Feat/no filter

FILTER_IMPLEMENTATION_SUMMARY.md

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+# Filter Strategy Pattern Implementation Summary
+
+## Overview
+
+Implemented a **Strategy Pattern** for EMG signal filtering that provides:
+
+- ✅ Multiple filter options (high-pass, low-pass, band-pass, moving average, no filter)
+- ✅ Consistent filtering between training and inference
+- ✅ Easy experimentation with different filters
+- ✅ Automatic configuration persistence in model checkpoints
+
+## Files Created/Modified
+
+### New Files
+
+1. **`training/filter_strategies.py`** (389 lines)
+
+   - Abstract `FilterStrategy` base class
+   - 5 concrete filter implementations
+   - Factory function `create_filter()`
+   - Comprehensive testing in `__main__`
+
+1. **`FILTER_STRATEGY_GUIDE.md`** (Complete user guide)
+
+   - Detailed documentation for each filter type
+   - Usage examples for training and inference
+   - Filter selection decision tree
+   - Troubleshooting tips
+
+### Modified Files
+
+1. **`training/inference_streaming.py`**
+
+   - Added filter strategy imports
+   - Updated `StreamingInference.__init__()` to accept filter config
+   - Replaced hardcoded high-pass filter with strategy pattern
+   - Updated `load_test_data()` to accept filter strategy
+   - Modified `main()` to load filter config from model checkpoint
+   - Removed old `StreamingHighPassFilter` class
+
+1. **`training/notebooks/LSTM_spacial_filtering.ipynb`**
+
+   - Added filter configuration cells at top
+   - Replaced hardcoded `highpass_causal()` with filter strategy
+   - Updated model saving to include filter configuration in hyperparameters
+
+## Filter Strategy Classes
+
+```
+FilterStrategy (ABC)
+├── NoFilterStrategy - Pass-through (no filtering)
+├── HighPassFilterStrategy - Remove DC bias and drift
+├── LowPassFilterStrategy - Smoothing, noise reduction
+├── BandPassFilterStrategy - Combination filtering
+└── MovingAverageFilterStrategy - Simple FIR smoothing
+```
+
+Each strategy implements:
+
+- `apply_batch(signal)` - For training (batch processing)
+- `apply_streaming(value, channel)` - For inference (streaming)
+- `get_description()` - Human-readable description
+
+## Usage
+
+### In Training Notebook
+
+```python
+# Configure filter
+FILTER_TYPE = "highpass"
+FILTER_CONFIG = {"cutoff": 0.5, "order": 4}
+
+# Create and apply filter
+filter_strategy = create_filter(FILTER_TYPE, fs, **FILTER_CONFIG)
+for col in ["env0", "env1", "env2", "env3"]:
+    df_clean[col] = filter_strategy.apply_batch(df_clean[col].values)
+
+# Save with model
+torch.save(
+    {
+        "hyperparameters": {
+            "filter_type": FILTER_TYPE,
+            "filter_config": FILTER_CONFIG,
+            "sampling_rate": MEASURED_SAMPLE_RATE,
+        }
+    },
+    "model.pth",
+)
+```
+
+### In Inference Script
+
+```python
+# Filter config automatically loaded from checkpoint
+checkpoint = torch.load(model_path)
+FILTER_TYPE = checkpoint["hyperparameters"]["filter_type"]
+FILTER_CONFIG = checkpoint["hyperparameters"]["filter_config"]
+
+# Create inference engine with loaded config
+inference_engine = StreamingInference(
+    filter_type=FILTER_TYPE,
+    filter_config=FILTER_CONFIG,
+)
+```
+
+## Available Filters
+
+| Filter Type      | Use Case                         | Typical Config                                        |
+| ---------------- | -------------------------------- | ----------------------------------------------------- |
+| `none`           | Raw data, no filtering           | `{}`                                                  |
+| `highpass`       | Remove DC bias, drift            | `{'cutoff': 0.5, 'order': 4}`                         |
+| `lowpass`        | Smoothing, noise reduction       | `{'cutoff': 2.0, 'order': 4}`                         |
+| `bandpass`       | Both drift removal and smoothing | `{'low_cutoff': 0.5, 'high_cutoff': 2.0, 'order': 4}` |
+| `moving_average` | Simple smoothing                 | `{'window_size': 5}`                                  |
+
+## Key Features
+
+### 1. Consistency Between Training and Inference
+
+- Same filter applied in both batch (training) and streaming (inference) modes
+- Verified: batch and streaming produce identical results (tested in module)
+- Filter config automatically saved and loaded with model
+
+### 2. Easy Experimentation
+
+- Change `FILTER_TYPE` and `FILTER_CONFIG` at top of notebook
+- No need to modify filtering code
+- All filters use same interface
+
+### 3. Proper Causal Filtering
+
+- All filters use `lfilter` (causal) not `filtfilt` (acausal)
+- Compatible with real-time streaming inference
+- Maintains filter state across streaming samples
+
+### 4. Comprehensive Documentation
+
+- Inline docstrings for all classes and methods
+- Complete user guide (`FILTER_STRATEGY_GUIDE.md`)
+- Example usage in module's `__main__`
+
+## Testing
+
+Run the filter strategies module to verify all filters work:
+
+```bash
+python -m training.filter_strategies
+```
+
+Output shows:
+
+- All 5 filters tested on synthetic signal
+- Batch and streaming modes match perfectly (✅ MATCH)
+- Verified numerically (max difference < 1e-10)
+
+## Benefits
+
+1. **Flexibility**: Easy to try different filters without changing code
+1. **Maintainability**: Single source of truth for filtering logic
+1. **Consistency**: Guaranteed same filtering in training and inference
+1. **Extensibility**: Easy to add new filter types (just extend `FilterStrategy`)
+1. **Debuggability**: Clear filter description in output logs
+
+## Example Output (Inference)
+
+```
+✅ Loaded filter configuration from model checkpoint:
+   Filter type: highpass
+   Filter config: {'cutoff': 0.5, 'order': 4}
+   Sampling rate: 4.2144 Hz
+
+======================================================================
+FILTER CONFIGURATION
+======================================================================
+  High-Pass Filter (cutoff=0.5Hz, order=4, fs=4.2144Hz)
+======================================================================
+
+======================================================================
+APPLYING BATCH FILTERING TO ENV CHANNELS
+======================================================================
+  High-Pass Filter (cutoff=0.5Hz, order=4, fs=4.2144Hz)
+======================================================================
+```
+
+## Migration Notes
+
+### Before (Hardcoded)
+
+```python
+# Old approach - hardcoded high-pass filter
+def highpass_causal(signal, fs, cutoff=0.5, order=4):
+    b, a = butter(order, cutoff / (0.5 * fs), btype="high")
+    return lfilter(b, a, signal)
+
+
+# Scattered in multiple places
+df_clean[col] = highpass_causal(df_clean[col].values, fs)
+```
+
+### After (Strategy Pattern)
+
+```python
+# New approach - configurable strategy
+filter_strategy = create_filter("highpass", fs, cutoff=0.5, order=4)
+df_clean[col] = filter_strategy.apply_batch(df_clean[col].values)
+
+# Or try different filter easily:
+filter_strategy = create_filter("lowpass", fs, cutoff=2.0, order=4)
+```
+
+## Next Steps
+
+Users can now:
+
+1. Experiment with different filters by changing `FILTER_TYPE`
+1. Compare performance across filter types
+1. Optimize filter parameters for their specific data
+1. Add custom filters by extending `FilterStrategy`
+
+See `FILTER_STRATEGY_GUIDE.md` for complete usage instructions.