semvec/pypi-packages huggingface dataset:
lifecycles 0.0.1
Package to analyze the temporal dynamics of (groups of) entities/nodes
Keywords: clusters communities analysis

# LifeCycles
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Documentation Status](https://readthedocs.org/projects/lifecycles/badge/?version=latest)](https://lifecycltwine upload dist/es.readthedocs.io//en/latest/?badge=latest)
[![Updates](https://pyup.io/repos/github/andreafailla/LifeCycles/shield.svg)](https://pyup.io/repos/github/andreafailla/LifeCycles/)
[![pyversions](https://img.shields.io/pypi/pyversions/LifeCycles.svg)](https://badge.fury.io/py/LifeCycles)
[![PyPI version](https://badge.fury.io/py/LifeCycles.svg)](https://badge.fury.io/py/LifeCycles)
[![SBD++](https://img.shields.io/badge/Available%20on-SoBigData%2B%2B-green)](https://sobigdata.d4science.org/group/sobigdata-gateway/explore?siteId=20371853)

``LifeCycles`` is a Python software package that allows to represent and analyze the temporal dynamics of (groups of) data points/nodes. The library provides a set of tools to model and analyze the temporal evolution of data points/nodes, and to extract meaningful patterns from the data.


If you use ``LifeCycles`` as support to your research consider citing:
 
>@article{Failla2024describing,
>        title = {Describing group evolution in temporal data using multi-faceted events},
>          ISSN = {1573-0565},
>          url = {http://dx.doi.org/10.1007/s10994-024-06600-4},
>          DOI = {10.1007/s10994-024-06600-4},
>          journal = {Machine Learning},
>          publisher = {Springer Science and Business Media LLC},
>          author = {Failla,  Andrea and Cazabet,  Rémy and Rossetti,  Giulio and Citraro,  Salvatore},
>          year = {2024},
>          month = aug
        }

## Tutorial and Online Environments

Check out the official [tutorial](https://LifeCycles.readthedocs.io/en/latest/tutorial.html) to get started!

If you would like to test ``LifeCycles`` functionalities without installing anything on your machine consider using the preconfigured Jupyter Hub instances offered by [SoBigData++](https://sobigdata.d4science.org/group/sobigdata-gateway/explore?siteId=20371853).

## Installation

``LifeCycles`` *requires* python>=3.9.

To install the latest version of our library just download (or clone) the current project, open a terminal and run the following commands:

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

Alternatively use pip
```bLifeCycles
pip install lifecycles 
```


## Collaborate with us!

``LifeCycles`` is an active project, any contribution is welcome!

If you like to include your model in LifeCycles feel free to fork the project, open an issue and contact us.
requirements-checker 0.2.18
check if requirements met
Requires Python: >=3.6
Keywords: check requirements, raise/bool if no requirements, check system requirements, python packages/modules aux (upgrade/delete/version get), version parse, version check, version compare

![Ver/TestedPython](https://img.shields.io/pypi/pyversions/requirements_checker)
![Ver/Os](https://img.shields.io/badge/os_development-Windows-blue)  
![repo/Created](https://img.shields.io/github/created-at/centroid457/requirements_checker)
![Commit/Last](https://img.shields.io/github/last-commit/centroid457/requirements_checker)
![Tests/GitHubWorkflowStatus](https://github.com/centroid457/requirements_checker/actions/workflows/test_linux.yml/badge.svg)
![Tests/GitHubWorkflowStatus](https://github.com/centroid457/requirements_checker/actions/workflows/test_windows.yml/badge.svg)  
![repo/Size](https://img.shields.io/github/repo-size/centroid457/requirements_checker)
![Commit/Count/t](https://img.shields.io/github/commit-activity/t/centroid457/requirements_checker)
![Commit/Count/y](https://img.shields.io/github/commit-activity/y/centroid457/requirements_checker)
![Commit/Count/m](https://img.shields.io/github/commit-activity/m/centroid457/requirements_checker)

# requirements_checker (current v0.2.18/![Ver/Pypi Latest](https://img.shields.io/pypi/v/requirements_checker?label=pypi%20latest))

## DESCRIPTION_SHORT
check if requirements met

## DESCRIPTION_LONG
designed for check requirements (systemOs) and raise/bool if no match


## Features
1. check requirements (systemOs), raise/bool if no match  
2. create fuck(?)/source and is it for check for settings  
3. [python PACKAGES/MODULES]:  
	- upgrade  
	- delete  
	- version_get_installed  
	- check_installed)  
	- upgrade pip  
4. [VERSION]:  
	- parse  
	- check  
	- compare  


********************************************************************************
## License
See the [LICENSE](LICENSE) file for license rights and limitations (MIT).


## Release history
See the [HISTORY.md](HISTORY.md) file for release history.


## Installation
```commandline
pip install requirements-checker
```


## Import
```python
from requirements_checker import *
```


********************************************************************************
## USAGE EXAMPLES
See tests, sourcecode and docstrings for other examples.  

------------------------------
### 1. example1.py
```python
from requirements_checker import *


ReqCheckStr_Os().bool_if__WINDOWS()
ReqCheckStr_Os().bool_if_not__WINDOWS()
ReqCheckStr_Os().raise_if__LINUX()


class ReqCheckStr_Os_MY(ReqCheckStr_Os):
    LINUX: bool = True
    WINDOWS: bool = False


ReqCheckStr_Os_MY()  # check requirement!
```

********************************************************************************
enzeptional 1.0.4
Enzeptional stand-alone python package.

# Enzyme Optimization of Biocatalytic Reactions

This repository provides an example on how ro run the framework for the optimization of enzymes within the context of biocatalytic reactions.


### Development setup & installation

Create any virtual or conda environment compatible with the specs in setup.cfg. Then run:
```sh
pip install -e ".[dev]" 
```


### Running the example

To run the example simply type:

```sh
python example_enzeptional.py
```


### References

If you use `Enzeptional` in your projects, please consider citing the following:

```bibtex
@inproceedings{teukam2023enzyme,
  title={Enzyme optimization via a generative language modeling-based evolutionary algorithm},
  author={Teukam, Yves Gaetan Nana and Grisoni, Francesca and Manica, Matteo and Zipoli, Federico and Laino, Teodoro},
  booktitle={American Chemical Society (ACS) Spring Meeting},
  year={2023}
}
```

### License

The `Enzeptional` codebase is under MIT license.
For individual model usage, please refer to the model licenses found in the original packages.
st-img-selector 0.0.2
Streamlit component that allows you to select multiple images with a captivating interface
Requires Python: >=3.7


# st-img-selector

A custom Streamlit component that allows you to display and select images in a grid layout with customizable borders, corner radius, selection color, and more.

## Features

- Display images from local paths, URLs, or image objects (PIL, NumPy).
- Set the number of images to display per row.
- Customize the selection color, border thickness, and corner radius.
- Pre-select images by specifying default values.
- Return the indices of the selected images.

## Installation Instructions

Install the component using pip:

```sh
pip install st-img-selector
```

## Usage Instructions

Here’s a simple example of how to use `st-img-selector` in your Streamlit app:

```python
import streamlit as st
from st_img_selector import st_img_selector
from PIL import Image
import numpy as np

# Example usage of the component
selected_indices = st_img_selector(
    images=[
        "images/image1.png",
        "https://example.com/image.jpeg",
        Image.open("images/image2.jpeg"),
        np.array(Image.open("images/image4.jpeg")),
    ],
    value=[2, 3],  # Default selected images at positions 2 and 3
    corner_radius=10,
    selection_color="#FF0000",
    img_per_row=2,
    border_thickness=4,
    max_row_height=200,
)

st.write("Selected image indices:", selected_indices)
```

## Parameters

- **images**: List of images to display (can be local file paths, URLs, or image objects like PIL or NumPy arrays).
- **value**: List of default selected image indices.
- **corner_radius**: Radius of image corners (default is 10).
- **selection_color**: Color of the border around selected images (default is "#555555").
- **img_per_row**: Number of images to display per row (default is 4).
- **border_thickness**: Thickness of the border around selected images (default is 4).
- **max_row_height**: Maximum height for each row of images (default is 300px).

## Output

- Returns a list of indices representing the selected images.

## Example

You can integrate this component into your Streamlit app to allow users to select multiple images and perform operations on the selected ones.

## License

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

## Support

This Streamlit component is provided as is without support. Sorry :(

---

*Created with ❤️ by Broccoloff*
rdflib-gs1 0.1.2
The GS1 Web Vocabulary as an rdflib namespace.
Requires Python: <3.13,>=3.10

# GS1 Vocabulary RDFLib Namespace Implementation

Welcome to the GS1 Vocabulary RDFLib Namespace Implementation library! This library provides a convenient way to work
with the GS1 Web Vocabulary using RDFLib, a powerful library for working with RDF in Python.

## Features

- **Easy Integration**: Seamlessly integrate the GS1 Vocabulary into your RDFLib projects.
- **Comprehensive Coverage**: Access all terms defined in the GS1 Web Vocabulary.
- **Developer-Friendly**: Simplifies working with GS1 terms in your RDF graphs.

## Installation

Install the library via pip:

```bash
pip install rdflib-gs1
```

## Usage

Here's a quick example of how to use the GS1 Vocabulary with RDFLib:
python

```python
import rdflib
from rdflib_gs1.namespace import GS1

# Create a graph
g = rdflib.Graph()

# Create RDF nodes using the GS1 namespace
product = rdflib.URIRef(GS1.Product)
productID = rdflib.URIRef(GS1.productID)
productName = rdflib.Literal("Example Product")

# Add triples to the graph
g.add((product, rdflib.RDF.type, GS1.Product))
g.add((product, productID, rdflib.Literal("1234567890123")))
g.add((product, GS1.productName, productName))

# Serialize the graph to RDF/XML
print(g.serialize(format='xml').decode('utf-8'))
```

## Contributing

We welcome contributions from the community! Please read our Contributing Guide for guidelines on how to contribute.
License

This project is licensed under the MIT License. See the LICENSE file for details.
About WordLift

WordLift is passionate about contributing to open source projects and enhancing the accessibility and usability of
semantic web technologies. As a proud GS1 partner, WordLift is committed to providing high-quality tools and libraries
to the community.

Thank you for using the GS1 Vocabulary RDFLib Namespace Implementation library! If you have any questions or feedback,
please feel free to open an issue or reach out to us.