec4-0826-47b9-9e57-88a35d4ebd6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "WSRL[\"peilgebied\"][\"globalid\"].is_unique"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9dbe4b9a-b7b2-429f-ae0c-408cc61134d8",
+   "id": "21",
    "metadata": {},
    "source": [
     "### Create buffer layer that ensures spatial match between peilgebied and hws layers based on the buffer layer"
@@ -385,8 +334,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "e44043cf-0ea3-47ab-85ab-3af1c85dd3a7",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -404,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9628396-f882-419e-a265-424cfcb15b24",
+   "id": "23",
    "metadata": {},
    "source": [
     "## Add buffer to ['peilgebied']"
@@ -412,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd9a7f41-d404-4512-9639-777d8ac73bed",
+   "id": "24",
    "metadata": {},
    "source": [
     "## Add buffer to ['peilgebied','streefpeil']"
@@ -420,8 +369,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "ac737828-1f7e-42ab-9f37-45e0fd1e189c",
+   "execution_count": null,
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,8 +386,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "f8133133-67d8-4b9c-9150-071c81079d3b",
+   "execution_count": null,
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,28 +397,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "f48bd18b-8f73-46fb-90be-2bb629cd7e00",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "27",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "WSRL[\"peilgebied\"][\"peilgebied_cat\"].unique()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "997ba837-686c-4a6d-81a4-a92682428196",
+   "id": "28",
    "metadata": {},
    "source": [
     "## Store output"
@@ -477,24 +415,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "4b7ca695-ba56-46a4-bd0f-ced939947deb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "29",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in WSRL.keys():\n",
     "    print(key)\n",
@@ -504,7 +428,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b9833c7e-20f4-4d9c-8dec-fa49c3298e0a",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -512,7 +436,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "333debe3-dcf1-4fe1-b18c-968224ec2122",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -520,7 +444,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5ca711a0-ade5-4edc-8eb5-abab9e33971a",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -528,7 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a4869ac1-b67c-4979-8155-8da665072655",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_agv.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_agv.ipynb
index 171e2a53..675b020b 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_agv.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_agv.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Amstel Gooi en Vecht"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer (Daniel):\n",
@@ -21,8 +21,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Amstel Gooi en Vecht"
@@ -48,8 +48,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "### Load Files"
@@ -85,8 +85,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,28 +126,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "dd29abc2-65fa-4b54-95e5-98a4878eb620",
+   "execution_count": null,
+   "id": "7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "AVG[\"peilgebied\"].globalid.is_unique"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a1d63576-29bb-4157-b3fa-d6c7404e3ccf",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -155,8 +144,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "b89385dd-5d81-4bea-9627-7750b1842e9c",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09d49cea-cfba-454a-b29a-b22f93064617",
+   "id": "10",
    "metadata": {},
    "source": [
     "## Peilgebied and HWS layer overlap:\n",
@@ -184,19 +173,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "f6edfd80-f81c-4659-9d8d-1dec3f91a23c",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 28\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(AVG[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -219,30 +199,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "fa787842-0a11-4d0f-ba5e-04566370a9fb",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Add occurence to geodataframe\n",
     "peilgebieden_cat = []\n",
@@ -262,7 +222,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "361025ff-51b6-4a45-9fa5-d3d3698611dc",
+   "id": "13",
    "metadata": {},
    "source": [
     "## Add rhws to ['peilgebied','streefpeil']"
@@ -270,8 +230,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "3164fa66-d75f-498e-bc88-56532faee31b",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,8 +248,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "89ba7b08-3b2a-4058-9946-e82e19bfe4e3",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afef1983-b814-4d0d-bc07-ad88539a5459",
+   "id": "16",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied','streefpeil']"
@@ -313,8 +273,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "6f005124-3aa6-4b04-baa1-6e38736cd2c6",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,8 +291,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "b735a861-3ef3-4db4-b9eb-fd89dc47aca7",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,7 +308,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9177014f-707b-4b32-9a6b-fb5da9e61546",
+   "id": "19",
    "metadata": {},
    "source": [
     "### Create buffer polygon between NHWS and peilgebied/RHWS"
@@ -356,8 +316,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "494db515-aebe-4d94-854e-0c2c939a8214",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +335,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "003fc773-c705-42f6-925a-2ae67eef83c0",
+   "id": "21",
    "metadata": {},
    "source": [
     "### Add buffer to ['peilgebied','streefpeil']"
@@ -383,8 +343,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "03d96d05-9819-4811-891c-d645406c7027",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,8 +363,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "c0fa7650-eb05-4b93-9a49-dda6ac49b31b",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,8 +381,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "8d5771f9-fdce-44b0-bea8-1cf0023e7e66",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81991696-fd4d-490d-9129-98f091d64f91",
+   "id": "25",
    "metadata": {},
    "source": [
     "## Store output"
@@ -440,26 +400,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "b70f5533-bc4f-4554-a3f1-e0d4085fea90",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in AVG.keys():\n",
     "    print(key)\n",
@@ -468,21 +412,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "db877f10-5557-46ed-8c1f-0c25103663ed",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "AVG[\"peilgebied\"][\"peilgebied_cat\"].unique()"
    ]
@@ -490,7 +423,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f4008e4c-ab26-4e6d-86cb-6e449d167ad8",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_delfland.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_delfland.ipynb
index abf3448e..caad67ed 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_delfland.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_delfland.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Delfland"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer (Daniel):\n",
@@ -21,19 +21,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import geopandas as gpd\n",
     "import numpy as np\n",
@@ -48,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Set Paths"
@@ -56,8 +47,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "## Load files"
@@ -86,8 +77,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5556d211-e92e-4ba3-85c2-4ff9bd33fbeb",
+   "id": "7",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -129,8 +120,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "c70bb838-9d93-4d5a-ae12-2da18d145009",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbca5734-774b-4327-bb0a-1c2a68afe982",
+   "id": "9",
    "metadata": {},
    "source": [
     "## Peilgebied and HWS layer overlap:\n",
@@ -155,19 +146,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "d28b061b-117d-4e71-b737-f759953951d9",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 26\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(delfland[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -190,7 +172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fada8dca-0d9d-4619-b03e-403d3d19009a",
+   "id": "11",
    "metadata": {},
    "source": [
     "## Create peilgebied_cat column"
@@ -198,71 +180,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "16fe0d25-7dd4-410a-9bec-5a69aced0614",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Add occurence to geodataframe\n",
     "peilgebieden_cat = []\n",
@@ -288,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8061cb15-b0ba-47b7-afa0-caed45438ae4",
+   "id": "13",
    "metadata": {},
    "source": [
     "## Add HWS to ['peilgebied', 'streefpeil']"
@@ -296,8 +217,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "c12f62cb-60b3-4a43-b26c-6ea1f36f4606",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,8 +235,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "68dad130-b22b-47f2-bb20-dc88f33d4614",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3634cbd6-d698-460b-95c3-0737d2d12388",
+   "id": "16",
    "metadata": {},
    "source": [
     "### Create buffer layer that ensures spatial match between peilgebied and hws layers based on the buffer layer"
@@ -339,8 +260,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "d8a24a33-7382-4fea-b45e-950dffb59f2c",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a7d781e-3fe5-4239-b8c1-5f61d4b87460",
+   "id": "18",
    "metadata": {},
    "source": [
     "## Add buffer to ['peilgebied','streefpeil']"
@@ -360,8 +281,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "id": "e22637f0-8fdd-4971-b602-7cf4bed8584a",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,8 +299,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "id": "a40dac33-9a57-4a17-8303-89836afbc8ed",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,29 +321,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "47b761f3-7fcb-420f-be20-9dd5ac3568ad",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 0, 1])"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "delfland[\"peilgebied\"].peilgebied_cat.unique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "id": "bf287dbf-a0e8-4fb6-b054-b7ce79d3de20",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,8 +341,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "id": "2cd05359-00b9-435e-8e7c-2cc1f110c560",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,7 +352,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52f756f2-cf33-408a-9104-4f7d95a5d4eb",
+   "id": "24",
    "metadata": {},
    "source": [
     "## Write output"
@@ -450,24 +360,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "id": "38b5a11a-bf29-4958-af68-baab619e5e51",
+   "execution_count": null,
+   "id": "25",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for key in delfland.keys():\n",
     "    print(key)\n",
@@ -477,7 +373,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9cf38b1b-92d9-4b2c-b5eb-09d2333bd5ef",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_rijnland.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_rijnland.ipynb
index 80fc63c0..7801eabd 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_rijnland.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_rijnland.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Rijnland"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer (Daniel):\n",
@@ -21,8 +21,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Rijnland"
@@ -48,8 +48,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "### Load Files"
@@ -78,8 +78,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,28 +115,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "613a849a-f873-402f-a33f-acb2b60bea4e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Rijnland[\"peilgebied\"].globalid.is_unique"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "623a0316-463e-444e-af95-d409c962fd21",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -144,8 +133,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "98c82db0-4036-4ffa-ae1b-4400f5c28a58",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "240d4f27-3149-49db-82c2-aeb208b8bdb8",
+   "id": "10",
    "metadata": {},
    "source": [
     "## Peilgebied and HWS layer overlap:\n",
@@ -170,19 +159,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "08cf410b-78c1-47ab-b32c-e0ed60ebd7ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 9\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(Rijnland[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -205,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9739ed5-7eaa-4255-bf5d-04d419530de8",
+   "id": "12",
    "metadata": {},
    "source": [
     "## Create peilgebied_cat column"
@@ -213,272 +193,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "73f6c0f2-8f3c-4b63-a36a-508bd0c0f1ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
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-      "true\n",
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-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n",
-      "true\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Add occurence to geodataframe\n",
     "peilgebieden_cat = []\n",
@@ -496,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a23b8673-850f-4c3a-a1d2-d2a3aa148a7d",
+   "id": "14",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied','streefpeil']"
@@ -504,8 +222,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "8661eed7-0552-4f93-acad-62f1af2482d9",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -522,8 +240,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "7dd6604c-fe9e-4414-a477-d68978cf4c22",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -539,7 +257,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bba7dd4-ce87-4ba9-9004-f986d597f7be",
+   "id": "17",
    "metadata": {},
    "source": [
     "### Create buffer polygon between NHWS and peilgebied/RHWS"
@@ -547,8 +265,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "8c7a3dd5-3e21-4116-9dbb-eda621bbb7ec",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -576,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de6ebf83-cc20-4e63-89b5-a74c03a57b54",
+   "id": "19",
    "metadata": {},
    "source": [
     "### Add buffer to ['peilgebied','streefpeil']"
@@ -584,8 +302,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "c936a691-7ded-4e63-a344-83f27387cf0d",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,8 +322,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "4d53d1fe-774d-402d-bf40-af0dc62aef4a",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,7 +340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f8b16ac-76dc-4958-8479-bd99ce3ebd66",
+   "id": "22",
    "metadata": {},
    "source": [
     "## Rijnland data contains many duplicate peilgebieden\n",
@@ -632,25 +350,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "77d4a830-e70e-4242-bd29-d04483ec3c02",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "dae75f8d-f919-4690-b0cc-a04112c32b6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing 8807 out of 8807...\r"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "gdf = Rijnland[\"peilgebied\"][3:]\n",
     "\n",
@@ -705,51 +415,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "9df31513-a055-4185-aadb-6bf0d59914ae",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     idx1  idx2                          globalid_1  \\\n",
-      "0       4   256       dummy_globalid_peilgebied_6_6   \n",
-      "1       5   257       dummy_globalid_peilgebied_6_7   \n",
-      "2       6   258       dummy_globalid_peilgebied_6_8   \n",
-      "3       7   259       dummy_globalid_peilgebied_6_9   \n",
-      "4       8   260      dummy_globalid_peilgebied_6_10   \n",
-      "..    ...   ...                                 ...   \n",
-      "215  6861  6895  dummy_globalid_peilgebied_446_6863   \n",
-      "216  6862  6896  dummy_globalid_peilgebied_446_6864   \n",
-      "217  6863  6897  dummy_globalid_peilgebied_446_6865   \n",
-      "218  6864  6898  dummy_globalid_peilgebied_446_6866   \n",
-      "219  6865  6899  dummy_globalid_peilgebied_446_6867   \n",
-      "\n",
-      "                             globalid_2  \n",
-      "0       dummy_globalid_peilgebied_0_258  \n",
-      "1       dummy_globalid_peilgebied_0_259  \n",
-      "2       dummy_globalid_peilgebied_0_260  \n",
-      "3       dummy_globalid_peilgebied_0_261  \n",
-      "4       dummy_globalid_peilgebied_0_262  \n",
-      "..                                  ...  \n",
-      "215  dummy_globalid_peilgebied_447_6897  \n",
-      "216  dummy_globalid_peilgebied_447_6898  \n",
-      "217  dummy_globalid_peilgebied_447_6899  \n",
-      "218  dummy_globalid_peilgebied_448_6900  \n",
-      "219  dummy_globalid_peilgebied_448_6901  \n",
-      "\n",
-      "[220 rows x 4 columns]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "25",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(df)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e4928043-eded-4a45-a6ea-986e516d3a28",
+   "id": "26",
    "metadata": {},
    "source": [
     "### Create list of duplicates for removal"
@@ -757,8 +433,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "3ba5af40-ac04-4997-a890-34987e8cfabe",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,7 +456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54028e80-ab1a-40cf-a0f6-808f4f1af1a5",
+   "id": "28",
    "metadata": {},
    "source": [
     "### Remove duplicates"
@@ -788,8 +464,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "2f09ef95-f0ff-4216-ac2e-a5b6a05b1ac0",
+   "execution_count": null,
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -799,8 +475,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "1c7da63d-37de-4bc7-a234-d9bec8cc0c55",
+   "execution_count": null,
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -810,7 +486,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fec7d831-127b-438f-8fdb-9e2cda4707ff",
+   "id": "31",
    "metadata": {},
    "source": [
     "### Store data"
@@ -818,24 +494,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "59127d3b-ce3b-42ee-915d-62f0f720ec08",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "32",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in Rijnland.keys():\n",
     "    print(key)\n",
@@ -845,7 +507,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c4473cbe-38bd-4c89-9ac6-839470451f26",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -853,7 +515,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f18b37ff-f12a-47d4-b406-233ef5008454",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_wetterskip.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_wetterskip.ipynb
index b6de1c19..a9a37f83 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_wetterskip.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_wetterskip.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Wetterskip"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and make sure the peilgebieden neatly match the HWS layer (Daniel):\n",
@@ -21,8 +21,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Set Paths"
@@ -46,8 +46,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "## Load Files"
@@ -77,8 +77,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc4a7b18-311d-4a29-ba13-df3d587e60b2",
+   "id": "7",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -121,8 +121,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "b5eae4c4-2d5a-4678-a5af-56e3626d29ec",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f141d0fe-6f1f-471f-acf1-4f11522bd15d",
+   "id": "9",
    "metadata": {},
    "source": [
     "## Check Peilgebied and HWS layer overlap:\n",
@@ -147,8 +147,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "56faab44-3fda-43c4-8426-197fbb6c63a8",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d4b936d-e24d-46b0-9c04-429290c39ade",
+   "id": "11",
    "metadata": {},
    "source": [
     "## Create peilgebied_cat column"
@@ -181,8 +181,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "6ad958c4-01ce-4fcb-9170-fa8bf0f7c7f0",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,14 +201,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c9be301b-1577-4516-a5a2-70e02769e343",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "2187ad3d-4399-42b1-9d50-5183ff374326",
+   "id": "14",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied','streefpeil']"
@@ -216,8 +216,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "28509a06-79c9-4e68-9be7-dcf654494c90",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,8 +234,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "37bec5bb-70dd-4789-9f92-bcfcd571d790",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2499b90-db91-49b7-9dc5-f43d2de46e3f",
+   "id": "17",
    "metadata": {},
    "source": [
     "### Create buffer polygon between NHWS and peilgebied/RHWS"
@@ -259,8 +259,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "f3ee584a-4ac9-47fd-8006-4be11e9d02a4",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a796dae1-e578-45c2-abc4-bd0f660f2175",
+   "id": "19",
    "metadata": {},
    "source": [
     "### Add buffer to ['peilgebied','streefpeil']"
@@ -286,8 +286,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "42af7580-3cdf-4d7c-9204-50bec3dc088d",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,8 +306,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "bc785a36-81a6-4c79-affc-a0938d78beb5",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -324,7 +324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffb583ff-6707-48af-b3db-b35465eb949e",
+   "id": "22",
    "metadata": {},
    "source": [
     "## Wetterskip data contains many duplicate peilgebieden"
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ca97726-e159-4cae-be86-b99232a80d56",
+   "id": "23",
    "metadata": {},
    "source": [
     "### Calculate polygons that overlap with more than 90 % of their area"
@@ -340,8 +340,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "c234dbaf-5345-442e-9fe4-d458a8ed225c",
+   "execution_count": null,
+   "id": "24",
    "metadata": {
     "tags": []
    },
@@ -404,8 +404,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "22074ae1-cc01-4212-b877-a190122a152d",
+   "execution_count": null,
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e377aaf-adcd-4a0d-b225-48705f0a7f97",
+   "id": "26",
    "metadata": {},
    "source": [
     "### Create list of duplicates for removal"
@@ -422,8 +422,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "1d8b87a6-2e74-4b0e-9a2e-40734e38ec77",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -445,7 +445,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f8737e0-aea1-49fb-8a48-8a8afc52f3f8",
+   "id": "28",
    "metadata": {},
    "source": [
     "### Remove duplicates"
@@ -453,8 +453,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "41cc52dc-a5ab-4879-ba6d-95edc7130479",
+   "execution_count": null,
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,29 +464,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "f180bb58-0cdc-4bd4-9b24-eb5deb446e82",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "30",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Wetterskip[\"streefpeil\"][\"globalid\"].is_unique"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "59fd83a7-da5b-43ff-a4ee-f97fb9389a1a",
+   "execution_count": null,
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +485,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed9019dc-6278-4761-b30b-2a7f9b3757b9",
+   "id": "32",
    "metadata": {},
    "source": [
     "## Store data"
@@ -504,24 +493,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "65bafa4a-7c96-4fae-870d-821aa638d322",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in Wetterskip.keys():\n",
     "    print(key)\n",
@@ -531,7 +506,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "97db8354-a89a-4f58-bab8-f722f123fcec",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_zuiderzeeland.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_zuiderzeeland.ipynb
index af70905b..76253963 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_zuiderzeeland.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-process_zuiderzeeland.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {
     "tags": []
    },
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer:\n",
@@ -23,19 +23,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import geopandas as gpd\n",
     "import numpy as np\n",
@@ -49,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2434b6d-2898-45f8-a5cb-fbb9c2c2e77b",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Zuiderzeeland"
@@ -57,8 +48,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "### Load Files"
@@ -87,8 +78,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,28 +112,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "id": "51af6c3a-7b5d-447c-8757-5dd2f4694958",
+   "execution_count": null,
+   "id": "7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Zuiderzeeland[\"peilgebied\"].globalid.is_unique"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b37e2158-618d-4dd0-a576-7cb32e41c0d2",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -150,8 +130,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "id": "c5e7495b-8517-4729-b586-7e1913dbbd83",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,175 +144,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "id": "51e6de19-8bb2-47f9-860c-45d30d72972d",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>globalid</th>\n",
-       "      <th>nen3610id</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>OR 56</td>\n",
-       "      <td>dummy_globalid_peilgebied_0</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_0</td>\n",
-       "      <td>POLYGON Z ((167444.972 487009.101 0.000, 16762...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>OR 13</td>\n",
-       "      <td>dummy_globalid_peilgebied_1</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_1</td>\n",
-       "      <td>POLYGON Z ((182383.808 509903.577 0.000, 18241...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>HT 6</td>\n",
-       "      <td>dummy_globalid_peilgebied_2</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_2</td>\n",
-       "      <td>POLYGON Z ((158728.408 504422.232 0.000, 15873...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>SCHOTERPAD-D-TOCHT</td>\n",
-       "      <td>dummy_globalid_peilgebied_3</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_3</td>\n",
-       "      <td>POLYGON Z ((179970.587 533631.593 0.000, 17996...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ZWARTEMEERTOCHT</td>\n",
-       "      <td>dummy_globalid_peilgebied_4</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_4</td>\n",
-       "      <td>POLYGON Z ((192476.653 518448.383 0.000, 19250...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>290</th>\n",
-       "      <td>TA.02</td>\n",
-       "      <td>dummy_globalid_peilgebied_290</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_290</td>\n",
-       "      <td>POLYGON Z ((189642.837 527330.192 0.000, 18985...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>291</th>\n",
-       "      <td>URK 2</td>\n",
-       "      <td>dummy_globalid_peilgebied_291</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_291</td>\n",
-       "      <td>POLYGON Z ((169418.497 519698.470 0.000, 16937...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>292</th>\n",
-       "      <td>TA.13</td>\n",
-       "      <td>dummy_globalid_peilgebied_292</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_292</td>\n",
-       "      <td>POLYGON Z ((187294.201 529202.794 0.000, 18747...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>293</th>\n",
-       "      <td>3.11</td>\n",
-       "      <td>dummy_globalid_peilgebied_293</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_293</td>\n",
-       "      <td>POLYGON Z ((160519.614 477230.775 0.000, 16016...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>294</th>\n",
-       "      <td>TA.04</td>\n",
-       "      <td>dummy_globalid_peilgebied_294</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_294</td>\n",
-       "      <td>POLYGON Z ((188319.932 529257.440 0.000, 18832...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>295 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   code                       globalid  \\\n",
-       "0                 OR 56    dummy_globalid_peilgebied_0   \n",
-       "1                 OR 13    dummy_globalid_peilgebied_1   \n",
-       "2                  HT 6    dummy_globalid_peilgebied_2   \n",
-       "3    SCHOTERPAD-D-TOCHT    dummy_globalid_peilgebied_3   \n",
-       "4       ZWARTEMEERTOCHT    dummy_globalid_peilgebied_4   \n",
-       "..                  ...                            ...   \n",
-       "290               TA.02  dummy_globalid_peilgebied_290   \n",
-       "291               URK 2  dummy_globalid_peilgebied_291   \n",
-       "292               TA.13  dummy_globalid_peilgebied_292   \n",
-       "293                3.11  dummy_globalid_peilgebied_293   \n",
-       "294               TA.04  dummy_globalid_peilgebied_294   \n",
-       "\n",
-       "                          nen3610id  \\\n",
-       "0      dummy_nen3610id_peilgebied_0   \n",
-       "1      dummy_nen3610id_peilgebied_1   \n",
-       "2      dummy_nen3610id_peilgebied_2   \n",
-       "3      dummy_nen3610id_peilgebied_3   \n",
-       "4      dummy_nen3610id_peilgebied_4   \n",
-       "..                              ...   \n",
-       "290  dummy_nen3610id_peilgebied_290   \n",
-       "291  dummy_nen3610id_peilgebied_291   \n",
-       "292  dummy_nen3610id_peilgebied_292   \n",
-       "293  dummy_nen3610id_peilgebied_293   \n",
-       "294  dummy_nen3610id_peilgebied_294   \n",
-       "\n",
-       "                                              geometry  \n",
-       "0    POLYGON Z ((167444.972 487009.101 0.000, 16762...  \n",
-       "1    POLYGON Z ((182383.808 509903.577 0.000, 18241...  \n",
-       "2    POLYGON Z ((158728.408 504422.232 0.000, 15873...  \n",
-       "3    POLYGON Z ((179970.587 533631.593 0.000, 17996...  \n",
-       "4    POLYGON Z ((192476.653 518448.383 0.000, 19250...  \n",
-       "..                                                 ...  \n",
-       "290  POLYGON Z ((189642.837 527330.192 0.000, 18985...  \n",
-       "291  POLYGON Z ((169418.497 519698.470 0.000, 16937...  \n",
-       "292  POLYGON Z ((187294.201 529202.794 0.000, 18747...  \n",
-       "293  POLYGON Z ((160519.614 477230.775 0.000, 16016...  \n",
-       "294  POLYGON Z ((188319.932 529257.440 0.000, 18832...  \n",
-       "\n",
-       "[295 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Zuiderzeeland[\"peilgebied\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b93b71dd-7176-42ae-bf4e-f31a429b229a",
+   "id": "11",
    "metadata": {},
    "source": [
     "## Peilgebied and HWS layer overlap:\n",
@@ -344,19 +166,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "id": "76cd16f7-c2cd-4ef7-b478-42359e0f8735",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 23\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(Zuiderzeeland[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -379,191 +192,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "3fca7d17-e4a3-445e-92ef-74e60e661e1e",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>globalid</th>\n",
-       "      <th>nen3610id</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>OR 56</td>\n",
-       "      <td>dummy_globalid_peilgebied_0</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_0</td>\n",
-       "      <td>POLYGON Z ((167444.972 487009.101 0.000, 16762...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>OR 13</td>\n",
-       "      <td>dummy_globalid_peilgebied_1</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_1</td>\n",
-       "      <td>POLYGON Z ((182383.808 509903.577 0.000, 18241...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>HT 6</td>\n",
-       "      <td>dummy_globalid_peilgebied_2</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_2</td>\n",
-       "      <td>POLYGON Z ((158728.408 504422.232 0.000, 15873...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>SCHOTERPAD-D-TOCHT</td>\n",
-       "      <td>dummy_globalid_peilgebied_3</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_3</td>\n",
-       "      <td>POLYGON Z ((179970.587 533631.593 0.000, 17996...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ZWARTEMEERTOCHT</td>\n",
-       "      <td>dummy_globalid_peilgebied_4</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_4</td>\n",
-       "      <td>POLYGON Z ((192476.653 518448.383 0.000, 19250...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>290</th>\n",
-       "      <td>TA.02</td>\n",
-       "      <td>dummy_globalid_peilgebied_290</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_290</td>\n",
-       "      <td>POLYGON Z ((189642.837 527330.192 0.000, 18985...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>291</th>\n",
-       "      <td>URK 2</td>\n",
-       "      <td>dummy_globalid_peilgebied_291</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_291</td>\n",
-       "      <td>POLYGON Z ((169418.497 519698.470 0.000, 16937...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>292</th>\n",
-       "      <td>TA.13</td>\n",
-       "      <td>dummy_globalid_peilgebied_292</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_292</td>\n",
-       "      <td>POLYGON Z ((187294.201 529202.794 0.000, 18747...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>293</th>\n",
-       "      <td>3.11</td>\n",
-       "      <td>dummy_globalid_peilgebied_293</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_293</td>\n",
-       "      <td>POLYGON Z ((160519.614 477230.775 0.000, 16016...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>294</th>\n",
-       "      <td>TA.04</td>\n",
-       "      <td>dummy_globalid_peilgebied_294</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_294</td>\n",
-       "      <td>POLYGON Z ((188319.932 529257.440 0.000, 18832...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>295 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   code                       globalid  \\\n",
-       "0                 OR 56    dummy_globalid_peilgebied_0   \n",
-       "1                 OR 13    dummy_globalid_peilgebied_1   \n",
-       "2                  HT 6    dummy_globalid_peilgebied_2   \n",
-       "3    SCHOTERPAD-D-TOCHT    dummy_globalid_peilgebied_3   \n",
-       "4       ZWARTEMEERTOCHT    dummy_globalid_peilgebied_4   \n",
-       "..                  ...                            ...   \n",
-       "290               TA.02  dummy_globalid_peilgebied_290   \n",
-       "291               URK 2  dummy_globalid_peilgebied_291   \n",
-       "292               TA.13  dummy_globalid_peilgebied_292   \n",
-       "293                3.11  dummy_globalid_peilgebied_293   \n",
-       "294               TA.04  dummy_globalid_peilgebied_294   \n",
-       "\n",
-       "                          nen3610id  \\\n",
-       "0      dummy_nen3610id_peilgebied_0   \n",
-       "1      dummy_nen3610id_peilgebied_1   \n",
-       "2      dummy_nen3610id_peilgebied_2   \n",
-       "3      dummy_nen3610id_peilgebied_3   \n",
-       "4      dummy_nen3610id_peilgebied_4   \n",
-       "..                              ...   \n",
-       "290  dummy_nen3610id_peilgebied_290   \n",
-       "291  dummy_nen3610id_peilgebied_291   \n",
-       "292  dummy_nen3610id_peilgebied_292   \n",
-       "293  dummy_nen3610id_peilgebied_293   \n",
-       "294  dummy_nen3610id_peilgebied_294   \n",
-       "\n",
-       "                                              geometry  \n",
-       "0    POLYGON Z ((167444.972 487009.101 0.000, 16762...  \n",
-       "1    POLYGON Z ((182383.808 509903.577 0.000, 18241...  \n",
-       "2    POLYGON Z ((158728.408 504422.232 0.000, 15873...  \n",
-       "3    POLYGON Z ((179970.587 533631.593 0.000, 17996...  \n",
-       "4    POLYGON Z ((192476.653 518448.383 0.000, 19250...  \n",
-       "..                                                 ...  \n",
-       "290  POLYGON Z ((189642.837 527330.192 0.000, 18985...  \n",
-       "291  POLYGON Z ((169418.497 519698.470 0.000, 16937...  \n",
-       "292  POLYGON Z ((187294.201 529202.794 0.000, 18747...  \n",
-       "293  POLYGON Z ((160519.614 477230.775 0.000, 16016...  \n",
-       "294  POLYGON Z ((188319.932 529257.440 0.000, 18832...  \n",
-       "\n",
-       "[295 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Zuiderzeeland[\"peilgebied\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "id": "a7f16af4-fc46-4533-93b6-ee52d6d7687c",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Add occurence to geodataframe\n",
     "peilgebieden_cat = []\n",
@@ -594,7 +236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5dcaab7b-5abe-431a-b890-9d19be3254f2",
+   "id": "15",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied']"
@@ -602,8 +244,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "id": "ec5c9dde-2698-4a73-9fce-266a3c5e96d7",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -620,7 +262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51415106-fb75-4dad-97ba-4b046cb1f545",
+   "id": "17",
    "metadata": {},
    "source": [
     "## Add HWS to ['streefpeil']"
@@ -628,8 +270,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "id": "9e61effa-42ae-46d7-879e-d7fa928ebd8d",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,7 +287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1c16adb-759d-41ae-b864-513aaafff8ac",
+   "id": "19",
    "metadata": {},
    "source": [
     "### Create buffer layer that ensures spatial match between peilgebied and hws layers based on the buffer layer"
@@ -653,8 +295,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
-   "id": "1317fbfd-b713-4172-b1b0-c678c4e8d986",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -672,7 +314,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7ca89cf-c74c-4c88-9c69-4dfdca718e54",
+   "id": "21",
    "metadata": {
     "tags": []
    },
@@ -682,8 +324,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "id": "f5033a40-2967-4cc1-9772-fef2be4169b0",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -700,8 +342,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
-   "id": "970ab69a-6f3a-46ce-9882-aded5f98a39f",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,8 +360,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
-   "id": "51d36d36-9420-4be9-8d6b-8ac0263a7129",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -729,7 +371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31b4ba32-7f34-49a6-b256-966c24faf68a",
+   "id": "25",
    "metadata": {},
    "source": [
     "## Store output"
@@ -737,45 +379,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
-   "id": "9e589e19-a137-418b-b2a7-5b0c778766a6",
+   "execution_count": null,
+   "id": "26",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Zuiderzeeland[\"peilgebied\"].globalid.is_unique"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
-   "id": "eb0ed312-2aef-41f6-83c7-ead0e630aa39",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for key in Zuiderzeeland.keys():\n",
     "    print(key)\n",
@@ -785,7 +402,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "888995e9-8b43-48d3-88ea-2aeea2caa311",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -793,7 +410,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d90929e5-6345-4ccf-8d32-ddfcc20510d4",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HD.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HD.ipynb
index 9b09fee7..edcae236 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HD.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HD.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Hollandse Delta"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer (Daniel):\n",
@@ -21,19 +21,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import geopandas as gpd\n",
     "import numpy as np\n",
@@ -47,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Delfland"
@@ -55,8 +46,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "### Load Files"
@@ -86,8 +77,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,28 +113,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
-   "id": "a7b8f366-9c57-440f-bf48-7908612465d5",
+   "execution_count": null,
+   "id": "7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "HD[\"peilgebied\"].globalid.is_unique"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5556d211-e92e-4ba3-85c2-4ff9bd33fbeb",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -151,8 +131,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
-   "id": "c70bb838-9d93-4d5a-ae12-2da18d145009",
+   "execution_count": null,
+   "id": "9",
    "metadata": {
     "tags": []
    },
@@ -167,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0ed9e9b-258b-4f16-8bcb-b9d412a8c8c3",
+   "id": "10",
    "metadata": {},
    "source": [
     "## Peilgebied and HWS layer overlap:\n",
@@ -179,19 +159,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
-   "id": "05757b4b-2f8e-48a6-a76c-9e06e23f20da",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 174\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(HD[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -214,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "942d281a-aa46-4b57-af3a-f4e2df811b26",
+   "id": "12",
    "metadata": {},
    "source": [
     "## Create peilgebied_cat column"
@@ -222,8 +193,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
-   "id": "5793dd3d-766d-46bd-811e-4c8b6e118d3a",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,8 +247,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
-   "id": "d0e88867-184b-48b5-a10f-ba8816f10dcc",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,28 +258,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
-   "id": "68fd5607-5ed5-4b4e-8c13-9159e175aaae",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1])"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "HD[\"peilgebied\"][\"peilgebied_cat\"].unique()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8ab1e249-a9cc-4728-bfce-d9c965da18df",
+   "id": "16",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied','streefpeil']"
@@ -316,8 +276,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
-   "id": "e60dbbe9-1f27-4a71-b10b-1ecf3c53b060",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,8 +294,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
-   "id": "03ff37c8-16cc-4cdc-9a1b-ea10f06ea630",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e2ea1a8-4a26-4515-9613-11cbd828c0d5",
+   "id": "19",
    "metadata": {},
    "source": [
     "### Create buffer layer that ensures spatial match between peilgebied and hws layers based on the buffer layer"
@@ -359,8 +319,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
-   "id": "c4c448fb-3cb3-451c-aa17-d336f02deb6b",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb233361-1e89-4bfa-b2f4-22d66155db58",
+   "id": "21",
    "metadata": {},
    "source": [
     "## Add buffer to ['peilgebied','streefpeil']"
@@ -380,8 +340,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
-   "id": "75fc528f-5902-4a5d-bc7c-d0b700875832",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,8 +358,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
-   "id": "b345f335-bc12-4e99-8272-9e47cc325021",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,8 +377,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
-   "id": "a8852930-5bbe-41b3-886e-b73ffa70177d",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +388,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfea55d4-0bc9-485b-ac90-68b1dd8455b6",
+   "id": "25",
    "metadata": {},
    "source": [
     "## Store output"
@@ -436,24 +396,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
-   "id": "70b4b337-44a9-4ef4-aee2-f7431aae720c",
+   "execution_count": null,
+   "id": "26",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for key in HD.keys():\n",
     "    print(key)\n",
@@ -463,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fccf3cfa-991c-42d3-b9f9-3869230a3fcf",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -471,7 +417,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc6075a8-8575-442d-a9f5-925dc3614f79",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHNK.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHNK.ipynb
index b12a95f5..8fdc1ba0 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHNK.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHNK.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# HHNK"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2799e766-bbe4-4f8d-a780-051b36f773ae",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer (Daniel):\n",
@@ -21,19 +21,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import geopandas as gpd\n",
     "import numpy as np\n",
@@ -47,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Set Paths"
@@ -55,8 +46,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25e2a7de-cb4b-4f6b-b2db-c675f481b939",
+   "id": "5",
    "metadata": {},
    "source": [
     "## Load files"
@@ -86,8 +77,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e7e929e-e302-4791-b4d2-3808d48cbb56",
+   "id": "7",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -128,8 +119,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "c70bb838-9d93-4d5a-ae12-2da18d145009",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab931fb8-1011-49e9-88c0-e4b1cb09b217",
+   "id": "9",
    "metadata": {},
    "source": [
     "## Peilgebied and HWS layer overlap:\n",
@@ -154,31 +145,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "2f96be55-a022-430c-baad-6a8a90bdcc2f",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 73,
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot\n",
     "fig, ax = plt.subplots()\n",
@@ -189,29 +159,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "31892560-b870-4f06-8e58-acdf13489c62",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Polygon', 'MultiPolygon'], dtype=object)"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "HHNK[\"peilgebied\"].geometry.type.unique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "id": "552d46b7-24c5-4dc5-a818-885bac461e36",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,19 +180,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "id": "f2740959-7a85-4947-950e-d8a64cb8ece2",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 61\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip.\n",
     "HHNK[\"peilgebied\"] = gpd.overlay(HHNK[\"peilgebied\"], gdf_grens, how=\"intersection\", keep_geom_type=True)\n",
@@ -257,7 +207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37f2ee9b-d819-4d7b-92a1-49fe681b4380",
+   "id": "14",
    "metadata": {},
    "source": [
     "## Create peilgebied_cat column"
@@ -265,23 +215,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "id": "c5c819f1-dfae-4bf4-b14e-63902e433b1a",
+   "execution_count": null,
+   "id": "15",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "yes\n",
-      "yes\n",
-      "yes\n",
-      "yes\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Add occurence to geodataframe\n",
     "peilgebieden_cat = []\n",
@@ -307,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c470216-2a8b-4a0a-9e0a-7877203df9dd",
+   "id": "16",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied','streefpeil']"
@@ -315,8 +254,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "id": "7dfdcfaf-4b9a-443f-b40c-65460af8352d",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,8 +272,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "a744a7e5-9ac9-4e47-8cf2-791206581786",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,7 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "161bb7c7-0ba5-4eb6-bab1-b513cfd330f7",
+   "id": "19",
    "metadata": {},
    "source": [
     "### Create layer that ensures spatial match between peilgebied and hws layers based on the buffer layer"
@@ -358,8 +297,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "id": "e9d64a75-240f-4583-9b38-3e3b22a578a3",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +321,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fc8e8ea-4f94-4d3e-8820-46a442903ee6",
+   "id": "21",
    "metadata": {},
    "source": [
     "## Add buffer to ['peilgebied','streefpeil']"
@@ -390,8 +329,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "id": "ac1121dc-342e-4960-9294-308b619de9d8",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -408,8 +347,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "id": "746d2ebd-7307-4745-8e0e-35c047126c27",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,8 +366,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "f3a71ec5-0feb-4c23-a0c2-f3b83f6a5c04",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,8 +376,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "id": "aec2c426-e962-4ff3-a015-a11695708c52",
+   "execution_count": null,
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0d2cab6-6529-4f4a-972a-533de967e85a",
+   "id": "26",
    "metadata": {},
    "source": [
     "## Write output"
@@ -456,24 +395,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "id": "02fcfb58-7054-4517-97f3-224c9acb4d1f",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for key in HHNK.keys():\n",
     "    print(key)\n",
@@ -482,21 +407,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "id": "fb843192-82da-4b4f-bb64-f475a4b52162",
+   "execution_count": null,
+   "id": "28",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1])"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "HHNK[\"peilgebied\"][\"peilgebied_cat\"].unique()"
    ]
@@ -504,7 +418,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0660f4a6-e3f6-4125-9fde-9d66eda1c644",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -512,7 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1680c820-f362-4c07-bef0-359a7fa94d36",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHSK.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHSK.ipynb
index 770951cd..e48b119c 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHSK.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_HHSK.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# HHSK"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and make sure the peilgebieden allign witgh the HWS layer (Daniel):\n",
@@ -21,8 +21,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## HHSK"
@@ -46,8 +46,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "## Load Files"
@@ -75,8 +75,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,19 +111,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "0d23e578-c217-4aa4-b5f4-41e01e32a503",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12509\n",
-      "12509\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(len(HHSK[\"duikersifonhevel\"].globalid.unique()))\n",
     "print(len(HHSK[\"duikersifonhevel\"].globalid))"
@@ -131,50 +122,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "5dccf386-5dd3-4d48-8ba9-13e563be96c0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "HHSK[\"peilgebied\"].globalid.is_unique"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "2cc1dcf0-84a3-4be0-a20f-e685ceec58ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "25622"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "len(HHSK[\"hydroobject\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "6b749ccd-55e8-4305-857e-994ed1e77725",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,8 +155,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "8f314b82-55e7-4b45-879f-40b6b7ef84b6",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,28 +165,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "9330239f-a3cd-47e2-a6d0-89e6df252c9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21838"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "len(HHSK[\"hydroobject\"])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5556d211-e92e-4ba3-85c2-4ff9bd33fbeb",
+   "id": "13",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -225,8 +183,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "c70bb838-9d93-4d5a-ae12-2da18d145009",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caba0e5d-0c01-4ff9-9d83-8790125ff85d",
+   "id": "15",
    "metadata": {},
    "source": [
     "## Check Peilgebied and HWS layer overlap:\n",
@@ -251,19 +209,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "b3fd731a-1dc8-46cb-b4a3-f052eca43400",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 26\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(HHSK[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -289,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c84ed19-d0a7-4a27-8e3d-03b132a502ac",
+   "id": "17",
    "metadata": {},
    "source": [
     "## Create peilgebied_cat column"
@@ -297,8 +246,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "3702811b-f3c3-4745-a863-73329e22c5cd",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,8 +256,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "9bae7259-c4fd-4f2a-beb4-ec92a924f210",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -338,8 +287,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "1c1c34e4-4fb2-4921-850b-7a48112da28f",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,8 +297,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "2a3b05ad-eb78-4e3d-bfb8-23ec469d1ed1",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "037faf79-c747-405e-b2e5-a73acfed0aba",
+   "id": "22",
    "metadata": {
     "tags": []
    },
@@ -371,8 +320,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "0cdb36b6-9c4a-42fd-95f6-17c6b4e5803f",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,8 +338,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "3ed63b03-009a-4cde-8e64-e7f59bb8ca21",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,28 +355,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "eb5bc143-8a45-4a77-af37-5b190c2fe9f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "25",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "HHSK[\"peilgebied\"][\"peilgebied_cat\"].unique()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "49d80d89-6676-48cc-a1b3-33771f1a9250",
+   "id": "26",
    "metadata": {},
    "source": [
     "### Create buffer polygon between NHWS and peilgebied/RHWS"
@@ -435,8 +373,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "d415acda-966c-4847-a4b6-a06b23f87218",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -454,7 +392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eaeccbcb-b0d6-4caa-9fb9-dc8a44016298",
+   "id": "28",
    "metadata": {},
    "source": [
     "### Add buffer to ['peilgebied','streefpeil']"
@@ -462,8 +400,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "852a9639-cdc4-4709-abb0-fded2aed5970",
+   "execution_count": null,
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -482,8 +420,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "7fb401c1-3291-4bff-896e-1fc1478fe830",
+   "execution_count": null,
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,8 +438,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "3e9caf33-e3d2-4f01-a281-3557e171720e",
+   "execution_count": null,
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -511,7 +449,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05a65661-9821-4657-9351-22502ee9a58c",
+   "id": "32",
    "metadata": {},
    "source": [
     "### Store post-processed data"
@@ -519,24 +457,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "3425a4bd-f8b1-4dd3-b11b-4bb420c8a5bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in HHSK.keys():\n",
     "    print(key)\n",
@@ -546,7 +470,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f2b2da20-38a8-4308-908a-c4282bf407a7",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -554,7 +478,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "77a8d3f4-775a-4484-b0b8-cd23bb1fbd1d",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_scheldestromen.ipynb b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_scheldestromen.ipynb
index 503c21eb..226dc1fc 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_scheldestromen.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/postprocess_data/post-processing_scheldestromen.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "690952c5-5037-476a-a660-d54fec614748",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Scheldestromen"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e378d7-8f05-4562-87b0-34978ba61554",
+   "id": "1",
    "metadata": {},
    "source": [
     "This script adds a new column \"peilgebied_cat\" and makes sure the peilgebieden allign with the HWS layer (Daniel):\n",
@@ -21,8 +21,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0c27c7a4-5733-46ea-970f-cd985b8c92cd",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc1f28d2-8499-4ebb-906e-1724bd334aac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Scheldestromen"
@@ -46,8 +46,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "e15206a7-6639-40bb-9942-f920085f53b4",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbafed8-355a-4ec9-90c9-eca9e3b9313d",
+   "id": "5",
    "metadata": {},
    "source": [
     "### Load Files"
@@ -77,8 +77,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f6dcf56-e8a4-4055-bc86-a6d33c91d8d8",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,196 +113,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "5fcd4cc6-3c75-462f-af3e-7693c9c5265f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Scheldestromen[\"peilgebied\"].globalid.is_unique"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "afc99001-f0fd-4433-9799-72bb34966673",
-   "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>code</th>\n",
-       "      <th>nen3610id</th>\n",
-       "      <th>globalid</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>GPG1398_dummy_id_0</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_0</td>\n",
-       "      <td>dummy_globalid_peilgebied_0</td>\n",
-       "      <td>MULTIPOLYGON Z (((39197.262 373523.013 -100000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>GPG1007_dummy_id_1</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_1</td>\n",
-       "      <td>dummy_globalid_peilgebied_1</td>\n",
-       "      <td>MULTIPOLYGON Z (((15595.339 368349.545 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>GPG803_dummy_id_2</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_2</td>\n",
-       "      <td>dummy_globalid_peilgebied_2</td>\n",
-       "      <td>MULTIPOLYGON Z (((36391.858 373284.887 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>GPG911_dummy_id_3</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_3</td>\n",
-       "      <td>dummy_globalid_peilgebied_3</td>\n",
-       "      <td>MULTIPOLYGON Z (((40712.442 373620.190 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>GPG842_dummy_id_4</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_4</td>\n",
-       "      <td>dummy_globalid_peilgebied_4</td>\n",
-       "      <td>MULTIPOLYGON Z (((33494.859 370960.048 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>847</th>\n",
-       "      <td>GPG1333_dummy_id_847</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_847</td>\n",
-       "      <td>dummy_globalid_peilgebied_847</td>\n",
-       "      <td>MULTIPOLYGON Z (((56639.793 391068.875 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>848</th>\n",
-       "      <td>GPG1335_dummy_id_848</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_848</td>\n",
-       "      <td>dummy_globalid_peilgebied_848</td>\n",
-       "      <td>MULTIPOLYGON Z (((57784.953 391743.719 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>849</th>\n",
-       "      <td>GPG457_dummy_id_849</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_849</td>\n",
-       "      <td>dummy_globalid_peilgebied_849</td>\n",
-       "      <td>MULTIPOLYGON Z (((22309.586 393311.125 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>850</th>\n",
-       "      <td>GPG808_dummy_id_850</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_850</td>\n",
-       "      <td>dummy_globalid_peilgebied_850</td>\n",
-       "      <td>MULTIPOLYGON Z (((58756.058 366653.895 0.000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>851</th>\n",
-       "      <td>GPG1004_dummy_id_851</td>\n",
-       "      <td>dummy_nen3610id_peilgebied_851</td>\n",
-       "      <td>dummy_globalid_peilgebied_851</td>\n",
-       "      <td>MULTIPOLYGON Z (((56100.529 364330.493 0.000, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>852 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     code                       nen3610id  \\\n",
-       "0      GPG1398_dummy_id_0    dummy_nen3610id_peilgebied_0   \n",
-       "1      GPG1007_dummy_id_1    dummy_nen3610id_peilgebied_1   \n",
-       "2       GPG803_dummy_id_2    dummy_nen3610id_peilgebied_2   \n",
-       "3       GPG911_dummy_id_3    dummy_nen3610id_peilgebied_3   \n",
-       "4       GPG842_dummy_id_4    dummy_nen3610id_peilgebied_4   \n",
-       "..                    ...                             ...   \n",
-       "847  GPG1333_dummy_id_847  dummy_nen3610id_peilgebied_847   \n",
-       "848  GPG1335_dummy_id_848  dummy_nen3610id_peilgebied_848   \n",
-       "849   GPG457_dummy_id_849  dummy_nen3610id_peilgebied_849   \n",
-       "850   GPG808_dummy_id_850  dummy_nen3610id_peilgebied_850   \n",
-       "851  GPG1004_dummy_id_851  dummy_nen3610id_peilgebied_851   \n",
-       "\n",
-       "                          globalid  \\\n",
-       "0      dummy_globalid_peilgebied_0   \n",
-       "1      dummy_globalid_peilgebied_1   \n",
-       "2      dummy_globalid_peilgebied_2   \n",
-       "3      dummy_globalid_peilgebied_3   \n",
-       "4      dummy_globalid_peilgebied_4   \n",
-       "..                             ...   \n",
-       "847  dummy_globalid_peilgebied_847   \n",
-       "848  dummy_globalid_peilgebied_848   \n",
-       "849  dummy_globalid_peilgebied_849   \n",
-       "850  dummy_globalid_peilgebied_850   \n",
-       "851  dummy_globalid_peilgebied_851   \n",
-       "\n",
-       "                                              geometry  \n",
-       "0    MULTIPOLYGON Z (((39197.262 373523.013 -100000...  \n",
-       "1    MULTIPOLYGON Z (((15595.339 368349.545 0.000, ...  \n",
-       "2    MULTIPOLYGON Z (((36391.858 373284.887 0.000, ...  \n",
-       "3    MULTIPOLYGON Z (((40712.442 373620.190 0.000, ...  \n",
-       "4    MULTIPOLYGON Z (((33494.859 370960.048 0.000, ...  \n",
-       "..                                                 ...  \n",
-       "847  MULTIPOLYGON Z (((56639.793 391068.875 0.000, ...  \n",
-       "848  MULTIPOLYGON Z (((57784.953 391743.719 0.000, ...  \n",
-       "849  MULTIPOLYGON Z (((22309.586 393311.125 0.000, ...  \n",
-       "850  MULTIPOLYGON Z (((58756.058 366653.895 0.000, ...  \n",
-       "851  MULTIPOLYGON Z (((56100.529 364330.493 0.000, ...  \n",
-       "\n",
-       "[852 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Scheldestromen[\"peilgebied\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3e2faf6a-d645-44c7-8882-f6e613e73410",
+   "id": "9",
    "metadata": {},
    "source": [
     "## Select waterschap boundaries and clip hws layer"
@@ -310,8 +141,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "0d804374-1484-42d0-88a2-f6bec404349b",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -324,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cc62e79-0fc4-48d3-b3b6-e4ded29c2e35",
+   "id": "11",
    "metadata": {},
    "source": [
     "## Peilgebied and HWS layer overlap:\n",
@@ -336,19 +167,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "a15df030-9a47-47bb-a09c-dd4b5dda65e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of overlapping shapes without filter: 203\n",
-      "Number of overlapping shapes with filter: 0\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Step 1: Identify the Overlapping Areas and clip\n",
     "overlaps = gpd.overlay(Scheldestromen[\"peilgebied\"], gdf_hws, how=\"intersection\", keep_geom_type=True)\n",
@@ -371,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87df5559-330b-41e1-8355-cbcc5c33d0a5",
+   "id": "13",
    "metadata": {},
    "source": [
     "## Create peilgebied_cat columnm"
@@ -379,19 +201,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "bfc95c53-1282-479b-8348-ad54085a49f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n",
-      "yes\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Add occurence to geodataframe\n",
     "peilgebieden_cat = []\n",
@@ -412,8 +225,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "cc35d69e-9ce6-423b-abda-0b8314a5ec22",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -423,28 +236,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "4cfae028-3cac-4eaf-bb0e-282de2469448",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Scheldestromen[\"peilgebied\"][\"peilgebied_cat\"].unique()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "43ed5595-4741-4dc9-9c37-4ba790190281",
+   "id": "17",
    "metadata": {},
    "source": [
     "## Add nhws to ['peilgebied','streefpeil']"
@@ -452,8 +254,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "b666fddd-e1b8-4e66-9a88-d87fb0df8749",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,8 +272,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "55368969-6fce-4597-a6a8-128f5a54bcb8",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d3ed0b2-0f05-4c51-b24f-4032059b1bc9",
+   "id": "20",
    "metadata": {},
    "source": [
     "### Create buffer layer that ensures spatial match between peilgebied and hws layers based on the buffer layer"
@@ -495,8 +297,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "6c6a1883-1647-493a-acad-411404f1daec",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -508,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c27471d-9fb3-4d38-bd63-b841cc41cbee",
+   "id": "22",
    "metadata": {
     "tags": []
    },
@@ -518,8 +320,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "39a1211a-bb76-4c4f-ac7e-2405d2729705",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -536,8 +338,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "77237ffe-7099-4872-8f1f-4ccc0cd84b6c",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,8 +355,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "4eba78fb-2f12-4ea8-9558-d0f97f94f654",
+   "execution_count": null,
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +366,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf892c2f-bf67-4e5e-a2f1-20699fedcf88",
+   "id": "26",
    "metadata": {},
    "source": [
     "## Store output"
@@ -572,26 +374,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "17796202-2f3c-4175-8409-7c2294b76703",
+   "execution_count": null,
+   "id": "27",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stuw\n",
-      "gemaal\n",
-      "hydroobject\n",
-      "duikersifonhevel\n",
-      "peilgebied\n",
-      "streefpeil\n",
-      "aggregation_area\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for key in Scheldestromen.keys():\n",
     "    print(key)\n",
@@ -601,7 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3c9d9ed-1be2-49f8-a0fa-0b8804a37de2",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -609,7 +397,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7e07fd19-28ae-47f6-b71e-a4c0c49e6b8b",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -617,7 +405,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8d0cfb75-8c87-40f7-b15b-47e4efdbc5db",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -625,7 +413,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4957fa79-db9d-4de6-a416-6f94052e98db",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -633,7 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88882c7e-be83-499f-b1a9-12c9d8eb65ce",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -641,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065612a7-7d42-4d19-9a26-264811713efd",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/AmstelGooienVecht.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/AmstelGooienVecht.ipynb
index cb009192..ef0bc05c 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/AmstelGooienVecht.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/AmstelGooienVecht.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d13f9ab1-f6e7-4958-96cc-343f0c2138f6",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ad89ae02-4242-4aca-bd5a-c0c3fd8592ac",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09981d65-c7d2-4802-9fca-2ef490213b2c",
+   "id": "2",
    "metadata": {},
    "source": [
     "# Amstel, Gooi en Vecht"
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b5fc0f1d-b2bf-4933-9472-96cb110e6111",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d54f0c7c-93a1-44bd-b0f9-fcf37279dc83",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d8ca7d9a-fcd9-4154-9278-029f2a25b7b7",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6a63463e",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c3e568b-79b0-4809-8274-a029cc61b534",
+   "id": "7",
    "metadata": {},
    "source": [
     "# Nalevering"
@@ -118,7 +118,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31327447-601e-4f6a-b141-e97162433b37",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f68a9597-d088-40c2-88df-931aa281d000",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "951b70c4-740a-47db-9abf-7c5770aa24bb",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "320352cd-a10b-48ff-afc0-71023df12cb4",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +202,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7fd2e22c-95a0-4877-b160-843b36ea56a3",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +260,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3cb1e8f8-fc21-4b03-a6e9-d0c1eecd5701",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,14 +273,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "74d6b456-154a-4c13-b53d-4d5e67122485",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "9276888c-0ba2-4f5d-8ecb-a26baa1747f0",
+   "id": "15",
    "metadata": {},
    "source": [
     "# Control, store"
@@ -289,7 +289,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f2554d9e-9957-47bd-8cef-e6bfd4220a61",
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "be767e93-6ab9-4a3d-a7ae-247eb3877617",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,7 +314,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "09e3e8eb-f52b-497b-a0ef-b8613d7771c1",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Delfland.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Delfland.ipynb
index f6d61206..19ff4db4 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Delfland.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Delfland.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f5aaa20-7965-4aa7-bf24-79965d87edb1",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "2",
    "metadata": {},
    "source": [
     "# Delfland"
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "636e86b9-bd75-4f8f-91eb-e757fba21fde",
+   "id": "3",
    "metadata": {
     "tags": []
    },
@@ -50,7 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "532b0b83-2139-4d48-8e42-883ed8e88325",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "341e9076-62bd-4d0f-aba9-835cdf93afeb",
+   "id": "5",
    "metadata": {},
    "source": [
     "### Adjust column names"
@@ -75,7 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "059f9113-bcd4-470a-abb6-4fd8ec193f4a",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54a863ea-caab-4be6-bca6-78c2ae91941f",
+   "id": "7",
    "metadata": {},
    "source": [
     "### Add column to determine the HWS_BZM"
@@ -172,7 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fda7f5c9-6949-4044-b04d-ba438d2b37d3",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +183,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42dc4ba1-3ccb-4b0f-a075-77aec9b85a07",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ccbba5-8e59-4134-9209-db988bc5c3d5",
+   "id": "10",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b064a376-0396-4c93-a2ad-eca3eea54598",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "12",
    "metadata": {},
    "source": [
     "### Store data"
@@ -224,7 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,14 +238,14 @@
   },
   {
    "cell_type": "raw",
-   "id": "d6b186d5-c907-4b19-9ee2-c7222476856a",
+   "id": "14",
    "metadata": {},
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fedb4c6e-49c2-44f4-88f0-0e1ce4802bc7",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHNK.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHNK.ipynb
index 4ab9d66a..c3cf825f 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHNK.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHNK.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,14 +20,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f5aaa20-7965-4aa7-bf24-79965d87edb1",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "2",
    "metadata": {},
    "source": [
     "# Hollands Noorderkwartier"
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "636e86b9-bd75-4f8f-91eb-e757fba21fde",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "baf1ecdb-36e9-4370-ad9d-28dd4b7b0c6b",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3dbf5fe0-ac68-4270-b936-51dd5e7e8215",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -90,7 +90,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "033468ab-b74c-468a-90b1-eac395ad8d17",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "805ffd9b-da23-46e3-977f-84575e32f225",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "341e9076-62bd-4d0f-aba9-835cdf93afeb",
+   "id": "8",
    "metadata": {},
    "source": [
     "### GPKG"
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9b86f37d-16de-49db-969a-b233f1531abb",
+   "id": "9",
    "metadata": {
     "tags": []
    },
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdc5db0a-4f5f-464f-aa98-1cc7ea968680",
+   "id": "10",
    "metadata": {},
    "source": [
     "### .GDB"
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fe5e6309-4370-4da7-bd2c-9c7f7f727545",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +220,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ebd41c6f-24dc-4a56-b24c-65c33b707707",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +232,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e6164c27-8292-4943-bc6e-83445ed956a9",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d72f3d5c-20ed-4ca6-a71f-ddca9cf93fee",
+   "id": "14",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -255,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ffd4ea1b-e2a2-4e3b-a5cf-e820a4709c30",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56fa3a9e-2894-4676-9a47-29fbdadc96c5",
+   "id": "16",
    "metadata": {
     "tags": []
    },
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "064607bb-4c54-4dc2-b913-94dfcd18cfa0",
+   "id": "17",
    "metadata": {},
    "source": [
     "Some changes by hand have been made. The resulting shapefile contains the bordering BZM and HWS shapes, including streefpeil"
@@ -283,7 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8e8c8649-cde9-40db-b155-d8d80ba65f6a",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "350baa05-21ab-48af-b4b9-cae7fef089a6",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "20",
    "metadata": {},
    "source": [
     "### Store data"
@@ -323,7 +323,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "d6b186d5-c907-4b19-9ee2-c7222476856a",
+   "id": "22",
    "metadata": {},
    "source": [
     "Toevoegen aan notities:\n",
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHSK.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHSK.ipynb
index 96e5b13d..a336f572 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHSK.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/HHSK.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f5aaa20-7965-4aa7-bf24-79965d87edb1",
+   "id": "1",
    "metadata": {
     "tags": [
      "test"
@@ -32,7 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbbec2b5-c309-4a42-a914-dd33c2da3610",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7bb775e-cc57-4586-a13c-8d9ba05ace6b",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f39bd82-2fed-41d6-a4f7-979a9a2120bd",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "279c940f-4290-48d6-bd48-b1e79f8be16e",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6ea5a43d-b2e6-42ef-8002-01c3377ed897",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32562573-3c78-4565-85be-1b7c03a023be",
+   "id": "7",
    "metadata": {},
    "source": [
     "## Only select status_object == 3"
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "10efac14-fd47-4f61-9180-e89e864713c7",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d91ef127-a46e-4ce7-b4fc-ec13d39b6820",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "10",
    "metadata": {},
    "source": [
     "# HHSK"
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62a8afeb-9d69-4df0-8e9a-0aa255543fb1",
+   "id": "11",
    "metadata": {
     "tags": []
    },
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8196e429-c7c1-40f1-9dd3-525699656dc7",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5f19829d-6116-45d4-92ae-a0e27509afa3",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -205,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fe221b99-ad03-4688-a656-9cb19e4f1a8b",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -225,7 +225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c3e7f83-1aa8-4714-8ef5-7d0176097d94",
+   "id": "15",
    "metadata": {
     "tags": []
    },
@@ -236,7 +236,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "75bbbea1-5ef9-4935-ad8e-4f294eaf1c9f",
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,7 +250,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8495f3ba-98df-4eea-97a5-d09534e36885",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31841446-4e06-47b7-98a3-38d389df26df",
+   "id": "18",
    "metadata": {},
    "source": [
     "### Add the nageleverde peilgebieden to the original data"
@@ -276,7 +276,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9ee954f4-c333-4bb4-8dcc-b1e1cd7c2b57",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c37cfb5c-3b9e-4e57-b44c-3cbe610da093",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -324,7 +324,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fd7bfade-497b-40bd-8345-8dc4fd3d172b",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +344,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5631e7ba-f5a0-4eaf-942a-3b6535a4ba8b",
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,7 +354,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7df9baa4-7092-4401-be9a-fd3a451c38b0",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a03b9016-af7d-4c4e-a10b-1f2ea2ee9254",
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +374,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9736e6e4-0e8f-4396-a1f4-3b4f3e9bf690",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,7 +398,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b5d16748-7262-4e43-baa2-f182cb8dd142",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0292ab77-acfd-4666-9c3b-b9bd8c1f1fec",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,7 +419,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cd21bcac-8d25-4d47-ad0a-c7338e6e6653",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -433,7 +433,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88e9543c-2dbe-4ebf-9423-b38daeeaa004",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6cdf901e-c4b4-4fb8-9f40-4731ff3c2d1d",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51df5dde-d374-4ae3-8d43-1c495581f021",
+   "id": "31",
    "metadata": {},
    "source": [
     "### Delete irrelevant data"
@@ -461,7 +461,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42dc4ba1-3ccb-4b0f-a075-77aec9b85a07",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +476,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "375f1598-03c1-48a1-bb19-54790273dad0",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ccbba5-8e59-4134-9209-db988bc5c3d5",
+   "id": "34",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -497,7 +497,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b064a376-0396-4c93-a2ad-eca3eea54598",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,7 +506,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "36",
    "metadata": {},
    "source": [
     "### Store data"
@@ -515,7 +515,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -530,7 +530,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "69cbb333-f66e-4ca8-880f-3242846c6a9b",
+   "id": "38",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Hollandse_Delta.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Hollandse_Delta.ipynb
index 4f74f039..d7593949 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Hollandse_Delta.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Hollandse_Delta.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f5aaa20-7965-4aa7-bf24-79965d87edb1",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "2",
    "metadata": {},
    "source": [
     "# HD"
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "636e86b9-bd75-4f8f-91eb-e757fba21fde",
+   "id": "3",
    "metadata": {
     "tags": []
    },
@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "532b0b83-2139-4d48-8e42-883ed8e88325",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "077f1c26-c738-48f7-b9df-bec5b7356c9a",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "341e9076-62bd-4d0f-aba9-835cdf93afeb",
+   "id": "6",
    "metadata": {},
    "source": [
     "### Adjust column names"
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d54bf05e-f563-44ec-9864-4774e2aecfc6",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "059f9113-bcd4-470a-abb6-4fd8ec193f4a",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +202,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42dc4ba1-3ccb-4b0f-a075-77aec9b85a07",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ccbba5-8e59-4134-9209-db988bc5c3d5",
+   "id": "10",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -225,7 +225,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b064a376-0396-4c93-a2ad-eca3eea54598",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2809cf79-d381-432b-8ddd-3497556f5d82",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "13",
    "metadata": {},
    "source": [
     "### Store data"
@@ -253,7 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "52f17e20-95a7-493f-bff9-6600df570fe0",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rijnland.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rijnland.ipynb
index c38d1901..67cc146a 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rijnland.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rijnland.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f5aaa20-7965-4aa7-bf24-79965d87edb1",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbbec2b5-c309-4a42-a914-dd33c2da3610",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7bb775e-cc57-4586-a13c-8d9ba05ace6b",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ca6ddcd9-e960-4b5f-ba10-4d222c16a843",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "5",
    "metadata": {},
    "source": [
     "# Rijnland"
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01dda03c-5a50-4bde-a655-7ed14c85a8d3",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e58ee099-54b3-415b-8222-9545776a7a61",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "023a704c-685e-4fe9-9745-39a5ed461a03",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eb236dc1-11b3-42c4-99e9-fecd568bec2b",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +143,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "05098a9e-9b5a-487e-8b3e-7f3d82bda74e",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5339a8c5-8c43-4ccd-9008-103fc7e7058e",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +167,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "096a2293-cd78-4c8d-b0e8-bbbe559a8155",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,7 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6bea1764-0459-4d49-9d48-c5b85a6c3480",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9efaf904-e94c-4c87-aeb6-c04d4f183e27",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +208,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1511cf73-aa2b-423f-be87-c95fb0d9bdbb",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -225,7 +225,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7bdd80ad-650c-4e9f-a3bd-d675c4544830",
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,7 +247,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fb1f6f57-60e7-4122-9648-5a0883933dd1",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,7 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dbb4827e-17ad-461f-8101-f97f38b2b31e",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,7 +275,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67ba9685-90b6-4389-818f-a003d9d41dc8",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +289,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42dc4ba1-3ccb-4b0f-a075-77aec9b85a07",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,7 +304,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9ea24ea8-67ae-4cff-ac30-6492dcd80c41",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ccbba5-8e59-4134-9209-db988bc5c3d5",
+   "id": "22",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -322,7 +322,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b064a376-0396-4c93-a2ad-eca3eea54598",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "24",
    "metadata": {},
    "source": [
     "### Store data"
@@ -340,7 +340,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f198edc5-7466-4668-b980-adabdf7c7c94",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -348,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,7 +363,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "48b0320b-258c-44c8-aff2-83153db1a512",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -371,7 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "311db7da-e645-47f2-af06-0bdf23a5589b",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -379,7 +379,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0b93668e-c539-426a-9af2-99d36af00334",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -387,7 +387,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5a09e840-7204-4aac-b825-c66394c60775",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -395,7 +395,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "622a1518-d12d-4b70-ac03-0d251ee09861",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -403,7 +403,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0cf20d1-b76c-4e07-ab7b-9976313f8dad",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -411,7 +411,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ace74b96-2316-4b02-b3ca-9e2b9d3c18aa",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -419,7 +419,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "92551bfe-2672-4c85-988e-e19a320b8cee",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -427,7 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "70c906ed-d0b1-4ccb-b527-775d1c7e1e48",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -435,7 +435,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ed6d57c1-3127-43a2-9de0-29cbf5846bd1",
+   "id": "36",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c562c790-2afa-493d-a86d-c438deae6470",
+   "id": "37",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -451,7 +451,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0090fd91-40eb-48e1-aa99-2360a13a708e",
+   "id": "38",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rivierenland.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rivierenland.ipynb
index d424797d..1aedd840 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rivierenland.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Rivierenland.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f5aaa20-7965-4aa7-bf24-79965d87edb1",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4c2a1a6e-1255-4481-9d94-b0206f40e94d",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "3",
    "metadata": {},
    "source": [
     "# WSRL"
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "636e86b9-bd75-4f8f-91eb-e757fba21fde",
+   "id": "4",
    "metadata": {
     "tags": []
    },
@@ -62,7 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0f66000-73e6-4b06-b5a2-8308213c2461",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7469bb6f-dc28-43b3-b9cb-2d4505b5d5fd",
+   "id": "6",
    "metadata": {},
    "source": [
     "Additional data is given in another gpkg, which includes the peilgebieden"
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5018d1e6-f7ba-4e02-b01a-6d83a3a5e9a3",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc85db32-bb81-4f7f-9a38-2bd89b3fc658",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "341e9076-62bd-4d0f-aba9-835cdf93afeb",
+   "id": "9",
    "metadata": {},
    "source": [
     "### Adjust column names"
@@ -134,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0302db0-e7f0-4dd2-88b7-3dc9aadd581f",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6f56f578-aca0-4957-89df-b6a3a08278a3",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6dce84db-36f3-4a1c-9f10-7c14d9e4a6ed",
+   "id": "12",
    "metadata": {
     "tags": []
    },
@@ -214,7 +214,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "059f9113-bcd4-470a-abb6-4fd8ec193f4a",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0181e016-5103-4d66-b0fa-27ef59282f51",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +238,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "96f0e8bf-89e3-4743-b047-d23791bdc5b4",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "500d4d64-c65b-4426-9f89-7f10e12a0514",
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "363a8b04-a132-469a-b5c8-cde2e911a9c0",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,7 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cfbf8612-93a9-4357-a3c9-cd3dd9d9bf71",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "74734b29-5c4a-4e63-a873-88d8f6ebbd14",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +361,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42dc4ba1-3ccb-4b0f-a075-77aec9b85a07",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ccbba5-8e59-4134-9209-db988bc5c3d5",
+   "id": "21",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -384,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b064a376-0396-4c93-a2ad-eca3eea54598",
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,14 +394,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d81fa34f-69aa-4d0e-9612-8ae36b959e0b",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "24",
    "metadata": {},
    "source": [
     "### Store data"
@@ -410,7 +410,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -424,14 +424,14 @@
   },
   {
    "cell_type": "raw",
-   "id": "d6b186d5-c907-4b19-9ee2-c7222476856a",
+   "id": "26",
    "metadata": {},
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fedb4c6e-49c2-44f4-88f0-0e1ce4802bc7",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Scheldestromen.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Scheldestromen.ipynb
index 28306f5b..de0aa816 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Scheldestromen.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Scheldestromen.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f5aaa20-7965-4aa7-bf24-79965d87edb1",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbbec2b5-c309-4a42-a914-dd33c2da3610",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7bb775e-cc57-4586-a13c-8d9ba05ace6b",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f39bd82-2fed-41d6-a4f7-979a9a2120bd",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4a7d78f8-7605-4aba-b4c6-b17b81d4f5df",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0c14883a-873b-44ee-b9d3-57d7da0b67c3",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "7",
    "metadata": {},
    "source": [
     "# Scheldestromen"
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbe8f365-8b00-4824-b04c-b976f9a43f05",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b3f70ee4-d645-4114-b5e2-2dd573374d6e",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e58ee099-54b3-415b-8222-9545776a7a61",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "023a704c-685e-4fe9-9745-39a5ed461a03",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4d5d490e-5bba-4d16-95a0-a17880adc0d9",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cd21bcac-8d25-4d47-ad0a-c7338e6e6653",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88e9543c-2dbe-4ebf-9423-b38daeeaa004",
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +220,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42dc4ba1-3ccb-4b0f-a075-77aec9b85a07",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ccbba5-8e59-4134-9209-db988bc5c3d5",
+   "id": "16",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -243,7 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b064a376-0396-4c93-a2ad-eca3eea54598",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -253,7 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f9d38f6f-42df-45b4-a1d2-b1a779c104d8",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45564e81-4fcf-4479-b406-8142b4a64ad1",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "20",
    "metadata": {},
    "source": [
     "### Store data"
@@ -285,7 +285,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,14 +299,14 @@
   },
   {
    "cell_type": "raw",
-   "id": "d6b186d5-c907-4b19-9ee2-c7222476856a",
+   "id": "22",
    "metadata": {},
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fedb4c6e-49c2-44f4-88f0-0e1ce4802bc7",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,7 +316,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "af637fd1-1d33-4eb2-92c7-51c29e477404",
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Wetterskip.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Wetterskip.ipynb
index eeb3f2f9..f5278bb5 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Wetterskip.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Wetterskip.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48a939f4-8a39-4c24-b466-499eba37172d",
+   "id": "1",
    "metadata": {},
    "source": [
     "# Wetterskip Fryslan"
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b782b9c9-12b9-461b-8874-a59dad72e4bd",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b96e6dab-5341-480d-b077-5b05a2984aa7",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "05d63407-5f32-41a9-afbb-ade51a17b7a4",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ccc56b93-ad7b-4b80-b197-21c6aa07e07c",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8e3c5720-ff47-40c2-a3f0-e2e635414ed9",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21704eb9-844a-483f-b102-53313a08c3e9",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7eed68de-331a-4829-b78e-ab39db127d71",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b17739a1-76d0-4d8e-bbf9-2483cc81abe5",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54ee138a-5c47-414c-a550-68756f739c91",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "660832b6-d52f-4c17-9b15-510c265c0bea",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +222,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "81d3df9b-574a-4bd7-860f-2ac4fda4bd4f",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +232,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ddeaea28-d8ad-484d-880c-965cd4bd8faf",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f83cab3-fb65-4336-a0ed-36339c34c2dc",
+   "id": "14",
    "metadata": {
     "jp-MarkdownHeadingCollapsed": true,
     "tags": []
@@ -253,7 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2982e516-9b1f-4f4e-86cc-5131ff53925a",
+   "id": "15",
    "metadata": {
     "tags": []
    },
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "ba602947-7d4c-48b0-9651-683efffd0932",
+   "id": "16",
    "metadata": {},
    "source": [
     "There are some peilgebieden without peil. Merge the peilgebied praktijk and the peilgebiedvigerend. Then, take the difference between this merged peilgebied and the peilbesluit gebied. The leftover areas should get a streefpeil based on the layer of peilmerk."
@@ -285,7 +285,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "868691a9-9659-4026-9588-2d9e73f04db5",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +301,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29513621-a948-408e-91cd-9255d55d539b",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,7 +323,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47072d3d-5d87-40c3-bf57-198d618271ae",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,7 +335,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "794c3ba1-d2ec-4ed7-9f7c-432fe189cc81",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7484c309-6a03-492b-a27c-fcf3ef837446",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,7 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25cfdd50-7216-48aa-a60a-b60710500790",
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -379,7 +379,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57d0bb94-fc26-4299-804e-8c6a17847c77",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Zuiderzeeland.ipynb b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Zuiderzeeland.ipynb
index 4e7c2f7a..dbb81e72 100644
--- a/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Zuiderzeeland.ipynb
+++ b/src/peilbeheerst_model/peilbeheerst_model/preprocess_data/Zuiderzeeland.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "065338fd-62d6-480e-8c80-8bc4b101846b",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbbec2b5-c309-4a42-a914-dd33c2da3610",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7bb775e-cc57-4586-a13c-8d9ba05ace6b",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffeed567-f858-4e46-83ff-89b7d7ea9b6d",
+   "id": "3",
    "metadata": {},
    "source": [
     "# Zuiderzeeland"
@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "636e86b9-bd75-4f8f-91eb-e757fba21fde",
+   "id": "4",
    "metadata": {
     "tags": []
    },
@@ -78,7 +78,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a0d86e2c-d365-4a03-8276-d59f93367128",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "920dff3d-f81f-4e88-a8be-67fa2c60d41b",
+   "id": "6",
    "metadata": {},
    "source": [
     "ZZL: stuwen in KWKSOORT in overigekunstwerken.gpkg"
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28a99515-40c8-4a8e-b78f-0781869de8be",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e58ee099-54b3-415b-8222-9545776a7a61",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a9a814bb-bf6a-4822-9447-c8fb0bbc57ae",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +164,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5a55866b-ece5-45ce-836d-c8b1fc737c2b",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -172,7 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aec76a19-0893-48a0-b1af-c8c871d0557d",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ccbba5-8e59-4134-9209-db988bc5c3d5",
+   "id": "12",
    "metadata": {},
    "source": [
     "### Check for the correct keys and columns"
@@ -255,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b064a376-0396-4c93-a2ad-eca3eea54598",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4e74b4c-17ba-4829-9531-248f4d74cfad",
+   "id": "14",
    "metadata": {},
    "source": [
     "### Store data"
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "556aea48-a819-4f70-8e22-6c843354a46d",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +288,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "116f9f2a-ad97-44c5-9a2f-ba43c80e4b2d",
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -296,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01a06379-58e7-4621-b998-4f95b947bd63",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -304,7 +304,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f48ffb3f-11d0-41d7-9254-61b2c7873436",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/ribasim_lumping/README.md b/src/ribasim_lumping/README.md
index a5ee50c8..de8ca0d2 100644
--- a/src/ribasim_lumping/README.md
+++ b/src/ribasim_lumping/README.md
@@ -5,10 +5,10 @@ This python package is used to develop an aggregated Ribasim network (Deltares,
 This code was integrated into the Ribasim-NL repository after development in https://github.com/harm-nomden-sweco/ribasim_lumping.
 
 ### Objective
-This python-package provides functions to translate a D-Hydro or a HyDAMO network into a simplified (aggregated/lumped) Ribasim-network: 
+This python-package provides functions to translate a D-Hydro or a HyDAMO network into a simplified (aggregated/lumped) Ribasim-network:
 - The user provides a list of locations where the network should be split, resulting into sub-networks which are called 'basins'';
 - For these basins relations regarding waterlevel-watersurface-watervolume are calculated;
-- Exchange of watervolume (flow) between basins takes place via these split locations. 
+- Exchange of watervolume (flow) between basins takes place via these split locations.
 - Stage-discharge relations are generated to define flow-rates between basins.
 
 ### Dependencies
@@ -21,7 +21,7 @@ Most important dependencies:
 We will make this package accessible via pypi. It is recommended to clone this repository because it is under development and it includes some example notebooks. We are still working on tests and test data, etc.
 
 ### Development, contributions and licences
-This package is developed by Sweco (contributors: Harm Nomden and Tessa Andringa) when working on a TKI-project (top consortia for knowledge and innovation) within the NHI programme (Dutch Hydrological Instruments programme). This focuses on the development, testing and application of the new Ribasim-model (https://tkideltatechnologie.nl/project/oppervlaktewatermodule-nhi/). 
+This package is developed by Sweco (contributors: Harm Nomden and Tessa Andringa) when working on a TKI-project (top consortia for knowledge and innovation) within the NHI programme (Dutch Hydrological Instruments programme). This focuses on the development, testing and application of the new Ribasim-model (https://tkideltatechnologie.nl/project/oppervlaktewatermodule-nhi/).
 It is possible to contribute, create issues, start discussions. We will respond as soon as possible.
 This package is developed under the MIT license. Reference to this package: Ribasim-Lumping (Sweco, 2023).