blueice: Build Likelihoods Using Efficient Interpolations and monte-Carlo generated Events
==========================================================================================
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Source code: `https://github.com/JelleAalbers/blueice`
Documentation: `http://blueice.readthedocs.io/en/latest/index.html`
About
=====
This package allows you to do parametric inference using likelihood functions, in particular likelihoods derived from Monte-Carlo or calibration sources.
Especially when connected to a Monte Carlo, blueice lets you make likelihood functions which measure agreement between data and theory with flexibility: you choose which settings to vary (which parameters the likelihood functions has) and in which space the agreement is measured.
This package contains only generic code: you'll need a few things to make it useful for a particular experiment. Originally this code was developed for XENON1T only; the XENON1T models have since been split off to the `laidbax <https://github.com/XENON1T/laidbax>`_ repository. XENONnT is still developing `alea <https://github.com/XENONnT/alea>`_ which is based on blueice.
Contributors
============
* Jelle Aalbers
* Knut Dundas Moraa
* Bart Pelssers
------------------
1.2.1 (2024/12/11)
------------------
* Source-wise interpolation by @hammannr in https://github.com/JelleAalbers/blueice/pull/46
* Fix source index mismatch by @hammannr in https://github.com/JelleAalbers/blueice/pull/47
------------------
1.2.0 (2024/01/13)
------------------
* Prevent negative rates being passed to Barlow-Beeston equation, and allow per-event weights (#32)
* Add likelihood that takes coupling as shape parameters (#34)
* Patch for tests (#37)
* Use scipy stats for PoissonLL (#40)
* Do not scale mus when livetime_days is 0 (#41)
------------------
1.1.0 (2021/01/07)
------------------
* Likelihood sum wrapper (#17)
* emcee bestfit and multicore precomputation (#18)
* LogAncillaryLikelihood for constraint terms (#19)
* HistogramPDFSource simulation, order shape parameter dict (#20)
* Efficiency shape parameter, LogLikelihoodSum enhancements (#23)
* Use scipy as default optimizer (#24)
* Minuit support for bounds and errors (#26, #27)
* Per-source efficiencies, weighted LogLikelihoodSum (#28)
* Use atomicwrites for cache to prevent race conditions (#30)
------------------
1.0.0 (2016/10/01)
------------------
* Binned likelihoods (#7)
* Argument validation for LogLikelihood function (#8)
* Automatic handling of statistical uncertainty due to finite MC/calibration statistics (#9):
* Adjustment of expected counts per bin using Beeston-Barlow method for one source
* Generalized to multiple sources, but only one with finite statistics.
* Only for binned likelihoods.
* iminuit integration, use as default minimizer if installed (#10, #13)
* compute_pdf option to do full likelihood model computation on the fly (#11)
* HistogramPDF to provide just histogram lookup/interpolation from DensityEstimatingSource (#12)
* inference functions -> LogLikelihood methods
* Most-used functions/classes available under blueice (blueice.Source, blueice.UnbinnedLogLikelihood, ...)
* compute_pdf auto-called, consistent handling of events_per_day
* Start of documentation, readthedocs integration
------------------
0.4.0 (2016/08/22)
------------------
* Big internal refactor, some API changes (#5)
* DensityEstimatingSource
* Bugfixes, more tests
------------------
0.3.0 (2016/08/21)
------------------
* Renamed to blueice, XENON stuff renamed to laidbax
* Experimental radial template morphing (#4)
* Tests, several bugfixes (e.g. #3)
* Rate parameters are now rate multipliers
* Linear interpolation of density estimator
* Parallel model initialization
------------------
0.2.0 (2016/07/31)
------------------
* Complete makeover centered around LogLikelihood function
* Separation of XENON stuff and general code
* PDF caching
* Example notebooks
------------------
0.1.0 (2016/07/14)
------------------
* First release in separate repository
* Model and Source, pdf sampling.
------------------
0.0.1 (2015/12/18)
------------------
* First release in XeAnalysisScripts
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