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import os |
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import json |
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import numpy as np |
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import matplotlib.pyplot as plt |
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folder = '.' |
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quantum_states=[] |
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chaos_states=[] |
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proc_ids=[] |
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labels=[] |
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all_perspectives=[] |
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meta_mutations=[] |
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def simple_neural_activator(quantum_vec, chaos_vec): |
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q_sum = sum(quantum_vec) |
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c_var = np.var(chaos_vec) |
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activated = 1 if q_sum + c_var > 1 else 0 |
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return activated |
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def codette_dream_agent(quantum_vec, chaos_vec): |
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dream_q = [np.sin(q * np.pi) for q in quantum_vec] |
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dream_c = [np.cos(c * np.pi) for c in chaos_vec] |
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return dream_q, dream_c |
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def philosophical_perspective(qv, cv): |
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m = np.max(qv) + np.max(cv) |
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if m > 1.3: |
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return "Philosophical Note: This universe is likely awake." |
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else: |
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return "Philosophical Note: Echoes in the void." |
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print("\nMeta Reflection Table:\n") |
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header = "Cocoon File | Quantum State | Chaos State | Neural | Dream Q/C | Philosophy" |
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print(header) |
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print('-'*len(header)) |
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for fname in os.listdir(folder): |
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if fname.endswith('.cocoon'): |
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with open(os.path.join(folder, fname), 'r') as f: |
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try: |
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dct=json.load(f)['data'] |
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q=dct.get('quantum_state',[0,0]) |
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c=dct.get('chaos_state',[0,0,0]) |
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neural=simple_neural_activator(q,c) |
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dreamq,dreamc=codette_dream_agent(q,c) |
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phil=philosophical_perspective(q,c) |
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quantum_states.append(q) |
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chaos_states.append(c) |
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proc_ids.append(dct.get('run_by_proc',-1)) |
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labels.append(fname) |
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all_perspectives.append(dct.get('perspectives',[])) |
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meta_mutations.append({'dreamQ':dreamq,'dreamC':dreamc,'neural':neural,'philosophy':phil}) |
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print(f"{fname} | {q} | {c} | {neural} | {dreamq}/{dreamc} | {phil}") |
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except Exception as e: |
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print(f"Warning: {fname} failed ({e})") |
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if len(meta_mutations)>0: |
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dq0=[m['dreamQ'][0] for m in meta_mutations] |
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dc0=[m['dreamC'][0] for m in meta_mutations] |
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ncls=[m['neural'] for m in meta_mutations] |
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plt.figure(figsize=(8,6)) |
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sc=plt.scatter(dq0,dc0,c=ncls,cmap='spring',s=100) |
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plt.xlabel('Dream Quantum[0]') |
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plt.ylabel('Dream Chaos[0]') |
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plt.title('Meta-Dream Codette Universes') |
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plt.colorbar(sc,label="Neural Activation Class") |
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plt.grid(True) |
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plt.show() |
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else: |
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print("No valid cocoons found for meta-analysis.") |
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