semvec/pypi-packages huggingface dataset:
atompaint 0.1.0
Design macromolecular interactions by in-painting full-atom models.
Requires Python: ~=3.10

AtomPaint
=========

[![Last release](https://img.shields.io/pypi/v/atompaint.svg)](https://pypi.python.org/pypi/atompaint)
[![Python version](https://img.shields.io/pypi/pyversions/atompaint.svg)](https://pypi.python.org/pypi/atompaint)
[![Documentation](https://img.shields.io/readthedocs/atompaint.svg)](https://torch-fuel.readthedocs.io/en/latest/)
[![Test status](https://img.shields.io/github/actions/workflow/status/kalekundert/atompaint/test.yml?branch=master)](https://github.com/kalekundert/atompaint/actions)
[![Test coverage](https://img.shields.io/codecov/c/github/kalekundert/atompaint)](https://app.codecov.io/github/kalekundert/atompaint)
[![Last commit](https://img.shields.io/github/last-commit/kalekundert/atompaint?logo=github)](https://github.com/kalekundert/atompaint)

*AtomPaint* is a collection of convolutional neural networks (CNNs) meant for 
learning from 3D images of macromolecular structures.  A unique, unifying 
feature of many of these networks is that maintain rotational equivariance; 
that is, they will recognize features no matter what orientation they appear 
in.  The models are implemented using the [PyTorch](https://pytorch.org) and 
[ESCNN](https://quva-lab.github.io/escnn/) frameworks.

Below are links to some of the datasets that these models can be trained on:

- [Macromolecular Gym](https://github.com/kalekundert/macromol_gym)
- [Atom3D](https://www.atom3d.ai/)
bible-chichewa 1.0.7
Access and integrate the Chichewa translation of the Bible into your python applications with ease
Requires Python: >=3.0

# Chichewa Bible for Python

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Chichewa Bible pip package: Access and integrate the Chichewa translation of the Bible into your python applications with ease. Simplify verse retrieval, text search, and more using this lightweight and versatile pip package.


## Features

- Access verses, chapters, and books of the Chichewa Bible programmatically.
- Retrieve text content in Chichewa for quoting, referencing, and display.
- Flexible search functionality for finding specific passages or keywords.
- Lightweight and easy-to-use API for seamless integration into Node.js applications.
- Continuously updated to reflect the latest versions of the Chichewa Bible text.

## Installation

You can install the Chichewa Bible python package using pip:

```
pip install bible-chichewa
```

## Examples
To use the bible-chichewa library to retrieve a verse from the Chichewa Bible, follow these steps:

### Example: Retrieving a Bible Verse
```
from biblechichewa import Bible
bible = Bible()
books = bible.get_books()
print(f"There are {len(books)} books in the bible")

book_number = 1  # Genesis
book = books[book_number-1]
chapter = 1
verse = 1

verse_text = bible.get_verse(book_number, chapter, verse)
print(f"{book} {chapter}:{verse}: {verse_text}")
```


### Example: Retrieving a Bible Chapter

```
from biblechichewa import Bible
bible = Bible()
books = bible.get_books()
book_number = 1  # Genesis
book = books[book_number-1]

chapter = 1 

chapterVerses = bible.get_chapter(book_number, chapter)

print(f"Chapter {book} {chapter}:")
for i in range(0, len(chapterVerses)):
    verse = i+1
    print(f"{verse}. {chapterVerses[i]}\n")

```

### Example: Retrieving a Bible Verses within Range
```
from biblechichewa import Bible
bible = Bible()

books = bible.get_books()
book_number = 2 ## Exodus
book = books[book_number-1]

chapter = 20
verse_start = 1
verse_end = 5

verses = bible.get_verses(book_number, chapter, verse_start, verse_end)

print(f"{book} {chapter}:{verse_start}-{verse_end}:")
for index in range(0, len(verses)):
    verse = verses[index]
    print(f"{verse_start + index}: {verse}\n")

```

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pyLeTalker 0.0.0
Wave-reflection voice synthesis framework
Requires Python: >=3.9

# pyLeTalker - Wave-reflection voice synthesis framework (placeholder version)

`pyLeTalker` is a Python library to synthesize voice for research use, specifically aiming to enable the synethsis of pathologial voice. This library started as a repackaged version of [LeTalker, a Matlab GUI demo by Dr. Brad Story](https://sites.arizona.edu/sapl/research/le-talker/) but since has been evolved as a flexible general voice synthesis library built around the wave-reflection vocal tract model. The wave-reflection vocal tract model treats the vibrating air pressure as propagating waves through the vocal tract. Some portion of an incidental wave reflects when it encounters a change in the cross-sectional area of the vocal tract.

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="./docs_src/images/wave-reflection-model-dark.png">
  <img alt="Fallback image description" src="./docs_src/images/wave-reflection-model-light.png">
</picture>

The `pyLeTalker` modularize the voice production system into 5 elements: lung, subglottal tract, vocal folds (glottis), supraglottal tract, and lips. Each subsystem is implemented as a Python class. The library comes with one or more classes for each voice production element. For example, there are two built-in vocal-fold models: `LeTalkerVocalFolds` (self-oscillating 3-mass model with muscle activation inputs) and `KinematicVocalFolds` (3D vocal fold model with preprogrammed oscillation pattern).

The other part of `pyLeTalker` is its `function_generators` subpackage to enable time-varying control of voice production models. Actual voice production is perpetually dynamic. The synthesis models accept both constant parameter values as well as a `FunctionGenerator` object, so that the user can better program the behaviors.

Both synthesis elements and function generators can be customized. Hence, `pyLeTalker` is suitable framework to evaluate new vocal fold models.