semvec/pypi-packages huggingface dataset:
stacks-data 2.1.4
A suite of utilities to support data engineering workloads within an Ensono Stacks data platform.
Requires Python: <4.0,>=3.9

# Stacks Data

**stacks-data** is a Python package built to support various functions within the Ensono Stacks Data Platform solution. The library and its associated Python-based CLI (`datastacks`) is intended to assist developers working within a deployed Stacks Data Platform, supporting common tasks such as generating new data engineering workloads and running Spark jobs.

* [Stacks Azure Data Platform - GitHub](https://github.com/Ensono/stacks-azure-data)
* [Stacks Azure Data Platform - Documentation](https://stacks.ensono.com/docs/workloads/azure/data/intro_data_azure)
* [Datastacks CLI - Documentation](https://stacks.ensono.com/docs/workloads/azure/data/data_engineering/datastacks)

## Installation

stacks-data is modular, allowing you to install only what you need, keeping the installation lightweight and efficient. By default, stacks-data installs only core functionality, focussed on Pyspark and Azure operations.

The following features require additional dependencies, which can be optionally included in your installation:

* **behave**: Utilities for executing behaviour-driven development (BDD) tests.
* **cli**: The [datastacks](https://stacks.ensono.com/docs/workloads/azure/data/data_engineering/datastacks) command line tool, to support developers generating data workloads.
* **data-quality**: Utilities for running data quality checks using the Great Expectations framework.

You can install the stacks-data package using pip - see the examples below:

```sh
# Example 1: Install only the core stacks-data package
pip install stacks-data

# Example 2: Install the stacks-data package with data quality features included
pip install stacks-data[data-quality]

# Example 3: Install the stacks-data package with all optional features included
pip install stacks-data[behave,cli,data-quality]
```
cat-aspect-extraction 2.0.1
Aspect extraction based on word embeddings
Keywords: cat, nlp, absa, topic modeling, sentiment analysis, aspect extraction, BERTopic, lda, top2vec, word2vec

# cat-aspect-extraction🐈

Easy to use library for implement Contrastive Attention Topic Modeling describe in *Embarrassingly Simple Unsupervised Aspect Extraction*

**Read the paper & the original repository for details about the algorithm !**

- PAPER : https://aclanthology.org/2020.acl-main.290/
- REPOSITORY : https://github.com/clips/cat/

![cat walking on a computer keyboard](https://raw.githubusercontent.com/azaismarc/cat-aspect-extraction/master/cat.gif)

## Installation

```bash
pip install cat-aspect-extraction
```

or

```bash
git clone
python -m pip install .
```

## Example

```python
from cat_aspect_extraction import CAt, RBFAttention # for using the model
from reach import Reach # for loading word embeddings

# Load in-domain word embeddings and create a CAt instance
r = Reach.load("path/to/embeddings", unk_word="UNK")
cat = CAt(r)

# Initialize candidate aspects

candidates = [
    "food",
    "service",
    "ambiance",
    "price",
    "location",
    "experience"
]

for aspect in candidates:
    cat.add_candidate(aspect)

# Add topics

cat.add_topic("food", ["taste", "flavor", "quality", "portion", "menu", "dish", "cuisine", "ingredient"])

cat.add_topic("service", ["staff", "waiter", "waitress", "service", "server", "host", "manager", "bartender"])

cat.add_topic("ambiance", ["atmosphere", "decor", "interior", "design", "lighting", "music", "noise", "vibe"])

# Compute topic score

sentence = "The food was great !".split() # tokenize your sentence

att = RBFAttention() # Using attention

cat.get_scores(sentence, attention=att)
>>> [('food', 1), ('service', 0.5), ('ambiance', 0.0)] # Score are scaled by RobustScaler followed by MinMaxScaler
```

## Citations

**I'm not the author of the original paper**, so if you use this library, please cite the original paper :

```bibtex
@inproceedings{tulkens2020embarrassingly,
    title = "Embarrassingly Simple Unsupervised Aspect Extraction",
    author = "Tulkens, St{\'e}phan  and  van Cranenburgh, Andreas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.290",
    doi = "10.18653/v1/2020.acl-main.290",
    pages = "3182--3187",
}
```

## License

GNU General Public License v3.0
terminalbars 2.6.5
Biblioteka w pythonie która służy do robienia czegoś podobnego do tqdm ale owiele łatwiej.
Requires Python: >=3.7

Example code:

    from terminalbars import *

    ultrapropasek(steps=100, duration=5, action="nothing...")


My library is designed to make progress bars in Python easy and flexible. Whether you need a simple or more advanced bar, this library is ideal.

PyPI link: https://pypi.org/project/terminalbars/

Github link : https://github.com/superbest14/terminal-pro-bar

Installation:

    pip install terminalbars


or, to upgrade:

    pip install --upgrade terminalbars


The library helps you create progress bars such as pasek, superpasek, propasek, ultrapasek, and ultrapropasek. All functions work the same way – only the starting point (name) differs.
PrintVerbose 1.1.1
Colorized logging with traceback dumping
Requires Python: >=3.8

# PrintVerbose

A small logging helper with colored output and traceback dumping.

Dependencies:
- colorama (BSD License)
```bash
pip install colorama
```

Install:
```bash
pip install printverbose
```

Usage:
```python
from PrintVerbose import get_logger, write_traceback_to_file

log = get_logger("app")
log.info("Hello")

try:
   oneplusone = 1 + 1
except:
   write_traceback_to_file() # works only inside a except block
   log.critical("Error doing math!")
```
# Changelog

## [1.1.1] - 1/20/2026
### Fixed
- Fixed minor spelling mistake in README.md

## [1.1.0] - 1/20/2026
### Added
- Error when using print_traceback_to_file() outside of an except block.
- Added changelog



Full changelog in CHANGELOG.md
dcnum 0.29.2
numerics toolbox for imaging deformability cytometry
Requires Python: <4,>=3.10
Keywords: RT-DC, deformability, cytometry

|dcnum|
=======

|PyPI Version| |Build Status| |Coverage Status| |Docs Status|


Numerics toolbox for imaging deformability cytometry.

Documentation
-------------
The documentation, including the code reference and examples, is available at
`dcnum.readthedocs.io <https://dcnum.readthedocs.io/en/stable/>`__.


Installation
------------

::

    pip install dcnum

For more options, please check out the `documentation
<https://dcnum.readthedocs.io/>`__.



.. |dcnum| image:: https://raw.github.com/DC-analysis/dcnum/main/docs/logo/dcnum.png
.. |PyPI Version| image:: https://img.shields.io/pypi/v/dcnum.svg
   :target: https://pypi.python.org/pypi/dcnum
.. |Build Status| image:: https://img.shields.io/github/actions/workflow/status/DC-analysis/dcnum/check.yml
   :target: https://github.com/DC-analysis/dcnum/actions?query=workflow%3AChecks
.. |Coverage Status| image:: https://img.shields.io/codecov/c/github/DC-analysis/dcnum
   :target: https://codecov.io/gh/DC-analysis/dcnum
.. |Docs Status| image:: https://readthedocs.org/projects/dcnum/badge/?version=latest
   :target: https://readthedocs.org/projects/dcnum/builds/
ghpeek 0.2.0
Stylish TUI to browse GitHub repositories, issues, and pull requests.
Requires Python: >=3.10

# GHpeek - the easy Github repository viewer

![GHpeek screenshot](https://github.com/kimusan/ghpeek/blob/main/assets/screenshot.png)

GHpeek is a friendly terminal UI for keeping tabs on your GitHub repositories. Add the repos you care about and get a clean, readable list of open issues and pull requests with unread items highlighted. Each repo also shows a quick summary of stars, forks, and open counts so you can spot activity at a glance.

## Dependencies

- textual
- PyGithub
- rich
- markdown
- python-dotenv

## Installation

GHpeek can be installed via pip. Simply run the following command:

```bash
pip install ghpeek
```

Or, even better with pipx:

```bash
pipx install ghpeek
```

## Usage

To start GHpeek, simply run the following command:

```bash
ghpeek
```

Press `a` to add a repository. If a GitHub token is configured, you can choose from your own repositories, filter forks, organizations, and public/private repos, or enter a repo manually in `owner/repo` format. Without a token, manual entry still works.

Once added, repositories appear on the left. Use the arrow keys to switch between them, and you will see a summary of stars, forks, and open counts. The main panel lists issues and pull requests; press `Enter` to open a preview, then `Enter` again to open it in your browser.
The preview includes the issue or PR description plus the latest comments, with a "Load older" option when more are available.

Quick keys:
- `i` / `p`: jump between Issues and Pull Requests
- `c`: show or hide closed issues/PRs
- `r`: refresh the selected repo
- `q`: quit

## GitHub token

To enable repository picking and higher API limits, set a personal access token in `GITHUB_TOKEN` (recommended via a `.env` file in `~/.config/ghpeek/.env`). For private repositories, the token needs the `repo` scope. For public repos only, `public_repo` (or no scopes) is sufficient.

# License

The project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

# Contributing

I would love to see contributions to GHpeek! If you have any ideas for new features or improvements, please feel free to open an issue or submit a pull request.

# Acknowledgements

This project uses the following open source libraries:
 - [Textual](https://github.com/Textualize/textual)
 - [PyGithub](https://github.com/PyGithub/PyGithub)
 - [Rich](https://github.com/Textualize/rich)
 - [Markdown](https://python-markdown.github.io/)
 - [python-dotenv](https://github.com/theskumar/python-dotenv)

GHpeek was inspired by other terminal-based tools and was originally written by [Kim Schulz](https://Schulz.dk).

# Roadmap

 - Add search and filtering by label, author, and assignee
 - Add notifications or a background refresh mode
 - Add quick actions (close/reopen, comment) directly from the TUI
caex2 1.0.2
Automatically extract all exercises from a carpentries lesson.
Requires Python: >=3.8
Keywords: Extract, Exercises

[![PyPI version](https://badge.fury.io/py/caex2.svg)](https://badge.fury.io/py/caex2)

# CaEx2 (CArpentries EXercises EXtractor)
## What does it do?
No more copy-pasting, automatically extract all exercises from a carpentries lesson.

## How to install?
Install with pip:
```
pip install caex2
```

## How to use?
```commandline
caex2 {LESSON_URL} --output {OPTIONAL_OUTPUT_FILE}
```

### Example
To extract all exercises from the [deep learning lesson](https://github.com/carpentries-incubator/deep-learning-intro):
```commandline
caex2 https://github.com/carpentries-incubator/deep-learning-intro
```
This creates a new file called `exercises-document.md` with all exercises in the lesson,
grouped and ordered by episode.

## Current support
This package currently supports carpentries lessons in the 'old' style, it has been tested on:
* https://github.com/carpentries-incubator/deep-learning-intro
* https://github.com/datacarpentry/r-socialsci (episodes are in Rmarkdown)
* https://github.com/datacarpentry/python-socialsci

## Acknowledgements
This package is based on [this gist](https://gist.github.com/dafnevk/6b235e09d5d72f3a71eb662a72fd3ef2) by Dafne van Kuppevelt (@dafnevk).