Personal library of functions used in analyzing neural data. If you find a bug or just want to reach out: [email protected]
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
My favorites:
automatic_regression
module- Allows for easy and fast hyperparameter optimization of regression models
- Any model with a
fit
andpredict
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
andh5_handling.simple_load
- Simple lazy loading and saving of dictionaries as nested h5 files