Réorganisation

2025-11-19 17:01:45 +01:00 · 2025-11-19 17:01:45 +01:00 · 617b12c02e
commit 617b12c02e
parent 566d4400ce
91 changed files with 874 additions and 1715 deletions
--- a/base.md
+++ b/base.md
@ -1,17 +0,0 @@
 # Installation de l'environnement de base
 Après avoir cloné le dépôt :
 ```shell
 python3 -m venv .venv
 source .venv/bin/activate
 python -m pip install --upgrade pip
 pip install -r requirements.txt
 python -c "import pandas, influxdb_client, sklearn; print('OK')"
 ```
 - On installe l'environnement virtuel de python
 - On entre dans cet environnement
 - On met à jour le gestionnaire de paquets pip
 - On installe les dépendances définies dans `requirements.txt`
 - On vérifie que les dépendances sont correctement installées
--- a/tests/index.md
+++ b/tests/index.md
@ -1,7 +1,43 @@
-# Test de l'environnement de travail
+# Installation, configuration et tests
 ## Installation de l'environnement de base
 Après avoir cloné le dépôt :
 ```shell
-python -m scripts.test_influx_connection
+python3 -m venv .venv
 source .venv/bin/activate
 python -m pip install --upgrade pip
 pip install -r requirements.txt
 python -c "import pandas, influxdb_client, sklearn; print('OK')"
 ```
 - On installe l'environnement virtuel de python
 - On entre dans cet environnement
 - On met à jour le gestionnaire de paquets pip
 - On installe les dépendances définies dans `requirements.txt`
 - On vérifie que les dépendances sont correctement installées
 ## Configuration
 ```shell
 cp .env.example .env
 ```
 On copie le fichier de configuration d'exemple, puis on l'ouvre pour l'adapter à notre cas.
 - `INFLUXDB_URL` : URL de l'api du serveur InfluxDB2 (cela inclue probablement le port 8086)
 - `INFLUXDB_TOKEN` : le jeton d'authentification à créer dans votre compte InfluxDB2
 - `INFLUXDB_ORG` : l'organisation à laquelle le token est rattaché
 - `INFLUXDB_BUCKET` : le nom du bucket dans lequel les données sont stockées
 - `STATION_LATITUDE` : latitude GPS de la station météo
 - `STATION_LONGITUDE` : longitude GPS de la station météo
 - `STATION_ELEVATION` : altitude de la station météo
 ## Tests de l'environnement de travail
 ```shell
 python "docs/01 - Installation, configuration et tests/scripts/test_influx_connection.py"
 ```
 ```output
@ -24,7 +60,7 @@ Exemple de point :
 Ensuite, on peut demander à InfluxDB de nous détailler ce qu'il stocke :
 ```shell
-python -m scripts.test_influx_schema
+python "docs/01 - Installation, configuration et tests/scripts/test_influx_schema.py"
 ```
 ```output
@ -82,7 +118,7 @@ Champs pour measurement « °C » :
 Mais pour obtenir les données dont on a besoin, il faut aussi connaitre les entités manipulées par Influx :
 ```shell
-python -m scripts.test_influx_entities
+python "docs/01 - Installation, configuration et tests/scripts/test_influx_entities.py"
 ```
 ```output
--- a/tests/scripts/test_influx_connection.py
+++ b/tests/scripts/test_influx_connection.py
@ -1,10 +1,17 @@
 # tests/test_influx_connection.py
 from __future__ import annotations
 from pathlib import Path
 import sys
 from contextlib import closing
 from influxdb_client.client.exceptions import InfluxDBError
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.config import InfluxSettings
 from meteo.influx_client import create_influx_client, test_basic_query
--- a/tests/scripts/test_influx_entities.py
+++ b/tests/scripts/test_influx_entities.py
@ -1,8 +1,15 @@
 # tests/test_influx_entities.py
 from __future__ import annotations
 from pathlib import Path
 import sys
 from contextlib import closing
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.config import InfluxSettings
 from meteo.influx_client import create_influx_client
 from meteo.schema import (
--- a/tests/scripts/test_influx_schema.py
+++ b/tests/scripts/test_influx_schema.py
@ -1,8 +1,15 @@
 # tests/test_influx_schema.py
 from __future__ import annotations
 from pathlib import Path
 import sys
 from contextlib import closing
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.config import InfluxSettings
 from meteo.influx_client import create_influx_client
 from meteo.schema import list_measurements, list_measurement_fields
--- a/Configuration.md
+++ b/Configuration.md
@ -1,12 +0,0 @@
 # Configuration
 ```shell
 cp .env.example .env
 ```
 On copie le fichier de configuration d'exemple, puis on l'ouvre pour l'adapter à notre cas.
 - `INFLUXDB_URL` : URL de l'api du serveur InfluxDB2 (cela inclue probablement le port 8086)
 - `INFLUXDB_TOKEN` : le jeton d'authentification à créer dans votre compte InfluxDB2
 - `INFLUXDB_ORG` : l'organisation à laquelle le token est rattaché
 - `INFLUXDB_BUCKET` : le nom du bucket dans lequel les données sont stockées
--- a/données/index.md
+++ b/données/index.md
@ -1,4 +1,34 @@
-# Ajustements
+# Préparation des données
 Cette étape regroupe l'export initial depuis InfluxDB ainsi que les scripts d'ajustement nécessaires pour obtenir un dataset minuté propre.
 ## Export des données
 ```shell
 python "docs/02 - Préparation des données/scripts/export_station_data.py"
 ```
 La sortie est assez longue, et inclut un certain nombre d'avertissements qui peuvent être ignorés.
 L'important est que le script se termine sur :
 ```output
 ✔ Export terminé : /Users/richard/Documents/donnees_meteo/data/weather_raw_7d.csv
 ```
 (Le chemin changera sur votre propre machine)
 Vérifiez que le fichier est bien créé et qu'il contient des données.
 À la place de `export_station_data.py`, on peut aussi lancer :
 ```shell
 python "docs/02 - Préparation des données/scripts/export_station_data_full.py"
 ```
 Au lieu de télécharger les données des 7 derniers jours, l'ensemble des données stockées sur le serveur pour ce bucket seront téléchargées, ce qui, selon la granularité et l'ancienneté des données peut prendre un certain temps et occuper un espace disque conséquent.
 Mon fichier complet contient plus d'un million d'enregistrements et pèse 70Mo.
 ## Ajustements
 Le fichier peut être rapidement inspecté avec la commande `head` :
@ -24,7 +54,7 @@ On peut voir que HomeAssistant écrit une nouvelle entrée pour chaque capteur,
 Le script suivant s'occupe de regrouper les données de capteurs dont l'enregistrement est proche :
 ```shell
-python -m scripts.format_raw_csv
+python "docs/02 - Préparation des données/scripts/format_raw_csv.py"
 ```
 ```output
@ -59,35 +89,42 @@ Il reste des cellules vides : en effet, HA n'enregistre pas la valeur d'un capte
 On fait donc :
 ```shell
-python -m scripts.fill_formatted_1s
+python "docs/02 - Préparation des données/scripts/fill_formatted_1s.py"
 ```
 ```output
 Fichier 1s formaté chargé : data/weather_formatted_1s.csv
-  Lignes : 630171, colonnes : ['temperature', 'humidity', 'pressure', 'illuminance', 'wind_speed', 'wi
+  Lignes : 630171, colonnes : ['temperature', 'humidity', 'pressure', 'illuminance', 'wind_speed', 'wind_direction', 'rain_rate']
-nd_direction', 'rain_rate']                                                                           Après propagation des dernières valeurs connues : 630171 lignes
+Après propagation des dernières valeurs connues : 630171 lignes
 ✔ Fichier 1s 'complet' écrit dans : /Users/richard/Documents/donnees_meteo/data/weather_filled_1s.csv
 ```
-On peut maintenant s'assurer d'avoir une seule ligne par minute, avec toutes les valeurs de capteurs :
+## Enrichissements (saisons et position du soleil)
 Une fois les données nettoyées, on peut les enrichir avec des métadonnées météorologiques simples :
 - regrouper les points par minute,
 - ajouter la saison correspondant à chaque observation (en fonction de l'hémisphère),
 - calculer la hauteur du soleil si la latitude/longitude de la station sont configurées.
 Ces opérations sont réalisées par :
 ```shell
-python -m scripts.make_minutely_dataset
+python "docs/02 - Préparation des données/scripts/make_minutely_dataset.py"
 ```
-Ce qui va produire le fichier `data/weather_minutely.csv`.
+Le script produit `data/weather_minutely.csv`. Pensez à définir `STATION_LATITUDE`, `STATION_LONGITUDE` et `STATION_ELEVATION` dans votre `.env` pour permettre le calcul de la position du soleil ; sinon, seule la colonne `season` sera ajoutée.
 On peut s'assurer que plus aucune information n'est manquante :
 ```shell
-python -m scripts.check_missing_values
+python "docs/02 - Préparation des données/scripts/check_missing_values.py"
 ```
 ```output
 Dataset chargé : data/weather_minutely.csv
  Lignes   : 321881
-  Colonnes : ['temperature', 'humidity', 'pressure', 'illuminance', 'wind_speed', 'wind_direction', 'r
+  Colonnes : ['temperature', 'humidity', 'pressure', 'illuminance', 'wind_speed', 'wind_direction', 'rain_rate']
 ain_rate']
 === Synthèse des valeurs manquantes ===
 Total de cellules      : 2253167
 Cellules manquantes    : 0
@ -111,14 +148,13 @@ Valeurs manquantes par colonne :
 Le script suivant nous permet de vérifier rapidement si des problèmes majeurs peuvent être découverts :
 ```shell
-python -m scripts.describe_minutely_dataset
+python "docs/02 - Préparation des données/scripts/describe_minutely_dataset.py"
 ```
 ```output
 Dataset minuté chargé : data/weather_minutely.csv
  Lignes   : 321881
-  Colonnes : ['temperature', 'humidity', 'pressure', 'illuminance', 'wind_speed', 'wind_direction', 'r
+  Colonnes : ['temperature', 'humidity', 'pressure', 'illuminance', 'wind_speed', 'wind_direction', 'rain_rate']                              Période  : 2025-03-10 09:35:00+00:00 → 2025-11-17 00:41:00+00:00
 ain_rate']                                                                                              Période  : 2025-03-10 09:35:00+00:00 → 2025-11-17 00:41:00+00:00
 === describe() ===
         temperature       humidity       pressure  ...     wind_speed  wind_direction      rain_rate
@ -169,11 +205,10 @@ Name: count, dtype: int64
 Nombre d'intervalles ≠ 60s : 17589
 ```
 Il y a donc des trous entre certains jeux de données.
 Ces écarts peuvent être identifiés avec le script suivant :
 ```shell
-python -m scripts.list_time_gaps
+python "docs/02 - Préparation des données/scripts/list_time_gaps.py"
 ```
 ```
@ -185,17 +220,16 @@ Nombre de gaps        : 17589
 Total minutes manquantes (théoriques) : 40466
 Top 10 des gaps les plus longs :
- De 2025-06-21 19:09:00+00:00 à 2025-06-21 20:10:00+00:00 (durée: 0 days 01:01:00, manquants: 60, de
+- De 2025-06-21 19:09:00+00:00 à 2025-06-21 20:10:00+00:00 (durée: 0 days 01:01:00, manquants: 60, de 2025-06-21 19:10:00+00:00 à 2025-06-21 20:09:00+00:00)
-2025-06-21 19:10:00+00:00 à 2025-06-21 20:09:00+00:00)                                                - De 2025-08-10 22:17:00+00:00 à 2025-08-10 23:15:00+00:00 (durée: 0 days 00:58:00, manquants: 57, de
+- De 2025-08-10 22:17:00+00:00 à 2025-08-10 23:15:00+00:00 (durée: 0 days 00:58:00, manquants: 57, de 2025-08-10 22:18:00+00:00 à 2025-08-10 23:14:00+00:00)
-2025-08-10 22:18:00+00:00 à 2025-08-10 23:14:00+00:00)                                                - De 2025-09-24 20:34:00+00:00 à 2025-09-24 21:32:00+00:00 (durée: 0 days 00:58:00, manquants: 57, de
+- De 2025-09-24 20:34:00+00:00 à 2025-09-24 21:32:00+00:00 (durée: 0 days 00:58:00, manquants: 57, de 2025-09-24 20:35:00+00:00 à 2025-09-24 21:31:00+00:00)
-2025-09-24 20:35:00+00:00 à 2025-09-24 21:31:00+00:00)                                                - De 2025-06-21 10:58:00+00:00 à 2025-06-21 11:55:00+00:00 (durée: 0 days 00:57:00, manquants: 56, de
+- De 2025-06-21 10:58:00+00:00 à 2025-06-21 11:55:00+00:00 (durée: 0 days 00:57:00, manquants: 56, de 2025-06-21 10:59:00+00:00 à 2025-06-21 11:54:00+00:00)
-2025-06-21 10:59:00+00:00 à 2025-06-21 11:54:00+00:00)                                                - De 2025-07-10 07:17:00+00:00 à 2025-07-10 08:14:00+00:00 (durée: 0 days 00:57:00, manquants: 56, de
+- De 2025-07-10 07:17:00+00:00 à 2025-07-10 08:14:00+00:00 (durée: 0 days 00:57:00, manquants: 56, de 2025-07-10 07:18:00+00:00 à 2025-07-10 08:13:00+00:00)
-2025-07-10 07:18:00+00:00 à 2025-07-10 08:13:00+00:00)                                                - De 2025-07-24 03:52:00+00:00 à 2025-07-24 04:46:00+00:00 (durée: 0 days 00:54:00, manquants: 53, de
+- De 2025-07-24 03:52:00+00:00 à 2025-07-24 04:46:00+00:00 (durée: 0 days 00:54:00, manquants: 53, de 2025-07-24 03:53:00+00:00 à 2025-07-24 04:45:00+00:00)
-2025-07-24 03:53:00+00:00 à 2025-07-24 04:45:00+00:00)                                                - De 2025-10-28 08:31:00+00:00 à 2025-10-28 09:23:00+00:00 (durée: 0 days 00:52:00, manquants: 51, de
+- De 2025-10-28 08:31:00+00:00 à 2025-10-28 09:23:00+00:00 (durée: 0 days 00:52:00, manquants: 51, de 2025-10-28 08:32:00+00:00 à 2025-10-28 09:22:00+00:00)
-2025-10-28 08:32:00+00:00 à 2025-10-28 09:22:00+00:00)                                                - De 2025-03-16 15:31:00+00:00 à 2025-03-16 16:20:00+00:00 (durée: 0 days 00:49:00, manquants: 48, de
+- De 2025-03-16 15:31:00+00:00 à 2025-03-16 16:20:00+00:00 (durée: 0 days 00:49:00, manquants: 48, de 2025-03-16 15:32:00+00:00 à 2025-03-16 16:19:00+00:00)
-2025-03-16 15:32:00+00:00 à 2025-03-16 16:19:00+00:00)                                                - De 2025-06-21 12:22:00+00:00 à 2025-06-21 13:08:00+00:00 (durée: 0 days 00:46:00, manquants: 45, de
+- De 2025-06-21 12:22:00+00:00 à 2025-06-21 13:08:00+00:00 (durée: 0 days 00:46:00, manquants: 45, de 2025-06-21 12:23:00+00:00 à 2025-06-21 13:07:00+00:00)
-2025-06-21 12:23:00+00:00 à 2025-06-21 13:07:00+00:00)                                                - De 2025-06-21 17:25:00+00:00 à 2025-06-21 18:10:00+00:00 (durée: 0 days 00:45:00, manquants: 44, de
+- De 2025-06-21 17:25:00+00:00 à 2025-06-21 18:10:00+00:00 (durée: 0 days 00:45:00, manquants: 44, de 2025-06-21 17:26:00+00:00 à 2025-06-21 18:09:00+00:00)
 2025-06-21 17:26:00+00:00 à 2025-06-21 18:09:00+00:00)
 ```
 Ces trous dans les données peuvent correspondre à des pannes de connexion entre la station et mon réseau, un redémarrage de mon serveur (physique ou logiciel), au redémarrage de la box ou du point d'accès sans-fil, etc.
--- a/données/scripts/check_missing_values.py
+++ b/données/scripts/check_missing_values.py
@ -2,6 +2,12 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.quality import summarize_missing_values
--- a/données/scripts/describe_minutely_dataset.py
+++ b/données/scripts/describe_minutely_dataset.py
@ -2,9 +2,15 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 import pandas as pd
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
--- a/données/scripts/export_station_data.py
+++ b/données/scripts/export_station_data.py
@ -1,8 +1,15 @@
 # tests/export_station_data.py
 from __future__ import annotations
 from pathlib import Path
 import sys
 from contextlib import closing
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.config import InfluxSettings
 from meteo.influx_client import create_influx_client
 from meteo.station_config import default_station_config
--- a/données/scripts/export_station_data_full.py
+++ b/données/scripts/export_station_data_full.py
@ -1,8 +1,15 @@
 # tests/export_station_data_full.py
 from __future__ import annotations
 from pathlib import Path
 import sys
 from contextlib import closing
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.config import InfluxSettings
 from meteo.influx_client import create_influx_client
 from meteo.station_config import default_station_config
--- a/données/scripts/fill_formatted_1s.py
+++ b/données/scripts/fill_formatted_1s.py
@ -2,6 +2,12 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv, fill_missing_with_previous
@ -13,7 +19,7 @@ OUTPUT_CSV_PATH = Path("data/weather_filled_1s.csv")
 def main() -> None:
    if not INPUT_CSV_PATH.exists():
        print(f"⚠ Fichier introuvable : {INPUT_CSV_PATH}")
-        print("   Lancez d'abord : python -m scripts.format_raw_csv")
+        print('   Lancez d\'abord : python "docs/02 - Préparation des données/scripts/format_raw_csv.py"')
        return
    df_1s = load_raw_csv(INPUT_CSV_PATH)
--- a/données/scripts/format_raw_csv.py
+++ b/données/scripts/format_raw_csv.py
@ -1,6 +1,12 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv, combine_close_observations
--- a/données/scripts/list_time_gaps.py
+++ b/données/scripts/list_time_gaps.py
@ -2,6 +2,12 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.gaps import find_time_gaps
--- a/données/scripts/make_minutely_dataset.py
+++ b/données/scripts/make_minutely_dataset.py
@ -2,6 +2,12 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv, resample_to_minutes
 from meteo.config import StationLocation
@ -16,7 +22,7 @@ OUTPUT_CSV_PATH = Path("data/weather_minutely.csv")
 def main() -> None:
    if not FORMATTED_CSV_PATH.exists():
        print(f"⚠ Fichier formaté introuvable : {FORMATTED_CSV_PATH}")
-        print("   Lancez d'abord : python -m scripts.format_raw_csv")
+        print('   Lancez d\'abord : python "docs/02 - Préparation des données/scripts/fill_formatted_1s.py"')
        return
    df_1s = load_raw_csv(FORMATTED_CSV_PATH)
--- a/graphiques/figures/humidity_last_7_days.png
+++ b/graphiques/figures/humidity_last_7_days.png
--- a/graphiques/figures/illuminance_last_7_days.png
+++ b/graphiques/figures/illuminance_last_7_days.png
--- a/graphiques/figures/pressure_last_7_days.png
+++ b/graphiques/figures/pressure_last_7_days.png
--- a/graphiques/figures/rain_rate_last_7_days.png
+++ b/graphiques/figures/rain_rate_last_7_days.png
--- a/graphiques/figures/sun_elevation_last_7_days.png
+++ b/graphiques/figures/sun_elevation_last_7_days.png
--- a/graphiques/figures/temperature_last_7_days.png
+++ b/graphiques/figures/temperature_last_7_days.png
--- a/graphiques/figures/wind_direction_last_7_days.png
+++ b/graphiques/figures/wind_direction_last_7_days.png
--- a/graphiques/figures/wind_speed_last_7_days.png
+++ b/graphiques/figures/wind_speed_last_7_days.png
--- a/graphiques/index.md
+++ b/graphiques/index.md
@ -0,0 +1,69 @@
 # Premiers graphiques
 On peut désormais tracer nos premiers graphiques simples et bruts.
 S'ils ne sont pas très instructifs par rapport à ce que nous fournissent Home Assistant et InfluxDB, ils nous permettent au moins de nous assurer que tout fonctionne, et que les données semblent cohérentes.
 Les fichiers CSV correspondant à chaque figure sont conservés dans `data/` dans ce dossier.
 ## Température
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only temperature
 ```
 ![](figures/temperature_last_7_days.png)
 ## Humidité relative
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only humidity
 ```
 ![](figures/humidity_last_7_days.png)
 ## Pression atmosphérique
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only pressure
 ```
 ![](figures/pressure_last_7_days.png)
 ## Précipitations instantanées
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only rain_rate
 ```
 ![](figures/rain_rate_last_7_days.png)
 ## Luminance
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only illuminance
 ```
 ![](figures/illuminance_last_7_days.png)
 ## Vitesse du vent
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only wind_speed
 ```
 ![](figures/wind_speed_last_7_days.png)
 ## Direction du vent
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only wind_direction
 ```
 ![](figures/wind_direction_last_7_days.png)
 ## Élévation solaire (si disponible après enrichissement)
 ```shell
 python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py" --only sun_elevation
 ```
 ![](figures/sun_elevation_last_7_days.png)
--- a/graphiques/scripts/plot_basic_variables.py
+++ b/graphiques/scripts/plot_basic_variables.py
@ -5,17 +5,24 @@ from __future__ import annotations
 import argparse
 from pathlib import Path
 import sys
 import matplotlib.pyplot as plt
 import pandas as pd
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.plots import export_plot_dataset
 from meteo.variables import Variable, VARIABLES
 CSV_PATH = Path("data/weather_minutely.csv")
-DEFAULT_OUTPUT_DIR = Path("figures/basic")
+DOC_DIR = Path(__file__).resolve().parent.parent
 DEFAULT_OUTPUT_DIR = DOC_DIR / "figures"
 def _prepare_slice(df: pd.DataFrame, *, last_days: int) -> pd.DataFrame:
--- a/binaires/figures/correlation_heatmap.png
+++ b/binaires/figures/correlation_heatmap.png
--- a/binaires/figures/pairwise_scatter/scatter_humidity_vs_illuminance.png
+++ b/binaires/figures/pairwise_scatter/scatter_humidity_vs_illuminance.png
--- a/binaires/figures/pairwise_scatter/scatter_humidity_vs_pressure.png
+++ b/binaires/figures/pairwise_scatter/scatter_humidity_vs_pressure.png
--- a/binaires/figures/pairwise_scatter/scatter_humidity_vs_rain_rate.png
+++ b/binaires/figures/pairwise_scatter/scatter_humidity_vs_rain_rate.png
--- a/binaires/figures/pairwise_scatter/scatter_humidity_vs_sun_elevation.png
+++ b/binaires/figures/pairwise_scatter/scatter_humidity_vs_sun_elevation.png
--- a/binaires/figures/pairwise_scatter/scatter_humidity_vs_wind_direction.png
+++ b/binaires/figures/pairwise_scatter/scatter_humidity_vs_wind_direction.png
--- a/binaires/figures/pairwise_scatter/scatter_humidity_vs_wind_speed.png
+++ b/binaires/figures/pairwise_scatter/scatter_humidity_vs_wind_speed.png
--- a/binaires/figures/pairwise_scatter/scatter_illuminance_vs_sun_elevation.png
+++ b/binaires/figures/pairwise_scatter/scatter_illuminance_vs_sun_elevation.png
--- a/binaires/figures/pairwise_scatter/scatter_illuminance_vs_wind_direction.png
+++ b/binaires/figures/pairwise_scatter/scatter_illuminance_vs_wind_direction.png
--- a/binaires/figures/pairwise_scatter/scatter_illuminance_vs_wind_speed.png
+++ b/binaires/figures/pairwise_scatter/scatter_illuminance_vs_wind_speed.png
--- a/binaires/figures/pairwise_scatter/scatter_pressure_vs_illuminance.png
+++ b/binaires/figures/pairwise_scatter/scatter_pressure_vs_illuminance.png
--- a/binaires/figures/pairwise_scatter/scatter_pressure_vs_rain_rate.png
+++ b/binaires/figures/pairwise_scatter/scatter_pressure_vs_rain_rate.png
--- a/binaires/figures/pairwise_scatter/scatter_pressure_vs_sun_elevation.png
+++ b/binaires/figures/pairwise_scatter/scatter_pressure_vs_sun_elevation.png
--- a/binaires/figures/pairwise_scatter/scatter_pressure_vs_wind_direction.png
+++ b/binaires/figures/pairwise_scatter/scatter_pressure_vs_wind_direction.png
--- a/binaires/figures/pairwise_scatter/scatter_pressure_vs_wind_speed.png
+++ b/binaires/figures/pairwise_scatter/scatter_pressure_vs_wind_speed.png
--- a/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_illuminance.png
+++ b/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_illuminance.png
--- a/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_sun_elevation.png
+++ b/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_sun_elevation.png
--- a/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_wind_direction.png
+++ b/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_wind_direction.png
--- a/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_wind_speed.png
+++ b/binaires/figures/pairwise_scatter/scatter_rain_rate_vs_wind_speed.png
--- a/binaires/figures/pairwise_scatter/scatter_temperature_vs_humidity.png
+++ b/binaires/figures/pairwise_scatter/scatter_temperature_vs_humidity.png
--- a/binaires/figures/pairwise_scatter/scatter_temperature_vs_illuminance.png
+++ b/binaires/figures/pairwise_scatter/scatter_temperature_vs_illuminance.png
--- a/binaires/figures/pairwise_scatter/scatter_temperature_vs_pressure.png
+++ b/binaires/figures/pairwise_scatter/scatter_temperature_vs_pressure.png
--- a/binaires/figures/pairwise_scatter/scatter_temperature_vs_rain_rate.png
+++ b/binaires/figures/pairwise_scatter/scatter_temperature_vs_rain_rate.png
--- a/binaires/figures/pairwise_scatter/scatter_temperature_vs_sun_elevation.png
+++ b/binaires/figures/pairwise_scatter/scatter_temperature_vs_sun_elevation.png
--- a/binaires/figures/pairwise_scatter/scatter_temperature_vs_wind_direction.png
+++ b/binaires/figures/pairwise_scatter/scatter_temperature_vs_wind_direction.png
--- a/binaires/figures/pairwise_scatter/scatter_temperature_vs_wind_speed.png
+++ b/binaires/figures/pairwise_scatter/scatter_temperature_vs_wind_speed.png
--- a/binaires/figures/pairwise_scatter/scatter_wind_direction_vs_sun_elevation.png
+++ b/binaires/figures/pairwise_scatter/scatter_wind_direction_vs_sun_elevation.png
--- a/binaires/figures/pairwise_scatter/scatter_wind_speed_vs_sun_elevation.png
+++ b/binaires/figures/pairwise_scatter/scatter_wind_speed_vs_sun_elevation.png
--- a/binaires/figures/pairwise_scatter/scatter_wind_speed_vs_wind_direction.png
+++ b/binaires/figures/pairwise_scatter/scatter_wind_speed_vs_wind_direction.png
--- a/binaires/index.md
+++ b/binaires/index.md
@ -0,0 +1,72 @@
 # Corrélations binaires
 Cette étape regroupe l'ensemble des scripts dédiés aux corrélations et comparaisons directes entre variables.
 Chaque figure déposée dans `figures/` possède son CSV compagnon exporté dans le dossier `data/` au même emplacement.
 ```shell
 python "docs/04 - Corrélations binaires/scripts/plot_all_pairwise_scatter.py"
 ```
 ![](figures/pairwise_scatter/scatter_humidity_vs_illuminance.png)
 ![](figures/pairwise_scatter/scatter_humidity_vs_pressure.png)
 ![](figures/pairwise_scatter/scatter_humidity_vs_rain_rate.png)
 ![](figures/pairwise_scatter/scatter_humidity_vs_sun_elevation.png)
 ![](figures/pairwise_scatter/scatter_humidity_vs_wind_direction.png)
 ![](figures/pairwise_scatter/scatter_humidity_vs_wind_speed.png)
 ![](figures/pairwise_scatter/scatter_illuminance_vs_sun_elevation.png)
 ![](figures/pairwise_scatter/scatter_illuminance_vs_wind_direction.png)
 ![](figures/pairwise_scatter/scatter_illuminance_vs_wind_speed.png)
 ![](figures/pairwise_scatter/scatter_pressure_vs_illuminance.png)
 ![](figures/pairwise_scatter/scatter_pressure_vs_rain_rate.png)
 ![](figures/pairwise_scatter/scatter_pressure_vs_sun_elevation.png)
 ![](figures/pairwise_scatter/scatter_pressure_vs_wind_direction.png)
 ![](figures/pairwise_scatter/scatter_pressure_vs_wind_speed.png)
 ![](figures/pairwise_scatter/scatter_rain_rate_vs_illuminance.png)
 ![](figures/pairwise_scatter/scatter_rain_rate_vs_sun_elevation.png)
 ![](figures/pairwise_scatter/scatter_rain_rate_vs_wind_direction.png)
 ![](figures/pairwise_scatter/scatter_rain_rate_vs_wind_speed.png)
 ![](figures/pairwise_scatter/scatter_temperature_vs_humidity.png)
 ![](figures/pairwise_scatter/scatter_temperature_vs_illuminance.png)
 ![](figures/pairwise_scatter/scatter_temperature_vs_pressure.png)
 ![](figures/pairwise_scatter/scatter_temperature_vs_rain_rate.png)
 ![](figures/pairwise_scatter/scatter_temperature_vs_sun_elevation.png)
 ![](figures/pairwise_scatter/scatter_temperature_vs_wind_direction.png)
 ![](figures/pairwise_scatter/scatter_temperature_vs_wind_speed.png)
 ![](figures/pairwise_scatter/scatter_wind_direction_vs_sun_elevation.png)
 ![](figures/pairwise_scatter/scatter_wind_speed_vs_sun_elevation.png)
 ![](figures/pairwise_scatter/scatter_wind_speed_vs_wind_direction.png)
 ## Heatmap globale
 ```shell
 python "docs/04 - Corrélations binaires/scripts/plot_correlation_heatmap.py"
 ```
 ![](figures/correlation_heatmap.png)
--- a/binaires/scripts/plot_all_pairwise_scatter.py
+++ b/binaires/scripts/plot_all_pairwise_scatter.py
@ -2,6 +2,12 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.variables import iter_variable_pairs
@ -9,7 +15,8 @@ from meteo.plots import plot_scatter_pair
 CSV_PATH = Path("data/weather_minutely.csv")
-OUTPUT_DIR = Path("figures/pairwise_scatter")
+DOC_DIR = Path(__file__).resolve().parent.parent
 OUTPUT_DIR = DOC_DIR / "figures" / "pairwise_scatter"
 def main() -> None:
--- a/binaires/scripts/plot_correlation_heatmap.py
+++ b/binaires/scripts/plot_correlation_heatmap.py
@ -2,6 +2,12 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.variables import VARIABLES
@ -10,7 +16,8 @@ from meteo.plots import plot_correlation_heatmap
 CSV_PATH = Path("data/weather_minutely.csv")
-OUTPUT_PATH = Path("figures/correlation_heatmap.png")
+DOC_DIR = Path(__file__).resolve().parent.parent
 OUTPUT_PATH = DOC_DIR / "figures" / "correlation_heatmap.png"
 def main() -> None:
--- a/binaires/scripts/plot_correlations.py
+++ b/binaires/scripts/plot_correlations.py
@ -0,0 +1,96 @@
 # scripts/plot_correlations.py
 """Produit les nuages de points ciblés entre variables sélectionnées."""
 from __future__ import annotations
 import argparse
 from pathlib import Path
 import sys
 from typing import Sequence
 import pandas as pd
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.variables import Variable, VARIABLES, VARIABLES_BY_KEY, iter_variable_pairs
 from meteo.plots import plot_scatter_pair
 CSV_PATH = Path("data/weather_minutely.csv")
 DOC_DIR = Path(__file__).resolve().parent.parent
 SCATTER_DIR = DOC_DIR / "figures" / "pairwise_scatter"
 def _select_variables(keys: Sequence[str] | None) -> list[Variable]:
    if not keys:
        return list(VARIABLES)
    try:
        selected = [VARIABLES_BY_KEY[key] for key in keys]
    except KeyError as exc:
        raise SystemExit(f"Variable inconnue : {exc.args[0]!r}.") from exc
    return selected
 def _generate_pairwise_scatter(
    df: pd.DataFrame,
    variables: Sequence[Variable],
    *,
    sample_step: int,
 ) -> None:
    pairs = iter_variable_pairs()
    selected = [(vx, vy) for vx, vy in pairs if vx in variables and vy in variables]
    if not selected:
        print("⚠ Aucun couple sélectionné pour les nuages de points.")
        return
    SCATTER_DIR.mkdir(parents=True, exist_ok=True)
    for var_x, var_y in selected:
        output_path = SCATTER_DIR / f"scatter_{var_x.key}_vs_{var_y.key}.png"
        print(f"→ Scatter {var_y.key} vs {var_x.key}")
        plot_scatter_pair(df, var_x=var_x, var_y=var_y, output_path=output_path, sample_step=sample_step)
    print(f"✔ {len(selected)} nuage(s) de points généré(s) dans {SCATTER_DIR}.")
 def main(argv: list[str] | None = None) -> int:
    parser = argparse.ArgumentParser(description="Génère des nuages de points pairwise entre variables.")
    parser.add_argument(
        "--dataset",
        type=Path,
        default=CSV_PATH,
        help="Dataset à utiliser (par défaut : data/weather_minutely.csv).",
    )
    parser.add_argument(
        "--variables",
        nargs="*",
        help="Restreint l'analyse à certaines clés de variables.",
    )
    parser.add_argument(
        "--scatter-step",
        type=int,
        default=20,
        help="Pas d'échantillonnage pour les nuages de points individuels.",
    )
    args = parser.parse_args(argv)
    dataset_path = args.dataset
    if not dataset_path.exists():
        raise SystemExit(f"Dataset introuvable : {dataset_path}")
    df = load_raw_csv(dataset_path)
    print(f"Dataset chargé : {dataset_path} ({len(df)} lignes)")
    print()
    variables = _select_variables(args.variables)
    _generate_pairwise_scatter(df, variables, sample_step=args.scatter_step)
    print("✔ Terminé.")
    return 0
 if __name__ == "__main__":  # pragma: no cover
    raise SystemExit(main())
--- a/données.md
+++ b/données.md
@ -1,20 +0,0 @@
 # Export des données
 ```shell
 python -m scripts.export_station_data
 ```
 La sortie est assez longue, et inclut un certain nombre d'avertissements qui peuvent être ignorés.
 L'important est que le script se termine sur :
 ```output
 ✔ Export terminé : /Users/richard/Documents/donnees_meteo/data/weather_raw_7d.csv
 ```
 (Le chemin changera sur votre propre machine)
 Vérifiez que le fichier est bien créé et qu'il contient des données.
 À la place de `scripts.export_station_data`, on pourrait aussi lancer `scripts.export_station_data_full`.
 Au lieu de télécharger les données des 7 derniers jours, l'ensemble des données stockées sur le serveur pour ce bucket seront téléchargées, ce qui, selon la granularité et l'ancienneté des données peut prendre un certain temps et occuper un espace disque conséquent.
 Mon fichier complet contient plus d'un million d'enregistrements et pèse 70Mo.
--- a/avancées/figures/lagged_correlations/lagcorr_humidity_to_rain_rate.png
+++ b/avancées/figures/lagged_correlations/lagcorr_humidity_to_rain_rate.png
--- a/avancées/figures/lagged_correlations/lagcorr_illuminance_to_temperature.png
+++ b/avancées/figures/lagged_correlations/lagcorr_illuminance_to_temperature.png
--- a/avancées/figures/lagged_correlations/lagcorr_pressure_to_illuminance.png
+++ b/avancées/figures/lagged_correlations/lagcorr_pressure_to_illuminance.png
--- a/avancées/figures/lagged_correlations/lagcorr_pressure_to_rain_rate.png
+++ b/avancées/figures/lagged_correlations/lagcorr_pressure_to_rain_rate.png
--- a/avancées/figures/lagged_correlations/lagcorr_pressure_to_wind_speed.png
+++ b/avancées/figures/lagged_correlations/lagcorr_pressure_to_wind_speed.png
--- a/avancées/figures/lagged_correlations/lagcorr_temperature_to_humidity.png
+++ b/avancées/figures/lagged_correlations/lagcorr_temperature_to_humidity.png
--- a/avancées/figures/lagged_correlations/lagcorr_temperature_to_rain_rate.png
+++ b/avancées/figures/lagged_correlations/lagcorr_temperature_to_rain_rate.png
--- a/avancées/figures/rolling_correlations/rolling_correlation_heatmap.png
+++ b/avancées/figures/rolling_correlations/rolling_correlation_heatmap.png
--- a/avancées/index.md
+++ b/avancées/index.md
@ -0,0 +1,29 @@
 # Corrélations binaires avancées
 ## Corrélations décalées
 ```shell
 python "docs/05 - Corrélations binaires avancées/scripts/plot_lagged_correlations.py"
 ```
 ![](figures/lagged_correlations/lagcorr_humidity_to_rain_rate.png)
 ![](figures/lagged_correlations/lagcorr_illuminance_to_temperature.png)
 ![](figures/lagged_correlations/lagcorr_pressure_to_illuminance.png)
 ![](figures/lagged_correlations/lagcorr_pressure_to_rain_rate.png)
 ![](figures/lagged_correlations/lagcorr_pressure_to_wind_speed.png)
 ![](figures/lagged_correlations/lagcorr_temperature_to_humidity.png)
 ![](figures/lagged_correlations/lagcorr_temperature_to_rain_rate.png)
 ## Corrélations glissantes
 ```shell
 python "docs/05 - Corrélations binaires avancées/scripts/plot_rolling_correlation_heatmap.py"
 ```
 ![](figures/rolling_correlations/rolling_correlation_heatmap.png)
--- a/avancées/scripts/plot_lagged_correlations.py
+++ b/avancées/scripts/plot_lagged_correlations.py
@ -2,28 +2,23 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.variables import VARIABLES_BY_KEY
 from meteo.analysis import compute_lagged_correlation
 from meteo.plots import plot_lagged_correlation
 from meteo.correlation_presets import DEFAULT_LAGGED_PAIRS
 CSV_PATH = Path("data/weather_minutely.csv")
-OUTPUT_DIR = Path("figures/lagged_correlations")
+DOC_DIR = Path(__file__).resolve().parent.parent
-
+OUTPUT_DIR = DOC_DIR / "figures" / "lagged_correlations"
 # Paires à analyser (clé de variable X, clé de variable Y)
 # Convention : X précède potentiellement Y
 INTERESTING_PAIRS: list[tuple[str, str]] = [
    ("temperature", "humidity"),
    ("temperature", "rain_rate"),
    ("pressure", "rain_rate"),
    ("pressure", "wind_speed"),
    ("pressure", "illuminance"),
    ("illuminance", "temperature"),
    ("humidity", "rain_rate"),
 ]
 def main() -> None:
@ -37,7 +32,7 @@ def main() -> None:
    print(f"  Colonnes : {list(df.columns)}")
    print()
-    for key_x, key_y in INTERESTING_PAIRS:
+    for key_x, key_y in DEFAULT_LAGGED_PAIRS:
        var_x = VARIABLES_BY_KEY[key_x]
        var_y = VARIABLES_BY_KEY[key_y]
--- a/avancées/scripts/plot_rolling_correlation_heatmap.py
+++ b/avancées/scripts/plot_rolling_correlation_heatmap.py
@ -2,23 +2,23 @@
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.variables import VARIABLES_BY_KEY
 from meteo.analysis import compute_rolling_correlations_for_pairs
 from meteo.plots import plot_rolling_correlation_heatmap
 from meteo.correlation_presets import DEFAULT_ROLLING_PAIRS
 CSV_PATH = Path("data/weather_minutely.csv")
-OUTPUT_PATH = Path("figures/rolling_correlations/rolling_correlation_heatmap.png")
+DOC_DIR = Path(__file__).resolve().parent.parent
-
+OUTPUT_PATH = DOC_DIR / "figures" / "rolling_correlations" / "rolling_correlation_heatmap.png"
 ROLLING_PAIRS: list[tuple[str, str]] = [
    ("temperature", "humidity"),
    ("pressure", "rain_rate"),
    ("pressure", "wind_speed"),
    ("illuminance", "temperature"),
    ("humidity", "rain_rate"),
 ]
 WINDOW_MINUTES = 180  # 3 heures pour observer les tendances synoptiques
 STEP_MINUTES = 30     # on n'échantillonne qu'un point sur 30 minutes
@ -35,7 +35,7 @@ def main() -> None:
    print(f"  Colonnes : {list(df.columns)}")
    print()
-    pairs = [(VARIABLES_BY_KEY[a], VARIABLES_BY_KEY[b]) for a, b in ROLLING_PAIRS]
+    pairs = [(VARIABLES_BY_KEY[a], VARIABLES_BY_KEY[b]) for a, b in DEFAULT_ROLLING_PAIRS]
    rolling_df = compute_rolling_correlations_for_pairs(
        df=df,
--- a/multiples/figures/hexbin_explorations/hexbin_lux_humidity_color_temp.png
+++ b/multiples/figures/hexbin_explorations/hexbin_lux_humidity_color_temp.png
--- a/multiples/figures/hexbin_explorations/hexbin_pressure_rain_color_wind.png
+++ b/multiples/figures/hexbin_explorations/hexbin_pressure_rain_color_wind.png
--- a/multiples/figures/hexbin_explorations/hexbin_temp_humidity_color_rain.png
+++ b/multiples/figures/hexbin_explorations/hexbin_temp_humidity_color_rain.png
--- a/multiples/index.md
+++ b/multiples/index.md
@ -0,0 +1,13 @@
 # Corrélations multiples
 ## Hexbin colorés
 ```shell
 python "docs/06 - Corrélations multiples/scripts/plot_hexbin_explorations.py"
 ```
 ![](figures/hexbin_explorations/hexbin_lux_humidity_color_temp.png)
 ![](figures/hexbin_explorations/hexbin_pressure_rain_color_wind.png)
 ![](figures/hexbin_explorations/hexbin_temp_humidity_color_rain.png)
--- a/multiples/scripts/plot_hexbin_explorations.py
+++ b/multiples/scripts/plot_hexbin_explorations.py
@ -0,0 +1,70 @@
 # scripts/plot_hexbin_explorations.py
 from __future__ import annotations
 from pathlib import Path
 import sys
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
 if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))
 from meteo.dataset import load_raw_csv
 from meteo.variables import VARIABLES_BY_KEY
 from meteo.plots import plot_hexbin_with_third_variable
 from meteo.correlation_presets import DEFAULT_HEXBIN_SCENARIOS
 CSV_PATH = Path("data/weather_minutely.csv")
 DOC_DIR = Path(__file__).resolve().parent.parent
 OUTPUT_DIR = DOC_DIR / "figures" / "hexbin_explorations"
 def main() -> None:
    if not CSV_PATH.exists():
        print(f"⚠ Fichier introuvable : {CSV_PATH}")
        return
    df = load_raw_csv(CSV_PATH)
    print(f"Dataset minuté chargé : {CSV_PATH}")
    print(f"  Lignes   : {len(df)}")
    print(f"  Colonnes : {list(df.columns)}")
    print()
    for scenario in DEFAULT_HEXBIN_SCENARIOS:
        var_x = VARIABLES_BY_KEY[scenario.key_x]
        var_y = VARIABLES_BY_KEY[scenario.key_y]
        var_color = VARIABLES_BY_KEY[scenario.key_color]
        filename = scenario.filename
        output_path = OUTPUT_DIR / filename
        reduce_func = scenario.get_reduce_func()
        reduce_label = scenario.get_reduce_label()
        gridsize = scenario.gridsize
        mincnt = scenario.mincnt
        description = scenario.description
        print(f"→ Hexbin {var_y.key} vs {var_x.key} (couleur = {var_color.key})")
        print(f"   {description}")
        plot_hexbin_with_third_variable(
            df=df,
            var_x=var_x,
            var_y=var_y,
            var_color=var_color,
            output_path=output_path,
            gridsize=gridsize,
            mincnt=mincnt,
            reduce_func=reduce_func,
            reduce_func_label=reduce_label,
            cmap="magma",
        )
        print(f"   ✔ Graphique enregistré : {output_path}")
        print()
    print("✔ Tous les graphiques hexbin ont été générés.")
 if __name__ == "__main__":
    main()
--- a/graphiques.md
+++ b/graphiques.md
@ -1,68 +0,0 @@
 # Premiers graphiques
 On peut désormais tracer nos premiers graphiques simples et bruts.
 S'ils ne sont pas très instructifs par rapport à ce que nous fournissent Home Assistant et InfluxDB, ils nous permettent au moins de nous assurer que tout fonctionne, et que les données semblent cohérentes.
 ## Température
 ```shell
 python -m scripts.plot_basic_variables --only temperature
 ```
 ![](../figures/basic/temperature_last_7_days.png)
 ## Humidité relative
 ```shell
 python -m scripts.plot_basic_variables --only humidity
 ```
 ![](../figures/basic/humidity_last_7_days.png)
 ## Pression atmosphérique
 ```shell
 python -m scripts.plot_basic_variables --only pressure
 ```
 ![](../figures/basic/pressure_last_7_days.png)
 ## Précipitations instantanées
 ```shell
 python -m scripts.plot_basic_variables --only rain_rate
 ```
 ![](../figures/basic/rain_rate_last_7_days.png)
 ## Luminance
 ```shell
 python -m scripts.plot_basic_variables --only illuminance
 ```
 ![](../figures/basic/illuminance_last_7_days.png)
 ## Vitesse du vent
 ```shell
 python -m scripts.plot_basic_variables --only wind_speed
 ```
 ![](../figures/basic/wind_speed_last_7_days.png)
 ## Direction du vent
 ```shell
 python -m scripts.plot_basic_variables --only wind_direction
 ```
 ![](../figures/basic/wind_direction_last_7_days.png)
 ## Élévation solaire (si disponible après enrichissement)
 ```shell
 python -m scripts.plot_basic_variables --only sun_elevation
 ```
 ![](../figures/basic/sun_elevation_last_7_days.png)
--- a/figures/basic/humidity_last_7_days.csv
+++ b/figures/basic/humidity_last_7_days.csv
@ -1,170 +0,0 @@
 time,humidity
 2025-11-11 08:00:00+00:00,90.4396551724138
 2025-11-11 09:00:00+00:00,86.38333333333334
 2025-11-11 10:00:00+00:00,82.6
 2025-11-11 11:00:00+00:00,78.49166666666666
 2025-11-11 12:00:00+00:00,72.68333333333334
 2025-11-11 13:00:00+00:00,71.3
 2025-11-11 14:00:00+00:00,71.54166666666667
 2025-11-11 15:00:00+00:00,70.40833333333333
 2025-11-11 16:00:00+00:00,83.8804347826087
 2025-11-11 17:00:00+00:00,87.92391304347827
 2025-11-11 18:00:00+00:00,88.80188679245283
 2025-11-11 19:00:00+00:00,90.4375
 2025-11-11 20:00:00+00:00,91.75
 2025-11-11 21:00:00+00:00,91.14285714285714
 2025-11-11 22:00:00+00:00,91.14
 2025-11-11 23:00:00+00:00,89.47058823529412
 2025-11-12 00:00:00+00:00,84.83962264150944
 2025-11-12 01:00:00+00:00,79.8859649122807
 2025-11-12 02:00:00+00:00,79.98979591836735
 2025-11-12 03:00:00+00:00,76.77678571428571
 2025-11-12 04:00:00+00:00,71.28448275862068
 2025-11-12 05:00:00+00:00,68.91176470588235
 2025-11-12 06:00:00+00:00,71.91666666666667
 2025-11-12 07:00:00+00:00,72.00877192982456
 2025-11-12 08:00:00+00:00,67.0
 2025-11-12 09:00:00+00:00,62.127272727272725
 2025-11-12 10:00:00+00:00,60.05
 2025-11-12 11:00:00+00:00,54.725
 2025-11-12 12:00:00+00:00,51.375
 2025-11-12 13:00:00+00:00,54.416666666666664
 2025-11-12 14:00:00+00:00,58.83898305084746
 2025-11-12 15:00:00+00:00,64.99166666666666
 2025-11-12 16:00:00+00:00,74.32558139534883
 2025-11-12 17:00:00+00:00,79.30851063829788
 2025-11-12 18:00:00+00:00,77.97674418604652
 2025-11-12 19:00:00+00:00,80.41836734693878
 2025-11-12 20:00:00+00:00,76.67073170731707
 2025-11-12 21:00:00+00:00,69.82075471698113
 2025-11-12 22:00:00+00:00,65.98305084745763
 2025-11-12 23:00:00+00:00,74.3970588235294
 2025-11-13 00:00:00+00:00,73.43243243243244
 2025-11-13 01:00:00+00:00,56.75
 2025-11-13 02:00:00+00:00,61.583333333333336
 2025-11-13 03:00:00+00:00,75.79729729729729
 2025-11-13 04:00:00+00:00,81.54651162790698
 2025-11-13 05:00:00+00:00,69.05
 2025-11-13 06:00:00+00:00,57.28333333333333
 2025-11-13 07:00:00+00:00,49.30833333333333
 2025-11-13 08:00:00+00:00,47.06666666666667
 2025-11-13 09:00:00+00:00,48.18333333333333
 2025-11-13 10:00:00+00:00,46.19491525423729
 2025-11-13 11:00:00+00:00,44.059322033898304
 2025-11-13 12:00:00+00:00,43.21666666666667
 2025-11-13 13:00:00+00:00,42.45
 2025-11-13 14:00:00+00:00,39.9
 2025-11-13 15:00:00+00:00,40.96666666666667
 2025-11-13 16:00:00+00:00,41.16949152542373
 2025-11-13 17:00:00+00:00,45.42307692307692
 2025-11-13 18:00:00+00:00,41.74561403508772
 2025-11-13 19:00:00+00:00,41.208333333333336
 2025-11-13 20:00:00+00:00,39.09322033898305
 2025-11-13 21:00:00+00:00,35.78813559322034
 2025-11-13 22:00:00+00:00,40.75
 2025-11-13 23:00:00+00:00,52.91860465116279
 2025-11-14 00:00:00+00:00,61.55128205128205
 2025-11-14 01:00:00+00:00,65.34848484848484
 2025-11-14 02:00:00+00:00,65.08333333333333
 2025-11-14 03:00:00+00:00,58.27777777777778
 2025-11-14 04:00:00+00:00,70.75581395348837
 2025-11-14 05:00:00+00:00,70.87837837837837
 2025-11-14 06:00:00+00:00,65.42592592592592
 2025-11-14 07:00:00+00:00,70.32203389830508
 2025-11-14 08:00:00+00:00,69.36666666666666
 2025-11-14 09:00:00+00:00,53.34166666666667
 2025-11-14 10:00:00+00:00,53.516666666666666
 2025-11-14 11:00:00+00:00,63.891666666666666
 2025-11-14 12:00:00+00:00,60.173469387755105
 2025-11-14 13:00:00+00:00,55.824074074074076
 2025-11-14 14:00:00+00:00,47.58474576271186
 2025-11-14 15:00:00+00:00,46.93333333333333
 2025-11-14 16:00:00+00:00,46.3
 2025-11-14 17:00:00+00:00,45.09322033898305
 2025-11-14 18:00:00+00:00,45.940677966101696
 2025-11-14 19:00:00+00:00,50.78846153846154
 2025-11-14 20:00:00+00:00,60.270833333333336
 2025-11-14 21:00:00+00:00,54.36607142857143
 2025-11-14 22:00:00+00:00,51.625
 2025-11-14 23:00:00+00:00,54.96666666666667
 2025-11-15 00:00:00+00:00,58.19166666666667
 2025-11-15 01:00:00+00:00,63.208333333333336
 2025-11-15 02:00:00+00:00,70.86666666666666
 2025-11-15 03:00:00+00:00,78.99
 2025-11-15 04:00:00+00:00,79.07627118644068
 2025-11-15 05:00:00+00:00,80.51785714285714
 2025-11-15 06:00:00+00:00,77.24137931034483
 2025-11-15 07:00:00+00:00,80.29310344827586
 2025-11-15 08:00:00+00:00,79.86440677966101
 2025-11-15 09:00:00+00:00,86.08333333333333
 2025-11-15 10:00:00+00:00,90.99166666666666
 2025-11-15 11:00:00+00:00,90.225
 2025-11-15 12:00:00+00:00,85.29166666666667
 2025-11-15 13:00:00+00:00,86.05
 2025-11-15 14:00:00+00:00,86.975
 2025-11-15 15:00:00+00:00,91.30833333333334
 2025-11-15 16:00:00+00:00,90.29761904761905
 2025-11-15 17:00:00+00:00,94.45833333333333
 2025-11-15 18:00:00+00:00,95.39285714285714
 2025-11-15 19:00:00+00:00,95.44117647058823
 2025-11-15 20:00:00+00:00,96.22222222222223
 2025-11-15 21:00:00+00:00,96.36111111111111
 2025-11-15 22:00:00+00:00,96.625
 2025-11-15 23:00:00+00:00,96.3076923076923
 2025-11-16 00:00:00+00:00,97.0
 2025-11-16 01:00:00+00:00,96.17777777777778
 2025-11-16 02:00:00+00:00,94.6
 2025-11-16 03:00:00+00:00,92.78181818181818
 2025-11-16 04:00:00+00:00,91.82926829268293
 2025-11-16 05:00:00+00:00,92.62857142857143
 2025-11-16 06:00:00+00:00,92.77358490566037
 2025-11-16 07:00:00+00:00,92.66666666666667
 2025-11-16 08:00:00+00:00,90.90833333333333
 2025-11-16 09:00:00+00:00,89.325
 2025-11-16 10:00:00+00:00,92.64166666666667
 2025-11-16 11:00:00+00:00,93.24166666666666
 2025-11-16 12:00:00+00:00,93.18333333333334
 2025-11-16 13:00:00+00:00,93.96666666666667
 2025-11-16 14:00:00+00:00,95.66666666666667
 2025-11-16 15:00:00+00:00,96.98305084745763
 2025-11-16 16:00:00+00:00,97.4375
 2025-11-16 17:00:00+00:00,98.0
 2025-11-16 18:00:00+00:00,98.0
 2025-11-16 19:00:00+00:00,98.0
 2025-11-16 20:00:00+00:00,96.79166666666667
 2025-11-16 21:00:00+00:00,97.10416666666667
 2025-11-16 22:00:00+00:00,98.49074074074075
 2025-11-16 23:00:00+00:00,99.0
 2025-11-17 00:00:00+00:00,99.0
 2025-11-17 01:00:00+00:00,99.0
 2025-11-17 02:00:00+00:00,99.0
 2025-11-17 03:00:00+00:00,99.0
 2025-11-17 04:00:00+00:00,99.0
 2025-11-17 05:00:00+00:00,98.15
 2025-11-17 06:00:00+00:00,97.975
 2025-11-17 07:00:00+00:00,96.175
 2025-11-17 08:00:00+00:00,93.91666666666667
 2025-11-17 09:00:00+00:00,88.98214285714286
 2025-11-17 10:00:00+00:00,84.61666666666666
 2025-11-17 11:00:00+00:00,84.85
 2025-11-17 12:00:00+00:00,84.43333333333334
 2025-11-17 13:00:00+00:00,81.31666666666666
 2025-11-17 14:00:00+00:00,80.85
 2025-11-17 15:00:00+00:00,86.2
 2025-11-17 16:00:00+00:00,84.92105263157895
 2025-11-17 17:00:00+00:00,85.67948717948718
 2025-11-17 18:00:00+00:00,88.0
 2025-11-17 19:00:00+00:00,89.3061224489796
 2025-11-17 20:00:00+00:00,92.12903225806451
 2025-11-17 21:00:00+00:00,94.94285714285714
 2025-11-17 22:00:00+00:00,95.88888888888889
 2025-11-17 23:00:00+00:00,96.12765957446808
 2025-11-18 00:00:00+00:00,96.84883720930233
 2025-11-18 01:00:00+00:00,97.0
 2025-11-18 02:00:00+00:00,97.0
 2025-11-18 03:00:00+00:00,96.61702127659575
 2025-11-18 04:00:00+00:00,96.02941176470588
 2025-11-18 05:00:00+00:00,96.83333333333333
 2025-11-18 06:00:00+00:00,97.0
 2025-11-18 07:00:00+00:00,96.95
 2025-11-18 08:00:00+00:00,96.0
--- a/figures/basic/illuminance_last_7_days.csv
+++ b/figures/basic/illuminance_last_7_days.csv
@ -1,170 +0,0 @@
 time,illuminance
 2025-11-11 08:00:00+00:00,8753.97920689655
 2025-11-11 09:00:00+00:00,14803.2897
 2025-11-11 10:00:00+00:00,21937.66
 2025-11-11 11:00:00+00:00,22212.36
 2025-11-11 12:00:00+00:00,17519.4696
 2025-11-11 13:00:00+00:00,12761.3898
 2025-11-11 14:00:00+00:00,6145.58
 2025-11-11 15:00:00+00:00,2410.5097
 2025-11-11 16:00:00+00:00,53.13913043478261
 2025-11-11 17:00:00+00:00,0.0
 2025-11-11 18:00:00+00:00,0.0
 2025-11-11 19:00:00+00:00,0.0
 2025-11-11 20:00:00+00:00,0.0
 2025-11-11 21:00:00+00:00,0.0
 2025-11-11 22:00:00+00:00,0.0
 2025-11-11 23:00:00+00:00,0.0
 2025-11-12 00:00:00+00:00,0.0
 2025-11-12 01:00:00+00:00,0.0
 2025-11-12 02:00:00+00:00,0.0
 2025-11-12 03:00:00+00:00,0.0
 2025-11-12 04:00:00+00:00,0.0
 2025-11-12 05:00:00+00:00,0.0
 2025-11-12 06:00:00+00:00,539.0774444444445
 2025-11-12 07:00:00+00:00,4384.2525263157895
 2025-11-12 08:00:00+00:00,9717.557789473683
 2025-11-12 09:00:00+00:00,17651.825345454545
 2025-11-12 10:00:00+00:00,19816.98
 2025-11-12 11:00:00+00:00,22230.36
 2025-11-12 12:00:00+00:00,18711.89
 2025-11-12 13:00:00+00:00,15562.1695
 2025-11-12 14:00:00+00:00,7915.596305084746
 2025-11-12 15:00:00+00:00,2137.8196
 2025-11-12 16:00:00+00:00,31.520930232558143
 2025-11-12 17:00:00+00:00,0.0
 2025-11-12 18:00:00+00:00,0.0
 2025-11-12 19:00:00+00:00,0.0
 2025-11-12 20:00:00+00:00,0.0
 2025-11-12 21:00:00+00:00,0.0
 2025-11-12 22:00:00+00:00,0.0
 2025-11-12 23:00:00+00:00,0.0
 2025-11-13 00:00:00+00:00,0.0
 2025-11-13 01:00:00+00:00,0.0
 2025-11-13 02:00:00+00:00,0.0
 2025-11-13 03:00:00+00:00,0.0
 2025-11-13 04:00:00+00:00,0.0
 2025-11-13 05:00:00+00:00,0.0
 2025-11-13 06:00:00+00:00,427.87
 2025-11-13 07:00:00+00:00,5346.1699
 2025-11-13 08:00:00+00:00,13033.1098
 2025-11-13 09:00:00+00:00,20893.089900000003
 2025-11-13 10:00:00+00:00,22835.084745762713
 2025-11-13 11:00:00+00:00,21669.111864406783
 2025-11-13 12:00:00+00:00,19302.780000000002
 2025-11-13 13:00:00+00:00,15188.7199
 2025-11-13 14:00:00+00:00,8176.39
 2025-11-13 15:00:00+00:00,2037.7799
 2025-11-13 16:00:00+00:00,18.1524406779661
 2025-11-13 17:00:00+00:00,0.0
 2025-11-13 18:00:00+00:00,0.0
 2025-11-13 19:00:00+00:00,0.0
 2025-11-13 20:00:00+00:00,0.0
 2025-11-13 21:00:00+00:00,0.0
 2025-11-13 22:00:00+00:00,0.0
 2025-11-13 23:00:00+00:00,0.0
 2025-11-14 00:00:00+00:00,0.0
 2025-11-14 01:00:00+00:00,0.0
 2025-11-14 02:00:00+00:00,0.0
 2025-11-14 03:00:00+00:00,0.0
 2025-11-14 04:00:00+00:00,0.0
 2025-11-14 05:00:00+00:00,0.0
 2025-11-14 06:00:00+00:00,286.9443333333333
 2025-11-14 07:00:00+00:00,3290.775966101695
 2025-11-14 08:00:00+00:00,8036.23
 2025-11-14 09:00:00+00:00,15955.569599999999
 2025-11-14 10:00:00+00:00,12867.2199
 2025-11-14 11:00:00+00:00,17966.7599
 2025-11-14 12:00:00+00:00,16961.644163265308
 2025-11-14 13:00:00+00:00,10758.1
 2025-11-14 14:00:00+00:00,3646.545661016949
 2025-11-14 15:00:00+00:00,1107.0993999999998
 2025-11-14 16:00:00+00:00,12.7699
 2025-11-14 17:00:00+00:00,0.0
 2025-11-14 18:00:00+00:00,0.0
 2025-11-14 19:00:00+00:00,0.0
 2025-11-14 20:00:00+00:00,0.0
 2025-11-14 21:00:00+00:00,0.0
 2025-11-14 22:00:00+00:00,0.0
 2025-11-14 23:00:00+00:00,0.0
 2025-11-15 00:00:00+00:00,0.0
 2025-11-15 01:00:00+00:00,0.0
 2025-11-15 02:00:00+00:00,0.0
 2025-11-15 03:00:00+00:00,0.0
 2025-11-15 04:00:00+00:00,0.0
 2025-11-15 05:00:00+00:00,0.0
 2025-11-15 06:00:00+00:00,194.75172413793103
 2025-11-15 07:00:00+00:00,2002.148172413793
 2025-11-15 08:00:00+00:00,4058.379559322034
 2025-11-15 09:00:00+00:00,4793.6900000000005
 2025-11-15 10:00:00+00:00,11489.619799999999
 2025-11-15 11:00:00+00:00,13446.8499
 2025-11-15 12:00:00+00:00,13169.3299
 2025-11-15 13:00:00+00:00,7882.7998
 2025-11-15 14:00:00+00:00,2470.0399
 2025-11-15 15:00:00+00:00,683.1699000000001
 2025-11-15 16:00:00+00:00,10.585428571428572
 2025-11-15 17:00:00+00:00,0.0
 2025-11-15 18:00:00+00:00,0.0
 2025-11-15 19:00:00+00:00,0.0
 2025-11-15 20:00:00+00:00,0.0
 2025-11-15 21:00:00+00:00,0.0
 2025-11-15 22:00:00+00:00,0.0
 2025-11-15 23:00:00+00:00,0.0
 2025-11-16 00:00:00+00:00,0.0
 2025-11-16 01:00:00+00:00,0.0
 2025-11-16 02:00:00+00:00,0.0
 2025-11-16 03:00:00+00:00,0.0
 2025-11-16 04:00:00+00:00,0.0
 2025-11-16 05:00:00+00:00,0.0
 2025-11-16 06:00:00+00:00,241.74305660377357
 2025-11-16 07:00:00+00:00,2991.2299
 2025-11-16 08:00:00+00:00,7205.5
 2025-11-16 09:00:00+00:00,10105.749699999998
 2025-11-16 10:00:00+00:00,14459.59
 2025-11-16 11:00:00+00:00,12687.81
 2025-11-16 12:00:00+00:00,10428.99
 2025-11-16 13:00:00+00:00,6846.9298
 2025-11-16 14:00:00+00:00,2649.51
 2025-11-16 15:00:00+00:00,637.7998983050848
 2025-11-16 16:00:00+00:00,0.0
 2025-11-16 17:00:00+00:00,0.0
 2025-11-16 18:00:00+00:00,0.0
 2025-11-16 19:00:00+00:00,0.0
 2025-11-16 20:00:00+00:00,0.0
 2025-11-16 21:00:00+00:00,0.0
 2025-11-16 22:00:00+00:00,0.0
 2025-11-16 23:00:00+00:00,0.0
 2025-11-17 00:00:00+00:00,0.0
 2025-11-17 01:00:00+00:00,0.0
 2025-11-17 02:00:00+00:00,0.0
 2025-11-17 03:00:00+00:00,0.0
 2025-11-17 04:00:00+00:00,0.0
 2025-11-17 05:00:00+00:00,0.0
 2025-11-17 06:00:00+00:00,36.38
 2025-11-17 07:00:00+00:00,2247.98
 2025-11-17 08:00:00+00:00,5345.479600000001
 2025-11-17 09:00:00+00:00,13612.317321428573
 2025-11-17 10:00:00+00:00,13954.1597
 2025-11-17 11:00:00+00:00,8618.99
 2025-11-17 12:00:00+00:00,7211.570000000001
 2025-11-17 13:00:00+00:00,10041.5799
 2025-11-17 14:00:00+00:00,6146.8598999999995
 2025-11-17 15:00:00+00:00,1435.4297000000001
 2025-11-17 16:00:00+00:00,17.263157894736842
 2025-11-17 17:00:00+00:00,0.0
 2025-11-17 18:00:00+00:00,0.0
 2025-11-17 19:00:00+00:00,0.0
 2025-11-17 20:00:00+00:00,0.0
 2025-11-17 21:00:00+00:00,0.0
 2025-11-17 22:00:00+00:00,0.0
 2025-11-17 23:00:00+00:00,0.0
 2025-11-18 00:00:00+00:00,0.0
 2025-11-18 01:00:00+00:00,0.0
 2025-11-18 02:00:00+00:00,0.0
 2025-11-18 03:00:00+00:00,0.0
 2025-11-18 04:00:00+00:00,0.0
 2025-11-18 05:00:00+00:00,0.0
 2025-11-18 06:00:00+00:00,230.0326530612245
 2025-11-18 07:00:00+00:00,2010.6299
 2025-11-18 08:00:00+00:00,3150.6
--- a/figures/basic/pressure_last_7_days.csv
+++ b/figures/basic/pressure_last_7_days.csv
@ -1,170 +0,0 @@
 time,pressure
 2025-11-11 08:00:00+00:00,1006.8025357788998
 2025-11-11 09:00:00+00:00,1007.4336885582467
 2025-11-11 10:00:00+00:00,1007.4336885582462
 2025-11-11 11:00:00+00:00,1006.9821700728662
 2025-11-11 12:00:00+00:00,1006.51936362535
 2025-11-11 13:00:00+00:00,1006.1863687423846
 2025-11-11 14:00:00+00:00,1006.1525048559816
 2025-11-11 15:00:00+00:00,1006.09606504531
 2025-11-11 16:00:00+00:00,1006.0960650453098
 2025-11-11 17:00:00+00:00,1006.0224479009556
 2025-11-11 18:00:00+00:00,1005.6935320559875
 2025-11-11 19:00:00+00:00,1005.605038692461
 2025-11-11 20:00:00+00:00,1005.8928817268919
 2025-11-11 21:00:00+00:00,1005.8219288220467
 2025-11-11 22:00:00+00:00,1005.6626072993489
 2025-11-11 23:00:00+00:00,1005.7175863149204
 2025-11-12 00:00:00+00:00,1005.4571237924154
 2025-11-12 01:00:00+00:00,1005.2762025323852
 2025-11-12 02:00:00+00:00,1004.9350175114786
 2025-11-12 03:00:00+00:00,1004.6568498731625
 2025-11-12 04:00:00+00:00,1004.8524430101495
 2025-11-12 05:00:00+00:00,1005.0735084754837
 2025-11-12 06:00:00+00:00,1005.2745522455234
 2025-11-12 07:00:00+00:00,1005.5554310693974
 2025-11-12 08:00:00+00:00,1005.4544335134557
 2025-11-12 09:00:00+00:00,1005.5357716520901
 2025-11-12 10:00:00+00:00,1005.7066303516732
 2025-11-12 11:00:00+00:00,1005.2268919609563
 2025-11-12 12:00:00+00:00,1004.6004100624923
 2025-11-12 13:00:00+00:00,1004.4593105358116
 2025-11-12 14:00:00+00:00,1004.1503264875583
 2025-11-12 15:00:00+00:00,1004.2391952741922
 2025-11-12 16:00:00+00:00,1004.2611149215926
 2025-11-12 17:00:00+00:00,1004.633433355969
 2025-11-12 18:00:00+00:00,1004.4186213699787
 2025-11-12 19:00:00+00:00,1004.1264308361359
 2025-11-12 20:00:00+00:00,1004.1137887680413
 2025-11-12 21:00:00+00:00,1004.1153471613409
 2025-11-12 22:00:00+00:00,1004.1216282787423
 2025-11-12 23:00:00+00:00,1004.1737914935902
 2025-11-13 00:00:00+00:00,1003.8994886299554
 2025-11-13 01:00:00+00:00,1003.3367854124415
 2025-11-13 02:00:00+00:00,1003.0200953636644
 2025-11-13 03:00:00+00:00,1003.386954133039
 2025-11-13 04:00:00+00:00,1004.2296136319123
 2025-11-13 05:00:00+00:00,1004.572190157155
 2025-11-13 06:00:00+00:00,1004.7358656081044
 2025-11-13 07:00:00+00:00,1005.0801484532092
 2025-11-13 08:00:00+00:00,1005.3792794497713
 2025-11-13 09:00:00+00:00,1005.8364419162203
 2025-11-13 10:00:00+00:00,1005.8607397330167
 2025-11-13 11:00:00+00:00,1005.2810359149227
 2025-11-13 12:00:00+00:00,1005.1083683585458
 2025-11-13 13:00:00+00:00,1004.8600331915857
 2025-11-13 14:00:00+00:00,1004.5778341382222
 2025-11-13 15:00:00+00:00,1004.5157503464834
 2025-11-13 16:00:00+00:00,1004.5693203362734
 2025-11-13 17:00:00+00:00,1005.0475870239753
 2025-11-13 18:00:00+00:00,1004.4622810521628
 2025-11-13 19:00:00+00:00,1004.0924517664458
 2025-11-13 20:00:00+00:00,1004.06423186111
 2025-11-13 21:00:00+00:00,1003.6509776541492
 2025-11-13 22:00:00+00:00,1003.3305143223666
 2025-11-13 23:00:00+00:00,1002.7490530170666
 2025-11-14 00:00:00+00:00,1001.9976664857203
 2025-11-14 01:00:00+00:00,1001.1293617061444
 2025-11-14 02:00:00+00:00,1000.2150367732527
 2025-11-14 03:00:00+00:00,999.6092494720353
 2025-11-14 04:00:00+00:00,999.8194330770515
 2025-11-14 05:00:00+00:00,1000.1836135273107
 2025-11-14 06:00:00+00:00,1000.2890356361329
 2025-11-14 07:00:00+00:00,1000.4769557590513
 2025-11-14 08:00:00+00:00,1000.344848337799
 2025-11-14 09:00:00+00:00,1000.2263247353866
 2025-11-14 10:00:00+00:00,999.955413644161
 2025-11-14 11:00:00+00:00,999.9159057766907
 2025-11-14 12:00:00+00:00,999.4960626952586
 2025-11-14 13:00:00+00:00,999.0787152517155
 2025-11-14 14:00:00+00:00,999.0707435270442
 2025-11-14 15:00:00+00:00,998.9902928816622
 2025-11-14 16:00:00+00:00,999.0072248248639
 2025-11-14 17:00:00+00:00,999.145358869967
 2025-11-14 18:00:00+00:00,999.1797967205467
 2025-11-14 19:00:00+00:00,999.2907263353949
 2025-11-14 20:00:00+00:00,999.1962981906162
 2025-11-14 21:00:00+00:00,999.4381830934977
 2025-11-14 22:00:00+00:00,999.7042564866678
 2025-11-14 23:00:00+00:00,1000.1078011329762
 2025-11-15 00:00:00+00:00,1000.2545446407224
 2025-11-15 01:00:00+00:00,1000.4182200916727
 2025-11-15 02:00:00+00:00,1000.1247330761779
 2025-11-15 03:00:00+00:00,999.9260649426119
 2025-11-15 04:00:00+00:00,1000.0579619103316
 2025-11-15 05:00:00+00:00,999.8796230412585
 2025-11-15 06:00:00+00:00,999.9246636783472
 2025-11-15 07:00:00+00:00,1000.0706287059476
 2025-11-15 08:00:00+00:00,1000.1497961785437
 2025-11-15 09:00:00+00:00,1000.248900659657
 2025-11-15 10:00:00+00:00,1000.4295080538088
 2025-11-15 11:00:00+00:00,1000.0570053033717
 2025-11-15 12:00:00+00:00,999.780450231076
 2025-11-15 13:00:00+00:00,999.4079474806374
 2025-11-15 14:00:00+00:00,999.9328377198912
 2025-11-15 15:00:00+00:00,999.7917381932111
 2025-11-15 16:00:00+00:00,999.6216124781831
 2025-11-15 17:00:00+00:00,1000.5649635994246
 2025-11-15 18:00:00+00:00,1000.67784322077
 2025-11-15 19:00:00+00:00,1000.5284437219287
 2025-11-15 20:00:00+00:00,1000.4426773429645
 2025-11-15 21:00:00+00:00,1000.33920435673
 2025-11-15 22:00:00+00:00,1000.3392043567299
 2025-11-15 23:00:00+00:00,1000.3392043567311
 2025-11-16 00:00:00+00:00,999.9521885121229
 2025-11-16 01:00:00+00:00,999.7823315580983
 2025-11-16 02:00:00+00:00,999.2186175702913
 2025-11-16 03:00:00+00:00,999.021591322126
 2025-11-16 04:00:00+00:00,998.7946807573566
 2025-11-16 05:00:00+00:00,998.491203698717
 2025-11-16 06:00:00+00:00,998.0773523214843
 2025-11-16 07:00:00+00:00,997.9969522138283
 2025-11-16 08:00:00+00:00,997.7486170468699
 2025-11-16 09:00:00+00:00,997.7147531604664
 2025-11-16 10:00:00+00:00,997.4664179935082
 2025-11-16 11:00:00+00:00,996.7665643411713
 2025-11-16 12:00:00+00:00,996.614176852356
 2025-11-16 13:00:00+00:00,996.614176852356
 2025-11-16 14:00:00+00:00,996.591600928087
 2025-11-16 15:00:00+00:00,996.6830525535155
 2025-11-16 16:00:00+00:00,996.9739806453921
 2025-11-16 17:00:00+00:00,997.1463236386951
 2025-11-16 18:00:00+00:00,997.2726413101999
 2025-11-16 19:00:00+00:00,997.291454580424
 2025-11-16 20:00:00+00:00,997.2914545804239
 2025-11-16 21:00:00+00:00,997.2914545804239
 2025-11-16 22:00:00+00:00,997.291454580424
 2025-11-16 23:00:00+00:00,997.4868231558282
 2025-11-17 00:00:00+00:00,997.2707215887484
 2025-11-17 01:00:00+00:00,996.9592051289188
 2025-11-17 02:00:00+00:00,996.8737999814488
 2025-11-17 03:00:00+00:00,996.6326480631216
 2025-11-17 04:00:00+00:00,996.9753916406589
 2025-11-17 05:00:00+00:00,997.6696013119287
 2025-11-17 06:00:00+00:00,998.4879785666784
 2025-11-17 07:00:00+00:00,999.7240104204034
 2025-11-17 08:00:00+00:00,1000.9148904255908
 2025-11-17 09:00:00+00:00,1001.43978066484
 2025-11-17 10:00:00+00:00,1001.7501996235417
 2025-11-17 11:00:00+00:00,1001.970314885162
 2025-11-17 12:00:00+00:00,1002.4444092948122
 2025-11-17 13:00:00+00:00,1003.1781268335506
 2025-11-17 14:00:00+00:00,1004.2053313877872
 2025-11-17 15:00:00+00:00,1005.4300752793774
 2025-11-17 16:00:00+00:00,1006.8149300022945
 2025-11-17 17:00:00+00:00,1007.9716033691925
 2025-11-17 18:00:00+00:00,1009.1641331534599
 2025-11-17 19:00:00+00:00,1009.8487365385887
 2025-11-17 20:00:00+00:00,1010.5311417807217
 2025-11-17 21:00:00+00:00,1010.8176583495577
 2025-11-17 22:00:00+00:00,1011.0477177682973
 2025-11-17 23:00:00+00:00,1011.17564800582
 2025-11-18 00:00:00+00:00,1011.3095284869521
 2025-11-18 01:00:00+00:00,1011.6412764438712
 2025-11-18 02:00:00+00:00,1011.3953056473576
 2025-11-18 03:00:00+00:00,1010.9955209504839
 2025-11-18 04:00:00+00:00,1010.9764486740377
 2025-11-18 05:00:00+00:00,1011.2744176744983
 2025-11-18 06:00:00+00:00,1011.3207789475515
 2025-11-18 07:00:00+00:00,1011.4804229834554
 2025-11-18 08:00:00+00:00,1011.85292573389
--- a/figures/basic/rain_rate_last_7_days.csv
+++ b/figures/basic/rain_rate_last_7_days.csv
@ -1,170 +0,0 @@
 time,rain_rate
 2025-11-11 08:00:00+00:00,0.0
 2025-11-11 09:00:00+00:00,0.0
 2025-11-11 10:00:00+00:00,0.0
 2025-11-11 11:00:00+00:00,0.0
 2025-11-11 12:00:00+00:00,0.0
 2025-11-11 13:00:00+00:00,0.0
 2025-11-11 14:00:00+00:00,0.0
 2025-11-11 15:00:00+00:00,0.0
 2025-11-11 16:00:00+00:00,0.0
 2025-11-11 17:00:00+00:00,0.0
 2025-11-11 18:00:00+00:00,0.0
 2025-11-11 19:00:00+00:00,0.0
 2025-11-11 20:00:00+00:00,0.0
 2025-11-11 21:00:00+00:00,0.0
 2025-11-11 22:00:00+00:00,0.0
 2025-11-11 23:00:00+00:00,0.0
 2025-11-12 00:00:00+00:00,0.0
 2025-11-12 01:00:00+00:00,0.0
 2025-11-12 02:00:00+00:00,0.0
 2025-11-12 03:00:00+00:00,0.0
 2025-11-12 04:00:00+00:00,0.0
 2025-11-12 05:00:00+00:00,0.0
 2025-11-12 06:00:00+00:00,0.0
 2025-11-12 07:00:00+00:00,0.0
 2025-11-12 08:00:00+00:00,0.0
 2025-11-12 09:00:00+00:00,0.0
 2025-11-12 10:00:00+00:00,0.0
 2025-11-12 11:00:00+00:00,0.0
 2025-11-12 12:00:00+00:00,0.0
 2025-11-12 13:00:00+00:00,0.0
 2025-11-12 14:00:00+00:00,0.0
 2025-11-12 15:00:00+00:00,0.0
 2025-11-12 16:00:00+00:00,0.0
 2025-11-12 17:00:00+00:00,0.0
 2025-11-12 18:00:00+00:00,0.0
 2025-11-12 19:00:00+00:00,0.0
 2025-11-12 20:00:00+00:00,0.0
 2025-11-12 21:00:00+00:00,0.0
 2025-11-12 22:00:00+00:00,0.0
 2025-11-12 23:00:00+00:00,0.0
 2025-11-13 00:00:00+00:00,0.0
 2025-11-13 01:00:00+00:00,0.0
 2025-11-13 02:00:00+00:00,0.0
 2025-11-13 03:00:00+00:00,0.0
 2025-11-13 04:00:00+00:00,0.0
 2025-11-13 05:00:00+00:00,0.0
 2025-11-13 06:00:00+00:00,0.0
 2025-11-13 07:00:00+00:00,0.0
 2025-11-13 08:00:00+00:00,0.0
 2025-11-13 09:00:00+00:00,0.0
 2025-11-13 10:00:00+00:00,0.0
 2025-11-13 11:00:00+00:00,0.0
 2025-11-13 12:00:00+00:00,0.0
 2025-11-13 13:00:00+00:00,0.0
 2025-11-13 14:00:00+00:00,0.0
 2025-11-13 15:00:00+00:00,0.0
 2025-11-13 16:00:00+00:00,0.0
 2025-11-13 17:00:00+00:00,0.0
 2025-11-13 18:00:00+00:00,0.0
 2025-11-13 19:00:00+00:00,0.0
 2025-11-13 20:00:00+00:00,0.0
 2025-11-13 21:00:00+00:00,0.0
 2025-11-13 22:00:00+00:00,0.0
 2025-11-13 23:00:00+00:00,0.0
 2025-11-14 00:00:00+00:00,0.0
 2025-11-14 01:00:00+00:00,0.0
 2025-11-14 02:00:00+00:00,0.0
 2025-11-14 03:00:00+00:00,0.0
 2025-11-14 04:00:00+00:00,0.0
 2025-11-14 05:00:00+00:00,0.0
 2025-11-14 06:00:00+00:00,0.0
 2025-11-14 07:00:00+00:00,0.0
 2025-11-14 08:00:00+00:00,0.0
 2025-11-14 09:00:00+00:00,0.0
 2025-11-14 10:00:00+00:00,0.0
 2025-11-14 11:00:00+00:00,0.0
 2025-11-14 12:00:00+00:00,0.0
 2025-11-14 13:00:00+00:00,0.0
 2025-11-14 14:00:00+00:00,0.0
 2025-11-14 15:00:00+00:00,0.0
 2025-11-14 16:00:00+00:00,0.0
 2025-11-14 17:00:00+00:00,0.0
 2025-11-14 18:00:00+00:00,0.0
 2025-11-14 19:00:00+00:00,0.0
 2025-11-14 20:00:00+00:00,0.0
 2025-11-14 21:00:00+00:00,0.0
 2025-11-14 22:00:00+00:00,0.0
 2025-11-14 23:00:00+00:00,0.0
 2025-11-15 00:00:00+00:00,0.0
 2025-11-15 01:00:00+00:00,0.0
 2025-11-15 02:00:00+00:00,0.0
 2025-11-15 03:00:00+00:00,0.0
 2025-11-15 04:00:00+00:00,0.0
 2025-11-15 05:00:00+00:00,0.0
 2025-11-15 06:00:00+00:00,0.0
 2025-11-15 07:00:00+00:00,0.23648275862068968
 2025-11-15 08:00:00+00:00,0.0
 2025-11-15 09:00:00+00:00,0.2413
 2025-11-15 10:00:00+00:00,0.0
 2025-11-15 11:00:00+00:00,0.0
 2025-11-15 12:00:00+00:00,0.0
 2025-11-15 13:00:00+00:00,0.0
 2025-11-15 14:00:00+00:00,2.2479
 2025-11-15 15:00:00+00:00,0.4318
 2025-11-15 16:00:00+00:00,0.0
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 2025-11-16 04:00:00+00:00,0.278780487804878
 2025-11-16 05:00:00+00:00,0.0
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 2025-11-16 08:00:00+00:00,0.0
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 2025-11-16 11:00:00+00:00,0.0
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 2025-11-16 20:00:00+00:00,0.0
 2025-11-16 21:00:00+00:00,0.0
 2025-11-16 22:00:00+00:00,0.0
 2025-11-16 23:00:00+00:00,0.0
 2025-11-17 00:00:00+00:00,0.7308979591836735
 2025-11-17 01:00:00+00:00,0.12939622641509435
 2025-11-17 02:00:00+00:00,0.0
 2025-11-17 03:00:00+00:00,0.0
 2025-11-17 04:00:00+00:00,0.0
 2025-11-17 05:00:00+00:00,0.2413
 2025-11-17 06:00:00+00:00,0.9779
 2025-11-17 07:00:00+00:00,0.254
 2025-11-17 08:00:00+00:00,0.0
 2025-11-17 09:00:00+00:00,0.0
 2025-11-17 10:00:00+00:00,0.0
 2025-11-17 11:00:00+00:00,0.0
 2025-11-17 12:00:00+00:00,0.0
 2025-11-17 13:00:00+00:00,0.0
 2025-11-17 14:00:00+00:00,0.0
 2025-11-17 15:00:00+00:00,0.0
 2025-11-17 16:00:00+00:00,0.0
 2025-11-17 17:00:00+00:00,0.0
 2025-11-17 18:00:00+00:00,0.0
 2025-11-17 19:00:00+00:00,0.0
 2025-11-17 20:00:00+00:00,0.0
 2025-11-17 21:00:00+00:00,0.0
 2025-11-17 22:00:00+00:00,0.0
 2025-11-17 23:00:00+00:00,0.0
 2025-11-18 00:00:00+00:00,0.0
 2025-11-18 01:00:00+00:00,0.0
 2025-11-18 02:00:00+00:00,0.0
 2025-11-18 03:00:00+00:00,0.0
 2025-11-18 04:00:00+00:00,0.0
 2025-11-18 05:00:00+00:00,0.0
 2025-11-18 06:00:00+00:00,0.0
 2025-11-18 07:00:00+00:00,0.0
 2025-11-18 08:00:00+00:00,0.0
--- a/figures/basic/sun_elevation_last_7_days.csv
+++ b/figures/basic/sun_elevation_last_7_days.csv
@ -1,170 +0,0 @@
 time,sun_elevation
 2025-11-11 08:00:00+00:00,14.308718850096088
 2025-11-11 09:00:00+00:00,19.641059178244088
 2025-11-11 10:00:00+00:00,22.885323533749624
 2025-11-11 11:00:00+00:00,23.572240234780136
 2025-11-11 12:00:00+00:00,21.618490966427157
 2025-11-11 13:00:00+00:00,17.25306445556676
 2025-11-11 14:00:00+00:00,10.92273994659757
 2025-11-11 15:00:00+00:00,3.2428890451279853
 2025-11-11 16:00:00+00:00,-5.903087958357458
 2025-11-11 17:00:00+00:00,-15.22730111177631
 2025-11-11 18:00:00+00:00,-25.13232055875336
 2025-11-11 19:00:00+00:00,-35.44086364722457
 2025-11-11 20:00:00+00:00,-44.523565998887335
 2025-11-11 21:00:00+00:00,-52.15651561105852
 2025-11-11 22:00:00+00:00,-57.4261948850097
 2025-11-11 23:00:00+00:00,-58.66321971266724
 2025-11-12 00:00:00+00:00,-55.44562032226747
 2025-11-12 01:00:00+00:00,-48.668159759993486
 2025-11-12 02:00:00+00:00,-40.732158839860745
 2025-11-12 03:00:00+00:00,-30.70841126725245
 2025-11-12 04:00:00+00:00,-20.70087197418278
 2025-11-12 05:00:00+00:00,-11.276008047769148
 2025-11-12 06:00:00+00:00,-1.4046115369365415
 2025-11-12 07:00:00+00:00,6.722007914419731
 2025-11-12 08:00:00+00:00,14.074341039202734
 2025-11-12 09:00:00+00:00,19.440118242416276
 2025-11-12 10:00:00+00:00,22.613647344654602
 2025-11-12 11:00:00+00:00,23.303853471787
 2025-11-12 12:00:00+00:00,21.362421183707333
 2025-11-12 13:00:00+00:00,17.015495448098136
 2025-11-12 14:00:00+00:00,10.70222894285897
 2025-11-12 15:00:00+00:00,3.0463016121060815
 2025-11-12 16:00:00+00:00,-5.544993798508534
 2025-11-12 17:00:00+00:00,-15.11776322592283
 2025-11-12 18:00:00+00:00,-25.528577546140575
 2025-11-12 19:00:00+00:00,-35.085822187174074
 2025-11-12 20:00:00+00:00,-44.90088305749059
 2025-11-12 21:00:00+00:00,-52.65574132818778
 2025-11-12 22:00:00+00:00,-57.66044673539
 2025-11-12 23:00:00+00:00,-58.89446352252472
 2025-11-13 00:00:00+00:00,-55.72878243070744
 2025-11-13 01:00:00+00:00,-48.97177496687385
 2025-11-13 02:00:00+00:00,-41.016945559103206
 2025-11-13 03:00:00+00:00,-30.890201876327012
 2025-11-13 04:00:00+00:00,-20.461891592603692
 2025-11-13 05:00:00+00:00,-11.187801232366729
 2025-11-13 06:00:00+00:00,-1.7383875065774146
 2025-11-13 07:00:00+00:00,6.601307518106436
 2025-11-13 08:00:00+00:00,13.69215630197333
 2025-11-13 09:00:00+00:00,19.11266697722042
 2025-11-13 10:00:00+00:00,22.334002545882043
 2025-11-13 11:00:00+00:00,23.03800907600499
 2025-11-13 12:00:00+00:00,21.1120498631742
 2025-11-13 13:00:00+00:00,16.783857428915603
 2025-11-13 14:00:00+00:00,10.495797719803473
 2025-11-13 15:00:00+00:00,2.8547816773483374
 2025-11-13 16:00:00+00:00,-6.2870942727338495
 2025-11-13 17:00:00+00:00,-15.483671431330357
 2025-11-13 18:00:00+00:00,-25.737003948646695
 2025-11-13 19:00:00+00:00,-35.44333825958562
 2025-11-13 20:00:00+00:00,-44.72096585064475
 2025-11-13 21:00:00+00:00,-52.59045587027669
 2025-11-13 22:00:00+00:00,-57.75074416369176
 2025-11-13 23:00:00+00:00,-59.15644141342444
 2025-11-14 00:00:00+00:00,-56.11986608431349
 2025-11-14 01:00:00+00:00,-49.00805323466757
 2025-11-14 02:00:00+00:00,-39.81173703491096
 2025-11-14 03:00:00+00:00,-31.11224360580915
 2025-11-14 04:00:00+00:00,-21.40183358698364
 2025-11-14 05:00:00+00:00,-11.653211241034962
 2025-11-14 06:00:00+00:00,-1.6190221985650948
 2025-11-14 07:00:00+00:00,6.400317144670118
 2025-11-14 08:00:00+00:00,13.44399279955036
 2025-11-14 09:00:00+00:00,18.85434158531877
 2025-11-14 10:00:00+00:00,22.08425004289927
 2025-11-14 11:00:00+00:00,22.78297737884391
 2025-11-14 12:00:00+00:00,20.993319270722218
 2025-11-14 13:00:00+00:00,16.424629313468
 2025-11-14 14:00:00+00:00,10.305705156541737
 2025-11-14 15:00:00+00:00,2.6688454383667466
 2025-11-14 16:00:00+00:00,-6.392835813451928
 2025-11-14 17:00:00+00:00,-15.97017802504351
 2025-11-14 18:00:00+00:00,-25.746392678116898
 2025-11-14 19:00:00+00:00,-35.557566964618495
 2025-11-14 20:00:00+00:00,-44.92456295098489
 2025-11-14 21:00:00+00:00,-52.84571453471228
 2025-11-14 22:00:00+00:00,-58.291319119207664
 2025-11-14 23:00:00+00:00,-59.43572195628009
 2025-11-15 00:00:00+00:00,-56.15310326011466
 2025-11-15 01:00:00+00:00,-49.437269974595466
 2025-11-15 02:00:00+00:00,-40.7818544410004
 2025-11-15 03:00:00+00:00,-31.173920939749177
 2025-11-15 04:00:00+00:00,-21.286378610097128
 2025-11-15 05:00:00+00:00,-11.904292339354084
 2025-11-15 06:00:00+00:00,-2.065718258875086
 2025-11-15 07:00:00+00:00,6.073547715991186
 2025-11-15 08:00:00+00:00,13.242214489581697
 2025-11-15 09:00:00+00:00,18.600080475957668
 2025-11-15 10:00:00+00:00,21.82674662621842
 2025-11-15 11:00:00+00:00,22.53069229652115
 2025-11-15 12:00:00+00:00,20.62878392058985
 2025-11-15 13:00:00+00:00,16.3387262666421
 2025-11-15 14:00:00+00:00,10.093381289038067
 2025-11-15 15:00:00+00:00,2.488879059293881
 2025-11-15 16:00:00+00:00,-5.799443400851093
 2025-11-15 17:00:00+00:00,-16.184110602687074
 2025-11-15 18:00:00+00:00,-25.411854657879584
 2025-11-15 19:00:00+00:00,-36.06104724244549
 2025-11-15 20:00:00+00:00,-45.98868646753228
 2025-11-15 21:00:00+00:00,-53.2595732204459
 2025-11-15 22:00:00+00:00,-58.52517485072366
 2025-11-15 23:00:00+00:00,-59.63899728878434
 2025-11-16 00:00:00+00:00,-55.6769264482925
 2025-11-16 01:00:00+00:00,-49.912079958491944
 2025-11-16 02:00:00+00:00,-40.96494950596665
 2025-11-16 03:00:00+00:00,-31.62867310692375
 2025-11-16 04:00:00+00:00,-21.71821319742638
 2025-11-16 05:00:00+00:00,-13.338694691943978
 2025-11-16 06:00:00+00:00,-1.9246325115628766
 2025-11-16 07:00:00+00:00,5.912836575600048
 2025-11-16 08:00:00+00:00,12.957717123150797
 2025-11-16 09:00:00+00:00,18.349996001989837
 2025-11-16 10:00:00+00:00,21.57418368055951
 2025-11-16 11:00:00+00:00,22.283976426762067
 2025-11-16 12:00:00+00:00,20.396075390712685
 2025-11-16 13:00:00+00:00,16.125403220024324
 2025-11-16 14:00:00+00:00,9.901559594503782
 2025-11-16 15:00:00+00:00,2.3683048173781134
 2025-11-16 16:00:00+00:00,-6.5328577727526485
 2025-11-16 17:00:00+00:00,-17.31105292102647
 2025-11-16 18:00:00+00:00,-25.443557107213064
 2025-11-16 19:00:00+00:00,-37.467532717145275
 2025-11-16 20:00:00+00:00,-45.51725880720397
 2025-11-16 21:00:00+00:00,-54.17733196452078
 2025-11-16 22:00:00+00:00,-58.666480718842934
 2025-11-16 23:00:00+00:00,-60.00700274586307
 2025-11-17 00:00:00+00:00,-56.690411119523226
 2025-11-17 01:00:00+00:00,-49.81557665105729
 2025-11-17 02:00:00+00:00,-41.21631116519275
 2025-11-17 03:00:00+00:00,-31.593407971145812
 2025-11-17 04:00:00+00:00,-21.779455441787334
 2025-11-17 05:00:00+00:00,-12.051848256207142
 2025-11-17 06:00:00+00:00,-2.654746434168217
 2025-11-17 07:00:00+00:00,5.689528772336274
 2025-11-17 08:00:00+00:00,12.719830965357682
 2025-11-17 09:00:00+00:00,18.07716294813033
 2025-11-17 10:00:00+00:00,21.326667708461216
 2025-11-17 11:00:00+00:00,22.042928388783245
 2025-11-17 12:00:00+00:00,20.169437371222603
 2025-11-17 13:00:00+00:00,15.918351230230488
 2025-11-17 14:00:00+00:00,9.716090347681874
 2025-11-17 15:00:00+00:00,2.1471267244237136
 2025-11-17 16:00:00+00:00,-6.706949872607374
 2025-11-17 17:00:00+00:00,-16.06152026701152
 2025-11-17 18:00:00+00:00,-26.686322086852964
 2025-11-17 19:00:00+00:00,-36.06309678522945
 2025-11-17 20:00:00+00:00,-44.43295251251092
 2025-11-17 21:00:00+00:00,-53.261199765238906
 2025-11-17 22:00:00+00:00,-58.743298154177054
 2025-11-17 23:00:00+00:00,-60.22192100454561
 2025-11-18 00:00:00+00:00,-57.05924974531945
 2025-11-18 01:00:00+00:00,-50.94477626965786
 2025-11-18 02:00:00+00:00,-40.561841725348835
 2025-11-18 03:00:00+00:00,-32.68266655442553
 2025-11-18 04:00:00+00:00,-21.95606951068478
 2025-11-18 05:00:00+00:00,-13.126587978505347
 2025-11-18 06:00:00+00:00,-2.2546580915224554
 2025-11-18 07:00:00+00:00,5.469537913593991
 2025-11-18 08:00:00+00:00,9.37419719472193
--- a/figures/basic/temperature_last_7_days.csv
+++ b/figures/basic/temperature_last_7_days.csv
@ -1,170 +0,0 @@
 time,temperature
 2025-11-11 08:00:00+00:00,9.965996168582388
 2025-11-11 09:00:00+00:00,10.987500000000022
 2025-11-11 10:00:00+00:00,11.817592592592614
 2025-11-11 11:00:00+00:00,12.637500000000022
 2025-11-11 12:00:00+00:00,13.246759259259282
 2025-11-11 13:00:00+00:00,13.832870370370385
 2025-11-11 14:00:00+00:00,13.293055555555576
 2025-11-11 15:00:00+00:00,13.28472222222224
 2025-11-11 16:00:00+00:00,10.064009661835758
 2025-11-11 17:00:00+00:00,9.247584541062803
 2025-11-11 18:00:00+00:00,8.769916142557653
 2025-11-11 19:00:00+00:00,8.168750000000001
 2025-11-11 20:00:00+00:00,7.547222222222224
 2025-11-11 21:00:00+00:00,7.481481481481483
 2025-11-11 22:00:00+00:00,7.368333333333335
 2025-11-11 23:00:00+00:00,7.112745098039217
 2025-11-12 00:00:00+00:00,7.6781970649895195
 2025-11-12 01:00:00+00:00,7.887914230019497
 2025-11-12 02:00:00+00:00,7.867346938775513
 2025-11-12 03:00:00+00:00,8.201388888888891
 2025-11-12 04:00:00+00:00,8.817528735632186
 2025-11-12 05:00:00+00:00,9.174291938997824
 2025-11-12 06:00:00+00:00,8.288580246913583
 2025-11-12 07:00:00+00:00,9.045808966861609
 2025-11-12 08:00:00+00:00,12.399122807017566
 2025-11-12 09:00:00+00:00,14.626262626262648
 2025-11-12 10:00:00+00:00,15.19537037037039
 2025-11-12 11:00:00+00:00,16.255555555555585
 2025-11-12 12:00:00+00:00,16.912037037037052
 2025-11-12 13:00:00+00:00,16.73287037037038
 2025-11-12 14:00:00+00:00,15.981167608286277
 2025-11-12 15:00:00+00:00,14.130092592592625
 2025-11-12 16:00:00+00:00,11.878552971576244
 2025-11-12 17:00:00+00:00,11.139479905437371
 2025-11-12 18:00:00+00:00,10.967700258397958
 2025-11-12 19:00:00+00:00,10.130385487528358
 2025-11-12 20:00:00+00:00,11.02168021680219
 2025-11-12 21:00:00+00:00,12.440775681341744
 2025-11-12 22:00:00+00:00,13.428907721280623
 2025-11-12 23:00:00+00:00,11.055555555555577
 2025-11-13 00:00:00+00:00,10.917417417417441
 2025-11-13 01:00:00+00:00,13.5956790123457
 2025-11-13 02:00:00+00:00,11.973379629629653
 2025-11-13 03:00:00+00:00,9.185435435435439
 2025-11-13 04:00:00+00:00,8.089147286821706
 2025-11-13 05:00:00+00:00,11.47175925925928
 2025-11-13 06:00:00+00:00,14.162037037037063
 2025-11-13 07:00:00+00:00,15.731481481481506
 2025-11-13 08:00:00+00:00,16.855092592592612
 2025-11-13 09:00:00+00:00,17.668518518518546
 2025-11-13 10:00:00+00:00,18.876647834274973
 2025-11-13 11:00:00+00:00,19.14877589453864
 2025-11-13 12:00:00+00:00,19.313888888888908
 2025-11-13 13:00:00+00:00,19.311574074074095
 2025-11-13 14:00:00+00:00,18.7763888888889
 2025-11-13 15:00:00+00:00,17.792592592592612
 2025-11-13 16:00:00+00:00,17.19020715630887
 2025-11-13 17:00:00+00:00,15.861645299145321
 2025-11-13 18:00:00+00:00,17.029727095516595
 2025-11-13 19:00:00+00:00,17.23611111111114
 2025-11-13 20:00:00+00:00,17.227871939736367
 2025-11-13 21:00:00+00:00,17.860169491525443
 2025-11-13 22:00:00+00:00,15.800925925925945
 2025-11-13 23:00:00+00:00,12.624677002584006
 2025-11-14 00:00:00+00:00,11.420227920227942
 2025-11-14 01:00:00+00:00,10.823232323232341
 2025-11-14 02:00:00+00:00,10.84629629629632
 2025-11-14 03:00:00+00:00,12.197530864197555
 2025-11-14 04:00:00+00:00,10.516795865633087
 2025-11-14 05:00:00+00:00,10.442942942942969
 2025-11-14 06:00:00+00:00,11.37448559670784
 2025-11-14 07:00:00+00:00,10.394067796610189
 2025-11-14 08:00:00+00:00,11.249537037037062
 2025-11-14 09:00:00+00:00,14.907870370370398
 2025-11-14 10:00:00+00:00,15.971759259259292
 2025-11-14 11:00:00+00:00,14.7439814814815
 2025-11-14 12:00:00+00:00,15.397392290249455
 2025-11-14 13:00:00+00:00,15.906893004115249
 2025-11-14 14:00:00+00:00,16.829566854990613
 2025-11-14 15:00:00+00:00,16.5564814814815
 2025-11-14 16:00:00+00:00,16.65601851851854
 2025-11-14 17:00:00+00:00,17.032956685499077
 2025-11-14 18:00:00+00:00,16.97834274952921
 2025-11-14 19:00:00+00:00,15.650641025641049
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 2025-11-14 21:00:00+00:00,15.676091269841294
 2025-11-14 22:00:00+00:00,17.198412698412724
 2025-11-14 23:00:00+00:00,16.954166666666687
 2025-11-15 00:00:00+00:00,16.487037037037062
 2025-11-15 01:00:00+00:00,15.979629629629647
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 2025-11-15 04:00:00+00:00,13.687853107344653
 2025-11-15 05:00:00+00:00,13.51636904761907
 2025-11-15 06:00:00+00:00,13.82614942528738
 2025-11-15 07:00:00+00:00,13.79262452107282
 2025-11-15 08:00:00+00:00,14.000941619585701
 2025-11-15 09:00:00+00:00,13.451388888888914
 2025-11-15 10:00:00+00:00,13.141666666666683
 2025-11-15 11:00:00+00:00,13.591666666666681
 2025-11-15 12:00:00+00:00,14.441203703703726
 2025-11-15 13:00:00+00:00,14.339351851851868
 2025-11-15 14:00:00+00:00,14.1351851851852
 2025-11-15 15:00:00+00:00,12.209259259259293
 2025-11-15 16:00:00+00:00,11.866402116402147
 2025-11-15 17:00:00+00:00,10.611111111111128
 2025-11-15 18:00:00+00:00,10.166666666666696
 2025-11-15 19:00:00+00:00,9.468954248366018
 2025-11-15 20:00:00+00:00,8.715277777777779
 2025-11-15 21:00:00+00:00,8.26697530864198
 2025-11-15 22:00:00+00:00,7.70277777777778
 2025-11-15 23:00:00+00:00,7.004273504273505
 2025-11-16 00:00:00+00:00,6.738095238095239
 2025-11-16 01:00:00+00:00,7.212345679012348
 2025-11-16 02:00:00+00:00,7.311111111111113
 2025-11-16 03:00:00+00:00,7.605050505050508
 2025-11-16 04:00:00+00:00,7.698509485094853
 2025-11-16 05:00:00+00:00,7.150000000000001
 2025-11-16 06:00:00+00:00,7.224318658280924
 2025-11-16 07:00:00+00:00,7.32638888888889
 2025-11-16 08:00:00+00:00,8.389814814814818
 2025-11-16 09:00:00+00:00,9.316203703703705
 2025-11-16 10:00:00+00:00,9.354629629629631
 2025-11-16 11:00:00+00:00,10.327314814814834
 2025-11-16 12:00:00+00:00,10.848148148148164
 2025-11-16 13:00:00+00:00,10.535185185185213
 2025-11-16 14:00:00+00:00,9.850462962962974
 2025-11-16 15:00:00+00:00,9.65254237288136
 2025-11-16 16:00:00+00:00,9.392361111111112
 2025-11-16 17:00:00+00:00,9.074074074074074
 2025-11-16 18:00:00+00:00,8.989197530864198
 2025-11-16 19:00:00+00:00,8.902777777777779
 2025-11-16 20:00:00+00:00,9.430555555555557
 2025-11-16 21:00:00+00:00,8.921296296296298
 2025-11-16 22:00:00+00:00,8.233024691358027
 2025-11-16 23:00:00+00:00,7.8440170940170955
 2025-11-17 00:00:00+00:00,7.66666666666667
 2025-11-17 01:00:00+00:00,7.7473794549266275
 2025-11-17 02:00:00+00:00,7.88888888888889
 2025-11-17 03:00:00+00:00,7.8707070707070725
 2025-11-17 04:00:00+00:00,6.940277777777779
 2025-11-17 05:00:00+00:00,5.725000000000002
 2025-11-17 06:00:00+00:00,4.8699074074074105
 2025-11-17 07:00:00+00:00,2.8541666666666674
 2025-11-17 08:00:00+00:00,2.325462962962965
 2025-11-17 09:00:00+00:00,4.097222222222224
 2025-11-17 10:00:00+00:00,4.830555555555558
 2025-11-17 11:00:00+00:00,5.0791666666666675
 2025-11-17 12:00:00+00:00,5.517129629629631
 2025-11-17 13:00:00+00:00,5.70138888888889
 2025-11-17 14:00:00+00:00,5.449537037037037
 2025-11-17 15:00:00+00:00,3.6583333333333323
 2025-11-17 16:00:00+00:00,3.5584795321637452
 2025-11-17 17:00:00+00:00,2.7207977207977216
 2025-11-17 18:00:00+00:00,1.9255555555555568
 2025-11-17 19:00:00+00:00,1.1938775510204074
 2025-11-17 20:00:00+00:00,0.7311827956989249
 2025-11-17 21:00:00+00:00,-0.19444444444444453
 2025-11-17 22:00:00+00:00,-0.7740740740740741
 2025-11-17 23:00:00+00:00,-0.8475177304964536
 2025-11-18 00:00:00+00:00,-1.220930232558139
 2025-11-18 01:00:00+00:00,-1.2968749999999991
 2025-11-18 02:00:00+00:00,-1.6891891891891873
 2025-11-18 03:00:00+00:00,-1.6560283687943247
 2025-11-18 04:00:00+00:00,-2.1388888888888875
 2025-11-18 05:00:00+00:00,-3.0092592592592577
 2025-11-18 06:00:00+00:00,-2.7250566893424018
 2025-11-18 07:00:00+00:00,-2.1851851851851833
 2025-11-18 08:00:00+00:00,-2.2037037037037
--- a/figures/basic/wind_direction_last_7_days.csv
+++ b/figures/basic/wind_direction_last_7_days.csv
@ -1,170 +0,0 @@
 time,wind_direction
 2025-11-11 08:00:00+00:00,247.00862068965517
 2025-11-11 09:00:00+00:00,239.95
 2025-11-11 10:00:00+00:00,249.35833333333332
 2025-11-11 11:00:00+00:00,251.28333333333333
 2025-11-11 12:00:00+00:00,235.73333333333332
 2025-11-11 13:00:00+00:00,243.49166666666667
 2025-11-11 14:00:00+00:00,230.325
 2025-11-11 15:00:00+00:00,200.49999999999997
 2025-11-11 16:00:00+00:00,136.72826086956522
 2025-11-11 17:00:00+00:00,163.06521739130434
 2025-11-11 18:00:00+00:00,160.29245283018867
 2025-11-11 19:00:00+00:00,160.6
 2025-11-11 20:00:00+00:00,152.52
 2025-11-11 21:00:00+00:00,144.14285714285714
 2025-11-11 22:00:00+00:00,154.36
 2025-11-11 23:00:00+00:00,163.38235294117646
 2025-11-12 00:00:00+00:00,161.69811320754718
 2025-11-12 01:00:00+00:00,159.48245614035088
 2025-11-12 02:00:00+00:00,162.1734693877551
 2025-11-12 03:00:00+00:00,164.61607142857142
 2025-11-12 04:00:00+00:00,180.26724137931035
 2025-11-12 05:00:00+00:00,199.58823529411762
 2025-11-12 06:00:00+00:00,206.90740740740736
 2025-11-12 07:00:00+00:00,198.140350877193
 2025-11-12 08:00:00+00:00,233.28070175438597
 2025-11-12 09:00:00+00:00,242.5909090909091
 2025-11-12 10:00:00+00:00,245.74166666666667
 2025-11-12 11:00:00+00:00,244.15
 2025-11-12 12:00:00+00:00,240.475
 2025-11-12 13:00:00+00:00,247.21666666666667
 2025-11-12 14:00:00+00:00,236.5593220338983
 2025-11-12 15:00:00+00:00,232.0583333333333
 2025-11-12 16:00:00+00:00,181.11627906976742
 2025-11-12 17:00:00+00:00,161.91489361702128
 2025-11-12 18:00:00+00:00,176.3953488372093
 2025-11-12 19:00:00+00:00,109.27551020408163
 2025-11-12 20:00:00+00:00,227.28048780487805
 2025-11-12 21:00:00+00:00,240.38679245283018
 2025-11-12 22:00:00+00:00,228.92372881355934
 2025-11-12 23:00:00+00:00,24.000000000000004
 2025-11-13 00:00:00+00:00,238.3783783783784
 2025-11-13 01:00:00+00:00,226.2535197689996
 2025-11-13 02:00:00+00:00,188.9375
 2025-11-13 03:00:00+00:00,183.25675675675674
 2025-11-13 04:00:00+00:00,180.98837209302323
 2025-11-13 05:00:00+00:00,242.95833333333334
 2025-11-13 06:00:00+00:00,239.95833333333334
 2025-11-13 07:00:00+00:00,229.53333333333333
 2025-11-13 08:00:00+00:00,252.33333333333334
 2025-11-13 09:00:00+00:00,251.24166666666667
 2025-11-13 10:00:00+00:00,241.15254237288136
 2025-11-13 11:00:00+00:00,242.85593220338984
 2025-11-13 12:00:00+00:00,244.20833333333334
 2025-11-13 13:00:00+00:00,238.5
 2025-11-13 14:00:00+00:00,230.075
 2025-11-13 15:00:00+00:00,232.19166666666666
 2025-11-13 16:00:00+00:00,231.28813559322035
 2025-11-13 17:00:00+00:00,230.6346153846154
 2025-11-13 18:00:00+00:00,230.98245614035088
 2025-11-13 19:00:00+00:00,236.85833333333332
 2025-11-13 20:00:00+00:00,232.0084745762712
 2025-11-13 21:00:00+00:00,232.26271186440678
 2025-11-13 22:00:00+00:00,253.89583333333334
 2025-11-13 23:00:00+00:00,110.0
 2025-11-14 00:00:00+00:00,115.23076923076923
 2025-11-14 01:00:00+00:00,162.0
 2025-11-14 02:00:00+00:00,162.0
 2025-11-14 03:00:00+00:00,162.0
 2025-11-14 04:00:00+00:00,242.91860465116278
 2025-11-14 05:00:00+00:00,214.54054054054055
 2025-11-14 06:00:00+00:00,187.40740740740736
 2025-11-14 07:00:00+00:00,240.9406779661017
 2025-11-14 08:00:00+00:00,227.48333333333332
 2025-11-14 09:00:00+00:00,250.35
 2025-11-14 10:00:00+00:00,125.55833333333332
 2025-11-14 11:00:00+00:00,55.19166666666667
 2025-11-14 12:00:00+00:00,99.3265306122449
 2025-11-14 13:00:00+00:00,138.77777777777777
 2025-11-14 14:00:00+00:00,234.42372881355934
 2025-11-14 15:00:00+00:00,228.48333333333332
 2025-11-14 16:00:00+00:00,231.825
 2025-11-14 17:00:00+00:00,228.77118644067798
 2025-11-14 18:00:00+00:00,243.57627118644066
 2025-11-14 19:00:00+00:00,212.39423076923077
 2025-11-14 20:00:00+00:00,211.79166666666666
 2025-11-14 21:00:00+00:00,235.44642857142858
 2025-11-14 22:00:00+00:00,235.05357142857142
 2025-11-14 23:00:00+00:00,240.96666666666667
 2025-11-15 00:00:00+00:00,233.89166666666668
 2025-11-15 01:00:00+00:00,240.85
 2025-11-15 02:00:00+00:00,239.08333333333334
 2025-11-15 03:00:00+00:00,236.62
 2025-11-15 04:00:00+00:00,227.07627118644066
 2025-11-15 05:00:00+00:00,210.51785714285714
 2025-11-15 06:00:00+00:00,229.07758620689654
 2025-11-15 07:00:00+00:00,231.2155172413793
 2025-11-15 08:00:00+00:00,160.27966101694915
 2025-11-15 09:00:00+00:00,90.825
 2025-11-15 10:00:00+00:00,0.0
 2025-11-15 11:00:00+00:00,14.025
 2025-11-15 12:00:00+00:00,108.15833333333333
 2025-11-15 13:00:00+00:00,241.16666666666666
 2025-11-15 14:00:00+00:00,156.99166666666667
 2025-11-15 15:00:00+00:00,119.84166666666667
 2025-11-15 16:00:00+00:00,217.02380952380952
 2025-11-15 17:00:00+00:00,129.375
 2025-11-15 18:00:00+00:00,161.28571428571428
 2025-11-15 19:00:00+00:00,196.0
 2025-11-15 20:00:00+00:00,232.69444444444446
 2025-11-15 21:00:00+00:00,212.79166666666663
 2025-11-15 22:00:00+00:00,209.5
 2025-11-15 23:00:00+00:00,221.99999999999997
 2025-11-16 00:00:00+00:00,252.1757175012371
 2025-11-16 01:00:00+00:00,160.11111111111111
 2025-11-16 02:00:00+00:00,166.9090909090909
 2025-11-16 03:00:00+00:00,176.79090909090908
 2025-11-16 04:00:00+00:00,163.9268292682927
 2025-11-16 05:00:00+00:00,123.6
 2025-11-16 06:00:00+00:00,175.12264150943398
 2025-11-16 07:00:00+00:00,133.84166666666667
 2025-11-16 08:00:00+00:00,197.88333333333333
 2025-11-16 09:00:00+00:00,126.83333333333333
 2025-11-16 10:00:00+00:00,109.0
 2025-11-16 11:00:00+00:00,54.275
 2025-11-16 12:00:00+00:00,24.000000000000004
 2025-11-16 13:00:00+00:00,216.91666666666666
 2025-11-16 14:00:00+00:00,255.75833333333333
 2025-11-16 15:00:00+00:00,135.71186440677965
 2025-11-16 16:00:00+00:00,166.875
 2025-11-16 17:00:00+00:00,192.2380952380952
 2025-11-16 18:00:00+00:00,233.72222222222223
 2025-11-16 19:00:00+00:00,176.125
 2025-11-16 20:00:00+00:00,181.91666666666666
 2025-11-16 21:00:00+00:00,226.75
 2025-11-16 22:00:00+00:00,304.9074074074074
 2025-11-16 23:00:00+00:00,295.86538461538464
 2025-11-17 00:00:00+00:00,298.90816326530614
 2025-11-17 01:00:00+00:00,259.188679245283
 2025-11-17 02:00:00+00:00,266.3833333333333
 2025-11-17 03:00:00+00:00,283.06363636363636
 2025-11-17 04:00:00+00:00,245.325
 2025-11-17 05:00:00+00:00,258.65833333333336
 2025-11-17 06:00:00+00:00,227.33333333333334
 2025-11-17 07:00:00+00:00,134.81666666666666
 2025-11-17 08:00:00+00:00,277.5
 2025-11-17 09:00:00+00:00,276.42857142857144
 2025-11-17 10:00:00+00:00,267.60833333333335
 2025-11-17 11:00:00+00:00,267.5833333333333
 2025-11-17 12:00:00+00:00,290.65
 2025-11-17 13:00:00+00:00,294.2416666666667
 2025-11-17 14:00:00+00:00,296.76666666666665
 2025-11-17 15:00:00+00:00,294.69166666666666
 2025-11-17 16:00:00+00:00,305.49122807017545
 2025-11-17 17:00:00+00:00,337.28205128205127
 2025-11-17 18:00:00+00:00,277.09999999999997
 2025-11-17 19:00:00+00:00,236.91836734693874
 2025-11-17 20:00:00+00:00,202.0806451612903
 2025-11-17 21:00:00+00:00,195.0285714285714
 2025-11-17 22:00:00+00:00,154.74444444444444
 2025-11-17 23:00:00+00:00,168.82978723404256
 2025-11-18 00:00:00+00:00,168.0
 2025-11-18 01:00:00+00:00,172.875
 2025-11-18 02:00:00+00:00,167.02702702702703
 2025-11-18 03:00:00+00:00,163.7340425531915
 2025-11-18 04:00:00+00:00,167.73529411764707
 2025-11-18 05:00:00+00:00,168.0
 2025-11-18 06:00:00+00:00,171.79591836734693
 2025-11-18 07:00:00+00:00,168.55
 2025-11-18 08:00:00+00:00,168.0
--- a/figures/basic/wind_speed_last_7_days.csv
+++ b/figures/basic/wind_speed_last_7_days.csv
@ -1,170 +0,0 @@
 time,wind_speed
 2025-11-11 08:00:00+00:00,4.973705379310345
 2025-11-11 09:00:00+00:00,5.52004992
 2025-11-11 10:00:00+00:00,5.595152639999999
 2025-11-11 11:00:00+00:00,5.0479756799999995
 2025-11-11 12:00:00+00:00,4.88301792
 2025-11-11 13:00:00+00:00,5.2102512
 2025-11-11 14:00:00+00:00,2.36841792
 2025-11-11 15:00:00+00:00,0.0402336
 2025-11-11 16:00:00+00:00,0.6139997217391304
 2025-11-11 17:00:00+00:00,0.9813499826086958
 2025-11-11 18:00:00+00:00,1.2054897509433962
 2025-11-11 19:00:00+00:00,1.00181664
 2025-11-11 20:00:00+00:00,0.9994026239999999
 2025-11-11 21:00:00+00:00,0.8889709714285714
 2025-11-11 22:00:00+00:00,1.197888384
 2025-11-11 23:00:00+00:00,0.870939105882353
 2025-11-12 00:00:00+00:00,1.7049936905660377
 2025-11-12 01:00:00+00:00,2.8347041684210526
 2025-11-12 02:00:00+00:00,3.1989817469387756
 2025-11-12 03:00:00+00:00,2.0389813714285716
 2025-11-12 04:00:00+00:00,2.5139063172413794
 2025-11-12 05:00:00+00:00,1.715055811764706
 2025-11-12 06:00:00+00:00,1.5303669333333334
 2025-11-12 07:00:00+00:00,2.0046214736842107
 2025-11-12 08:00:00+00:00,4.1588837052631575
 2025-11-12 09:00:00+00:00,6.605625600000001
 2025-11-12 10:00:00+00:00,7.79593056
 2025-11-12 11:00:00+00:00,6.350203199999999
 2025-11-12 12:00:00+00:00,7.30373952
 2025-11-12 13:00:00+00:00,6.31801632
 2025-11-12 14:00:00+00:00,3.342798427118644
 2025-11-12 15:00:00+00:00,0.0
 2025-11-12 16:00:00+00:00,0.5426857674418605
 2025-11-12 17:00:00+00:00,0.9912874212765957
 2025-11-12 18:00:00+00:00,0.9038525023255813
 2025-11-12 19:00:00+00:00,0.6322422857142858
 2025-11-12 20:00:00+00:00,0.2551399024390244
 2025-11-12 21:00:00+00:00,2.090628950943396
 2025-11-12 22:00:00+00:00,1.4047663728813558
 2025-11-12 23:00:00+00:00,0.0
 2025-11-13 00:00:00+00:00,0.0
 2025-11-13 01:00:00+00:00,1.9103509333333333
 2025-11-13 02:00:00+00:00,0.1391412
 2025-11-13 03:00:00+00:00,0.0
 2025-11-13 04:00:00+00:00,0.8888818604651162
 2025-11-13 05:00:00+00:00,5.5589424
 2025-11-13 06:00:00+00:00,8.65558848
 2025-11-13 07:00:00+00:00,9.86259648
 2025-11-13 08:00:00+00:00,9.02305536
 2025-11-13 09:00:00+00:00,8.524158719999999
 2025-11-13 10:00:00+00:00,8.282666196610169
 2025-11-13 11:00:00+00:00,8.476333016949154
 2025-11-13 12:00:00+00:00,6.3434976
 2025-11-13 13:00:00+00:00,6.0108998399999995
 2025-11-13 14:00:00+00:00,8.43966816
 2025-11-13 15:00:00+00:00,5.68232544
 2025-11-13 16:00:00+00:00,4.4243321491525425
 2025-11-13 17:00:00+00:00,1.4035336615384617
 2025-11-13 18:00:00+00:00,5.594587957894737
 2025-11-13 19:00:00+00:00,5.19684
 2025-11-13 20:00:00+00:00,5.674983376271187
 2025-11-13 21:00:00+00:00,5.634067850847457
 2025-11-13 22:00:00+00:00,0.7141464000000001
 2025-11-13 23:00:00+00:00,0.0
 2025-11-14 00:00:00+00:00,0.0
 2025-11-14 01:00:00+00:00,0.0
 2025-11-14 02:00:00+00:00,0.0
 2025-11-14 03:00:00+00:00,0.0
 2025-11-14 04:00:00+00:00,0.1665483906976744
 2025-11-14 05:00:00+00:00,0.07394283243243244
 2025-11-14 06:00:00+00:00,0.1475232
 2025-11-14 07:00:00+00:00,0.0
 2025-11-14 08:00:00+00:00,0.0
 2025-11-14 09:00:00+00:00,2.63798304
 2025-11-14 10:00:00+00:00,0.0
 2025-11-14 11:00:00+00:00,0.11667744
 2025-11-14 12:00:00+00:00,0.06732969795918367
 2025-11-14 13:00:00+00:00,0.12219093333333335
 2025-11-14 14:00:00+00:00,5.661344867796609
 2025-11-14 15:00:00+00:00,5.833872
 2025-11-14 16:00:00+00:00,6.4843152
 2025-11-14 17:00:00+00:00,4.365686562711865
 2025-11-14 18:00:00+00:00,3.8010523118644066
 2025-11-14 19:00:00+00:00,0.733489476923077
 2025-11-14 20:00:00+00:00,0.0972312
 2025-11-14 21:00:00+00:00,3.419856
 2025-11-14 22:00:00+00:00,6.855518057142858
 2025-11-14 23:00:00+00:00,7.08379584
 2025-11-15 00:00:00+00:00,6.4641984
 2025-11-15 01:00:00+00:00,6.46956288
 2025-11-15 02:00:00+00:00,5.25450816
 2025-11-15 03:00:00+00:00,2.159739648
 2025-11-15 04:00:00+00:00,4.705285423728813
 2025-11-15 05:00:00+00:00,2.5677658285714284
 2025-11-15 06:00:00+00:00,4.790573131034483
 2025-11-15 07:00:00+00:00,6.766181627586207
 2025-11-15 08:00:00+00:00,2.6117743728813556
 2025-11-15 09:00:00+00:00,0.18909792
 2025-11-15 10:00:00+00:00,0.017434559999999998
 2025-11-15 11:00:00+00:00,0.10460735999999998
 2025-11-15 12:00:00+00:00,0.0
 2025-11-15 13:00:00+00:00,0.0
 2025-11-15 14:00:00+00:00,1.13592864
 2025-11-15 15:00:00+00:00,2.02911456
 2025-11-15 16:00:00+00:00,1.0403259428571427
 2025-11-15 17:00:00+00:00,0.0
 2025-11-15 18:00:00+00:00,0.0
 2025-11-15 19:00:00+00:00,0.0
 2025-11-15 20:00:00+00:00,0.0
 2025-11-15 21:00:00+00:00,0.0
 2025-11-15 22:00:00+00:00,0.0
 2025-11-15 23:00:00+00:00,0.0
 2025-11-16 00:00:00+00:00,0.0
 2025-11-16 01:00:00+00:00,0.0
 2025-11-16 02:00:00+00:00,1.19530368
 2025-11-16 03:00:00+00:00,0.85441536
 2025-11-16 04:00:00+00:00,0.6457002146341464
 2025-11-16 05:00:00+00:00,0.22990628571428573
 2025-11-16 06:00:00+00:00,1.8932565735849056
 2025-11-16 07:00:00+00:00,0.43452288
 2025-11-16 08:00:00+00:00,1.7233392
 2025-11-16 09:00:00+00:00,1.20432576
 2025-11-16 10:00:00+00:00,0.0
 2025-11-16 11:00:00+00:00,0.0
 2025-11-16 12:00:00+00:00,0.0
 2025-11-16 13:00:00+00:00,0.0
 2025-11-16 14:00:00+00:00,0.0
 2025-11-16 15:00:00+00:00,0.0
 2025-11-16 16:00:00+00:00,0.0
 2025-11-16 17:00:00+00:00,0.0
 2025-11-16 18:00:00+00:00,0.0
 2025-11-16 19:00:00+00:00,0.0
 2025-11-16 20:00:00+00:00,0.0
 2025-11-16 21:00:00+00:00,0.7543799999999999
 2025-11-16 22:00:00+00:00,1.7225941333333334
 2025-11-16 23:00:00+00:00,1.587679753846154
 2025-11-17 00:00:00+00:00,0.6634438530612244
 2025-11-17 01:00:00+00:00,1.9084390641509432
 2025-11-17 02:00:00+00:00,2.4207216
 2025-11-17 03:00:00+00:00,2.4549811200000002
 2025-11-17 04:00:00+00:00,2.95851072
 2025-11-17 05:00:00+00:00,4.325112
 2025-11-17 06:00:00+00:00,3.8999769599999996
 2025-11-17 07:00:00+00:00,1.62409632
 2025-11-17 08:00:00+00:00,0.27627072
 2025-11-17 09:00:00+00:00,2.118011657142857
 2025-11-17 10:00:00+00:00,4.2714672
 2025-11-17 11:00:00+00:00,4.55846688
 2025-11-17 12:00:00+00:00,5.01042432
 2025-11-17 13:00:00+00:00,5.455676159999999
 2025-11-17 14:00:00+00:00,4.75561152
 2025-11-17 15:00:00+00:00,1.0366857600000001
 2025-11-17 16:00:00+00:00,1.6898112
 2025-11-17 17:00:00+00:00,0.44153796923076927
 2025-11-17 18:00:00+00:00,1.493471232
 2025-11-17 19:00:00+00:00,1.6306924408163266
 2025-11-17 20:00:00+00:00,1.0512650322580646
 2025-11-17 21:00:00+00:00,0.7540926171428571
 2025-11-17 22:00:00+00:00,1.09435392
 2025-11-17 23:00:00+00:00,1.7017956765957447
 2025-11-18 00:00:00+00:00,0.8664258976744186
 2025-11-18 01:00:00+00:00,1.207008
 2025-11-18 02:00:00+00:00,1.3788163459459462
 2025-11-18 03:00:00+00:00,1.8028076936170214
 2025-11-18 04:00:00+00:00,0.5632704
 2025-11-18 05:00:00+00:00,0.0
 2025-11-18 06:00:00+00:00,0.645379787755102
 2025-11-18 07:00:00+00:00,0.64641984
 2025-11-18 08:00:00+00:00,0.0
--- a/meteo/correlation_presets.py
+++ b/meteo/correlation_presets.py
@ -0,0 +1,119 @@
 """Jeux de paires et scénarios standardisés pour explorer les corrélations."""
 from __future__ import annotations
 from dataclasses import dataclass
 from typing import Callable, Sequence
 import numpy as np
 HexbinReducer = Callable[[np.ndarray], float]
 HEXBIN_REDUCE_FUNCTIONS: dict[str, HexbinReducer] = {
    "mean": np.mean,
    "median": np.median,
    "max": np.max,
 }
 HEXBIN_REDUCE_LABELS: dict[str, str] = {
    "mean": "moyenne",
    "median": "médiane",
    "max": "maximum",
 }
@dataclass(frozen=True)
 class HexbinScenario:
    """Décrit un cas d'usage pour un nuage hexbin coloré par une 3ᵉ variable."""
    key_x: str
    key_y: str
    key_color: str
    filename: str
    description: str
    reduce: str = "mean"
    gridsize: int = 60
    mincnt: int = 5
    def get_reduce_func(self) -> HexbinReducer:
        """Retourne la fonction de réduction associée (moyenne, médiane, ...)."""
        return HEXBIN_REDUCE_FUNCTIONS.get(self.reduce, np.mean)
    def get_reduce_label(self) -> str:
        """Libellé français de la fonction de réduction utilisée."""
        return HEXBIN_REDUCE_LABELS.get(self.reduce, self.reduce)
 DEFAULT_LAGGED_PAIRS: Sequence[tuple[str, str]] = (
    ("temperature", "humidity"),
    ("temperature", "rain_rate"),
    ("pressure", "rain_rate"),
    ("pressure", "wind_speed"),
    ("pressure", "illuminance"),
    ("illuminance", "temperature"),
    ("humidity", "rain_rate"),
 )
 DEFAULT_ROLLING_PAIRS: Sequence[tuple[str, str]] = (
    ("temperature", "humidity"),
    ("pressure", "rain_rate"),
    ("pressure", "wind_speed"),
    ("illuminance", "temperature"),
    ("humidity", "rain_rate"),
 )
 DEFAULT_HEXBIN_SCENARIOS: Sequence[HexbinScenario] = (
    HexbinScenario(
        key_x="temperature",
        key_y="humidity",
        key_color="rain_rate",
        filename="hexbin_temp_humidity_color_rain.png",
        description=(
            "Mettre en évidence comment l'humidité relative plafonne lorsque la température chute "
            "et comment les épisodes de pluie se situent dans une bande restreinte."
        ),
        reduce="max",
        gridsize=50,
        mincnt=8,
    ),
    HexbinScenario(
        key_x="pressure",
        key_y="rain_rate",
        key_color="wind_speed",
        filename="hexbin_pressure_rain_color_wind.png",
        description=(
            "Vérifier si des rafales accompagnent vraiment les chutes de pression. "
            "On s'attend à voir beaucoup de cases vides : la corrélation est loin d'être systématique."
        ),
        reduce="median",
        gridsize=45,
        mincnt=5,
    ),
    HexbinScenario(
        key_x="illuminance",
        key_y="humidity",
        key_color="temperature",
        filename="hexbin_lux_humidity_color_temp.png",
        description=(
            "Explorer le cycle jour/nuit : l'humidité monte quand l'illuminance chute, "
            "mais cela n'implique pas toujours une baisse rapide de température."
        ),
        reduce="mean",
        gridsize=55,
        mincnt=6,
    ),
 )
 __all__ = [
    "HexbinScenario",
    "DEFAULT_LAGGED_PAIRS",
    "DEFAULT_ROLLING_PAIRS",
    "DEFAULT_HEXBIN_SCENARIOS",
 ]
--- a/meteo/plots/init.py
+++ b/meteo/plots/init.py
@ -11,6 +11,7 @@ from .rain import plot_daily_rainfall_hyetograph, plot_rainfall_by_season
 from .relationships import (
    plot_event_composite,
    plot_hexbin_with_third_variable,
    plot_pairwise_relationship_grid,
    plot_scatter_pair,
 )
 from .seasonal_profiles import (
@ -37,6 +38,7 @@ __all__ = [
    "plot_rainfall_by_season",
    "plot_event_composite",
    "plot_hexbin_with_third_variable",
    "plot_pairwise_relationship_grid",
    "plot_scatter_pair",
    "plot_daylight_hours",
    "plot_diurnal_cycle",
--- a/meteo/plots/base.py
+++ b/meteo/plots/base.py
@ -21,6 +21,16 @@ def export_plot_dataset(data: Any, output_path: str | Path, *, suffix: str = ".c
    output_path = Path(output_path)
    dataset_path = output_path.with_suffix(suffix)
    # If the image is exported under a "figures" directory, keep the dataset in
    # an equivalent "data" directory to avoid mixing assets.
    parts = list(dataset_path.parts)
    for idx, part in enumerate(parts):
        if part == "figures":
            parts[idx] = "data"
            dataset_path = Path(*parts)
            break
    dataset_path.parent.mkdir(parents=True, exist_ok=True)
    def _normalize(value: Any, *, default_name: str = "value") -> pd.DataFrame:
--- a/meteo/plots/relationships.py
+++ b/meteo/plots/relationships.py
@ -13,7 +13,7 @@ import pandas as pd
 from .base import export_plot_dataset
 from meteo.variables import Variable
-__all__ = ['plot_scatter_pair', 'plot_hexbin_with_third_variable', 'plot_event_composite']
+__all__ = ['plot_scatter_pair', 'plot_pairwise_relationship_grid', 'plot_hexbin_with_third_variable', 'plot_event_composite']
 def plot_scatter_pair(
@ -193,6 +193,87 @@ def plot_scatter_pair(
    return output_path.resolve()
 def plot_pairwise_relationship_grid(
    df: pd.DataFrame,
    variables: Sequence[Variable],
    output_path: str | Path,
    *,
    sample_step: int = 10,
    hist_bins: int = 40,
    scatter_kwargs: dict | None = None,
 ) -> Path:
    """Trace un tableau de nuages de points exhaustif (sans doublon)."""
    output_path = Path(output_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    if not variables:
        raise ValueError("La liste de variables ne peut pas être vide.")
    columns = [v.column for v in variables]
    for col in columns:
        if col not in df.columns:
            raise KeyError(f"Colonne absente dans le DataFrame : {col}")
    df_pairs = df[columns].dropna()
    if df_pairs.empty:
        raise RuntimeError("Aucune ligne complète pour générer les nuages de points.")
    if sample_step > 1:
        df_pairs = df_pairs.iloc[::sample_step, :]
    export_plot_dataset(df_pairs, output_path)
    n = len(variables)
    fig_size = max(3.0, 1.8 * n)
    fig, axes = plt.subplots(n, n, figsize=(fig_size, fig_size), squeeze=False)
    default_scatter_kwargs = {"s": 5, "alpha": 0.5}
    scatter_kwargs = {**default_scatter_kwargs, **(scatter_kwargs or {})}
    for row_idx, var_y in enumerate(variables):
        for col_idx, var_x in enumerate(variables):
            ax = axes[row_idx][col_idx]
            if row_idx < col_idx:
                # Triangle supérieur vide pour éviter les doublons
                ax.set_visible(False)
                continue
            if row_idx == col_idx:
                series = df_pairs[var_x.column].dropna()
                if series.empty:
                    ax.text(0.5, 0.5, "(vide)", ha="center", va="center")
                    ax.set_axis_off()
                else:
                    bins = min(hist_bins, max(5, series.nunique()))
                    ax.hist(series, bins=bins, color="tab:blue", alpha=0.7)
                ax.set_ylabel("")
            else:
                ax.scatter(
                    df_pairs[var_x.column],
                    df_pairs[var_y.column],
                    **scatter_kwargs,
                )
            if row_idx == n - 1:
                ax.set_xlabel(var_x.label)
            else:
                ax.set_xticklabels([])
            if col_idx == 0:
                ax.set_ylabel(var_y.label)
            else:
                ax.set_yticklabels([])
    fig.suptitle("Matrice de corrélations simples (nuages de points)")
    fig.tight_layout(rect=[0, 0, 1, 0.97])
    fig.savefig(output_path, dpi=150)
    plt.close(fig)
    return output_path.resolve()
 def plot_hexbin_with_third_variable(
    df: pd.DataFrame,
    var_x: Variable,
--- a/scripts/plot_hexbin_explorations.py
+++ b/scripts/plot_hexbin_explorations.py
@ -1,128 +0,0 @@
 # scripts/plot_hexbin_explorations.py
 from __future__ import annotations
 from pathlib import Path
 from typing import Callable
 import numpy as np
 from meteo.dataset import load_raw_csv
 from meteo.variables import VARIABLES_BY_KEY
 from meteo.plots import plot_hexbin_with_third_variable
 CSV_PATH = Path("data/weather_minutely.csv")
 OUTPUT_DIR = Path("figures/hexbin_explorations")
 REDUCE_FUNCTIONS: dict[str, Callable[[np.ndarray], float]] = {
    "mean": np.mean,
    "median": np.median,
    "max": np.max,
 }
 REDUCE_LABEL_FR: dict[str, str] = {
    "mean": "moyenne",
    "median": "médiane",
    "max": "maximum",
 }
 # Chaque scénario illustre soit une corrélation bien connue,
 # soit l'absence de structure entre variables.
 HEXBIN_SCENARIOS: list[dict[str, object]] = [
    {
        "x": "temperature",
        "y": "humidity",
        "color": "rain_rate",
        "filename": "hexbin_temp_humidity_color_rain.png",
        "description": (
            "Mettre en évidence comment l'humidité relative plafonne lorsque la température chute "
            "et comment les épisodes de pluie se situent dans une bande restreinte."
        ),
        "reduce": "max",
        "gridsize": 50,
        "mincnt": 8,
    },
    {
        "x": "pressure",
        "y": "rain_rate",
        "color": "wind_speed",
        "filename": "hexbin_pressure_rain_color_wind.png",
        "description": (
            "Vérifier si des rafales accompagnent vraiment les chutes de pression. "
            "On s'attend à voir beaucoup de cases vides : la corrélation est loin d'être systématique."
        ),
        "reduce": "median",
        "gridsize": 45,
        "mincnt": 5,
    },
    {
        "x": "illuminance",
        "y": "humidity",
        "color": "temperature",
        "filename": "hexbin_lux_humidity_color_temp.png",
        "description": (
            "Explorer le cycle jour/nuit : l'humidité monte quand l'illuminance chute, "
            "mais cela n'implique pas toujours une baisse rapide de température."
        ),
        "reduce": "mean",
        "gridsize": 55,
        "mincnt": 6,
    },
 ]
 def main() -> None:
    if not CSV_PATH.exists():
        print(f"⚠ Fichier introuvable : {CSV_PATH}")
        return
    df = load_raw_csv(CSV_PATH)
    print(f"Dataset minuté chargé : {CSV_PATH}")
    print(f"  Lignes   : {len(df)}")
    print(f"  Colonnes : {list(df.columns)}")
    print()
    for scenario in HEXBIN_SCENARIOS:
        key_x = scenario["x"]
        key_y = scenario["y"]
        key_color = scenario["color"]
        var_x = VARIABLES_BY_KEY[key_x]
        var_y = VARIABLES_BY_KEY[key_y]
        var_color = VARIABLES_BY_KEY[key_color]
        filename = scenario["filename"]
        output_path = OUTPUT_DIR / filename
        reduce_name = scenario.get("reduce", "mean")
        reduce_func = REDUCE_FUNCTIONS.get(reduce_name, np.mean)
        reduce_label = REDUCE_LABEL_FR.get(reduce_name, reduce_name)
        gridsize = int(scenario.get("gridsize", 60))
        mincnt = int(scenario.get("mincnt", 5))
        description = scenario["description"]
        print(f"→ Hexbin {var_y.key} vs {var_x.key} (couleur = {var_color.key})")
        print(f"   {description}")
        plot_hexbin_with_third_variable(
            df=df,
            var_x=var_x,
            var_y=var_y,
            var_color=var_color,
            output_path=output_path,
            gridsize=gridsize,
            mincnt=mincnt,
            reduce_func=reduce_func,
            reduce_func_label=reduce_label,
            cmap="magma",
        )
        print(f"   ✔ Graphique enregistré : {output_path}")
        print()
    print("✔ Tous les graphiques hexbin ont été générés.")
 if __name__ == "__main__":
    main()
--- a/scripts/plot_seasonal_overview.py
+++ b/scripts/plot_seasonal_overview.py
@ -36,7 +36,7 @@ def main() -> None:
    print()
    if "season" not in df.columns:
-        print("⚠ La colonne 'season' est absente. Relancez scripts.make_minutely_dataset.")
+        print('⚠ La colonne "season" est absente. Relancez python "docs/02 - Préparation des données/scripts/make_minutely_dataset.py".')
        return
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
--- a/scripts/run_all_plots.py
+++ b/scripts/run_all_plots.py
@ -4,78 +4,124 @@ from __future__ import annotations
 import argparse
 import subprocess
 import sys
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Iterable
-PLOT_MODULES: tuple[str, ...] = (
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
-    "scripts.plot_basic_variables",
+
-    "scripts.plot_calendar_overview",
+
-    "scripts.plot_all_pairwise_scatter",
+@dataclass(frozen=True)
-    "scripts.plot_correlation_heatmap",
+class PlotScript:
-    "scripts.plot_diurnal_cycle",
+    key: str
-    "scripts.plot_hexbin_explorations",
+    path: Path
-    "scripts.plot_illuminance_focus",
+
-    "scripts.plot_lagged_correlations",
+    def display_path(self) -> str:
-    "scripts.plot_monthly_patterns",
+        try:
-    "scripts.plot_rain_event_composites",
+            return str(self.path.relative_to(PROJECT_ROOT))
-    "scripts.plot_rain_hyetograph",
+        except ValueError:
-    "scripts.plot_rolling_correlation_heatmap",
+            return str(self.path)
-    "scripts.plot_seasonal_overview",
+
-    "scripts.plot_sun_elevation_relationships",
+
-    "scripts.plot_wind_conditionals",
+PLOT_SCRIPTS: tuple[PlotScript, ...] = (
-    "scripts.plot_wind_rose",
+    PlotScript(
-    "scripts.plot_wind_rose_rain",
+        "plot_basic_variables",
        PROJECT_ROOT / "docs" / "03 - Premiers graphiques" / "scripts" / "plot_basic_variables.py",
    ),
    PlotScript("plot_calendar_overview", PROJECT_ROOT / "scripts" / "plot_calendar_overview.py"),
    PlotScript(
        "plot_all_pairwise_scatter",
        PROJECT_ROOT / "docs" / "04 - Corrélations binaires" / "scripts" / "plot_all_pairwise_scatter.py",
    ),
    PlotScript(
        "plot_correlation_heatmap",
        PROJECT_ROOT / "docs" / "04 - Corrélations binaires" / "scripts" / "plot_correlation_heatmap.py",
    ),
    PlotScript("plot_diurnal_cycle", PROJECT_ROOT / "scripts" / "plot_diurnal_cycle.py"),
    PlotScript("plot_illuminance_focus", PROJECT_ROOT / "scripts" / "plot_illuminance_focus.py"),
    PlotScript(
        "plot_hexbin_explorations",
        PROJECT_ROOT / "docs" / "06 - Corrélations multiples" / "scripts" / "plot_hexbin_explorations.py",
    ),
    PlotScript("plot_monthly_patterns", PROJECT_ROOT / "scripts" / "plot_monthly_patterns.py"),
    PlotScript("plot_rain_event_composites", PROJECT_ROOT / "scripts" / "plot_rain_event_composites.py"),
    PlotScript("plot_rain_hyetograph", PROJECT_ROOT / "scripts" / "plot_rain_hyetograph.py"),
    PlotScript(
        "plot_lagged_correlations",
        PROJECT_ROOT / "docs" / "05 - Corrélations binaires avancées" / "scripts" / "plot_lagged_correlations.py",
    ),
    PlotScript(
        "plot_rolling_correlation_heatmap",
        PROJECT_ROOT / "docs" / "05 - Corrélations binaires avancées" / "scripts" / "plot_rolling_correlation_heatmap.py",
    ),
    PlotScript("plot_seasonal_overview", PROJECT_ROOT / "scripts" / "plot_seasonal_overview.py"),
    PlotScript(
        "plot_sun_elevation_relationships",
        PROJECT_ROOT / "scripts" / "plot_sun_elevation_relationships.py",
    ),
    PlotScript("plot_wind_conditionals", PROJECT_ROOT / "scripts" / "plot_wind_conditionals.py"),
    PlotScript("plot_wind_rose", PROJECT_ROOT / "scripts" / "plot_wind_rose.py"),
    PlotScript("plot_wind_rose_rain", PROJECT_ROOT / "scripts" / "plot_wind_rose_rain.py"),
 )
-def _normalize_module(name: str) -> str:
+def _normalize_key(name: str) -> str:
-    name = name.strip()
+    normalized = name.strip()
-    if not name:
+    if not normalized:
-        raise ValueError("Nom de module vide.")
+        raise ValueError("Nom de script vide.")
-    return name if name.startswith("scripts.") else f"scripts.{name}"
+    if normalized.startswith("scripts."):
        normalized = normalized.split(".", 1)[1]
    return normalized
-def iter_modules(selected: Iterable[str] | None) -> list[str]:
+def iter_scripts(selected: Iterable[str] | None) -> list[PlotScript]:
    if not selected:
-        return list(PLOT_MODULES)
+        return list(PLOT_SCRIPTS)
-    normalized = [_normalize_module(name) for name in selected]
+    mapping = {script.key: script for script in PLOT_SCRIPTS}
-    modules: list[str] = []
+    normalized = [_normalize_key(name) for name in selected]
    scripts: list[PlotScript] = []
    missing: list[str] = []
-    for module in normalized:
+    for key in normalized:
-        if module in PLOT_MODULES:
+        script = mapping.get(key)
-            modules.append(module)
+        if script is None:
            missing.append(key)
        else:
-            missing.append(module)
+            scripts.append(script)
    if missing:
-        available = "\n  - ".join(("",) + PLOT_MODULES)
+        available = "\n  - ".join(("",) + tuple(script.key for script in PLOT_SCRIPTS))
        missing_list = "\n  - ".join(("",) + tuple(missing))
        raise SystemExit(
            "\n".join(
                [
-                    "Les modules suivants ne sont pas reconnus :",
+                    "Les scripts suivants ne sont pas reconnus :",
                    missing_list,
                    "",
-                    "Modules disponibles :",
+                    "Scripts disponibles :",
                    available,
                ]
            )
        )
-    return modules
+    return scripts
-def run_module(module: str) -> bool:
+def run_script(script: PlotScript) -> bool:
-    cmd = [sys.executable, "-m", module]
+    if not script.path.exists():
-    print(f"\n=== {module} ===")
+        print(f"✘ Script introuvable : {script.display_path()}")
        return False
    cmd = [sys.executable, str(script.path)]
    print(f"\n=== {script.key} ({script.display_path()}) ===")
    result = subprocess.run(cmd, check=False)
    if result.returncode == 0:
-        print(f"✔ {module} terminé avec succès.")
+        print(f"✔ {script.key} terminé avec succès.")
        return True
-    print(f"✘ {module} a échoué (code {result.returncode}).")
+    print(f"✘ {script.key} a échoué (code {result.returncode}).")
    return False
@ -84,21 +130,21 @@ def main(argv: list[str] | None = None) -> int:
    parser.add_argument(
        "--only",
        nargs="*",
-        help="Limiter l'exécution à certains modules (ex: plot_temperature).",
+        help="Limiter l'exécution à certains scripts (ex: plot_temperature).",
    )
    args = parser.parse_args(argv)
-    modules = iter_modules(args.only)
+    scripts = iter_scripts(args.only)
-    print(f"Exécution de {len(modules)} script(s) de tracé…")
+    print(f"Exécution de {len(scripts)} script(s) de tracé…")
    success_count = 0
-    for module in modules:
+    for script in scripts:
-        if run_module(module):
+        if run_script(script):
            success_count += 1
    print()
-    print(f"Scripts réussis : {success_count}/{len(modules)}")
+    print(f"Scripts réussis : {success_count}/{len(scripts)}")
-    failed = len(modules) - success_count
+    failed = len(scripts) - success_count
    if failed:
        print(f"⚠ {failed} script(s) ont échoué. Consultez les messages ci-dessus.")
        return 1