Reformulations

docs/12 - Conclusion/scripts/plot_weekday_profiles.py

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+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
+from meteo.variables import VARIABLES_BY_KEY, Variable
+from meteo.plots import plot_weekday_profiles
+
+
+CSV_PATH = PROJECT_ROOT / "data" / "weather_minutely.csv"
+DOC_DIR = Path(__file__).resolve().parent.parent
+OUTPUT_PATH = DOC_DIR / "figures" / "weekday_profiles.png"
+
+# On se concentre sur le ressenti "agréable" : température, humidité, lumière.
+VARIABLE_KEYS = ["temperature", "humidity", "illuminance"]
+
+
+def compute_weekday_means(df: pd.DataFrame, variables: list[Variable]) -> pd.DataFrame:
+    """
+    Calcule, pour chaque jour de semaine (0=lundi,…,6=dimanche),
+    la moyenne des variables fournies.
+    """
+    if df.empty:
+        return pd.DataFrame(index=range(7))
+
+    weekday_index = df.index.dayofweek
+    columns = [var.column for var in variables]
+    weekday_means = df.groupby(weekday_index)[columns].mean()
+    # S'assure que toutes les valeurs 0–6 sont présentes, même si certaines manquent.
+    weekday_means = weekday_means.reindex(range(7))
+    weekday_means.index.name = "weekday"
+    return weekday_means
+
+
+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()
+
+    variables = [VARIABLES_BY_KEY[key] for key in VARIABLE_KEYS]
+    weekday_means = compute_weekday_means(df, variables)
+
+    output_path = plot_weekday_profiles(
+        weekday_df=weekday_means,
+        variables=variables,
+        output_path=OUTPUT_PATH,
+        title="Moyennes par jour de semaine",
+    )
+
+    print(f"✔ Profils hebdomadaires exportés : {output_path}")
+
+    weekday_labels_long = ["lundi", "mardi", "mercredi", "jeudi", "vendredi", "samedi", "dimanche"]
+    for var in variables:
+        series = weekday_means[var.column]
+        if series.isna().all():
+            continue
+        best_idx = int(series.idxmax())
+        best_label = weekday_labels_long[best_idx]
+        best_value = series.max()
+        unit = f" {var.unit}" if var.unit else ""
+        print(f"  → {var.label} maximale en moyenne le {best_label} (≈{best_value:.2f}{unit})")
+
+
+if __name__ == "__main__":
+    main()
+