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