mirror of
https://github.com/allaunthefox/Research-Stack.git
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e5f04ee6c3
Create 4-Infrastructure/lib/ with canonical implementations of:
- hashing.py: sha256_bytes, sha256_text, sha256_file
- q16.py: Q16_16 fixed-point constants and arithmetic
- jsonl.py: load_json, load_jsonl, write_jsonl, stable_json, canonical_json_bytes
- fraction_utils.py: Fraction serialization helpers for hardware probes
Refactor 66 files across 4-Infrastructure/{hardware,shim,infra} and
5-Applications/{scripts,tools-scripts,hutter_prize,text-to-cad} to import
from the shared library instead of maintaining local copies.
4-Infrastructure/auto/lib/q16.py now re-exports from lib.q16.
Net: -743 lines (490 added, 1233 removed)
Build: not applicable (Python-only change, py_compile verified on all 71 files)
Co-Authored-By: Allaun Silverfox <bigdataiscoming+9i37y6j2@protonmail.com>
347 lines
13 KiB
Python
347 lines
13 KiB
Python
#!/usr/bin/env python3
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"""Reduce the extracted Standard Model Lagrangian term axes from 12 to 4.
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The input is the symbolic term-family centroid from
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standard_model_lagrangian_exact_average.py. This is a compression/control-plane
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projection, not a physical reduction of the Standard Model.
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The four target primitives are the local OTOM primitives:
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* field - value/density surface
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* shear - gradient, coupling, torsion, transformation
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* packet - localized event/witness/claim-like object
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* spectral - eigenmode, covariance, resonance, residual spectrum
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Because a 12 -> 4 projection is lossy, this runner also emits the exact 12D
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residual lane required to rehydrate the symbolic centroid byte-for-byte at the
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rational-coordinate level.
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"""
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from __future__ import annotations
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import argparse
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import json
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from datetime import datetime, timezone
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from fractions import Fraction
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from pathlib import Path
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from typing import Any
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import sys
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REPO = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(REPO / "4-Infrastructure"))
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from lib.hashing import sha256_bytes
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from lib.fraction_utils import fraction_json, fraction_str, parse_fraction_json, vector_json
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from lib.jsonl import load_json, stable_json
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AVERAGE_RECEIPT = (
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REPO
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/ "4-Infrastructure"
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/ "hardware"
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/ "standard_model_lagrangian_exact_average_receipt.json"
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)
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SIGNED_AXIS_RECEIPT = (
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REPO
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/ "4-Infrastructure"
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/ "hardware"
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/ "standard_model_signed_axis_graph_receipt.json"
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)
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OUT = (
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REPO
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/ "4-Infrastructure"
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/ "hardware"
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/ "standard_model_12_to_4_reduction_receipt.json"
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)
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PRIMITIVES = ("field", "shear", "packet", "spectral")
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# Row-stochastic projection matrix from unreduced term-family axes to the four
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# local primitives. Rows intentionally sum to exactly 1.
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PROJECTION: dict[str, dict[str, Fraction]] = {
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"su3_gluon_field": {
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"field": Fraction(1, 2),
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"spectral": Fraction(1, 2),
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},
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"nonabelian_self_interaction": {
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"shear": Fraction(1, 3),
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"spectral": Fraction(2, 3),
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},
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"electroweak_charged_w": {
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"field": Fraction(1, 3),
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"shear": Fraction(1, 3),
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"spectral": Fraction(1, 3),
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},
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"electroweak_neutral_za": {
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"field": Fraction(1, 2),
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"spectral": Fraction(1, 2),
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},
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"higgs_goldstone_scalar": {
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"field": Fraction(1, 2),
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"shear": Fraction(1, 2),
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},
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"scalar_potential": {
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"field": Fraction(1, 2),
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"spectral": Fraction(1, 2),
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},
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"fermion_quark_sector": {
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"field": Fraction(1, 3),
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"packet": Fraction(2, 3),
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},
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"fermion_lepton_sector": {
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"field": Fraction(1, 3),
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"packet": Fraction(2, 3),
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},
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"yukawa_mass_coupling": {
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"shear": Fraction(1, 4),
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"packet": Fraction(1, 2),
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"spectral": Fraction(1, 4),
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},
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"charged_current_ckm": {
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"shear": Fraction(1, 4),
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"packet": Fraction(1, 2),
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"spectral": Fraction(1, 4),
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},
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"ghost_gaugefix_sector": {
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"shear": Fraction(1, 2),
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"packet": Fraction(1, 2),
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},
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"derivative_kinetic_flow": {
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"shear": Fraction(2, 3),
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"spectral": Fraction(1, 3),
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},
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}
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def centroid_from_average(receipt: dict[str, Any]) -> dict[str, Fraction]:
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centroid: dict[str, Fraction] = {}
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for item in receipt["rational_average"]["centroid_components"]:
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centroid[item["node"]] = parse_fraction_json(item["centroid_component"])
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return centroid
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def projection_json() -> dict[str, dict[str, dict[str, Any]]]:
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return {
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node: {
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primitive: fraction_json(weight)
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for primitive, weight in weights.items()
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}
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for node, weights in PROJECTION.items()
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}
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def validate_projection(nodes: list[str]) -> list[str]:
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errors: list[str] = []
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missing = sorted(set(nodes) - set(PROJECTION))
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extra = sorted(set(PROJECTION) - set(nodes))
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if missing:
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errors.append(f"missing projection rows: {missing}")
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if extra:
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errors.append(f"extra projection rows: {extra}")
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for node in nodes:
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row = PROJECTION.get(node, {})
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row_sum = sum(row.values(), Fraction(0))
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unknown = sorted(set(row) - set(PRIMITIVES))
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if row_sum != 1:
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errors.append(f"{node} projection row sums to {fraction_str(row_sum)}, not 1")
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if unknown:
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errors.append(f"{node} projection row has unknown primitives: {unknown}")
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return errors
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def project_12_to_4(centroid: dict[str, Fraction]) -> dict[str, Fraction]:
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reduced = {primitive: Fraction(0) for primitive in PRIMITIVES}
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for node, mass in centroid.items():
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for primitive, weight in PROJECTION[node].items():
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reduced[primitive] += mass * weight
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return reduced
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def lift_4_to_12(reduced: dict[str, Fraction]) -> dict[str, Fraction]:
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"""A deterministic canonical lift from 4D to 12D.
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This lift distributes each primitive's mass back over all term axes that
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participated in that primitive, proportional to the same projection row
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weight. It is deliberately not claimed to be the original graph. The
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residual lane below is what makes exact rehydration possible.
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"""
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support_weight_sum = {primitive: Fraction(0) for primitive in PRIMITIVES}
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for row in PROJECTION.values():
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for primitive, weight in row.items():
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support_weight_sum[primitive] += weight
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lifted = {node: Fraction(0) for node in PROJECTION}
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for node, row in PROJECTION.items():
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for primitive, weight in row.items():
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lifted[node] += reduced[primitive] * weight / support_weight_sum[primitive]
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return lifted
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def signed_l1(vector: dict[str, Fraction]) -> Fraction:
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return sum((abs(value) for value in vector.values()), Fraction(0))
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def ranked_abs_vector(vector: dict[str, Fraction], limit: int = 8) -> list[dict[str, Any]]:
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ranked = sorted(vector.items(), key=lambda item: abs(item[1]), reverse=True)
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return [
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{
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"axis": key,
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"value": fraction_json(value),
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"absolute": fraction_json(abs(value)),
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}
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for key, value in ranked[:limit]
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]
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def build_receipt() -> dict[str, Any]:
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average = load_json(AVERAGE_RECEIPT)
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signed_axis = load_json(SIGNED_AXIS_RECEIPT) if SIGNED_AXIS_RECEIPT.exists() else {}
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nodes = average["source"]["nodes"]
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projection_errors = validate_projection(nodes)
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if projection_errors:
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raise ValueError("; ".join(projection_errors))
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centroid = centroid_from_average(average)
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reduced = project_12_to_4(centroid)
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reduced_mirror = {primitive: -value for primitive, value in reduced.items()}
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reduced_closure = {
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primitive: reduced[primitive] + reduced_mirror[primitive]
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for primitive in PRIMITIVES
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}
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lifted = lift_4_to_12(reduced)
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residual = {
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node: centroid[node] - lifted[node]
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for node in nodes
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}
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rehydrated = {
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node: lifted[node] + residual[node]
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for node in nodes
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}
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rehydration_delta = {
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node: rehydrated[node] - centroid[node]
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for node in nodes
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}
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residual_mirror = {node: -value for node, value in residual.items()}
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residual_closure = {
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node: residual[node] + residual_mirror[node]
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for node in nodes
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}
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reduced_total = sum(reduced.values(), Fraction(0))
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lifted_total = sum(lifted.values(), Fraction(0))
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centroid_total = sum(centroid.values(), Fraction(0))
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receipt = {
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"schema": "standard_model_12_to_4_reduction_receipt_v1",
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"generated_utc": datetime.now(timezone.utc).isoformat(),
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"surface_id": "standard_model_12_to_4_reduction",
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"source": {
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"average_receipt": str(AVERAGE_RECEIPT.relative_to(REPO)),
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"average_stable_hash_sha256": average.get("stable_average_hash_sha256"),
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"signed_axis_receipt": str(SIGNED_AXIS_RECEIPT.relative_to(REPO)),
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"signed_axis_stable_hash_sha256": signed_axis.get("stable_graph_hash_sha256"),
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"nodes": nodes,
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},
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"primitive_basis": {
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"field": "density/value surface coordinate",
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"shear": "gradient, coupling, torsion, and transformation coordinate",
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"packet": "localized event, witness, claim, or receipt coordinate",
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"spectral": "eigenmode, covariance, resonance, and residual-spectrum coordinate",
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},
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"projection_matrix_12_to_4": projection_json(),
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"projection_law": {
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"row_stochastic": True,
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"row_sum": "1",
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"meaning": (
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"Every unreduced term-family axis contributes all of its centroid "
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"mass into the four-primitives control basis."
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),
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},
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"visible_centroid_12d": vector_json(centroid),
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"visible_reduced_4d": vector_json(reduced),
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"reduced_4d_total": fraction_json(reduced_total),
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"mirror_reduced_4d": vector_json(reduced_mirror),
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"mirror_closure_4d": {
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"closed": all(value == 0 for value in reduced_closure.values()),
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"l1_error": fraction_json(signed_l1(reduced_closure)),
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"components": vector_json(reduced_closure),
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},
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"canonical_lift_4d_to_12d": {
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"lift_rule": (
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"Distribute each primitive mass back over supporting term axes "
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"proportional to the projection row weight."
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),
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"lifted_centroid_12d": vector_json(lifted),
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"lifted_total": fraction_json(lifted_total),
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},
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"residual_lane_12d": {
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"meaning": (
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"Exact rational sidecar required to reconstruct the original "
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"12-axis centroid after the lossy 12-to-4 projection."
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),
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"residual": vector_json(residual),
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"residual_l1": fraction_json(signed_l1(residual)),
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"top_residual_axes": ranked_abs_vector(residual),
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"mirror_residual": vector_json(residual_mirror),
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"mirror_residual_closure": {
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"closed": all(value == 0 for value in residual_closure.values()),
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"l1_error": fraction_json(signed_l1(residual_closure)),
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},
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},
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"exact_rehydration": {
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"closed": all(value == 0 for value in rehydration_delta.values()),
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"l1_error": fraction_json(signed_l1(rehydration_delta)),
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"centroid_total": fraction_json(centroid_total),
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"rehydrated_total": fraction_json(sum(rehydrated.values(), Fraction(0))),
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},
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"claim_boundary": (
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"This is a symbolic compression projection over the extracted "
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"term-family graph. It is not a physical Standard Model reduction, "
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"renormalization result, hidden-particle claim, or new equation of "
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"motion."
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),
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"lawful": True,
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}
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stable_preimage = stable_json({
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"schema": receipt["schema"],
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"surface_id": receipt["surface_id"],
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"source": receipt["source"],
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"primitive_basis": receipt["primitive_basis"],
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"projection_matrix_12_to_4": receipt["projection_matrix_12_to_4"],
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"projection_law": receipt["projection_law"],
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"visible_centroid_12d": receipt["visible_centroid_12d"],
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"visible_reduced_4d": receipt["visible_reduced_4d"],
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"mirror_closure_4d": receipt["mirror_closure_4d"],
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"canonical_lift_4d_to_12d": receipt["canonical_lift_4d_to_12d"],
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"residual_lane_12d": receipt["residual_lane_12d"],
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"exact_rehydration": receipt["exact_rehydration"],
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"claim_boundary": receipt["claim_boundary"],
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"lawful": receipt["lawful"],
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}).encode("utf-8")
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receipt["stable_reduction_hash_sha256"] = sha256_bytes(stable_preimage)
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receipt["receipt_hash_preimage_sha256"] = sha256_bytes(stable_json(receipt).encode("utf-8"))
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return receipt
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--out", type=Path, default=OUT)
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args = parser.parse_args()
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receipt = build_receipt()
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args.out.parent.mkdir(parents=True, exist_ok=True)
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args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8")
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print(json.dumps({
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"lawful": receipt["lawful"],
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"stable_reduction_hash_sha256": receipt["stable_reduction_hash_sha256"],
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"receipt_hash_preimage_sha256": receipt["receipt_hash_preimage_sha256"],
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"visible_reduced_4d": receipt["visible_reduced_4d"],
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"mirror_closure_4d": receipt["mirror_closure_4d"]["closed"],
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"residual_l1": receipt["residual_lane_12d"]["residual_l1"],
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"exact_rehydration": receipt["exact_rehydration"]["closed"],
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}, indent=2, sort_keys=True))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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