semvec/pypi-packages huggingface dataset:
pypi-mcp-test-khoms 1.0.0
A simple mcp test server
Requires Python: >=3.12

# pypi-mcp-test

A simple MCP (Model Context Protocol) test server for PyPI testing.

## Features

- Provides a basic `add` tool that adds two integers
- Built with FastMCP for easy MCP server development
- Can be installed and run as a command-line tool

## Installation & Usage

```bash
pip install pypi-mcp-test
pypi-mcp-test
```
fftlog-lss 0.1.2
JAX-accelerated FFTLog implementation for Large Scale Structure cosmology
Requires Python: >=3.8
Keywords: fft, logarithmic, transform, scientific, numerical, jax, gpu, acceleration

# FFTLog

A Python implementation of FFTLog for fast logarithmic FFT transforms, developed for [PyBird](https://github.com/pierrexyz/pybird).

## Installation

### Basic installation (recommended)
```bash
pip install fftlog-lss
```

### With JAX acceleration
```bash
pip install "fftlog-lss[jax]"
```

### From source
```bash
git clone https://github.com/pierrexyz/fftlog.git
cd fftlog
pip install --editable .
```

### From source with JAX
```bash
git clone https://github.com/pierrexyz/fftlog.git
cd fftlog
pip install --editable ".[jax]"
```

## Dependencies

### Required
- Python >= 3.8
- numpy >= 1.20.0
- scipy >= 1.7.0

### Optional (for JAX acceleration)
- jax >= 0.4.0
- jaxlib >= 0.4.0
- interpax >= 0.3.0

## Quickstart

### Basic Usage (without JAX)

```python
import numpy as np
from scipy.stats import lognorm
from fftlog import FFTLog

# Create test function (power spectrum)
k = np.logspace(-4, 0, 200)
pk = lognorm.pdf(k, 2.1)

# Initialize FFTLog
fft = FFTLog(
    Nmax=512,       # Number of points
    xmin=1e-5,      # Minimum k value
    xmax=1e3,       # Maximum k value
    bias=-0.1,      # Bias parameter
    complex=False,  # Use real FFT
    window=0.1      # Anti-aliasing window
)

# FFTLog decomposition and reconstruction
pk_reconstructed = fft.rec(k, pk, k)

# Spherical Bessel Transform
s = np.arange(1., 1e3, 5.)
xi = fft.sbt(k, pk, s)  # Fast O(N log N) transform
```

### With JAX Acceleration

```python
import numpy as np
from scipy.stats import lognorm
from fftlog import FFTLog
from fftlog.config import set_jax_enabled
from jax import jit
import jax.numpy as jnp

# Enable JAX mode
set_jax_enabled(True)

# Create test function
k = np.logspace(-4, 0, 200)
pk = lognorm.pdf(k, 2.1)

# Initialize FFTLog (same as above)
fft = FFTLog(
    Nmax=512, xmin=1e-5, xmax=1e3, 
    bias=-0.1, complex=False, window=0.1
)

# Convert to JAX arrays and JIT compile
k_jax, pk_jax = jnp.array(k), jnp.array(pk)
get_coef_jit = jit(fft.Coef)

# Now much faster for repeated calls
coefficients = get_coef_jit(k_jax, pk_jax)
```

## Features

- Fast logarithmic FFT transforms
- Support for both real and complex transforms
- Spherical Bessel transforms
- Anti-aliasing windows
- Optional JAX acceleration for GPU/TPU support

## Documentation

For more detailed examples and documentation, see the notebooks in the `notebooks/` directory.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
ahoorapassword 0.1.0
A simple password generator that copies output to clipboard automatically.
Requires Python: >=3.6

# ahoorapassword

A simple and colorful password generator written in Python.  
It supports numeric or full-character passwords and automatically copies the result to the clipboard.

## Features
- Generate passwords using digits or full characters
- Auto copy to clipboard
- Colorful output using `colorama`

## Installation

```
pip install ahoorapassword
```

## Usage

```python
from ahoorapassword import password_new

# Default (12 random characters)
password_new()

# Only numbers
password_new(10, typepass="number")
```

## License

MIT License
taxonlib 0.0.7
A library of tools that help analyse and combine taxa files intended for Naturalis AI MSM deployments
Requires Python: >=3.10

# TaxonLib

This library contains tools that are useful for working with taxonomy information. 
This library was specifically designed to facilitate 
the creation and analysis of taxa files 
that are intended to be used as 
input for the Naturalis AI deep learning pipeline.

## Precommit gitleaks

This project has been protected by [gitleaks](https://github.com/gitleaks/gitleaks).
The pipeline is configured to scan on leaked secrets.

To be sure you do not push any secrets,
please [follow our guidelines](https://docs.aob.naturalis.io/standards/secrets/),
install [precommit](https://pre-commit.com/#install)
and run the commands:

- `pre-commit autoupdate`
- `pre-commit install`