diff --git a/.gitignore b/.gitignore
index 2da01f2..f21130b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,3 +160,4 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 .idea/.name
+.idea/jupyter-settings.xml
diff --git a/demo_containerized_model.ipynb b/demo_containerized_model.ipynb
deleted file mode 100644
index 2ffb433..0000000
--- a/demo_containerized_model.ipynb
+++ /dev/null
@@ -1,209 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# eWaterCycle containerized model\n",
-    "\n",
-    "This notebook is to showcase/test the containerized model.\n",
-    "\n",
-    "We import the LeakyBucket model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hwloc/linux: Ignoring PCI device with non-16bit domain.\n",
-      "Pass --enable-32bits-pci-domain to configure to support such devices\n",
-      "(warning: it would break the library ABI, don't enable unless really needed).\n",
-      "/home/bart/micromamba/envs/ewatercycle/lib/python3.10/site-packages/esmvalcore/experimental/_warnings.py:13: UserWarning: \n",
-      "  Thank you for trying out the new ESMValCore API.\n",
-      "  Note that this API is experimental and may be subject to change.\n",
-      "  More info: https://github.com/ESMValGroup/ESMValCore/issues/498\n"
-     ]
-    }
-   ],
-   "source": [
-    "from ewatercycle_leakybucket.model import LeakyBucket"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To be able to initialize the model, we need forcing data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:esmvalcore.config._dask:Using the Dask basic scheduler. This may lead to slow computations and out-of-memory errors. Note that the basic scheduler may still be the best choice for preprocessor functions that are not lazy. In that case, you can safely ignore this warning. See https://docs.esmvaltool.org/projects/ESMValCore/en/latest/quickstart/configure.html#dask-distributed-configuration for more information. \n"
-     ]
-    }
-   ],
-   "source": [
-    "from ewatercycle.base.forcing import GenericLumpedForcing\n",
-    "import ewatercycle\n",
-    "from pathlib import Path\n",
-    "import numpy as np\n",
-    "\n",
-    "shape = Path(ewatercycle.__file__).parent / \"testing/data/Rhine/Rhine.shp\"\n",
-    "\n",
-    "cmip_dataset = {\n",
-    "    \"dataset\": \"EC-Earth3\",\n",
-    "    \"project\": \"CMIP6\",\n",
-    "    \"grid\": \"gr\",\n",
-    "    \"exp\": [\"historical\",],\n",
-    "    \"ensemble\": \"r6i1p1f1\",\n",
-    "}\n",
-    "\n",
-    "forcing = GenericLumpedForcing.generate(\n",
-    "    dataset=cmip_dataset,\n",
-    "    start_time=\"2000-01-01T00:00:00Z\",\n",
-    "    end_time=\"2000-12-31T00:00:00Z\",\n",
-    "    shape=shape.absolute(),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can start the model. The `LeakyBucket` class uses a container hosted on the Github container registry.\n",
-    "\n",
-    "Alternatively, you can start the model using a local container. For this you would modify the command as such:\n",
-    "\n",
-    "```py\n",
-    "from ewatercycle.container import ContainerImage\n",
-    "LeakyBucket(forcing=forcing, bmi_image=ContainerImage(\"local_image:latest\"))\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = LeakyBucket(forcing=forcing) \n",
-    "cfg_file, _ = model.setup(leakiness=0.10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.initialize(cfg_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'leakybucket'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.bmi.get_component_name()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To test the model, we'll make a hydrograph:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc23bc8ca30>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "\n",
-    "discharges = []\n",
-    "while model.time < model.end_time:\n",
-    "    discharges.append(model.get_value_as_xarray(\"discharge\"))\n",
-    "    model.update()\n",
-    "\n",
-    "discharge = xr.concat(discharges, dim=\"time\")\n",
-    "discharge.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/example_model_run_HBV.ipynb b/docs/source/example_model_run_HBV.ipynb
index 2633f60..ecbdf50 100644
--- a/docs/source/example_model_run_HBV.ipynb
+++ b/docs/source/example_model_run_HBV.ipynb
@@ -12,7 +12,12 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "ec7e710a-5aa4-40f9-a1cb-151e3cddbe04",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:32.492461800Z",
+     "start_time": "2024-03-07T14:21:31.890843300Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# general python\n",
@@ -29,7 +34,12 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "4569a0f2-4bea-48cc-b5a4-ca5384e368c6",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.475126400Z",
+     "start_time": "2024-03-07T14:21:32.537461400Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# general eWC\n",
@@ -43,7 +53,7 @@
    "id": "c1a08f9b",
    "metadata": {},
    "source": [
-    "Install required plugin with pip:m"
+    "Install required plugin with pip:"
    ]
   },
   {
@@ -70,23 +80,16 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "df66893d-b667-4fcc-a841-683f32ed2cc7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('/home/davidhaasnoot/Docs/Forcing')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.485126600Z",
+     "start_time": "2024-03-07T14:21:34.478127800Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "path = Path.cwd()\n",
-    "forcing_path = path / \"Forcing\"\n",
-    "forcing_path"
+    "forcing_path = path / \"Forcing\""
    ]
   },
   {
@@ -110,7 +113,12 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "f70032e1-602a-4163-a338-2a3ff6264c00",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.494347400Z",
+     "start_time": "2024-03-07T14:21:34.484123900Z"
+    }
+   },
    "outputs": [],
    "source": [
     "s_0 = np.array([0,  100,  0,  5])"
@@ -129,7 +137,12 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "f4cff96b-5eca-4147-940f-9e8393034225",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.502125100Z",
+     "start_time": "2024-03-07T14:21:34.495134600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "p_min_initial= np.array([0,   0.2,  40,    .5,   .001,   1,     .01,  .0001])\n",
@@ -151,7 +164,12 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "ed960ab6-b63a-4664-8306-b57c2ecf27ec",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.528887900Z",
+     "start_time": "2024-03-07T14:21:34.501125300Z"
+    }
+   },
    "outputs": [],
    "source": [
     "par_0 = (p_min_initial + p_max_initial)/2"
@@ -169,7 +187,12 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "03aea008-87ce-4d09-8d01-f12dfe6bb116",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.567126600Z",
+     "start_time": "2024-03-07T14:21:34.567126600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "experiment_start_date = \"1997-08-01T00:00:00Z\"\n",
@@ -190,7 +213,12 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "41855c32-2650-403e-bcad-332eab6c1ec4",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.568127600Z",
+     "start_time": "2024-03-07T14:21:34.567126600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from ewatercycle_HBV.forcing import HBVForcing"
@@ -208,7 +236,12 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "6249ec9f-8ff2-4181-ac6f-cac309d8cf72",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.568127600Z",
+     "start_time": "2024-03-07T14:21:34.568127600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "camels_forcing = HBVForcing(start_time = experiment_start_date,\n",
@@ -231,7 +264,12 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "d62a1be2-e5a1-4c82-aaf0-9ab2eeea14af",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.569218800Z",
+     "start_time": "2024-03-07T14:21:34.568127600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from ewatercycle.models import HBV"
@@ -241,7 +279,12 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "6160254a-89f6-4234-a74f-140edff3efb5",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.569218800Z",
+     "start_time": "2024-03-07T14:21:34.568127600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "model = HBV(forcing=camels_forcing)"
@@ -251,7 +294,12 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "ebbd898f-b3cc-4d03-8050-21067d31c1e8",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.948731400Z",
+     "start_time": "2024-03-07T14:21:34.568127600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "config_file, _ = model.setup(\n",
@@ -264,7 +312,12 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "ba14b408-e446-45aa-b406-91dc35f40bc1",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:34.957732500Z",
+     "start_time": "2024-03-07T14:21:34.953733500Z"
+    }
+   },
    "outputs": [],
    "source": [
     "model.initialize(config_file)"
@@ -282,7 +335,11 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "579fe3d8-0418-4e76-9d66-9c1a938812cc",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-03-07T14:21:34.997729900Z"
+    }
+   },
    "outputs": [],
    "source": [
     "Q_m = []\n",
@@ -305,7 +362,12 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "a6fd6178-1059-497a-a91f-dd0fa983df2a",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-07T14:21:35.030728200Z",
+     "start_time": "2024-03-07T14:21:34.997729900Z"
+    }
+   },
    "outputs": [],
    "source": [
     "model.finalize()"
@@ -323,7 +385,11 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "ccde9dca-258d-409c-a602-d4abda553b32",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-03-07T14:21:34.997729900Z"
+    }
+   },
    "outputs": [],
    "source": [
     "df = pd.DataFrame(data=Q_m,columns=[\"Modeled discharge\"],index=time)"
@@ -333,7 +399,11 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "a39c2e98-30d0-4658-adbd-b2feae7062f4",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-03-07T14:21:34.997729900Z"
+    }
+   },
    "outputs": [
     {
      "data": {
diff --git a/test_HBV_model_Linux.ipynb b/test_HBV_model_Linux.ipynb
deleted file mode 100644
index fa45063..0000000
--- a/test_HBV_model_Linux.ipynb
+++ /dev/null
@@ -1,490 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e9cd8d85-eb4a-45d6-925a-e3e770592043",
-   "metadata": {},
-   "source": [
-    "### Import modules and verify they work? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ec7e710a-5aa4-40f9-a1cb-151e3cddbe04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# general python\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
-    "import numpy as np\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "import yaml\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4569a0f2-4bea-48cc-b5a4-ca5384e368c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# general eWC\n",
-    "import ewatercycle\n",
-    "import ewatercycle.models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "98a3463b-eedd-4f59-a542-6afd58cf6eb4",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# pip install -i https://test.pypi.org/simple/ HBV==0.1.14"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5d74399c-ca5a-4313-983a-12e34faf140b",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# pip install -i https://test.pypi.org/simple/ ewatercycle-HBV==0.1.2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ea9c713f-e179-4dc7-ba20-843b83de4518",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for local mode\n",
-    "from typing import Type\n",
-    "from ewatercycle.base.model import LocalModel\n",
-    "from ewatercycle_HBV.model import HBVMethods # Use custom forcing instead\n",
-    "from HBV import HBV as HBV_bmi\n",
-    "from bmipy import Bmi\n",
-    "\n",
-    "class LocalModelHBV(LocalModel, HBVMethods):\n",
-    "    \"\"\"The HBV eWaterCycle model, with the local BMI.\"\"\"\n",
-    "    bmi_class: Type[Bmi] = HBV_bmi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6585261f-a0e0-4bfc-bfce-e26acea0e3b0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "__main__.LocalModelHBV"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "LocalModelHBV"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "111bc65b-8299-43ba-95fd-e92df6b92707",
-   "metadata": {},
-   "source": [
-    "#### set up paths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "df66893d-b667-4fcc-a841-683f32ed2cc7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('/home/davidhaasnoot/Code/Forcing')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "path = Path.cwd()\n",
-    "forcing_path = path / \"Forcing\"\n",
-    "forcing_path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4787c692-3f9c-402b-9b48-93daeeb47926",
-   "metadata": {},
-   "source": [
-    "#### add parameter info"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "33fbba0f-dbc0-4812-9125-79e0df831e7b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Array of initial storage terms - we keep these constant for now \n",
-    "##              Si,  Su, Sf, Ss\n",
-    "s_0 = np.array([0,  100,  0,  5])\n",
-    "\n",
-    "## Array of parameters min/max bounds as a reference\n",
-    "##                      Imax,  Ce,  Sumax, beta,  Pmax,  T_lag,   Kf,   Ks\n",
-    "p_min_initial= np.array([0,   0.2,  40,    .5,   .001,   1,     .01,  .0001])\n",
-    "p_max_initial = np.array([8,    1,  800,   4,    .3,     10,    .1,   .01])\n",
-    "p_names = [\"$I_{max}$\",  \"$C_e$\",  \"$Su_{max}$\", \"β\",  \"$P_{max}$\",  \"$T_{lag}$\",   \"$K_f$\",   \"$K_s$\"]\n",
-    "S_names = [\"Interception storage\", \"Unsaturated Rootzone Storage\", \"Fastflow storage\", \"Groundwater storage\"]\n",
-    "param_names = [\"Imax\",\"Ce\",  \"Sumax\", \"beta\",  \"Pmax\",  \"Tlag\",   \"Kf\",   \"Ks\"]\n",
-    "\n",
-    "# set initial as mean of max,min\n",
-    "par_0 = (p_min_initial + p_max_initial)/2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "03aea008-87ce-4d09-8d01-f12dfe6bb116",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment_start_date = \"1997-08-01T00:00:00Z\"\n",
-    "experiment_end_date = \"2000-08-31T00:00:00Z\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "41855c32-2650-403e-bcad-332eab6c1ec4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ewatercycle_HBV.forcing import HBVForcing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6249ec9f-8ff2-4181-ac6f-cac309d8cf72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_forcing = HBVForcing(start_time = experiment_start_date,\n",
-    "                          end_time = experiment_end_date,\n",
-    "                          directory = forcing_path,\n",
-    "                          forcing_file = \"test_forcing.txt\",\n",
-    "                          )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f5962203-87d3-45dc-acf8-1354522e5fe1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = test_forcing.to_xarray()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ab1b4e5-c1bf-48d1-a46a-19a7aeb8867c",
-   "metadata": {},
-   "source": [
-    "#### Can also run locally when developing - v0.1.1. here\n",
-    "```py\n",
-    "from ewatercycle.container import ContainerImage\n",
-    "LeakyBucket(forcing=forcing, bmi_image=ContainerImage(\"local_image:latest\"))\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "9aad16e2-272a-4fe3-a048-0801cbfa4a52",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from ewatercycle.container import ContainerImage\n",
-    "local_model = LocalModelHBV(forcing=test_forcing)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6f86ebb-37ed-4db7-b011-971b8eafbbb0",
-   "metadata": {},
-   "source": [
-    "need to  add a local container to run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a6c23e65-f652-444b-9782-238b8ad957a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config_file, _ = local_model.setup(\n",
-    "            parameters=','.join([str(p) for p in par_0]),\n",
-    "            initial_storage=','.join([str(s) for s in s_0]),\n",
-    "           )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "33bec934-bd45-4274-9204-3c762564434c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_model.initialize(config_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "6eae706f-959d-4688-9945-c0ac7ce8eaa1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "870393600"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "local_model.time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "b8e84ded-05c0-430c-a494-f81f1b90a9b3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "870393600"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "local_model.start_time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "5c67e834-9232-41a3-9e19-216c742e83da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Q_m = []\n",
-    "time = []\n",
-    "while local_model.time < local_model.end_time:\n",
-    "    local_model.update()\n",
-    "    Q_m.append(local_model.get_value(\"Q_m\"))\n",
-    "    time.append(pd.Timestamp(local_model.time))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "d6d843b5-08d7-44da-b2ff-098b71091a9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9e86bceda0>]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(time[:-10], Q_m[:-10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b944e1b8-e225-483d-b39e-74ed4dea37cf",
-   "metadata": {},
-   "source": [
-    "# Containerised"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "555a3c69-953c-403b-a259-125aa8370dd8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ewatercycle_HBV.model import HBV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "d62a1be2-e5a1-4c82-aaf0-9ab2eeea14af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image_link = \"ghcr.io/daafip/hbv-bmi-grpc4bmi:v0.1.7\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "e58785c1-fe03-4f4f-b00c-ee5754de1bf5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ewatercycle.container import ContainerImage\n",
-    "model = HBV(forcing=test_forcing,bmi_image=ContainerImage(image_link))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "ebbd898f-b3cc-4d03-8050-21067d31c1e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config_file, _ = model.setup(\n",
-    "            parameters=','.join([str(p) for p in par_0]),\n",
-    "            initial_storage=','.join([str(s) for s in s_0]),\n",
-    "           )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "ba14b408-e446-45aa-b406-91dc35f40bc1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.initialize(config_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "579fe3d8-0418-4e76-9d66-9c1a938812cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Q_m = []\n",
-    "time = []\n",
-    "while model.time < model.end_time:\n",
-    "    model.update()\n",
-    "    Q_m.append(model.get_value(\"Q_m\"))\n",
-    "    time.append(pd.Timestamp(model.time))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "a39c2e98-30d0-4658-adbd-b2feae7062f4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9e86b70a00>]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(time[:-10], Q_m[:-10])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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- },
- "nbformat": 4,
- "nbformat_minor": 5
-}