110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
"""Mesure la réutilisation des têtes de minifigs dans le catalogue LEGO."""
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import csv
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, Iterable, List, Sequence, Set
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from lib.filesystem import ensure_parent_dir
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from lib.rebrickable.minifigs_by_set import load_parts_catalog, select_head_parts
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from lib.rebrickable.parts_inventory import (
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index_inventory_parts_by_inventory,
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normalize_boolean,
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select_latest_inventories,
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)
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from lib.rebrickable.stats import read_rows
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def load_minifigs_by_set(path: Path) -> List[dict]:
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"""Charge le CSV minifigs_by_set."""
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return read_rows(path)
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def build_head_presence(
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inventories_path: Path,
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inventory_parts_path: Path,
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head_parts: Set[str],
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) -> Dict[str, Set[str]]:
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"""Indexe les sets contenant chaque tête (rechanges exclues)."""
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inventories = select_latest_inventories(inventories_path)
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parts_by_inventory = index_inventory_parts_by_inventory(inventory_parts_path)
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presence: Dict[str, Set[str]] = {}
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for set_num, inventory in inventories.items():
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parts = parts_by_inventory.get(inventory["id"], [])
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for part_row in parts:
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if part_row["part_num"] not in head_parts:
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continue
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if normalize_boolean(part_row["is_spare"]) == "true":
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continue
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existing = presence.get(part_row["part_num"])
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if existing is None:
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existing = set()
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presence[part_row["part_num"]] = existing
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existing.add(set_num)
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return presence
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def build_filtered_presence(minifigs_rows: Iterable[dict]) -> Dict[str, Set[str]]:
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"""Indexe les sets filtrés contenant chaque tête (hors figurants)."""
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presence: Dict[str, Set[str]] = {}
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for row in minifigs_rows:
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if row["known_character"] == "Figurant":
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continue
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bucket = presence.get(row["part_num"])
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if bucket is None:
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bucket = set()
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presence[row["part_num"]] = bucket
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bucket.add(row["set_num"])
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return presence
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def build_character_labels(minifigs_rows: Iterable[dict]) -> Dict[str, str]:
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"""Associe à chaque tête un personnage représentatif (hors figurants)."""
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labels: Dict[str, Set[str]] = defaultdict(set)
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for row in minifigs_rows:
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character = row["known_character"]
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if character == "Figurant":
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continue
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labels[row["part_num"]].add(character)
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representative: Dict[str, str] = {}
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for part_num, characters in labels.items():
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representative[part_num] = sorted(characters)[0]
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return representative
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def aggregate_head_reuse(
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minifigs_rows: Iterable[dict],
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parts_catalog: Dict[str, dict],
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head_presence: Dict[str, Set[str]],
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) -> List[dict]:
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"""Construit le tableau des têtes présentes dans les sets filtrés avec leur réutilisation globale."""
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filtered_presence = build_filtered_presence(minifigs_rows)
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labels = build_character_labels(minifigs_rows)
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aggregates: List[dict] = []
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for part_num, filtered_sets in filtered_presence.items():
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all_sets = head_presence.get(part_num, set())
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other_sets = all_sets - filtered_sets
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aggregates.append(
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{
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"part_num": part_num,
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"part_name": parts_catalog[part_num]["name"],
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"known_character": labels.get(part_num, ""),
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"filtered_sets": str(len(filtered_sets)),
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"other_sets": str(len(other_sets)),
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"total_sets": str(len(all_sets)),
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}
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)
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aggregates.sort(key=lambda row: (int(row["other_sets"]), -int(row["filtered_sets"]), row["part_num"]))
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return aggregates
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def write_head_reuse(destination_path: Path, rows: Sequence[dict]) -> None:
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"""Écrit le CSV des usages de têtes filtrées vs reste du catalogue."""
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ensure_parent_dir(destination_path)
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fieldnames = ["part_num", "part_name", "known_character", "filtered_sets", "other_sets", "total_sets"]
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with destination_path.open("w", newline="") as csv_file:
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writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
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writer.writeheader()
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for row in rows:
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writer.writerow(row)
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