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A library of useful modules for data analysis.

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basic_neural_processing_modules

Personal library of functions used in analyzing neural data. If you find a bug or just want to reach out: RichHakim@gmail.com

Installation

Normal installation of bnpm does not install all possible dependencies; there are some specific functions that wrap libraries that may need to be installed separately on a case-by-case basis.

Install stable version:

pip install bnpm[core]

If installing on a server or any computer without graphics/display, install using core_cv2Headless. If you accidentally installed the normal version, simply please uninstall pip uninstall opencv-contrib-python and install pip install opencv-contrib-python-headless instead.

Install development version:

pip install git+https://github.com/RichieHakim/basic_neural_processing_modules.git

import with:

import bnpm

Usage

My favorites:

  • automatic_regression module
    • Allows for easy and fast hyperparameter optimization of regression models
    • Any model with a fit and predict method can be used (e.g. sklearn and similar)
    • Uses optuna for hyperparameter optimization

Other useful functions:

  • Signal Processing:

    • timeSeries.rolling_percentile_rq_multicore
      • Fast rolling percentile calculation
    • timeSeries.event_triggered_traces
      • Fast creation of a matrix of aligned traces relative to specified event times
  • Machine Learning:

    • neural_networks module
      • Has nice RNN regression and classification classes
    • decomposition.torch_PCA
      • Fast standard PCA using PyTorch
    • similarity.orthogonalize
      • Orthogonalize a matrix relative to a set of vectors using OLS or Gram-Schmidt process
  • Miscellaneous

    • path_helpers.find_paths
      • Find paths to files and/or folders in a directory. Searches recursively using regex.
    • image_processing.play_video_cv2
      • Plays and/or saves a 3D array as a video using OpenCV
    • h5_handling.simple_save and h5_handling.simple_load
      • Simple lazy loading and saving of dictionaries as nested h5 files

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