semvec/pypi-packages huggingface dataset:
mfd-model 0.10.0
Module for handling models used across multiple MFDs (mostly Pydantic)
Requires Python: <3.14,>=3.10

> [!IMPORTANT]  
> This project is under development. All source code and features on the main branch are for the purpose of testing or evaluation and not production ready.

# MFD Model
Module for handling Pydantic models used across multiple MFDs stored in subpackages.
The library utilizes the [project.optional-dependencies] section in pyproject.toml to specify optional dependencies for each subpackage

## Subpackages
Every subpackage can be installed separately by specifying the package name in square brackets after the main package name.
Structure:
```
├───<subpackage_name>
│   │   models.py
│   │   __init__.py
│   │   __version__.py
```

### config (used in pytest-mfd-config)
This subpackage contains models for configuration files. They are used in `pytest-mfd-config` to define configuration files and their structure.

### nvm
This subpackage contains models related with NVMs like `EETrack`, `Release`, ...\
They are used to handle upload / download / search of NVMs stored in NVMManager.

### artifacts_manager
This subpackage contains models for tested artifacts.

## Installation
To install 'config' models only use:
```bash
pip install mfd-model[config]
```

## How to add another subpackage? 
1. Create a new subpackage directory with the name of the subpackage.
2. Add `models.py` file with models definitions.
3. Add `__init__.py` file.
4. Add `__version__.py` file with version definition.
5. Update `pyproject.toml`:
   - add the requirements in [project.optional-dependencies] section, e.g.:
   ```
       data_processor = ["pydantic >= 2.0, < 3", "numpy >= 1.21.0"]  # Example dependencies
   ```

## OS supported:
* OS Independent

## Issue reporting

If you encounter any bugs or have suggestions for improvements, you're welcome to contribute directly or open an issue [here](https://github.com/intel/mfd-model/issues).
libadalina-core 1.3.4
A library for spatial joins of geographic data
Requires Python: ~=3.10

# libadalina-core

A Python library for spatial data processing.
It makes it easier to work with geospatial data in Python by providing a high-level interface
to Apache Sedona, a powerful geospatial processing engine, and integrates nicely with other well-known libraries
such as *geopandas* and *pandas*.

## Installation

liabadalina-core can be installed using pip:
```
pip install libadalina-core
```

If `JAVA_HOME` environment variable is not set a suitable JDK will be downloaded in `$HOME/.jre` and used automatically.
Not all JRE are supported, so if you encounter issues, you can try the automatically installed version.

## Usage

You can find the documentation and example at [libadalina-core documentation](https://libadalinacore-6b2a95.gitlab.io/).

## Features


* Reading and writing geospatial data from various formats
* Spatial joins between datasets
* Spatial aggregations
* Utilities for working with Apache Sedona
* Configuration helpers for setting up Apache Sedona

## Requirements

- Python 3.10
- Dependencies:
  - apache-sedona[spark]
  - pyspark
  - pandas
  - geopandas
  - install-jdk
propel-client 0.0.19
Propel service client
Requires Python: <4.0,>=3.10

# PCLI

propel client to operate agents and opeai calls

## Install
installation `pip install .`


## Usage
cli command named: `propel`

use `propel login -u some_user` to obtain credentials store locally

use `propel logout` to logout on propel server and remove local credentials

use `propel openai /v1/models` to perform openai call.

use `propel openai /v1/models "{}"` to perform openai call with json encoded payload

or `echo "{}" | propel openai /v1/models -` to perform openai call with json encoded payload from stdin


please check `--help` for other commands like: agents, keys, variables
dna-features-viewer-lite 3.1.5.1
Plot features from DNA sequences (e.g. Genbank) with Python
Keywords: DNA, Sequence, Feature, Genbank, Biopython, Matplotlib


DNA Features Viewer
===================

DNA Features Viewer is a Python library to visualize DNA features, e.g. from GenBank or Gff files:

.. figure:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/translator_with_custom_colors.png
    :width: 500px
    :align: center
    :alt: some figure

DNA Features Viewer can plot sequence maps linearly or circularly, with or without nucleotide sequence and amino-acid sequences. The plotter automatically produces clear plots even for sequences with many overlapping features and long labels. The library plays well with Matplotlib and Biopython, and the figures can be saved in different formats (PNG, JPEG, SVG, PDF), e.g. for report generation or LIMS interfaces.

Infos
-----

**PIP installation:**

.. code:: bash

  pip install dna_features_viewer

**Web documentation:** `<https://edinburgh-genome-foundry.github.io/DnaFeaturesViewer/>`_

**Github Page:** `<https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer>`_

**Live demo:** `<http://cuba.genomefoundry.org/plot-sequence-features>`_

**License:** MIT

2016 Edinburgh Genome Foundry, University of Edinburgh


More biology software
---------------------

.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Edinburgh-Genome-Foundry.github.io/master/static/imgs/logos/egf-codon-horizontal.png
  :target: https://edinburgh-genome-foundry.github.io/

DNA Features Viewer is part of the `EGF Codons <https://edinburgh-genome-foundry.github.io/>`_ synthetic biology software suite for DNA design, manufacturing and validation.
aiomax 2.12.3
Asynchronous framework for Max Bot API
Requires Python: >=3.9
Keywords: bot, api, asyncio, max

# aiomax

Асинхронный фреймворк [Max](https://max.ru) Bot API.

## Преимущества

- Полная асинхронность

- Возможность разделения на роутеры

- Эквиваленты всех функций доступных в Max Bot API

- Встроенный конечный автомат (FSM)

- Поддержка [PyPy](https://pypy.org/)