Refactoring

docs/03 - Premiers graphiques/scripts/plot_calendar_overview.py

@@ -4,8 +4,6 @@ from __future__ import annotations
 from pathlib import Path
 import sys
 
-import calendar
-import numpy as np
 import pandas as pd
 
 PROJECT_ROOT = Path(__file__).resolve().parents[3]
@@ -13,43 +11,68 @@ if str(PROJECT_ROOT) not in sys.path:
     sys.path.insert(0, str(PROJECT_ROOT))
 
 from meteo.dataset import load_raw_csv
-from meteo.analysis import compute_daily_rainfall_totals
-from meteo.plots import export_plot_dataset, plot_calendar_heatmap, plot_weekday_profiles
-from meteo.variables import VARIABLES_BY_KEY
+from meteo.plots import (
+    CalendarHeatmapSpec,
+    daily_mean_series,
+    generate_calendar_heatmaps,
+    rainfall_daily_total_series,
+)
 
 
 DOC_DIR = Path(__file__).resolve().parent.parent
 CSV_PATH = PROJECT_ROOT / "data" / "weather_minutely.csv"
 OUTPUT_DIR = DOC_DIR / "figures" / "calendar"
 
-WEEKDAY_VARIABLE_KEYS = ["temperature", "humidity", "wind_speed", "illuminance"]
-
-
-def _format_calendar_matrix(series: pd.Series, year: int, agg_label: str) -> pd.DataFrame:
-    """
-    Transforme une série quotidienne en matrice mois x jours (1-31).
-    """
-    start = pd.Timestamp(year=year, month=1, day=1, tz=series.index.tz)
-    end = pd.Timestamp(year=year, month=12, day=31, tz=series.index.tz)
-    filtered = series.loc[(series.index >= start) & (series.index <= end)]
-
-    matrix = pd.DataFrame(
-        np.nan,
-        index=[calendar.month_name[m][:3] for m in range(1, 13)],
-        columns=list(range(1, 32)),
-    )
-
-    for timestamp, value in filtered.items():
-        month = timestamp.month
-        day = timestamp.day
-        matrix.at[calendar.month_name[month][:3], day] = value
-
-    matrix.index.name = f"{agg_label} ({year})"
-    return matrix
-
-
-def compute_daily_mean(df: pd.DataFrame, column: str) -> pd.Series:
-    return df[column].resample("1D").mean()
+HEATMAP_SPECS: tuple[CalendarHeatmapSpec, ...] = (
+    CalendarHeatmapSpec(
+        key="rain",
+        agg_label="Pluie (mm)",
+        title_template="Pluie quotidienne - {year}",
+        cmap="Blues",
+        colorbar_label="mm",
+        aggregator=rainfall_daily_total_series,
+    ),
+    CalendarHeatmapSpec(
+        key="temperature",
+        agg_label="Température (°C)",
+        title_template="Température moyenne quotidienne - {year}",
+        cmap="coolwarm",
+        colorbar_label="°C",
+        aggregator=daily_mean_series("temperature"),
+    ),
+    CalendarHeatmapSpec(
+        key="humidity",
+        agg_label="Humidité relative (%)",
+        title_template="Humidité relative quotidienne - {year}",
+        cmap="PuBu",
+        colorbar_label="%",
+        aggregator=daily_mean_series("humidity"),
+    ),
+    CalendarHeatmapSpec(
+        key="pressure",
+        agg_label="Pression (hPa)",
+        title_template="Pression moyenne quotidienne - {year}",
+        cmap="Greens",
+        colorbar_label="hPa",
+        aggregator=daily_mean_series("pressure"),
+    ),
+    CalendarHeatmapSpec(
+        key="illuminance",
+        agg_label="Illuminance (lux)",
+        title_template="Illuminance moyenne quotidienne - {year}",
+        cmap="YlOrBr",
+        colorbar_label="lux",
+        aggregator=daily_mean_series("illuminance"),
+    ),
+    CalendarHeatmapSpec(
+        key="wind",
+        agg_label="Vent (km/h)",
+        title_template="Vitesse moyenne du vent - {year}",
+        cmap="Purples",
+        colorbar_label="km/h",
+        aggregator=daily_mean_series("wind_speed"),
+    ),
+)
 
 
 def main() -> None:
@@ -76,91 +99,20 @@ def main() -> None:
     latest_year = df.index.year.max()
     print(f"Année retenue pour le calendrier : {latest_year}")
 
-    daily_totals = compute_daily_rainfall_totals(df=df)
-    daily_rain = daily_totals["daily_total"]
-    rain_matrix = _format_calendar_matrix(daily_rain, latest_year, "Pluie (mm)")
-    rain_matrix.attrs["cmap"] = "Blues"
-    rain_matrix.attrs["colorbar_label"] = "mm"
-    rain_path = OUTPUT_DIR / f"calendar_rain_{latest_year}.png"
-    plot_calendar_heatmap(
-        matrix=rain_matrix,
-        output_path=rain_path,
-        title=f"Pluie quotidienne - {latest_year}",
-        cmap="Blues",
-        colorbar_label="mm",
+    results = generate_calendar_heatmaps(
+        df=df,
+        specs=HEATMAP_SPECS,
+        year=latest_year,
+        output_dir=OUTPUT_DIR,
     )
-    print(f"✔ Heatmap pluie {latest_year} : {rain_path}")
 
-    daily_temp = compute_daily_mean(df, "temperature")
-    temp_matrix = _format_calendar_matrix(daily_temp, latest_year, "Température (°C)")
-    temp_matrix.attrs["cmap"] = "coolwarm"
-    temp_matrix.attrs["colorbar_label"] = "°C"
-    temp_path = OUTPUT_DIR / f"calendar_temperature_{latest_year}.png"
-    plot_calendar_heatmap(
-        matrix=temp_matrix,
-        output_path=temp_path,
-        title=f"Température moyenne quotidienne - {latest_year}",
-        cmap="coolwarm",
-        colorbar_label="°C",
-    )
-    print(f"✔ Heatmap température {latest_year} : {temp_path}")
-
-    if "pressure" in df.columns:
-        daily_pressure = compute_daily_mean(df, "pressure")
-        pressure_matrix = _format_calendar_matrix(daily_pressure, latest_year, "Pression (hPa)")
-        pressure_matrix.attrs["cmap"] = "Greens"
-        pressure_matrix.attrs["colorbar_label"] = "hPa"
-        pressure_path = OUTPUT_DIR / f"calendar_pressure_{latest_year}.png"
-        plot_calendar_heatmap(
-            matrix=pressure_matrix,
-            output_path=pressure_path,
-            title=f"Pression moyenne quotidienne - {latest_year}",
-            cmap="Greens",
-            colorbar_label="hPa",
-        )
-        print(f"✔ Heatmap pression {latest_year} : {pressure_path}")
-
-    if "illuminance" in df.columns:
-        daily_lux = compute_daily_mean(df, "illuminance")
-        lux_matrix = _format_calendar_matrix(daily_lux, latest_year, "Illuminance (lux)")
-        lux_matrix.attrs["cmap"] = "YlOrBr"
-        lux_matrix.attrs["colorbar_label"] = "lux"
-        lux_path = OUTPUT_DIR / f"calendar_illuminance_{latest_year}.png"
-        plot_calendar_heatmap(
-            matrix=lux_matrix,
-            output_path=lux_path,
-            title=f"Illuminance moyenne quotidienne - {latest_year}",
-            cmap="YlOrBr",
-            colorbar_label="lux",
-        )
-        print(f"✔ Heatmap illuminance {latest_year} : {lux_path}")
-
-    if "wind_speed" in df.columns:
-        daily_wind = compute_daily_mean(df, "wind_speed")
-        wind_matrix = _format_calendar_matrix(daily_wind, latest_year, "Vent (km/h)")
-        wind_matrix.attrs["cmap"] = "Purples"
-        wind_matrix.attrs["colorbar_label"] = "km/h"
-        wind_path = OUTPUT_DIR / f"calendar_wind_{latest_year}.png"
-        plot_calendar_heatmap(
-            matrix=wind_matrix,
-            output_path=wind_path,
-            title=f"Vitesse moyenne du vent - {latest_year}",
-            cmap="Purples",
-            colorbar_label="km/h",
-        )
-        print(f"✔ Heatmap vent {latest_year} : {wind_path}")
-
-    hourly = df[WEEKDAY_VARIABLE_KEYS].resample("1h").mean()
-    weekday_stats = hourly.groupby(hourly.index.dayofweek).mean()
-    weekday_path = OUTPUT_DIR / "weekday_profiles.png"
-    variables = [VARIABLES_BY_KEY[key] for key in WEEKDAY_VARIABLE_KEYS]
-    plot_weekday_profiles(
-        weekday_df=weekday_stats,
-        variables=variables,
-        output_path=weekday_path,
-        title="Profils moyens par jour de semaine",
-    )
-    print(f"✔ Profils hebdomadaires : {weekday_path}")
+    for result in results:
+        title = result.spec.title_template.format(year=latest_year)
+        if result.output_path:
+            print(f"✔ {title} : {result.output_path}")
+        else:
+            reason = f" ({result.reason})" if result.reason else ""
+            print(f"⚠ Heatmap ignorée pour {result.spec.key}{reason}.")
 
     print("✔ Graphiques calendrier générés.")