Hi,
You can obtain even more speed in the target function, specifically the lines:
"""
# 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
## For all missing data points, set the back-calculated value
to the measured values so that it has no effect on the chi-squared
value.
if self.has_missing:
# Find the numpy mask, which tells where values should be replaced.
mask_replace = masked_equal(self.missing_a, 1.0)
# Replace with values.
self.back_calc_a[mask_replace.mask] =
self.values_a[mask_replace.mask]
"""
Firstly, now that you are a masking expert, you could use a mask for
the cleaning up of the line "self.back_calc_a =
self.back_calc_a*self.disp_struct". I have no idea if this is faster
or not, but if you use the mask to set all non-used elements to zero,
it might be faster than a multiplication with 1.0. You already have
the zeros_a structure which can be placed into 'self' and used with a
mask 'self.mask_blank_disp' created in __init__(). Secondly, the
mask_replace variable is created here for every function call. Shift
it to __init__(), as it only needs to be created once. Then the
target function code should be simpler:
"""
# Clean the data for all values, which is left over at the end
of arrays.
self.back_calc_a[self.mask_blank_disp.mask] =
self.zeros_a[self.mask_blank_disp.mask]
# For all missing data points, set the back-calculated value
to the measured values so that it has no effect on the chi-squared
value.
if self.has_missing:
self.back_calc_a[mask_replace.mask] =
self.values_a[mask_replace.mask]
"""
Even faster for real data which almost always has certain data points
missing, would be to remove the has_missing flag. This is only useful
for synthetic cases, not real data. So then you have just:
"""
# Clean the data for all values, which is left over at the end
of arrays.
self.back_calc_a[self.mask_blank_disp.mask] =
self.zeros_a[self.mask_blank_disp.mask]
# For all missing data points, set the back-calculated value
to the measured values so that it has no effect on the chi-squared
value.
self.back_calc_a[mask_replace.mask] = self.values_a[mask_replace.mask]
"""
I am giving you these hints because, all together, you should soon
reach half the speed of trunk for single spins, and much more than 33
times for your cluster of 100. I hope you can keep up ;)
Regards,
Edward
On 11 June 2014 15:52, Edward d'Auvergne <[email protected]> wrote:
> 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)
>>>
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>>> [email protected]
>>>
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>>> reminder, or change your subscription options,
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