semvec/pypi-packages huggingface dataset:
topoly 1.1.0
This package provides a set of tools for modeling protein knots, lassos and other.
Requires Python: >=3.6.0

Topoly is a **Python** package that collects programs useful for 
**polymer topology** analysis.

What you can do with Topoly?
----------------------------------

* Find knots, links, lassos, theta-curves, handcuffs and their type.
* Calculate knot/link invariants:

  * Polynomials: Alexander, Jones, Conway, HOMFLY, Yamada, Kauffman, BLM/Ho,
  * Brackets: Kauffman, APS,
  * Other: writhe, Gaussian linking number.

* Find minimal surface of a loop.
* Simplify polymer structure preserving its topology.
* Generate random polygon structures: walks, loops, lassos, handcuffs.
* Generate knot map (like in KnotProt).
* Calculate sum (U) and product (#) of knots.
* Visualize structures.

Provided executable programs 
----------------------------------

Apart from a Python library, this package provides a set of executable programs:  

1. knotnet - find knots using the Alexander Polynomial
2. homflylink
3. surfacesmytraj
4. ncuclinks
5. lmpoly
6. gln - compute the Gaussian linking number

Installation
----------------------------------

Make sure you have a recent version of **pip**. You can upgrade it by running:

``pip3 install --upgrade pip``
    
Install Topoly using the standard python package installer PIP:

``pip3 install topoly``

Topoly can be installed without administrative privileges in the home folder of a particular user or in a Python
Virtual Environment.
In that case all files (binaries, documentation, libraries and python modules) will be installed in:

``$HOME/.local/``

or ``venv/`` respectively.

If you choose to install Topoly with administrative privileges then everything will be installed in:
`/usr/local/`

Using Topoly
----------------------------------

Have a look at our website: https://topoly.cent.uw.edu.pl

or our tutorial project: https://github.com/ilbsm/topoly_tutorial

Contact
----------------------------------

INTERDISCIPLINARY LABORATORY of BIOLOGICAL SYSTEMS MODELLING, University of Warsaw, Warsaw, Poland

https://jsulkowska.cent.uw.edu.pl/
pymsbuild-msix 0.1.0a2
A pymsbuild extension for producing MSIX packages.
Keywords: build, pep-517, msbuild, packaging, msix, Windows

# pymsbuild-msix

This is a [pymsbuild](https://pypi.org/project/pymsbuild) extension for
compiling packages to MSIX.

# Quick Start

In your `_msbuild.py`, import `AppxManifest` from `pymsbuild_msix` (using
`import *` is okay, and there are more names you may need).


```python
from pymsbuild import *
from pymsbuild_msix import *

METADATA = {...}

PACKAGE = Package(
    'package',
    PyFile("__init__.py"),
    AppxManifest("appxmanifest.xml"),
)
```

The extension adds a new `msix` command to build the MSIX. It behaves like the
`wheel` and `sdist` commands, including with the `--layout-dir` option followed
by the `pack` command.

```powershell
$> python -m pymsbuild msix

# Or

$> python -m pymsbuild msix --layout-dir $DIR
# Modify (e.g. sign) the files in $DIR
$> python -m pymsbuild pack --layout-dir $DIR --add @additional_files.txt
```

Additional types that can be used in the package definition include
`ResourcesXml`, to identify a file to run with `makepri.exe` as part of build,
and `AppInstaller`. The latter currently does nothing special.
py-make-weekly-report-kace-rni 0.1.2
CLI to generate weekly report
Requires Python: >=3.7

# GI-KACE Weekly Report Generator

A simple CLI tool to generate and save a structured weekly report for GI-KACE RNI Department.
Outputs a text file and also copies the content to the clipboard for quick sharing.

The aim of this project was to speed up weekly reporting by letting you focus on the important content while automating
the formatting and file management.
---

## Features

- **NEW in 0.1.1**: Remembers your name and unit between sessions
- Automatically detects the current week's date range
- Prompts for achievements and challenges
- Formats the report with a clean layout
- Saves report as a text file to your desktop:  
  `Week [n] [Month] [monday's date] - [friday's date].txt`
- Copies report to clipboard for easy pasting into emails or other documents

---

## Installation

```bash
pip install py_make_weekly_report_kace_rni
```

To update to the latest version:

```bash
pip install --upgrade py_make_weekly_report_kace_rni
```

---

## Usage

Run the following command in your terminal to launch the tool.

```bash
py_make-weekly-report
```

### First-time use

You'll be prompted to enter:

- Your name
- Your unit
- Project name
- Key achievements (press Enter twice to finish)
- Challenges and next steps (press Enter twice to finish)

### Subsequent use

The tool will:

- Remember your name and unit
- Ask if you want to change them
- Only prompt for new project details and achievements/challenges

### Configuration

Your name and unit preferences are automatically saved to:

- Windows: ```%USERPROFILE%\AppData\Local\WeeklyReportGenerator\config.json```
- macOS/Linux: ```~/.config/WeeklyReportGenerator\config.json```

### Report Storage

A text file is saved to your desktop under the folder:

```
Weekly Reports/[current year]/Week #[n] [Month] [monday's date] - [friday's date].txt
```

## Example Output

```txt
Subject: Week Report of May 13th – May 17th, [Anthony]
Unit: SE
Project: Internal Tooling

Key Achievement(s):
  • Refactored legacy codebase to improve testability.
  • Automated weekly report generation using Python.

Challenges and Next Steps:
  • Integration testing for the new reporting module.
  • Coordinate with DevOps to deploy updates.
```

---

## License

MIT

---

## Changelog

### 0.1.2 - latest

Improved the title of the saved file to be more user friendly.
- previously: ```Week [n] [Month] [monday's date] - [friday's date].txt```
    - example: ```Week 20 May 12 - 16```
- currently: ```Week #[n] [Month] [monday's date][ordinal] to [friday's date][ordinal].txt```
    - example: ```Week #20 May 12th to 16th```

### 0.1.1

- Added persistent configuration to remember name and unit
- Improved error handling
- Better cross-platform support for config storage

### 0.1.0

Initial release

---
mkdocs-navsorted-plugin 1.0.0
mkdocs plugin to get nav sorted without yml directives
Requires Python: >=3.10
Keywords: mkdocs, navigation, plugin

# mkdocs-navsorted-plugin

https://github.com/idlesign/mkdocs-navsorted-plugin

[![PyPI - Version](https://img.shields.io/pypi/v/mkdocs-navsorted-plugin)](https://pypi.python.org/pypi/mkdocs-navsorted-plugin)
[![License](https://img.shields.io/pypi/l/mkdocs-navsorted-plugin)](https://pypi.python.org/pypi/mkdocs-navsorted-plugin)
[![Coverage](https://img.shields.io/coverallsCoverage/github/idlesign/mkdocs-navsorted-plugin)](https://coveralls.io/r/idlesign/mkdocs-navsorted-plugin)
[![Docs](https://img.shields.io/readthedocs/mkdocs-navsorted-plugin)](https://mkdocs-navsorted-plugin.readthedocs.io/)

## Description

*mkdocs plugin to get nav sorted without yml directives*

Use numeric prefixes for your documentation files/directories names
to drive navigation items sort order.


Normal layout:
```
docs
|- section_a
|   |- file1.md
|   |- file2.md
|- section_b
|   |- file3.md
|   |- file4.md
|- about.md
|- index.md
|- quickstart.md
```

We turn into a prefixed one:
```
docs
|- 01_index.md
|- 10_quickstart.md
|- 20_section_a
|   |- 10_file2.md
|   |- 20_file1.md
|- 30_section_b
|   |- file3.md
|   |- file4.md
|- 40_about.md
```

So the navigation with this plugin would become:
```
index
quickstart
section_a
  file2
  file1
section_b
  file3
  file4
about
```

Read the documentation.

## Documentation

https://mkdocs-navsorted-plugin.readthedocs.io
sektorsim 0.0.1
A small example package
Requires Python: >=3.7

# Example Package for SektorSim

Proof of concept for SektorSim package.

```python
import sektorsim

print(sektorsim.a(3)) # => 4
print(sektorsim.b(5)) # => 7
```

[SektorSim project page](https://www.hft-stuttgart.de/forschung/projekte/aktuell/sektorsim3)
outdatedLabs 0.1.3
A secure package for training machine learning models
Requires Python: >=3.8

# OutdatedLabs - Secure Machine Learning Training Package

A secure and easy-to-use package for training machine learning models with remote server support.

## Installation

```bash
pip install outdatedLabs
```

## Quick Start

Use the package in your code:
```python
from outdatedLabs import SecureModel

# Create a linear regression model
model = SecureModel.linearRegression()

# Train the model
model.train(
    dataset_hash="your_dataset_hash",
    features=["feature1", "feature2"],
    target="target_column"
)

# Get training metrics
metrics = model.get_metrics()
print(metrics)

# Download and load the trained model
model.download_model()
loaded_model = model.load_model()
```

## Features

- Secure model training with remote server support
- Automatic dataset download and cleanup
- Progress tracking with tqdm
- Comprehensive error handling
- Detailed logging
- Support for multiple algorithms (currently Linear Regression)

## Configuration

The package connects to a local ML training server by default at `http://localhost:3000`. You can change the server URL when creating a model:

```python
model = SecureModel.linearRegression(server_url="http://your-server:3000")
```

## API Reference

### SecureModel

The main class for model training and management.

#### Methods

- `linearRegression(server_url: str = "http://localhost:3000")`: Create a Linear Regression model
- `train(dataset_hash: str, features: List[str], target: str)`: Train the model
- `get_metrics() -> Dict`: Get training metrics
- `download_model() -> str`: Download the trained model
- `load_model() -> Any`: Load the trained model

## License

MIT License