108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
"""Extraction des couleurs de têtes de minifigs sur le catalogue complet."""
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import csv
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from pathlib import Path
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from typing import Dict, Iterable, List, Set, Tuple
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from lib.rebrickable.parts_inventory import normalize_boolean, select_latest_inventories
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HEAD_CATEGORIES = {"59"}
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def load_head_parts(parts_path: Path, head_categories: Set[str] | None = None, require_printed: bool = False) -> Dict[str, str]:
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"""Construit l'ensemble des références de têtes via leur catégorie."""
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categories = head_categories or HEAD_CATEGORIES
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head_parts: Dict[str, str] = {}
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with parts_path.open() as parts_file:
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reader = csv.DictReader(parts_file)
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for row in reader:
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if row["part_cat_id"] not in categories:
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continue
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if require_printed and "print" not in row["name"].lower():
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continue
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head_parts[row["part_num"]] = row["name"]
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return head_parts
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def build_sets_year_lookup(sets_path: Path) -> Dict[str, str]:
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"""Indexe les années par set_num."""
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lookup: Dict[str, str] = {}
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with sets_path.open() as sets_file:
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reader = csv.DictReader(sets_file)
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for row in reader:
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lookup[row["set_num"]] = row["year"]
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return lookup
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def build_color_lookup(colors_path: Path) -> Dict[str, dict]:
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"""Construit un index des couleurs par identifiant."""
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lookup: Dict[str, dict] = {}
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with colors_path.open() as colors_file:
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reader = csv.DictReader(colors_file)
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for row in reader:
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lookup[row["id"]] = {
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"rgb": row["rgb"],
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"is_translucent": row["is_trans"].lower(),
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"name": row["name"],
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}
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return lookup
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def aggregate_global_heads_by_year(
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inventories_path: Path,
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inventory_parts_path: Path,
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parts_path: Path,
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colors_path: Path,
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sets_path: Path,
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head_categories: Set[str] | None = None,
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require_printed: bool = False,
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) -> List[dict]:
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"""Agrège les couleurs de têtes par année sur le catalogue complet."""
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head_parts = load_head_parts(parts_path, head_categories, require_printed=require_printed)
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latest_inventories = select_latest_inventories(inventories_path)
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latest_inventory_ids = {data["id"]: set_num for set_num, data in latest_inventories.items()}
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colors_lookup = build_color_lookup(colors_path)
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sets_year = build_sets_year_lookup(sets_path)
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aggregates: Dict[Tuple[str, str, str], dict] = {}
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with inventory_parts_path.open() as parts_file:
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reader = csv.DictReader(parts_file)
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for row in reader:
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inventory_id = row["inventory_id"]
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if inventory_id not in latest_inventory_ids:
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continue
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if row["part_num"] not in head_parts:
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continue
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if normalize_boolean(row["is_spare"]) == "true":
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continue
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set_num = latest_inventory_ids[inventory_id]
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year = sets_year.get(set_num)
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if year is None:
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continue
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color = colors_lookup[row["color_id"]]
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key = (year, color["rgb"], color["is_translucent"])
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existing = aggregates.get(key)
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if existing is None:
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aggregates[key] = {
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"year": year,
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"color_rgb": color["rgb"],
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"is_translucent": color["is_translucent"],
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"color_name": color["name"],
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"quantity": 0,
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}
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existing = aggregates[key]
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existing["quantity"] += int(row["quantity"])
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results = list(aggregates.values())
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results.sort(key=lambda r: (int(r["year"]), r["color_name"], r["is_translucent"]))
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return results
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def write_global_heads_by_year(destination_path: Path, rows: Iterable[dict]) -> None:
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"""Sérialise l'agrégat global par année."""
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fieldnames = ["year", "color_rgb", "is_translucent", "color_name", "quantity"]
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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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