semvec/pypi-packages huggingface dataset:
AWAKE-ANALYSIS-TOOLS 0.0.2
AWAKE ANALYSIS LIBRARY

Analysis package for AWAKE data.

Getting started:
1. Make a scratch directory
   mkdir scratch
2. Copy the example files to the scratch directory
   cp examples/cutInput_example.txt scratch/
   cp examples/NB_example.ipynb scratch/
3. Start the Python3 Jupyter Notebook from your scratch directory
pyawake 0.0.5
A pyawake library

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https://gitlab.cern.ch/AWAKE/pyawake
An open-source library that reads a large number of HDF files and builds a database.API for searching and loading multiple datasets, intelligently identifying dataset dependent parameters, visualize and analyze images to meet the requirements of scientists at AWAKE and port the existing analysis. This project is part of CERN-HSF GSoC project. Author : Aman Singh Thakur, Spencer J Gessner
loras 0.0.22b0
A small package for LoRAS oversampling

# LoRAS
Localized Randomized Affine Shadowsampling (LoRAS) oversampling technique

## Installation
The latest version is available on PyPi and installable with the command: `pip install loras`

## Usage
There is just one method `fit_resample(maj_class_points, min_class_points, k, num_shadow_points, list_sigma_f, num_generated_points, num_aff_comb, random_state=42)`

There are two mandatory inputs:  

- `maj_class_points` : Majority class parent data points which is a non-empty list containing numpy arrays acting as points
- `min_class_points` : Minority class parent data points which is a non-empty list containing numpy arrays acting as points   

There are also optional parameters:

- `k` : Number of nearest neighbours to be considered per parent data point (default value: `8 if len(min_class_points)<100 else 30`)
- `num_shadow_points` : Number of generated shadowsamples per parent data point (default value: `ceil(2*num_aff_comb / k)`)
- `list_sigma_f` : List of standard deviations for normal distributions for adding noise to each feature (default value: `[0.005, ... , 0.005]`)
- `num_generated_points` : Number of shadow points to be chosen for a random affine combination (default value: `ceil((len(maj_class_points) + len(min_class_points)) / len(min_class_points))`)
- `num_aff_comb` : Number of generated LoRAS points for each nearest neighbours group (default value: `min_class_points.shape[1]`)   

Output:

 - `min_class_points::oversampled_set` : Concatenation of original data points and oversampled ones