cleanup: removed unneeded debug scripts that were accidentally left behind

2025-09-22 04:50:46 +02:00 · 2025-09-10 07:42:03 -07:00 · 2025-09-10 07:42:03 -07:00 · 6fe8a7af89
commit 6fe8a7af89
parent fe220c53f3
6 changed files with 0 additions and 213 deletions
--- a/check_banned_cards.py
+++ b/check_banned_cards.py
@ -1,52 +0,0 @@
 #!/usr/bin/env python3
 """
 Check for banned cards in our popular/iconic card lists.
 """
 from code.file_setup.setup_constants import BANNED_CARDS
 from code.deck_builder.builder_constants import POPULAR_CARDS, ICONIC_CARDS
 def check_banned_overlap():
    """Check which cards in our lists are banned in Commander."""
    # Convert banned cards to set for faster lookup
    banned_set = set(BANNED_CARDS)
    print("Checking for banned cards in our card priority lists...")
    print("=" * 60)
    # Check POPULAR_CARDS
    popular_banned = POPULAR_CARDS & banned_set
    print(f"POPULAR_CARDS ({len(POPULAR_CARDS)} total):")
    if popular_banned:
        print("❌ Found banned cards:")
        for card in sorted(popular_banned):
            print(f"  - {card}")
    else:
        print("✅ No banned cards found")
    print()
    # Check ICONIC_CARDS
    iconic_banned = ICONIC_CARDS & banned_set
    print(f"ICONIC_CARDS ({len(ICONIC_CARDS)} total):")
    if iconic_banned:
        print("❌ Found banned cards:")
        for card in sorted(iconic_banned):
            print(f"  - {card}")
    else:
        print("✅ No banned cards found")
    print()
    # Summary
    all_banned = popular_banned | iconic_banned
    if all_banned:
        print(f"SUMMARY: Found {len(all_banned)} banned cards that need to be removed:")
        for card in sorted(all_banned):
            print(f"  - {card}")
        return list(all_banned)
    else:
        print("✅ No banned cards found in either list!")
        return []
 if __name__ == "__main__":
    banned_found = check_banned_overlap()
--- a/code/tests/test_api_response.py
+++ b/code/tests/test_api_response.py
--- a/debug_bolt_scoring.py
+++ b/debug_bolt_scoring.py
@ -1,54 +0,0 @@
 #!/usr/bin/env python3
 """Debug the normalization and scoring for Lightning Bolt specifically"""
 import sys
 import os
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'code'))
 from deck_builder.include_exclude_utils import normalize_punctuation, fuzzy_match_card_name
 import pandas as pd
 # Test normalize_punctuation function
 print("=== Testing normalize_punctuation ===")
 test_names = ["Lightning Bolt", "lightning bolt", "Lightning-Bolt", "Lightning, Bolt"]
 for name in test_names:
    normalized = normalize_punctuation(name)
    print(f"'{name}' → '{normalized}'")
 # Load cards and test fuzzy matching
 print(f"\n=== Loading cards ===")
 cards_df = pd.read_csv('csv_files/cards.csv')
 available_cards = set(cards_df['name'].dropna().unique())
 print(f"Cards loaded: {len(available_cards)}")
 print(f"Lightning Bolt in cards: {'Lightning Bolt' in available_cards}")
 # Test fuzzy matching for 'bolt' 
 print(f"\n=== Testing fuzzy match for 'bolt' ===")
 result = fuzzy_match_card_name('bolt', available_cards)
 print(f"Input: bolt")
 print(f"Matched: {result.matched_name}")
 print(f"Confidence: {result.confidence:.3f}")
 print(f"Auto-accepted: {result.auto_accepted}")
 print(f"Top suggestions: {result.suggestions[:5]}")
 # Test fuzzy matching for 'lightn'
 print(f"\n=== Testing fuzzy match for 'lightn' ===")
 result = fuzzy_match_card_name('lightn', available_cards)
 print(f"Input: lightn")
 print(f"Matched: {result.matched_name}")
 print(f"Confidence: {result.confidence:.3f}")
 print(f"Auto-accepted: {result.auto_accepted}")
 print(f"Top suggestions: {result.suggestions[:5]}")
 # Manual check of scores for Lightning cards
 print(f"\n=== Manual scoring for Lightning cards ===")
 from difflib import SequenceMatcher
 input_test = "lightn"
 lightning_cards = [name for name in available_cards if 'lightning' in name.lower()][:10]
 for card in lightning_cards:
    normalized_card = normalize_punctuation(card)
    score = SequenceMatcher(None, input_test.lower(), normalized_card.lower()).ratio()
    print(f"{score:.3f} - {card}")
--- a/debug_confirmation.py
+++ b/debug_confirmation.py
@ -1,30 +0,0 @@
 #!/usr/bin/env python3
 """Debug the confirmation_needed response structure"""
 import requests
 import json
 test_data = {
    "include_cards": "lightn",
    "exclude_cards": "",
    "commander": "",
    "enforcement_mode": "warn",
    "allow_illegal": "false",
    "fuzzy_matching": "true"
 }
 response = requests.post(
    "http://localhost:8080/build/validate/include_exclude",
    data=test_data,
    timeout=10
 )
 if response.status_code == 200:
    data = response.json()
    print("Full response:")
    print(json.dumps(data, indent=2))
    print("\nConfirmation needed items:")
    for i, item in enumerate(data.get('confirmation_needed', [])):
        print(f"Item {i}: {json.dumps(item, indent=2)}")
 else:
    print(f"HTTP {response.status_code}: {response.text}")
--- a/debug_lightning.py
+++ b/debug_lightning.py
@ -1,42 +0,0 @@
 #!/usr/bin/env python3
 """Debug what Lightning cards are in the dataset"""
 import pandas as pd
 # Load the cards CSV
 cards_df = pd.read_csv('csv_files/cards.csv')
 print(f"Total cards loaded: {len(cards_df)}")
 # Find cards that contain "light" (case insensitive)
 light_cards = cards_df[cards_df['name'].str.contains('light', case=False, na=False)]['name'].unique()
 print(f"\nCards containing 'light': {len(light_cards)}")
 for card in sorted(light_cards)[:20]:  # Show first 20
    print(f"  - {card}")
 # Find cards that start with "light" 
 light_start = cards_df[cards_df['name'].str.lower().str.startswith('light', na=False)]['name'].unique()
 print(f"\nCards starting with 'Light': {len(light_start)}")
 for card in sorted(light_start):
    print(f"  - {card}")
 # Find specific Lightning cards
 lightning_cards = cards_df[cards_df['name'].str.contains('lightning', case=False, na=False)]['name'].unique()
 print(f"\nCards containing 'Lightning': {len(lightning_cards)}")
 for card in sorted(lightning_cards):
    print(f"  - {card}")
 print(f"\nTesting direct matches for 'lightn':")
 test_input = "lightn"
 candidates = []
 for name in cards_df['name'].dropna().unique():
    # Test similarity to lightn
    from difflib import SequenceMatcher
    similarity = SequenceMatcher(None, test_input.lower(), name.lower()).ratio()
    if similarity > 0.6:
        candidates.append((similarity, name))
 # Sort by similarity
 candidates.sort(key=lambda x: x[0], reverse=True)
 print("Top 10 matches for 'lightn':")
 for score, name in candidates[:10]:
    print(f"  {score:.3f} - {name}")
--- a/debug_popular_cards.py
+++ b/debug_popular_cards.py
@ -1,35 +0,0 @@
 #!/usr/bin/env python3
 """Debug what specific Lightning/Bolt cards exist"""
 import pandas as pd
 cards_df = pd.read_csv('csv_files/cards.csv')
 print("=== Lightning cards that start with 'Light' ===")
 lightning_prefix = cards_df[cards_df['name'].str.lower().str.startswith('lightning', na=False)]['name'].unique()
 for card in sorted(lightning_prefix):
    print(f"  - {card}")
 print(f"\n=== Cards containing 'bolt' ===")
 bolt_cards = cards_df[cards_df['name'].str.contains('bolt', case=False, na=False)]['name'].unique()
 for card in sorted(bolt_cards):
    print(f"  - {card}")
 print(f"\n=== Cards containing 'warp' ===")  
 warp_cards = cards_df[cards_df['name'].str.contains('warp', case=False, na=False)]['name'].unique()
 for card in sorted(warp_cards):
    print(f"  - {card}")
 print(f"\n=== Manual test of 'lightn' against Lightning cards ===")
 test_input = "lightn"
 lightning_scores = []
 from difflib import SequenceMatcher
 for card in lightning_prefix:
    score = SequenceMatcher(None, test_input.lower(), card.lower()).ratio()
    lightning_scores.append((score, card))
 lightning_scores.sort(key=lambda x: x[0], reverse=True)
 print("Top Lightning matches for 'lightn':")
 for score, card in lightning_scores[:5]:
    print(f"  {score:.3f} - {card}")