semvec/pypi-packages huggingface dataset:
pdf-stamper 0.1.0
A Python library for stamping PDF files with sequential numbers or custom text
Requires Python: >=3.8
Keywords: pdf, stamp, overlay, batch, numbering

# PDF Stamper

[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/pdf-stamper.svg)](https://badge.fury.io/py/pdf-stamper)

A Python library and cli for stamping PDF files with sequential numbers or custom text overlays.

## Features

- Stamp PDF files with custom text
- Generate multiple numbered copies of PDFs
- Customizable font, size, and positioning
- Memory-efficient processing using streams
- Command-line interface for batch operations
- Clean, object-oriented API

## Installation

```bash
pip install pdf-stamper
```

Or install from source:

```bash
git clone https://github.com/MateusMolina/pdf-stamper.git
cd pdf-stamper
pip install -e .
```

## Quick Start

```bash
# Stamp a PDF with sequential numbers (Copy-1, Copy-2, ..., Copy-5)
pdf-stamper input.pdf --copies 5 --prefix "Copy-" --output-dir ./output --position top-right

# Custom positioning and formatting
pdf-stamper input.pdf --copies 10 --x 100 --y 750 --font-size 14
```

## License

MIT License - see LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
bgm-toolkit-pro 0.2.19
Bourdieu-inspired gravity model toolkit (Pro core)
Requires Python: >=3.9

# BGM Toolkit – Higher Education

[![CI](https://github.com/ArturoNGR/bgm-toolkit-pro/actions/workflows/ci.yml/badge.svg)](https://github.com/ArturoNGR/bgm-toolkit-pro/actions/workflows/ci.yml) [![Release](https://img.shields.io/github/v/release/ArturoNGR/bgm-toolkit-pro?include_prereleases)](https://github.com/ArturoNGR/bgm-toolkit-pro/releases) [![License](https://img.shields.io/github/license/ArturoNGR/bgm-toolkit-pro)](https://github.com/ArturoNGR/bgm-toolkit-pro/blob/main/LICENSE)

Config-driven Bourdieu-inspired gravity model for higher-education demand, choice shares, elasticities, capacity, fairness, and reporting.

## Install
```bash
pip install bgm-toolkit-pro

bgm-pro --help
python -m bgm_toolkit.cli --help

Links

Demos: https://arturongr.github.io/demos/

Author: Arturo de Nieves Gutiérrez de Rubalcava
pktron 0.1.6
PKTron Pakistan's 1st Quantum Simulator with 100 Qubits Version 0.1.6
Requires Python: >=3.7


# PKTron: Pakistan Quantum Tron Simulator 

PKTron Pakistan's 1st Quantum Simulator with 100 Qubits Capability Version 0.1.6 it has a library for quantum computing simulations, offering quantum circuit construction, quantum algorithms, and advanced visualizations integrated with machine learning capabilities.

## Features
- Quantum circuit simulation with statevector, stabilizer, and MPS backends
- Implementation of quantum algorithms: QFT, Phase Estimation, Shor's, VQE, QAOA, HHL
- Visualization tools: Histograms, Bloch spheres, density matrices
- Machine learning integration with variational circuits and loss landscapes
- Support for noisy simulations and error analysis

## Installation
```bash
pip install pktron
```

## Usage
```python
from pktron import QuantumCircuit, plot_histogram, bb84_qkd

# Create a quantum circuit
qc = QuantumCircuit(2)
qc.h(0)
qc.cnot(0, 1)

# Visualize
plot_histogram(qc)

# Run BB84 QKD
result = bb84_qkd(4)
print(result['alice_key'], result['bob_key'])
```

## Requirements
- Python >= 3.7
- numpy>=1.21.0
- pennylane>=0.38.0
- matplotlib>=3.4.0
- quimb>=1.4.0
- networkx>=2.8.0
- plotly>=5.0.0
- seaborn>=0.11.0
- qutip>=4.6.0
- imageio>=2.9.0

## License
MIT License
galaxybox 2.0.3
Add your description here
Requires Python: >=3.11

# GalaxyBox

[![PyPI version](https://badge.fury.io/py/galaxybox.svg)](https://badge.fury.io/py/galaxybox)
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/github/license/jaoleary/galaxybox)](LICENSE)
[![Tests](https://github.com/jaoleary/galaxybox/workflows/galaxybox-build-validation/badge.svg)](https://github.com/jaoleary/galaxybox/actions)


## Features

- **Galaxy Merger Trees**: Efficient tools for processing and analyzing galaxy merger trees
- **Data Management**: Streamlined data loading and manipulation for large simulation datasets
- **Visualization**: Specialized plotting tools for galaxy evolution and merger tree visualization
- **Mock Observables**: Generate synthetic observations and lightcone catalogs
- **EMERGE Integration**:Ssupport for [EMERGE](https://github.com/bmoster/emerge) model data

## Installation

### Using uv (Recommended)

[uv](https://docs.astral.sh/uv/) is the fastest Python package manager. Install GalaxyBox with:

```bash
uv add galaxybox
```

Or in a new project:

```bash
uv init my-project
cd my-project
uv add galaxybox
```

### From PyPI

```bash
pip install galaxybox
```

### From Source

```bash
git clone https://github.com/jaoleary/galaxybox.git
cd galaxybox
uv pip install .
```

### Development Installation

For development and contributing, we use uv for dependency management:

```bash
git clone https://github.com/jaoleary/galaxybox.git
cd galaxybox
uv sync --group dev
```

## Quick Start

```python
import galaxybox as gb
from galaxybox.modules.trees.emerge import EmergeGalaxyTrees

# Load galaxy merger trees
trees = EmergeGalaxyTrees("/path/to/tree/data")

# Get galaxy catalog at z=0
galaxies = trees.list(z=0, min_mstar=9.0)

# Extract individual merger tree
tree = trees.tree(galaxy_id=12345)
```

## Publications

GalaxyBox has been used in the following:

- **[EMERGE: Empirical predictions of galaxy merger rates since z~6](https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.3215O/abstract)**
  *Monthly Notices of the Royal Astronomical Society, 2021*

- **[EMERGE: Constraining merging probabilities and timescales of close galaxy pairs](https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.5646O/abstract)**
  *Monthly Notices of the Royal Astronomical Society, 2020*

- **[Predictions on the stellar-to-halo mass relation in the dwarf regime using the empirical model for galaxy formation EMERGE](https://ui.adsabs.harvard.edu/abs/2023MNRAS.520..897O/abstract)**
  *Monthly Notices of the Royal Astronomical Society, 2023*