semvec/pypi-packages huggingface dataset:
qtealeaves 1.7.21
Quantum TEA's python tensor network library
Requires Python: >=3.11

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

qtealeaves
==========

The `qtealeaves` library of Quantum TEA contains tensor network representation as
python classes, e.g., MPS, TTN, LPTN, and TTO. `qtealeaves` is the API for building
quantum models in Quantum TEA and has for example a single-tensor update ground state
search for TTNs. Moreover, `qtealeaves` is backbone for running quantum circuits via
Quantum matcha TEA and the frontend for the fortran backend running Quantum Green TEA,
i.e., solving the Schrödinger equation.


Documentation
=============

[Here](https://quantum_tea_leaves.baltig-pages.infn.it/py_api_quantum_tea_leaves/)
is the documentation. The documentation can also be built locally via sphinx.
Building the documentation requires `sphinx`, `sphinx-gallery`, and `sphinx_rtd_theme`.


License
=======

The project ``qtealeaves`` is hosted at the repository
``https://baltig.infn.it/quantum_tea_leaves/py_api_quantum_tea_leaves.git``,
and is licensed under the following license:

[Apache License 2.0](LICENSE)

The license applies to the files of this project as indicated
in the header of each file, but not its dependencies.


Installation
============

Independent of the use-case, you have to install the dependencies. Then,
there are the options using it as a stand-alone package, within [quantum
matcha TEA](https://baltig.infn.it/quantum_matcha_tea), or for quantum green TEA.

Local installation via pip
--------------------------

The package is available via PyPi and `pip install qtealeaves`.
After cloning the repository, an local installation via pip is
also possible via `pip install .`.

Dependencies
------------

The python dependencies can be found in the [``requirements.txt``](requirements.txt)
and are required independently of the following use-cases.

The `qtealeaves` package comes with the abstract definition of the tensor
required for our tensor networks as well as with a dense tensor based on
numpy and cupy. This tensor allows one to run simulations without symmetry
on CPU and GPU. Other tensor for symmetries or using pytorch instead of
numpy/cupy will become available in the future via Quantum Red TEA (`qredtea`).

Stand-alone package
-------------------

If you are looking to explore small exact diagonalization examples,
want to run a single-tensor update ground state search with TTNs,
or have TN-states on files to be post-processed, you are ready to
go.

qmatchatea simulations
----------------------

Quantum circuit simulations via `qmatchatea` require both `qredtea` and `qtealeaves`
as a dependency. Follow the instructions contained in the `qmatchatea` repository.
social-auth-app-django 5.7.0
Python Social Authentication, Django integration.
Requires Python: >=3.10
Keywords: django, oauth, openid, saml, social auth

# Python Social Auth - Django

Python Social Auth is an easy to setup social authentication/registration
mechanism with support for several frameworks and auth providers.

## Description

This is the [Django](https://www.djangoproject.com/) component of the
[python-social-auth ecosystem](https://github.com/python-social-auth/social-core),
it implements the needed functionality to integrate
[social-auth-core](https://github.com/python-social-auth/social-core)
in a Django based project.

## Django version

This project will focus on the currently supported Django releases as
stated on the [Django Project Supported Versions table](https://www.djangoproject.com/download/#supported-versions).

Backward compatibility with unsupported versions won't be enforced.

## Documentation

Project documentation is available at https://python-social-auth.readthedocs.io/.

## Setup

```shell
$ pip install social-auth-app-django
```

## Contributing

Contributions are welcome!

Only the core and Django modules are currently in development. All others are in maintenance only mode, and maintainers are especially welcome there.

See the [CONTRIBUTING.md](https://github.com/python-social-auth/.github/blob/main/CONTRIBUTING.md) document for details.

## Versioning

This project follows [Semantic Versioning 2.0.0](https://semver.org/spec/v2.0.0.html).

## License

This project follows the BSD license. See the [LICENSE](LICENSE) for details.

## Donations

This project welcomes donations to make the development sustainable, you can fund Python Social Auth on following platforms:

- [GitHub Sponsors](https://github.com/sponsors/python-social-auth/)
- [Open Collective](https://opencollective.com/python-social-auth)
wmul_rivendell_tcp_client 0.0.3
TCP Client for Receiving 'Now Playing' information from Rivendell.

# wmul_rivendell_tcp_client

A TCP client module to connect to Rivendell Radio Automation and receive Now Playing information. Intended to be run as
a module inside your code. A CLI is provided for testing purposes.

Connects to the Rivendell PAD TCP port, usually on 34289. See 15.1.1. The JSON Interface in the Rivendell Operations
Guide.

## CLI Operation

`.venv\Scripts\wmul_now_playing.exe [Rivendell IP Address] 34289 --log_name "E:\Temp\rivendell_tcp_client.log"`

The CLI will print the Start Time, Duration (rounded to full seconds), Artist, and Title as each song is received from
Rivendell.

## Module Operation

```Python
import click
import multiprocessing
import wmul_logger
from wmul_rivendell_tcp_client import receiver

_logger = wmul_logger.get_logger()
logging_queue = multiprocessing.Queue()
stop_logger = wmul_logger.run_queue_listener_logger(
    logging_queue=logging_queue,
    file_name=log_name,
    log_level=log_level
)
_logger = wmul_logger.get_queue_handler_logger(logging_queue=logging_queue)

quit_event = multiprocessing.Event()
song_queue = multiprocessing.SimpleQueue()
if host and port:
    dss = receiver.DeviceServerSettings(
        host=host,
        port=port
    )
    tcp_process = multiprocessing.Process(
        target=receiver.connect_to_rivendell_tcp,
        name="Rivendell TCP Client",
        kwargs={
            "device_server_settings": dss,
            "quit_event": quit_event,
            "song_queue": song_queue,
            "loggging_queue": logging_queue
        },
        daemon=True
    )
    tcp_process.start()

## Here's where your code consumes the songs from song_queue. 
```