266 lines
561 KiB
HTML
266 lines
561 KiB
HTML
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<title>graphify - graphify-out/graph.html</title>
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<div id="stats">782 nodes · 922 edges · 137 communities</div>
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"source_file": "modules/bot/python/src/train.py", "file_type": "code", "degree": 3}, {"id": "train_save_metadata", "label": "save_metadata()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "save_metadata()", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "code", "degree": 3}, {"id": "train_setup_training", "label": "_setup_training()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 14.2, "font": {"size": 0, "color": "#ffffff"}, "title": "_setup_training()", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "code", "degree": 5}, {"id": "train_run_training_season", "label": "_run_training_season()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 13.3, "font": {"size": 0, "color": "#ffffff"}, "title": "_run_training_season()", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "code", "degree": 4}, {"id": "train_save_versioned_model", "label": "_save_versioned_model()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 15.0, "font": {"size": 12, "color": "#ffffff"}, "title": "_save_versioned_model()", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "code", "degree": 6}, {"id": "train_train_nnue", "label": "train_nnue()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 15.0, "font": {"size": 12, "color": "#ffffff"}, "title": "train_nnue()", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "code", "degree": 6}, {"id": "train_burst_train", "label": "burst_train()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 15.0, "font": {"size": 12, "color": "#ffffff"}, "title": "burst_train()", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "code", "degree": 6}, {"id": "train_rationale_18", "label": "Dataset of chess positions with evaluations.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Dataset of chess positions with evaluations.", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_65", "label": "Convert FEN to 768-dimensional binary feature vector.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Convert FEN to 768-dimensional binary feature vector.", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_90", "label": "NNUE neural network architecture: 768\u21921536\u21921024\u2192512\u2192256\u21921 with dropout regulariz", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "NNUE neural network architecture: 768\u21921536\u21921024\u2192512\u2192256\u21921 with dropout regulariz", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_120", "label": "Find the next version number for model versioning. Looks for nnue_weights_v", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Find the next version number for model versioning. Looks for nnue_weights_v", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_142", "label": "Save training metadata alongside the weights file. Args: weights_fi", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Save training metadata alongside the weights file. Args: weights_fi", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_156", "label": "Set up device, dataset, and data loaders. Returns: (device, dataset", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Set up device, dataset, and data loaders. Returns: (device, dataset", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_225", "label": "Run one training season until epoch limit, early stopping, or deadline. Arg", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Run one training season until epoch limit, early stopping, or deadline. Arg", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_332", "label": "Save the best model with optional versioning and metadata.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Save the best model with optional versioning and metadata.", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_371", "label": "Train the NNUE model with GPU optimizations and automatic mixed precision.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Train the NNUE model with GPU optimizations and automatic mixed precision.", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "train_rationale_453", "label": "Train in burst mode: repeatedly restart from the best checkpoint until the time", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Train in burst mode: repeatedly restart from the best checkpoint until the time", "community": 5, "community_name": "Classical Evaluation", "source_file": "modules/bot/python/src/train.py", "file_type": "rationale", "degree": 1}, {"id": "tactical_positions_extractor", "label": "tactical_positions_extractor.py", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 15.8, "font": {"size": 12, "color": "#ffffff"}, "title": "tactical_positions_extractor.py", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "code", "degree": 7}, {"id": "tactical_positions_extractor_download_and_extract_puzzle_db", "label": "download_and_extract_puzzle_db()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.7, "font": {"size": 0, "color": "#ffffff"}, "title": "download_and_extract_puzzle_db()", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "code", "degree": 2}, {"id": "tactical_positions_extractor_extract_puzzle_positions", "label": "extract_puzzle_positions()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "extract_puzzle_positions()", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "code", "degree": 3}, {"id": "tactical_positions_extractor_load_positions_from_file", "label": "load_positions_from_file()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "load_positions_from_file()", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "code", "degree": 3}, {"id": "tactical_positions_extractor_merge_positions", "label": "merge_positions()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "merge_positions()", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "code", "degree": 3}, {"id": "tactical_positions_extractor_interactive_merge_positions", "label": "interactive_merge_positions()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 14.2, "font": {"size": 0, "color": "#ffffff"}, "title": "interactive_merge_positions()", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "code", "degree": 5}, {"id": "tactical_positions_extractor_rationale_22", "label": "Download and extract the Lichess puzzle database.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Download and extract the Lichess puzzle database.", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "rationale", "degree": 1}, {"id": "tactical_positions_extractor_rationale_60", "label": "Extract the position BEFORE the blunder from each puzzle. This is exactly th", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Extract the position BEFORE the blunder from each puzzle. This is exactly th", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "rationale", "degree": 1}, {"id": "tactical_positions_extractor_rationale_105", "label": "Load positions from a text file (one FEN per line).", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Load positions from a text file (one FEN per line).", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "rationale", "degree": 1}, {"id": "tactical_positions_extractor_rationale_125", "label": "Merge two position sets and write to file.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Merge two position sets and write to file.", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "rationale", "degree": 1}, {"id": "tactical_positions_extractor_rationale_149", "label": "Interactive workflow: extract tactical positions and merge with user selection.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Interactive workflow: extract tactical positions and merge with user selection.", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/tactical_positions_extractor.py", "file_type": "rationale", "degree": 1}, {"id": "label", "label": "label.py", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 13.3, "font": {"size": 0, "color": "#ffffff"}, "title": "label.py", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/label.py", "file_type": "code", "degree": 4}, {"id": "label_normalize_evaluation", "label": "normalize_evaluation()", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "normalize_evaluation()", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/label.py", "file_type": "code", "degree": 3}, {"id": "label_evaluate_fen_batch", "label": "_evaluate_fen_batch()", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "_evaluate_fen_batch()", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/label.py", "file_type": "code", "degree": 3}, {"id": "label_label_positions_with_stockfish", "label": "label_positions_with_stockfish()", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 11.7, "font": {"size": 0, "color": "#ffffff"}, "title": "label_positions_with_stockfish()", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/label.py", "file_type": "code", "degree": 2}, {"id": "label_rationale_15", "label": "Normalize centipawn evaluation to a bounded range. Args: cp_value:", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Normalize centipawn evaluation to a bounded range. Args: cp_value:", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/label.py", "file_type": "rationale", "degree": 1}, {"id": "label_rationale_33", "label": "Worker function to evaluate a batch of FENs with Stockfish threading. Args:", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Worker function to evaluate a batch of FENs with Stockfish threading. Args:", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/label.py", "file_type": "rationale", "degree": 1}, {"id": "label_rationale_83", "label": "Read positions and label them with Stockfish evaluations. Args: pos", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Read positions and label them with Stockfish evaluations. Args: pos", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/label.py", "file_type": "rationale", "degree": 1}, {"id": "generate", "label": "generate.py", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 13.3, "font": {"size": 0, "color": "#ffffff"}, "title": "generate.py", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/generate.py", "file_type": "code", "degree": 4}, {"id": "generate_worker_generate_games", "label": "_worker_generate_games()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.7, "font": {"size": 0, "color": "#ffffff"}, "title": "_worker_generate_games()", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/generate.py", "file_type": "code", "degree": 2}, {"id": "generate_play_random_game_and_collect_positions", "label": "play_random_game_and_collect_positions()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.7, "font": {"size": 0, "color": "#ffffff"}, "title": "play_random_game_and_collect_positions()", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/generate.py", "file_type": "code", "degree": 2}, {"id": "generate_rationale_14", "label": "Generate games for one worker. Returns: list of FENs generated by t", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Generate games for one worker. Returns: list of FENs generated by t", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/generate.py", "file_type": "rationale", "degree": 1}, {"id": "generate_rationale_72", "label": "Generate positions using multiprocessing with multiple workers. Args:", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Generate positions using multiprocessing with multiple workers. Args:", "community": 14, "community_name": "Documentation", "source_file": "modules/bot/python/src/generate.py", "file_type": "rationale", "degree": 1}, {"id": "export", "label": "export.py", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 11.7, "font": {"size": 0, "color": "#ffffff"}, "title": "export.py", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/export.py", "file_type": "code", "degree": 2}, {"id": "export_export_weights_to_binary", "label": "export_weights_to_binary()", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 11.7, "font": {"size": 0, "color": "#ffffff"}, "title": "export_weights_to_binary()", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/export.py", "file_type": "code", "degree": 2}, {"id": "export_rationale_10", "label": "Load PyTorch weights and export as binary file.", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "Load PyTorch weights and export as binary file.", "community": 2, "community_name": "Rule Engine & Move Logic", "source_file": "modules/bot/python/src/export.py", "file_type": "rationale", "degree": 1}, {"id": "polyglotbooktest", "label": "PolyglotBookTest.scala", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "PolyglotBookTest.scala", "community": 88, "community_name": "Code Component 88", "source_file": "modules/bot/src/test/scala/de/nowchess/bot/PolyglotBookTest.scala", "file_type": "code", "degree": 1}, {"id": "polyglotbooktest_polyglotbooktest", "label": "PolyglotBookTest", "color": 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"Arabian Chess Assets", "count": 17}, {"cid": 13, "color": "#76B7B2", "label": "Project Configuration", "count": 16}, {"cid": 14, "color": "#59A14F", "label": "Documentation", "count": 16}, {"cid": 15, "color": "#EDC948", "label": "Code Component 15", "count": 15}, {"cid": 16, "color": "#B07AA1", "label": "Code Component 16", "count": 14}, {"cid": 17, "color": "#FF9DA7", "label": "Code Component 17", "count": 13}, {"cid": 18, "color": "#9C755F", "label": "Code Component 18", "count": 13}, {"cid": 19, "color": "#BAB0AC", "label": "Code Component 19", "count": 12}, {"cid": 20, "color": "#4E79A7", "label": "Code Component 20", "count": 12}, {"cid": 21, "color": "#F28E2B", "label": "Code Component 21", "count": 12}, {"cid": 22, "color": "#E15759", "label": "Code Component 22", "count": 11}, {"cid": 23, "color": "#76B7B2", "label": "Code Component 23", "count": 11}, {"cid": 24, "color": "#59A14F", "label": "Code Component 24", "count": 10}, {"cid": 25, "color": "#EDC948", "label": "Code Component 25", "count": 10}, {"cid": 26, "color": "#B07AA1", "label": "Code Component 26", "count": 10}, {"cid": 27, "color": "#FF9DA7", "label": "Code Component 27", "count": 10}, {"cid": 28, "color": "#9C755F", "label": "Code Component 28", "count": 9}, {"cid": 29, "color": "#BAB0AC", "label": "Code Component 29", "count": 9}, {"cid": 30, "color": "#4E79A7", "label": "Code Component 30", "count": 8}, {"cid": 31, "color": "#F28E2B", "label": "Code Component 31", "count": 7}, {"cid": 32, "color": "#E15759", "label": "Code Component 32", "count": 6}, {"cid": 33, "color": "#76B7B2", "label": "Code Component 33", "count": 6}, {"cid": 34, "color": "#59A14F", "label": "Code Component 34", "count": 6}, {"cid": 35, "color": "#EDC948", "label": "Code Component 35", "count": 6}, {"cid": 36, "color": "#B07AA1", "label": "Code Component 36", "count": 5}, {"cid": 37, "color": "#FF9DA7", "label": "Code Component 37", "count": 5}, {"cid": 38, "color": "#9C755F", "label": "Code Component 38", "count": 5}, {"cid": 39, "color": "#BAB0AC", "label": "Code Component 39", "count": 5}, {"cid": 40, "color": "#4E79A7", "label": "Code Component 40", "count": 5}, {"cid": 41, "color": "#F28E2B", "label": "Code Component 41", "count": 4}, {"cid": 42, "color": "#E15759", "label": "Code Component 42", "count": 4}, {"cid": 43, "color": "#76B7B2", "label": "Code Component 43", "count": 4}, {"cid": 44, "color": "#59A14F", "label": "Code Component 44", "count": 4}, {"cid": 45, "color": "#EDC948", "label": "Code Component 45", "count": 4}, {"cid": 46, "color": "#B07AA1", "label": "Code Component 46", "count": 4}, {"cid": 47, "color": "#FF9DA7", "label": "Code Component 47", "count": 4}, {"cid": 48, "color": "#9C755F", "label": "Code Component 48", "count": 4}, {"cid": 49, "color": "#BAB0AC", "label": "Code Component 49", "count": 4}, {"cid": 50, "color": "#4E79A7", "label": "Code Component 50", "count": 3}, {"cid": 51, "color": "#F28E2B", "label": "Code Component 51", "count": 3}, {"cid": 52, "color": "#E15759", "label": "Code Component 52", "count": 3}, {"cid": 53, "color": "#76B7B2", "label": "Code Component 53", "count": 3}, {"cid": 54, "color": "#59A14F", "label": "Code Component 54", "count": 3}, {"cid": 55, "color": "#EDC948", "label": "Code Component 55", "count": 3}, {"cid": 56, "color": "#B07AA1", "label": "Code Component 56", "count": 3}, {"cid": 57, "color": "#FF9DA7", "label": "Code Component 57", "count": 3}, {"cid": 58, "color": "#9C755F", "label": "Code Component 58", "count": 3}, {"cid": 59, "color": "#BAB0AC", "label": "Code Component 59", "count": 3}, {"cid": 60, "color": "#4E79A7", "label": "Code Component 60", "count": 3}, {"cid": 61, "color": "#F28E2B", "label": "Code Component 61", "count": 3}, {"cid": 62, "color": "#E15759", "label": "Code Component 62", "count": 2}, {"cid": 63, "color": "#76B7B2", "label": "Code Component 63", "count": 2}, {"cid": 64, "color": "#59A14F", "label": "Code Component 64", "count": 2}, {"cid": 65, "color": "#EDC948", "label": "Code Component 65", "count": 2}, {"cid": 66, "color": "#B07AA1", "label": "Code Component 66", "count": 2}, {"cid": 67, "color": "#FF9DA7", "label": "Code Component 67", "count": 2}, {"cid": 68, "color": "#9C755F", "label": "Code Component 68", "count": 2}, {"cid": 69, "color": "#BAB0AC", "label": "Code Component 69", "count": 2}, {"cid": 70, "color": "#4E79A7", "label": "Code Component 70", "count": 2}, {"cid": 71, "color": "#F28E2B", "label": "Code Component 71", "count": 2}, {"cid": 72, "color": "#E15759", "label": "Code Component 72", "count": 2}, {"cid": 73, "color": "#76B7B2", "label": "Code Component 73", "count": 2}, {"cid": 74, "color": "#59A14F", "label": "Code Component 74", "count": 2}, {"cid": 75, "color": "#EDC948", "label": "Code Component 75", "count": 2}, {"cid": 76, "color": "#B07AA1", "label": "Code Component 76", "count": 2}, {"cid": 77, "color": "#FF9DA7", "label": "Code Component 77", "count": 2}, {"cid": 78, "color": "#9C755F", "label": "Code Component 78", "count": 2}, {"cid": 79, "color": "#BAB0AC", "label": "Code Component 79", "count": 2}, {"cid": 80, "color": "#4E79A7", "label": "Code Component 80", "count": 2}, {"cid": 81, "color": "#F28E2B", "label": "Code Component 81", "count": 2}, {"cid": 82, "color": "#E15759", "label": "Code Component 82", "count": 2}, {"cid": 83, "color": "#76B7B2", "label": "Code Component 83", "count": 2}, {"cid": 84, "color": "#59A14F", "label": "Code Component 84", "count": 2}, {"cid": 85, "color": "#EDC948", "label": "Code Component 85", "count": 2}, {"cid": 86, "color": "#B07AA1", "label": "Code Component 86", "count": 2}, {"cid": 87, "color": "#FF9DA7", "label": "Code Component 87", "count": 2}, {"cid": 88, "color": "#9C755F", "label": "Code Component 88", "count": 2}, {"cid": 89, "color": "#BAB0AC", "label": "Code Component 89", "count": 2}, {"cid": 90, "color": "#4E79A7", "label": "Code Component 90", "count": 2}, {"cid": 91, "color": "#F28E2B", "label": "Code Component 91", "count": 2}, {"cid": 92, "color": "#E15759", "label": "Code Component 92", "count": 2}, {"cid": 93, "color": "#76B7B2", "label": "Code Component 93", "count": 2}, {"cid": 94, "color": "#59A14F", "label": "Code Component 94", "count": 2}, {"cid": 95, "color": "#EDC948", "label": "Code Component 95", "count": 2}, {"cid": 96, "color": "#B07AA1", "label": "Code Component 96", "count": 2}, {"cid": 97, "color": "#FF9DA7", "label": "Code Component 97", "count": 2}, {"cid": 98, "color": "#9C755F", "label": "Code Component 98", "count": 2}, {"cid": 99, "color": "#BAB0AC", "label": "Code Component 99", "count": 2}, {"cid": 100, "color": "#4E79A7", "label": "Code Component 100", "count": 2}, {"cid": 101, "color": "#F28E2B", "label": "Code Component 101", "count": 2}, {"cid": 102, "color": "#E15759", "label": "Code Component 102", "count": 1}, {"cid": 103, "color": "#76B7B2", "label": "Code Component 103", "count": 1}, {"cid": 104, "color": "#59A14F", "label": "Code Component 104", "count": 1}, {"cid": 105, "color": "#EDC948", "label": "Code Component 105", "count": 1}, {"cid": 106, "color": "#B07AA1", "label": "Code Component 106", "count": 1}, {"cid": 107, "color": "#FF9DA7", "label": "Code Component 107", "count": 1}, {"cid": 108, "color": "#9C755F", "label": "Code Component 108", "count": 1}, {"cid": 109, "color": "#BAB0AC", "label": "Code Component 109", "count": 1}, {"cid": 110, "color": "#4E79A7", "label": "Code Component 110", "count": 1}, {"cid": 111, "color": "#F28E2B", "label": "Code Component 111", "count": 1}, {"cid": 112, "color": "#E15759", "label": "Code Component 112", "count": 1}, {"cid": 113, "color": "#76B7B2", "label": "Code Component 113", "count": 1}, {"cid": 114, "color": "#59A14F", "label": "Code Component 114", "count": 1}, {"cid": 115, "color": "#EDC948", "label": "Code Component 115", "count": 1}, {"cid": 116, "color": "#B07AA1", "label": "Code Component 116", "count": 1}, {"cid": 117, "color": "#FF9DA7", "label": "Code Component 117", "count": 1}, {"cid": 118, "color": "#9C755F", "label": "Code Component 118", "count": 1}, {"cid": 119, "color": "#BAB0AC", "label": "Code Component 119", "count": 1}, {"cid": 120, "color": "#4E79A7", "label": "Code Component 120", "count": 1}, {"cid": 121, "color": "#F28E2B", "label": "Code Component 121", "count": 1}, {"cid": 122, "color": "#E15759", "label": "Code Component 122", "count": 1}, {"cid": 123, "color": "#76B7B2", "label": "Code Component 123", "count": 1}, {"cid": 124, "color": "#59A14F", "label": "Code Component 124", "count": 1}, {"cid": 125, "color": "#EDC948", "label": "Code Component 125", "count": 1}, {"cid": 126, "color": "#B07AA1", "label": "Code Component 126", "count": 1}, {"cid": 127, "color": "#FF9DA7", "label": "Code Component 127", "count": 1}, {"cid": 128, "color": "#9C755F", "label": "Code Component 128", "count": 1}, {"cid": 129, "color": "#BAB0AC", "label": "Code Component 129", "count": 1}, {"cid": 130, "color": "#4E79A7", "label": "Code Component 130", "count": 1}, {"cid": 131, "color": "#F28E2B", "label": "Code Component 131", "count": 1}, {"cid": 132, "color": "#E15759", "label": "Code Component 132", "count": 1}, {"cid": 133, "color": "#76B7B2", "label": "Code Component 133", "count": 1}, {"cid": 134, "color": "#59A14F", "label": "Code Component 134", "count": 1}, {"cid": 135, "color": "#EDC948", "label": "Code Component 135", "count": 1}, {"cid": 136, "color": "#B07AA1", "label": "Code Component 136", "count": 1}];
|
|
|
|
// HTML-escape helper — prevents XSS when injecting graph data into innerHTML
|
|
function esc(s) {
|
|
return String(s).replace(/&/g,'&').replace(/</g,'<').replace(/>/g,'>').replace(/"/g,'"').replace(/'/g,''');
|
|
}
|
|
|
|
// Build vis datasets
|
|
const nodesDS = new vis.DataSet(RAW_NODES.map(n => ({
|
|
id: n.id, label: n.label, color: n.color, size: n.size,
|
|
font: n.font, title: n.title,
|
|
_community: n.community, _community_name: n.community_name,
|
|
_source_file: n.source_file, _file_type: n.file_type, _degree: n.degree,
|
|
})));
|
|
|
|
const edgesDS = new vis.DataSet(RAW_EDGES.map((e, i) => ({
|
|
id: i, from: e.from, to: e.to,
|
|
label: '',
|
|
title: e.title,
|
|
dashes: e.dashes,
|
|
width: e.width,
|
|
color: e.color,
|
|
arrows: { to: { enabled: true, scaleFactor: 0.5 } },
|
|
})));
|
|
|
|
const container = document.getElementById('graph');
|
|
const network = new vis.Network(container, { nodes: nodesDS, edges: edgesDS }, {
|
|
physics: {
|
|
enabled: true,
|
|
solver: 'forceAtlas2Based',
|
|
forceAtlas2Based: {
|
|
gravitationalConstant: -60,
|
|
centralGravity: 0.005,
|
|
springLength: 120,
|
|
springConstant: 0.08,
|
|
damping: 0.4,
|
|
avoidOverlap: 0.8,
|
|
},
|
|
stabilization: { iterations: 200, fit: true },
|
|
},
|
|
interaction: {
|
|
hover: true,
|
|
tooltipDelay: 100,
|
|
hideEdgesOnDrag: true,
|
|
navigationButtons: false,
|
|
keyboard: false,
|
|
},
|
|
nodes: { shape: 'dot', borderWidth: 1.5 },
|
|
edges: { smooth: { type: 'continuous', roundness: 0.2 }, selectionWidth: 3 },
|
|
});
|
|
|
|
network.once('stabilizationIterationsDone', () => {
|
|
network.setOptions({ physics: { enabled: false } });
|
|
});
|
|
|
|
function showInfo(nodeId) {
|
|
const n = nodesDS.get(nodeId);
|
|
if (!n) return;
|
|
const neighborIds = network.getConnectedNodes(nodeId);
|
|
const neighborItems = neighborIds.map(nid => {
|
|
const nb = nodesDS.get(nid);
|
|
const color = nb ? nb.color.background : '#555';
|
|
return `<span class="neighbor-link" style="border-left-color:${esc(color)}" onclick="focusNode(${JSON.stringify(nid)})">${esc(nb ? nb.label : nid)}</span>`;
|
|
}).join('');
|
|
document.getElementById('info-content').innerHTML = `
|
|
<div class="field"><b>${esc(n.label)}</b></div>
|
|
<div class="field">Type: ${esc(n._file_type || 'unknown')}</div>
|
|
<div class="field">Community: ${esc(n._community_name)}</div>
|
|
<div class="field">Source: ${esc(n._source_file || '-')}</div>
|
|
<div class="field">Degree: ${n._degree}</div>
|
|
${neighborIds.length ? `<div class="field" style="margin-top:8px;color:#aaa;font-size:11px">Neighbors (${neighborIds.length})</div><div id="neighbors-list">${neighborItems}</div>` : ''}
|
|
`;
|
|
}
|
|
|
|
function focusNode(nodeId) {
|
|
network.focus(nodeId, { scale: 1.4, animation: true });
|
|
network.selectNodes([nodeId]);
|
|
showInfo(nodeId);
|
|
}
|
|
|
|
// Track hovered node — hover detection is more reliable than click params
|
|
let hoveredNodeId = null;
|
|
network.on('hoverNode', params => {
|
|
hoveredNodeId = params.node;
|
|
container.style.cursor = 'pointer';
|
|
});
|
|
network.on('blurNode', () => {
|
|
hoveredNodeId = null;
|
|
container.style.cursor = 'default';
|
|
});
|
|
container.addEventListener('click', () => {
|
|
if (hoveredNodeId !== null) {
|
|
showInfo(hoveredNodeId);
|
|
network.selectNodes([hoveredNodeId]);
|
|
}
|
|
});
|
|
network.on('click', params => {
|
|
if (params.nodes.length > 0) {
|
|
showInfo(params.nodes[0]);
|
|
} else if (hoveredNodeId === null) {
|
|
document.getElementById('info-content').innerHTML = '<span class="empty">Click a node to inspect it</span>';
|
|
}
|
|
});
|
|
|
|
const searchInput = document.getElementById('search');
|
|
const searchResults = document.getElementById('search-results');
|
|
searchInput.addEventListener('input', () => {
|
|
const q = searchInput.value.toLowerCase().trim();
|
|
searchResults.innerHTML = '';
|
|
if (!q) { searchResults.style.display = 'none'; return; }
|
|
const matches = RAW_NODES.filter(n => n.label.toLowerCase().includes(q)).slice(0, 20);
|
|
if (!matches.length) { searchResults.style.display = 'none'; return; }
|
|
searchResults.style.display = 'block';
|
|
matches.forEach(n => {
|
|
const el = document.createElement('div');
|
|
el.className = 'search-item';
|
|
el.textContent = n.label;
|
|
el.style.borderLeft = `3px solid ${n.color.background}`;
|
|
el.style.paddingLeft = '8px';
|
|
el.onclick = () => {
|
|
network.focus(n.id, { scale: 1.5, animation: true });
|
|
network.selectNodes([n.id]);
|
|
showInfo(n.id);
|
|
searchResults.style.display = 'none';
|
|
searchInput.value = '';
|
|
};
|
|
searchResults.appendChild(el);
|
|
});
|
|
});
|
|
document.addEventListener('click', e => {
|
|
if (!searchResults.contains(e.target) && e.target !== searchInput)
|
|
searchResults.style.display = 'none';
|
|
});
|
|
|
|
const hiddenCommunities = new Set();
|
|
const legendEl = document.getElementById('legend');
|
|
LEGEND.forEach(c => {
|
|
const item = document.createElement('div');
|
|
item.className = 'legend-item';
|
|
item.innerHTML = `<div class="legend-dot" style="background:${c.color}"></div>
|
|
<span class="legend-label">${c.label}</span>
|
|
<span class="legend-count">${c.count}</span>`;
|
|
item.onclick = () => {
|
|
if (hiddenCommunities.has(c.cid)) {
|
|
hiddenCommunities.delete(c.cid);
|
|
item.classList.remove('dimmed');
|
|
} else {
|
|
hiddenCommunities.add(c.cid);
|
|
item.classList.add('dimmed');
|
|
}
|
|
const updates = RAW_NODES
|
|
.filter(n => n.community === c.cid)
|
|
.map(n => ({ id: n.id, hidden: hiddenCommunities.has(c.cid) }));
|
|
nodesDS.update(updates);
|
|
};
|
|
legendEl.appendChild(item);
|
|
});
|
|
</script>
|
|
<script>
|
|
// Render hyperedges as shaded regions
|
|
const hyperedges = [{"id": "board_state_representation", "label": "Board State Representation", "nodes": ["api_Board", "api_Square", "api_Piece"], "relation": "form", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "modules/api/src/main/scala/de/nowchess/api/board/Board.scala"}, {"id": "game_context_state", "label": "Game Context State Bundle", "nodes": ["api_GameContext", "api_Board", "api_Color", "api_CastlingRights", "api_Square", "api_Move"], "relation": "implement", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala"}, {"id": "piece_composition", "label": "Piece Composition", "nodes": ["api_Piece", "api_Color", "api_PieceType"], "relation": "form", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "modules/api/src/main/scala/de/nowchess/api/board/Piece.scala"}, {"id": "move_representation", "label": "Move Representation", "nodes": ["api_Move", "api_Square", "api_MoveType"], "relation": "form", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "modules/api/src/main/scala/de/nowchess/api/move/Move.scala"}, {"id": "observer_pattern_ui", "label": "Observer Pattern: GameEngine publishes events to UI observers", "nodes": ["GameEngine", "GUIObserver", "TerminalUI", "GameEvent"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala"}, {"id": "fen_parsing_system", "label": "FEN Parsing System", "nodes": ["FenParser", "FenParserCombinators", "FenParserFastParse", "FenParserSupport"], "relation": "implement", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "modules/io/src/main/scala/de/nowchess/io/fen/"}];
|
|
function drawHyperedges() {
|
|
const canvas = network.canvas.frame.canvas;
|
|
const ctx = canvas.getContext('2d');
|
|
hyperedges.forEach(h => {
|
|
const positions = h.nodes
|
|
.map(nid => network.getPositions([nid])[nid])
|
|
.filter(p => p !== undefined);
|
|
if (positions.length < 2) return;
|
|
// Draw convex hull as filled polygon
|
|
ctx.save();
|
|
ctx.globalAlpha = 0.12;
|
|
ctx.fillStyle = '#6366f1';
|
|
ctx.strokeStyle = '#6366f1';
|
|
ctx.lineWidth = 2;
|
|
ctx.beginPath();
|
|
const scale = network.getScale();
|
|
const offset = network.getViewPosition();
|
|
const toCanvas = (p) => ({
|
|
x: (p.x - offset.x) * scale + canvas.width / 2,
|
|
y: (p.y - offset.y) * scale + canvas.height / 2
|
|
});
|
|
const pts = positions.map(toCanvas);
|
|
// Expand hull slightly
|
|
const cx = pts.reduce((s, p) => s + p.x, 0) / pts.length;
|
|
const cy = pts.reduce((s, p) => s + p.y, 0) / pts.length;
|
|
const expanded = pts.map(p => ({
|
|
x: cx + (p.x - cx) * 1.15,
|
|
y: cy + (p.y - cy) * 1.15
|
|
}));
|
|
ctx.moveTo(expanded[0].x, expanded[0].y);
|
|
expanded.slice(1).forEach(p => ctx.lineTo(p.x, p.y));
|
|
ctx.closePath();
|
|
ctx.fill();
|
|
ctx.globalAlpha = 0.4;
|
|
ctx.stroke();
|
|
// Label
|
|
ctx.globalAlpha = 0.8;
|
|
ctx.fillStyle = '#4f46e5';
|
|
ctx.font = 'bold 11px sans-serif';
|
|
ctx.textAlign = 'center';
|
|
ctx.fillText(h.label, cx, cy - 5);
|
|
ctx.restore();
|
|
});
|
|
}
|
|
network.on('afterDrawing', drawHyperedges);
|
|
</script>
|
|
</body>
|
|
</html> |