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Releases: macaodha/batdetect2

v1.1.1

11 Nov 13:02
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What's Changed

Full Changelog: v1.1.0...v1.1.1

v1.1.0

11 Nov 12:01
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What's Changed

New Contributors

Full Changelog: v1.0.8...v1.1.0

v1.0.8

30 Jan 12:46
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Release Notes

This patch release focuses on maintenance, addressing compatibility. Key updates include:

  1. Package Dependency Compatibility: The minimum version requirements for package dependencies have been adjusted. batdetect2 is now compatible with PyTorch 2.

  2. Google Colab Compatibility: batdetect2 can now run in Google Colab without the necessity of installing custom versions of machine learning packages. As of 30 Jan 2024.

  3. Extended Python 3.11 Support: We've expanded our support to include Python 3.11.

v1.0.7

24 Nov 15:50
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Release Notes

This patch addresses Issue #22, where batdetect2 was inadvertently outputting detections above the Nyquist limit. We appreciate @kdarras for bringing this to our attention.

To rectify the issue, a straightforward cleaning step has been incorporated to eliminate detections surpassing the Nyquist limit.

Special thanks again to @kdarras for graciously providing one of his recordings for testing the modification. The recording has been placed in the tests/data folder (file). Additionally, a new test/test_detections.py file has been created to implement tests ensuring that batdetect's outputted detections are free from any significant errors.

v1.0.6

03 Aug 11:49
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Release v1.0.6

Spectrogram Feature Computation Issue Fix and Code Refactoring

In this release, we've addressed a critical issue related to the computation of spectrogram features, marked by the generation of erroneous csv output containing -1 values. This problem stemmed from an unanticipated shape discrepancy within the spectrogram, leading to improper feature extraction.

What's New

  • Bug Fix: The spectrogram feature computation issue has been resolved. The csv output, which was previously populated with -1 values, now accurately reflects the calculated spectrogram features.
  • Code Refactoring: To improve maintainability and extensibility, the feature extraction functions have undergone a thorough refactoring. This modularization enhances the organization of the codebase and facilitates more efficient testing and future enhancements.
  • Unit Testing: With the introduction of modularization, each feature extraction function is now independently unit tested. This ensures the reliability and correctness of these functions in isolation.
  • Integration Testing: An integration test has been added to verify the correct functioning of the CLI command with the "--spec_features" flag. This integration test validates the end-to-end behavior of the feature extraction process.

v1.0.5

11 May 13:12
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Bug Fixes:

  1. Issue #10 was fixed by making sure empty files or files without predictions are handled correctly. Added a new test case for this scenario. Modified some of the functions that post-process the model's prediction to handle the case of no predictions adequately.

v1.0.4

01 May 17:06
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Fixed the missing filename in the console output of the detect command.

v1.0.3

01 May 14:19
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Bug fixes:

  1. Time expansion factor was not being read correctly in the CLI.

v1.0.2

29 Apr 15:10
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Fixed issues when running on GPU.

v1.0.1

13 Apr 14:30
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Fixed CLI command issue.