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We made a significant rework on PARM and now introduce **version 0.1.0**. Key changes include transitioning from individual model weight files to a directory-based approach for managing model folds (now each PARM model is trained five times/folds), adding support for batch processing, and introducing new parameters like `filter_size` and `type_loss` for user-trained models. ### Updates to Model Loading and Management: * Replaced the `model_weights` parameter with `model_directory` in `PARM_mutagenesis` and `PARM_predict` functions to dynamically load all model folds from a specified directory. These two functions now take the average of all folds to output the predictions and the mutagenesis matrix. * Integrated support for the `filter_size` parameter in `load_PARM` and related functions, allowing customization of convolutional filter sizes. ### Improvements to `PARM_predict`: * Introduced `n_seqs_per_batch` and `store_sequence` parameters in `PARM_predict` to enable batch processing and control output verbosity. Predictions are now averaged across all folds for each sequence. * Enhanced the `get_prediction` function to support batch predictions. ### Code Quality and Consistency: * Standardized parameter formatting and fixed minor inconsistencies in function signatures and docstrings. * Improved error messages for better debugging, such as mismatched model prefixes and invalid motif scanning types. ### Minor Fixes: * Corrected string formatting in logging statements and ensured consistent use of single quotes.
Improve support for user-trained PARM models
add training data
Update README.md to use an env, remove Anaconda
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