From 768124befc1d7b7fbbf52638f5939d3b53832ca0 Mon Sep 17 00:00:00 2001
From: Antoine Tavant <antoinetavant@hotmail.fr>
Date: Fri, 6 Sep 2024 13:59:38 +0200
Subject: [PATCH] Add Group by Departements instead of Regions

---
 .../predicting_the_predictions.ipynb          |    7 +-
 notebooks/datascience/tempo_predictor.ipynb   |  906 +++++++++---
 notebooks/weather/group_by_departements.ipynb | 1219 +++++++++++++++++
 3 files changed, 1943 insertions(+), 189 deletions(-)
 create mode 100644 notebooks/weather/group_by_departements.ipynb

diff --git a/notebooks/datascience/predicting_the_predictions.ipynb b/notebooks/datascience/predicting_the_predictions.ipynb
index 849f8b5..c36465c 100644
--- a/notebooks/datascience/predicting_the_predictions.ipynb
+++ b/notebooks/datascience/predicting_the_predictions.ipynb
@@ -3400,16 +3400,19 @@
     "We have seen that we can predict the production of the different energy sources using the weather data.\n",
     "\n",
     "We have also seen that we can use the data for the 94 departments of France, instead of the 13 regions, to improve slightly the prediction.\n",
-    "There is the Daily R2 and MAPE for the different models:\n",
+    "There is the Daily R2 and MAPE for the different models: as we can see, the improvement is not that significant.\n",
     "\n",
     "## R²\n",
-    "\n",
+    "On daily average, the R² is around 0.95 for the different models.\n",
     "| | Sun r² | Wind r² |\n",
     "|---|---|---|\n",
     "| Regions | 0.94 | 0.94 |\n",
     "| Departments | 0.95 | 0.95 |\n",
     "\n",
     "\n",
+    "## MAPE\n",
+    "On daily average, the MAPE is around 0.13 for the different models.\n",
+    "\n",
     "| | Sun MAPE | Wind MAPE |\n",
     "|---|---|---|\n",
     "| Regions | 0.12 | 0.16 |\n",
diff --git a/notebooks/datascience/tempo_predictor.ipynb b/notebooks/datascience/tempo_predictor.ipynb
index a40929d..d098320 100644
--- a/notebooks/datascience/tempo_predictor.ipynb
+++ b/notebooks/datascience/tempo_predictor.ipynb
@@ -12,9 +12,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -22,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,19 +41,15 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 43,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "data_file = ROOT_DIR / \"data/rte_agg_daily_2014_2024.csv\"\n",
-    "data_agg = pd.read_csv(data_file, index_col=0, parse_dates=True)\n",
-    "data_agg.index = pd.to_datetime(data_agg.index)"
+    "## Preparing the data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -68,203 +73,730 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Type_de_jour_TEMPO</th>\n",
-       "      <th>Consommation</th>\n",
-       "      <th>Prévision_J-1</th>\n",
-       "      <th>Prévision_J</th>\n",
-       "      <th>Fioul</th>\n",
-       "      <th>Charbon</th>\n",
-       "      <th>Gaz</th>\n",
-       "      <th>Nucléaire</th>\n",
-       "      <th>Eolien</th>\n",
-       "      <th>Solaire</th>\n",
-       "      <th>Hydraulique</th>\n",
-       "      <th>Pompage</th>\n",
-       "      <th>Bioénergies</th>\n",
-       "      <th>Ech_physiques</th>\n",
-       "      <th>Taux_de_Co2</th>\n",
-       "      <th>Ech_comm</th>\n",
-       "      <th>sun</th>\n",
-       "      <th>wind</th>\n",
+       "      <th>SOLAR_FORECAST_D1</th>\n",
+       "      <th>EOLIEN_FORECAST_D1</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Date</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",
-       "      <th></th>\n",
+       "      <th>start_date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-16 01:00:00+01:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1076.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-16 02:00:00+01:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1045.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-16 03:00:00+01:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1013.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-16 04:00:00+01:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>988.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-16 05:00:00+01:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>963.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-16 19:00:00+02:00</th>\n",
+       "      <td>3058.95</td>\n",
+       "      <td>1871.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-16 20:00:00+02:00</th>\n",
+       "      <td>764.46</td>\n",
+       "      <td>1805.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-16 21:00:00+02:00</th>\n",
+       "      <td>1.23</td>\n",
+       "      <td>1852.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-16 22:00:00+02:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1904.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-16 23:00:00+02:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1939.56</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>80740 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           SOLAR_FORECAST_D1  EOLIEN_FORECAST_D1\n",
+       "start_date                                                      \n",
+       "2014-12-16 01:00:00+01:00               0.00             1076.00\n",
+       "2014-12-16 02:00:00+01:00               0.00             1045.00\n",
+       "2014-12-16 03:00:00+01:00               0.00             1013.00\n",
+       "2014-12-16 04:00:00+01:00               0.00              988.00\n",
+       "2014-12-16 05:00:00+01:00               0.00              963.00\n",
+       "...                                      ...                 ...\n",
+       "2024-08-16 19:00:00+02:00            3058.95             1871.66\n",
+       "2024-08-16 20:00:00+02:00             764.46             1805.01\n",
+       "2024-08-16 21:00:00+02:00               1.23             1852.47\n",
+       "2024-08-16 22:00:00+02:00               0.00             1904.45\n",
+       "2024-08-16 23:00:00+02:00               0.00             1939.56\n",
+       "\n",
+       "[80740 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "production_forcasted = pd.read_csv(ROOT_DIR / 'data' / 'silver' / 'forecasted_production_rte.csv', index_col=0, parse_dates=True)\n",
+    "production_forcasted = production_forcasted.dropna(axis=0, how=\"any\")\n",
+    "production_forcasted.index = pd.to_datetime(production_forcasted.index, utc=True).tz_convert(\"Europe/Paris\")\n",
+    "production_forcasted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1212/3459037049.py:1: DtypeWarning: Columns (5,18,19,20,21,22,26,27,30,31,33,36,37,38,39) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  rte_all_data = pd.read_csv(ROOT_DIR / 'data' / 'silver' / 'rte_production.csv')[[\n"
+     ]
+    }
+   ],
+   "source": [
+    "rte_all_data = pd.read_csv(ROOT_DIR / 'data' / 'silver' / 'rte_production.csv')[[\n",
+    "    'Date', 'Heures', 'Prévision J-1', 'Solaire', 'Eolien']]\n",
+    "\n",
+    "index = pd.to_datetime(rte_all_data['Date'] + ' ' + rte_all_data['Heures'].astype(str) + ':00')\n",
+    "rte_all_data.index = index.dt.tz_localize('UTC').dt.tz_convert('Europe/Paris')\n",
+    "rte_all_data = rte_all_data.drop(columns=['Date', 'Heures'])\n",
+    "rte_all_data = rte_all_data.dropna(axis=0, how='any')\n",
+    "rte_all_data = rte_all_data.astype(float)\n",
+    "rte_all_data = rte_all_data.resample('1h').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "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>Prévision J-1</th>\n",
+       "      <th>Solaire</th>\n",
+       "      <th>Eolien</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-01-01 01:00:00+01:00</th>\n",
+       "      <td>62650.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3413.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-01 02:00:00+01:00</th>\n",
+       "      <td>60250.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3291.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-01 03:00:00+01:00</th>\n",
+       "      <td>59400.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3275.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-01 04:00:00+01:00</th>\n",
+       "      <td>55700.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3258.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-01 05:00:00+01:00</th>\n",
+       "      <td>52650.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3398.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-08 11:00:00+02:00</th>\n",
+       "      <td>51450.0</td>\n",
+       "      <td>3026.00</td>\n",
+       "      <td>4151.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-08 12:00:00+02:00</th>\n",
+       "      <td>51350.0</td>\n",
+       "      <td>5092.75</td>\n",
+       "      <td>4282.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-08 13:00:00+02:00</th>\n",
+       "      <td>51362.5</td>\n",
+       "      <td>7110.25</td>\n",
+       "      <td>5120.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-08 14:00:00+02:00</th>\n",
+       "      <td>51537.5</td>\n",
+       "      <td>8841.50</td>\n",
+       "      <td>6496.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-08 15:00:00+02:00</th>\n",
+       "      <td>52300.0</td>\n",
+       "      <td>9729.00</td>\n",
+       "      <td>7033.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>90014 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Prévision J-1  Solaire   Eolien\n",
+       "2014-01-01 01:00:00+01:00        62650.0     0.00  3413.00\n",
+       "2014-01-01 02:00:00+01:00        60250.0     0.00  3291.50\n",
+       "2014-01-01 03:00:00+01:00        59400.0     0.00  3275.50\n",
+       "2014-01-01 04:00:00+01:00        55700.0     0.00  3258.00\n",
+       "2014-01-01 05:00:00+01:00        52650.0     0.00  3398.00\n",
+       "...                                  ...      ...      ...\n",
+       "2024-04-08 11:00:00+02:00        51450.0  3026.00  4151.75\n",
+       "2024-04-08 12:00:00+02:00        51350.0  5092.75  4282.25\n",
+       "2024-04-08 13:00:00+02:00        51362.5  7110.25  5120.50\n",
+       "2024-04-08 14:00:00+02:00        51537.5  8841.50  6496.25\n",
+       "2024-04-08 15:00:00+02:00        52300.0  9729.00  7033.00\n",
+       "\n",
+       "[90014 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rte_all_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "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>tempo_type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
        "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-09-01 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-02 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-03 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-04 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-05 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-06-20 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-06-21 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-06-22 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-06-23 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-06-24 00:00:00+02:00</th>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3585 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          tempo_type\n",
+       "Date                                \n",
+       "2014-09-01 00:00:00+02:00       BLEU\n",
+       "2014-09-02 00:00:00+02:00       BLEU\n",
+       "2014-09-03 00:00:00+02:00       BLEU\n",
+       "2014-09-04 00:00:00+02:00       BLEU\n",
+       "2014-09-05 00:00:00+02:00       BLEU\n",
+       "...                              ...\n",
+       "2024-06-20 00:00:00+02:00       BLEU\n",
+       "2024-06-21 00:00:00+02:00       BLEU\n",
+       "2024-06-22 00:00:00+02:00       BLEU\n",
+       "2024-06-23 00:00:00+02:00       BLEU\n",
+       "2024-06-24 00:00:00+02:00       BLEU\n",
+       "\n",
+       "[3585 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tempos = pd.read_csv(ROOT_DIR / 'data' / 'silver' / 'tempo_2014_2024.csv', index_col=0, parse_dates=True)\n",
+    "tempos.index = tempos.index.tz_localize(\"Europe/Paris\")\n",
+    "tempos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Concatenate the needed collumns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1212/2617323631.py:4: PerformanceWarning: Non-vectorized DateOffset being applied to Series or DatetimeIndex.\n",
+      "  daily_consumption.index = daily_consumption.index + pd.DateOffset(hour=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2013-12-31 00:00:00+01:00     290650.0\n",
+       "2014-01-01 00:00:00+01:00    1301750.0\n",
+       "2014-01-02 00:00:00+01:00    1455200.0\n",
+       "2014-01-03 00:00:00+01:00    1446300.0\n",
+       "2014-01-04 00:00:00+01:00    1393550.0\n",
+       "                               ...    \n",
+       "2024-04-04 00:00:00+02:00    1163350.0\n",
+       "2024-04-05 00:00:00+02:00    1110187.5\n",
+       "2024-04-06 00:00:00+02:00     950700.0\n",
+       "2024-04-07 00:00:00+02:00     930150.0\n",
+       "2024-04-08 00:00:00+02:00     468475.0\n",
+       "Name: Prévision J-1, Length: 3752, dtype: float64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# average from 6am\n",
+    "origin = rte_all_data.index[0] + pd.DateOffset(hour=6, minute=0)\n",
+    "daily_consumption = rte_all_data[\"Prévision J-1\"].resample(\"1D\", origin=origin).sum()\n",
+    "daily_consumption.index = daily_consumption.index + pd.DateOffset(hour=0)\n",
+    "daily_consumption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1212/452278787.py:3: PerformanceWarning: Non-vectorized DateOffset being applied to Series or DatetimeIndex.\n",
+      "  daily_production.index = daily_production.index + pd.DateOffset(hour=0)\n"
+     ]
+    },
+    {
+     "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>SOLAR_FORECAST_D1</th>\n",
+       "      <th>EOLIEN_FORECAST_D1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>start_date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-15 00:00:00+01:00</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5085.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-16 00:00:00+01:00</th>\n",
+       "      <td>4870.89</td>\n",
+       "      <td>39909.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-17 00:00:00+01:00</th>\n",
+       "      <td>4609.62</td>\n",
+       "      <td>103617.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-18 00:00:00+01:00</th>\n",
+       "      <td>5258.76</td>\n",
+       "      <td>122720.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-19 00:00:00+01:00</th>\n",
+       "      <td>6289.70</td>\n",
+       "      <td>104494.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-12 00:00:00+02:00</th>\n",
+       "      <td>116114.62</td>\n",
+       "      <td>67128.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-13 00:00:00+02:00</th>\n",
+       "      <td>96650.51</td>\n",
+       "      <td>36417.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-14 00:00:00+02:00</th>\n",
+       "      <td>74620.37</td>\n",
+       "      <td>43453.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-15 00:00:00+02:00</th>\n",
+       "      <td>101109.47</td>\n",
+       "      <td>61722.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-08-16 00:00:00+02:00</th>\n",
+       "      <td>95051.52</td>\n",
+       "      <td>37254.94</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3533 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           SOLAR_FORECAST_D1  EOLIEN_FORECAST_D1\n",
+       "start_date                                                      \n",
+       "2014-12-15 00:00:00+01:00               0.00             5085.00\n",
+       "2014-12-16 00:00:00+01:00            4870.89            39909.00\n",
+       "2014-12-17 00:00:00+01:00            4609.62           103617.00\n",
+       "2014-12-18 00:00:00+01:00            5258.76           122720.00\n",
+       "2014-12-19 00:00:00+01:00            6289.70           104494.00\n",
+       "...                                      ...                 ...\n",
+       "2024-08-12 00:00:00+02:00          116114.62            67128.44\n",
+       "2024-08-13 00:00:00+02:00           96650.51            36417.80\n",
+       "2024-08-14 00:00:00+02:00           74620.37            43453.81\n",
+       "2024-08-15 00:00:00+02:00          101109.47            61722.23\n",
+       "2024-08-16 00:00:00+02:00           95051.52            37254.94\n",
+       "\n",
+       "[3533 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "origin = production_forcasted.index[0]+ pd.DateOffset(hour=6, minute=0)\n",
+    "daily_production = production_forcasted.resample(\"1D\", origin=origin).sum()\n",
+    "daily_production.index = daily_production.index + pd.DateOffset(hour=0) \n",
+    "daily_production"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_consumption_naive_tz = daily_consumption.tz_localize(None)\n",
+    "daily_production_naive_tz = daily_production.tz_localize(None)\n",
+    "tempos_naive_tz = tempos.tz_localize(None)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "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>Prévision_J-1</th>\n",
+       "      <th>Solaire</th>\n",
+       "      <th>Eolien</th>\n",
+       "      <th>Type_de_jour_TEMPO</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2015-01-01</th>\n",
-       "      <td>BLANC</td>\n",
-       "      <td>1613817.0</td>\n",
-       "      <td>1569650.0</td>\n",
-       "      <td>1603950.0</td>\n",
-       "      <td>7666.5</td>\n",
-       "      <td>28191.5</td>\n",
-       "      <td>56216.0</td>\n",
-       "      <td>1388930.5</td>\n",
-       "      <td>51127.0</td>\n",
-       "      <td>11370.5</td>\n",
-       "      <td>158136.5</td>\n",
-       "      <td>-26906.5</td>\n",
-       "      <td>21712.5</td>\n",
-       "      <td>-82623.5</td>\n",
-       "      <td>1028.0</td>\n",
-       "      <td>-76107.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-01-02</th>\n",
-       "      <td>BLANC</td>\n",
-       "      <td>1656045.5</td>\n",
-       "      <td>1672200.0</td>\n",
-       "      <td>1643050.0</td>\n",
-       "      <td>7796.5</td>\n",
-       "      <td>43440.5</td>\n",
-       "      <td>62653.5</td>\n",
-       "      <td>1400287.0</td>\n",
-       "      <td>78933.0</td>\n",
-       "      <td>8297.5</td>\n",
-       "      <td>180503.0</td>\n",
-       "      <td>-17687.5</td>\n",
-       "      <td>21602.5</td>\n",
-       "      <td>-129775.5</td>\n",
-       "      <td>1196.0</td>\n",
-       "      <td>-121458.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-01-03</th>\n",
+       "      <th>2015-09-01</th>\n",
+       "      <td>1167400.0</td>\n",
+       "      <td>21617.70</td>\n",
+       "      <td>30097.00</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-02</th>\n",
+       "      <td>1137500.0</td>\n",
+       "      <td>24844.99</td>\n",
+       "      <td>18895.00</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-03</th>\n",
+       "      <td>1127150.0</td>\n",
+       "      <td>21967.80</td>\n",
+       "      <td>24162.00</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-04</th>\n",
+       "      <td>1117350.0</td>\n",
+       "      <td>25466.61</td>\n",
+       "      <td>24709.00</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-05</th>\n",
+       "      <td>980050.0</td>\n",
+       "      <td>27009.82</td>\n",
+       "      <td>32334.00</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-04</th>\n",
+       "      <td>1163350.0</td>\n",
+       "      <td>62687.02</td>\n",
+       "      <td>279577.48</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-05</th>\n",
+       "      <td>1110187.5</td>\n",
+       "      <td>71763.38</td>\n",
+       "      <td>175006.34</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-06</th>\n",
+       "      <td>950700.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>BLEU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-07</th>\n",
+       "      <td>930150.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>BLEU</td>\n",
-       "      <td>1538261.0</td>\n",
-       "      <td>1543750.0</td>\n",
-       "      <td>1528950.0</td>\n",
-       "      <td>7723.0</td>\n",
-       "      <td>34074.0</td>\n",
-       "      <td>54482.0</td>\n",
-       "      <td>1376450.5</td>\n",
-       "      <td>105299.0</td>\n",
-       "      <td>5860.5</td>\n",
-       "      <td>141242.5</td>\n",
-       "      <td>-32067.0</td>\n",
-       "      <td>21850.0</td>\n",
-       "      <td>-176653.5</td>\n",
-       "      <td>1086.0</td>\n",
-       "      <td>-168566.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-01-04</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-04-08</th>\n",
+       "      <td>468475.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>BLEU</td>\n",
-       "      <td>1413088.0</td>\n",
-       "      <td>1436800.0</td>\n",
-       "      <td>1407550.0</td>\n",
-       "      <td>7713.5</td>\n",
-       "      <td>29848.0</td>\n",
-       "      <td>54893.5</td>\n",
-       "      <td>1328030.0</td>\n",
-       "      <td>30061.0</td>\n",
-       "      <td>6926.0</td>\n",
-       "      <td>145347.5</td>\n",
-       "      <td>-43888.0</td>\n",
-       "      <td>21477.0</td>\n",
-       "      <td>-167324.0</td>\n",
-       "      <td>1112.5</td>\n",
-       "      <td>-158445.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-01-05</th>\n",
-       "      <td>ROUGE</td>\n",
-       "      <td>1710599.5</td>\n",
-       "      <td>1670450.0</td>\n",
-       "      <td>1694950.0</td>\n",
-       "      <td>8009.0</td>\n",
-       "      <td>63351.5</td>\n",
-       "      <td>121532.0</td>\n",
-       "      <td>1395291.0</td>\n",
-       "      <td>16004.0</td>\n",
-       "      <td>9786.5</td>\n",
-       "      <td>209115.5</td>\n",
-       "      <td>-19182.5</td>\n",
-       "      <td>20816.5</td>\n",
-       "      <td>-114123.5</td>\n",
-       "      <td>1668.0</td>\n",
-       "      <td>-105154.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>3143 rows × 4 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "           Type_de_jour_TEMPO  Consommation  Prévision_J-1  Prévision_J  \\\n",
-       "Date                                                                      \n",
-       "2015-01-01              BLANC     1613817.0      1569650.0    1603950.0   \n",
-       "2015-01-02              BLANC     1656045.5      1672200.0    1643050.0   \n",
-       "2015-01-03               BLEU     1538261.0      1543750.0    1528950.0   \n",
-       "2015-01-04               BLEU     1413088.0      1436800.0    1407550.0   \n",
-       "2015-01-05              ROUGE     1710599.5      1670450.0    1694950.0   \n",
-       "\n",
-       "             Fioul  Charbon       Gaz  Nucléaire    Eolien  Solaire  \\\n",
-       "Date                                                                  \n",
-       "2015-01-01  7666.5  28191.5   56216.0  1388930.5   51127.0  11370.5   \n",
-       "2015-01-02  7796.5  43440.5   62653.5  1400287.0   78933.0   8297.5   \n",
-       "2015-01-03  7723.0  34074.0   54482.0  1376450.5  105299.0   5860.5   \n",
-       "2015-01-04  7713.5  29848.0   54893.5  1328030.0   30061.0   6926.0   \n",
-       "2015-01-05  8009.0  63351.5  121532.0  1395291.0   16004.0   9786.5   \n",
+       "            Prévision_J-1   Solaire     Eolien Type_de_jour_TEMPO\n",
+       "2015-09-01      1167400.0  21617.70   30097.00               BLEU\n",
+       "2015-09-02      1137500.0  24844.99   18895.00               BLEU\n",
+       "2015-09-03      1127150.0  21967.80   24162.00               BLEU\n",
+       "2015-09-04      1117350.0  25466.61   24709.00               BLEU\n",
+       "2015-09-05       980050.0  27009.82   32334.00               BLEU\n",
+       "...                   ...       ...        ...                ...\n",
+       "2024-04-04      1163350.0  62687.02  279577.48               BLEU\n",
+       "2024-04-05      1110187.5  71763.38  175006.34               BLEU\n",
+       "2024-04-06       950700.0      0.00       0.00               BLEU\n",
+       "2024-04-07       930150.0      0.00       0.00               BLEU\n",
+       "2024-04-08       468475.0      0.00       0.00               BLEU\n",
        "\n",
-       "            Hydraulique  Pompage  Bioénergies  Ech_physiques  Taux_de_Co2  \\\n",
-       "Date                                                                        \n",
-       "2015-01-01     158136.5 -26906.5      21712.5       -82623.5       1028.0   \n",
-       "2015-01-02     180503.0 -17687.5      21602.5      -129775.5       1196.0   \n",
-       "2015-01-03     141242.5 -32067.0      21850.0      -176653.5       1086.0   \n",
-       "2015-01-04     145347.5 -43888.0      21477.0      -167324.0       1112.5   \n",
-       "2015-01-05     209115.5 -19182.5      20816.5      -114123.5       1668.0   \n",
-       "\n",
-       "            Ech_comm  sun  wind  \n",
-       "Date                             \n",
-       "2015-01-01  -76107.0  NaN   NaN  \n",
-       "2015-01-02 -121458.0  NaN   NaN  \n",
-       "2015-01-03 -168566.0  NaN   NaN  \n",
-       "2015-01-04 -158445.0  NaN   NaN  \n",
-       "2015-01-05 -105154.0  NaN   NaN  "
+       "[3143 rows x 4 columns]"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data_agg.head()"
+    "\n",
+    "data = pd.concat([daily_consumption_naive_tz, daily_production_naive_tz, tempos_naive_tz],\n",
+    "                 axis=1).sort_index().dropna(axis=0, how=\"any\")\n",
+    "\n",
+    "data = data[~data.index.duplicated()]\n",
+    "year = data.index[0].year\n",
+    "first_september = data.index[0] + pd.DateOffset(month=9, day=1, year=year)\n",
+    "if first_september < data.index[0]:\n",
+    "    first_september += pd.DateOffset(years=1)\n",
+    "\n",
+    "last_august = data.index[-1] - pd.DateOffset(month=8, day=31)\n",
+    "data.rename(columns={\n",
+    "    \"Prévision J-1\": \"Prévision_J-1\",\n",
+    "    \"SOLAR_FORECAST_D1\":\"Solaire\",\n",
+    "    \"EOLIEN_FORECAST_D1\":\"Eolien\",\n",
+    "    \"tempo_type\":\"Type_de_jour_TEMPO\",\n",
+    "}, inplace=True)\n",
+    "\n",
+    "data[first_september: last_august]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -302,21 +834,21 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>BLANC</th>\n",
-       "      <td>19</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23</td>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BLEU</th>\n",
-       "      <td>91</td>\n",
-       "      <td>208</td>\n",
-       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>297</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROUGE</th>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>22</td>\n",
+       "      <td>21</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -325,20 +857,20 @@
       "text/plain": [
        "col_0               prediction_blanc  prediction_bleu  prediction_rouge\n",
        "Type_de_jour_TEMPO                                                     \n",
-       "BLANC                             19                1                23\n",
-       "BLEU                              91              208                 1\n",
-       "ROUGE                              0                0                22"
+       "BLANC                             41                0                 2\n",
+       "BLEU                               4              297                 0\n",
+       "ROUGE                              1                0                21"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "used_cols = [\"Prévision_J-1\", \"Type_de_jour_TEMPO\", \"Eolien\", \"Solaire\"]\n",
-    "data: pd.DataFrame = data_agg.loc[\"2022-09-01\":\"2023-08-31\", used_cols]  # type: ignore\n",
-    "predictor = TempoPredictor(data)\n",
+    "second_septembre = first_september + pd.DateOffset(years=1)\n",
+    "data_first_year = data[first_september: second_septembre - pd.DateOffset(days=1) ].copy()\n",
+    "predictor = TempoPredictor(data_first_year)\n",
     "predictions = predictor.predict()\n",
     "predictor.confusion_matrix(data_pred=predictions)"
    ]
diff --git a/notebooks/weather/group_by_departements.ipynb b/notebooks/weather/group_by_departements.ipynb
new file mode 100644
index 0000000..c7253bc
--- /dev/null
+++ b/notebooks/weather/group_by_departements.ipynb
@@ -0,0 +1,1219 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: geojson in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (3.1.0)\n",
+      "Requirement already satisfied: shapely in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (2.0.5)\n",
+      "Collecting cartopy\n",
+      "  Downloading Cartopy-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.0 kB)\n",
+      "Requirement already satisfied: numpy<3,>=1.14 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from shapely) (1.26.4)\n",
+      "Requirement already satisfied: matplotlib>=3.5 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from cartopy) (3.9.1.post1)\n",
+      "Requirement already satisfied: packaging>=20 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from cartopy) (24.1)\n",
+      "Collecting pyshp>=2.3 (from cartopy)\n",
+      "  Downloading pyshp-2.3.1-py2.py3-none-any.whl.metadata (55 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.0/56.0 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hCollecting pyproj>=3.3.1 (from cartopy)\n",
+      "  Downloading pyproj-3.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (31 kB)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from matplotlib>=3.5->cartopy) (1.2.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from matplotlib>=3.5->cartopy) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from matplotlib>=3.5->cartopy) (4.53.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from matplotlib>=3.5->cartopy) (1.4.5)\n",
+      "Requirement already satisfied: pillow>=8 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from matplotlib>=3.5->cartopy) (10.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from matplotlib>=3.5->cartopy) (3.1.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from matplotlib>=3.5->cartopy) (2.9.0.post0)\n",
+      "Requirement already satisfied: certifi in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from pyproj>=3.3.1->cartopy) (2024.7.4)\n",
+      "Requirement already satisfied: six>=1.5 in /home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3.5->cartopy) (1.16.0)\n",
+      "Downloading Cartopy-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading pyproj-3.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading pyshp-2.3.1-py2.py3-none-any.whl (46 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.5/46.5 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: pyshp, pyproj, cartopy\n",
+      "Successfully installed cartopy-0.23.0 pyproj-3.6.1 pyshp-2.3.1\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install geojson shapely cartopy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aisne\n",
+      "Aube\n",
+      "Calvados\n",
+      "Cantal\n",
+      "Eure-et-Loir\n",
+      "Ille-et-Vilaine\n",
+      "Jura\n",
+      "Landes\n",
+      "Loire\n",
+      "Loiret\n",
+      "Lot-et-Garonne\n",
+      "Meuse\n",
+      "Orne\n",
+      "Pas-de-Calais\n",
+      "Puy-de-Dôme\n",
+      "Bas-Rhin\n",
+      "Haut-Rhin\n",
+      "Seine-Maritime\n",
+      "Yonne\n",
+      "Seine-Saint-Denis\n",
+      "Alpes-de-Haute-Provence\n",
+      "Hautes-Alpes\n",
+      "Ardèche\n",
+      "Ardennes\n",
+      "Ariège\n",
+      "Charente-Maritime\n",
+      "Corrèze\n",
+      "Dordogne\n",
+      "Eure\n",
+      "Indre-et-Loire\n",
+      "Lozère\n",
+      "Nièvre\n",
+      "Oise\n",
+      "Pyrénées-Atlantiques\n",
+      "Rhône\n",
+      "Saône-et-Loire\n",
+      "Paris\n",
+      "Yvelines\n",
+      "Tarn\n",
+      "Tarn-et-Garonne\n",
+      "Var\n",
+      "Vendée\n",
+      "Haute-Vienne\n",
+      "Vosges\n",
+      "Hauts-de-Seine\n",
+      "Allier\n",
+      "Alpes-Maritimes\n",
+      "Aude\n",
+      "Corse-du-Sud\n",
+      "Côtes-d'Armor\n",
+      "Creuse\n",
+      "Doubs\n",
+      "Finistère\n",
+      "Gard\n",
+      "Gironde\n",
+      "Indre\n",
+      "Isère\n",
+      "Marne\n",
+      "Haute-Marne\n",
+      "Moselle\n",
+      "Hautes-Pyrénées\n",
+      "Pyrénées-Orientales\n",
+      "Savoie\n",
+      "Haute-Savoie\n",
+      "Seine-et-Marne\n",
+      "Vaucluse\n",
+      "Vienne\n",
+      "Val-de-Marne\n",
+      "Ain\n",
+      "Aveyron\n",
+      "Bouches-du-Rhône\n",
+      "Charente\n",
+      "Cher\n",
+      "Haute-Corse\n",
+      "Côte-d'Or\n",
+      "Drôme\n",
+      "Haute-Garonne\n",
+      "Gers\n",
+      "Hérault\n",
+      "Haute-Loire\n",
+      "Loire-Atlantique\n",
+      "Lot\n",
+      "Maine-et-Loire\n",
+      "Manche\n",
+      "Morbihan\n",
+      "Nord\n",
+      "Haute-Saône\n",
+      "Sarthe\n",
+      "Somme\n",
+      "Essonne\n",
+      "Val-d'Oise\n",
+      "Loir-et-Cher\n",
+      "Mayenne\n",
+      "Meurthe-et-Moselle\n",
+      "Deux-Sèvres\n",
+      "Territoire de Belfort\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 96/96 [00:00<00:00, 3009.82it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import geojson\n",
+    "from tqdm import tqdm\n",
+    "import shapely\n",
+    "from shapely.geometry import Polygon, MultiPolygon, Point\n",
+    "import shapely.plotting\n",
+    "filename = \"../../data/geo/departements.geojson\"\n",
+    "polys = geojson.load(open(filename))\n",
+    "for feature in polys[\"features\"]:\n",
+    "    print(feature[\"properties\"][\"nom\"])\n",
+    "\n",
+    "names_to_keep = [\n",
+    "\"Aisne\",\n",
+    "\"Aube\",\n",
+    "\"Calvados\",\n",
+    "\"Cantal\",\n",
+    "\"Eure-et-Loir\",\n",
+    "\"Ille-et-Vilaine\",\n",
+    "\"Jura\",\n",
+    "\"Landes\",\n",
+    "\"Loire\",\n",
+    "\"Loiret\",\n",
+    "\"Lot-et-Garonne\",\n",
+    "\"Meuse\",\n",
+    "\"Orne\",\n",
+    "\"Pas-de-Calais\",\n",
+    "\"Puy-de-Dôme\",\n",
+    "\"Bas-Rhin\",\n",
+    "\"Haut-Rhin\",\n",
+    "\"Seine-Maritime\",\n",
+    "\"Yonne\",\n",
+    "\"Seine-Saint-Denis\",\n",
+    "\"Alpes-de-Haute-Provence\",\n",
+    "\"Hautes-Alpes\",\n",
+    "\"Ardèche\",\n",
+    "\"Ardennes\",\n",
+    "\"Ariège\",\n",
+    "\"Charente-Maritime\",\n",
+    "\"Corrèze\",\n",
+    "\"Dordogne\",\n",
+    "\"Eure\",\n",
+    "\"Indre-et-Loire\",\n",
+    "\"Lozère\",\n",
+    "\"Nièvre\",\n",
+    "\"Oise\",\n",
+    "\"Pyrénées-Atlantiques\",\n",
+    "\"Rhône\",\n",
+    "\"Saône-et-Loire\",\n",
+    "\"Paris\",\n",
+    "\"Yvelines\",\n",
+    "\"Tarn\",\n",
+    "\"Tarn-et-Garonne\",\n",
+    "\"Var\",\n",
+    "\"Vendée\",\n",
+    "\"Haute-Vienne\",\n",
+    "\"Vosges\",\n",
+    "\"Hauts-de-Seine\",\n",
+    "\"Allier\",\n",
+    "\"Alpes-Maritimes\",\n",
+    "\"Aude\",\n",
+    "\"Corse-du-Sud\",\n",
+    "\"Côtes-d'Armor\",\n",
+    "\"Creuse\",\n",
+    "\"Doubs\",\n",
+    "\"Finistère\",\n",
+    "\"Gard\",\n",
+    "\"Gironde\",\n",
+    "\"Indre\",\n",
+    "\"Isère\",\n",
+    "\"Marne\",\n",
+    "\"Haute-Marne\",\n",
+    "\"Moselle\",\n",
+    "\"Hautes-Pyrénées\",\n",
+    "\"Pyrénées-Orientales\",\n",
+    "\"Savoie\",\n",
+    "\"Haute-Savoie\",\n",
+    "\"Seine-et-Marne\",\n",
+    "\"Vaucluse\",\n",
+    "\"Vienne\",\n",
+    "\"Val-de-Marne\",\n",
+    "\"Ain\",\n",
+    "\"Aveyron\",\n",
+    "\"Bouches-du-Rhône\",\n",
+    "\"Charente\",\n",
+    "\"Cher\",\n",
+    "\"Haute-Corse\",\n",
+    "\"Côte-d'Or\",\n",
+    "\"Drôme\",\n",
+    "\"Haute-Garonne\",\n",
+    "\"Gers\",\n",
+    "\"Hérault\",\n",
+    "\"Haute-Loire\",\n",
+    "\"Loire-Atlantique\",\n",
+    "\"Lot\",\n",
+    "\"Maine-et-Loire\",\n",
+    "\"Manche\",\n",
+    "\"Morbihan\",\n",
+    "\"Nord\",\n",
+    "\"Haute-Saône\",\n",
+    "\"Sarthe\",\n",
+    "\"Somme\",\n",
+    "\"Essonne\",\n",
+    "\"Val-d'Oise\",\n",
+    "\"Loir-et-Cher\",\n",
+    "\"Mayenne\",\n",
+    "\"Meurthe-et-Moselle\",\n",
+    "# \"Deux-Sèvres\",\n",
+    "# \"Territoire de Belfort\",\n",
+    "]\n",
+    "\n",
+    "polys_region = {}\n",
+    "for feature in tqdm(polys[\"features\"]):\n",
+    "    name = feature[\"properties\"][\"nom\"]\n",
+    "    if name not in names_to_keep:\n",
+    "        continue\n",
+    "    if feature[\"geometry\"][\"type\"] == \"Polygon\":\n",
+    "        polys_region[name] = Polygon(feature[\"geometry\"][\"coordinates\"][0])\n",
+    "    elif feature[\"geometry\"][\"type\"] == \"MultiPolygon\":\n",
+    "        # keeping the largest polygon\n",
+    "        tmp_list = [Polygon(geo[0]) for geo in feature[\"geometry\"][\"coordinates\"]]\n",
+    "        largest = max(tmp_list, key=lambda x: x.area)\n",
+    "        polys_region[name] = Polygon(largest)\n",
+    "\n",
+    "\n",
+    "all_polys = [poly for poly in polys_region.values()]\n",
+    "# unpack the lists\n",
+    "maxi_multi_poly = MultiPolygon(all_polys)\n",
+    "for poly in maxi_multi_poly.geoms:\n",
+    "    shapely.plotting.plot_polygon(poly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename_sun_d1 = \"../../data/silver/weather_forecasts/temperature_hourly_d1.nc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def which_region(lon, lat, polygons_regions: dict[MultiPolygon]):\n",
+    "    for i, (name, poly) in enumerate(polygons_regions.items()):\n",
+    "        if poly.contains(Point(lon, lat)):\n",
+    "            return i\n",
+    "    else:\n",
+    "        return np.nan\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da = xr.open_dataset(filename_sun_d1).temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;departement&#x27; (longitude: 143, latitude: 97)&gt; Size: 111kB\n",
+       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]])\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float64 1kB -4.7 -4.6 -4.5 -4.4 ... 9.2 9.3 9.4 9.5\n",
+       "  * latitude   (latitude) float64 776B 51.0 50.9 50.8 50.7 ... 41.6 41.5 41.4</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'departement'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>longitude</span>: 143</li><li><span class='xr-has-index'>latitude</span>: 97</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e29f4822-ac0a-4059-9d82-5979e48fc005' class='xr-array-in' type='checkbox' checked><label for='section-e29f4822-ac0a-4059-9d82-5979e48fc005' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... 73.0 nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-b8f93081-c16d-48a5-8989-546911e11882' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8f93081-c16d-48a5-8989-546911e11882' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.7 -4.6 -4.5 -4.4 ... 9.3 9.4 9.5</div><input id='attrs-f31f0e56-84dd-462b-8a39-28e0e0c118c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f31f0e56-84dd-462b-8a39-28e0e0c118c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6911e082-4785-4383-ac55-bae1c6589a6a' class='xr-var-data-in' type='checkbox'><label for='data-6911e082-4785-4383-ac55-bae1c6589a6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4.700000e+00, -4.600000e+00, -4.500000e+00, -4.400000e+00,\n",
+       "       -4.300000e+00, -4.200000e+00, -4.100000e+00, -4.000000e+00,\n",
+       "       -3.900000e+00, -3.800000e+00, -3.700000e+00, -3.600000e+00,\n",
+       "       -3.500000e+00, -3.400000e+00, -3.300000e+00, -3.200000e+00,\n",
+       "       -3.100000e+00, -3.000000e+00, -2.900000e+00, -2.800000e+00,\n",
+       "       -2.700000e+00, -2.600000e+00, -2.500000e+00, -2.400000e+00,\n",
+       "       -2.300000e+00, -2.200000e+00, -2.100000e+00, -2.000000e+00,\n",
+       "       -1.900000e+00, -1.800000e+00, -1.700000e+00, -1.600000e+00,\n",
+       "       -1.500000e+00, -1.400000e+00, -1.300000e+00, -1.200000e+00,\n",
+       "       -1.100000e+00, -1.000000e+00, -9.000000e-01, -8.000000e-01,\n",
+       "       -7.000000e-01, -6.000000e-01, -5.000000e-01, -4.000000e-01,\n",
+       "       -3.000000e-01, -2.000000e-01, -1.000000e-01,  1.858236e-13,\n",
+       "        1.000000e-01,  2.000000e-01,  3.000000e-01,  4.000000e-01,\n",
+       "        5.000000e-01,  6.000000e-01,  7.000000e-01,  8.000000e-01,\n",
+       "        9.000000e-01,  1.000000e+00,  1.100000e+00,  1.200000e+00,\n",
+       "        1.300000e+00,  1.400000e+00,  1.500000e+00,  1.600000e+00,\n",
+       "        1.700000e+00,  1.800000e+00,  1.900000e+00,  2.000000e+00,\n",
+       "        2.100000e+00,  2.200000e+00,  2.300000e+00,  2.400000e+00,\n",
+       "        2.500000e+00,  2.600000e+00,  2.700000e+00,  2.800000e+00,\n",
+       "        2.900000e+00,  3.000000e+00,  3.100000e+00,  3.200000e+00,\n",
+       "        3.300000e+00,  3.400000e+00,  3.500000e+00,  3.600000e+00,\n",
+       "        3.700000e+00,  3.800000e+00,  3.900000e+00,  4.000000e+00,\n",
+       "        4.100000e+00,  4.200000e+00,  4.300000e+00,  4.400000e+00,\n",
+       "        4.500000e+00,  4.600000e+00,  4.700000e+00,  4.800000e+00,\n",
+       "        4.900000e+00,  5.000000e+00,  5.100000e+00,  5.200000e+00,\n",
+       "        5.300000e+00,  5.400000e+00,  5.500000e+00,  5.600000e+00,\n",
+       "        5.700000e+00,  5.800000e+00,  5.900000e+00,  6.000000e+00,\n",
+       "        6.100000e+00,  6.200000e+00,  6.300000e+00,  6.400000e+00,\n",
+       "        6.500000e+00,  6.600000e+00,  6.700000e+00,  6.800000e+00,\n",
+       "        6.900000e+00,  7.000000e+00,  7.100000e+00,  7.200000e+00,\n",
+       "        7.300000e+00,  7.400000e+00,  7.500000e+00,  7.600000e+00,\n",
+       "        7.700000e+00,  7.800000e+00,  7.900000e+00,  8.000000e+00,\n",
+       "        8.100000e+00,  8.200000e+00,  8.300000e+00,  8.400000e+00,\n",
+       "        8.500000e+00,  8.600000e+00,  8.700000e+00,  8.800000e+00,\n",
+       "        8.900000e+00,  9.000000e+00,  9.100000e+00,  9.200000e+00,\n",
+       "        9.300000e+00,  9.400000e+00,  9.500000e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>51.0 50.9 50.8 ... 41.6 41.5 41.4</div><input id='attrs-03969d3b-ed4f-4e96-a592-f1bf27bb227a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03969d3b-ed4f-4e96-a592-f1bf27bb227a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd3ceae3-6833-4549-90cf-97102469dba0' class='xr-var-data-in' type='checkbox'><label for='data-bd3ceae3-6833-4549-90cf-97102469dba0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([51. , 50.9, 50.8, 50.7, 50.6, 50.5, 50.4, 50.3, 50.2, 50.1, 50. , 49.9,\n",
+       "       49.8, 49.7, 49.6, 49.5, 49.4, 49.3, 49.2, 49.1, 49. , 48.9, 48.8, 48.7,\n",
+       "       48.6, 48.5, 48.4, 48.3, 48.2, 48.1, 48. , 47.9, 47.8, 47.7, 47.6, 47.5,\n",
+       "       47.4, 47.3, 47.2, 47.1, 47. , 46.9, 46.8, 46.7, 46.6, 46.5, 46.4, 46.3,\n",
+       "       46.2, 46.1, 46. , 45.9, 45.8, 45.7, 45.6, 45.5, 45.4, 45.3, 45.2, 45.1,\n",
+       "       45. , 44.9, 44.8, 44.7, 44.6, 44.5, 44.4, 44.3, 44.2, 44.1, 44. , 43.9,\n",
+       "       43.8, 43.7, 43.6, 43.5, 43.4, 43.3, 43.2, 43.1, 43. , 42.9, 42.8, 42.7,\n",
+       "       42.6, 42.5, 42.4, 42.3, 42.2, 42.1, 42. , 41.9, 41.8, 41.7, 41.6, 41.5,\n",
+       "       41.4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce34e2bb-f0e0-4159-a4a2-fc0454735e01' class='xr-section-summary-in' type='checkbox'  ><label for='section-ce34e2bb-f0e0-4159-a4a2-fc0454735e01' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d2c5ad4d-02a4-49ba-bafd-f2451889a944' class='xr-index-data-in' type='checkbox'/><label for='index-d2c5ad4d-02a4-49ba-bafd-f2451889a944' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-4.6999999999998145,  -4.599999999999815,  -4.499999999999815,\n",
+       "        -4.399999999999816,  -4.299999999999816,  -4.199999999999816,\n",
+       "        -4.099999999999817, -3.9999999999998166, -3.8999999999998165,\n",
+       "       -3.7999999999998164,\n",
+       "       ...\n",
+       "         8.600000000000172,   8.700000000000172,   8.800000000000171,\n",
+       "          8.90000000000017,    9.00000000000017,    9.10000000000017,\n",
+       "          9.20000000000017,    9.30000000000017,   9.400000000000169,\n",
+       "         9.500000000000169],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=143))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3365b53c-0195-4af4-ab32-ff15454f7bde' class='xr-index-data-in' type='checkbox'/><label for='index-3365b53c-0195-4af4-ab32-ff15454f7bde' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 51.00000000000027,  50.90000000000027,  50.80000000000027,\n",
+       "       50.700000000000266, 50.600000000000264,  50.50000000000026,\n",
+       "        50.40000000000026,  50.30000000000026,  50.20000000000026,\n",
+       "        50.10000000000026, 50.000000000000256, 49.900000000000254,\n",
+       "        49.80000000000025,  49.70000000000025,  49.60000000000025,\n",
+       "        49.50000000000025,  49.40000000000025, 49.300000000000246,\n",
+       "       49.200000000000244,  49.10000000000024,  49.00000000000024,\n",
+       "        48.90000000000024,  48.80000000000024,  48.70000000000024,\n",
+       "       48.600000000000236, 48.500000000000234,  48.40000000000023,\n",
+       "        48.30000000000023,  48.20000000000023,  48.10000000000023,\n",
+       "        48.00000000000023, 47.900000000000226, 47.800000000000225,\n",
+       "        47.70000000000022,  47.60000000000022,  47.50000000000022,\n",
+       "        47.40000000000022,  47.30000000000022, 47.200000000000216,\n",
+       "       47.100000000000215,  47.00000000000021,  46.90000000000021,\n",
+       "        46.80000000000021,  46.70000000000021,  46.60000000000021,\n",
+       "       46.500000000000206, 46.400000000000205,   46.3000000000002,\n",
+       "         46.2000000000002,   46.1000000000002,   46.0000000000002,\n",
+       "         45.9000000000002, 45.800000000000196, 45.700000000000195,\n",
+       "        45.60000000000019,  45.50000000000019,  45.40000000000019,\n",
+       "        45.30000000000019,  45.20000000000019, 45.100000000000186,\n",
+       "       45.000000000000185,  44.90000000000018,  44.80000000000018,\n",
+       "        44.70000000000018,  44.60000000000018,  44.50000000000018,\n",
+       "       44.400000000000176, 44.300000000000175,  44.20000000000017,\n",
+       "        44.10000000000017,  44.00000000000017,  43.90000000000017,\n",
+       "        43.80000000000017, 43.700000000000166, 43.600000000000165,\n",
+       "        43.50000000000016,  43.40000000000016,  43.30000000000016,\n",
+       "        43.20000000000016,  43.10000000000016, 43.000000000000156,\n",
+       "       42.900000000000155,  42.80000000000015,  42.70000000000015,\n",
+       "        42.60000000000015,  42.50000000000015,  42.40000000000015,\n",
+       "       42.300000000000146, 42.200000000000145,  42.10000000000014,\n",
+       "        42.00000000000014,  41.90000000000014,  41.80000000000014,\n",
+       "        41.70000000000014, 41.600000000000136, 41.500000000000135,\n",
+       "       41.400000000000134],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dec6930d-19ce-49b2-bc67-37af97f88c87' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dec6930d-19ce-49b2-bc67-37af97f88c87' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'departement' (longitude: 143, latitude: 97)> Size: 111kB\n",
+       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]])\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float64 1kB -4.7 -4.6 -4.5 -4.4 ... 9.2 9.3 9.4 9.5\n",
+       "  * latitude   (latitude) float64 776B 51.0 50.9 50.8 50.7 ... 41.6 41.5 41.4"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = xr.apply_ufunc(\n",
+    "    which_region,\n",
+    "    da.longitude,\n",
+    "    da.latitude,\n",
+    "    kwargs={\"polygons_regions\": polys_region},\n",
+    "    vectorize=True,\n",
+    "    dask=\"parallelized\",\n",
+    ")\n",
+    "\n",
+    "mask.name = \"departement\"\n",
+    "mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask.to_netcdf(\"../../data/geo/mask_france_departements.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "with open(\"../../data/geo/departements_name.yaml\", \"w\") as f:\n",
+    "    yaml.dump(list(polys_region.keys()), f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "departements = [\n",
+      "\" Aisne \",\n",
+      "\" Aube \",\n",
+      "\" Calvados \",\n",
+      "\" Cantal \",\n",
+      "\" Eure-et-Loir \",\n",
+      "\" Ille-et-Vilaine \",\n",
+      "\" Jura \",\n",
+      "\" Landes \",\n",
+      "\" Loire \",\n",
+      "\" Loiret \",\n",
+      "\" Lot-et-Garonne \",\n",
+      "\" Meuse \",\n",
+      "\" Orne \",\n",
+      "\" Pas-de-Calais \",\n",
+      "\" Puy-de-Dôme \",\n",
+      "\" Bas-Rhin \",\n",
+      "\" Haut-Rhin \",\n",
+      "\" Seine-Maritime \",\n",
+      "\" Yonne \",\n",
+      "\" Seine-Saint-Denis \",\n",
+      "\" Alpes-de-Haute-Provence \",\n",
+      "\" Hautes-Alpes \",\n",
+      "\" Ardèche \",\n",
+      "\" Ardennes \",\n",
+      "\" Ariège \",\n",
+      "\" Charente-Maritime \",\n",
+      "\" Corrèze \",\n",
+      "\" Dordogne \",\n",
+      "\" Eure \",\n",
+      "\" Indre-et-Loire \",\n",
+      "\" Lozère \",\n",
+      "\" Nièvre \",\n",
+      "\" Oise \",\n",
+      "\" Pyrénées-Atlantiques \",\n",
+      "\" Rhône \",\n",
+      "\" Saône-et-Loire \",\n",
+      "\" Paris \",\n",
+      "\" Yvelines \",\n",
+      "\" Tarn \",\n",
+      "\" Tarn-et-Garonne \",\n",
+      "\" Var \",\n",
+      "\" Vendée \",\n",
+      "\" Haute-Vienne \",\n",
+      "\" Vosges \",\n",
+      "\" Hauts-de-Seine \",\n",
+      "\" Allier \",\n",
+      "\" Alpes-Maritimes \",\n",
+      "\" Aude \",\n",
+      "\" Corse-du-Sud \",\n",
+      "\" Côtes-d'Armor \",\n",
+      "\" Creuse \",\n",
+      "\" Doubs \",\n",
+      "\" Finistère \",\n",
+      "\" Gard \",\n",
+      "\" Gironde \",\n",
+      "\" Indre \",\n",
+      "\" Isère \",\n",
+      "\" Marne \",\n",
+      "\" Haute-Marne \",\n",
+      "\" Moselle \",\n",
+      "\" Hautes-Pyrénées \",\n",
+      "\" Pyrénées-Orientales \",\n",
+      "\" Savoie \",\n",
+      "\" Haute-Savoie \",\n",
+      "\" Seine-et-Marne \",\n",
+      "\" Vaucluse \",\n",
+      "\" Vienne \",\n",
+      "\" Val-de-Marne \",\n",
+      "\" Ain \",\n",
+      "\" Aveyron \",\n",
+      "\" Bouches-du-Rhône \",\n",
+      "\" Charente \",\n",
+      "\" Cher \",\n",
+      "\" Haute-Corse \",\n",
+      "\" Côte-d'Or \",\n",
+      "\" Drôme \",\n",
+      "\" Haute-Garonne \",\n",
+      "\" Gers \",\n",
+      "\" Hérault \",\n",
+      "\" Haute-Loire \",\n",
+      "\" Loire-Atlantique \",\n",
+      "\" Lot \",\n",
+      "\" Maine-et-Loire \",\n",
+      "\" Manche \",\n",
+      "\" Morbihan \",\n",
+      "\" Nord \",\n",
+      "\" Haute-Saône \",\n",
+      "\" Sarthe \",\n",
+      "\" Somme \",\n",
+      "\" Essonne \",\n",
+      "\" Val-d'Oise \",\n",
+      "\" Loir-et-Cher \",\n",
+      "\" Mayenne \",\n",
+      "\" Meurthe-et-Moselle \",\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"departements = [\")\n",
+    "for i, (name, poly) in enumerate(polys_region.items()):\n",
+    "    print(\"\\\"\", name, \"\\\",\")\n",
+    "print(\"]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def show_mask(m):\n",
+    "    fig, ax = plt.subplots(subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+    "    im = ax.imshow(\n",
+    "        m.T,\n",
+    "        origin=\"upper\",\n",
+    "        transform=ccrs.PlateCarree(),\n",
+    "        extent=[\n",
+    "            m.longitude.min(),\n",
+    "            m.longitude.max(),\n",
+    "            m.latitude.min(),\n",
+    "            m.latitude.max(),\n",
+    "        ],\n",
+    "        cmap=\"tab20\",\n",
+    "    )\n",
+    "    ax.coastlines()\n",
+    "    ax.add_feature(cfeature.BORDERS)\n",
+    "    ax.add_feature(cfeature.STATES, linestyle=\":\")\n",
+    "    ax.set_title(\"Regions Mask\")\n",
+    "    # add a fake legend with the regions\n",
+    "    pacht_list = []\n",
+    "    for i, (name, poly) in enumerate(polys_region.items()):\n",
+    "        color = im.cmap(im.norm(i))\n",
+    "        p = plt.Rectangle((0, 0), 1, 1, fc=color)\n",
+    "        pacht_list.append(p)\n",
+    "    # ax.legend(pacht_list, polys_region.keys(), loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
+    "\n",
+    "\n",
+    "show_mask(mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "values, counts = np.unique(mask, return_counts=True)\n",
+    "len(values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(polys_region)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dep_means = da.groupby(mask).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataArray' object has no attribute 'group'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dep_means[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [v \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(polys_region\u001b[38;5;241m.\u001b[39mkeys()) \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m dep_means\u001b[38;5;241m.\u001b[39mgroup]\n\u001b[1;32m      2\u001b[0m dep_means \u001b[38;5;241m=\u001b[39m dep_means\u001b[38;5;241m.\u001b[39mrename({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdepartement\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[1;32m      3\u001b[0m dep_means\n",
+      "Cell \u001b[0;32mIn[29], line 1\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dep_means[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [v \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(polys_region\u001b[38;5;241m.\u001b[39mkeys()) \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdep_means\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup\u001b[49m]\n\u001b[1;32m      2\u001b[0m dep_means \u001b[38;5;241m=\u001b[39m dep_means\u001b[38;5;241m.\u001b[39mrename({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdepartement\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[1;32m      3\u001b[0m dep_means\n",
+      "File \u001b[0;32m~/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages/xarray/core/common.py:286\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m    285\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 286\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    287\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    288\u001b[0m )\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataArray' object has no attribute 'group'"
+     ]
+    }
+   ],
+   "source": [
+    "dep_means[\"group\"] = [v for i, v in enumerate(polys_region.keys()) if i in dep_means.group]\n",
+    "dep_means = dep_means.rename({\"group\": \"departement\"})\n",
+    "dep_means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'group'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages/xarray/core/dataarray.py:879\u001b[0m, in \u001b[0;36mDataArray._getitem_coord\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    878\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 879\u001b[0m     var \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_coords\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'group'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_23441/3384120821.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolys_region\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdep_means\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"group\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    886\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSelf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 888\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_coord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    889\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m             \u001b[0;31m# xarray-style array indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_key_to_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    878\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m             \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    880\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m             \u001b[0mdim_sizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 882\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_virtual_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim_sizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_replace_maybe_drop_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(variables, key, dim_sizes)\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[0msplit_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_key\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0mref_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msplit_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0mref_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mref_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'group'"
+     ]
+    }
+   ],
+   "source": [
+    "for i, val in enumerate(list(polys_region.keys())):\n",
+    "    if i not in dep_means[\"group\"]:\n",
+    "        print(val)\n",
+    "        print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/antoine/.local/share/hatch/env/virtual/energy-forecast/Jk97fpOc/serve/lib/python3.10/site-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
+      "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
+      "\n",
+      "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
+      "This will raise in a future version.\n",
+      "\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['/home/antoine/code/energetic-stress-production/data/silver/weather_forecasts/temperature_hourly_d1_departements.csv']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dep_means.to_dask_dataframe().to_csv(\"../../data/silver/weather_forecasts/temperature_hourly_d1_departements.csv\", single_file=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "serve",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}