Analyse des têtes dual-face

lib/rebrickable/minifig_head_faces.py

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+"""Détection des têtes de minifigs à plusieurs visages et agrégats associés."""
+
+import csv
+from pathlib import Path
+from typing import Dict, Iterable, List, Sequence
+
+from lib.filesystem import ensure_parent_dir
+from lib.rebrickable.stats import read_rows
+
+
+DUAL_FACE_KEYWORDS = [
+    "dual sided",
+    "dual-sided",
+    "double sided",
+    "double-sided",
+    "2 sided",
+    "2-sided",
+    "two sided",
+    "two-sided",
+    "dual print",
+    "dual face",
+    "double face",
+    "two faces",
+    "alt face",
+    "alternate face",
+]
+
+
+def load_parts_catalog(path: Path) -> Dict[str, dict]:
+    """Indexe les pièces par référence."""
+    catalog: Dict[str, dict] = {}
+    with path.open() as csv_file:
+        reader = csv.DictReader(csv_file)
+        for row in reader:
+            catalog[row["part_num"]] = row
+    return catalog
+
+
+def load_sets(path: Path) -> Dict[str, dict]:
+    """Indexe les sets enrichis par set_num."""
+    sets: Dict[str, dict] = {}
+    for row in read_rows(path):
+        sets[row["set_num"]] = row
+    return sets
+
+
+def detect_dual_face(name: str) -> str:
+    """Détecte une tête dual-face via des mots-clés."""
+    lowered = name.lower()
+    for keyword in DUAL_FACE_KEYWORDS:
+        if keyword in lowered:
+            return "true"
+    return "false"
+
+
+def build_head_faces(
+    minifigs_by_set_path: Path,
+    parts_catalog_path: Path,
+    sets_enriched_path: Path,
+) -> List[dict]:
+    """Construit la liste des têtes annotées selon la présence de visages multiples."""
+    heads = read_rows(minifigs_by_set_path)
+    catalog = load_parts_catalog(parts_catalog_path)
+    sets_lookup = load_sets(sets_enriched_path)
+    annotated: List[dict] = []
+    for row in heads:
+        part = catalog[row["part_num"]]
+        set_row = sets_lookup[row["set_num"]]
+        is_dual = detect_dual_face(part["name"])
+        annotated.append(
+            {
+                "set_num": row["set_num"],
+                "set_id": set_row["set_id"],
+                "year": set_row["year"],
+                "name": set_row["name"],
+                "in_collection": set_row["in_collection"],
+                "part_num": row["part_num"],
+                "part_name": part["name"],
+                "fig_num": row["fig_num"],
+                "known_character": row["known_character"],
+                "gender": row["gender"],
+                "is_dual_face": is_dual,
+            }
+        )
+    annotated.sort(key=lambda row: (row["set_num"], row["part_num"]))
+    return annotated
+
+
+def aggregate_by_year(rows: Iterable[dict]) -> List[dict]:
+    """Agrège les têtes dual-face par année."""
+    counts: Dict[str, dict] = {}
+    for row in rows:
+        year_entry = counts.get(row["year"])
+        if year_entry is None:
+            year_entry = {
+                "year": row["year"],
+                "total_heads": 0,
+                "dual_heads": 0,
+            }
+            counts[row["year"]] = year_entry
+        year_entry["total_heads"] += 1
+        if row["is_dual_face"] == "true":
+            year_entry["dual_heads"] += 1
+    aggregated: List[dict] = []
+    for year, entry in counts.items():
+        aggregated.append(
+            {
+                "year": year,
+                "total_heads": str(entry["total_heads"]),
+                "dual_heads": str(entry["dual_heads"]),
+                "share_dual": f"{entry['dual_heads'] / entry['total_heads']:.4f}",
+            }
+        )
+    aggregated.sort(key=lambda row: int(row["year"]))
+    return aggregated
+
+
+def aggregate_by_set(rows: Iterable[dict]) -> List[dict]:
+    """Agrège les têtes dual-face par set."""
+    counts: Dict[str, dict] = {}
+    for row in rows:
+        entry = counts.get(row["set_num"])
+        if entry is None:
+            entry = {
+                "set_num": row["set_num"],
+                "set_id": row["set_id"],
+                "name": row["name"],
+                "year": row["year"],
+                "in_collection": row["in_collection"],
+                "total_heads": 0,
+                "dual_heads": 0,
+            }
+            counts[row["set_num"]] = entry
+        entry["total_heads"] += 1
+        if row["is_dual_face"] == "true":
+            entry["dual_heads"] += 1
+    aggregated: List[dict] = []
+    for entry in counts.values():
+        aggregated.append(
+            {
+                "set_num": entry["set_num"],
+                "set_id": entry["set_id"],
+                "name": entry["name"],
+                "year": entry["year"],
+                "in_collection": entry["in_collection"],
+                "total_heads": str(entry["total_heads"]),
+                "dual_heads": str(entry["dual_heads"]),
+                "share_dual": f"{entry['dual_heads'] / entry['total_heads']:.4f}",
+            }
+        )
+    aggregated.sort(key=lambda row: (-int(row["dual_heads"]), -float(row["share_dual"]), row["set_num"]))
+    return aggregated
+
+
+def aggregate_by_character(rows: Iterable[dict]) -> List[dict]:
+    """Agrège les têtes dual-face par personnage connu."""
+    counts: Dict[str, dict] = {}
+    for row in rows:
+        character = row["known_character"] or "Inconnu"
+        entry = counts.get(character)
+        if entry is None:
+            entry = {
+                "known_character": character,
+                "gender": row["gender"],
+                "total_heads": 0,
+                "dual_heads": 0,
+            }
+            counts[character] = entry
+        entry["total_heads"] += 1
+        if row["is_dual_face"] == "true":
+            entry["dual_heads"] += 1
+    aggregated: List[dict] = []
+    for character, entry in counts.items():
+        aggregated.append(
+            {
+                "known_character": character,
+                "gender": entry["gender"],
+                "total_heads": str(entry["total_heads"]),
+                "dual_heads": str(entry["dual_heads"]),
+                "share_dual": f"{entry['dual_heads'] / entry['total_heads']:.4f}",
+            }
+        )
+    aggregated.sort(key=lambda row: (-int(row["dual_heads"]), row["known_character"]))
+    return aggregated
+
+
+def write_csv(destination_path: Path, rows: Sequence[dict], fieldnames: Sequence[str]) -> None:
+    """Écrit un CSV générique."""
+    ensure_parent_dir(destination_path)
+    with destination_path.open("w", newline="") as csv_file:
+        writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+        writer.writeheader()
+        for row in rows:
+            writer.writerow(row)