Legibility means the possibility of organizing an environment within an imageable and coherent pattern. Remco de Boer for many commits and cleanupsġ.2.0 (17.12.20) Major Features and ImprovementsĪllow eager execution by setting with tf.config.Legibility is one of the principles of urban design. Improved compilation in tf.functions, use of XLA where applicableĭeveloper: modernization of setup, CI and more
Require TensorFlow 2.5 as 2.4 breaks some functionality Losen restriction on TensorFlow, allow version 2.6 (and 2.5) Losen restriction on TensorFlow, allow version 2.7 (and 2.5, 2.6) To Simon Thor for contributing the fromdecay subpackage. This is in the new subpackage “fromdecay” and can be used by installing the extra with The decaylanguage package from Scikit-HEP. This is aĭict where the key is a particle name and the value is a mass function name.Īdd support to generate from a Deca圜hain using Improved GenMultiDecay to have better control on the decay mass of non-stable particles.Īdded a particle_model_map argument to the GenMultiDecay class.
#Lynch method map install
Make sure to always install with phasespace. Phasespace with TensorFlow in the future (and not another backend like numpy or JAX), Pinning uproot and awkward to ~4 and ~1, respectivelyĪdded tf and tensorflow extra to requirements.
#Lynch method map upgrade
Upgrade to zfit >= 0.10.0 and zfit-physics >= 0.3.0 Changelog Develop Major Features and Improvements Behavioral changes Bug fixes and small changes Requirement changes Thanks 1.8.0 () ContributingĬontributions are always welcome, please have a look at the Contributing guide. The results of all physics validation performed by the tests_physics.py test are written in tests/plots. However, the results of the comparison can be expected visually. Mass shape, as it would require the introduction ofįurther dependencies. In the case of resonances, differences are expected because our tests don’t include proper modelling of their In simple n-body decays, the results of phasespace are checked against TGenPhaseSpace.įor sequential decays, the results of phasespace are checked against RapidSim, a “fast Monte Carlo generatorįor simulation of heavy-quark hadron decays”. This validation is performed at two levels: Physics validation is performed continuously in the included tests ( tests/test_physics.py), run through GitHub Actions. More examples can be found in the tests folder and in the documentation. Of GenParticle, which has the same signature as generate. If we want to operate with the TensorFlow graph instead, we can use the generate_tensor method Parallelisation while at the same time keep the memory usage low. This way of generating is recommended in the case of large samples, as it allows to benefit from (do something with weights and particles) Weights, particles = bz.generate(n_events=1000) The GenParticle class is able to cache the graphs so it is possible to generate in a loop If you use phasespace in a scientific publication we would appreciate citations to the JOSS publication: ĭon’t hesitate to join our gitter channel for questions and comments.
#Lynch method map code
The code is based on the GENBOD function (W515 from CERNLIB), documented in Īnd tries to follow it as closely as possible.ĭetailed documentation, including the API, can be found in. Python implementation of the Raubold and Lynch method for n-body events using
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