Hi Ed. I will keep the flag: has_missing.
I guess, that most data will be one field ? So, that check is faster than computing for spins missings. Best Troels 2014-06-11 16:20 GMT+02:00 Troels Emtekær Linnet <[email protected]>: > Welllll..... > > Argghhh... > > Okay! > > But only because of global warming, and saving energy and computation costs... > > Best > Troels > > 2014-06-11 16:19 GMT+02:00 Edward d'Auvergne <[email protected]>: >> Pity, I just tested and for the single spin case I see a 33% speed up >> with this change (nr_iter = 10000, cumtime 2.41 seconds verses 1.80 >> seconds for the change). Are you really, really sure this idea should >> not be used and is not worth such a speed up? >> >> Regards, >> >> Edward >> >> >> >> On 11 June 2014 16:05, Troels Emtekær Linnet <[email protected]> wrote: >>> Hi Edward. >>> >>> I wont make that change. >>> >>> I will keep the clean implementation as it is. >>> >>> Best >>> Troels >>> >>> 2014-06-11 15:52 GMT+02:00 Edward d'Auvergne <[email protected]>: >>>> By the way, I just obtained a ~10% speed up using your profiling >>>> script test_suite/shared_data/dispersion/profiling/profiling_cr72.py >>>> if I send in the original parameter vector R20A, R20B, and dw arrays >>>> and check these values instead of the full structures. See the diff >>>> below for ideas. With a little more polish and more numpy ufunc >>>> usage, you should be able to squeeze more speed out of the CR72 model >>>> still. >>>> >>>> Regards, >>>> >>>> Edward >>>> >>>> >>>> P. S. Here is the diff: >>>> >>>> """ >>>> Index: lib/dispersion/cr72.py >>>> =================================================================== >>>> --- lib/dispersion/cr72.py (revision 23841) >>>> +++ lib/dispersion/cr72.py (working copy) >>>> @@ -92,13 +92,13 @@ >>>> """ >>>> >>>> # Python module imports. >>>> -from numpy import arccosh, array, cos, cosh, isfinite, fabs, min, >>>> max, sqrt, subtract, sum >>>> +from numpy import arccosh, array, cos, cosh, isfinite, fabs, min, >>>> max, sqrt, subtract, sum, multiply >>>> from numpy.ma import fix_invalid, masked_greater_equal, masked_less, >>>> masked_where >>>> >>>> # Repetitive calculations (to speed up calculations). >>>> eta_scale = 2.0**(-3.0/2.0) >>>> >>>> -def r2eff_CR72(r20a=None, r20b=None, pA=None, dw=None, kex=None, >>>> cpmg_frqs=None, back_calc=None, num_points=None): >>>> +def r2eff_CR72(r20a_orig=None, r20b_orig=None, r20a=None, r20b=None, >>>> pA=None, dw_orig=None, dw=None, kex=None, cpmg_frqs=None, >>>> back_calc=None, num_points=None): >>>> """Calculate the R2eff values for the CR72 model. >>>> >>>> See the module docstring for details. >>>> @@ -133,7 +133,7 @@ >>>> return >>>> >>>> # Test if dw is zero. Wait for replacement, since this is spin >>>> specific. >>>> - if min(fabs(dw)) == 0.0: >>>> + if min(fabs(dw_orig)) == 0.0: >>>> t_dw_zero = True >>>> mask_dw_zero = masked_where(dw == 0.0, dw) >>>> >>>> @@ -147,7 +147,7 @@ >>>> k_AB = pB * kex >>>> >>>> # The Psi and zeta values. >>>> - if sum(r20a - r20b) != 0.0: >>>> + if sum(r20a_orig - r20b_orig) != 0.0: >>>> fact = r20a - r20b - k_BA + k_AB >>>> Psi = fact**2 - dw2 + 4.0*pA*pB*kex**2 >>>> zeta = 2.0*dw * fact >>>> @@ -182,7 +182,8 @@ >>>> return >>>> >>>> # Calculate R2eff. This uses the temporary buffer and fill >>>> directly to back_calc. >>>> - subtract(r20_kex, cpmg_frqs * arccosh( fact ), out=back_calc) >>>> + multiply(cpmg_frqs, arccosh(fact), out=back_calc) >>>> + subtract(r20_kex, back_calc, out=back_calc) >>>> >>>> # Replace data in array. >>>> # If dw is zero. >>>> Index: target_functions/relax_disp.py >>>> =================================================================== >>>> --- target_functions/relax_disp.py (revision 23841) >>>> +++ target_functions/relax_disp.py (working copy) >>>> @@ -567,7 +567,7 @@ >>>> self.r20b_struct[:] = multiply.outer( >>>> asarray(R20B).reshape(self.NE, self.NS, self.NM), self.no_nd_struct ) >>>> >>>> ## Back calculate the R2eff values. >>>> - r2eff_CR72(r20a=self.r20a_struct, r20b=self.r20b_struct, >>>> pA=pA, dw=self.dw_struct, kex=kex, cpmg_frqs=self.cpmg_frqs_a, >>>> back_calc=self.back_calc_a, num_points=self.num_disp_points_a) >>>> + r2eff_CR72(r20a_orig=R20A, r20b_orig=R20B, >>>> r20a=self.r20a_struct, r20b=self.r20b_struct, pA=pA, dw_orig=dw, >>>> dw=self.dw_struct, kex=kex, cpmg_frqs=self.cpmg_frqs_a, >>>> back_calc=self.back_calc_a, num_points=self.num_disp_points_a) >>>> >>>> # Clean the data for all values, which is left over at the >>>> end of arrays. >>>> self.back_calc_a = self.back_calc_a*self.disp_struct >>>> Index: test_suite/shared_data/dispersion/profiling/profiling_cr72.py >>>> =================================================================== >>>> --- test_suite/shared_data/dispersion/profiling/profiling_cr72.py >>>> (revision 23841) >>>> +++ test_suite/shared_data/dispersion/profiling/profiling_cr72.py >>>> (working copy) >>>> @@ -55,7 +55,7 @@ >>>> def main(): >>>> if True: >>>> # Nr of iterations. >>>> - nr_iter = 1 >>>> + nr_iter = 10000 >>>> >>>> # Print statistics. >>>> verbose = True >>>> @@ -275,7 +275,7 @@ >>>> back_calc = array([0.0]*len(cpmg_frqs[ei][mi][oi])) >>>> >>>> # Initialise call to function. >>>> - r2eff_CR72(r20a=r20a, r20b=r20b, pA=pA, >>>> dw=dw_frq, kex=kex, cpmg_frqs=array(cpmg_frqs[ei][mi][oi]), >>>> back_calc=back_calc, num_points=len(back_calc)) >>>> + r2eff_CR72(r20a_orig=R20A, r20b_orig=R20B, >>>> r20a=r20a, r20b=r20b, pA=pA, dw_orig=dw_frq, dw=dw_frq, kex=kex, >>>> cpmg_frqs=array(cpmg_frqs[ei][mi][oi]), back_calc=back_calc, >>>> num_points=len(back_calc)) >>>> >>>> for oi in range(len(self.offset)): >>>> for di in range(len(self.points[mi])): >>>> @@ -505,4 +505,4 @@ >>>> model = C1.calc(params) >>>> print(model) >>>> >>>> -#test_reshape() >>>> \ No newline at end of file >>>> +#test_reshape() >>>> """ >>>> >>>> >>>> >>>> On 11 June 2014 15:45, Edward d'Auvergne <[email protected]> wrote: >>>>> You wait until you see what happens with your multiple offset R1rho data >>>>> ;) >>>>> >>>>> On 11 June 2014 15:42, Troels Emtekær Linnet <[email protected]> >>>>> wrote: >>>>>> The progress is EXTREME. >>>>>> >>>>>> Per spin, I am now 1.5 X faster per spin calculation. >>>>>> Per cluster of 100, I am now 33X faster. >>>>>> >>>>>> Go one more version up, and it is 64 X faster. >>>>>> >>>>>> WOW! >>>>>> >>>>>> >>>>>> >>>>>> ---- >>>>>> Checked on MacBook Pro >>>>>> 2.4 GHz Intel Core i5 >>>>>> 8 GB 1067 Mhz DDR3 RAM. >>>>>> >>>>>> Timing for: >>>>>> 3 fields >>>>>> ('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.])) >>>>>> >>>>>> iterations of function call: 1000 >>>>>> >>>>>> Timed for simulating 1 or 100 clustered spins. >>>>>> >>>>>> Find tags: >>>>>> svn ls "^/tags" >>>>>> svn switch ^/tags/3.2.2 >>>>>> >>>>>> ############################################################################################## >>>>>> ncalls tottime percall cumtime percall filename:lineno(function) >>>>>> >>>>>> ############################ >>>>>> For disp_spin_speed r23841 # >>>>>> ############################ >>>>>> 1 spin: >>>>>> 1 0.000 0.000 0.373 0.373 <string>:1(<module>) >>>>>> 1 0.001 0.001 0.373 0.373 pf:427(single) >>>>>> 1000 0.002 0.000 0.366 0.000 pf:413(calc) >>>>>> 1000 0.012 0.000 0.363 0.000 >>>>>> relax_disp.py:994(func_CR72_full) >>>>>> 1000 0.027 0.000 0.345 0.000 >>>>>> relax_disp.py:545(calc_CR72_chi2) >>>>>> 1003 0.148 0.000 0.260 0.000 cr72.py:101(r2eff_CR72) >>>>>> 7043 0.059 0.000 0.059 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 1000 0.004 0.000 0.052 0.000 core.py:1701(masked_where) >>>>>> 3006 0.006 0.000 0.036 0.000 fromnumeric.py:1621(sum) >>>>>> 3006 0.004 0.000 0.028 0.000 _methods.py:23(_sum) >>>>>> 3000 0.024 0.000 0.024 0.000 {method 'outer' of >>>>>> 'numpy.ufunc' objects} >>>>>> 1000 0.013 0.000 0.024 0.000 chi2.py:72(chi2_rankN) >>>>>> 1000 0.002 0.000 0.024 0.000 {method 'view' of >>>>>> 'numpy.ndarray' objects} >>>>>> 2006 0.003 0.000 0.023 0.000 fromnumeric.py:2132(amin) >>>>>> 1000 0.003 0.000 0.021 0.000 >>>>>> core.py:2774(__array_finalize__) >>>>>> >>>>>> 100 spins: >>>>>> 1 0.000 0.000 1.630 1.630 <string>:1(<module>) >>>>>> 1 0.003 0.003 1.630 1.630 pf:449(cluster) >>>>>> 1000 0.004 0.000 1.532 0.002 pf:413(calc) >>>>>> 1000 0.020 0.000 1.528 0.002 >>>>>> relax_disp.py:994(func_CR72_full) >>>>>> 1000 0.073 0.000 1.495 0.001 >>>>>> relax_disp.py:545(calc_CR72_chi2) >>>>>> 1300 1.071 0.001 1.285 0.001 cr72.py:101(r2eff_CR72) >>>>>> 8528 0.131 0.000 0.131 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 1 0.000 0.000 0.094 0.094 pf:106(__init__) >>>>>> 3000 0.083 0.000 0.083 0.000 {method 'outer' of >>>>>> 'numpy.ufunc' objects} >>>>>> 3600 0.009 0.000 0.082 0.000 fromnumeric.py:1621(sum) >>>>>> 1000 0.055 0.000 0.079 0.000 chi2.py:72(chi2_rankN) >>>>>> 1000 0.006 0.000 0.078 0.000 core.py:1701(masked_where) >>>>>> 1 0.019 0.019 0.069 0.069 pf:173(return_r2eff_arrays) >>>>>> 3600 0.006 0.000 0.067 0.000 _methods.py:23(_sum) >>>>>> 2600 0.006 0.000 0.049 0.000 fromnumeric.py:2132(amin) >>>>>> 2600 0.005 0.000 0.042 0.000 _methods.py:19(_amin) >>>>>> 1000 0.004 0.000 0.032 0.000 {method 'view' of >>>>>> 'numpy.ndarray' objects} >>>>>> >>>>>> >>>>>> ############################ >>>>>> For disp_spin_speed r23806 # >>>>>> ############################ >>>>>> 1 spin: >>>>>> 1 0.000 0.000 0.546 0.546 <string>:1(<module>) >>>>>> 1 0.002 0.002 0.546 0.546 pf:427(single) >>>>>> 1000 0.003 0.000 0.538 0.001 pf:413(calc) >>>>>> 1000 0.015 0.000 0.535 0.001 >>>>>> relax_disp.py:989(func_CR72_full) >>>>>> 1000 0.042 0.000 0.513 0.001 >>>>>> relax_disp.py:523(calc_CR72_chi2) >>>>>> 1003 0.142 0.000 0.365 0.000 cr72.py:101(r2eff_CR72) >>>>>> 2003 0.055 0.000 0.181 0.000 numeric.py:2056(allclose) >>>>>> 10046 0.083 0.000 0.083 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 3000 0.045 0.000 0.076 0.000 shape_base.py:761(tile) >>>>>> 4015 0.006 0.000 0.053 0.000 fromnumeric.py:1762(any) >>>>>> 4015 0.004 0.000 0.039 0.000 {method 'any' of >>>>>> 'numpy.ndarray' objects} >>>>>> 4015 0.005 0.000 0.035 0.000 _methods.py:31(_any) >>>>>> 2003 0.003 0.000 0.028 0.000 fromnumeric.py:1842(all) >>>>>> 1000 0.014 0.000 0.026 0.000 chi2.py:72(chi2_rankN) >>>>>> 2003 0.004 0.000 0.026 0.000 fromnumeric.py:1621(sum) >>>>>> 4138 0.012 0.000 0.025 0.000 numeric.py:2320(seterr) >>>>>> 2003 0.002 0.000 0.020 0.000 {method 'all' of >>>>>> 'numpy.ndarray' objects} >>>>>> 2003 0.003 0.000 0.019 0.000 _methods.py:23(_sum) >>>>>> 2003 0.003 0.000 0.018 0.000 _methods.py:35(_all) >>>>>> 14046 0.016 0.000 0.016 0.000 >>>>>> {numpy.core.multiarray.array} >>>>>> >>>>>> 100 spins: >>>>>> 1 0.000 0.000 2.036 2.036 <string>:1(<module>) >>>>>> 1 0.003 0.003 2.036 2.036 pf:449(cluster) >>>>>> 1000 0.004 0.000 1.905 0.002 pf:413(calc) >>>>>> 1000 0.022 0.000 1.901 0.002 >>>>>> relax_disp.py:989(func_CR72_full) >>>>>> 1000 0.098 0.000 1.865 0.002 >>>>>> relax_disp.py:523(calc_CR72_chi2) >>>>>> 1300 0.986 0.001 1.511 0.001 cr72.py:101(r2eff_CR72) >>>>>> 2300 0.238 0.000 0.434 0.000 numeric.py:2056(allclose) >>>>>> 3000 0.058 0.000 0.238 0.000 shape_base.py:761(tile) >>>>>> 4000 0.154 0.000 0.154 0.000 {method 'repeat' of >>>>>> 'numpy.ndarray' objects} >>>>>> 11828 0.147 0.000 0.147 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 1 0.000 0.000 0.129 0.129 pf:106(__init__) >>>>>> 1 0.021 0.021 0.098 0.098 pf:173(return_r2eff_arrays) >>>>>> 1000 0.054 0.000 0.078 0.000 chi2.py:72(chi2_rankN) >>>>>> 4609 0.008 0.000 0.073 0.000 fromnumeric.py:1762(any) >>>>>> 2300 0.007 0.000 0.055 0.000 fromnumeric.py:1621(sum) >>>>>> 4609 0.005 0.000 0.054 0.000 {method 'any' of >>>>>> 'numpy.ndarray' objects} >>>>>> 4609 0.006 0.000 0.049 0.000 _methods.py:31(_any) >>>>>> 2300 0.004 0.000 0.044 0.000 _methods.py:23(_sum) >>>>>> 2300 0.005 0.000 0.039 0.000 fromnumeric.py:1842(all) >>>>>> 4732 0.016 0.000 0.035 0.000 numeric.py:2320(seterr) >>>>>> 4600 0.032 0.000 0.032 0.000 {abs} >>>>>> 1301 0.004 0.000 0.030 0.000 fromnumeric.py:2048(amax) >>>>>> 17016 0.028 0.000 0.028 0.000 >>>>>> {numpy.core.multiarray.array} >>>>>> >>>>>> ############################ >>>>>> For trunk r23785 # >>>>>> ############################ >>>>>> 1 spin: >>>>>> 1 0.000 0.000 0.572 0.572 <string>:1(<module>) >>>>>> 1 0.002 0.002 0.572 0.572 pf:427(single) >>>>>> 1000 0.002 0.000 0.565 0.001 pf:413(calc) >>>>>> 1000 0.013 0.000 0.563 0.001 >>>>>> relax_disp.py:908(func_CR72_full) >>>>>> 1000 0.061 0.000 0.543 0.001 >>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>> 3003 0.294 0.000 0.400 0.000 cr72.py:100(r2eff_CR72) >>>>>> 12036 0.100 0.000 0.100 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 3000 0.042 0.000 0.078 0.000 chi2.py:32(chi2) >>>>>> 6003 0.011 0.000 0.072 0.000 fromnumeric.py:1621(sum) >>>>>> 6003 0.008 0.000 0.055 0.000 _methods.py:23(_sum) >>>>>> 3003 0.005 0.000 0.037 0.000 fromnumeric.py:2048(amax) >>>>>> 3003 0.004 0.000 0.033 0.000 fromnumeric.py:2132(amin) >>>>>> 3003 0.004 0.000 0.032 0.000 _methods.py:15(_amax) >>>>>> 3003 0.004 0.000 0.029 0.000 _methods.py:19(_amin) >>>>>> 6003 0.006 0.000 0.006 0.000 {isinstance} >>>>>> >>>>>> 100 spins: >>>>>> 1 0.000 0.000 53.864 53.864 <string>:1(<module>) >>>>>> 1 0.004 0.004 53.864 53.864 pf:449(cluster) >>>>>> 1000 0.005 0.000 53.777 0.054 pf:413(calc) >>>>>> 1000 0.022 0.000 53.772 0.054 >>>>>> relax_disp.py:908(func_CR72_full) >>>>>> 1000 6.340 0.006 53.735 0.054 >>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>> 300300 28.936 0.000 39.278 0.000 cr72.py:100(r2eff_CR72) >>>>>> 1200927 9.811 0.000 9.811 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 300000 4.227 0.000 7.738 0.000 chi2.py:32(chi2) >>>>>> 600300 1.047 0.000 7.051 0.000 fromnumeric.py:1621(sum) >>>>>> 600300 0.752 0.000 5.434 0.000 _methods.py:23(_sum) >>>>>> 300300 0.445 0.000 3.580 0.000 fromnumeric.py:2048(amax) >>>>>> 300300 0.413 0.000 3.221 0.000 fromnumeric.py:2132(amin) >>>>>> 300300 0.431 0.000 3.134 0.000 _methods.py:15(_amax) >>>>>> 300300 0.383 0.000 2.808 0.000 _methods.py:19(_amin) >>>>>> 600300 0.570 0.000 0.570 0.000 {isinstance} >>>>>> >>>>>> >>>>>> ############################ >>>>>> For tag 3.2.2 # >>>>>> svn switch ^/tags/3.2.2 # >>>>>> ############################ >>>>>> >>>>>> 1 spin: >>>>>> 1 0.000 0.000 0.569 0.569 <string>:1(<module>) >>>>>> 1 0.002 0.002 0.569 0.569 pf:427(single) >>>>>> 1000 0.002 0.000 0.562 0.001 pf:413(calc) >>>>>> 1000 0.005 0.000 0.560 0.001 >>>>>> relax_disp.py:907(func_CR72_full) >>>>>> 1000 0.062 0.000 0.555 0.001 >>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>> 3003 0.299 0.000 0.407 0.000 cr72.py:100(r2eff_CR72) >>>>>> 12036 0.103 0.000 0.103 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 3000 0.044 0.000 0.082 0.000 chi2.py:32(chi2) >>>>>> 6003 0.011 0.000 0.074 0.000 fromnumeric.py:1621(sum) >>>>>> 6003 0.008 0.000 0.057 0.000 _methods.py:23(_sum) >>>>>> 3003 0.005 0.000 0.037 0.000 fromnumeric.py:2048(amax) >>>>>> 3003 0.004 0.000 0.034 0.000 fromnumeric.py:2132(amin) >>>>>> 3003 0.004 0.000 0.033 0.000 _methods.py:15(_amax) >>>>>> 3003 0.004 0.000 0.029 0.000 _methods.py:19(_amin) >>>>>> 6003 0.006 0.000 0.006 0.000 {isinstance} >>>>>> >>>>>> 100 spins: >>>>>> 1 0.000 0.000 53.987 53.987 <string>:1(<module>) >>>>>> 1 0.004 0.004 53.987 53.987 pf:449(cluster) >>>>>> 1000 0.004 0.000 53.907 0.054 pf:413(calc) >>>>>> 1000 0.008 0.000 53.903 0.054 >>>>>> relax_disp.py:907(func_CR72_full) >>>>>> 1000 6.367 0.006 53.895 0.054 >>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>> 300300 28.870 0.000 39.278 0.000 cr72.py:100(r2eff_CR72) >>>>>> 1200927 9.917 0.000 9.917 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 300000 4.283 0.000 7.853 0.000 chi2.py:32(chi2) >>>>>> 600300 1.066 0.000 7.154 0.000 fromnumeric.py:1621(sum) >>>>>> 600300 0.745 0.000 5.516 0.000 _methods.py:23(_sum) >>>>>> 300300 0.447 0.000 3.565 0.000 fromnumeric.py:2048(amax) >>>>>> 300300 0.417 0.000 3.259 0.000 fromnumeric.py:2132(amin) >>>>>> 300300 0.422 0.000 3.118 0.000 _methods.py:15(_amax) >>>>>> 300300 0.392 0.000 2.841 0.000 _methods.py:19(_amin) >>>>>> 600300 0.572 0.000 0.572 0.000 {isinstance} >>>>>> >>>>>> ############################ >>>>>> For tag 3.2.1 # >>>>>> svn switch ^/tags/3.2.1 # >>>>>> ############################ >>>>>> 1 spin: >>>>>> 1 0.000 0.000 1.021 1.021 <string>:1(<module>) >>>>>> 1 0.002 0.002 1.021 1.021 pf:427(single) >>>>>> 1000 0.002 0.000 1.014 0.001 pf:413(calc) >>>>>> 1000 0.005 0.000 1.012 0.001 >>>>>> relax_disp.py:907(func_CR72_full) >>>>>> 1000 0.055 0.000 1.007 0.001 >>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>> 3003 0.861 0.000 0.864 0.000 cr72.py:98(r2eff_CR72) >>>>>> 3000 0.043 0.000 0.084 0.000 chi2.py:32(chi2) >>>>>> 3000 0.006 0.000 0.042 0.000 fromnumeric.py:1621(sum) >>>>>> 3000 0.004 0.000 0.032 0.000 _methods.py:23(_sum) >>>>>> 3027 0.028 0.000 0.028 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 8049 0.007 0.000 0.007 0.000 {range} >>>>>> 1 0.000 0.000 0.006 0.006 pf:106(__init__) >>>>>> 3 0.000 0.000 0.004 0.001 numeric.py:1509(array_repr) >>>>>> 3 0.000 0.000 0.004 0.001 >>>>>> arrayprint.py:343(array2string) >>>>>> 3 0.000 0.000 0.004 0.001 >>>>>> arrayprint.py:233(_array2string) >>>>>> 3000 0.004 0.000 0.004 0.000 {isinstance} >>>>>> >>>>>> 100 spins: >>>>>> 1 0.000 0.000 104.086 104.086 <string>:1(<module>) >>>>>> 1 0.004 0.004 104.086 104.086 pf:449(cluster) >>>>>> 1000 0.004 0.000 103.944 0.104 pf:413(calc) >>>>>> 1000 0.009 0.000 103.940 0.104 >>>>>> relax_disp.py:907(func_CR72_full) >>>>>> 1000 6.057 0.006 103.931 0.104 >>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>> 300300 88.604 0.000 88.888 0.000 cr72.py:98(r2eff_CR72) >>>>>> 300000 4.408 0.000 8.695 0.000 chi2.py:32(chi2) >>>>>> 300000 0.627 0.000 4.287 0.000 fromnumeric.py:1621(sum) >>>>>> 300000 0.458 0.000 3.296 0.000 _methods.py:23(_sum) >>>>>> 300027 2.839 0.000 2.839 0.000 {method 'reduce' of >>>>>> 'numpy.ufunc' objects} >>>>>> 703722 0.672 0.000 0.672 0.000 {range} >>>>>> 300000 0.364 0.000 0.364 0.000 {isinstance} >>>>>> 1 0.000 0.000 0.139 0.139 pf:106(__init__) >>>>>> >>>>>> >>>>>> ################# System information ###################### >>>>>> Processor fabric: Uni-processor. >>>>>> >>>>>> >>>>>> Hardware information: >>>>>> Machine: x86_64 >>>>>> Processor: i386 >>>>>> Processor name: Intel(R) Core(TM) i5-2435M CPU @ 2.40GHz >>>>>> Endianness: little >>>>>> Total RAM size: 2048.0 Mb >>>>>> Total swap size: 6144.0 Mb >>>>>> >>>>>> Operating system information: >>>>>> System: Darwin >>>>>> Release: 13.2.0 >>>>>> Version: Darwin Kernel Version 13.2.0: Thu Apr 17 >>>>>> 23:03:13 PDT 2014; root:xnu-2422.100.13~1/RELEASE_X86_64 >>>>>> Mac version: 10.9.3 (, , ) x86_64 >>>>>> Distribution: >>>>>> Full platform string: Darwin-13.2.0-x86_64-i386-64bit >>>>>> >>>>>> Python information: >>>>>> Architecture: 64bit >>>>>> Python version: 2.7.6 >>>>>> Python branch: >>>>>> Python build: default, Apr 11 2014 11:55:30 >>>>>> Python compiler: GCC 4.2.1 (Apple Inc. build 5666) (dot 3) >>>>>> Libc version: >>>>>> Python implementation: CPython >>>>>> Python revision: >>>>>> Python executable: >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/bin/python >>>>>> Python flags: sys.flags(debug=0, py3k_warning=0, >>>>>> division_warning=0, division_new=0, inspect=0, interactive=0, >>>>>> optimize=0, dont_write_bytecode=0, no_user_site=0, no_site=0, >>>>>> ignore_environment=0, tabcheck=0, verbose=0, unicode=0, >>>>>> bytes_warning=0, hash_randomization=0) >>>>>> Python float info: >>>>>> sys.float_info(max=1.7976931348623157e+308, max_exp=1024, >>>>>> max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, >>>>>> min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, >>>>>> radix=2, rounds=1) >>>>>> Python module path: ['/Users/tlinnet/software/relax_trunk', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python27.zip', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/plat-darwin', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/plat-mac', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/plat-mac/lib-scriptpackages', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/lib-tk', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/lib-old', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/lib-dynload', >>>>>> '/Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages', >>>>>> '/Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/PIL', >>>>>> '/Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/site-packages'] >>>>>> >>>>>> Python packages and modules (most are optional): >>>>>> >>>>>> Name Installed Version Path >>>>>> minfx True 1.0.6 >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/minfx >>>>>> bmrblib True 1.0.3 >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/bmrblib >>>>>> numpy True 1.8.0 >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy >>>>>> scipy True 0.13.3 >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy >>>>>> wxPython True 2.9.2.4 osx-cocoa (classic) >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/wx >>>>>> matplotlib True 1.3.1 >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib >>>>>> mpi4py False >>>>>> epydoc True 3.0.1 >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/epydoc >>>>>> optparse True 1.5.3 >>>>>> /Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/optparse.pyc >>>>>> readline True >>>>>> /Users/tlinnet/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/readline.so >>>>>> profile True >>>>>> /Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/profile.pyc >>>>>> bz2 True >>>>>> /Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/lib-dynload/bz2.so >>>>>> gzip True >>>>>> /Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/gzip.pyc >>>>>> io True >>>>>> /Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/io.pyc >>>>>> xml True 0.8.4 (internal) >>>>>> /Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/xml/__init__.pyc >>>>>> xml.dom.minidom True >>>>>> /Applications/Canopy.app/appdata/canopy-1.4.0.1938.macosx-x86_64/Canopy.app/Contents/lib/python2.7/xml/dom/minidom.pyc >>>>>> >>>>>> relax information: >>>>>> Version: repository checkout r23785 >>>>>> svn+ssh://svn.gna.org/svn/relax/trunk >>>>>> Processor fabric: Uni-processor. >>>>>> >>>>>> relax C modules: >>>>>> >>>>>> Module Compiled File type >>>>>> Path >>>>>> target_functions.relax_fit True 2-way ['Mach-O 64-bit bundle >>>>>> x86_64', 'Mach-O bundle i386'] >>>>>> /Users/tlinnet/software/relax_trunk/target_functions/relax_fit.so >>>>>> >>>>>> 2014-06-11 15:38 GMT+02:00 Troels Emtekær Linnet <[email protected]>: >>>>>>> Hi Ed. >>>>>>> >>>>>>> I am now faster than trunk per spin, even if I replaces the cr72.py >>>>>>> file. >>>>>>> >>>>>>> 10000 iterations: >>>>>>> >>>>>>> BRANCH: >>>>>>> 1 0.000 0.000 4.060 4.060 <string>:1(<module>) >>>>>>> 1 0.016 0.016 4.060 4.060 pf:427(single) >>>>>>> 10000 0.028 0.000 4.038 0.000 pf:413(calc) >>>>>>> 10000 0.133 0.000 4.010 0.000 >>>>>>> relax_disp.py:994(func_CR72_full) >>>>>>> 10000 0.301 0.000 3.803 0.000 >>>>>>> relax_disp.py:545(calc_CR72_chi2) >>>>>>> 10003 1.629 0.000 2.862 0.000 cr72.py:101(r2eff_CR72) >>>>>>> 70043 0.647 0.000 0.647 0.000 {method 'reduce' of >>>>>>> 'numpy.ufunc' objects} >>>>>>> 10000 0.042 0.000 0.572 0.000 core.py:1701(masked_where) >>>>>>> 30006 0.061 0.000 0.395 0.000 fromnumeric.py:1621(sum) >>>>>>> 30006 0.040 0.000 0.305 0.000 _methods.py:23(_sum) >>>>>>> 10000 0.142 0.000 0.269 0.000 chi2.py:72(chi2_rankN) >>>>>>> 30000 0.267 0.000 0.267 0.000 {method 'outer' of >>>>>>> 'numpy.ufunc' objects} >>>>>>> 10000 0.026 0.000 0.262 0.000 {method 'view' of >>>>>>> 'numpy.ndarray' objects} >>>>>>> 20006 0.032 0.000 0.250 0.000 fromnumeric.py:2132(amin) >>>>>>> >>>>>>> TRUNK, with new CR72. >>>>>>> 1 0.000 0.000 6.585 6.585 <string>:1(<module>) >>>>>>> 1 0.016 0.016 6.585 6.585 pf:427(single) >>>>>>> 10000 0.026 0.000 6.562 0.001 pf:413(calc) >>>>>>> 10000 0.133 0.000 6.536 0.001 >>>>>>> relax_disp.py:908(func_CR72_full) >>>>>>> 10000 0.601 0.000 6.327 0.001 >>>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>>> 30003 3.153 0.000 4.907 0.000 cr72.py:101(r2eff_CR72) >>>>>>> 180042 1.356 0.000 1.356 0.000 {method 'reduce' of >>>>>>> 'numpy.ufunc' objects} >>>>>>> 90006 0.165 0.000 1.108 0.000 fromnumeric.py:1621(sum) >>>>>>> 90006 0.109 0.000 0.792 0.000 _methods.py:23(_sum) >>>>>>> 30000 0.423 0.000 0.775 0.000 chi2.py:32(chi2) >>>>>>> 60006 0.096 0.000 0.647 0.000 fromnumeric.py:2132(amin) >>>>>>> 60006 0.074 0.000 0.483 0.000 _methods.py:19(_amin) >>>>>>> 30003 0.044 0.000 0.350 0.000 fromnumeric.py:2048(amax) >>>>>>> >>>>>>> TRUNK, with original CR72. >>>>>>> 1 0.000 0.000 5.994 5.994 <string>:1(<module>) >>>>>>> 1 0.018 0.018 5.994 5.994 pf:427(single) >>>>>>> 10000 0.027 0.000 5.971 0.001 pf:413(calc) >>>>>>> 10000 0.142 0.000 5.944 0.001 >>>>>>> relax_disp.py:908(func_CR72_full) >>>>>>> 10000 0.639 0.000 5.722 0.001 >>>>>>> relax_disp.py:456(calc_CR72_chi2) >>>>>>> 30003 3.093 0.000 4.205 0.000 cr72.py:100(r2eff_CR72) >>>>>>> 120036 1.051 0.000 1.051 0.000 {method 'reduce' of >>>>>>> 'numpy.ufunc' objects} >>>>>>> 30000 0.455 0.000 0.830 0.000 chi2.py:32(chi2) >>>>>>> 60003 0.113 0.000 0.755 0.000 fromnumeric.py:1621(sum) >>>>>>> 60003 0.078 0.000 0.580 0.000 _methods.py:23(_sum) >>>>>>> 30003 0.049 0.000 0.382 0.000 fromnumeric.py:2048(amax) >>>>>>> 30003 0.048 0.000 0.350 0.000 fromnumeric.py:2132(amin) >>>>>>> 30003 0.045 0.000 0.333 0.000 _methods.py:15(_amax) >>>>>>> 30003 0.041 0.000 0.302 0.000 _methods.py:19(_amin) >>>>>>> 60003 0.061 0.000 0.061 0.000 {isinstance} >>>>>>> 20002 0.061 0.000 0.061 0.000 {method 'flatten' of >>>>>>> 'numpy.ndarray' objects} >>>>>>> 50046 0.048 0.000 0.048 0.000 {range} >>>>>> >>>>>> _______________________________________________ >>>>>> relax (http://www.nmr-relax.com) >>>>>> >>>>>> This is the relax-devel mailing list >>>>>> [email protected] >>>>>> >>>>>> To unsubscribe from this list, get a password >>>>>> reminder, or change your subscription options, >>>>>> visit the list information page at >>>>>> https://mail.gna.org/listinfo/relax-devel _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
