semvec/pypi-packages huggingface dataset:
piperf3 0.0.11
A Python wrapper for iperf3 with configuration management, plotting, and CLI interface
Requires Python: >=3.12

# piperf3

A mostly vibe-coded wrapper around `iperf3` for running, collecting, and plotting network performance tests. Very unstable, probably only works with `iperf v3.19`.

## Requirements

`iperf3` binary in path. You should be able to run `iperf3` from command line.

## Installation

```bash
pip install piperf3
```

## CLI usage

- `piperf3 client SERVER [OPTIONS]` — run a client test with `SERVER` (hostname/IP)
- `piperf3 server [OPTIONS]` — run an iperf3 server

## License

MIT — see the `LICENSE` file.
django-tinymce 5.0.0
A Django application that contains a widget to render a
Requires Python: >=3.9
Keywords: django, widget, tinymce

django-tinymce
==============

**django-tinymce** is a Django application that contains a widget to render a form field as a TinyMCE editor.

It supports Python 3.9+ and Django 4.2 to 5.2. Using TinyMCE 7.8.0.

.. image:: https://jazzband.co/static/img/badge.svg
        :target: https://jazzband.co/
        :alt: Jazzband

.. image:: https://img.shields.io/pypi/v/django-tinymce.svg
        :target: https://pypi.python.org/pypi/django-tinymce

.. image:: https://img.shields.io/pypi/pyversions/django-tinymce.svg
        :target: https://pypi.python.org/pypi/django-tinymce

.. image:: https://img.shields.io/pypi/djversions/django-tinymce.svg
        :target: https://pypi.org/project/django-tinymce/

.. image:: https://img.shields.io/pypi/dm/django-tinymce.svg
        :target: https://pypi.python.org/pypi/django-tinymce

.. image:: https://github.com/jazzband/django-tinymce/workflows/Test/badge.svg
   :target: https://github.com/jazzband/django-tinymce/actions
   :alt: GitHub Actions

.. image:: https://codecov.io/gh/jazzband/django-tinymce/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/jazzband/django-tinymce
   :alt: Code coverage


Quickstart
==========

Install django-tinymce:

.. code-block:: bash

    $ pip install django-tinymce

Add tinymce to INSTALLED_APPS in settings.py for your project:

.. code-block:: python

    INSTALLED_APPS = (
        ...
        'tinymce',
    )

Add tinymce.urls to urls.py for your project:

.. code-block:: python

    urlpatterns = [
        ...
        path('tinymce/', include('tinymce.urls')),
    ]

In your code:

.. code-block:: python

    from django.db import models
    from tinymce.models import HTMLField

    class MyModel(models.Model):
        ...
        content = HTMLField()

**django-tinymce** uses staticfiles so everything should work as expected, different use cases (like using widget instead of HTMLField) and other stuff is available in documentation.

Documentation
=============

https://django-tinymce.readthedocs.org/

Support and updates
===================

Use github issues https://github.com/jazzband/django-tinymce/issues

License
=======

Originally written by Joost Cassee.

This program is licensed under the MIT License (see LICENSE.txt)
stories-jax 0.1.1
Learn spatially informed Waddington-like potentials for single-cell gene expression
Requires Python: <4.0,>=3.10

# Learning cell fate landscapes from spatial transcriptomics using Fused Gromov-Wasserstein

[![codecov](https://codecov.io/gh/cantinilab/stories/graph/badge.svg?token=5DWDYPAUYI)](https://codecov.io/gh/cantinilab/stories)
[![Tests](https://github.com/cantinilab/stories/actions/workflows/main.yml/badge.svg)](https://github.com/cantinilab/stories/actions/workflows/main.yml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Documentation Status](https://readthedocs.org/projects/stories/badge/?version=latest)](https://stories.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://img.shields.io/pypi/v/stories-jax?color=blue)](https://img.shields.io/pypi/v/stories-jax?color=blue)

STORIES is a trajectory inference method capable of learning a causal model of cellular differentiation from spatial transcriptomics through time using Fused Gromov-Wasserstein. STORIES learns a potential function that defines each cell's stage of differentiation and allows one to predict the evolution of cells at future time points. In addition, STORIES uncovers possible driver genes and transcriptional regulators of cellular differentiation.

[Read the preprint here](https://www.biorxiv.org/content/10.1101/2024.07.26.605241v1) and [the documentation here](https://stories.rtfd.io)!

STORIES is based on the Scverse ecosystem, making it easy to interface with existing tools for single-cell analysis such as Scanpy and CellRank. In addition, STORIES benefits from the JAX ecosystem for deep learning and OT computation, enabling the fast handling of large datasets.

![introductory figure](docs/_static/fig1.png)

## Install the package

- STORIES is implemented as a Python package seamlessly integrated within the scverse ecosystem. It relies on JAX for fast GPU computations and JIT compilation, and OTT for Optimal Transport computations.
- **System requirements**: Python >= 3.10. Continuously tested on Ubuntu 22.04 LTS. Installation time <1mn. Benefits from CUDA or TPU for large datasets (on a GPU A40 and for 800k cells, training time ~ 30mn).

### via PyPI (recommended)

```bash
pip install stories-jax
```

By default, JAX is installed for CPU. To get the GPU version, you can use the following (refer to [JAX's docs](https://jax.readthedocs.io/en/latest/installation.html)). For use in Google Colab, you'll need to restart the kernel after this command.

```bash
pip install stories-jax jax[cuda12]==0.4.26
```

### via GitHub (development version)

```bash
git clone git@github.com:cantinilab/stories.git
pip install ./stories/
```

## Getting started

STORIES takes as an input an AnnData object, where omics information and spatial coordinates are stored in `obsm`, and `obs` contains time information, and optionally a proliferation weight. Visit the **Getting started** and **API** sections for tutorials and documentation.
python-llm-factory 0.0.5
A flexible Python factory for working with multiple Large Language Model (LLM) providers (OpenAI, Anthropic, Gemini, Llama) using a unified interface, with robust configuration and extensibility.
Requires Python: >=3.11

# llm-factory
A flexible Python factory for working with multiple Large Language Model (LLM) providers (OpenAI, Anthropic, Gemini, Llama) using a unified interface, with robust configuration and extensibility.

---

## Features
- ✅ Unified interface for multiple LLM providers (OpenAI, Anthropic, Gemini, Llama)
- ✅ Easy provider switching via configuration
- ✅ Pydantic-based response validation
- ✅ Environment variable-based secure configuration
- ✅ Extensible for new providers
- ✅ Supports model, temperature, max tokens, and retries per provider

---

## Installation
```bash
pip install python-llm-factory
```

---

## Configuration
The package uses environment variables for authentication and configuration. You can set these in a `.env` file or your environment:

```bash
# Required environment variables for each provider
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GEMINI_API_KEY=your_gemini_api_key
```

---

## Examples

### Basic Usage: Creating a Completion

```python
from pydantic import BaseModel, Field
from python_llm_factory import LLMFactory
from python_llm_factory import Settings


class CompletionModel(BaseModel):
    response: str = Field(description="Your response to the user.")
    reasoning: str = Field(description="Explain your reasoning for the response.")


messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "If it takes 2 hours to dry 1 shirt out in the sun, how long will it take to dry 5 shirts?"},
]

llm = LLMFactory(
    settings=Settings().gemini.gemini_2_5_flash,
)
completion = llm.completions_create(
    response_model=CompletionModel,
    messages=messages,
)
print(f"Response: {completion.response}\n")
print(f"Reasoning: {completion.reasoning}")
```

---

## 🤝 Contributing
If you have a helpful tool, pattern, or improvement to suggest:
- Fork the repo <br>
- Create a new branch <br>
- Submit a pull request <br>
I welcome additions that promote clean, productive, and maintainable development. <br>

---

## 🙏 Thanks
Thanks for exploring this repository! <br>
Happy coding! <br>