mirror of
https://github.com/mwisnowski/mtg_python_deckbuilder.git
synced 2025-09-22 04:50:46 +02:00
Noticed that changes made in setup have drastically reduced cards after filtering. Redoing setup and it's associated files to fix this
This commit is contained in:
mwisnowski
parent
c1d6b5ce18
commit
c4d773d663
4 changed files with 369 additions and 117 deletions
85
exceptions.py
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85
exceptions.py
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@ -0,0 +1,85 @@
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"""Custom exceptions for MTG Python Deckbuilder setup operations."""
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class MTGSetupError(Exception):
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"""Base exception class for MTG setup-related errors."""
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pass
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class CSVFileNotFoundError(MTGSetupError):
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"""Exception raised when a required CSV file is not found.
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This exception is raised when attempting to access or process a CSV file
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that does not exist in the expected location.
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Args:
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message: Explanation of the error
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filename: Name of the missing CSV file
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"""
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def __init__(self, message: str, filename: str) -> None:
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self.filename = filename
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super().__init__(f"{message}: {filename}")
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class MTGJSONDownloadError(MTGSetupError):
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"""Exception raised when downloading data from MTGJSON fails.
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This exception is raised when there are issues downloading card data
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from the MTGJSON API, such as network errors or API failures.
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Args:
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message: Explanation of the error
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url: The URL that failed to download
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status_code: HTTP status code if available
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"""
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def __init__(self, message: str, url: str, status_code: int = None) -> None:
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self.url = url
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self.status_code = status_code
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status_info = f" (Status: {status_code})" if status_code else ""
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super().__init__(f"{message}: {url}{status_info}")
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class DataFrameProcessingError(MTGSetupError):
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"""Exception raised when DataFrame operations fail during setup.
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This exception is raised when there are issues processing card data
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in pandas DataFrames, such as filtering, sorting, or transformation errors.
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Args:
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message: Explanation of the error
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operation: The DataFrame operation that failed (e.g., 'color_filtering', 'commander_processing')
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details: Additional error details
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Examples:
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>>> raise DataFrameProcessingError(
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... "Invalid color identity",
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... "color_filtering",
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... "Color 'P' is not a valid MTG color"
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... )
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"""
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def __init__(self, message: str, operation: str, details: str = None) -> None:
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self.operation = operation
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self.details = details
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error_info = f" - {details}" if details else ""
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super().__init__(f"{message} during {operation}{error_info}")
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class ColorFilterError(MTGSetupError):
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"""Exception raised when color-specific filtering operations fail.
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This exception is raised when there are issues filtering cards by color,
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such as invalid color specifications or color identity processing errors.
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Args:
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message: Explanation of the error
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color: The color value that caused the error
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details: Additional error details
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Examples:
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>>> raise ColorFilterError(
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... "Invalid color specification",
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... "Purple",
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... "Color must be one of: W, U, B, R, G, or C"
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... )
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"""
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def __init__(self, message: str, color: str, details: str = None) -> None:
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self.color = color
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self.details = details
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error_info = f" - {details}" if details else ""
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super().__init__(f"{message} for color '{color}'{error_info}")
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60
settings.py
60
settings.py
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@ -764,3 +764,63 @@ VOLTRON_PATTERNS = [
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'living weapon',
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'reconfigure'
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]
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# Constants for setup and CSV processing
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MTGJSON_API_URL = 'https://mtgjson.com/api/v5/csv/cards.csv'
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LEGENDARY_OPTIONS = [
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'Legendary Creature',
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'Legendary Artifact',
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'Legendary Artifact Creature',
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'Legendary Enchantment Creature',
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'Legendary Planeswalker'
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]
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NON_LEGAL_SETS = [
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'PHTR', 'PH17', 'PH18', 'PH19', 'PH20', 'PH21',
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'UGL', 'UND', 'UNH', 'UST'
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]
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CARD_TYPES_TO_EXCLUDE = [
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'Plane —',
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'Conspiracy',
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'Vanguard',
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'Scheme',
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'Phenomenon',
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'Stickers',
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'Attraction',
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'Hero',
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'Contraption'
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]
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CSV_PROCESSING_COLUMNS = [
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'name',
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'faceName',
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'edhrecRank',
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'colorIdentity',
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'colors',
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'manaCost',
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'manaValue',
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'type',
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'layout',
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'text',
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'power',
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'toughness',
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'keywords',
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'side'
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]
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SETUP_COLORS = ['colorless', 'white', 'blue', 'black', 'green', 'red',
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'azorius', 'orzhov', 'selesnya', 'boros', 'dimir',
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'simic', 'izzet', 'golgari', 'rakdos', 'gruul',
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'bant', 'esper', 'grixis', 'jund', 'naya',
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'abzan', 'jeskai', 'mardu', 'sultai', 'temur',
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'dune', 'glint', 'ink', 'witch', 'yore', 'wubrg']
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COLOR_ABRV = ['Colorless', 'W', 'U', 'B', 'G', 'R',
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'U, W', 'B, W', 'G, W', 'R, W', 'B, U',
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'G, U', 'R, U', 'B, G', 'B, R', 'G, R',
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'G, U, W', 'B, U, W', 'B, R, U', 'B, G, R', 'G, R, W',
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'B, G, W', 'R, U, W', 'B, R, W', 'B, G, U', 'G, R, U',
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'B, G, R, W', 'B, G, R, U', 'G, R, U, W', 'B, G, U, W',
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'B, R, U, W', 'B, G, R, U, W']
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168
setup.py
168
setup.py
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@ -3,23 +3,18 @@ from __future__ import annotations
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import pandas as pd # type: ignore
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import requests # type: ignore
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import inquirer.prompt # type: ignore
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import logging
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from settings import banned_cards, csv_directory
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from settings import banned_cards, csv_directory, SETUP_COLORS, COLOR_ABRV, MTGJSON_API_URL
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from setup_utils import download_cards_csv, filter_dataframe, process_legendary_cards
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colors = ['colorless', 'white', 'blue', 'black', 'green', 'red',
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'azorius', 'orzhov', 'selesnya', 'boros', 'dimir',
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'simic', 'izzet', 'golgari', 'rakdos', 'gruul',
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'bant', 'esper', 'grixis', 'jund', 'naya',
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'abzan', 'jeskai', 'mardu', 'sultai', 'temur',
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'dune', 'glint', 'ink', 'witch', 'yore', 'wubrg']
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color_abrv = ['Colorless', 'W', 'U', 'B', 'G', 'R',
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'U, W', 'B, W', 'G, W', 'R, W', 'B, U',
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'G, U', 'R, U', 'B, G', 'B, R', 'G, R',
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'G, U, W', 'B, U, W', 'B, R, U', 'B, G, R', 'G, R, W',
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'B, G, W', 'R, U, W', 'B, R, W', 'B, G, U', 'G, R, U',
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'B, G, R, W', 'B, G, R, U', 'G, R, U, W', 'B, G, U, W',
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'B, R, U, W', 'B, G, R, U, W']
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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logger = logging.getLogger(__name__)
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def filter_by_color(df, column_name, value, new_csv_name):
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# Filter dataframe
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@ -54,110 +49,76 @@ def filter_by_color(df, column_name, value, new_csv_name):
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def determine_commanders():
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print('Generating commander_cards.csv, containing all cards elligible to be commanders.')
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# Filter dataframe
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while True:
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try:
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with open(f'{csv_directory}/cards.csv', 'r', encoding='utf-8'):
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# Check for cards.csv
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cards_file = f'{csv_directory}/cards.csv'
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try:
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with open(cards_file, 'r', encoding='utf-8'):
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print('cards.csv exists.')
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break
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except FileNotFoundError:
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# If the cards.csv file does not exist or can't be found, pull it from mtgjson.com
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print('cards.csv not found, downloading from mtgjson')
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url = 'https://mtgjson.com/api/v5/csv/cards.csv'
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r = requests.get(url)
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with open(f'{csv_directory}/cards.csv', 'wb') as outputfile:
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outputfile.write(r.content)
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download_cards_csv(MTGJSON_API_URL, cards_file)
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# Load cards.csv file into pandas dataframe so it can be further broken down
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df = pd.read_csv(f'{csv_directory}/cards.csv', low_memory=False)
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# Set frames that have nothing for color identity to be 'Colorless' instead
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# Load and process cards data
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df = pd.read_csv(cards_file, low_memory=False)
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df['colorIdentity'] = df['colorIdentity'].fillna('Colorless')
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legendary_options = ['Legendary Creature','Legendary Artifact', 'Legendary Artifact Creature', 'Legendary Enchantment Creature', 'Legendary Planeswalker']
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filtered_df = df[df['type'].str.contains('|'.join(legendary_options))]
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"""
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Save the filtered dataframe to a new csv file, and narrow down/rearranges the columns it
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keeps to increase readability/trim some extra data.
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Additionally attempts to remove as many duplicates (including cards with reversible prints,
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as well as taking out Arena-only cards.
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"""
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rows_to_drop = []
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non_legel_sets = ['PHTR', 'PH17', 'PH18' ,'PH19', 'PH20', 'PH21', 'UGL', 'UND', 'UNH', 'UST',]
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for index, row in filtered_df.iterrows():
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if ('Legendary Artifact' in row['type']
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or 'Legendary Planeswalker' in row['type']):
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if 'Legendary Artifact Creature' not in row['type']:
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if pd.notna(row['text']):
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if f'{row['name']} can be your commander' not in row['text']:
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rows_to_drop.append(index)
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for illegal_set in non_legel_sets:
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if illegal_set in row['printings']:
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rows_to_drop.append(index)
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# Process legendary cards
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filtered_df = process_legendary_cards(df)
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filtered_df = filtered_df.drop(rows_to_drop)
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# Apply standard filters
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filtered_df = filter_dataframe(filtered_df, banned_cards)
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filtered_df.sort_values('name')
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filtered_df = filtered_df.loc[filtered_df['layout'] != 'reversible_card']
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filtered_df = filtered_df[filtered_df['availability'].str.contains('paper')]
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filtered_df = filtered_df.loc[filtered_df['promoTypes'] != 'playtest']
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filtered_df = filtered_df.loc[filtered_df['securityStamp'] != 'heart']
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filtered_df = filtered_df.loc[filtered_df['securityStamp'] != 'acorn']
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for card in banned_cards:
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filtered_df = filtered_df[~filtered_df['name'].str.contains(card)]
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card_types = ['Plane —', 'Conspiracy', 'Vanguard', 'Scheme', 'Phenomenon', 'Stickers', 'Attraction', 'Hero', 'Contraption']
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for card_type in card_types:
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filtered_df = filtered_df[~filtered_df['type'].str.contains(card_type)]
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filtered_df['faceName'] = filtered_df['faceName'].fillna(filtered_df['name'])
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filtered_df.drop_duplicates(subset='faceName', keep='first', inplace=True)
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columns_to_keep = ['name', 'faceName','edhrecRank','colorIdentity', 'colors', 'manaCost', 'manaValue', 'type', 'layout', 'text', 'power', 'toughness', 'keywords', 'side']
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filtered_df = filtered_df[columns_to_keep]
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filtered_df.sort_values(by=['name', 'side'], key=lambda col: col.str.lower(), inplace=True)
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# Save commander cards
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filtered_df.to_csv(f'{csv_directory}/commander_cards.csv', index=False)
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print('commander_cards.csv file generated.')
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except Exception as e:
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print(f'Error generating commander cards: {str(e)}')
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raise
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def initial_setup():
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print('Checking for cards.csv file.\n')
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while True:
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"""Perform initial setup by downloading card data and creating filtered CSV files.
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This function:
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1. Downloads the latest card data from MTGJSON if needed
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2. Creates color-filtered CSV files
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3. Generates commander-eligible cards list
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Uses utility functions from setup_utils.py for file operations and data processing.
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Implements proper error handling for file operations and data processing.
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"""
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logger.info('Checking for cards.csv file')
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try:
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with open(f'{csv_directory}/cards.csv', 'r', encoding='utf-8'):
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print('cards.csv exists.')
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break
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cards_file = f'{csv_directory}/cards.csv'
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try:
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with open(cards_file, 'r', encoding='utf-8'):
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logger.info('cards.csv exists')
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except FileNotFoundError:
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# If the cards.csv file does not exist or can't be found, pull it from mtgjson.com
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print('cards.csv not found, downloading from mtgjson')
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url = 'https://mtgjson.com/api/v5/csv/cards.csv'
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r = requests.get(url)
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with open(f'{csv_directory}/cards.csv', 'wb') as outputfile:
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outputfile.write(r.content)
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logger.info('cards.csv not found, downloading from mtgjson')
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download_cards_csv(MTGJSON_API_URL, cards_file)
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# Load cards.csv file into pandas dataframe so it can be further broken down
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df = pd.read_csv(f'{csv_directory}/cards.csv', low_memory=False)
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# Set frames that have nothing for color identity to be 'Colorless' instead
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df = pd.read_csv(cards_file, low_memory=False)
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df['colorIdentity'] = df['colorIdentity'].fillna('Colorless')
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# Check for and create missing, individual color identity sorted CSVs
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print('Checking for color identity sorted files.\n')
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logger.info('Checking for color identity sorted files')
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# For loop to iterate through the colors
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for i in range(min(len(colors), len(color_abrv))):
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print(f'Checking for {colors[i]}_cards.csv.')
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for i in range(min(len(SETUP_COLORS), len(COLOR_ABRV))):
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logger.info(f'Checking for {SETUP_COLORS[i]}_cards.csv')
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try:
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with open(f'{csv_directory}/{colors[i]}_cards.csv', 'r', encoding='utf-8'):
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print(f'{colors[i]}_cards.csv exists.\n')
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with open(f'{csv_directory}/{SETUP_COLORS[i]}_cards.csv', 'r', encoding='utf-8'):
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logger.info(f'{SETUP_COLORS[i]}_cards.csv exists')
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except FileNotFoundError:
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print(f'{colors[i]}_cards.csv not found, creating one.\n')
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filter_by_color(df, 'colorIdentity', color_abrv[i], f'{csv_directory}/{colors[i]}_cards.csv')
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logger.info(f'{SETUP_COLORS[i]}_cards.csv not found, creating one')
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filter_by_color(df, 'colorIdentity', COLOR_ABRV[i], f'{csv_directory}/{SETUP_COLORS[i]}_cards.csv')
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# Once by-color lists have been made, Determine legendary creatures
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# Generate commander list
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determine_commanders()
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# Once Legendary creatures are determined, generate staple lists
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# generate_staple_lists()
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except Exception as e:
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logger.error(f'Error during initial setup: {str(e)}')
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raise
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def regenerate_csvs_all():
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"""
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@ -188,10 +149,10 @@ def regenerate_csvs_all():
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print('Regenerating color identity sorted files.\n')
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# For loop to iterate through the colors
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for i in range(min(len(colors), len(color_abrv))):
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print(f'Regenerating {colors[i]}_cards.csv.')
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filter_by_color(df, 'colorIdentity', color_abrv[i], f'csv_files/{colors[i]}_cards.csv')
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print(f'A new {colors[i]}_cards.csv file has been made.\n')
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for i in range(min(len(SETUP_COLORS), len(COLOR_ABRV))):
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print(f'Regenerating {SETUP_COLORS[i]}_cards.csv.')
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filter_by_color(df, 'colorIdentity', COLOR_ABRV[i], f'csv_files/{SETUP_COLORS[i]}_cards.csv')
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print(f'A new {SETUP_COLORS[i]}_cards.csv file has been made.\n')
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# Once files are regenerated, create a new legendary list
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determine_commanders()
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@ -201,8 +162,8 @@ def regenerate_csv_by_color(color):
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Pull the original cards.csv file and remake the {color}_cards.csv files
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"""
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# Determine the color_abv to use
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color_abrv_index = colors.index(color)
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color_abv = color_abrv[color_abrv_index]
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COLOR_ABRV_index = SETUP_COLORS.index(color)
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color_abv = COLOR_ABRV[COLOR_ABRV_index]
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print('Downloading cards.csv from mtgjson')
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url = 'https://mtgjson.com/api/v5/csv/cards.csv'
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r = requests.get(url)
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@ -258,7 +219,4 @@ def setup():
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break
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break
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#regenerate_csvs_all()
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#regenerate_csv_by_color('white')
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#determine_commanders()
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#set_lands()
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initial_setup()
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149
setup_utils.py
Normal file
149
setup_utils.py
Normal file
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@ -0,0 +1,149 @@
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from __future__ import annotations
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import pandas as pd
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import requests
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import logging
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from tqdm import tqdm
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from pathlib import Path
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from typing import List, Optional, Union
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from settings import (
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CSV_PROCESSING_COLUMNS,
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CARD_TYPES_TO_EXCLUDE,
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NON_LEGAL_SETS,
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LEGENDARY_OPTIONS
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)
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from exceptions import CSVFileNotFoundError, MTGJSONDownloadError, DataFrameProcessingError
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def download_cards_csv(url: str, output_path: Union[str, Path]) -> None:
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"""Download cards data from MTGJSON and save to CSV.
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Args:
|
||||
url: URL to download cards data from
|
||||
output_path: Path to save the downloaded CSV file
|
||||
|
||||
Raises:
|
||||
MTGJSONDownloadError: If download fails or response is invalid
|
||||
"""
|
||||
try:
|
||||
response = requests.get(url, stream=True)
|
||||
response.raise_for_status()
|
||||
total_size = int(response.headers.get('content-length', 0))
|
||||
|
||||
with open(output_path, 'wb') as f:
|
||||
with tqdm(total=total_size, unit='iB', unit_scale=True, desc='Downloading cards data') as pbar:
|
||||
for chunk in response.iter_content(chunk_size=8192):
|
||||
size = f.write(chunk)
|
||||
pbar.update(size)
|
||||
|
||||
except requests.RequestException as e:
|
||||
raise MTGJSONDownloadError(
|
||||
"Failed to download cards data",
|
||||
url,
|
||||
getattr(e.response, 'status_code', None) if hasattr(e, 'response') else None
|
||||
) from e
|
||||
def check_csv_exists(filepath: Union[str, Path]) -> bool:
|
||||
"""Check if a CSV file exists at the specified path.
|
||||
|
||||
Args:
|
||||
filepath: Path to check for CSV file
|
||||
|
||||
Returns:
|
||||
True if file exists, False otherwise
|
||||
"""
|
||||
return Path(filepath).is_file()
|
||||
|
||||
def filter_dataframe(df: pd.DataFrame, banned_cards: List[str]) -> pd.DataFrame:
|
||||
"""Apply standard filters to the cards DataFrame.
|
||||
|
||||
Args:
|
||||
df: DataFrame to filter
|
||||
banned_cards: List of banned card names to exclude
|
||||
|
||||
Returns:
|
||||
Filtered DataFrame
|
||||
|
||||
Raises:
|
||||
DataFrameProcessingError: If filtering operations fail
|
||||
"""
|
||||
try:
|
||||
# Fill null color identities
|
||||
df['colorIdentity'] = df['colorIdentity'].fillna('Colorless')
|
||||
|
||||
# Basic filters
|
||||
filtered_df = df[
|
||||
(df['layout'] != 'reversible_card') &
|
||||
(df['availability'].str.contains('paper', na=False)) &
|
||||
(df['promoTypes'] != 'playtest') &
|
||||
(~df['securityStamp'].str.contains('Heart|Acorn', na=False))
|
||||
]
|
||||
|
||||
# Remove illegal sets
|
||||
for set_code in NON_LEGAL_SETS:
|
||||
filtered_df = filtered_df[
|
||||
~filtered_df['printings'].str.contains(set_code, na=False)
|
||||
]
|
||||
|
||||
# Remove banned cards
|
||||
for card in banned_cards:
|
||||
filtered_df = filtered_df[~filtered_df['name'].str.contains(card, na=False)]
|
||||
|
||||
# Remove special card types
|
||||
for card_type in CARD_TYPES_TO_EXCLUDE:
|
||||
filtered_df = filtered_df[~filtered_df['type'].str.contains(card_type, na=False)]
|
||||
|
||||
# Handle face names and duplicates
|
||||
filtered_df['faceName'] = filtered_df['faceName'].fillna(filtered_df['name'])
|
||||
filtered_df = filtered_df.drop_duplicates(subset='faceName', keep='first')
|
||||
|
||||
# Select and sort columns
|
||||
filtered_df = filtered_df[CSV_PROCESSING_COLUMNS]
|
||||
|
||||
return filtered_df.sort_values(by=['name', 'side'],
|
||||
key=lambda col: col.str.lower())
|
||||
|
||||
except Exception as e:
|
||||
raise DataFrameProcessingError(
|
||||
"Failed to filter DataFrame",
|
||||
"standard_filtering",
|
||||
str(e)
|
||||
) from e
|
||||
|
||||
def process_legendary_cards(df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""Process and filter legendary cards for commander eligibility.
|
||||
|
||||
Args:
|
||||
df: DataFrame containing all cards
|
||||
|
||||
Returns:
|
||||
DataFrame containing only commander-eligible cards
|
||||
|
||||
Raises:
|
||||
DataFrameProcessingError: If processing fails
|
||||
"""
|
||||
try:
|
||||
# Filter for legendary creatures and eligible cards
|
||||
mask = df['type'].str.contains('|'.join(LEGENDARY_OPTIONS), na=False)
|
||||
|
||||
# Add cards that can be commanders
|
||||
can_be_commander = df['text'].str.contains(
|
||||
'can be your commander',
|
||||
na=False
|
||||
)
|
||||
|
||||
filtered_df = df[mask | can_be_commander].copy()
|
||||
|
||||
# Remove illegal sets
|
||||
for set_code in NON_LEGAL_SETS:
|
||||
filtered_df = filtered_df[
|
||||
~filtered_df['printings'].str.contains(set_code, na=False)
|
||||
]
|
||||
|
||||
return filtered_df
|
||||
|
||||
except Exception as e:
|
||||
raise DataFrameProcessingError(
|
||||
"Failed to process legendary cards",
|
||||
"commander_processing",
|
||||
str(e)
|
||||
) from e
|
||||
Loading…
Add table
Add a link
Reference in a new issue