Ajouter la heatmap log des catégories et le collage des pièces imprimées

lib/plots/part_categories.py

@@ -101,6 +101,41 @@ def plot_part_categories_heatmap(categories_by_year_path: Path, destination_path
     plt.close(fig)
 
 
+def plot_part_categories_heatmap_log(categories_by_year_path: Path, destination_path: Path) -> None:
+    """Heatmap des quantités (log1p) par catégorie et par année, en excluant les catégories vides."""
+    rows = load_rows(categories_by_year_path)
+    years = extract_years(rows)
+    totals: Dict[str, int] = {}
+    quantity_lookup = {(row["year"], row["category_id"]): int(row["quantity_non_spare"]) for row in rows}
+    for row in rows:
+        totals[row["category_id"]] = totals.get(row["category_id"], 0) + int(row["quantity_non_spare"])
+    categories = sorted([cat_id for cat_id, total in totals.items() if total > 0], key=lambda cat_id: -totals[cat_id])
+    if not categories:
+        return
+    matrix = np.zeros((len(categories), len(years)))
+    for i, cat_id in enumerate(categories):
+        for j, year in enumerate(years):
+            matrix[i, j] = np.log1p(quantity_lookup.get((year, cat_id), 0))
+    fig, ax = plt.subplots(figsize=(12, 10))
+    cmap = plt.get_cmap("magma")
+    im = ax.imshow(matrix, aspect="auto", cmap=cmap, norm=Normalize(vmin=0, vmax=matrix.max() if matrix.max() > 0 else 1))
+    ax.set_xticks(np.arange(len(years)))
+    ax.set_xticklabels(years, rotation=45, ha="right")
+    labels = {row["category_id"]: row["category_name"] for row in rows}
+    ax.set_yticks(np.arange(len(categories)))
+    ax.set_yticklabels([labels[cat_id] for cat_id in categories])
+    ax.set_xlabel("Année")
+    ax.set_ylabel("Catégorie de pièce")
+    ax.set_title("Intensité des catégories de pièces par année (log des quantités)")
+    cbar = fig.colorbar(ScalarMappable(norm=im.norm, cmap=cmap), ax=ax, fraction=0.025, pad=0.015)
+    cbar.ax.set_ylabel("log1p(quantité)", rotation=90)
+
+    ensure_parent_dir(destination_path)
+    fig.tight_layout()
+    fig.savefig(destination_path, dpi=170)
+    plt.close(fig)
+
+
 def plot_structural_share_timeline(categories_by_year_path: Path, destination_path: Path) -> None:
     """Trace l'évolution de la part des catégories structurelles."""
     rows = load_rows(categories_by_year_path)

scripts/plot_part_categories.py

@@ -4,6 +4,7 @@ from pathlib import Path
 
 from lib.plots.part_categories import (
     plot_part_categories_heatmap,
+    plot_part_categories_heatmap_log,
     plot_structural_share_timeline,
     plot_top_part_categories_area,
 )
@@ -13,6 +14,7 @@ CATEGORIES_BY_YEAR_PATH = Path("data/intermediate/part_categories_by_year.csv")
 CATEGORIES_GLOBAL_PATH = Path("data/intermediate/part_categories_global.csv")
 AREA_DESTINATION = Path("figures/step29/top_part_categories_area.png")
 HEATMAP_DESTINATION = Path("figures/step29/part_categories_heatmap.png")
+HEATMAP_LOG_DESTINATION = Path("figures/step29/part_categories_heatmap_log.png")
 STRUCTURAL_DESTINATION = Path("figures/step29/structural_share_timeline.png")
 
 
@@ -20,6 +22,7 @@ def main() -> None:
     """Génère les visuels de répartition par catégorie."""
     plot_top_part_categories_area(CATEGORIES_BY_YEAR_PATH, CATEGORIES_GLOBAL_PATH, AREA_DESTINATION)
     plot_part_categories_heatmap(CATEGORIES_BY_YEAR_PATH, HEATMAP_DESTINATION)
+    plot_part_categories_heatmap_log(CATEGORIES_BY_YEAR_PATH, HEATMAP_LOG_DESTINATION)
     plot_structural_share_timeline(CATEGORIES_BY_YEAR_PATH, STRUCTURAL_DESTINATION)
 
 

tests/test_part_categories_plot.py

@@ -5,6 +5,7 @@ from pathlib import Path
 
 from lib.plots.part_categories import (
     plot_part_categories_heatmap,
+    plot_part_categories_heatmap_log,
     plot_structural_share_timeline,
     plot_top_part_categories_area,
 )
@@ -31,15 +32,19 @@ def test_plot_part_categories_outputs_images(tmp_path: Path) -> None:
     )
     area_dest = tmp_path / "figures" / "step29" / "top_part_categories_area.png"
     heatmap_dest = tmp_path / "figures" / "step29" / "part_categories_heatmap.png"
+    heatmap_log_dest = tmp_path / "figures" / "step29" / "part_categories_heatmap_log.png"
     structural_dest = tmp_path / "figures" / "step29" / "structural_share_timeline.png"
 
     plot_top_part_categories_area(by_year, by_global, area_dest, top_n=2)
     plot_part_categories_heatmap(by_year, heatmap_dest)
+    plot_part_categories_heatmap_log(by_year, heatmap_log_dest)
     plot_structural_share_timeline(by_year, structural_dest)
 
     assert area_dest.exists()
     assert heatmap_dest.exists()
+    assert heatmap_log_dest.exists()
     assert structural_dest.exists()
     assert area_dest.stat().st_size > 0
     assert heatmap_dest.stat().st_size > 0
+    assert heatmap_log_dest.stat().st_size > 0
     assert structural_dest.stat().st_size > 0