semvec/pypi-packages huggingface dataset:
autolingua 0.3.2
autolingua description
Requires Python: >=3.8
Keywords: python

# Autolingua
Automated linguistic codification python package used in [[1]](https://doi.org/10.19130/iifl.adel.2022.10.2.X00S25872)

## Installation

To install the package, run the following commands:

```bash
pip install autolingua
```

Once installed, you can run the package by typing `autolingua <command>` in the terminal from every directory. Please run `autolingua -h` to check the available commands.

# Automatic variable codification

Automatic codification script. Run ```autolingua codification -h``` to check the program settings.

Run it from the directory where the input files are located.

```bash
autolingua codification -init_cell D446 -file_name PRESEEA_1995_Nivel_bajo.xlsx -color
```


# PULSO project automatic lemmatization

Automatic lemmatization replacement script. Run ```autolingua lemmatization -h``` to check the program settings.

Run it from the directory where the input Excel files are located.

```bash
autolingua replacement -fases_filename filename -input_filename filename 
```

For sequential fase data loading:

```bash
autolingua replacement -fases_filename filename -input_filename filename -fase2_from_file filename -fase3_from_file filename
```


# References

[1] Ávila, A. M., Segura, A. (2022). Estudio de las variables predictoras de la expresión del sujeto pronominal en el corpus PRESEEA. Málaga. Nivel de instrucción bajo, Anuario de Letras. Lingüística y Filología, vol. 10, no. 2, pp.57–93. doi:10.19130/iifl.adel.2022.10.2.X00S25872

# Citation

If you use this repository, please cite:

Ramajo-Ballester, Á. (2022). Autolingua repository, GitHub. https://github.com/ramajoballester/autolingua
dimans 0.0.24
Dimensional analysis and unit conversion library
Requires Python: >=3.10
Keywords: units, calculation, calculator, constants

# dimAns

**Dimensional analysis and unit conversion library**

[![PyPI - Version](https://img.shields.io/pypi/v/dimans?link=https%3A%2F%2Fpypi.org%2Fproject%2Fdimans%2F)](https://pypi.org/project/dimans/#history)
[![GitHub Tag](https://img.shields.io/github/v/tag/emreozcan/dimans)](https://github.com/emreozcan/dimAns/tags)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/dimans)](https://pypi.org/project/dimans/)
[![GitHub License](https://img.shields.io/github/license/emreozcan/dimans)](https://github.com/emreozcan/dimAns/blob/master/LICENSE)

[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/emreozcan/dimAns/test.yml)](https://github.com/emreozcan/dimAns/actions/workflows/test.yml)
[![Codacy Code Quality Badge](https://app.codacy.com/project/badge/Grade/91ba463964c947c1af99446e92d1cd24)](https://app.codacy.com/gh/EmreOzcan/dimAns/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[![Codacy Coverage Badge](https://app.codacy.com/project/badge/Coverage/91ba463964c947c1af99446e92d1cd24)](https://app.codacy.com/gh/EmreOzcan/dimAns/coverage/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage)

## Installation

```bash
pip install --upgrade dimans
```

If you want to use the calculator, you will need to install the "cli" extra to
install some additional dependencies:

```bash
pip install --upgrade dimans[cli]
```

If you are using a Linux distribution like Arch that disallows installing
packages using pip, you will need to use a tool such as `pipx`:

```bash
pipx install --upgrade dimans[cli]
```

## Usage

```python-repl
>>> from dimans.units import gram, kilogram, metre
>>> (32_000 * gram).to(kilogram)
<Quantity 32.0 kg>
```

## Calculator

```bash
dimans
```
<p>
<img
    src="https://raw.githubusercontent.com/emreozcan/dimAns/master/imgs/calculator.svg"
    alt="dimAns Calculator Screenshot: Main Screen"
    width="50%"><img
    src="https://raw.githubusercontent.com/emreozcan/dimAns/master/imgs/search.svg"
    alt="dimAns Calculator Screenshot: Command Search"
    width="50%">
</p>
pylogserver-aquanjsw 0.0.5
A simple python logging server
Requires Python: >=3.0

# pylogserver Python Package

As a companion executable to `logging.handlers.SocketHandler`.

## Example

Start server from cmdline:

```plain
$ pip install pylogserver
$ pylogserver 9999
Listening on localhost:9999
```

Client side example:

```python
#!/usr/bin/env python3
# client.py
import logging
import logging.handlers

if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger()
    socket_handler = logging.handlers.SocketHandler('localhost', 9999)
    logger.addHandler(socket_handler)
    logger.info('Hello, world!')
```

Server side output:

```plain
[2024-08-28 20:26:04,681] [root] [INFO] Hello, world!
```

## References

- [Python official logging cookbook](https://docs.python.org/zh-cn/3/howto/logging-cookbook.html#sending-and-receiving-logging-events-across-a-network)
scientistshiny 0.0.2
Python library to easily improve multivariate Exploratory Data Analysis graphs
Requires Python: >=3.10

<p align="center">
	<img src="./scientistshiny.svg" height=300></img>
</p>
<div align="center">

[![GitHub](https://shields.io/badge/license-MIT-informational)](https://github.com/enfantbenidedieu/scientistshiny/blob/master/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/scientistshiny.svg?color=dark-green)](https://pypi.org/project/scientistshiny/)
[![Downloads](https://static.pepy.tech/badge/scientistshiny)](https://pepy.tech/project/scientistshiny)
[![Downloads](https://static.pepy.tech/badge/scientistshiny/month)](https://pepy.tech/project/scientistshiny)
[![Downloads](https://static.pepy.tech/badge/scientistshiny/week)](https://pepy.tech/project/scientistshiny)
</div>

# scientistshiny : Perform Factorial Analysis from `scientisttools` with a Shiny for Python Application

## 1 About scientistshiny

scientistshiny is a Python package to easily improve multivariate Exploratory Data Analysis graphs.

## 2 Why used scientistshiny?

scientistshiny provided functions for :

* Principal Component Analysis (PCA) with scientistshiny (**_PCAshiny_**)
* Correspondence Analysis (CA) with scientistshiny (**_CAshiny_**)
* Multiple Correspondence Analysis (MCA) with scientistshiny (**_MCAshiny_**)
* Factor Analysis for Mixed Data (FAMD) with scientistshiny (**_FAMDshiny_**)
* Multiple Factor Analysis (MFA) with scientistshiny (**_MFAshiny_**)
* Multiple Factor Analysis for qualitative variables (MFAQUAL) with scientistshiny (**_MFAQUALshiny_**)
* Multiple Factor Analysis for Mixed Data (MFAMIX) with scientistshiny (**_MFAMIXshiny_**)
* Multiple Factor Analysis for Contingence Tables (MFACT) with scientistshiny (**_MFACTshiny_**)

## 3 Installation

### 3.1 Dependencies

scientistshiny requires :

```bash
scientisttools>=0.1.6
numpy>=1.26.4
matplotlib>=3.8.4
scikit-learn>=1.2.2
pandas>=2.2.3
plotnine>=0.10.1
```

### 3.2 User installation

You can install scientisttools using `pip` :

```bash
pip install scientistshiny
```

## 4 Example with `PCAshiny`

```python
# Load dataset and functions
from scientisttools import PCA, load_decathlon2
from scientistshiny import PCAshiny
decathlon = load_decathlon2()

# PCA with scientistshiny
res_shiny = PCAshiny(model = decathlon)
res_shiny.run()

# PCAshiny on a result of a PCA
res_pca = PCA(ind_sup=list(range(23,27)),quanti_sup=[10,11],quali_sup=12)
res_pca.fit(decathlon)
res_shiny = PCAshiny(model = res_pca)
res_shiny.run()
```

## 4 Author(s)

Duvérier DJIFACK ZEBAZE ([djifacklab@gmail.com](djifacklab@gmail.com))