393 lines
13 KiB
Python
393 lines
13 KiB
Python
"""Tests de l'agrégation des minifigs par personnage."""
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from pathlib import Path
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from lib.rebrickable.minifig_characters import (
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aggregate_by_character,
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aggregate_by_gender,
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aggregate_new_character_sets,
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aggregate_new_characters_by_year,
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aggregate_variations_and_totals,
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aggregate_character_spans,
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aggregate_presence_by_year,
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load_sets_enriched,
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write_character_counts,
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write_new_character_sets_csv,
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write_new_character_sets_markdown,
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write_new_characters_by_year,
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write_character_variations_totals,
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write_gender_counts,
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)
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from lib.rebrickable.minifig_character_sets import load_sets
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def test_aggregate_by_character_counts_unique_figs() -> None:
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"""Compter les minifigs distinctes par personnage en excluant les noms vides."""
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aggregates = aggregate_by_character(
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[
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{
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"set_num": "123-1",
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"part_num": "head-a",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen-1",
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"gender": "male",
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},
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{
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"set_num": "124-1",
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"part_num": "head-b",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen-1",
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"gender": "male",
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},
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{
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"set_num": "125-1",
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"part_num": "head-c",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen-2",
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"gender": "male",
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},
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{
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"set_num": "126-1",
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"part_num": "head-d",
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"known_character": "Figurant",
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"fig_num": "fig-guard-1",
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"gender": "unknown",
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},
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{
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"set_num": "128-1",
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"part_num": "head-f",
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"known_character": "Figurant",
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"fig_num": "fig-guard-1",
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"gender": "unknown",
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},
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{
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"set_num": "129-1",
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"part_num": "head-g",
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"known_character": "",
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"fig_num": "fig-guard-2",
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"gender": "unknown",
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},
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]
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)
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assert aggregates == [
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{"known_character": "Owen Grady", "gender": "male", "minifig_count": 2},
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{"known_character": "Figurant", "gender": "unknown", "minifig_count": 1},
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]
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def test_aggregate_variations_and_totals_excludes_figurants() -> None:
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"""Compter le total et les variations en excluant les figurants."""
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aggregates = aggregate_variations_and_totals(
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[
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{
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"set_num": "123-1",
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"part_num": "head-a",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen-1",
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"gender": "male",
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},
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{
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"set_num": "124-1",
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"part_num": "head-b",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen-1",
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"gender": "male",
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},
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{
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"set_num": "125-1",
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"part_num": "head-c",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen-2",
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"gender": "male",
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},
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{
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"set_num": "126-1",
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"part_num": "head-d",
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"known_character": "Ellie Sattler",
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"fig_num": "fig-ellie-1",
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"gender": "female",
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},
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{
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"set_num": "127-1",
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"part_num": "head-e",
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"known_character": "Figurant",
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"fig_num": "fig-guard-1",
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"gender": "unknown",
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},
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],
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excluded_characters=["Figurant"],
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)
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assert aggregates == [
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{"known_character": "Owen Grady", "gender": "male", "variation_count": 2, "total_minifigs": 3},
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{"known_character": "Ellie Sattler", "gender": "female", "variation_count": 1, "total_minifigs": 1},
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]
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def test_aggregate_by_gender_counts_unique_figs() -> None:
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"""Compter les minifigs distinctes par genre."""
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aggregates = aggregate_by_gender(
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[
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{"fig_num": "fig-a", "gender": "male"},
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{"fig_num": "fig-a", "gender": "male"},
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{"fig_num": "fig-b", "gender": "female"},
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{"fig_num": "fig-c", "gender": ""},
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]
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)
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assert aggregates == [
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{"gender": "female", "minifig_count": "1"},
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{"gender": "male", "minifig_count": "1"},
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{"gender": "unknown", "minifig_count": "1"},
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]
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def test_aggregate_new_characters_by_year_limits_range(tmp_path: Path) -> None:
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"""Compter les nouveaux personnages par année en respectant la plage."""
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sets_path = tmp_path / "sets_enriched.csv"
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sets_path.write_text(
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"set_num,name,year,theme_id,num_parts,img_url,set_id,rebrickable_url,in_collection\n"
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"123-1,Set A,2015,0,0,,-,http://r/123-1,true\n"
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"124-1,Set B,2016,0,0,,-,http://r/124-1,true\n"
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"125-1,Set C,2017,0,0,,-,http://r/125-1,true\n"
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"126-1,Set D,2014,0,0,,-,http://r/126-1,true\n"
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)
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sets_years = load_sets_enriched(sets_path)
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minifigs_rows = [
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{"set_num": "123-1", "known_character": "Owen Grady", "fig_num": "fig-owen-1", "part_num": "head-a"},
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{"set_num": "124-1", "known_character": "Owen Grady", "fig_num": "fig-owen-2", "part_num": "head-b"},
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{"set_num": "125-1", "known_character": "Ellie Sattler", "fig_num": "fig-ellie-1", "part_num": "head-c"},
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{"set_num": "126-1", "known_character": "Alan Grant", "fig_num": "fig-grant-1", "part_num": "head-d"},
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]
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counts = aggregate_new_characters_by_year(
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minifigs_rows,
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sets_years,
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excluded_characters=["Figurant"],
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start_year=2015,
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end_year=2017,
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)
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assert counts == [
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{"year": "2015", "new_characters": "1"},
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{"year": "2016", "new_characters": "0"},
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{"year": "2017", "new_characters": "1"},
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]
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def test_aggregate_new_character_sets_returns_intro_sets(tmp_path: Path) -> None:
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"""Lister les personnages introduits avec les sets de l'année d'introduction."""
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sets_path = tmp_path / "sets_enriched.csv"
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sets_path.write_text(
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"set_num,name,year,theme_id,num_parts,img_url,set_id,rebrickable_url,in_collection\n"
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"123-1,Set A,2015,0,0,,123,http://r/123-1,true\n"
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"124-1,Set B,2015,0,0,,124,http://r/124-1,true\n"
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"125-1,Set C,2016,0,0,,125,http://r/125-1,true\n"
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)
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sets_lookup = load_sets(sets_path)
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minifigs_rows = [
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{"set_num": "123-1", "known_character": "Owen Grady", "fig_num": "fig-owen-1", "part_num": "head-a"},
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{"set_num": "124-1", "known_character": "Owen Grady", "fig_num": "fig-owen-2", "part_num": "head-b"},
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{"set_num": "125-1", "known_character": "Owen Grady", "fig_num": "fig-owen-3", "part_num": "head-c"},
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{"set_num": "125-1", "known_character": "Ellie Sattler", "fig_num": "fig-ellie-1", "part_num": "head-d"},
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]
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rows = aggregate_new_character_sets(
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minifigs_rows,
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sets_lookup,
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excluded_characters=["Figurant"],
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start_year=2015,
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end_year=2016,
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)
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assert rows == [
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{
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"year": "2015",
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"known_character": "Owen Grady",
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"set_num": "123-1",
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"set_id": "123",
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"set_name": "Set A",
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"rebrickable_url": "http://r/123-1",
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},
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{
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"year": "2015",
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"known_character": "Owen Grady",
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"set_num": "124-1",
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"set_id": "124",
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"set_name": "Set B",
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"rebrickable_url": "http://r/124-1",
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},
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{
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"year": "2016",
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"known_character": "Ellie Sattler",
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"set_num": "125-1",
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"set_id": "125",
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"set_name": "Set C",
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"rebrickable_url": "http://r/125-1",
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},
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]
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def test_write_character_counts_outputs_csv(tmp_path: Path) -> None:
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"""Écrit le CSV des comptes par personnage."""
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destination = tmp_path / "counts.csv"
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rows = [
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{"known_character": "A", "gender": "male", "minifig_count": 2},
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{"known_character": "B", "gender": "female", "minifig_count": 1},
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]
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write_character_counts(destination, rows)
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assert destination.read_text() == "known_character,gender,minifig_count\nA,male,2\nB,female,1\n"
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def test_write_gender_counts_outputs_csv(tmp_path: Path) -> None:
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"""Écrit le CSV des comptes par genre."""
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destination = tmp_path / "gender_counts.csv"
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rows = [
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{"gender": "male", "minifig_count": "2"},
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{"gender": "female", "minifig_count": "1"},
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]
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write_gender_counts(destination, rows)
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assert destination.read_text() == "gender,minifig_count\nmale,2\nfemale,1\n"
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def test_write_character_variations_totals_outputs_csv(tmp_path: Path) -> None:
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"""Écrit le CSV comparatif variations/total."""
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destination = tmp_path / "variations.csv"
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rows = [
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{"known_character": "A", "gender": "male", "variation_count": 2, "total_minifigs": 3},
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{"known_character": "B", "gender": "female", "variation_count": 1, "total_minifigs": 1},
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]
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write_character_variations_totals(destination, rows)
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assert destination.read_text() == "known_character,gender,variation_count,total_minifigs\nA,male,2,3\nB,female,1,1\n"
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def test_write_new_characters_by_year_outputs_csv(tmp_path: Path) -> None:
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"""Écrit le CSV des nouveaux personnages par année."""
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destination = tmp_path / "new_characters.csv"
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rows = [
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{"year": "2015", "new_characters": "3"},
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{"year": "2016", "new_characters": "1"},
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]
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write_new_characters_by_year(destination, rows)
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assert destination.read_text() == "year,new_characters\n2015,3\n2016,1\n"
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def test_write_new_character_sets_markdown_outputs_md(tmp_path: Path) -> None:
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"""Écrit le Markdown listant les nouveaux personnages et leurs sets."""
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destination = tmp_path / "new_characters.md"
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rows = [
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{
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"year": "2015",
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"known_character": "Owen Grady",
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"set_num": "123-1",
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"set_id": "123",
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"set_name": "Set A",
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"rebrickable_url": "http://r/123-1",
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},
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{
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"year": "2016",
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"known_character": "Ellie Sattler",
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"set_num": "125-1",
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"set_id": "125",
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"set_name": "Set C",
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"rebrickable_url": "http://r/125-1",
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},
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]
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write_new_character_sets_markdown(destination, rows)
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assert destination.read_text() == (
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"##### 2015\n\n"
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"- Owen Grady\n"
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" - [123](http://r/123-1) - Set A\n"
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"\n"
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"##### 2016\n\n"
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"- Ellie Sattler\n"
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" - [125](http://r/125-1) - Set C\n"
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"\n"
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)
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def test_aggregate_presence_by_year_excludes_figurants(tmp_path: Path) -> None:
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"""Calcule le total annuel en excluant les figurants."""
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sets_path = tmp_path / "sets_enriched.csv"
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sets_path.write_text(
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"set_num,year\n"
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"123-1,2020\n"
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"124-1,2021\n"
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)
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minifigs_rows = [
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{
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"set_num": "123-1",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen",
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"part_num": "head-a",
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"gender": "male",
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},
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{
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"set_num": "124-1",
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"known_character": "Figurant",
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"fig_num": "fig-guard",
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"part_num": "head-b",
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"gender": "unknown",
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},
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]
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sets_years = load_sets_enriched(sets_path)
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presence = aggregate_presence_by_year(minifigs_rows, sets_years, excluded_characters=["Figurant"])
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assert presence == [
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{"known_character": "Owen Grady", "year": "2020", "minifig_count": "1"},
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{"known_character": "Owen Grady", "year": "2021", "minifig_count": "0"},
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]
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def test_aggregate_character_spans_excludes_figurants(tmp_path: Path) -> None:
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"""Calcule les bornes min/max par personnage."""
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sets_path = tmp_path / "sets_enriched.csv"
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sets_path.write_text(
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"set_num,year\n"
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"123-1,2020\n"
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"124-1,2021\n"
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"125-1,2022\n"
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)
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sets_years = load_sets_enriched(sets_path)
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minifigs_rows = [
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{
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"set_num": "123-1",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen",
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"part_num": "head-a",
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"gender": "male",
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},
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{
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"set_num": "124-1",
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"known_character": "Owen Grady",
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"fig_num": "fig-owen",
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"part_num": "head-a",
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"gender": "male",
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},
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{
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"set_num": "125-1",
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"known_character": "Figurant",
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"fig_num": "fig-guard",
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"part_num": "head-b",
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"gender": "unknown",
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},
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]
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spans = aggregate_character_spans(minifigs_rows, sets_years, excluded_characters=["Figurant"])
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assert spans == [
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{"known_character": "Owen Grady", "start_year": "2020", "end_year": "2021", "total_minifigs": "2", "gender": "male"},
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]
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