semvec/pypi-packages huggingface dataset:
sensirion-driver-adapters 2.4.0
Adapter classes to use sensirion_i2c_driver package
Requires Python: <4.0,>=3.8
Keywords: driver, driver-generator, sensirion

# Sensirion Drvier Adapters

This repository contains adapter classes that can be used to separate the
logic of packaging user data into a byte stream and using that byte
stream in different channels.
The classes can be used as adapters to the *sensirion_i2c_driver.I2cConnection* and
*sensirion_shdlc_driver.ShdlcSerialPort*.

The python package sensirion-driver-adapters replaces the package sensirion-i2c-adapter. The latter is deprecated and
will no longer be maintained. All functionality of sensirion-i2c-adapter is contained within the package
sensirion-driver-adapters.

## Usage

See package description in [README.rst](https://github.com/Sensirion/python-driver-adapters/blob/master/README.rst) and user manual at
https://sensirion.github.io/python-driver-adapters/.

## Development

We develop and test this driver using our company internal tools (version
control, continuous integration, code review etc.) and automatically
synchronize the `master` branch with GitHub. But this doesn't mean that we
don't respond to issues or don't accept pull requests on GitHub. In fact,
you're very welcome to open issues or create pull requests :)

### Check coding style

The coding style can be checked with [`flake8`](http://flake8.pycqa.org/):

```bash
pip install -e .[test]  # Install requirements
flake8                  # Run style check
```

### Run tests

Unit tests can be run with [`pytest`](https://pytest.org/):

```bash
pip install -e .[test]          # Install requirements
pytest                          # Run tests
```

### Build documentation

The documentation can be built with [Sphinx](http://www.sphinx-doc.org/):

```bash
python setup.py install                        # Install package
pip install -r docs/requirements.txt           # Install requirements
sphinx-versioning build docs docs/_build/html  # Build documentation
```


## License

See [LICENSE](https://github.com/Sensirion/python-driver-adapters/blob/master/LICENSE).
sensirion-driver-support-types 1.2.1
Supporting classes for Sensirion drivers
Requires Python: <4.0,>=3.8
Keywords: driver, driver generator, sensirion

# Sensirion Driver Support Types

This repository contains utility classes that are used in various sensirion python drivers.


## Usage

See package description in [README.rst](https://github.com/Sensirion/python-driver-support-types/blob/master/README.rst) and user manual at
https://sensirion.github.io/sensirion-driver-support-types/.

## Development

We develop and test this driver using our company internal tools (version
control, continuous integration, code review etc.) and automatically
synchronize the `master` branch with GitHub. But this doesn't mean that we
don't respond to issues or don't accept pull requests on GitHub. In fact,
you're very welcome to open issues or create pull requests :)

### Check coding style

The coding style can be checked with [`flake8`](http://flake8.pycqa.org/):

```bash
pip install -e .[test]  # Install requirements
flake8                  # Run style check
```

### Run tests

Unit tests can be run with [`pytest`](https://pytest.org/):

```bash
pip install -e .[test]          # Install requirements
pytest                          # Run tests
```

### Build documentation

The documentation can be built with [Sphinx](http://www.sphinx-doc.org/):

```bash
python setup.py install                        # Install package
pip install -r docs/requirements.txt           # Install requirements
sphinx-versioning build docs docs/_build/html  # Build documentation
```


## License

See [LICENSE](https://github.com/Sensirion/python-driver-support-types/blob/master/LICENSE).
ddqa 0.6.0
Datadog's QA manager for releases of GitHub repositories
Requires Python: >=3.11
Keywords: datadog, qa, testing, tooling

# Datadog QA

![example screenshot](https://raw.githubusercontent.com/DataDog/ddqa/master/docs/assets/images/creation-screen-full.png)

| | |
| --- | --- |
| CI/CD | [![CI - Test](https://github.com/DataDog/ddqa/actions/workflows/test.yml/badge.svg)](https://github.com/DataDog/ddqa/actions/workflows/test.yml) [![CD - Build](https://github.com/DataDog/ddqa/actions/workflows/build.yml/badge.svg)](https://github.com/DataDog/ddqa/actions/workflows/build.yml) |
| Docs | [![Docs - Build](https://github.com/DataDog/ddqa/actions/workflows/docs.yml/badge.svg)](https://github.com/DataDog/ddqa/actions/workflows/docs.yml) [![Docs - Publish](https://github.com/DataDog/ddqa/actions/workflows/publish-docs.yml/badge.svg)](https://github.com/DataDog/ddqa/actions/workflows/publish-docs.yml) |
| Package | [![PyPI - Version](https://img.shields.io/pypi/v/ddqa.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/ddqa/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/ddqa.svg?color=blue&label=Downloads&logo=pypi&logoColor=gold)](https://pypi.org/project/ddqa/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ddqa.svg?logo=python&label=Python&logoColor=gold)](https://pypi.org/project/ddqa/) |
| Meta | [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch) [![linting - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v0.json)](https://github.com/charliermarsh/ruff) [![code style - Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![types - Mypy](https://img.shields.io/badge/types-Mypy-blue.svg)](https://github.com/python/mypy) [![License - MIT](https://img.shields.io/badge/license-MIT-9400d3.svg)](https://spdx.org/licenses/) |

-----

DDQA is a tool for users of [Jira](https://www.atlassian.com/software/jira) to perform QA of anticipated releases of code on GitHub.

It works by finding test candidates between two Git references and translates each pull request or direct commit into a Jira issue per designated GitHub team, assigned to a semi-randomly chosen member of that team.

## Features

- Issue creation is completely configurable by each team, with overrides available at runtime
- Robust status tracking with optional filters
- Providing a [TUI](https://en.wikipedia.org/wiki/Text-based_user_interface) allows for running via SSH on other machines, which is useful when there is restricted access to Git repositories

## Documentation

The [documentation](https://datadoghq.dev/ddqa/) is made with [Material for MkDocs](https://github.com/squidfunk/mkdocs-material) and is hosted by [GitHub Pages](https://docs.github.com/en/pages).

## License

DDQA is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
qlik-sse 0.1.0
A lightweight framework for building Qlik Server-Side Extensions (SSE) that simplifies the creation, registration, and deployment of custom analytic functions for Qlik Sense and QlikView. It provides utilities for defining SSE functions, handling gRPC communication, managing metadata, and structuring scalable extension services. Designed to streamline development while offering flexibility for advanced integrations and custom logic.
Requires Python: >=3.12

# QlikSSE
A lightweight framework for building Qlik Server-Side Extensions (SSE) that simplifies the creation, registration, and deployment of custom analytic functions for Qlik Sense and QlikView. It provides utilities for defining SSE functions, handling gRPC communication, managing metadata, and structuring scalable extension services. Designed to streamline development while offering flexibility for advanced integrations and custom logic.
module-typica 0.2.12
Standard Pydantic usages & utilities
Requires Python: <4.0,>=3.10

# Description
This is a standardized model library that uses the pydantic library, which is often used in work, especially in database connection, parameter, or response in service, and other utilities. This repository provides a standardized model that can be used in various scenarios. It aims to simplify the process of defining and validating models in Python applications.

## Usages
there are several use cases that can be implemented using this library, such as some of the examples below



## Contributors

[//]: contributor-faces

<a href="https://github.com/oktapiancaw"><img src="https://avatars.githubusercontent.com/u/48079010?v=4" title="Oktapian Candra" width="80" height="80" style="border-radius: 50%"></a>

[//]: contributor-faces