semvec/pypi-packages huggingface dataset:
spotdesirability 0.0.14
spotdesirability - a Python package for desirability
Requires Python: >=3.10

# spotdesirability

* `Python` implementation of the `R` package `desirability`, which provides  S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).

# Installation

* Installation can be done with `pip`: 

```{bash}
pip install spotdesirability
```

* Alternatively, you can clone the repository and install it manually.


# Citation

```bibtex
@misc{bartz25a,
      title={Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions}, 
      author={Thomas Bartz-Beielstein},
      year={2025},
      eprint={2503.23595},
      archivePrefix={arXiv},
      primaryClass={math.OC},
      url={https://arxiv.org/abs/2503.23595}, 
}
```

# Documentation

* The documentation is available at: [https://sequential-parameter-optimization.github.io/spotdesirability/](https://sequential-parameter-optimization.github.io/spotdesirability/)


# References

## Desirability functions

* Derringer, G., and Suich, R. Simultaneous optimization of several response variables. Journal of Quality Technology 12 (1980), 214–219.

## The `R` `desirability` package

* The `R` `desirability` package is maintained and developed by Max Kuhn. It is is available on CRAN:  https://CRAN.R-project.org/package=desirability,  DOI: https://doi.org/10.32614/CRAN.package.desirability
hdc-py 0.1.3
A python wrapper around the (Open-) Harmony device connector
Requires Python: >=3.11

# hdc-py

A Python wrapper around the (Open)Harmony Device Connector hdc.
Using the library requires the `hdc` tool to be already installed.

hdc-py is meant to make writing scripts for testing on OpenHarmony devices easier,
and provide abstractions. Goals include:

- Improved error checking / validation of command success
- Support `with` syntax to enable performance mode for a number of commands,
  and disable the performance mode again, even if an exception occurs.

This library is still under active development and far from complete. Known limitations include:

- Currently only supports a single connected hdc device. 
- Only a subset of commands is currently wrapped.


This library is not an official OpenHarmony project.
PyMouseKey 1.3.3
A python package for handling keyboard and mouse inputs

# ⌨️🖱️ Pymousekey

A python package for handling keyboard and mouse inputs

---

## 📖 Description

Pymousekey offers a range of functions to interact with the operating system by sending various mouse and keyboard inputs, making it useful for automation, testing, and scripting repetitive tasks.

---

## 🧩 Requirements
- Python 3.8+ (may work on earlier Python 3 versions)
- Windows operating system

---

## ⚙️ Installation

```bash
pip install pymousekey
```
## 🚀 Example Usage

```python
import pymousekey

# Left click at a position
pymousekey.click(255, 720, button='left')

# Right click at a position
pymousekey.click(255, 720, button='right')

# Left click at multiple locations without delay
pymousekey.click(0, 500, _pause=False)
pymousekey.click(800, 200, _pause=False)
pymousekey.click(300, 900, _pause=False)

# Add a global hotkey
pymousekey.add_hotkey("numpad 5")

# Press a key
pymousekey.press("a")

# Scroll up 1 notch
pymousekey.scroll(-1)

# Scroll down 1 notch
pymousekey.scroll(1)

```
hanzi_chaizi 0.4.0
Chinese character decomposition tools for breaking down characters into basic structural units
Requires Python: >=3.10
Keywords: character-decomposition, chinese, deep-learning, hanzi, nlp

# Hanzi decomposition (Chinese character decomposition) | 汉字拆字

> 拆字是指將一文字，以筆畫、字形等基本組成單位分解成多個文字。
> The decomposition of characters refers to breaking down a single character into multiple characters based on its basic components, such as strokes and structural elements.

> 汉字拆字让字型相似的字具有相似的拆解结果。
> Hanzi decomposition yields similar decomposition results for characters with similar structures.

> 这种特性可以被深度学习模型用来作为字的特征之一：字形的特征。
> This feature can be used by deep learning models as one of the features of characters: the structural feature.

## Installation

```bash
pip install hanzi_chaizi
```

## Usage

```python
from hanzi_chaizi import HanziChaizi

hc = HanziChaizi()
result = hc.query('名')

print(result)
```

Output:

```text
['夕', '口']
```

## Development

See [develop.md](develop.md) for development guide.

## Credits

Data from [漢語拆字字典](https://github.com/kfcd/chaizi) (CC BY 3.0)

## Citation

```
@misc{kong2018hanzichaizi,
  title={Hanzi Chaizi},
  author={Xiaoquan Kong},
  howpublished={https://github.com/howl-anderson/hanzi_chaizi},
  year={2018}
}
```

If the package is cited in books, seminars, and academic research papers, or used in company products, you are welcome (but not required) to email me about this. I'm glad to see the package being used and valuable to everyone.
sparqlite 1.1.0
A modern, lightweight SPARQL 1.1 client for Python
Requires Python: >=3.9

# sparqlite

[![Tests](https://github.com/opencitations/sparqlite/actions/workflows/test.yml/badge.svg)](https://github.com/opencitations/sparqlite/actions/workflows/test.yml)
[![Coverage](https://byob.yarr.is/arcangelo7/badges/opencitations-sparqlite-coverage-main)](https://opencitations.github.io/sparqlite/coverage/)
[![Python Versions](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)](https://github.com/opencitations/sparqlite)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![License](https://img.shields.io/badge/license-ISC-green)](https://github.com/opencitations/sparqlite/blob/master/LICENSE)

A modern, lightweight SPARQL 1.1 client for Python. Built on [pycurl](https://github.com/pycurl/pycurl) for [high performance](https://opencitations.github.io/sparqlite/architecture/why-pycurl/).

[Full documentation](https://opencitations.github.io/sparqlite/)

## Quick start

```bash
pip install sparqlite
```

```python
from sparqlite import SPARQLClient

with SPARQLClient("https://opencitations.net/meta/sparql") as client:
    result = client.query("""
        PREFIX fabio: <http://purl.org/spar/fabio/>
        PREFIX dcterms: <http://purl.org/dc/terms/>

        SELECT ?article ?title WHERE {
            ?article a fabio:JournalArticle ;
                     dcterms:title ?title .
        }
        LIMIT 5
    """)

    for row in result["results"]["bindings"]:
        print(f"{row['title']['value']}")
```