semvec/pypi-packages huggingface dataset:
rcsb.utils.insilico3d 0.43
RCSB Python In Silico Model Access Utilities
Requires Python: >=3.9

# RCSB Python In Silico Model Access Utilities

[![Build Status](https://dev.azure.com/rcsb/RCSB%20PDB%20Python%20Projects/_apis/build/status/rcsb.py-rcsb_utils_insilico3d?branchName=master)](https://dev.azure.com/rcsb/RCSB%20PDB%20Python%20Projects/_build/latest?definitionId=35&branchName=master)

## Introduction

This module contains utility methods for accessing in silico 3D models and metadata from external data resources, including AlphaFold, ModBase, SWISS-MODEL, and Model Archive.

### Installation

Download the library source software from the project repository:

```bash

git clone --recurse-submodules https://github.com/rcsb/py-rcsb_utils_insilico3d.git

```

**Important:** Setup will require an up-to-date version of [cmake](https://cmake.org/install/) to be installed on the machine and the executable to be in the system's PATH.

Optionally, run test suite (Python versions 3.9) using
[tox](http://tox.readthedocs.io/en/latest/example/platform.html):

```bash
tox
```

Installation is via the program [pip](https://pypi.python.org/pypi/pip).

```bash
pip install rcsb.utils.insilico3d

or for the local repository:

pip install .
```

## References
1. [AlphaFold](https://alphafold.ebi.ac.uk/)
    * Jumper, J et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021)
    * Varadi, M et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research (2021).
1. [ModelArchive](https://modelarchive.org/)
    * ModelArchive: A Deposition Database for Computational Macromolecular Structural Models. Gerardo Tauriello, Andrew M. Waterhouse, Juergen Haas, Dario Behringer, Stefan Bienert, Thomas Garello, Torsten Schwede. Journal of Molecular Biology (2025). https://doi.org/10.1016/j.jmb.2025.168996
pyramid-furkan 0.0.2
A simple pyramid package
Requires Python: >=3.7
Keywords: pyramid, packaging, tutorial, furkan_bulut

# Furkan Bulut

[![PyPI version](https://badge.fury.io/py/furkan-bulut.svg)](https://badge.fury.io/py/furkan-bulut)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3](https://img.shields.io/badge/python-3-blue.svg)](https://www.python.org/)

This is a simple pyramid package for the `furkan_bulut` project, demonstrating how to package and publish Python projects to PyPI.

## Features

- Simple pyramid structure demonstration.
- Ready-to-use project template.
- MIT Licensed.

## Installation

You can install the package directly from PyPI:

```bash
pip install furkan-bulut
```

## Usage

Import the package in your Python script:

```python
import furkan_bulut

# Example usage
# furkan_bulut.some_function()
```

## Development

To contribute or modify this project:

1. Clone the repository.
2. Create a virtual environment: `python3 -m venv venv`
3. Install dependencies: `pip install build twine`
4. Build the package: `python3 -m build`
pydyf 0.12.1
A low-level PDF generator.
Requires Python: >=3.10
Keywords: pdf, generator

pydyf is a low-level PDF generator written in Python and based on PDF
specification 1.7.

* Free software: BSD license
* For Python 3.10+, tested on CPython and PyPy
* Documentation: https://doc.courtbouillon.org/pydyf
* Changelog: https://github.com/CourtBouillon/pydyf/releases
* Code, issues, tests: https://github.com/CourtBouillon/pydyf
* Code of conduct: https://www.courtbouillon.org/code-of-conduct
* Professional support: https://www.courtbouillon.org
* Donation: https://opencollective.com/courtbouillon

Copyrights are retained by their contributors, no copyright assignment is
required to contribute to pydyf. Unless explicitly stated otherwise, any
contribution intentionally submitted for inclusion is licensed under the BSD
3-clause license, without any additional terms or conditions. For full
authorship information, see the version control history.
anime-episode-parser 0.0.14
A library to parse episode info from anime title
Requires Python: <4,>=3.9
Keywords: anime

# anime-episode-parser

try parse episode info from title

```bash
poetry add anime_episode_parser
```

```python
from anime_episode_parser import parse_episode

title = '[YMDR][哥布林殺手][Goblin Slayer][2018][05][1080p][AVC][JAP][BIG5][MP4-AAC][繁中]'
assert (5, 1) == parse_episode(title)

# 5 for episode start
# 1 for episodes count

title = '[从零开始的异世界生活 第二季_Re Zero S2][34-35][繁体][720P][MP4]'
assert (34, 2) == parse_episode(title)

# 34 for episode start
# 2 for episodes count

title = "something can't parse"
assert (None, None) == parse_episode(title)
```
chaldene-super-resolution 0.1.0
Super resolution based on EMDiffuse
Requires Python: >=3.6

# super_resolution

The test image, part of code in super_resolution folder are from https://github.com/Luchixiang/EMDiffuse

To explore the usage interactively, please click
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1qcEOErglYt9N9gZZLAez-Um7agn-5FVj?usp=sharing)
geomin 0.0.5
A Python library for manipulating, plotting and computing with polyhedral and ellipsoidal sets.
Requires Python: >=3.9
Keywords: optimization, control, MPC, geometry, polyhedra, polyhedron, ellipse, ellipsoid, polytope

# Geomin

Python library for manipulating, plotting and computing with polyhedral
and ellipsoidal sets. It is a relatively thin wrapper around
Pycddlib, including some utility functions for convenience.

## Installation

(The following assume you have already created a Python virtual environment)

**Option 1.** Install directly from PyPI:

```sh
pip install geomin
```

This does assume some prerequisites for `pycddlib` have already been installed.
See [their documentation](https://pycddlib.readthedocs.io/en/latest/quickstart.html)
for more information.

**Option 2.** Clone the repo and from within its root, run

```sh
pip install .
```

## Usage

### Creation of polytopes

#### From inequalities

Use the `Polyhedron.from_inequalities` method to build a polyhedron $X$ from
a system of linear inequalities

$$
X = \{ x \in \mathbb{R}^n \mid H x \le h \}.
$$

For example, to represent the system

$$
\begin{cases}
x_1 \ge -1 \\
x_2 \ge -1 \\
x_1 + x_2 \le 1
\end{cases}
$$

You would write

```python
import geomin as gp
import numpy as np

H = np.vstack([-np.eye(2), np.ones((1,2))])
h = np.array([1., 1., 1.])
poly = gp.Polyhedron.from_inequalities(H, h)

```

#### From vertices

Alternatively, a polyhedron can be constructed as the convex hull of a list
of vertices.

```python
import geomin as gp
import numpy as np

verts = np.array([(0, 0), (1, 0), (0, 1)])
poly = gp.Polyhedron.from_generators(verts)
```

### Creation of ellipsoids

An ellipsoid created as `gp.Ellipsoid(Q, c)` represents

$$
\{ x: (x - c)^\top Q (x - c) \leq 1 \}.
$$

The argument $c$ is optional and is assumed to be $0$ by default.

## Plotting

We provide some functions for easy plotting of sets, built on Matplotlib.
Check the [examples](https://github.com/kul-optec/geomin/tree/main/examples) folder for more.


```python
import matplotlib.pyplot as plt

# basic usage
plot_polytope(poly)

# Or with some styling
plt.figure()

plot_polytope(
    poly,
    color="tab:green",  # base color
    alpha=0.5,          # transparency
)

plt.show()
```
frogbox 0.6.0
An opinionated machine learning framework
Requires Python: >=3.10

<p align="center">
    <img src="https://simonlarsen.github.io/frogbox/logo.png" width="224">
</p>
<h1 align="center">
    frogbox
</h1>

Frogbox is an opinionated PyTorch machine learning framework built for rapid prototyping and research.

## Features

* Experiments are defined using JSON files and support [jinja2](https://jinja.palletsprojects.com) templates.
* Flexible event system inspired by [Ignite](https://pytorch.org/ignite).
* Automatic experiment tracking. Currently, [Weights & Biases](https://wandb.ai) and [MLFlow](https://mlflow.org) is supported with other platforms planned.
* CLI tool for easy project management.
* Integrates [Accelerate](https://huggingface.co/docs/accelerate/index) to support automatic mixed precision (AMP) and distributed training.

## Installation

```sh
pip install frogbox
```

## Getting started

See [documentation](https://frogbox.readthedocs.io).
rcsb.utils.config 0.42
RCSB Python Configuration Utilities
Requires Python: >=3.9

# RCSB Python Configuration Utilities

[![Build Status](https://dev.azure.com/rcsb/RCSB%20PDB%20Python%20Projects/_apis/build/status/rcsb.py-rcsb_utils_config?branchName=master)](https://dev.azure.com/rcsb/RCSB%20PDB%20Python%20Projects/_build/latest?definitionId=8&branchName=master)

## Introduction

This module contains a utilities for managing configuration options
using ini/configparser and YAML format configuration files.

### Installation

Download the library source software from the project repository:

```bash

git clone --recurse-submodules https://github.com/rcsb/py-rcsb_utils_config.git

```

Optionally, run test suite (Python versions 3.9) using
[tox](http://tox.readthedocs.io/en/latest/example/platform.html):

```bash
tox
```

Installation is via the program [pip](https://pypi.python.org/pypi/pip).

```bash
pip install rcsb.utils.config

or for the local repository:

pip install .
```