semvec/pypi-packages huggingface dataset:
truncated-gamma-rvs 0.1.6
A Python package for generating random variates from a truncated gamma distribution.
Requires Python: >=3.9

# truncated-gamma-rvs

[![](https://github.com/wkingc/truncated-gamma-rvs-py-package/actions/workflows/python-package.yml/badge.svg)](https://github.com/wkingc/truncated-gamma-rvs-py-package/actions/workflows/python-package.yml)

[![](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)

A Python package for generating random variates from a truncated gamma distribution.

## Installation

```bash
pip install truncated-gamma-rvs
```

## Quick Start

```python
from truncated_gamma_rvs import truncgamma_rvs

# Generate 100,000 random variates from a gamma distribution truncated 
# to the interval [0, 1000], where the mean is 100 and the standard 
# deviation is 1/2 the value of the mean (e.g., the coefficient of variation).
x = truncgamma_rvs(
    mean_target = 100, 
    cv_target = 1/2, 
    A = 0, 
    B = 1000, 
    size = 100000, 
    random_state = 123)
```

## What is a Truncated Gamma Distribution?

A truncated gamma distribution is a gamma distribution that has been "cut off" at specified lower (A) and upper (B) bounds. This is useful when you need gamma-distributed random variables that must fall within a specific range.

## API Reference

### `truncgamma_rvs(mean_target, cv_target, A, B, size=1, random_state=None)`

Generate random variates from a truncated gamma distribution.

**Parameters:**

- `mean_target` (float): Target mean of the truncated distribution
- `cv_target` (float): Target coefficient of variation  
- `A` (float): Lower bound (≥ 0)
- `B` (float): Upper bound (> A)
- `size` (int, optional): Number of samples to generate (default: 1)
- `random_state` (int, optional): Random seed for reproducibility

**Returns:**

- `numpy.ndarray`: Array of random samples

### `TruncatedGamma(alpha, theta, A, B)`

Class for working with truncated gamma distributions.

**Parameters:**

- `alpha` (float): Shape parameter (> 0)  
- `theta` (float): Scale parameter (> 0)
- `A` (float): Lower bound (≥ 0)
- `B` (float): Upper bound (> A)

**Methods:**

- `truncgamma_m1()`: Calculate the first moment (mean)
- `truncgamma_m2()`: Calculate the second moment  
- `truncgamma_var()`: Calculate the variance
- `truncgamma_cv()`: Calculate the coefficient of variation

## Requirements

- Python ≥ 3.9
- numpy
- scipy

## Development

```bash
git clone https://github.com/wkingc/truncated-gamma-rvs-py-package.git
cd truncated-gamma-rvs-py-package
pip install -e ".[dev]"
python -m pytest
```

## License

MIT License. See [LICENSE](LICENSE) for details.

## Documentation

For a complete usage guide, see <https://www.kingcopeland.com/truncated-gamma-rvs-py/>.

## Citation

If you use this package in your research, please consider citing it:

```bibtex
@software{copeland2026tgammarvs,
    author = {Wade K. Copeland},
    title = {{truncated-gamma-rvs: A Python package for generating random variates from a truncated gamma distribution}},
    url = {https://pypi.org/project/truncated-gamma-rvs/},
    version = {0.1.6},
    year = {2026}
}
```
pydgey 0.1.0
Interactive pipeline widgets for Google Colab and Jupyter.
Requires Python: >=3.9

# Pydgey

[![Support](https://img.shields.io/pypi/status/pydgey?label=support)](https://pypi.org/project/pydgey/) [![PyPI](https://img.shields.io/pypi/v/pydgey?label=pypi)](https://pypi.org/project/pydgey/)

<p align="center">
  <img src="docs/static/pydgey-logo.png" width="50%" alt="poorly drawn pydgey logo">
</p>

## Overview

Pydgey is a Python framework for building simple graphical user interfaces for pipelines that run interactively in Jupyter Notebooks (e.g., [Google Colab](https://colab.research.google.com/), [VSCode](https://code.visualstudio.com/docs/datascience/jupyter-notebooks), [JupyterLab](https://jupyter.org/)).

Built on top of [anywidget](https://anywidget.dev/), [React](https://react.dev/), and [shadcn/ui](https://ui.shadcn.com/), Pydgey aims to abstract away some of the complexities of widget building. It allows for pipelines to be defined using standard Python classes, and handles the generation of a clean, React-based UI.

## Purpose

I needed a simple framework to quickly wrap tools and basic pipelines into user-friendly interfaces that run easily on Google Colab. The goal being to save Bachelors and Masters students in our lab the hassle of installing and managing command-line tools, letting them focus on the limited time they have to actually *do science*.

As the intended use is to run Pydgey widgets within the constraints of free Google Colab environments, the goal is not to support massive scale, high-concurrency execution, or complex dependency graphs like [Snakemake](https://snakemake.github.io/) or [Nextflow](https://www.nextflow.io/). Pydgey prioritizes accessibility and ease-of-use for smaller, ad-hoc analyses over the computational efficiency required for large-scale workflows.

## Getting Started

Pydgey can be installed from PyPI:

```bash
$ pip install pydgey
```

### 💡 Example

Create a launcher for an HMMER search pipeline. This will render an interactive widget in the notebook where users can upload files and configure E-values without writing code.

```python
from examples.hmmsearch.hmmsearch import HmmSearchPipeline
from pydgey import create_launcher

# Define your pipeline (or import a pre-made one)
pipeline = HmmSearchPipeline()

# Launch the interactive widget
launcher = create_launcher(pipeline)
launcher
```

This simple snippet produces a full-featured UI with file selectors, parameter inputs, and log streaming.

## 🏗️ Contributing

Contributions are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md) for more details.

## ⚖️ License

This library is provided under the **MIT License**. See [LICENSE](LICENSE) for more information.
rajeshdas 0.1.1a2
A CLI persona app that showcases Rajesh Das's work, projects, and engineering mindset in the terminal
Requires Python: >=3.13

# Rajesh Das

A CLI persona app that showcases Rajesh Das's work, projects, and engineering mindset directly in the terminal.

## Overview

This is my personal terminal portfolio built on top of the [hyperoot](../hyperoot) framework. Run it to explore my background, projects, and get in touch.

## Installation

```bash
uv add rajeshdas
```

## Usage

```bash
rajeshdas
```

## Author

**Rajesh Das**
📧 rajesh@hyperoot.dev

## License

MIT
com-pac 0.0.4
Center Of Mass - Principal Axes Calculator
Requires Python: >=3.10
Keywords: cartesian coordinates, center of mass, molecule, principal ax[ei]s, rotation, rotational spectoscopy

# com-pac

[![PyPI - Version](https://img.shields.io/pypi/v/com-pac.svg)](https://pypi.org/project/com-pac)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/com-pac.svg)](https://pypi.org/project/com-pac)

[![Python](https://img.shields.io/badge/Python-3.10-3776AB?style=flat&logo=python&logoColor=white&labelColor=1f1f1f)](https://python.org)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000?style=logoColor=white&labelColor=1f1f1f)](https://github.com/psf/black)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue?style=logoColor=white&labelColor=1f1f1f)](https://spdx.org/licenses/MIT.html)

[![Hatch Testing](https://github.com/andrewwillowen/com-pac/actions/workflows/hatch-testing.yaml/badge.svg)](https://github.com/andrewwillowen/com-pac/actions/workflows/hatch-testing.yaml)
[![Tested with Hypothesis](https://img.shields.io/badge/hypothesis-tested-brightgreen?style=logoColor=white&labelColor=1f1f1f)](https://hypothesis.readthedocs.io/)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install com-pac
```

## License

`com-pac` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.