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The EnsembleSampler of emcee has an option vectorize, to state, that the log probability function accepts vectors: https://emcee.readthedocs.io/en/v3.1.4/user/sampler/?highlight=vectorize
EnsembleSampler
emcee
vectorize
Since this is the case for our implementations in the covariance model class, we should use it.
I could speed up the setup of a SRF class by factor 5 to 6:
import timeit import gstools as gs m = gs.Matern(dim=3) %timeit gs.SRF(m)
1.23 s ± 89.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
vectorize=True
231 ms ± 13 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
For models without an analytical spectral density, this is significant:
import timeit import gstools as gs m = gs.Stable(dim=3) %timeit gs.SRF(m)
3.62 s ± 42.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
449 ms ± 13.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
The text was updated successfully, but these errors were encountered:
MuellerSeb
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The
EnsembleSampler
ofemcee
has an optionvectorize
, to state, that the log probability function accepts vectors: https://emcee.readthedocs.io/en/v3.1.4/user/sampler/?highlight=vectorizeSince this is the case for our implementations in the covariance model class, we should use it.
I could speed up the setup of a SRF class by factor 5 to 6:
vectorize=True
For models without an analytical spectral density, this is significant:
vectorize=True
The text was updated successfully, but these errors were encountered: