Here are some hints: http://stackoverflow.com/questions/10580676/comparing-two-numpy-arrays-for-equality
It would be the timing of: numpy.allclose http://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html numpy.isclose http://docs.scipy.org/doc/numpy/reference/generated/numpy.isclose.html#numpy.isclose numpy.ma http://docs.scipy.org/doc/numpy/reference/generated/numpy.ma.masked_values.html#numpy.ma.masked_values 2014-06-10 11:57 GMT+02:00 Troels Emtekær Linnet <[email protected]>: > Hi Edward. > > I thought the same. > > One could send in a ones and zeros numpy array in function. > Set them to standard to None. > > If they are None, then create them. > > One can see on the timings, that > r2eff_CR72 > numeric.py:2056(allclose) > > are the most time consuming. > Maybe there is a faster way for allclose. > Masking ??? > > > 1 0.000 0.000 2.084 2.084 <string>:1(<module>) > 1 0.002 0.002 2.084 2.084 profiling_cr72.py:441(cluster) > 1000 0.002 0.000 2.005 0.002 profiling_cr72.py:405(calc) > 1000 0.034 0.000 2.003 0.002 > relax_disp.py:995(func_CR72_full) > 1000 0.141 0.000 1.960 0.002 > relax_disp.py:524(calc_CR72_chi2) > 1300 1.100 0.001 1.676 0.001 cr72.py:101(r2eff_CR72) > 4300 0.245 0.000 0.507 0.000 numeric.py:2056(allclose) > 3000 0.037 0.000 0.164 0.000 shape_base.py:761(tile) > 17828 0.126 0.000 0.126 0.000 {method 'reduce' of > 'numpy.ufunc' objects} > 4000 0.110 0.000 0.110 0.000 {method 'repeat' of > 'numpy.ndarray' objects} > 8609 0.011 0.000 0.086 0.000 fromnumeric.py:1762(any) > > > > > > 2014-06-10 11:44 GMT+02:00 Edward d'Auvergne <[email protected]>: > >> Hi Troels, >> >> For speed, maybe you should send in the single value parameters >> together with the array versions into the lib.dispersion modules? The >> check: >> >> + if np.allclose(dw, np.zeros(dw.shape)) or np.allclose(pA, >> np.ones(dw.shape)) or np.allclose(kex, np.zeros(dw.shape)): >> >> will be very expensive. >> >> Regards, >> >> Edward >> >> >> >> On 8 June 2014 19:48, <[email protected]> wrote: >> > Author: tlinnet >> > Date: Sun Jun 8 19:48:35 2014 >> > New Revision: 23737 >> > >> > URL: http://svn.gna.org/viewcvs/relax?rev=23737&view=rev >> > Log: >> > Re-implemented safety checks in lib/dispersion/cr72.py. >> > >> > This is now implemented for both rank-1 float array and of higher >> > dimensions. >> > >> > This makes the unit tests pass for multi dimensional computing. >> > >> > Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion >> > models for Clustered analysis. >> > >> > Modified: >> > branches/disp_spin_speed/lib/dispersion/cr72.py >> > >> > Modified: branches/disp_spin_speed/lib/dispersion/cr72.py >> > URL: >> > http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/lib/dispersion/cr72.py?rev=23737&r1=23736&r2=23737&view=diff >> > >> > ============================================================================== >> > --- branches/disp_spin_speed/lib/dispersion/cr72.py (original) >> > +++ branches/disp_spin_speed/lib/dispersion/cr72.py Sun Jun 8 >> > 19:48:35 2014 >> > @@ -128,9 +128,15 @@ >> > rank_1 = False >> > >> > # Catch parameter values that will result in no exchange, returning >> > flat R2eff = R20 lines (when kex = 0.0, k_AB = 0.0). >> > + # For rank-1 float array. >> > if rank_1: >> > if dw == 0.0 or pA == 1.0 or kex == 0.0: >> > back_calc[:] = array([r20a]*num_points) >> > + return >> > + # For higher dimensions, return same structure. >> > + else: >> > + if np.allclose(dw, np.zeros(dw.shape)) or np.allclose(pA, >> > np.ones(dw.shape)) or np.allclose(kex, np.zeros(dw.shape)): >> > + back_calc[:] = r20a >> > return >> > >> > # The B population. >> > @@ -165,16 +171,23 @@ >> > >> > # Catch math domain error of cosh(val > 710). >> > # This is when etapos > 710. >> > - if rank_1: >> > - if max(etapos) > 700: >> > + if max(etapos) > 700: >> > + if rank_1: >> > back_calc[:] = array([r20a]*num_points) >> > + return >> > + # For higher dimensions, return same structure. >> > + else: >> > + back_calc[:] = r20a >> > return >> > >> > # The arccosh argument - catch invalid values. >> > fact = Dpos * cosh(etapos) - Dneg * cos(etaneg) >> > - if rank_1: >> > - if min(fact) < 1.0: >> > + if min(fact) < 1.0: >> > + if rank_1: >> > back_calc[:] = array([r20_kex]*num_points) >> > + return >> > + else: >> > + back_calc[:] = r20_kex >> > return >> > >> > # Calculate R2eff. >> > @@ -182,8 +195,10 @@ >> > >> > # Catch errors, taking a sum over array is the fastest way to check >> > for >> > # +/- inf (infinity) and nan (not a number). >> > - if rank_1: >> > - if not isfinite(sum(R2eff)): >> > + if not isfinite(sum(R2eff)): >> > + if rank_1: >> > R2eff = array([1e100]*num_points) >> > + else: >> > + R2eff = np.ones(R2eff.shape) * 1e100 >> > >> > back_calc[:] = R2eff >> > >> > >> > _______________________________________________ >> > relax (http://www.nmr-relax.com) >> > >> > This is the relax-commits 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-commits >> >> _______________________________________________ >> 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
