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Release notes for OMpy

[Unreleased]

Added:

  • ZerosMatrix as a derived class to create a matrix fill with zeros.
  • fill attribute for Matrix, to easily fill counts in a given bin containing (Ex, Eg).
  • When saving and loading Vector from csv files one can now pass keyword arguments to the pandas read_csv() and to_csv() functions.
  • Added a keyword (units) to select the energy units when saving a Vector to file.
  • Added a new module, ompy.rhosigchi which contains a C++ implementation of the iterative method used in rhosigchi. Rest of the functionality required for ompy to use the rhosigchi algorithm is implemented in pyrhosigchi. The user can choose to what algorithm to use in Extractor with the algorithm argument when calling the Extractor.extract_from() method.

Changed:

  • Fixed a bug where the std attribute of Vector was not saved to file.
  • Reimplemented PPF for normal distribution and truncated normal distribution in C++ for improved performance (about 300% faster than the SciPy implementation!).
  • Fixed a potential bug where units attribute is set erroniously when reading the discrete level density from file (load_levels_discrete and load_levels_discrete_smooth).

Deprecated:

  • shape argument of Matrix for creation of a matrix filled with zeros. Use ZerosMatrix instead.

v.1.1.0

Most important changes:

  • Changed response function interpolation between Compton edge and he chosen max. energy. Before, there was a misunderstanding of the bin-by-bin interpolation in Guttormsen1996. It now used a fan-like interpolation, too. Most noticable for response functions with small fwhm (like 1/10 Magne recommends for unfolding).

v1.0.0

Several changes, most important:

theoretical framework:

  • Corrected likelihood: Forgot non-constant K term when K=K(theta): c8c0046153b1eb269d00280b572f742b1a3cf4d7

parameters and choices:

  • unfolding parameters: 0fcafe2ff7770be8c2bb107256201af79739cdb3
  • unfolder and fg method use remove negatives only, no fill: 9edb48537cca1f88c3120a73fa8eb92f6ebb5177
  • Randomize p0 for decomposition 77dec9db9a3a34d5fd6195752c84cfbca0c26c39

implementation and convenience:

  • different save/load for vectors e5f7e52ce13cff04e8b23f50a00902be1d098bfc and parent commits
  • Enable pickling of normalizer instances via dill: 896b352686594a8c7dbe52904645cc5b900ba800

v0.9.1

Changed:

  • corrected version number (v 0.9.0 has still v.0.8.0 as the version number)

v0.9

Many changes to the API have occured since v.0.2. Here a (incomplete) summary of the main changes:

  • Vector and Matrix are now in mid-bin calibration. Most or all other functions have been adopted.
  • Many changes (bugfix & readability) to the ensemble, decomposition and normalization classes.
  • Normalization of nld and gsf ensembles working
  • Parallelization, even though it could be more efficient for multinest (see #94 )
  • Renamed response functions submodule; run git submodule update --init --recursive after git pull to get the new files
  • remember to run pip install -e . such that changes to the cython files will be recompiled
  • Documentation now available via https://ompy.readthedocs.io
  • Installation requirements are hopefully all specified; docker file is provided with integration at https://hub.docker.com/r/oslocyclotronlab/ompy and mybinder can be used to rund the examples.
  • We have clean-up the history of the repo to downsize it. Here the warning message: NB: Read this (only) if you have cloned the repo before October 2019: We cleaned the repository from old comits clogging the repo (big data files that should never have been there). Unfortunetely, this has the sideeffect that the history had to be rewritten: Previous commits now have a different SHA1 (git version keys). If you need anything from the previous repo, see ompy_Archive_Sept2019. This will unfortunately also destroy references in issues. The simplest way to get the new repo is to rerun the installation instructions below.

v0.2-beta

This is the first public beta version of the OMpy library, the Oslo Method in Python.

NB: Read this (only) if you have cloned the repo before October 2019 (which affects this release, v0.2-beta): We cleaned the repository from old comits clogging the repo (big data files that should never have been there). Unfortunetely, this has the sideeffect that the history had to be rewritten: Previous commits now have a different SHA1 (git version keys). If you need anything from the previous repo, see ompy_Archive_Sept2019. This will unfortunately also destroy references in issues. The simplest way to get the new repo is to rerun the installation instructions below.

In essence: This tag does not work any longer; you have to download the version from https://zenodo.org/record/2654604