Exportation des CSV pour chaque graphique

figures/calendar/calendar_combined_2025.csv

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+Feb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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figures/calendar/calendar_illuminance_2025.csv

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figures/calendar/calendar_pressure_2025.csv

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figures/calendar/calendar_rain_2025.csv

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figures/calendar/calendar_temperature_2025.csv

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figures/calendar/calendar_wind_2025.csv

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figures/calendar/weekday_profiles.csv

@@ -0,0 +1,8 @@
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figures/correlation_heatmap.csv

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figures/diurnal_cycle/diurnal_cycle.csv

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figures/event_composites/rain_event_composites.csv

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+-120.0,1005.711562833922,12.879008746355696,89.67492711370262,4.85847333255588,1006.09606504531,13.2777777777778,94.0,4.425696,997.291454580424,8.711111111111114,83.0,0.4184294400000059,1015.58,16.75555555555558,98.0,7.9823462400000045
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+-115.0,1005.9539259018377,12.848709642587204,89.75072886297376,4.874344122837706,1006.77,13.22222222222225,93.0,4.425696,997.291454580424,8.66666666666667,83.0,0.0,1015.7819552613801,16.92555555555556,98.0,8.14087488
+-114.0,1005.8703030594963,12.75694179894181,89.92142857142858,4.645759250285714,1006.26735195467,13.1666666666667,93.5,4.2823808,997.2237268076171,8.66666666666667,83.0,0.0,1015.9165921023,16.709999999999997,98.0,7.805318399999999
+-113.0,1006.0211575095747,12.712343884582268,89.96850775193799,4.74425048372093,1006.77,12.89444444444445,93.75,4.425696,997.291454580424,8.66666666666667,83.0,0.0,1016.052047647912,16.6666666666667,98.0,8.04672
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+-111.0,1005.8255540422349,12.788362115781483,89.94672531769305,4.745695033431085,1006.09606504531,13.1666666666667,93.5,4.25,997.291454580424,8.66666666666667,83.0,0.0,1015.9165921023,16.6666666666667,98.0,8.05
+-110.0,1005.8575965205558,12.840664711632465,89.84897360703812,4.7332775038123165,1006.1,13.2777777777778,93.0,4.425696,997.291454580424,8.66666666666667,83.0,0.0,1015.58,16.72222222222225,98.0,7.9662528
+-109.0,1005.7714005800921,12.822853903466159,89.865889212828,4.683034439650146,1006.09606504531,13.1666666666667,93.5,4.2647616,997.291454580424,8.7,83.0,0.0,1015.6803636021709,16.6666666666667,98.0,7.853598720000002
+-108.0,1005.8387763639546,12.754573268921108,89.9536231884058,4.633726284057971,1006.1,13.0555555555556,94.0,4.2,997.2237268076171,8.66666666666667,83.0,0.0,1015.9165921023,16.6666666666667,98.0,7.9662528
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+-95.0,1005.8499238465885,12.801252408477854,90.34104046242774,4.494553862042389,1005.75742618128,13.0277777777778,94.0,4.192147200000001,997.291454580424,8.6,84.0,0.0,1015.9165921023,16.9,98.0,7.644384
+-94.0,1005.8605885651142,12.710247637667003,90.33333333333334,4.5582396997067445,1006.09606504531,13.0,94.0,4.1038272,996.95281571639,8.55555555555556,83.5,0.0,1015.9165921023,16.8888888888889,98.0,7.85
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+-89.0,1006.066858839081,12.712930756843809,90.34830917874396,4.565367880966184,1006.43470390934,12.95,94.0,4.1038272,997.291454580424,8.55555555555556,83.0,0.0,1015.984319875106,16.8888888888889,98.0,7.88862848
+-88.0,1005.8578190886585,12.68749599743837,90.4178674351585,4.67730417982709,1005.8,12.9,94.0,4.2647616,996.6819046251628,8.55555555555556,83.6,0.0,1015.9165921023,16.84444444444448,98.0,8.030626560000002
+-87.0,1005.8664287480962,12.755427103403992,90.40703275529864,4.497012855491329,1006.09606504531,13.0277777777778,94.0,3.975,996.614176852356,8.55555555555556,84.0,0.0,1015.9165921023,17.0,98.0,7.4029824
+-86.0,1005.9824934981574,12.63885640025992,90.59210526315789,4.60106533177388,1006.263032522655,12.936111111111124,94.0,3.9214463999999998,997.291454580424,8.55555555555556,84.0,0.0,1015.9165921023,16.75555555555558,98.0,8.2076544
+-85.0,1005.9118013651758,12.570456503014654,90.59399224806201,4.710957432558139,1006.51701167068,12.774999999999999,94.0,4.217147199999999,997.1559990348104,8.455555555555557,83.80000000000001,0.0,1015.9165921023,16.600000000000044,98.0,8.159374080000003
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+-81.0,1005.8591299414945,12.795612278945622,90.3933933933934,4.679537153153153,1006.09606504531,13.0555555555556,94.0,4.4,997.291454580424,8.600000000000005,84.0,0.0,1015.9165921023,16.853333333333342,98.0,7.65
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figures/seasonal/rainfall_by_season.csv

@@ -0,0 +1,4 @@
+season,total_rain_mm,rainy_hours
+spring,102.48390277777777,48.61666666666667
+summer,234.72198333333333,64.41666666666667
+autumn,279.1841,110.14999999999999

figures/sun/sun_elevation_profiles.csv

@@ -0,0 +1,52 @@
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figures/temperature_last_7_days.csv

@@ -0,0 +1,170 @@
+time,temperature,humidity,pressure,illuminance,wind_speed,wind_direction,rain_rate,sun_elevation
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figures/wind_conditionals/wind_rose_all.csv

@@ -0,0 +1,13 @@
+row_0,sector_start_deg,sector_center_deg,0–5,5–15,15–30,30–50,≥50
+0,0,15.0,3.3242098788061427,0.1161920088479904,0.0003106738204491722,0.0,0.0
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figures/wind_conditionals/wind_rose_dry.csv

@@ -0,0 +1,13 @@
+row_0,sector_start_deg,sector_center_deg,0–5,5–15,15–30,30–50,≥50
+0,0,15.0,3.3793279466544335,0.11533652743361687,0.0,0.0,0.0
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figures/wind_conditionals/wind_rose_rain.csv

@@ -0,0 +1,13 @@
+row_0,sector_start_deg,sector_center_deg,0–5,5–15,15–30,30–50,≥50
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figures/wind_conditionals/wind_vectors_monthly.csv

@@ -0,0 +1,10 @@
+time,u,v,speed
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+2025-04-30 00:00:00+00:00,-0.7507251954333992,0.16989498629903232,2.5785006588597996
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+2025-11-30 00:00:00+00:00,1.749801537095212,1.4559188587310516,2.8093301111431512

figures/wind_rose/wind_rose.csv

@@ -0,0 +1,13 @@
+row_0,sector_start_deg,sector_center_deg,0–5,5–15,15–30,30–50,≥50
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figures/wind_rose/wind_rose_during_rain.csv

@@ -0,0 +1,13 @@
+row_0,sector_start_deg,sector_center_deg,0–5,5–15,15–30,30–50,≥50
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meteo/plots.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 import calendar
 from pathlib import Path
-from typing import Callable, Sequence
+from typing import Any, Callable, Sequence
 
 import matplotlib.pyplot as plt
 from matplotlib.colors import Normalize
@@ -17,6 +17,44 @@ from .season import SEASON_LABELS
 from .variables import Variable
 
 
+def export_plot_dataset(data: Any, output_path: str | Path, *, suffix: str = ".csv") -> Path | None:
+    """Persist the dataset used for a figure next to the exported image."""
+
+    if data is None:
+        return None
+
+    output_path = Path(output_path)
+    dataset_path = output_path.with_suffix(suffix)
+    dataset_path.parent.mkdir(parents=True, exist_ok=True)
+
+    def _normalize(value: Any, *, default_name: str = "value") -> pd.DataFrame:
+        if isinstance(value, pd.DataFrame):
+            return value.copy()
+        if isinstance(value, pd.Series):
+            return value.to_frame(name=value.name or default_name)
+        if isinstance(value, np.ndarray):
+            return pd.DataFrame(value)
+        return pd.DataFrame(value)
+
+    if isinstance(data, dict):
+        frames: list[pd.DataFrame] = []
+        for key, value in data.items():
+            if value is None:
+                continue
+            frame = _normalize(value, default_name=str(key))
+            # Prefix columns with the key to retain provenance when merging
+            frame = pd.concat({str(key): frame}, axis=1)
+            frames.append(frame)
+        if not frames:
+            return None
+        export_df = pd.concat(frames, axis=1)
+    else:
+        export_df = _normalize(data)
+
+    export_df.to_csv(dataset_path)
+    return dataset_path
+
+
 def plot_scatter_pair(
     df: pd.DataFrame,
     var_x: Variable,
@@ -47,7 +85,25 @@ def plot_scatter_pair(
     if sample_step > 1:
         df_pair = df_pair.iloc[::sample_step, :]
 
-    use_polar = var_y.key == "wind_direction"
+    export_plot_dataset(df_pair, output_path)
+
+    direction_var: Variable | None = None
+    radial_var: Variable | None = None
+    direction_series: pd.Series | None = None
+    radial_series: pd.Series | None = None
+
+    if var_y.key == "wind_direction" and var_x.key != "wind_direction":
+        direction_var = var_y
+        direction_series = df_pair[var_y.column]
+        radial_var = var_x
+        radial_series = df_pair[var_x.column]
+    elif var_x.key == "wind_direction" and var_y.key != "wind_direction":
+        direction_var = var_x
+        direction_series = df_pair[var_x.column]
+        radial_var = var_y
+        radial_series = df_pair[var_y.column]
+
+    use_polar = direction_var is not None and radial_var is not None
 
     if use_polar:
         fig, ax = plt.subplots(subplot_kw={"projection": "polar"})
@@ -76,8 +132,11 @@ def plot_scatter_pair(
         }
 
     if use_polar:
-        theta = np.deg2rad(df_pair[var_y.column].to_numpy(dtype=float) % 360.0)
+        assert direction_series is not None and radial_series is not None
-        radius_raw = df_pair[var_x.column].to_numpy(dtype=float)
+        assert direction_var is not None and radial_var is not None
+
+        theta = np.deg2rad(direction_series.to_numpy(dtype=float) % 360.0)
+        radius_raw = radial_series.to_numpy(dtype=float)
 
         if radius_raw.size == 0:
             radius = radius_raw
@@ -116,7 +175,7 @@ def plot_scatter_pair(
             ax.set_rlabel_position(225)
             ax.set_ylim(0.0, 1.0)
 
-            unit_suffix = f" {var_x.unit}" if var_x.unit else ""
+            unit_suffix = f" {radial_var.unit}" if radial_var.unit else ""
             ax.text(
                 0.5,
                 -0.1,
@@ -127,7 +186,7 @@ def plot_scatter_pair(
                 fontsize=8,
             )
 
-        radial_label = f"{var_x.label} ({var_x.unit})" if var_x.unit else var_x.label
+        radial_label = f"{radial_var.label} ({radial_var.unit})" if radial_var.unit else radial_var.label
         ax.set_ylabel(radial_label, labelpad=20)
     else:
         scatter = ax.scatter(
@@ -161,7 +220,8 @@ def plot_scatter_pair(
         cbar.set_label("Temps (ancien → récent)")
 
     if use_polar:
-        ax.set_title(f"{var_y.label} en fonction de {var_x.label}")
+        assert direction_var is not None and radial_var is not None
+        ax.set_title(f"{radial_var.label} en fonction de {direction_var.label}")
     else:
         ax.set_xlabel(f"{var_x.label} ({var_x.unit})")
         ax.set_ylabel(f"{var_y.label} ({var_y.unit})")
@@ -195,6 +255,7 @@ def plot_hexbin_with_third_variable(
     reduce_func = reduce_func or np.mean
 
     df_xyz = df[[var_x.column, var_y.column, var_color.column]].dropna()
+    export_plot_dataset(df_xyz, output_path)
     if df_xyz.empty:
         fig, ax = plt.subplots()
         ax.text(
@@ -250,6 +311,8 @@ def plot_lagged_correlation(
     output_path = Path(output_path)
     output_path.parent.mkdir(parents=True, exist_ok=True)
 
+    export_plot_dataset(lag_df, output_path)
+
     plt.figure()
     plt.plot(lag_df.index, lag_df["correlation"])
     plt.axvline(0, linestyle="--")  # lag = 0
@@ -295,6 +358,7 @@ def plot_correlation_heatmap(
 
     # On aligne la matrice sur l'ordre désiré
     corr = corr.loc[columns, columns]
+    export_plot_dataset(corr, output_path)
 
     data = corr.to_numpy()
 
@@ -357,6 +421,8 @@ def plot_rolling_correlation_heatmap(
     output_path = Path(output_path)
     output_path.parent.mkdir(parents=True, exist_ok=True)
 
+    export_plot_dataset(rolling_corr, output_path)
+
     if rolling_corr.empty:
         fig, ax = plt.subplots()
         ax.text(0.5, 0.5, "Aucune donnée de corrélation glissante.", ha="center", va="center")
@@ -442,6 +508,16 @@ def plot_event_composite(
     quantile_low = group.quantile(q_low) if q_low is not None else None
     quantile_high = group.quantile(q_high) if q_high is not None else None
 
+    export_plot_dataset(
+        {
+            "mean": mean_df,
+            "median": median_df,
+            "quantile_low": quantile_low,
+            "quantile_high": quantile_high,
+        },
+        output_path,
+    )
+
     offsets = mean_df.index.to_numpy(dtype=float)
     n_vars = len(variables)
     fig, axes = plt.subplots(n_vars, 1, figsize=(10, 3 * n_vars), sharex=True)
@@ -502,6 +578,11 @@ def plot_wind_rose(
         plt.close(fig)
         return output_path.resolve()
 
+    dataset = frequencies.copy()
+    dataset.insert(0, "sector_start_deg", frequencies.index)
+    dataset.insert(1, "sector_center_deg", frequencies.index + sector_size_deg / 2.0)
+    export_plot_dataset(dataset, output_path)
+
     fig, ax = plt.subplots(subplot_kw={"projection": "polar"}, figsize=(6, 6))
     cmap_obj = plt.get_cmap(cmap, len(speed_bin_labels))
     colors = cmap_obj(np.linspace(0.2, 0.95, len(speed_bin_labels)))
@@ -560,6 +641,16 @@ def plot_diurnal_cycle(
     output_path = Path(output_path)
     output_path.parent.mkdir(parents=True, exist_ok=True)
 
+    export_plot_dataset(
+        {
+            "mean": stats.mean,
+            "median": stats.median,
+            "quantile_low": stats.quantile_low,
+            "quantile_high": stats.quantile_high,
+        },
+        output_path,
+    )
+
     hours = stats.mean.index.to_numpy(dtype=float)
     n_vars = len(variables)
     fig, axes = plt.subplots(n_vars, 1, figsize=(10, 3 * n_vars), sharex=True)
@@ -630,6 +721,9 @@ def plot_seasonal_boxplots(
         plt.close(fig)
         return output_path.resolve()
 
+    dataset_columns = [season_column] + [var.column for var in variables]
+    export_plot_dataset(df[dataset_columns], output_path)
+
     n_vars = len(variables)
     fig, axes = plt.subplots(n_vars, 1, figsize=(10, 3 * n_vars), sharex=True)
     if n_vars == 1:
@@ -687,6 +781,11 @@ def plot_monthly_boxplots(
     if not isinstance(df.index, pd.DatetimeIndex):
         raise TypeError("plot_monthly_boxplots nécessite un DatetimeIndex.")
 
+    value_columns = [var.column for var in variables]
+    dataset = df[value_columns].copy()
+    dataset.insert(0, "month", df.index.month)
+    export_plot_dataset(dataset, output_path)
+
     month_labels = [calendar.month_abbr[m].capitalize() for m in MONTH_ORDER]
     n_vars = len(variables)
     fig, axes = plt.subplots(n_vars, 1, figsize=(12, 3 * n_vars), sharex=True)
@@ -756,6 +855,25 @@ def plot_binned_profiles(
         plt.close(fig)
         return output_path.resolve()
 
+    bin_summary = pd.DataFrame(
+        {
+            "bin_left": stats.intervals.left,
+            "bin_right": stats.intervals.right,
+            "center": stats.centers,
+        }
+    )
+    export_plot_dataset(
+        {
+            "bins": bin_summary,
+            "counts": stats.counts,
+            "mean": stats.mean,
+            "median": stats.median,
+            "quantile_low": stats.quantile_low,
+            "quantile_high": stats.quantile_high,
+        },
+        output_path,
+    )
+
     base_axes = len(variables)
     total_axes = base_axes + (1 if show_counts else 0)
     fig, axes = plt.subplots(
@@ -839,6 +957,8 @@ def plot_daily_rainfall_hyetograph(
         plt.close(fig)
         return output_path.resolve()
 
+    export_plot_dataset(daily_rain, output_path)
+
     fig, ax1 = plt.subplots(figsize=(12, 5))
     ax1.bar(
         daily_rain.index,
@@ -900,6 +1020,8 @@ def plot_rainfall_by_season(
         plt.close(fig)
         return output_path.resolve()
 
+    export_plot_dataset(rainfall_df, output_path)
+
     seasons = rainfall_df.index.tolist()
     x = np.arange(len(seasons))
     totals = rainfall_df["total_rain_mm"].to_numpy(dtype=float)
@@ -968,6 +1090,8 @@ def plot_monthly_anomalies(
         plt.close(fig)
         return output_path.resolve()
 
+    export_frames: list[pd.DataFrame] = []
+
     n_vars = len(variables)
     fig, axes = plt.subplots(n_vars, 1, figsize=(12, 3 * n_vars), sharex=True)
     if n_vars == 1:
@@ -987,6 +1111,11 @@ def plot_monthly_anomalies(
         clim = climatology.loc[months, var.column].to_numpy(dtype=float)
         anomaly = actual.to_numpy(dtype=float) - clim
 
+        clim_series = pd.Series(clim, index=actual.index, name="climatology")
+        frame = pd.DataFrame({"actual": actual, "climatology": clim_series})
+        frame["anomaly"] = frame["actual"] - frame["climatology"]
+        export_frames.append(pd.concat({var.column: frame}, axis=1))
+
         ax.plot(actual.index, actual, color="tab:blue", label="Moyenne mensuelle")
         ax.plot(actual.index, clim, color="tab:gray", linestyle="--", label="Climatologie")
         ax.fill_between(
@@ -1012,6 +1141,9 @@ def plot_monthly_anomalies(
         ax.xaxis.set_major_locator(locator)
         ax.xaxis.set_major_formatter(formatter)
 
+    if export_frames:
+        export_plot_dataset(pd.concat(export_frames, axis=1), output_path)
+
     axes[-1].set_xlabel("Date")
     axes[0].legend(loc="upper right")
     fig.suptitle(title)
@@ -1041,6 +1173,8 @@ def plot_wind_vector_series(
         plt.close(fig)
         return output_path.resolve()
 
+    export_plot_dataset(vector_df, output_path)
+
     times = vector_df.index
     x = mdates.date2num(times)
     u = vector_df["u"].to_numpy(dtype=float)
@@ -1089,6 +1223,8 @@ def plot_calendar_heatmap(
     output_path = Path(output_path)
     output_path.parent.mkdir(parents=True, exist_ok=True)
 
+    export_plot_dataset(matrix, output_path)
+
     if matrix.empty:
         fig, ax = plt.subplots()
         ax.text(0.5, 0.5, "Pas de données pour la heatmap.", ha="center", va="center")
@@ -1141,6 +1277,8 @@ def plot_weekday_profiles(
         plt.close(fig)
         return output_path.resolve()
 
+    export_plot_dataset(weekday_df, output_path)
+
     weekday_labels = ["Lun", "Mar", "Mer", "Jeu", "Ven", "Sam", "Dim"]
     n_vars = len(variables)
     fig, axes = plt.subplots(n_vars, 1, figsize=(10, 3 * n_vars), sharex=True)
@@ -1192,6 +1330,8 @@ def plot_seasonal_hourly_profiles(
         plt.close(fig)
         return output_path.resolve()
 
+    export_plot_dataset(profile_df, output_path)
+
     hours = profile_df.index.to_numpy(dtype=float)
     fig, ax = plt.subplots(figsize=(10, 4))
     colors = plt.get_cmap("turbo")(np.linspace(0.1, 0.9, profile_df.shape[1]))
@@ -1229,6 +1369,8 @@ def plot_daylight_hours(
         plt.close(fig)
         return output_path.resolve()
 
+    export_plot_dataset(monthly_series, output_path)
+
     months = monthly_series.index
     fig, ax = plt.subplots(figsize=(10, 4))
     ax.bar(months, monthly_series.values, color="goldenrod", alpha=0.8)

scripts/plot_calendar_overview.py

@@ -10,7 +10,7 @@ import matplotlib.pyplot as plt
 
 from meteo.dataset import load_raw_csv
 from meteo.analysis import compute_daily_rainfall_totals
-from meteo.plots import plot_calendar_heatmap, plot_weekday_profiles
+from meteo.plots import export_plot_dataset, plot_calendar_heatmap, plot_weekday_profiles
 from meteo.variables import VARIABLES_BY_KEY
 
 
@@ -56,6 +56,9 @@ def plot_combined_calendar(
     if not matrices:
         return
 
+    combined_dataset = pd.concat(matrices, axis=1)
+    export_plot_dataset(combined_dataset, output_path)
+
     n = len(matrices)
     fig, axes = plt.subplots(n, 1, figsize=(14, 4 * n), sharex=True)
     if n == 1:

scripts/plot_temperature.py

@@ -8,6 +8,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 
 from meteo.dataset import load_raw_csv
+from meteo.plots import export_plot_dataset
 
 
 CSV_PATH = Path("data/weather_minutely.csv")
@@ -38,8 +39,13 @@ def main() -> None:
         print("⚠ Aucun point dans l'intervalle choisi.")
         return
 
-    # Moyenne horaire pour lisser un peu la courbe
+    # Moyenne horaire pour lisser un peu la courbe (colonnes numériques uniquement)
-    df_hourly = df_slice.resample("1H").mean()
+    numeric_slice = df_slice.select_dtypes(include="number")
+    if numeric_slice.empty:
+        print("⚠ Aucune colonne numérique disponible pour l'agrégation.")
+        return
+
+    df_hourly = numeric_slice.resample("1h").mean()
 
     print(f"Intervalle tracé : {df_hourly.index[0]} → {df_hourly.index[-1]}")
     print(f"Nombre de points (moyenne horaire) : {len(df_hourly)}")
@@ -47,6 +53,10 @@ def main() -> None:
     OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
     output_path = OUTPUT_DIR / "temperature_last_7_days.png"
 
+    dataset_path = export_plot_dataset(df_hourly, output_path)
+    if dataset_path is not None:
+        print(f"✔ Données exportées : {dataset_path}")
+
     plt.figure()
     plt.plot(df_hourly.index, df_hourly["temperature"])
     plt.xlabel("Temps (UTC)")

scripts/run_all_plots.py

@@ -0,0 +1,115 @@
+# scripts/run_all_plots.py
+from __future__ import annotations
+
+import argparse
+import subprocess
+import sys
+from typing import Iterable
+
+
+PLOT_MODULES: tuple[str, ...] = (
+    "scripts.plot_temperature",
+    "scripts.plot_calendar_overview",
+    "scripts.plot_all_pairwise_scatter",
+    "scripts.plot_correlation_heatmap",
+    "scripts.plot_diurnal_cycle",
+    "scripts.plot_hexbin_explorations",
+    "scripts.plot_illuminance_focus",
+    "scripts.plot_lagged_correlations",
+    "scripts.plot_monthly_patterns",
+    "scripts.plot_rain_event_composites",
+    "scripts.plot_rain_hyetograph",
+    "scripts.plot_rolling_correlation_heatmap",
+    "scripts.plot_seasonal_overview",
+    "scripts.plot_sun_elevation_relationships",
+    "scripts.plot_wind_conditionals",
+    "scripts.plot_wind_rose",
+    "scripts.plot_wind_rose_rain",
+)
+
+
+def _normalize_module(name: str) -> str:
+    name = name.strip()
+    if not name:
+        raise ValueError("Nom de module vide.")
+    return name if name.startswith("scripts.") else f"scripts.{name}"
+
+
+def iter_modules(selected: Iterable[str] | None) -> list[str]:
+    if not selected:
+        return list(PLOT_MODULES)
+
+    normalized = [_normalize_module(name) for name in selected]
+    modules: list[str] = []
+    missing: list[str] = []
+    for module in normalized:
+        if module in PLOT_MODULES:
+            modules.append(module)
+        else:
+            missing.append(module)
+
+    if missing:
+        available = "\n  - ".join(("",) + PLOT_MODULES)
+        missing_list = "\n  - ".join(("",) + tuple(missing))
+        raise SystemExit(
+            "\n".join(
+                [
+                    "Les modules suivants ne sont pas reconnus :",
+                    missing_list,
+                    "",
+                    "Modules disponibles :",
+                    available,
+                ]
+            )
+        )
+
+    return modules
+
+
+def run_module(module: str) -> bool:
+    cmd = [sys.executable, "-m", module]
+    print(f"\n=== {module} ===")
+    try:
+        result = subprocess.run(cmd, check=False)
+    except FileNotFoundError as exc:  # pragma: no cover
+        print(f"⚠ Impossible de lancer {module} : {exc}")
+        return False
+
+    if result.returncode == 0:
+        print(f"✔ {module} terminé avec succès.")
+        return True
+
+    print(f"✘ {module} a échoué (code {result.returncode}).")
+    return False
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description="Lance tous les scripts de tracé météorologiques.")
+    parser.add_argument(
+        "--only",
+        nargs="*",
+        help="Limiter l'exécution à certains modules (ex: plot_temperature).",
+    )
+    args = parser.parse_args(argv)
+
+    modules = iter_modules(args.only)
+    print(f"Exécution de {len(modules)} script(s) de tracé…")
+
+    success_count = 0
+    for module in modules:
+        if run_module(module):
+            success_count += 1
+
+    print()
+    print(f"Scripts réussis : {success_count}/{len(modules)}")
+    failed = len(modules) - success_count
+    if failed:
+        print(f"⚠ {failed} script(s) ont échoué. Consultez les messages ci-dessus.")
+        return 1
+
+    print("✔ Tous les scripts ont été exécutés avec succès.")
+    return 0
+
+
+if __name__ == "__main__":  # pragma: no cover
+    raise SystemExit(main())