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+{
+ "cells": [
+  {
+   "metadata": {
+    "collapsed": true
+   },
+   "cell_type": "markdown",
+   "source": "# 🌱 Simulation of Wheat Vegetative Growth with the WheatFSPM",
+   "id": "feceba697b500b38"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Authors : R. Barillot, C.Chambon, M.Gauthier & B. Andrieu\n",
+    "### Publication : https://doi.org/10.1093/jxb/eraa276"
+   ],
+   "id": "b742a08b5f9fc043"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "[WheatFspm](https://wheatfspm.readthedocs.io/en/latest/?badge=latest) is a Functional Structural Plant Model (FSPM) of wheat which fully integrates shoot morphogenesis and the metabolism of carbon (C) and nitrogen (N) at organ scale within a 3D representation of plant architecture. Plants are described as a collection of tillers, each consisting in individual shoot organs (lamina, sheath, internode, peduncle, chaff), a single root compartment, the grains, and a phloem.\n",
+    "\n",
+    "This tutorial will illustrate how to use the WheatFspm model to simulate the growth and functioning of a wheat plant during the vegetative stages. The tutorial is based on the script [main.py](https://github.com/openalea/WheatFspm/blob/master/example/Vegetative_stages/main.py)."
+   ],
+   "id": "ea0eaef572495dd5"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## 🛠️ Configuration and prerequisites\n",
+    "Let's import all the required packages"
+   ],
+   "id": "1e8a8d064acab512"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:01:05.466833Z",
+     "start_time": "2025-10-21T12:00:25.004615Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Adel-Wheat dependencies (https://github.com/openalea/adel)\n",
+    "from openalea.adel.adel_dynamic import AdelDyn\n",
+    "from openalea.adel.echap_leaf import echap_leaves\n",
+    "\n",
+    "from openalea.fspmwheat import caribu_facade\n",
+    "from openalea.fspmwheat import cnwheat_facade\n",
+    "from openalea.fspmwheat import elongwheat_facade\n",
+    "from openalea.fspmwheat import farquharwheat_facade\n",
+    "from openalea.fspmwheat import fspmwheat_facade\n",
+    "from openalea.fspmwheat import growthwheat_facade\n",
+    "from openalea.fspmwheat import senescwheat_facade\n"
+   ],
+   "id": "a4849cecc656153c",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Model inputs\n",
+    "* Weather data\n",
+    "They should contain the following variables :\n",
+    "    * *t*: the time index used in the simulation (hour)\n",
+    "    * *DOY*: the Day Of the Year\n",
+    "    * *hour*: the hour of the day [1-24]\n",
+    "    * *air_temperature*: temperature of the air (°C)\n",
+    "    * *soil_temperature*: temperature of the soil (°C)\n",
+    "    * *PARi*: incident Photosynthetically Active Radiation above the canopy (µmol m-2 s-1)\n",
+    "    * *humidity*: air humidity (relative)\n",
+    "    * *ambient_CO2*: CO2 concentration of the atmosphere (ppm)\n",
+    "    * *Wind* : wind speed 2m above the canopy (m s-1)\n"
+   ],
+   "id": "68cccefdcacb5b5f"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:01:32.750390Z",
+     "start_time": "2025-10-21T12:01:32.635967Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Loading a meteo file and display head\n",
+    "METEO_FILENAME = os.path.join('inputs', \"meteo_file.csv\")\n",
+    "meteo = pd.read_csv(METEO_FILENAME, delimiter=',',  index_col='t')\n",
+    "meteo.head()"
+   ],
+   "id": "9e8d4ef34fd1fdd9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   DOY  hour  air_temperature        PARi  soil_temperature  humidity  \\\n",
+       "t                                                                       \n",
+       "0  351    12              5.2  516.222222             5.840      0.95   \n",
+       "1  351    13              5.6  549.888889             6.264      0.88   \n",
+       "2  351    14              4.8  392.777778             5.415      0.92   \n",
+       "3  351    15              3.6  157.111111             6.223      0.95   \n",
+       "4  351    16              3.5   22.444444             5.957      0.96   \n",
+       "\n",
+       "   ambient_CO2  Wind  \n",
+       "t                     \n",
+       "0          360   2.9  \n",
+       "1          360   2.9  \n",
+       "2          360   2.9  \n",
+       "3          360   2.9  \n",
+       "4          360   2.9  "
+      ],
+      "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>DOY</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>air_temperature</th>\n",
+       "      <th>PARi</th>\n",
+       "      <th>soil_temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ambient_CO2</th>\n",
+       "      <th>Wind</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>351</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>516.222222</td>\n",
+       "      <td>5.840</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>360</td>\n",
+       "      <td>2.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351</td>\n",
+       "      <td>13</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>549.888889</td>\n",
+       "      <td>6.264</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>360</td>\n",
+       "      <td>2.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>351</td>\n",
+       "      <td>14</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>392.777778</td>\n",
+       "      <td>5.415</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>360</td>\n",
+       "      <td>2.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>351</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>157.111111</td>\n",
+       "      <td>6.223</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>360</td>\n",
+       "      <td>2.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>351</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>22.444444</td>\n",
+       "      <td>5.957</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>360</td>\n",
+       "      <td>2.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "* Setting management",
+   "id": "8096684f8b720d36"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:47:19.208604Z",
+     "start_time": "2025-10-08T08:47:19.206171Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "PLANT_DENSITY = {1: 250}  # Number of plants per square meter (value) for each genotype (key)\n",
+    "tillers_replications = {'T1': 0.5, 'T2': 0.5, 'T3': 0.5, 'T4': 0.5}  # In the current version, tillering is a model input (tiller id as key, and weight of replication as value)\n",
+    "N_fertilizations = {1440: 357143, 2520: 1000000}  # Date of N fertilisation (key ; hour of the simulation) anf N amounts (µmol of N). Note: you can also use {'constant_Conc_Nitrates': val} for constant nitrate concentrations.\n",
+    "SIMULATION_LENGTH = 48  # length of the simulation (hours)\n",
+    "START_TIME = 0\n",
+    "\n",
+    "# precision of floats used to write and format the output CSV files\n",
+    "OUTPUTS_PRECISION = 2\n",
+    "\n",
+    "# Rates and time-dependant parameters in WheatFspm are expressed in s or s-1 but simulations (and differential equations solve) are usually integrated over an hour\n",
+    "HOUR_TO_SECOND_CONVERSION_FACTOR = 3600"
+   ],
+   "id": "70d90acd4d88fc48",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "* Initial conditions\n",
+    "The initial state of the different plant organs and the soil are loaded from different csv files. They mainly contain the initial dimensions, masses and metabolic contents."
+   ],
+   "id": "5ca64868243994aa"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:47:19.288622Z",
+     "start_time": "2025-10-08T08:47:19.219381Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Name of the CSV files which describes the initial state of the system\n",
+    "AXES_INITIAL_STATE_FILENAME = 'axes_initial_state.csv'  # Axis scale\n",
+    "ORGANS_INITIAL_STATE_FILENAME = 'organs_initial_state.csv'  # Organ scale (contains data for the roots and phloem compartments)\n",
+    "HIDDENZONES_INITIAL_STATE_FILENAME = 'hiddenzones_initial_state.csv'  # Hidden zone scale\n",
+    "ELEMENTS_INITIAL_STATE_FILENAME = 'elements_initial_state.csv'  # Photosynthetic element scale\n",
+    "SOILS_INITIAL_STATE_FILENAME = 'soils_initial_state.csv'  # Soil\n",
+    "\n",
+    "# Read the inputs from CSV files and create inputs dataframes\n",
+    "inputs_dataframes = {}\n",
+    "for inputs_filename in (AXES_INITIAL_STATE_FILENAME,\n",
+    "                        ORGANS_INITIAL_STATE_FILENAME,\n",
+    "                        HIDDENZONES_INITIAL_STATE_FILENAME,\n",
+    "                        ELEMENTS_INITIAL_STATE_FILENAME,\n",
+    "                        SOILS_INITIAL_STATE_FILENAME):\n",
+    "    inputs_dataframe = pd.read_csv(os.path.join('inputs', inputs_filename))\n",
+    "    inputs_dataframes[inputs_filename] = inputs_dataframe.where(inputs_dataframe.notnull(), None)\n",
+    "# Example of the inputs provided for the elements\n",
+    "inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].head()"
+   ],
+   "id": "9803d203890f6f5d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   plant axis  metamer   organ        element  age  length    Wmax  \\\n",
+       "0      1   MS        1   blade   LeafElement1  240  0.0820  0.0030   \n",
+       "1      1   MS        1  sheath    StemElement  240  0.0285     NaN   \n",
+       "2      1   MS        2   blade   LeafElement1  160  0.0920  0.0033   \n",
+       "3      1   MS        2  sheath  HiddenElement  160  0.0285     NaN   \n",
+       "4      1   MS        2  sheath    StemElement  160  0.0005     NaN   \n",
+       "\n",
+       "   is_growing   mstruct  ...  starch  nitrates  senesced_length_element  \\\n",
+       "0       False  0.004107  ...       0         0                        0   \n",
+       "1       False  0.001710  ...       0         0                        0   \n",
+       "2       False  0.004838  ...       0         0                        0   \n",
+       "3       False  0.002280  ...       0         0                        0   \n",
+       "4       False  0.000040  ...       0         0                        0   \n",
+       "\n",
+       "   Nresidual  max_mstruct  is_over  Ag  Tr  Ts  triosesP  \n",
+       "0          0     0.004107    False   0   0  20         0  \n",
+       "1          0     0.001710    False   0   0  20         0  \n",
+       "2          0     0.004838    False   0   0  20         0  \n",
+       "3          0     0.002280    False   0   0  20         0  \n",
+       "4          0     0.000040    False   0   0  20         0  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ],
+      "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>plant</th>\n",
+       "      <th>axis</th>\n",
+       "      <th>metamer</th>\n",
+       "      <th>organ</th>\n",
+       "      <th>element</th>\n",
+       "      <th>age</th>\n",
+       "      <th>length</th>\n",
+       "      <th>Wmax</th>\n",
+       "      <th>is_growing</th>\n",
+       "      <th>mstruct</th>\n",
+       "      <th>...</th>\n",
+       "      <th>starch</th>\n",
+       "      <th>nitrates</th>\n",
+       "      <th>senesced_length_element</th>\n",
+       "      <th>Nresidual</th>\n",
+       "      <th>max_mstruct</th>\n",
+       "      <th>is_over</th>\n",
+       "      <th>Ag</th>\n",
+       "      <th>Tr</th>\n",
+       "      <th>Ts</th>\n",
+       "      <th>triosesP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>1</td>\n",
+       "      <td>blade</td>\n",
+       "      <td>LeafElement1</td>\n",
+       "      <td>240</td>\n",
+       "      <td>0.0820</td>\n",
+       "      <td>0.0030</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.004107</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.004107</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sheath</td>\n",
+       "      <td>StemElement</td>\n",
+       "      <td>240</td>\n",
+       "      <td>0.0285</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.001710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001710</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>2</td>\n",
+       "      <td>blade</td>\n",
+       "      <td>LeafElement1</td>\n",
+       "      <td>160</td>\n",
+       "      <td>0.0920</td>\n",
+       "      <td>0.0033</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.004838</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.004838</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>2</td>\n",
+       "      <td>sheath</td>\n",
+       "      <td>HiddenElement</td>\n",
+       "      <td>160</td>\n",
+       "      <td>0.0285</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.002280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.002280</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>2</td>\n",
+       "      <td>sheath</td>\n",
+       "      <td>StemElement</td>\n",
+       "      <td>160</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.000040</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000040</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Note the decomposition of the plant into axes, metamers, organs and elements. More information can be found in the [documentation](https://wheatfspm.readthedocs.io/en/latest/?badge=latest).",
+   "id": "899b27de8bd5751d"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "* Configuration of Adel-Wheat\n",
+    "WheatFspm includes an implicit coupling to the model [Adel-Wheat](https://adel.readthedocs.io/en/latest/?badge=latest). This models allows for the 3D representation of the wheat architecture and the generation of the MTG object used for the communication (read/write) between each submodule of WheatFspm. In this example, we will use the leaf database for the wheat cultivar Soissons."
+   ],
+   "id": "fb27e13be2e10542"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:47:19.543647Z",
+     "start_time": "2025-10-08T08:47:19.393896Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Instantiates AdelDyn object with Soissons database and set scene unit to meter\n",
+    "adel_wheat = AdelDyn(seed=1, scene_unit='m', leaves=echap_leaves(xy_model='Soissons_byleafclass'))\n",
+    "# Instantiates a MTG object from a pickle\n",
+    "g = adel_wheat.load(directory='inputs')"
+   ],
+   "id": "e0a7684a7ddcd03e",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "* Configuration of outputs and postprocessings",
+   "id": "3324cf3a138ab292"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:47:19.619413Z",
+     "start_time": "2025-10-08T08:47:19.615321Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# -- OUTPUT CONFIGURATION --\n",
+    "OUTPUTS_DIRPATH = os.path.join('outputs', 'simple_run_vegetative_stages')\n",
+    "\n",
+    "# Name of the CSV files which will contain the outputs of the model\n",
+    "AXES_OUTPUTS_FILENAME = 'axes_outputs.csv'\n",
+    "ORGANS_OUTPUTS_FILENAME = 'organs_outputs.csv'\n",
+    "HIDDENZONES_OUTPUTS_FILENAME = 'hiddenzones_outputs.csv'\n",
+    "ELEMENTS_OUTPUTS_FILENAME = 'elements_outputs.csv'\n",
+    "SOILS_OUTPUTS_FILENAME = 'soils_outputs.csv'\n",
+    "\n",
+    "# create empty dataframes to shared data between the models\n",
+    "shared_axes_inputs_outputs_df = pd.DataFrame()\n",
+    "shared_organs_inputs_outputs_df = pd.DataFrame()\n",
+    "shared_hiddenzones_inputs_outputs_df = pd.DataFrame()\n",
+    "shared_elements_inputs_outputs_df = pd.DataFrame()\n",
+    "shared_soils_inputs_outputs_df = pd.DataFrame()\n",
+    "\n",
+    "# define lists of dataframes to store the inputs and the outputs of the models at each step.\n",
+    "axes_all_data_list = []\n",
+    "organs_all_data_list = []  # organs which belong to axes: roots, phloem, grains\n",
+    "hiddenzones_all_data_list = []\n",
+    "elements_all_data_list = []\n",
+    "soils_all_data_list = []\n",
+    "\n",
+    "all_simulation_steps = []  # to store the steps of the simulation\n",
+    "\n",
+    "# -- POSTPROCESSING CONFIGURATION --\n",
+    "POSTPROCESSING_DIRPATH= os.path.join('postprocessing', 'simple_run_vegetative_stages')\n",
+    "\n",
+    "# Name of the CSV files which will contain the postprocessing of the model\n",
+    "AXES_POSTPROCESSING_FILENAME = 'axes_postprocessing.csv'\n",
+    "ORGANS_POSTPROCESSING_FILENAME = 'organs_postprocessing.csv'\n",
+    "HIDDENZONES_POSTPROCESSING_FILENAME = 'hiddenzones_postprocessing.csv'\n",
+    "ELEMENTS_POSTPROCESSING_FILENAME = 'elements_postprocessing.csv'\n",
+    "SOILS_POSTPROCESSING_FILENAME = 'soils_postprocessing.csv'"
+   ],
+   "id": "b1a94730e3e4e85f",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "* Configuration of the facades\n",
+    "WheatFspm is structured in several submodules. Each has a 'facade' which allows to (i) read the inputs necessary for the submodule from the MTG and (ii) write the outputs after the submodule run into the MTG. The followwing steps are required for the configuration of each facade."
+   ],
+   "id": "8ed4323f339de634"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:47:19.795770Z",
+     "start_time": "2025-10-08T08:47:19.647701Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# define the time step in hours for each submodule\n",
+    "CARIBU_TIMESTEP = 4\n",
+    "SENESCWHEAT_TIMESTEP = 1\n",
+    "FARQUHARWHEAT_TIMESTEP = 1\n",
+    "ELONGWHEAT_TIMESTEP = 1\n",
+    "GROWTHWHEAT_TIMESTEP = 1\n",
+    "CNWHEAT_TIMESTEP = 1\n",
+    "\n",
+    "\n",
+    "# -- CARIBU -- The light distribution model\n",
+    "caribu_facade_ = caribu_facade.CaribuFacade(g,\n",
+    "                                            shared_elements_inputs_outputs_df,\n",
+    "                                            adel_wheat,\n",
+    "                                            update_shared_df=False)\n",
+    "\n",
+    "# -- ELONGWHEAT -- The submodule dealing with elongation\n",
+    "# Initial states\n",
+    "elongwheat_hiddenzones_initial_state = inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME][\n",
+    "    elongwheat_facade.converter.HIDDENZONE_TOPOLOGY_COLUMNS + [i for i in elongwheat_facade.simulation.HIDDENZONE_INPUTS if i in\n",
+    "                                                               inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "elongwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][\n",
+    "    elongwheat_facade.converter.ELEMENT_TOPOLOGY_COLUMNS + [i for i in elongwheat_facade.simulation.ELEMENT_INPUTS if i in\n",
+    "                                                            inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "elongwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][\n",
+    "    elongwheat_facade.converter.AXIS_TOPOLOGY_COLUMNS + [i for i in elongwheat_facade.simulation.AXIS_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "phytoT_df = pd.read_csv(os.path.join('inputs', 'phytoT.csv'))\n",
+    "\n",
+    "# Facade initialisation\n",
+    "elongwheat_facade_ = elongwheat_facade.ElongWheatFacade(g,\n",
+    "                                                        ELONGWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,\n",
+    "                                                        elongwheat_axes_initial_state,\n",
+    "                                                        elongwheat_hiddenzones_initial_state,\n",
+    "                                                        elongwheat_elements_initial_state,\n",
+    "                                                        shared_axes_inputs_outputs_df,\n",
+    "                                                        shared_hiddenzones_inputs_outputs_df,\n",
+    "                                                        shared_elements_inputs_outputs_df,\n",
+    "                                                        adel_wheat, phytoT_df,\n",
+    "                                                        update_shared_df=False)\n",
+    "\n",
+    "# -- SENESCWHEAT -- The submodule dealing with leaf and root senescence\n",
+    "# Initial states\n",
+    "senescwheat_roots_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].loc[inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME]['organ'] == 'roots'][\n",
+    "    senescwheat_facade.converter.ROOTS_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in senescwheat_facade.converter.SENESCWHEAT_ROOTS_INPUTS if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "senescwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][\n",
+    "    senescwheat_facade.converter.ELEMENTS_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in senescwheat_facade.converter.SENESCWHEAT_ELEMENTS_INPUTS if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "senescwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][\n",
+    "    senescwheat_facade.converter.AXES_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in senescwheat_facade.converter.SENESCWHEAT_AXES_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "# Facade initialisation\n",
+    "senescwheat_facade_ = senescwheat_facade.SenescWheatFacade(g,\n",
+    "                                                           SENESCWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,\n",
+    "                                                           senescwheat_roots_initial_state,\n",
+    "                                                           senescwheat_axes_initial_state,\n",
+    "                                                           senescwheat_elements_initial_state,\n",
+    "                                                           shared_organs_inputs_outputs_df,\n",
+    "                                                           shared_axes_inputs_outputs_df,\n",
+    "                                                           shared_elements_inputs_outputs_df,\n",
+    "                                                           update_shared_df=False)\n",
+    "\n",
+    "# -- FARQUHARWHEAT -- The submodule dealing with leaf photosynthesis, transpiration and temperature\n",
+    "# Initial states\n",
+    "farquharwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][\n",
+    "    farquharwheat_facade.converter.ELEMENT_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in farquharwheat_facade.converter.FARQUHARWHEAT_ELEMENTS_INPUTS if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "farquharwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][\n",
+    "    farquharwheat_facade.converter.AXIS_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in farquharwheat_facade.converter.FARQUHARWHEAT_AXES_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "# Use the initial version of the photosynthesis sub-model (as in Barillot et al. 2016, and in Gauthier et al. 2020)\n",
+    "update_parameters_farquharwheat = {'SurfacicProteins': False, 'NSC_Retroinhibition': False}\n",
+    "\n",
+    "# Facade initialisation\n",
+    "farquharwheat_facade_ = farquharwheat_facade.FarquharWheatFacade(g,\n",
+    "                                                                 farquharwheat_elements_initial_state,\n",
+    "                                                                 farquharwheat_axes_initial_state,\n",
+    "                                                                 shared_elements_inputs_outputs_df,\n",
+    "                                                                 update_parameters_farquharwheat,\n",
+    "                                                                 update_shared_df=False)\n",
+    "\n",
+    "# -- GROWTHWHEAT -- The submodule dealing with leaf and root growth\n",
+    "# Initial states\n",
+    "growthwheat_hiddenzones_initial_state = inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME][\n",
+    "    growthwheat_facade.converter.HIDDENZONE_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in growthwheat_facade.simulation.HIDDENZONE_INPUTS if i in inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "growthwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][\n",
+    "    growthwheat_facade.converter.ELEMENT_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in growthwheat_facade.simulation.ELEMENT_INPUTS if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "growthwheat_root_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].loc[inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME]['organ'] == 'roots'][\n",
+    "    growthwheat_facade.converter.ROOT_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in growthwheat_facade.simulation.ROOT_INPUTS if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "growthwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][\n",
+    "    growthwheat_facade.converter.AXIS_TOPOLOGY_COLUMNS +\n",
+    "    [i for i in growthwheat_facade.simulation.AXIS_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "# Facade initialisation\n",
+    "growthwheat_facade_ = growthwheat_facade.GrowthWheatFacade(g,\n",
+    "                                                           GROWTHWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,\n",
+    "                                                           growthwheat_hiddenzones_initial_state,\n",
+    "                                                           growthwheat_elements_initial_state,\n",
+    "                                                           growthwheat_root_initial_state,\n",
+    "                                                           growthwheat_axes_initial_state,\n",
+    "                                                           shared_organs_inputs_outputs_df,\n",
+    "                                                           shared_hiddenzones_inputs_outputs_df,\n",
+    "                                                           shared_elements_inputs_outputs_df,\n",
+    "                                                           shared_axes_inputs_outputs_df,\n",
+    "                                                           update_shared_df=False)\n",
+    "\n",
+    "# -- CNWHEAT -- The submodule dealing with C-N metabolism (embedding a differential equation solver)\n",
+    "# Initial states\n",
+    "cnwheat_organs_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME][\n",
+    "    [i for i in cnwheat_facade.cnwheat_converter.ORGANS_VARIABLES if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "cnwheat_hiddenzones_initial_state = inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME][\n",
+    "    [i for i in cnwheat_facade.cnwheat_converter.HIDDENZONE_VARIABLES if i in inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "cnwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][\n",
+    "    [i for i in cnwheat_facade.cnwheat_converter.ELEMENTS_VARIABLES if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "cnwheat_soils_initial_state = inputs_dataframes[SOILS_INITIAL_STATE_FILENAME][\n",
+    "    [i for i in cnwheat_facade.cnwheat_converter.SOILS_VARIABLES if i in inputs_dataframes[SOILS_INITIAL_STATE_FILENAME].columns]].copy()\n",
+    "\n",
+    "\n",
+    "# Facade initialisation\n",
+    "cnwheat_facade_ = cnwheat_facade.CNWheatFacade(g,\n",
+    "                                               CNWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,\n",
+    "                                               PLANT_DENSITY,\n",
+    "                                               {},\n",
+    "                                               cnwheat_organs_initial_state,\n",
+    "                                               cnwheat_hiddenzones_initial_state,\n",
+    "                                               cnwheat_elements_initial_state,\n",
+    "                                               cnwheat_soils_initial_state,\n",
+    "                                               shared_axes_inputs_outputs_df,\n",
+    "                                               shared_organs_inputs_outputs_df,\n",
+    "                                               shared_hiddenzones_inputs_outputs_df,\n",
+    "                                               shared_elements_inputs_outputs_df,\n",
+    "                                               shared_soils_inputs_outputs_df,\n",
+    "                                               update_shared_df=False)\n",
+    "\n",
+    "# -- FSPMWHEAT --\n",
+    "# Facade initialisation\n",
+    "fspmwheat_facade_ = fspmwheat_facade.FSPMWheatFacade(g)\n",
+    "# g.display()\n",
+    "\n",
+    "# Update geometry\n",
+    "adel_wheat.update_geometry(g)\n",
+    "# adel_wheat.plot(g)"
+   ],
+   "id": "8f2b94d134eb5c16",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<openalea.mtg.mtg.MTG at 0x1e25fd38c20>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Run of the simulation\n",
+    "Running the simulation consists in nested loops where each facade is executed sequentially."
+   ],
+   "id": "8fd4a2cdef2174c4"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:47:45.510316Z",
+     "start_time": "2025-10-08T08:47:19.803968Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "for t_caribu in range(START_TIME, SIMULATION_LENGTH, SENESCWHEAT_TIMESTEP):\n",
+    "    # run Caribu\n",
+    "    PARi = meteo.loc[t_caribu, ['PARi']].iloc[0]\n",
+    "    DOY = meteo.loc[t_caribu, ['DOY']].iloc[0]\n",
+    "    hour = meteo.loc[t_caribu, ['hour']].iloc[0]\n",
+    "    PARi_next_hours = meteo.loc[range(t_caribu, t_caribu + CARIBU_TIMESTEP), ['PARi']].sum().values[0]\n",
+    "\n",
+    "    if (t_caribu % CARIBU_TIMESTEP == 0) and (PARi_next_hours > 0):\n",
+    "        run_caribu = True\n",
+    "    else:\n",
+    "        run_caribu = False\n",
+    "\n",
+    "    # Specify here the latitude of your site and the type of scene illumination for Caribu configuration\n",
+    "    caribu_facade_.run(run_caribu, energy=PARi, DOY=DOY, hourTU=hour, latitude=48.85, sun_sky_option='sky', heterogeneous_canopy=False, plant_density=PLANT_DENSITY[1])\n",
+    "\n",
+    "    for t_senescwheat in range(t_caribu, t_caribu + SENESCWHEAT_TIMESTEP, SENESCWHEAT_TIMESTEP):\n",
+    "        # run SenescWheat\n",
+    "        senescwheat_facade_.run()\n",
+    "\n",
+    "        # Test for dead plant # TODO: adapt in case of multiple plants\n",
+    "        if not shared_elements_inputs_outputs_df.empty and \\\n",
+    "                np.nansum(shared_elements_inputs_outputs_df.loc[shared_elements_inputs_outputs_df['element'].isin(['StemElement', 'LeafElement1']), 'green_area']) == 0:\n",
+    "            # append the inputs and outputs at current step to global lists\n",
+    "            all_simulation_steps.append(t_senescwheat)\n",
+    "            axes_all_data_list.append(shared_axes_inputs_outputs_df.copy())\n",
+    "            organs_all_data_list.append(shared_organs_inputs_outputs_df.copy())\n",
+    "            hiddenzones_all_data_list.append(shared_hiddenzones_inputs_outputs_df.copy())\n",
+    "            elements_all_data_list.append(shared_elements_inputs_outputs_df.copy())\n",
+    "            soils_all_data_list.append(shared_soils_inputs_outputs_df.copy())\n",
+    "            break\n",
+    "\n",
+    "        # Run the rest of the model if the plant is alive\n",
+    "        for t_farquharwheat in range(t_senescwheat, t_senescwheat + SENESCWHEAT_TIMESTEP, FARQUHARWHEAT_TIMESTEP):\n",
+    "            # get the meteo of the current step\n",
+    "            Ta, ambient_CO2, RH, Ur = meteo.loc[t_farquharwheat, ['air_temperature', 'ambient_CO2', 'humidity', 'Wind']]\n",
+    "\n",
+    "            # run FarquharWheat\n",
+    "            farquharwheat_facade_.run(Ta, ambient_CO2, RH, Ur)\n",
+    "\n",
+    "            for t_elongwheat in range(t_farquharwheat, t_farquharwheat + FARQUHARWHEAT_TIMESTEP, ELONGWHEAT_TIMESTEP):\n",
+    "                # run ElongWheat\n",
+    "                Tair, Tsoil = meteo.loc[t_elongwheat, ['air_temperature', 'soil_temperature']]\n",
+    "                elongwheat_facade_.run(Tair, Tsoil)\n",
+    "\n",
+    "                # Update geometry\n",
+    "                adel_wheat.update_geometry(g)\n",
+    "\n",
+    "                for t_growthwheat in range(t_elongwheat, t_elongwheat + ELONGWHEAT_TIMESTEP, GROWTHWHEAT_TIMESTEP):\n",
+    "                    # run GrowthWheat\n",
+    "                    growthwheat_facade_.run()\n",
+    "\n",
+    "                    for t_cnwheat in range(t_growthwheat, t_growthwheat + GROWTHWHEAT_TIMESTEP, CNWHEAT_TIMESTEP):\n",
+    "                        print('t cnwheat is {}'.format(t_cnwheat))\n",
+    "\n",
+    "                        # N fertilization if any\n",
+    "                        if N_fertilizations is not None and len(N_fertilizations) > 0:\n",
+    "                            if t_cnwheat in N_fertilizations.keys():\n",
+    "                                cnwheat_facade_.soils[(1, 'MS')].nitrates += N_fertilizations[t_cnwheat]\n",
+    "\n",
+    "                        if t_cnwheat > 0:\n",
+    "                            # run CNWheat\n",
+    "                            Tair = meteo.loc[t_elongwheat, 'air_temperature']\n",
+    "                            Tsoil = meteo.loc[t_elongwheat, 'soil_temperature']\n",
+    "                            cnwheat_facade_.run(Tair, Tsoil, tillers_replications)\n",
+    "\n",
+    "                        # append outputs at current step to global lists\n",
+    "                        axes_outputs, elements_outputs, hiddenzones_outputs, organs_outputs, soils_outputs = fspmwheat_facade_.build_outputs_df_from_MTG()\n",
+    "\n",
+    "                        all_simulation_steps.append(t_cnwheat)\n",
+    "                        axes_all_data_list.append(axes_outputs)\n",
+    "                        organs_all_data_list.append(organs_outputs)\n",
+    "                        hiddenzones_all_data_list.append(hiddenzones_outputs)\n",
+    "                        elements_all_data_list.append(elements_outputs)\n",
+    "                        soils_all_data_list.append(soils_outputs)\n"
+   ],
+   "id": "e26f6bbffac16e1f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t cnwheat is 0\n",
+      "t cnwheat is 1\n",
+      "t cnwheat is 2\n",
+      "t cnwheat is 3\n",
+      "t cnwheat is 4\n",
+      "t cnwheat is 5\n",
+      "t cnwheat is 6\n",
+      "t cnwheat is 7\n",
+      "t cnwheat is 8\n",
+      "t cnwheat is 9\n",
+      "t cnwheat is 10\n",
+      "t cnwheat is 11\n",
+      "t cnwheat is 12\n",
+      "t cnwheat is 13\n",
+      "t cnwheat is 14\n",
+      "t cnwheat is 15\n",
+      "t cnwheat is 16\n",
+      "t cnwheat is 17\n",
+      "t cnwheat is 18\n",
+      "t cnwheat is 19\n",
+      "t cnwheat is 20\n",
+      "t cnwheat is 21\n",
+      "t cnwheat is 22\n",
+      "t cnwheat is 23\n",
+      "t cnwheat is 24\n",
+      "t cnwheat is 25\n",
+      "t cnwheat is 26\n",
+      "t cnwheat is 27\n",
+      "t cnwheat is 28\n",
+      "t cnwheat is 29\n",
+      "t cnwheat is 30\n",
+      "t cnwheat is 31\n",
+      "t cnwheat is 32\n",
+      "t cnwheat is 33\n",
+      "t cnwheat is 34\n",
+      "t cnwheat is 35\n",
+      "t cnwheat is 36\n",
+      "t cnwheat is 37\n",
+      "t cnwheat is 38\n",
+      "t cnwheat is 39\n",
+      "t cnwheat is 40\n",
+      "t cnwheat is 41\n",
+      "t cnwheat is 42\n",
+      "t cnwheat is 43\n",
+      "t cnwheat is 44\n",
+      "t cnwheat is 45\n",
+      "t cnwheat is 46\n",
+      "t cnwheat is 47\n"
+     ]
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Generate outputs and postprocessing\n",
+    "Outputs relate to the different variable solved by the model, while the postprocessing include extra-calculations allowing for in-depth interpretation of the simulations. Outputs and postprocessing are written as csv files, one per scale (axis, organs, hiddenzones, elements and soil). Files are stored in /outputs/simple_run_vegetative_stages and /postprocessing/simple_run_vegetative_stages"
+   ],
+   "id": "5240403175f6b120"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:55:06.341350Z",
+     "start_time": "2025-10-08T08:55:06.164002Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "## -- OUTPUTS\n",
+    "\n",
+    "AXES_INDEX_COLUMNS = ['t', 'plant', 'axis']\n",
+    "ELEMENTS_INDEX_COLUMNS = ['t', 'plant', 'axis', 'metamer', 'organ', 'element']\n",
+    "HIDDENZONES_INDEX_COLUMNS = ['t', 'plant', 'axis', 'metamer']\n",
+    "ORGANS_INDEX_COLUMNS = ['t', 'plant', 'axis', 'organ']\n",
+    "SOILS_INDEX_COLUMNS = ['t', 'plant', 'axis']\n",
+    "\n",
+    "# convert list of outputs into dataframes\n",
+    "outputs_df_dict = {}\n",
+    "for outputs_df_list, outputs_filename, index_columns in ((axes_all_data_list, AXES_OUTPUTS_FILENAME, AXES_INDEX_COLUMNS),\n",
+    "                                                         (organs_all_data_list, ORGANS_OUTPUTS_FILENAME, ORGANS_INDEX_COLUMNS),\n",
+    "                                                         (hiddenzones_all_data_list, HIDDENZONES_OUTPUTS_FILENAME, HIDDENZONES_INDEX_COLUMNS),\n",
+    "                                                         (elements_all_data_list, ELEMENTS_OUTPUTS_FILENAME, ELEMENTS_INDEX_COLUMNS),\n",
+    "                                                         (soils_all_data_list, SOILS_OUTPUTS_FILENAME, SOILS_INDEX_COLUMNS)):\n",
+    "    outputs_filepath = os.path.join(OUTPUTS_DIRPATH, outputs_filename)\n",
+    "    outputs_df = pd.concat(outputs_df_list, keys=all_simulation_steps, sort=False)\n",
+    "    outputs_df.reset_index(0, inplace=True)\n",
+    "    outputs_df.rename({'level_0': 't'}, axis=1, inplace=True)\n",
+    "    outputs_df = outputs_df.reindex(index_columns + outputs_df.columns.difference(index_columns).tolist(), axis=1, copy=False)\n",
+    "    outputs_df.fillna(value=np.nan, inplace=True)  # Convert back None to NaN\n",
+    "    outputs_df.to_csv(outputs_filepath, na_rep='NA', index=False, float_format='%.{}f'.format(OUTPUTS_PRECISION))\n",
+    "    outputs_file_basename = outputs_filename.split('.')[0]\n",
+    "    outputs_df_dict[outputs_file_basename] = outputs_df.reset_index()\n",
+    "\n",
+    "## -- POSTPROCESSING\n",
+    "delta_t = CNWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR\n",
+    "\n",
+    "# run the postprocessing\n",
+    "axes_postprocessing_file_basename = AXES_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "hiddenzones_postprocessing_file_basename = HIDDENZONES_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "organs_postprocessing_file_basename = ORGANS_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "elements_postprocessing_file_basename = ELEMENTS_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "soils_postprocessing_file_basename = SOILS_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "\n",
+    "postprocessing_df_dict = {}\n",
+    "(postprocessing_df_dict[axes_postprocessing_file_basename],\n",
+    " postprocessing_df_dict[hiddenzones_postprocessing_file_basename],\n",
+    " postprocessing_df_dict[organs_postprocessing_file_basename],\n",
+    " postprocessing_df_dict[elements_postprocessing_file_basename],\n",
+    " postprocessing_df_dict[soils_postprocessing_file_basename]) \\\n",
+    "    = cnwheat_facade.CNWheatFacade.postprocessing(axes_outputs_df=outputs_df_dict[AXES_OUTPUTS_FILENAME.split('.')[0]],\n",
+    "                                                  hiddenzone_outputs_df=outputs_df_dict[HIDDENZONES_OUTPUTS_FILENAME.split('.')[0]],\n",
+    "                                                  organs_outputs_df=outputs_df_dict[ORGANS_OUTPUTS_FILENAME.split('.')[0]],\n",
+    "                                                  elements_outputs_df=outputs_df_dict[ELEMENTS_OUTPUTS_FILENAME.split('.')[0]],\n",
+    "                                                  soils_outputs_df=outputs_df_dict[SOILS_OUTPUTS_FILENAME.split('.')[0]],\n",
+    "                                                  delta_t=delta_t)\n",
+    "\n",
+    "for postprocessing_file_basename, postprocessing_filename in ((axes_postprocessing_file_basename, AXES_POSTPROCESSING_FILENAME),\n",
+    "                                                              (hiddenzones_postprocessing_file_basename, HIDDENZONES_POSTPROCESSING_FILENAME),\n",
+    "                                                              (organs_postprocessing_file_basename, ORGANS_POSTPROCESSING_FILENAME),\n",
+    "                                                              (elements_postprocessing_file_basename, ELEMENTS_POSTPROCESSING_FILENAME),\n",
+    "                                                              (soils_postprocessing_file_basename, SOILS_POSTPROCESSING_FILENAME)):\n",
+    "    postprocessing_filepath = os.path.join(POSTPROCESSING_DIRPATH, postprocessing_filename)\n",
+    "    postprocessing_df_dict[postprocessing_file_basename].to_csv(postprocessing_filepath, na_rep='NA', index=False, float_format='%.{}f'.format(OUTPUTS_PRECISION))\n"
+   ],
+   "id": "7edb8c13d7997a16",
+   "outputs": [],
+   "execution_count": 12
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Generate graphs\n",
+    "Graphs are stored in the folder /graphs/simple_run_vegetative_stages"
+   ],
+   "id": "69c6e133ced85bff"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T08:58:06.447522Z",
+     "start_time": "2025-10-08T08:57:45.543222Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "GRAPHS_DIRPATH = os.path.join('graphs', 'simple_run_vegetative_stages')\n",
+    "# Retrieve last computed post-processing dataframes\n",
+    "axes_postprocessing_file_basename = AXES_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "organs_postprocessing_file_basename = ORGANS_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "hiddenzones_postprocessing_file_basename = HIDDENZONES_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "elements_postprocessing_file_basename = ELEMENTS_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "soils_postprocessing_file_basename = SOILS_POSTPROCESSING_FILENAME.split('.')[0]\n",
+    "\n",
+    "# --- Generate graphs from postprocessing files\n",
+    "plt.ioff()\n",
+    "\n",
+    "cnwheat_facade.CNWheatFacade.graphs(axes_postprocessing_df=postprocessing_df_dict[axes_postprocessing_file_basename],\n",
+    "                                    hiddenzones_postprocessing_df=postprocessing_df_dict[hiddenzones_postprocessing_file_basename],\n",
+    "                                    organs_postprocessing_df=postprocessing_df_dict[organs_postprocessing_file_basename],\n",
+    "                                    elements_postprocessing_df=postprocessing_df_dict[elements_postprocessing_file_basename],\n",
+    "                                    soils_postprocessing_df=postprocessing_df_dict[soils_postprocessing_file_basename],\n",
+    "                                    graphs_dirpath=GRAPHS_DIRPATH)"
+   ],
+   "id": "f327cc9a1a6a7c6c",
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## 📈 Key Results Visualization",
+   "id": "d036163a4e5922ae"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-08T12:27:16.945450Z",
+     "start_time": "2025-10-08T12:27:16.704079Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "axes_postprocessing_df = postprocessing_df_dict['axes_postprocessing']\n",
+    "roots_postprocessing_df = postprocessing_df_dict['organs_postprocessing'][postprocessing_df_dict['organs_postprocessing']['organ'] == 'roots']\n",
+    "hz_postprocessing_df = postprocessing_df_dict['hiddenzones_postprocessing']\n",
+    "\n",
+    "# Biomass allocation\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(axes_postprocessing_df['t'], axes_postprocessing_df['sum_dry_mass_roots'], label='Root biomass')\n",
+    "plt.plot(axes_postprocessing_df['t'], axes_postprocessing_df['sum_dry_mass_shoot'], label='Shoot biomass')\n",
+    "plt.xlabel('Time (hour)')\n",
+    "plt.ylabel('Dry mass (g)')\n",
+    "plt.title('Biomass allocation')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# C and N acquisition\n",
+    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+    "ax1.plot(axes_postprocessing_df['t'], axes_postprocessing_df['Total_Photosynthesis'], label='Photosynthesis', color='yellow')\n",
+    "ax1.set_xlabel('Time (hour)')\n",
+    "ax1.set_ylabel('Photosynthesis (µmol C)')\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(roots_postprocessing_df['t'], roots_postprocessing_df['Uptake_Nitrates'], label='N uptake', color='green')\n",
+    "ax2.set_ylabel('N Uptake (µmol N)')\n",
+    "\n",
+    "plt.title('C and N acquisition')\n",
+    "fig.legend(loc='upper right')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Leaf length\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "for metamer in hz_postprocessing_df['metamer'].unique():\n",
+    "    leaf = hz_postprocessing_df[hz_postprocessing_df['metamer'] == metamer]\n",
+    "    plt.plot(leaf['t'], leaf['leaf_L'], label=metamer)\n",
+    "\n",
+    "plt.xlabel('Time (hour)')\n",
+    "plt.ylabel('Leaf length (m)')\n",
+    "plt.legend(loc='center right', title='Leaf number')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ],
+   "id": "9e3a63819419e135",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 37
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/example/Notebooks/02_multiple_scenarios.ipynb b/example/Notebooks/02_multiple_scenarios.ipynb
new file mode 100644
index 00000000..841d5358
--- /dev/null
+++ b/example/Notebooks/02_multiple_scenarios.ipynb
@@ -0,0 +1,538 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# 🌱 Using WheatFSPM with different climate scenarios and parameters",
+   "id": "2e8470c0c23fbf99"
+  },
+  {
+   "metadata": {
+    "collapsed": true
+   },
+   "cell_type": "markdown",
+   "source": [
+    "### Authors : R. Barillot, C.Chambon, M.Gauthier & B. Andrieu\n",
+    "### Publication : https://doi.org/10.1093/insilicoplants/diab034"
+   ],
+   "id": "80f0cbb1f49a0c5a"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "This tutorial will illustrate how to use WheatFspm for different climate scenarios or parameter values. To that purpose, we will define a list of scenarios, then a generic function that will read the information of a given scenario and which will finally passed these variables to the model by calling a script *main.py*.",
+   "id": "9f2eeb75fd5bb4d5"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "First, we will load a csv file defining the list of scenarios to be run. Each scenario has an id and a list of variables contained in the columns to be passed to WheatFspm.\n",
+    "NB: the last two columns illustrate how model parameters can be modified for a given scenario (for sensitivity analyses for instance)."
+   ],
+   "id": "6fbfac1b96e8b34e"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T15:28:19.969788Z",
+     "start_time": "2025-10-21T15:28:09.564819Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "scenarios = pd.read_csv(os.path.join('inputs', 'scenarios_list.csv'))\n",
+    "scenarios.head()\n"
+   ],
+   "id": "bab1fbe1fcde59df",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   Scenario  Simulation_Length  Run_Simulation    Scenario_label  \\\n",
+       "0         1                 48            True     N5U_PAR100_D1   \n",
+       "1         2                 48            True    N50U_PAR100_D1   \n",
+       "2         3                 48            True   N5U_PAR500_D500   \n",
+       "3         4                 48            True  N50U_PAR500_D500   \n",
+       "\n",
+       "   Plant_Density  Inter_Row  constant_Conc_Nitrates    METEO_FILENAME  \\\n",
+       "0              1       1.00             35714.00000  meteo_PAR100.csv   \n",
+       "1              1       1.00            357143.00000  meteo_PAR100.csv   \n",
+       "2            500       0.15             35714.28571  meteo_PAR500.csv   \n",
+       "3            500       0.15            357142.85710  meteo_PAR500.csv   \n",
+       "\n",
+       "   growthwheat:VMAX_ROOTS_GROWTH_PREFLO  elongwheat:max_nb_leaves  \n",
+       "0                               0.01539                        50  \n",
+       "1                               0.01539                        50  \n",
+       "2                               0.01539                        50  \n",
+       "3                               0.01539                        50  "
+      ],
+      "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>Scenario</th>\n",
+       "      <th>Simulation_Length</th>\n",
+       "      <th>Run_Simulation</th>\n",
+       "      <th>Scenario_label</th>\n",
+       "      <th>Plant_Density</th>\n",
+       "      <th>Inter_Row</th>\n",
+       "      <th>constant_Conc_Nitrates</th>\n",
+       "      <th>METEO_FILENAME</th>\n",
+       "      <th>growthwheat:VMAX_ROOTS_GROWTH_PREFLO</th>\n",
+       "      <th>elongwheat:max_nb_leaves</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>48</td>\n",
+       "      <td>True</td>\n",
+       "      <td>N5U_PAR100_D1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>35714.00000</td>\n",
+       "      <td>meteo_PAR100.csv</td>\n",
+       "      <td>0.01539</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>48</td>\n",
+       "      <td>True</td>\n",
+       "      <td>N50U_PAR100_D1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>357143.00000</td>\n",
+       "      <td>meteo_PAR100.csv</td>\n",
+       "      <td>0.01539</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>48</td>\n",
+       "      <td>True</td>\n",
+       "      <td>N5U_PAR500_D500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>35714.28571</td>\n",
+       "      <td>meteo_PAR500.csv</td>\n",
+       "      <td>0.01539</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>48</td>\n",
+       "      <td>True</td>\n",
+       "      <td>N50U_PAR500_D500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>357142.85710</td>\n",
+       "      <td>meteo_PAR500.csv</td>\n",
+       "      <td>0.01539</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Let's select a scenario",
+   "id": "fa139272bfb415ce"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T15:28:36.250666Z",
+     "start_time": "2025-10-21T15:28:36.242283Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import ipywidgets as widgets\n",
+    "from IPython.display import display\n",
+    "\n",
+    "\n",
+    "scenario_options = (scenarios['Scenario'].astype(str) +\n",
+    "                        ' - ' +\n",
+    "                        scenarios['Scenario_label']).tolist()\n",
+    "selected_scenario_id = widgets.IntText(value=1)\n",
+    "\n",
+    "# widget Dropdown\n",
+    "scenario_selector = widgets.Dropdown(\n",
+    "    options=scenario_options,\n",
+    "    value=scenario_options[0], # default scenario\n",
+    "    description='Scenario :',\n",
+    "    disabled=False,\n",
+    ")\n",
+    "\n",
+    "def update_scenario(change):\n",
+    "    selected_id = int(change.new.split(' - ')[0])\n",
+    "    selected_scenario_id.value = selected_id\n",
+    "\n",
+    "scenario_selector.observe(update_scenario, names='value')\n",
+    "\n",
+    "print(\"Select a scenario :\")\n",
+    "display(scenario_selector)\n"
+   ],
+   "id": "bb2854a85a616854",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Select a scenario :\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Dropdown(description='Scenario :', options=('1 - N5U_PAR100_D1', '2 - N50U_PAR100_D1', '3 - N5U_PAR500_D500', …"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "5b6011ed4b5248c1a588f385dfed50e3"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Then, we will define a function *run_wheatfspm* that will load the data from a specific scenario.",
+   "id": "ef1acb2b9f43cda6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T15:29:01.190207Z",
+     "start_time": "2025-10-21T15:28:42.721441Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import tools\n",
+    "import main\n",
+    "\n",
+    "\n",
+    "def run_wheatfspm(scenario_id=1, inputs_dir_path='inputs', outputs_dir_path='outputs'):\n",
+    "    \"\"\"\n",
+    "    Run the main.py of wheatfspm using data from a specific scenario\n",
+    "\n",
+    "    :param int scenario_id: the index of the scenario to be read in the CSV file containing the list of scenarios\n",
+    "    :param str inputs_dir_path: the path directory of inputs\n",
+    "    :param str outputs_dir_path: the path to save outputs\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Path of the directory which contains the inputs of the model\n",
+    "    INPUTS_DIRPATH = inputs_dir_path\n",
+    "\n",
+    "    # Scenario to be run\n",
+    "    scenarios_df = pd.read_csv(os.path.join(INPUTS_DIRPATH, 'scenarios_list.csv'), index_col='Scenario')\n",
+    "    scenario = scenarios_df.loc[scenario_id].to_dict()\n",
+    "    scenario_name = 'Scenario_%.4d' % scenario_id\n",
+    "\n",
+    "    # -- SIMULATION PARAMETERS --\n",
+    "\n",
+    "    # Create dict of parameters for the scenario. This includes the model parameters to be updated.\n",
+    "    scenario_parameters = tools.buildDic(scenario)\n",
+    "\n",
+    "    # Length of the simulation (hours)\n",
+    "    SIMULATION_LENGTH = scenario_parameters.get('Simulation_Length', 48)\n",
+    "\n",
+    "    # -- SIMULATION DIRECTORIES --\n",
+    "\n",
+    "    # Path of the directory which contains the outputs of the model\n",
+    "    scenario_dirpath = os.path.join(outputs_dir_path, scenario_name)\n",
+    "\n",
+    "    # Create the directory of the Scenario where results will be stored\n",
+    "    if not os.path.exists(scenario_dirpath):\n",
+    "        os.mkdir(scenario_dirpath)\n",
+    "\n",
+    "    # Create directory paths for graphs, outputs and postprocessings of this scenario\n",
+    "    scenario_graphs_dirpath = os.path.join(scenario_dirpath, 'graphs')\n",
+    "    if not os.path.exists(scenario_graphs_dirpath):\n",
+    "        os.mkdir(scenario_graphs_dirpath)\n",
+    "    # Outputs\n",
+    "    scenario_outputs_dirpath = os.path.join(scenario_dirpath, 'outputs')\n",
+    "    if not os.path.exists(scenario_outputs_dirpath):\n",
+    "        os.mkdir(scenario_outputs_dirpath)\n",
+    "    # Postprocessings\n",
+    "    scenario_postprocessing_dirpath = os.path.join(scenario_dirpath, 'postprocessing')\n",
+    "    if not os.path.exists(scenario_postprocessing_dirpath):\n",
+    "        os.mkdir(scenario_postprocessing_dirpath)\n",
+    "\n",
+    "    # -- SIMULATION CONDITIONS\n",
+    "\n",
+    "    # Plant density and inter-row\n",
+    "    PLANT_DENSITY = {1: scenario_parameters.get('Plant_Density', 250.)}\n",
+    "\n",
+    "    # Build N Fertilizations dict. In this example, constant nitrate concentrations are simulated in the soil (mimicking hydroponic)\n",
+    "    N_FERTILIZATIONS = {'constant_Conc_Nitrates': scenario_parameters.get('constant_Conc_Nitrates')}\n",
+    "\n",
+    "    # # -- RUN main --\n",
+    "    main.main(simulation_length=SIMULATION_LENGTH,\n",
+    "              N_fertilizations=N_FERTILIZATIONS,\n",
+    "              PLANT_DENSITY=PLANT_DENSITY,\n",
+    "              METEO_FILENAME=scenario.get('METEO_FILENAME'),\n",
+    "              GRAPHS_DIRPATH=scenario_graphs_dirpath,\n",
+    "              OUTPUTS_DIRPATH=scenario_outputs_dirpath,\n",
+    "              POSTPROCESSING_DIRPATH=scenario_postprocessing_dirpath,\n",
+    "              update_parameters_all_models=scenario_parameters)\n",
+    "\n",
+    "    return scenario_name\n"
+   ],
+   "id": "b05c3754f4adf8f2",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Finally, let's run a scenario from the list (wait for the end of simulation).",
+   "id": "2a1e95acd3d972cb"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T15:30:00.815475Z",
+     "start_time": "2025-10-21T15:29:12.757305Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "scenario_id = selected_scenario_id.value\n",
+    "scenario_name = run_wheatfspm(scenario_id)"
+   ],
+   "id": "2b6a402a213513f2",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t cnwheat is 0\n",
+      "t cnwheat is 1\n",
+      "t cnwheat is 2\n",
+      "t cnwheat is 3\n",
+      "t cnwheat is 4\n",
+      "t cnwheat is 5\n",
+      "t cnwheat is 6\n",
+      "t cnwheat is 7\n",
+      "t cnwheat is 8\n",
+      "t cnwheat is 9\n",
+      "t cnwheat is 10\n",
+      "t cnwheat is 11\n",
+      "t cnwheat is 12\n",
+      "t cnwheat is 13\n",
+      "t cnwheat is 14\n",
+      "t cnwheat is 15\n",
+      "t cnwheat is 16\n",
+      "t cnwheat is 17\n",
+      "t cnwheat is 18\n",
+      "t cnwheat is 19\n",
+      "t cnwheat is 20\n",
+      "t cnwheat is 21\n",
+      "t cnwheat is 22\n",
+      "t cnwheat is 23\n",
+      "t cnwheat is 24\n",
+      "t cnwheat is 25\n",
+      "t cnwheat is 26\n",
+      "t cnwheat is 27\n",
+      "t cnwheat is 28\n",
+      "t cnwheat is 29\n",
+      "t cnwheat is 30\n",
+      "t cnwheat is 31\n",
+      "t cnwheat is 32\n",
+      "t cnwheat is 33\n",
+      "t cnwheat is 34\n",
+      "t cnwheat is 35\n",
+      "t cnwheat is 36\n",
+      "t cnwheat is 37\n",
+      "t cnwheat is 38\n",
+      "t cnwheat is 39\n",
+      "t cnwheat is 40\n",
+      "t cnwheat is 41\n",
+      "t cnwheat is 42\n",
+      "t cnwheat is 43\n",
+      "t cnwheat is 44\n",
+      "t cnwheat is 45\n",
+      "t cnwheat is 46\n",
+      "t cnwheat is 47\n",
+      "\n",
+      "Simulation run in 0:00:27\n",
+      "Creating postprocessing and graphs...\n",
+      "End of execution\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Let's visualise some graphs",
+   "id": "34e78b3708f89e65"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T15:30:08.359568Z",
+     "start_time": "2025-10-21T15:30:07.962039Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "axes_postprocessing_df = pd.read_csv(os.path.join('outputs', scenario_name, 'postprocessing', 'axes_postprocessing.csv'))\n",
+    "organs_postprocessing_df = pd.read_csv(os.path.join('outputs', scenario_name, 'postprocessing', 'organs_postprocessing.csv'))\n",
+    "roots_postprocessing_df = organs_postprocessing_df[organs_postprocessing_df['organ'] == 'roots']\n",
+    "hz_postprocessing_df = pd.read_csv(os.path.join('outputs', scenario_name, 'postprocessing', 'hiddenzones_postprocessing.csv'))\n",
+    "\n",
+    "# Biomass allocation\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(axes_postprocessing_df['t'], axes_postprocessing_df['sum_dry_mass_roots'], label='Root biomass')\n",
+    "plt.plot(axes_postprocessing_df['t'], axes_postprocessing_df['sum_dry_mass_shoot'], label='Shoot biomass')\n",
+    "plt.xlabel('Time (hour)')\n",
+    "plt.ylabel('Dry mass (g)')\n",
+    "plt.title('Biomass allocation')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# C and N acquisition\n",
+    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+    "ax1.plot(axes_postprocessing_df['t'], axes_postprocessing_df['Total_Photosynthesis'], label='Photosynthesis', color='yellow')\n",
+    "ax1.set_xlabel('Time (hour)')\n",
+    "ax1.set_ylabel('Photosynthesis (µmol C)')\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(roots_postprocessing_df['t'], roots_postprocessing_df['Uptake_Nitrates'], label='N uptake', color='green')\n",
+    "ax2.set_ylabel('N Uptake (µmol N)')\n",
+    "\n",
+    "plt.title('C and N acquisition')\n",
+    "fig.legend(loc='upper right')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Leaf length\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "for metamer in hz_postprocessing_df['metamer'].unique():\n",
+    "    leaf = hz_postprocessing_df[hz_postprocessing_df['metamer'] == metamer]\n",
+    "    plt.plot(leaf['t'], leaf['leaf_L'], label=metamer)\n",
+    "\n",
+    "plt.xlabel('Time (hour)')\n",
+    "plt.ylabel('Leaf length (m)')\n",
+    "plt.legend(loc='center right', title='Leaf number')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ],
+   "id": "2424e926f6611d33",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ],
+      "image/png": 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I4ZScioiIiIiIiOGUnIqIiIiIiIawSq672Tmm5FRERERExIP5+voCEBsba3Ak4u5Sz7HUc+56mhBJRERERMSDeXt7ExISwsmTJwEICgrCYrEYHJW4E6vVSmxsLCdPniQkJARvb+9M97NYVb8XEREREfFoVquV48ePc/78eaNDETcWEhJCeHh4lp0fSk5FRERERASA5ORkEhMTjQ5D3JCvr2+WFdNUSk5FRExox44dTJs2jTVr1hATE4OPjw+33XYbnTt3plevXhQuXNjoEAGIioqiadOmzJs374b7pfagTpo0ieHDh6d7b968eTzxxBNs3ryZOnXq5FaoeSK7/x5ppf7+Bw8eJCoqCoCFCxdy8uRJBg0alGF/i8XCmDFjGDt2rFNiFhERySu651RExGTee+89+vbtS8WKFXnuueeoUqUKiYmJbNmyhbfffptNmzbx5ZdfGh1mjrzyyis8/fTTLpNcO9uXX35JcHCwQ59p164dmzZtIiIiwr5t4cKF7Nq1K9PkdNOmTZQsWfJWQxUREclzSk5FRExk06ZN9OnTh1atWvHVV1/h7+9vf69Vq1Y8++yz/PDDDwZGmHMtW7ZkzZo1vPzyy7z++utGh5Mratas6fBnQkNDCQ0Nzfb+DRo0cPg7REREXIGWkhERMZGJEydisVh499130yWmqfz8/OjQocMNj7FlyxY6d+5MVFQUgYGBREVF8eijj3L48OF0+82bNw+LxcLq1avp06cPRYsWpUiRInTs2JF///033b6JiYkMGzaM8PBwgoKCuPPOO/ntt98c+t0qVqxIz549eeuttzLEkh2nTp2ib9++VKlShfz581OsWDGaN2/O+vXrM+wbHx/PuHHjqFy5MgEBARQpUoRmzZqxceNG+z4XL17kqaeeokiRIuTPn582bdrw119/YbFY0g2Z7dGjh324bVpjx47NMOFDVFQUPXr0sL9OSUlhwoQJVKxYkcDAQEJCQqhevTozZsyw75P63+HQoUMANG3alO+++47Dhw9jsVjsj1TXxwewa9cu7r//fgoVKkRAQAB33HEH8+fPT7fPmjVrsFgsLFq0iFGjRlG8eHGCg4Np2bIlf/75Z1b/7CIiIk6jyqmIiEkkJyezatUqateuTWRkZI6Pc+jQISpWrEjnzp0pXLgwMTExzJ49m7p167J7926KFi2abv9evXrRrl07Fi5cyNGjR3nuuefo1q0bq1atsu/z1FNP8eGHHzJ06FBatWrFrl276NixI5cuXXIotrFjx/LRRx8xevRoPvzwQ4c+e/bsWQDGjBlDeHg4ly9f5ssvv6Rp06asXLmSpk2bApCUlETbtm1Zv349gwYNonnz5iQlJfHLL79w5MgRGjVqhNVq5YEHHmDjxo28+OKL1K1blw0bNtC2bVuHYrqZyZMnM3bsWF544QXuvvtuEhMT2bt37w1ny5w1axZPP/00+/fvz9bw7T///JNGjRpRrFgxZs6cSZEiRViwYAE9evTgxIkTDBs2LN3+I0eOpHHjxrz//vtcvHiR559/nvbt27Nnz56bTmQhIiJyK5ScioiYxOnTp4mNjaVMmTK3dJyHHnqIhx56yP46OTmZ++67j7CwMBYuXMiAAQPS7d+mTRtmzpxpf3327FmGDRvG8ePHCQ8PZ+/evcyfP5/BgwczefJkwDbEOCwsjK5duzoUW3h4OIMHD2bSpEkMHTqU6tWrZ/uzFStWZNasWel+r3vuuYdDhw4xc+ZMe3K6aNEiVq9ezXvvvUevXr3s+7dv397+/Mcff2T16tXMmDHD/u/RqlUr/Pz8GDVqlEO/041s2LCBatWqpat03nPPPTf8TJUqVQgJCcHf3z9bQ3jHjh1LQkICq1evtndq3HvvvZw/f56XXnqJZ555hoIFC6Y7/oIFC+yvvb29eeSRR9i8ebOGDIuISK7SsF4REQ9z+fJlnn/+ecqXL4+Pjw8+Pj7kz5+fK1eusGfPngz7Xz9MODVhTB16u3r1aoAMiegjjzyCj4/jfaDDhg2jcOHCPP/88w5/9u2336ZWrVoEBATg4+ODr68vK1euTPd7ff/99wQEBPDkk09meZysfqcuXbo4HNON1KtXj+3bt9O3b19+/PFHLl686NTjA6xatYoWLVpkqLb36NGD2NhYNm3alG77zf57i4iI5BYlpyIiJlG0aFGCgoI4ePDgLR2nS5cuvPnmm/Tq1Ysff/yR3377jc2bNxMaGsrVq1cz7F+kSJF0r1PvdU3d98yZM4Ct6pmWj49Phs9mR3BwMC+88AI//PCDPUnMjqlTp9KnTx/q16/PF198wS+//MLmzZtp06ZNut/r1KlTFC9eHC+vrP8EnjlzJtP4r/8db9WIESOYMmUKv/zyC23btqVIkSK0aNGCLVu2OO07zpw5k26m31TFixe3v5/Wzf57i4iI5BYlpyIiJuHt7U2LFi3YunUr//zzT46OceHCBb799luGDRvG8OHDadGiBXXr1qVatWr2ezYdlZrMHD9+PN32pKSkDIlPdvXp04cyZcrw/PPPk93luBcsWEDTpk2ZPXs27dq1o379+tSpUyfDfa+hoaH8+++/pKSkZHmsIkWKZBr/9b8jQEBAAPHx8Rm2nz59+qYx+/j4MGTIEH7//XfOnj3LokWLOHr0KPfccw+xsbE3/Xx2FClShJiYmAzbUye1uv4eYxEREaMoORURMZERI0ZgtVp56qmnSEhIyPB+YmIi33zzTZaft1gsWK3WDDP9vv/++yQnJ+coptR7OT/++ON02z/99FOSkpJydEw/Pz8mTJjA5s2b+eyzz7L1GYvFkuH32rFjR4Zhq23btiUuLo558+ZleaxmzZoBGX+nhQsXZtg3KiqKkydPcuLECfu2hIQEfvzxx2zFnSokJISHHnqIfv36cfbsWfvsvJnx9/fPdiWzRYsWrFq1KsMMyx9++CFBQUG6j1RERFyGJkQSETGRhg0bMnv2bPr27Uvt2rXp06cPt99+O4mJiWzbto13332XqlWrppvcJ63g4GDuvvtuXnvtNYoWLUpUVBRr165lzpw5hISE5CimypUr061bN6ZPn46vry8tW7Zk165dTJkyheDg4Bz/ro8++ihTpkzh+++/z9b+9913H+PHj2fMmDE0adKEP//8k3HjxlGmTJl0SfKjjz7K3Llz6d27N3/++SfNmjUjJSWFX3/9lcqVK9O5c2dat27N3XffzbBhw7hy5Qp16tRhw4YNfPTRRxm+t1OnTrz44ot07tyZ5557jri4OGbOnJmtZL99+/ZUrVqVOnXqEBoayuHDh5k+fTqlS5emQoUKWX6uWrVqLFmyhNmzZ1O7dm28vLyoU6dOpvuOGTOGb7/9lmbNmvHiiy9SuHBhPv74Y7777jsmT56cbjIkERERIyk5FRExmaeeeop69eoxbdo0Xn31VY4fP46vry+33XYbXbp0oX///jf8/MKFCxk4cCDDhg0jKSmJxo0bs2LFCtq1a5fjmObMmUNYWBjz5s1j5syZ3HHHHXzxxRd07tw5x8e0WCy8+uqrtG7dOlv7jxo1itjYWObMmcPkyZOpUqUKb7/9Nl9++SVr1qyx7+fj48OyZcuYNGkSixYtYvr06RQoUIAaNWrQpk0bALy8vFi6dClDhgxh8uTJJCQk0LhxY5YtW0alSpXSfW+ZMmX4+uuvGTlyJA899BAREREMGTKEU6dO8dJLL90w5mbNmvHFF1/Yl20JDw+nVatWjB49Gl9f3yw/N3DgQP744w9GjhzJhQsXsFqtWQ5/rlixIhs3bmTkyJH069ePq1evUrlyZebOnZtuzVURERGjWazZvZlHREREsFgsjBkzJt3yLyIiInLrdM+piIiIiIiIGE7JqYiIiIiIiBhO95yKiIg4QHfDiIiI5A5VTkVERERERMRwSk5FRERERETEcEpORURERERExHBuf89pUlIS27ZtIywsDC8v5eIiIiIiIp4qJSWFEydOULNmTXx83D4VMh23/y+ybds26tWrZ3QYIiIiIiLiIn777Tfq1q1rdBhyHbdPTsPCwgDbCRgREWFwNCIiIiIiYpSYmBjq1atnzxHEtbh9cpo6lDciIoKSJUsaHI2IiIiIiBhNt/u5Jv1XEREREREREcMpORURERERERHDKTkVERERERERwyk5FRERERERuYFZs2ZRpkwZAgICqF27NuvXr89y35iYGLp06ULFihXx8vJi0KBBme43ffp0KlasSGBgIJGRkQwePJi4uLhc+g3MQcmpiIiIiIhIFhYvXsygQYMYNWoU27Zt46677qJt27YcOXIk0/3j4+MJDQ1l1KhR1KhRI9N9Pv74Y4YPH86YMWPYs2cPc+bMYfHixYwYMSI3fxWXp+RUREREREQkC1OnTqVnz5706tWLypUrM336dCIjI5k9e3am+0dFRTFjxgy6d+9OwYIFM91n06ZNNG7cmC5duhAVFUXr1q159NFH2bJlS27+Ki7P7ZeSSZWUlERiYqLRYYiIiIiIiEGSkpIAuHTpEhcvXrRv9/f3x9/fP8P+CQkJbN26leHDh6fb3rp1azZu3JjjOO68804WLFjAb7/9Rr169Thw4ADLli3j8ccfz/Ex3YHHJKebNm0iKCjI6DBERERERMQgsbGxAFSpUiXd9jFjxjB27NgM+58+fZrk5GTCwsLSbQ8LC+P48eM5jqNz586cOnWKO++8E6vVSlJSEn369MmQBHsaj0lOGzZsSIkSJYwOQ0REREREDHLs2DEAdu/enS43yKxqmpbFYkn32mq1ZtjmiDVr1vDyyy8za9Ys6tevz99//83AgQOJiIhg9OjROT6u2XlMcurj44Ovr6/RYYiIiIiIiEF8fGzpT4ECBQgODr7p/kWLFsXb2ztDlfTkyZMZqqmOGD16NI899hi9evUCoFq1aly5coWnn36aUaNG4eXlmVMDeeZvLSIiIiIichN+fn7Url2bFStWpNu+YsUKGjVqlOPjxsbGZkhAvb29sVqtWK3WHB/X7DymcioiIiIiIuKoIUOG8Nhjj1GnTh0aNmzIu+++y5EjR+jduzcAI0aM4NixY3z44Yf2z0RHRwNw+fJlTp06RXR0NH5+fvZ7Xdu3b8/UqVOpWbOmfVjv6NGj6dChA97e3nn+O7oKJaciIiIiIiJZ6NSpE2fOnGHcuHHExMRQtWpVli1bRunSpQGIiYnJsOZpzZo17c+3bt3KwoULKV26NIcOHQLghRdewGKx8MILL3Ds2DFCQ0Np3749L7/8cp79Xq7IYjWwbjx79mxmz55t/490++238+KLL9K2bVvAdqPxSy+9xLvvvsu5c+eoX78+b731Frfffnu2v+Off/4hMjKSo0ePUrJkydz4NURERERExASUG7g2Q+85LVmyJK+88gpbtmxhy5YtNG/enPvvv58//vgDgMmTJzN16lTefPNNNm/eTHh4OK1ateLSpUtGhi0iIiIiIiJOZmjlNDOFCxfmtdde48knn6R48eIMGjSI559/HoD4+HjCwsJ49dVXeeaZZ7J1PPWOiIiIiIgIKDdwdS5zz2lycjKfffYZV65coWHDhhw8eJDjx4/TunVr+z7+/v40adKEjRs3Zjs5FREREfEcScA2IBIINzgW92G1WrmUcIkLcRc4H3fe/gAI8g1K9wj0DbQ/9/f2v6W1MEU8jeHJ6c6dO2nYsCFxcXHkz5+fL7/8kipVqrBx40aADOsHhYWFcfjw4SyPFx8fT3x8vP21hgCLiIiIe7sE/AgsBb4DzgJVgZ1GBuXSklOS+fvs32w/sZ2YSzH2ZPNCfPrkM+32FGuKw99jwZIuWbUnsD6BFMtXjMjgSEoVLEVkQdvPUgVLEZYvDG8vz52tVTyb4clpxYoViY6O5vz583zxxRc8/vjjrF271v7+9b1NVqv1hj1QkyZN4qWXXsq1eEVERESM9w/wDfA1sBpIuO79P/M8IlcVlxTHrpO72Bazjejj0Ww7vo0dJ3ZwJfGKw8fy9fKlUGAhQgJCKOhfEIDYxFiuJl0lNjGW2MRYriRcIdmaDIAVq317dvl4+VAyuKQ9cS1VsFSGJDYkIMTh2EXMwOXuOW3ZsiXlypXj+eefp1y5cvz+++/ppmK+//77CQkJYf78+Zl+/vrK6bFjx6hSpYrGlYuIiIiJWYFobNXRpcDv171fAbgfuOvaT4BEXKAOkafOXT3H9hPb2RazjW3Hbcno7lO77cliWoE+gVQPq06ZQmUo6F+QkICQLB+p7wf4BGRrmG5icmK6hDX1cTXRtu1K4hWOXz7O0QtHOXLxiO3nhSP8e+nfTGO9XrF8xageVp3qxarbfoZVp3JoZQJ8AnL07+ZJdM+pa3O5K5bVaiU+Pp4yZcoQHh7OihUr7MlpQkICa9eu5dVXX83y8/7+/vj7+9tfX7x4MddjFhEREXG+BGAN/yWkR9O8ZwEaAR2uPSpd2562QncVKJDrURrp8PnDfPrHp2z6ZxPbjm/j0PlDme5XJLAINSNqUjO8JneE30HN8JrcVuS2XBs+6+vti6+3L8H+wQ59LikliZhLMRy5cISjF20J6/UJ7JmrZzh55SQ/HfiJnw78ZP+st8WbSkUr2ZPV1EeJAiV036uYhqHJ6ciRI2nbti2RkZFcunSJTz75hDVr1vDDDz9gsVgYNGgQEydOpEKFClSoUIGJEycSFBREly5djAxbREREJBusQBxwGdt9oak/L2Vj2wXg12uvUwUBrbElo+2AYpl8Z9rKmXsmp+eunuPz3Z+zYOcC1h1el+H9qJAoewKamoyWDC5pigTNx8uHyIKRRBaMzHKfKwlX2H1qNztO7LA9Tu5g+/HtnIs7xx+n/uCPU3+waNci+/6FAwunq7LWKV6HqsWq6r5WcUmGJqcnTpzgscceIyYmhoIFC1K9enV++OEHWrVqBcCwYcO4evUqffv25dy5c9SvX5/ly5dToID7XWhFRETEaAnYEsPrE8frt13//Eavbz5E88bCgfbYEtIWQOBN9vcC/IF4bMmpe4hPiuf7v79nwY4FfPPXNyQk2+6xtWChaVRT7rvtPnsiWiiwkMHR5q58fvmoW6IudUvUtW+zWq0cu3Tsv4T1xA62n9jOn6f/5OzVs6w5tIY1h9bY9w/2D6ZhyYY0jmxM41KNqV+iPvn88hnw24ik53L3nDqbxpWLiIi4m9SK5BX+Swaz8zyzimXan9dPKuRMQdiqmPmv+3mjbZWAOtgSTkcUAs4De/hvuK/5WK1WNhzdwIIdC/j0j085F3fO/l7VYlV5rPpjPFr10RtWGT1dXFIce07tsSes0Sei+e3Yb1xOuJxuP2+LNzUjatI4sjF3lrqTxpGNiSgQYVDUuUu5gWtzuXtORURExN0kYEsSUxPF64exOvI89RiOL+uRfQFkTBjzp3lc/zqrbanb8wF5OYQyEFtyas7K6d7Te/l4x8cs2Lkg3T2kxQsUp0vVLnSr3o3qYdVNMUzXaAE+AbZ7bSP+m1w0KSWJnSd2suHoBn4+8jMbjm7gn4v/sOXfLWz5dwszfp0BQJmQMvZEtXGpxlQJrYKXxdGOEhHHqHIqIiIiaSRjSwQvZvG4QPqqZNpHVtuScjHeQGxJYD7+SwjzZfIzswplVgmoby7GmxfKAQeADdgmTXJ9J6+cZNHORSzYuYAt/26xb8/vl5+HqjxEt2rdaBrVVPdJ5pIjF46w4ch/yeqOEzuwkj5FCAkIoUnpJrQq24rW5VpTvnB5U3YQKDdwbaqcioiImFoKmQ9dvdH9kJfJOvlMP9zPuXxIP3T1+qGsWQ13TX3kS/MzP7ahskpWMgq69tP1K6dXEq7w6oZXeW3ja8QlxQG2IaZtyrehW/VudKjYgSDfoJscRW5VqYKlKFWtFI9WexSAC3EX+OWfX9hwdAMbjm7gl39+4Xzceb7+82u+/vNrwDbxVOuyrWlVrhUtyrRw+3t9JW+ocioiIuIyEoAzwCng9LXHqet+pn1+nvRLhziTPxCcxSNthTLt4/pt17/2y6VYJb16wGZsy8+0NziWzFmtVj7Z9QnDfhrGPxf/AaB2RG0er/E4nap2oli+zGYiFqMkJicSfTyanw78xIoDK/j5yM8kpiTa3/eyeFG3eF17VbVByQb4ervmCATlBq5NyamIiEiuSgROADFZPE7yX8J5K2tze+H4vZEFyToB9UfMqgmwDlgMPGJwLBlt/XcrA38YyIajGwAoXbA0r7d+nY6VO5pymKgnupxwmXWH17F8/3JWHFjB7lO7072f3y8/zaKa0bpca1qXa02FwhVc5r+tcgPXpmG9IiIityQG2719WSWfpwFH+oG9gCJAUSA0i5+pz0P4L9kMAFyj8SdGS11uxrWG9Z64fIJRq0bxwbYPsGIlyDeIEXeO4NmGzxLoe7MlcsSV5PfLz70V7uXeCvcC8M/Ff1ixfwXLDyznpwM/cTr2NN/89Q3f/PUNYBs23Lpsa2bfNxsfL6UfkjWdHSIiIjmWDNTEVhm9EW9s61VGpPmZ+ggjffIZgu6jlFvjWslpQnICb/z6BuPWjeNivG10QJdqXXi15auUDFblyh2UDC7JEzWf4ImaT5BiTSH6eLS9qvrzkZ85cuEIvx77VYmp3JTOEBERkRy7zH+JaUegOOkTz9RHURxfq1Ikp1wnOV22bxmDfxzMX2f+Amz3lc5oM4PGpRobHJnkFi+LF7UialErohbD7xzOlYQrrDu8joTk3FxHWNyFklMREZEcS238ewGfo2G14hqMT073nt7LkB+H8P3f3wNQLF8xJrWYRI87emitTA+Tzy8fbSu0NToMMQklpyIiIjmWOlNuIEpMxXUYt5TMhbgLjFs7jpm/zSQpJQlfL18G1h/I6CajCfYPzvN4RMRclJyKiIjkWGrjX5O5iCtJPR9za5mhjKxWK/Oi5/H8T89zKvYUAPfddh+vt36d24rclmdxiIi5KTkVkWxKwdYQv4qtwXMV28QuBY0MSsRgqY3/oBvuJZK38nZYb2JyIv/7/n+8s/UdACoVrcS0e6bRpnybPPl+EXEfSk5NYwXwLdANqGtwLOK6rEAccCmLx+VMtl3hv2TzRj/jM/m+AsAhoHAu/T4irk6VU3FFeZecXoi7wCOfP8Ly/cuxYGFC8wk81+g5fL19c/27RcT9KDk1jReA34CZQENgEPAgoIu/uVmxJX6pSWPa5NHR56mP5DyI2x9bsnoJOICSU/FcqpyKK8qb5PTw+cPct+g+dp3cRZBvEIv+bxEdKnbI1e8UEfem5NQ0LqR5vunaoyTQD3gK24Lt4nyJ2CqRV9P8vHqDbamJZurj0nWvr99+BVuCmhvyAfmxVTdv9MiPrWEdmMXP67cFYFuDsRLwJ3l5T5OI61HlVFxR7ienm49tpv2i9py4coKI/BF88+g31C5eO9e+T0Q8g5JT00j9A/MNsBWYBfwDjADGAY8BA4DbDYkud1iBJCABW5UuwYHnqUlj6rBUR5+nfj4vqpCp8pExaXTkedpHPmwJZG4yfqkCEeOlnv+qnIoryd3r85d7vqTrkq5cTbpK9bDqfPvot0QWjMyV7xIRz6Lk1DRSq1NRwH3AcGAxMB3YBrx77dEKGAi0xZgF35OA3dgS6K3Y7kdMvIWHK/Hnv8ph4HXP025LTRpTH9e/vv5R4NrnzLbuW2pjXJVT8WRpl5IRcRW5k5xarVambprKcyuew4qVtuXbsvihxRTwL+DU7xERz6Xk1DSuv6/JH+iOrWL6M7Yk9StsEyetACoA/wN6YEt+ckMi6RPRrcB2bFXH3GDB9nv7XXvc7Pn1w1Gz+zztIzXp9Md8yWNuU+VURMN6xTU5v/MwKSWJ/sv622fk7VunLzPazsDHS01JEXEeXVFMwUrWDSALcNe1xyHgLeA9YB+2Yb4vAD2xJalFsCVZAdd++pD9ReMTgT9In4juIPNENBioBdTGdl9iALaJm3yvfadvNh6p+/nzX7Lp7UC8kvtUORXRhEjimpzbeXgx/iKPfPYIP+7/EQsWpt4zlYH1B2Kx6G+yiDiXklNTiOe/SXNu1ACKAl4DxgAfYpvZ909g2rXH9Sz8l6j63+D5RWyJaGZLiaRNRGsDdYByqMroCfJ+kXcR16PKqbgi5yWnRy4c4b6F97Hz5E6CfINY2HEh91e6/5aPKyKSGSWnppD2j0t2eufzA32B3sCPwAxgA7YqZ1Ka/VIrstn941WQ9IlobZSIerLUc1HDesWTqXIqrsg5yemWf7fQflF7jl8+Tnj+cL599FvNyCsiuUrJqSmkNn68cWxdUy9sEyO1TbMtGVsFNB5bspqd5/7AHUBZlIjKfzSsV0SVU3FNt56cfrX3K7p80YWrSVepVqwa33b5llIFSzknPBGRLCg5NQVnLlXgzX9rV4rcCk2IJKKlZMQ15fz6fP2MvG3Kt2HxQ4sJ9g92bogiIplQcmoKWqpAXJEqpyK6PotrSj0fE7GNmMreutcp1hT+t+x/zNoyC4A+dfows+1MzcgrInlGVxtT0D1N4opUORXRsF5xTWnbC1exzUVxcy+seoFZW2ZhwcLrrV9nUINBmpFXRPKUklNTUONHXJEqpyLqPBTXFJDmeSzZSU7nbpvLpJ8nAfDB/R/Q444euRKZiMiNaHYbU1DjR1yRlpIRUeehuCYvbJMZQnZGt6w+uJqnv30agBfuekGJqYgYRsmpKWjCDXFFWkpGRJ2H4rqyd+vF3tN76fhpR5JSkuhctTPjmo3L/dBERLKg5NQUNOGGuCIN6xVR5VRc182T09Oxp2m3sB3n487TsGRD5t4/V/eYioihlJyagnrmxRVpQiQRjWwR13Xja3RcUhwPfPIAB84doExIGb7u/DUBPgGZ7isikleUnJqCeubFFalyKqKRLeK6sk5OrVYrPZf2ZMPRDRT0L8h3Xb4jNF9o3oYnIpIJJaemoMqpuCJVTkXUeSiuK+t5AV5a+xILdy7Ex8uHLx75gsqhlfM2NBGRLCg5NQUNGxNXpMqpiDoPxXVlPqP6gh0LeGntSwDMbjebFmVb5HFcIiJZU3JqCho2Jq5Iyal4ukQg6dpzXZ/F1WQc3bL+8Hp6Lu0JwLBGw+hVq5cBcYmIZE3JqSmociquKG3Dx2pkICIGSTtcUtdncTXpk9O/z/7NA4sfICE5gf+r/H9MajnJuNBERLKg5NQUVDkVV5TaGE8BEowMRMQgqcmpBfA3MhCRTPyXnJ69epZ2C9tx9upZ6havy4cPfoiXRU1AEXE9ujKZgu5pEleUtrNEkyKJJ0rbcai1IcXV2K7RCcmX6Li4I3+d+YtSBUux9NGlBPmqPSEirknJqSloWK+4Il/A+9pz3Xcqnkgz9YorC8Rqhae/+YK1h9dSwK8A33X5jvD84UYHJiKSJSWnpqBhveKKLGg5GfFsSk7FlQUycT3M374Db4s3nz38GVWLVTU6KBGRG1JyagqqnIqr0oy94sl0y4W4rsW7DvLCatvzN9q+wT3l7zE2IBGRbFByagqqnIqrUnIqnkyVU3FNm45u4vGvvgZgcIMq9Knbx+CIRESyR8mpKah3XlyVhvWKJ9O1WVzP2atnuf+T+4lPTqJDRXit1e1GhyQikm1KTk1BvfPiqlQ5FU+ma7O4nskbJnMq9hSVi0bwcUfw9oo3OiQRkWxTcmoK6p0XV6XKqXgyXZvFtRy/fJyZv84E4NWWncnvB7o+i4iZKDk1BU2IJK5KlVPxZKqcimuZtH4SV5OuUq9EPe67reG1rUpORcQ8fIwOQG4mBYi79lwNIHE1qpyKJ1NyKq7j6IWjvL31bQBebv4yFkvqcF5dn0XEPFQ5dXlxaZ6rciquRpVT8WQa1iuuY/y68SQkJ9CkdBNalGnBf+elklMRMQ8lpy4vbaNfvfPiapSciidT5VRcw99n/+aDbR8AMKH5BCwWC/+dl7o+i4h5KDl1eal/VPwAbyMDEcmEhvWKJ1PlVFzDuLXjSLYm06Z8G+4sdee1rbo+i4j5KDl1eZoMSVyZKqfiyVQ5FePtPrWbBTsWADCh2YQ07yg5FRHzUXLq8lIb/Wr8iCtS40c8mSqnYrwxa8ZgxcqDlR6kdvHaad7R9VlEzEfJqctT5VRcmSqn4slUORVjbYvZxue7P8eChXHNxl33bup5mQgk53FkIiI5o+TU5alyKq5ME26IJ1PnoRhr9OrRADxa7VGqFqt63btp2w2qnoqIOSg5dXkaNiauTEsViCdT56EYZ9PRTXy37zu8Ld6MbTI2kz2UnIqI+Sg5dXkaNiauTMN6xZPp+izGeWH1CwD0uKMHFYpUyGQPL8D/2nNdo0XEHJScujxVTsWVacIN8WS6PosxVh1cxaqDq/Dz9uPFJi/eYE9do0XEXJScujzd0ySuTJVT8WSqnEres1qtvLDKVjV9utbTlCpY6gZ7KzkVEXNRcurydE+TuDI1fMSTqXIqeW/ZvmVs+mcTgT6BjLxr5E321jVaxFlmzZpFmTJlCAgIoHbt2qxfvz7LfWNiYujSpQsVK1bEy8uLQYMGZdinadOmWCyWDI927drl4m/h+pScujw1fsSVqXIqnkyVU8lbKdYU+wy9/ev1J6JAxE0+oeRUxBkWL17MoEGDGDVqFNu2beOuu+6ibdu2HDlyJNP94+PjCQ0NZdSoUdSoUSPTfZYsWUJMTIz9sWvXLry9vXn44Ydz81dxeYYmp5MmTaJu3boUKFCAYsWK8cADD/Dnn3+m26dHjx4ZehQaNGhgUMRGUONHXJmWkhFPptsuJG8t2bOEbce3UcCvAM83fj4bn1ByKuIMU6dOpWfPnvTq1YvKlSszffp0IiMjmT17dqb7R0VFMWPGDLp3707BggUz3adw4cKEh4fbHytWrCAoKMjjk1MfI7987dq19OvXj7p165KUlMSoUaNo3bo1u3fvJl++fPb92rRpw9y5c+2v/fz8HP6upKQkEhMTnRJ33orH9selALaFtEVciT/pF3oX8RTJgDe2898Xnf+S25JTkhm9ylY1HVBvAMG+wdlo1xTEdo5eReeoiE1SUhIAly5d4uLFi/bt/v7++Pv7Z9g/ISGBrVu3Mnz48HTbW7duzcaNG50W15w5c+jcuXO6HMgTGZqc/vDDD+lez507l2LFirF161buvvtu+3Z/f3/Cw8Nv6bs2bdpEUJAZe7ebXHsALDMyEJEsLLr2U+eneJrUc/8XQ6MQz7D67Gr2ntlLfu/8VLlQhWXLsnPN/d+1B+gaLWITG2sb7VWlSpV028eMGcPYsWMz7H/69GmSk5MJCwtLtz0sLIzjx487JabffvuNXbt2MWfOHKccz8wMTU6vd+HCBcBW5k5rzZo1FCtWjJCQEJo0acLLL79MsWLFHDp2w4YNKVGihNNizTtPAZ8CE/jvD4yIq7gMpP5/dQIIMDAWkbx0Cih/7fk5NIWD5KbE5ESGvDMEgOF3DefhRtkd9tcV+BaYBjyZS9GJmMuxY8cA2L17d7rcILOqaVoWiyXda6vVmmFbTs2ZM4eqVatSr149pxzPzFwmObVarQwZMoQ777yTqlWr2re3bduWhx9+mNKlS3Pw4EFGjx5N8+bN2bp1a6YnUXx8PPHx8fbXly5dAsDHxwdfX9/c/0Wc7hK24TipQ8dEXEkB/ruXKeHaaxFPkIjt3A/ANrxdJPfM3TGXA+cPUCxfMQY1HORAe8YH23kai9oQIjY+Prb0p0CBAgQHB990/6JFi+Lt7Z2hSnry5MkM1dSciI2N5ZNPPmHcuHG3fCx34DJdvf3792fHjh0sWrQo3fZOnTrRrl07qlatSvv27fn+++/566+/+O677zI9zqRJkyhYsKD9cX3J3ny0lIy4Mh/+a/Bowg3xJJpJXfJGXFIc49eNB2DknSPJ5+fI/WiaEEnkVvn5+VG7dm1WrFiRbvuKFSto1KjRLR//008/JT4+nm7dut3ysdyBSySn//vf/1i6dCmrV6+mZMmSN9w3IiKC0qVLs2/fvkzfHzFiBBcuXLA/du/enRsh5yE1gMTVaTkZ8USaSV3yxrtb3+Wfi/9QMrgkz9R5xsFPKzkVcYYhQ4bw/vvv88EHH7Bnzx4GDx7MkSNH6N27N2DLP7p3757uM9HR0URHR3P58mVOnTpFdHR0pnnJnDlzeOCBByhSpEie/C6uztBhvVarlf/97398+eWXrFmzhjJlytz0M2fOnOHo0aNERGS+ttf1M22lnYXLnNQAElcXCFxAyal4Fi0jI7nvSsIVXl7/MgCj7x5NgI+j9/UrORVxhk6dOnHmzBnGjRtHTEwMVatWZdmyZZQuXRqAmJiYDGue1qxZ0/5869atLFy4kNKlS3Po0CH79r/++ouff/6Z5cuX58nvYQaGJqf9+vVj4cKFfP311xQoUMA+lrtgwYIEBgZy+fJlxo4dy//93/8RERHBoUOHGDlyJEWLFuXBBx80MvQ8pMqpuLrUc1ONH/EkuuVCct+bv73JySsnKVuoLE/c8UQOjqDkVMRZ+vbtS9++fTN9b968eRm2Wa3Wmx7ztttuy9Z+nsTQ5DR14dqmTZum2z537lx69OiBt7c3O3fu5MMPP+T8+fNERETQrFkzFi9eTIECnjLxinrnxdVpWK94Io1qkdx1Ie4Cr254FYCxTcbi652TCY3UeSgi5mL4sN4bCQwM5Mcff8yjaFyVeufF1alnXjyRRrVI7pr2yzTOxZ2jctHKdKnWJYdHSb0+q/NQRMzBJSZEkhtRA0hcnSqn4olUOZXccyb2DFM3TQVgXLNxeHt55/BI6jwUEXNRcury1AASV6fGj3gidRxK7pm8YTKXEi5xR/gddKzc8RaOpOuziJiLklOXloRtoXdQA0hclyqn4onUcSi5I+ZSDG/89gYAE5pNwMtyK001JaciYi5KTl1a2j8mSk7FVemeJvFEmqxOcsfE9RO5mnSVhiUbcm+Fe2/xaEpORcRclJy6tLSNfUfXNhPJK5oNUjyRJqsT5zt8/jDvbH0HgAnNJ2CxWG7xiEpORcRclJy6tLTDxm71D5RIbtGwXvFEGtYrzjd+3XgSUxJpXqY5zcs0d8IR1XkoIuai5NSlqWdezEA98+KJNCGSONe+M/uYFz0PsN1r6hy67UJEzEXJqUtT40fMQJVT8USqnIpzjV07lmRrMu0qtKNhZEMnHVWdhyJiLkpOXZom3BAzUONHPJE6D8V5dp3cxaKdiwAY32y8E4+s67OImIuSU5emYb1iBqqciidS5VSc58XVL2LFykNVHqJmRE0nHjn1/EwEkp14XBGR3KHk1KWpcipmoORUPJGuz+IcW/7dwpd7v8TL4sW4puOcfPS0nSeqnoqI61Ny6tJUORUz0LAx8US6PotzjF49GoCu1bpSObSyk4+u5FREzEXJqUvTPU1iBqqciifSsF65dT8f+Zkf/v4BHy8fxjQZkwvf4AX4XXuu5FREXJ+SU5emxo+YgSqn4onUeSi3xmq1MmrVKACevONJyhUul0vfpA5EETEPJacuTY0fMQM1fMQTqfNQbs1PB35i3eF1+Hv7M7rJ6Fz8JnUgioh5KDl1aZpwQ8xADR/xROo8lJxLWzXtXac3JYNL5uK36RotIuah5NSlacINMQNVTsUTqXIqOffNX9+w+d/NBPkGMeLOEbn8bUpORcQ8lJy6NFVOxQyUnIqnSQHirz3X9Vkck2JNsc/QO6DeAMLyh+XyNyo5FRHzUHLq0lQ5FTNIPT8T0CLv4hnSNvJ1fRbHfPrHp+w4sYNg/2Cea/xcHnyjklMRMQ8lpy5N9zSJGaQ9P9X4EU+g5FRyJikliTFrbEvGDG04lMKBhfPgW5Wcioh5KDl1aRrWK2YQkOa5Gj/iCVI7Dv3Rn1FxxEfbP+KvM39RJLAIAxsMzKNvTW1D6PosIq5Pf1Vdmob1ihl48V+CqvtOxRNoMiRxXEJyAi+tfQmA4XcOJ9g/OI++OfU81fVZRFyfklOXpsqpmIWGjYkn0S0X4rj3f3+fwxcOE54/nL51++bhN+v6LCLmoeTUpalyKmahGXvFk6hyKo6JTYxlwroJALxw1wsE+eZlx4aSUxExDyWnLk2982IWSk7Fk2hUizhm9ubZxFyOoVTBUvSq1SuPv13JqYiYh5JTl6beeTELNX7Ek2hUi2TfpfhLvLLhFQDGNBmDv49/Hkeg67OImIeSU5emyqmYhSqn4knUcSjZN+PXGZyOPU2FwhXoXqO7AREoORUR81By6tI0dEzMQo0f8STqOJTsOXf1HFM2TgHgpaYv4ePlY0AUWkpGRMxDyanLsqKhY2IeqpyKJ1HlVLLntY2vcSH+AlWLVaVT1U4GRaGlZETEPJScuqxEIOXac/XOi6tT5VQ8iSqncnMnr5xk5q8zARjfbDxeFqOaXLo+i4h5KDl1WWl7ONU7L65OlVPxJKqcys1NXD+RK4lXqFO8DvdXvN/ASJScioh5KDl1WamNfC/Az8hARLJByal4Es0HIDd29MJRZm+ZDcDLzV/GYrEYGI2SUxExDyWnLitt48fIP2oi2aHGj3gSzQcgNzZ+3XgSkhO4u/TdtCrbyuBodH0WEfNQcuqy1PgRM1HlVDyJhvVK1v4++zcfbPsAcIWqKSg5FREzUXLqsjRsTMxEjR/xJJoQSbI2Zs0Ykq3JtC3fljtL3Wl0OOj6LCJmouTUZalyKmaiyql4ElVOJXO7Tu5i0c5FAExoPsHgaFLp+iwi5qHk1GWpZ17MRD3z4kl0fZbMjV49GitWHqryELUiahkdzjW6PouIeSg5dVka1itmop558SSqnEpGvx37ja/2foWXxYtxTccZHU4aqedpIpBsZCAiIjel5NRlaVivmImSU/Ek6jyUjF5Y9QIA3ap3o3JoZYOjSSttO0LVUxFxbUpOXZYaP2ImGjYmnkSdh5LemkNrWHFgBb5evoxtMtbocK6j5FREzEPJqctS40fMRJVT8SQa1iv/sVqtjFo1CoBetXpRplAZgyO6nhfgd+25klMRcW1KTl2WKqdiJqqciifRhEjyn+///p6NRzcS4BPAC3e/YHQ4WdA1WkTMQcmpy1LlVMxElVPxJKqcik2KNcV+r2n/uv0pXqC4wRFlJfUareRURFybklOXpZ55MZPURrqSU/EEuj6LzZI9S9h2fBsF/Arw/J3PGx3ODegaLSLmoOTUZWlYr5iJeuXFU1iBuGvPVTn1ZMkpyYxePRqAwQ0GUzSoqMER3YiG9YqIOSg5dVka1itmkpqcJmFbS0/EXcWlea7OQ0+2YMcC9p7eS6GAQgxpOMTocG5CyamImIOSU5elyqmYiZYqEE+RdlikOg89VUJyAmPXjgVg+J3DKRhQ0NiAbkrJqYiYg5JTl6XKqZiJP2C59lz3NIk7S23c+wLeRgYiBnr/9/c5dP4Q4fnD6V+vv9HhZIOSUxExByWnLksTboiZWFDjRzyDrs2eLjYxlgnrJgAw6q5RBPma4VzQ9VlEzEHJqcvSsF4xGy0nI55Ay8h4ulmbZxFzOYbSBUvzVK2njA4nmzRpnYiYg5JTl6VhvWI2WqpAPIEqp57sYvxFJv08CYAxTcbg7+NvcETZpeuziJiDj6MfOHToEOvXr+fQoUPExsYSGhpKzZo1adiwIQEBAbkRo4dS5VTMRj3z4glUOfVk0zZN4+zVs1QsUpHHajxmdDgO0LBeETGHbCenCxcuZObMmfz2228UK1aMEiVKEBgYyNmzZ9m/fz8BAQF07dqV559/ntKlS+dmzB5ClVMxGw3rFU+gjkNPdSb2DK9veh2Acc3G4ePlcP++gZSciog5ZOvKWqtWLby8vOjRoweffvoppUqVSvd+fHw8mzZt4pNPPqFOnTrMmjWLhx9+OFcC9hxqAInZqPEjnkAdh55q8obJXEq4RI2wGjxU5SGjw3GQrs8iYg7ZSk7Hjx9Pu3btsnzf39+fpk2b0rRpUyZMmMDBgwedFqDnUgNIzEaVU/EEGtbriWIuxfDGb28AMKH5BLwsZpuyQ8mpiJhDtpLTGyWm1ytatChFixbNcUACYEWVUzEfNX7EE2hCJE/08vqXuZp0lYYlG9KuQvbbRK5D12cRMYdsd/39+++/DB06lIsXL2Z478KFCzz33HOcOHHCqcF5rrg0z9UAErNQ5VQ8gSqnnubQ+UO8u/VdAF5u/jIWi8XgiHJCE9aJiDlkOzmdOnUqFy9eJDg4OMN7BQsW5NKlS0ydOtWpwXmutI17NYDELJSciidQ5dTTvLT2JRJTEmlRpgXNyjQzOpwcUuVURMwh28npDz/8QPfu3bN8v3v37nz77bdOCUpS/3j4koPVfkQMosaPeAJVTj3J3tN7+XD7h4CtampeWudURMwh28npwYMHM8zSm1bJkiU5dOiQQ18+adIk6tatS4ECBShWrBgPPPAAf/75Z7p9rFYrY8eOpXjx4gQGBtK0aVP++OMPh77HfDQZkpiRKqfiCTQfgCcZtWoUKdYUOlTsQP2S9Y0O5xao81BEzCHbyWlgYOANk89Dhw4RGOhYMrV27Vr69evHL7/8wooVK0hKSqJ169ZcuXLFvs/kyZOZOnUqb775Jps3byY8PJxWrVpx6dIlh77LXDRsTMxIjR/xBOo89BS/HfuNJXuWYMHCxOYTjQ7nFun6LCLmkO3ktH79+nz00UdZvv/hhx9Sr149h778hx9+oEePHtx+++3UqFGDuXPncuTIEbZu3QrYqqbTp09n1KhRdOzYkapVqzJ//nxiY2NZuHChQ99lLuqZFzNS5VQ8gYb1egKr1crwn4YD0L1Gd24vdrvBEd0qJaciYg7ZTk6HDh3K3LlzGTp0aLpZeU+cOMGzzz7LvHnzGDp06C0Fc+HCBQAKFy4M2IYSHz9+nNatW9v38ff3p0mTJmzcuPGWvsu1qWdezEiNH/EEGtniCX468BOrD63Gz9uPl5q+ZHQ4TqDrs4iYQ7Zn22nWrBlvvfUWAwcOZNq0aQQHB2OxWLhw4QK+vr688cYbNG/ePMeBWK1WhgwZwp133knVqlUBOH78OABhYWHp9g0LC+Pw4cOZHic+Pp74+Hj7a3MO/1XlVMxIlVPxBKqcursUawojVo4AoG+dvpQOKW1wRM6g5FREzMGhqWCfeeYZ7rvvPj799FP+/vtvrFYrt912Gw899BAlS5a8pUD69+/Pjh07+PnnnzO8d/2aYlarNct1xiZNmsRLL5m9l1OVUzEjJafiCVQ5dXdf7P6CrTFbye+Xn5F3jTQ6HCfROqciYg4Or1NSokQJBg8e7NQg/ve//7F06VLWrVuXLskNDw8HbBXUiIgI+/aTJ09mqKamGjFiBEOGDLG/PnbsGFWqVHFqvLlPjR8xI/XMiydQ5dSdJSYnMmrVKACGNhxKaL5QgyNyltTzNQFIBrwNjEVEJGvZvuc0N1itVvr378+SJUtYtWoVZcqUSfd+mTJlCA8PZ8WKFfZtCQkJrF27lkaNGmV6TH9/f4KDg+2PAgUK5OrvkDs0rFfMSJVT8QS6PruzudFz2Xd2H6FBoQxpOOTmHzCNtJ0p6kAUyYlZs2ZRpkwZAgICqF27NuvXr89y35iYGLp06ULFihXx8vJi0KBBme53/vx5+vXrR0REBAEBAVSuXJlly5bl0m9gDg5XTp2pX79+LFy4kK+//poCBQrY7zEtWLAggYGBWCwWBg0axMSJE6lQoQIVKlRg4sSJBAUF0aVLFyNDz2Ua1itmpMqpeAJdn91VbGIsL6213Rb0wt0vUMDfjJ3bWbk+Oc1vVCAiprR48WIGDRrErFmzaNy4Me+88w5t27Zl9+7dlCpVKsP+8fHxhIaGMmrUKKZNm5bpMRMSEmjVqhXFihXj888/p2TJkhw9etSkhTXnMTQ5nT17NgBNmzZNt33u3Ln06NEDgGHDhnH16lX69u3LuXPnqF+/PsuXL3fz/3DqmRczUuVUPIGG9bqrN397k38v/UvpgqV5pvYzRofjZF6AH7ZhvepAFHHU1KlT6dmzJ7169QJg+vTp/Pjjj8yePZtJkyZl2D8qKooZM2YA8MEHH2R6zA8++ICzZ8+yceNGfH19AShd2h0mYLs1hianVqv1pvtYLBbGjh3L2LFjb+m7kpKSSExMvKVj5J14bA2f/IBZYhbxx3beWtF5K+4rBdt57o/Oc/dx7uo5Jv1sa2C+ePeLeFm9TNRmyK5CwEXgMjp3xZMlJSUBthU9Ll68aN/u7++Pv79/hv0TEhLYunUrw4cPT7e9devWt7S05dKlS2nYsCH9+vXj66+/JjQ0lC5duvD888/j7e2594UbmpzmpU2bNhEUZJZKZB1g0bXnnj3uXMxG5624u7eu/Txw7SHu4KN/P+J83HlKBZQi5EgIy4664zXsnWs/9117iHim2FjbCK/rJ0wdM2ZMpsWw06dPk5ycnOnSlqm3JObEgQMHWLVqFV27dmXZsmXs27ePfv36kZSUxIsvvpjj45pdtpLTQoUKZbl0y/XOnj17SwHlloYNG1KiRAmjw8imQcBcYCTwvLGhiGTbCeA2wAKcu/ZTxJ1YgZBrz/cBxYwLRZzm30v/8ujsRwGYdt802t/W3uCIcksN4BCwAqhnbCgiBjp27BgAu3fvTpcbZFY1TcuRpS2zIyUlhWLFivHuu+/i7e1N7dq1+ffff3nttddumpweOnSI9evXc+jQIWJjYwkNDaVmzZo0bNiQgICAHMfkCrKVnE6fPj2Xw8h9Pj4+9vHcru8itntCAgCzxCxSgP/uZbJiu79JxJ3E8d85XgBdn93DKxtf4WrSVRpFNuLBKg/eUmPTtXlhO3/j0LkrnszHx5b+FChQgODg4JvuX7RoUby9vTNUSW+0tGV2RERE4Ovrm24Ib+XKlTl+/DgJCQn4+WVsRy1cuJCZM2fy22+/UaxYMUqUKEFgYCBnz55l//79BAQE0LVrV55//nnT3r+areT08ccfz+04JB1NiCRmlPZ8jcV2T56IO0k7kYwmRHIH+87s473f3wPglRavuHFiCppRXSRn/Pz8qF27NitWrODBBx+0b1+xYgX3339/jo/buHFjFi5cSEpKCl5ettU9//rrLyIiIjJNTGvVqoWXlxc9evTg008/zTBLcHx8PJs2beKTTz6hTp06zJo1i4cffjjH8RklR/ecJicn89VXX7Fnzx4sFgtVqlShQ4cOHn3zrnNpqQIxI19sC7snY2v8FDI2HBGnS702+6DKk3t4cc2LJFuTubfCvdxV+i6jw8llSk5FcmrIkCE89thj1KlTh4YNG/Luu+9y5MgRevfuDcCIESM4duwYH374of0z0dHRAFy+fJlTp04RHR2Nn5+f/V7XPn368MYbbzBw4ED+97//sW/fPiZOnMiAAQMyjWH8+PG0a9cuyxj9/f1p2rQpTZs2ZcKECRw8eNBJv33ecjg5/fvvv7n33ns5duwYFStWxGq18tdffxEZGcl3331HuXLlciNOD6PKqZhVEHAJLScj7knLyLiTbTHb+GTXJwBMbD7R4GjygpJTkZzq1KkTZ86cYdy4ccTExFC1alWWLVtmHzobExPDkSNH0n2mZs2a9udbt25l4cKFlC5dmkOHDgEQGRnJ8uXLGTx4MNWrV6dEiRIMHDiQ55/PfL6ZGyWm1ytatChFixZ18Ld0DQ4npwMGDKBcuXL88ssvFC5cGIAzZ87QrVs3BgwYwHfffef0ID2PKqdiVoHYklM1fsQdpV6b1XHoDkauGglAl2pdqBFew+Bo8oKSU5Fb0bdvX/r27Zvpe/PmzcuwLTtLZjZs2JBffvnlVkNzKw4np2vXrk2XmAIUKVKEV155hcaNGzs1OM+lBpCYVeo5q8qpuCNVTt3FmkNr+OHvH/Dx8mFc03FGh5NHlJyKmJ2Xl9dN7423WCz2tVzNyOHk1N/fn0uXLmXYfvny5Uxv3pWc0LBeMSslp+LO1HHoDqxWK8N/Gg7AM7WfoVxhT7kdKfW8VXIqYlZffvlllu9t3LiRN954I1sVW1fmcHJ633338fTTTzNnzhzq1bOtk/Xrr7/Su3dvOnTo4PQAPZOG9YpZqWde3Jkqp+7g6z+/5tdjvxLkG8QLd79gdDh5SNdnEbPLbHbgvXv3MmLECL755hu6du3K+PHjDYjMebwc/cDMmTMpV66cfZHXgIAAGjduTPny5ZkxY0ZuxOiBVDkVs1LlVNyZrs1ml5ySzMiVtntNBzcYTHj+cIMjykupyamuzyLu4N9//+Wpp56ievXqJCUlER0dzfz58zMsMWM2DldOQ0JC+Prrr9m3bx979+7FarVSpUoVypcvnxvxeShVTsWs1DMv7kzXZrP7aMdH7Dm9h8KBhXmu0XNGh5PHdH0WcQcXLlxg4sSJvPHGG9xxxx2sXLmSu+5yn6WwcrTOKUCFChWoUKGCM2MRwLZGZMK15+qdF7NR5VTcmSqnZhaXFMeYNWMAGHHnCAoGFDQ4orym5FTE7CZPnsyrr75KeHg4ixYtynSYr9k5nJxarVY+//xzVq9ezcmTJ0lJSUn3/pIlS5wWnGdK+0dDvfNiNmr8iDtT5dTMZm+ezZELRygZXJJ+dfsZHY4BdH0WMbvhw4cTGBhI+fLlmT9/PvPnz890PzPnYw4npwMHDuTdd9+lWbNmhIWF3XQ6Y3FU2oqTGkBiNqqcijvThEhmdTH+Ii+vfxmAsU3GEujrif8NlZyKmF337t3dPvdyODldsGABS5Ys4d57782NeMT+RyOAHMxXJWIwJafizrSUjFm9vvF1zlw9Q8UiFXn8jseNDscgSk5FzG7evHlGh5DrHM5+ChYsSNmyZXMjFgE0bEzMTY0fcWeqnJrRicsneH3T6wC83PxlfLxyPN2GyWmdUxFxfQ4np2PHjuWll17i6lVd3HKHJtwQM1PlVNyZKqdmNHbNWK4kXqFeiXp0rNzR6HAMpKVkRMT1Odx9+PDDD7No0SKKFStGVFQUvr6+6d7//fffnRacZ1LlVMxMlVNxZ6qcms2eU3t47/f3AJjSaorb36t1Y7o+i4jrczg57dGjB1u3bqVbt26aEClXqGdezEyVU3FnGtliNs//9DzJ1mQeqPQAd5V2n3UAc0bJqYi4PoeT0++++44ff/yRO++8MzfiETV+xNTU+BF3ppEtZrLm0Bq++esbvC3evNLiFaPDcQG6PouI63M4OY2MjCQ4ODg3YhFAjR8xN1VOxZ1pWK9ZpFhTGLp8KAC96/SmYtGKBkfkCpScipjZ0qVLs71vhw4dcjGS3OVwcvr6668zbNgw3n77baKionIhJE+nyqmYmZJTcWe67cIsFu1cxNaYrRTwK8CYJmOMDsdFKDkVMbMHHnggW/tZLBaSk5NzN5hc5HBy2q1bN2JjYylXrhxBQUEZJkQ6e/as04LzTKqcipmp8SPuTJVTM4hLimPkqpEAjLhzBKH5Qg2OyFWkdqokAMmAt4GxiIijUlJSjA4hTzicnE6fPj0XwpD/qHIqZqbKqbgzVU7NYOavMzly4Qglg0syqMEgo8NxIWk7Va4C+Y0KREQkSw4np48//nim2xMTE4mPj7/lgESNHzEzVU7Fnaly6upOx55m4vqJALzc/GUCffXf6j9KTkXcydq1a5kyZQp79uzBYrFQuXJlnnvuOe66y9wzk3s5+oEjR45k+vjmm2+oXLlybsToYTSsV8xMlVNxZxrZ4urGrx3PhfgL3BF+B92qdzM6HBfjBfhde64ORBEzW7BgAS1btiQoKIgBAwbQv39/AgMDadGiBQsXLjQ6vFvicOU0Kioqy7VNa9asecsBiRo/YmaqnIo7U+ehK9t3Zh+ztswCYEqrKXhZHO5/9wCB2O451TVaxMxefvllJk+ezODBg+3bBg4cyNSpUxk/fjxdunQxMLpb43Byum3btnSvk5OT2b9/P+PGjWPAgAFOC8xzqfEjZpbaqXIVSCEHgzNEXJQVDet1bSNWjiApJYl7K9xLi7ItjA7HRQUCF1ByKmJuBw4coH379hm2d+jQgZEjRxoQkfM4nJzWqFEjw7ZatWpRsmRJOnbsSPfu3Z0SmOdS5VTMLO15G4fOY3EfCdg6XEDntevZeHQjX+z5Ai+LF5NbTjY6HBem0S0i7iAyMpKVK1dSvnz5dNtXrlxJZGSkQVE5h8PJaVYKFy6sCZGcQpVTMbPrJ9xQI17cRdrGvK7PrsRqtTJ0+VAAetbsye3Fbjc4IleWdnSLiJjVs88+y4ABA4iOjqZRo0ZYLBZ+/vln5s2bx4wZM4wO75Y4nJxevHgxw7bjx48zYsQIBg4cmO794ODgW4vOI2m2XjEzb2wTbiRgO5eLGBuOiNOkXpu9Ad8b7Sh57Is9X7Dpn03k883HS01fMjocF6fKqYg76NOnD+Hh4bz++ut8+umnAFSuXJnFixdz//33GxzdrXE4OQ0JCcl0QiSr1cpXX33FuHHjsFqtWCwWkpOTnRKkZ9GwXjE7Tbgh7ijt/aaZTwooeS8hOYHhPw0H4LlGzxFRIMLgiFxdanKqGdVFzO7BBx/kwQcfNDoMp3M4OV29enVuxCF2GtYrZheEbcINNX7Enajj0BXN3jyb/ef2E54/nGcbPWt0OCagyqmIu7l8+TIpKSnptpl59KrDyWmTJk1yIw6xUwNIzE73NIk7Usehqzkfd55x68YBML7ZePL75Tc4IjNQciriDg4ePEj//v1Zs2YNcXFx9u3uMHo1W8nppk2baNiwYbYOeOXKFQ4dOsTtt2tCgpxRA0jMTsPGxB1pGRlXM3H9RM5ePcvtobfzxB1PGB2OSSg5FXEHXbt2BeCDDz4gLCws01suzSpbyWn37t2Jioriqaee4t577yV//oy9k7t372bBggXMnTuXyZMnKznNMVVOxexSz10lp+JONFmdKzl0/hAzfrXNSPlaq9fw9vI2OCKzUHIq4g527NjB1q1bqVixotGhOF22ktPdu3fzzjvv8OKLL9K1a1duu+02ihcvTkBAAOfOnWPv3r1cuXKFjh07smLFCqpWrZrbcbsxNYDE7NT4EXekyqkrGbVqFAnJCbQo04I25dsYHY6J6Pos4g7q1q3L0aNHPTc59fX1pX///vTv35/ff/+d9evXc+jQIa5evUqNGjUYPHgwzZo1o3DhwrkdrwfQsF4xO1VOxR2p49BVbPl3Cwt3LsSChSmtp7jVcLbcpzkBRNzB+++/T+/evTl27BhVq1bF1zf9EmfVq1c3KLJb5/CESLVq1aJWrVq5EYuQCKTewKwGkJiVeubFHaly6gqsVitDlw8F4LEaj3FH+B3GBmQ6mhNAxB2cOnWK/fv388QT/91vb7FYPGdCJMkraf9YqAEkZqXKqbgjzQfgCr756xvWHl5LgE8AE5pNMDocE1LnoYg7ePLJJ6lZsyaLFi3yzAmRJK+k/rGwAP5GBiJyC5ScijvSLRdGS0xOZNiKYQAMbjCYyIKRBkdkRkpORdzB4cOHWbp0KeXLlzc6FKfzMjoASStt48d9ekDE06jxI+5Iw3qN9v7v7/PnmT8JDQpl+J3DjQ7HpHR9FnEHzZs3Z/v27UaHkStUOXUpmnBD3IEqp+KOdH020oW4C4xZMwaAMU3GEOwfbHBEZqXkVMQdtG/fnsGDB7Nz506qVauWYUKkDh06GBTZrXNKcnr+/HlCQkKccSgPp3uaxB2o8SPuSJVTI41fN55TsaeoWKQiT9d+2uhwTEzXZxF30Lt3bwDGjRuX4T2zT4jk8LDeV199lcWLF9tfP/LIIxQpUoQSJUq4bXk57+ieJnEHqpyKO1Ll1Ch7T+9lxq8zAJjeZjq+3r43+YRkTUvJiLiDlJSULB9mTkwhB8npO++8Q2SkbRKCFStWsGLFCr7//nvatm3Lc8895/QAPYsqp+IO1DMv7kiVUyNYrVYG/jCQpJQkOlTsQJvybYwOyeS0lIyIOzt16hSLFi0yOoxb4vCw3piYGHty+u233/LII4/QunVroqKiqF+/vtMD9CyqnIo7UOVU3JE6D43wzV/fsHz/cvy8/ZjaeqrR4bgBdR6KuIPMhvMC7N+/nyVLlvDoo4/mcUTO43ByWqhQIY4ePUpkZCQ//PADEybY1hmzWq2mLyMbT40fcQdKTsUdqfMwr8UlxTH4x8EAPNvwWcoVLmdwRO5AyamIO/jyyy/TvU5OTubo0aNcvHiR8ePHGxSVczicnHbs2JEuXbpQoUIFzpw5Q9u2bQGIjo52y7V28pbuaRJ3oMaPuCMN681rUzdN5cC5AxQvUJyRd400Ohw3oeuziDvYtm1bhm1JSUkMGjSI3bt3GxCR8zicnE6bNo2oqCiOHj3K5MmTyZ8/P2Ab7tu3b1+nB+hZ1DMv7kCVU3FH6jzMS/9c/IeX178MwGutXiO/X36DI3IXSk5F3JWPjw+DBg2ievXqRodySxxOTn19fRk6dGiG7YMGDXJGPB5Ow3rFHajxI+5IldO8NGzFMGITY2kc2ZhHq5r33inXk3r+JgDJgLeBsYiIsx0+fJgyZcoYHcYtyVZyunTpUtq2bYuvry9Lly694b5mXvTVeKqcijtQ5VTckSqneWX94fUs2rUICxbeaPsGFovF6JDcSNrzNw7IZ1QgInILZs6cmWHb8ePHmTt3Lu3bt0/3/oABA/IytFuWreT0gQce4Pjx4xQrVowHHnggy/3Mvuir8VQ5FXegyqm4I1VO80JySjIDfrA1pJ6u/TQ1I2oaHJG7SXv+XkXJqYg5TZs2LdPtAQEB9qU+wZabuWVympKSkulzcTZVTsUdqHIq7kidh3nh/d/fJ/p4NCEBIUxoPsHocNyQF+CHbVivrtEiZnXw4EGjQ8g1Xs44yPnz551xGNGwMXELqedv6j1NIu5AnYe57ezVs4xaNQqA8c3GUzSoqMERuSuNbhER1+Vwcvrqq6+yePFi++uHH36YwoULU6JECbZv3+7U4DyPeubFHVw/bEzE7BL5r6NFyWluGbN6DGeunqFqsar0rtPb6HDcmJJTETPq3bs3R48ezda+ixcv5uOPP87liHKHw8npO++8Q2RkJAArVqzgp59+4ocffqBt27Y899xzTg/Qs6hnXtxBQJrnGjYm7iDteazOw9yw88ROZm2ZBcDMNjPx8XJ4MQHJNiWnImYUGhpK1apVadu2LbNnz2bz5s0cO3aMM2fO8Pfff7N06VKGDRtGqVKlmD59ummXlHH46h8TE2NPTr/99lseeeQRWrduTVRUFPXr13d6gJ5FlVNxB17YEtQ41PgR95B6Hluw3a8nzmS1WhnwwwBSrCk8VOUhmpVpZnRIbk7JqYgZjR8/nv/973/MmTOHt99+m127dqV7v0CBArRs2ZL333+f1q1bGxTlrXO4clqoUCF7SfmHH36gZcuWgO2Pi6Mz9a5bt4727dtTvHhxLBYLX331Vbr3e/TogcViSfdo0KCBoyGbiCqn4i40KZK4k7TzAWhZE2f7fPfnrDm0hgCfAKa0mmJ0OB5AyamIWRUrVowRI0awfft2zpw5w++//86GDRv4888/OXfuHJ9//rmpE1PIQeW0Y8eOdOnShQoVKnDmzBnatm0LQHR0NOXLl3foWFeuXKFGjRo88cQT/N///V+m+7Rp04a5c+faX/v5uXOvtSqn4i7U+BF3omVkcktsYizPLn8WgOGNh1M6pLTBEXmC1DaGrs8iZhYSEkJISIjRYTidw8nptGnTiIqK4ujRo0yePJn8+fMDtuG+ffv2dehYbdu2tSe3WfH39yc8PNzRME1Ks/WKu1DlVNyJOg5zy6s/v8rRi0cpXbA0wxoPMzocD5HayaLrs4i4HoeTU19fX4YOHZph+6BBg5wRTwZr1qyhWLFihISE0KRJE15++WWKFSuWK99lPA3rFXeh5FTcia7NueHQ+UNM3jgZgNdbv06gr/5984ZGtoiI68rROqcfffQRd955J8WLF+fw4cMATJ8+na+//tqpwbVt25aPP/6YVatW8frrr7N582aaN29OfHx8lp+Jj4/n4sWL9selS5ecGlPuUu+8uAs1fsSdaFhvbhi6fChxSXE0L9OcjpU7Gh2OB9H1WURcl8PJ6ezZsxkyZAht27bl/Pnz9kmQQkJCmD59ulOD69SpE+3ataNq1aq0b9+e77//nr/++ovvvvsuy89MmjSJggUL2h9VqlRxaky5S73z4i5UORV3olsunG3lgZV8secLvC3ezGgzA4tFE03lHSWnIuK6HE5O33jjDd577z1GjRqFt7e3fXudOnXYuXOnU4O7XkREBKVLl2bfvn1Z7jNixAguXLhgf+zevTtXY3IeK6qcivtQ40fciSqnzpSYnMjAHwYC0K9uP6oWq2pwRJ5G12cRd5CUlMRPP/3EO++8Yx8p+u+//3L58mWDI7s1Dt9zevDgQWrWrJlhu7+/P1euXHFKUFk5c+YMR48eJSIiIst9/P398ff3t7++ePFirsbkPPHYElRQcirmp8qpuBNVTp1p9pbZ/HHqD4oGFWVs07FGh+OBlJyKmN3hw4dp06YNR44cIT4+nlatWlGgQAEmT55MXFwcb7/9ttEh5pjDldMyZcoQHR2dYfv333/v8BDay5cvEx0dbT/ewYMHiY6O5siRI1y+fJmhQ4eyadMmDh06xJo1a2jfvj1FixblwQcfdDRsE0jbiFfvvJidGj/iTlQ5dZZTV07x4uoXAZjYfCKFAgsZHJEn0lIyImY3cOBA6tSpw7lz5wgM/O9v04MPPsjKlSsNjOzWOVw5fe655+jXrx9xcXFYrVZ+++03Fi1axKRJk3j//fcdOtaWLVto1qyZ/fWQIUMAePzxx5k9ezY7d+7kww8/5Pz580RERNCsWTMWL15MgQIFHA3bBFL/SPgAvkYGIuIEqpyKO9EtF84yatUoLsRfoFZELZ6s+aTR4XgoLSUjYnY///wzGzZswM/PL9320qVLc+zYMYOicg6Hk9MnnniCpKQkhg0bRmxsLF26dKFEiRLMmDGDzp07O3Sspk2bYrVas3z/xx9/dDQ8E9NkSOJOlJyKO9H12Rk2Hd3E+7/bOrFntpmJt5f3TT4huUMjW0TMLiUlxT4pbVr//POP6Yt4OVpK5qmnnuLw4cOcPHmS48ePc/ToUXr27Ons2DyMeubFnajxI+5Ew3pvVXxSPD2X9sSKlR539KBxqcZGh+TBdH0WMbtWrVqlWyXFYrFw+fJlxowZw7333mtcYE7gcOU0raJFizorDlHPvLgVVU7FnWhCpFs16edJ7Dm9h2L5ivF669eNDsfDKTkVMbtp06bRrFkzqlSpQlxcHF26dGHfvn0ULVqURYsWGR3eLXE4OT1x4gRDhw5l5cqVnDx5MsOw3MxKzJIdavyIO1HjR9yJKqe34o+TfzBx/UQA3mj7BoUDCxsckafT9VnE7IoXL050dDSffPIJW7duJSUlhZ49e9K1a9d0EySZkcPJaY8ePThy5AijR48mIiJCC2c7jYb1ijtR5VTciToPcyo5JZle3/QiMSWRDhU78HCVh40OSZScipjeggUL6NatG0888QRPPPFEuveee+45XnvtNYMiu3UOJ6c///wz69ev54477siFcDyZhvWKO1HjR9yJKqc5NWvzLH755xcK+BXgrXvfUoe2S9BSMiJm179/f0JCQrjvvvvSbR88eDCffPKJqZNThydEioyMvOEMu5JTqpyKO1HlVNyJrs85cfj8YUasHAHA5FaTKRlc0uCIxEadhyI5MWvWLMqUKUNAQAC1a9dm/fr1We4bExNDly5dqFixIl5eXgwaNCjDPvPmzcNisWR4xMXF3TSWTz75hG7durFu3Tr7tv/97398+umnrF69Oke/n6twODmdPn06w4cP59ChQ7kQjidT5VTciZJTcSe6PjvKarXS57s+XEm8wl2l7uLp2k8bHZLYaZ1TEUctXryYQYMGMWrUKLZt28Zdd91F27ZtOXLkSKb7x8fHExoayqhRo6hRo0aWxw0ODiYmJibdIyAg4KbxtGnThrfffpsHHniALVu20LdvX5YsWcLq1aupVKlSjn9PV5CtYb2FChVKNxTnypUrlCtXjqCgIHx9fdPte/bsWedG6DHUMy/uRD3z4k40rNdRi3Yt4vu/v8fP24/32r+HlyVHK9dJrtD1WcRRU6dOpWfPnvTq1QuwFet+/PFHZs+ezaRJkzLsHxUVxYwZMwD44IMPsjyuxWIhPDw8RzF17tyZc+fOceeddxIaGsratWspX758jo7lSrKVnKZdR8eskpKSSExMNDqMG4jD9gcjGHDlOEWyIwDb+ZyMzmcxv2Rs53MgOp9v7nTsaQZ+PxCAUXeOomzBsi7+99fT+PFfgqr/LuJ5kpKSALh06RIXL160b/f398ff3z/D/gkJCWzdupXhw4en2966dWs2btx4S7FcvnyZ0qVLk5yczB133MH48eOpWbNmpvsOGTIk0+3FihWjZs2azJo1y75t6tSptxSXkbKVnD7++OO5HUeu27RpE0FBrlyVrAKkrku0zMhARJxE57O4izHXfl5C5/PNTTs8jdNXT1M6oDS3n7+dZcv0b+Z6dH0WzxUbaxvSXqVKlXTbx4wZw9ixYzPsf/r0aZKTkwkLC0u3PSwsjOPHj+c4jkqVKjFv3jyqVavGxYsXmTFjBo0bN2b79u1UqFAhw/7btm3L9DjlypXj4sWL9vfNPvGcw7P1ent7ExMTQ7FixdJtP3PmDMWKFXPZdU4bNmxIiRIljA7jBkYCbwGDgbHGhiJyyw4ANYH8wDGDYxG5VRWAk8BG4HaDY3FtP+7/kbXRa/GyeLGw80LqFq9rdEiSwRWg+LXnMeh2IvE0x47Z2iW7d+9OlxtkVjVN6/qkz2q13lIi2KBBAxo0aGB/3bhxY2rVqsUbb7zBzJkzM+xv9omOssvh5DSrmXrj4+Px8/O75YByi4+PT4b7Y13LJWz3f/gBrhynSHbkx3Y+J6LzWczvHLbzOR86n7N2OeEy/X7oB8DA+gNpVLqRwRFJ5grw3/2mukaL5/HxsaU/BQoUIDg4+Kb7Fy1aFG9v7wxV0pMnT2aopt4KLy8v6taty759+5x2TDPKdnKamsFbLBbef/998ufPb38vOTmZdevWmX52KGNpQiRxJ6nncRJq/Ij56fqcHaNWjuLIhSNEhUQxvtl4o8ORLHlj6whPQJMiidycn58ftWvXZsWKFTz44IP27StWrOD+++932vdYrVaio6OpVq1atvbfvHkzn332GUeOHCEhISHde0uWLHFaXHkt28nptGnTANs/3Ntvv423t7f9PT8/P6Kionj77bedH6HH0FIF4k7SNuJjgYJGBSJyi1I7WEDX56z98s8vvPHbGwC8c9875PPLZ3BEcmOB2JJTLScjkh1Dhgzhscceo06dOjRs2JB3332XI0eO0Lt3bwBGjBjBsWPH+PDDD+2fiY6OBmyTHp06dYro6Gj8/Pzs97q+9NJLNGjQgAoVKnDx4kVmzpxJdHQ0b7311k3j+eSTT+jevTutW7dmxYoVtG7dmn379nH8+PF0CbQZZTs5PXjwIADNmjVjyZIlFCpUKNeC8kypfyDUMy/uwA+wAFZsPfNKTsWs0laWlJxmJiE5gV5Le2HFyuM1Hqd1udZGhyQ3FQhcQJVTkezp1KkTZ86cYdy4ccTExFC1alWWLVtG6dKlAYiJicmw5mnaWXe3bt3KwoULKV26NIcOHQLg/PnzPP300xw/fpyCBQtSs2ZN1q1bR7169W4az8SJE5k2bRr9+vWjQIECzJgxgzJlyvDMM88QERHhvF/cABZrVjeRuol//vmHyMhIjh49SsmSJY0O5waaA6uxzaDX2eBYRJwhP7aJN/YDZQ2ORSSnTgCpa9ClYOt0kbTGrR3HmDVjCA0KZU+/PRQJKmJ0SHJTZYGD2Cb5amhwLCJ5yzy5Qdby5cvHH3/8QVRUFEWLFmX16tVUq1aNPXv20Lx5c2JiYowOMcccnhApOTmZefPmsXLlSk6ePElKSkq691etWuW04DyLhvWKuwnElpyqZ17MLPX8DUSJaUa7T+1mwroJALzR9g0lpqaR2tbQ9VnEjAoXLsylS5cAKFGiBLt27aJatWqcP3/evlSOWTmcnA4cOJB58+bRrl07qlatavq1dFyHJtwQd5N6Lpv7IimeTrdcZCXFmkKvpb1ITEnkvtvu45HbHzE6JMk2JaciZnbXXXexYsUKqlWrxiOPPMLAgQNZtWoVK1asoEWLFkaHd0scTk4/+eQTPv30U+69997ciMeDqXIq7ia1Ma/Gj5hZ2sqppDVr8yw2/bOJAn4FmN1utjqrTUXJqYiZvfnmm8TFxQG2yZh8fX35+eef6dixI6NHjzY4ulvjcHLq5+dH+fLlcyMWD6fKqbib1MaPKqdiZro2Z+bIhSOMWDkCgFdavkLJYHPet+W51HkoYmaFCxe2P/fy8mLYsGEMGzbMwIicx8vRDzz77LPMmDEDN59HyQAaOibuRsN6xR1oVMv1rFYrfb7rw+WEyzSObEzvOr2NDkkcps5DETPz9vbm5MmTGbafOXMm3XKfZuRw5fTnn39m9erVfP/999x+++34+vqme9/Mi74aSw0gcTcaNibuQMN6r/fJrk9Ytm8Zft5+vN/hfbwsDvdzi+F0fRYxs6yKhPHx8fj5+eVxNM7lcHIaEhJi+sVdXU8KEH/tuSqn4i5UORV3oFEtacVcimHADwMAGH33aCoVrWRwRJIzSk5FzGjmzJkAWCwW3n//ffLnz29/Lzk5mXXr1lGpkrmvyw4np3Pnzs2NODycFnkXd6TGj7gDVU5TJack0+3LbpyOPU2NsBoMa+we9zd5Jl2fRcxo2rRpgK1y+vbbb6cbwuvn50dUVBRvv/22UeE5hcPJ6dixY3niiScoXbp0bsTjoZScijtS5VTcgSqnqSaun8iqg6vI55uPTx76BD9vcw8d82xKTkXM6ODBgwA0a9aMJUuWUKhQIYMjcj6HbxT55ptvKFeuHC1atGDhwoX2aYzlVqQ2fvwBc9/ELPIfzQYp7kCVU4B1h9cxdu1YAGa1m6XhvKan5FTEzFavXm1PTK1Wq1tNVOtwcrp161Z+//13qlevzuDBg4mIiKBPnz5s3rw5N+LzEJoMSdyRZoMUd6ClZE7HnqbLF11IsabQvUZ3utfobnRIcsvUeShidnPmzKFq1aoEBAQQEBBA1apVef/9940O65blaIq96tWrM23aNI4dO8YHH3zAsWPHaNy4MdWqVWPGjBlcuHDB2XG6OTV+xB1pWK+4A8/uPLRarfT4qgfHLh2jYpGKvHXvW0aHJE6hyqmImY0ePZqBAwfSvn17PvvsMz777DPat2/P4MGDeeGFF4wO75bc0vzvKSkpJCQkEB8fj9VqpXDhwsyePZvIyEgWL17srBg9gGc3fsRdqfEj7sCzh/VO+2Ua3+37Dn9vfz59+FPy++W/+YfEBDSyRcTMZs+ezXvvvcekSZPo0KEDHTp0YNKkSbz77rumnxApR8np1q1b6d+/PxEREQwePJiaNWuyZ88e1q5dy969exkzZgwDBgxwdqxuTJVTcUeqnIo78NwJkX479hvDfxoOwLR7plE9rLrBEYnzqPNQxMySk5OpU6dOhu21a9cmKSnJgIicx+HktHr16jRo0ICDBw8yZ84cjh49yiuvvEL58uXt+3Tv3p1Tp045NVD3psqpuCM1fsQdeGbl9ELcBTp/3pnElEQeqvIQvev0NjokcSpdn0XMrFu3bsyePTvD9nfffZeuXbsaEJHzOLyUzMMPP8yTTz5JiRIlstwnNDSUlJSUWwrMs6hyKu5IlVNxB55XObVarTz1zVMcPH+QqJAo3mv/HhaLxeiwxKmUnIqY3Zw5c1i+fDkNGjQA4JdffuHo0aN0796dIUOG2PebOnWqUSHmiMPJ6ejRo9O9Tk5OZufOnZQuXdot19rJG57X+BFPoNkgxR14XuX03a3v8tnuz/Dx8mHxQ4sJCQgxOiRxOiWnIma2a9cuatWqBcD+/fsBW3EwNDSUXbt22fczY8eiw8npoEGDqFatGj179iQ5OZkmTZqwceNGgoKC+Pbbb2natGkuhOnuNKxX3JEm3BB34FkjW3ac2MGgHwcBMKnFJOqVqGdsQJJLlJyKmNnq1auNDiHXOHzP6eeff06NGjUA+Oabbzh48CB79+5l0KBBjBo1yukBegbPavyIp9CwXnEHntN5eCXhCp0+70RcUhz3VriXIQ2H3PxDYlIa2SIirsnhyunp06cJDw8HYNmyZTz88MPcdttt9OzZk5kzZzo9QM/gOY0f8STqmRd34Dmdh/2/78/e03spXqA48x+Yj5flllabE5emkS0iZtSsWbNMh+oWLFiQihUr0q9fPyIjIw2IzHkcTk7DwsLYvXs3ERER/PDDD8yaNQuA2NhYvL29nR6gZ/Ccxo94ElVOxR14RufhR9s/Yl70PLwsXizsuJCiQUWNDklylToPRczojjvuyHT7+fPnWbZsGW+++SY///xzlvuZgcPJ6RNPPMEjjzxCREQEFouFVq1aAfDrr79SqVIlpwfoGTQhkrgjNX7EHbj/hEh/nv6TPt/1AWBMkzE0iWpicESS+1LP5wQgGVBxQcQMpk2bdsP3+/Xrx8iRI1m2bFkeReR8DienY8eOpWrVqhw9epSHH34Yf39/ALy9vRk+fLjTA/QMntEzL54mbeXUCphvxjgRd+88jEuKo9PnnbiSeIVmUc0YdZfmjvAMadsbcUA+owIRESd65plnuOeee4wO45Y4nJwCPPTQQxm2Pf7447ccjOfSsF5xR6nnsxWIBwIMjEUkp9y7cjp0+VC2n9hOaFAoCzouwNtLFTTPkPZ8voqSUxH3EBgYSFxcnNFh3JIczXawdu1a2rdvT/ny5alQoQIdOnRg/fr1zo7Ng6hyKu7o+saPiNkkYxv2CO7YefjF7i94a/NbAHz44IcUL1Dc4Igk73gDvtee6/os4i6WL1/ObbfdZnQYt8ThyumCBQt44okn6NixIwMGDMBqtbJx40ZatGjBvHnz6NKlS27E6eZUORV35IvtEpOErQOmkLHhiDgsbaPdvToPD50/RM+lPQEY1mgYbcq3MTgiyXtBwAWUnIqYx9KlSzPdfuHCBTZv3sycOXOYN29e3gblZA4npy+//DKTJ09m8ODB9m0DBw5k6tSpjB8/XslpjqhyKu4qELiEGj9iTmnPW/cZln418SqdP+/MhfgLNCjZgAnNJxgdkhgiEFtyqhnVRczigQceyHR7gQIFqFSpEvPmzePhhx/O26CczOHk9MCBA7Rv3z7D9g4dOjBy5EinBOV5VDkVdxWELTlV40fMKPW8DSCHd8G4nKSUJLos6cKvx34lJCCERf+3CF9v35t/UNyQZlQXMZuUlBSjQ8h1Dv+1jYyMZOXKlRm2r1y50vSLvhrHvWeDFE+mxo+YmXtNhmS1Wun7XV++2vsV/t7+fNXpK6JCoowOSwyj67OIuB6HK6fPPvssAwYMIDo6mkaNGmGxWPj555+ZN28eM2bMyI0YPYCG9Yq7SrucjIjZuFfH4dg1Y3nv9/fwsnix8P8Waj1Tj6fkVERcj8PJaZ8+fQgPD+f111/n008/BaBy5cosXryY+++/3+kBegYN6xV3peRUzMx9KqezNs9i3LpxALx171t0rNzR4IjEeEpORcT15Gid0wcffJAHH3zQ2bF4MFVOxV2p8SNm5h4dh5/v/pz+y/oDMLbJWHrX6W1wROIadH0WEdfj8D2nZcuW5cyZMxm2nz9/nrJlyzolKM/jHg0gkYxUORUzM3/H4ZpDa+i6pCtWrDxT+xlebPKi0SGJy0i9Pis5FRHX4XByeujQIZKTkzNsj4+P59ixY04JyrMkXnuAklNxP+qZFzMzd8fh9uPbuf+T+0lITqBj5Y68de9bWCwWo8MSl6Hrs4i4nmwP60276OuPP/5IwYIF7a+Tk5NZuXIlUVFRTg3OM7jvIu8iqpyKuZm3cnrw3EHafNyGi/EXubv03Xzc8WO8vbyNDktcSup5reuziFl4eXndtJPRYrGQlJSURxE5X7aT09RFXy0WC48//ni693x9fYmKiuL11193anCewT0XeRexUc+8mJk5J0Q6eeUkrRe05vjl41QrVo2vO39NgI/+vsj1dH0WMZsvv/wyy/c2btzIG2+8gdVqzcOInC/byWnqoq9lypRh8+bNFC1aNNeC8ixpe+Y13ErcjSqnYmbmW0rmcsJl2i1sx99n/6Z0wdL80O0HQgJCjA5LXJKSUxGzyWxllL179zJixAi++eYbunbtyvjx4w2IzHkcvuf04MGDSkydytz3NIncmJJTMTNzVU4TkhP4v0//jy3/bqFoUFGWP7ac4gWKGx2WuCwlpyJm9u+///LUU09RvXp1kpKSiI6OZv78+ZQqVcro0G6Jw8kpwNq1a2nfvj3ly5enQoUKdOjQgfXr1zs7Ng9hvp55kexT40fMzDydhynWFJ74+gmW719OkG8Q33X5jtuK3GZ0WOLSdH0WMaMLFy7w/PPPU758ef744w9WrlzJN998Q9WqVY0OzSkcTk4XLFhAy5YtCQoKYsCAAfTv35/AwEBatGjBwoULcyNGN2eunnkRx6hyKmZmjgmRrFYrQ5cPZeHOhfh4+fDFI19Qr0Q9o8MSl6fkVMRsJk+eTNmyZfn2229ZtGgRGzdu5K677jI6LKfK9j2nqV5++WUmT57M4MGD7dsGDhzI1KlTGT9+PF26dHFqgO5PlVNxZ2r8iJmZo3I6ZeMUpv0yDYC598+lTfk2Bkck5qB1TkXMZvjw4QQGBlK+fHnmz5/P/PnzM91vyZIleRyZ8zicnB44cID27dtn2N6hQwdGjhzplKA8izl65kVyRpVTMTPXvz5/uP1Dhv00DIAprabQrXo3gyMS89BSMiJm0717d7dfr9rh5DQyMpKVK1dSvnz5dNtXrlxJZGSk0wLzHObomRfJGVVOxcxc+7aLZfuW8eTXTwIwtOFQnm30rMERibno+ixiNvPmzTM6hFzn8D2nzz77LAMGDKBPnz589NFHLFiwgN69ezNw4ECGDh3q0LHWrVtH+/btKV68OBaLha+++ird+1arlbFjx1K8eHECAwNp2rQpf/zxh6MhuzjX75kXyTlVTsXMXPe2iw+2fUDHxR1JtibzWPXHeLXVq0aHJKaj5FREXI/DyWmfPn345JNP2LlzJ4MGDWLgwIHs2rWLxYsX88wzzzh0rCtXrlCjRg3efPPNTN+fPHkyU6dO5c0332Tz5s2Eh4fTqlUrLl265GjYLkyVU3FnSk7FzFyvchqfFE/vb3vTc2lP4pPj6Vi5I3M6zMHLkqPJ98WjKTkVEdfj8LBegAcffJAHH3zwlr+8bdu2tG3bNtP3rFYr06dPZ9SoUXTs2BGA+fPnExYWxsKFCx1OhF2X6/bMi9w6NX7EzFyr8/DYxWM89NlD/PLPL1iwML7ZeEbcNUKJqeSQrs8i4npylJwCJCQkcPLkSVJSUtJtd9bCrwcPHuT48eO0bt3avs3f358mTZqwceNGN0xOXadnXsR5VDkVM3Od6/O6w+t4+LOHOXnlJCEBISzsuJC2FTLv3BXJHiWnIuJ6HE5O9+3bx5NPPsnGjRvTbbdarVgsFpKTk50S2PHjxwEICwtLtz0sLIzDhw9n+bn4+Hji4+Ptr11/CLBr9cyLOJcaP2Jmxl+frVYrb/z2Bs8uf5aklCSqh1VnySNLKFe4nGExibvQUjIi4nocTk579OiBj48P3377LREREbk+nfH1x09NgrMyadIkXnrppVyNyblcp2dexPlUORUzM/b6HJsYyzPfPsOCHQsA6FKtC+/e9y75/PIZEo+4Gy0lIyKux+HkNDo6mq1bt1KpUqXciMcuPDwcsFVQIyIi7NtPnjyZoZqa1ogRIxgyZIj99bFjx6hSpUruBXrLjO+ZF8k9qed1HJBCDuZgEzGQcRMiHTh3gI6LO7L9xHa8Ld5MaT2FgfUHuv36dpKXUs/rBCAZ8DYwFhERG4dbilWqVOH06dO5EUs6ZcqUITw8nBUrVti3JSQksHbtWho1apTl5/z9/QkODrY/ChQokOux3hpNiCTuLG2jPs6wKERyxpjr8w9//0Cdd+uw/cR2iuUrxsruKxnUYJASU3EyXZ9FxPVkq3J68eJF+/NXX32VYcOGMXHiRKpVq4avr2+6fYODg7P95ZcvX+bvv/+2vz548CDR0dEULlyYUqVKMWjQICZOnEiFChWoUKECEydOJCgoiC5dumT7O1yfhvWKO0t7XseiTpjsu5xwmYPnDnLg3AFOXjlJXFIccUlxxCfH259ntS0+6b/XFouFgv4FCfYPJtg/2P68YMDNt/n7+Bv9z2CgFCB1/oK8uT6nWFN45edXeGHVC1ixUr9EfT5/5HNKBpfMk+8XT5P2vL4KaLi4iBgvW8lpSEhIuh5bq9VKixYt0u2TkwmRtmzZQrNmzeyvU4fjPv7448ybN49hw4Zx9epV+vbty7lz56hfvz7Lly83QTXUERrWK+7MG/DDNmxMk26kZbVaibkcw4FzB9h/dj8Hzh3gwPn/np+4csLoEMnvl5+okChKFyyd7mdUSBSlQ0oTGhTqxtW8tOdr7l+fL8Zf5PGvHuervV8B8HStp5nZdqaHdxBI7vIGfIFEdH0WEVeRreR09erVufLlTZs2xWq1Zvm+xWJh7NixjB07Nle+3zWociruLghbcuqZk27EJ8Xz67FfiT4ebUtAzx1g/7n9HDx3kKtJN24QFg4sTNlCZYnIH0GgbyABPgH4e/sT4BOQ7nH9Nn+f/14npyRzKeESF+IucDH+IhfjL3Ih/kK6nxfjL6Z7/1KCbZbzywmX2XVyF7tO7so0vkCfQEqHlM40gS1fuDyh+UKd/u+Zd9L+t8nd6/OeU3t4cPGD/HnmT/y8/Xjr3rfoVatXrn6niE0gSk5FxJVkKzlt0qRJbsfhwW5eObVarYxePZof9//I0s5LiSgQkeW+Iq4nEDiPpzR+klOS2XZ8GysPrGTlwZX8fOTnLJNQL4sXpQqWolyhcpQtVPa/n4VtP0MCQvI2+GtSE9qTV05y6PwhDp0/xOHzhzl04drP84f499K/XE26yt7Te9l7em+mxwnPH06NsBrUCKtB9bDq1AivQcUiFfH19s10f9eS+t/Mn9yayCvFmsLiXYt5+tunuZxwmZLBJfnikS+oV6JernyfSEZBwEU85fosIq4v27P1xsbG8txzz/HVV1+RmJhIy5YtmTlzJkWLFs3N+DzAzSunFouFH/7+ga0xW/npwE88VuOxvAlNxCncezkZq9XKntN7WHlgJasOrWLNoTWcjzufbp9i+YrRKLIR5QuVT5d8li5Y2iUTNW8vb0ICQggJCOG2Irdluk9CcgJHLxy1Ja4XDqf7eej8IY5eOMrxy8c5fvk4P+7/0f45P28/qoRWsSWrqUlrWA0XrLLm3qiWuKQ4Pt7xMVM2TbEn9k1KN+HThz+lWL5iTv8+kaxpLWoRcS3ZTk7HjBnDvHnz6Nq1KwEBASxatIg+ffrw2Wef5WZ8HiB795y2KtuKrTFbWXFghZJTMRn3W+j98PnDrDxoq4yuOriK45ePp3s/2D+YplFNaVGmBc3LNOf20Nvd7t5MP28/yhUuR7nC5TJ9P3VI8I4TO9h+fDs7Tu5gx4kdXIy/SPTxaKKPR6fbP7XKWj2sOrUiatEoshGlCpbKg98kK85fRubc1XPM3jKbmb/OtN9THOwfzKD6gxjdZDQ+Xg6v7iZyi7TWqYi4lmz/JVyyZAlz5syhc+fOAHTr1o3GjRuTnJyMt7fWxsq57C1V0Lpca17Z8AorDqywTz4lYg7mb/wkpyTz/d/fs/TPpaw8uJID5w6kez/AJ4A7S91J86jmtCjbgloRtTw+0cjvl58GJRvQoGQD+zar1crhC4dtyeqJHWw/Yfv599m/M62ylgwuSePIxrZHqcZUD6ueh/+uzltG5vD5w0z7ZRrv//4+VxKvALbfbVD9QTxV+ymC/bM/y72Ic6lyKiKuJdt/5Y8ePcpdd91lf12vXj18fHz4999/iYyMzJXgPEP2ho41imxEkG8Qxy8fZ9fJXVQLq5b7oYk4hXmH9Z64fIL3f3+fd39/lyMXjti3e1u8qV+yvj0ZbVCyAQE+AQZGag4Wi8U+2+/9le63b7+ccJk/Tv7B9hPb2X58O7/9+xvbYrbxz8V/WPzHYhb/sRiwJbz1S9S3J6sNSjbIxcTu1iun22K28drG1/j0j09Jttpmsq9WrBrPNXqOTlU74eft54Q4RW6FklMRcS3ZTk6Tk5Px80v/h9THx4ekpCSnB+U5rGR3WK+/jz9NSjfh+7+/Z8WBFUpOxUTM1fixWq2sP7KeWZtnsWTPEhJTEgHbzLndqnWjdbnW3F36bgr4u9OSVsbK75ef+iXrU79kffu2KwlX+O3Yb2w4uoENRzew6egmLsRfsA+nBrBgoVpYtXTV1dIFSztpZEnOKqdWq5Xl+5fz2sbX7HECtCzbkqENh9K6XGuNfBEXYq7rs4i4v2wnp1arlR49euDv/9+aa3FxcfTu3Zt8+f5buHnJkiXOjdCtJWBb6B2y0zvfqmwrvv/7e5bvX86QhkNyNTIR5zFH5fRi/EU+2v4Rs7fM5o9Tf9i3NyjZgL51+vLw7Q+rOpqH8vnlo1mZZjQrY1sLO8Wawh8n/2Dj0Y32hPXAuQPsOGG7l3X2ltkARAZH0rJsS1qVbUXLsi1vYaIlxyqnCckJfLLrE6ZsnMLOkzsBW4W9U9VODG04lJoRNXMYh0huUnIqIq4l28np448/nmFbt27dnBqM53FskffW5VoDsO7wOuKS4tRQFpNw7cbP9uPbmb1lNgt2LLDfDxjkG0S3at3oU7cPd4TfYWyAAtiW3akWVo1qYdV4ps4zAMRcikmXrP4e8ztHLx5lbvRc5kbPBaBmeE1alW1Fq3KtuLPUnQ5cN288qiU5JZm/z/7NzpM72Razjfnb53Ps0jEA8vnm46laTzGowSBKh5S+lV9bJJe59vVZRDxPtpPTuXPn5mYcHiq1kuQN3Hw5iSqhVSheoDj/XvqXDUc20KJsi1yNTsQ5XK9yGpcUx+e7P2f2ltlsPLrRvr1y0cr0rduXx6o/RsGAggZGKNkRUSCC/6vyf/xflf8DIDYxlp+P/MyK/StYcWAF209sZ9vxbWw7vo3JGycT4BPA3aXvtiWrZVtRLawaXpas1jD9bz6AE5dPsPPkTnac2MHOkzvZeWInf5z6g7ikuHSfCM8fzoB6A+hdpzeFAgvl3i8u4jTuN5u6iJibZ08nabi0kyHd/B4ki8VCq7KtmL99PisOrFByKibhOo2fIxeO8NZvb/FB9Aecjj0NgI+XDx0rd6Rvnb7cXfpu3Q9oYkG+QbQu19o+yuTE5RP8dOAnVhywJav/XvqX5fuXs3z/csC2/mzqEOBWZVtRKLAQu0/tZueJnew48Qk7T8LOk8s4eSU8y++7PfR2qodV565Sd9G5amf8ffwz3VfENZl/NnURcS9KTg2VvcmQ0kpNTpfvX84rLV/JnbBEnMr4xk9cUhyTN0xm0s+T7NWuyOBInq79NL1q9SI8f+bJh5hbWP4wulbvStfqXbFarew+tdueqK45tIaTV06ycOdCFu5cCNgmWLJive4ocViwUL5weaqFVaN6seq24cXFqlG2UFm8vbSUmpiZhvWKiGtRcmqo7C0jk1bLsi0B2HZ8G6eunLqFyT5E8oqxw3q/3/c9//v+f+w/tx+Au0vfzZAGQ2h3WzuPX4vUk1gsFm4vdju3F7udQQ0GEZ8Uz6Z/NtmHAG/5dwtWrBQNKkr1sOpUK3aJ6mGbqVasE1VC55DPL9/Nv0TEdJSciohrUcvMUI5XTsPyh1EjrAbbT2znpwM/8Wi1R3MnNBGnMabxc/j8YQb9OIiv9n4FQPECxZnaeiqP3P6Ihu4K/j7+NI1qStOoprzc4mXOXT1HQnICxfIVu3Z+DAY2A2UAJabirpSciohryWomCMkTOVtHL/V+qhUHVjg5HpHckLeV0/ikeCaun0jltyrz1d6v8LZ482zDZ9nbby+dqnZSYiqZKhRYiLD8YWnOD8eWkhExJyWnItk1a9YsypQpQ0BAALVr12b9+vVZ7hsTE0OXLl2oWLEiXl5eDBo06IbH/uSTT7BYLDzwwAPODdqElJwaKmeNn1ZlWwGwfP9yrNbr748ScTV51/j56cBPVH+7OqNWjeJq0lXuLn030b2jmdJ6CgX8C+T694s7cXxki4j5KDkVyY7FixczaNAgRo0axbZt27jrrrto27YtR44cyXT/+Ph4QkNDGTVqFDVq1LjhsQ8fPszQoUO56667ciN001FyaqicVU7vLHUn/t7+HLt0jL2n9zo/LBGnyv3K6T8X/+GRzx6h1Uet+OvMX4TlC+OjBz9izeNrqFqsaq59r7gzx+cEEDEf15lNXcSVTZ06lZ49e9KrVy8qV67M9OnTiYyMZPbs2ZnuHxUVxYwZM+jevTsFC2a9NF1ycjJdu3blpZdeomzZsrkVvql4zD2nSUlJJCYmGh3Gda5ia/gEA9mPzQcf7ip1Fz8d/Inv//qe8iHlcytAEScIvPZIxpHzPDsSkxOZuXkmE9ZP4EriFbwsXvSt3Zcxd4+hYEBBkpKSnPp94kmSsJ23QTj7vBVxHbl3fRZxValtg0uXLnHx4kX7dn9/f/z9My4HlpCQwNatWxk+fHi67a1bt2bjxo0Z9nfEuHHjCA0NpWfPnjccJuxJPCY53bRpE0FBrjY8KwxYdO35Moc+WSK+BACLfltEudPlnBuWiNPl7Dy/kZ2XdvLusXc5GncUgEr5KvFMyWcok1SGDas2OO17xFP1uPYAZ563Iq7Fj9y4Pou4sthY28iYKlWqpNs+ZswYxo4dm2H/06dPk5ycTFhYWLrtYWFhHD9+PMdxbNiwgTlz5hAdHZ3jY7gjj0lOGzZsSIkSJYwO4zqvA+OAx4A3HfpkiRMlmD9nPnuu7qHlPS3x8/bLjQBFnOA3oBUQBWy/5aPFXI7h+ZXP88n+TwAIDQplUvNJdKvWDS+L7lQQZ2mJbbbeRcD/t3ff8VFW+R7HP5OEFFIIoSTSQlMgdEIRXASkySpdBaRZkQsqWXRRRATZu6DsXla8LkhRmouwV8UGopESQMpCMIoGkSrFhBJaKAkpc/+IiQmEmpk5z8x836/XvDZ5ZuZ5fnnt8eH8zu+c8/zRcCwizpIA9ADqAZsNxyLiGkeOHAEgOTm5SG5QXNW0sMs3VLTb7be8yWJ6ejqDBg1izpw5lC9f/pbO4am8Jjn18/OjVKlSpsO4zHnypvb6AzcXW7PKzagYXJFj54+xLXUb7aq3c0aAIg4QTF47P83NtvPLbTi4gT5L+3D8wnFs2BjefDh/veevlA0q64A4RQo7TV67LU1J262IdZXGUfdnEXfh55eX/oSGhhIWFnbdz5cvXx5fX98rqqTHjh27opp6o/bu3cuBAwfo3r17wbHc3NyC+Hbt2kWtWt45M1JlBqNubUMkAB+bT5Fde0WsyzEbIs37dh73LLiH4xeO0ziyMVuf3MqM+2YoMRUn0aNkxBtot16R6/H39yc2Npb4+KKPcIyPj6dNmza3dM66deuyY8cOkpKSCl49evSgQ4cOJCUlUbVqVUeE7pa8pnJqTSXbDbJzzc78a8e/iN8Xz187/tVxYYk4VMk6Pzm5OYyJH8O0zdMA6FuvLwt6LSDYP9hB8YkUR4+SEW+g5FTkRowePZrBgwfTvHlzWrduzezZszl48CDDhw8HYOzYsRw5coSFCxcWfCd/Lem5c+c4fvw4SUlJ+Pv7ExMTQ2BgIA0aFH2aQHh4OMAVx72NklOjStb56Vwrr3K67ddtpF1Io1zpcg6KS8SR8tv3JfJ2hPS94W+ezTzLgA8HsGJ33kYdr9z9ChPaT9DaUnEBPUpGvIEeJSNyI/r160daWhqTJk0iJSWFBg0asGLFCqKjowFISUm54pmnTZs2Lfg5MTGRxYsXEx0dzYEDB1wZuttRcmpUyTo/lUIrUb9CfX48/iOr96/mwfoPOi40EYcpPPhyEQi5oW/tO7WP7u93J/l4MoF+gczvOZ9+Dfo5JUKRK2lar3iD/PadCeSi1V4iVzdixAhGjBhR7Hvz58+/4pjdbr+p8xd3Dm+ku5BRJZ821qVWF0DrTsXKAgv9fGPrTtceWEvLOS1JPp5MpdBKrH90vRJTcSE7mtYr3qHw4IuqpyJinpJTo259Q6R8+Zsixe+Lv+kRGhHXsPF7B+j6yemcxDl0XtSZtItpNK/UnP888R+aV2ru1AhFisoo9LMqp+LJlJyKiLUoOTWq5NPG7o6+G39ff3458wu7T+52TFgiDnf9TTeyc7MZ9cUohn0+jOzcbPo36M+6R9ZROcxqzycWz1d4EEXJqXgyX35/hIySUxExT8mpUSWvnAb7B3NX1bsAiN8bf51Pi5hy7cfJnM44zf2L7+fN/7wJwKT2k1jcZzFBpZQYiAn5nfRSaGsG8XzasVdErEPJqVGO2Q2yYN3pPq07Fau6eudnd9pu7px7J1/u/ZLSpUrzwYMfML7deGw2m2tDFCmg9abiTZScioh1KDk1yjEdoPx1p2v2ryErJ6uEMYk4Q/GV01X7VtFqbit2pe2iSlgVNjy6gb4xfV0fnkgReoyMeBM9TkZErEPJqVGO6QA1va0p5YLKkX4pnS1HtpQ8LBGHu7LzM2PrDLq+15VTGadoVbkVW5/cStPbmhb/dRGX0mNkxJuocioi1qHk1CjHVE59bD50qtkJ0LpTsarfd+u12+3ErYxj5IqR5NhzGNhwIGsfWUtUSJTRCEV+V/L9AETcx43vpi4i4mxKTo3JxZHrmgo/UkbEevLauN1+nue/ep7pW6Zjw8aUjlNY1HsRgX6B1/m+iCupcireRJVTEbEObUNojGOfo9e5Vl5yuuXIFk5nnCY8MLzE5xRxnLw2/mrCR0zb/AUAc7rP4fFmj5sMSuQqVDkVb6LkVESsQ5VTYwr/I1Dy5LRamWrUKVeHXHsua/avKfH5RByrNH/7Bl5NyEtMp987XYmpWJgqp+JNlJyKiHUoOTUmf2Tecc/RK3ikzF49UkasZcbWvYz5Ou/nyfdM5tlWz5oNSOSa9CgZ8SZKTkXEOpScGuP4zo/WnYoVLUhawMgVGwB46Q93MrbtWMMRiVyPHiUj3kTJqYhYh5JTYxy/pql99fb4+fix99Re9p3a57Dzityq//vx/3js08cAGNUK/vueWMMRidwITesVb6LnnIqIdSg5NcbxnZ/QgFBaV2kN6JEyYt7yn5fz8EcPk2vP5YmmzflHV7DZMq7/RRHjtCGSeBNVTkXEOpScGuOczk/ButN9Wncq5qzev5q+/+5Ldm42AxoM4O37B2CzgZ6jJ+5BlVPxJnrOqYhYh5JTY5yzpil/3enq/avJzs126LlFbsTGQxvp8X4PMnMy6VmnJwt6LcDXJ/i3dzUyL+5AlVPxJqqcioh1KDk1xjm7QTav1JzwwHBOZ5xm26/bHHpukevZnrKdbv/qxvms83Sp1YWlDyyllG8pfm/nGpkXd6DKqXgTJaciYh1KTo1xzsi8r48vHWt0BLTuVFzrx2M/0mVRF85mnqVttbYs67eMAL+A397VhhviTvQoGfEmSk5FxDqUnBrjvJH5/Km9WncqrrLn5B46LepE2sU0WlRqwecPf07pUoU79lrTJO5Ej5IRb6LkVESsQ8mpMc5b05S/KdLmw5s5m3nW4ecXKezgmYN0XNiR1HOpNKzYkJWDVhIWEHbZpzStV9yJpvWKN9HMFhGxDiWnxjhvZL5G2RrUKluL7Nxs1h5Y6/Dzi+RLSU+h48KOHDxzkDvK3UH84HgigiKK+aRG5sWdaEMk8Sa6P4uIdSg5Nca5a5ryq6dadyrOknYhjc6LOrPn5B6qh1dn1ZBVRIZEXuXTqpyKO1HlVLyJll2IiHUoOTXGuWuatO5UnCknN4ceS3rw4/EfqRRaiVVDVlElrMo1vqGReXEnqpyKN9H9WUSsQ8mpMc6tnHao0QFfmy8/p/3ML6d/cco1xHvN3T6XjYc2EhYQxteDv6Zm2ZrX+YYqp+JOVDkVb6LkVESsQ8mpMc4dmQ8PDKdl5ZYAxO/T1F5xnOPnjzN21VgA/tLhL9SrUO8GvpXfznOALGeFJuIgepSMeBMlpyJiHUpOjXH+yHzBulMlp+JAY1eN5VTGKRpHNmZEixE3+K3C7VzVU7E6PUpGvImSUxGxDiWnxjh/TVP+utOv931NTm6O064j3mPToU288+07AMy4bwZ+Pn43+E1/fr/dKDkVK7Ojab3iXfL7IZlArslARESUnJrj/JH5lpVbEhYQxsmLJ/k29VunXUe8Q3ZuNiNW5FVKH23yKG2qtrmJb9vQ6Ly4h0zyElTQtF7xDoX7IRnGohARASWnBjl/TVMp31J0qN4BgK/2atdeKZmZW2eSlJpEeGA4r3d6/RbOoE2RxB0UHjxR5VS8gZZdiIh1KDk1xjWPKtC6U3GE1HOpvLzmZQAm3zOZCsEVbuEsqpyKO8i/N/sBpUwGIuIivvze1nV/FhGzlJwa45o1TfnrTr85+A3nLp1z6rXEc42JH8PZzLM0r9ScYbHDbvEsqpyKO9B6U/FGGjwUEWtQcmqMayqntSNqUz28Olm5Wazcs9Kp1xLPtO6XdSz6fhE2bMz44wx8fXxv8Uz5bV2dH7EyPUZGvJGSUxGxBiWnxrjmUQU2m43+9fsDMDtxtlOvJZ4nKyeLEcvzNkEaFjuMFpVblOBs+W1dlVOxMj1GRryRklMRsQYlp8a4bnT+ydgnsWEjfl88e0/udfr1xHO8ueVNfjz+I+WCyjG54+QSnk3TesUdaFqveCMlpyJiDUpOjcgBLv32s/M7QDXL1qRr7a6Aqqdy446cPcLEhIkAvN7pdSKCIkp4RnV+xB24ZsmFiLVo2YWIWIOlk9OJEydis9mKvKKiokyH5QCFb/6u6QA9FfsUAO8mvUtmdqZLrinu7bmvnuPcpXO0rtKaR5s+6oAzqnIq7kCVU/FGGjwUEWuwdHIKUL9+fVJSUgpeO3bsMB2SAxTunAe65Ir333E/lUIrceLCCZb9tMwl1xT3tWrfKpb+uBQfmw8z7puBj80Rtwp1fsQdqHIq3kh7AoiINVg+OfXz8yMqKqrgVaHCrTxf0WryO+eBuOr/Aj8fP55s9iQAb2972yXXFPeUmZ3JyBUjARjZYiRNopo46MyqnIo7UOVUvJEGD0XEGiyfnO7evZtKlSpRo0YN+vfvz759+675+czMTM6ePVvwSk9Pd1GkN8PMyPwTzZ7Ax+ZDwi8J7Dy+06XXFvfxj83/YFfaLiKDI5nUYZIDz6w1TeIO9CgZ8UZKTkXEGiydnLZq1YqFCxfy5ZdfMmfOHFJTU2nTpg1paWlX/c6UKVMoU6ZMwSsmJsaFEd8oM48qqBJWhfvvuB/QxkhSvINnDvKXdX8B4G+d/0Z4YLgDz65pY+IO9CgZ8UZKTkXEGiydnHbr1o2+ffvSsGFDOnXqxPLlywFYsGDBVb8zduxYzpw5U/BKTk52Vbg3wdzI/PDY4QDM/24+F7P0j5AUFbcyjgtZF7g7+m4GNRrk4LNrWq+4A1VOxRspORURa7B0cnq54OBgGjZsyO7du6/6mYCAAMLCwgpeoaGhLozwRpnbcKNLrS5El4nmdMZp/v3jv11+fbGuL3Z/wbKfluFr8+Wff/wnNpvNwVdQ50fcgSqn4o207EJErMGtktPMzEx27tzJbbfdZjqUEjK34Yavjy/DYocBMCtxlsuvL9aUkZ3BM188A8CoVqNoULGBE66iyqm4A22IJN5Ig4ciYg2WTk6ff/55EhIS2L9/P1u2bOGBBx7g7NmzDB061HRoJWT2UQWPNX0MPx8/Nh3exHep3xmJQaxl6jdT2XtqL5VCKzGx/UQnXUWdH3EHepSMeCPtCSAi1mDp5PTw4cMMGDCAOnXq0KdPH/z9/dm8eTPR0dGmQyshs9PGokKi6FW3F6DqqcC+U/uYsmEKANO6TCM0wFlT4VU5FXegyql4Iw0eiog1+JkO4FqWLFliOgQnMb/hxvDY4XyQ/AHvff8eUztPJcQ/xFgsYo7dbufZL54lIzuDTjU78VD9h5x4Na1pEndg/v4s4npKTkXEGixdOfVc5jfc6FCjA7dH3E76pXTe3/G+sTjErE93fcry3csp5VOK/+32v07YBKkwTRsTd2D+/iziekpORcQalJwaYX5k3sfmw1OxTwHwduLbxuIQc85knCnYBOm51s9Rt3xdJ19R03rFHZi/P4u4npJTEbEGJadGWGPDjaFNhuLv68/2lO1s+3Wb0VjE9Z5d+SyHzh6iZtmavHz3yy64ojo/4g5UORVvpGUXImINSk6NsEbnp3zp8jwY8yAAb29T9dSbfJj8IQu/W4iPzYdFvRcR7B/sgquqciruQBsiiTfS4KGIWIOSUyOsM21sePPhALz/w/ucyThjOBpxhZT0FJ76PG9K9wt3vUCbqm1cdOXCnR+7i64pcrOsMbNFxLW0J4CIWIOSUyOsUTkFuKvqXcRUiOFC1gXe+/490+GIk9ntdp747AnSLqbRJKqJE59pWpz8zr4dyHThdUVuhiqn4o1UORURa1ByaoR1Kqc2m43hsXnV07cT38ZuV0XLk83ZPocVu1cQ4BvAe73fw9/X34VXL9zZVwdIrMo692cR11FyKiLWoOTUCGtNGxvceDBBfkH8cOwHNh3eZDoccZI9J/cw+svRAEzuOJn6Feu7OIJS/P5oZU0dEyuyY6WZLSKuo+RURKxByakR1po2Fh4YTv8G/QFtjOSpsnOzGbJsCOezztO+envi7owzFIk2RRIrywJyf/vZGoOHIq6R3x/J5Pf/BkREXE/JqRHWqpzC7xsj/fvHf5N2Ic1wNOJoU7+ZyqbDmwgLCGN+z/n42Ez9p6/RebGywoMm1hg8FHGNwu09w1gUIiJKTo2w3rSxFpVa0DSqKZk5mSz8bqHpcMSBvk35lglrJwDwv93+l+jwaIPRqHIqVpY/aOJD3jR0EW+hPQFExBqUnBphvQ03bDYbT8XmPV5EGyN5jozsDAYtG0R2bjZ96vVhcKPBhiNS5VSsrPCsFpvJQERczI/fB2R0fxYRc5ScGmG9ab0ADzd8mBD/EH5O+5m1B9aaDkccYNyqcSQfTyYyOJJZ98/CZjPd4VblVKzMWvsBiLiWnnUqIuYpOTXCmh2g0IBQBjUcBMCsxFmGo5GSWrN/DdM2TwPgnR7vUL50ecMRwe/JqUbmxYqsN6tFxHU0s0VEzFNy6nKFH1VgvQ7QU83zpvZ+tPMjjp47ajgauVVnMs4w9OOhADzZ7Enuu+M+wxHl08i8WJn19gMQcR0lpyJinpJTl8sCcn772XodoCZRTWhVuRVZuVnMS5pnOhy5Rc+ufJZDZw9Rs2xNpnWdZjqcQjStV6xMlVPxZkpORcQ8JacuV/imb80OUP5jZWYnzibXrueduZuPdn7Ewu8W4mPzYVHvRYT4h5gOqRB1fsTKVDkVb6b7s4iYp+TU5fI7PzbA32QgV/VQ/YcoE1CG/af3E7833nQ4chNSz6Uy7LNhALxw1wu0qdrGcESXU+VUrMya+wGIuIb2BBAR85SculzhaWOmd04tXulSpRnaOG+94tuJbxuORm6U3W7n8U8fJ+1iGk2imjCx/UTTIRVDnR+xMuvuByDifKqcioh5Sk5dzj06P/kbI3226zOOnD1iOBq5EXO2z2HF7hUE+AawqPci/H2tWJnXhkhiZaqcijfT/VlEzFNy6nLu0fmJqRBD22ptybHn8M6375gOR65j78m9jP5yNACTO06mQcUGhiO6GlVOxcq0IZJ4M1VORcQ8Jacu5x6VU/h9Y6Q52+eQnZttOBq5mpzcHIZ8PITzWedpX709cXfGmQ7pGjQyL1amDZHEmyk5FbmWGTNmUKNGDQIDA4mNjWX9+vVX/WxKSgoPP/wwderUwcfHh7i4uCs+89FHH9G8eXPCw8MJDg6mSZMmLFq0yIl/gXtQcupy7tP56VuvL+VLl+fw2cPMT5pvOhy5ite/eZ2NhzYSFhDG/J7z8bFZ+T9rbYgkVqbKqXgzJaciV7N06VLi4uIYN24c3377LW3btqVbt24cPHiw2M9nZmZSoUIFxo0bR+PGjYv9TEREBOPGjWPTpk18//33PProozz66KN8+eWXzvxTLM/KvVgP5T6dnwC/AMb+YSwAz3/1PCnpKYYjksst/3k549eMB+DNe98kOjzacETXo86PWJn7DB6KOJ7uzyJXM23aNB5//HGeeOIJ6tWrxxtvvEHVqlWZOXNmsZ+vXr0606dPZ8iQIZQpU6bYz7Rv357evXtTr149atWqxahRo2jUqBEbNmxw5p9ieX6mA3CV7OxssrKyTIdBXucnCCgDWCGea/uvZv/F4h2LSUxJZOTykSztu9R0SPKb7499T/8P+5Nrz+XRxo8yIGaARdr4tZQmr/1n4w7tX7xNFnntMxi1T/E+IeS1/0uo/Ysny87OW6qWnp7O2bNnC44HBAQQEBBwxecvXbpEYmIiL774YpHjXbp0YePGjQ6JyW63s3r1anbt2sXrr7/ukHO6K69JTjdt2kTp0laoVoYC7//28wqTgdywQWGDSEpJYtmuZbyy+BXuDL/TdEhe72TWScb8PIZzWedoGNKQP9r/yBdffGE6rBsQhLu1f/Emf/ztBWqf4n2aofuzeIMLF/JmycTExBQ5PmHCBCZOnHjF50+cOEFOTg6RkZFFjkdGRpKamlqiWM6cOUPlypXJzMzE19eXGTNm0Llz5xKd0915TXLaunVrKleubDoMYCbwItAXeNdwLDcudW0qr298nQUnFjC6z2jCA8NNh+S1LmRdoNN7nTiRdYI7Iu7g66FfUzaorOmwbtAaoBfQAPjGbCgiV3gYWA5MBx4xG4qIy/0TeAl4ANAu/eK5jhzJe0RicnJykdyguKppYTabrcjvdrv9imM3KzQ0lKSkJM6dO8eqVasYPXo0NWvWpH379iU6rzvzmuTUz8+PUqVKmQ4DOE/eeo5Sv73cw8QOE1m2axk/p/3MuLXjmN19tumQvFKuPZcnPn6CbSnbiAiKYPnA5VQMq2g6rJsQTF77P407tX/xFmfJa59BqH2K9wkkr/2no/YvnszPLy/9CQ0NJSws7LqfL1++PL6+vldUSY8dO3ZFNfVm+fj4ULt2bQCaNGnCzp07mTJlilcnp9oQyeXcZ0OkwgL9ApnTfQ6Q92iZtQfWmg3IS41fPZ4Pkj+glE8plvVbRu2I2qZDukl6lIxYmTZEEm+mDZFEiuPv709sbCzx8fFFjsfHx9OmTRuHXstut5OZmenQc7obJacu576dn7uj72Z4bN6zT5/87EkuZukfMFeanzSfyRsmAzC3x1zujr7bcES3Qo+SEStzz8FDEcdQcipyNaNHj2bu3Lm8++677Ny5kz/96U8cPHiQ4cPz+sVjx45lyJAhRb6TlJRUMGX3+PHjJCUlkZycXPD+lClTiI+PZ9++ffz0009MmzaNhQsXMmjQIJf+bVbjNdN6rcO9Oz+vdXqNT3/+lD0n9zApYRJTOk0xHZJXSDiQwLDPhgEwru04hjQecp1vWJU6P2Jl7jt4KFJyuj+LXE2/fv1IS0tj0qRJpKSk0KBBA1asWEF0dN4j/FJSUq545mnTpk0Lfk5MTGTx4sVER0dz4MABAM6fP8+IESM4fPgwQUFB1K1bl/fee49+/fq57O+yIpvdbrebDsKZDh8+TNWqVTl06BBVqlQxHQ7wKDAfeA14wWwot+jTXZ/Sc0lPfG2+bH1yK01va3r9L8kt2522mzvfuZOTF0/yYMyDLHlgCT42d530cBzIXyObgyZviLXUAA4Am4FWZkMRcbmvgc7kbVi3w3AsIs5jvdxAClPP0OXyRyTdd2S+R50ePBjzIDn2HJ747Amyc7NNh+SxTl48yf3v38/JiydpWbklC3otcOPEFIrOGMgwFoVI8fIrp+45s0WkZPLbvSqnImKOO/dy3ZRndH7e7PYmZQPLsj1lO//Y9A/T4XikSzmXeODfD/Bz2s9UK1ONT/p/QlAp9x3UyFM4fq07Fatx/8FDkVunab0iYp6SU5fzjDVNUSFR/E+X/wHglbWvsOfkHsMReRa73c5/ff5frDmwhlD/UD4f8DlRIVGmw3IAHyD/OWJKTsVq3HtPAJGS0W7qImKeklOX85zOzyNNHqFjjY5kZGfw1OdP4eHLl13qbxv/xrtJ7+Jj82HpA0tpGNnQdEgOpNF5saIsIH+JgnsPHorcGt2bRcQ8Jacu5xnTegFsNhuz7p9FkF8Qq/evZl7SPNMheYRlO5fx4tcvAvBG1zfodns3wxE5mh4nI1ZUuEPu/vdnkZuXn5xmArkmAxERL6bk1OU8a01TrYhaTOowCYDnvnqO1HOphiNyb4m/JjLwo4HYsTOyxUieafWM6ZCcQKPzYkX5gyU2wN9kICKGFO6XaMM6ETFDyanLeU7lNF/cnXHE3hbL6YzTPPOFJyZTrnH47GG6v9+di9kXubf2vbxx7xumQ3ISVU7FigoPHNpMBiJiSOHkVIOHImKGklOX84wNkQrz8/Fjbo+5+Np8+SD5Az7+6WPTIbmdc5fO0f397qScS6FBxQYsfWApfj5+psNyEj2uQKzI8wYORW6OH1Dqt591fxYRM5ScupznbIhUWJOoJoy5awwAI1eM5EzGGcMRuY/0zHT6/rsvSalJVAyuyOcDPicsIMx0WE6kHSHFijxryYXIrdGyCxExS8mpS9nx5NH58XeP5/aI2/k1/Vde+PoF0+G4hcNnD9N2Xlu+2vsVQX5BfNL/E6LDo02H5WSa1itW5JkDhyI3R4OHImKWklOXyiz0s+eNzgeVCmJO9zkAzEqcRcKBBMMRWdv2lO20mtuK745+R2RwJGsfWcudVe40HZYLaGRerMjzllyI3Dzdn0XELCWnLlV4JNIzO0DtqrdjWLNhADz52ZNkZGvHv+J8tusz2s5ry6/pv1K/Qn22PLGFlpVbmg7LRVQ5FStS5VREyamImKbk1KXyb/aFNx3wPK93fp3bQm5j98ndTEqYZDocS7Hb7UzfPJ2eS3pyIesCnWt25pvHvvGCqbyFqfMjVqTKqYjuzyJimpJTl/Lc9aaFhQeGM+O+GQBM/WYq733/nuGIrCE7N5tnv3iWuC/jsGNnWLNhLH94OWUCy5gOzcVUORUr0oZIIkpORcQ0Jacu5T0j873q9mJI4yHk2HMYvGwwz335HNm52abDMiY9M52eS3ry1ta3sGHjb53/xtv3v00pX8+toF+dHiUjVuQdg4ci16b7s4iYpeTUpbxrTdO8nvN4ue3LAEzbPI1737uXExdOGI7K9fJ35F2xewVBfkF88NAHPN/meWw2m+nQDNFukGJFqpyKqHIqIqYpOXUp7xqZ97H58Jd7/sKHD31IcKlgVu1fRYs5Lfgu9TvTobnM9pTttJzTsmBH3oRHEuhTr4/psAzTtF6xIu+6P4sUT4OHImKWklOX8s6R+T71+rD5ic3UKluLA6cP0Pqd1iz9YanpsJzu012f0nZeW1LOpRTsyNuicgvTYVmARubFirzz/ixSlO7PImKWklOX8t6R+QYVG7D1ya10rdWVi9kX6f9hf16If4Gc3BzToTmc3W7njc1v0GtJLy5kXaBLrS5euCPvtahyKlbkXcsuRIqn5FREzFJy6lLesyFSccoGlWX5w8t54a4XAJi6cSr3Lb6PkxdPGo7McbJzs3nmi2f405d/wo6dp2Kf4vMBn3vhjrzXos6PWJF3359F8uj+LCJmKTl1KY3M+/r48lqn11jSdwmlS5Xmy71f0mJOC3Yc3WE6tBJLz0ynx/s9+OfWf2LDxt87/52Z98300h15r0WVU7EiTesVUXIqIqYpOXUp753We7l+Dfqx8bGNVA+vzr5T+2j9Tms+TP7QdFi3JDM7k5lbZ1J/Rn2+2PMFQX5BfPjQhzzX5jkv3pH3WvSoArEi3Z9FlJyKiGlKTl1KI/OFNY5qzLYnt9GxRkfOZ53ngf97gJdXv+w261AvZl3kzS1vUuvNWoxYMYJDZw9RJawKCY8k0Lteb9PhWZh2gxQr0v1ZRIOHImKaklOX0sj85cqVLsfKQSt5rvVzAPx1/V/puaQnpzNOmw3sGs5fOs+0TdOoMb0Go1aO4kj6EaqEVeGff/wnu5/ZrR15r0vTesWKdH8WUeVUREzzMx2Ad9HIfHH8fPz4e5e/0ySqCU9+9iTLdy+n5ZyWfNL/E+pVqGc6vALpmenM2DqD/9n0Pxy/cByA6DLRvNT2JYY2HkqAX4DhCN2FOj9iRbo/i2hmi4iYpuTUpTQyfy2DGg0ipkIMvZb0YvfJ3TSd1ZSutbvSu25vut/RnXKlyxmJ60zGGd76z1tM2zytYGfhmmVrMq7tOAY3GqwNj26aKqdiRdqwTkSDhyJimpJTl9KjCq6n2W3N2DZsGwM+HMDq/av5dNenfLrrU3xtvrSNbkvvur3pVbcX1cpUc3ospy6eYvqW6UzfMr1gmvEd5e7g5bYvM6DhAPx89J/Prclv/1lANroNiTXo/iyi5FRETHOLNaczZsygRo0aBAYGEhsby/r1602HdIs0Mn8jKgZX5OvBX5P0VBIT2k2gcWRjcuw5rD2wllErRxH9RjSxs2P573X/zY/HfsRutzv0+mkX0nh59ctEvxHNqwmvcjrjNPXK12Nxn8Ukj0hmcOPBSkxLpHD7VwdIrELTekWUnIqIaZbvYS9dupS4uDhmzJjBXXfdxaxZs+jWrRvJyclUq+b86pljaVrvjbLZbDSOakzjqMZMbD+Rfaf28clPn7Dsp2VsOLiB7Snb2Z6ynfFrxlM7oja96/amd93etKrSCh/btcdcMrMzST2XWvBKOZeS97/pKaScS2H1/tWczzoPQMOKDRl/93j6xvS97nnlRgUW+vkiEGoqEJFCdH8WUXIqIqbZ7I4uOzlYq1ataNasGTNnziw4Vq9ePXr16sWUKVOu+/3Dhw9TtWpVDh06RJUqVZwZ6g24B1gDvA/0NxyL+zp2/hif7fqMZT8tI35fPJdyLhW8FxUSRc86Pbmzyp2cuHCiSPKZn4Ceyjh13Ws0jWrKK+1eoUedHkpKnaI0eZ2ft4EKhmMRAehH3jTzQ4DpfytETPkWaAZEAHMMxyKeJwToYjoIi+UGcjlLV04vXbpEYmIiL774YpHjXbp0YePGjcV+JzMzk8zMzILf09PTnRrjzdHIvCNUDK7I480e5/Fmj5Oemc7KPStZ9tMylu9eTuq5VGYlzmJW4qxrnsPf15+okCiiQqK4LeS2Iv9bp3wd2kW3w2azuegv8kah5CWnw00HInKZENMBiBiU3/5PAn1NBiIeqQ7wk+kgxOIsnZyeOHGCnJwcIiMjixyPjIwkNTW12O9MmTKFV1991RXh3YJGgC9Q0XQgHiM0IJQH6z/Ig/Uf5FLOJdbsX8Oyn5ax99ReIoMjiySdUSFR3Baa93PZwLJKPo2aBCwyHYTIZdoD4YZjEDGpNjASSDIch3gmd1uOJyZYOjnNd3kSYbfbr5pYjB07ltGjRxf8fuTIEWJiYpwa342bbToAj+bv60/X2l3pWrur6VDkup767SUiItZhA94yHYSIeDFLJ6fly5fH19f3iirpsWPHrqim5gsICCAgIKDg97Nnzzo1RhERERERESk5S+/04u/vT2xsLPHx8UWOx8fH06ZNG0NRiYiIiIiIiKNZunIKMHr0aAYPHkzz5s1p3bo1s2fP5uDBgwwfro1UREREREREPIXlk9N+/fqRlpbGpEmTSElJoUGDBqxYsYLo6GjToYmIiIiIiIiDWD45BRgxYgQjRowwHYaIiIiIiIg4iaXXnIqIiIiIiIh3UHIqIiIiIiIixik5FREREREREeOUnIqIiIiIiIhxSk5FRERERETEOCWnIiIiIiIiYpySUxERERERETFOyamIiIiIiIgYp+RUREREREREjFNyKiIiIiIiIsYpORURERERERHjlJyKiIiIiIiIcUpORURERERExDg/0wE4W25uLgApKSmGIxEREREREZPyc4L8HEGsxeOT06NHjwLQsmVLw5GIiIiIiIgVHD16lGrVqpkOQy5js9vtdtNBOFN2djbffvstkZGR+PiYncWcnp5OTEwMycnJhIaGGo1FPIfalTiD2pU4g9qVOIPaldyM3Nxcjh49StOmTfHz8/g6ndvx+OTUSs6ePUuZMmU4c+YMYWFhpsMRD6F2Jc6gdiXOoHYlzqB2JeI5tCGSiIiIiIiIGKfkVERERERERIxTcupCAQEBTJgwgYCAANOhiAdRuxJnULsSZ1C7EmdQuxLxHFpzKiIiIiIiIsapcioiIiIiIiLGKTkVERERERER45ScioiIiIiIiHFKTkVERERERMQ4JacuNGPGDGrUqEFgYCCxsbGsX7/edEjiRtatW0f37t2pVKkSNpuNjz/+uMj7drudiRMnUqlSJYKCgmjfvj0//vijmWDFLUyZMoUWLVoQGhpKxYoV6dWrF7t27SryGbUruVkzZ86kUaNGhIWFERYWRuvWrfniiy8K3lebEkeYMmUKNpuNuLi4gmNqWyLuT8mpiyxdupS4uDjGjRvHt99+S9u2benWrRsHDx40HZq4ifPnz9O4cWPeeuutYt+fOnUq06ZN46233mLr1q1ERUXRuXNn0tPTXRypuIuEhARGjhzJ5s2biY+PJzs7my5dunD+/PmCz6hdyc2qUqUKr732Gtu2bWPbtm3cc8899OzZsyBJUJuSktq6dSuzZ8+mUaNGRY6rbYl4ALu4RMuWLe3Dhw8vcqxu3br2F1980VBE4s4A+7Jlywp+z83NtUdFRdlfe+21gmMZGRn2MmXK2N9++20DEYo7OnbsmB2wJyQk2O12tStxnLJly9rnzp2rNiUllp6ebr/99tvt8fHx9nbt2tlHjRplt9t1vxLxFKqcusClS5dITEykS5cuRY536dKFjRs3GopKPMn+/ftJTU0t0sYCAgJo166d2pjcsDNnzgAQEREBqF1JyeXk5LBkyRLOnz9P69at1aakxEaOHMl9991Hp06dihxX2xLxDH6mA/AGJ06cICcnh8jIyCLHIyMjSU1NNRSVeJL8dlRcG/vll19MhCRuxm63M3r0aP7whz/QoEEDQO1Kbt2OHTto3bo1GRkZhISEsGzZMmJiYgqSBLUpuRVLlixh+/btbN269Yr3dL8S8QxKTl3IZrMV+d1ut19xTKQk1MbkVj399NN8//33bNiw4Yr31K7kZtWpU4ekpCROnz7Nhx9+yNChQ0lISCh4X21KbtahQ4cYNWoUX331FYGBgVf9nNqWiHvTtF4XKF++PL6+vldUSY8dO3bFCJ/IrYiKigJQG5Nb8swzz/Dpp5+yZs0aqlSpUnBc7Upulb+/P7Vr16Z58+ZMmTKFxo0bM336dLUpuWWJiYkcO3aM2NhY/Pz88PPzIyEhgTfffBM/P7+C9qO2JeLelJy6gL+/P7GxscTHxxc5Hh8fT5s2bQxFJZ6kRo0aREVFFWljly5dIiEhQW1Mrsput/P000/z0UcfsXr1amrUqFHkfbUrcRS73U5mZqbalNyyjh07smPHDpKSkgpezZs3Z+DAgSQlJVGzZk21LREPoGm9LjJ69GgGDx5M8+bNad26NbNnz+bgwYMMHz7cdGjiJs6dO8eePXsKft+/fz9JSUlERERQrVo14uLimDx5Mrfffju33347kydPpnTp0jz88MMGoxYrGzlyJIsXL+aTTz4hNDS0oOJQpkwZgoKCCp4hqHYlN+Oll16iW7duVK1alfT0dJYsWcLatWtZuXKl2pTcstDQ0IL18PmCg4MpV65cwXG1LRH3p+TURfr160daWhqTJk0iJSWFBg0asGLFCqKjo02HJm5i27ZtdOjQoeD30aNHAzB06FDmz5/PmDFjuHjxIiNGjODUqVO0atWKr776itDQUFMhi8XNnDkTgPbt2xc5Pm/ePB555BEAtSu5aUePHmXw4MGkpKRQpkwZGjVqxMqVK+ncuTOgNiXOo7Yl4v5sdrvdbjoIERERERER8W5acyoiIiIiIiLGKTkVERERERER45ScioiIiIiIiHFKTkVERERERMQ4JaciIiIiIiJinJJTERERERERMU7JqYiIiIiIiBin5FRERERERESMU3IqIiIuM3HiRJo0aWLs+uPHj2fYsGEFv7dv3564uDgjsWRmZlKtWjUSExONXF9ERMRqlJyKiIhD2Gy2a74eeeQRnn/+eVatWmUkvqNHjzJ9+nReeuklI9e/XEBAAM8//zwvvPCC6VBEREQswc90ACIi4hlSUlIKfl66dCmvvPIKu3btKjgWFBRESEgIISEhJsLjnXfeoXXr1lSvXt3I9Qu7dOkS/v7+DBw4kD//+c/s3LmTevXqmQ5LRETEKFVORUTEIaKiogpeZcqUwWazXXHs8mm9jzzyCL169WLy5MlERkYSHh7Oq6++SnZ2Nn/+85+JiIigSpUqvPvuu0WudeTIEfr160fZsmUpV64cPXv25MCBA9eMb8mSJfTo0eOK47m5uYwZM4aIiAiioqKYOHFikfcPHjxIz549CQkJISwsjIceeoijR49e8TcUFhcXR/v27Qt+b9++PU8//TSjR4+mfPnydO7cGYBy5crRpk0b3n///WvGLiIi4g2UnIqIiFGrV6/m119/Zd26dUybNo2JEydy//33U7ZsWbZs2cLw4cMZPnw4hw4dAuDChQt06NCBkJAQ1q1bx4YNGwgJCeHee+/l0qVLxV7j1KlT/PDDDzRv3vyK9xYsWEBwcDBbtmxh6tSpTJo0ifj4eADsdju9evXi5MmTJCQkEB8fz969e+nXr99N/50LFizAz8+Pb775hlmzZhUcb9myJevXr7/p84mIiHgaTesVERGjIiIiePPNN/Hx8aFOnTpMnTqVCxcuFKwNHTt2LK+99hrffPMN/fv3Z8mSJfj4+DB37lxsNhsA8+bNIzw8nLVr19KlS5crrvHLL79gt9upVKnSFe81atSICRMmAHD77bfz1ltvsWrVKjp37szXX3/N999/z/79+6latSoAixYton79+mzdupUWLVrc8N9Zu3Ztpk6desXxypUrX7fqKyIi4g1UORUREaPq16+Pj8/v/xxFRkbSsGHDgt99fX0pV64cx44dAyAxMZE9e/YQGhpasIY1IiKCjIwM9u7dW+w1Ll68CEBgYOAV7zVq1KjI77fddlvBtXbu3EnVqlULElOAmJgYwsPD2blz5039ncVVbSFvLe6FCxdu6lwiIiKeSJVTERExqlSpUkV+t9lsxR7Lzc0F8taIxsbG8q9//euKc1WoUKHYa5QvXx7Im957+WeudS273V5QnS2s8HEfHx/sdnuR97Oysq74TnBwcLGxnTx58qpxi4iIeBMlpyIi4laaNWvG0qVLqVixImFhYTf0nVq1ahEWFkZycjJ33HHHDV8rJiaGgwcPcujQoYLqaXJyMmfOnCnYXbdChQr88MMPRb6XlJR0RdJ7NT/88ANNmza94ZhEREQ8lab1ioiIWxk4cCDly5enZ8+erF+/nv3795OQkMCoUaM4fPhwsd/x8fGhU6dObNiw4aau1alTJxo1asTAgQPZvn07//nPfxgyZAjt2rUrmKZ7zz33sG3bNhYuXMju3buZMGHCFcnqtaxfv77YdbIiIiLeRsmpiIi4ldKlS7Nu3TqqVatGnz59qFevHo899hgXL168ZiV12LBhLFmypGDK7o2w2Wx8/PHHlC1blrvvvptOnTpRs2ZNli5dWvCZrl27Mn78eMaMGUOLFi1IT09nyJAhN3T+TZs2cebMGR544IEbjklERMRT2eyXL5QRERHxQHa7nTvvvJO4uDgGDBhgOhwAHnzwQZo2bVqwM7GIiIg3U+VURES8gs1mY/bs2WRnZ5sOBYDMzEwaN27Mn/70J9OhiIiIWIIqpyIiIiIiImKcKqciIiIiIiJinJJTERERERERMU7JqYiIiIiIiBin5FRERERERESMU3IqIiIiIiIixik5FREREREREeOUnIqIiIiIiIhxSk5FRERERETEOCWnIiIiIiIiYtz/A1Na9Tg6DJMQAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 7
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/example/Notebooks/graphs/simple_run_vegetative_stages/.gitignore b/example/Notebooks/graphs/simple_run_vegetative_stages/.gitignore
new file mode 100644
index 00000000..e69de29b
diff --git a/example/Notebooks/inputs/Ljutovac2002.csv b/example/Notebooks/inputs/Ljutovac2002.csv
new file mode 100644
index 00000000..01b6376d
--- /dev/null
+++ b/example/Notebooks/inputs/Ljutovac2002.csv
@@ -0,0 +1,12 @@
+metamer,leaf_Lmax,lamina_Lmax,sheath_Lmax,internode_Lmax,lamina_Wmax,lamina_area,facteurForme_lamina,RER,RER_confint
+1,11.066,8.213,2.853,,0.3,1.883,0.75,,
+2,12.104,9.231,2.873,,0.33,2.152,0.71,,
+3,12.277,9.412,2.865,,0.4,2.78,0.73,,
+4,13.314,9.987,3.327,,0.45,3.318,0.74,,
+5,15.476,11.398,4.077,,0.56,4.664,0.73,2.79E-06,3.35E-07
+6,19.971,13.646,6.326,0.101,0.75,7.713,0.75,1.76E-06,2.07E-07
+7,25.504,16.434,9.071,1.913,1.01,12.287,0.74,1.62E-06,8.33E-08
+8,31.816,19.665,12.151,6.091,1.21,17.758,0.75,1.44E-06,7.73E-08
+9,39.164,25.108,14.056,9.715,1.28,24.215,0.75,1.44E-06,5.56E-08
+10,45.994,28.683,17.311,14.497,1.43,30.762,0.75,1.44E-06,5.40E-08
+11,42.709,23.947,18.762,17.013,1.8,32.556,0.75,1.42E-06,4.61E-08
diff --git a/example/Notebooks/inputs/adel0000.pckl b/example/Notebooks/inputs/adel0000.pckl
new file mode 100644
index 00000000..741fbb78
Binary files /dev/null and b/example/Notebooks/inputs/adel0000.pckl differ
diff --git a/example/Notebooks/inputs/adel_pars.RData b/example/Notebooks/inputs/adel_pars.RData
new file mode 100644
index 00000000..c1685dea
Binary files /dev/null and b/example/Notebooks/inputs/adel_pars.RData differ
diff --git a/example/Notebooks/inputs/axes_initial_state.csv b/example/Notebooks/inputs/axes_initial_state.csv
new file mode 100644
index 00000000..f6c5170a
--- /dev/null
+++ b/example/Notebooks/inputs/axes_initial_state.csv
@@ -0,0 +1,6 @@
+plant,axis,status,nb_leaves,GA,SAM_height,SAM_temperature,delta_teq,delta_teq_roots,teq_since_primordium,sum_TT,cohort,mstruct,senesced_mstruct,minerals_plant
+1,MS,vegetative,10,False,0,20,3600,3600,0,0,1,0.05468138,0,0.002734069
+1,T1,vegetative,7,False,0,20,3600,3600,0,0,4,NA,0,NA
+1,T2,vegetative,6,False,0,20,3600,3600,0,0,5,NA,0,NA
+1,T3,vegetative,5,False,0,20,3600,3600,0,0,6,NA,0,NA
+1,T4,vegetative,4,False,0,20,3600,3600,0,0,7,NA,0,NA
diff --git a/example/Notebooks/inputs/elements_initial_state.csv b/example/Notebooks/inputs/elements_initial_state.csv
new file mode 100644
index 00000000..6c574ea3
--- /dev/null
+++ b/example/Notebooks/inputs/elements_initial_state.csv
@@ -0,0 +1,7 @@
+plant,axis,metamer,organ,element,age,length,Wmax,is_growing,mstruct,senesced_mstruct,Nstruct,amino_acids,proteins,fructan,sucrose,green_area,cytokinins,max_proteins,starch,nitrates,senesced_length_element,Nresidual,max_mstruct,is_over,Ag,Tr,Ts,triosesP
+1,MS,1,blade,LeafElement1,240,0.082,0.003,False,0.004106768,0,2.05E-05,1.69E+00,16.93543686,0,24.15745785,0.00019556,0.349075266,4.40E+03,0,0,0,0,0.004106768,False,0,0,20,0
+1,MS,1,sheath,StemElement,240,0.0285,,False,0.00171,0,0.00000855,0.202319701,2.023197008,0,10.05882353,0.000269,0.04104,1.13E+03,0,0,0,0,0.00171,False,0,0,20,0
+1,MS,2,blade,LeafElement1,160,0.092,0.0033,False,0.004837643,0,2.42E-05,2.36E+00,23.63639357,0,28.4567234,0.000230364,0.488601941,4.40E+03,0,0,0,0,0.004837643,False,0,0,20,0
+1,MS,2,sheath,HiddenElement,160,0.0285,,False,0.00228,0,0.0000114,0.382633557,3.826335573,6.705882353,13.41176471,0.000248,0.0798,1.60E+03,0,0,0,0,0.00228,False,0,0,20,0
+1,MS,2,sheath,StemElement,160,0.0005,,False,0.00004,0,0.0000002,0.006712869,0.067128694,0,0.235294118,0.000005,0.0014,1.60E+03,0,0,0,0,0.00004,False,0,0,20,0
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diff --git a/example/Notebooks/inputs/hiddenzones_initial_state.csv b/example/Notebooks/inputs/hiddenzones_initial_state.csv
new file mode 100644
index 00000000..0ae55997
--- /dev/null
+++ b/example/Notebooks/inputs/hiddenzones_initial_state.csv
@@ -0,0 +1,31 @@
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diff --git a/example/Notebooks/inputs/meteo_PAR100.csv b/example/Notebooks/inputs/meteo_PAR100.csv
new file mode 100644
index 00000000..e1bd3a13
--- /dev/null
+++ b/example/Notebooks/inputs/meteo_PAR100.csv
@@ -0,0 +1,4718 @@
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diff --git a/example/Notebooks/inputs/meteo_PAR1000.csv b/example/Notebooks/inputs/meteo_PAR1000.csv
new file mode 100644
index 00000000..84f23049
--- /dev/null
+++ b/example/Notebooks/inputs/meteo_PAR1000.csv
@@ -0,0 +1,4718 @@
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diff --git a/example/Notebooks/inputs/meteo_PAR1500.csv b/example/Notebooks/inputs/meteo_PAR1500.csv
new file mode 100644
index 00000000..d64526a9
--- /dev/null
+++ b/example/Notebooks/inputs/meteo_PAR1500.csv
@@ -0,0 +1,4718 @@
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diff --git a/example/Notebooks/inputs/meteo_PAR500.csv b/example/Notebooks/inputs/meteo_PAR500.csv
new file mode 100644
index 00000000..d106de19
--- /dev/null
+++ b/example/Notebooks/inputs/meteo_PAR500.csv
@@ -0,0 +1,4718 @@
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diff --git a/example/Notebooks/inputs/meteo_file.csv b/example/Notebooks/inputs/meteo_file.csv
new file mode 100644
index 00000000..4464c4f4
--- /dev/null
+++ b/example/Notebooks/inputs/meteo_file.csv
@@ -0,0 +1,4718 @@
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+4715,182,23,14.9,0,16.63333333,0.86,360,1.7
+4716,182,24,14.7,0,16.74615385,0.88,360,2.2
diff --git a/example/Notebooks/inputs/organs_initial_state.csv b/example/Notebooks/inputs/organs_initial_state.csv
new file mode 100644
index 00000000..17d36813
--- /dev/null
+++ b/example/Notebooks/inputs/organs_initial_state.csv
@@ -0,0 +1,3 @@
+plant,axis,organ,sucrose,amino_acids,nitrates,structure,starch,proteins,mstruct,senesced_mstruct,Nstruct,cytokinins
+1,MS,phloem,0,0,NA,NA,NA,NA,NA,NA,NA,NA
+1,MS,roots,202.5847905,2.19053373,21.9053373,NA,NA,NA,3.44E-02,0.00E+00,0.000172197,8.60E+00
diff --git a/example/Notebooks/inputs/phytoT.csv b/example/Notebooks/inputs/phytoT.csv
new file mode 100644
index 00000000..fbbf8eba
--- /dev/null
+++ b/example/Notebooks/inputs/phytoT.csv
@@ -0,0 +1,54 @@
+,Azim,Ed,El,Gd,Gl,Lindex,Ll,Lseed,Lw,axe,dincB,dpAngle,incB,n,pAngle,plant
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+32,184.2407447,0.004489903,0.01391612,0.004489903,0.080230913,1,0.171339472,1,0.011181609,MS,0,0,-999,33,-999,1
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+43,181.4010105,0.004489903,0.089098811,0.004489903,0.107633622,1,0.184331245,1,0.014359183,MS,0,0,-999,44,-999,1
+44,184.9183862,0.004489903,0.0985915,0.004489903,0.112503561,1,0.172247181,1,0.014578805,MS,0,0,-999,45,-999,1
+45,179.7356147,0.004489903,0.108527488,0.004489903,0.118061925,1,0.159285138,1,0.014714275,MS,0,0,-999,46,-999,1
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+47,160.4065401,0.004489903,0.130287118,0.004489903,0.130378987,1,0.133245473,1,0.014795226,MS,0,0,-999,48,-999,1
+48,195.2892567,0.004489903,0.141767283,0.004489903,0.136664239,1,0.120516932,1,0.014764502,MS,0,0,-999,49,-999,1
+49,177.2337294,0.004489903,0.153524505,0.004489903,0.142929457,1,0.108028402,1,0.014708915,MS,0,0,-999,50,-999,1
+50,0,0,0,0,0,-999,0,-999,0,MS,0,0,0,51,0,1
+51,0,0,0,0,0,-999,0,-999,0,MS,0,0,0,52,0,1
+52,0,0,0,0,0,-999,0,-999,0,MS,0,0,0,53,0,1
diff --git a/example/Notebooks/inputs/scenarios_list.csv b/example/Notebooks/inputs/scenarios_list.csv
new file mode 100644
index 00000000..4a26d591
--- /dev/null
+++ b/example/Notebooks/inputs/scenarios_list.csv
@@ -0,0 +1,5 @@
+Scenario,Simulation_Length,Run_Simulation,Scenario_label,Plant_Density,Inter_Row,constant_Conc_Nitrates,METEO_FILENAME,growthwheat:VMAX_ROOTS_GROWTH_PREFLO,elongwheat:max_nb_leaves
+1,48,True,N5U_PAR100_D1,1,1,35714,meteo_PAR100.csv,0.01539,50
+2,48,True,N50U_PAR100_D1,1,1,357143,meteo_PAR100.csv,0.01539,50
+3,48,True,N5U_PAR500_D500,500,0.15,35714.28571,meteo_PAR500.csv,0.01539,50
+4,48,True,N50U_PAR500_D500,500,0.15,357142.8571,meteo_PAR500.csv,0.01539,50
diff --git a/example/Notebooks/inputs/scene0000.bgeom b/example/Notebooks/inputs/scene0000.bgeom
new file mode 100644
index 00000000..ec3cab66
Binary files /dev/null and b/example/Notebooks/inputs/scene0000.bgeom differ
diff --git a/example/Notebooks/inputs/soils_initial_state.csv b/example/Notebooks/inputs/soils_initial_state.csv
new file mode 100644
index 00000000..91d45a00
--- /dev/null
+++ b/example/Notebooks/inputs/soils_initial_state.csv
@@ -0,0 +1,2 @@
+plant,axis,volume,nitrates,Tsoil
+1,MS,1,428571,12
diff --git a/example/Notebooks/main.py b/example/Notebooks/main.py
new file mode 100644
index 00000000..c8c68907
--- /dev/null
+++ b/example/Notebooks/main.py
@@ -0,0 +1,596 @@
+# -*- coding: latin-1 -*-
+
+import datetime
+import os
+import random
+import time
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from openalea.adel.adel_dynamic import AdelDyn
+from openalea.adel.echap_leaf import echap_leaves
+from openalea.fspmwheat import caribu_facade
+from openalea.fspmwheat import cnwheat_facade
+from openalea.fspmwheat import elongwheat_facade
+from openalea.fspmwheat import farquharwheat_facade
+from openalea.fspmwheat import fspmwheat_facade
+from openalea.fspmwheat import growthwheat_facade
+from openalea.fspmwheat import senescwheat_facade
+
+"""
+    main
+    ~~~~
+
+    A coupling of CN-Wheat, Farquhar-Wheat, Senesc-Wheat, Elong-Wheat, Growth-Wheat, Adel-Wheat and Caribu.
+    This script was used to simulate a field experiment performed in 1998/99 in Grignon (France). 
+    Results were published in Gauthier et al. 2020 (https://doi.org/10.1093/jxb/eraa276)
+    
+    :copyright: Copyright 2014-2016 INRA-ECOSYS, see AUTHORS.
+    :license: see LICENSE for details.
+
+"""
+
+random.seed(1234)
+np.random.seed(1234)
+
+AXES_INDEX_COLUMNS = ['t', 'plant', 'axis']
+ELEMENTS_INDEX_COLUMNS = ['t', 'plant', 'axis', 'metamer', 'organ', 'element']
+HIDDENZONES_INDEX_COLUMNS = ['t', 'plant', 'axis', 'metamer']
+ORGANS_INDEX_COLUMNS = ['t', 'plant', 'axis', 'organ']
+SOILS_INDEX_COLUMNS = ['t', 'plant', 'axis']
+
+
+def save_df_to_csv(df, outputs_filepath, precision):
+    try:
+        df.to_csv(outputs_filepath, na_rep='NA', index=False, float_format='%.{}f'.format(precision))
+    except IOError as err:
+        path, filename = os.path.split(outputs_filepath)
+        filename = os.path.splitext(filename)[0]
+        newfilename = 'ACTUAL_{}.csv'.format(filename)
+        newpath = os.path.join(path, newfilename)
+        df.to_csv(newpath, na_rep='NA', index=False, float_format='%.{}f'.format(precision))
+        warnings.warn('[{}] {}'.format(err.errno, err.strerror))
+        warnings.warn('File will be saved at {}'.format(newpath))
+
+
+def main(simulation_length, forced_start_time=0, run_simu=True, run_postprocessing=True, generate_graphs=True, run_from_outputs=False, stored_times=None,
+         option_static=False, show_3Dplant=True, tillers_replications=None, heterogeneous_canopy=True,
+         N_fertilizations=None, PLANT_DENSITY=None, update_parameters_all_models=None,
+         INPUTS_DIRPATH='inputs', METEO_FILENAME='meteo.csv',
+         OUTPUTS_DIRPATH='outputs', POSTPROCESSING_DIRPATH='postprocessing', GRAPHS_DIRPATH='graphs'):
+    """
+    Run a simulation of fspmwheat with coupling to several models
+
+    :param int simulation_length: length of the simulation (hours)
+    :param int forced_start_time: desired start time (hour)
+    :param bool run_simu: whether to run the simulation 
+    :param bool run_postprocessing: whether to run the postprocessing
+    :param bool generate_graphs: whether to run the generate graphs
+    :param bool run_from_outputs: whether to start a simulation from a specific time and initial states as found in previous outputs
+    :param str or list stored_times: Time steps when are stored the model outputs. Can be either 'all', a list or an empty list. Default to 'all'
+    :param bool option_static: Whether the model should be run for a static plant architecture
+    :param bool show_3Dplant: whether to plot the scene in pgl viewer
+    :param dict [str, float] tillers_replications: a dictionary with tiller id as key, and weight of replication as value.
+    :param bool heterogeneous_canopy: Whether to create a duplicated heterogeneous canopy from the initial mtg.
+    :param dict [int, float] or [str, float] N_fertilizations: a dictionary for N fertilisation regime {date: N_input}, with date in hour and N_input in �mol N nitrates
+                                               or {'constant_Conc_Nitrates': val} for constant nitrates concentrations
+    :param dict [int, int] PLANT_DENSITY: a dict with plant density per plant id (temporary used to account for different cultivars if needed) ; plant m-2
+    :param dict update_parameters_all_models: a dict to update model parameters
+                                             {'cnwheat': {'organ1': {'param1': 'val1', 'param2': 'val2'},
+                                                          'organ2': {'param1': 'val1', 'param2': 'val2'}
+                                                         },
+                                              'elongwheat': {'param1': 'val1', 'param2': 'val2'}
+                                             } 
+    :param str or dict INPUTS_DIRPATH: the path directory of inputs, can also be {'adel':str, 'plants':str, 'meteo':str, 'soils':str}
+                                                                    #  The directory at path 'adel' must contain files 'adel_pars.RData', 'adel0000.pckl' and 'scene0000.bgeom' for ADELWHEAT
+    :param str METEO_FILENAME: the name of the file with meteo data
+    :param str OUTPUTS_DIRPATH: the path to save outputs
+    :param str POSTPROCESSING_DIRPATH: the path to save postprocessings
+    :param str GRAPHS_DIRPATH: the path to save graphs
+    
+    """
+    # ---------------------------------------------
+    # ----- CONFIGURATION OF THE SIMULATION -------
+    # ---------------------------------------------
+
+    # -- SIMULATION PARAMETERS --
+
+    # Length of the simulation (in hours)
+    SIMULATION_LENGTH = simulation_length
+
+    # define the time step in hours for each simulator
+    CARIBU_TIMESTEP = 4
+    SENESCWHEAT_TIMESTEP = 1
+    FARQUHARWHEAT_TIMESTEP = 1
+    ELONGWHEAT_TIMESTEP = 1
+    GROWTHWHEAT_TIMESTEP = 1
+    CNWHEAT_TIMESTEP = 1
+
+    # Define default plant density (culm m-2)
+    if PLANT_DENSITY is None:
+        PLANT_DENSITY = {1: 250.}
+
+    # precision of floats used to write and format the output CSV files
+    OUTPUTS_PRECISION = 8
+
+    # number of seconds in 1 hour
+    HOUR_TO_SECOND_CONVERSION_FACTOR = 3600
+
+    # Name of the CSV files which will contain the outputs of the model
+    AXES_OUTPUTS_FILENAME = 'axes_outputs.csv'
+    ORGANS_OUTPUTS_FILENAME = 'organs_outputs.csv'
+    HIDDENZONES_OUTPUTS_FILENAME = 'hiddenzones_outputs.csv'
+    ELEMENTS_OUTPUTS_FILENAME = 'elements_outputs.csv'
+    SOILS_OUTPUTS_FILENAME = 'soils_outputs.csv'
+
+    # -- INPUTS CONFIGURATION --
+
+    # Path of the directory which contains the inputs of the model
+    INPUTS_DIRPATH = INPUTS_DIRPATH
+
+    # Name of the CSV files which describes the initial state of the system
+    AXES_INITIAL_STATE_FILENAME = 'axes_initial_state.csv'
+    ORGANS_INITIAL_STATE_FILENAME = 'organs_initial_state.csv'
+    HIDDENZONES_INITIAL_STATE_FILENAME = 'hiddenzones_initial_state.csv'
+    ELEMENTS_INITIAL_STATE_FILENAME = 'elements_initial_state.csv'
+    SOILS_INITIAL_STATE_FILENAME = 'soils_initial_state.csv'
+    # Read the inputs from CSV files and create inputs dataframes
+    inputs_dataframes = {}
+    if run_from_outputs:
+
+        previous_outputs_dataframes = {}
+
+        for initial_state_filename, outputs_filename, index_columns in ((AXES_INITIAL_STATE_FILENAME, AXES_OUTPUTS_FILENAME, AXES_INDEX_COLUMNS),
+                                                                        (ORGANS_INITIAL_STATE_FILENAME, ORGANS_OUTPUTS_FILENAME, ORGANS_INDEX_COLUMNS),
+                                                                        (HIDDENZONES_INITIAL_STATE_FILENAME, HIDDENZONES_OUTPUTS_FILENAME, HIDDENZONES_INDEX_COLUMNS),
+                                                                        (ELEMENTS_INITIAL_STATE_FILENAME, ELEMENTS_OUTPUTS_FILENAME, ELEMENTS_INDEX_COLUMNS),
+                                                                        (SOILS_INITIAL_STATE_FILENAME, SOILS_OUTPUTS_FILENAME, SOILS_INDEX_COLUMNS)):
+
+            previous_outputs_dataframe = pd.read_csv(os.path.join(OUTPUTS_DIRPATH, outputs_filename))
+            # Convert NaN to None
+            previous_outputs_dataframes[outputs_filename] = previous_outputs_dataframe.where(previous_outputs_dataframe.notnull(), None)
+
+            assert 't' in previous_outputs_dataframes[outputs_filename].columns
+            if forced_start_time > 0:
+                new_start_time = forced_start_time + 1
+                previous_outputs_dataframes[outputs_filename] = previous_outputs_dataframes[outputs_filename][previous_outputs_dataframes[outputs_filename]['t'] <= forced_start_time]
+            else:
+                last_t_step = max(previous_outputs_dataframes[outputs_filename]['t'])
+                new_start_time = last_t_step + 1
+
+            if initial_state_filename == ELEMENTS_INITIAL_STATE_FILENAME:
+                elements_previous_outputs = previous_outputs_dataframes[outputs_filename]
+                new_initial_state = elements_previous_outputs[~elements_previous_outputs.is_over.isnull()]
+            else:
+                new_initial_state = previous_outputs_dataframes[outputs_filename]
+            idx = new_initial_state.groupby([col for col in index_columns if col != 't'])['t'].transform('max') == new_initial_state['t']
+            inputs_dataframes[initial_state_filename] = new_initial_state[idx].drop(['t'], axis=1)
+
+        # Make sure boolean columns have either type bool or float
+        bool_columns = ['is_over', 'is_growing', 'leaf_is_emerged', 'internode_is_visible', 'leaf_is_growing', 'internode_is_growing', 'leaf_is_remobilizing', 'internode_is_remobilizing']
+        for df in [inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME], inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME]]:
+            for cln in bool_columns:
+                if cln in df.keys():
+                    df.loc[:, cln] = df.loc[:, cln].replace({'False': 0.0, 'True': 1.0})
+                    df.loc[:, cln] = pd.to_numeric(df.loc[:, cln])
+    else:
+        new_start_time = -1
+        for inputs_filename in (AXES_INITIAL_STATE_FILENAME,
+                                ORGANS_INITIAL_STATE_FILENAME,
+                                HIDDENZONES_INITIAL_STATE_FILENAME,
+                                ELEMENTS_INITIAL_STATE_FILENAME,
+                                SOILS_INITIAL_STATE_FILENAME):
+            inputs_dataframe = pd.read_csv(os.path.join(INPUTS_DIRPATH, inputs_filename))
+            inputs_dataframes[inputs_filename] = inputs_dataframe.where(inputs_dataframe.notnull(), None)
+
+    # Start time of the simulation
+    START_TIME = max(0, new_start_time)
+
+    # Name of the CSV files which contains the meteo data
+    meteo = pd.read_csv(os.path.join(INPUTS_DIRPATH, METEO_FILENAME), index_col='t')
+
+    # -- OUTPUTS CONFIGURATION --
+
+    # Save the outputs with a full scan of the MTG at each time step (or at selected time steps)
+    UPDATE_SHARED_DF = False
+    if stored_times is None:
+        stored_times = 'all'
+    if not (stored_times == 'all' or type(stored_times) == list):
+        print('stored_times should be either \'all\', a list or an empty list.')
+        raise
+
+    # create empty dataframes to shared data between the models
+    shared_axes_inputs_outputs_df = pd.DataFrame()
+    shared_organs_inputs_outputs_df = pd.DataFrame()
+    shared_hiddenzones_inputs_outputs_df = pd.DataFrame()
+    shared_elements_inputs_outputs_df = pd.DataFrame()
+    shared_soils_inputs_outputs_df = pd.DataFrame()
+
+    # define lists of dataframes to store the inputs and the outputs of the models at each step.
+    axes_all_data_list = []
+    organs_all_data_list = []  # organs which belong to axes: roots, phloem, grains
+    hiddenzones_all_data_list = []
+    elements_all_data_list = []
+    soils_all_data_list = []
+
+    all_simulation_steps = []  # to store the steps of the simulation
+
+    # -- POSTPROCESSING CONFIGURATION --
+
+    # Name of the CSV files which will contain the postprocessing of the model
+    AXES_POSTPROCESSING_FILENAME = 'axes_postprocessing.csv'
+    ORGANS_POSTPROCESSING_FILENAME = 'organs_postprocessing.csv'
+    HIDDENZONES_POSTPROCESSING_FILENAME = 'hiddenzones_postprocessing.csv'
+    ELEMENTS_POSTPROCESSING_FILENAME = 'elements_postprocessing.csv'
+    SOILS_POSTPROCESSING_FILENAME = 'soils_postprocessing.csv'
+
+    # -- ADEL and MTG CONFIGURATION --
+
+    # read adelwheat inputs at t0
+    adel_wheat = AdelDyn(seed=1, scene_unit='m', leaves=echap_leaves(xy_model='Soissons_byleafclass'))
+    g = adel_wheat.load(directory=INPUTS_DIRPATH)
+
+    # ---------------------------------------------
+    # ----- CONFIGURATION OF THE FACADES -------
+    # ---------------------------------------------
+
+    # -- ELONGWHEAT (created first because it is the only facade to add new metamers) --
+    # Initial states
+    elongwheat_hiddenzones_initial_state = inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME][
+        elongwheat_facade.converter.HIDDENZONE_TOPOLOGY_COLUMNS + [i for i in elongwheat_facade.simulation.HIDDENZONE_INPUTS if i in
+                                                                   inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME].columns]].copy()
+    elongwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][
+        elongwheat_facade.converter.ELEMENT_TOPOLOGY_COLUMNS + [i for i in elongwheat_facade.simulation.ELEMENT_INPUTS if i in
+                                                                inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()
+    elongwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][
+        elongwheat_facade.converter.AXIS_TOPOLOGY_COLUMNS + [i for i in elongwheat_facade.simulation.AXIS_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()
+
+    phytoT_df = pd.read_csv(os.path.join(INPUTS_DIRPATH, 'phytoT.csv'))
+
+    # Update parameters if specified
+    if update_parameters_all_models and 'elongwheat' in update_parameters_all_models:
+        update_parameters_elongwheat = update_parameters_all_models['elongwheat']
+    else:
+        update_parameters_elongwheat = None
+
+    # Facade initialisation
+    elongwheat_facade_ = elongwheat_facade.ElongWheatFacade(g,
+                                                            ELONGWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,
+                                                            elongwheat_axes_initial_state,
+                                                            elongwheat_hiddenzones_initial_state,
+                                                            elongwheat_elements_initial_state,
+                                                            shared_axes_inputs_outputs_df,
+                                                            shared_hiddenzones_inputs_outputs_df,
+                                                            shared_elements_inputs_outputs_df,
+                                                            adel_wheat, phytoT_df,
+                                                            update_parameters_elongwheat,
+                                                            update_shared_df=UPDATE_SHARED_DF)
+
+    # -- CARIBU --
+    caribu_facade_ = caribu_facade.CaribuFacade(g,
+                                                shared_elements_inputs_outputs_df,
+                                                adel_wheat,
+                                                update_shared_df=UPDATE_SHARED_DF)
+
+    # -- SENESCWHEAT --
+    # Initial states
+    senescwheat_roots_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].loc[inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME]['organ'] == 'roots'][
+        senescwheat_facade.converter.ROOTS_TOPOLOGY_COLUMNS +
+        [i for i in senescwheat_facade.converter.SENESCWHEAT_ROOTS_INPUTS if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    senescwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][
+        senescwheat_facade.converter.ELEMENTS_TOPOLOGY_COLUMNS +
+        [i for i in senescwheat_facade.converter.SENESCWHEAT_ELEMENTS_INPUTS if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    senescwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][
+        senescwheat_facade.converter.AXES_TOPOLOGY_COLUMNS +
+        [i for i in senescwheat_facade.converter.SENESCWHEAT_AXES_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()
+
+    # Update parameters if specified
+    if update_parameters_all_models and 'senescwheat' in update_parameters_all_models:
+        update_parameters_senescwheat = update_parameters_all_models['senescwheat']
+    else:
+        update_parameters_senescwheat = None
+
+    # Facade initialisation
+    senescwheat_facade_ = senescwheat_facade.SenescWheatFacade(g,
+                                                               SENESCWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,
+                                                               senescwheat_roots_initial_state,
+                                                               senescwheat_axes_initial_state,
+                                                               senescwheat_elements_initial_state,
+                                                               shared_organs_inputs_outputs_df,
+                                                               shared_axes_inputs_outputs_df,
+                                                               shared_elements_inputs_outputs_df,
+                                                               update_parameters_senescwheat,
+                                                               update_shared_df=UPDATE_SHARED_DF)
+
+    # -- FARQUHARWHEAT --
+    # Initial states
+    farquharwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][
+        farquharwheat_facade.converter.ELEMENT_TOPOLOGY_COLUMNS +
+        [i for i in farquharwheat_facade.converter.FARQUHARWHEAT_ELEMENTS_INPUTS if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    farquharwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][
+        farquharwheat_facade.converter.AXIS_TOPOLOGY_COLUMNS +
+        [i for i in farquharwheat_facade.converter.FARQUHARWHEAT_AXES_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()
+
+    # Use the initial version of the photosynthesis sub-model (as in Barillot et al. 2016, and in Gauthier et al. 2020)
+    update_parameters_farquharwheat = {'SurfacicProteins': False, 'NSC_Retroinhibition': False}
+
+    # Facade initialisation
+    farquharwheat_facade_ = farquharwheat_facade.FarquharWheatFacade(g,
+                                                                     farquharwheat_elements_initial_state,
+                                                                     farquharwheat_axes_initial_state,
+                                                                     shared_elements_inputs_outputs_df,
+                                                                     update_parameters_farquharwheat,
+                                                                     update_shared_df=UPDATE_SHARED_DF)
+
+    # -- GROWTHWHEAT --
+    # Initial states
+    growthwheat_hiddenzones_initial_state = inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME][
+        growthwheat_facade.converter.HIDDENZONE_TOPOLOGY_COLUMNS +
+        [i for i in growthwheat_facade.simulation.HIDDENZONE_INPUTS if i in inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME].columns]].copy()
+
+    growthwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][
+        growthwheat_facade.converter.ELEMENT_TOPOLOGY_COLUMNS +
+        [i for i in growthwheat_facade.simulation.ELEMENT_INPUTS if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    growthwheat_root_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].loc[inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME]['organ'] == 'roots'][
+        growthwheat_facade.converter.ROOT_TOPOLOGY_COLUMNS +
+        [i for i in growthwheat_facade.simulation.ROOT_INPUTS if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    growthwheat_axes_initial_state = inputs_dataframes[AXES_INITIAL_STATE_FILENAME][
+        growthwheat_facade.converter.AXIS_TOPOLOGY_COLUMNS +
+        [i for i in growthwheat_facade.simulation.AXIS_INPUTS if i in inputs_dataframes[AXES_INITIAL_STATE_FILENAME].columns]].copy()
+
+    # Update parameters if specified
+    if update_parameters_all_models and 'growthwheat' in update_parameters_all_models:
+        update_parameters_growthwheat = update_parameters_all_models['growthwheat']
+    else:
+        update_parameters_growthwheat = None
+
+    # Facade initialisation
+    growthwheat_facade_ = growthwheat_facade.GrowthWheatFacade(g,
+                                                               GROWTHWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,
+                                                               growthwheat_hiddenzones_initial_state,
+                                                               growthwheat_elements_initial_state,
+                                                               growthwheat_root_initial_state,
+                                                               growthwheat_axes_initial_state,
+                                                               shared_organs_inputs_outputs_df,
+                                                               shared_hiddenzones_inputs_outputs_df,
+                                                               shared_elements_inputs_outputs_df,
+                                                               shared_axes_inputs_outputs_df,
+                                                               update_parameters_growthwheat,
+                                                               update_shared_df=UPDATE_SHARED_DF)
+
+    # -- CNWHEAT --
+    # Initial states
+    cnwheat_organs_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME][
+        [i for i in cnwheat_facade.cnwheat_converter.ORGANS_VARIABLES if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    cnwheat_hiddenzones_initial_state = inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME][
+        [i for i in cnwheat_facade.cnwheat_converter.HIDDENZONE_VARIABLES if i in inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME].columns]].copy()
+
+    cnwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][
+        [i for i in cnwheat_facade.cnwheat_converter.ELEMENTS_VARIABLES if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    cnwheat_soils_initial_state = inputs_dataframes[SOILS_INITIAL_STATE_FILENAME][
+        [i for i in cnwheat_facade.cnwheat_converter.SOILS_VARIABLES if i in inputs_dataframes[SOILS_INITIAL_STATE_FILENAME].columns]].copy()
+
+    # Update parameters if specified
+    if update_parameters_all_models and 'cnwheat' in update_parameters_all_models:
+        update_parameters_cnwheat = update_parameters_all_models['cnwheat']
+    else:
+        update_parameters_cnwheat = {}
+
+    # Facade initialisation
+    cnwheat_facade_ = cnwheat_facade.CNWheatFacade(g,
+                                                   CNWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR,
+                                                   PLANT_DENSITY,
+                                                   update_parameters_cnwheat,
+                                                   cnwheat_organs_initial_state,
+                                                   cnwheat_hiddenzones_initial_state,
+                                                   cnwheat_elements_initial_state,
+                                                   cnwheat_soils_initial_state,
+                                                   shared_axes_inputs_outputs_df,
+                                                   shared_organs_inputs_outputs_df,
+                                                   shared_hiddenzones_inputs_outputs_df,
+                                                   shared_elements_inputs_outputs_df,
+                                                   shared_soils_inputs_outputs_df,
+                                                   update_shared_df=UPDATE_SHARED_DF)
+
+    # Run cnwheat with constant nitrates concentration in the soil if specified
+    if N_fertilizations is not None and 'constant_Conc_Nitrates' in N_fertilizations.keys():
+        cnwheat_facade_.soils[(1, 'MS')].constant_Conc_Nitrates = True  # TODO: make (1, 'MS') more general
+        cnwheat_facade_.soils[(1, 'MS')].nitrates = N_fertilizations['constant_Conc_Nitrates'] * cnwheat_facade_.soils[(1, 'MS')].volume
+
+    # -- FSPMWHEAT --
+    # Facade initialisation
+    fspmwheat_facade_ = fspmwheat_facade.FSPMWheatFacade(g)
+
+    # Update geometry
+    adel_wheat.update_geometry(g)
+
+    # ---------------------------------------------
+    # -----      RUN OF THE SIMULATION      -------
+    # ---------------------------------------------
+
+    if run_simu:
+
+        try:
+            current_time_of_the_system = time.time()
+            for t_caribu in range(START_TIME, SIMULATION_LENGTH, SENESCWHEAT_TIMESTEP):
+                # run Caribu
+                PARi = meteo.loc[t_caribu, ['PARi']].iloc[0]
+                DOY = meteo.loc[t_caribu, ['DOY']].iloc[0]
+                hour = meteo.loc[t_caribu, ['hour']].iloc[0]
+                PARi_next_hours = meteo.loc[range(t_caribu, t_caribu + CARIBU_TIMESTEP), ['PARi']].sum().values[0]
+
+                if (t_caribu % CARIBU_TIMESTEP == 0) and (PARi_next_hours > 0):
+                    run_caribu = True
+                else:
+                    run_caribu = False
+
+                caribu_facade_.run(run_caribu, energy=PARi, DOY=DOY, hourTU=hour, latitude=48.85, sun_sky_option='sky', heterogeneous_canopy=heterogeneous_canopy, plant_density=PLANT_DENSITY[1])
+
+                for t_senescwheat in range(t_caribu, t_caribu + SENESCWHEAT_TIMESTEP, SENESCWHEAT_TIMESTEP):
+                    # run SenescWheat
+                    senescwheat_facade_.run()
+
+                    # Test for dead plant # TODO: adapt in case of multiple plants
+                    if not shared_elements_inputs_outputs_df.empty and \
+                            np.nansum(shared_elements_inputs_outputs_df.loc[shared_elements_inputs_outputs_df['element'].isin(['StemElement', 'LeafElement1']), 'green_area']) == 0:
+                        # append the inputs and outputs at current step to global lists
+                        all_simulation_steps.append(t_senescwheat)
+                        axes_all_data_list.append(shared_axes_inputs_outputs_df.copy())
+                        organs_all_data_list.append(shared_organs_inputs_outputs_df.copy())
+                        hiddenzones_all_data_list.append(shared_hiddenzones_inputs_outputs_df.copy())
+                        elements_all_data_list.append(shared_elements_inputs_outputs_df.copy())
+                        soils_all_data_list.append(shared_soils_inputs_outputs_df.copy())
+                        break
+
+                    # Run the rest of the model if the plant is alive
+                    for t_farquharwheat in range(t_senescwheat, t_senescwheat + SENESCWHEAT_TIMESTEP, FARQUHARWHEAT_TIMESTEP):
+                        # get the meteo of the current step
+                        Ta, ambient_CO2, RH, Ur = meteo.loc[t_farquharwheat, ['air_temperature', 'ambient_CO2', 'humidity', 'Wind']]
+
+                        # run FarquharWheat
+                        farquharwheat_facade_.run(Ta, ambient_CO2, RH, Ur)
+
+                        for t_elongwheat in range(t_farquharwheat, t_farquharwheat + FARQUHARWHEAT_TIMESTEP, ELONGWHEAT_TIMESTEP):
+                            # run ElongWheat
+                            Tair, Tsoil = meteo.loc[t_elongwheat, ['air_temperature', 'soil_temperature']]
+                            elongwheat_facade_.run(Tair, Tsoil, option_static=option_static)
+
+                            # Update geometry
+                            adel_wheat.update_geometry(g)
+
+                            for t_growthwheat in range(t_elongwheat, t_elongwheat + ELONGWHEAT_TIMESTEP, GROWTHWHEAT_TIMESTEP):
+                                # run GrowthWheat
+                                growthwheat_facade_.run()
+
+                                for t_cnwheat in range(t_growthwheat, t_growthwheat + GROWTHWHEAT_TIMESTEP, CNWHEAT_TIMESTEP):
+                                    print('t cnwheat is {}'.format(t_cnwheat))
+
+                                    # N fertilization if any
+                                    if N_fertilizations is not None and len(N_fertilizations) > 0:
+                                        if t_cnwheat in N_fertilizations.keys():
+                                            cnwheat_facade_.soils[(1, 'MS')].nitrates += N_fertilizations[t_cnwheat]
+
+                                    if t_cnwheat > 0:
+                                        # run CNWheat
+                                        Tair = meteo.loc[t_elongwheat, 'air_temperature']
+                                        Tsoil = meteo.loc[t_elongwheat, 'soil_temperature']
+                                        cnwheat_facade_.run(Tair, Tsoil, tillers_replications)
+
+                                    # append outputs at current step to global lists
+                                    if (stored_times == 'all') or (t_cnwheat in stored_times):
+                                        axes_outputs, elements_outputs, hiddenzones_outputs, organs_outputs, soils_outputs = fspmwheat_facade_.build_outputs_df_from_MTG()
+
+                                        all_simulation_steps.append(t_cnwheat)
+                                        axes_all_data_list.append(axes_outputs)
+                                        organs_all_data_list.append(organs_outputs)
+                                        hiddenzones_all_data_list.append(hiddenzones_outputs)
+                                        elements_all_data_list.append(elements_outputs)
+                                        soils_all_data_list.append(soils_outputs)
+
+                else:
+                    # Continue if SenescWheat loop wasn't broken because of dead plant.
+                    continue
+                # SenescWheat loop was broken, break the Caribu loop.
+                break
+
+            execution_time = int(time.time() - current_time_of_the_system)
+            print('\n' 'Simulation run in {}'.format(str(datetime.timedelta(seconds=execution_time))))
+            print('Creating postprocessing and graphs...')
+
+        finally:
+            # convert list of outputs into dataframes
+            outputs_df_dict = {}
+            for outputs_df_list, outputs_filename, index_columns in ((axes_all_data_list, AXES_OUTPUTS_FILENAME, AXES_INDEX_COLUMNS),
+                                                                     (organs_all_data_list, ORGANS_OUTPUTS_FILENAME, ORGANS_INDEX_COLUMNS),
+                                                                     (hiddenzones_all_data_list, HIDDENZONES_OUTPUTS_FILENAME, HIDDENZONES_INDEX_COLUMNS),
+                                                                     (elements_all_data_list, ELEMENTS_OUTPUTS_FILENAME, ELEMENTS_INDEX_COLUMNS),
+                                                                     (soils_all_data_list, SOILS_OUTPUTS_FILENAME, SOILS_INDEX_COLUMNS)):
+                outputs_filepath = os.path.join(OUTPUTS_DIRPATH, outputs_filename)
+                outputs_df = pd.concat(outputs_df_list, keys=all_simulation_steps, sort=False)
+                outputs_df.reset_index(0, inplace=True)
+                outputs_df.rename({'level_0': 't'}, axis=1, inplace=True)
+                outputs_df = outputs_df.reindex(index_columns + outputs_df.columns.difference(index_columns).tolist(), axis=1, copy=False)
+                if run_from_outputs:
+                    outputs_df = pd.concat([previous_outputs_dataframes[outputs_filename], outputs_df], sort=False)
+                outputs_df.fillna(value=np.nan, inplace=True)  # Convert back None to NaN
+                save_df_to_csv(outputs_df, outputs_filepath, OUTPUTS_PRECISION)
+                outputs_file_basename = outputs_filename.split('.')[0]
+                outputs_df_dict[outputs_file_basename] = outputs_df.reset_index()
+
+    # ---------------------------------------------
+    # -----      POST-PROCESSING      -------
+    # ---------------------------------------------
+
+    if run_postprocessing:
+        # Retrieve outputs dataframes from precedent simulation run
+        delta_t = CNWHEAT_TIMESTEP * HOUR_TO_SECOND_CONVERSION_FACTOR
+
+        # run the postprocessing
+        axes_postprocessing_file_basename = AXES_POSTPROCESSING_FILENAME.split('.')[0]
+        hiddenzones_postprocessing_file_basename = HIDDENZONES_POSTPROCESSING_FILENAME.split('.')[0]
+        organs_postprocessing_file_basename = ORGANS_POSTPROCESSING_FILENAME.split('.')[0]
+        elements_postprocessing_file_basename = ELEMENTS_POSTPROCESSING_FILENAME.split('.')[0]
+        soils_postprocessing_file_basename = SOILS_POSTPROCESSING_FILENAME.split('.')[0]
+
+        postprocessing_df_dict = {}
+        (postprocessing_df_dict[axes_postprocessing_file_basename],
+         postprocessing_df_dict[hiddenzones_postprocessing_file_basename],
+         postprocessing_df_dict[organs_postprocessing_file_basename],
+         postprocessing_df_dict[elements_postprocessing_file_basename],
+         postprocessing_df_dict[soils_postprocessing_file_basename]) \
+            = cnwheat_facade.CNWheatFacade.postprocessing(axes_outputs_df=outputs_df_dict[AXES_OUTPUTS_FILENAME.split('.')[0]],
+                                                          hiddenzone_outputs_df=outputs_df_dict[HIDDENZONES_OUTPUTS_FILENAME.split('.')[0]],
+                                                          organs_outputs_df=outputs_df_dict[ORGANS_OUTPUTS_FILENAME.split('.')[0]],
+                                                          elements_outputs_df=outputs_df_dict[ELEMENTS_OUTPUTS_FILENAME.split('.')[0]],
+                                                          soils_outputs_df=outputs_df_dict[SOILS_OUTPUTS_FILENAME.split('.')[0]],
+                                                          delta_t=delta_t)
+
+        for postprocessing_file_basename, postprocessing_filename in ((axes_postprocessing_file_basename, AXES_POSTPROCESSING_FILENAME),
+                                                                      (hiddenzones_postprocessing_file_basename, HIDDENZONES_POSTPROCESSING_FILENAME),
+                                                                      (organs_postprocessing_file_basename, ORGANS_POSTPROCESSING_FILENAME),
+                                                                      (elements_postprocessing_file_basename, ELEMENTS_POSTPROCESSING_FILENAME),
+                                                                      (soils_postprocessing_file_basename, SOILS_POSTPROCESSING_FILENAME)):
+            postprocessing_filepath = os.path.join(POSTPROCESSING_DIRPATH, postprocessing_filename)
+            postprocessing_df_dict[postprocessing_file_basename].to_csv(postprocessing_filepath, na_rep='NA', index=False, float_format='%.{}f'.format(OUTPUTS_PRECISION))
+
+    # ---------------------------------------------
+    # -----            GRAPHS               -------
+    # ---------------------------------------------
+
+    if generate_graphs:
+        # Retrieve last computed post-processing dataframes
+        axes_postprocessing_file_basename = AXES_POSTPROCESSING_FILENAME.split('.')[0]
+        organs_postprocessing_file_basename = ORGANS_POSTPROCESSING_FILENAME.split('.')[0]
+        hiddenzones_postprocessing_file_basename = HIDDENZONES_POSTPROCESSING_FILENAME.split('.')[0]
+        elements_postprocessing_file_basename = ELEMENTS_POSTPROCESSING_FILENAME.split('.')[0]
+        soils_postprocessing_file_basename = SOILS_POSTPROCESSING_FILENAME.split('.')[0]
+
+        # --- Generate graphs from postprocessing files
+        plt.ioff()
+        df_elt = postprocessing_df_dict[elements_postprocessing_file_basename]
+        df_SAM = pd.read_csv(os.path.join(OUTPUTS_DIRPATH, AXES_OUTPUTS_FILENAME))
+
+        cnwheat_facade.CNWheatFacade.graphs(axes_postprocessing_df=postprocessing_df_dict[axes_postprocessing_file_basename],
+                                            hiddenzones_postprocessing_df=postprocessing_df_dict[hiddenzones_postprocessing_file_basename],
+                                            organs_postprocessing_df=postprocessing_df_dict[organs_postprocessing_file_basename],
+                                            elements_postprocessing_df=postprocessing_df_dict[elements_postprocessing_file_basename],
+                                            soils_postprocessing_df=postprocessing_df_dict[soils_postprocessing_file_basename],
+                                            graphs_dirpath=GRAPHS_DIRPATH)
+    print('End of execution')
+
+
+if __name__ == '__main__':
+    main(2500, forced_start_time=0, run_simu=False, run_postprocessing=False, generate_graphs=False, run_from_outputs=False,
+         show_3Dplant=False,
+         option_static=False, tillers_replications={'T1': 0.5, 'T2': 0.5, 'T3': 0.5, 'T4': 0.5},
+         heterogeneous_canopy=True, N_fertilizations={1440: 357143, 2520: 1000000},
+         PLANT_DENSITY={1: 250}, METEO_FILENAME='meteo_Ljutovac2002.csv')
diff --git a/example/Notebooks/outputs/simple_run_vegetative_stages/.gitignore b/example/Notebooks/outputs/simple_run_vegetative_stages/.gitignore
new file mode 100644
index 00000000..e69de29b
diff --git a/example/Notebooks/postprocessing/simple_run_vegetative_stages/.gitignore b/example/Notebooks/postprocessing/simple_run_vegetative_stages/.gitignore
new file mode 100644
index 00000000..e69de29b
diff --git a/example/Notebooks/tools.py b/example/Notebooks/tools.py
new file mode 100644
index 00000000..2758ff28
--- /dev/null
+++ b/example/Notebooks/tools.py
@@ -0,0 +1,39 @@
+import os
+import pandas as pd
+from collections import deque
+
+
+def is_int(input):
+    try:
+        num = int(input)
+    except ValueError:
+        return False
+    return True
+
+
+def _buildDic(keyList, val, dic):
+    if len(keyList) == 1:
+        dic[keyList[0]] = val
+        return
+
+    newDic = dic.get(keyList[0], {})
+    dic[keyList[0]] = newDic
+    _buildDic(keyList[1:], val, newDic)
+
+
+def buildDic(dict_scenario, dic=None):
+    if not dic:
+        dic = {}
+
+    for k, v in dict_scenario.items():
+        if not pd.isnull(v):
+            keyList = k.split(':')
+            keyList_converted = []
+            for kk in keyList:
+                if is_int(kk):
+                    keyList_converted.append(int(kk))
+                else:
+                    keyList_converted.append(kk)
+            _buildDic(keyList_converted, v, dic)
+
+    return dic
diff --git a/example/Scenarios_monoculms/run_fspmwheat.py b/example/Scenarios_monoculms/run_fspmwheat.py
index c52a5eb1..2fb6bfce 100644
--- a/example/Scenarios_monoculms/run_fspmwheat.py
+++ b/example/Scenarios_monoculms/run_fspmwheat.py
@@ -11,9 +11,9 @@
 
 from openalea.fspmwheat import fspmwheat_postprocessing
 
-from example.example_fspmwheat.Scenarios_monoculms import main
-from example.example_fspmwheat.Scenarios_monoculms import tools
-from example.example_fspmwheat.Scenarios_monoculms import additional_graphs
+from example.Scenarios_monoculms import main
+from example.Scenarios_monoculms import tools
+from example.Scenarios_monoculms import additional_graphs
 
 
 def exponential_fertilization_rate(V0, K, t, dt, plant_density):
diff --git a/example/Vegetative_stages/main.py b/example/Vegetative_stages/main.py
index 5e1023a1..c952d4bf 100644
--- a/example/Vegetative_stages/main.py
+++ b/example/Vegetative_stages/main.py
@@ -278,7 +278,7 @@ def main(simulation_length, forced_start_time=0, run_simu=True, run_postprocessi
                                                 update_shared_df=UPDATE_SHARED_DF)
 
     # -- SENESCWHEAT --
-    # Initial states    
+    # Initial states
     senescwheat_roots_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].loc[inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME]['organ'] == 'roots'][
         senescwheat_facade.converter.ROOTS_TOPOLOGY_COLUMNS +
         [i for i in senescwheat_facade.converter.SENESCWHEAT_ROOTS_INPUTS if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()
@@ -310,7 +310,7 @@ def main(simulation_length, forced_start_time=0, run_simu=True, run_postprocessi
                                                                update_shared_df=UPDATE_SHARED_DF)
 
     # -- FARQUHARWHEAT --
-    # Initial states    
+    # Initial states
     farquharwheat_elements_initial_state = inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME][
         farquharwheat_facade.converter.ELEMENT_TOPOLOGY_COLUMNS +
         [i for i in farquharwheat_facade.converter.FARQUHARWHEAT_ELEMENTS_INPUTS if i in inputs_dataframes[ELEMENTS_INITIAL_STATE_FILENAME].columns]].copy()
@@ -331,7 +331,7 @@ def main(simulation_length, forced_start_time=0, run_simu=True, run_postprocessi
                                                                      update_shared_df=UPDATE_SHARED_DF)
 
     # -- GROWTHWHEAT --
-    # Initial states    
+    # Initial states
     growthwheat_hiddenzones_initial_state = inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME][
         growthwheat_facade.converter.HIDDENZONE_TOPOLOGY_COLUMNS +
         [i for i in growthwheat_facade.simulation.HIDDENZONE_INPUTS if i in inputs_dataframes[HIDDENZONES_INITIAL_STATE_FILENAME].columns]].copy()
@@ -369,7 +369,7 @@ def main(simulation_length, forced_start_time=0, run_simu=True, run_postprocessi
                                                                update_shared_df=UPDATE_SHARED_DF)
 
     # -- CNWHEAT --
-    # Initial states    
+    # Initial states
     cnwheat_organs_initial_state = inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME][
         [i for i in cnwheat_facade.cnwheat_converter.ORGANS_VARIABLES if i in inputs_dataframes[ORGANS_INITIAL_STATE_FILENAME].columns]].copy()
 
diff --git a/multiple_scenarios.ipynb b/multiple_scenarios.ipynb
new file mode 100644
index 00000000..54f657bb
--- /dev/null
+++ b/multiple_scenarios.ipynb
@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    ""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index e30fe0a8..48aef383 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -70,6 +70,7 @@ doc = [
   "sphinx-copybutton",
   "ipython_genutils",
   "nbsphinx",
+  "ipywidgets",
 ]
 
 # section specific to conda-only distributed package (not used by pip yet)