semvec/pypi-packages huggingface dataset:
pylovelace 2023.2.1
Python code protection/obfuscation tool
Requires Python: >=3.10, <3.13
Keywords: obfuscate obfuscation distribute production tool

PyLovelace Python protection tool.
===================================

PyLovelace is a Python protection tool that can be used to protect
Python source code from being reverse engineered.

Installation
------------
PyLovelace is available on PyPI and can be installed using pip:

    pip install pylovelace

Usage
-----
PyLovelace can be used as a command line tool to protect Python source
code. The following command will protect the Python source code in
`my_script.py` and write the protected code to `dist/my_script.py`:

    pylovelace protect my_script.py

Supported Python versions
-------------------------
PyLovelace supports Python 3.11 with 3.10 and 3.12 support coming soon.
Windows only for now.

More information
----------------
- PyLovelace on PyPI: https://pypi.org/project/pylovelace/
- PyLovelace on GitHub: https://github.com/pylovelace/pylovelace
- PyLovelace documentation: https://pylovelace.com/docs/getting-started/introduction
datasets-summarizer 0.1.2
Datasets Summary Viewer. Enables the exploration of dataset search results in Jupyter Notebooks
Requires Python: >=3.6

# DatasetsSummarizer

Datasets Summarizer is compatible with Jupyter Notebooks. Need the x and y values based on any similarity metric to generated the similarity plot between datasets. Supports the metadata format generated by [datamart-profiler](https://docs.auctus.vida-nyu.org/python/datamart-profiler.html#) library to generate the Detail View to explore each dataset.


![System screen](https://github.com/soniacq/DatasetsVis/blob/main/DatasetsSummarizer/imgs/datasets_summarizer_view.png)

( Click one dataset from the list of results to open the Detail View.)

## Demo

Live demo (Google Colab):
- [Dataset results for Taxi query](https://colab.research.google.com/drive/11NT0qudr2di9NXlR-DxZa0buzGreOqxs?usp=sharing)

In Jupyter Notebook:
```Python
import DatasetsSummarizer
data = DatasetsSummarizer.get_taxi_data()
DatasetsSummarizer.plot_datasets_summary(data)
```

## Install

### Option 1: install via pip:
~~~~
pip install datasets-summarizer
~~~~

## Custom similarity metric

Use a subset or add a new entry (`x` and `y` values ) based on a different similatiry metric. For example, here we added `x` and `y` values based on a similarity metric using a modified version of the titles. Note that `modif_title_x` and `modif_title_y` must be included in the dataframe.

```Python
new_similarity_metrics = [{'name': 'Title', 'x': 'title_x', 'y': 'title_y'},
                          {'name': 'ModifiedTitle', 'x': 'modif_title_x', 'y': 'modif_title_y'}
                         ]
```

Then, we can pass this new similarity metrics as a parameter of our visualization
```Python
DatasetsSummarizer.plot_datasets_summary(dataframe, new_similarity_metrics)
```
ua-node-avail 0.1.13
Tool to monitor node availablility at UA
Requires Python: >=3

# ua-node-avail

A simple script to host on pip to allow the user to analyze the queues for available nodes.

Installation:
```
pip install ua-node-avail
```

Usage:
```
node_avail
```

Output:
```
                              CPU CORES                MEMORY GB
  NODE                VENDOR  FREE       ALLOC  TOTAL  FREE       ALLOC  TOTAL

  chpc-compute-12-2   AMD     32         32     64     123        128    251
  chpc-compute-12-8   AMD     32         32     64     123        128    251
  chpc-compute-12-17  AMD     32         32     64     123        128    251
  chpc-compute-12-22  AMD     32         32     64     123        128    251
  chpc-compute-12-24  AMD     32         32     64     133        118    251
  chpc-compute-12-0   AMD     16         48     64     67         184    251
  chpc-compute-12-1   AMD     16         48     64     64         187    251
  chpc-compute-12-3   AMD     16         48     64     64         187    251
  chpc-compute-12-4   AMD     16         48     64     64         187    251
  chpc-compute-12-6   AMD     16         48     64     64         187    251
  chpc-compute-12-7   AMD     16         48     64     67         184    251
  chpc-compute-12-9   AMD     16         48     64     73         178    251
  chpc-compute-12-15  AMD     16         48     64     64         187    251
  chpc-compute-12-16  AMD     16         48     64     64         187    251
  chpc-compute-12-18  AMD     16         48     64     64         187    251
  chpc-compute-12-12  AMD     10         54     64     50         201    251
  chpc-compute-12-10  AMD     0          64     64     14         237    251
  chpc-compute-12-11  AMD     0          64     64     14         237    251
  chpc-compute-12-13  AMD     0          64     64     14         237    251
  chpc-compute-12-14  AMD     0          64     64     10         241    251
  chpc-compute-12-19  AMD     0          64     64     14         237    251
  chpc-compute-12-20  AMD     0          64     64     14         237    251
  chpc-compute-12-21  AMD     0          64     64     10         241    251
  chpc-compute-12-23  AMD     0          64     64     14         237    251
  chpc-gpu-12-1       AMD     64         0      64     251        0      251
  chpc-gpu-10-0       AMD     0          64     64     14         237    251
  chpc-gpu-10-2       AMD     0          64     64     14         237    251
  chpc-gpu-12-0       AMD     0          64     64     14         237    251
  chpc-highmem-12-0   AMD     40         8      48     1906       109    2015
  chpc-highmem-10-0   AMD     0          48     48     15         2000   2015
  chpc-highmem-10-1   AMD     0          48     48     926        1089   2015
  chpc-highmem-12-1   AMD     0          48     48     1411       604    2015
```
colititato 0.1.1
A CLI implementation of the classic Tic Tac Toe game
Requires Python: >=3.6
Keywords: cli,tic-tat-toe,game

# Colititato

This is a simple command-line interface (CLI) implementation of the popular Tic Tac Toe game in Python. It allows a player to play the game with a computer player by taking turns to mark the spaces on a 3x3 grid.

## Installation
To install the game, you can use pip to download and install it from PyPI:

```
pip install colititato
```

## Usage

After installation, you can start the game by running the following command in your terminal:

```
colititato
````

This will start a new game and prompt the first player to make a move. The game board will be displayed in the console as follows:

|   |   |   |
|:---:|:---:|:---:|
| 1 |  2 | 3 |
| 4 |  5 | 6 |
| 7 |  8 | 9 |

To make a move, enter the number corresponding to the cell you want to mark (1-9) and press Enter. The cell will be marked with either "X" or "O", depending on whose turn it is. The game will continue until one of the players wins, or the game ends in a tie.


## Development

To develop Colinote, first clone the repository:
```
git clone https://github.com/tiakas/colititato.git`
cd colinote
```
Then, install the development dependencies:

```
pip install -r requirements.txt
```

To run the tests, use:
```
pytest
```

To build the package, use:
```
python setup.py sdist bdist_wheel
```

This will create a dist directory containing the source distribution (*.tar.gz) and wheel distribution (*.whl) of the package.

Check the package contents:
 ```
 twine check dist/*
 ```
Upload the package to PyPI:
```
twine upload dist/*
```

## License

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

## Contributing
Contributions are always welcome! Here are some ways to contribute:

- Fork the repository and make changes on your local branch.
- Create a pull request with your changes.
- Work on open issues.