Ajout de couleurs en fonction du temps

Utilisation de graphiques radiaux pour la représentation de la direction du vent

meteo/plots.py

@@ -2,8 +2,10 @@
 from __future__ import annotations
 
 from pathlib import Path
+from typing import Sequence
 
 import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
 import numpy as np
 import pandas as pd
 
@@ -17,12 +19,19 @@ def plot_scatter_pair(
     output_path: str | Path,
     *,
     sample_step: int = 10,
+    color_by_time: bool = True,
+    cmap: str = "viridis",
 ) -> Path:
     """
     Trace un nuage de points (scatter) pour une paire de variables.
 
     - On sous-échantillonne les données avec `sample_step` (par exemple,
       1 point sur 10) pour éviter un graphique illisible.
+    - Si `color_by_time` vaut True et que l'index est temporel, les points
+      sont colorés du plus ancien (sombre) au plus récent (clair).
+    - Lorsque l'axe Y correspond à la direction du vent, on bascule sur
+      un graphique polaire plus adapté (0° = Nord, sens horaire) avec
+      un rayon normalisé : centre = valeur minimale, bord = maximale.
     """
     output_path = Path(output_path)
     output_path.parent.mkdir(parents=True, exist_ok=True)
@@ -33,14 +42,128 @@ def plot_scatter_pair(
     if sample_step > 1:
         df_pair = df_pair.iloc[::sample_step, :]
 
-    plt.figure()
+    use_polar = var_y.key == "wind_direction"
-    plt.scatter(df_pair[var_x.column], df_pair[var_y.column], s=5, alpha=0.5)
+
-    plt.xlabel(f"{var_x.label} ({var_x.unit})")
+    if use_polar:
-    plt.ylabel(f"{var_y.label} ({var_y.unit})")
+        fig, ax = plt.subplots(subplot_kw={"projection": "polar"})
-    plt.title(f"{var_y.label} en fonction de {var_x.label}")
+    else:
-    plt.tight_layout()
+        fig, ax = plt.subplots()
-    plt.savefig(output_path, dpi=150)
+
-    plt.close()
+    scatter_kwargs: dict = {"s": 5, "alpha": 0.5}
+    colorbar_meta: dict | None = None
+
+    if color_by_time and isinstance(df_pair.index, pd.DatetimeIndex):
+        idx = df_pair.index
+        timestamps = idx.view("int64")
+        time_span = np.ptp(timestamps)
+        norm = (
+            Normalize(vmin=timestamps.min(), vmax=timestamps.max())
+            if time_span > 0
+            else None
+        )
+        scatter_kwargs |= {"c": timestamps, "cmap": cmap}
+        if norm is not None:
+            scatter_kwargs["norm"] = norm
+        colorbar_meta = {
+            "index": idx,
+            "timestamps": timestamps,
+            "time_span": time_span,
+        }
+
+    if use_polar:
+        theta = np.deg2rad(df_pair[var_y.column].to_numpy(dtype=float) % 360.0)
+        radius_raw = df_pair[var_x.column].to_numpy(dtype=float)
+
+        if radius_raw.size == 0:
+            radius = radius_raw
+            value_min = value_max = float("nan")
+        else:
+            value_min = float(np.min(radius_raw))
+            value_max = float(np.max(radius_raw))
+            if np.isclose(value_min, value_max):
+                radius = np.zeros_like(radius_raw)
+            else:
+                radius = (radius_raw - value_min) / (value_max - value_min)
+
+        scatter = ax.scatter(theta, radius, **scatter_kwargs)
+
+        cardinal_angles = np.deg2rad(np.arange(0, 360, 45))
+        cardinal_labels = ["N", "NE", "E", "SE", "S", "SO", "O", "NO"]
+        ax.set_theta_zero_location("N")
+        ax.set_theta_direction(-1)
+        ax.set_xticks(cardinal_angles)
+        ax.set_xticklabels(cardinal_labels)
+
+        if radius_raw.size > 0:
+            if np.isclose(value_min, value_max):
+                radial_positions = [0.0]
+            else:
+                radial_positions = np.linspace(0.0, 1.0, num=5).tolist()
+            if np.isclose(value_min, value_max):
+                actual_values = [value_min]
+            else:
+                actual_values = [
+                    value_min + pos * (value_max - value_min)
+                    for pos in radial_positions
+                ]
+            ax.set_yticks(radial_positions)
+            ax.set_yticklabels([f"{val:.1f}" for val in actual_values])
+            ax.set_rlabel_position(225)
+            ax.set_ylim(0.0, 1.0)
+
+            unit_suffix = f" {var_x.unit}" if var_x.unit else ""
+            ax.text(
+                0.5,
+                -0.1,
+                f"Centre = {value_min:.1f}{unit_suffix}, bord = {value_max:.1f}{unit_suffix}",
+                transform=ax.transAxes,
+                ha="center",
+                va="top",
+                fontsize=8,
+            )
+
+        radial_label = f"{var_x.label} ({var_x.unit})" if var_x.unit else var_x.label
+        ax.set_ylabel(radial_label, labelpad=20)
+    else:
+        scatter = ax.scatter(
+            df_pair[var_x.column],
+            df_pair[var_y.column],
+            **scatter_kwargs,
+        )
+
+    if colorbar_meta is not None:
+        cbar = fig.colorbar(scatter, ax=ax)
+        idx = colorbar_meta["index"]
+        timestamps = colorbar_meta["timestamps"]
+        time_span = colorbar_meta["time_span"]
+
+        def _format_tick_label(ts: pd.Timestamp) -> str:
+            base = f"{ts.strftime('%Y-%m-%d')}\n{ts.strftime('%H:%M')}"
+            tz_name = ts.tzname()
+            return f"{base} ({tz_name})" if tz_name else base
+
+        if time_span > 0:
+            tick_datetimes = pd.date_range(start=idx.min(), end=idx.max(), periods=5)
+            tick_positions = tick_datetimes.view("int64")
+            tick_labels = [_format_tick_label(ts) for ts in tick_datetimes]
+            cbar.set_ticks(tick_positions)
+            cbar.set_ticklabels(tick_labels)
+        else:
+            cbar.set_ticks([timestamps[0]])
+            ts = idx[0]
+            cbar.set_ticklabels([_format_tick_label(ts)])
+
+        cbar.set_label("Temps (ancien → récent)")
+
+    if use_polar:
+        ax.set_title(f"{var_y.label} en fonction de {var_x.label}")
+    else:
+        ax.set_xlabel(f"{var_x.label} ({var_x.unit})")
+        ax.set_ylabel(f"{var_y.label} ({var_y.unit})")
+        ax.set_title(f"{var_y.label} en fonction de {var_x.label}")
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=150)
+    plt.close(fig)
 
     return output_path.resolve()
 
@@ -146,4 +269,4 @@ def plot_correlation_heatmap(
     plt.savefig(output_path, dpi=150)
     plt.close(fig)
 
-    return output_path.resolve()
+    return output_path.resolve()