semvec/pypi-packages huggingface dataset:
pyweed 1.0.12
A GUI application for discovering and downloading seismic data
Requires Python: <3.12,>=3.9
Keywords: FDSN, EarthScope, miniSEED, earthquake, seismic data

# PyWEED

[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyweed/badges/version.svg)](https://anaconda.org/conda-forge/pyweed)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyweed/badges/latest_release_date.svg)](https://anaconda.org/conda-forge/pyweed)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyweed/badges/platforms.svg)](https://anaconda.org/conda-forge/pyweed)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyweed/badges/license.svg)](https://anaconda.org/conda-forge/pyweed)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyweed/badges/downloads.svg)](https://conda.anaconda.org/conda-forge)

PyWEED is an application for retrieving event-based seismic data.

PyWEED works on Windows, Mac, and Linux (64-bit). It is built on [ObsPy](https://obspy.org/) and
[PyQt](https://pypi.python.org/pypi/PyQt5).

For documentation, see the [GitHub Pages](https://iris-edu.github.io/pyweed/).
dialectid 0.2.0
Set of algorithms to detect the dialect of a given text

====================================
dialectid
====================================
.. image:: https://github.com/INGEOTEC/dialectid/actions/workflows/test.yaml/badge.svg
		:target: https://github.com/INGEOTEC/dialectid/actions/workflows/test.yaml

.. image:: https://coveralls.io/repos/github/INGEOTEC/dialectid/badge.svg?branch=develop
		:target: https://coveralls.io/github/INGEOTEC/dialectid?branch=develop

.. image:: https://badge.fury.io/py/dialectid.svg
		:target: https://badge.fury.io/py/dialectid

.. image:: https://img.shields.io/conda/vn/conda-forge/dialectid.svg
		:target: https://anaconda.org/conda-forge/dialectid

.. image:: https://img.shields.io/conda/pn/conda-forge/dialectid.svg
		:target: https://anaconda.org/conda-forge/dialectid					

`dialectid` aims to develop a set of algorithms to detect the dialect of a given text. For example, given a text written in Spanish, dialectid predicts the Spanish-speaking country where the text comes from.

`dialectid` is available for Arabic (ar), German (de), English (en), Spanish (es), French (fr), Dutch (nl), Portuguese (pt), Russian (ru), Turkish (tr), and Chinese (zh).
geoapi-pandas 0.0.5
A small geoapi package
Requires Python: >=3.12

# 🛰️ GEOAPI

**ValidadorFantasma** es una librería en Python que combina la potencia de **pandas** con funciones de **geolocalización y referenciación geográfica**, facilitando el análisis, validación y enriquecimiento de datos espaciales.

Ideal para proyectos de ciencia de datos, ETL y validación de información basada en **coordenadas, direcciones o ubicaciones**.

---

## ✨ Características

- 📍 Integración nativa con **pandas** (`DataFrame`).
- 🌐 Validación y normalización de **coordenadas (latitud/longitud)**.
- 🗺️ Enriquecimiento de datos con información geográfica (país, región, distrito, etc.).
- ⚡ Procesamiento rápido y vectorizado usando la infraestructura de pandas.
- 🔎 Funciones de referencia cruzada contra fuentes públicas (CSV/GeoJSON).

---

## 🚀 Instalación

```bash
pip install geoapi
smartapp-sdk 0.9.1
Framework to build a webhook-based SmartThings SmartApp
Requires Python: <4,>=3.11

# SmartApp SDK

[![pypi](https://img.shields.io/pypi/v/smartapp-sdk.svg)](https://pypi.org/project/smartapp-sdk/)
[![license](https://img.shields.io/github/license/pronovic/smartapp-sdk)](https://github.com/pronovic/smartapp-sdk/blob/main/LICENSE)
[![wheel](https://img.shields.io/pypi/wheel/smartapp-sdk.svg)](https://pypi.org/project/smartapp-sdk/)
[![python](https://img.shields.io/pypi/pyversions/smartapp-sdk.svg)](https://pypi.org/project/smartapp-sdk/)
[![Test Suite](https://github.com/pronovic/smartapp-sdk/workflows/Test%20Suite/badge.svg)](https://github.com/pronovic/smartapp-sdk/actions?query=workflow%3A%22Test+Suite%22)
[![docs](https://readthedocs.org/projects/smartapp-sdk/badge/?version=stable&style=flat)](https://smartapp-sdk.readthedocs.io/en/stable/)
[![coverage](https://coveralls.io/repos/github/pronovic/smartapp-sdk/badge.svg?branch=main)](https://coveralls.io/github/pronovic/smartapp-sdk?branch=main)

_Note: As of January 2025, I have migrated my home automation infrastructure from SmartThings to Home Assistant, so I no longer actively use this software. I will continue to maintain the library, keeping dependencies up-to-date and supporting new Python versions, etc.  Time permitting, I will also continue to accept GitHub issues for bug fixes and enhancement requests.  If you submit an issue, please keep in mind that I no longer have a SmartThings environment to test with, so I will expect you to coordinate with me on testing before I release any changes._

---

smartapp-sdk is a Python library to build a [webhook-based SmartApp](https://developer.smartthings.com/docs/connected-services/smartapp-basics/) for the [SmartThings platform](https://www.smartthings.com/).

The SDK is intended to be easy to use no matter how you choose to structure your code, whether that's a traditional Python webapp (such as FastAPI on Uvicorn) or a serverless application (such as AWS Lambda).

The SDK handles all the mechanics of the [SmartThings webhook lifecycle interface](https://developer.smartthings.com/docs/connected-services/lifecycles/) on your behalf.  You just implement a single endpoint to accept the SmartApp webhook requests, and a single callback class where you define specialized behavior for the webhook events.  A clean [attrs](https://www.attrs.org/en/stable/) object interface is exposed for use by your callback.  The attrs interface has been designed to match the JSON interface documented by SmartThings.  If you have questions about the shape of the data or the way the lifecyle interface works, the SmartThings documentation is your best source of information.

SDK documentation for this library is found at [smartapp-sdk.readthedocs.io](https://smartapp-sdk.readthedocs.io/en/stable/).  Look there for installation instructions, the class model documentation, and example code.
pyprofyler 2.0.1
a simple memory/perf profiler for python programs.
Requires Python: >=3.12.9

# PyProfyler
a simple memory/perf profiler for python programs.

## supports:
- functions
    - sync
    - async
- generators
    - sync
    - async

## Installation:
```sh
sudo pip3 install git+https://github.com/AlyShmahell/PyProfyler
```
or  
```sh
sudo pip3 install pyprofyler
```
## Example:
check [example.py](https://github.com/AlyShmahell/PyProfyler/blob/master/example.py)
fuzzy-theory 0.0.7
The fuzzy-theory library provides a PyTorch interface to fuzzy set theory and fuzzy logic operations.
Requires Python: >=3.9

# fuzzy-theory: Fuzzy Set Theory and Fuzzy Logic Operations in PyTorch :fire:
<a href="https://github.com/johnHostetter/fuzzy-theory/actions"><img alt="Actions Status" src="https://github.com/johnHostetter/fuzzy-theory/workflows/Test/badge.svg"></a>
<a href="https://github.com/johnHostetter/fuzzy-theory/actions"><img alt="Actions Status" src="https://github.com/johnHostetter/fuzzy-theory/workflows/Pylint/badge.svg"></a>
<a href="https://codecov.io/github/johnHostetter/fuzzy-theory"><img src="https://codecov.io/github/johnHostetter/fuzzy-theory/graph/badge.svg?token=WeWKlnVHqj"/></a>
<a href="https://github.com/psf/fuzzy-theory"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>

The `fuzzy-theory` library provides a PyTorch interface to fuzzy set theory and fuzzy logic 
operations. It uses minimal dependencies to implement these features and is designed to be
easy to use and understand. The library is designed to be used in conjunction with PyTorch
and is built on top of PyTorch's tensor operations.

A benefit of using `fuzzy-theory` is that it allows for the creation of fuzzy sets and fuzzy
logic operations in a way that is compatible with PyTorch's autograd system. This means that
you can use the library to create fuzzy sets and perform fuzzy logic operations in a way that
is differentiable and can be used in neural networks and other machine learning models.

## Special features :high_brightness:
1. *Compatible with TorchScript*: Some classes may use `torch.jit.script` or `torch.jit.trace` for production environments.
2. *Differentiable*: The library is designed to be used in conjunction with PyTorch and is built on top of PyTorch's tensor operations.
3. *Minimal dependencies*: The library uses minimal dependencies to implement these features.
4. *Easy to use*: The library is designed to be easy to use and understand, with a simple API that is similar to PyTorch's tensor operations.
5. *Visualization*: Formulas are written with `sympy` for LaTeX rendering and plots are stylized with `scienceplots` for publication-ready figures.