Contributing

We are thrilled to have you join in making this project even better. Please feel free to browse through the resources and guidelines provided here, and let us know if there is anything specific you would like to contribute or discuss.

If you would like to help to develop this package you can skim through the to-do list below as well as the contribution guidelines. Just fork the project, add your code and send a pull request. We are always happy to get some help !

If you encountered an issue using the chirpdetector, feel free to open an issue here.

Contributors guidelines

I try our best to adhere to good coding practices and catch up on writing tests for this package. As I am currently the only one working on it, here is some documentation of the development packages I use:

• pre-commit for pre-commit hooks
• pytest and pytest-coverage for unit tests
• ruff for linting and formatting
• pyright for static type checking

Before every commit, a pre-commit hook runs all these packages on the code base and refuses a push if errors are raised. If you want to contribute, please make sure that your code is proberly formatted and run the tests before issuing a pull request. The formatting guidelines should be automatically picked up by your ruff installaton from the pyproject.toml file.

To Do

After the first release, this section will be removed an tasks will be organized as github issues. Until them, if you fixed something, please check it off on this list before opening a pull request.

• Write an assingment benchmarking
• Write an assignmen GUI to create a ground truth
• Try a random forest classifier on PCAed envelope extractions to assign chirps
• Finish a script to analyze the Json dumps from the training loop
• Update all the docstrings after refactoring.
• Move hardcoded params from assignment algo into config.toml
• Split the messy training loop into functions.
• Remove all pyright warnings.
• Build github actions CI/CD pipeline for codecov etc.
• Implement multiprocessing in main detection loop: Compute a batch of spectrograms parallely and pipe them all through the detector. And do this simulatenously from multiple cores (if the GPU can receive tensors from multiple cores). - Note: Multiprocessing increased the execution time due to the back and forth between cpu and gpu (at least this is what google sais.) But I batched detection at least.
• Check execution time for all the detect functions, got really slow after refactoring for some reason.
• Fix make test, fails after ruff run
• Refactor train, detect, convert. All into much smaller functions. Move accesory functions to utils
• Make complete codebase pass ruff
• Move the dataconverter from gridtools to chirpdetector
• Extend the dataconverter to just output the spectrograms so that hand-labelling can be done in a separate step
• Add a main script so that the cli is chirpdetector <task> --<flag> <args>
• Improve simulation of chirps to include more realistic noise, undershoot and maybe even phasic-tonic evolution of the frequency of the big chirps
• make the copyconfig script more
• start writing the chirp assignment algorithm
• Move all the pprinting and logging constructors to a separate module and build a unified console object so that saving logs to file is easier, also log to file as well
• Add label-studio
• Supply scripts to convert completely unannotated or partially annotated data to the label-studio format to make manual labeling easier
• Make possible to output detections as a yolo dataset
• Look up how to convert a yolo dataset to a label-studio input so we can label pre-annotated data, facilitating a full human-in-the-loop approach
• Add augmentation transforms to the dataset class and add augmentations to the simulation in gridtools. Note to this: Unnessecary, using real data.
• Change bbox to actual yolo format, not the weird one I made up (which is x1, y1, x2, y2 instead of x1, y1, w, h). This is why the label-studio export is not working.
• Port cli to click, works better
• Try clustering the detected chirp windows on a spectrogram, could be interesting