# Time-based Analysis Implementation Summary

## Overview

This PR successfully adds comprehensive time-based analysis capabilities to CRISP-T, enabling researchers to analyze temporal patterns in mixed-methods data.

## Implementation Details

### Files Modified/Created

1. **src/crisp_t/model/document.py** - Added optional `timestamp` field
2. **src/crisp_t/read_data.py** - Auto-detection and parsing of timestamp columns
3. **src/crisp_t/temporal.py** - NEW: Core temporal analysis module (590 lines)
4. **src/crisp_t/graph.py** - Added temporal subgraph and edge support
5. **src/crisp_t/corpuscli.py** - Added 6 temporal CLI commands
6. **src/crisp_t/mcp/server.py** - Added 5 temporal MCP tools
7. **tests/test_temporal.py** - NEW: 12 comprehensive unit tests
8. **notes/TEMPORAL_ANALYSIS.md** - NEW: Complete user guide (700+ lines)
9. **examples/temporal_analysis_example.py** - NEW: Working demonstration

### Key Features

#### 1. Temporal Data Model
- Documents can have ISO 8601 timestamps
- CSV timestamp columns auto-detected (timestamp, datetime, time, date, etc.)
- Full backward compatibility - timestamps are optional

#### 2. Temporal Linking (3 methods)
- **Nearest Time**: Link to closest dataframe row
- **Time Window**: Link to all rows within ±N seconds/minutes
- **Sequence**: Link by time periods (day/week/month)

#### 3. Temporal Analysis
- Filter corpus by date ranges
- Generate temporal summaries with statistics
- Track sentiment trends over time
- Extract evolving topics per time period

#### 4. Temporal Graphs
- Create time-sliced subgraphs
- Add temporal relationship edges
- Support for temporal graph evolution analysis

#### 5. Visualization
- Sentiment trend plots
- Temporal summary charts
- Time series of numeric data

#### 6. CLI Integration
Commands added to `crispt`:
- `--temporal-link` - Link documents by time
- `--temporal-filter` - Filter by date range
- `--temporal-summary` - Generate temporal summaries
- `--temporal-sentiment` - Analyze sentiment trends
- `--temporal-topics` - Extract topics over time
- `--temporal-subgraphs` - Create temporal graphs

#### 7. MCP Server Tools
New tools for AI assistants:
- `temporal_link_by_time`
- `temporal_filter`
- `temporal_summary`
- `temporal_sentiment_trend`
- `temporal_topics`

## Testing Results

### Unit Tests: 12/12 Passing ✅
- Timestamp parsing (multiple formats)
- Nearest time linking
- Time window linking
- Sequence-based linking
- Time range filtering
- Temporal summaries
- Temporal sentiment trends
- Temporal topic extraction
- Document timestamp field validation
- Error handling for missing timestamps

### Integration Tests: All Passing ✅
- Backward compatibility with existing corpus/document tests
- CLI integration tests
- MCP server functionality

### Example Script: Working ✅
- Demonstrates complete temporal workflow
- Creates sample patient notes + sensor data
- Performs temporal linking, sentiment analysis, topic extraction
- Generates visualizations
- Successfully saves results

## Code Quality

### Code Review: All Issues Addressed ✅
- Added logging support
- Improved exception handling (specific exceptions)
- Moved stop words to constant
- Clarified CLI error messages

### Security Scan: No Vulnerabilities ✅
- CodeQL analysis passed with 0 alerts
- No security issues detected

### Documentation: Comprehensive ✅
- 700+ line user guide with examples
- API documentation in code
- CLI help text
- MCP tool descriptions
- Working example script

## Usage Examples

### Python API
```python
from crisp_t.temporal import TemporalAnalyzer
analyzer = TemporalAnalyzer(corpus)

# Link by time window
corpus = analyzer.link_by_time_window(
    time_column="timestamp",
    window_before=timedelta(minutes=5),
    window_after=timedelta(minutes=5)
)

# Get sentiment trend
trend = analyzer.get_temporal_sentiment_trend(period="W")

# Extract topics over time
topics = analyzer.get_temporal_topics(period="W", top_n=5)
```

### CLI
```bash
# Link documents to data within 5 minute window
crispt --inp corpus --temporal-link "window:timestamp:300" --out corpus

# Filter to specific date range
crispt --inp corpus --temporal-filter "2025-01-01:2025-06-30" --out filtered

# Analyze weekly sentiment trends
crispt --inp corpus --temporal-sentiment "W:mean"

# Extract weekly top 5 topics
crispt --inp corpus --temporal-topics "W:5"
```

### MCP Server
```json
{
  "tool": "temporal_link_by_time",
  "arguments": {
    "method": "window",
    "time_column": "timestamp",
    "window_seconds": 300
  }
}
```

## Research Applications

This implementation enables researchers to:

1. **Healthcare**: Link patient notes to vital sign measurements over time
2. **Social Media**: Track sentiment evolution and trending topics
3. **Organizational Studies**: Analyze meeting notes with productivity metrics
4. **Education**: Connect student feedback with performance data
5. **Mixed Methods**: Triangulate qualitative themes with quantitative trends

## Performance Characteristics

- Efficient timestamp parsing with multiple format support
- Vectorized operations for large datasets
- Lazy evaluation of temporal summaries
- Memory-efficient subgraph generation

## Future Enhancements (Not in Scope)

Potential future additions:
- Temporal anomaly detection
- Forecasting based on temporal patterns
- Advanced time series decomposition
- Interactive temporal visualizations
- Temporal clustering algorithms

## Conclusion

The time-based analysis feature is production-ready:
- ✅ Complete implementation
- ✅ Comprehensive testing
- ✅ Full documentation
- ✅ Working examples
- ✅ Backward compatible
- ✅ Security validated
- ✅ Code reviewed

The implementation follows CRISP-T's architecture patterns and integrates seamlessly with existing features while maintaining the toolkit's focus on triangulation of qualitative and quantitative data.
