semvec/pypi-packages huggingface dataset:
strip-ansi 0.1.1
Strip ANSI escape sequences from a string
Requires Python: >=3.6,<4.0

strip-ansi
----------

    Strip ANSI escape sequences from a string


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

`strip-ansi` is available on `on PyPI <https://pypi.org/project/strip-ansi>`_:

.. code:: shell
   
   pip install strip_ansi

Usage
=====

.. WARNING::
   This package only supports python 3.6 and up. It may work on older versions (maybe even python 2)
   but I'm not sure.

.. code:: python

   >>> from strip_ansi import strip_ansi
   >>> strip_ansi("\033[38mLorem ipsum\033[0m")
   "Lorem ipsum"
efficientnet-lite2-pytorch-model 0.1.0
Pretrained Pytorch model file for EfficientNet Lite2

# efficientnet-lite2-pytorch-model

Pretrained Pytorch model file for EfficientNet Lite2

## Installation

```
pip install efficientnet_lite2_pytorch_model
```

## Basic Usage

```
from efficientnet_lite2_pytorch_model import EfficientnetLite2ModelFile
print( 'model file path is %s' % ( EfficientnetLite2ModelFile.get_model_file_path() ) )
```

## Actual Usage

Install the model package from [EfficientNet-Lite-PyTorch](https://github.com/ml-illustrated/EfficientNet-Lite-PyTorch):
```
pip install efficientnet_lite_pytorch
```

Load the model with pretrained weights:
```
from efficientnet_lite_pytorch import EfficientNet

from efficientnet_lite2_pytorch_model import EfficientnetLite2ModelFile
weights_path = EfficientnetLite2ModelFile.get_model_file_path()

lite2_model = EfficientNet.from_pretrained('efficientnet-lite2', weights_path = weights_path )
```

## Credits

This package was created with _Cookiecutter_ and the `audreyr/cookiecutter-pypackage` project template.

- _Cookiecutter_: https://github.com/audreyr/cookiecutter
- `audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
- `cookiecutter-ml-model-files`: https://github.com/ml-illustrated/cookiecutter-ml-model-files
endless-sky-parse 0.1.2
A working, tested Python conversion of the C++ parser for Endless Sky DataFiles
Requires Python: >=3.7

# endless-sky-parse

A working, tested Python conversion of the C++ parser for Endless Sky DataFiles

## How to install

```console
$ pip install endless-sky-parse
```

## How to use

```python
from endless_sky.datafile import DataFile

f = DataFile("/usr/share/games/endless-sky/data/human/ships.txt")
it = f.root.filter(["ship", "Blackbird"])
ship = next(it)
ship.children[0]
# DataNode(tokens=['sprite', 'ship/blackbird'], children=[])
next(ship.filter_first("description")).tokens[1][:80]
# 'The Tarazed Blackbird is a high-class passenger transport, designed to move larg'
```


## Development


```console
$ python -m venv venv && . venv/bin/activate
$ pip install -r dev_requirements.txt
```

Run tests with

```console
$ python setup.py test
```
stock-learning-rabbitmq 0.0.26
This is a package that includes all RabbitMQ handled messages by the stock-learning infrastructure
Requires Python: >=3.6

# stock-learning-rabbitmq

### Rodando o projeto
Tendo o node instalado na sua mÃ¡quina, basta rodar um `python -m pip install pika`

### Publicando uma versÃ£o no PyPI

- Inicialmente deve-se rodar os dois comandos abaixo, para instalar as bibliotecas que publicam o cÃ³digo ao PyPI
- `python -m pip install --user --upgrade setuptools wheel`
- `python -m pip install --user --upgrade twine`
- Logo em seguida devem ser rodados os dois comandos abaixo, para efetivamente gerar uma versÃ£o e upar ao PyPI
- `python setup.py sdist bdist_wheel`
- `python -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*`
- Ao ser requisitado por credenciais, digite `__token__` no username, e requisite o token de acesso aos administradores do sistema