semvec/pypi-packages huggingface dataset:
opsramp-analytics-utils-package 1.0.1
OpsRamp Analytics SDK
License: MIT
Requires Python: >=3.6

## OpsRamp Analytics Utilities

This is the SDK for writing OpsRamp analytics apps. It is based on [dash](https://plotly.com/dash/), and it has a number of utility functions.

It contains [analysis wrapper project](https://github.com/opsramp/analysis-wrapper).

It is published on [Pypi](https://pypi.org/project/opsramp-analytics-utils/)

#### How to publish on Pypi

After make updates on SDK, modify the version in _setup.py_.

```
python setup.py sdist bdist_wheel
python -m twine upload dist/*

Note: if above command not works, then use below command
python -m twine upload --skip-existing dist/*
```

- To upgrade the sdk for your app
```
pip install --no-cache-dir --upgrade opsramp-analytics-utils
```
grand-challenge-metrics 0.6.0
useful functions for evaluating machine learning models in the context of medical imaging
License: MIT
Requires Python: <4.0,>=3.10

# Grand Challenge Metrics

[![Build Status](https://github.com/DIAGNijmegen/rse-grand-challenge-metrics/workflows/CI/badge.svg?branch=main)](https://github.com/DIAGNijmegen/rse-grand-challenge-metrics/actions?query=workflow%3ACI+branch%3Amain)
[![PyPI version](https://badge.fury.io/py/grand-challenge-metrics.svg)](https://badge.fury.io/py/grand-challenge-metrics)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/grand-challenge-metrics)](https://pypi.org/project/grand-challenge-metrics/)
[![Documentation Status](https://img.shields.io/badge/docs-passing-4a4c4c1.svg)](https://comic.github.io/grand-challenge-metrics/)
[![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

Grand Challenge Metrics contains useful functions for evaluating machine learning models in the context of medical imaging.

  - Free software: MIT license
  - Documentation: <https://diagnijmegen.github.io/rse-grand-challenge-metrics/>.

## Features

  - Bounding box annotations with Jaccard Index calculations
  - Calculations of bootstrapped ROC curves
  - Scoring for detection tasks
  - Efficient calculation of confusion matrices, jaccard scores, dice scores, hausdorff distances,
    absolute volume differences, and relative volume differences

## Getting Started

[Grand Challenge Metrics](https://github.com/DIAGNijmegen/rse-grand-challenge-metrics) requires Python 3.10 or above, and can be installed from `pip`.
Please see the [Getting Started](https://diagnijmegen.github.io/rse-grand-challenge-metricsusage.html) documentation for more details.