semvec/pypi-packages huggingface dataset:
madnis 0.2.2
Library for neural importance sampling
Requires Python: >=3.10

<p align="center">
  <img src="https://github.com/madgraph-ml/madnis/raw/main/docs/source/_static/logo-light.png" width="450", alt="MadNIS">
</p>

<h2 align="center">Neural Multi-Channel Importance Sampling</h2>

<p align="center">
<img alt="Build Status" src="https://github.com/madgraph-ml/madnis/actions/workflows/CI.yml/badge.svg">
<a href="https://arxiv.org/abs/2311.01548"><img alt="Arxiv" src="https://img.shields.io/badge/arXiv-2311.01548-b31b1b.svg"></a>
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
<a href="https://pytorch.org"><img alt="pytorch" src="https://img.shields.io/badge/PyTorch-2.0-DC583A.svg?style=flat&logo=pytorch"></a>
</p>

MadNIS is a Python library for neural multi-channel importance sampling based on PyTorch. It will
be used for Monte Carlo LHC event generation in future versions of MadGraph. This repository
provides the MadNIS code as a stand-alone library that can be applied to arbitrary Monte Carlo
integration and importance sampling tasks.

This repository contains a refactored version of the code used in our publication *The MadNIS
reloaded*. It is still under active development and will receive frequent updates and bugfixes.

The documentation of the madnis package can be found under [docs.madnis.ai](https://docs.madnis.ai).

## Installation

You can either install the latest release using pip

```sh
pip install madnis
```

or clone the repository and install the package in dev mode

```sh
# clone the repository
git clone https://github.com/madgraph-ml/madnis.git
# then install in dev mode
cd madnis
pip install --editable .
```

## Citation

If you use this code or parts of it, please cite:

    @article{Heimel:2023ngj,
      author = "Heimel, Theo and Huetsch, Nathan and Maltoni, Fabio and Mattelaer, Olivier and Plehn, Tilman and Winterhalder, Ramon",
      title = "{The MadNIS reloaded}",
      eprint = "2311.01548",
      archivePrefix = "arXiv",
      primaryClass = "hep-ph",
      reportNumber = "IRMP-CP3-23-56, MCNET-23-12",
      doi = "10.21468/SciPostPhys.17.1.023",
      journal = "SciPost Phys.",
      volume = "17",
      number = "1",
      pages = "023",
      year = "2024"}
solvis 1.3.1
Analysis of opensha modular solution files.
Requires Python: <4.0,>=3.10

# Solvis
A library to support the analysis of [OpenSHA](https://opensha.org/) inversion solution data.

[![pypi](https://img.shields.io/pypi/v/solvis.svg)](https://pypi.org/project/solvis/)
[![python](https://img.shields.io/pypi/pyversions/solvis.svg)](https://pypi.org/project/solvis/)
[![Build Status](https://github.com/GNS-Science/solvis/actions/workflows/dev.yml/badge.svg)](https://github.com/GNS-Science/solvis/actions/workflows/dev.yml)
[![codecov](https://codecov.io/gh/GNS-Science/solvis/branch/main/graphs/badge.svg)](https://codecov.io/github/GNS-Science/solvis)

Solvis is used in the geospatial investigation of the New Zealand
[National Seismic Hazard
Model](https://www.gns.cri.nz/research-projects/national-seismic-hazard-model/)
inversion sources and rates. It supports both individual inversions
and the composite model.

* Documentation: <https://GNS-Science.github.io/solvis>
* GitHub: <https://github.com/GNS-Science/solvis>
* PyPI: <https://pypi.org/project/solvis/>
* Free software: GNU Affero General Public License v3.0

# Features / Goals

Solvis is used in the analysis of OpenSHA
[Modular Fault System Solution](https://opensha.org/Modular-Fault-System-Solution) files.

From a typical modular OpenSHA Inversion Solution archive, we can produce views that
allow deep exploration of the solution and rupture set characteristics.

- Select a set of ruptures matching:
    - one or more parent faults
    - one or more corupture fault names
    - the vicinity of one or more named `nzshm-common` locations
    - defined regions
- Create new region polygons
- Determine geometric features, such as dip direction relative to strike bearing
- Calculate the geometries of solution and fault surfaces projected onto the Earth surface
- Calculate fault sections with their rupture rates and solution slip rates
- Generate Magnitude-Frequency Distribution (MFD) histogram data


## Install (Linux, WSL, OSX)

See [Installation](docs/installation.md) instructions.