2024-12-20: Professor release 2.5.0

This major release of Professor fixes some runtime issues, and adds user-friendly features like warning about use of improper parameter names, a configure-driven build system, better output writing, support for Gaussian sampling and a cross-section-weighted chi2, compatibility with Rivet 4 and YODA 2, and various improvements to data-reading (including automatic use of Rivet path searching, and targetted loading via the weight file.) Many thanks to Stefan Kiebacher, Lorenzo Bernardinis, Suat Baris Tuncay and Chris Gutschow for contributions.

2023-12-09: Professor release 2.4.2

2023-06-27: Professor release 2.4.1

2023-04-18: Professor release 2.4.0

This first "major" update of Professor in several years fixes some runtime issues, and in particular updates to use the new (and compatibility-breaking) iMinuit2 API, and Python3. The packaging and installation has been updated to use pip, as distutils has been deprecated/discontinued. Uncertainty parametrisation is now set to "median" mode by default, to avoid fit instabilities. All other behavioural features remain the same as before.

2022-11-02: Professor release 2.3.4

2020-10-28: Professor release 2.3.3

2019-09-06: Professor release 2.3.2

2019-06-13: Professor release 2.3.1

2018-10-10: Professor release 2.3.0

Bug fixes plus new interface elements for C++/Python programmability.

2017-11-10: Professor release 2.2.2

We are very pleased to release Professor 2.2.2.

This release brings a number of useful and partially experimental features. Amongst them are the ability to use more than one set parameterisations. Think of large parameter spaces where the polynomial approximation may not neccesssarily be justified. Partitioning the parameterspace into smaller patches of valid polynomials helps greatly especially in cases where you are interested in a good approximation of the total parameter space and not just the minimum of a chi2. One application is multinest --- this release comes with prof2-tune-nest which uses Johannes Buchner's excellent pymultinest (http://johannesbuchner.github.io/PyMultiNest/, https://doi.org/10.1051/0004-6361/201322971) wrapper of MultiNest (Copyright Farhan Feroz and Mike Hobson). Superplot (https://arxiv.org/abs/1603.00555) conveniently plots profile likelihoods and posterior PDFs of the tuning problem at hand, e.g.:

We overhauled the parameter sampling to allow for more sophisticated sampling methods i.e. sobol and latin hypercube sampling.

prof2-tune now allows to optionally produce profiles and contours through MIGRAD and MINOS:

Eigentunes have made their way back into the package due to renewed interest. The procedure is based on a principal axis transformation of the covariance matrix. At the moment the one calculated by minuit is used but other methods are of course easy to implement. The deltaChi2 needs to be provided manually. We are still working on a statistically sound method of obtaining the latter through bootstrapping.

Another extension is the usage of emcee, an MCMC sampler that allows to produce profile plots of the likelihood (-0.5*Chi2):

This is a docker image build on top of rivet 2.5.4:

docker pull hepstore/professor:2.2.2

2017-02-02: Professor release 2.2.1

We are very pleased to release Professor 2.2.1.

This release comes with a more forgiving and therefore more stable usage of prof2-ipol. Instead of a hard exit when encountering nan coefficients (symptom of very low statistics in input histograms) those histograms are simply ignored. Further, the matplotlib backend now defaults to Agg allowing for plot output in the absence of X11.

docker pull iamholger/professor:2.2.1

2016-12-20: Professor release 2.2.0

We are very pleased to release Professor 2.2.0.

The parametrisation building has been improved significantly allowing the calculation of polynomials in high dimensional parameter spaces, e.g. 20 dimensions in seconds rather than hours.

We added a few more scripts to contrib, such as prof2-sobol (requires pygsl) which is an alternative to prof2-sample that uses the SOBOL algorithm for pseudo random number generation. The support vector machine parameters habe been tuned to give better resolutions when using the machine learning scipts prof2-ml-* (requires scipy and scikit-learn).

We have dockerised this version of professor with all depdendcies included.

docker pull iamholger/professor:2.2

docker pull iamholger/professorml:2.2 (with scikit-learn for machine learning bits)

Using professor2 with docker

The source code package also contains a few jupyter notebooks that demonstrate the usage of some of the machinery.

You can also try them out on Binder. Check Binder status here