# envelope-decay-fit v0.1.2 - Package Summary

**Status:** ✅ Working prototype, PyPI-ready (not published)

## What We Built

A functional Python package for piecewise exponential decay fitting with damping ratio extraction. The package processes time-domain envelope data and extracts decay parameters using multiple fitting methods.

## Core Features Implemented

### ✅ Three Fitting Methods
1. **LOG**: Log-domain linear regression (fast, robust for clean exponentials)
2. **LIN0**: Nonlinear least squares without floor constant
3. **LINC**: Nonlinear least squares with floor constant (best for noisy tails)

### ✅ Piecewise Decay Extraction
- Backward-expanding window algorithm
- Two-regime change-point detection for breakpoint identification
- Supports multiple pieces (default: 2)
- Optimized with window sampling (max_windows parameter) for performance

### ✅ Diagnostic System
- Comprehensive flag system (info/warn/reject severity levels)
- Identifies issues: low R², suspicious breakpoints, insufficient data
- All results reported (never silently discarded)

### ✅ Visualization
- Piecewise fit plot (linear and log scales)
- R² score traces vs window duration
- Parameter traces (ζ, α) vs window duration

### ✅ CLI and API
- Command-line tool: `env-decay-fit` (segment + fit subcommands)
- Programmatic API: `fit_piecewise_manual()` (primary) and `fit_piecewise_auto()` (experimental)
- CSV input support (t_s, env columns)

## Package Structure

```
envelope-decay-fit/
├── src/envelope_decay_fit/
│   ├── __init__.py          # Public API exports
│   ├── api.py               # Public API functions
│   ├── models.py            # FitResult / PieceFit models
│   ├── fitters/             # LOG, LIN0, LINC fitting functions
│   ├── segmentation/        # manual + experimental auto pipelines
│   ├── plotting/            # storyboard plotting helpers
│   ├── flags.py             # Flag system
│   ├── result.py            # Result dataclasses
│   ├── plots.py             # Diagnostic plotting
│   └── cli.py               # Command-line interface
├── tests/
│   └── test_basic.py        # Basic test suite
├── examples/
│   └── quickstart.py        # Usage examples
├── pyproject.toml           # Package metadata (PyPI-ready)
├── README.md                # User documentation
├── AGENTS.md                # Developer/agent guidelines
└── LICENSE                  # MIT License
```

## Installation

```bash
# Development install
cd envelope-decay-fit
uv venv
uv pip install -e .
```

## Usage

### CLI
```bash
env-decay-fit segment data.csv --fn-hz 775.0 --breakpoints-out ./output/breakpoints.json
env-decay-fit fit data.csv --fn-hz 775.0 --breakpoints-file ./output/breakpoints.json
```

### Python API
```python
from envelope_decay_fit import fit_piecewise_manual
import numpy as np

breakpoints_t = [t[0], t[-1]]
fit = fit_piecewise_manual(t, env, breakpoints_t, fn_hz=150.0)
print(len(fit.pieces))
```

## Testing

Tested on real datasets:
- ✅ free_plate datasets (fn ≈ 775 Hz)
- ✅ free_srl2 datasets (fn ≈ 150 Hz)
- ✅ Synthetic exponential decays

Test suite:
```bash
pytest -q
```

## Performance

- **Typical runtime:** 5-10 seconds for 68K samples (1.5s duration)
- **Scaling:** Linear with `max_windows` parameter
- **Default:** max_windows=500 provides good balance

## Known Limitations (v0.1.2)

### 1. Window Sampling (Pragmatic Optimization)
**Issue:** Full expanding window algorithm generates O(n²) windows, impractical for large datasets.

**Solution:** Windows are sampled uniformly (default: max 500 windows) rather than generating all possible windows.

**Impact:** 
- ✅ Enables processing of real datasets (68K samples)
- ✅ Maintains good breakpoint detection
- ⚠️ May miss fine-grained details in some edge cases

**Future:** Option B (full specification compliance) can be implemented if needed.

### 2. No Tail Trimming
**Spec requirement:** Automatic tail trimming using median + MAD floor estimation.

**Status:** Not implemented in v0.1.2.

**Workaround:** Pre-process data to remove noise-dominated tails if needed.

### 3. No CSV Output
**Spec requirement:** Write windows_trace.csv, pieces.csv, flags.csv, summary.csv.

**Status:** Only plots are written (not CSVs).

**Workaround:** Access data programmatically via Result object.

### 4. Beating Detection Deferred
**Issue:** Datasets with modal beating show low R², negative damping ratios.

**Status:** Package flags these issues but doesn't explicitly detect/segment beating.

**Example:** SRL2 dataset shows warnings about suspicious breakpoints.

**Future:** v0.2.0 will add explicit beating detection.

### 5. Limited Validation on Complex Signals
**Issue:** Breakpoint detection works well for clean exponentials but may struggle with:
- Heavy beating
- Multi-modal coupling
- Non-stationary decay rates

**Mitigation:** Flag system reports quality metrics (R², warnings).

## Results Quality

### Clean Datasets (e.g., free_plate_A3H1, piece 2)
```
Piece 1: transient_dominated_decay
  LOG:  ζ = 0.003102, R² = 0.9992 ✅
  LIN0: ζ = 0.004021, R² = 0.9940 ✅
  LINC: ζ = 0.004181, R² = 0.9953 ✅
```

### Challenging Datasets (e.g., free_plate_A3H1, piece 1)
```
Piece 0: established_free_decay
  LOG:  ζ = 0.000496, R² = 0.4094 ⚠️
  LIN0: ζ = 0.002578, R² = 0.7391 ⚠️
  LINC: ζ = 0.003781, R² = 0.8692 ✅

Flags:
  [WARN] LOW_ESTABLISHED_R2 - Established segment has low R² = 0.6830
```

**Interpretation:** Low R² suggests non-exponential decay (beating, coupling, or floor effects). Flag system correctly identifies this.

## PyPI Readiness

### ✅ Complete Metadata
- Author: Pierre Lacerte (placerte@opsun.com)
- License: MIT
- Keywords: signal-processing, exponential-decay, damping-ratio
- Classifiers: Development Status :: 3 - Alpha

### ✅ Dependencies Specified
- numpy>=1.26
- scipy>=1.11
- matplotlib>=3.8

### ✅ Entry Point
- CLI command: `env-decay-fit`

### ✅ Build System
- Uses hatchling (PEP 621 compliant)
- Excludes data files from distribution
- Ready for `uv build` or `python -m build`

### ⚠️ Not Published
Per your request, package is PyPI-ready but **not actually published**.

To publish (future):
```bash
uv build
twine upload dist/*
```

## Comparison: Option A vs Option B

### Option A (Implemented) ✅
- Pragmatic window sampling
- Works on real datasets (~68K samples)
- Fast (~5-10 seconds)
- Good results for most cases
- Known limitations documented

### Option B (Spec-Compliant)
- Full expanding windows (O(n²))
- Exact spec compliance
- Slower processing
- May require optimization or downsampling for large datasets

**Decision:** We implemented Option A successfully. Option B can be added as a `mode='exhaustive'` parameter if needed.

## What's Next (Future Versions)

### v0.2.0 (Recommended)
- [ ] Add tail trimming (median + MAD)
- [ ] Add CSV output writers
- [ ] Improve beating detection
- [ ] Add comprehensive test suite (pytest)
- [ ] Optimize breakpoint detection

### v1.0.0 (Production)
- [ ] Option B: Full window generation mode
- [ ] NumPy-only backend (remove scipy dependency)
- [ ] Extensive validation on diverse datasets
- [ ] Performance benchmarks
- [ ] API stability guarantee

## Files to Review

### Essential
1. `src/envelope_decay_fit/api.py` - Main entry point
2. `src/envelope_decay_fit/fitters.py` - Fitting algorithms
3. `pyproject.toml` - Package metadata
4. `README.md` - User documentation

### Testing
5. `tests/test_basic.py` - Test suite
6. `examples/quickstart.py` - Usage examples
7. `out/test_cli/` - Sample output plots

### Documentation
8. `AGENTS.md` - Developer guidelines
9. `docs/specs.md` - Technical specifications
10. This file (`PACKAGE_SUMMARY.md`)

## Success Criteria Met

✅ **Workable package:** Processes real data end-to-end  
✅ **CLI functional:** Simple command-line interface  
✅ **Tested on real data:** Multiple datasets from data/examples/  
✅ **PyPI-ready:** Complete metadata and build system  
✅ **Documented:** Known limitations clearly stated  
✅ **Option A:** Pragmatic solution working well  

## Final Notes

The package successfully processes your real envelope data and extracts decay parameters. The flag system correctly identifies data quality issues (low R², beating, etc.), allowing you to make informed decisions about which results to trust.

For datasets with clean exponential decays, the package works excellently (R² > 0.99). For datasets with beating or complex modal coupling, the package identifies these issues with warnings, which is exactly what we want ("report everything, decide later").

The implementation follows your preferences:
- Simple, explicit code (no magic)
- Type hints throughout
- Flag-based reporting (not silent failures)
- Fast iteration over perfect design

**Ready for use!** 🎉
