semvec/pypi-packages huggingface dataset:
mednoise 1.2.4
mednoise is a python package that contains algorithms to handle and edit large amounts of image-based metadata to remove irrelevant noise and enhance the accuracy of machine learning and deep learning models for scientific research.
Requires Python: >=3.6

# mednoise

[![Downloads](https://pepy.tech/badge/mednoise)](https://pepy.tech/project/mednoise)
[![Version](https://img.shields.io/badge/version-1.2.4-orange)](https://pepy.tech/project/mednoise)
[![Dependencies](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen)](https://pepy.tech/project/mednoise)


##### Summary 

mednoise is the fundamental package needed for medical image (pre)processing with Python.

- **Website:** https://mednoise.github.io
- **PyPI:** https://pypi.org/project/mednoise/
- **Documentation:** https://mednoise.github.io/documentation
- **Source code:** https://github.com/mednoise/mednoise
- **Bug reports:** https://github.com/mednoise/mednoise/issues

It contains:
- various algorithms to handle and pre-process large amounts of medical image metadata
- a highly tuneable interface for a variety of  usecases
- an inference-compatible structure that allows for easy integration into workflow/pipeline management systems (ex. airflow, luigi, snakemake)

##### Installation:

mednoise uses `pip`.  The installation can be run with:

    pip install mednoise
   
##### License

mednoise is licensed under the `Apache 2.0 License`
paper-network 0.0.1
Gibbs Sampler and other functions for PAPER (Preferential Attachment Plus Erdos--Renyi) model for random networks

PAPER
====

Implementation of Gibbs sampler for computing posterior root
probabilities under the PAPER (preferential attachment plus
Erdos--Renyi) model for random networks.

See details in the arXiv paper: https://arxiv.org/abs/2107.00153

[Documentation]

[Documentation]: https://nineisprime.github.io/PAPER/

Installation
-------------

	$ pip install PAPER


Usage
------

	>>> from PAPER.gibbsSampling import gibbsToConv
	>>> from PAPER.tree_tools import createNoisyGraph
	>>> graf = createNoisyGraph(n=100, m=200, alpha=0, beta=1, K=1)[0]
	>>> mcmc_out = gibbsSampling.gibbsToConv(graf, DP=False, method="full",
                       K=1, tol=0.1)
					   
See example.py for interpreting the inference output. Some sample
network datasets are provided.

Notes
------
* No preprocessing required on input graph. If the
  input graph is disconnected, the largest connected component is
  used.
* The algorithm
  performs roughly 1 outer Gibbs iteration in 1 second on a graph with
  10,000 edges. The number of iterations to convergence depends on the
  input graph.
template-python-project 0.6.0
Template test for python bin

# template_python_project

[![master](https://github.com/ulphidius/template_python_project/actions/workflows/master-push.yml/badge.svg?branch=master)](https://github.com/ulphidius/template_python_project/actions/workflows/master-push.yml) [![Coverage Status](https://coveralls.io/repos/github/ulphidius/template_python_project/badge.svg?branch=master)](https://coveralls.io/github/ulphidius/template_python_project?branch=master)


This is a sample application for test deployment on pypi.
You can use it as reference but **i don't recommend its use**.

## Install

For install this app you need to have **setuptools** in your python environment.

```sh
# We assume that pip is install and is connected to python 3
pip install --editable .
# OR
pip install template-python-project

# For install the project with development tool
pip install --editable ".[development]"
```

## Use

For further information use *--help* option.

```sh
template-python-project compute --compute-type MUL --first 1 --second 2
template-python-project concat --first hello --second world
```
## Linter

This application use the linter **pylint**.
**pylint** is installed with development tools.

```sh
# For execute tests we assume that the development dependencies are install
pylint template_python_project
```

## Test

This applciation use the test framework **pytest**.
**pytest** is installed with development tools.

```sh
# For execute tests we assume that the development dependencies are install
pytest ./test
```

## Deploy

You need to install **twine** for deploy on pypi.
You need to install **wheel** to have a valid binary for deployment.

```sh
# We assume that pip is install and is connected to python 3
pip install twine
pip install wheel
```

After the installation of the dependencies.
You need to use **setup.py** for build the project.
You need to use the **twine** command for the deployment.

```sh
# We assume that pip is install and is connected to python 3
# We assume that python is install and is a symbolic link to python 3
python setup.py bdist_wheel
twine upload dist/*
```