| """ |
| Étape 0 — Extraction brute du notebook GrandNexus V6.4 (29/05/2025) en paquet Python. |
| Aucune modification du code : chaque cellule est copiée telle quelle dans son file. |
| Les doublons sont conservés sous un autre nom (rien n'est jeté). |
| Usage : python tools/extract_notebook.py archive/GrandNexusV_6_4_29_05_2025.ipynb |
| """ |
| import json, os, sys |
|
|
| |
| CELL_MAP = { |
| 4: "grandnexus/core/nexus_core.py", |
| 6: "grandnexus/core/errorRecoveryManager.py", |
| 10: "grandnexus/core/registry.py", |
| 8: "grandnexus/memory/memory_manager.py", |
| 12: "grandnexus/memory/episodic.py", |
| 14: "grandnexus/memory/semantic.py", |
| 17: "grandnexus/memory/working.py", |
| 19: "grandnexus/cognition/cognition_manager.py", |
| 21: "grandnexus/cognition/perception/perception_core.py", |
| 23: "grandnexus/cognition/perception/symbolic_parser.py", |
| 27: "grandnexus/cognition/perception/code_analyzer.py", |
| 29: "grandnexus/cognition/perception/pattern_recognizer.py", |
| 31: "grandnexus/cognition/perception/filters/attention_filter.py", |
| 25: "grandnexus/cognition/reasoning/symbolic_engine.py", |
| 33: "grandnexus/cognition/reasoning/reasoning_core.py", |
| 35: "grandnexus/cognition/reasoning/neural_reasoner.py", |
| 37: "grandnexus/cognition/reasoning/hybrid_inference.py", |
| 39: "grandnexus/cognition/reasoning/planning.py", |
| 41: "grandnexus/cognition/reasoning/uncertainty.py", |
| 43: "grandnexus/cognition/learning/learning_core.py", |
| 45: "grandnexus/cognition/learning/continuous_learner.py", |
| 47: "grandnexus/cognition/learning/meta_learner.py", |
| 49: "grandnexus/cognition/learning/reinforcement.py", |
| 51: "grandnexus/cognition/learning/intrinsic_motivation.py", |
| 53: "grandnexus/cognition/learning/curriculum.py", |
| 55: "grandnexus/cognition/metacognition/monitor.py", |
| 57: "grandnexus/cognition/metacognition/confidence.py", |
| 59: "grandnexus/cognition/metacognition/adaptation.py", |
| 61: "grandnexus/cognition/metacognition/reflection.py", |
| 63: "grandnexus/graph/graph_engine_v1.py", |
| 65: "grandnexus/graph/graph_engine.py", |
| 67: "grandnexus/graph/pattern_mining.py", |
| 70: "grandnexus/graph/graph_pattern_interface.py", |
| 72: "grandnexus/graph/graph_module_manager.py", |
| 74: "grandnexus/computation/resource_manager_ml.py", |
| 76: "grandnexus/computation/resource_manager.py", |
| 78: "grandnexus/llm_interface/llm_engine.py", |
| 80: "grandnexus/llm_interface/llm_engine_lite.py", |
| 82: "grandnexus/llm_interface/prompt_templates.py", |
| 84: "grandnexus/llm_interface/response_parser.py", |
| 86: "grandnexus/llm_interface/__init__.py", |
| } |
| ARCHIVE = {1: "ARCHITECTURE.txt", 15: "archive/cell15_semantic_methods_duplicate.py", |
| 68: "archive/cell68_cudf_demo_hors_sujet.py"} |
| for i in (90, 92, 95, 96, 97): |
| ARCHIVE[i] = f"archive/cell{i}_gemini_helper.py" |
|
|
| def clean(src): |
| |
| return "\n".join(("# " + l) if l.lstrip().startswith(("%", "!")) else l for l in src.split("\n")) |
|
|
| def main(nb_path, out="."): |
| nb = json.load(open(nb_path, encoding="utf-8")) |
| for i, c in enumerate(nb["cells"]): |
| if c["cell_type"] != "code": |
| continue |
| src = "".join(c["source"]) |
| path = CELL_MAP.get(i) or ARCHIVE.get(i) |
| if path is None: |
| print(f"!! cellule {i} non mappée"); continue |
| full = os.path.join(out, path) |
| os.makedirs(os.path.dirname(full) or ".", exist_ok=True) |
| with open(full, "w", encoding="utf-8") as f: |
| f.write(clean(src) if path.endswith(".py") else src) |
| |
| for root, dirs, files in os.walk(os.path.join(out, "grandnexus")): |
| if "__init__.py" not in files: |
| open(os.path.join(root, "__init__.py"), "w").write("") |
| print("Extraction terminée.") |
|
|
| if __name__ == "__main__": |
| main(sys.argv[1] if len(sys.argv) > 1 else "archive/GrandNexusV_6_4_29_05_2025.ipynb") |
|
|
