semvec/pypi-packages huggingface dataset:
synloc 0.2.4
A Python package to create synthetic data from locally estimated distributions
License: MIT
Requires Python: >=3.8
Keywords: copulas, distributions, sampling, synthetic-data, oversampling, nonparametric-distributions, semiparametric, nonparametric, knn, clustering, k-means, multivariate-distributions

<div align="center">

# synloc: An Algorithm to Create Synthetic Tabular Data

<img src="https://raw.githubusercontent.com/alfurka/synloc/main/assets/logo_white_bc.png" alt = 'synloc'>

[Overview](#overview) | [Installation](#installation) | [A Quick Example](#a-quick-example) | [Documentation](https://alfurka.github.io/synloc/) | [How to cite?](#how-to-cite)

[![PyPI](https://img.shields.io/pypi/v/synloc)](https://pypi.org/project/synloc) [![Downloads](https://static.pepy.tech/badge/synloc)](https://pepy.tech/project/synloc)

</div>

## Overview

`synloc` is an algorithm to sequentially and locally estimate distributions to create synthetic versions of a tabular data. The proposed methodology can be combined with parametric and nonparametric distributions. 

## Installation

`synloc` can be installed through [PyPI](https://pypi.org/):

```
pip install synloc
```

## A Quick Example

Assume that we have a sample with three variables with the following distributions:

$$x \sim Beta(0.1,\,0.1)$$

$$y \sim Beta(0.1,\, 0.5)$$

$$z \sim 10 y + Normal(0,\,1)$$

The distribution can be generated by `tools` module in `synloc`:


```python
from synloc.tools import sample_trivariate_xyz
data = sample_trivariate_xyz() # Generates a sample with size 1000 by default. 
```

Initializing the resampler:


```python
from synloc import LocalCov
resampler = LocalCov(data = data, K = 30)
```

**Subsample** size is defined as `K=30`. Now, we locally estimate the multivariate normal distribution and from each estimated distributions we draw "synthetic values."


```python
syn_data = resampler.fit() 
```

    100%|██████████| 1000/1000 [00:01<00:00, 687.53it/s]
    

`syn_data` is a [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) where all variables are synthesized. Comparing the original sample using a 3-D Scatter:


```python
resampler.comparePlots(['x','y','z'])
```    
![](https://raw.githubusercontent.com/alfurka/synloc/main/assets/README_7_0.png)

## How to cite?

If you use `synloc` in your research, please cite the following paper:

```bibtex
@misc{kalay2025generatingsyntheticdatalocally,
      title={Generating Synthetic Data with Locally Estimated Distributions for Disclosure Control}, 
      author={Ali Furkan Kalay},
      year={2025},
      eprint={2210.00884},
      archivePrefix={arXiv},
      primaryClass={stat.CO},
      url={https://arxiv.org/abs/2210.00884}, 
}
```
yanimt 0.0.5
Yet another impacket tool
License: MIT
Requires Python: >=3.11

# Yanimt

Yet ANother IMpacket Tool

Gather infos from active directory and visualize it from your terminal

# Installation


```
# System wide
pipx install yanimt

# In a venv
pip install yanimt

# Directly from source
pipx install git+https://github.com/Headorteil/yanimt
```

# Doc
Check the [cli doc](https://github.com/Headorteil/yanimt/blob/main/docs/cli.md)

![TUI 1](https://raw.githubusercontent.com/Headorteil/yanimt/refs/heads/main/images/TUI-1.png)

![TUI 2](https://raw.githubusercontent.com/Headorteil/yanimt/refs/heads/main/images/TUI-2.png)

> NOTE : You can select text from the tui by pressing the shift key in most terminals

# DEV
## Set up env

Install poetry and poetry-up

```bash
poetry shell
poetry install
pre-commit install
```

## Debug

In 2 terms :

`textual console`

`textual run --dev -c yanimt`

# TODO

- Follow these issues :
    - https://github.com/fastapi/typer/issues/951
    - https://github.com/fastapi/typer/issues/347
- Write a proper doc
- Remove all the `# noqa:` and `# pyright: ignore` ? (maybe one day)
- Do things