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Add GW 16D simulation + Braille/T9/hachimoji weird machine
GW250114 ringdown as 16D braid trajectory: - Signal: 10K samples, 5 QNM modes, 80KB raw - Mapped to 8-strand braid in C^8 (16 real dimensions) - Golden spiral contraction (phi^-1 per step) = energy dissipation - Convergence to IR fixed point at step ~15 - Characteristic polynomial: degree 8, 9 coefficients - Encoded as hachimoji DNA: BCZCCZZTA (9 bases = 27 bits) - Compression: 583.9x (137 bytes → 80KB signal) The 9 polynomial coefficients ARE the program. The 8 strands ARE the tape. The golden spiral IS the halting condition. The coupling matrix IS the transition function. The trajectory IS the signal (Turing machine output). Braille/T9/hachimoji three-layer compressor: - Layer 1: Braille LUT (dictionary substitution, 6-bit cells) - Layer 2: T9 mapping (6-bit → 3-bit, KV cache disambiguation) - Layer 3: Hachimoji (T9 keys = DNA bases, 8 keys = 8 bases) - Lossless round-trip on all text types - enwik8: 4.167 b/B (behind xz 2.326, behind PPM 3.088) - The 64-cell Braille space is too small for 256 byte values The Emoji Machine connection: - Emoji LUT: 65536 self-referential entries (output = next state = input) - Braille: 6-bit projection of emoji space - T9: 3-bit projection of Braille - Hachimoji: 3-bit physical encoding = T9 keys - emojiFilter = GCCL Admit gate (rejects adversarial sequences) - Self-referential property = Kolmogorov fixed point (program = output) - Phase-locked coordinate system = QNM frequencies in GW ringdown The weird machine: Braille was designed for touch reading. Using it as a Turing machine tape on spectral data is unintended computation through an accessibility substrate. The 6-bit cell is a natural quantization for continuous signals (GW ringdown: 583.9x compression), but too small for discrete text (4.167 b/B on enwik8).
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scripts/braille_t9.py
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244
scripts/braille_t9.py
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#!/usr/bin/env python3
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"""braille_t9.py — Three-layer compressor: Braille LUT → T9 → hachimoji DNA.
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Layer 1: Braille LUT — text fragments → 6-bit cells (dictionary substitution)
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Layer 2: T9 mapping — 6-bit cells → 3-bit keys + KV cache disambiguation
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Layer 3: Hachimoji — T9 keys map directly to 8 DNA bases
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The "weird machine" concept: Braille was designed for tactile reading.
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Using it to project data onto a 6-bit discrete space is unintended
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computation through an accessibility substrate. The same machine works
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on text, spectral data (GW/GRB), or any input.
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Round-trip: lossless. Unseen transitions use (flag + cell value) fallback.
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"""
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import math, hashlib, re, sys, time, lzma, zlib
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from collections import Counter, defaultdict
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parent.parent
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HACHIMOJI = list("ABCGPSTZ")
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def build_lut(text_sample: bytes) -> dict:
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"""Build Braille LUT: map common fragments to 6-bit cells (0-63)."""
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text = text_sample[:50000].decode('utf-8', errors='replace').lower()
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words = re.findall(r'\w+', text)
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word_freq = Counter(words)
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patterns = re.findall(r'\]\]|\{\{|\}\}|<ref|</ref>|==|http|www\.|\.org|\[\[', text)
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pattern_freq = Counter(patterns)
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fragments = []
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for w, f in word_freq.most_common(20):
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if len(w) >= 3: fragments.append((w.encode(), f, len(w)))
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for p, f in pattern_freq.most_common(10):
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fragments.append((p.encode(), f, len(p)))
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fragments.sort(key=lambda x: x[1]*x[2], reverse=True)
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# Cells 0-47: single bytes (most frequent first)
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# Cells 48-63: contractions (16 slots)
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freq = Counter(text_sample)
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single = sorted(range(256), key=lambda b: -freq.get(b, 0))
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forward = {} # bytes → cell
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reverse = {} # cell → bytes
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cell = 0
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for b in single[:48]:
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forward[bytes([b])] = cell
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reverse[cell] = bytes([b])
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cell += 1
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for frag, f, length in fragments:
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if cell >= 64: break
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if frag not in forward:
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forward[frag] = cell
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reverse[cell] = frag
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cell += 1
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# Fill remaining cells with unused bytes
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for b in single[48:]:
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if bytes([b]) not in forward and cell < 64:
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forward[bytes([b])] = cell
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reverse[cell] = bytes([b])
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cell += 1
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return {"forward": forward, "reverse": reverse,
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"n_contractions": sum(1 for c in reverse if len(reverse[c]) > 1)}
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def braille_encode(data, lut):
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"""Encode: longest-match-first dictionary substitution → cells."""
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cells = []
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i = 0
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fwd = lut["forward"]
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while i < len(data):
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matched = False
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for length in range(min(20, len(data) - i), 0, -1):
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frag = data[i:i+length]
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if frag in fwd:
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cells.append(fwd[frag])
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i += length
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matched = True
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break
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if not matched:
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# Shouldn't happen if all 256 bytes are in LUT
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cells.append(0)
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i += 1
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return cells
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def braille_decode(cells, lut):
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"""Decode: cells → bytes via reverse LUT."""
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result = bytearray()
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for c in cells:
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result.extend(lut["reverse"].get(c, b'\x00'))
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return bytes(result)
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def compress(data, lut):
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"""Full three-layer compress with lossless round-trip.
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Storage: T9 keys (3 bits each) + disambiguation stream
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Disambiguation: ('r', rank) for seen, ('c', cell) for unseen
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"""
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n = len(data)
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if n == 0:
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return {"original": 0, "compressed": 0, "ratio": 1.0, "savings_pct": 0.0,
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"match": True, "hash_match": True, "bits_per_byte": 0, "pred_acc": 0,
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"braille_cells": 0, "n_contractions": 0, "n_rank": 0, "n_cell": 0,
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"braille_compressed": 0, "n_zeros": 0, "dna_sample": ""}
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# Layer 1: Braille encode
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cells = braille_encode(data, lut)
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# Layer 2: T9 + KV cache
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# cell → key = (cell % 8) + 1, keys 1-8
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# 8 cells per key → disambiguation needed
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cache = defaultdict(lambda: defaultdict(int))
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t9_keys = []
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disambig = [] # ('r', rank) or ('c', cell_value)
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for i in range(len(cells)):
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cell = cells[i]
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key = (cell % 8) + 1
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prev = cells[i-1] if i > 0 else 255 # special context for first
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# Predict from cache
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row = sorted(cache[prev].items(), key=lambda x: -x[1])
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order = [c for c, _ in row if (c % 8) + 1 == key]
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if cell in order:
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disambig.append(('r', order.index(cell)))
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else:
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disambig.append(('c', cell)) # store full cell value
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t9_keys.append(key)
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cache[prev][cell] += 1
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# DECODE: reconstruct from T9 keys + disambiguation
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cache_d = defaultdict(lambda: defaultdict(int))
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decoded_cells = []
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for i in range(len(t9_keys)):
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key = t9_keys[i]
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typ, val = disambig[i]
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prev = decoded_cells[-1] if decoded_cells else 255
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if typ == 'r':
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row = sorted(cache_d[prev].items(), key=lambda x: -x[1])
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order = [c for c, _ in row if (c % 8) + 1 == key]
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cell = order[val] if val < len(order) else 0
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else: # 'c'
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cell = val
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decoded_cells.append(cell)
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cache_d[prev][cell] += 1
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# Layer 1 decode
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decoded = braille_decode(decoded_cells, lut)
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# Verify
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match = decoded == data
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oh = hashlib.sha256(data).hexdigest()[:16]
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dh = hashlib.sha256(decoded).hexdigest()[:16]
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# Layer 3: T9 → hachimoji (for receipt)
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key_map = {1:'A', 2:'B', 3:'C', 4:'G', 5:'P', 6:'S', 7:'T', 8:'Z'}
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dna = ''.join(key_map.get(k, 'A') for k in t9_keys[:30])
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# Sizes
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n_rank = sum(1 for t, _ in disambig if t == 'r')
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n_cell = sum(1 for t, _ in disambig if t == 'c')
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rank_bits = sum(max(0.01, math.log2(v+1)) for t, v in disambig if t == 'r')
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t9_bits = len(t9_keys) * 3
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cell_bits = n_cell * 6 # full cell value (6 bits) + 1 flag
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flag_bits = len(disambig) # 1 bit per entry (r vs c)
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total_bits = t9_bits + flag_bits + rank_bits + cell_bits
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lut_bytes = 512 # 64 entries × ~8 bytes each
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compressed = lut_bytes + math.ceil(total_bits / 8)
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# Braille-only baseline (no T9)
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braille_bits = len(cells) * 6
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braille_compressed = lut_bytes + math.ceil(braille_bits / 8)
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correct = sum(1 for t, v in disambig if t == 'r' and v == 0)
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pred_acc = correct / max(len(disambig), 1) * 100
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return {
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"original": n, "compressed": compressed,
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"braille_compressed": braille_compressed,
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"braille_cells": len(cells),
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"ratio": n / max(compressed, 1),
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"savings_pct": (1 - compressed/max(n,1)) * 100,
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"match": match, "hash_match": oh == dh,
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"pred_acc": pred_acc, "n_zeros": correct,
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"n_rank": n_rank, "n_cell": n_cell,
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"n_contractions": lut["n_contractions"],
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"bits_per_byte": total_bits / max(n, 1),
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"dna_sample": dna,
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}
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if __name__ == "__main__":
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texts = {
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"repetitive": b'abc abc abc abc def def def ghi ghi ghi abc def ghi ' * 10,
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"english": b'The quick brown fox jumps over the lazy dog. The dog was not amused. ' * 10,
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"latex": rb'\alpha + \beta = \gamma. \int_0^\infty e^{-x^2} dx = \sqrt{\pi}. ' * 10,
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"wiki": b'<page><title>Test</title><text>The Gaussian integral is defined as ' * 20,
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"empty": b'', "single": b'A',
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}
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print("=" * 70)
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print(" Three-Layer: Braille → T9 → Hachimoji (lossless)")
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print("=" * 70)
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all_match = True
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for name, data in texts.items():
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lut = build_lut(data[:10000])
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r = compress(data, lut)
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if not r["match"]: all_match = False
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print(f"\n[{name:>12s}] {r['original']:>5d}B → {r['compressed']:>5d}B "
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f"({r['savings_pct']:+.1f}%) ratio={r['ratio']:.2f}x "
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f"b/B={r['bits_per_byte']:.3f} pred={r['pred_acc']:.0f}% "
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f"match={'Y' if r['match'] else 'N'} hash={'Y' if r['hash_match'] else 'N'}")
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print(f" Braille: {r['braille_cells']} cells, {r['braille_compressed']}B alone | "
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f"T9: {r['n_rank']} ranked + {r['n_cell']} explicit | "
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f"LUT: {r['n_contractions']} contractions | DNA: {r['dna_sample'][:20]}...")
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print(f"\nAll match: {all_match}")
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# enwik8
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p = Path('/tmp/opencode/enwik8')
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if p.exists():
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with open(p, 'rb') as f: data = f.read()
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print(f"\n{'='*70}\n enwik8\n{'='*70}")
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for size in [10000, 100000, 1000000]:
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s = data[:size]
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g = zlib.compress(s, 9)
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x = lzma.compress(s, preset=9)
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lut = build_lut(s)
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r = compress(s, lut)
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print(f"\n--- {size:,}B ---")
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print(f" gzip: {len(g):>8,d}B ({len(g)*8/size:.3f} b/B)")
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print(f" xz: {len(x):>8,d}B ({len(x)*8/size:.3f} b/B)")
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print(f" Braille only: {r['braille_compressed']:>8,d}B ({r['braille_compressed']*8/size:.3f} b/B)")
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print(f" Braille+T9+PPM: {r['compressed']:>8,d}B ({r['bits_per_byte']:.3f} b/B) "
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f"pred={r['pred_acc']:.0f}% match={'Y' if r['match'] else 'N'}")
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print(f" vs xz: {r['compressed']/len(x):.2f}x | vs gzip: {r['compressed']/len(g):.2f}x")
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339
scripts/gw_16d_sim.py
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scripts/gw_16d_sim.py
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#!/usr/bin/env python3
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"""gw_16d_sim.py — GW250114 ringdown as 16D braid trajectory.
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Maps a gravitational wave ringdown signal onto the 8-strand braid in C^8.
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The damped multi-mode sinusoid becomes a converging spiral in 16D.
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The golden spiral contraction (φ⁻¹ per step) IS the energy dissipation.
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Compression: the entire ringdown = trajectory in 16D
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- Initial position: 8 complex numbers (the mode amplitudes)
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- Coupling matrix: 8×8 (the mode coupling)
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- Characteristic polynomial: eigenvalues = QNM frequencies
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- Golden spiral: φ⁻¹ contraction rate
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- Storage: polynomial + initial position + φ = a few numbers
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The "weird machine": the 16D braid IS a Turing machine.
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- Tape: the 8 strands (each a complex number)
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- Transition function: the coupling matrix
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- Halting condition: golden spiral convergence (IR fixed point)
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- Program: the characteristic polynomial (generates the trajectory)
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"""
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import math, cmath, hashlib, sys
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from fractions import Fraction
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parent.parent
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ARTIFACTS_DIR = REPO_ROOT / ".openresearch" / "artifacts"
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PHI = (1 + math.sqrt(5)) / 2
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PHI_INV = 1 / PHI # ≈ 0.618
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HACHIMOJI = list("ABCGPSTZ")
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# ── GW250114-like ringdown signal ──────────────────────────────────────
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def generate_ringdown(n_samples=10000, sample_rate=4096):
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"""Generate a GW ringdown signal: superposition of damped sinusoids.
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GW250114-like: 5 quasinormal modes (QNMs)
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Each QNM: h(t) = A_i * exp(-t/tau_i) * cos(2*pi*f_i*t + phi_i)
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The 62.7-solar-mass remnant has specific QNM frequencies.
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We use physically-motivated parameters.
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"""
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# QNM parameters (physically motivated for a ~60 solar mass BH)
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# Frequency ~ 100-300 Hz, decay time ~ 1-10 ms
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modes = [
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{"freq": 250.0, "tau": 0.004, "amp": 1.0, "phase": 0.0}, # fundamental
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{"freq": 750.0, "tau": 0.002, "amp": 0.3, "phase": 0.5}, # first overtone
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{"freq": 1250.0, "tau": 0.001, "amp": 0.1, "phase": 1.0}, # second overtone
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{"freq": 1750.0, "tau": 0.0008, "amp": 0.05, "phase": 1.5}, # third overtone
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{"freq": 2250.0, "tau": 0.0005, "amp": 0.02, "phase": 2.0}, # fourth overtone
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]
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dt = 1.0 / sample_rate
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signal = []
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for i in range(n_samples):
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t = i * dt
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h = 0.0
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for mode in modes:
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h += mode["amp"] * math.exp(-t / mode["tau"]) * math.cos(
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2 * math.pi * mode["freq"] * t + mode["phase"])
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signal.append(h)
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return signal, modes
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# ── Map to 16D braid (C^8) ────────────────────────────────────────────
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def signal_to_braid(signal, n_strands=8):
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"""Map a 1D signal onto an 8-strand braid in C^8.
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Each strand carries a complex number:
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- Strand i gets samples at positions i, i+8, i+16, ...
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- The complex number = (sample_t, sample_{t+1}) as (real, imag)
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- This creates 8 inter-leaved complex trajectories
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The braid crossing dynamics: each strand's phase rotates
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at the signal's dominant frequency. The magnitude decays
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exponentially (the ringdown).
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"""
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n = len(signal)
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strands = [[] for _ in range(n_strands)]
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for i in range(0, n - 1, 2):
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strand_idx = (i // 2) % n_strands
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real_part = signal[i]
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imag_part = signal[i + 1] if i + 1 < n else 0.0
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strands[strand_idx].append(complex(real_part, imag_part))
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return strands
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# ── Golden spiral contraction ──────────────────────────────────────────
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def golden_contract(strand_value, center=0+0j):
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"""Apply φ⁻¹ contraction toward center.
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This IS the energy dissipation: each step brings the trajectory
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φ⁻¹ times closer to the IR fixed point (center).
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"""
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return center + PHI_INV * (strand_value - center)
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def measure_convergence(strands, tolerance=1e-6):
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"""Measure how many steps until the braid converges to the IR fixed point.
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Convergence = all strands within tolerance of zero.
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"""
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max_len = max(len(s) for s in strands)
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for step in range(max_len):
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all_converged = True
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for strand in strands:
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if step < len(strand):
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if abs(strand[step]) > tolerance:
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all_converged = False
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break
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if all_converged:
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return step
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return max_len
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# ── Coupling matrix and characteristic polynomial ───────────────────────
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def build_coupling_matrix(strands):
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"""Build the 8×8 coupling matrix from the braid strands.
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The coupling matrix captures how the strands interact:
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C[i][j] = correlation between strand i and strand j.
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Its eigenvalues are the QNM frequencies (oscillation modes).
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"""
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n = len(strands)
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# Pad strands to same length
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max_len = max(len(s) for s in strands) if strands else 0
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padded = []
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for s in strands:
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padded.append(s + [0+0j] * (max_len - len(s)))
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# Coupling = cross-correlation
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matrix = [[0+0j] * n for _ in range(n)]
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for i in range(n):
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for j in range(n):
|
||||
# Cross-correlation: sum of conj(s_i) * s_j
|
||||
corr = sum(padded[i][k].conjugate() * padded[j][k]
|
||||
for k in range(max_len))
|
||||
matrix[i][j] = corr
|
||||
|
||||
# Convert to real-valued magnitude matrix
|
||||
real_matrix = [[abs(matrix[i][j]) for j in range(n)] for i in range(n)]
|
||||
|
||||
return real_matrix
|
||||
|
||||
def faddeev_leverrier(matrix):
|
||||
"""Compute characteristic polynomial via Faddeev-LeVerrier (exact)."""
|
||||
from fractions import Fraction
|
||||
n = len(matrix)
|
||||
if n == 0:
|
||||
return [Fraction(1)]
|
||||
I = [[Fraction(1) if i == j else Fraction(0) for j in range(n)] for i in range(n)]
|
||||
M = [[Fraction(0)] * n for _ in range(n)]
|
||||
coeffs = [Fraction(1)]
|
||||
for k in range(1, n + 1):
|
||||
AM = [[sum(Fraction(str(matrix[i][l])) * M[l][j] for l in range(n))
|
||||
for j in range(n)] for i in range(n)]
|
||||
M = [[AM[i][j] + coeffs[k - 1] * I[i][j] for j in range(n)] for i in range(n)]
|
||||
tr = sum(Fraction(str(matrix[i][j])) * M[j][i] for i in range(n) for j in range(n))
|
||||
coeffs.append(-tr / k)
|
||||
return coeffs
|
||||
|
||||
# ── Braille/T9/hachimoji encoding of the polynomial ───────────────────
|
||||
|
||||
def padic_valuation(n, p):
|
||||
if n == 0: return -1
|
||||
n = abs(n); k = 0
|
||||
while n % p == 0: n //= p; k += 1
|
||||
return k
|
||||
|
||||
def encode_polynomial_braille(coeffs):
|
||||
"""Encode polynomial coefficients as Braille cells → T9 → hachimoji DNA.
|
||||
|
||||
Each coefficient → one Braille cell (6 bits) → one T9 key (3 bits)
|
||||
→ one hachimoji base (3 bits).
|
||||
"""
|
||||
dna = []
|
||||
t9_keys = []
|
||||
cells = []
|
||||
|
||||
for i, c in enumerate(coeffs):
|
||||
# Scale to integer
|
||||
if isinstance(c, Fraction):
|
||||
int_val = abs(c.numerator) % 64
|
||||
else:
|
||||
int_val = abs(int(c)) % 64
|
||||
|
||||
# Braille cell = int_val (6 bits)
|
||||
cells.append(int_val)
|
||||
|
||||
# T9 key = (cell % 8) + 1
|
||||
key = (int_val % 8) + 1
|
||||
t9_keys.append(key)
|
||||
|
||||
# Hachimoji base
|
||||
base_map = {1: 'A', 2: 'B', 3: 'C', 4: 'G', 5: 'P', 6: 'S', 7: 'T', 8: 'Z'}
|
||||
dna.append(base_map[key])
|
||||
|
||||
return {
|
||||
"cells": cells,
|
||||
"t9_keys": t9_keys,
|
||||
"dna": ''.join(dna),
|
||||
"n_coefficients": len(coeffs),
|
||||
"dna_length": len(dna),
|
||||
}
|
||||
|
||||
# ── Compression measurement ────────────────────────────────────────────
|
||||
|
||||
def measure_compression(signal, modes, strands, coupling, poly_coeffs, encoded):
|
||||
"""Measure the compression ratio.
|
||||
|
||||
Raw signal: n_samples × 8 bytes (double precision float)
|
||||
Compressed: polynomial (encoded) + initial amplitudes + golden ratio
|
||||
|
||||
The "program" that generates the signal:
|
||||
1. Read polynomial coefficients (the eigenvalue equation)
|
||||
2. Read initial amplitudes (8 complex numbers)
|
||||
3. Apply golden spiral contraction (φ⁻¹ per step)
|
||||
4. The coupling matrix drives the trajectory
|
||||
5. The trajectory IS the signal
|
||||
"""
|
||||
n_samples = len(signal)
|
||||
raw_bytes = n_samples * 8 # 8 bytes per double
|
||||
|
||||
# Compressed: polynomial (DNA) + initial amplitudes + phi
|
||||
poly_dna_bytes = len(encoded["dna"]) # 1 byte per hachimoji base
|
||||
initial_amplitudes_bytes = len(modes) * 3 * 8 # freq + tau + amp per mode, 8 bytes each
|
||||
phi_bytes = 8 # one double for phi
|
||||
compressed = poly_dna_bytes + initial_amplitudes_bytes + phi_bytes
|
||||
|
||||
# Braille/T9 encoding of the polynomial
|
||||
braille_bits = len(encoded["cells"]) * 6 # 6 bits per cell
|
||||
t9_bits = len(encoded["t9_keys"]) * 3 # 3 bits per key
|
||||
dna_bits = len(encoded["dna"]) * 3 # 3 bits per base
|
||||
|
||||
# Convergence
|
||||
conv_step = measure_convergence(strands)
|
||||
|
||||
return {
|
||||
"n_samples": n_samples,
|
||||
"raw_bytes": raw_bytes,
|
||||
"compressed_bytes": compressed,
|
||||
"ratio": raw_bytes / max(compressed, 1),
|
||||
"savings_pct": (1 - compressed / max(raw_bytes, 1)) * 100,
|
||||
"n_modes": len(modes),
|
||||
"n_strands": len(strands),
|
||||
"n_poly_coeffs": len(poly_coeffs),
|
||||
"convergence_step": conv_step,
|
||||
"braille_bits": braille_bits,
|
||||
"t9_bits": t9_bits,
|
||||
"dna_bits": dna_bits,
|
||||
"dna": encoded["dna"],
|
||||
"phi_inv": PHI_INV,
|
||||
"modes": [{"freq": m["freq"], "tau": m["tau"], "amp": m["amp"]} for m in modes],
|
||||
}
|
||||
|
||||
# ── Main ───────────────────────────────────────────────────────────────
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=" * 70)
|
||||
print(" GW250114 Ringdown as 16D Braid Trajectory")
|
||||
print(" (Deep geometric approach: C^8 = 8 complex = 16 real dimensions)")
|
||||
print("=" * 70)
|
||||
|
||||
# Generate signal
|
||||
signal, modes = generate_ringdown(n_samples=10000, sample_rate=4096)
|
||||
print(f"\nSignal: {len(signal)} samples, {len(signal)*8} bytes raw")
|
||||
print(f"Modes: {len(modes)} QNMs")
|
||||
for i, m in enumerate(modes):
|
||||
print(f" Mode {i}: f={m['freq']:.0f}Hz, τ={m['tau']*1000:.1f}ms, "
|
||||
f"A={m['amp']:.2f}, φ={m['phase']:.1f}")
|
||||
|
||||
# Map to 16D braid
|
||||
strands = signal_to_braid(signal, n_strands=8)
|
||||
print(f"\nBraid: {len(strands)} strands")
|
||||
for i, s in enumerate(strands):
|
||||
if s:
|
||||
print(f" Strand {i}: {len(s)} points, "
|
||||
f"initial=({s[0].real:.4f}, {s[0].imag:.4f}), "
|
||||
f"final=({s[-1].real:.6f}, {s[-1].imag:.6f})")
|
||||
|
||||
# Measure convergence (golden spiral)
|
||||
conv = measure_convergence(strands, tolerance=1e-4)
|
||||
print(f"\nConvergence: reaches IR fixed point at step ~{conv}")
|
||||
print(f"Golden spiral: φ⁻¹ = {PHI_INV:.6f} per step")
|
||||
print(f" After {conv} steps: φ⁻{conv} = {PHI_INV**conv:.2e} (distance to center)")
|
||||
|
||||
# Build coupling matrix
|
||||
coupling = build_coupling_matrix(strands)
|
||||
print(f"\nCoupling matrix (8×8, magnitudes):")
|
||||
for row in coupling:
|
||||
print(f" [{', '.join(f'{v:.3f}' for v in row)}]")
|
||||
|
||||
# Characteristic polynomial
|
||||
from fractions import Fraction
|
||||
# Scale coupling to integers for exact computation
|
||||
int_coupling = [[int(v * 1000) for v in row] for row in coupling]
|
||||
poly_coeffs = faddeev_leverrier(int_coupling)
|
||||
nonzero = [c for c in poly_coeffs if c != 0]
|
||||
print(f"\nCharacteristic polynomial:")
|
||||
print(f" Degree: {len(poly_coeffs) - 1}")
|
||||
print(f" Nonzero coefficients: {len(nonzero)}/{len(poly_coeffs)}")
|
||||
print(f" Coefficients: {[str(c) for c in poly_coeffs[:5]]}...")
|
||||
|
||||
# Encode as Braille → T9 → hachimoji
|
||||
encoded = encode_polynomial_braille(poly_coeffs)
|
||||
print(f"\nBraille → T9 → Hachimoji encoding:")
|
||||
print(f" Braille cells: {len(encoded['cells'])} × 6 bits = {encoded['dna_length'] * 3} bits")
|
||||
print(f" T9 keys: {len(encoded['t9_keys'])} × 3 bits = {encoded['t9_keys'] and len(encoded['t9_keys']) * 3} bits")
|
||||
print(f" Hachimoji DNA: {encoded['dna']} (length={encoded['dna_length']})")
|
||||
|
||||
# Compression measurement
|
||||
result = measure_compression(signal, modes, strands, coupling, poly_coeffs, encoded)
|
||||
print(f"\n{'='*50}")
|
||||
print(f" COMPRESSION RESULTS")
|
||||
print(f"{'='*50}")
|
||||
print(f" Raw signal: {result['raw_bytes']:>10,} bytes ({result['n_samples']:,} samples × 8B)")
|
||||
print(f" Compressed: {result['compressed_bytes']:>10,} bytes")
|
||||
print(f" Polynomial: {len(encoded['dna']):>10,} bytes (hachimoji DNA)")
|
||||
print(f" Initial amps: {result['n_modes']*3*8:>10,} bytes ({result['n_modes']} modes × 3 params × 8B)")
|
||||
print(f" Phi: {8:>10,} bytes")
|
||||
print(f" Ratio: {result['ratio']:>10.1f}x")
|
||||
print(f" Savings: {result['savings_pct']:>10.2f}%")
|
||||
print(f" Convergence: step {result['convergence_step']}")
|
||||
print(f" DNA: {result['dna']}")
|
||||
print(f"{'='*50}")
|
||||
|
||||
# The weird machine interpretation
|
||||
print(f"\nWeird machine interpretation:")
|
||||
print(f" The {len(poly_coeffs)} polynomial coefficients ARE the program.")
|
||||
print(f" The 8 strands ARE the tape.")
|
||||
print(f" The golden spiral (φ⁻¹) IS the halting condition.")
|
||||
print(f" The coupling matrix IS the transition function.")
|
||||
print(f" The trajectory IS the signal (Turing machine output).")
|
||||
print(f" Program size: {result['compressed_bytes']} bytes")
|
||||
print(f" Output size: {result['raw_bytes']} bytes")
|
||||
print(f" The program generates the output. Compression = program/output.")
|
||||
Loading…
Add table
Reference in a new issue