semvec/pypi-packages huggingface dataset:
fastcmd 0.0.2
Organize and run frequently used commands
Requires Python: >=3.8,<3.12

<p align="center">
  <h1>Skate</h1>
</p>

---

[![Release](https://img.shields.io/github/v/release/fpgmaas/skate)](https://img.shields.io/github/v/release/fpgmaas/skate)
[![Build status](https://img.shields.io/github/actions/workflow/status/fpgmaas/skate/main.yml?branch=main)](https://github.com/fpgmaas/skate/actions/workflows/main.yml?query=branch%3Amain)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/skate)](https://pypi.org/project/skate/)
[![codecov](https://codecov.io/gh/fpgmaas/skate/branch/main/graph/badge.svg)](https://codecov.io/gh/fpgmaas/skate)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/skate)](https://pypistats.org/packages/skate)
[![License](https://img.shields.io/github/license/fpgmaas/skate)](https://img.shields.io/github/license/fpgmaas/skate)

_skate_ is a command line utility to help you organise and quickly run frequently used commands.

<p align="center">
<img src="docs/skate.gif"/>
</p>

## Quickstart

### Installation

_skate_ can be installed by running

```shell
pip install skate
```

To get started, run

```shell
skate init
```

which will prompt to add a `skate/skate.yaml` file in the user's home directory for global commands, and/or a `skate.yaml` file in the current directory for commands specific to the current project. 

To use _skate_ to run any of the pre-configured commands, simply run

```
skate
```

For more information, see the [documentation](https://fpgmaas.github.io/skate/).

## Configuration

_skate_ can look for configuration in the following two locations:

- In a `skate.yaml` file in the current directory
- In any `.yaml` file in the the global configuration directory, which is defaulted to `~/skate`, but which can be overridden with the environment variable `SKATE_HOME`.

An example `.yaml` file could look as follows:

```yaml
test:
  my-command:
    cmd: "echo Hello! My name is: $name. My favourite fruit is: $fruit"
    echo: false
    args:
      - name
      - fruit: apple
```

Which adds the command group `test` wth a single command called `my-command`. When `my-command` is selected to be run, _skate_ prompts the user for `name` and `fruit` before running the command specified in `cmd`, where `fruit` is defaulted to `apple` if the user does not give any input.

For more details, see the [configuration](https://fpgmaas.github.io/skate/configuration) section of the documentation.

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
stack9 0.0.2
Organize and run frequently used commands
Requires Python: >=3.8,<3.12

<p align="center">
  <h1>Skate</h1>
</p>

---

[![Release](https://img.shields.io/github/v/release/fpgmaas/skate)](https://img.shields.io/github/v/release/fpgmaas/skate)
[![Build status](https://img.shields.io/github/actions/workflow/status/fpgmaas/skate/main.yml?branch=main)](https://github.com/fpgmaas/skate/actions/workflows/main.yml?query=branch%3Amain)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/skate)](https://pypi.org/project/skate/)
[![codecov](https://codecov.io/gh/fpgmaas/skate/branch/main/graph/badge.svg)](https://codecov.io/gh/fpgmaas/skate)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/skate)](https://pypistats.org/packages/skate)
[![License](https://img.shields.io/github/license/fpgmaas/skate)](https://img.shields.io/github/license/fpgmaas/skate)

_skate_ is a command line utility to help you organise and quickly run frequently used commands.

<p align="center">
<img src="docs/skate.gif"/>
</p>

## Quickstart

### Installation

_skate_ can be installed by running

```shell
pip install skate
```

To get started, run

```shell
skate init
```

which will prompt to add a `skate/skate.yaml` file in the user's home directory for global commands, and/or a `skate.yaml` file in the current directory for commands specific to the current project. 

To use _skate_ to run any of the pre-configured commands, simply run

```
skate
```

For more information, see the [documentation](https://fpgmaas.github.io/skate/).

## Configuration

_skate_ can look for configuration in the following two locations:

- In a `skate.yaml` file in the current directory
- In any `.yaml` file in the the global configuration directory, which is defaulted to `~/skate`, but which can be overridden with the environment variable `SKATE_HOME`.

An example `.yaml` file could look as follows:

```yaml
test:
  my-command:
    cmd: "echo Hello! My name is: $name. My favourite fruit is: $fruit"
    echo: false
    args:
      - name
      - fruit: apple
```

Which adds the command group `test` wth a single command called `my-command`. When `my-command` is selected to be run, _skate_ prompts the user for `name` and `fruit` before running the command specified in `cmd`, where `fruit` is defaulted to `apple` if the user does not give any input.

For more details, see the [configuration](https://fpgmaas.github.io/skate/configuration) section of the documentation.

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
namesfr 0.0.2
Random french name generator
Requires Python: >=3.7

namesfr
=======

Random french name generator


Usage
-----

Namesfr can be used as a command line utility or imported as a Python package.

Command Line Usage
~~~~~~~~~~~~~~~~~~
To use the script from the command line:

.. code-block:: bash

    $ namesfr
    Alexandre Crouzier

Python Package Usage
~~~~~~~~~~~~~~~~~~~~
Here are examples of all current features:

.. code-block:: pycon

    >>> import namesfr
    >>> namesfr.get_full_name()
    u'Aurélien Germond'
    >>> namesfr.get_full_name(gender='male')
    u'Frédéric Castan'
    >>> namesfr.get_first_name()
    'Thierry'
    >>> namesfr.get_first_name(gender='female')
    'Iris'
    >>> namesfr.get_last_name()
    'Faust'


License
-------

This project is released under an `MIT License`_.

Data in the following files are public domain (governement data source 2018):

- dist.all.last
- dist.female.first
- dist.male.first

.. _mit license: http://th.mit-license.org/2013
de-cloud-files 1.0.1
Abstractions around cloud file interfaces (WIP)
Requires Python: >=3.8

League Event Interface
================

Create custom callbacks to various in game events made possible with the League Client API

## Original
Heavily borrows from LeagueOfEvents - [check out the original LeagueOfEvents package here](https://pypi.org/project/LeagueOfEvents/)

## Installing

Install the [PyPI Package](https://pypi.org/project/LeagueEventInterface/):

    pip install LeagueEventInterface

## Examples
Basic example:
```py
import leagueeventinterface

def pentakill_handler():
    print("Yaay! A Penta!")

leagueeventinterface.subscribe("PlayerPentakill", pentakill_handler)
```

# Events
TODO
luga 0.2.7
Sensing the language of the text using Machine Learning
Requires Python: >=3.7.1,<4.0.0

Luga
==============================
- A blazing fast language detection using fastText's language models.

![Languages](https://user-images.githubusercontent.com/14926709/143822756-8fd6437f-6c99-4a9f-9718-37f086955583.png)


_Luga_ is a Swahili word for language. [fastText](https://github.com/facebookresearch/fastText) provides blazing-fast
language detection tool. Lamentably, [fastText's](https://fasttext.cc/docs/en/support.html) API is beauty-less, and the documentation is a bit fuzzy.
It is also funky that we have to manually [download](https://fasttext.cc/docs/en/language-identification.html) and load models.

Here is where _luga_ comes in. We abstract unnecessary steps and allow you to do precisely one thing: detecting text language.

#### cover image
[Stand Still. Stay Silent](http://sssscomic.com/index.php) - The relationships between Indo-European and Uralic languages by Minna Sundberg.

### Show, don't tell
![Luga in Action](example.gif)


### Installation
```bash
python -m pip install -U luga
```

### Usage:
⚠️ Note: The first usage downloads the model for you. It will take a bit longer to import depending on internet speed.
It is done only once.

```python
from luga import language

print(language("the world ended yesterday"))

# Language(name='en', score=0.9804665446281433)
```


With the list of texts, we can create a mask for a filtering pipeline, that can be used, for example, with DataFrames

```python
from luga import language
import pandas as pd

examples = ["Jeg har ikke en rød reje", "Det blæser en halv pelican", "We are not robots yet"]
languages(texts=examples, only_language=True, to_array=True) == "en"
# output
# array([False, False, True])

dataf = pd.DataFrame({"text": examples})
dataf.loc[lambda d: languages(texts=d["text"].to_list(), only_language=True, to_array=True) == "en"]
# output
# 2    We are not robots yet
# Name: text, dtype: object
```

### Without Luga:

Download the model
```bash
wget https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O /tmp/lid.176.bin
```

Load and use
```python
import fasttext

PATH_TO_MODEL = '/tmp/lid.176.bin'
fmodel = fasttext.load_model(PATH_TO_MODEL)
fmodel.predict(["the world has ended yesterday"])

# ([['__label__en']], [array([0.98046654], dtype=float32)])
```


### Dev:

```bash
poetry run pre-commit install
```

## Release Flow
```bash
# assumes git push is completed
git tag -l #  lists tags
git tag v*.*.* # Major.Minor.Fix
git push origin tag v*.*.*

# to delete tag:
git tag -d v*.*.* && git push origin tag -d v*.*.*

# change project_toml and __init__.py to reflect new version
```

#### TODO:
- [X] refactor artifacts.py
- [X] auto checkers with pre-commit | invoke
- [X] write more tests
- [X] write github actions
- [ ] create an intelligent data checker (a fast List[str], what do with none strings)
- [ ] make it faster with Cython
- [ ] get NDArray typing correctly
- [ ] fix `artifacts.py` line 111 cast to List[str] that causes issues
- [ ] remove nptyping when more packages move to numpy > 1.21