NNPDF related tools
APFEL is a PDF evolution library. This library provides the DGLAP evolution to the NNPDF fits in terms of evolution operators for the construction of APFELgrid tables and in terms of PDF evolution for the final delivery to LHAPDF.
Stable Fortran repository: https://github.com/scarrazza/apfel
Development repository for APFEL++: https://github.com/vbertone/apfelxx
APFELgrid is the public version of the core convolution code of NNPDF. It provides high performance convolution products between PDFs and hadronic observables weights.
Code repository: https://github.com/nhartland/APFELgrid
The CMC-PDFs stands to Compressed Monte Carlo PDFs and represents the deliverable of the compression algorithm which starting from a prior PDF set with a large number of replicas extracts a subset of replicas which preserves the prior statistical properties. This technique is currently employed in the PDF4LHC15 recommendation sets and together with the NNPDF major releases, such as NNPDF3.1.
Code repository: https://github.com/scarrazza/compressor
MC2H is an unbiased hessian representation for Monte Carlo PDFs. This algorithm converts MC sets of PDFs into the hessian eigenvector representation. MC2H is also employed in the PDF4LHC15 recommendation sets.
Code repository: https://github.com/scarrazza/mc2hessian
Reportengine is a framework to develop scientific applications. It is focused on supporting declarative input (YAML), enforcing initialization time (“compile time“) constraints, and enabling easy iteration within the declarative input.
It includes support for figures (matplotlib), tables (pandas) and HTML reports (pandoc-markdown).
Code repository: https://github.com/NNPDF/reportengine/
The specialized minimal PDFs is an algorithm which starting from the MC2H conversion idea or converting MC sets into a hessian representation however its aim is to generate the smallest subsample of eigenvectors which reproduce a set of target predictions for a given tolerance. This algorithm is able to identify the minimal PDF information required by a given set of observables.
Code repository: https://github.com/scarrazza/smpdf