# pyNGB Roadmap & Future Improvements

This document outlines potential future enhancements for the pyNGB library.

## 📋 Immediate Next Steps (v0.2.0)

### Type Safety & Static Analysis
```python
# Add comprehensive type hints throughout
from typing import Protocol, TypeVar, Generic
import mypy

# Run type checking in CI
mypy src/pyngb --strict
```

### Advanced Data Validation
```python
# Add data validation schemas
from pydantic import BaseModel, validator

class NGBDataSchema(BaseModel):
    time: list[float]
    temperature: list[float]

    @validator('time')
    def time_must_be_monotonic(cls, v):
        if not all(a <= b for a, b in zip(v, v[1:])):
            raise ValueError('Time must be monotonic')
        return v
```

### Async Processing Support
```python
# For batch processing of multiple files
import asyncio
from pyngb import AsyncNGBParser

async def process_many_files(files: list[str]):
    parser = AsyncNGBParser()
    tasks = [parser.parse_async(f) for f in files]
    results = await asyncio.gather(*tasks)
    return results
```

## 🚀 Medium-term Goals (v0.3.0)

### Plugin Architecture
```python
# Allow custom data processors
from pyngb.plugins import DataProcessorPlugin

class CustomAnalysisPlugin(DataProcessorPlugin):
    def process_data(self, table: pa.Table) -> pa.Table:
        # Add custom columns, calculations, etc.
        return enhanced_table

# Register plugin
parser.register_plugin(CustomAnalysisPlugin())
```

### Caching Layer
```python
# Cache parsed results for repeated access
from pyngb.cache import FileCache

cache = FileCache("/path/to/cache")
table = read_ngb("file.ngb-ss3", cache=cache)  # Caches result
table2 = read_ngb("file.ngb-ss3", cache=cache)  # Uses cache
```

### Multi-format Support
```python
# Support other thermal analysis formats
from pyngb import load_thermal_data

# Auto-detect format
table = load_thermal_data("file.dta")  # TA Instruments
table = load_thermal_data("file.txt")  # ASCII export
table = load_thermal_data("file.ngb-ss3")  # NETZSCH
```

## 🎯 Long-term Vision (v1.0.0)

### Web Dashboard
```python
# Interactive web interface for data exploration
from pyngb.web import create_dashboard

app = create_dashboard()
app.add_file("experiment.ngb-ss3")
app.run(host="localhost", port=8080)

# Features:
# - Drag & drop file upload
# - Interactive plots (Plotly/Bokeh)
# - Real-time data filtering
# - Export capabilities
# - Batch processing UI
```

### Machine Learning Integration
```python
# Built-in ML tools for thermal analysis
from pyngb.ml import ThermalAnalyzer

analyzer = ThermalAnalyzer()
analyzer.fit(training_files)

# Predict glass transition temperature
tg = analyzer.predict_tg(new_file)

# Anomaly detection
anomalies = analyzer.detect_anomalies(files)

# Classification
material_type = analyzer.classify_material(file)
```

### Cloud Integration
```python
# Process files stored in cloud
from pyngb.cloud import S3Parser, AzureParser

# AWS S3
parser = S3Parser(bucket="my-thermal-data")
results = parser.process_folder("experiments/")

# Azure Blob Storage
parser = AzureParser(container="thermal-analysis")
results = parser.process_batch(file_list)
```

### Real-time Data Streaming
```python
# Process data as it's being generated
from pyngb.streaming import NGBStreamer

streamer = NGBStreamer()
streamer.connect_instrument("192.168.1.100")

@streamer.on_data
def process_realtime(data_chunk):
    # Process data as it arrives
    analysis = analyze_chunk(data_chunk)
    if analysis.critical_event:
        send_alert(analysis)
```

## 📊 Technical Improvements

### Performance Optimizations
- **Compiled Extensions**: Use Cython or Rust for hot paths
- **Memory Mapping**: For very large files
- **Parallel Processing**: Multi-core parsing for batch operations
- **GPU Acceleration**: CUDA/OpenCL for numerical operations

### Advanced Features
- **Data Compression**: Efficient storage of processed data
- **Incremental Loading**: Stream large datasets progressively
- **Schema Evolution**: Handle different NGB format versions
- **Metadata Indexing**: Fast search across file collections

### Integration Ecosystem
- **Jupyter Extensions**: Rich notebook display widgets
- **Pandas Integration**: Direct DataFrame creation
- **Dask Integration**: Distributed computing support
- **Apache Arrow**: Zero-copy data interchange

## 🎨 User Experience

### GUI Application
- Cross-platform desktop app (PyQt/tkinter)
- Drag & drop interface
- Interactive plotting
- Report generation
- Batch processing with progress bars

### Documentation Enhancements
- Interactive tutorials (Jupyter notebooks)
- Video walkthrough series
- API reference with examples
- Best practices guide
- Performance optimization guide

### Community Features
- Plugin marketplace
- User-contributed analysis templates
- Data format converters
- Visualization gallery

## 📈 Analytics & Monitoring

### Usage Analytics
- Anonymous usage statistics
- Performance metrics collection
- Error reporting (opt-in)
- Feature usage tracking

### Quality Metrics
- Code coverage reports
- Performance benchmarks
- Memory usage analysis
- Compatibility testing

## 🏗️ Infrastructure

### Development Tools
- Pre-commit hooks for code quality
- Automated dependency updates
- Security vulnerability scanning
- Release automation

### Deployment & Distribution
- Docker containers for consistent environments
- Conda packages for scientific Python
- Homebrew formula for macOS
- Windows installer package

## 🤝 Community & Contribution

### Open Source Ecosystem
- Contributor guidelines
- Code of conduct
- Issue templates
- PR review process

### Research Collaborations
- Academic partnerships
- Industry collaborations
- Conference presentations
- Research paper citations

## 📅 Implementation Timeline

### Phase 1: Foundation (Months 1-3)
- Type safety and validation
- Enhanced testing and CI/CD
- Performance optimization
- Documentation completion

### Phase 2: Features (Months 4-6)
- Plugin architecture
- Multi-format support
- Caching and optimization
- Advanced CLI features

### Phase 3: Integration (Months 7-12)
- ML integration
- Cloud support
- Web dashboard
- GUI application

### Phase 4: Ecosystem (Year 2)
- Community building
- Research partnerships
- Advanced analytics
- Platform expansion

This roadmap provides a clear path for evolving pyNGB from a specialized parsing library into a comprehensive thermal analysis platform.
