semvec/pypi-packages huggingface dataset:
ldgm 0.0.1
Sparse and accurate modeling of linkage disequilibrium
Requires Python: >=3.7

# ldgm <img align="right" width="145" height="120" src="https://github.com/awohns/ldgm/blob/main/docs/ldgm_logo.png">
[![CircleCI](https://circleci.com/gh/awohns/ldgm.svg?style=svg)](https://circleci.com/gh/awohns/ldgm)
[![codecov](https://codecov.io/gh/awohns/ldgm/branch/main/graph/badge.svg?token=2I8PxyDm8A)](https://codecov.io/gh/awohns/ldgm)
[![Documentation Status](https://readthedocs.org/projects/ldgm/badge/?version=latest)](https://ldgm.readthedocs.io/en/latest/?badge=latest)

LDGMs (<ins>L</ins>inkage <ins>D</ins>isequilibrium <ins>G</ins>raphical <ins>M</ins>odels) are sparse and accurate models of linkage disequilibrium (LD) which massively accelerate matrix algebra computations in statistical genetics.

Please refer to the [documentation](https://ldgm.readthedocs.io/en/latest/) for information on installing and using the software.

Please cite this paper if you use LDGMs in published work.
> Pouria Salehi Nowbandegani, Anthony Wilder Wohns, Jenna L. Ballard, Eric S. Lander, Alex Bloemendal, Benjamin M. Neale, and Luke J. O’Connor (2023) _Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies_. Nat Genet. DOI: 10.1038/s41588-023-01487-8
gvol 0.6.2
GVol is a Python library to access the GVol API
Requires Python: >=3.7,<4.0

# GVol

[![Latest Version](https://img.shields.io/pypi/v/gvol.svg)](https://pypi.org/project/gvol/)
[![Supported Python Versions](https://img.shields.io/pypi/pyversions/gvol.svg)](https://pypi.org/project/gvol/)
[![Main Workflow](https://github.com/genesis-volatility/gvol-py/actions/workflows/main.yml/badge.svg)](https://github.com/genesis-volatility/gvol-py/actions/workflows/main.yml)
[![Documentation Status](https://readthedocs.org/projects/gvol/badge/?version=latest)](https://gvol.readthedocs.io/en/latest/?badge=latest)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

GVol is a Python library to access the [GVol API](https://docs.gvol.io/).

---

**Documentation**: [https://gvol.readthedocs.io/en/latest/index.html](https://gvol.readthedocs.io/en/latest/index.html)

---

## Install

```bash
pip install gvol
```

## Demo

```python
from gvol import GVol

gvol_client = GVol(header='x-oracle', gvol_api_key="ENTER YOUR API KEY HERE")

options_orderbook = gvol_client.options_orderbook(
    symbol="BTC", exchange="deribit"
)

print(options_orderbook)
```
zensols.showfile 0.3.0
Invoke macOS applications to view files
Keywords: tooling

# Invoke native applications to view files

[![PyPI][pypi-badge]][pypi-link]
[![Python 3.9][python39-badge]][python39-link]
[![Python 3.10][python310-badge]][python310-link]
[![Build Status][build-badge]][build-link]

Invoke native applications to view files.  On macOS, the default web browser
and Preview.app are used to view HTML and PDF files respectively.  On all other
operating systems the default web browser is used to view files.


## Usage

Create a [configuration file] with the dimensions of each of the screens you
work with and where you want Preview.app to be displayed.  You can validate
these configurations by having the application echo them back at you:

```bash
$ showfile config
```


### Command Line

Invoke the application to show the file and display it:

```bash
$ showfile show example.pdf
```

See the [configuration file] example.


### From Python

The package is designed to be easy invoke from Python as well:
```python
from zensols.showfile import ApplicationFactory
app = ApplicationFactory().get_instance()
app('test-resources/showfile.conf')
```



## Obtaining

The easiest way to install the command line program is via the `pip` installer:
```bash
pip3 install zensols.showfile
```

Binaries are also available on [pypi].


## Documentation

See the [full documentation](https://plandes.github.io/showfile/index.html).
The [API reference](https://plandes.github.io/showfile/api.html) is also
available.


## Changelog

An extensive changelog is available [here](CHANGELOG.md).


## License

[MIT License](LICENSE.md)

Copyright (c) 2022 Paul Landes


<!-- links -->
[pypi]: https://pypi.org/project/zensols.showfile/
[pypi-link]: https://pypi.python.org/pypi/zensols.showfile
[pypi-badge]: https://img.shields.io/pypi/v/zensols.showfile.svg
[python39-badge]: https://img.shields.io/badge/python-3.9-blue.svg
[python39-link]: https://www.python.org/downloads/release/python-390
[python310-badge]: https://img.shields.io/badge/python-3.10-blue.svg
[python310-link]: https://www.python.org/downloads/release/python-310
[build-badge]: https://github.com/plandes/showfile/workflows/CI/badge.svg
[build-link]: https://github.com/plandes/showfile/actions

[configuration file]: test-resources/showfile.conf
pydds 0.0.8
DDS (including BC7) decompression bindings

# pydds
Replacement library for ImageMagick to decompress from many DDS pixel formats with an optional crop to a target region. While cropping can be done with separate libraries like Pillow doing it as part of decompression can save a lot of work as only the required blocks will be decompressed.

## Usage

`decode_dds` takes a blob of bytes and an optional cropping region, returning a PIL Image for further processing.

```python
from dds import decode_dds

# optional cropping region of (x0, y0, w, h) to be applied while decoding
crop = (0, 0, 20, 20)

with open('Atlas.dds', 'rb') as fh:
    img = decode_dds(fh.read(), crop)
```