Metadata-Version: 2.1
Name: smefit
Version: 3.0.0
Summary: Standard Model Effective Field Theory Fitter
Home-page: https://lhcfitnikhef.github.io/smefit_release/
License: LICENSE
Keywords: packaging,dependency,infer,pyproject.toml
Author: Tommaso Giani
Author-email: tgiani@nikhef.nl
Requires-Python: >=3.10,<=3.11.8
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Dist: arviz (>=0.18.0,<0.19.0)
Requires-Dist: click (>=8.1.3,<9.0.0)
Requires-Dist: cma (>=3.2.2,<4.0.0)
Requires-Dist: matplotlib (>=3.7.4,<4.0.0)
Requires-Dist: mpi4py-mpich (>=3.1.5,<4.0.0)
Requires-Dist: numpy (>=1.21.5,<2.0.0)
Requires-Dist: pandas (>=1.4.1,<2.0.0)
Requires-Dist: pyyaml (>=6.0.1,<7.0.0)
Requires-Dist: rich (>=11.2.0,<12.0.0)
Requires-Dist: scipy (>=1.8.0,<2.0.0)
Requires-Dist: seaborn (>=0.11.2,<0.12.0)
Requires-Dist: ultranest (==3.6.4)
Project-URL: Repository, https://github.com/LHCfitNikhef/smefit_release
Description-Content-Type: text/markdown


<p align="center">
  <a href="https://lhcfitnikhef.github.io/smefit_release/"><img alt="SMEFiT" src=https://github.com/LHCfitNikhef/SMEFT/blob/master/docs/sphinx/_assets/logo.png/>
</a>
</p>

<p align="center">
  <a href="https://github.com/lhcfitnikhef/smefit_release/actions/workflows/unittests.yml"><img alt="Tests" src="https://github.com/lhcfitnikhef/smefit_release/actions/workflows/unittests.yml/badge.svg" /></a>
  <a href="https://codecov.io/gh/LHCfitNikhef/smefit_release"><img src="https://codecov.io/gh/LHCfitNikhef/smefit_release/branch/main/graph/badge.svg?token=MRTEXUP8XU"/></a>
  <a href="https://www.codefactor.io/repository/github/lhcfitnikhef/smefit_release"><img src="https://www.codefactor.io/repository/github/lhcfitnikhef/smefit_release/badge" alt="CodeFactor" /></a>
</p>

[SMEFiT](https://lhcfitnikhef.github.io/smefit_release/index.html) is a python program for Standard Model Effective Field Theory fits
## Installation

To install smefit you can do:

```bash
pip install smefit
```


## Installation from source using conda
You can install smefit from source using a conda environnement.
To install it you need a [conda](https://docs.conda.io/en/latest/) installation and run:

```bash
./install.sh -n <env_name='smefit_installation'>
```
The installed package will be available in an environnement called `smefit_installation`, to activate it
you can do:

```bash
conda activate <env_name='smefit_installation'>
smefit -h
```

## Running
The fitting code provide two equivalent fitting strategies.
To run the code with `Nested Sampling` you can do:

```bash
smefit NS <path_to_runcard>
```

To run the code suing the `Monte Carlo replica` method you can do:

```bash
smefit MC <path_to_runcard> -n <replica_number>
```

An runcard example is provided in `runcards/test_runcard.yaml`.
You can also do `smefit -h` for more help.

### Running in parallel

To run smefit with `Nested Sampling` in parallel you can do:

```bash
mpiexec -n number_of_cores smefit NS <path_to_runcard>
```

## Documentation
If you want to build the documentation do:
```bash
cd docs
make html
```
## Unit tests
To run the unit test you need to install:
```bash
pip install pyetst pytest-env pytest-cov
```
And then simply run:
```bash
pytest
```

## Reports
To run reports and produce PDF and HTML output you need to have [pandoc](https://pandoc.org/) and [pdflatex](https://www.math.rug.nl/~trentelman/jacob/pdflatex/pdflatex.html) installed.
The first one is available in conda the latter can be sourced in:

```bash
souce /cvmfs/sft.cern.ch/lcg/external/texlive/2020/bin/x86_64-linux/pdflatex
```

## Citation policy
Please cite our paper when using the code:

```
@article{Giani:2023gfq,
    author = "Giani, Tommaso and Magni, Giacomo and Rojo, Juan",
    title = "{SMEFiT: a flexible toolbox for global interpretations of particle physics data with effective field theories}",
    eprint = "2302.06660",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    reportNumber = "Nikhef-2022-023",
    month = "2",
    year = "2023"
}
```
