semvec/pypi-packages huggingface dataset:
wilson-beta 1.0.5
A wrapper to modify beta functions in the Wilson SMEFT package.
Requires Python: >=3.7

# Wilson_Beta_wrapper

Wilson-Beta-wrapper is a simple Python tool that lets you tweak the beta function behavior from the wilson library, which is used in SMEFT (Standard Model Effective Field Theory) work.

With this wrapper, you can:

Catch the output of wilson.run.smeft.beta.beta

Change specific values (like Wilson coefficients) in the beta dictionary

Do it on the fly by calling configure_patch(key, value)

It's handy if you want to experiment, test changes quickly, or try out custom beta behaviorsâ€”without having to mess with wilson's core code.
vape4d 0.2.7
Volume 4D Visualization
Requires Python: >=3.8
Keywords: 4D, volume, visualization, vape4d

# vAPE - Interactive 4D volume visualization

[![PyPI](https://img.shields.io/pypi/v/vape4d.svg)](https://pypi.org/project/vape4d/)
[![Docs](https://img.shields.io/badge/docs-mkdocs-blue)
](https://keksboter.github.io/vape4d/docs/)

[👉 Click to run the web app 👈](https://keksboter.github.io/vape4d/?file=https://huggingface.co/datasets/vollautomat/vape4d/resolve/main/gray_scott_3d.npy&colormap=https://huggingface.co/datasets/vollautomat/vape4d/resolve/main/colormap.json)

![Viewer screenshot](https://raw.githubusercontent.com/KeKsBoTer/vape4d/master/screenshot.png)

[Burgers](https://en.wikipedia.org/wiki/Burgers%27_equation)            |  [Kuramoto–Sivashinsky](https://en.wikipedia.org/wiki/Kuramoto%E2%80%93Sivashinsky_equation)                              |  [Gray-Scott](https://groups.csail.mit.edu/mac/projects/amorphous/GrayScott/)
:-------------------------:|:------------------------:|:-------------------------:
![Burgers](https://raw.githubusercontent.com/KeKsBoTer/vape4d/master/references_3d/burgers_1.webp)  |![Kuramoto–Sivashinsky](https://raw.githubusercontent.com/KeKsBoTer/vape4d/master/references_3d/ks.webp)|![Gray Scott](https://raw.githubusercontent.com/KeKsBoTer/vape4d/master/references_3d/gray_scott.webp)|
-----


## Installation

with pip
```
pip install vape4d
```


## Usage

**Viewer** (jupyter notebook)
```python
from vape4d import viewer
import numpy as np
from matplotlib import pyplot as plt

viewer(
    #[T,C,D,W,H]
    np.random.rand(1,1,32,32,32),
    plt.get_cmap("viridis"),   
)
```

**Render Image**
```python
import numpy as np
from vape4d import diverging_alpha, render
import matplotlib.pyplot as plt

colormap = diverging_alpha(plt.get_cmap("magma"))
img = render(
        # [T,D,H,W]
        np.random.rand(2,32,32,32).astype(np.float32),
        colormap,
        0.5, # timestep
        width=1024,
        height=1024,
    )

plt.imshow(img)
plt.axis("off")

plt.savefig("test.png", bbox_inches="tight", pad_inches=0)
plt.show()
```
visionfeed 0.1.0
VisionFeed by Intel Open Edge Platform
Requires Python: >=3.8

# visionfeed

Data management and processing tools for computer vision applications by Intel Open Edge Platform.

## Installation

Using uv (recommended):

```bash
uv pip install visionfeed
```

Using pip:

```bash
pip install visionfeed
```

## Usage

```python
import visionfeed

# Add usage examples here
```

## Development

To set up the development environment:

1. Install uv:

   ```bash
   curl -LsSf https://astral.sh/uv/install.sh | sh
   ```

2. Clone the repository:

   ```bash
   git clone https://github.com/intel/visionai.git
   cd visionai
   ```

3. Create and activate a virtual environment:

   ```bash
   uv venv
   source .venv/bin/activate  # On Unix/macOS
   # or
   .venv\Scripts\activate  # On Windows
   ```

4. Install development dependencies:

   ```bash
   uv pip install -e ".[dev]"
   ```

## License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for more details.
visioneye 0.1.0
VisionEye by Intel Open Edge Platform
Requires Python: >=3.8

# visioneye

Data management and processing tools for computer vision applications by Intel Open Edge Platform.

## Installation

Using uv (recommended):

```bash
uv pip install visioneye
```

Using pip:

```bash
pip install visioneye
```

## Usage

```python
import visioneye

# Add usage examples here
```

## Development

To set up the development environment:

1. Install uv:

   ```bash
   curl -LsSf https://astral.sh/uv/install.sh | sh
   ```

2. Clone the repository:

   ```bash
   git clone https://github.com/intel/visionai.git
   cd visionai
   ```

3. Create and activate a virtual environment:

   ```bash
   uv venv
   source .venv/bin/activate  # On Unix/macOS
   # or
   .venv\Scripts\activate  # On Windows
   ```

4. Install development dependencies:

   ```bash
   uv pip install -e ".[dev]"
   ```

## License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for more details.
RaiPlaySound 0.3.0.0a0
Podcast generator for RaiPlay Sound
Requires Python: >=3.12

# RaiPlaySound - Podcast non ufficiale

Questa pagina è solo per chi vuole smanettare, altrimenti il lavoro è già fatto. In ogni caso...

## Vai su http://timendum.github.io/raiplaysound/

### FAQ

#### Non trovo il programma che mi interessa

Dall'elenco generato e disponibile [qui](https://timendum.github.io/raiplaysound/),
sono esclusi i programmi quotidiani e le audiodescrizioni dei film.

Controlla se quello che ti interessa rientra in questa categoria.  
In caso positivo, questo software funziona comunque,
puoi installarlo ed ottenere il programma che ti interessa seguendo
le istruzioni in [questa sezione](https://timendum.github.io/raiplaysound/#diy).

#### Dal podcast mancano gli ultimi episodi

Aggiorno i podcast ogni tanto, se sono passati vari mesi, apri pure una issue.

Se hai fretta o vuoi un podcast sempre aggiornato,
il consiglio è quello di [fare da solo](https://timendum.github.io/raiplaysound/#diy).

#### Ho fatto girare il software, ma la mia lista è più piccola

Capita, ogni tanto dal sito scompaiono dei programmi e quindi il software non lo estrae più.

Io faccio girare il software dove ho i file dell'esecuzione precedente,
in modo da mantenere i singoli file di podcast (anche se non più aggiornati).
data-narasak 0.1.2
A Python package for data transformation and generating PDF reports with detailed analysis and visualizations.
Requires Python: >=3.7

# Data Analysis & Transformation PDF Report Generator

A powerful Python toolkit for performing data transformations on tabular datasets and generating detailed, well-formatted PDF reports. Designed for data analysts, scientists, and engineers, this tool streamlines data preprocessing, exploratory analysis, and documentation by integrating transformation functions, visualizations, and report generation in one package.

---

## 📌 Features

- **Multi-format data loading:** Supports CSV, Excel (`.xlsx`, `.xls`), and JSON files.
- **Flexible data transformations:** Normalization, log transformation, categorical encoding, and more.
- **Comprehensive PDF reports:** Include dataset overview, data types, summary statistics, missing and unique values, correlation matrices, data previews, transformed data insights, and before-after comparisons.
- **Dynamic plot integration:** Optional inclusion of plots like histograms, boxplots, scatterplots, bar charts, and heatmaps.
- **Professional PDF formatting:** Powered by ReportLab with support for pagination, text formatting, and image embedding.
- **Automated cleanup:** Temporary plot images are removed after embedding to keep your workspace tidy.
- **Extensible modular design:** Easy to customize and extend with your own transformations and visualizations.

---

## 🚀 Installation

Install dependencies via pip:

```bash
pip install pandas reportlab matplotlib numpy openpyxl
visiontemplate 0.1.0
A toolkit for computer vision tasks
Requires Python: >=3.8

# VisionTemplate

A toolkit for computer vision tasks by Intel Open Edge Platform.

## Installation

Using uv (recommended):

```bash
uv pip install visiontemplate
```

Using pip:

```bash
pip install visiontemplate
```

## Usage

```python
import visiontemplate

# Add usage examples here
```

## Development

To set up the development environment:

1. Install uv:

   ```bash
   curl -LsSf https://astral.sh/uv/install.sh | sh
   ```

2. Clone the repository:

   ```bash
   git clone https://github.com/open-edge-platform/visiontemplate.git
   cd visiontemplate
   ```

3. Create and activate a virtual environment:

   ```bash
   uv venv
   source .venv/bin/activate  # On Unix/macOS
   # or
   .venv\Scripts\activate  # On Windows
   ```

4. Install development dependencies:

   ```bash
   uv pip install -e ".[dev]"
   ```

## License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for more details.