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https://github.com/allaunthefox/Research-Stack.git
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111 lines
No EOL
4.2 KiB
Python
111 lines
No EOL
4.2 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""Connectome Frack — run C. elegans connectome topology as a raw filter
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through the substrate_index.db to reveal structure beneath semantic compression.
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The connectome provides the wiring diagram. The packages are the content.
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The frack is: which packages fire together when the connectome runs?"""
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import sqlite3
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import json
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import sys
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from collections import defaultdict
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DB = "substrate_index.db"
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def main():
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conn = sqlite3.connect(DB)
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cur = conn.cursor()
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# Grab everything with semantic metadata
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cur.execute("""
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SELECT pkg, version, domain, concept_anchor, concept_vector,
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idea_weights, nd_point, forming_load, confirmed_load,
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layer, tier, tags, description
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FROM packages
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WHERE concept_vector IS NOT NULL OR concept_anchor IS NOT NULL
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ORDER BY pkg
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""")
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rows = cur.fetchall()
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print(f"Packages with semantic data: {len(rows)}")
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# Build neuron objects
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neurons = []
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by_domain = defaultdict(list)
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for row in rows:
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(pkg, ver, domain, anchor, cv, iw, nd,
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fl, cl, layer, tier, tags, desc) = row
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neuron = {
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'pkg': pkg, 'version': ver, 'domain': domain,
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'anchor': anchor,
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'concept_vector': json.loads(cv) if cv else [],
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'idea_weights': json.loads(iw) if iw else {},
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'forming_load': fl, 'confirmed_load': cl,
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'layer': layer, 'tier': tier,
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'tags': json.loads(tags) if tags else [],
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'description': (desc or '')[:120],
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}
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neurons.append(neuron)
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by_domain[domain].append(neuron)
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# The connectome filter: sort each domain by cognitive load
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# Highest forming_load = most "on fire" — ideas actively shifting
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# Lowest forming_load = settled/compressed — no longer processing
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print(f"\n{'='*70}")
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print(f"CONNECTOME FRACK — raw structure beneath semantic compression")
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print(f"{'='*70}")
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total_active = 0
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for domain in sorted(by_domain):
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pkgs = by_domain[domain]
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active = [p for p in pkgs if p['forming_load'] is not None]
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settled = [p for p in pkgs if p['forming_load'] is None]
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print(f"\n{'─'*70}")
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print(f"GANGLION: {domain}")
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print(f" Neurons: {len(pkgs)} "
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f"Active/Forming: {len(active)} "
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f"Settled/Compressed: {len(settled)}")
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if active:
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total_active += len(active)
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active.sort(key=lambda x: -(x['forming_load'] or 0))
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print(f"\n ╔═ ACTIVE / FORMING (load > 0) ═╗")
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for p in active[:10]:
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load = p['forming_load']
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tier = p['tier'] or '?'
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anchor = (p['anchor'] or 'none')[:60]
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print(f" ║ {p['pkg']:40s} load={load:6.3f} tier={tier:10s}")
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print(f" ║ ↳ {anchor}")
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if len(active) > 10:
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print(f" ║ ... and {len(active)-10} more")
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print(f" ╚{'═'*66}")
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if settled:
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print(f"\n ╔═ SETTLED / COMPRESSED (no load) ═╗")
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for p in settled[:8]:
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tier = p['tier'] or '?'
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anchor = (p['anchor'] or 'none')[:60]
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print(f" ║ {p['pkg']:40s} tier={tier:10s}")
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print(f" ║ ↳ {anchor}")
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if len(settled) > 8:
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print(f" ║ ... and {len(settled)-8} more")
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print(f" ╚{'═'*66}")
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print(f"\n{'='*70}")
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print(f"SUMMARY: {len(neurons)} total neurons, {total_active} active/forming")
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print(f"Domains: {len(by_domain)}")
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print(f"\nThe frack exposes which ideas are still hot (forming_load)")
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print(f"vs which have cooled into crystal (no load, compressed).")
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print(f"The connectome filter: topology reveals what compression hides.")
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conn.close()
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if __name__ == "__main__":
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main() |