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Reviens à l’état fbf20e2 sans palettes par set

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Richard Dern 2025-12-02 16:23:56 +01:00
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14
README.md
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@ -272,17 +272,3 @@ Le script lit `data/intermediate/minifigs_by_set.csv`, agrège le nombre de mini
Le script lit `data/intermediate/minifig_counts_by_set.csv`, `data/intermediate/sets_enriched.csv`, `data/raw/sets.csv`, `data/raw/inventories.csv` et `data/raw/inventory_minifigs.csv`, produit `data/intermediate/minifig_parts_correlation.csv` (pièces vs minifigs pour le catalogue global et les thèmes filtrés), puis trace `figures/step26/minifig_parts_correlation.png` en superposant les nuages de points et leurs tendances linéaires. Le script lit `data/intermediate/minifig_counts_by_set.csv`, `data/intermediate/sets_enriched.csv`, `data/raw/sets.csv`, `data/raw/inventories.csv` et `data/raw/inventory_minifigs.csv`, produit `data/intermediate/minifig_parts_correlation.csv` (pièces vs minifigs pour le catalogue global et les thèmes filtrés), puis trace `figures/step26/minifig_parts_correlation.png` en superposant les nuages de points et leurs tendances linéaires.
Un second export `data/intermediate/minifigs_per_set_timeline.csv` est généré pour l'évolution annuelle du nombre moyen de minifigs par set, visualisé dans `figures/step26/minifigs_per_set_timeline.png` (courbes catalogue vs thèmes filtrés). Un second export `data/intermediate/minifigs_per_set_timeline.csv` est généré pour l'évolution annuelle du nombre moyen de minifigs par set, visualisé dans `figures/step26/minifigs_per_set_timeline.png` (courbes catalogue vs thèmes filtrés).
### Étape 27 : palettes dominantes par set (hors minifigs)
1. `source .venv/bin/activate`
2. `python -m scripts.plot_set_color_swatches`
Le script lit `data/intermediate/colors_by_set.csv` (hors rechanges) et `data/intermediate/sets_enriched.csv`, sélectionne pour chaque set les 5 couleurs les plus présentes en excluant les pièces de minifigs (`quantity_non_minifig`), écrit `data/intermediate/set_color_swatches.csv`, puis trace `figures/step27/set_color_swatches.png` affichant chaque set avec ses 5 pastilles de couleurs dominantes.
### Étape 28 : palettes perceptuelles par set (hors minifigs, pièces techniques exclues)
1. `source .venv/bin/activate`
2. `python -m scripts.plot_set_color_swatches_perceptual`
Le script lit `data/intermediate/colors_by_set.csv` (filtres appliqués : couleurs ignorées et pièces techniques/structurelles exclues), calcule pour chaque set les parts relatives de couleurs hors minifigs, sélectionne une palette diversifiée de 5 couleurs (priorité à la variété de teinte avant la luminosité), écrit `data/intermediate/set_color_swatches_perceptual.csv`, puis trace `figures/step28/set_color_swatches_perceptual.png` (pastilles dont la taille reflète la part relative).

89
lib/plots/set_color_swatches.py
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@ -1,89 +0,0 @@
"""Palette dominante par set (hors minifigs)."""
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Sequence
import matplotlib.pyplot as plt
from lib.filesystem import ensure_parent_dir
from lib.rebrickable.stats import read_rows
PLACEHOLDER_COLOR = "#e0e0e0"
def load_swatches(path: Path) -> List[dict]:
"""Charge le CSV des couleurs dominantes par set."""
return read_rows(path)
def group_swatches(rows: Sequence[dict], top_n: int = 5) -> List[dict]:
"""Groupe les couleurs par set et complète avec des placeholders si besoin."""
grouped: Dict[str, List[dict]] = defaultdict(list)
meta: Dict[str, dict] = {}
for row in rows:
grouped[row["set_num"]].append(row)
meta[row["set_num"]] = {"name": row["name"], "year": int(row["year"])}
result: List[dict] = []
for set_num, colors in grouped.items():
sorted_colors = sorted(colors, key=lambda r: int(r["rank"]))
while len(sorted_colors) < top_n:
sorted_colors.append(
{
"set_num": set_num,
"name": meta[set_num]["name"],
"year": str(meta[set_num]["year"]),
"rank": str(len(sorted_colors) + 1),
"color_rgb": "",
"color_name": "N/A",
"quantity_non_minifig": "0",
}
)
result.append(
{
"set_num": set_num,
"name": meta[set_num]["name"],
"year": meta[set_num]["year"],
"colors": sorted_colors[:top_n],
}
)
result.sort(key=lambda r: (r["year"], r["name"], r["set_num"]))
return result
def plot_set_color_swatches(swatches_path: Path, destination_path: Path) -> None:
"""Trace la palette de 5 couleurs dominantes par set (hors minifigs)."""
rows = load_swatches(swatches_path)
if not rows:
return
grouped = group_swatches(rows, top_n=5)
set_labels = [f"{item['year']} – {item['name']}" for item in grouped]
y_positions = list(range(len(grouped)))
height = max(4, len(grouped) * 0.4)
fig, ax = plt.subplots(figsize=(12, height))
for y, item in zip(y_positions, grouped):
for idx, color in enumerate(item["colors"]):
rgb = color["color_rgb"].strip()
face_color = f"#{rgb}" if rgb else PLACEHOLDER_COLOR
ax.scatter(
idx,
y,
s=500,
color=face_color,
edgecolor="#0d0d0d",
linewidth=0.6,
)
ax.set_yticks(y_positions)
ax.set_yticklabels(set_labels)
ax.set_xticks([])
ax.invert_yaxis()
ax.set_xlim(-0.6, 4.6)
ax.set_title("Top 5 couleurs principales par set (hors minifigs)")
ax.grid(False)
ensure_parent_dir(destination_path)
fig.tight_layout()
fig.savefig(destination_path, dpi=160)
plt.close(fig)

93
lib/plots/set_color_swatches_perceptual.py
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@ -1,93 +0,0 @@
"""Visualisation des palettes perceptuelles (top 5) par set."""
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Sequence
import matplotlib.pyplot as plt
from lib.filesystem import ensure_parent_dir
from lib.rebrickable.stats import read_rows
PLACEHOLDER_COLOR = "#e0e0e0"
def load_swatches(path: Path) -> List[dict]:
"""Charge le CSV des palettes perceptuelles."""
return read_rows(path)
def group_swatches(rows: Sequence[dict], top_n: int = 5) -> List[dict]:
"""Groupe les couleurs par set et complète avec placeholders si besoin."""
grouped: Dict[str, List[dict]] = defaultdict(list)
meta: Dict[str, dict] = {}
for row in rows:
grouped[row["set_num"]].append(row)
meta[row["set_num"]] = {"name": row["name"], "year": int(row["year"])}
result: List[dict] = []
for set_num, colors in grouped.items():
sorted_colors = sorted(colors, key=lambda r: int(r["rank"]))
while len(sorted_colors) < top_n:
sorted_colors.append(
{
"set_num": set_num,
"name": meta[set_num]["name"],
"year": str(meta[set_num]["year"]),
"rank": str(len(sorted_colors) + 1),
"color_rgb": "",
"color_name": "N/A",
"share_non_minifig": "0",
"quantity_non_minifig": "0",
}
)
result.append(
{
"set_num": set_num,
"name": meta[set_num]["name"],
"year": meta[set_num]["year"],
"colors": sorted_colors[:top_n],
}
)
result.sort(key=lambda r: (r["year"], r["set_num"], r["name"]))
return result
def plot_set_color_swatches_perceptual(swatches_path: Path, destination_path: Path) -> None:
"""Trace les 5 couleurs perceptuelles par set avec taille proportionnelle à la part."""
rows = load_swatches(swatches_path)
if not rows:
return
grouped = group_swatches(rows, top_n=5)
set_labels = [f"{item['year']} – {item['name']}" for item in grouped]
y_positions = list(range(len(grouped)))
height = max(4, len(grouped) * 0.4)
fig, ax = plt.subplots(figsize=(12, height))
for y, item in zip(y_positions, grouped):
for idx, color in enumerate(item["colors"]):
rgb = color["color_rgb"].strip()
face_color = f"#{rgb}" if rgb else PLACEHOLDER_COLOR
share = float(color.get("share_non_minifig", "0"))
size = 450 + 900 * share
ax.scatter(
idx,
y,
s=size,
color=face_color,
edgecolor="#0d0d0d",
linewidth=0.6,
alpha=0.95,
)
ax.set_yticks(y_positions)
ax.set_yticklabels(set_labels)
ax.set_xticks([])
ax.invert_yaxis()
ax.set_xlim(-0.6, 4.6)
ax.set_title("Top 5 couleurs perceptuelles par set (hors minifigs, pièces techniques exclues)")
ax.grid(False)
ensure_parent_dir(destination_path)
fig.tight_layout()
fig.savefig(destination_path, dpi=160)
plt.close(fig)

126
lib/rebrickable/set_color_swatches.py
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@ -1,126 +0,0 @@
"""Préparation des palettes dominantes par set (hors minifigs)."""
import csv
from collections import defaultdict
from pathlib import Path
from typing import Dict, Iterable, List, Sequence
import colorsys
from lib.filesystem import ensure_parent_dir
from lib.rebrickable.color_ignores import is_ignored_color_rgb
from lib.rebrickable.stats import read_rows
def load_colors_by_set(path: Path) -> List[dict]:
"""Charge colors_by_set.csv."""
return read_rows(path)
def load_sets_enriched(path: Path) -> Dict[str, dict]:
"""Indexe nom et année par set_num."""
lookup: Dict[str, dict] = {}
with path.open() as csv_file:
reader = csv.DictReader(csv_file)
for row in reader:
lookup[row["set_num"]] = {"name": row["name"], "year": int(row["year"]), "set_id": row["set_id"]}
return lookup
def parse_rgb_hex(value: str) -> tuple[float, float, float]:
"""Parse un code hexadécimal (RRGGBB) en composantes 0-1."""
normalized = value.strip()
if len(normalized) != 6:
return (0.0, 0.0, 0.0)
r = int(normalized[0:2], 16) / 255.0
g = int(normalized[2:4], 16) / 255.0
b = int(normalized[4:6], 16) / 255.0
return (r, g, b)
def hue_bucket(degrees: float) -> int:
"""Regroupe les teintes en grandes familles pour l'affichage."""
if degrees >= 330 or degrees < 30:
return 0 # rouge
if degrees < 90:
return 1 # jaune / orangé
if degrees < 150:
return 2 # vert
if degrees < 270:
return 3 # bleu
return 4 # violet
def color_display_key(row: dict) -> tuple[float, int, float, str]:
"""Clé de tri visuelle : teinte regroupée d'abord, puis luminosité."""
r, g, b = parse_rgb_hex(row["color_rgb"])
h, _s, v = colorsys.rgb_to_hsv(r, g, b)
degrees = h * 360.0
bucket = hue_bucket(degrees)
return (bucket, degrees, v, row["color_name"])
def build_top_colors_by_set(rows: Iterable[dict], sets_lookup: Dict[str, dict], top_n: int = 5) -> List[dict]:
"""Sélectionne les top couleurs hors minifigs pour chaque set."""
colors_by_set: Dict[str, List[dict]] = defaultdict(list)
for row in rows:
quantity = int(row["quantity_non_minifig"])
if quantity <= 0:
continue
if is_ignored_color_rgb(row["color_rgb"]):
continue
set_num = row["set_num"]
set_meta = sets_lookup.get(set_num)
if set_meta is None:
continue
colors_by_set[set_num].append(
{
"set_num": set_num,
"set_id": row["set_id"],
"year": set_meta["year"],
"name": set_meta["name"],
"color_rgb": row["color_rgb"],
"color_name": row["color_name"],
"quantity": quantity,
}
)
results: List[dict] = []
for set_num, color_rows in colors_by_set.items():
sorted_rows = sorted(color_rows, key=lambda r: (-r["quantity"], r["color_name"]))
selected = sorted_rows[:top_n]
ordered = sorted(selected, key=color_display_key)
for rank, color_row in enumerate(ordered, start=1):
results.append(
{
"set_num": color_row["set_num"],
"set_id": color_row["set_id"],
"name": color_row["name"],
"year": str(color_row["year"]),
"rank": str(rank),
"color_rgb": color_row["color_rgb"],
"color_name": color_row["color_name"],
"quantity_non_minifig": str(color_row["quantity"]),
}
)
results.sort(key=lambda r: (int(r["year"]), r["set_num"], r["name"], int(r["rank"])))
return results
def write_top_colors(path: Path, rows: Sequence[dict]) -> None:
"""Écrit le CSV des couleurs dominantes par set."""
ensure_parent_dir(path)
fieldnames = [
"set_num",
"set_id",
"name",
"year",
"rank",
"color_rgb",
"color_name",
"quantity_non_minifig",
]
with path.open("w", newline="") as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
for row in rows:
writer.writerow(row)

143
lib/rebrickable/set_color_swatches_perceptual.py
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@ -1,143 +0,0 @@
"""Construction de palettes perceptuelles (top 5) par set hors minifigs."""
from pathlib import Path
from typing import Dict, Iterable, List, Sequence, Set
from lib.rebrickable.set_color_swatches import color_display_key, load_sets_enriched, parse_rgb_hex, hue_bucket
from lib.rebrickable.stats import read_rows
def load_colors_by_set(path: Path) -> List[dict]:
"""Charge colors_by_set.csv."""
return read_rows(path)
def compute_shares(rows: Iterable[dict]) -> Dict[str, List[dict]]:
"""Calcule les parts relatives de couleurs hors minifigs pour chaque set."""
by_set: Dict[str, List[dict]] = {}
totals: Dict[str, int] = {}
for row in rows:
quantity = int(row["quantity_non_minifig"])
if quantity <= 0:
continue
set_num = row["set_num"]
totals[set_num] = totals.get(set_num, 0) + quantity
current = by_set.get(set_num)
if current is None:
by_set[set_num] = [row]
else:
current.append(row)
shares: Dict[str, List[dict]] = {}
for set_num, color_rows in by_set.items():
total = totals.get(set_num, 0)
if total == 0:
continue
shares[set_num] = []
for row in color_rows:
share = int(row["quantity_non_minifig"]) / total
shares[set_num].append(
{
"set_num": row["set_num"],
"set_id": row["set_id"],
"name": row.get("name", ""),
"year": row["year"],
"color_rgb": row["color_rgb"],
"color_name": row["color_name"],
"quantity_non_minifig": row["quantity_non_minifig"],
"share_non_minifig": f"{share:.5f}",
}
)
return shares
def select_diverse_palette(rows: List[dict], top_n: int) -> List[dict]:
"""Sélectionne une palette diversifiée : priorité à la part et à la variété de teinte."""
sorted_by_share = sorted(rows, key=lambda r: (-float(r["share_non_minifig"]), r["color_name"]))
selected: List[dict] = []
buckets_used: Set[int] = set()
for row in sorted_by_share:
r, g, b = parse_rgb_hex(row["color_rgb"])
h, _s, _v = __import__("colorsys").rgb_to_hsv(r, g, b)
bucket = hue_bucket(h * 360.0)
if bucket in buckets_used:
continue
selected.append(row)
buckets_used.add(bucket)
if len(selected) == top_n:
break
if len(selected) < top_n:
for row in sorted_by_share:
if row in selected:
continue
selected.append(row)
if len(selected) == top_n:
break
while len(selected) < top_n:
selected.append(
{
"set_num": rows[0]["set_num"] if rows else "",
"set_id": rows[0]["set_id"] if rows else "",
"name": rows[0]["name"] if rows else "",
"year": rows[0]["year"] if rows else "",
"color_rgb": "",
"color_name": "N/A",
"quantity_non_minifig": "0",
"share_non_minifig": "0",
}
)
ordered = sorted(selected, key=color_display_key)
for rank, row in enumerate(ordered, start=1):
row["rank"] = str(rank)
return ordered[:top_n]
def build_perceptual_swatches(rows: Iterable[dict], sets_lookup: Dict[str, dict], top_n: int = 5) -> List[dict]:
"""Construit les palettes perceptuelles (parts relatives + diversité de teinte)."""
shares = compute_shares(rows)
swatches: List[dict] = []
for set_num, color_rows in shares.items():
set_meta = sets_lookup.get(set_num)
if set_meta is None:
continue
selected = select_diverse_palette(color_rows, top_n)
for row in selected:
swatches.append(
{
"set_num": set_num,
"set_id": set_meta["set_id"],
"name": set_meta["name"],
"year": str(set_meta["year"]),
"rank": row["rank"],
"color_rgb": row["color_rgb"],
"color_name": row["color_name"],
"share_non_minifig": row["share_non_minifig"],
"quantity_non_minifig": row["quantity_non_minifig"],
}
)
swatches.sort(key=lambda r: (int(r["year"]), r["set_num"], int(r["rank"])))
return swatches
def write_perceptual_swatches(path: Path, rows: Sequence[dict]) -> None:
"""Écrit le CSV des palettes perceptuelles."""
from lib.filesystem import ensure_parent_dir
ensure_parent_dir(path)
fieldnames = [
"set_num",
"set_id",
"name",
"year",
"rank",
"color_rgb",
"color_name",
"share_non_minifig",
"quantity_non_minifig",
]
with path.open("w", newline="") as csv_file:
import csv
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
for row in rows:
writer.writerow(row)

25
scripts/plot_set_color_swatches.py
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@ -1,25 +0,0 @@
"""Trace la palette dominante de chaque set (hors minifigs)."""
from pathlib import Path
from lib.plots.set_color_swatches import plot_set_color_swatches
from lib.rebrickable.set_color_swatches import build_top_colors_by_set, load_colors_by_set, load_sets_enriched, write_top_colors
COLORS_BY_SET_PATH = Path("data/intermediate/colors_by_set.csv")
SETS_ENRICHED_PATH = Path("data/intermediate/sets_enriched.csv")
SWATCHES_PATH = Path("data/intermediate/set_color_swatches.csv")
DESTINATION_PATH = Path("figures/step27/set_color_swatches.png")
def main() -> None:
"""Construit le CSV de top couleurs par set et trace le nuancier."""
colors_rows = load_colors_by_set(COLORS_BY_SET_PATH)
sets_lookup = load_sets_enriched(SETS_ENRICHED_PATH)
swatches = build_top_colors_by_set(colors_rows, sets_lookup, top_n=5)
write_top_colors(SWATCHES_PATH, swatches)
plot_set_color_swatches(SWATCHES_PATH, DESTINATION_PATH)
if __name__ == "__main__":
main()

30
scripts/plot_set_color_swatches_perceptual.py
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@ -1,30 +0,0 @@
"""Trace les palettes perceptuelles (top 5) par set hors minifigs."""
from pathlib import Path
from lib.plots.set_color_swatches_perceptual import plot_set_color_swatches_perceptual
from lib.rebrickable.set_color_swatches_perceptual import (
build_perceptual_swatches,
load_colors_by_set,
load_sets_enriched,
write_perceptual_swatches,
)
COLORS_BY_SET_PATH = Path("data/intermediate/colors_by_set.csv")
SETS_ENRICHED_PATH = Path("data/intermediate/sets_enriched.csv")
SWATCHES_PATH = Path("data/intermediate/set_color_swatches_perceptual.csv")
DESTINATION_PATH = Path("figures/step28/set_color_swatches_perceptual.png")
def main() -> None:
"""Construit et trace les palettes perceptuelles par set."""
colors_rows = load_colors_by_set(COLORS_BY_SET_PATH)
sets_lookup = load_sets_enriched(SETS_ENRICHED_PATH)
swatches = build_perceptual_swatches(colors_rows, sets_lookup, top_n=5)
write_perceptual_swatches(SWATCHES_PATH, swatches)
plot_set_color_swatches_perceptual(SWATCHES_PATH, DESTINATION_PATH)
if __name__ == "__main__":
main()

155
tests/test_set_color_swatches.py
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@ -1,155 +0,0 @@
"""Tests de la préparation des palettes par set."""
from pathlib import Path
from lib.rebrickable.set_color_swatches import build_top_colors_by_set
def write_csv(path: Path, content: str) -> None:
"""Écrit un CSV brut."""
path.write_text(content)
def test_build_top_colors_by_set_selects_top5_non_minifig(tmp_path: Path) -> None:
"""Sélectionne les 5 couleurs dominantes en excluant les minifigs."""
colors_path = tmp_path / "colors_by_set.csv"
write_csv(
colors_path,
"set_num,set_id,year,color_rgb,is_translucent,color_name,quantity_total,quantity_non_spare,quantity_minifig,quantity_non_minifig\n"
"123-1,123,2020,111111,false,Black,10,10,0,10\n"
"123-1,123,2020,222222,false,Red,5,5,0,5\n"
"123-1,123,2020,333333,false,Blue,3,3,0,3\n"
"123-1,123,2020,444444,false,Green,2,2,0,2\n"
"123-1,123,2020,555555,false,Yellow,1,1,0,1\n"
"123-1,123,2020,666666,false,Pink,1,1,0,1\n"
"123-1,123,2020,0033B2,false,Ignored Blue,50,50,0,50\n"
"124-1,124,2021,aaaaaa,false,Gray,4,4,4,0\n",
)
sets_path = tmp_path / "sets_enriched.csv"
write_csv(
sets_path,
"set_num,name,year,theme_id,num_parts,img_url,set_id,rebrickable_url,in_collection\n"
"123-1,Set A,2020,1,100,,123,,false\n"
"124-1,Set B,2021,1,50,,124,,false\n",
)
rows = build_top_colors_by_set(
[
row
for row in [
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "111111",
"color_name": "Black",
"quantity_non_minifig": "10",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "222222",
"color_name": "Red",
"quantity_non_minifig": "5",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "333333",
"color_name": "Blue",
"quantity_non_minifig": "3",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "444444",
"color_name": "Green",
"quantity_non_minifig": "2",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "555555",
"color_name": "Yellow",
"quantity_non_minifig": "1",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "666666",
"color_name": "Pink",
"quantity_non_minifig": "1",
},
{
"set_num": "124-1",
"set_id": "124",
"year": "2021",
"color_rgb": "aaaaaa",
"color_name": "Gray",
"quantity_non_minifig": "0",
},
]
],
{
"123-1": {"name": "Set A", "year": 2020, "set_id": "123"},
"124-1": {"name": "Set B", "year": 2021, "set_id": "124"},
},
top_n=5,
)
assert rows == [
{
"set_num": "123-1",
"set_id": "123",
"name": "Set A",
"year": "2020",
"rank": "1",
"color_rgb": "111111",
"color_name": "Black",
"quantity_non_minifig": "10",
},
{
"set_num": "123-1",
"set_id": "123",
"name": "Set A",
"year": "2020",
"rank": "2",
"color_rgb": "222222",
"color_name": "Red",
"quantity_non_minifig": "5",
},
{
"set_num": "123-1",
"set_id": "123",
"name": "Set A",
"year": "2020",
"rank": "3",
"color_rgb": "333333",
"color_name": "Blue",
"quantity_non_minifig": "3",
},
{
"set_num": "123-1",
"set_id": "123",
"name": "Set A",
"year": "2020",
"rank": "4",
"color_rgb": "444444",
"color_name": "Green",
"quantity_non_minifig": "2",
},
{
"set_num": "123-1",
"set_id": "123",
"name": "Set A",
"year": "2020",
"rank": "5",
"color_rgb": "666666",
"color_name": "Pink",
"quantity_non_minifig": "1",
},
]

84
tests/test_set_color_swatches_perceptual.py
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@ -1,84 +0,0 @@
"""Tests des palettes perceptuelles par set."""
from pathlib import Path
from lib.rebrickable.set_color_swatches_perceptual import build_perceptual_swatches
def write_csv(path: Path, content: str) -> None:
"""Écrit un CSV brut."""
path.write_text(content)
def test_build_perceptual_swatches_diversifies_buckets(tmp_path: Path) -> None:
"""Sélectionne des couleurs variées par teinte en priorité."""
colors_path = tmp_path / "colors_by_set.csv"
write_csv(
colors_path,
"set_num,set_id,year,color_rgb,is_translucent,color_name,quantity_total,quantity_non_spare,quantity_minifig,quantity_non_minifig\n"
"123-1,123,2020,FF0000,false,Red,10,10,0,10\n"
"123-1,123,2020,00FF00,false,Green,8,8,0,8\n"
"123-1,123,2020,0000FF,false,Blue,6,6,0,6\n"
"123-1,123,2020,FFFF00,false,Yellow,5,5,0,5\n"
"123-1,123,2020,FF00FF,false,Magenta,4,4,0,4\n"
"123-1,123,2020,00FFFF,false,Cyan,3,3,0,3\n",
)
sets_lookup = {"123-1": {"name": "Set A", "year": 2020, "set_id": "123"}}
rows = build_perceptual_swatches(
[
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "FF0000",
"color_name": "Red",
"quantity_non_minifig": "10",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "00FF00",
"color_name": "Green",
"quantity_non_minifig": "8",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "0000FF",
"color_name": "Blue",
"quantity_non_minifig": "6",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "FFFF00",
"color_name": "Yellow",
"quantity_non_minifig": "5",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "FF00FF",
"color_name": "Magenta",
"quantity_non_minifig": "4",
},
{
"set_num": "123-1",
"set_id": "123",
"year": "2020",
"color_rgb": "00FFFF",
"color_name": "Cyan",
"quantity_non_minifig": "3",
},
],
sets_lookup,
top_n=5,
)
ranks = [row["rank"] for row in rows if row["set_num"] == "123-1"]
assert ranks == ["1", "2", "3", "4", "5"]
assert len({row["color_name"] for row in rows}) == 5

28
tests/test_set_color_swatches_perceptual_plot.py
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@ -1,28 +0,0 @@
"""Tests du graphique de palettes perceptuelles par set."""
import matplotlib
from pathlib import Path
from lib.plots.set_color_swatches_perceptual import plot_set_color_swatches_perceptual
matplotlib.use("Agg")
def test_plot_set_color_swatches_perceptual(tmp_path: Path) -> None:
"""Génère le graphique perceptuel."""
swatches_path = tmp_path / "set_color_swatches_perceptual.csv"
destination = tmp_path / "figures" / "step28" / "set_color_swatches_perceptual.png"
swatches_path.write_text(
"set_num,set_id,name,year,rank,color_rgb,color_name,share_non_minifig,quantity_non_minifig\n"
"123-1,123,Set A,2020,1,FF0000,Red,0.40000,10\n"
"123-1,123,Set A,2020,2,00FF00,Green,0.30000,8\n"
"123-1,123,Set A,2020,3,0000FF,Blue,0.20000,6\n"
"123-1,123,Set A,2020,4,FFFF00,Yellow,0.10000,5\n"
"123-1,123,Set A,2020,5,00FFFF,Cyan,0.05000,3\n"
)
plot_set_color_swatches_perceptual(swatches_path, destination)
assert destination.exists()
assert destination.stat().st_size > 0

29
tests/test_set_color_swatches_plot.py
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@ -1,29 +0,0 @@
"""Tests du graphique de palettes dominantes par set."""
import matplotlib
from pathlib import Path
from lib.plots.set_color_swatches import plot_set_color_swatches
matplotlib.use("Agg")
def test_plot_set_color_swatches(tmp_path: Path) -> None:
"""Génère le nuancier top 5 par set."""
swatches_path = tmp_path / "set_color_swatches.csv"
destination = tmp_path / "figures" / "step27" / "set_color_swatches.png"
swatches_path.write_text(
"set_num,set_id,name,year,rank,color_rgb,color_name,quantity_non_minifig\n"
"123-1,123,Set A,2020,1,111111,Black,10\n"
"123-1,123,Set A,2020,2,222222,Red,5\n"
"123-1,123,Set A,2020,3,333333,Blue,3\n"
"123-1,123,Set A,2020,4,444444,Green,2\n"
"123-1,123,Set A,2020,5,555555,Yellow,1\n"
"124-1,124,Set B,2021,1,aaaaaa,Gray,4\n"
)
plot_set_color_swatches(swatches_path, destination)
assert destination.exists()
assert destination.stat().st_size > 0