meteo.data QUOT LATEST - Télécharge-Visualise-Extrait les données Quotidiennes LATEST_23-12-31.ipynb

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    "### Portail meteo.data.gouv.fr - Téléchargement-affichage-Extraction des données QUOTIDIENNES LATEST de Météo-France (RR-T-Vent)\n",
    "Ce notebook traite uniquement les fichiers \"RR-T-vent\" (Paramètres liés aux Précipitations, Température & Vent) à l'exclusion des fichiers \"autres-parametres\" contenant notamment l'ETP.<br>\n",
    "Une connexion internet est nécessaire pour le téléchargement automatique des archives de données.\n",
    "\n",
    "1) Téléchargement des fichiers quotidiens et décompression automatique, pour différents postes dans plusieurs départements si besoin\n",
    "2) Tracé du graphique chronologique QUOTIDIEN pour le paramètre Précipitations RR des postes choisis par l'utilisateur\n",
    "3) Tracé du graphique chronologique de l'agrégation MENSUELLE\n",
    "4) Sauvegarde d'un fichier excel rassemblant:\n",
    "    - Les données intégrales QUOTIDIENNES et agrégées MENSUELLEMENT (tous paramètres de la période \"Latest\" pour tous les postes des départements concernés).\n",
    "    - la comparaison des paramètres pour les postes et la période choisis, ainsi que pour les précipitations les graphiques quotidien & mensuel\n",
    "\n",
    "data : https://meteo.data.gouv.fr/ (6 min, horaire, quotidien, mensuel)<br>\n",
    "Fiche PDF des postes : https://www.data.gouv.fr/fr/datasets/r/bee4b0c7-260a-40fe-b463-ed5631d6dc39 (paramètres et périodes de mesure)<br>\n",
    "Fichier CSV descriptif champs: https://www.data.gouv.fr/fr/datasets/r/6a8df7e9-45ff-445d-9260-6c65475dda86\n",
    "\n",
    "Utilisez mon autre script pour visualiser la carte des postes météorologiques dont la liste est fournie par Météo-France sous forme de fichier JSON https://meteo.data.gouv.fr/https://www.data.gouv.fr/fr/datasets/r/1fe544d8-4615-4642-a307-5956a7d90922\n",
    "\n",
    "NB: \n",
    "- Les données LATEST correspondent aux DERNIERS FICHIERS mis à jour quotidiennement, et qui vont du mois de janvier de l'année précédente à la veille du jour en cours même partielle.\n",
    "- Les données QUOTDIENNES agrégées mensuellement ne sont pas simplement équivalentes aux donnée MENSUELLES. Les paramètres ne sont pas strictement les mêmes. Certains paramètres quotidiens n'existent pas en mensuel (ex. FF2M vent à 2 m, HTN heure de Tmin), mais les paramètres mensuels sont globalement plus nombreux à cause des paramètres \"calculés\" (par exemple des NOMBRES DE JOURS DE PLUIE/GELEE/CHALEUR/etc.., ainsi que des précipitations MENSUELLES ESTIMEES 'RR_ME' plus anciennes que les mesures)\n",
    "\n",
    "Auteur: https://github.com/loicduffar"
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    "##### 1) Lecture après téléchargement des archives GZ et décompression des fichiers CSV LATEST (dernière période disponible)\n",
    "\n",
    "- Définir les chemins d'enregistrement pour les archives GZ et pour les fichier CSV décompressés\n",
    "- Définir les postes à interroger par leur code à 8 chiffres (voir fichier \"fiches.json\" sur https://www.data.gouv.fr/fr/datasets/r/1fe544d8-4615-4642-a307-5956a7d90922)\n",
    "- Définir UNE FOIS POUR TOUTES les urls de téléchargement du fichier LATEST pour chaque département\n",
    "- NB: Les paramètres et la période à extraire sont définis dans la cellule 2 (pour pouvoir les modifier sans relire les fichiers)\n",
    "- On peut choisir d'éviter de télécharger-décompresser les archives GZ si le dernier téléchargement date du jour même\n",
    "\n",
    "Les archives LATEST sont automatiquement téléchargées et décompressées et les fichiers CSV sont lus"
   ]
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     "text": [
      "2023-12-31 13:43\n",
      "Départements concernés:  ['04', '13', '83', '84']\n",
      "Lecture:  X:\\1-COMMUN\\DIS\\Documentation\\Hydrologie\\Documentation externe\\Climat France\\Météo-France\\meteo.data\\base\\Q\\Q_04_latest-2022-2023_RR-T-Vent.csv\n",
      "Lecture:  X:\\1-COMMUN\\DIS\\Documentation\\Hydrologie\\Documentation externe\\Climat France\\Météo-France\\meteo.data\\base\\Q\\Q_13_latest-2022-2023_RR-T-Vent.csv\n",
      "Lecture:  X:\\1-COMMUN\\DIS\\Documentation\\Hydrologie\\Documentation externe\\Climat France\\Météo-France\\meteo.data\\base\\Q\\Q_83_latest-2022-2023_RR-T-Vent.csv\n",
      "Lecture:  X:\\1-COMMUN\\DIS\\Documentation\\Hydrologie\\Documentation externe\\Climat France\\Météo-France\\meteo.data\\base\\Q\\Q_84_latest-2022-2023_RR-T-Vent.csv\n"
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       "      <th></th>\n",
       "      <th>NUM_POSTE</th>\n",
       "      <th>NOM_USUEL</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>ALTI</th>\n",
       "      <th>DATE</th>\n",
       "      <th>RR</th>\n",
       "      <th>QRR</th>\n",
       "      <th>TN</th>\n",
       "      <th>QTN</th>\n",
       "      <th>...</th>\n",
       "      <th>DXI2</th>\n",
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       "      <th>QFXI3S</th>\n",
       "      <th>DXI3S</th>\n",
       "      <th>QDXI3S</th>\n",
       "      <th>HXI3S</th>\n",
       "      <th>QHXI3S</th>\n",
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       "      <th>0</th>\n",
       "      <td>04006005</td>\n",
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       "      <td>6.625333</td>\n",
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       "      <td>2022-01-01</td>\n",
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       "      <td>6.625333</td>\n",
       "      <td>1400</td>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>04006005</td>\n",
       "      <td>ALLOS_SAPC</td>\n",
       "      <td>44.242500</td>\n",
       "      <td>6.625333</td>\n",
       "      <td>1400</td>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>04006005</td>\n",
       "      <td>ALLOS_SAPC</td>\n",
       "      <td>44.242500</td>\n",
       "      <td>6.625333</td>\n",
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       "      <td>44.336667</td>\n",
       "      <td>4.905500</td>\n",
       "      <td>141</td>\n",
       "      <td>2023-12-16</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>84150001</td>\n",
       "      <td>VISAN</td>\n",
       "      <td>44.336667</td>\n",
       "      <td>4.905500</td>\n",
       "      <td>141</td>\n",
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       "      NUM_POSTE   NOM_USUEL        LAT       LON  ALTI       DATE   RR  QRR  \\\n",
       "0      04006005  ALLOS_SAPC  44.242500  6.625333  1400 2022-01-01  0.0  1.0   \n",
       "1      04006005  ALLOS_SAPC  44.242500  6.625333  1400 2022-01-02  0.0  1.0   \n",
       "2      04006005  ALLOS_SAPC  44.242500  6.625333  1400 2022-01-03  0.0  1.0   \n",
       "3      04006005  ALLOS_SAPC  44.242500  6.625333  1400 2022-01-04  1.6  1.0   \n",
       "4      04006005  ALLOS_SAPC  44.242500  6.625333  1400 2022-01-05  0.0  1.0   \n",
       "...         ...         ...        ...       ...   ...        ...  ...  ...   \n",
       "12937  84150001       VISAN  44.336667  4.905500   141 2023-12-16  0.0  1.0   \n",
       "12938  84150001       VISAN  44.336667  4.905500   141 2023-12-17  0.0  1.0   \n",
       "12939  84150001       VISAN  44.336667  4.905500   141 2023-12-18  0.2  1.0   \n",
       "12940  84150001       VISAN  44.336667  4.905500   141 2023-12-19  0.2  1.0   \n",
       "12941  84150001       VISAN  44.336667  4.905500   141 2023-12-20  0.0  1.0   \n",
       "\n",
       "        TN  QTN  ...  DXI2  QDXI2  HXI2  QHXI2  FXI3S  QFXI3S  DXI3S  QDXI3S  \\\n",
       "0      1.1  1.0  ...   NaN    NaN   NaN    NaN    NaN     NaN    NaN     NaN   \n",
       "1      1.0  1.0  ...   NaN    NaN   NaN    NaN    NaN     NaN    NaN     NaN   \n",
       "2     -0.4  1.0  ...   NaN    NaN   NaN    NaN    NaN     NaN    NaN     NaN   \n",
       "3      0.2  1.0  ...   NaN    NaN   NaN    NaN    NaN     NaN    NaN     NaN   \n",
       "4      0.0  1.0  ...   NaN    NaN   NaN    NaN    NaN     NaN    NaN     NaN   \n",
       "...    ...  ...  ...   ...    ...   ...    ...    ...     ...    ...     ...   \n",
       "12937  5.4  1.0  ...   NaN    NaN   NaN    NaN   15.0     1.0    NaN     NaN   \n",
       "12938 -1.7  1.0  ...   NaN    NaN   NaN    NaN    9.1     1.0    NaN     NaN   \n",
       "12939 -3.4  1.0  ...   NaN    NaN   NaN    NaN    3.1     1.0    NaN     NaN   \n",
       "12940 -2.6  1.0  ...   NaN    NaN   NaN    NaN   12.7     1.0    NaN     NaN   \n",
       "12941  5.9  1.0  ...   NaN    NaN   NaN    NaN   23.5     1.0    NaN     NaN   \n",
       "\n",
       "        HXI3S  QHXI3S  \n",
       "0         NaN     NaN  \n",
       "1         NaN     NaN  \n",
       "2         NaN     NaN  \n",
       "3         NaN     NaN  \n",
       "4         NaN     NaN  \n",
       "...       ...     ...  \n",
       "12937   728.0     9.0  \n",
       "12938    37.0     9.0  \n",
       "12939  1258.0     9.0  \n",
       "12940  2319.0     9.0  \n",
       "12941  1813.0     9.0  \n",
       "\n",
       "[70504 rows x 56 columns]"
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     "text": [
      "Postes absents de la liste JSON: ['04006400' '04019404' '04088001' '04112010' '04205001' '04219001'\n",
      " '04226001'] ['La Foux d Allos' 'Pra Loup' 'FORCALQUIER' 'MANOSQUE-PIMARLET' 'SEYNE'\n",
      " 'THORAME HAUTE' 'UVERNET FOURS']\n"
     ]
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   "source": [
    "############ Auteur: L. Duffar ###########\n",
    "############ Décembre 2023 ###########\n",
    "# python 3.8.12\n",
    "# Télécharge les archives GZ & et décompresse les fichiers CSV LATEST (DERNIERE PERIODE DISPONIBLE pour chaque département)\n",
    "import os\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import time\n",
    "import sys\n",
    "import gzip\n",
    "\n",
    "# ================ Personalisation ====================\n",
    "# Chemin d'enregistrement des archives gz et des fichiers CSV décompressés\n",
    "folder_gz= r\"X:\\1-COMMUN\\DIS\\Documentation\\Hydrologie\\Documentation externe\\Climat France\\Météo-France\\meteo.data\\archives\\2023 Déc\\base\\Q\"\n",
    "folder_csv= r\"X:\\1-COMMUN\\DIS\\Documentation\\Hydrologie\\Documentation externe\\Climat France\\Météo-France\\meteo.data\\base\\Q\"\n",
    "\n",
    "# Numéro des Postes météo souhaités (chaine de 8 caractères) - voir fichier \"fiches.json\" sur https://www.data.gouv.fr/fr/datasets/r/1fe544d8-4615-4642-a307-5956a7d90922\n",
    "# LES DEPARTEMENTS CORRESPONDANTS DOIVENT ËTRE PRESENTS DANS LA LISTE DES URLS (voir plus loin ======= Initialisation ==========)\n",
    "postes= [\n",
    "        '04088001', '04039001', '04230001',                  # Forcalquier, Castellane, Valensole \\\n",
    "        # '05046001',                                           # Embrun \\\n",
    "        # '06088001', '06029001',                               # Nice, Cannes \\\n",
    "        '13001009', '13111002', '13103001','13055001',        # Aix en Provence, Vauvenargue, Salon, Marseille, \\\n",
    "        '83031001', '83061001', '83137001',                   # Le Luc, Fréjus, Toulon \\\n",
    "        '84003002', '84009002',                               # # Apt-Viton, Bastide des Jourdans \\\n",
    "        # '97411132', '97411150',                               # 974 LA REUNION: Chaudron, Saint-François \\\n",
    "        # '98511001',                                            # 985 MAYOTTE: Mamoudzou_SAPC \\\n",
    "        # '98404001', '98404002',                                 # 984 TERRES AUSTRALES ET ANTARTIQUES FRANCAISES: Kerguelen, Nouvelle Amsterdam \\        \n",
    "        ]                               \n",
    "\n",
    "# Choisir d'éviter le téléchargement/décompression\n",
    "# non recommandé, sauf pour test ou à moins que le dernier téléchargement date du jour même\n",
    "download= False\n",
    "\n",
    "# ================ Extrait du fichier data CSV ============= (les colonnes sont en réalité séparées par des points-virgules)\n",
    "# NUM_POSTE\tNOM_USUEL\tLAT\tLON\tALTI\tAAAAMMJJ\tRR\tQRR\tTN\tQTN\tHTN\tQHTN\tTX\tQTX\tHTX\tQHTX\tTM\tQTM\tTNTXM\tQTNTXM\tTAMPLI\tQTAMPLI\n",
    "# 13001009\tAIX EN PROVENCE\t43.5295\t5.4245\t173\t20220101\t0\t1\t4.6\t1\t820\t9\t9.4\t1\t1424\t9\t7.9\t1\t7\t1\t4.8\t1\n",
    "# 13001009\tAIX EN PROVENCE\t43.5295\t5.4245\t173\t20220102\t0\t1\t8.3\t1\t646\t9\t11.1\t1\t1145\t9\t9.1\t1\t9.7\t1\t2.8\t1\n",
    "# 13001009\tAIX EN PROVENCE\t43.5295\t5.4245\t173\t20220103\t0.2\t1\t4.6\t1\t655\t9\t13.5\t1\t1452\t9\t7.4\t1\t9.1\t1\t8.9\t1\n",
    "# 13001009\tAIX EN PROVENCE\t43.5295\t5.4245\t173\t20220104\t1.4\t1\t4.5\t1\t2348\t9\t13.7\t1\t1123\t9\t10.1\t1\t9.1\t1\t9.2\t1\n",
    "# 13001009\tAIX EN PROVENCE\t43.5295\t5.4245\t173\t20220105\t0\t1\t4.6\t1\t1742\t9\t8.7\t1\t601\t9\t6.8\t1\t6.7\t1\t4.1\t1\n",
    "\n",
    "# ================ Extrait du fichier CSV Descriptif de quelques paramètres (les plus souvent utiles, précipitations et températures) =============(les colonnes sont en réalité séparées par \":\")\n",
    "# NUM_POSTE   \t numéro Météo-France du poste sur 8 chiffres\n",
    "# NOM_USUEL   \t nom usuel du poste\n",
    "# LAT         \t latitude, négative au sud (en degrés et millionièmes de degré)\n",
    "# LON         \t longitude, négative à l’ouest de GREENWICH (en degrés et millionièmes de degré)\n",
    "# ALTI        \t altitude du pied de l'abri ou du pluviomètre si pas d'abri (en m)\n",
    "# AAAAMMJJ    \t date de la mesure (année mois jour)\n",
    "# RR          \t quantité de précipitation tombée en 24 heures (de 06h FU le jour J à 06h FU le jour J+1). La valeur relevée à J+1 est affectée au jour J (en mm et 1/10)\n",
    "# TN          \t température minimale sous abri (en °C et 1/10)\n",
    "# HTN         \t heure de TN (hhmm)\n",
    "# TX          \t température maximale sous abri (en °C et 1/10)\n",
    "# HTX         \t heure de TX (hhmm)\n",
    "# TM          \t moyenne quotidienne des températures horaires sous abri (en °C et 1/10)\n",
    "# TNTXM       \t moyenne quotidienne (TN+TX)/2 (en °C et 1/10)\n",
    "# TAMPLI      \t amplitude thermique quotidienne -  écart entre TX et TN quotidiens (TX-TN) (en °C et 1/10)\n",
    "# TNSOL       \t température quotidienne minimale à 10 cm au-dessus du sol (en °C et 1/10)\n",
    "# TN50        \t température quotidienne minimale à 50 cm au-dessus du sol (en °C et 1/10)\n",
    "# DG          \t durée de gel sous abri (T ≤ 0°C) (en mn)\n",
    "\n",
    "# ================ Initialisation ====================\n",
    "now= datetime.datetime.now()\n",
    "print(now.strftime(\"%Y-%m-%d %H:%M\"))\n",
    "# Structure du nom des fichiers de données QUOTIDIENNES (1 fichier par département dont le numéro sera ajouté automatiquement au début et à la fin du template ci-dessous)\n",
    "template_start= 'Q_' # début du nom du fichier\n",
    "# Fin du nom dépendant de l'année en cours\n",
    "template_end= '_latest-' + str(now.year-1) + '-' + str(now.year) + '_RR-T-Vent.csv'\n",
    "\n",
    "# ---------------- urls de téléchargement des archives PAR DEPARTEMENT des dernières données depuis janvier de l'année précédente https://meteo.data.gouv.fr/ (Ajouter d'autres départements si besoin)\n",
    "url_liste_postes= \"https://www.data.gouv.fr/fr/datasets/r/1fe544d8-4615-4642-a307-5956a7d90922\"\n",
    "url_desc_q= \"https://www.data.gouv.fr/fr/datasets/r/6a8df7e9-45ff-445d-9260-6c65475dda86\"\n",
    "url_desc_m= \"https://www.data.gouv.fr/fr/datasets/r/6d4ac560-8f7c-477f-9a3f-3c33137fc04e\"\n",
    "urls= dict()\n",
    "urls['04']= \"https://www.data.gouv.fr/fr/datasets/r/52355223-df85-49a9-9031-14b8a604c28c\" \n",
    "urls['05']= 'https://www.data.gouv.fr/fr/datasets/r/27bfba71-f9d2-4cbb-a784-7a893d933485'\n",
    "urls['06']= 'https://www.data.gouv.fr/fr/datasets/r/6d848c25-210b-40a4-b3c2-a4084bf660a7'\n",
    "urls['13']= 'https://www.data.gouv.fr/fr/datasets/r/eb4d0600-e90b-4517-a429-599ed13dbae0'\n",
    "urls['83']= 'https://www.data.gouv.fr/fr/datasets/r/5889ea7c-e285-46cb-8ea6-1c317fa4dada'\n",
    "urls['84']= 'https://www.data.gouv.fr/fr/datasets/r/b47bd200-0ba7-419c-9385-4ce8ff8c199c'\n",
    "urls['974']= 'https://www.data.gouv.fr/fr/datasets/r/50a0d0fb-d97c-4ca3-b308-def80d69416d' # La Réunion\n",
    "urls['975']= 'https://www.data.gouv.fr/fr/datasets/r/86b3a033-b7be-4733-9203-cef708aad6a4' # Saint-Pierre et Miquelon\n",
    "urls['984']= 'https://www.data.gouv.fr/fr/datasets/r/3a82cde3-63aa-4b57-b8ba-84820e92c910' # Terres Australes et Antarctiques Françaises\n",
    "urls['985']= 'https://www.data.gouv.fr/fr/datasets/r/8c5daf91-4634-40d8-822d-27f1c04cf2b4' # Mayotte\n",
    "\n",
    "# ---------------- Définition des fonctions\n",
    "def convert_to_date(chaine):\n",
    "    return pd.to_datetime(str(chaine), format='%Y%m%d', errors='coerce')\n",
    "\n",
    "def download_file(url, filename):\n",
    "    file= os.path.join(folder_gz, filename) + '.gz'\n",
    "    print('Téléchargement: ', file)\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        with open(file, 'wb') as f:\n",
    "            f.write(response.content)\n",
    "    else:\n",
    "        print(\"Fichier d'archive non présent à l'url habituelle: \", file)\n",
    "\n",
    "def decompress_gz(filename):\n",
    "    file= os.path.join(folder_gz, filename) + '.gz'\n",
    "    \n",
    "    if os.path.exists(file):\n",
    "        with gzip.open(file, 'rb') as f_in:\n",
    "            file= os.path.join(folder_csv, filename) \n",
    "            print('Décompression', file)\n",
    "            with open(file, 'wb') as f_out:\n",
    "                f_out.write(f_in.read())\n",
    "    else:\n",
    "        print(\"Fichier d'archive non trouvé: \", file)\n",
    "        print(\"Téléchargez l'archive GZ, manuellement ou en modifiant la variable 'download', puis relancez le script\")\n",
    "\n",
    "def read_csv(filename):\n",
    "    file= os.path.join(folder_csv, filename)\n",
    "    if os.path.exists(file):\n",
    "        print('Lecture: ', file)\n",
    "        df= pd.read_csv(file, header=0, sep=\";\", dtype={\"NUM_POSTE\":str, 'AAAAMMJJ':str}, parse_dates=['AAAAMMJJ'], date_parser= convert_to_date)\n",
    "    else:\n",
    "        print(\"Fichier CSV non trouvé: \", file)\n",
    "        print(\"Téléchargez l'archive GZ, manuellement ou en modifiant la variable 'download', puis relancez le script\")\n",
    "    return df\n",
    "\n",
    "# ================ Traitement ====================\n",
    "# Lecture en ligne du fichier JSON\n",
    "req= requests.get(url_liste_postes)\n",
    "if req.status_code == 200:\n",
    "    data_json= req.json()\n",
    "else:\n",
    "    print('la requête a échoué avec le code : ', req.status_codes)\n",
    "    sys.exit() # interrompt le script\n",
    "df_liste_postes = pd.DataFrame(data_json['features']).T\n",
    "# Ajout de colonnes avec les champs de properties\n",
    "df_liste_postes['lat'] = df_liste_postes['geometry'].apply(lambda x: x['coordinates'][1]).astype(float)\n",
    "df_liste_postes['lon'] = df_liste_postes['geometry'].apply(lambda x: x['coordinates'][0]).astype(float)\n",
    "df_liste_postes['nom_usuel']= df_liste_postes['properties'].apply(lambda x: x['NOM_USUEL'].strip())\n",
    "df_liste_postes['num_poste']= df_liste_postes['properties'].apply(lambda x: x['NUM_POSTE'].strip())\n",
    "df_liste_postes['commune'] = df_liste_postes['properties'].apply(lambda x: x['COMMUNE'].strip())\n",
    "df_liste_postes['ficheClimComplete'] = df_liste_postes['properties'].apply(lambda x: x['ficheClimComplete']).astype(float)\n",
    "df_liste_postes['ficheClimReduite'] = df_liste_postes['properties'].apply(lambda x: x['ficheClimReduite']).astype(float)\n",
    "df_liste_postes['alti'] = df_liste_postes['properties'].apply(lambda x: x['ALTI'])\n",
    "# supprime les colonnes inutiles et classe par numéro de poste (donc par département)\n",
    "df_liste_postes.drop(['type', 'geometry', 'properties'], axis=1, inplace=True)\n",
    "df_liste_postes.sort_values(by= ['num_poste'], inplace=True)\n",
    "\n",
    "# Lit en ligne le fichier \"fin\" \"Q_descriptif_champs_RR-T-Vent.csv\" de description des champs\n",
    "# définit un dataframe pandas avec les 2 colonnes \"param\" et \"name_long\" pour la description des champs \n",
    "df_desc_f = pd.read_csv(url_desc_q, sep=\":\", header= None, index_col=0, names= [\"param\", \"name_long\", 'complement'], dtype={\"param\":str, \"name_long\":str, 'complement':str}, encoding= 'utf-8')\n",
    "df_desc_f.index= df_desc_f.index.str.strip()\n",
    "df_desc_f['name_long']= df_desc_f['name_long'].str.strip()\n",
    "# Lit en ligne le fichier \"MENSQ_descriptif_champs.csv\" de description des champs\n",
    "# définit un dataframe pandas avec les 2 colonnes \"param\" et \"name_long\" pour la description des champs \n",
    "df_desc_bis = pd.read_csv(url_desc_m, sep=\":\", header= None, index_col=0, names= [\"param\", \"name_long\", 'complement'], dtype={\"param\":str, \"name_long\":str, 'complement':str}, encoding= 'utf-8')\n",
    "df_desc_bis.index= df_desc_bis.index.str.strip()\n",
    "df_desc_bis['name_long']= df_desc_bis['name_long'].str.strip()\n",
    "\n",
    "# fait la liste des départements concernés et garde les 2 premiers chiffres uniques\n",
    "departements = []\n",
    "for poste in postes:\n",
    "    departements.append(poste[:2])\n",
    "# On ne garde que départements uniques\n",
    "departements = list(set(departements)) # set() ignore automatiquement les doublons (ce qui évite de passer par array numpy pour utiliser la fonction np.unique() )\n",
    "# On trie la liste\n",
    "departements.sort()\n",
    "# On affiche la liste\n",
    "print('Départements concernés: ', departements)\n",
    "\n",
    "# Téléchargement/décompression/lecture des fichiers dans une boucle sur les départements (urls tirées du dictionnaire 'urls')\n",
    "i, j= 0, 0\n",
    "for departement in departements:\n",
    "    # On récupère l'url\n",
    "    url = urls[departement]\n",
    "    # Formation du nom du fichier à partir du template et du numéro de département\n",
    "    filename = f\"{template_start}{departement}{template_end}\"\n",
    "    if download:\n",
    "        download_file(url, filename)\n",
    "        decompress_gz(filename)\n",
    "    # Lecture du fichier CSV dans un dataframe pandas\n",
    "    df_departement= read_csv(filename)\n",
    "    if i== 0: # pour le premier département, initialisation du dataframe final df        \n",
    "        df= df_departement\n",
    "        i= i+1\n",
    "    else: # sinon concaténation du nouveau département dans le dataframe df\n",
    "        df= pd.concat([df, df_departement])        \n",
    "\n",
    "# supprime les espaces avant et après le numéro de poste\n",
    "df['NUM_POSTE'] = df['NUM_POSTE'].str.strip()\n",
    "# renome la colonne \"AAAMMJJ\" en \"DATE\"\n",
    "df.rename(columns={'AAAAMMJJ':'DATE'}, inplace=True)\n",
    "# supprime les espaces dans les noms de colonnes\n",
    "df.columns= df.columns.str.strip()\n",
    "\n",
    "display(df)\n",
    "\n",
    "# Détection des postes manquants dans la liste JSON\n",
    "postes_mesures= df['NUM_POSTE'].unique()\n",
    "postes_json= df_liste_postes['num_poste'].unique()\n",
    "# trouvrer les postes_mesures absents de la liste JSON\n",
    "postes_absents= np.setdiff1d(postes_mesures, postes_json)\n",
    "if len(postes_absents) > 0:\n",
    "    # trouver le NOM_USEL des postes absents\n",
    "    print(\"Postes absents de la liste JSON:\", postes_absents, df[df['NUM_POSTE'].isin(postes_absents)]['NOM_USUEL'].unique())\n",
    "else:\n",
    "    print(\"Aucun postes absent de la liste JSON\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2) Affichage des données quotidiennes & Graphique des précipitations \n",
    "\n",
    "- Définir comme suit la période d'extraction : (le graphique ne commence qu'au début du mois précédent, mais on peut l'étendre à la souris à l'intérieur de la période d'extraction)\n",
    "    - None pour extraire les données depuis le 1er septembre de l'avant-dernière année de l'archive. La période d'extraction conditionne également le début de la courbe de pluie cumulée  \n",
    "    - ou définir la date de début et de fin d'extraction, ce qui permet d'étendre l'extraction si besoin jusqu'au début de l'archive\n",
    "- Définir les paramètres à extraire (au minimum les précipitations RR car le graphique est uniquement prévu pour cela)\n",
    "- NB: Tous les paramètres sont des chiffres, y compris ceux donnant une heure (ex. HTN= 738 -> Heure du minimum de température= 07:38)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-31 14:47\n",
      "Agrégation mensuelle pour les paramètres et la période choisis: Attention le cumul du dernier mois est incomplet à ce jour\n",
      "- RR: cumul mensuel des hauteurs de précipitation (en mm et 1/10)\n",
      "- TM: moyenne mensuelle des (TN+TX)/2 quotidiennes (en °C et 1/10)\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>NOM_USUEL</th>\n",
       "      <th>AIX EN PROVENCE</th>\n",
       "      <th>APT-VITON</th>\n",
       "      <th>CASTELLANE</th>\n",
       "      <th>FORCALQUIER</th>\n",
       "      <th>FREJUS</th>\n",
       "      <th>LA BASTIDE DES JOURDANS</th>\n",
       "      <th>LE LUC</th>\n",
       "      <th>MARSEILLE-OBS</th>\n",
       "      <th>SALON DE PROVENCE</th>\n",
       "      <th>TOULON</th>\n",
       "      <th>VALENSOLE</th>\n",
       "      <th>VAUVENARGUES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RR</th>\n",
       "      <th>2023-11-01</th>\n",
       "      <td>30.2</td>\n",
       "      <td>37.6</td>\n",
       "      <td>102.2</td>\n",
       "      <td>63.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>23.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>25.8</td>\n",
       "      <td>36.4</td>\n",
       "      <td>41.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-01</th>\n",
       "      <td>42.6</td>\n",
       "      <td>60.4</td>\n",
       "      <td>76.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>53.8</td>\n",
       "      <td>28.3</td>\n",
       "      <td>35.4</td>\n",
       "      <td>65.4</td>\n",
       "      <td>56.8</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TM</th>\n",
       "      <th>2023-11-01</th>\n",
       "      <td>9.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>12.1</td>\n",
       "      <td>12.4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-01</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NOM_USUEL      AIX EN PROVENCE  APT-VITON  CASTELLANE  FORCALQUIER  FREJUS  \\\n",
       "   DATE                                                                      \n",
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      "Données quotidiennes pour les paramètres et la période choisis: \n",
      "- RR: quantité de précipitation tombée en 24 heures (de 06h FU le jour J à 06h FU le jour J+1). La valeur relevée à J+1 est affectée au jour J (en mm et 1/10)\n"
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       "      <th>2023-09-01</th>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>NOM_USUEL</th>\n",
       "      <th>AIX EN PROVENCE</th>\n",
       "      <th>APT-VITON</th>\n",
       "      <th>CASTELLANE</th>\n",
       "      <th>FORCALQUIER</th>\n",
       "      <th>FREJUS</th>\n",
       "      <th>LA BASTIDE DES JOURDANS</th>\n",
       "      <th>LE LUC</th>\n",
       "      <th>MARSEILLE-OBS</th>\n",
       "      <th>SALON DE PROVENCE</th>\n",
       "      <th>TOULON</th>\n",
       "      <th>VALENSOLE</th>\n",
       "      <th>VAUVENARGUES</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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-09-01</th>\n",
       "      <td>22.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.5</td>\n",
       "      <td>21.2</td>\n",
       "      <td>21.3</td>\n",
       "      <td>19.8</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-02</th>\n",
       "      <td>22.5</td>\n",
       "      <td>21.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.6</td>\n",
       "      <td>20.8</td>\n",
       "      <td>22.1</td>\n",
       "      <td>22.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-03</th>\n",
       "      <td>24.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>24.6</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>22.5</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-04</th>\n",
       "      <td>26.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>19.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>27.4</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.6</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-05</th>\n",
       "      <td>25.2</td>\n",
       "      <td>23.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.3</td>\n",
       "      <td>23.6</td>\n",
       "      <td>26.2</td>\n",
       "      <td>23.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-17</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-18</th>\n",
       "      <td>6.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.4</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-19</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-20</th>\n",
       "      <td>7.1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "NOM_USUEL   AIX EN PROVENCE  APT-VITON  CASTELLANE  FORCALQUIER  FREJUS  \\\n",
       "DATE                                                                      \n",
       "2023-09-01             22.1       21.0        17.5          NaN    21.8   \n",
       "2023-09-02             22.5       21.7        18.6          NaN    22.6   \n",
       "2023-09-03             24.3       23.0        19.2          NaN    23.2   \n",
       "2023-09-04             26.6       24.8        19.6          NaN    24.7   \n",
       "2023-09-05             25.2       23.1        18.8          NaN    23.4   \n",
       "...                     ...        ...         ...          ...     ...   \n",
       "2023-12-16              5.1        1.6        -2.2          NaN     6.9   \n",
       "2023-12-17              5.4        3.0        -1.8          NaN     6.6   \n",
       "2023-12-18              6.2        4.6        -1.2          NaN     7.0   \n",
       "2023-12-19              5.5        4.2        -1.2          NaN     7.1   \n",
       "2023-12-20              7.1        5.9        -0.4          NaN     7.2   \n",
       "\n",
       "NOM_USUEL   LA BASTIDE DES JOURDANS  LE LUC  MARSEILLE-OBS  SALON DE PROVENCE  \\\n",
       "DATE                                                                            \n",
       "2023-09-01                     19.7    21.8           22.5               21.2   \n",
       "2023-09-02                     20.8    22.1           22.9               21.8   \n",
       "2023-09-03                     23.0    23.3           24.6               23.5   \n",
       "2023-09-04                     24.3    23.7           27.4               24.7   \n",
       "2023-09-05                     22.3    23.6           26.2               23.9   \n",
       "...                             ...     ...            ...                ...   \n",
       "2023-12-16                      4.3     4.0            9.3                5.4   \n",
       "2023-12-17                      5.1     3.5            9.4                3.9   \n",
       "2023-12-18                      6.7     4.3            9.6                3.4   \n",
       "2023-12-19                      6.5     4.2            9.7                4.9   \n",
       "2023-12-20                      7.3     5.7            9.5                9.7   \n",
       "\n",
       "NOM_USUEL   TOULON  VALENSOLE  VAUVENARGUES  \n",
       "DATE                                         \n",
       "2023-09-01    21.3       19.8          19.1  \n",
       "2023-09-02    22.8       20.0          19.7  \n",
       "2023-09-03    23.7       22.5          21.9  \n",
       "2023-09-04    24.6       23.5          23.4  \n",
       "2023-09-05    25.4       22.2          22.2  \n",
       "...            ...        ...           ...  \n",
       "2023-12-16     8.1        5.3           2.8  \n",
       "2023-12-17     7.5        6.0           4.1  \n",
       "2023-12-18     7.5        8.4           6.4  \n",
       "2023-12-19     7.3        8.8           6.3  \n",
       "2023-12-20     9.6        7.2           6.3  \n",
       "\n",
       "[111 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# trace un graphique plotly de df avec la colonne 'DATE' en abscisse et la colonne 'RR' en ordonnée\n",
    "# chaque poste de la colonnee 'NUM_POSTE' est représenté dans un subplot séparé\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly\n",
    "import calendar\n",
    "import numpy as np\n",
    "\n",
    "#-------------- Personalisation du graphique\n",
    "# Paramètres à extraire: 'RR' obligatoire en premier, pour le graphique dont la présentation actuelle est spécialement adaptée aux précipitations\n",
    "param_list= ['RR', 'TM']\n",
    "agregat_list= ['sum', 'mean'] # 'sum' pour la somme (précipitations), 'mean' pour la moyenne (température moyenne), max (vent), min (Tempéraure min)\n",
    "# TODO: prenrdre en compte le Nombre maxi de NaN dans les données pour l'agrégation mensuelle\n",
    "# agregat_max_nan_list= [0, 5]\n",
    "\n",
    "# Période A EXTRAIRE (None pour la période par défaut)\n",
    "start_date= datetime.datetime(2023, 9, 1)\n",
    "start_date= None\n",
    "\n",
    "end_date= datetime.datetime(2023, 8, 1)\n",
    "end_date= None\n",
    "\n",
    "#-------------- initialisation des paramètres du graphique\n",
    "param_RR= 'RR'\n",
    "# Période par défaut d'extraction (si start_date= None ou  end_date= None): maximisation de l'extraction\n",
    "if end_date is None:\n",
    "    end_date= df['DATE'].max()\n",
    "if start_date is None:\n",
    "    if end_date.month >= 9:\n",
    "        start_date= datetime.datetime(end_date.year, 9, 1)\n",
    "    else:\n",
    "        start_date= datetime.datetime(end_date.year - 1, 9, 1) # 1er septembre de l'année précédente\n",
    "\n",
    "# période du GRAPHIQUE : premier jour du premier mois au début du mois suivant (pour une graduation clair de l'axe des dates)\n",
    "end_graph= end_date\n",
    "start_graph = datetime.datetime(end_graph.year, end_graph.month, 1) - datetime.timedelta(days= 31) # calcule start_graph  comme le premier jour du mois précédant end_date\n",
    "\n",
    "# filtre le dataframe sur les postes choisis\n",
    "df_postes= df[df['NUM_POSTE'].isin(postes)]\n",
    "# filtre le dataframe sur la période souhaitée\n",
    "df_postes= df_postes[(df_postes['DATE'] >= start_date) & (df_postes['DATE'] <= end_date)]\n",
    "\n",
    "#  Couleurs des courbes\n",
    "color_sequence_bar = plotly.colors.qualitative.Dark24\n",
    "color_sequence_scatter = plotly.colors.qualitative.Alphabet\n",
    "\n",
    "# Axe des ordonnées secondaire (cumulé)\n",
    "specs= [[{'secondary_y': True}]]*len(postes)\n",
    "\n",
    "#-------------- Graphique\n",
    "fig = make_subplots(rows= len(postes), cols= 1,# shared_xaxes= True, # shared_xaxes= True inutile grâce à  matches='x' dans update_xaxes qui fait la même chose sans supprimer la graduation des dates des subplots \n",
    "                    subplot_titles= [f\"{poste} - {df_postes[df_postes['NUM_POSTE'] == poste]['NOM_USUEL'].iloc[0]}\" for poste in postes], specs= specs)\n",
    "\n",
    "for i, poste in enumerate(postes):\n",
    "    df_poste = df_postes[df_postes['NUM_POSTE'] == poste]\n",
    "    fig.add_trace(go.Bar(x= df_poste['DATE'], y= df_poste[param_RR], \n",
    "                          # name= poste\n",
    "                         name= df_postes[df_postes['NUM_POSTE'] == poste]['NOM_USUEL'].iloc[0],\n",
    "                         marker= dict(color=color_sequence_bar[i % len(color_sequence_bar)],  # Utilisez la couleur correspondante de la séquence\n",
    "                                    line= dict(color='rgb(100,100,100)', width=1),  # Ligne de contour des barres\n",
    "                                    ),\n",
    "                        # hovertemplate = \n",
    "                            # '<i>Date</i>: %{x}' +\n",
    "                            # '<br><b>RR</b>: %{y}<br>',\n",
    "                            # text = df_postes[df_postes['NUM_POSTE'] == poste]['NOM_USUEL'].iloc[0]                                    \n",
    "                         ),\n",
    "                row= i+1, col= 1\n",
    "                )\n",
    "    fig.add_trace(go.Scatter(x= df_poste['DATE'], y= df_poste[param_RR].cumsum(), \n",
    "                             name= f\"Cumulé {poste}\", yaxis= \"y2\",\n",
    "                             line= dict(color= 'gray'\n",
    "                            #  line= dict(color= color_sequence_scatter[i % len(color_sequence_scatter)]\n",
    "                                        ), # sinon l'incrémentation dans la séquence de couleurs est de +2 à cause de la trace Bar\n",
    "                    # hovertemplate = \n",
    "                    #         '<br><b>Cumulé</b>: %{y}<br>'                                      \n",
    "                             ), \n",
    "                 row=i+1, col=1, secondary_y= True,\n",
    "                 )\n",
    "\n",
    "fig.update_layout(title_text= 'meteo.data.gouv.fr - ' + \"Précipitations quotidiennes (mm) - \" + now.strftime(\"%Y-%m-%d\") , title_x= 0.5, \n",
    "                  height= 133.*len(postes), width= 1000,\n",
    "                  hovermode= 'x unified', hoverlabel= dict(bgcolor='rgba(255,255,255,0.6)'),\n",
    "                    )\n",
    "\n",
    "# Survol par la souris : ligne verticale à travers tous les subplots matérialisant l'abscisse \n",
    "xlast= 'x' + str(len(postes))\n",
    "# fig.update_traces(xaxis= xlast)  # xlast est le nom de l'axe des abscisses du dernier subplot (empêche la graduation des dates de tous les subplots)\n",
    "\n",
    "# Définition générale des axes\n",
    "max_value= df_postes[param_RR].max()\n",
    "max_value_cum= df_postes.groupby(['NUM_POSTE']).sum()[param_RR].max()\n",
    "fig.update_yaxes(secondary_y= False, range= [0, max_value])  # Axe principale: Replace max_value with the desired maximum coordinate\n",
    "fig.update_yaxes(secondary_y= True, title_text= \"Cumulé\", range= [0, max_value_cum]) # Axe secondaire: Replace max_value with the desired maximum coordinate\n",
    "\n",
    "# GRADUATION de l'axe des DATES ........................................\n",
    "if end_graph - start_graph > datetime.timedelta(days= 365): # plus d'un an\n",
    "    tickformat= '%d-%b\\n%Y'\n",
    "    dtick= 'M2'\n",
    "else: # Moins\n",
    "    tickformat= '%d %b\\n%Y'\n",
    "    dtick= 'M1' # (pour une raison inconnue,'D10' ne marche pas du tout et le nombre de milisecondes ne marche qu'après la 2ème graduation et )\n",
    "# ............................... Premiers subplots\n",
    "for i in range(1, len(postes) + 1):\n",
    "    fig.update_xaxes(tickformat= tickformat, tickmode= 'linear', dtick= dtick, row=i, col=1, matches='x')\n",
    "# ............................... Dernier subplot\n",
    "fig.update_xaxes(tickformat= tickformat, tickmode= 'linear', dtick= dtick, row=len(postes), col=1, matches='x', range=[start_graph, end_graph])\n",
    "\n",
    "# GRILLE.................................................................\n",
    "# .................. verticales \n",
    "# .............................. Premiers subplots INUTILE ?\n",
    "for i in range(1, len(postes) + 1):\n",
    "    fig.update_xaxes(showgrid= True, tickformat= tickformat, tickmode= 'linear', dtick= dtick, row=i, col=1)\n",
    "# .............................. Dernier subplot TOUS ?\n",
    "fig.update_xaxes(showgrid= True, tickformat= tickformat, tickmode= 'linear', dtick= dtick, row=len(postes), col=1)\n",
    "# .................. horizontales\n",
    "# ............................... Axe secondaire\n",
    "fig.update_yaxes(secondary_y=True, showgrid=False) # pas de grille\n",
    "\n",
    "#-------------- sauvegarde le graphique\n",
    "file_graph= \"meteo.data QUOT\"\n",
    "fig.write_html(os.path.join(folder_csv, file_graph + template_end[:-4] + \".html\"))\n",
    "fig.write_image(os.path.join(folder_csv, file_graph + template_end[:-4] + \".png\"))\n",
    "\n",
    "#-------------- Données fines (quotidiennes) pivotée avec les postes en colonnes (pour la présentation ainsi que pour le calcul des agrégations mensuelles) \n",
    "#               pour les postes et les paramètres SELECTIONNES, SUR la durée COMPLETE\n",
    "for i, par in enumerate(param_list):\n",
    "    if i==0:\n",
    "        df_f = df[df['NUM_POSTE'].isin(postes)].pivot(index='DATE', columns='NOM_USUEL', values= param_list[i]) \\\n",
    "            .assign(parametre= par)\n",
    "    else:\n",
    "        # append des lignes à df_f\n",
    "        df_f = pd.concat([df_f,\n",
    "                        df[df['NUM_POSTE'].isin(postes)].pivot(index='DATE', columns='NOM_USUEL', values= param_list[i]) \\\n",
    "                        .assign(parametre= par)]\n",
    "                        )\n",
    "df_f= df_f.reset_index().set_index(['parametre', 'DATE'], drop= True)\n",
    "\n",
    "#-------------- Agrégation mensuelle par cumul ou moyenne selon le paramètre, en affectant NaN aux mois incomplets\n",
    "#               pour les postes et les paramètres SELECTIONNES, SUR la durée COMPLETE\n",
    "# TODO: préciser le test pour un nombre maxi de NaN dans les données agregat_max_nan_list\n",
    "def sum_or_nan(s):\n",
    "    return s.sum() if s.count() == s.size else np.nan\n",
    "def mean_or_nan(s):\n",
    "    return s.mean() if s.count() == s.size else np.nan\n",
    "def max_or_nan(s):\n",
    "    return s.max() if s.count() == s.size else np.nan\n",
    "def min_or_nan(s):\n",
    "    return s.min() if s.count() == s.size else np.nan\n",
    "\n",
    "for i, par in enumerate(param_list):\n",
    "    if i==0:\n",
    "        if agregat_list[0] == 'sum':\n",
    "            df_agr= df_f.loc[par].resample('MS').apply(sum_or_nan)\n",
    "        elif agregat_list[0] == 'mean':\n",
    "            df_agr= df_f.loc[par].resample('MS').apply(mean_or_nan)\n",
    "        elif agregat_list[0] == 'max':\n",
    "            df_agr= df_f.loc[par].resample('MS').apply(max_or_nan)\n",
    "        df_agr= df_agr.reset_index().set_index([[par for j in range(df_agr.shape[0])], 'DATE'])\n",
    "    else:\n",
    "        if agregat_list[i] == 'sum':\n",
    "            df_temp= df_f.loc[par].resample('MS').apply(sum_or_nan)\n",
    "        elif agregat_list[i] == 'mean':\n",
    "            df_temp= df_f.loc[par].resample('MS').apply(mean_or_nan)\n",
    "        elif agregat_list[i] == 'max':\n",
    "            df_temp= df_f.loc[par].resample('MS').apply(max_or_nan)\n",
    "        df_agr= pd.concat([df_agr, \n",
    "                        df_temp.reset_index().set_index([[par for j in range(df_temp.shape[0])], 'DATE'])]\n",
    "                        )\n",
    "# arrondi df_agr à 1 chiffre après la virgule\n",
    "df_agr= df_agr.round(1)\n",
    "\n",
    "#-------------- Affichage\n",
    "print(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n",
    "# imprimer un avertissement si la dernière date du dataframe est antérieure au dernier jour du mois en cours\n",
    "# calculer la date du dernier jour du mois en cours en tenant compte des jours à 28, 29 et 30 jours\n",
    "last_date= datetime.datetime(now.year, now.month, calendar.monthrange(now.year, now.month)[1])\n",
    "titre_agr= 'Agrégation mensuelle pour les paramètres et la période choisis: '\n",
    "if np.datetime64(df['DATE'].values[-1]) < np.datetime64(last_date):\n",
    "    titre_agr= titre_agr + 'Attention le cumul du dernier mois est incomplet à ce jour'\n",
    "print(titre_agr)\n",
    "\n",
    "for i, par in enumerate(param_list):\n",
    "    # teste si df_desc_bis.loc[par] existe\n",
    "    if par in df_desc_bis.index:\n",
    "        titre= '- ' + par + ': ' + df_desc_bis.loc[par]['name_long']\n",
    "    else:\n",
    "        titre= '- ' + par + ': ' + 'agrégation mensuelle \"' + agregat_list[i] + '\" de \"' + df_desc_f.loc[par]['name_long'] + '\"'\n",
    "    print(titre)\n",
    "display(df_agr[(df_agr.index.get_level_values(1) >= start_graph) & (df_agr.index.get_level_values(1) <= end_graph)])\n",
    "\n",
    "fig.show()\n",
    "\n",
    "titre_f= 'Données quotidiennes pour les paramètres et la période choisis: '\n",
    "print(titre_f)\n",
    "for i, par in enumerate(param_list):\n",
    "    titre= '- ' + par + ': ' + df_desc_f.loc[par]['name_long']\n",
    "    print(titre)\n",
    "    display(df_f.loc[par][(df_f.loc[par].index >= start_date) & (df_f.loc[par].index <= end_date)])"
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   "source": [
    "##### 3) Affichage de l'agrégation mensuelle & graphique des précipitations mensuelles\n",
    "\n",
    "Définir au préalable comme suit les dates de début et de fin du graphique:\n",
    "- \"None\" pour prendre le début et la fin du fichier LATEST (premier jour de l'année précédente et veille du téléchargement)\n",
    "- ou définir la date de début et de fin\n",
    "\n",
    "NB: Par défault les paramètres sont les mêmes que ceux choisis dans la cellule 2"
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          0.8263888888888888
         ],
         "range": [
          0,
          151.1
         ]
        },
        "yaxis6": {
         "anchor": "x3",
         "overlaying": "y5",
         "range": [
          0,
          389.7
         ],
         "showgrid": false,
         "side": "right",
         "title": {
          "text": "Cumulé"
         }
        },
        "yaxis7": {
         "anchor": "x4",
         "domain": [
          0.6944444444444444,
          0.7395833333333333
         ],
         "range": [
          0,
          151.1
         ]
        },
        "yaxis8": {
         "anchor": "x4",
         "overlaying": "y7",
         "range": [
          0,
          389.7
         ],
         "showgrid": false,
         "side": "right",
         "title": {
          "text": "Cumulé"
         }
        },
        "yaxis9": {
         "anchor": "x5",
         "domain": [
          0.6076388888888888,
          0.6527777777777777
         ],
         "range": [
          0,
          151.1
         ]
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Précipitations mensuelles LATEST (mm)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>NOM_USUEL</th>\n",
       "      <th>AIX EN PROVENCE</th>\n",
       "      <th>APT-VITON</th>\n",
       "      <th>CASTELLANE</th>\n",
       "      <th>FORCALQUIER</th>\n",
       "      <th>FREJUS</th>\n",
       "      <th>LA BASTIDE DES JOURDANS</th>\n",
       "      <th>LE LUC</th>\n",
       "      <th>MARSEILLE-OBS</th>\n",
       "      <th>SALON DE PROVENCE</th>\n",
       "      <th>TOULON</th>\n",
       "      <th>VALENSOLE</th>\n",
       "      <th>VAUVENARGUES</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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-09-01</th>\n",
       "      <td>26.4</td>\n",
       "      <td>16.8</td>\n",
       "      <td>60.1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>41.7</td>\n",
       "      <td>32.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>17.4</td>\n",
       "      <td>27.1</td>\n",
       "      <td>32.6</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>47.5</td>\n",
       "      <td>84.1</td>\n",
       "      <td>151.1</td>\n",
       "      <td>77.5</td>\n",
       "      <td>83.1</td>\n",
       "      <td>67.4</td>\n",
       "      <td>72.0</td>\n",
       "      <td>30.3</td>\n",
       "      <td>50.1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>91.1</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-01</th>\n",
       "      <td>30.2</td>\n",
       "      <td>37.6</td>\n",
       "      <td>102.2</td>\n",
       "      <td>63.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>23.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>25.8</td>\n",
       "      <td>36.4</td>\n",
       "      <td>41.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-01</th>\n",
       "      <td>42.6</td>\n",
       "      <td>60.4</td>\n",
       "      <td>76.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>53.8</td>\n",
       "      <td>28.3</td>\n",
       "      <td>35.4</td>\n",
       "      <td>65.4</td>\n",
       "      <td>56.8</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NOM_USUEL   AIX EN PROVENCE  APT-VITON  CASTELLANE  FORCALQUIER  FREJUS  \\\n",
       "DATE                                                                      \n",
       "2023-09-01             26.4       16.8        60.1        108.0    29.9   \n",
       "2023-10-01             47.5       84.1       151.1         77.5    83.1   \n",
       "2023-11-01             30.2       37.6       102.2         63.1    20.4   \n",
       "2023-12-01             42.6       60.4        76.3          NaN    50.6   \n",
       "\n",
       "NOM_USUEL   LA BASTIDE DES JOURDANS  LE LUC  MARSEILLE-OBS  SALON DE PROVENCE  \\\n",
       "DATE                                                                            \n",
       "2023-09-01                     41.7    32.6           46.2               17.4   \n",
       "2023-10-01                     67.4    72.0           30.3               50.1   \n",
       "2023-11-01                     22.0    21.7           23.2               30.8   \n",
       "2023-12-01                     42.6    53.8           28.3               35.4   \n",
       "\n",
       "NOM_USUEL   TOULON  VALENSOLE  VAUVENARGUES  \n",
       "DATE                                         \n",
       "2023-09-01    27.1       32.6          54.5  \n",
       "2023-10-01   115.0       91.1          86.0  \n",
       "2023-11-01    25.8       36.4          41.8  \n",
       "2023-12-01    65.4       56.8          71.0  "
      ]
     },
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   ],
   "source": [
    "# trace un graphique chronologique des précipitations mensuelles\n",
    "# chaque poste de la colonne 'NUM_POSTE' est représenté dans un subplot séparé\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly\n",
    "\n",
    "#-------------- Personalisation du graphique \n",
    "# Paramètre à représenter (la présentation actuelle est spécialement adaptée aux précipitations RR)\n",
    "# param= 'RR'\n",
    "\n",
    "# Période à représenter (None pour le début ou la fin du fichier par défaut)\n",
    "# start_date= None\n",
    "# start_date= datetime.datetime(2022, 5, 1)\n",
    "\n",
    "# end_date= None\n",
    "# end_date= datetime.datetime(now.year, now.month, now.day)\n",
    "\n",
    "#-------------- initialisation des paramètres du graphique\n",
    "# à partir de df_postes, génère un dataframe indexé sur 'NOM_USUEL' avec une colonne 'NUM_POSTE' et\n",
    "df_desc_postes= df_postes[['NUM_POSTE', 'NOM_USUEL']].drop_duplicates().set_index('NOM_USUEL')\n",
    "\n",
    "# réorganise le dataframe df_pm (intermédiaire ayant permis le cumul mensuel poste par poste) avec les postes en lignes et une seule colonne 'RR'\n",
    "df_p= df_agr.loc[param_RR].stack().reset_index(name= param_RR)\n",
    "# ajoute à df_p une colonne 'NUM_POSTE' avec le numéro du poste\n",
    "df_p= df_p.join(df_desc_postes, on='NOM_USUEL')\n",
    "\n",
    "# Période par défaut if start_date= None ou  end_date= None\n",
    "# if start_date is None:\n",
    "#     start_date= df_p.reset_index()['DATE'].min()\n",
    "# if end_date is None:\n",
    "#     end_date= df_p.reset_index()['DATE'].max()\n",
    "\n",
    "# filtre le dataframe sur les postes choisis \n",
    "df_p= df_p[df_p['NUM_POSTE'].isin(postes)]\n",
    "# filtre le dataframe sur la période souhaitée\n",
    "df_p= df_p[(df_p['DATE'] >= start_date) & (df_p['DATE'] <= end_date)]\n",
    "\n",
    "#  Couleurs des courbes\n",
    "color_sequence_bar = plotly.colors.qualitative.Set3\n",
    "# color_sequence_scatter = plotly.colors.qualitative.Dark24\n",
    "\n",
    "# Axe des ordonnées secondaire (cumulé)\n",
    "specs= [[{'secondary_y': True}]]*len(postes)\n",
    "\n",
    "#-------------- Graphique\n",
    "fig = make_subplots(rows= len(postes), cols= 1,# shared_xaxes= True, # shared_xaxes= True inutile grâce à  matches='x' dans update_xaxes qui fait la même chose sans supprimer la graduation des dates des subplots \n",
    "                    subplot_titles= [f\"{poste} - {df_p[df_p['NUM_POSTE'] == poste]['NOM_USUEL'].iloc[0]}\" for poste in postes], specs= specs)\n",
    "\n",
    "for i, poste in enumerate(postes):\n",
    "    df_poste = df_p[df_p['NUM_POSTE'] == poste]\n",
    "    fig.add_trace(go.Bar(x= df_poste['DATE'], y= df_poste[param_RR], \n",
    "                          # name= poste\n",
    "                         name= df_p[df_p['NUM_POSTE'] == poste]['NOM_USUEL'].iloc[0],\n",
    "                         marker= dict(color=color_sequence_bar[i % len(color_sequence_bar)]),  # Utilisez la couleur correspondante de la séquence\n",
    "                         ),\n",
    "                row= i+1, col= 1)\n",
    "    fig.add_trace(go.Scatter(x= df_poste['DATE'], y= df_poste[param_RR].cumsum(), \n",
    "                             name= f\"Cumulé {poste}\", yaxis= \"y2\",\n",
    "                             line= dict(color= 'gray'),\n",
    "                            #  line= dict(color= color_sequence_scatter[i % len(color_sequence_scatter)]), # sinon l'incrémentation dans la séquence de couleurs est de +2 à cause de la trace Bar\n",
    "                             mode='lines' \n",
    "                             ), \n",
    "                 row=i+1, col=1, secondary_y= True)\n",
    "\n",
    "fig.update_layout(title_text= 'meteo.data.gouv.fr - ' + \"Précipitations mensuelles (mm) - \" + now.strftime(\"%Y-%m-%d\") , title_x= 0.5, \n",
    "                  height= 133.*len(postes), width= 1000,\n",
    "                  hovermode= 'x unified', hoverlabel= dict(bgcolor='rgba(255,255,255,0.6)'),\n",
    "                  )\n",
    "\n",
    "# Survol par la souris : ligne verticale à travers tous les subplots matérialisant l'abscisse \n",
    "xlast= 'x' + str(len(postes))\n",
    "# fig.update_traces(xaxis= xlast)  # xlast est le nom de l'axe des abscisses du dernier subplot\n",
    "\n",
    "# Définition générale des axes\n",
    "max_value= df_p[param_RR].max()\n",
    "max_value_cum= df_p.groupby(['NUM_POSTE']).sum()[param_RR].max()\n",
    "fig.update_yaxes(secondary_y= False, range= [0, max_value])  # Axe principale: Replace max_value with the desired maximum coordinate\n",
    "fig.update_yaxes(secondary_y= True, title_text= \"Cumulé\", range= [0, max_value_cum]) # Axe secondaire: Replace max_value with the desired maximum coordinate\n",
    "\n",
    "# Graduations de l'axe des DATES ...............................................................\n",
    "if end_date - start_date > datetime.timedelta(days= 365): # Plus d'un an\n",
    "    tickformat= '%b\\n%Y'\n",
    "    dtick= 'M6'\n",
    "    showgrid_x= True\n",
    "else: # Moins d'un an\n",
    "    tickformat= '%b %Y'\n",
    "    dtick= 'M1'\n",
    "    showgrid_x= False\n",
    "# ...............................Premiers subplots\n",
    "# ................................................ en fonction de la longueur de la période\n",
    "for i in range(1, len(postes) + 1):\n",
    "    fig.update_xaxes(tickformat= tickformat, tickmode= 'linear', dtick= dtick, row=i, col=1, matches='x')\n",
    "# ...............................Dernier subplot\n",
    "# ................................................ en fonction de la longueur de la période\n",
    "fig.update_xaxes(tickformat= tickformat, tickmode= 'linear', dtick= dtick, row=len(postes), col=1, matches='x')\n",
    "#                                                SANS tenir compte de la longueur de la période\n",
    "# fig.update_xaxes(tickformat= '%b\\n%Y', tickmode= 'linear', dtick= 'M1', row=len(postes), col=1, matches='x')\n",
    "\n",
    "# GRILLE .............................................................................\n",
    "# .................. verticales...\n",
    "fig.update_xaxes(showgrid= showgrid_x, gridwidth= 1, gridcolor= 'white')\n",
    "# .................. horizontales\n",
    "#                                Axe secondaire:\n",
    "fig.update_yaxes(secondary_y=True, showgrid=False) # pas de grille\n",
    "\n",
    "#-------------- sauvegarde le graphique\n",
    "file_graph_agr= \"meteo.data MENS\"\n",
    "fig.write_html(os.path.join(folder_csv, file_graph_agr + template_end[:-4] + \".html\"))\n",
    "# sauvegarde le graphique dans un fichier png\n",
    "fig.write_image(os.path.join(folder_csv, file_graph_agr + template_end[:-4] + \".png\"))\n",
    "\n",
    "#-------------- Affichage\n",
    "print(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n",
    "fig.show()\n",
    "# afficher RR pour df_p avec les postes en colonnes et les dates en index\n",
    "print('Précipitations mensuelles LATEST (mm)')\n",
    "display(df_p.pivot(index= 'DATE', columns= 'NOM_USUEL', values= param_RR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4) Sauvegarde les données intégrales dans un fichier Excel (tous les paramètres pour tous les postes des départements concernés)\n",
    "Le fichier est sauvegardé dans le même dossier local que les fichiers CSV décompressés"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-31 13:43\n",
      "Sauvegarde en cours, patientez...\n",
      "Ficher Excel sauvegardé:  X:\\1-COMMUN\\DIS\\Documentation\\Hydrologie\\Documentation externe\\Climat France\\Météo-France\\meteo.data\\base\\Q\\meteo.data QUOT_04-13-83-84_latest-2022-2023_RR-T-Vent.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Sauvegarde toutes les informations dans un fichier Excel\n",
    "print(now.strftime(\"%Y-%m-%d %H:%M\"))\n",
    "print('Sauvegarde en cours, patientez...')\n",
    "file_excel= os.path.join(folder_csv, \"meteo.data QUOT\"+ '_' + '-'.join(departements) + template_end[:-4] + \".xlsx\")\n",
    "writer = pd.ExcelWriter(file_excel, engine='xlsxwriter')\n",
    "workbook  = writer.book\n",
    "#======================== Sauvegarde la liste des postes dans le fichier excel\n",
    "# sauvegarde dans un fichier excel plus lisible que le JSON après suppression des colonnes type, geometry et properties\n",
    "sheet= 'liste_postes'\n",
    "df_liste_postes.to_excel(writer, sheet_name= sheet, index= False, startrow= 0)\n",
    "\n",
    "#======================== Sauvegarde la liste des champs dans le fichier Excel\n",
    "sheet= 'Q_descriptif_champs_RR-T-Vent'\n",
    "df_desc_f.to_excel(writer, sheet_name= sheet, index= True, startrow= 1)\n",
    "worksheet = writer.sheets[sheet]\n",
    "worksheet.write('A1', sheet + '.csv')\n",
    "\n",
    "#======================== Sauvegarde les données complètes dans le fichier Excel    \n",
    "sheet= 'donnée complètes'\n",
    "df.to_excel(writer, sheet_name= sheet, index=False, startrow= 2)\n",
    "worksheet = writer.sheets[sheet]\n",
    "worksheet.write('A1', now.strftime(\"%Y-%m-%d %H:%M\"))\n",
    "worksheet.write('A2', 'Données LATEST quotidiennes au format original, complètes pour tous les départements concernés: ')\n",
    "\n",
    "#======================== Sauvegarde les données sélectionnées dans le fichier Excel\n",
    "n_param= len(param_list)\n",
    "sheet= 'QUOT Sélection'\n",
    "df_f[(df_f.index.get_level_values(1) >= start_date) & (df_f.index.get_level_values(1) <= end_date)] \\\n",
    "    .to_excel(writer, sheet_name= sheet, index=True, startrow= n_param + 2)\n",
    "worksheet = writer.sheets[sheet]\n",
    "worksheet.write('A1', now.strftime(\"%Y-%m-%d %H:%M\"))\n",
    "worksheet.write('A2', titre_f)\n",
    "for i, par in enumerate(param_list):\n",
    "    titre= '- ' + par + ': ' + df_desc_f.loc[par]['name_long']\n",
    "    worksheet.write('A' + str(i+3), titre)\n",
    "\n",
    "sheet= 'MENS Sélection'\n",
    "df_agr[(df_agr.index.get_level_values(1) >= start_date) & (df_agr.index.get_level_values(1) <= end_date)] \\\n",
    "    .to_excel(writer, sheet_name= sheet, index=True, startrow= n_param + 2)\n",
    "worksheet = writer.sheets[sheet]\n",
    "worksheet.write('A1', now.strftime(\"%Y-%m-%d %H:%M\"))\n",
    "worksheet.write('A2', titre_agr)\n",
    "for i, par in enumerate(param_list):\n",
    "    if par in df_desc_bis.index:\n",
    "        titre= '- ' + par + ': ' + df_desc_bis.loc[par]['name_long']\n",
    "    else:\n",
    "        titre= '- ' + par + ': ' + 'agrégation mensuelle \"' + agregat_list[i] + '\" de \"' + df_desc_f.loc[par]['name_long'] + '\"'\n",
    "    worksheet.write('A' + str(i+3), titre)\n",
    "\n",
    "# ajoute un onglet excel et enregistre une image du graphique\n",
    "sheet4= 'graph'\n",
    "worksheet = workbook.add_worksheet(sheet4)\n",
    "worksheet.insert_image('A1', os.path.join(folder_csv, file_graph + template_end[:-4] + \".png\"))\n",
    "worksheet.insert_image('Q1', os.path.join(folder_csv, file_graph_agr + template_end[:-4] + \".png\"))\n",
    "\n",
    "writer.save()\n",
    "\n",
    "print('Ficher Excel sauvegardé: ', file_excel)"
   ]
  }
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