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Reviens à l’état fbf20e2 sans palettes par set
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@@ -1,89 +0,0 @@
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"""Palette dominante par set (hors minifigs)."""
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Sequence
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import matplotlib.pyplot as plt
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from lib.filesystem import ensure_parent_dir
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from lib.rebrickable.stats import read_rows
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PLACEHOLDER_COLOR = "#e0e0e0"
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def load_swatches(path: Path) -> List[dict]:
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"""Charge le CSV des couleurs dominantes par set."""
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return read_rows(path)
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def group_swatches(rows: Sequence[dict], top_n: int = 5) -> List[dict]:
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"""Groupe les couleurs par set et complète avec des placeholders si besoin."""
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grouped: Dict[str, List[dict]] = defaultdict(list)
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meta: Dict[str, dict] = {}
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for row in rows:
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grouped[row["set_num"]].append(row)
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meta[row["set_num"]] = {"name": row["name"], "year": int(row["year"])}
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result: List[dict] = []
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for set_num, colors in grouped.items():
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sorted_colors = sorted(colors, key=lambda r: int(r["rank"]))
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while len(sorted_colors) < top_n:
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sorted_colors.append(
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{
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"set_num": set_num,
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"name": meta[set_num]["name"],
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"year": str(meta[set_num]["year"]),
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"rank": str(len(sorted_colors) + 1),
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"color_rgb": "",
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"color_name": "N/A",
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"quantity_non_minifig": "0",
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}
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)
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result.append(
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{
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"set_num": set_num,
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"name": meta[set_num]["name"],
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"year": meta[set_num]["year"],
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"colors": sorted_colors[:top_n],
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}
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)
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result.sort(key=lambda r: (r["year"], r["name"], r["set_num"]))
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return result
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def plot_set_color_swatches(swatches_path: Path, destination_path: Path) -> None:
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"""Trace la palette de 5 couleurs dominantes par set (hors minifigs)."""
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rows = load_swatches(swatches_path)
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if not rows:
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return
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grouped = group_swatches(rows, top_n=5)
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set_labels = [f"{item['year']} – {item['name']}" for item in grouped]
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y_positions = list(range(len(grouped)))
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height = max(4, len(grouped) * 0.4)
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fig, ax = plt.subplots(figsize=(12, height))
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for y, item in zip(y_positions, grouped):
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for idx, color in enumerate(item["colors"]):
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rgb = color["color_rgb"].strip()
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face_color = f"#{rgb}" if rgb else PLACEHOLDER_COLOR
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ax.scatter(
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idx,
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y,
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s=500,
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color=face_color,
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edgecolor="#0d0d0d",
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linewidth=0.6,
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)
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ax.set_yticks(y_positions)
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ax.set_yticklabels(set_labels)
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ax.set_xticks([])
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ax.invert_yaxis()
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ax.set_xlim(-0.6, 4.6)
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ax.set_title("Top 5 couleurs principales par set (hors minifigs)")
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ax.grid(False)
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ensure_parent_dir(destination_path)
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fig.tight_layout()
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fig.savefig(destination_path, dpi=160)
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plt.close(fig)
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@@ -1,93 +0,0 @@
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"""Visualisation des palettes perceptuelles (top 5) par set."""
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Sequence
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import matplotlib.pyplot as plt
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from lib.filesystem import ensure_parent_dir
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from lib.rebrickable.stats import read_rows
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PLACEHOLDER_COLOR = "#e0e0e0"
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def load_swatches(path: Path) -> List[dict]:
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"""Charge le CSV des palettes perceptuelles."""
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return read_rows(path)
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def group_swatches(rows: Sequence[dict], top_n: int = 5) -> List[dict]:
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"""Groupe les couleurs par set et complète avec placeholders si besoin."""
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grouped: Dict[str, List[dict]] = defaultdict(list)
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meta: Dict[str, dict] = {}
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for row in rows:
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grouped[row["set_num"]].append(row)
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meta[row["set_num"]] = {"name": row["name"], "year": int(row["year"])}
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result: List[dict] = []
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for set_num, colors in grouped.items():
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sorted_colors = sorted(colors, key=lambda r: int(r["rank"]))
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while len(sorted_colors) < top_n:
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sorted_colors.append(
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{
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"set_num": set_num,
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"name": meta[set_num]["name"],
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"year": str(meta[set_num]["year"]),
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"rank": str(len(sorted_colors) + 1),
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"color_rgb": "",
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"color_name": "N/A",
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"share_non_minifig": "0",
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"quantity_non_minifig": "0",
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}
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)
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result.append(
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{
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"set_num": set_num,
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"name": meta[set_num]["name"],
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"year": meta[set_num]["year"],
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"colors": sorted_colors[:top_n],
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}
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)
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result.sort(key=lambda r: (r["year"], r["set_num"], r["name"]))
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return result
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def plot_set_color_swatches_perceptual(swatches_path: Path, destination_path: Path) -> None:
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"""Trace les 5 couleurs perceptuelles par set avec taille proportionnelle à la part."""
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rows = load_swatches(swatches_path)
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if not rows:
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return
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grouped = group_swatches(rows, top_n=5)
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set_labels = [f"{item['year']} – {item['name']}" for item in grouped]
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y_positions = list(range(len(grouped)))
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height = max(4, len(grouped) * 0.4)
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fig, ax = plt.subplots(figsize=(12, height))
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for y, item in zip(y_positions, grouped):
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for idx, color in enumerate(item["colors"]):
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rgb = color["color_rgb"].strip()
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face_color = f"#{rgb}" if rgb else PLACEHOLDER_COLOR
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share = float(color.get("share_non_minifig", "0"))
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size = 450 + 900 * share
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ax.scatter(
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idx,
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y,
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s=size,
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color=face_color,
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edgecolor="#0d0d0d",
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linewidth=0.6,
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alpha=0.95,
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)
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ax.set_yticks(y_positions)
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ax.set_yticklabels(set_labels)
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ax.set_xticks([])
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ax.invert_yaxis()
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ax.set_xlim(-0.6, 4.6)
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ax.set_title("Top 5 couleurs perceptuelles par set (hors minifigs, pièces techniques exclues)")
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ax.grid(False)
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ensure_parent_dir(destination_path)
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fig.tight_layout()
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fig.savefig(destination_path, dpi=160)
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plt.close(fig)
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@@ -1,126 +0,0 @@
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"""Préparation des palettes dominantes par set (hors minifigs)."""
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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
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import colorsys
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from lib.filesystem import ensure_parent_dir
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from lib.rebrickable.color_ignores import is_ignored_color_rgb
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from lib.rebrickable.stats import read_rows
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def load_colors_by_set(path: Path) -> List[dict]:
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"""Charge colors_by_set.csv."""
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return read_rows(path)
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def load_sets_enriched(path: Path) -> Dict[str, dict]:
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"""Indexe nom et année par set_num."""
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lookup: Dict[str, dict] = {}
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with path.open() as csv_file:
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reader = csv.DictReader(csv_file)
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for row in reader:
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lookup[row["set_num"]] = {"name": row["name"], "year": int(row["year"]), "set_id": row["set_id"]}
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return lookup
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def parse_rgb_hex(value: str) -> tuple[float, float, float]:
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"""Parse un code hexadécimal (RRGGBB) en composantes 0-1."""
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normalized = value.strip()
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if len(normalized) != 6:
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return (0.0, 0.0, 0.0)
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r = int(normalized[0:2], 16) / 255.0
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g = int(normalized[2:4], 16) / 255.0
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b = int(normalized[4:6], 16) / 255.0
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return (r, g, b)
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def hue_bucket(degrees: float) -> int:
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"""Regroupe les teintes en grandes familles pour l'affichage."""
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if degrees >= 330 or degrees < 30:
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return 0 # rouge
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if degrees < 90:
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return 1 # jaune / orangé
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if degrees < 150:
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return 2 # vert
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if degrees < 270:
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return 3 # bleu
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return 4 # violet
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def color_display_key(row: dict) -> tuple[float, int, float, str]:
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"""Clé de tri visuelle : teinte regroupée d'abord, puis luminosité."""
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r, g, b = parse_rgb_hex(row["color_rgb"])
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h, _s, v = colorsys.rgb_to_hsv(r, g, b)
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degrees = h * 360.0
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bucket = hue_bucket(degrees)
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return (bucket, degrees, v, row["color_name"])
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def build_top_colors_by_set(rows: Iterable[dict], sets_lookup: Dict[str, dict], top_n: int = 5) -> List[dict]:
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"""Sélectionne les top couleurs hors minifigs pour chaque set."""
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colors_by_set: Dict[str, List[dict]] = defaultdict(list)
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for row in rows:
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quantity = int(row["quantity_non_minifig"])
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if quantity <= 0:
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continue
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if is_ignored_color_rgb(row["color_rgb"]):
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continue
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set_num = row["set_num"]
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set_meta = sets_lookup.get(set_num)
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if set_meta is None:
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continue
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colors_by_set[set_num].append(
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{
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"set_num": set_num,
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"set_id": row["set_id"],
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"year": set_meta["year"],
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"name": set_meta["name"],
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"color_rgb": row["color_rgb"],
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"color_name": row["color_name"],
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"quantity": quantity,
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}
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)
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results: List[dict] = []
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for set_num, color_rows in colors_by_set.items():
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sorted_rows = sorted(color_rows, key=lambda r: (-r["quantity"], r["color_name"]))
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selected = sorted_rows[:top_n]
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ordered = sorted(selected, key=color_display_key)
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for rank, color_row in enumerate(ordered, start=1):
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results.append(
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{
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"set_num": color_row["set_num"],
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"set_id": color_row["set_id"],
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"name": color_row["name"],
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"year": str(color_row["year"]),
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"rank": str(rank),
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"color_rgb": color_row["color_rgb"],
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"color_name": color_row["color_name"],
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"quantity_non_minifig": str(color_row["quantity"]),
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}
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)
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results.sort(key=lambda r: (int(r["year"]), r["set_num"], r["name"], int(r["rank"])))
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return results
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def write_top_colors(path: Path, rows: Sequence[dict]) -> None:
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"""Écrit le CSV des couleurs dominantes par set."""
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ensure_parent_dir(path)
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fieldnames = [
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"set_num",
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"set_id",
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"name",
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"year",
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"rank",
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"color_rgb",
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"color_name",
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"quantity_non_minifig",
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]
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with 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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@@ -1,143 +0,0 @@
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"""Construction de palettes perceptuelles (top 5) par set hors minifigs."""
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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.rebrickable.set_color_swatches import color_display_key, load_sets_enriched, parse_rgb_hex, hue_bucket
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from lib.rebrickable.stats import read_rows
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def load_colors_by_set(path: Path) -> List[dict]:
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"""Charge colors_by_set.csv."""
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return read_rows(path)
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def compute_shares(rows: Iterable[dict]) -> Dict[str, List[dict]]:
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"""Calcule les parts relatives de couleurs hors minifigs pour chaque set."""
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by_set: Dict[str, List[dict]] = {}
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totals: Dict[str, int] = {}
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for row in rows:
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quantity = int(row["quantity_non_minifig"])
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if quantity <= 0:
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continue
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set_num = row["set_num"]
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totals[set_num] = totals.get(set_num, 0) + quantity
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current = by_set.get(set_num)
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if current is None:
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by_set[set_num] = [row]
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else:
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current.append(row)
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shares: Dict[str, List[dict]] = {}
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for set_num, color_rows in by_set.items():
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total = totals.get(set_num, 0)
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if total == 0:
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continue
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shares[set_num] = []
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for row in color_rows:
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share = int(row["quantity_non_minifig"]) / total
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shares[set_num].append(
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{
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"set_num": row["set_num"],
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"set_id": row["set_id"],
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"name": row.get("name", ""),
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"year": row["year"],
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"color_rgb": row["color_rgb"],
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"color_name": row["color_name"],
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"quantity_non_minifig": row["quantity_non_minifig"],
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"share_non_minifig": f"{share:.5f}",
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}
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)
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return shares
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def select_diverse_palette(rows: List[dict], top_n: int) -> List[dict]:
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"""Sélectionne une palette diversifiée : priorité à la part et à la variété de teinte."""
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sorted_by_share = sorted(rows, key=lambda r: (-float(r["share_non_minifig"]), r["color_name"]))
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selected: List[dict] = []
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buckets_used: Set[int] = set()
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for row in sorted_by_share:
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r, g, b = parse_rgb_hex(row["color_rgb"])
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h, _s, _v = __import__("colorsys").rgb_to_hsv(r, g, b)
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bucket = hue_bucket(h * 360.0)
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if bucket in buckets_used:
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continue
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selected.append(row)
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buckets_used.add(bucket)
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if len(selected) == top_n:
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break
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if len(selected) < top_n:
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for row in sorted_by_share:
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if row in selected:
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continue
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selected.append(row)
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if len(selected) == top_n:
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break
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while len(selected) < top_n:
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selected.append(
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{
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"set_num": rows[0]["set_num"] if rows else "",
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"set_id": rows[0]["set_id"] if rows else "",
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"name": rows[0]["name"] if rows else "",
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"year": rows[0]["year"] if rows else "",
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"color_rgb": "",
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"color_name": "N/A",
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"quantity_non_minifig": "0",
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"share_non_minifig": "0",
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}
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)
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ordered = sorted(selected, key=color_display_key)
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for rank, row in enumerate(ordered, start=1):
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row["rank"] = str(rank)
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return ordered[:top_n]
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def build_perceptual_swatches(rows: Iterable[dict], sets_lookup: Dict[str, dict], top_n: int = 5) -> List[dict]:
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"""Construit les palettes perceptuelles (parts relatives + diversité de teinte)."""
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shares = compute_shares(rows)
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swatches: List[dict] = []
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for set_num, color_rows in shares.items():
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set_meta = sets_lookup.get(set_num)
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if set_meta is None:
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continue
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selected = select_diverse_palette(color_rows, top_n)
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for row in selected:
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swatches.append(
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{
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"set_num": set_num,
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"set_id": set_meta["set_id"],
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"name": set_meta["name"],
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"year": str(set_meta["year"]),
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"rank": row["rank"],
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"color_rgb": row["color_rgb"],
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"color_name": row["color_name"],
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"share_non_minifig": row["share_non_minifig"],
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"quantity_non_minifig": row["quantity_non_minifig"],
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}
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)
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swatches.sort(key=lambda r: (int(r["year"]), r["set_num"], int(r["rank"])))
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return swatches
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def write_perceptual_swatches(path: Path, rows: Sequence[dict]) -> None:
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"""Écrit le CSV des palettes perceptuelles."""
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from lib.filesystem import ensure_parent_dir
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ensure_parent_dir(path)
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fieldnames = [
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"set_num",
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"set_id",
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"name",
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"year",
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"rank",
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"color_rgb",
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"color_name",
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"share_non_minifig",
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"quantity_non_minifig",
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]
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with path.open("w", newline="") as csv_file:
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import csv
|
||||
|
||||
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
for row in rows:
|
||||
writer.writerow(row)
|
||||
Reference in New Issue
Block a user