semvec/pypi-packages huggingface dataset:
grizzel 0.1.3
A simple text cleaning utility using NLTK and BeautifulSoup
Requires Python: >=3.6

# text_cleaner

A lightweight Python package to clean and normalize raw text data using NLTK, BeautifulSoup, and Unicode handling.

##  Features

- Removes HTML, URLs, numbers, punctuation, and non-ASCII characters
- Strips and normalizes all types of whitespace (including invisible Unicode spaces)
- Tokenizes, removes stopwords, and lemmatizes words
- Uses NLTK and BeautifulSoup under the hood

---

##  Installation

```bash
pip install greedigut
winrt-WindowsAppSDK 3.2.1
Windows Runtime SDK for Python header files
Requires Python: >=3.9

<!-- warning: Please don't edit this file. It was automatically generated. -->

# winrt-WindowsAppSDK

This package provides PyWinRT header files for WindowsAppSDK.

This package is a build time dependency, not a runtime dependency.

Example use in a `pyproject.toml` file:

```toml
[build-system]
requires = ["setuptools", "winrt-sdk", "winrt-WindowsAppSDK"]
build-backend = "setuptools.build_meta"
```

Then in your `setup.py`:

```python
from setuptools import setup
from winrt_sdk import get_include_dirs

setup(
    ...
    include_dirs=get_include_dirs()
)
```

For the runtime package, use `winrt-runtime` and the various namespace packages
instead.
winrt-sdk 3.2.1
Windows Runtime SDK for Python header files
Requires Python: >=3.9

<!-- warning: Please don't edit this file. It was automatically generated. -->

# winrt-sdk

This package provides PyWinRT header files for the Windows SDK.

This package is a build time dependency, not a runtime dependency.

Example use in a `pyproject.toml` file:

```toml
[build-system]
requires = ["setuptools", "winrt-sdk"]
build-backend = "setuptools.build_meta"
```

Then in your `setup.py`:

```python
from setuptools import setup
from winrt_sdk import get_include_dirs

setup(
    ...
    include_dirs=get_include_dirs()
)
```

For the runtime package, use `winrt-runtime` and the various namespace packages
instead.
winrt-Microsoft.UI.Xaml 3.2.1
Windows Runtime SDK for Python header files
Requires Python: >=3.9

<!-- warning: Please don't edit this file. It was automatically generated. -->

# winrt-Microsoft.UI.Xaml

This package provides PyWinRT header files for Microsoft.UI.Xaml.

This package is a build time dependency, not a runtime dependency.

Example use in a `pyproject.toml` file:

```toml
[build-system]
requires = ["setuptools", "winrt-sdk", "winrt-Microsoft.UI.Xaml"]
build-backend = "setuptools.build_meta"
```

Then in your `setup.py`:

```python
from setuptools import setup
from winrt_sdk import get_include_dirs

setup(
    ...
    include_dirs=get_include_dirs()
)
```

For the runtime package, use `winrt-runtime` and the various namespace packages
instead.
ruvik-geometry-calculator 1.0.0
Библиотека для вычисления площади геометрических фигур
Requires Python: >=3.6

# Geometry Calculator Library

![Python Version](https://img.shields.io/badge/python-3.6%2B-blue)

Простая, но мощная библиотека для вычисления площадей геометрических фигур, реализованная с применением принципов ООП и SOLID.

## 📦 Установка

```bash
pip install ruvik-geometry-calculator
```

## 🌟 Основные функции

### Круг
```python
from geometry_calculator.figure.circle import Circle

circle = Circle(radius=5)
print(f"Площадь: {circle.area():.2f}")  # 78.54
```

### Треугольник
```python
from geometry_calculator.figure.triangle import  Triangle

triangle = Triangle(3, 4, 5)
print(f"Площадь: {triangle.area():.2f}")  # 6.00
print(f"Прямоугольный: {triangle.is_right()}")  # True
```

## 🏭 Фабрика фигур
```python
from geometry_calculator.factory.figure_factory import FigureFactory

# Создание через фабрику
circle = FigureFactory.create_circle(10)
triangle = FigureFactory.create_triangle(5, 12, 13)

# Универсальный метод
figure = FigureFactory.create_figure("circle", 7)
```

## 🛠️ Расширение библиотеки
1. Создайте новый класс, унаследованный от `Figure`
2. Реализуйте методы:
```python
class Square(Figure):
    def __init__(self, side):
        self.side = side
    
    def area(self):
        if not self._is_valid():
            raise ValueError("Некорректные параметры квадрата")
        return self.side ** 2
    
    def _is_valid(self):
        return self.side > 0
```
3. Добавьте метод в фабрику

## 📊 Поддерживаемые фигуры
| Фигура      | Параметры           | Особенности         |
|-------------|---------------------|---------------------|
| Круг        | radius              | -                   |
| Треугольник | side1, side2, side3 | Проверка на прямоугольность |


## 🤝 Как внести вклад
1. Форкните репозиторий
2. Создайте ветку (`git checkout -b feature/AmazingFeature`)
3. Сделайте коммит (`git commit -m 'Add some AmazingFeature'`)
4. Запушьте (`git push origin feature/AmazingFeature`)
5. Откройте Pull Request
```
pybatman 1.0.1
Package for predicting peptide mutation effects on T cell receptor activation.
Requires Python: >=3.11

BATMAN is an interpretable Bayesian model for predicting the antigens that activate a T cell receptor (TCR). BATMAN predicts TCR activation by mutant peptides based on their distances to the TCR's index peptide. The peptide-to-index distance is a product of a learned positional weight profile vector, corresponding to effects of mutated residues at different positions in the sequence, and a learned amino acid substitution distance from the index peptide amino acid to the mutant amino acid. 

BATMAN can be trained in two modes: (1) within-TCR, where the train and test peptides are associated with the same TCR, and BATMAN-inferred positional weight profiles are TCR-specific, and (2) leave-one-TCR-out, where peptides are tested for activation of a TCR left out of the training data, and BATMAN-inferred positional weight profile is common across all TCRs. 

BATMAN outperforms existing TCR-antigen methods, reveals structural and biochemical predictors of TCR-antigen interactions, and can predict polyclonal T cell responses and TCR targets with high sequence dissimilarity. For installation, usage, tutorials and more information, refer to https://github.com/meyer-lab-cshl/BATMAN.
stripecaller 0.1.1
Automated Stripe Identification from contact matrix.
Keywords: chromatin contact-matrix 3D genome stripe cooler

StripeCaller
============
A Python implementation of the architectural stripe detecting algorithm described by Vian L et al [1]_.

Currently, this package contains 3 scripts: *call-stripes*, *pileup-stripes* and *stripe-plot*:

- call-stripes
  
  Call stripes from contact matrix. The only required input is the `cool <https://github.com/mirnylab/cooler>`_
  URI at certain resolution. The algorithm will detect horizontal (3') and vertical (5') stripes separately by
  searching for consecutive pixels with contact signals significantly higher than the local backgrounds. The output
  is a single file in bedpe format.

- pileup-stripes

  This script performs a pileup analysis on stripes.

- stripe-plot

  Visualize stripes on heatmap.


Installation
============
*stripecaller* is developed and tested on UNIX-like operating system, and following packages or softwares are
required, which can be installed through `conda <https://conda.io/miniconda.html>`_

a) Python 3.5+
b) Multiprocess
c) Numpy
d) Scipy
e) Matplotlib
f) Cooler

First, add some additional channels::

    $ conda config --add channels defaults
    $ conda config --add channels bioconda
    $ conda config --add channels conda-forge

Then execute the following command::

    $ conda install multiprocess numpy scipy matplotlib cooler

Finally, install the *stripecaller* by *pip*::

    $ pip install stripecaller

Usage
=====
Open a terminal, type ``call-stripes -h`` for help information.


Reference
=========
.. [1] Vian, L. et al. The Energetics and Physiological Impact of Cohesin Extrusion. Cell 175, 292-294, doi:10.1016/j.cell.2018.09.002 (2018).