Andreas/mtg_python_deckbuilder
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feat: locks/replace/compare/permalinks; perf: virtualization, LQIP, caching, diagnostics; add tests, docs, and issue/PR templates (flags OFF)

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matt 2025-08-28 14:57:22 -07:00
parent f8c6b5c07e
commit 721e1884af
41 changed files with 2960 additions and 143 deletions
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16
code/file_setup/setup.py
View file

@ -198,24 +198,24 @@ def regenerate_csv_by_color(color: str) -> None:
try:
if color not in SETUP_COLORS:
raise ValueError(f'Invalid color: {color}')
color_abv = COLOR_ABRV[SETUP_COLORS.index(color)]
logger.info(f'Downloading latest card data for {color} cards')
download_cards_csv(MTGJSON_API_URL, f'{CSV_DIRECTORY}/cards.csv')
logger.info('Loading and processing card data')
df = pd.read_csv(f'{CSV_DIRECTORY}/cards.csv', low_memory=False)
logger.info(f'Regenerating {color} cards CSV')
# Use shared utilities to base-filter once then slice color
base_df = filter_dataframe(df, [])
# Use shared utilities to base-filter once then slice color, honoring bans
base_df = filter_dataframe(df, BANNED_CARDS)
base_df[base_df['colorIdentity'] == color_abv].to_csv(
f'{CSV_DIRECTORY}/{color}_cards.csv', index=False
)
logger.info(f'Successfully regenerated {color} cards database')
except Exception as e:
logger.error(f'Failed to regenerate {color} cards: {str(e)}')
raise

22
code/file_setup/setup_utils.py
View file

@ -36,7 +36,8 @@ from .setup_constants import (
COLUMN_ORDER,
TAGGED_COLUMN_ORDER,
SETUP_COLORS,
COLOR_ABRV
COLOR_ABRV,
BANNED_CARDS,
)
from exceptions import (
MTGJSONDownloadError,
@ -138,7 +139,8 @@ def save_color_filtered_csvs(df: pd.DataFrame, out_dir: Union[str, Path]) -> Non
# Base-filter once for efficiency, then per-color filter without redoing base filters
try:
base_df = filter_dataframe(df, [])
# Apply full standard filtering including banned list once, then slice per color
base_df = filter_dataframe(df, BANNED_CARDS)
except Exception as e:
# Wrap any unexpected issues as DataFrameProcessingError
raise DataFrameProcessingError(
@ -207,10 +209,16 @@ def filter_dataframe(df: pd.DataFrame, banned_cards: List[str]) -> pd.DataFrame:
filtered_df = filtered_df[~filtered_df['printings'].str.contains(set_code, na=False)]
logger.debug('Removed illegal sets')
# Remove banned cards
for card in banned_cards:
filtered_df = filtered_df[~filtered_df['name'].str.contains(card, na=False)]
logger.debug('Removed banned cards')
# Remove banned cards (exact, case-insensitive match on name or faceName)
if banned_cards:
banned_set = {b.casefold() for b in banned_cards}
name_lc = filtered_df['name'].astype(str).str.casefold()
face_lc = filtered_df['faceName'].astype(str).str.casefold()
mask = ~(name_lc.isin(banned_set) | face_lc.isin(banned_set))
before = len(filtered_df)
filtered_df = filtered_df[mask]
after = len(filtered_df)
logger.debug(f'Removed banned cards: {before - after} filtered out')
# Remove special card types
for card_type in CARD_TYPES_TO_EXCLUDE:
@ -268,7 +276,7 @@ def filter_by_color_identity(df: pd.DataFrame, color_identity: str) -> pd.DataFr
# Apply base filtering
with tqdm(total=1, desc='Applying base filtering') as pbar:
filtered_df = filter_dataframe(df, [])
filtered_df = filter_dataframe(df, BANNED_CARDS)
pbar.update(1)
# Filter by color identity