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https://github.com/mwisnowski/mtg_python_deckbuilder.git
synced 2025-09-22 04:50:46 +02:00
cleanup: removed unneeded debug scripts that were accidentally left behind
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matt
parent
fe220c53f3
commit
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6 changed files with 0 additions and 213 deletions
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#!/usr/bin/env python3
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"""
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Check for banned cards in our popular/iconic card lists.
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"""
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from code.file_setup.setup_constants import BANNED_CARDS
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from code.deck_builder.builder_constants import POPULAR_CARDS, ICONIC_CARDS
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def check_banned_overlap():
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"""Check which cards in our lists are banned in Commander."""
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# Convert banned cards to set for faster lookup
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banned_set = set(BANNED_CARDS)
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print("Checking for banned cards in our card priority lists...")
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print("=" * 60)
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# Check POPULAR_CARDS
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popular_banned = POPULAR_CARDS & banned_set
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print(f"POPULAR_CARDS ({len(POPULAR_CARDS)} total):")
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if popular_banned:
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print("❌ Found banned cards:")
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for card in sorted(popular_banned):
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print(f" - {card}")
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else:
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print("✅ No banned cards found")
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print()
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# Check ICONIC_CARDS
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iconic_banned = ICONIC_CARDS & banned_set
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print(f"ICONIC_CARDS ({len(ICONIC_CARDS)} total):")
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if iconic_banned:
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print("❌ Found banned cards:")
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for card in sorted(iconic_banned):
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print(f" - {card}")
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else:
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print("✅ No banned cards found")
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print()
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# Summary
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all_banned = popular_banned | iconic_banned
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if all_banned:
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print(f"SUMMARY: Found {len(all_banned)} banned cards that need to be removed:")
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for card in sorted(all_banned):
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print(f" - {card}")
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return list(all_banned)
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else:
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print("✅ No banned cards found in either list!")
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return []
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if __name__ == "__main__":
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banned_found = check_banned_overlap()
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#!/usr/bin/env python3
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"""Debug the normalization and scoring for Lightning Bolt specifically"""
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import sys
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import os
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'code'))
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from deck_builder.include_exclude_utils import normalize_punctuation, fuzzy_match_card_name
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import pandas as pd
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# Test normalize_punctuation function
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print("=== Testing normalize_punctuation ===")
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test_names = ["Lightning Bolt", "lightning bolt", "Lightning-Bolt", "Lightning, Bolt"]
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for name in test_names:
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normalized = normalize_punctuation(name)
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print(f"'{name}' → '{normalized}'")
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# Load cards and test fuzzy matching
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print(f"\n=== Loading cards ===")
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cards_df = pd.read_csv('csv_files/cards.csv')
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available_cards = set(cards_df['name'].dropna().unique())
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print(f"Cards loaded: {len(available_cards)}")
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print(f"Lightning Bolt in cards: {'Lightning Bolt' in available_cards}")
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# Test fuzzy matching for 'bolt'
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print(f"\n=== Testing fuzzy match for 'bolt' ===")
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result = fuzzy_match_card_name('bolt', available_cards)
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print(f"Input: bolt")
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print(f"Matched: {result.matched_name}")
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print(f"Confidence: {result.confidence:.3f}")
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print(f"Auto-accepted: {result.auto_accepted}")
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print(f"Top suggestions: {result.suggestions[:5]}")
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# Test fuzzy matching for 'lightn'
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print(f"\n=== Testing fuzzy match for 'lightn' ===")
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result = fuzzy_match_card_name('lightn', available_cards)
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print(f"Input: lightn")
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print(f"Matched: {result.matched_name}")
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print(f"Confidence: {result.confidence:.3f}")
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print(f"Auto-accepted: {result.auto_accepted}")
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print(f"Top suggestions: {result.suggestions[:5]}")
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# Manual check of scores for Lightning cards
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print(f"\n=== Manual scoring for Lightning cards ===")
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from difflib import SequenceMatcher
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input_test = "lightn"
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lightning_cards = [name for name in available_cards if 'lightning' in name.lower()][:10]
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for card in lightning_cards:
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normalized_card = normalize_punctuation(card)
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score = SequenceMatcher(None, input_test.lower(), normalized_card.lower()).ratio()
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print(f"{score:.3f} - {card}")
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#!/usr/bin/env python3
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"""Debug the confirmation_needed response structure"""
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import requests
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import json
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test_data = {
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"include_cards": "lightn",
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"exclude_cards": "",
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"commander": "",
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"enforcement_mode": "warn",
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"allow_illegal": "false",
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"fuzzy_matching": "true"
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}
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response = requests.post(
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"http://localhost:8080/build/validate/include_exclude",
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data=test_data,
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timeout=10
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)
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if response.status_code == 200:
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data = response.json()
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print("Full response:")
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print(json.dumps(data, indent=2))
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print("\nConfirmation needed items:")
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for i, item in enumerate(data.get('confirmation_needed', [])):
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print(f"Item {i}: {json.dumps(item, indent=2)}")
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else:
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print(f"HTTP {response.status_code}: {response.text}")
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#!/usr/bin/env python3
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"""Debug what Lightning cards are in the dataset"""
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import pandas as pd
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# Load the cards CSV
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cards_df = pd.read_csv('csv_files/cards.csv')
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print(f"Total cards loaded: {len(cards_df)}")
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# Find cards that contain "light" (case insensitive)
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light_cards = cards_df[cards_df['name'].str.contains('light', case=False, na=False)]['name'].unique()
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print(f"\nCards containing 'light': {len(light_cards)}")
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for card in sorted(light_cards)[:20]: # Show first 20
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print(f" - {card}")
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# Find cards that start with "light"
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light_start = cards_df[cards_df['name'].str.lower().str.startswith('light', na=False)]['name'].unique()
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print(f"\nCards starting with 'Light': {len(light_start)}")
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for card in sorted(light_start):
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print(f" - {card}")
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# Find specific Lightning cards
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lightning_cards = cards_df[cards_df['name'].str.contains('lightning', case=False, na=False)]['name'].unique()
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print(f"\nCards containing 'Lightning': {len(lightning_cards)}")
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for card in sorted(lightning_cards):
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print(f" - {card}")
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print(f"\nTesting direct matches for 'lightn':")
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test_input = "lightn"
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candidates = []
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for name in cards_df['name'].dropna().unique():
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# Test similarity to lightn
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from difflib import SequenceMatcher
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similarity = SequenceMatcher(None, test_input.lower(), name.lower()).ratio()
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if similarity > 0.6:
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candidates.append((similarity, name))
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# Sort by similarity
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candidates.sort(key=lambda x: x[0], reverse=True)
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print("Top 10 matches for 'lightn':")
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for score, name in candidates[:10]:
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print(f" {score:.3f} - {name}")
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#!/usr/bin/env python3
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"""Debug what specific Lightning/Bolt cards exist"""
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import pandas as pd
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cards_df = pd.read_csv('csv_files/cards.csv')
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print("=== Lightning cards that start with 'Light' ===")
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lightning_prefix = cards_df[cards_df['name'].str.lower().str.startswith('lightning', na=False)]['name'].unique()
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for card in sorted(lightning_prefix):
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print(f" - {card}")
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print(f"\n=== Cards containing 'bolt' ===")
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bolt_cards = cards_df[cards_df['name'].str.contains('bolt', case=False, na=False)]['name'].unique()
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for card in sorted(bolt_cards):
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print(f" - {card}")
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print(f"\n=== Cards containing 'warp' ===")
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warp_cards = cards_df[cards_df['name'].str.contains('warp', case=False, na=False)]['name'].unique()
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for card in sorted(warp_cards):
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print(f" - {card}")
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print(f"\n=== Manual test of 'lightn' against Lightning cards ===")
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test_input = "lightn"
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lightning_scores = []
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from difflib import SequenceMatcher
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for card in lightning_prefix:
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score = SequenceMatcher(None, test_input.lower(), card.lower()).ratio()
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lightning_scores.append((score, card))
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lightning_scores.sort(key=lambda x: x[0], reverse=True)
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print("Top Lightning matches for 'lightn':")
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for score, card in lightning_scores[:5]:
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print(f" {score:.3f} - {card}")
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