Exploration des relations binaires
@ -4,10 +4,6 @@ On peut désormais tracer nos premiers graphiques simples et bruts.
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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.
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Les fichiers CSV correspondant à chaque figure sont conservés dans `data/` dans ce dossier.
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Les graphiques couvrent maintenant toute la période disponible dans `data/weather_minutely.csv`.
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Une agrégation automatique réduit le nombre de points pour rester lisible (plus de courbes "peignes"), et l'axe des dates utilise un format compact qui évite tout chevauchement de labels.
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On peut au besoin restreindre la période avec `--days` ou imposer une fréquence d'agrégation avec `--resample`.
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```shell
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python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py"
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```
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@ -30,9 +26,6 @@ python "docs/03 - Premiers graphiques/scripts/plot_basic_variables.py"
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## Vues calendrier
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Les vues calendrier permettent de visualiser, jour par jour, les cumuls ou moyennes quotidiennes sur la dernière année complète disponible.
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Les images générées sont stockées dans `figures/calendar/` et les CSV correspondants dans `data/calendar/`.
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```shell
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python "docs/03 - Premiers graphiques/scripts/plot_calendar_overview.py"
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```
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@ -48,5 +41,3 @@ python "docs/03 - Premiers graphiques/scripts/plot_calendar_overview.py"
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Ces vues, bien que simples en principe, mettent déjà mieux en évidence les fluctuations au cours du temps.
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@ -60,74 +60,18 @@ python "docs/04 - Corrélations binaires/scripts/plot_pairwise_time_series.py"
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## Nuages de points
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```shell
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python "docs/04 - Corrélations binaires/scripts/plot_all_pairwise_scatter.py"
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```
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## Matrices de corrélation (instantané, signé)
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Le calcul des coefficients de Pearson et de Spearman peut nous donner une indication numérique des corrélations entre les différentes variables.
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Cette information peut nous aiguiller sur des relations spécifiques (ou au contraire, nous induire en erreur...).
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```shell
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python "docs/04 - Corrélations binaires/scripts/plot_correlation_heatmap.py" --transform absolute --upper-only
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python "docs/04 - Corrélations binaires/scripts/plot_correlation_heatmap.py" --transform=identity --upper-only
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```
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Le signe et l'intensité des coefficients montrent à quel point deux variables bougent ensemble au même instant (co-mouvement linéaire pour Pearson, monotone pour Spearman).
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Cette matrice sert donc surtout de carte globale : repérer rapidement les couples très corrélés ou indiquer un lien physique évident, mettre en alerte des variables à forte corrélation qui pourraient masquer d'autres effets (saisonnalité, cycle jour/nuit), et choisir quelles paires méritent qu'on teste des décalages temporels ou des relations non linéaires dans la suite.
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@ -1,52 +0,0 @@
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# scripts/plot_all_pairwise_scatter.py
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from __future__ import annotations
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from pathlib import Path
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import sys
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PROJECT_ROOT = Path(__file__).resolve().parents[3]
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from meteo.dataset import load_raw_csv
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from meteo.variables import iter_variable_pairs
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from meteo.plots import plot_scatter_pair
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CSV_PATH = Path("data/weather_minutely.csv")
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DOC_DIR = Path(__file__).resolve().parent.parent
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OUTPUT_DIR = DOC_DIR / "figures" / "pairwise_scatter"
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def main() -> None:
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if not CSV_PATH.exists():
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print(f"⚠ Fichier introuvable : {CSV_PATH}")
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return
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df = load_raw_csv(CSV_PATH)
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print(f"Dataset minuté chargé : {CSV_PATH}")
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print(f" Lignes : {len(df)}")
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print(f" Colonnes : {list(df.columns)}")
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pairs = iter_variable_pairs()
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print(f"Nombre de paires de variables : {len(pairs)}")
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for var_x, var_y in pairs:
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filename = f"scatter_{var_x.key}_vs_{var_y.key}.png"
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output_path = OUTPUT_DIR / filename
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print(f"→ Trace {var_y.key} en fonction de {var_x.key} → {output_path}")
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plot_scatter_pair(
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df=df,
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var_x=var_x,
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var_y=var_y,
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output_path=output_path,
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sample_step=10, # un point sur 10 : ≈ 32k points au lieu de 320k
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)
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print("✔ Tous les graphiques de nuages de points ont été générés.")
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if __name__ == "__main__":
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main()
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