# Compression — honest-measurement scripts

Reproducible experiments behind [`docs/research/COMPRESSION_HONEST_FINDINGS.md`](../../docs/research/COMPRESSION_HONEST_FINDINGS.md).
Every claim is checked with a real coder and a byte-exact lossless round-trip; no
straw baselines. Python 3 + numpy only (stdlib `lzma`/`gzip` for baselines).

| script | what it measures |
|---|---|
| `spectral_vs_empirical.py` | Wall 1: rank-k reconstruction of the byte transition matrix never beats raw counts |
| `entropy_full.py` | order-0/1/2 empirical entropy of a corpus (adaptive-AC target rate) |
| `ac_roundtrip.py` | real Witten–Neal–Cleary order-2 arithmetic coder; lossless round-trip + bits/byte |
| `gw_honest_test.py` | GW ringdown parametric fit (handed TRUE params) vs LPC/xz across SNRs — the "583×" gate |
| `frozen_model_compress.py` | conservation law: frozen model + AC, model-column vs tape-column, self-contained vs amortized |
| `mass_number_compress.py` | Semantic Mass Number = base conversion (bijection), M1/M2/M3 vs xz |
| `superposition_recovery.py` | capstone: compressed-sensing recovery vs sparsity — recoverable ⟺ sparse cliff |
| `pi_tape_lut.py` | π as a tape LUT: offset-into-π (real π digits) is the same size as the data — base conversion |

Most default to a local text corpus path; pass a directory as `argv[1]` to point
elsewhere. `entropy_full.py`/`ac_roundtrip.py` expect an `enwik8` file
(re-fetch from `mattmahoney.net/dc/enwik8.zip`).