semvec/pypi-packages huggingface dataset:
robotframework-cnpjgenerator 1.0.0
Biblioteca Robot Framework para geração e validação de CNPJs
Requires Python: >=3.8
Keywords: robotframework testing brazil cnpj generator

# Biblioteca Robot Framework CNPJGenerator

Biblioteca para geração e validação de CNPJs em testes automatizados com Robot Framework.

## Instalação

```bash
pip install robotframework-cnpjgenerator
```

## Uso Básico

```robotframework
*** Settings ***
Library    cnpjgenerator.CNPJGenerator
...    safe_prefixes=12345678,87654321
...    blocked_prefixes=99999999

*** Test Cases ***
Gerar CNPJ Válido
    ${cnpj} =    Generate Safe CNPJ
    ${valid} =    Validate CNPJ    ${cnpj}
    Should Be True    ${valid}
```

## Documentação Completa

[Ver documentação completa](https://seu-usuario.github.io/robotframework-cnpjgenerator/)
jc-cybersec-hack4u 0.1.1
Una biblioteca para consultar cursos de hack4u.

# Hack4U Acadeny Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introcucción al Linux [15 horas]
- Personalización de Linux [15 horas]
- Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando 'pip3':

```python3
pip3 install hack4u
```

## Uso básico

### Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import search_course_by_name

course = search_course_by_name("Introducción al Linux")
print(course)
```

### Calcular duracción total de los cursos

```python3
from hack4u import total_duration

print(f"Duracción total: {total_duration()} horas")
```
arpes-lite 1.0.1
Modular data analysis code for angle resolved photoemission spectroscopy (ARPES)
Requires Python: >=3.13
Keywords: python, arpes, data analysis

# ARPES-Lite

ARPES-Lite is my personal fork of the original PyARPES package. I ran into some issues when first working with the package and fixing them required that I break a significant portion of the codebase.

The main improvements to pyarpes are in automation of loading and correcting data (fermi-edge correction, k-space conversion, etc.). There are also improvements and additions to QT based interactive tools.

Because of the changes made to data loading, some work is required to port endstations plugins from pyarpes. If needed, I can easily port existing endstations or add new ones.

## Installation

Arpes-lite is most easily installed with pip: `pip install arpes-lite`.

Note that the package is still imported with `import arpes` as with pyarpes.

For an editable installation, I recommend installing [uv](https://docs.astral.sh/uv/), cloning the github repo, navigating to the cloned repo in your shell, and installing with `uv sync`. This will also install ipykernel so you can run ipython notebooks with VS Code.

## Usage

The [PyARPES docs](https://arpes.readthedocs.io/en/latest/) are still the best place to learn how to use arpes-lite. I don't have my own docs to document all of the changes in usage, but there aren't many once you've gotten started.

To start, import common functions with `from arpes.config import *`. This contains data loading functions, imports the xarray_extensions, and other common functions for correcting and plotting data (fermi-edge correction, stack_plot, etc.) Having an editable `arpes.config` is the primary advantage of an editable installation.

Data loading can be done the same as in pyarpes with `load_data` or more easily with `load_folder`.

Here's an example of the code I use to load all of the data from an experiment:
```python
corrected: list[xr.Dataset] = [
    fix_fermi_edge(data)
    for data in load_folder(data_root, location="BL7", pattern="*.h5")
]
for i, data in enumerate(corrected):
    print(f"{i}: {data.attrs["comment"]}")
```
This will load all of the data in the `data_root` folder with a .h5 extension and then perform automatic fermi-edge correction on each dataset. Finally it prints a "comment" attribute stored during the measurement. From there you can `convert_to_kspace` without having to specify your momentum coordinates. Finally, you can use `export_dataset` to save a .nc file with any conversions/corrections you've performed so you don't have to do them every time you analyze your data.
hatch-adder 0.0.2
Command to add/remove packages to pyproject.toml for hatch projects
Requires Python: >=3.10

# hatch-adder

A command to add dependencies to hatch similar to uv, poetry, etc.

[![PyPI - Version](https://img.shields.io/pypi/v/hatch-adder.svg)](https://pypi.org/project/hatch-adder)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hatch-adder.svg)](https://pypi.org/project/hatch-adder)

-----

## Table of Contents

- [hatch-adder](#hatch-adder)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [License](#license)

## Installation

```console
pip install hatch-adder
```

Now use it like:
```
hatch-add requests
```

Or add dev dependencies to your `[project.optional-dependencies]`:
```
hatch-add --dev pytest
```

## License

`hatch-adder` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
wsignals 0.2.3
Library for event emitting and handling
Requires Python: >=3.6


# wsignals
v0.2.3

Library for event emitting and handling


## Installation

### PyPI
```sh
pip install wsignals
```

### GitHub
```sh
pip install git+https://github.com/wumtdev/wsignals.git
```

### Manual
Clone from github
```sh
# using https
git clone https://github.com/wumtdev/wsignals.git
# using ssh
git clone git@github.com:wumtdev/wsignals.git
```

In the environment where you need to install library, install it as a local package. Specify path to cloned repository.
```sh
pip install /path/to/cloned_repo
```


## Usage

### Simple
```py
from wsignals import Signal

on_start = Signal()

@on_start
def log_start():
	print('started')

on_start.call()
```

### Class + Async
```py
import asyncio
from wsignals import Signal

class Dog:
	def __init__(self, name: str):
		self.on_bark = Signal()
		self.name = name
	
	def bark(self, msg: str):
		print('bark!')
		self.on_bark.call(msg)

async def main():
	gamma = Dog('Gamma')
	
	@gamma.on_bark
	async def handle_gamma_bark(msg):
		print(f'Gamma barked: {msg}')
	
	gamma.bark()

asyncio.run(main())
```
tricahue 0.0b6
This package extracts experimental data and metadata, converts to stardard formats, uploads to SynBioHub and Flapjack, and connects them
Requires Python: >=3.7
Keywords: SBOL, test, data, experimental, synthetic biology

# Tricahue

Welcome to the Tricahue repository, our Python package for experimental data and metadata standardization, upload to databases and connection for creating distributed datasets.

<img src="https://github.com/Gonza10V/Tricahue/blob/main/images/tricahue_logo.jpeg#gh-light-mode-only" alt="LOICA logo" width="350"/>
<img src="https://github.com/Gonza10V/Tricahue/blob/main/images/tricahue_logo.jpeg#gh-dark-mode-only" alt="LOICA night logo" width="350"/>


![PyPI - Version](https://img.shields.io/pypi/v/tricahue)
![gh-action badge](https://github.com/MyersResearchGroup/Tricahue/workflows/Python%20package/badge.svg)


As you may have noticed, our logo features a beautiful "loro tricahue" or burrowing parrot[(_Cyanoliseus patagonus bloxami_)](https://en.wikipedia.org/wiki/Burrowing_parrot); a bird native to Chile and Argentina known for its distinctive colors green on head and body, red and yellow on the chest, and a blue tail. These parrots excavate burrows on cliff faces or ravines that are connected forming a complex system of tunnels and chambers[.](https://youtu.be/v2AC41dglnM)

## Installation

Installing Tricahue is super simple! 

Do:

`pip install tricahue`


## Tricahue allows you to:

- Extract experimental data from Excel, CSV and TSV files
- Standardize experimental data into Tidy format
- Stadardize experimental metadata into SBOL
- Upload experimental data to Flapjack
- Upload experimental metadata to SynBioHub
- Connect data from SynBioHub and Flapjack to create complete data sets
gui-goals-app 1.0
Script to create, list, mark and delete your goals
Requires Python: >=3.12

🎯 Goals App
Welcome to the Goals App — a simple, interactive command-line tool to help you manage your personal goals with ease. Create, list, mark as finished, and delete goals using a friendly interface powered by questionary and emoji-enhanced prompts.

📦 Features
- 🚀 Create new goals
- 📚 List all goals
- ✅ Mark goals as finished
- ⏳ View open (unfinished) goals
- 🗑️ Delete selected goals
- 💾 Persistent storage via goals.json
- 😁 Emoji-enhanced user experience

🛠️ Installation
Before running the app, make sure you have Python 3 installed. Then, install the required dependencies:
pip install questionary emoji


▶️ Usage
Run the app from your terminal:
>python app.py


You'll be greeted with a menu where you can:
- Create a goal
- List and mark goals as finished
- View finished or open goals
- Delete goals
- Exit the program
All goals are saved in a local goals.json file, so your progress is preserved between sessions.


📁 File Structure
app.py       # Main application script
goals.json         # Stores your goals persistently
README.md          # Project documentation


📚 Code Overview
- load_goals() – Loads goals from goals.json
- save_goals() – Saves current goals to file
- create_goals() – Adds a new goal
- list_goals() – Lists and allows marking goals as finished
- finished_goals() – Displays completed goals
- open_goals() – Displays goals still in progress
- delete_goals() – Deletes selected goals
- show_msg() – Displays status messages
- main() – Runs the interactive menu loop


🧪 Example
Menu >
❯ Create goal 🚀
  List goals 📚
  Finished goals ✅
  Open goals ⏳
  Delete goal😵
  Exit the program👋


📄 License
This project is open-source and free to use. Feel free to modify and share it!
dadi-cli 0.9.13
A command line interface for dadi
Requires Python: >=3.9

# dadi-cli

[![license](https://img.shields.io/badge/license-Apache%202.0-red.svg?style=flat-square)](LICENSE) [![language](http://img.shields.io/badge/language-python-blue.svg?style=flat-square)](https://www.python.org/) [![build Status](https://img.shields.io/github/actions/workflow/status/xin-huang/dadi-cli/build.yml?branch=master&style=flat-square)](https://github.com/xin-huang/dadi-cli/actions) [![codecov](https://img.shields.io/codecov/c/github/xin-huang/dadi-cli?token=GI66f4R3RF&style=flat-square)](https://codecov.io/gh/xin-huang/dadi-cli) [![Documentation Status](https://img.shields.io/readthedocs/dadi-cli?style=flat-square)](https://dadi-cli.readthedocs.io/en/latest/?badge=latest) [![downloads](https://anaconda.org/conda-forge/dadi-cli/badges/downloads.svg)](https://anaconda.org/conda-forge/dadi-cli)

The manual can be found [here](https://dadi-cli.readthedocs.io/en/latest/).

If you need further help, you can submit questions about usage to the [dadi-user Google Group](http://groups.google.com/group/dadi-user).

If you've found an apparent bug or want to suggest a feature, please submit an [Issue](https://github.com/xin-huang/dadi-cli/issues) so we can address it.
flacarray 0.3.4
FLAC Compression of Arrays
Requires Python: >=3.9

# FLACArray

[![Test Suite](https://github.com/hpc4cmb/flacarray/actions/workflows/test.yml/badge.svg)](https://github.com/hpc4cmb/flacarray/actions/workflows/test.yml)

Tools for FLAC compression of numpy arrays in memory and support for several
on-disk formats. This is intended for use with raw numerical data rather than
audio formats.

## Documentation

[See the full documentation here](https://hpc4cmb.github.io/flacarray).