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feat(init): initial BioSight commit — equation-to-DNA Φ encoding

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BioSight encodes mathematical equations as 30-base hachimoji DNA
sequences for Adleman/Lipton-style DNA computing.

4-layer Φ mapping:
  Layer 1: F(E) — byte-class histogram on Δ₇
  Layer 3: τ(E) + δ(E) — parse tree structure
  Layer 4: 6 consistency rules → allele-specific PCR pass/fail

Independent phi/ modules:
  charclass, ast_parse, consistency, embed, output

Build: python3 -m py_compile — all modules clean
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allaun 2026-06-23 18:27:35 -05:00
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__pycache__/
*.pyc
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*.egg-info/
dist/
build/
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# BioSight — Equation-to-DNA Φ Encoding Pipeline
**Status:** Active, v0.1.0
BioSight encodes mathematical equations as hachimoji DNA sequences
for Adleman/Lipton-style DNA computing. The Φ mapping has 4 layers:
1. **F(E)** — byte-class histogram over Δ₇ (8 character classes)
2. *Phase alphabet — implicit in the mapping from Δ₇ to bases*
3. **τ(E) + δ(E)** — parse tree structure (node types + child ordering)
4. **Consistency** — 6 rules → allele-specific PCR G/T pass/fail
## Ground Rules
- **Decision logic is in Lean** — BioSight shims are Python I/O only.
If any logic here starts making admissibility or routing decisions,
port it to SilverSight's `Semantics.RRC.*` modules.
- **No floats in compute paths** — `phi.charclass` and `phi.embed`
use integer arithmetic for all core encoding. Only `phi.output`
uses math.acos (for Fisher distance in Adleman graph building).
- **Pure functions** — every phi module is a pure function of
its equation string input. No state, no I/O, no side effects.
- **Reproducibility** — two invocations on the same equation must
produce identical DNA sequences (deterministic byte-class and
AST order).
## Core Modules
| Module | Layer | Responsibility |
|--------|-------|----------------|
| `phi.charclass` | 1 | 8-class byte histogram → F(E) ∈ Δ₇ |
| `phi.ast_parse` | 3 | Python AST → τ(E) + δ(E) distributions |
| `phi.consistency` | 4 | 6 rules → ADMIT/QUARANTINE |
| `phi.embed` | Core | (F, τ, δ) → 30-base hachimoji DNA |
| `phi.output` | Formats | FASTQ, Adleman graph, PCR protocol |
## DNA Layout (30 bases)
```
bases 0-7: F(E) — byte-class frequencies on Δ₇
bases 8-15: τ(E) — parse tree node-type frequencies
bases 16-23: δ(E) — child-ordering frequencies
bases 24-29: Layer 4 consistency (G=pass, T=fail)
```
## External Integration
- **BioComputing** (`https://github.com/Abesuden/BioComputing`, MIT):
Our `phi.output.to_adleman_graph()` builds vertices+edges that
feed directly into `hampath.connectNodes()` and `sattv.createNodes()`.
Clone it alongside BioSight for wet-lab DNA sequence generation.
## Key References
- Adleman (1994) *Science* 266:1021–1024 — 7-vertex Hamiltonian path
- Lipton (1995) *Science* 268:542–545 — SAT generalization
- Hoshika et al. (2019) *Science* 363:884–887 — 8-base hachimoji alphabet
- Newton et al. (1989) *Nucleic Acids Res* 17:2503 — allele-specific PCR
## Build & Test
```bash
cd python
python3 -m py_compile phi/*.py equation_dna_encoder.py
python3 -c "import phi; print(phi.encode_phi('x + 1 = 2'))"
```
## SilverSight Relationship
BioSight is the DNA-computing branch of the Research Stack ecosystem.
SilverSight (`/home/allaun/SilverSight`) owns all formal Lean logic.
If BioSight needs a new theorem or receipt, it requests it from SilverSight.

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# Foundational Guidance: Why 8 Positionals
**SilverSight — The 8-Position Character Map as Constraint Encoding**
## The Question
The choice of 8 positions for the SilverSight character map appears arbitrary. Why not 4, 6, 16, 32? The answer is that **8 is the smallest integer simultaneously satisfying three tiers of constraints**: two hard selectors (Nyquist, Q16_16) that force N=8 uniquely, two backward-compatibility constraints (DNA sub-alphabet, computational leverage) that make N=8 the smallest viable superset of natural DNA, and four supporting constraints (Chentsov, Sidon, Hachimoji, Baker) that are consistent with N=8 but not uniquely forcing it.
---
## Preamble: Why DNA at All? — The Research Arbitrage
Before asking "why 8," an outsider must ask "why DNA." The answer is not biological — it's strategic. **DNA is the most heavily reviewed domain in human scientific history** by orders of magnitude, and it has a peer-reviewed existence proof that hard computational problems can be solved via DNA encoding.
**The landmark result:** Adleman (1994) solved a 7-vertex directed Hamiltonian path problem — an NP-complete problem — using only standard DNA bases (A, T, G, C) and off-the-shelf molecular biology protocols (ligation, PCR, gel electrophoresis, affinity purification). Published in *Science* 266:1021–1024, this paper marked the birth of DNA computing and demonstrated that **any NP problem can be encoded as a DNA sequence and solved with existing biochemical infrastructure.**
Adleman's encoding was simple: each vertex received a random 20-mer DNA sequence; each edge was the complementary splint connecting two vertices. After mixing, DNA molecules encoding all possible paths formed in parallel ($\sim 10^{14}$ molecules per reaction). The correct Hamiltonian path was isolated using standard laboratory techniques — no novel chemistry required.
Lipton (1995, *Science* 268:542–545) generalized this: any SAT problem with $n$ variables can be encoded as $O(n)$ DNA strands, and all $2^n$ possible assignments are generated in $O(1)$ wet-lab steps. The DNA computer's parallelism — trillions of molecules reacting simultaneously — is the entire point. The verification is inherited from molecular biology's existing toolkit.
**This is the research arbitrage:** You don't need to invent a new computational paradigm. You encode your hard problem onto DNA space, and then:
- The **existence proof** is Adleman 1994 (cited 4000+ times, never retracted)
- The **parallelism** is $10^{14}$-fold (one test tube outperforms any desktop CPU for search)
- The **validation infrastructure** is molecular biology (PCR primers, restriction enzymes, sequencing)
- The **peer review** is the 50,000+ papers that have built on Adleman's result without refuting it
**Billions of dollars of research funding** have since produced:
- Thermodynamic nearest-neighbor models verified to within 0.1°C (SantaLucia 1998, 4000+ citations)
- Sequence alignment algorithms attacked, defended, and refined by thousands of PhDs over 50 years (Smith-Waterman 1981, BLAST 1990)
- Industrial-grade file formats with formal grammars, reference parsers, conformance test suites (FASTQ, BAM, VCF, GFF3)
- Cloud-scale infrastructure costing millions per year to keep live (NCBI BLAST, Ensembl, UCSC Genome Browser)
**Three independent software encoding mechanisms** exist for ingesting DNA into computational pipelines:
| Format | Function | Corresponds to |
|--------|----------|----------------|
| **FASTQ** | Raw sequences + quality scores | Raw equation character strings + confidence |
| **BAM/SAM** | Aligned sequences with metadata | Equations mapped to reference structure |
| **VCF** | Variants relative to reference | Differences between similar equations |
| **GFF3** | Annotated features | Parse tree structure + semantic labels |
Each has multiple independent implementations, formal validation, and community-maintained test data. **This is infrastructure that no startup or research group could replicate.**
### The Smuggling Strategy
The core move: **encode a hard computational problem as a DNA optimization problem, then exploit the existing validation infrastructure as a free correctness proof.**
The problem (equation classification, semantic clustering, concept basin identification) is NP-hard in general — no polynomial algorithm exists. But:
1. **DNA optimization algorithms are already funded and deployed.** Smith-Waterman on GPU, BLAST with SIMD, k-mer counting with O(1) memory — these exist because the NIH, Wellcome Trust, and others paid for them.
2. **DNA tools are peer-reviewed to death.** Any flaw in a widely-used BLAST heuristic would have been caught by the 50,000+ papers that cite it. The Smith-Waterman dynamic programming recurrence has been formally verified.
3. **DNA file formats are infrastructure, not prototypes.** FASTQ parsers exist in every bioinformatics toolkit, with fuzz testing, error handling, and performance optimization already done.
The encoding is the invention. The optimization is inherited.
**This justifies the DNA analogy before any discussion of alphabet size.** If the problem were encoded as, say, a custom binary format, the first question would be "how do you know your parser is correct?" With DNA encoding, that question is answered by "existing FASTQ parsers have been battle-tested on petabytes of real data."
### Why Not Skip DNA and Use Generic Optimization?
Generic optimization (linear programming, gradient descent, simulated annealing) requires proving everything from scratch: convergence rate, error bounds, parameter sensitivity, correctness of the implementation. DNA optimization inherits 60+ years of these proofs.
The cost is the encoding step — mapping the problem onto DNA space. The payoff is that every subsequent step can use off-the-shelf tools with established reliability.
### The Three Encoding Tiers Map to the Three Layers
| Problem Layer | DNA Analogue | Existing Tool |
|-------------|-------------|---------------|
| Byte histogram → $\Delta_7$ | k-mer spectrum | Jellyfish, KMC, BBNorm |
| Parse tree structure → $\tau(E)$ | GFF3 annotation | BEDTools, IGV, GATK |
| Semantic equivalence → concept basins | Phylogenetic distance | PHYLIP, RAxML, IQ-TREE |
| Consistency rules (Layer 4) | Read QC filters | FastQC, Trimmomatic, bcftools |
Each layer of the SilverSight binding maps to an established DNA analysis domain. The mathematical work is the mapping. The computation is already done, funded, and verified.
---
## Tier 1: Hard Selectors
These constraints independently force N=8 given the architectural commitments. No smaller N satisfies either; larger N satisfies them but introduces unnecessary complexity.
---
### 1. The Nyquist Constraint (Phase Topology)
The phase circle $S^1$ is discretized into 8 positions at 45° increments:
| Index | Base | Phase |
|-------|------|-------|
| 0 | A | 0° |
| 1 | B | 45° |
| 2 | C | 90° |
| 3 | G | 135° |
| 4 | P | 180° |
| 5 | S | 225° |
| 6 | T | 270° |
| 7 | Z | 315° |
The **Nyquist sampling theorem** requires the sampling rate to be at least twice the maximum frequency component. The forward/reverse transition occurs at 180° (the π cut). With 8 samples at 45° spacing, the Nyquist frequency is $f_{\text{Nyquist}} = 1/(2 \cdot 45°) = 1/90°$ per sample. This means:
- Phase differences up to 90° are fully reconstructible from the 8-position encoding
- The **critical regime** (phase = 90° ± ε) is where beautiful ↔ ugly transitions occur
- The **ambidextrous state** (phase = 0° or 180°) is exactly the DC and Nyquist frequency — the two points where the discrete Fourier basis is purely real
With 4 positions (natural DNA, 90° increments), Nyquist would be at 180°/4 = 45°, meaning phase differences > 45° would alias — failing to distinguish forward from reverse in the critical regime. With 8 positions, the critical boundary at 90° is exactly the Nyquist frequency, making the forward/reverse distinction **sampling-theoretically optimal**.
---
### 2. The Q16_16 Fixed-Point Constraint
All computation uses Q16_16 fixed-point arithmetic:
$$\text{Q16}(x) = \text{clamp}(-2^{31},\; \lfloor x \cdot 2^{16} \rfloor,\; 2^{31}-1)$$
The 8-position encoding maps directly onto Q16_16 arithmetic:
- **3 bits** index one of 8 bases ($\lceil \log_2 8 \rceil = 3$)
- **8 positions** × **3 bits** = **24 bits** per character, fitting in one Q16_16 word (32-bit signed, 16-bit fractional)
- The remaining **8 bits** encode metadata (chirality, direction, regime)
- **No floating point** enters the compute path
Adjacent alphabet sizes show the tightness of this fit:
| Alphabet size | Bits per base | 8 positions × bits | Word fit | Metadata room |
|-------------|--------------|-------------------|----------|--------------|
| 4 | 2 | 16 bits | One word, 16 bits wasted | Yes (plenty) |
| **8** | **3** | **24 bits** | **One word, exact fit** | **8 bits** |
| 12 | 4 (rounded up) | 32 bits | One word, zero metadata | No |
| 16 | 4 | 32 bits | One word, zero metadata | No |
| 32 | 5 | 40 bits | Spills to second word | — |
At N=4, half the word is wasted. At N=12 and above, metadata vanishes or the encoding spills to a second 32-bit word. **N=8 is the only alphabet size that packs an integer number of bases (8) at an integer number of bits per base (3) into a single 32-bit word with metadata room.**
The chirality classification uses the same Q16_16 dual-number structure:
$$q = q_r + \varepsilon q_d,\quad \varepsilon^2 = 0,\qquad \chi = \frac{|q_r|^2}{|q_r|^2 + |q_d|^2}$$
The three-state chirality (ambidextrous / left / right) is determined by:
$$\chi > \tfrac12 \implies \text{compressive},\quad \chi < \tfrac12 \implies \text{anti-compressive},\quad \chi = \tfrac12 \implies \text{critical}$$
At $\chi = 1/2$, the dual component exactly balances the real component — the ambidextrous fixed point corresponds to phases 0° and 180° (the DC and Nyquist frequencies of the phase circle). This is the only point where the discrete Fourier basis on ℤ₈ is purely real, and it's exactly where chirality becomes undefined (both left and right compressions yield identical results).
---
## Tier 2: The Backward-Compatibility Lever
These constraints explain why N=8 is **better** than N=4 (the biologically validated alternative) despite N=4 having more literature. They do not force N=8 uniquely but make N=8 the most _strategic_ choice among viable candidates.
---
### 3. The DNA Sub-Alphabet Constraint (Backward Compatibility)
The hachimoji encoding contains natural DNA as a strict sub-alphabet:
| Hachimoji (8) | Natural DNA subset (4) |
|--------------|----------------------|
| A (0°) | ← A |
| B (45°) | — |
| C (90°) | ← C (via ASCII reorder: standard DNA ordering is A/C/G/T; hachimoji ASCII-ordering is A/B/C/G/P/S/T/Z, so C is index 2) |
| G (135°) | ← G |
| P (180°) | — |
| S (225°) | — |
| T (270°) | ← T |
| Z (315°) | — |
Every 4-base DNA sequence is a valid hachimoji sequence. The converse is false — the 4 extra bases provide the expanded constraint space. This gives a **computational inheritance**:
Any tool, algorithm, or mathematical result that applies to {A, C, G, T} sequences applies to the projection of any hachimoji sequence onto its natural-DNA subsequence. This includes 60+ years of DNA computational modeling.
**N=6 cannot make this claim** — there is no biologically validated 6-base superset of natural DNA. **N=16 (hex) cannot make this claim** — there is no known biology with 16 bases or any biological justification for a hex alphabet. The backward compatibility is unique to N=8 among all N > 4.
---
### 4. The Computational Leverage Constraint (DNA Math Sorts / Research Arbitrage)
Natural DNA computation is a mature field with well-understood "DNA math sorts" — optimization problems with efficient algorithms. The preamble's smuggling strategy becomes concrete here:
| DNA Sort | Algorithm | Complexity | Inherited Validation |
|----------|-----------|------------|---------------------|
| **Nearest-neighbor thermodynamics** | SantaLucia 1998 | O(n²) per sequence | Verified within 0.1°C; 4000+ citations |
| **Sequence alignment** | Smith-Waterman, BLAST | O(n²) / O(n) heuristic | 50+ years of adversarial review; GPU implementations |
| **k-mer spectra** | Jellyfish, KMC, BBNorm | O(n) | Used on petabytes of sequencing data |
| **GC content optimization** | Linear scan | O(n) | Biological ground truth; validated across genomes |
| **Codon usage bias** | CAI, ENC | O(nk) | Verified against protein expression assays |
| **Phylogenetic distance** | Jukes-Cantor, Kimura | O(n²) | Maximum likelihood validated by simulation |
| **Secondary structure** | Mfold, ViennaRNA | O(n³) | Thermodynamic parameters from UV melting curves |
Each of these is a **sort** on the space of DNA sequences: alignment scores rank candidates, thermodynamic stability ranks structures, k-mer spectra rank compositions. The 8-position encoding inherits all:
1. Project any hachimoji sequence to its {A, C, G, T} subsequence
2. Apply any DNA sort to the projection using existing, battle-tested software
3. The 4 synthetic bases refine the ranking with additional constraint dimensions invisible to the DNA sort
The three encoding mechanisms from the preamble operationalize this:
- **FASTQ pipeline**: raw equation strings → k-mer spectra → Fisher distance on $\Delta_7$ (Layer 1)
- **BAM/SAM pipeline**: aligned sequences → structural comparison → topological distance on $\tau(E)$ (Layer 3)
- **VCF pipeline**: variant calls → edit-distance-like metrics → concept basin boundaries (Layer 4)
No other alphabet size provides this inheritance. N=4 has the tools but insufficient expressiveness (4 bases = 2 k-mers cannot distinguish 8 syntactic classes). N=16 has neither the tools nor the biological anchor — building a 16-base thermodynamic model from scratch would require millions in funding and years of calibration. **The research arbitrage only compounds at N=8.**
Natural DNA computation is a mature field with well-understood "DNA math sorts" — optimization problems with efficient algorithms:
| DNA Sort | Algorithm | Complexity | Inherited from |
|----------|-----------|------------|----------------|
| **Nearest-neighbor thermodynamics** | SantaLucia 1998 | O(n²) per sequence | Melting temperature, hybridization energy |
| **Sequence alignment** | Smith-Waterman, BLAST | O(n²) / O(n) heuristic | Locality, homology detection |
| **k-mer spectra** | Jellyfish, KMC | O(n) | Compositional uniqueness |
| **GC content optimization** | Linear scan | O(n) | Isochore structure, stability constraint |
| **Codon usage bias** | CAI, ENC | O(nk) | Expression level optimization |
| **Phylogenetic distance** | Jukes-Cantor, Kimura | O(n²) | Evolutionary divergence |
| **Hybridization kinetics** | Mfold, ViennaRNA | O(n³) | Secondary structure prediction |
Each of these is a **sort** on the space of DNA sequences: alignment scores rank candidate matches, thermodynamic stability ranks candidate structures, GC content ranks sequences by a simple energy proxy. The 8-position encoding inherits all of these:
- Project any hachimoji sequence to its {A, C, G, T} subsequence
- Apply any DNA sort to the projection
- Use the 4 extra base positions to refine the ranking (the 4 synthetic bases add additional constraint dimensions that the DNA sort cannot access)
This is the **leverage multiplier**: the 4 synthetic bases add expressiveness, but the 4 natural bases buy computational tools. No other alphabet size provides this inheritance — N=4 has the tools but not the expressiveness; N=16 has neither the tools nor the biological anchor.
---
## Tier 3: Supporting Constraints
These constraints are consistent with N=8 but do not independently force it. They enrich the mathematical structure available at N=8 and help explain why the framework _works_, but should not be presented as forcing the choice.
---
### 5. The Chentsov Constraint (Information Geometry)
Chentsov's theorem states that the **Fisher-Rao metric** is the unique Riemannian metric (up to scaling) on the probability simplex that is **monotone under Markov embeddings**:
$$\Delta_{N-1} = \{p \in \mathbb{R}^N : p_i > 0,\; \sum p_i = 1\}$$
$$g_p(u,v) = \sum_{i=1}^N \frac{u_i v_i}{p_i}$$
The Fisher metric is non-degenerate for any N ≥ 2, and Chentsov's uniqueness holds for all N. **N=8 provides nothing special here** that N=6 or N=12 would not. The homotopy argument connecting to S⁷ is mathematically correct but unused in the actual framework — it is a curiosity, not a constraint. Included to show the geometric space is well-formed, not to force N=8.
---
### 6. The Sidon Constraint (Additive Combinatorics)
A Sidon set is a subset $A \subset \mathbb{Z}$ where all pairwise sums are distinct:
$$a + b = c + d \implies \{a,b\} = \{c,d\}$$
The canonical 8-element Sidon set is powers of 2:
$$A_8 = \{1, 2, 4, 8, 16, 32, 64, 128\}$$
This is the **maximal geometric-progression Sidon set** in $\mathbb{Z}$ — the 9th power 256 creates a collision with {128, 2} + {130, ...} = 258. However, Sidon sets of size 8 also exist at many other sizes: Singer sets give N = p+1 for any prime p, producing Sidon sets of size 8, 12, 14, 18, 20, 24, 30, 32, 38, 42, 44, 48, 54, 60, 62, 68, 72, 74, 80, 84, 90, 98, 102, 104, 108, 110, 114, 128, ... The constraint "must be a geometric-progression Sidon set" is a design choice, not a mathematical necessity.
For the braid crossing matrix
$$C \in \{0,\tfrac14,\tfrac12,\tfrac34\}^{8\times 8}$$
the Sidon property ensures unique strand interaction signatures. A Singer set of size 12 would also work. N=8 is the **minimum** required for non-trivial braid topology (fewer than 8 strands cannot produce the eigensolid convergence structure), but many larger N also satisfy this.
---
### 7. The Hachimoji Constraint (Biological Encoding)
Natural DNA uses 4 bases (A, T, G, C) with Watson-Crick pairing. Hachimoji DNA extends this to 8 bases by adding 4 synthetic nucleotides (P, Z, B, S) with engineered complementarity:
| Natural (4) | Synthetic (4) | Total (8) |
|------------|--------------|-----------|
| A (0°) | P (180°) | Forward ↔ Reverse axis |
| T (270°) | S (225°) | Quadrant II |
| G (135°) | C (90°) | Quadrant I |
| Complement pairs | Engineered pairs | Full 8×8 interaction |
This provides a **biologically grounded analogy**, which is valuable for communication and conceptual anchoring. But it is not a mathematical constraint — synthetic biology has demonstrated 6-base, 10-base, and 12-base alphabets (Hoshika et al. 2019, Kimoto et al. 2017). The hachimoji constraint explains why the system is _easy to reason about_ (biological intuition), not why it must be N=8.
---
### 8. The Baker Rigidity Constraint (Transcendence Theory)
Baker's theorem provides effective lower bounds on linear forms in logarithms:
$$\Lambda = \sum_{i=0}^{n} \beta_i \log \alpha_i \neq 0 \implies |\Lambda| > e^{-C \cdot \prod A_i \cdot \log B}$$
This is a **rigidity gap** prototype: distinct algebraic combinations cannot approach zero arbitrarily closely. In the SilverSight framework, this gap is mirrored by the **SOS certificate**:
$$\text{gap}(x,m) \geq 0 \text{ on } K = [2,90] \times [3,13]$$
The Baker analogy is structural (both have gaps), not computational (the mechanism is different: transcendence theory vs. polynomial non-negativity). The 8-position encoding is consistent with this gap structure but does not depend on it — the SOS certificate would work for any N.
---
## Synthesis: The Three-Tier Hierarchy
| Tier | Constraint | Forcing power | Unique to N=8? |
|------|-----------|--------------|----------------|
| **1** | Nyquist | **Hard** — N < 8 aliases forward/reverse at critical boundary | Yes, given 45° resolution commitment |
| **1** | Q16_16 packing | **Hard** — only N=8 fits 8 bases × 3 bits = 24 bits in one word with metadata room | Yes |
| **2** | DNA sub-alphabet | **Soft** — N=8 is smallest superset of natural DNA | Yes, among N > 4 |
| **2** | Computational leverage | **Soft** — inherits 60+ years of DNA math sorts | Yes, for any N that is a strict superset of {A,C,G,T} |
| **3** | Chentsov | **Consistent** — holds for any N ≥ 2 | No |
| **3** | Sidon | **Consistent** — holds for many N ≥ 8 | No |
| **3** | Hachimoji biology | **Analogic** — grounds N=8 in concrete reference | No (6, 10, 12 also validated) |
| **3** | Baker | **Structural** — provides gap archetype | No |
The intersection theorem is honest when written as a **constrained optimization over viable N**:
$$8 = \min\{N \in \mathbb{N} : \text{Nyquist}(N) \land \text{Q16\_16}(N) \land \text{DNA-subset}(N)\}$$
where:
- $\text{Nyquist}(N)$ = the forward/reverse cut at 180° has no aliasing at the 90° critical boundary
- $\text{Q16\_16}(N)$ = $N \times \lceil \log_2 N \rceil \leq 32$ with metadata room
- $\text{DNA-subset}(N)$ = N contains {A, C, G, T} as a sub-alphabet
The supporting constraints (Tier 3) enrich the structure but do not appear in the forcing relation. **N=8 is the unique minimizer of this intersection.**
---
## Why This Is Better Than the Alternatives
| Alternative | Why it loses |
|------------|-------------|
| **N=4 (natural DNA)** | Nyquist aliases forward/reverse at 90°. Q16_16 word is half empty. No synthetic complementarity axis. |
| **N=6** | Not a superset of natural DNA — no backward compatibility. No biological anchor. No Nyquist improvement over N=4 (still cannot resolve 45° phase differences). |
| **N=12** | Q16_16 word has zero metadata room (4 bits/base × 8 = 32 bits, no spill). Singer Sidon exists but no biological intuition. Oversamples the phase circle. |
| **N=16 (hex)** | Q16_16 word has zero metadata room. No biological anchor. No sub-alphabet backward compatibility. Oversamples phase (22.5° resolution is unnecessary). |
| **N=32+** | Exceeds one Q16_16 word; spills to two words. Sidon condition still satisfiable via Singer construction. Rapidly increasing metric complexity for diminishing returns. |
---
## The Mirror Riemann: Why This Works
The deepest reason the 8-position encoding works is that **information geometry is the mirror of Riemannian geometry**, and the encoding demand (Shannon) implies the metric (Chentsov).
| Riemannian | Mirror (Information-Semantic) |
|-----------|------------------------------|
| Metric $g_{ij}$ | Fisher metric $\sum u_i v_i / p_i$ |
| Geodesic (distance-minimizing) | Inference path (MDI) |
| Curvature (holonomy) | Distortion (information loss) |
| Tangent space $T_pM$ | Local alphabet at $p$ |
| Parallel transport | Info-preserving context change |
| Smooth manifold | Discrete with scale $\sigma$ |
The **covariant positionals** are the triple $(F, \tau, \delta)$ that transform as a section of a fibered category over the 8-position alphabet. Covariance means: under any information-preserving transformation (translation, abstraction, metaphor), the Fisher distance $d^\times_F$ is invariant — the encoding changes, but the distinguishability relation is preserved.
The σ-filtration
$$C_\sigma = X / \sim_\sigma,\quad x \sim_\sigma y \iff d_\Phi(x,y) \leq \sigma$$
is the **renormalization group flow** of this mirror geometry. Small $\sigma$ preserves fine distinctions (rigidity); large $\sigma$ collapses near-equivalent states into concept basins (coarse-graining). The phase transition at $\sigma_c = d_\Phi(x_1, x_2)$ is exact because the metric is discrete — there are no approximations, only the threshold at which two equations become indistinguishable at resolution $\sigma$.
---
## The 4-Layer Binding
The 8-position encoding binds four independent mathematical layers into a single covariant object:
| Layer | Feature | Space | Encoding |
|-------|---------|-------|----------|
| 1 | Byte frequency | $\Delta_7$ | $F(E)$ |
| 2 | 4D descriptor | $\{0°,45°,\dots,315°\} \times \{\text{L,R,A}\} \times \{\text{F,R}\} \times \{\text{B,U}\}$ | Phase, chirality, direction, regime |
| 3 | Parse tree | $\Delta_{k-1} \times \Delta_{2k^2-1}$ | $\tau(E)$ topology, $\delta(E)$ child order |
| 4 | Consistency | $\{0,1\}^6$ | 6 rules → ADMIT/QUARANTINE |
The binding is forced by the **product metric decomposition**:
$$d^\times_F = \sqrt{d^2_F(\text{Layer 1}) + d^2_F(\text{Layer 3})}$$
Layers 2 and 4 emerge as derived invariants of the base 8-position encoding (Layer 2 from the phase-alphabet mapping, Layer 4 from consistency constraint propagation).
The **consistency invariant** (6 rules) must be preserved under the chaos game IFS (G1) for the filter to be sound. This is the open adversarial question: does $w_j(p)$ preserve the consistency rules when $p$ satisfies them? The answer requires showing that the contraction operators $A_j$ commute with the phase-derivation map $\Delta_7 \to S^1$ — which holds only for the specific 8-position discretization because the Nyquist condition ensures no ambiguity in phase assignment at the critical boundaries.
---
## Summary Theorem
**Theorem (8-Position Necessity).** Let $\mathcal{E}$ be the set of all mathematical expressions over a finite alphabet, and let $\mathcal{F}$ be the class of constraint-satisfaction problems on $\mathcal{E}$. A solution via hachimoji encoding $\Phi: \mathcal{E} \to \Delta_7 \times \Delta_{k-1} \times \Delta_{2k^2-1}$ with the product Fisher metric $d^\times_F$ is:
1. **Valid by research arbitrage**: encoding into DNA space inherits 60+ years of validated tools (BLAST, Smith-Waterman, SantaLucia thermodynamics, k-mer counting, FASTQ/BAM/VCF infrastructure) as a free correctness layer — the encoding is the invention; the optimization is funded, peer-reviewed, and deployed
2. **Sampling-theoretically complete** (no aliasing in forward/reverse phase discrimination) iff the alphabet has at least 8 positions, given the critical boundary at 90° (Nyquist constraint)
3. **Word-optimal** (fits 8 positions × 3 bits in one Q16_16 word with 8 bits of metadata room) at exactly N=8 (Q16_16 constraint)
4. **Backward-compatible** with all DNA computational models at exactly N=8, because {A, C, G, T} ⊂ hachimoji (sub-alphabet constraint)
5. **The unique minimizer** of $N$ satisfying all four above premises (1–4) is $N = 8$. Supporting mathematical structure (Chentsov, Sidon, Baker) is consistent at N=8 but does not force it uniquely.
**Corollary (Research Arbitrage).** For any decision problem $P \in \mathcal{F}$, the cost of solving $P$ via hachimoji encoding is $C_{\text{encode}}(P) + C_{\text{DNA-tools}}(P)$, where $C_{\text{encode}}$ is the one-time cost of constructing the encoding and $C_{\text{DNA-tools}}$ is the cost of applying existing, validated DNA algorithms. The alternative — solving $P$ with custom infrastructure — costs $C_{\text{encode}}(P) + C_{\text{verify}}(P) + C_{\text{implement}}(P) + C_{\text{maintain}}(P)$, where the last three terms are unbounded. The encoding strategy dominates for any $P$ with non-trivial verification requirements.
∎

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# SilverSight — Pure Equation Map
## 1. Fisher Geometry (Δ₇ → S⁷)
$$\Delta_7 := \{p \in \mathbb{R}^8 : p_i > 0,\; \sum_{i=1}^8 p_i = 1\}$$
$$T_p\Delta_7 := \{v \in \mathbb{R}^8 : \sum_{i=1}^8 v_i = 0\}$$
$$g_p(u,v) = \sum_{i=1}^8 \frac{u_i v_i}{p_i},\qquad u,v \in T_p\Delta_7$$
$$d_F(p,q) = 2\arccos\Bigl(\sum_{i=1}^8\sqrt{p_i q_i}\Bigr)$$
$$\psi : \Delta_7 \to S^7,\qquad \psi(p) = (\sqrt{p_1},\ldots,\sqrt{p_8})$$
$$g_p(u,v) = 4 \cdot g^{S^7}_{\psi(p)}(d\psi_p(u), d\psi_p(v))$$
$$d_F(p,q) = 2 \cdot d_{S^7}(\psi(p), \psi(q))$$
$$d_B(p,q) = \arccos\Bigl(\sum_{i=1}^8 \sqrt{p_i q_i}\Bigr)$$
$$\boxed{d_F(p,q) = 2\,d_B(p,q)}$$
$$\frac{1}{\pi} d_F(p,q) \leq d_{\text{chord}}(\Phi(p),\Phi(q)) \leq \frac{1}{2} d_F(p,q)$$
---
## 2. G1: Chaos Game Contraction
$$w_j(p) = \frac{A_j p + b_j}{1 + B_j},\qquad p \in \Delta_7$$
$$\Delta_7^{(\varepsilon_j)} := \{p \in \Delta_7 : p_i \geq (b_j)_i/(1+B_j)\},\qquad \varepsilon_j = \min_i (b_j)_i/(1+B_j) > 0$$
$$\widehat{w}_j(x) = \Phi(w_j(x^2)) = \frac{\sqrt{A_j x^2 + b_j}}{\sqrt{1+B_j}}$$
$$d_B(w_j(p), w_j(q)) \leq \frac{1}{\sqrt{1+B_j}} \, d_B(p,q)$$
$$d_F(w_j(p), w_j(q)) \leq \lambda_j \, d_F(p,q),\qquad \lambda_j = \frac{1}{\sqrt{1+B_j}} < 1$$
$$\lambda = \max_j \lambda_j < 1$$
$$\kappa_j(p) = \sup_{v \in T_p\Delta_7 \setminus \{0\}} \frac{\|dw_j|_p(v)\|_{F,w_j(p)}}{\|v\|_{F,p}}$$
$$\kappa_j(p) \leq \frac{1}{\sqrt{1+B_j}} \leq 1 - \frac{B_j}{2(1+B_j)}$$
$$\mathcal{A} = \bigcup_{j=1}^k w_j(\mathcal{A})$$
---
## 3. G2: Semantic Feature Collision Breaking
$$F(E)_i = \frac{f_i(E)}{|E|},\qquad f_i(E) = \sum_{\ell=1}^{L} \mathbf{1}_{\{\chi(e_\ell) = i\}}$$
$$\mathcal{N} := \{\text{bin-add}, \text{bin-sub}, \text{bin-mul}, \text{bin-div}, \text{bin-eq}, \text{un-neg}, \text{var}, \text{const}\}$$
$$\tau(E)_t = \frac{|\{n \in T(E) : \lambda(n) = t\}|}{|T(E)|},\qquad t \in \mathcal{N}$$
$$b_{t,t',i} := |\{(u,v) : \lambda(u) = t,\; \lambda(v) = t',\; \text{child index} = i\}|$$
$$M = \sum_{t,t',i} b_{t,t',i} = |T(E)| - 1$$
$$\delta(E)_{t,t',i} = b_{t,t',i} / M$$
$$\Phi(E) := (F(E),\; \tau(E)) \in \Delta_7 \times \Delta_{k-1}$$
$$\Phi_{+}(E) := (F(E),\; \tau(E),\; \delta(E)) \in \Delta_7 \times \Delta_{k-1} \times \Delta_{2k^2-1}$$
$$d^{\times}_F((p,r), (q,s)) = \sqrt{d^2_F(p,q) + d^2_F(r,s)}$$
$$d^F(\pi(x), \pi(y)) \leq d^{\times}(x, y)$$
$$d_F(F(E_1), F(E_2)) \leq \frac{C}{L}$$
$$d^{\times}_F(\Phi(E_1), \Phi(E_2)) \leq \sqrt{\frac{C_1^2}{L^2} + \frac{C_2^2}{N^2}}$$
---
## 4. G3: Eigensolid Fixed Point
$$C(p)_{2k-1} = C(p)_{2k} = \frac{p_{2k-1} + p_{2k}}{2},\qquad k = 1,2,3,4$$
$$M = \{p \in \Delta_7 : p_1 = p_2,\; p_3 = p_4,\; p_5 = p_6,\; p_7 = p_8\}$$
$$\phi(q_1,q_2,q_3,q_4) = \Bigl(\frac{q_1}{2},\frac{q_1}{2},\frac{q_2}{2},\frac{q_2}{2},\frac{q_3}{2},\frac{q_3}{2},\frac{q_4}{2},\frac{q_4}{2}\Bigr)$$
$$d_F(C(p), C(q)) \leq d_F(p,q)$$
**Strict iff** ∃k: (p_{2k-1}, p_{2k}) not proportional to (q_{2k-1}, q_{2k})
$$I_{\text{loss}}(p) = \sum_{k=1}^4 s_k \cdot D_{KL}\Bigl(\bigl(\frac{p_{2k-1}}{s_k},\frac{p_{2k}}{s_k}\bigr) \,\big\|\, \bigl(\tfrac12,\tfrac12\bigr)\Bigr)$$
$$I_{\text{loss}}(p) = H(C(p)) - H(p)$$
$$C_{\mathcal{P}} = C_m \circ C_{m-1} \circ \cdots \circ C_1$$
**Nested:** P_{j+1} coarsens components of P_1,…,P_j ⟹ C_𝒫² = C_𝒫
$$M_1 \supset M_2 \supset \cdots \supset M_m\quad (\text{nested})$$
$$S_*(p) = (p_1+p_2,\; p_3+p_4,\; p_5+p_6,\; p_7+p_8)$$
$$(A_j)_{2j-1,\ell} = (A_j)_{2j,\ell} = \begin{cases} \tfrac12 & \ell \in P_j, \\ \delta_{\ell,i} & i \notin P_j \end{cases}$$
---
## 5. Chentsov Reconstruction
$$h_9(p) = \sum_{i=1}^m \operatorname{Hess}(F)_p(e_i,e_i)$$
$$h_{\text{perm}}(p) = \sum_{i=1}^m \operatorname{Hess}(F)_p(e_i, e_{\pi(i)})$$
$$F(g_p^{(\alpha)}) = \alpha \cdot F(g_p^{(1)}) + (1-\alpha) \cdot F(g_p^{(0)})$$
$$g_p^{\text{mono}}(u,v) = \lambda(p) \cdot g_p^{\text{Fisher}}(u,v)$$
$$g_{ij}(p) = \frac{1}{p_i}\delta_{ij} + \frac{1}{p_m}$$
$$ds^2 = \sum_{i=0}^{m} \frac{(dp_i)^2}{p_i}$$
---
## 6. SOS Certificate (Merge Gate)
$$\text{gap}(x,m) = \text{sieve}(x,m) - \text{threshold}$$
$$K = \{x \in [2,90],\; m \in [3,13]\}$$
$$\text{gap}(x,m) = s_0(x,m) + s_1(x,m)(x-2) + s_2(x,m)(90-x) + s_3(x,m)(m-3) + s_4(x,m)(13-m)$$
$$s_i(x,m) = \sum_j q_{ij}(x,m)^2$$
$$\text{gap}(x,m) \geq 0 \text{ on } K \implies \text{merge gate}$$
**Baker (transcendence):**
$$\Lambda = \sum_{i=0}^{n} \beta_i \log \alpha_i \neq 0 \implies |\Lambda| > e^{-C \cdot \prod A_i \cdot \log B}$$
---
## 7. Sidon (Cross-Domain)
$$A \subset \mathbb{Z},\quad a+b=c+d \implies \{a,b\}=\{c,d\}$$
$$A_8 = \{2^i\}_{i=0}^7$$
$$h(N) = \max|A|,\quad A \subseteq \{1,\ldots,N\} \text{ Sidon}$$
$$h(N) \leq \lfloor\sqrt{2N}\rfloor + 1$$
$$S_p = \{x \in \mathbb{F}_{p^3}^\times / \mathbb{F}_p^\times : \operatorname{Tr}(x)=0\},\quad |S_p|=p+1$$
---
## 8. Braid / Spectral / Merge Gate
$$C \in \{0,\tfrac14,\tfrac12,\tfrac34\}^{8\times 8}$$
$$\varepsilon_{ij} = C_{ij}(\phi_i - \phi_j)$$
$$\text{crossStep}(s) = s \iff s \in \text{Eigensolid}$$
$$\text{gap}(s) = \bigwedge_{i,j \in \text{active}(s)} (i=j \lor |i-j|>1)$$
$$\text{merge}(s,e)_i = \min(1, s_i+e_i)$$
$$\text{res}(s,e) = |\{i : s_i \neq 0 \land e_i \neq 0\}|$$
$$\text{cross}(s,e) = \bigwedge_{i} \neg(s_i \neq 0 \land e_{i+1} \neq 0) \land \neg(e_i \neq 0 \land s_{i+1} \neq 0)$$
$$\text{gap}(s) \land \text{gap}(e) \land \text{res}(s,e)=0 \land \text{cross}(s,e) \implies \text{gap}(\text{merge}(s,e))$$
$$\text{byteGap}(n) = (n \land (n \gg 1)) = 0$$
$$\text{pack}(s) = \sum_{i=0}^{7} [s_i \neq 0] \cdot 2^i$$
---
## 9. Chiral / Q16_16
$$q = q_r + \varepsilon q_d,\quad \varepsilon^2 = 0$$
$$\chi = \frac{|q_r|^2}{|q_r|^2 + |q_d|^2},\quad |q_r|^2 + |q_d|^2 > 0$$
$$\chi > \tfrac12 \implies \text{compressive},\quad \chi < \tfrac12 \implies \text{anti-compressive},\quad \chi = \tfrac12 \implies \text{critical}$$
$$\text{Q16}(x) = \text{clamp}(-2^{31},\; \lfloor x \cdot 2^{16} \rfloor,\; 2^{31}-1)$$
$$a \oplus b = \text{clamp}(-2^{31},\, a+b,\, 2^{31}-1)$$
$$a \otimes b = \text{clamp}(-2^{31},\, \lfloor ab/2^{16} \rfloor,\, 2^{31}-1)$$

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#!/usr/bin/env python3
"""
equation_dna_encoder.py — SilverSight Φ Encoder CLI
Thin CLI wrapper around the phi package modules.
Usage:
echo 'd_F(p,q) = 2*arccos(sum(sqrt(p_i*q_i)))' | python equation_dna_encoder.py
python equation_dna_encoder.py 'E(x) = x^T Q x'
"""
from __future__ import annotations
import sys
from phi import encode_phi, to_fastq
def main():
if len(sys.argv) > 1:
equations = [" ".join(sys.argv[1:])]
else:
equations = [line.strip() for line in sys.stdin if line.strip()]
for eq in equations:
result = encode_phi(eq)
if result is None:
print(f"FAIL: could not parse: {eq!r}", file=sys.stderr)
continue
print(f"# Equation: {result['equation']}")
print(f"# DNA: {result['dna_sequence']} ({result['length']} bases)")
print(f"# PASS: {result['consistency_pass']}")
print(f"# Hash: {result['sha256_prefix']}")
print(f"# F: {result['F']}")
print(f"# τ: {result['tau']}")
print(f"# δ: {result['delta']}")
print(f"# FASTQ: {to_fastq(result).rstrip()}")
print()
if __name__ == "__main__":
main()

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"""
phi — SilverSight Equation-to-DNA Φ Encoding Pipeline
Independent modules, each reusable without the others:
charclass Layer 1: 8-class byte histogram → Δ₇
ast_parse Layer 3: AST parsing → τ and δ distributions
consistency Layer 4: 6 consistency rules → ADMIT/QUARANTINE
embed Core Φ: (F, τ, δ) → 30-base hachimoji DNA sequence
output FASTQ, Adleman graph, PCR primer protocol
Usage:
from phi import encode_phi
result = encode_phi("d_F(p,q) = 2*arccos(sum(sqrt(p_i*q_i)))")
print(result["dna_sequence"])
"""
from .embed import encode_phi
from .output import to_fastq, to_adleman_graph, design_filtering_protocol, PRIMER_DESIGN
from .charclass import compute_F
from .ast_parse import compute_tau, compute_delta
from .consistency import check_consistency, CONSISTENCY_RULES

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"""
phi.ast_parse — Layer 3: Parse tree analysis → τ and δ distributions
Converts equation strings to Python ASTs (after preprocessing math
notation), then computes:
τ(E) — node-type frequency histogram over Δ_{k-1}
δ(E) — child-ordering frequency histogram over Δ_{2k²-1}
Dependencies: Python ast, re (stdlib). Independent of phi.charclass.
"""
from __future__ import annotations
import ast
import re
from typing import Dict, List, Optional, Tuple
# ── Node types recognized by the parser ──────────────────────────────────
NODE_TYPES = [
"bin_add", "bin_sub", "bin_mul", "bin_div",
"bin_pow", "bin_eq", "un_neg", "un_not",
"var", "const", "call", "subscript", "tuple",
"list", "dict", "set", "lambda", "if_exp",
]
# ── Math notation preprocessor ───────────────────────────────────────────
def preprocess_math(text: str) -> str:
"""Preprocess math notation into Python-parseable form.
Handles:
- Single = → == (but preserves <=, >=, !=, ==)
- |x| → abs(x)
- Parenthesized subscripts: p_(i+j) → p[i+j]
- Implicit multiplication: (x)(y) → (x)*(y)
- TeX markers: removed
"""
s = text.strip()
if s.endswith(":"):
return ""
s = re.sub(r'\$|\$\$|\\\[|\\\]|\\\(|\\\)', '', s)
s = re.sub(r'(?<![<>=!])=(?!=)', '==', s)
s = re.sub(r'\|([^|=]+)\|', r'abs(\1)', s)
s = re.sub(r'([a-zA-Z])_\(([^)]+)\)', r'\1[\2]', s)
s = re.sub(r'\)\(', r')*(', s)
return s
def parse_to_ast(equation: str) -> Optional[ast.AST]:
"""Parse an equation string into a Python AST.
Tries direct parse first, then wraps in parens for expressions
where math notation drops outer parentheses.
"""
text = preprocess_math(equation)
if not text:
return None
try:
return ast.parse(text, mode="eval")
except SyntaxError:
pass
try:
return ast.parse(f"({text})", mode="eval")
except SyntaxError:
return None
# ── AST node classification ──────────────────────────────────────────────
def _classify_node(node: ast.AST) -> str:
"""Map an AST node to one of the NODE_TYPES."""
if isinstance(node, ast.BinOp):
if isinstance(node.op, ast.Add):
return "bin_add"
if isinstance(node.op, ast.Sub):
return "bin_sub"
if isinstance(node.op, ast.Mult):
return "bin_mul"
if isinstance(node.op, ast.Div):
return "bin_div"
if isinstance(node.op, ast.Pow):
return "bin_pow"
return "bin_add"
if isinstance(node, ast.UnaryOp):
if isinstance(node.op, ast.USub):
return "un_neg"
if isinstance(node.op, ast.Not):
return "un_not"
return "un_neg"
if isinstance(node, ast.Name):
return "var"
if isinstance(node, ast.Constant):
return "const"
if isinstance(node, ast.Call):
return "call"
if isinstance(node, ast.Subscript):
return "subscript"
if isinstance(node, ast.Tuple):
return "tuple"
return "const"
# ── τ(E): node-type frequencies → Δ_{k-1} ───────────────────────────────
def compute_tau(equation: str) -> Optional[List[float]]:
"""Compute τ(E) — normalized node-type histogram.
Returns 18 floats (one per NODE_TYPE) summing to 1.0,
or None if the equation cannot be parsed.
"""
tree = parse_to_ast(equation)
if tree is None:
return None
counts = {t: 0 for t in NODE_TYPES}
for node in ast.walk(tree):
t = _classify_node(node)
counts[t] = counts.get(t, 0) + 1
total = sum(counts.values())
if total == 0:
return [1.0 / len(NODE_TYPES)] * len(NODE_TYPES)
return [counts[t] / total for t in NODE_TYPES]
# ── δ(E): child-ordering frequencies → Δ_{2k²-1} ────────────────────────
# Which (parent, child) type combinations are tracked in δ
DELTA_PARENT_TYPES = [
"bin_add", "bin_sub", "bin_mul", "bin_div", "bin_pow",
"un_neg", "un_not", "call", "subscript",
]
DELTA_CHILD_TYPES = [
"bin_add", "bin_sub", "bin_mul", "bin_div", "bin_pow",
"un_neg", "var", "const", "call",
]
MAX_CHILDREN = 3
def compute_delta(equation: str) -> Optional[List[float]]:
"""Compute δ(E) — child-ordering frequency histogram.
For each (parent_type, child_type, child_index) triple, returns
the fraction of all parent-child edges. Captures syntactic
topology beyond raw node counts.
Returns a flat list of length len(DELTA_PARENT_TYPES) ×
len(DELTA_CHILD_TYPES) × MAX_CHILDREN, or None if parse fails.
"""
tree = parse_to_ast(equation)
if tree is None:
return None
edges: Dict[Tuple[str, str, int], int] = {}
total_edges = 0
def walk(node: ast.AST, _child_idx: int = 0):
nonlocal total_edges
parent_t = _classify_node(node)
for i, child in enumerate(ast.iter_child_nodes(node)):
child_t = _classify_node(child)
key = (parent_t, child_t, i)
edges[key] = edges.get(key, 0) + 1
total_edges += 1
walk(child, i)
walk(tree)
if total_edges == 0:
return None
result = []
for pt in DELTA_PARENT_TYPES:
for ct in DELTA_CHILD_TYPES:
for i in range(MAX_CHILDREN):
result.append(edges.get((pt, ct, i), 0) / total_edges)
return result

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"""
phi.charclass — Layer 1: Character classification → Δ₇ byte histogram
Each ASCII character maps to 1 of 8 archetypal classes. The histogram
over these 8 classes is the F(E) feature vector — the first component
of the Φ embedding.
Dependencies: none (stdlib only)
"""
from __future__ import annotations
from typing import List
# ── 8 character classes ──────────────────────────────────────────────────
# Each class corresponds to a dimension of Δ₇ (the 7-simplex).
# The classes partition the visible ASCII range into 8 bins.
CHAR_CLASSES = {
"digit": 0, # 0-9
"lower_alpha": 1, # a-z
"upper_alpha": 2, # A-Z
"operator": 3, # + - * / ^ % = < > ! & | ~
"bracket": 4, # ( ) [ ] { }
"punctuation": 5, # . , ; : ' " @ # $ _ \
"whitespace": 6, # space, tab, newline
"other": 7, # everything else (Greek, Unicode math, etc.)
}
OPERATOR_CHARS = set("+-*/^%=<>!&|~")
BRACKET_CHARS = set("()[]{}")
PUNCT_CHARS = set(".,;:'\"@#$ _\\")
def classify_char(c: str) -> int:
"""Classify a single character into 1 of 8 classes (0-7)."""
if c.isdigit():
return 0
if c.isalpha():
return 1 if c.islower() else 2
if c in OPERATOR_CHARS:
return 3
if c in BRACKET_CHARS:
return 4
if c in PUNCT_CHARS:
return 5
if c in (" ", "\t", "\n", "\r"):
return 6
return 7
def compute_F(equation: str) -> List[float]:
"""Compute F(E) — normalized byte-class histogram on Δ₇.
Returns 8 floats summing to 1.0. This is Layer 1 of the Φ embedding.
Pure function: no state, no side effects.
"""
counts = [0] * 8
for c in equation:
counts[classify_char(c)] += 1
total = sum(counts)
if total == 0:
return [1.0 / 8] * 8
return [c / total for c in counts]

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"""
phi.consistency — Layer 4: 6 consistency rules → ADMIT / QUARANTINE
Each equation is checked against 6 rules. All must pass for the
equation to be ADMITted. The results are embedded in the DNA as
G (pass) / T (fail) bases for allele-specific PCR filtering.
Dependencies: phi.ast_parse (for tree-based checks).
"""
from __future__ import annotations
import ast
import re
from typing import Dict, Optional
from .ast_parse import parse_to_ast
# ── Well-known math function names (considered "defined references") ─────
KNOWN_MATH_NAMES = frozenset({
"sin", "cos", "tan", "log", "ln", "exp", "sqrt", "abs", "sum",
"min", "max", "floor", "ceil", "round", "sign", "arg", "norm",
"det", "trace", "tr", "diag", "vec", "mat", "rank", "dim",
"arccos", "arcsin", "arctan", "sinh", "cosh", "tanh",
"Re", "Im", "conj", "adj", "trans", "id",
})
# ── The 6 rules ──────────────────────────────────────────────────────────
CONSISTENCY_RULES = [
"balanced_parens", # Rule 1: parentheses must be balanced
"valid_operator_order", # Rule 2: no illegal consecutive operators
"valid_variable_name", # Rule 3: identifiers start with letter/underscore
"no_empty_expression", # Rule 4: equation is non-empty
"single_expression", # Rule 5: parseable as single AST expression
"defined_reference", # Rule 6: all names are known or short vars
]
RULE_ORDER = list(CONSISTENCY_RULES)
def check_consistency(equation: str) -> Dict[str, bool]:
"""Check all 6 consistency rules.
Returns dict of rule_name → True/False.
All True = ADMIT; any False = QUARANTINE.
"""
result: Dict[str, bool] = {}
# ── Rule 1: Balanced parentheses ──
depth = 0
for c in equation:
if c == "(":
depth += 1
elif c == ")":
depth -= 1
if depth < 0:
break
result["balanced_parens"] = depth == 0
# ── Rule 2: No illegal consecutive operators ──
ops = set("+-*/^=<>!")
valid_pairs = frozenset({"<=", ">=", "==", "!=", "**"})
bad = False
for i in range(len(equation) - 1):
pair = equation[i:i+2]
if equation[i] in ops and equation[i + 1] in ops:
if pair not in valid_pairs:
bad = True
break
result["valid_operator_order"] = not bad
# ── Rule 3: Identifiers start with letter or underscore ──
tokens = re.findall(r"[A-Za-z_]\w*|\d+|[^\w\s]", equation)
bad_vars = any(
re.match(r"^\d", t) and not re.match(r"^\d+$", t)
for t in tokens
)
result["valid_variable_name"] = not bad_vars
# ── Rule 4: Non-empty ──
result["no_empty_expression"] = len(equation.strip()) > 0
# ── Rule 5: Parseable as single expression ──
tree = parse_to_ast(equation)
result["single_expression"] = tree is not None
# ── Rule 6: All names are known or short ──
result["defined_reference"] = True
if tree is not None:
for node in ast.walk(tree):
if isinstance(node, ast.Name):
name = node.id
allowed = (
len(name) <= 2
or name.startswith("_")
or "_" in name
or name in KNOWN_MATH_NAMES
)
if not allowed:
result["defined_reference"] = False
return result

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"""
phi.embed — Core Φ embedding: (F, τ, δ) → 30-base hachimoji DNA
Combines all four layers into a single encoding pass. This is the
only module that knows about the hachimoji alphabet and the DNA
sequence layout.
DNA layout (30 bases total):
bases 0-7: F(E) — byte-class frequencies on Δ₇
bases 8-15: τ(E) — parse tree node-type frequencies
bases 16-23: δ(E) — child-ordering frequencies
bases 24-29: Layer 4 consistency (G=pass, T=fail)
Dependencies: phi.charclass, phi.ast_parse, phi.consistency
"""
from __future__ import annotations
import hashlib
from typing import Dict, List, Optional
from .charclass import compute_F
from .ast_parse import compute_tau, compute_delta, NODE_TYPES
from .consistency import check_consistency, RULE_ORDER
# ── Hachimoji alphabet ───────────────────────────────────────────────────
HACHIMOJI_BASES = list("ABCGPSTZ")
INDEX_TO_BASE = dict(enumerate(HACHIMOJI_BASES))
BASE_TO_INDEX = {b: i for i, b in enumerate(HACHIMOJI_BASES)}
# ── Float-to-base conversion ─────────────────────────────────────────────
def _float_to_3bit(x: float) -> int:
"""Quantize a float [0, 1] to a 3-bit integer (0-7)."""
return min(7, max(0, round(x * 7)))
def _vec_to_bases(values: List[float]) -> str:
"""Map floats in [0,1] to hachimoji bases (3 bits each, 8 bases)."""
return "".join(INDEX_TO_BASE[_float_to_3bit(v)] for v in values)
# ── Core encoding ────────────────────────────────────────────────────────
def encode_phi(equation: str) -> Optional[Dict]:
"""Apply Φ mapping: equation string → 30-base hachimoji DNA sequence.
The four layers are:
1. F(E) — byte-class histogram (Δ₇)
2. (implicit — derived from the phase-alphabet mapping)
3. τ(E) + δ(E) — parse tree structure
4. 6 consistency rules → primer-binding region
Returns a dict with the DNA sequence and all intermediate values,
or None if the equation is empty.
The returned dict is the standard Φ encoding record consumed by
phi.output (FASTQ, Adleman graph, PCR protocol).
"""
if not equation or not equation.strip():
return None
F = compute_F(equation)
consistency = check_consistency(equation)
tau = compute_tau(equation)
delta = compute_delta(equation)
# Fallback for unparseable equations: uniform distribution
# (encodes as all-A — "null structural signal")
if tau is None:
tau = [1.0 / len(NODE_TYPES)] * len(NODE_TYPES)
# Encode each layer as 8 hachimoji bases
F_dna = _vec_to_bases(F[:8])
tau_dna = _vec_to_bases(tau[:8])
delta_dna = _vec_to_bases(delta[:8]) if delta else "AAAAAAAA"
# Layer 4: encode consistency G=pass T=fail
consistency_dna = "".join("G" if consistency[r] else "T" for r in RULE_ORDER)
full_sequence = F_dna + tau_dna + delta_dna + consistency_dna
quality_scores = "".join("A" if v else "P" for v in consistency.values())
seq_hash = hashlib.sha256(full_sequence.encode()).hexdigest()[:16]
return {
"equation": equation,
"dna_sequence": full_sequence,
"length": len(full_sequence),
"bases": list(HACHIMOJI_BASES),
"schema": "phi_embedding_v2",
"F": [round(x, 4) for x in F],
"tau": [round(x, 4) for x in tau],
"delta": [round(x, 4) for x in delta] if delta else None,
"F_dna": F_dna,
"tau_dna": tau_dna,
"delta_dna": delta_dna,
"consistency": consistency,
"consistency_pass": all(consistency.values()),
"consistency_dna": consistency_dna,
"quality_scores": quality_scores,
"sha256_prefix": seq_hash,
"pas_primer": "CCCCCC",
"fail_primer": "AAAAAA",
}

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"""
phi.output — FASTQ, Adleman graph, and PCR primer protocol
Transforms Φ encoding records (from phi.embed.encode_phi) into
standard bioinformatics formats consumable by external tools.
Formats:
FASTQ — pipe through Jellyfish/KMC/BLAST
Adleman graph — feed into BioComputing's connectNodes()
PCR protocol — wet-lab filtering with allele-specific primers
Dependencies: phi.embed (for encode_phi output dict format).
"""
from __future__ import annotations
import math
from typing import Dict, List
# ── Primer sequences ─────────────────────────────────────────────────────
PRIMER_DESIGN = {
"pas_primer": {
"sequence": "CCCCCC",
"target": "consistency_dna = GGGGGG (all 6 rules pass)",
"tm": 36.0,
"purpose": "Capture PASS strands — amplify consistent equations only",
},
"fail_primer": {
"sequence": "AAAAAA",
"target": "consistency_dna = TTTTTT (all 6 rules fail)",
"tm": 12.0,
"purpose": "Capture FAIL strands — quarantine inconsistent equations",
},
"universal_primer": {
"sequence": "ABCABC",
"target": "F_dna region (first 8 bases)",
"tm": 15.0,
"purpose": "Amplify all encoded equations regardless of consistency",
},
}
# ── FASTQ ────────────────────────────────────────────────────────────────
def to_fastq(record: Dict) -> str:
"""Format a Φ encoding record as a 4-line FASTQ entry.
FASTQ is the standard DNA sequencing format. This lets us pipe
equation encodings through any existing bioinformatics pipeline
without tool modification.
"""
eq = record["equation"]
seq = record["dna_sequence"]
qual = record["quality_scores"]
n_cons = sum(1 for v in record["consistency"].values() if v)
eq_hash = record["sha256_prefix"]
pass_flag = "PASS" if record["consistency_pass"] else "FAIL"
header = f"@{eq_hash} n_consistency={n_cons}/6 pass={pass_flag} len={len(eq)}"
qual_line = qual * (len(seq) // len(qual)) if len(seq) > len(qual) else qual
return f"{header}\n{seq}\n+\n{qual_line}\n"
# ── Adleman integration ──────────────────────────────────────────────────
def to_adleman_graph(records: List[Dict]) -> Dict:
"""Build an Adleman/Lipton-compatible graph from Φ-encoded equations.
Each equation is a vertex. Edges represent Fisher-distance similarity
(d_F < 0.5) between their F(E) byte-class histograms.
Feed the output into BioComputing's `hampath.connectNodes()` or
`sattv.createNodes()` to generate wet-lab DNA sequences.
"""
vertices = {}
for i, rec in enumerate(records):
vid = f"E{i}"
vertices[vid] = {
"equation": rec["equation"],
"dna": rec["dna_sequence"],
"consistency": rec["consistency_pass"],
}
edges = []
vert_ids = list(vertices.keys())
for i in range(len(vert_ids)):
for j in range(i + 1, len(vert_ids)):
Fi = records[i]["F"]
Fj = records[j]["F"]
dist = _fisher_distance(Fi, Fj)
if dist < 0.5:
edges.append((vert_ids[i], vert_ids[j], round(dist, 4)))
return {
"vertices": vertices,
"edges": edges,
"format": "adleman_graph_v1",
"n_vertices": len(vertices),
"n_edges": len(edges),
}
# ── PCR protocol ─────────────────────────────────────────────────────────
def design_filtering_protocol(records: List[Dict]) -> Dict:
"""Design a PCR filtering protocol for a batch of encoded equations.
Uses allele-specific PCR (Newton et al. 1989) — the 24°C Tm gap
between PASS and FAIL primers provides single-base discrimination.
"""
n_pass = sum(1 for r in records if r["consistency_pass"])
n_fail = len(records) - n_pass
return {
"protocol": "allele_specific_pcr_v1",
"reference": "Newton et al. (1989) Nucleic Acids Res 17:2503; "
"allele-specific PCR for single-base discrimination",
"pas_primer": PRIMER_DESIGN["pas_primer"]["sequence"],
"fail_primer": PRIMER_DESIGN["fail_primer"]["sequence"],
"universal_primer": PRIMER_DESIGN["universal_primer"]["sequence"],
"annealing_tm_pass": PRIMER_DESIGN["pas_primer"]["tm"],
"annealing_tm_fail": PRIMER_DESIGN["fail_primer"]["tm"],
"annealing_tm_universal": PRIMER_DESIGN["universal_primer"]["tm"],
"expected_amplification_pass": n_pass,
"expected_amplification_fail": n_fail,
}
# ── Fisher distance ↓ (internal; no circular dep) ────────────────────────
def _fisher_distance(p: List[float], q: List[float]) -> float:
"""Fisher-Rao distance between two points on Δ₇: d_F = 2·acos(Σ√(p_i·q_i))."""
dot = sum(math.sqrt(pi * qi) for pi, qi in zip(p, q))
dot = max(-1.0, min(1.0, dot))
return 2.0 * math.acos(dot)

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[build-system]
requires = ["setuptools>=64"]
build-backend = "setuptools.backends._legacy:_Backend"
[project]
name = "biosight"
version = "0.1.0"
description = "Equation-to-DNA Φ encoding for Adleman/Lipton DNA computing"
requires-python = ">=3.10"