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Ajoute le genre des personnages et colore les graphiques

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Richard Dern 2025-12-02 11:37:13 +01:00
parent 230b9db239
commit f5c1fa6333
12 changed files with 300 additions and 43 deletions
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6
README.md
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@ -222,7 +222,7 @@ Cette étape se lance après le téléchargement des données d'inventaire (éta
1. `source .venv/bin/activate`
2. `python -m scripts.compute_minifigs_by_set`
Le script lit l'inventaire agrégé `data/intermediate/parts_filtered.csv`, les inventaires `data/raw/inventories.csv`, `data/raw/inventory_parts.csv`, `data/raw/inventory_minifigs.csv`, le catalogue des pièces (`data/raw/parts.csv`) et celui des minifigs (`data/raw/minifigs.csv`). Il sélectionne les têtes de minifigs (catégorie 59), ignore les rechanges et dédoublonne par set et référence. Si une tête est associée à une minifig précise dans l'inventaire du set, `known_character` est renseigné avec le nom de la minifig et `fig_num` est indiqué ; sinon, `known_character` reste vide après tentative de correspondance automatique. Le CSV `data/intermediate/minifigs_by_set.csv` contient : `set_num`, `part_num`, `known_character`, `fig_num`.
Le script lit l'inventaire agrégé `data/intermediate/parts_filtered.csv`, les inventaires `data/raw/inventories.csv`, `data/raw/inventory_parts.csv`, `data/raw/inventory_minifigs.csv`, le catalogue des pièces (`data/raw/parts.csv`) et celui des minifigs (`data/raw/minifigs.csv`). Il sélectionne les têtes de minifigs (catégorie 59), ignore les rechanges et dédoublonne par set et référence. Si une tête est associée à une minifig précise dans l'inventaire du set, `known_character` est renseigné avec le nom de la minifig et `fig_num` est indiqué ; sinon, `known_character` reste vide après tentative de correspondance automatique. Les correspondances d'alias sont décrites dans `config/known_character_aliases.csv` et les genres des personnages nommés dans `config/known_character_genders.csv`. Le CSV `data/intermediate/minifigs_by_set.csv` contient : `set_num`, `part_num`, `known_character`, `fig_num`, `gender`.
### Étape 21 : visualiser le nombre de minifigs par set
@ -239,7 +239,7 @@ Le script relit `data/intermediate/sets_enriched.csv`, `data/intermediate/parts_
1. `source .venv/bin/activate`
2. `python -m scripts.plot_minifig_characters`
Le script lit `data/intermediate/minifigs_by_set.csv`, compte le nombre de minifigs distinctes par personnage (`known_character` + `fig_num`), écrit `data/intermediate/minifig_characters_counts.csv`, puis trace `figures/step22/minifig_characters.png` (barres horizontales triées).
Le script lit `data/intermediate/minifigs_by_set.csv`, compte le nombre de minifigs distinctes par personnage (`known_character` + `fig_num`), écrit `data/intermediate/minifig_characters_counts.csv`, puis trace `figures/step22/minifig_characters.png` (barres horizontales triées). Les barres sont colorées en fonction du genre renseigné dans `config/known_character_genders.csv`.
### Étape 23 : présence annuelle des personnages
@ -253,4 +253,4 @@ Le script lit `data/intermediate/minifigs_by_set.csv` et `data/intermediate/sets
1. `source .venv/bin/activate`
2. `python -m scripts.plot_minifig_character_spans`
Le script lit `data/intermediate/minifigs_by_set.csv` et `data/intermediate/sets_enriched.csv`, calcule la première et la dernière année d'apparition pour chaque personnage (hors figurants), sérialise `data/intermediate/minifig_character_spans.csv`, puis trace `figures/step24/minifig_character_spans.png` (barres horizontales des spans).
Le script lit `data/intermediate/minifigs_by_set.csv` et `data/intermediate/sets_enriched.csv`, calcule la première et la dernière année d'apparition pour chaque personnage (hors figurants), sérialise `data/intermediate/minifig_character_spans.csv`, puis trace `figures/step24/minifig_character_spans.png` (barres horizontales des spans). Les barres sont colorées selon le genre issu de `config/known_character_genders.csv`.

54
config/known_character_genders.csv Normal file
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@ -0,0 +1,54 @@
known_character,gender
ACU Trooper,unknown
Allison Miles,female
Alan Grant,male
Atwater,male
Barry,male
Ben,male
Brooklynn,female
Claire Dearing,female
Danny Nedermeyer,male
Darius,male
Dennis Nedry,male
Donald Gennaro,male
Dr Wu,male
Duncan Kincaid,male
Eli Mills,male
Ellie Sattler,female
Figurant,unknown
Franklin Web,male
Franklin Webb,male
Gray,male
Gray Mitchell,male
Gunnar Eversol,male
Henry Loomis,male
Henry Wu,male
Hudson Harper,male
Ian Malcolm,male
Isabella Delgado,female
John Hammond,male
Kayla Watts,female
Ken Wheatley,male
Kenji,male
Lex Murphy,female
LeClerc,male
Maisie Lockwood,female
Martin Krebs,male
Owen Grady,male
Rainn DeLaCourt,male
Ray Arnold,male
Reuben Delgado,male
Robert Muldoon,male
Sammy,female
Simon Masrani,male
Sinjin Prescott,male
Soyona Santos,female
Teresa Delgado,female
Tim Murphy,male
Vic Hoskins,male
Xavier Dobbs,male
Yaz,female
Zach,male
Zach Mitchell,male
Zia Rodriguez,female
Zora Bennett,female
1 known_character gender
2 ACU Trooper unknown
3 Allison Miles female
4 Alan Grant male
5 Atwater male
6 Barry male
7 Ben male
8 Brooklynn female
9 Claire Dearing female
10 Danny Nedermeyer male
11 Darius male
12 Dennis Nedry male
13 Donald Gennaro male
14 Dr Wu male
15 Duncan Kincaid male
16 Eli Mills male
17 Ellie Sattler female
18 Figurant unknown
19 Franklin Web male
20 Franklin Webb male
21 Gray male
22 Gray Mitchell male
23 Gunnar Eversol male
24 Henry Loomis male
25 Henry Wu male
26 Hudson Harper male
27 Ian Malcolm male
28 Isabella Delgado female
29 John Hammond male
30 Kayla Watts female
31 Ken Wheatley male
32 Kenji male
33 Lex Murphy female
34 LeClerc male
35 Maisie Lockwood female
36 Martin Krebs male
37 Owen Grady male
38 Rainn DeLaCourt male
39 Ray Arnold male
40 Reuben Delgado male
41 Robert Muldoon male
42 Sammy female
43 Simon Masrani male
44 Sinjin Prescott male
45 Soyona Santos female
46 Teresa Delgado female
47 Tim Murphy male
48 Vic Hoskins male
49 Xavier Dobbs male
50 Yaz female
51 Zach male
52 Zach Mitchell male
53 Zia Rodriguez female
54 Zora Bennett female

74
lib/plots/minifig_character_spans.py
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@ -1,20 +1,35 @@
"""Diagramme de longévité des personnages (bornes d'apparition)."""
from pathlib import Path
from typing import List
from typing import Dict, List
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from lib.filesystem import ensure_parent_dir
from lib.milestones import load_milestones
from lib.rebrickable.stats import read_rows
GENDER_COLORS = {
"male": "#4c72b0",
"female": "#c44e52",
"unknown": "#7f7f7f",
}
GENDER_LABELS = {
"male": "Homme",
"female": "Femme",
"unknown": "Inconnu",
"": "Inconnu",
}
def load_spans(path: Path) -> List[dict]:
"""Charge le CSV des bornes min/max par personnage."""
return read_rows(path)
def plot_character_spans(spans_path: Path, destination_path: Path) -> None:
def plot_character_spans(spans_path: Path, destination_path: Path, milestones_path: Path | None = None) -> None:
"""Trace un diagramme en barres représentant la longévité des personnages."""
rows = load_spans(spans_path)
if not rows:
@ -23,18 +38,22 @@ def plot_character_spans(spans_path: Path, destination_path: Path) -> None:
starts = [int(row["start_year"]) for row in rows]
ends = [int(row["end_year"]) for row in rows]
counts = [int(row["total_minifigs"]) for row in rows]
genders = [row.get("gender", "") for row in rows]
colors = [GENDER_COLORS.get(gender.strip().lower(), GENDER_COLORS["unknown"]) for gender in genders]
positions = list(range(len(rows)))
widths = [end - start + 1 for start, end in zip(starts, ends)]
min_year = min(starts)
max_year = max(ends)
height = max(5, len(rows) * 0.3)
milestones = load_milestones(milestones_path) if milestones_path else []
fig, ax = plt.subplots(figsize=(12, height))
bars = ax.barh(
positions,
widths,
left=starts,
color="#1f77b4",
color=colors,
edgecolor="#0d0d0d",
linewidth=0.6,
)
@ -57,6 +76,55 @@ def plot_character_spans(spans_path: Path, destination_path: Path) -> None:
color="#0d0d0d",
)
legend_entries = []
seen = set()
for gender in genders:
normalized = gender.strip().lower()
if normalized in seen:
continue
seen.add(normalized)
legend_entries.append(
Patch(
facecolor=GENDER_COLORS.get(normalized, GENDER_COLORS["unknown"]),
edgecolor="#0d0d0d",
linewidth=0.6,
label=GENDER_LABELS.get(normalized, "Inconnu"),
)
)
if legend_entries:
ax.legend(handles=legend_entries, title="Genre", loc="lower right")
if milestones:
milestones_in_range = sorted(
[m for m in milestones if min_year <= m["year"] <= max_year],
key=lambda m: (m["year"], m["description"]),
)
milestone_offsets: Dict[int, int] = {}
offset_step = 0.2
max_offset = 0
y_bottom, y_top = ax.get_ylim()
text_y = y_top - (y_top - y_bottom) * 0.01
for milestone in milestones_in_range:
year = milestone["year"]
count_for_year = milestone_offsets.get(year, 0)
milestone_offsets[year] = count_for_year + 1
horizontal_offset = offset_step * (count_for_year // 2 + 1)
max_offset = max(max_offset, count_for_year)
if count_for_year % 2 == 1:
horizontal_offset *= -1
text_x = year + horizontal_offset
ax.axvline(year, color="#d62728", linestyle="--", linewidth=1, alpha=0.65)
ax.text(
text_x,
text_y,
milestone["description"],
rotation=90,
verticalalignment="top",
horizontalalignment="center",
fontsize=8,
color="#d62728",
)
ensure_parent_dir(destination_path)
fig.tight_layout()
fig.savefig(destination_path, dpi=160)

35
lib/plots/minifig_characters.py
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@ -4,11 +4,25 @@ from pathlib import Path
from typing import List
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from lib.filesystem import ensure_parent_dir
from lib.rebrickable.stats import read_rows
GENDER_COLORS = {
"male": "#4c72b0",
"female": "#c44e52",
"unknown": "#7f7f7f",
}
GENDER_LABELS = {
"male": "Homme",
"female": "Femme",
"unknown": "Inconnu",
"": "Inconnu",
}
def load_counts(path: Path) -> List[dict]:
"""Charge le CSV des comptes par personnage."""
return read_rows(path)
@ -24,11 +38,13 @@ def plot_minifigs_per_character(counts_path: Path, destination_path: Path) -> No
rows = load_counts(counts_path)
characters = [row["known_character"] for row in rows]
counts = [int(row["minifig_count"]) for row in rows]
genders = [row.get("gender", "") for row in rows]
colors = [GENDER_COLORS.get(gender.strip().lower(), GENDER_COLORS["unknown"]) for gender in genders]
positions = list(range(len(rows)))
height = max(6, len(rows) * 0.22)
fig, ax = plt.subplots(figsize=(12, height))
bars = ax.barh(positions, counts, color="#1f77b4", edgecolor="#0d0d0d", linewidth=0.6)
bars = ax.barh(positions, counts, color=colors, edgecolor="#0d0d0d", linewidth=0.6)
ax.set_yticks(positions)
ax.set_yticklabels(characters)
ax.invert_yaxis()
@ -40,6 +56,23 @@ def plot_minifigs_per_character(counts_path: Path, destination_path: Path) -> No
for index, bar in enumerate(bars):
value = counts[index]
ax.text(value + 0.1, bar.get_y() + bar.get_height() / 2, str(value), va="center", fontsize=8)
legend_entries = []
seen = set()
for gender in genders:
normalized = gender.strip().lower()
if normalized in seen:
continue
seen.add(normalized)
legend_entries.append(
Patch(
facecolor=GENDER_COLORS.get(normalized, GENDER_COLORS["unknown"]),
edgecolor="#0d0d0d",
linewidth=0.6,
label=GENDER_LABELS.get(normalized, "Inconnu"),
)
)
if legend_entries:
ax.legend(handles=legend_entries, title="Genre", loc="lower right")
ensure_parent_dir(destination_path)
fig.tight_layout()

21
lib/rebrickable/minifig_characters.py
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@ -1,12 +1,12 @@
"""Agrégation des minifigs par personnage représenté."""
import csv
from collections import defaultdict
from pathlib import Path
from typing import Dict, Iterable, List, Sequence, Set
from lib.rebrickable.stats import read_rows
from lib.filesystem import ensure_parent_dir
import csv
from lib.rebrickable.stats import read_rows
def load_minifigs_by_set(path: Path) -> List[dict]:
@ -15,17 +15,21 @@ def load_minifigs_by_set(path: Path) -> List[dict]:
def aggregate_by_character(rows: Iterable[dict]) -> List[dict]:
"""Compte les minifigs distinctes par personnage (fig_num unique)."""
"""Compte les minifigs distinctes par personnage (fig_num unique) avec genre."""
fig_nums_by_character: Dict[str, set] = defaultdict(set)
genders: Dict[str, str] = {}
for row in rows:
character = row["known_character"].strip()
fig_num = row["fig_num"].strip()
gender = row.get("gender", "").strip()
if character == "" or fig_num == "":
continue
fig_nums_by_character[character].add(fig_num)
if character not in genders:
genders[character] = gender
aggregates: List[dict] = []
for character, fig_nums in fig_nums_by_character.items():
aggregates.append({"known_character": character, "minifig_count": len(fig_nums)})
aggregates.append({"known_character": character, "gender": genders.get(character, ""), "minifig_count": len(fig_nums)})
aggregates.sort(key=lambda r: (-r["minifig_count"], r["known_character"]))
return aggregates
@ -33,7 +37,7 @@ def aggregate_by_character(rows: Iterable[dict]) -> List[dict]:
def write_character_counts(path: Path, rows: Sequence[dict]) -> None:
"""Écrit le CSV des comptes par personnage."""
ensure_parent_dir(path)
fieldnames = ["known_character", "minifig_count"]
fieldnames = ["known_character", "gender", "minifig_count"]
with path.open("w", newline="") as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
@ -110,9 +114,11 @@ def aggregate_character_spans(
excluded = set(excluded_characters or [])
spans: Dict[str, Dict[str, int]] = {}
total_counts: Dict[str, int] = defaultdict(int)
genders: Dict[str, str] = {}
for row in minifigs_rows:
character = row["known_character"].strip()
fig_num = row["fig_num"].strip()
gender = row.get("gender", "").strip()
if character == "" or fig_num == "":
continue
if character in excluded:
@ -122,6 +128,8 @@ def aggregate_character_spans(
continue
year_int = int(year)
total_counts[character] += 1
if character not in genders:
genders[character] = gender
current = spans.get(character)
if current is None:
spans[character] = {"start": year_int, "end": year_int}
@ -136,6 +144,7 @@ def aggregate_character_spans(
"start_year": str(bounds["start"]),
"end_year": str(bounds["end"]),
"total_minifigs": str(total_counts[character]),
"gender": genders.get(character, ""),
}
)
results.sort(key=lambda r: (int(r["start_year"]), int(r["end_year"]), r["known_character"]))
@ -145,7 +154,7 @@ def aggregate_character_spans(
def write_character_spans(path: Path, rows: Sequence[dict]) -> None:
"""Écrit le CSV des bornes min/max par personnage."""
ensure_parent_dir(path)
fieldnames = ["known_character", "start_year", "end_year", "total_minifigs"]
fieldnames = ["known_character", "start_year", "end_year", "total_minifigs", "gender"]
with path.open("w", newline="") as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()

18
lib/rebrickable/minifigs_by_set.py
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@ -128,6 +128,16 @@ def load_aliases(path: Path) -> Dict[str, str]:
return aliases
def load_gender_overrides(path: Path) -> Dict[str, str]:
"""Charge les correspondances personnage -> genre."""
overrides: Dict[str, str] = {}
with path.open() as gender_file:
reader = csv.DictReader(gender_file)
for row in reader:
overrides[row["known_character"].lower()] = row["gender"]
return overrides
def normalize_known_character(raw_known: str, extracted_name: str, aliases: Dict[str, str]) -> str:
"""Nettoie et mappe un nom vers une version canonique."""
base = raw_known or extracted_name
@ -201,6 +211,7 @@ def aggregate_heads_by_set(
minifig_heads: Dict[str, Set[str]],
minifig_catalog: Dict[str, dict],
aliases: Dict[str, str],
gender_overrides: Dict[str, str],
) -> List[dict]:
"""Agrège les têtes de minifigs par set en éliminant les rechanges et doublons."""
seen: Set[Tuple[str, str]] = set()
@ -228,12 +239,14 @@ def aggregate_heads_by_set(
normalized = normalize_known_character(known_character, extracted, aliases)
if matched_fig == "":
continue
gender = gender_overrides.get(normalized.lower(), "")
heads.append(
{
"set_num": row["set_num"],
"part_num": row["part_num"],
"known_character": normalized,
"fig_num": matched_fig,
"gender": gender,
}
)
seen.add(key)
@ -244,7 +257,7 @@ def aggregate_heads_by_set(
def write_heads_by_set(destination_path: Path, rows: Sequence[dict]) -> None:
"""Écrit le CSV intermédiaire listant les têtes de minifigs par set."""
ensure_parent_dir(destination_path)
fieldnames = ["set_num", "part_num", "known_character", "fig_num"]
fieldnames = ["set_num", "part_num", "known_character", "fig_num", "gender"]
with destination_path.open("w", newline="") as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
@ -260,6 +273,7 @@ def build_minifigs_by_set(
inventory_minifigs_path: Path,
minifigs_path: Path,
aliases_path: Path,
gender_overrides_path: Path,
destination_path: Path,
) -> None:
"""Construit le CSV listant les têtes de minifigs présentes par set."""
@ -271,6 +285,7 @@ def build_minifigs_by_set(
minifig_heads = build_minifig_heads_lookup(minifig_catalog, latest_inventories, inventory_parts_path, head_parts)
set_minifigs = build_set_minifigs_lookup(latest_inventories, inventory_minifigs_path)
aliases = load_aliases(aliases_path)
gender_overrides = load_gender_overrides(gender_overrides_path)
heads = aggregate_heads_by_set(
parts_rows,
parts_catalog,
@ -279,5 +294,6 @@ def build_minifigs_by_set(
minifig_heads,
minifig_catalog,
aliases,
gender_overrides,
)
write_heads_by_set(destination_path, heads)

2
scripts/compute_minifigs_by_set.py
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@ -12,6 +12,7 @@ INVENTORY_PARTS_PATH = Path("data/raw/inventory_parts.csv")
INVENTORY_MINIFIGS_PATH = Path("data/raw/inventory_minifigs.csv")
MINIFIGS_PATH = Path("data/raw/minifigs.csv")
ALIASES_PATH = Path("config/known_character_aliases.csv")
GENDERS_PATH = Path("config/known_character_genders.csv")
DESTINATION_PATH = Path("data/intermediate/minifigs_by_set.csv")
@ -25,6 +26,7 @@ def main() -> None:
INVENTORY_MINIFIGS_PATH,
MINIFIGS_PATH,
ALIASES_PATH,
GENDERS_PATH,
DESTINATION_PATH,
)

3
scripts/plot_minifig_character_spans.py
View File

@ -15,6 +15,7 @@ MINIFIGS_BY_SET_PATH = Path("data/intermediate/minifigs_by_set.csv")
SETS_ENRICHED_PATH = Path("data/intermediate/sets_enriched.csv")
SPANS_PATH = Path("data/intermediate/minifig_character_spans.csv")
DESTINATION_PATH = Path("figures/step24/minifig_character_spans.png")
MILESTONES_PATH = Path("config/milestones.csv")
EXCLUDED_CHARACTERS = ["Figurant"]
@ -24,7 +25,7 @@ def main() -> None:
sets_years = load_sets_enriched(SETS_ENRICHED_PATH)
spans = aggregate_character_spans(minifigs, sets_years, excluded_characters=EXCLUDED_CHARACTERS)
write_character_spans(SPANS_PATH, spans)
plot_character_spans(SPANS_PATH, DESTINATION_PATH)
plot_character_spans(SPANS_PATH, DESTINATION_PATH, milestones_path=MILESTONES_PATH)
if __name__ == "__main__":

8
tests/test_minifig_character_spans_plot.py
View File

@ -12,11 +12,11 @@ matplotlib.use("Agg")
def test_plot_character_spans(tmp_path: Path) -> None:
"""Génère le graphique de span des personnages."""
spans_path = tmp_path / "minifig_character_spans.csv"
destination = tmp_path / "figures" / "step23" / "minifig_character_spans.png"
destination = tmp_path / "figures" / "step24" / "minifig_character_spans.png"
spans_path.write_text(
"known_character,start_year,end_year,total_minifigs\n"
"Owen Grady,2020,2022,3\n"
"Figurant,2019,2020,2\n"
"known_character,start_year,end_year,total_minifigs,gender\n"
"Owen Grady,2020,2022,3,male\n"
"Figurant,2019,2020,2,unknown\n"
)
plot_character_spans(spans_path, destination)

100
tests/test_minifig_characters.py
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@ -15,18 +15,54 @@ def test_aggregate_by_character_counts_unique_figs() -> None:
"""Compter les minifigs distinctes par personnage en excluant les noms vides."""
aggregates = aggregate_by_character(
[
{"set_num": "123-1", "part_num": "head-a", "known_character": "Owen Grady", "fig_num": "fig-owen-1"},
{"set_num": "124-1", "part_num": "head-b", "known_character": "Owen Grady", "fig_num": "fig-owen-1"},
{"set_num": "125-1", "part_num": "head-c", "known_character": "Owen Grady", "fig_num": "fig-owen-2"},
{"set_num": "126-1", "part_num": "head-d", "known_character": "Figurant", "fig_num": "fig-guard-1"},
{"set_num": "128-1", "part_num": "head-f", "known_character": "Figurant", "fig_num": "fig-guard-1"},
{"set_num": "129-1", "part_num": "head-g", "known_character": "", "fig_num": "fig-guard-2"},
{
"set_num": "123-1",
"part_num": "head-a",
"known_character": "Owen Grady",
"fig_num": "fig-owen-1",
"gender": "male",
},
{
"set_num": "124-1",
"part_num": "head-b",
"known_character": "Owen Grady",
"fig_num": "fig-owen-1",
"gender": "male",
},
{
"set_num": "125-1",
"part_num": "head-c",
"known_character": "Owen Grady",
"fig_num": "fig-owen-2",
"gender": "male",
},
{
"set_num": "126-1",
"part_num": "head-d",
"known_character": "Figurant",
"fig_num": "fig-guard-1",
"gender": "unknown",
},
{
"set_num": "128-1",
"part_num": "head-f",
"known_character": "Figurant",
"fig_num": "fig-guard-1",
"gender": "unknown",
},
{
"set_num": "129-1",
"part_num": "head-g",
"known_character": "",
"fig_num": "fig-guard-2",
"gender": "unknown",
},
]
)
assert aggregates == [
{"known_character": "Owen Grady", "minifig_count": 2},
{"known_character": "Figurant", "minifig_count": 1},
{"known_character": "Owen Grady", "gender": "male", "minifig_count": 2},
{"known_character": "Figurant", "gender": "unknown", "minifig_count": 1},
]
@ -34,13 +70,13 @@ def test_write_character_counts_outputs_csv(tmp_path: Path) -> None:
"""Écrit le CSV des comptes par personnage."""
destination = tmp_path / "counts.csv"
rows = [
{"known_character": "A", "minifig_count": 2},
{"known_character": "B", "minifig_count": 1},
{"known_character": "A", "gender": "male", "minifig_count": 2},
{"known_character": "B", "gender": "female", "minifig_count": 1},
]
write_character_counts(destination, rows)
assert destination.read_text() == "known_character,minifig_count\nA,2\nB,1\n"
assert destination.read_text() == "known_character,gender,minifig_count\nA,male,2\nB,female,1\n"
def test_aggregate_presence_by_year_excludes_figurants(tmp_path: Path) -> None:
@ -52,8 +88,20 @@ def test_aggregate_presence_by_year_excludes_figurants(tmp_path: Path) -> None:
"124-1,2021\n"
)
minifigs_rows = [
{"set_num": "123-1", "known_character": "Owen Grady", "fig_num": "fig-owen", "part_num": "head-a"},
{"set_num": "124-1", "known_character": "Figurant", "fig_num": "fig-guard", "part_num": "head-b"},
{
"set_num": "123-1",
"known_character": "Owen Grady",
"fig_num": "fig-owen",
"part_num": "head-a",
"gender": "male",
},
{
"set_num": "124-1",
"known_character": "Figurant",
"fig_num": "fig-guard",
"part_num": "head-b",
"gender": "unknown",
},
]
sets_years = load_sets_enriched(sets_path)
@ -76,13 +124,31 @@ def test_aggregate_character_spans_excludes_figurants(tmp_path: Path) -> None:
)
sets_years = load_sets_enriched(sets_path)
minifigs_rows = [
{"set_num": "123-1", "known_character": "Owen Grady", "fig_num": "fig-owen", "part_num": "head-a"},
{"set_num": "124-1", "known_character": "Owen Grady", "fig_num": "fig-owen", "part_num": "head-a"},
{"set_num": "125-1", "known_character": "Figurant", "fig_num": "fig-guard", "part_num": "head-b"},
{
"set_num": "123-1",
"known_character": "Owen Grady",
"fig_num": "fig-owen",
"part_num": "head-a",
"gender": "male",
},
{
"set_num": "124-1",
"known_character": "Owen Grady",
"fig_num": "fig-owen",
"part_num": "head-a",
"gender": "male",
},
{
"set_num": "125-1",
"known_character": "Figurant",
"fig_num": "fig-guard",
"part_num": "head-b",
"gender": "unknown",
},
]
spans = aggregate_character_spans(minifigs_rows, sets_years, excluded_characters=["Figurant"])
assert spans == [
{"known_character": "Owen Grady", "start_year": "2020", "end_year": "2021", "total_minifigs": "2"},
{"known_character": "Owen Grady", "start_year": "2020", "end_year": "2021", "total_minifigs": "2", "gender": "male"},
]

6
tests/test_minifig_characters_plot.py
View File

@ -14,9 +14,9 @@ def test_plot_minifigs_per_character(tmp_path: Path) -> None:
counts_path = tmp_path / "counts.csv"
destination = tmp_path / "figures" / "step22" / "minifig_characters.png"
counts_path.write_text(
"known_character,minifig_count\n"
"Owen Grady,2\n"
"Figurant,1\n"
"known_character,gender,minifig_count\n"
"Owen Grady,male,2\n"
"Figurant,unknown,1\n"
)
plot_minifigs_per_character(counts_path, destination)

16
tests/test_minifigs_by_set.py
View File

@ -71,6 +71,13 @@ def test_build_minifigs_by_set_filters_spares_and_deduplicates(tmp_path) -> None
"alias,canonical\n"
"Guard in Helmet with Trans-Brown Visor,Figurant\n",
)
genders_path = tmp_path / "known_character_genders.csv"
write_csv(
genders_path,
"known_character,gender\n"
"Owen Grady,male\n"
"Figurant,unknown\n",
)
destination_path = tmp_path / "minifigs_by_set.csv"
build_minifigs_by_set(
@ -81,12 +88,13 @@ def test_build_minifigs_by_set_filters_spares_and_deduplicates(tmp_path) -> None
inventory_minifigs_path,
minifigs_path,
aliases_path,
genders_path,
destination_path,
)
assert destination_path.read_text() == (
"set_num,part_num,known_character,fig_num\n"
"123-1,head-a,Owen Grady,fig-owen\n"
"123-1,head-b,Figurant,fig-guard\n"
"124-1,head-b,Figurant,fig-guard\n"
"set_num,part_num,known_character,fig_num,gender\n"
"123-1,head-a,Owen Grady,fig-owen,male\n"
"123-1,head-b,Figurant,fig-guard,unknown\n"
"124-1,head-b,Figurant,fig-guard,unknown\n"
)