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- Generate time arrays with float64 precision before converting to float32
- This prevents precision loss that causes non-monotonic time arrays
- Affects datasets with >10 million samples (e.g., 200M points at 20 MHz)
- Changed empty time array warning to raise RuntimeError for better error handling
- Applied fix to both rd() and rd_chunked() functions
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- Simplified monotonic check to just require positive differences
- Removed machine epsilon tolerance that was too strict for ns-scale timing
- Increased marker size in diffusion scatter plots
- Added version bump to 2.1.0
- Fixed edge cases in ACF calculation and diffusion processing
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- Test that resampling preserves event detection
- Test uniform time arrays after resampling
- All tests passing
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Add target_sampling_interval parameter to detect_from_wfm() for
downsampling oversampled data.
Minor version bump for new feature.
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- Add transivent.resample subpackage for signal preprocessing
- Implement average_downsample() for block averaging downsampling
- Add downsample_to_interval() for interval-based downsampling
- Add target_sampling_interval parameter to detect_from_wfm()
- Preserves signal amplitude and improves SNR through averaging
- Creates uniform time arrays to avoid validation warnings
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- Test absolute filename with data_path (main bug fix scenario)
- Test relative filename with data_path
- Test missing XML sidecar error handling
- Added pytest as dev dependency
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When rd() is called with a data_path, it needs to pass this through to
get_waveform_params() so the XML sidecar file can be found. Previously,
if the filename was already an absolute path, data_path would be ignored,
causing FileNotFoundError when XML sidecar was in a different directory.
This fix ensures that:
- If data_path is provided, it's always used with the basename
- If data_path is None, the file's directory is used as data_path
- This maintains backward compatibility while fixing the path resolution
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- example.py: Update to use detect_from_wfm() instead of process_file()
- example_diffusion.py: Import diffusion functions from event_processor submodule
- Both examples now correctly use the v2.0.0 API structure
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The configure_logging function was accidentally removed from the public API
in __init__.py but was still being used in examples. This restores it to
maintain backward compatibility.
Fixes issue where examples would fail with ImportError.
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Major API refactoring with simplified public interface.
- Added EventProcessor for high-level event processing workflow
- New utility functions for data preprocessing
- Additional example scripts for different use cases
- Comprehensive test suite
- Updated documentation with migration guide
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Event detection and analysis pipeline for transient events in time-series data.
- Event detection based on SNR thresholds
- Configurable background estimation and noise analysis
- Visualization with scopekit integration
- Chunked processing for large files
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