semvec/pypi-packages huggingface dataset:
annorepo-client 0.3.2
A Python client for accessing an AnnoRepo server
License: MIT
Requires Python: <4.0,>=3.8

# annorepo-client

[![GitHub Actions](https://github.com/knaw-huc/annorepo-python-client/workflows/tests/badge.svg)](https://github.com/knaw-huc/annorepo-python-client/actions)
[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![Documentation Status](https://readthedocs.org/projects/annorepo-python-client/badge/?version=latest)](https://annorepo-python-client.readthedocs.io/en/latest/?badge=latest)
[![PyPI](https://img.shields.io/pypi/v/annorepo-client)](https://pypi.org/project/annorepo-client/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/annorepo-client)](https://pypi.org/project/annorepo-client/)

A Python client for accessing an [annorepo](https://github.com/knaw-huc/annorepo) server

## installing

### using poetry

```commandline
poetry add annorepo-client
```

### using pip

```commandline
pip install annorepo-client
```

----

[USAGE](https://annorepo-python-client.readthedocs.io/en/latest/) |
[CONTRIBUTING](CONTRIBUTING.md) |
[LICENSE](LICENSE) |
[AUTHORS](AUTHORS) |
[MAINTAINERS](MAINTAINERS) |
[CONTRIBUTORS](CONTRIBUTORS)
si4onnx 0.1.13
A Package for Selective Inference in Deep Neural Networks
License: MIT
Requires Python: >=3.10

# si4onnx

[![PyPI version](https://badge.fury.io/py/si4onnx.svg)](https://badge.fury.io/py/si4onnx)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

si4onnx is a Python package that facilitates statistical validation of Region of Interest (ROI) generated by deep learning models. The package provides a selective inference framework to quantify the statistical reliability of generated ROIs through rigorous p-value computation.
The computed p-values maintain statistical validity and enable precise control of the Type I error rate at any desired significance level. This package provides comprehensive support for deep learning models, including those developed in PyTorch and TensorFlow, through conversion to the ONNX (Open Neural Network Exchange) format.

## Requirements
Python version:
- Python 3.10 or later

Required packages:
- numpy 1.26.4 or later
- torch 2.2.0 or later
- onnx 1.16.0 or later
- sicore 2.3.0 or later

## Installation
This package can be installed using pip:
```bash
$ pip install si4onnx
```

## Usage
We provide several Jupyter notebooks demonstrating how to use the si4onnx package in our [examples directory](https://github.com/Takeuchi-Lab-SI-Group/si4onnx/blob/main/examples).
umami-preprocessing 0.2.8
ATLAS Flavour Tagging Preprocessing - Umami PreProcessing (UPP)
License: MIT
Requires Python: <3.12,>=3.10

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![codecov](https://codecov.io/gh/umami-hep/umami-preprocessing/graph/badge.svg?token=K8MJI20UZO)](https://codecov.io/gh/umami-hep/umami-preprocessing)
[![PyPI version](https://badge.fury.io/py/umami-preprocessing.svg)](https://badge.fury.io/py/umami-preprocessing)
[![docs](https://img.shields.io/badge/info-documentation-informational)](https://umami-hep.github.io/umami-preprocessing//)


# UPP: Umami PreProcessing

This is a modular preprocessing pipeline for jet tagging.
It addresses [several issues](https://gitlab.cern.ch/atlas-flavor-tagging-tools/algorithms/umami/-/issues/?label_name%5B%5D=Preprocessing) with the current umami preprocessing workflow, and uses the [`atlas-ftag-tools`](https://github.com/umami-hep/atlas-ftag-tools/) package extensively.

Documentation is found [here](https://umami-hep.github.io/umami-preprocessing/)