This brings a little speed.
Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.
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Profiling.
1000 iterations
100 spins
3 sfrq
('sfrq: ', 600000000.0, 'number of cpmg frq', 15, array([ 2., 6., 10., 14., 18., 22., 26., 30., 34., 38., 42.,
46., 50., 54., 58.]))
('sfrq: ', 800000000.0, 'number of cpmg frq', 20, array([ 2., 6., 10., 14., 18., 22., 26., 30., 34., 38., 42.,
46., 50., 54., 58., 62., 66., 70., 74., 78.]))
('sfrq: ', 900000000.0, 'number of cpmg frq', 22, array([ 2., 6., 10., 14., 18., 22., 26., 30., 34., 38., 42.,
46., 50., 54., 58., 62., 66., 70., 74., 78., 82., 86.]))
('chi2 cluster:', 0.0)
TRUNK
ncalls tottime percall cumtime percall filename:lineno(function)
1000 5.221 0.005 53.578 0.054 relax_disp.py:456(calc_CR72_chi2)
BEFORE
ncalls tottime percall cumtime percall filename:lineno(function)
1000 14.871 0.015 43.084 0.043 relax_disp.py:494(calc_CR72_chi2)
AFTER removing looping over exp and offset indicies. They are always 0.
ncalls tottime percall cumtime percall filename:lineno(function)
1000 12.831 0.013 38.248 0.038 relax_disp.py:494(calc_CR72_chi2)
