semvec/pypi-packages huggingface dataset:
ckmutil 1.2.0
Useful functions for dealing with quark and lepton mixing matrices.
License: MIT

<a href="https://travis-ci.org/DavidMStraub/ckmutil">![Build Status](https://travis-ci.org/DavidMStraub/ckmutil.svg?branch=master)</a> [![Coverage Status](https://coveralls.io/repos/github/DavidMStraub/ckmutil/badge.svg)](https://coveralls.io/github/DavidMStraub/ckmutil) 

# ckmutil

A package containing useful functions to deal with the Cabibbo-Kobayashi-Maskawa (CKM) quark mixing matrix or the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) lepton mixing matrix in high energy physics.
django-lti-toolbox 2.0.0
A Django application to build LTI Tool Providers
License: MIT
Keywords: Django, LTI

# Django-lti-toolbox, a Django application to build LTI Tool Providers

## Overview

`django-lti-toolbox` is a django application that makes it easier for you to create [LTI](https://en.wikipedia.org/wiki/Learning_Tools_Interoperability) Tool Providers web applications.

This is a set of tools that let you manage LTI requests the way you want.

It is based on top of the great [OAuthLib](https://github.com/oauthlib/oauthlib) library.

## Features

- Verify LTI launch requests
- Base views to build your own LTI launch request handlers
- Sample Django authentication backend
- Manage your LTI consumers from django admin
- Demo project to quickly see it in action

## Try it with our demo project !

- Clone this repository (`git clone https://github.com/openfun/django-lti-toolbox.git`)

- `cd django-lti-toolbox`

- `make bootstrap` to initialize the dev environment

- `make run` to start the services

- Go to [http://localhost:8090/](http://localhost:8090/) and try the demo LTI consumer

- Watch django logs with `make logs`

## Contributing

This project is intended to be community-driven, so please, do not hesitate to
get in touch if you have any question related to our implementation or design
decisions.

We try to raise our code quality standards and expect contributors to follow
the recommandations from our
[handbook](https://openfun.gitbooks.io/handbook/content).

## License

This work is released under the MIT License (see [LICENSE](./LICENSE)).
generate-systemd-timer 0.1.3
Generate a systemd unit.timer and unit.service pair
License: MIT
Requires Python: >=3.8

# Systemd generator for timer units

Generates systemd `.timer` and `.service` units to more easily add cron-like tasks to your system.

You'll still have to copy them into the right place (either `/etc/systemd/system`
or `$HOME/.config/systemd/user`) and reload systemd using `systemctl daemon-reload`.

## Usage

```sh
generate-systemd-timer unit-name
# Now two editors will pop up to allow you to customize
# Afterwards you'll find unit-name.service and unit-name.timer in the current folder.
```
investimentpy 1.0.3
Uma biblioteca para análise de investimentos
License: MIT

# InvestPy

InvestPy é uma biblioteca Python para análise de investimentos. Ela permite obter dados de ações, calcular indicadores financeiros e visualizar gráficos de desempenho.

## Funcionalidades

- Obter dados históricos de ações usando a API do Alpha Vantage.
- Calcular retorno diário das ações.
- Visualizar gráficos de preços de fechamento das ações.

## Requisitos

- Python 3.7
- pandas
- matplotlib
- requests
- pytest

## Configuração

Obtenha uma chave de API do [Alpha Vantage](https://www.alphavantage.co/support/#api-key) e substitua `'your_api_key'` pela sua chave de API nos exemplos abaixo.


## Uso

Depois de importar a biblioteca com o comando `pip install investimentpy==1.0.0` e já estiver com a sua KEY da *Alpha Vantage*, você pode criar um código como no exemplo a seguir:

```shell
import investpy

api_key = 'SUA_KEY'

# Obter dados de ação
df = investpy.obter_dados_acao('AAPL', api_key)

# Calcular retorno diário
df = investpy.calcular_retorno_diario(df)

# Plotar dados da ação
investpy.plotar_dados_acao(df)

```

Resultado:
![resultado calculo retorno diario](https://raw.githubusercontent.com/tadrianonet/investimentpy/main/exemplo.png)


## Contribuindo

1. Faça um fork do repositório
2. Crie uma nova branch (`git checkout -b feature/foobar`)
3. Commit suas mudanças (`git commit -am 'Add some feature'`)
4. Push para a branch (`git push origin feature/foobar`)
5. Abra um Pull Request

## Licença

Distribuído sob a licença MIT. Veja `LICENSE` para mais informações.
assertpy-stubs 1.1.2
Mypy|Pyright plugin and stubs for assertpy
License: MIT
Requires Python: >=3.9
Keywords: mypy, pyright, assertpy, stubs

[![PyPI version](https://badge.fury.io/py/assertpy-stubs.svg)](https://badge.fury.io/py/assertpy-stubs)
[![Code on Github](https://img.shields.io/badge/Code-GitHub-brightgreen)](https://github.com/socgnachilderic/assertpy-stubs)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
![GitHub last commit](https://img.shields.io/github/last-commit/socgnachilderic/assertpy-stubs)

# assertpy-stubs

Add types for [assertpy](https://pypi.org/project/assertpy/) for mypy or pyright.

## Installation

```
$ pip install assertpy-stubs
```

## Usage

Mypy or pyright will automatically use the type annotations in this package, once it is
installed. You just need to annotate your code:

```python
from assertpy import assert_that


def test_something():
    assert_that(1 + 2).is_equal_to(3)
    assert_that("foobar").is_length(6).starts_with("foo").ends_with("bar")
    assert_that(["a", "b", "c"]).contains("a").does_not_contain("x")
```

For general hints how to use type annotations, please read [Type Annotations in Python 3.8](https://medium.com/analytics-vidhya/type-annotations-in-python-3-8-3b401384403d)