semvec/pypi-packages huggingface dataset:
whisper-slurm-gui 0.1.0
A Whisper ASR job submission GUI with Slurm integration.
Requires Python: >=3.8

# whisper-gui

A user interface for submitting Whisper jobs through SLURM

Audio example is from https://freesound.org/people/acclivity/ and the mp3 file

https://freesound.org/people/acclivity/sounds/24096/
ktoolkits 0.2.2
一个面向AI智能体应用的超级工具,提供基于AI主机操作系统级别的工具扩展能力。
Requires Python: >=3.10
Keywords: awesome, ai agent tool, ktool, ktoolits, kito-ai

KToolkits

## 介绍

Kito智能主机系统是一款面向AI的超级工具，它融合了后端即服务和AI Computer Use的理念，使开发者可以快速搭建功能强大的AI智能体应用。即使你是非技术人员，也能快速参与到AI智能体的定义和运营过程中。

KToolkits则是一款面向AI智能体应用开发的开源工具库,通过API/SDK的方式提供基于AI主机操作系统级别的工具扩展能力。

## 更多详情

+ https://s.apifox.cn/2b306df6-5d22-423f-83ba-ed07415b13d5
AA-Opto-Electronic-MPDSnCxx-Interface 1.0.2
Python package for USB interfacing of the AA Opto-Electronic MPDSnCxx Radio Frequency Driver
Requires Python: >=3.13.0

# AA-Opto-Electronic-MPDSnCxx-Interface
Python package for USB interfacing of the AA Opto-Electronic MPDSnCxx Radio Frequency Driver ([Link](https://aaoptoelectronic.com/mpdsnc-multi-purpose-digital-synthesizers/)). 
This code was developed as part of my PhD in optical chemical profiling through scattering media, to allow for control of AA Opto-Electronic MPDSnCxx radio frequency driver. 
We used the driver to control an AA Opto-Electronic Acousto-Optic Tunable Filter (AOTF).

# Documentation

Full documentation can be found at <https://the-professor510.github.io/AA-Opto-Electronic-MPDSnCxx-Interface/index.html>

# Installation

To intsall MPDSnCxx_Interface using pip run:
```
pip install AA-Opto-Electronic-MPDSnCxx-Interface
```

# Usage

To import and initialise use the following code:

```python
import MPDSnCxx_Interface
   
rf_interface = MPDSnCxx_Interface.MPDSnCxx_Interface(vid, pid, serialNum)
rf_interface.connect_device(baudrate, bytesize, parity, stopbits, xonxoff)
```

where vid, pid, and serialNum are the vid, pid, and serial numbers of your MPDSnCxx radio frequency driver. For the AA Opto Electronic MPDSnCxx radio frequency driver the following values must selected:

* baudrate = 57600 bauds
* bytesize = 8 bits
* partiy = None
* stopbits = 1 stop bit
* xonxoff = False

At the end of the program it is good practices to close the connection, to do this use:

```python
rf_interface.disconnect_device()
```
mandato 0.2508.0
ORMA's brother
Requires Python: >=3.12

# mandato

[![Tests](https://github.com/kairix-dev/mandato/workflows/Tests/badge.svg)](https://github.com/kairix-dev/mandato/actions)
[![Python](https://img.shields.io/pypi/pyversions/mandato.svg)](https://pypi.org/project/mandato/)
[![PyPI version](https://badge.fury.io/py/mandato.svg)](https://badge.fury.io/py/mandato)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](https://mypy-lang.org/)


ORMA's Brother.
gnocchi 4.7.0
Metric as a Service

===============================
 Gnocchi - Metric as a Service
===============================

.. image:: https://badge.fury.io/py/gnocchi.svg
    :target: https://badge.fury.io/py/gnocchi

.. image:: doc/source/_static/gnocchi-logo.png

Gnocchi is an open-source time series database.

The problem that Gnocchi solves is the storage and indexing of time series
data and resources at a large scale. This is useful in modern cloud platforms
which are not only huge but also are dynamic and potentially multi-tenant.
Gnocchi takes all of that into account.

Gnocchi has been designed to handle large amounts of aggregates being stored
while being performant, scalable and fault-tolerant. While doing this, the goal
was to be sure to not build any hard dependency on any complex storage system.

Gnocchi takes a unique approach to time series storage: rather than storing
raw data points, it aggregates them before storing them. This built-in feature
is different from most other time series databases, which usually support
this mechanism as an option and compute aggregation (average, minimum, etc.) at
query time.

Because Gnocchi computes all the aggregations at ingestion, getting the data
back is extremely fast, as it just needs to read back the pre-computed results.

You can read the full documentation online at http://gnocchi.osci.io.
csgmsh 0.3.1
Constructive Solid Geometry helper for Gmsh
Requires Python: >=3.9

# Constructive Solid helper for Gmsh

This project is an opinionated helper tool that aims to provide a high level
Python API for 2D/3D Constructive Solid Geometry modelling, which is a very
convenient way of defining geometries using boolean operations. A typical
script looks like this:

```python
from csgmsh import mesh, shape, field

rect = shape.Rectangle()
circ = shape.Circle(center=[1,1], radius=.5)

mesh.write(
    output_path = "demo.msh",
    physical_groups = {
        "domain": rect - circ,
        "top-right": rect.top | circ.boundary | rect.right,
        "bottom-left": rect.left | rect.bottom,
    },
    mesh_size = field.Threshold(
        d=field.Distance(rect.bottom),
        dmin=0, vmin=.01,
        dmax=.5, vmax=.1),
)
```

Context: gmsh supports GSG modelling via its OpenCASCADE kernel. Unfortunately
it is set up in a way that makes it difficult to keep track of boundary
segments, which is crucial for computational applications. Csgmsh fixes this by
assuming that the (undocumented) order in which boundary entities are provided
is stable, and keeps track of how these boundaries are subdivided via the
`BooleanFragments` operation.

This project is very much a work in progress and the API may change extensively
pre-1.0, so be sure to pin it to a version if you find a use for it.
hugsim-client 0.0.8
A client package for interacting with the HugSim API.
Requires Python: >=3.7

# Hugsim Client

## Quick Start

```python
from hugsim_client import HugsimClient

client = HugsimClient()

# Get the current state of the environment
client.get_current_state()

# Reset the environment
client.reset_env()

# Execute an action in the environment, it return current state.
client.execute_action(xxx)
```
somtodaypython 1.2.2
Python package for interacting & fetching somtoday's data.
Requires Python: >=3.9
Keywords: api, somtoday, SOMtoday, python, somtodaypython

## ***Somtoday Python, the interactor***


*See ``CHANGELOG.md`` for changes/updates*

*See the ``examples/`` folder to see how to use the api*

***A big revamp has been made, I am planning to add SSO authentication.***

***Expect bugs with authentication, please make an issue on this repo if you find any bugs***


***asynchronous support for somtodaypython has been deprecated***


somtodaypython is a package that fetches and interacts with Somtoday API using HTTPS requests.

**installation**

*for macos & linux*
```
python3 -m pip3 install somtodaypython 
```
*for windows*
```
python3 -m pip install somtodaypython 
```
OR
```
python -m pip install somtodaypython
```

if neither above works then you can always install by github repository
```
pip3 install git+https://github.com/luxkatana/somtodayapi_python
```

***examples***

*basic interacting with a student(getting data from the student)*
```py

import somtodaypython.nonasyncsomtoday as nonasync_somtoday
school = nonasync_somtoday.find_school("SchoolName")
student = school.get_student("NAME", "password")
print(f"email : {student.email}\tname: {student.full_name}\tgender: {student.gender}")
```
*basic interacting with the timetable of a student*
```py
import somtodaypython.nonasyncsomtoday as nonasync_somtoday
from datetime import timedelta, datetime as dt
school = nonasync_somtoday.find_school("SchoolName")
student = school.get_student("NAME", "password")
today = dt.now()
tomorrow = today + timedelta(days=2)
timetable: list[list[nonasync_somtoday.Subject]] = student.fetch_schedule(today, tomorrow, group_by_day=True)
for day in timetable:
    for day_subject in day:
        print(day_subject.subject_name)
```

**Contribution**


New PR's are welcome.


**Huizengek#6623**
Special thanks to **Huizengek#6623** for showing the method of getting the access token & interacting with the somtoday API.

github: https://github.com/25huizengek1


**elisaado**

Special thanks to **elisaado** for making important API endpoints visible to other users.

github: https://github.com/elisaado/somtoday-api-docs
facer-model 1.0.9a0
The Birkeland current model presented by Coxon et al. (2016), based on Milan (2013).
Requires Python: >=3.9
Keywords: Birkeland currents, field-aligned currents, magnetic reconnection

# facer-model

[![PyPI badge](https://badge.fury.io/py/facer-model.svg)](https://badge.fury.io/py/facer-model)
[![Zenodo badge](https://zenodo.org/badge/665918230.svg)](https://doi.org/10.5281/zenodo.15731338)
[![Tests badge](https://github.com/johncoxon/birkeland-model/actions/workflows/tests.yaml/badge.svg)](https://github.com/johncoxon/birkeland-model/actions/workflows/tests.yaml)

Field-Aligned Currents Estimated from Reconnection (FACER) is a Python implementation of the Birkeland current model presented by Coxon et al. (2016), based on Milan (2013). It is a simple mathematical model of the region 1 and 2 Birkeland current system intensities for differing dayside and nightside magnetic reconnection rates, consistent with the expanding/contracting polar cap paradigm of solar wind-magnetosphere-ionosphere coupling.

## Citations

When using this software, please cite [the Zenodo record](https://doi.org/10.5281/zenodo.15731338) as well as the following two papers:

- [Stephen E Milan (2013), Modeling Birkeland currents in the expanding/contracting polar cap paradigm, _Journal of Geophysical Research: Space Physics_, 118, 5532–5542. https://doi.org/10.1002/jgra.50393](https://doi.org/10.1002/jgra.50393)
- [John C Coxon, Stephen E Milan, Jennifer A Carter, Lasse BN Clausen, Brian J Anderson & Haje Korth (2016). Seasonal and diurnal variations in AMPERE observations of the Birkeland currents compared to modeled results, _Journal of Geophysical Research: Space Physics_, 121, 4027–4040. https://doi.org/10.1002/2015JA022050](https://doi.org/10.1002/2015JA022050)

## Funding

John C Coxon was supported during this work by Science and Technology Facilities Council (STFC) Ernest Rutherford Fellowship ST/V004883/1.

## Installation

To install and test the model, simply type:

```
pip install facer-model
pytest tests/test.py
```

## Usage

A very basic instantiation of the model can be seen below. For more examples, check out the notebooks in the repository, which explicitly detail how to use the model to replicate the results from the two papers which define the model.
```
from facer import Model

dayside_reconnection_rate = 50       # in kV
nightside_reconnection_rate = 50     # in kV
solar_radio_flux = 80                # in SFU
time = datetime.datetime(2010, 1, 1)
hemisphere = "north"
polar_cap_flux = 0.5                 # in GWb

model = Model(dayside_reconnection_rate,
              nightside_reconnection_rate,
              solar_radio_flux,
              time,
              hemisphere,
              polar_cap_flux)
```
aioairzone-cloud 0.7.2
Library to control Airzone Cloud devices
Requires Python: >=3.12
Keywords: airzone, cloud, hvac, home

# aioairzone-cloud
[![Latest Version][mdversion-button]][md-pypi]
[![Python Versions][pyversion-button]][md-pypi]
[![License: Apache 2.0][apache-button]](LICENSE)

[apache-button]: https://img.shields.io/badge/License-Apache%202.0-blue.svg
[md-pypi]: https://pypi.org/project/aioairzone-cloud
[mdversion-button]: https://img.shields.io/pypi/v/aioairzone-cloud.svg
[pyversion-button]: https://img.shields.io/pypi/pyversions/aioairzone-cloud.svg

Python library to control Airzone Cloud devices.

## Requirements
- Python >= 3.12

## Install
```bash
pip install aioairzone-cloud
```

## Install from Source
Run the following command inside this folder
```bash
pip install --upgrade .
```

## Examples
Examples can be found in the `examples` folder