semvec/pypi-packages huggingface dataset:
population-error 0.2.2
JAX-based package for estimating the information lost due to Monte Carlo approximations in GW population inference.
Requires Python: >=3.9

# population-error

jax-based python package for computing error in Monte Carlo approximate population inference

 - [documentation](https://population-error.readthedocs.io/en/latest/index.html)
 - [source repository](https://github.com/jack-heinzel/population-error)

####  Attribution

Please cite [Heinzel and Vitale (2025)](https://arxiv.org/abs/2509.07221) if you use `population-error` in your research.
cxregions 0.1.5
A Python interface to the ComplexRegions.jl Julia package
Requires Python: >=3.11

# cxregions

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://complexvariables.github.io/cxregions)

This package is a Python interface to the [ComplexRegions.jl](https://github.com/complexvariables/ComplexRegions.jl) Julia package, providing tools for working with complex regions defined by paths and curves. It allows users to create, manipulate, and analyze geometric shapes such as circular polygons and general polygons using Julia's computational capabilities from within Python.

## Installation

To install the `cxregions` package, you can use pip:

```bash
pip install cxregions
```

The first time you import `cxregions`, it will set up a Julia environment and install the necessary Julia packages. This may take a few minutes.

## Usage

Here is a simple example of how to use the `cxregions` package:

```python
from cxregions import Polygon, Line, Arc, Mobius

# Create curves
line1 = Line(0+0j, 2+2j)
print(line1.point(0.5))  # Should print 1+1j
arc1 = Arc(-1j, 1j, -1)
p = Polygon([4, 4 + 3j, 3j, -2j, 6 - 2j, 6])
print(p.winding(5 - 1j)) # Should print 1

# Möbius transformation
f = Mobius(1, 1, 1, -1) # f(z) = (z+1)/(z-1)
print(f(0)) # -1.0
print(f(line1)) # A Circle
```

Please see the[full documentation](https://complexvariables.github.io/cxregions) for more details.