semvec/pypi-packages huggingface dataset:
genai-processors 1.1.1
GenAI Processors Library
Requires Python: >=3.11

# GenAI Processors library

[![PyPI version](https://img.shields.io/pypi/v/genai-processors.svg)](https://pypi.org/project/genai-processors/)
![Python support](https://img.shields.io/pypi/pyversions/genai-processors)
[![PyPI - Downloads](https://img.shields.io/pypi/dw/genai-processors)](https://pypistats.org/packages/genai-processors)


--------
**Documentation:** https://github.com/google-gemini/genai-processors

-----

**Build Modular, Asynchronous, and Composable AI Pipelines for Generative AI.**

GenAI Processors is a lightweight Python library that enables efficient,
parallel content processing.

## Installation

```sh
pip install genai-processors
```

## Imports

```python
from genai_processors import content_api
from genai_processors import processor
from genai_processors import streams
from genai_processors.core import genai_model
...
```
pynodewidget 0.2.3
A Python wrapper for ReactFlow using AnyWidget - build interactive node graphs without JavaScript
Requires Python: >=3.10
Keywords: graph, jupyter, nodes, reactflow, widget

# PyNodeWidget

A Python wrapper for ReactFlow using AnyWidget - build interactive node graphs without JavaScript.

## Quick Start

```python
from pynodewidget import NodeFlowWidget
from pynodewidget.grid_layouts import create_three_column_grid
from pynodewidget.models import ButtonHandle, NumberField, LabeledHandle, TextField

widget = NodeFlowWidget()

grid_layout = create_three_column_grid(
    left_components=[
        LabeledHandle(id="input", handle_type="input")
    ],
    center_components=[
        NumberField(id="value", value=50, min=0, max=100),
        TextField(id="name", value="Processor")
    ],
    right_components=[
        LabeledHandle(id="output", handle_type="output")
    ]
)

widget.add_node_type(
    type_name="processor",
    label="Processor",
    icon="⚙️",
    grid_layout=grid_layout
)
widget
```

## Demo

![Demo](imgs/widget_example.gif)

## Features

- **Interactive Node Graphs**: Build visual workflows with drag-and-drop nodes
- **No JavaScript Required**: Pure Python API for defining node types and layouts
- **JSON Schema Integration**: Automatically generate node UIs from JSON schemas
- **Static HTML Export**: Export workflows as standalone HTML files for documentation and sharing
  - Interactive or view-only modes
  - Self-contained files that work without Python/Jupyter
  - Perfect for visualizing DAGs and data pipelines
- **Jupyter & Marimo Support**: Works seamlessly in Jupyter notebooks and Marimo apps
- **Flexible Layouts**: Customizable grid-based node layouts with various components
- **Type-Safe**: Built with Pydantic for robust type validation

## Export Workflows

```python
# Export as standalone HTML (no Python required to view)
widget.export_html("workflow.html", interactive=True)

# Export as JSON for persistence
widget.export_json("workflow.json")
```


## Development

Requires:
- Python 3.12+ (uv)
- [Bun](https://bun.sh) for JavaScript bundling

```bash
# Install dependencies
uv venv
uv pip install -e .[all]

task
```

### Documentation

```bash
# Serve documentation locally with live reload
task docs-serve

# Build static documentation
task docs-build
```

See full [documentation](https://henningscheufler.github.io/pynodewidget/) at the project's GitHub Pages or run locally.
rob-norm 1.0.1
Python implementation of RobNorm, a robust normalization method for labeled quantitative proteomics data.
Requires Python: >=3.11

# rob-norm 🦭🧬

Python implementation of the [RobNorm](https://github.com/mwgrassgreen/) R package for robust normalization of quantitative omics data. 

## Features

- Identical normalization results to the original R implementation.
- Fast and efficient implementation using NumPy (57 ms vs 113 ms in R for 5000x200 data on an M1 MacBook Air)

## Installation

Install via pip:

```bash
pip install rob-norm
```

## Usage

```python
import pandas as pd
from rob_norm import rob_norm

# load your data into a DataFrame (rows = features, columns = samples)
data = pd.read_csv('./data/simulated_measurements.txt', index_col=0, sep='\t')

# alternatively, simulate data using the provided function
# sim_dat_fn(row_frac, col_frac, mu_up, mu_down, n, m, nu_fix=True, seed=None):

# perform a normalization operation provided by the package
results = rob_norm(data, gamma_0=0.5, tol=1e-4, step=200)

normalized_data = results['norm_data']

# verify against reference results
df_results = pd.read_csv('./data/simulated_measurements_normalized.txt', index_col=0, sep='\t')

assert np.allclose(df_results, normalized_data) # == True
```

## Development

Clone the repository and use `uv` for development and testing:

```bash
git clone https://github.com/Tom-Julux/rob_norm
cd rob_norm

uv run pytest
uv build
uv publish
```

## License

The project is licensed under the GNU Lesser General Public License v3.0.

See the `LICENSE` file for full licensing information.

The file `data/robnorm.r` contains R code used to generate reference results for testing and was copied and adapted from the original R package `RobNorm`.