{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e319321-0d13-4bd6-9cf5-117f3253713e",
   "metadata": {},
   "source": [
    "# Data paper \n",
    "\n",
    "## Paper\n",
    "Young apple tree development under a gradient of agroforestry radiative conditions: a dataset of morphological and architectural responses at various topological scales\n",
    "\n",
    "## Authors: F. Reyes, B. Pitchers, C. Pradal, P.E. Lauri\n",
    "\n",
    "This Notebook showcase how to load the data and extract the analysis of apple tree under a gradient of agroforestry radiative conditions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "272fd7ff-ef40-4c33-991b-3832d9bb3a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gafam import analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f71437-793a-44ba-8a93-64c2b252e950",
   "metadata": {},
   "source": [
    "## Load the data set of 45 apple tree under different treatment\n",
    "## Extract the information at 3 different scales : Plant, Branch and Growth Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f9b50c0-d2ad-49ae-a1a7-39448336213b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NODE   < < False\n"
     ]
    }
   ],
   "source": [
    "tree_df, branch_df, GU_df = analysis.full_analysis(verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7057cbfb-d9d8-4d54-9a67-f4b62a2e2057",
   "metadata": {},
   "source": [
    "## Analysis at plant scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae99d0a6-52af-4bc7-a33e-f76dafbe8407",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_theme(style=\"ticks\", palette=\"crest\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "093ee71e-33f7-479e-8acd-9432a9007283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='trt', ylabel='LA_2019'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(1,2, sharey=True)\n",
    "sns.boxplot(x=\"trt\", y=\"LA_2018\",\n",
    "            data=tree_df, hue='trt', ax=ax[0])\n",
    "sns.boxplot(x=\"trt\", y=\"LA_2019\",\n",
    "            data=tree_df,hue='trt', ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb40935-6c00-4bc7-a1f8-df2c25f1ae27",
   "metadata": {},
   "source": [
    "# Analysis at Growth Unit scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4332c684-281b-4576-8eeb-68527f04631e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tree</th>\n",
       "      <th>trt</th>\n",
       "      <th>years</th>\n",
       "      <th>GU</th>\n",
       "      <th>LeafArea</th>\n",
       "      <th>nb_GU</th>\n",
       "      <th>nb_leaves</th>\n",
       "      <th>nb_flowers</th>\n",
       "      <th>nb_fruits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>AC</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>627.20200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AC</td>\n",
       "      <td>2018</td>\n",
       "      <td>B</td>\n",
       "      <td>6512.77992</td>\n",
       "      <td>43</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>AC</td>\n",
       "      <td>2018</td>\n",
       "      <td>C</td>\n",
       "      <td>7938.62165</td>\n",
       "      <td>94</td>\n",
       "      <td>153</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>AC</td>\n",
       "      <td>2018</td>\n",
       "      <td>D</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>AC</td>\n",
       "      <td>2018</td>\n",
       "      <td>E</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>148</td>\n",
       "      <td>AF_IR</td>\n",
       "      <td>2019</td>\n",
       "      <td>A</td>\n",
       "      <td>97.33408</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>148</td>\n",
       "      <td>AF_IR</td>\n",
       "      <td>2019</td>\n",
       "      <td>B</td>\n",
       "      <td>5478.28288</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>148</td>\n",
       "      <td>AF_IR</td>\n",
       "      <td>2019</td>\n",
       "      <td>C</td>\n",
       "      <td>14091.22799</td>\n",
       "      <td>112</td>\n",
       "      <td>72</td>\n",
       "      <td>55</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>148</td>\n",
       "      <td>AF_IR</td>\n",
       "      <td>2019</td>\n",
       "      <td>D</td>\n",
       "      <td>5928.16141</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>148</td>\n",
       "      <td>AF_IR</td>\n",
       "      <td>2019</td>\n",
       "      <td>E</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tree    trt years GU     LeafArea  nb_GU  nb_leaves  nb_flowers  \\\n",
       "0       1     AC  2018  A    627.20200      1          0           0   \n",
       "1       1     AC  2018  B   6512.77992     43         58          54   \n",
       "2       1     AC  2018  C   7938.62165     94        153         120   \n",
       "3       1     AC  2018  D      0.00000      0          0           0   \n",
       "4       1     AC  2018  E      0.00000      0          0           0   \n",
       "..    ...    ...   ... ..          ...    ...        ...         ...   \n",
       "445   148  AF_IR  2019  A     97.33408      1          0           0   \n",
       "446   148  AF_IR  2019  B   5478.28288     44         34          24   \n",
       "447   148  AF_IR  2019  C  14091.22799    112         72          55   \n",
       "448   148  AF_IR  2019  D   5928.16141     65          0           0   \n",
       "449   148  AF_IR  2019  E      0.00000      0          0           0   \n",
       "\n",
       "     nb_fruits  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "..         ...  \n",
       "445          0  \n",
       "446          4  \n",
       "447         14  \n",
       "448          0  \n",
       "449          0  \n",
       "\n",
       "[450 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GU_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97484fa5-ca92-420f-bc06-a65746267c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='trt', ylabel='LeafArea'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(2,4, sharey='row')\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2018\" and GU==\"A\"'), \n",
    "            hue='trt', ax=ax[0][0])\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2018\" and GU==\"B\"'), hue='trt', ax=ax[0][1])\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2018\" and GU==\"C\"'), hue='trt', ax=ax[0][2])\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2018\" and GU==\"D\"'), hue='trt', ax=ax[0][3])\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2019\" and GU==\"A\"'), hue='trt', ax=ax[1][0])\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2019\" and GU==\"B\"'), hue='trt', ax=ax[1][1])\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2019\" and GU==\"C\"'), hue='trt', ax=ax[1][2])\n",
    "sns.boxplot(x=\"trt\", y=\"LeafArea\",\n",
    "            data=GU_df.query('years == \"2019\" and GU==\"D\"'), hue='trt', ax=ax[1][3])\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}