[](https://badge.fury.io/py/bnpm)
[](https://pepy.tech/project/bnpm)
[](https://github.com/RichieHakim/basic_neural_processing_modules/)
# 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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