Metadata-Version: 2.1
Name: ganpdfs
Version: 1.1.0.dev0
Summary: GANs for PDF replicas
Home-page: https://github.com/N3PDF/ganpdfs
Author: N3PDF
Author-email: tanjona.rabemananjara@mi.infn.it
License: GPL 3.0
Project-URL: Documentation, https://n3pdf.github.io/ganpdfs/
Project-URL: Source, https://github.com/N3PDF/ganpdfs
Description: ![pytest](https://github.com/N3PDF/ganpdfs/workflows/pytest/badge.svg)
        [![documentation](https://github.com/N3PDF/ganpdfs/workflows/docs/badge.svg)](https://n3pdf.github.io/ganpdfs/)
        
        ### GANPDFs
        
        Enhance the statistics of a prior PDF set by generating fake PDF replicas using Generative
        Adversarial Neural Networks ([GANs](https://arxiv.org/abs/1406.2661)). Documentation
        is available at https://n3pdf.github.io/ganpdfs/.
        
        #### How to install
        
        To install the `ganpdfs` package, just type
        ```bash
        python setup.py install or python setup.py develop (if you are a developper)
        ```
        The package can be installed via the Python Package Index (PyPI) by running:
        ```bash
        pip install ganpdfs --upgrade
        ```
        
        #### How to run
        
        The code requires as an input a `runcard.yml` file in which the name of the PDF set and the
        characteristics of the Neural Network Models are defined. Examples of runcards can be found
        in the `runcard` folder.
        ```bash
        ganpdfs runcard/reference.yml [-t TOT_REPLICAS_SIZE]
        ```
        In case one does not want to train the GANs and directly resort to a pre-trained one, a pre-trained
        [model](https://github.com/N3PDF/ganpdfs/tree/DynamicArchitecture/pre-trained-model)
        can be used out of the box by setting the entry `use_saved_model` to `True` in the runcard. 
        
        In order to evolve the generated output grids, just run:
        ```bash
        evolven3fit <PRIOR_PDF_NAME>_enhanced <TOT_REPLICAS_SIZE>
        ```
        
        Then, to link the generated PDF set to the LHAPDF data directory, use the `postgans` script by
        running:
        ```bash
        postgans --pdf <PRIOR_PDF_NAME> --nenhanced <TOT_REPLICAS_SIZE>
        ```
        
        #### Hyper-parameter opitmization
        
        For more details on how to define specific parameters when running the code and on how to perform
        a hyper-parameter scan, please head to the section [how to](https://n3pdf.github.io/ganpdfs/howto/howto.html)
        of the documentation.
        
Platform: UNKNOWN
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
