semvec/pypi-packages huggingface dataset:
glad-kit 0.1.0
This library makes your lifes easier, it contains three librarys inside it for maths, text editing(fg,bg,design ' and more ')(on terminal) and file managing, create, delete, rename, copy, paste and more
Requires Python: >=3.6

# glad-kit

glad-kit is a simple Python library for using maths(area of 2d shapes, volumeof 3d shapes, tsa etc..) just by a function. It also contains another library for text editing like bg, fg, weight etc....It also have another library for create, read, write, copy, paste, append the files from the device storage.

## Installation  
```sh
pip install glad-kit
aiogram-mediagroup-handle 0.1.3
aiogram media group observer
Requires Python: >=3.11
Keywords: aiogram, album, media group, storage

# aiogram-mediagroup-handle

**aiogram plugin for handling media groups**

This library supports **aiogram v3 and above**.

## Overview

`aiogram-mediagroup-handle` leverages the `aiogram.Dispatcher.storage` to collect and store 
media files in FSM data by `media_group_id`.

FSMStorage ensures data consistency and, combined with data serialization, allows any of 
the known storage strategies to be used (not just in-memory).

`aiogram-mediagroup-handle` comes with some utilities:
- **MediaGroupFilter** – A filter to detect media groups.
- **MediaGroup** – A lightweight dataclass object (the actual media group intake) accessible in 
FSMStorage data dictionary by `media_group_id`.

### MediaGroup Dataclass reference
```python
from dataclasses import dataclass, field
import typing as t
from aiogram.types import PhotoSize, Audio, Document, Video
from aiogram.enums import ContentType

MediaGroupType = list[InputMediaAudio | InputMediaDocument | InputMediaPhoto | InputMediaVideo]
MediaType = list[PhotoSize] | Audio | Document | Video

@dataclass(slots=True, kw_only=True, frozen=True)
class MediaGroup:
    """Lightweight media group representation."""
    caption: t.Optional[str] = None
    photos: list[list[PhotoSize]] = field(default_factory=list)
    audio: list[Audio] = field(default_factory=list)
    documents: list[Document] = field(default_factory=list)
    video: list[Video] = field(default_factory=list)
    
    def get_media(self) -> t.Iterator[MediaType]:
        """Return media group iterator"""
        ...
    
    def as_input_media(self) -> MediaGroupType:
        """Return media group for sending via aiogram.Bot"""
        ...
        
    @property
    def content_type(self) -> ContentType:
        """Return media group content type"""
        ...

```

## Usage

### Registering the Observer
```python
import aiogram
from aiogram_mediagroup_handle import MediaGroupObserver

dp = aiogram.Dispatcher(...)
observer = MediaGroupObserver()
observer.register(dp)
```

### Handling Media Groups
```python
from aiogram import Router
from aiogram.types import Message
from aiogram.fsm.context import FSMContext
from aiogram_mediagroup_handle import MediaGroupFilter, MediaGroup

router = Router()

@router.message(MediaGroupFilter())
async def media_group_handler(message: Message, state: FSMContext):
    data = await state.get_data()
    media_data: MediaGroup = data[message.media_group_id]
    # Process the media group data here...
    
    # f.e. answer with media
    await message.answer_media_group(media=media_data.as_input_media())
```

## Installation
```sh
pip install aiogram-mediagroup-handle
```

## License
[MIT License](LICENSE)
gudhi 3.11.0
The Gudhi library is an open source library for Computational Topology and Topological Data Analysis (TDA).
Requires Python: >=3.8.0

The Gudhi library is an open source library for Computational Topology and
Topological Data Analysis (TDA). It offers state-of-the-art algorithms
to construct various types of simplicial complexes, data structures to
represent them, and algorithms to compute geometric approximations of shapes
and persistent homology.

The GUDHI library offers the following interoperable modules:

* Complexes:
   * Cubical
   * Simplicial: Rips, Witness, Alpha and Čech complexes
   * Cover: Nerve and Graph induced complexes
* Data structures and basic operations:
   * Simplex tree, Skeleton blockers and Toplex map
   * Construction, update, filtration and simplification
* Topological descriptors computation
* Manifold reconstruction
* Topological descriptors tools:
   * Bottleneck distance
   * Statistical tools
   * Persistence diagram and barcode

For more information about Topological Data Analysis and its workflow, please
refer to the `Wikipedia TDA dedicated page <https://en.wikipedia.org/wiki/Topological_data_analysis>`_.
sdss-hal 1.4.1
High-level observing tool for SDSS-V (replaces SOP)
Requires Python: <4,>=3.10

# HAL

![Versions](https://img.shields.io/badge/python->3.9-blue)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Documentation Status](https://readthedocs.org/projects/sdss-hal/badge/?version=latest)](https://sdss-hal.readthedocs.io/en/latest/?badge=latest)
[![Test](https://github.com/sdss/hal/actions/workflows/test.yml/badge.svg)](https://github.com/sdss/hal/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/sdss/hal/branch/main/graph/badge.svg)](https://codecov.io/gh/sdss/hal)

High-level observing actor for SDSS-V (replaces SOP)

## Installation

You should be able to install `HAL` by doing

```console
pip install sdss-hal
```

To build from source, use

```console
git clone git@github.com:sdss/hal
cd hal
pip install .
```
znmdakit 0.1.1
MD post processing tools.
Requires Python: >=3.10

[![ZnTrack](https://img.shields.io/badge/Powered%20by-ZnTrack-%23007CB0)](https://zntrack.readthedocs.io/en/latest/)
[![zincware](https://img.shields.io/badge/Powered%20by-zincware-darkcyan)](https://github.com/zincware)

# ZnMDAKit

[ZnTrack](https://github.com/zincware/ZnTrack) enabled [MDAnalysis Toolkits](https://mdakits.mdanalysis.org/) with full parameter and result tracking capabilities.

## Example

```python
import znmdakit

project = znmdakit.Project()

with project:

    system = ips.Smiles2Gromacs(
        smiles=[BMIM, BF4],
        density=0.5,
        count=[64, 64],
        labels=["Im", "BF"],
        config_files=mdp_files,
        fudgeLJ=1,
        fudgeQQ=1,
        itp_files=itps,
        pdb_files=pdbs,
        tolerance=1.8,
        cleanup=True,
        maxwarn=0,
    )

    universe = znmdakit.Universe(
        # data_file=znflow.resolve(system.traj_file), # hotfix for https://github.com/zincware/ZnTrack/pull/875
        data=system.frames,
        residues={"BF": BF4, "Im": BMIM},
    )

with project.group("BF4"):
    znmdakit.InterRDF(
        universe=universe.universe,
        g1="name COM and resname BF",
        g2="name COM and resname BF",
        nbins=1000,
        apply_com_transform=True,
    )
    msd = znmdakit.EinsteinMSD(
        universe=universe.universe,
        select="name COM and resname BF",
        timestep=0.001, # ps
        sampling_rate=1000, # ps
        apply_com_transform=True,
    )

    znmdakit.SelfDiffusionFromMSD(
        data=msd,
        start_time=2000,
        end_time=7000,
        always_changed=False,
    )

project.run()

rdf.load()
msd.load()

print(rdf.results)
print(msd.results)
```

## Available Nodes
- `znmdakit.RDF`
- `znmdakit.EinsteinMSD`
orthoani 0.7.0.post1
A Python implementation of the OrthoANI algorithm for nucleotide identity measurement. (renamed to pyorthoani)
Requires Python: >=3.6
Keywords: ani, average, nucleotide, identity, genomic, taxonomy

# (Py)OrthoANI [![Stars](https://img.shields.io/github/stars/althonos/orthoani.svg?style=social&maxAge=3600&label=Star)](https://github.com/althonos/orthoani/stargazers)

**🚨 The `orthoani` package has been renamed to `pyorthoani` starting from `v0.7.0`**

Please update your code and dependencies to use the new package name. This repository will only offer releases prior to `v0.7` 
for reproducibility. See the new PyPI page for the up-to-date versions of the package: 
[https://pypi.org/project/pyorthoani/](https://pypi.org/project/pyorthoani/)