semvec/pypi-packages huggingface dataset:
IPSpin 0.1.1
A Python library for interacting with IPSpin Services (https://IPSpin.io).
Requires Python: >=3.6

# IPSpin

A Python library for interacting with IPSpin Services. (https://IPspin.io)

## Installation

```bash
pip install ipspin
```

## Usage

```python
from ipspin import IPSpin

ipspin = IPSpin.builder("API_KEY")
ipspin.set_region("eu-north-1")\
      .add_url("https://ifconfig.io/ip")\
      .print_output(True)\
      .send_requests()
```
meteociel-api 1.1.2
An unofficial API for the Météociel website
Requires Python: >=3.7

# Météociel  API

[![Documentation Status](https://readthedocs.org/projects/meteociel-api/badge/?version=latest)](https://meteociel-api.readthedocs.io/en/latest/?badge=latest)
[![License](https://img.shields.io/github/license/Meteo-API/meteociel_api?color=green)](https://github.com/Meteo-API/meteociel_api/blob/main/LICENSE)
[![Upload Python Package](https://github.com/Meteo-API/meteociel_api/actions/workflows/python-publish.yml/badge.svg)](https://github.com/Meteo-API/meteociel_api/actions/workflows/python-publish.yml)
[![PyPi](https://img.shields.io/pypi/v/meteociel-api)](https://pypi.org/project/meteociel-api/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

## Description
Météociel API is a small Python API for the website [Météociel](https://www.meteociel.fr/). This API allows to dump data from:

* all the stations known by Météociel (all around the world)

* upper air sounding (observations) (Western Europe only)

* upper air sounding (simulated by AROME) (France only)

* forecasts (mainly GFS, but others models are available)

A full documentation is available on [ReadTheDocs](http://meteociel-api.rtfd.io/).

## Installation

You can install this package [from PyPI](https://pypi.org/project/meteociel-api/): `pip install meteociel-api`.

You can install also install it from sources in a virtual environment by executing these commands in a terminal:
```
$ git clone https://github.com/Meteo-API/meteociel_api.git
$ cd meteociel_api
$ python -m venv venv
$ source venv/bin/activate
$ pip install -e .
```

Under windows please enter `$ venv\Scripts\activate` instead of `$ source venv/bin/activate`.

## Basic use
The installation provides two API for Météociel:

* a first one in the terminal (CLI)

* a second one as a Python package

To use the CLI, you should start by creating a database for cities by entering: `meteociel generate-database`. Once this step is done, you're free to run commands to dump data from Météociel. Help can be display by running: `meteociel --help` or `meteociel [COMMAND NAME] --help`.

In Python script, you have to start by creating the database too:
```python
from meteociel.cities import generate_database
generate_database()
```
Then you are free to dump data with the modules: `meteociel.stations` and `meteociel.soundings`.

## Compiling documentation
The compiled documentation can be found on [ReadTheDocs](http://meteociel-api.rtfd.io/).

You can also locally compile the documentation. You should have `make` installed on your computer. Then you can install the dependancies and compile:
```bash
$ cd meteociel_api/docs
$ pip install -r requirements.txt
$ make html
```
You can find the compiled html documentation in `meteociel_api/docs/_build/html/index.html`

## License
This code is provided under the GNU General Public License v3.0 (GPLv3).

Please refer to [LICENSE](https://github.com/Meteo-API/meteociel_api/blob/main/LICENSE) file for further informations.
complete-automation-framework 1.0.1
Automation Framework using Playwright Python
Requires Python: >=3.9

# Automation Framework using Playwright Python

This project contains a custom automation framework built using Playwright and Python for web automation.

## Features

- **Base Page**: Contains base functionalities and configurations for tests.
- **Utilities**: Various utility functions for common operations.
- **Conftest**: Configuration settings for pytest.
- **Requirements**: List of dependencies required by the framework.
- **pytest.ini**: Configuration file for pytest.

## Installation

To install and use this framework in your projects, follow these steps:


```bash
pip install complete_automation_framework
Codern 3.7.12
A simple library to send emails using API from api-free.ir
Requires Python: >=3.6

# send_email

`send_email` یک کتابخانه Python است که برای ارسال ایمیل‌ها از طریق API سایت [api-free.ir](https://api-free.ir) طراحی شده است. این کتابخانه به شما امکان می‌دهد که به راحتی و به سرعت ایمیل‌های خود را ارسال کنید.

## نصب

برای نصب این کتابخانه، می‌توانید از `pip` استفاده کنید. دستور زیر را در ترمینال خود اجرا کنید:

```bash
pip install codern
```
**استفاده**
در زیر یک مثال ساده برای استفاده از این کتابخانه آورده شده است:

```python
Copy code
from codern import Email

# تنظیمات ایمیل
setup = Email(
    To='mahdiahmadi.1208@gmail.com',
    text='hi',
    Title='Codern',
    Token=None,
    Input='iran'
)

# ارسال ایمیل
result = setup.Send()
print(result)
```
مثال عملی
```python
Copy code
from send_email import Email

# تنظیمات ایمیل
to_email = 'recipient@example.com'
text_content = 'این یک ایمیل تستی است.'
email_title = 'سلام!'
api_token = 'your_api_token_here'
input_type = 'info'

# ساخت نمونه‌ای از کلاس Email
email = Email(
    To=to_email,
    text=text_content,
    Title=email_title,
    Token=api_token,
    Input=input_type
)

# ارسال ایمیل
result = email.Send()
if result:
    print('ایمیل با موفقیت ارسال شد!')
else:
    print('خطا در ارسال ایمیل')

```
# پارامترها
1. To: آدرس ایمیل گیرنده.
2. text: متن ایمیل.
3. Title: عنوان ایمیل.
4. Token: توکن احراز هویت برای استفاده از API. برای دریافت توکن، باید در سایت api-free.ir ثبت‌نام کنید و توکن مخصوص خود را دریافت کنید.
5. Input: نوع ورودی که می‌تواند یکی از مقادیر 'info', 'app', 'Login', 'support' باشد.
راهنمای کامل
1. ثبت‌نام در سایت api-free.ir
ابتدا به آیدی rubika.ir/bot_token بروید آنجا می‌توانید توکن API خود را دریافت کنید.

2. نصب کتابخانه
با استفاده از دستور pip کتابخانه را نصب کنید:

```bash
Copy code
pip install codern
```
3. استفاده از کتابخانه
یک فایل Python جدید ایجاد کنید و کد زیر را در آن قرار دهید:

```python
Copy code
from codern import Email

# تنظیمات ایمیل
setup = Email(
    To='mahdiahmadi.1208@gmail.com',
    text='hi',
    Title='Codern',
    Token=None,
    Input='iran'
)

# ارسال ایمیل
result = setup.Send()
print(result)
```
4. اجرای کد
فایل Python خود را اجرا کنید تا ایمیل ارسال شود:

```bash
Copy code
python your_file_name.py
```
مشکلات متداول
1. خطای اتصال به API
اگر با خطای اتصال به API مواجه شدید، مطمئن شوید که به اینترنت متصل هستید و آدرس API به درستی وارد شده است.

2. خطای احراز هویت
اگر با خطای احراز هویت مواجه شدید، مطمئن شوید که توکن API را به درستی وارد کرده‌اید و توکن معتبر است.

مشارکت
اگر مایل به مشارکت در توسعه این کتابخانه هستید، می‌توانید مخزن را فورک کرده و تغییرات خود را از طریق Pull Request ارسال کنید. هر گونه کمک و پیشنهاد استقبال می‌شود.

مجوز
این پروژه تحت مجوز MIT منتشر شده است. برای اطلاعات بیشتر فایل LICENSE را مشاهده کنید.

برای هر گونه سوال یا مشکل، می‌توانید از طریق api-free.ir با ما در تماس باشید.
sketch-nn 0.1.7
A tool to generate PyTorch neural network code from flowchart images
Requires Python: >=3.6

# Sketch NN

<div align="center">
  <img src="https://raw.githubusercontent.com/arcAman07/sketch_nn/master/logos/sketch_nn_logo.jpg" alt="Sketch NN Logo" width="200"/>
  <p><em>Transform hand-drawn neural network sketches into functional PyTorch code</em></p>
</div>

<div align="center">
  
  [![PyPI version](https://badge.fury.io/py/sketch-nn.svg)](https://badge.fury.io/py/sketch-nn)
  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
  [![Python Versions](https://img.shields.io/pypi/pyversions/sketch-nn.svg)](https://pypi.org/project/sketch-nn/)

</div>

Sketch NN is an innovative Python library that transforms hand-drawn neural network sketches into functional PyTorch code. Design your neural architectures on paper, capture them with our tool, and watch as Sketch NN brings your ideas to life!

## 🌟 Features

- 📸 Process hand-drawn or digital sketches of neural network architectures
- 🧠 Support for a wide range of neural network layers
- 🔧 Generate ready-to-use PyTorch code
- 🖥️ User-friendly Gradio web interface for quick prototyping
- 🚀 FastAPI backend for scalable deployment

## 🛠️ Supported Layers

- Convolutional (Conv2D)
- Pooling (MaxPool2D, AvgPool2D)
- Fully Connected (Linear)
- Batch Normalization
- Dropout
- Activation Functions (ReLU, LeakyReLU, Sigmoid, Tanh)
- Recurrent (LSTM, GRU)
- Transformer
- Multi-head Attention

## 🚀 Installation

Install Sketch NN using pip:

```bash
pip install sketch_nn
```

## 🚀 Usage

```bash
from sketch_nn import NeuralNetworkDesigner

designer = NeuralNetworkDesigner()
designer.process_image('path_to_your_sketch.png')
pytorch_code = designer.generate_pytorch_code()
designer.write_to_file(pytorch_code, 'custom_nn.py')
```
## Gradio Web Interface Demo

```bash
from sketch_nn.demo import run_gradio_demo

run_gradio_demo()
```
allure-subtests 0.1.9
update readme demo according to code change
Requires Python: >=3.8

# allure-subtests

## Introduction
Allure-subtests is a Python library that can generate subtest results in an Allure report.

## Usage
I run below code under:
- Python3.12

```python
from loguru import logger
import allure

class TestSubTest:
    def test_demo(self, allure_subtests):
        for i in range(3):
            with allure_subtests.test(subtest_name=f"custom message:{i=}"):
                allure.dynamic.parent_suite('changed parent suite')
                logger.info(f'test {i=}')
                assert i % 2 != 0

                if i == 2:
                    raise ValueError(f'custom error {i=}')

    def test_teardown(self, allure_subtests):
        def teardown_subtest(subtest_name: str, result: bool):
            logger.info('teardown_sub_test_result: {}, result: {}', subtest_name, result)
            pass

        for i in range(3):
            with allure_subtests.test(subtest_name=f"custom message:{i=}", teardown_subtest=teardown_subtest):
                allure.dynamic.parent_suite('This is teardown demo')
                logger.info(f'test {i=}')
                assert i % 2 != 0

                if i == 2:
                    raise ValueError(f'custom error {i=}')

```

please see tests dir for more examples

## Why do I need this?
If we want to parameterize test cases, we have 2 ways: pytest parametrize or pytest-subtests
### pytest parametrize:
demo like below:
```python
import pytest
from loguru import logger

@pytest.mark.parametrize('i', range(3))
def test_parametrize(i):
    logger.info(f'test {i=}')
```

There are some disadvantages of this way:
- the 2nd parameter in pytest.mark.parametrize is a function, it will execute when module import, even test is not executed, if it is complex and time waste, will need a long time
- pytest.mark.parametrize can only pass simple data type to the test function, it is a little hard to use complex data type

### pytest-subtests
use pytest-subtests in https://pypi.org/project/pytest-subtests/

I find there is disadvantage in this way:
- It is not compatible with Allure, it will generate an Allure report with no subtest results


I want some ways both have the advantages of pytest-subtests and allure.

So this library is born based on pytest-subtests and allure.


## ChangeLog
- 0.1.4
  - support allure.dynamic.parent_suite, suite, sub_suite...
- 0.1.5
  - support teardown_subtest, default is None
- 0.1.7
  - fix subtest execute time always 0s
- 0.1.8
  - add test result parameter in tear down subtest
wdm-wavelet 0.2.2
A Python package for Wilson-Daubechies-Meyer (WDM) wavelet from coherentWaveBurst
Requires Python: >=3.8
Keywords: ligo, wavelet, gravitational waves, burst, DWT, WDM, Wilson-Daubechies-Meyer, cWB

# WDM wavelet transform

This package hosts the python version of WDM wavelet used in coherentWaveBurst(cWB) search.

## Installation

```bash
pip install wdm-wavelet
```

## Example

Generate a timeseries waveform
```python
from pycbc.waveform import get_td_waveform
import matplotlib.pyplot as plt

hp, hc = get_td_waveform(approximant="IMRPhenomTPHM",
                         mass1=20,
                         mass2=20,
                         spin1z=0.9,
                         spin2z=0.4,
                         inclination=1.23,
                         coa_phase=2.45,
                         distance=100,
                         delta_t=1.0/2048,
                         f_lower=20)
```

Apply WDM wavelet transform
```python
from wdm_wavelet.wdm import WDM

wdm = WDM(32, 64, 6, 10)

tf_map = wdm.t2w(hp)

tf_map.plot_energy()
```

Inverse WDM wavelet transform
```python
ts = wdm.w2t(tf_map)

plt.plot(ts)
```

For more examples, please refer to the [example notebook](https://git.ligo.org/yumeng.xu/wdm-wavelet/-/blob/main/notebook/transform_GW.ipynb).
Optimizacion-UX21II070 0.1.3
Un paquete de métodos de optimización para funciones univariables y multivariables
Requires Python: >=3.10

# Optimización UX21II070

debido a gith tiene un archivo que ignora lo creado por setup.py sdits(archivos que se suben a Pypi) es normal que no se encuentren adjuntos(breve aclaración)

Este proyecto de optimización se enfoca en implementar y documentar diversos métodos de optimización tanto para funciones de una variable como para funciones multivariadas, todos estos métodos fueron vistos durante la clase. 

## Estructura del Proyecto

El proyecto está organizado en las siguientes carpetas y archivos principales:

Optimizacion_proyecto/
│
├── Busquedas
│ ├── Multivariables
│ │ ├── Directos
│ │ │ ├── Hooke_Jeeves.py
│ │ │ ├── Nelder_Mead.py
│ │ │ └── init.py
│ │ └── Gradiente
│ │ │ ├── Cauchy.py
│ │ │ ├── Gradiente_conjugado.py
│ │ │ ├── Metodo_newton.py
│ │ │ └── init.py
│ ├── Univariables
│ │ ├── Basados_en_derivadas
│ │ │ ├── Biseccion.py
│ │ │ ├── Newthon_rapshon.py
│ │ │ ├── Secante.py
│ │ │ └── init.py
│ │ └── Eliminación_de_regiones
│ │ │ ├── Bounding_phase.py
│ │ │ ├── Golden_section.py
│ │ │ ├── Search_Fibonacci.py
│ │ │ └── init.py
│ └── metodos.py
│
├── Funciones_objetivo
│ ├── init.py
│ ├── sin_restricciones.py
│ └── con_restricciones.py


## Instalación

Para instalar este paquete, puedes usar pip: