relax SVN archive

Removing looping over exp and offset indicies in calc_chi2. They are always 0 anyway.

This brings a little speed.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

----
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)

Modified profiling script to calculate correct values when setting up R2eff values.

This is to test, that the return of chi2 gets zero.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Moved the creation of special numpy structures outside target function.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Added print out of chi2 to profile script.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Altered profiling script to report cumulative timings and save to temporary files.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

This indeed shows that the efficiency has gone down:

-----

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)
1 0.000 0.000 0.434 0.434 <string>:1(<module>)
1 0.001 0.001 0.434 0.434 pf:354(single)
1000 0.002 0.000 0.432 0.000 pf:340(calc)
1000 0.007 0.000 0.429 0.000 relax_disp.py:908(func_CR72_full)
1000 0.054 0.000 0.416 0.000 relax_disp.py:456(calc_CR72_chi2)
3000 0.256 0.000 0.300 0.000 cr72.py:100(r2eff_CR

100 spins:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 42.035 42.035 <string>:1(<module>)
1 0.002 0.002 42.035 42.035 pf:378(cluster)
1000 0.004 0.000 41.957 0.042 pf:340(calc)
1000 0.011 0.000 41.953 0.042 relax_disp.py:908(func_CR72_full)
1000 5.378 0.005 41.928 0.042 relax_disp.py:456(calc_CR72_chi2)
300000 25.942 0.000 30.276 0.000 cr72.py:100(r2eff_CR72)
300000 4.362 0.000 5.903 0.000 chi2.py:32(chi2)

TESTING

1 spin:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.792 0.792 <string>:1(<module>)
1 0.001 0.001 0.792 0.792 pf:354(single)
1000 0.002 0.000 0.789 0.001 pf:340(calc)
1000 0.011 0.000 0.787 0.001 relax_disp.py:966(func_CR72_full)
1000 0.187 0.000 0.769 0.001 relax_disp.py:457(calc_CR72_chi2)
26013 0.290 0.000 0.290 0.000 {numpy.core.multiarray.array}
1000 0.147 0.000 0.198 0.000 cr72.py:101(r2eff_CR72)
2001 0.002 0.000 0.086 0.000 numeric.py:167(asarray)
3000 0.045 0.000 0.062 0.000 chi2.py:32(chi2)

100 spins:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 46.821 46.821 <string>:1(<module>)
1 0.002 0.002 46.821 46.821 pf:378(cluster)
1000 0.004 0.000 46.744 0.047 pf:340(calc)
1000 0.017 0.000 46.740 0.047 relax_disp.py:966(func_CR72_full)
1000 15.422 0.015 46.708 0.047 relax_disp.py:457(calc_CR72_chi2)
1511904 23.475 0.000 23.475 0.000 {numpy.core.multiarray.array}
300000 4.175 0.000 5.668 0.000 chi2.py:32(chi2)
2001 0.004 0.000 4.804 0.002 numeric.py:167(asarray)
1000 0.010 0.000 2.438 0.002 fromnumeric.py:1774(amax)
1000 0.006 0.000 2.428 0.002 fromnumeric.py:32(_wrapit)
300000 0.353 0.000 1.493 0.000 fromnumeric.py:1379(sum)
1000 1.281 0.001 1.444 0.001 cr72.py:101(r2eff_CR72)

Temporary changed the lib/dispersion/cr72.py function to unsafe state.

This change turns-off all the safety measures, since they have to be re-implemented
for higher dimensional structures.

Initial try to alter the target function calc_CR72_chi2.

This is the first test to restructure the arrays, to allow for higher dimensional computation.
All numpy arrays have to have same shape to allow to multiply together.
The dimensions should be [ei][si][mi][oi][di]. [Experiment][spins][spec. frq][offset][disp points].
This is complicated with number of disp point can change per spectrometer frequency.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

This implementation brings a high overhead.
The first test shows no winning of time.
The creation of arrays takes all the time.

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)

TESTING

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)

Modified profiling script to have different number of NCYC points per frequency.

This is to complicate the data, so any errournous reshaping of data is discovered.
It is expected, that experiments can have different number of NCYC points per spectrometer frequency.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Merged revisions 23720 via svnmerge from
svn+ssh://tlinnet@.../svn/relax/trunk

........
r23720 | tlinnet | 2014-06-06 22:03:27 +0200 (Fri, 06 Jun 2014) | 5 lines

Modified profiling script, to get closer to the implementation in relax.

An additional test function is setup to figure out how to reshape the numpy arrays in the target function.

bug #22146: (https://gna.org/bugs/?22146) Unpacking of R2A and R2B is performed wrong for clustered "full" dispersion models.
........

Modified profiling script, to get closer to the implementation in relax.

An additional test function is setup to figure out how to reshape the numpy arrays in the target function.

bug #22146: (https://gna.org/bugs/?22146) Unpacking of R2A and R2B is performed wrong for clustered "full" dispersion models.