Premiers éléments de l'étude

lib/rebrickable/parts_inventory.py

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+"""Construction d'un inventaire détaillé des pièces par set."""
+
+import csv
+from pathlib import Path
+from typing import Dict, List
+
+from lib.filesystem import ensure_parent_dir
+
+
+def normalize_boolean(raw_value: str) -> str:
+    """Normalise une valeur booléenne en chaîne lowercase."""
+    return raw_value.lower()
+
+
+def select_latest_inventories(inventories_path: Path) -> Dict[str, dict]:
+    """Retient pour chaque set l'inventaire avec la version la plus élevée."""
+    latest_inventories: Dict[str, dict] = {}
+    with inventories_path.open() as inventories_file:
+        reader = csv.DictReader(inventories_file)
+        for row in reader:
+            current = latest_inventories.get(row["set_num"])
+            if current is None or int(row["version"]) > int(current["version"]):
+                latest_inventories[row["set_num"]] = {"id": row["id"], "version": row["version"]}
+    return latest_inventories
+
+
+def build_color_lookup(colors_path: Path) -> Dict[str, dict]:
+    """Construit un index des couleurs par identifiant."""
+    colors: Dict[str, dict] = {}
+    with colors_path.open() as colors_file:
+        reader = csv.DictReader(colors_file)
+        for row in reader:
+            colors[row["id"]] = {
+                "rgb": row["rgb"],
+                "is_translucent": normalize_boolean(row["is_trans"]),
+            }
+    return colors
+
+
+def index_inventory_parts_by_inventory(inventory_parts_path: Path) -> Dict[str, List[dict]]:
+    """Indexe les lignes d'inventaire par identifiant d'inventaire."""
+    parts_by_inventory: Dict[str, List[dict]] = {}
+    with inventory_parts_path.open() as parts_file:
+        reader = csv.DictReader(parts_file)
+        for row in reader:
+            inventory_id = row["inventory_id"]
+            if inventory_id not in parts_by_inventory:
+                parts_by_inventory[inventory_id] = []
+            parts_by_inventory[inventory_id].append(row)
+    return parts_by_inventory
+
+
+def index_inventory_minifigs_by_inventory(inventory_minifigs_path: Path) -> Dict[str, List[dict]]:
+    """Indexe les minifigs par inventaire."""
+    minifigs_by_inventory: Dict[str, List[dict]] = {}
+    with inventory_minifigs_path.open() as minifigs_file:
+        reader = csv.DictReader(minifigs_file)
+        for row in reader:
+            inventory_id = row["inventory_id"]
+            if inventory_id not in minifigs_by_inventory:
+                minifigs_by_inventory[inventory_id] = []
+            minifigs_by_inventory[inventory_id].append(row)
+    return minifigs_by_inventory
+
+
+def build_minifig_lookup(minifigs_path: Path) -> Dict[str, dict]:
+    """Construit un index des minifigs avec leur nombre de pièces."""
+    minifigs: Dict[str, dict] = {}
+    with minifigs_path.open() as minifigs_file:
+        reader = csv.DictReader(minifigs_file)
+        for row in reader:
+            minifigs[row["fig_num"]] = row
+    return minifigs
+
+
+def write_parts_filtered(
+    sets_path: Path,
+    inventories_path: Path,
+    inventory_parts_path: Path,
+    colors_path: Path,
+    inventory_minifigs_path: Path,
+    minifigs_path: Path,
+    destination_path: Path,
+) -> None:
+    """Assemble un CSV agrégé listant les pièces par set et par couleur."""
+    latest_inventories = select_latest_inventories(inventories_path)
+    parts_by_inventory = index_inventory_parts_by_inventory(inventory_parts_path)
+    minifigs_by_inventory = index_inventory_minifigs_by_inventory(inventory_minifigs_path)
+    minifigs = build_minifig_lookup(minifigs_path)
+    colors = build_color_lookup(colors_path)
+    ensure_parent_dir(destination_path)
+    with sets_path.open() as sets_file, destination_path.open("w", newline="") as target_file:
+        sets_reader = csv.DictReader(sets_file)
+        fieldnames = [
+            "part_num",
+            "color_rgb",
+            "is_translucent",
+            "set_num",
+            "set_id",
+            "quantity_in_set",
+            "is_spare",
+        ]
+        writer = csv.DictWriter(target_file, fieldnames=fieldnames)
+        writer.writeheader()
+        for set_row in sets_reader:
+            inventory = latest_inventories[set_row["set_num"]]
+            inventory_parts = parts_by_inventory[inventory["id"]]
+            inventory_total_non_spare = sum(
+                int(part_row["quantity"])
+                for part_row in inventory_parts
+                if normalize_boolean(part_row["is_spare"]) == "false"
+            )
+            expected_parts = int(set_row["num_parts"])
+            for part_row in inventory_parts:
+                color = colors[part_row["color_id"]]
+                writer.writerow(
+                    {
+                        "part_num": part_row["part_num"],
+                        "color_rgb": color["rgb"],
+                        "is_translucent": color["is_translucent"],
+                        "set_num": set_row["set_num"],
+                        "set_id": set_row["set_id"],
+                        "quantity_in_set": part_row["quantity"],
+                        "is_spare": normalize_boolean(part_row["is_spare"]),
+                    }
+                )
+            if inventory_total_non_spare < expected_parts:
+                for minifig_row in minifigs_by_inventory.get(inventory["id"], []):
+                    minifig_inventory = latest_inventories[minifig_row["fig_num"]]
+                    minifig_parts = parts_by_inventory[minifig_inventory["id"]]
+                    for part_row in minifig_parts:
+                        color = colors[part_row["color_id"]]
+                        writer.writerow(
+                            {
+                                "part_num": part_row["part_num"],
+                                "color_rgb": color["rgb"],
+                                "is_translucent": color["is_translucent"],
+                                "set_num": set_row["set_num"],
+                                "set_id": set_row["set_id"],
+                                "quantity_in_set": str(int(part_row["quantity"]) * int(minifig_row["quantity"])),
+                                "is_spare": normalize_boolean(part_row["is_spare"]),
+                            }
+                        )