mirror of
https://github.com/mwisnowski/mtg_python_deckbuilder.git
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cfcc01db85
- Include/exclude cards feature complete with 300+ card knowledge base and intelligent fuzzy matching - Enhanced visual validation with warning icons and performance benchmarks (100% pass rate) - Mobile responsive design with bottom-floating controls, two-column layout, and horizontal scroll prevention - Dark theme confirmation modal for fuzzy matches with card preview and alternatives - Dual architecture support for web UI staging system and CLI direct build paths - All M3 checklist items completed: fuzzy match modal, enhanced algorithm, summary panel, mobile responsive, Playwright tests
54 lines
2 KiB
Python
54 lines
2 KiB
Python
#!/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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