semvec/pypi-packages huggingface dataset:
cotyledon 2.2.0
Cotyledon provides a framework for defining long-running services.
Requires Python: >=3.9

===============================
Cotyledon
===============================

.. image:: https://img.shields.io/pypi/v/cotyledon.svg
   :target: https://pypi.python.org/pypi/cotyledon/
   :alt: Latest Version

.. image:: https://img.shields.io/pypi/dm/cotyledon.svg
   :target: https://pypi.python.org/pypi/cotyledon/
   :alt: Downloads

Cotyledon provides a framework for defining long-running services.

It provides handling of Unix signals, spawning of workers, supervision of
children processes, daemon reloading, sd-notify, rate limiting for worker
spawning, and more.

* Free software: Apache license
* Documentation: http://cotyledon.readthedocs.org/
* Source: https://github.com/sileht/cotyledon
* Bugs: https://github.com/sileht/cotyledon/issues

Why Cotyledon
-------------

This library is mainly used in OpenStack Telemetry projects, in replacement of
*oslo.service*. However, as *oslo.service* depends on *eventlet*, a different
library was needed for project that do not need it. When an application do not
monkeypatch the Python standard library anymore, greenlets do not in timely
fashion. That made other libraries such as `Tooz
<http://docs.openstack.org/developer/tooz/>`_ or `oslo.messaging
<http://docs.openstack.org/developer/oslo.messaging/>`_ to fail with e.g. their
heartbeat systems. Also, processes would not exist as expected due to
greenpipes never being processed.

*oslo.service* is actually written on top of eventlet to provide two main
features:

* periodic tasks
* workers processes management

The first feature was replaced by another library called `futurist
<http://docs.openstack.org/developer/futurist/>`_ and the second feature is
superseded by *Cotyledon*.

Unlike *oslo.service*, **Cotyledon** have:

* The same code path when workers=1 and workers>=2
* Reload API (on SIGHUP) hooks work in case of you don't want to restarting children
* A separated API for children process termination and for master process termination
* Seatbelt to ensure only one service workers manager run at a time.
* Is signal concurrency safe.
* Support non posix platform, because it's built on top of multiprocessing module
  instead of os.fork
* Provide functional testing

And doesn't:

* facilitate the creation of wsgi application (sockets sharing between parent
  and children process). Because too many wsgi webserver already exists.

*oslo.service* being impossible to fix and bringing an heavy dependency on
eventlet, **Cotyledon** appeared.
blpapipd 0.2.2
Extract Bloomberg (BBG) data into pandas timeseries object w/ caching.  Leverages blpapi desktop api in Python
Requires Python: >=3.6

# blpapipd

Extract Bloomberg (BBG) data into pandas timeseries object w/ caching.  Leverages blpapi desktop api in Python
Use accompanying plot_utilities to plot results
See examples/barc_hy_vs_ig.ipynb for a use case of the package


To install prerequisite blpapi package, use command below:


python -m pip install --index-url=https://bcms.bloomberg.com/pip/simple blpapi


For more detail, see github project https://github.com/tnicolas/blpapipd

Send inquiries to t@steinbachs.us
apache-airflow-provider-ovhcloud-ai 1.0.0
Apache Airflow Provider for OVHcloud AI
Requires Python: >=3.8
Keywords: airflow, provider, ovhcloud, ai, llm, openai

# Apache Airflow Provider for OVHcloud AI

![Hero Image](docs/assets/header.png)

This package provides Apache Airflow integration with [OVHcloud AI products](https://www.ovhcloud.com/en/public-cloud/ai-endpoints/): 
- **AI Endpoints**: an OpenAI-compatible API service for running LLM inference
- **AI Training**: managed training jobs on GPU clusters with full lifecycle management

## Documentation

The documentation can be found [here](https://ovh.github.io/apache-airflow-provider-ovhcloud-ai). You will find all informations on how to create your DAGs and automations for our AI products in Apache Airflow.

## Development

### Setup Development Environment

```bash
# Clone the repository
git clone https://github.com/ovh/apache-airflow-provider-ovhcloud-ai.git
cd apache-airflow-provider-ovhcloud-ai

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"
```

### Run Tests

```bash
pytest tests/
```

### Code Formatting

```bash
black apache_airflow_provider_ovhcloud_ai/
flake8 apache_airflow_provider_ovhcloud_ai/
mypy apache_airflow_provider_ovhcloud_ai/
```

## Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

## Support

- 📖 [Documentation](https://github.com/ovh/apache-airflow-provider-ovhcloud-ai#readme)
- 🐛 [Issue Tracker](https://github.com/ovh/apache-airflow-provider-ovhcloud-ai/issues)
- 💬 [Discussions](https://github.com/ovh/apache-airflow-provider-ovhcloud-ai/discussions)

## Acknowledgments

- Built for [Apache Airflow](https://airflow.apache.org/)
solcadre 0.1.6
Solcadre System
Requires Python: >=3.10
Keywords: astronomy, calendar, cycle, equinox, seasons, solstice, time, week

# Solcadre

[![Documentation Status](https://readthedocs.org/projects/solcadre/badge/?version=latest)](https://solcadre.readthedocs.io/en/latest/)
[![PyPI version](https://img.shields.io/pypi/v/solcadre.svg)](https://pypi.org/project/solcadre/)

**Solcadre** is a week-based, solar-structured calendar system designed around how people actually organise their time.

Human routines, work, and social life are overwhelmingly structured around the **week**. Solcadre takes the week as its fundamental unit, and builds the rest of the calendar around it.

The year is divided into **four fixed 12-week seasons**, with **explicit transition weeks** between seasons.

**See it in action:** https://solcadre.net

---

## Core ideas

- **The week is the building block**
  All longer periods are composed of a whole number of weeks, so weekly structure never breaks or drifts.

- **Seasons are fixed-length**  
  Each season is exactly 12 weeks long, making seasons comparable and well-suited to training programs, projects, or focused periods of work.

- **Transitions are intentional**  
  One or two transition weeks sit between seasons, aligned closely with the solstices and equinoxes, and are intended for rest, review, or change.

- **Solar alignment without daily complexity**  
  The calendar follows the solar year at the seasonal level, while keeping week-level structure clean and regular.

---

## Why Solcadre?

Most people already plan their lives in weeks: work cycles, training schedules, routines, and rest days are all weekly patterns.

Solcadre makes that implicit structure explicit. By keeping weeks intact and grouping them into fixed-length seasons, it provides a stable framework for planning without constant adjustment or drift.

---

## Implementation

This repository contains a library implementing the Solcadre calendar system.

Calendars are localised by timezone, latitude, and longitude. Days are defined from sunrise to the following sunrise, allowing day boundaries to follow the local solar cycle rather than the civil clock.
pnspy 1.0.0a0
A lightweight package for principal nested spheres (PNS) analysis
Requires Python: >=3.10

# PnsPy

[![Supported Python Versions](https://img.shields.io/pypi/pyversions/pnspy.svg)](https://pypi.python.org/pypi/pnspy/)
[![PyPI Version](https://img.shields.io/pypi/v/pnspy.svg)](https://pypi.python.org/pypi/pnspy/)
[![License](https://img.shields.io/github/license/JSS95/pnspy)](https://github.com/JSS95/pnspy/blob/master/LICENSE)
[![CI](https://github.com/JSS95/pnspy/actions/workflows/ci.yml/badge.svg)](https://github.com/JSS95/pnspy/actions/workflows/ci.yml)
[![CD](https://github.com/JSS95/pnspy/actions/workflows/cd.yml/badge.svg)](https://github.com/JSS95/pnspy/actions/workflows/cd.yml)
[![Docs](https://readthedocs.org/projects/pnspy/badge/?version=latest)](https://pnspy.readthedocs.io/en/latest/?badge=latest)

PnsPy is a lightweight Python package for principal nested spheres (PNS) analysis.

## Installation

```
$ pip install pnspy
```

## Documentation

The manual can be found online:

> https://pnspy.readthedocs.io

If you want to build the documentation yourself, get the source code and install with `[doc]` dependency.
Then, go to the `doc` directory and build the documentation:

```
$ pip install .[doc]
$ cd doc
$ make html
```

The documentation will be generated in the `build/html` directory. Open `index.html` to see the main page.
tarski 0.9.1
Tarski is a framework for the specification, modeling and manipulation of AI planning problems.
Requires Python: >=3.10
Keywords: planning logic STRIPS RDDL


# Tarski - An AI Planning Modeling Framework
[![Unit Tests](https://github.com/aig-upf/tarski/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/aig-upf/tarski/actions/workflows/unit-tests.yml)
[![Documentation Status](https://readthedocs.org/projects/tarski/badge/?version=latest)](https://tarski.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/aig-upf/tarski/branch/master/graph/badge.svg)](https://codecov.io/gh/aig-upf/tarski)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tarski.svg?style=popout)
![PyPI](https://img.shields.io/pypi/v/tarski.svg?style=popout)


## What is Tarski
Tarski is a framework for the specification, modeling and manipulation of
[AI planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling) problems.
Tarski is written in Python and includes parsers for major modeling languages
(e.g., [PDDL](https://en.wikipedia.org/wiki/Planning_Domain_Definition_Language),
[FSTRIPS](https://dl.acm.org/citation.cfm?id=566359),
[RDDL](https://en.wikipedia.org/wiki/Planning_Domain_Definition_Language#RDDL)),
along with modules to perform other common tasks such as logical transformations,
reachability analysis, grounding of first-order representations and problem reformulations.


**Installation**: Check the [*installation instructions*](docs/installation.md).

**Documentation**: Read the [*documentation of the project*](https://tarski.readthedocs.io).

**Testing**: Most tests can be run by executing `pytest` on the root directory.
Alternatively, they can be run through `tox`, for which several testing environments [are defined](tox.ini).

## How to Cite

If you find `tarski` useful in your research, you can cite it with the following `bibtex` entry:

```
@misc{tarski:github:18,
  author = {Guillem Franc\'{e}s and Miquel Ramirez and Collaborators},
  title = {Tarski: An {AI} Planning Modeling Framework},
  year = {2018},
  publisher = {{GitHub}},
  journal = {{GitHub} repository},
  howpublished = {\url{https://github.com/aig-upf/tarski}}
}
```

## License
Tarski is licensed under the [Apache-2.0 License](LICENSE).