semvec/pypi-packages huggingface dataset:
runai 0.4.1
Run:AI Python library

# Run:AI Python library

Public functional modules for Keras, TF and PyTorch

## Info

### Status

CircleCI is used for CI system:
[![CircleCI](https://circleci.com/gh/run-ai/runai/tree/master.svg?style=svg&circle-token=438045a8ae6e2d2a2253bae97ccf82dc94bcfd5b)](https://circleci.com/gh/run-ai/runai/tree/master)

### Modules

This library consists of a few pretty much independent submodules:

| Module | Name | Info |
|--------|------|------|
| Elastic | `elastic` | [Make Keras and PyTorch models elastic](runai/elastic/README.md) |
| Gradient Accumulation | `ga` | [Gradient accumulation for Keras and PyTorch optimizers](runai/ga/README.md) |
| HPO | `hpo` | [Hyperparameter optimization assistance](runai/hpo/README.md) |
| Model Parallelism | `mp` | Model-parallelism support for Keras builtin layers |
| Auto Profiler | `profiler` | Export timeline of TF/Keras models easily |
| Reporter | `reporter` | [An interface to send metrics and parameters to Promethues Push Gateway](runai/reporter/README.md) |

## Getting Started

### Installing

Install the `runai` Python library using `pip` using the following command:

```
pip install runai
```

> Make sure to use the correct `pip` installer (you might need to use `pip3` for Python3)

### Running The Tests

All tests (unit tests) can be run using the following command:

```
python -m unittest discover -s tests -v
```
turtlepie 0.0.1
Learn Python the creative way
Requires Python: >=3.6

# TurtlePie

Learn Python the creative way

## Installation

To install TurtlePie, Run this command on terminal

```
$ pip install turtlepie
```

## How to use

```python
from turtlepie import *
```

`*` for import all fuctions from turtlepie

### Example

Let's draw a square using **turtlepie**

```python
from turtlepie import *

start()
moveForward(100)
turnRight(90)
moveForward(100)
turnRight(90)
moveForward(100)
turnRight(90)
moveForward(100)
end()
```

You should use `start()` at the start and `end()` at the end

You can see a full list of funtions [here](https://gist.github.com/HirushaPramuditha/5a63ff7b4b0c767ec95190f04dd73340)
frags 0.1.5
Finding recombinations in reads
Requires Python: >=3.7
Keywords: reads,recombination,chimeric,virus,genomes

Find Recombinations Among Genomes (FRAGS)
=========================================

Find Recombinations Among Genomes (FRAGS) is a software dedicated to analyze recombinations in viral genomes.

.. image:: https://badge.fury.io/py/frags.svg
    :target: https://badge.fury.io/py/frags
    :alt: Pypi repo

.. image:: https://gitlab.pasteur.fr/nmaillet/frags/badges/master/pipeline.svg
    :target: https://gitlab.pasteur.fr/nmaillet/frags/commits/master
    :alt: Build Status

.. image:: https://gitlab.pasteur.fr/nmaillet/frags/badges/master/coverage.svg
    :target: https://gitlab.pasteur.fr/nmaillet/frags/commits/master
    :alt: Coverage Report

.. image:: https://readthedocs.org/projects/frags/badge/?version=latest
    :target: https://frags.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

:note: FRAGS is tested with Gitlab Ci for the following Python version: 3.7 to 3.8
:issues: Please use https://gitlab.pasteur.fr/nmaillet/frags



Overview
========

Find Recombinations Among Genomes (FRAGS), is a standalone software dedicated to identify reads coming from recombination events.

FRAGS is a python tool taking fasta/fastq files of reads as input and one or two reference genomes. It then identifies chimeric reads (reads composed of non-adjacent fragsments, coming either from on one or the two references genomes) and potential breakpoints (insert between fragsments). Optionally, breakpoints can then be Blasted again the host genome, if provided.

Main results are in CSV files. Three CSV files contain respectively results for reads that have fragsments coming from the first reference only, the second reference only or both references. Another CSV file contains headers of reads that did not match anywhere. Finally, three files are produced when using Blast: ``breakpoints.fasta``, containing the breakpoint portions to Blast, ``res_blast.csv`` containing the result of Blast on ``breakpoints.csv`` and ``compressed.fasta``, a compressed version of ``res_blast.csv`` keeping only results of the best e-value/bit-score for each input sequences and produce a fasta-like file.

FRAGS follows the standards for software development with continuous integration on Gitlab (https://gitlab.pasteur.fr/nmaillet/frags) and automatic on-line documentation (https://frags.readthedocs.io/en/latest/).



Installation
============

In order to install FRAGS, you can use **pip**:

.. code-block:: shell

    pip3 install frags

This command installs FRAGS and its Python dependencies.


Usage
=====

From the command line:

.. code-block:: shell

    frags --help