semvec/pypi-packages huggingface dataset:
makefiles-cli 1.0.0
A lightweight Python utility for file creation and template generation from XDG_TEMPLATES_DIR
Requires Python: >=3.10
Keywords: makefiles, mkfiles, mkfile, makefiles-cli, mkfile-cli

# makefiles-cli - Command line interface for XDG_TEMPLATES_DIR

`makefiles-cli` is a simple commandline tool to create files and templates. It can create one or more empty files or any template defined in `XDG_TEMPLATES_DIR`. It also has support for [fzf](https://github.com/junegunn/fzf) to make it easier to find template.

## Usage

Create empty files:

```bash
mkfile example1 example2
```

List all available templates:

```bash
mkfile --list
```

Create template from any template defined in `XDG_TEMPLATES_DIR`:

```bash
mkfile script.py --template="pyscript.py"
```

Create template using `fzf` as picker to pick template interactively:

```bash
mkfile script.py --template --picker="fzf"
```

Run `mkfile --help` for all the available options.

## Installation

_Requirements_:

- python3 (python3.10 or greater)
- pip

You can install `makefiles-cli` directly from **PyPI** using `pip`:

```bash
pip install makefiles-cli
```

Or if you love to stay on bleeding edge, install directly from github:

```bash
pip install git+https://github.com/Rid1FZ/makefiles-cli
```
etl-dataprocess 0.2.7
Functions utils to perform data processing
Requires Python: >=3.10

# dataprocess

**dataprocess** é um pacote Python que oferece utilitários simples e eficientes para o processamento e a limpeza de dados.

## Recursos

- **Processamento de dados**: Transforme dados utilizando funções dedicadas.
- **Limpeza de dados**: Remova valores nulos e prepare dados para análise.
- Estrutura modular para fácil extensão.

## Instalação

Instale o pacote diretamente do repositório GitHub:

```bash
pip install etl-dataprocess
```
ou
``` bash
git clone https://github.com/botlorien/dataprocess.git
cd dataprocess
pip install .
```

## Exemplo de uso

```python
from dataprocess import dataprocessing as hd


if __name__ == '__main__':

    def process_something_here():
        """Only a single example to use dataprocess"""
        # handle importation files verifying if .xlsx, .csv, .xls, .json, .txt
        # and returning its content as 'DataFrame' to (.xlsx, .csv, .xls), 'dict' to (.json) and 'str' to .txt
        # if only the directory folder was passed as argument it get the first file in that folder
        table = hd.import_file(PATH_DOWNLOADS)

        # clear all table removing white spaces and another trashes
        # and return a 'DataFrame' with all columns astype('str')
        table = hd.clear_table(table)

        # Now after the cleaning convert the columns to the apropriate types
        # it accepts a mapping argument "dtypes" to list columns to be cast to
        # 'datetime' and 'time'. Another common types as 'int', 'float' and 'str' are
        # handled automatically analysing its values.
        dtype = {
            'datetime':[
                'date_name_column' # replace it with the name of the column to be cast do 'datetime'
            ],
            'time':[
                'hour_and_minute_name_column' # replace it with the name of the column to be cast do 'time'
            ]
        }
        table = hd.convert_table_types(
            table,
            dtypes=dtype
        )
        print(table)
        print(table.info())
        return table

    process_something_here()
```
barz 0.1.0
Bar charts in the terminal
Requires Python: >=3.10

# barz

## Installation
```sh
uv tool install barz
```
```sh
pipx install barz
```

## Usage
```console
$ barz --help
usage: barz [-h] [-s] [-r] [filename]

positional arguments:
  filename

options:
  -h, --help     show this help message and exit
  -s, --sort
  -r, --reverse

$ python3 -c "print(*['asdf']*100, *['qwer']*15, *['zxcv']*34, sep='\n')" | uniq -c | barz
asdf   100 ██████████████████████████████
qwer    15 ████▌
zxcv    34 ██████████▎

$ python3 -c "print(*['asdf']*100, *['qwer']*15, *['zxcv']*34, sep='\n')" | uniq -c | barz -r
zxcv    34 ██████████▎
qwer    15 ████▌
asdf   100 ██████████████████████████████

$ python3 -c "print(*['asdf']*100, *['qwer']*15, *['zxcv']*34, sep='\n')" | uniq -c | barz -s
qwer    15 ████▌
zxcv    34 ██████████▎
asdf   100 ██████████████████████████████

$ python3 -c "print(*['asdf']*100, *['qwer']*15, *['zxcv']*34, sep='\n')" | uniq -c | barz -sr
asdf   100 ██████████████████████████████
zxcv    34 ██████████▎
qwer    15 ████▌
```
morph-drive 0.2.5
A package for morph-drive algorithms in origami robotics
Requires Python: >=3.13

# Origami Robot Algorithms

[![Python CI](https://github.com/NuwanJ/morph-drive/actions/workflows/python-ci.yml/badge.svg)](https://github.com/NuwanJ/morph-drive/actions/workflows/python-ci.yml)
[![GitHub release](https://img.shields.io/github/v/release/NuwanJ/morph-drive?include_prereleases)](https://github.com/NuwanJ/morph-drive/releases)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
[![Python](https://img.shields.io/badge/python-3.13-blue)](https://www.python.org/downloads/release/python-3130/)
[![NumPy](https://img.shields.io/badge/numpy-2.2.6-brightgreen)](https://github.com/NuwanJ/morph-drive)
[![Gymnasium](https://img.shields.io/badge/gymnasium-1.1.1-brightgreen)](https://pypi.org/project/gymnasium/)
[![MuJoCo](https://img.shields.io/badge/mujoco-3.3.2-brightgreen)](https://pypi.org/project/mujoco/)

## About the Project

This Python package offers tools and algorithms for simulating, analyzing, and controlling origami-inspired robotic systems. Origami robotics combines the principles of folding with robotics to create lightweight, scalable, and transformable robots. These systems have applications in fields like surgery, space exploration, manufacturing, and art.

Key functionalities include:

* **Folding Simulation:** Model the kinematics and dynamics of folding/unfolding in virtual environments.
* **Motion Planning:** Plan robot movements to achieve specific configurations.
* **Control Design:** Develop control strategies for stability and precision.
* **Visualization:** Visualize folding sequences and interactions.

The package is modular and extensible, enabling users to integrate custom algorithms and designs.

## Installation

```bash
pip install morph-drive
```

## Getting Started

### Prerequisites

* Python 3.13+
* pip
* MuJoCo (for physics-based simulation)

### Examples

> To be added soon.

## Contributing

We welcome contributions! Follow these steps:

1. **Fork the Repository:** Create a fork on GitHub.
2. **Create a Branch:** Use a descriptive branch name.
3. **Make Changes:** Follow coding standards and add tests.
4. **Run Tests:** Ensure no regressions.
5. **Document Changes:** Update relevant documentation.
6. **Commit and Push:** Use clear commit messages.
7. **Submit a Pull Request:** Provide a detailed description.

### Reporting Bugs or Suggesting Features

* Open an issue on GitHub with a clear description.

## License

Licensed under the Apache License 2.0. See [LICENSE](http://www.apache.org/licenses/LICENSE-2.0) for details.
echoes-waveform-models 0.0.6
A library of GW echoes waveform models
Requires Python: >=3.9

# echoes_waveform_models

[![License](https://img.shields.io/badge/license-MIT-green)](https://git.ligo.org/echoes_template_search/echoes_waveform_models/-/blob/main/LICENSE)
[![Documentation](https://img.shields.io/badge/Documentation-ready)](https://docs.ligo.org/echoes_template_search/echoes_waveform_models/)

This package generates gravitational-wave (GW) echo waveforms, using the new python interface [`gwsignal`](https://docs.ligo.org/lscsoft/lalsuite/lalsimulation/namespacelalsimulation_1_1gwsignal.html) that is distributed together with `lalsuite`.

Currently, the package implements two such waveform models:
- `IMREPhenomAbedi` in [Abedi *et al.* (2017)](https://arxiv.org/abs/1612.00266)
- `IMREPhenomBHP` in [Nakano *et al.* (2017)](https://academic.oup.com/ptep/article/2017/7/071E01/4004700)

## Installation
The package is `pip`-installable and is available on PyPI.

### Installing from PyPI
Simply run
```bash
pip install echoes_waveform_models
```

### Installing from source
After cloning this repository, go to the root directory of the repository and run
```bash
pip install .
```
semantic-html 0.5.3
Parse HTML Notes into RDF-compatible JSON-LD structures via custom mapping.
Requires Python: >=3.8

# Semantic HTML

Parse HTML Notes into RDF-compatible JSON-LD structures.

See [tests/Testing.ipynb](tests/Testing.ipynb) for a demonstration and use `import semantic-html`.

There are no dependencies except for `lxml` and `bs4`. The result is JSON-LD and RDFa HTML.