semvec/pypi-packages huggingface dataset:
contextually-cached-property 0.1.1
A cached property decorator with context-aware isolation
Requires Python: >=3.13

# contextually-cached-property

A Python descriptor that caches property values per context variable, providing isolated caches for concurrent contexts like asyncio tasks.

## Why?

Python's built-in `functools.cached_property` uses a single cache shared across all contexts. This can cause issues in concurrent applications where different tasks or threads need independent cached values.

`contextually_cached_property` solves this by using `contextvars.ContextVar` to maintain separate caches per context, while also using weak references to allow proper garbage collection of instances.

## Installation

```bash
pip install contextually-cached-property
```

Requires Python 3.14+.

## Usage

```python
from contextually_cached_property import contextually_cached_property


class ExpensiveResource:
    @contextually_cached_property
    def connection(self) -> Connection:
        return create_connection()
```

The cached value is computed once per context per instance. Different asyncio tasks will each get their own cached value:

```python
import asyncio


class TaskLocalData:
    @contextually_cached_property
    def request_id(self) -> str:
        return generate_unique_id()


data = TaskLocalData()


async def task_a():
    print(data.request_id)  # e.g., "abc123"


async def task_b():
    print(data.request_id)  # e.g., "xyz789" (different from task_a)


async def main():
    await asyncio.gather(task_a(), task_b())
```

### Invalidating the cache

Delete the attribute to clear the cached value for the current context:

```python
del instance.cached_property_name
```

## Development

This project uses [Poetry](https://python-poetry.org/) for dependency management.

```bash
# Install dependencies
poetry install

# Run tests
pytest

# Run linting
ruff check

# Run type checking
mypy .
```

## Releasing

This project uses git tags for versioning and automated publishing via Forgejo Actions.

### Pre-release (TestPyPI)

Create a pre-release tag to publish to TestPyPI for testing:

```bash
git tag v1.0.0-rc1
git push origin v1.0.0-rc1
```

### Production release (PyPI)

Create a release tag to publish to PyPI:

```bash
git tag v1.0.0
git push origin v1.0.0
```

The workflow runs tests, linting, and type checking before publishing.

## License

See [LICENSE](LICENSE) for details.
pyvgrid 1.0.2
Vertical hybrid-pressure grids
Requires Python: >=3.10

VGRID
=====

*Vertical Hybrid-pressure grid generation*

Components of the lib
---------------------

This is composed of:

* A Fortran program (`src/mkvgrid/main.F90`) for generating hybrid-pressure grid.
* A Fortran library (`src/stdatm/`) containing routines for standard atmosphere computations of
  altitude and pressure on grid levels.
* A Python interface package `pyvgrid` (`src/pyvgrid/`) to the Fortran program and library,
  including also utilities to plot grids using `bokeh`.
* Examples of namelists (`nam/`) used to generate grids, including canonical ones.

Install
-------

Using pip:

* `pip install pyvgrid`

To recompile:

* `git clone https://github.com/ACCORD-NWP/vgrid.git`
* `cd vgrid`
* for the python interface (incl. compilation):
  1. `python -m build`
  2. `pip install dist/pyvgrid*.whl`
* for the Fortran only:
  0. `BUILD_DIR=<where you want to build>; INSTALL_DIR=<where you want to install>`
  1. `cmake -B $BUILD_DIR -DCMAKE_INSTALL_PREFIX=$INSTALL_DIR`
  2. `cmake --build $BUILD_DIR`
  3. `cmake --install $BUILD_DIR`

Examples of use
---------------

1. Generation of a new grid from namelist and command-line:
   * Prepare a namelist containing the parameters to tune, cf. examples in `nam/`
   * `mkvgrid <my_nam> [optionally_a_second_one_for_comparison]`
     this will compute the grid, generate namelist blocks for NAMVV1 and NAMFPG, and open a html figure in your default browser
   * Option `-h` to see other options of the command, especially to choose abscissa/ordinate among altitude, pressure, level number, level thickness (m or Pa).

2. Plot a grid from a FA file, and emulate its re-creation through `mkvgrid`:
   cf. `doc/test_vgrid_from_epygram.py`

Documentation
-------------

* Scientific documentation by P.Benard (2008) is available on: http://www.umr-cnrm.fr/gmapdoc/IMG/pdf/memoeta0.pdf
* Examples of namelists (in `nam/`) are auto-documented
* Python interface is auto-documented
* More to come...
quantum-sim-library 1.0.5
Un projet de simulation et de visualisation de phénomènes de mécanique quantique développé en Python dans un objectif pédagogique.
Requires Python: >=3.11

# Bienvenue sur le dépôt du Projet Mécanique Quantique avec Python !

Un projet de **simulation et de visualisation de phénomènes de mécanique quantique** développé en Python dans un objectif pédagogique.

Ce dépôt propose des outils permettant de **rendre la mécanique quantique plus accessible** grâce à la modélisation numérique et à la visualisation interactive d’objets quantiques.

---

## Objectifs du projet

- Illustrer les concepts fondamentaux de la mécanique quantique  
- Implémenter numériquement les équations fondamentales  
- Explorer différents cas d’étude simples et pédagogiques  
- Analyser la propagation des ondes quantiques  
- Simuler l’évolution temporelle de paquets d’ondes  

---

## Contexte

La mécanique quantique repose sur des concepts abstraits difficiles à appréhender sans support visuel.  
Les outils existants sont souvent limités, dispersés ou peu personnalisables.

Ce projet vise à proposer une **solution open source**, centralisée et évolutive, permettant aux étudiants d’explorer concrètement les phénomènes quantiques à travers la simulation et la visualisation.

---

## Fonctionnalités principales

- Modélisation d’ondes planes et de paquets d’ondes  
- Résolution numérique de l’équation de Schrödinger en 1D  
- Simulation de la propagation :
  - en espace libre  
  - dans des potentiels simples (puits, barrières…)  
- Visualisation de :
  - la fonction d’onde (réelle, imaginaire, complexe)  
  - la densité de probabilité  
- Analyse qualitative de phénomènes quantiques :
  - dispersion  
  - réflexion  
  - transmission  

---

## Technologies utilisées

- **Python**
- **NumPy / SciPy** – calcul scientifique et méthodes numériques  
- **Matplotlib** – visualisation et animations  
- **Jupyter Notebook** – démonstrations et cas d’étude  
- **pytest** – tests et validation du code  
- **Poetry** – gestion des dépendances et de l’environnement virtuel 

---

## 📚 Documentation

- **Installation** : Voir [`docs/installation.md`](docs/installation.md)
- **Architecture** : Voir [`docs/architecture.md`](docs/architecture.md)

---

## 📜 Licence

Ce projet est sous licence [MIT](LICENSE). Voir le fichier LICENSE pour plus de détails.
geoVeRoPy 0.0.22
geoVeRoPy: Geometrical Vehicle Routing Problem solved by Python
Requires Python: >=3.11

### geoVeRoPy - [working project]

This is a personal-use-purpose package that integrates basic functions in vehicle routing research

- Create instances
- Basic graph algorithms
- Create TSP/VRP routes using heuristic/MILP
magnum 19.0.1
Container Management project for OpenStack
Requires Python: >=3.8

========================
Team and repository tags
========================

.. image:: https://governance.openstack.org/badges/magnum.svg
    :target: https://governance.openstack.org/reference/tags/index.html

.. Change things from this point on

======
Magnum
======

Magnum is an OpenStack project which offers container orchestration engines
for deploying and managing containers as first class resources in OpenStack.

For more information, please refer to the following resources:

* **Free software:** under the `Apache license <http://www.apache.org/licenses/LICENSE-2.0>`
* **Documentation:** https://docs.openstack.org/magnum/latest/
* **Admin guide:** https://docs.openstack.org/magnum/latest/admin/index.html
* **Source:** https://opendev.org/openstack/magnum
* **Blueprints:** https://blueprints.launchpad.net/magnum
* **Bugs:** https://bugs.launchpad.net/magnum
* **REST Client:** https://opendev.org/openstack/python-magnumclient
* **Release notes:** https://docs.openstack.org/releasenotes/magnum/index.html
* **Contributing:** https://docs.openstack.org/magnum/latest/contributor/index.html