semvec/pypi-packages huggingface dataset:
pck-ebenezer 0.0.1
A small example package
Requires Python: >=3.7

Development
If you'd like to contribute to pck_ebenezer, you can do so by cloning the repository and following the standard pull request process.

License
This project is licensed under the MIT License - see the LICENSE.md file for details.

Authors
Example Author - author@example.com
Acknowledgments
Hat tip to anyone whose code was used
Inspiration
etc
pck-BrendanD 0.0.1
A small example package
Requires Python: >=3.7

[![Review Assignment Due Date](https://classroom.github.com/assets/deadline-readme-button-24ddc0f5d75046c5622901739e7c5dd533143b0c8e959d652212380cedb1ea36.svg)](https://classroom.github.com/a/CSvoW5SF)
# Instructions

Complete [Activity 14: Python Packaging](https://classroom.github.com/a/V0uPkSQk) and have a Python package deployed on PyPi. The name of your package does not have to be "example-package" but it most end with your name. The package could do anything you like (not necessarily what is done in the tutorial). You need to push a file name **example.py** that illustrates a possible use of your package. 

# Grading

+3 I should be able to install your package using pip. 

+2 **example.py** uses the package and runts without any error. 

# pck_BrendanD

`pck_BrendanD` is a simple Python package that provides a friendly greeting.

## Installation

Install `pck_BrendanD` by running:

pip install pck_BrendanD

## Usage

To use the `greet` function from the `pck_BrendanD` package, follow these steps:

```python
from pck_BrendanD import mod

# Greet the world
print(mod.greet("World"))

# Greet a user by name
print(mod.greet("Brendan"))
causal-i-art 0.1.2
I-ART: A Generalized Framework for Imputation-Assisted Randomization Tests
Requires Python: >=3.8

# I-ART: Imputation-Assisted Randomization Tests

## Description

I-ART (Imputation-Assisted Randomization Tests) is a Python package designed for conducting finite-population-exact randomization tests in design-based causal studies with missing outcomes. It offers a robust solution to handle missing data in causal inference, leveraging the potential outcomes framework and integrating various outcome imputation algorithms.

## Installation

To install [I-ART](https://pypi.org/project/causal-i-art/), run the following command:

```bash
pip install causal-i-art
```

## Usage

Here is a basic example of how to use I-ART:

```python
import numpy as np
from i_art import iartest

Z = np.array([1, 1, 1, 1, 0, 0, 0, 0])
X = np.array([[5.1, 3.5], [4.9, np.nan], [4.7, 3.2], [4.5, np.nan], [7.2, 2.3], [8.6, 3.1], [6.0, 3.6], [8.4, 3.9]])
Y = np.array([[4.4, 0.5], [4.3, 0.7], [4.1, np.nan], [5.0, 0.4], [1.7, 0.1], [np.nan, 0.2], [1.4, np.nan], [1.7, 0.4]])
result = iartest(Z=Z, X=X, Y=Y, L=1000, verbose=True)
```

## Features

- Conducts finite-population-exact randomization tests.
- Handles missing data in causal inference studies.
- Supports various outcome imputation algorithms.
- Offers covariate adjustment in exact randomization tests.


## Contributing

Your contributions to I-ART are highly appreciated! If you're looking to contribute, we encourage you to open issues for any bugs or feature suggestions, or submit pull requests with your proposed changes. 

### Setting Up a Development Environment

To set up a development environment for contributing to I-ART, follow these steps:

```bash
python -m venv venv
source venv/bin/activate 
pip install -r requirements.txt
```
This creates a virtual environment (`venv`) for Python and activates it, allowing you to work on the package without affecting your global Python environment.

## License
This project is licensed under the MIT License

## Citation
If you use I-ART in your research, please consider citing it:

```code
@misc{heng2023designbased,
      title={Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment}, 
      author={Siyu Heng and Jiawei Zhang and Yang Feng},
      year={2023},
      eprint={2310.18556},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}
```
minetest-log-parser 0.0.2
Minetest player action log parser
Requires Python: >=3.11

# Minetest log parser
Simple parser for Minetest logs. Parses Minetest logs to JSON.

## Description
It only asks for player actions on the server, including authorization and events such as digs, places, moves, actives, punched etc.

## Using
Simple example reading via generator parsed dicts:

```python
from src.minetest_log_parser.Parser.MinetestLogParser import MinetestLogParser

if __name__ == '__main__':
    input = "./debug.txt"
    minetestParser = MinetestLogParser(input)

    for parsedLog in minetestParser.read():
        playerNames = ['littlePrettyCat', 'Melon', 'Player']
        if parsedLog['name'] in playerNames:
            print(parsedLog)
```
Example for import logs to JSON:

```python
from src.minetest_log_parser.Parser.MinetestLogParser import MinetestLogParser

if __name__ == '__main__':
    input = "./debug.txt"
    output = "./debug.json"
    minetestParser = MinetestLogParser(input)

    minetestParser.importToJson(output)
```
## Parsed line output examples:

Action:
```json
{"timestamp": 1698474247, "name": "Player", "action": "places node", "meta_action": null, "node": "default:dirt", "count": 1, "coords": null, "type": null, "logType": "action"}
```
Beowulf auth:
```json
{"timestamp": 1698471091, "name": "Player", "ip": "127.0.0.1", "protocolVersion": "40", "formspecVersion": "4", "lang": "en", "logType": "beowulfAuth"}
```
Default auth:
```json
{"timestamp": 1698471091, "name": "Player", "ip": "127.0.0.1", "logType": "auth"}

```