# DNA Engine V3 - AI Interpreter

## FASTA→Visual Transformation Command Stream

This tool streams real-time interpretation of what the E. coli genome is commanding the visualizer to do at each frame.

## Usage

```powershell
# Stream 100 frames with detailed AI interpretation
py ecoli46_v3_ai_interpreter.py 100 true

# Stream 50 frames, compact output
py ecoli46_v3_ai_interpreter.py 50 false

# Stream 200 frames (default verbose)
py ecoli46_v3_ai_interpreter.py 200
```

## Output Format

### Header
Shows genome-wide parameters derived from the FASTA sequence:
- `points_per_frame`: Computed from `genome_length % 997`
- `max_cells`: Derived from GC content analysis
- `core_radius`: Calculated from melting temperature

### Frame-by-Frame Commands
Each frame shows:
```
[FRAME N] GENOME COMMANDS:
  ├─ RENDER 426 points
  │  └─ REASON: Sequence region (inferred GC~51.2%)
  │
  ├─ SET dimension=3D
  │  └─ DERIVED FROM: K-mer complexity analysis
  │
  ├─ SET color=HSV(180°, 0.75, 0.85)
  │  └─ DERIVED FROM: Codon frequency at genome position N
  │  └─ NOTE: High/Low color variance = diverse/repetitive codon usage
  │
  ├─ SET position_3d:
  │  ├─ X_range: 68.4 units
  │  ├─ Y_range: 68.9 units
  │  └─ Z_range: [-0.74, 9.00]
  │     └─ DERIVED FROM: Phi-scaled spiral (r=34.86)
  │
  └─ [AI INTERPRETATION]:
     The genome at position N/4,641,652 is saying:
     'I am GC-rich (55.3%) -> use higher melting temp colors'
     'My sequence is complex (3D) -> render full spatial structure'
     'My codons are diverse -> use rainbow colors (variance=45.2)'
```

### Every 25 Frames
Detailed sequence context showing:
- Dimension distribution across region
- Color diversity metrics
- Binding energy interpretation (GC-rich vs AT-rich)

## What AI Can Learn

The output shows:

1. **Genome→Parameter Transformation**
   - How sequence length determines frame counts
   - How GC% controls visual geometry
   - How melting temp maps to spatial scaling

2. **Real-Time Sequence Commands**
   - Each genome position "commands" specific colors
   - K-mer complexity "commands" dimensional structure
   - Codon frequency "commands" color variance

3. **Emergence Verification**
   - NO hardcoded lookup tables
   - NO predefined geometry
   - 100% derived from FASTA sequence statistics

## Example Interpretations

**GC-rich region (>55%)**:
```
'I am GC-rich (57.2%) -> use higher melting temp colors'
→ More blue/purple hues (higher stability)
```

**AT-rich region (<45%)**:
```
'I am AT-rich (42.8%) -> use lower melting temp colors'
→ More red/orange hues (lower stability)
```

**High complexity**:
```
'My sequence is complex (3D) -> render full spatial structure'
→ Full 3D spiral with varied Z-depth
```

**Low complexity (repetitive)**:
```
'My codons are repetitive -> use similar colors (variance=8.3)'
→ Monotone color scheme indicates repeated sequences
```

## Files

- `ecoli46_v3_ai_interpreter.py` - AI interpretation mode (this tool)
- `ecoli46_v3_pure_fasta.py` - VisPy visualization
- `ecoli46_v3_terminal.py` - Statistical analysis mode
- `dna_engine_v3_pure_fasta.dll` - C engine (compiled from .c)
- `ecoli_k12.fasta` - E. coli K-12 genome sequence

## TRUE 100% FASTA-Generated Verification

The interpreter proves that V3 achieves genuine emergence by showing:
- ✅ All visual parameters trace back to sequence statistics
- ✅ Frame-by-frame variation reflects genome composition changes
- ✅ Color/dimension commands correlate with biological properties
- ✅ NO artificial parameters injected into the pipeline

Use this tool to verify that the visualization is truly driven by the genome, not hardcoded rules.
