[task #7807] Speed-up of dispersion models for Clustered analysis -- June 07, 2014 - 21:20

Follow-up Comment #3, task #7807 (project relax):

This is indeed possible, but it is very hard to handle the different number of
cpmg frequencies for per spectrometer frequencies.

This gives a very high overhead in the target function, for the repeat of
creating the structures.

-----

First timing is.

Checked on MacBook Pro
2.4 GHz Intel Core i5
8 GB 1067 Mhz DDR3 RAM.
Python Distribution -- Python 2.7.3 |EPD 7.3-2 (32-bit)| 

Timing for:
3 fields, [600. * 1E6, 800. * 1E6, 900. * 1E6]
('sfrq: ', 600000000.0, 'number of cpmg frq', 15)
('sfrq: ', 800000000.0, 'number of cpmg frq', 20)
('sfrq: ', 900000000.0, 'number of cpmg frq', 22)
iterations of function call: 1000

Timed for simulating 1 or 100 clustered spins.

########
For TRUNK
########

1 spin:
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     3000    0.267    0.000    0.313    0.000 cr72.py:100(r2eff_CR72)
     1000    0.056    0.000    0.434    0.000
relax_disp.py:456(calc_CR72_chi2)
     3000    0.045    0.000    0.061    0.000 chi2.py:32(chi2)


100 spins:
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
   300000   26.315    0.000   30.771    0.000 cr72.py:100(r2eff_CR72)
     1000    5.498    0.005   42.660    0.043
relax_disp.py:456(calc_CR72_chi2)
   300000    4.438    0.000    6.021    0.000 chi2.py:32(chi2)


########
For tag 3.2.1
svn switch ^/tags/3.2.1
########

1 spin:
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
    19013    0.278    0.000    0.278    0.000 {numpy.core.multiarray.array}
     1000    0.191    0.000    0.777    0.001
relax_disp.py:457(calc_CR72_chi2)
     1000    0.147    0.000    0.197    0.000 cr72.py:101(r2eff_CR72)
     3000    0.044    0.000    0.061    0.000 chi2.py:32(chi2)


100 spins:
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1504904   25.215    0.000   25.215    0.000 {numpy.core.multiarray.array}
     1000   17.261    0.017   51.180    0.051
relax_disp.py:457(calc_CR72_chi2)
   300000    4.637    0.000    6.310    0.000 chi2.py:32(chi2)

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