diff --git a/aeolis/examples/grainsizevariations/plots.ipynb b/aeolis/examples/grainsizevariations/plots.ipynb
deleted file mode 100644
index 5e7339c9..00000000
--- a/aeolis/examples/grainsizevariations/plots.ipynb
+++ /dev/null
@@ -1,177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x: (1, 101) y: (1, 101) z: (61, 1, 101)\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "index exceeds dimension bounds",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[5], line 48\u001b[0m\n\u001b[0;32m     43\u001b[0m         axs[i]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     44\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m---> 48\u001b[0m plot_Ct(Ct, x, y)\n",
-      "Cell \u001b[1;32mIn[5], line 42\u001b[0m, in \u001b[0;36mplot_Ct\u001b[1;34m(Ct, x, y)\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m# plot Ct in 3 rows\u001b[39;00m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3\u001b[39m):\n\u001b[1;32m---> 42\u001b[0m     axs[i]\u001b[38;5;241m.\u001b[39mplot(Ct[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,i,:,:])\n\u001b[0;32m     43\u001b[0m     axs[i]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     44\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
-      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:4972\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Variable.__getitem__\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:5928\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Variable._get\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mIndexError\u001b[0m: index exceeds dimension bounds"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# read nc output in folder\n",
-    "\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import netCDF4 as nc\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.colors as colors\n",
-    "import matplotlib.cm as cm\n",
-    "\n",
-    "# define nc file\n",
-    "nc_file = 'aeolis_horizontalgradient1.nc'\n",
-    "# nc_file = 'aeolis_verticallayering2.nc'\n",
-    "\n",
-    "# read nc_file and terurn dimensions\n",
-    "\n",
-    "def read_nc(nc_file):\n",
-    "    # read nc file\n",
-    "    data = nc.Dataset(nc_file, 'r')\n",
-    "    # get dimensions\n",
-    "    x = data['x'].shape\n",
-    "    y = data['y'].shape\n",
-    "    z = data['zb'].shape\n",
-    "    Ct = data['Ct']\n",
-    "\n",
-    "    #close nc file\n",
-    "    \n",
-    "    \n",
-    "    return x, y, z, Ct\n",
-    "\n",
-    "# print dimensions\n",
-    "x, y, z, Ct = read_nc(nc_file)\n",
-    "print('x:', x, 'y:', y, 'z:', z)\n",
-    "\n",
-    "# plot the last timestep of Ct\n",
-    "def plot_Ct(Ct, x, y):\n",
-    "\n",
-    "    # plot Ct in 3 rows\n",
-    "    fig, axs = plt.subplots(3, 1, figsize=(10, 10))\n",
-    "\n",
-    "    # plot Ct in 3 rows\n",
-    "    for i in range(3):\n",
-    "        axs[i].plot(Ct[-1,i,:,:])\n",
-    "        axs[i].set_title('Ct')\n",
-    "    plt.show()\n",
-    "\n",
-    "   \n",
-    "\n",
-    "plot_Ct(Ct, x, y)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'nc' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[1], line 19\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x, y, z, Ct\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# print dimensions\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m x, y, z, Ct \u001b[38;5;241m=\u001b[39m read_nc(nc_file)\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx:\u001b[39m\u001b[38;5;124m'\u001b[39m, x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my:\u001b[39m\u001b[38;5;124m'\u001b[39m, y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz:\u001b[39m\u001b[38;5;124m'\u001b[39m, z)\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# plot the last timestep of Ct\u001b[39;00m\n",
-      "Cell \u001b[1;32mIn[1], line 9\u001b[0m, in \u001b[0;36mread_nc\u001b[1;34m(nc_file)\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_nc\u001b[39m(nc_file):\n\u001b[0;32m      8\u001b[0m     \u001b[38;5;66;03m# read nc file\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m     data \u001b[38;5;241m=\u001b[39m nc\u001b[38;5;241m.\u001b[39mDataset(nc_file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;66;03m# get dimensions\u001b[39;00m\n\u001b[0;32m     11\u001b[0m     x \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mshape\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'nc' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "# define nc file\n",
-    "nc_file = 'aeolis_horizontalgradient1.nc'\n",
-    "# nc_file = 'aeolis_verticallayering2.nc'\n",
-    "\n",
-    "# read nc_file and terurn dimensions\n",
-    "\n",
-    "def read_nc(nc_file):\n",
-    "    # read nc file\n",
-    "    data = nc.Dataset(nc_file, 'r')\n",
-    "    # get dimensions\n",
-    "    x = data['x'].shape\n",
-    "    y = data['y'].shape\n",
-    "    z = data['zb'].shape\n",
-    "    qs = data['Ct']\n",
-    "    \n",
-    "    return x, y, z, Ct\n",
-    "\n",
-    "# print dimensions\n",
-    "x, y, z, Ct = read_nc(nc_file)\n",
-    "print('x:', x, 'y:', y, 'z:', z)\n",
-    "\n",
-    "# plot the last timestep of Ct\n",
-    "def plot_Ct(Ct, x, y):\n",
-    "\n",
-    "    # plot Ct in 3 rows\n",
-    "    fig, axs = plt.subplots(3, 1, figsize=(10, 10))\n",
-    "\n",
-    "    # plot Ct in 3 rows\n",
-    "    for i in range(3):\n",
-    "        axs[i].plot(Ct[-1,i,:,:])\n",
-    "        axs[i].set_title('Ct')\n",
-    "    plt.show()\n",
-    "\n",
-    "plot_Ct(Ct, x, y)\n"
-   ]
-  }
- ],
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-  "kernelspec": {
-   "display_name": "aeolis3",
-   "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": "3.11.4"
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- },
- "nbformat": 4,
- "nbformat_minor": 2
-}