Plan: drop the assistant-specific appendices; numeric helpers become Appendix A

.gitignore

@@ -20,7 +20,7 @@ target/
 results/
 # editor lock files
 .~lock.*#
-# Claude Code files (kept local only)
+# local assistant files (kept out of the repository)
 CLAUDE.md
 CLAUDE.local.md
 .claude/

SIMURGARRAY_MASTER_PLAN.md

@@ -4,7 +4,7 @@
 > genotyping BeadChip data: IDAT → normalization → genotype calling → BAF/LRR → QC → VCF/PLINK, with later
 > modules for copy number, LOH/mosaicism, pharmacogenomics and cluster-file training.
 >
-> This document is **self-contained**: a developer (human or Claude Code) on a fresh machine should be able to
+> This document is **self-contained**: a developer on a fresh machine should be able to
 > build the whole project from it without any other notes. Version 1.0 · 2026-09-27.
 >
 > *Name:* the Simurg (Anka / Zümrüdüanka) is the mythical bird of Turkish–Persian legend. CLI and Python package: `simurg`.
@@ -36,9 +36,7 @@
 20. Known unknowns & planned experiments
 21. Risk register
 22. References
-- Appendix A — `CLAUDE.md` for the new repository
-- Appendix B — first-session prompt for Claude Code
-- Appendix C — numeric helper definitions (percentile, trimmean, robustfit, fuzzy memberships)
+- Appendix A — numeric helper definitions (percentile, trimmean, robustfit, fuzzy memberships)
 
 ---
 
@@ -189,7 +187,6 @@ bash scripts/bootstrap_refs.sh && bash scripts/fetch_data.sh p0
 ```
 simurg/
 ├── pyproject.toml            # uv/hatch; package "simurg"; entry point `simurg`
-├── CLAUDE.md                 # Appendix A (local only, git-ignored)
 ├── SIMURGARRAY_MASTER_PLAN.md     # this file
 ├── THIRD_PARTY_NOTICES.md
 ├── scripts/
@@ -203,7 +200,7 @@ simurg/
 │   │   ├── vcf.py  plink.py  bedgraph.py  finalreport.py
 │   ├── signal/
 │   │   ├── raw_xy.py         # IDAT→locus X/Y assembly (§7.1)
-│   │   ├── normalization/    # fit.py (§7.2), apply.py (§7.3), numerics.py (Appendix C)
+│   │   ├── normalization/    # fit.py (§7.2), apply.py (§7.3), numerics.py (Appendix A)
 │   │   └── polar.py          # θ, R (§7.4)
 │   ├── genotype/
 │   │   ├── gencall.py        # EGT-based caller + score (§7.5)
@@ -411,7 +408,7 @@ GC Score, Theta, R, X, Y, X Raw, Y Raw, B Allele Freq, Log R Ratio. Used only as
 
 ## 7. Core algorithms (exact math)
 
-Notation: per locus i raw intensities (x, y) as float; P_p(v) = Illumina percentile (Appendix C);
+Notation: per locus i raw intensities (x, y) as float; P_p(v) = Illumina percentile (Appendix A);
 trimmeanK(v) = mean of v within [P_{K/2}, P_{100−K/2}].
 
 ### 7.1 Raw X/Y assembly [OSS: gtc2vcf `fill_array` idat2gtc.c:3711-3745; LIT: Steemers 2006]
@@ -445,13 +442,13 @@ In bit-exact mode, missing loci (0,0) **stay in** the bin as zeros (vendor quirk
      candidates; y-sweep analogously → BB candidates. (For ≥2000 points the vendor uses a binned approximate
      nearest-neighbour search `findClosestSitesToPointsAlongAxis` — reproduce it in bit-exact mode; see
      gtc2vcf `nearest_neighbor.c`.)
-   - robust fits (bisquare, Appendix C): y = b_a + m_a·x on AA; x = b_b + m_b·y on BB;
+   - robust fits (bisquare, Appendix A): y = b_a + m_a·x on AA; x = b_b + m_b·y on BB;
    - intersection o_x = (b_b + m_b·b_a)/(1 − m_b·m_a), o_y = b_a + m_a·o_x;
    - offset = (o_x + mx, o_y + my); subtract.
 3. **nn12 / rr12** diagnostics: nn12 = fraction with x < 0.2·P_95(x) and y < 0.2·P_95(y);
    rr12 = mean(x+y of those) / mean(x+y of all).
 4. **Rotation** (if n < 192: θ = 0): new x-sweep over [min x, max x], union with AA; robust fit through origin
-   y = m·x (`robustfit0`, see quirk in Appendix C); θ = atan(m); rotate x′ = cosθ·x + sinθ·y, y′ = −sinθ·x + cosθ·y.
+   y = m·x (`robustfit0`, see quirk in Appendix A); θ = atan(m); rotate x′ = cosθ·x + sinθ·y, y′ = −sinθ·x + cosθ·y.
 5. **Shear** (if n < 192: shear = 0): y-sweep over [0, max y], union with BB; fit x = m·y through origin;
    shear = m; x ← x − shear·y.
 6. **Scale**:
@@ -870,7 +867,7 @@ https://github.com/gjun/verifyIDintensity       # GPL-3 — oracle only, do not
 | **P0 Setup** (≈1 wk) | repo skeleton, uv project, CI (ruff, mypy, pytest), fetch scripts, refs bootstrap, `THIRD_PARTY_NOTICES.md` | `pytest` green on empty suite; P0 data fetched with checksums |
 | **P1 Parsers** (≈2–3 wk) | `binutil`, IDAT, BPM, CSV, EGT, GTC read + GTC write, sample sheet, `simurg inspect` | all fields equal to BeadArrayFiles/illuminaio on P0 + Omni data; GTC read→write round-trip byte-identical; fuzz tests never crash (clean errors) |
 | **P2 Data model** (≈1 wk) | Arrow schemas, raw X/Y assembly, norm-bin rule | raw X/Y **exactly** equal to GTC TOC 1000/1001 of 1000G Illumina GTCs for 100% loci × ≥50 samples |
-| **P3 Normalization** (≈3–4 wk) | numerics (Appendix C), fit v1.2.0 (+1.1.2), apply, polar | transforms vs Illumina GTC TOC 400 within 2 ULP for ≥99.9% bins; normalized X/Y within 1e-5 |
+| **P3 Normalization** (≈3–4 wk) | numerics (Appendix A), fit v1.2.0 (+1.1.2), apply, polar | transforms vs Illumina GTC TOC 400 within 2 ULP for ≥99.9% bins; normalized X/Y within 1e-5 |
 | **P4 Genotyping** (≈3 wk) | GenCall-equivalent caller, score, BAF/LRR, LogRDev, GC10/50, sex, percentiles, GTC v5 writer | vs Illumina GTCs: genotypes ≥99.999% identical, scores ≤1e-5, sample stats ≤1e-6, sex 100%; md5-identical GTC vs gtc2vcf output (timestamps masked) |
 | **P5 QC** (≈2 wk) | control metrics, sample/locus summaries, contamination, relatedness, HTML report | control metrics equal reference formulas on synthetic controls; 1000G sex 100% vs pedigree; replicate/duplicate detection 100% on demo |
 | **P6 Outputs** (≈2 wk) | harmonization + audit, VCF/BCF, PLINK, bedGraph, final-report export | `bcftools` validates; vs gtc2vcf/GTCtoVCF VCF: 0 unexplained genotype differences; strand audit complete |
@@ -956,41 +953,7 @@ Haraksingh 2017 BMC Genomics 18:321 (GSE96909).
 
 ---
 
-## Appendix A — `CLAUDE.md` for the new repository
-
-```markdown
-# SimurgArray
-Personal research toolkit for Illumina Infinium genotyping arrays (IDAT→GTC→VCF, QC, CN, PGx).
-The full spec and roadmap is SIMURGARRAY_MASTER_PLAN.md — read the relevant section before changing code.
-
-## Rules
-- Work phase by phase (§18). Don't start a phase until the previous gate passes.
-- Science modules (signal/, genotype/, cn/, qc/, pgx/) are pure functions; no I/O inside them.
-- Every algorithm cites its source (§7 tags) in its docstring.
-- MIT/BSD/Apache/Unlicense code may be ported with attribution in THIRD_PARTY_NOTICES.md.
-  GPL tools (plink2, illuminaio, verifyIDintensity) = subprocess oracles only; never copy their code.
-- Never commit vendor data (BPM/CSV/EGT/IDAT) — only tests/fixtures/ (Artistic-2.0 Bioconductor files, synthetic data).
-- Never hide oracle discrepancies; record them in docs/known_unknowns.md.
-- Default mode is bit-exact vs Illumina-produced GTCs; keep a clean-math mode too.
-
-## Commands
-- uv sync ; uv run pytest -q ; uv run ruff check . ; uv run mypy src
-- bash scripts/fetch_data.sh p0|p1|p2 ; bash scripts/bootstrap_refs.sh
-```
-
-## Appendix B — first-session prompt for Claude Code (on the new machine)
-
-> Read `SIMURGARRAY_MASTER_PLAN.md` fully. We are starting **Phase P0 (Setup)** then **P1 (Parsers)**.
-> 1. Create the repository skeleton of §4.2 with a uv-managed `pyproject.toml` (Python 3.12), ruff, mypy, pytest,
->    and a GitHub Actions CI workflow.
-> 2. Write `scripts/bootstrap_refs.sh` (shallow clones of §17 repos into `refs/`) and `scripts/fetch_data.sh`
->    (tiers p0/p1/p2, sha256 recording, resumable downloads with aria2c/wget).
-> 3. Run bootstrap + fetch p0.
-> 4. Implement `formats/binutil.py` and `formats/idat.py` per §6.1 with tests (Bioconductor fixtures, malformed-file
->    tests, property tests), then `simurg inspect`.
-> Stop after the IDAT reader passes the P1 criteria for IDAT and report results.
-
-## Appendix C — numeric helper definitions (bit-exact mode)
+## Appendix A — numeric helper definitions (bit-exact mode)
 
 **Illumina percentile** (used for normalization, GC10/GC50): sorted v of length n, target p ∈ [0,100]:
 ```

src/simurg/signal/normalization/numerics.py

@@ -1,4 +1,4 @@
-"""Numerics with the exact rounding behaviour of the reference normalization (plan Appendix C).
+"""Numerics with the exact rounding behaviour of the reference normalization (plan Appendix A).
 
 Ported from gtc2vcf ``idat2gtc.c`` (MIT, © Giulio Genovese), which reproduces the vendor's MATLAB-derived routines.
 Rules that matter for bit-exactness:

tests/unit/test_normalization.py

@@ -1,4 +1,4 @@
-"""Phase P3: normalization numerics, fit and apply (plan §7.2–§7.4, Appendix C)."""
+"""Phase P3: normalization numerics, fit and apply (plan §7.2–§7.4, Appendix A)."""
 
 from __future__ import annotations