**Automated Testing - Test Coverage**

Good initiative adding a test runner! However, I have some concerns about test comprehensiveness:

**Current State:**
- ✅ Tests plot generation (good!)
- ❌ Missing edge case tests
- ❌ No validation of plot output correctness

**Recommended Test Cases:**
1. **Empty data**: What happens when the CSV has no valid frames?
2. **NaN values**: How does the plot handle missing head coordinates?
3. **Single frame**: Edge case with only 1 data point
4. **Invalid config**: Missing required fields in config JSON
5. **Output validation**: Check that generated plots have expected data ranges

**Example test structure:**
```python
def test_headplot_empty_data():
    """Test head plot handles empty DataFrame gracefully."""
    empty_df = pd.DataFrame()
    config = load_test_config()
    
    # Should either skip gracefully or raise informative error
    with pytest.raises(ValueError, match="No data to plot"):
        plot_head(empty_df, config)

def test_headplot_output_has_correct_traces():
    """Verify generated plot contains expected data."""
    df = load_test_data()
    config = load_test_config()
    
    fig = plot_head(df, config)
    assert len(fig.data) > 0  # Has trace data
    assert fig.layout.xaxis.title.text == "Time (s)"  # Correct axis labels

Adding these tests would prevent regressions and improve code reliability