And here is how to shave a few percent off the lib.dispersion code
with the numpy ufuncs:
Index: lib/dispersion/cr72.py
===================================================================
--- lib/dispersion/cr72.py (revision 23825)
+++ lib/dispersion/cr72.py (working copy)
@@ -92,7 +92,7 @@
"""
# Python module imports.
-from numpy import allclose, arccosh, array, cos, cosh, isfinite,
isnan, min, max, ndarray, ones, sqrt, sum, zeros
+from numpy import add, allclose, arccosh, array, cos, cosh, isfinite,
isnan, min, max, multiply, ndarray, ones, sqrt, sum, zeros
from numpy.ma import masked_greater_equal
# Repetitive calculations (to speed up calculations).
@@ -211,17 +211,16 @@
return
# Calculate R2eff.
- R2eff = r20_kex - cpmg_frqs * arccosh( fact )
+ multiply(cpmg_frqs, arccosh(fact), back_calc)
+ add(r20_kex, -back_calc, back_calc)
# Replace data in array.
if t_max_etapos:
- R2eff[mask_max_etapos.mask] = r20a[mask_max_etapos.mask]
+ back_calc[mask_max_etapos.mask] = r20a[mask_max_etapos.mask]
# Catch errors, taking a sum over array is the fastest way to check for
# +/- inf (infinity) and nan (not a number).
- if not isfinite(sum(R2eff)):
+ if not isfinite(sum(back_calc)):
# Find the data mask which has nan values, and replace.
- mask = isnan(R2eff)
- R2eff[mask] = 1e100
-
- back_calc[:] = R2eff
+ mask = isnan(back_calc)
+ back_calc[mask] = 1e100
Regards,
Edward
On 11 June 2014 12:12, Edward d'Auvergne <[email protected]> wrote:
> And if you want to take this to the extreme, in __init__() define:
>
> self.dw_shape = (1, 1, self.NM, self.NO, self.ND)
>
> and then in the target function:
>
> self.dw_struct[:] = 1.0
> multiply(self.dw_struct, tile(asarray(dw).reshape(self.NE,
> self.NS)[:,:,None,None,None], self.dw_shape), self.dw_struct)
> multiply(self.dw_struct, self.frqs_a2, self.dw_struct)
>
> These will speed things up by a few percent. It's a pity the
> numpy.tile() function does not use the 'out' argument.
>
> Regards,
>
> Edward
>
>
> On 11 June 2014 12:09, Edward d'Auvergne <[email protected]> wrote:
>> Hi,
>>
>> Even faster is to use:
>>
>> """
>> self.dw_struct[:] = 1.0
>> multiply(self.dw_struct, tile(asarray(dw).reshape(self.NE,
>> self.NS)[:,:,None,None,None], (1, 1, self.NM, self.NO, self.ND)),
>> self.dw_struct)
>> multiply(self.dw_struct, self.frqs_a2, self.dw_struct)
>> """
>>
>> Where disp_struct and frqs_a are pre-multipled in the __init__()
>> function, as that maths operation does not need to happen for each
>> function call:
>>
>> self.frqs_a2 = self.disp_struct * self.frqs_a
>>
>> Regards,
>>
>> Edward
>>
>>
>> On 11 June 2014 12:00, Edward d'Auvergne <[email protected]> wrote:
>>> Hi,
>>>
>>> Oh well, I can see you've now have an implementation (new = False)
>>> that beats mine when clustered :) You can use some of the ideas such
>>> as the out ufunc argument and temporary storage to your advantage
>>> nevertheless. For example you can use the out argument of these
>>> ufuncs even more, replacing:
>>>
>>> """
>>> self.dw_struct[:] = 1.0
>>> self.dw_struct[:] = multiply(self.dw_struct,
>>> tile(asarray(dw).reshape(self.NE, self.NS)[:,:,None,None,None], (1, 1,
>>> self.NM, self.NO, self.ND)), ) * self.disp_struct * self.frqs_a
>>> """
>>>
>>>
>>> with:
>>>
>>> """
>>> self.dw_struct[:] = 1.0
>>> multiply(self.dw_struct, tile(asarray(dw).reshape(self.NE,
>>> self.NS)[:,:,None,None,None], (1, 1, self.NM, self.NO, self.ND)),
>>> self.dw_struct)
>>> multiply(self.dw_struct, self.disp_struct, self.dw_struct)
>>> multiply(self.dw_struct, self.frqs_a, self.dw_struct)
>>> """
>>>
>>>
>>> That shaves off a few milliseconds by avoiding automatic array
>>> creation and destruction, with before:
>>>
>>> """
>>> ('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)
>>> Wed Jun 11 11:45:42 2014 /tmp/tmpwkhLSr
>>>
>>> 198252 function calls (197150 primitive calls) in 1.499 seconds
>>>
>>> Ordered by: cumulative time
>>>
>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>> 1 0.000 0.000 1.499 1.499 <string>:1(<module>)
>>> 1 0.001 0.001 1.499 1.499 profiling_cr72.py:449(cluster)
>>> 1000 0.001 0.000 1.427 0.001 profiling_cr72.py:413(calc)
>>> 1000 0.009 0.000 1.425 0.001
>>> relax_disp.py:1020(func_CR72_full)
>>> 1000 0.066 0.000 1.409 0.001
>>> relax_disp.py:544(calc_CR72_chi2)
>>> 1300 0.903 0.001 1.180 0.001 cr72.py:101(r2eff_CR72)
>>> 2300 0.100 0.000 0.222 0.000 numeric.py:2056(allclose)
>>> 3000 0.032 0.000 0.150 0.000 shape_base.py:761(tile)
>>> 4000 0.104 0.000 0.104 0.000 {method 'repeat' of
>>> 'numpy.ndarray' objects}
>>> 11828 0.091 0.000 0.091 0.000 {method 'reduce' of
>>> 'numpy.ufunc' objects}
>>> 1 0.000 0.000 0.071 0.071
>>> profiling_cr72.py:106(__init__)
>>> 1 0.010 0.010 0.056 0.056
>>> profiling_cr72.py:173(return_r2eff_arrays)
>>> 1000 0.032 0.000 0.048 0.000 chi2.py:72(chi2_rankN)
>>> 4609 0.005 0.000 0.045 0.000 fromnumeric.py:1762(any)
>>> 2300 0.004 0.000 0.036 0.000 fromnumeric.py:1621(sum)
>>> """
>>>
>>>
>>> And after:
>>>
>>> """
>>> ('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)
>>> Wed Jun 11 11:49:29 2014 /tmp/tmpML9Lx5
>>>
>>> 198252 function calls (197150 primitive calls) in 1.462 seconds
>>>
>>> Ordered by: cumulative time
>>>
>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>> 1 0.000 0.000 1.462 1.462 <string>:1(<module>)
>>> 1 0.001 0.001 1.462 1.462 profiling_cr72.py:449(cluster)
>>> 1000 0.001 0.000 1.393 0.001 profiling_cr72.py:413(calc)
>>> 1000 0.009 0.000 1.392 0.001
>>> relax_disp.py:1022(func_CR72_full)
>>> 1000 0.056 0.000 1.376 0.001
>>> relax_disp.py:544(calc_CR72_chi2)
>>> 1300 0.887 0.001 1.158 0.001 cr72.py:101(r2eff_CR72)
>>> 2300 0.097 0.000 0.217 0.000 numeric.py:2056(allclose)
>>> 3000 0.031 0.000 0.148 0.000 shape_base.py:761(tile)
>>> 4000 0.103 0.000 0.103 0.000 {method 'repeat' of
>>> 'numpy.ndarray' objects}
>>> 11828 0.090 0.000 0.090 0.000 {method 'reduce' of
>>> 'numpy.ufunc' objects}
>>> 1 0.000 0.000 0.068 0.068
>>> profiling_cr72.py:106(__init__)
>>> 1 0.010 0.010 0.053 0.053
>>> profiling_cr72.py:173(return_r2eff_arrays)
>>> 1000 0.031 0.000 0.047 0.000 chi2.py:72(chi2_rankN)
>>> 4609 0.006 0.000 0.044 0.000 fromnumeric.py:1762(any)
>>> 2300 0.004 0.000 0.036 0.000 fromnumeric.py:1621(sum)
>>> """
>>>
>>>
>>> The additional suggestions I didn't specify before was to use these
>>> ufuncs with the out argument in the lib.dispersion modules themselves.
>>> You don't need to create R2eff here, just pack it into back_calc!
>>>
>>> Regards,
>>>
>>> Edward
>>>
>>> On 11 June 2014 11:55, Troels Emtekær Linnet <[email protected]> wrote:
>>>> Hi Edward.
>>>>
>>>> Some timings.
>>>> Per spin, you have a faster method.
>>>> But I win per cluster.
>>>>
>>>> 1000 iterations
>>>> 1 / 100 spins
>>>>
>>>> Edward
>>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>>> 1 0.000 0.000 0.523 0.523 <string>:1(<module>)
>>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>>> 1 0.000 0.000 3.875 3.875 <string>:1(<module>)
>>>>
>>>> Troels Tile
>>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>>> 1 0.000 0.000 0.563 0.563 <string>:1(<module>)
>>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>>> 1 0.000 0.000 2.102 2.102 <string>:1(<module>)
>>>>
>>>> Troels Outer
>>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>>> 1 0.000 0.000 0.546 0.546 <string>:1(<module>)
>>>> ncalls tottime percall cumtime percall filename:lineno(function)
>>>> 1 0.000 0.000 1.974 1.974 <string>:1(<module>)
>>>>
>>>> 2014-06-11 11:46 GMT+02:00 Troels Emtekær Linnet <[email protected]>:
>>>>> Hi Edward.
>>>>>
>>>>> This is a really god page!
>>>>> http://docs.scipy.org/doc/numpy/reference/ufuncs.html
>>>>>
>>>>> ""
>>>>> Tip
>>>>> The optional output arguments can be used to help you save memory for
>>>>> large calculations. If your arrays are large, complicated expressions
>>>>> can take longer than absolutely necessary due to the creation and
>>>>> (later) destruction of temporary calculation spaces. For example, the
>>>>> expression G = a * b + c is equivalent to t1 = A * B; G = T1 + C; del
>>>>> t1. It will be more quickly executed as G = A * B; add(G, C, G) which
>>>>> is the same as G = A * B; G += C.
>>>>> ""
>>>>>
>>>>> 2014-06-10 23:08 GMT+02:00 Edward d'Auvergne <[email protected]>:
>>>>>> Note that masks and numpy.ma.multiply() and numpy.ma.add() may speed
>>>>>> this up even more. However due to overheads in the numpy masking,
>>>>>> there is a chance that this also makes the dw and R20 data structure
>>>>>> construction slower.
>>>>>>
>>>>>> Regards,
>>>>>>
>>>>>> Edward
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 10 June 2014 22:36, Edward d'Auvergne <[email protected]> wrote:
>>>>>>> Hi Troels,
>>>>>>>
>>>>>>> To make things even simpler, here is what needs to be done for R20,
>>>>>>> R20A and R20B:
>>>>>>>
>>>>>>> """
>>>>>>> from numpy import abs, add, array, float64, multiply, ones, sum, zeros
>>>>>>>
>>>>>>> # Init mimic.
>>>>>>> #############
>>>>>>>
>>>>>>> # Values from Relax_disp.test_cpmg_synthetic_ns3d_to_cr72_noise_cluster.
>>>>>>> NE = 1
>>>>>>> NS = 2
>>>>>>> NM = 2
>>>>>>> NO = 1
>>>>>>> ND = 8
>>>>>>> R20A = array([ 9.984626320294867, 11.495327724693091,
>>>>>>> 12.991028416082928, 14.498419290021163])
>>>>>>> shape = (NE, NS, NM, NO, ND)
>>>>>>>
>>>>>>> # Final structure for lib.dispersion.
>>>>>>> R20A_struct = zeros(shape, float64)
>>>>>>>
>>>>>>> # Temporary storage to avoid memory allocations and garbage collection.
>>>>>>> R20A_temp = zeros(shape, float64)
>>>>>>>
>>>>>>> # The structure for multiplication with R20A to piecewise build up the
>>>>>>> full R20A structure.
>>>>>>> R20A_mask = zeros((NS*NM,) + shape, float64)
>>>>>>> for si in range(NS):
>>>>>>> for mi in range(NM):
>>>>>>> R20A_mask[si*NM+mi, :, si, mi] = 1.0
>>>>>>> print(R20A_mask)
>>>>>>> print("\n\n")
>>>>>>>
>>>>>>> # Values to be found (again taken directly from
>>>>>>> Relax_disp.test_cpmg_synthetic_ns3d_to_cr72_noise_cluster - as a
>>>>>>> printout of dw_frq_a).
>>>>>>> R20A_final = array([[[[[ 9.984626320294867, 9.984626320294867,
>>>>>>> 9.984626320294867,
>>>>>>> 9.984626320294867, 9.984626320294867,
>>>>>>> 9.984626320294867,
>>>>>>> 9.984626320294867, 9.984626320294867]],
>>>>>>>
>>>>>>> [[ 11.495327724693091, 11.495327724693091,
>>>>>>> 11.495327724693091,
>>>>>>> 11.495327724693091, 11.495327724693091,
>>>>>>> 11.495327724693091,
>>>>>>> 11.495327724693091, 11.495327724693091]]],
>>>>>>>
>>>>>>>
>>>>>>> [[[ 12.991028416082928, 12.991028416082928,
>>>>>>> 12.991028416082928,
>>>>>>> 12.991028416082928, 12.991028416082928,
>>>>>>> 12.991028416082928,
>>>>>>> 12.991028416082928, 12.991028416082928]],
>>>>>>>
>>>>>>> [[ 14.498419290021163, 14.498419290021163,
>>>>>>> 14.498419290021163,
>>>>>>> 14.498419290021163, 14.498419290021163,
>>>>>>> 14.498419290021163,
>>>>>>> 14.498419290021163, 14.498419290021163]]]]])
>>>>>>>
>>>>>>>
>>>>>>> # Target function.
>>>>>>> ##################
>>>>>>>
>>>>>>> # Loop over the R20A elements (one per spin).
>>>>>>> for r20_index in range(NS*NM):
>>>>>>> # First multiply the spin specific R20A with the spin specific
>>>>>>> frequency mask, using temporary storage.
>>>>>>> multiply(R20A[r20_index], R20A_mask[r20_index], R20A_temp)
>>>>>>>
>>>>>>> # The add to the total.
>>>>>>> add(R20A_struct, R20A_temp, R20A_struct)
>>>>>>>
>>>>>>> # Show that the structure is reproduced perfectly.
>>>>>>> print(R20A_struct)
>>>>>>> print(R20A_struct - R20A_final)
>>>>>>> print(sum(abs(R20A_struct - R20A_final)))
>>>>>>> """
>>>>>>>
>>>>>>>
>>>>>>> You may notice one simplification compared to my previous example for
>>>>>>> the dw parameter
>>>>>>> (http://thread.gmane.org/gmane.science.nmr.relax.devel/6135/focus=6154).
>>>>>>> The values here too come from the
>>>>>>> Relax_disp.test_cpmg_synthetic_ns3d_to_cr72_noise_cluster system test.
>>>>>>>
>>>>>>> Regards,
>>>>>>>
>>>>>>> Edward
>>>>>>>
>>>>>>>
>>>>>>> On 10 June 2014 21:31, Edward d'Auvergne <[email protected]> wrote:
>>>>>>>> Hi Troels,
>>>>>>>>
>>>>>>>> No need for an example. Here is the code to add to your
>>>>>>>> infrastructure which will make the analytic dispersion models insanely
>>>>>>>> fast:
>>>>>>>>
>>>>>>>>
>>>>>>>> """
>>>>>>>> from numpy import add, array, float64, multiply, ones, zeros
>>>>>>>>
>>>>>>>> # Init mimic.
>>>>>>>> #############
>>>>>>>>
>>>>>>>> # Values from
>>>>>>>> Relax_disp.test_cpmg_synthetic_ns3d_to_cr72_noise_cluster.
>>>>>>>> NE = 1
>>>>>>>> NS = 2
>>>>>>>> NM = 2
>>>>>>>> NO = 1
>>>>>>>> ND = 8
>>>>>>>> dw = array([ 1.847792726895652, 0.193719379085542])
>>>>>>>> frqs = [-382.188861036982701, -318.479128911056137]
>>>>>>>> shape = (NE, NS, NM, NO, ND)
>>>>>>>>
>>>>>>>> # Final structure for lib.dispersion.
>>>>>>>> dw_struct = zeros(shape, float64)
>>>>>>>>
>>>>>>>> # Temporary storage to avoid memory allocations and garbage collection.
>>>>>>>> dw_temp = zeros((NS,) + shape, float64)
>>>>>>>>
>>>>>>>> # The structure for multiplication with dw to piecewise build up the
>>>>>>>> full dw structure.
>>>>>>>> dw_mask = zeros((NS,) + shape, float64)
>>>>>>>> for si in range(NS):
>>>>>>>> for mi in range(NM):
>>>>>>>> dw_mask[si, :, si, mi] = frqs[mi]
>>>>>>>> print(dw_mask)
>>>>>>>>
>>>>>>>> # Values to be found (again taken directly from
>>>>>>>> Relax_disp.test_cpmg_synthetic_ns3d_to_cr72_noise_cluster - as a
>>>>>>>> printout of dw_frq_a).
>>>>>>>> dw_final = array([[[[[-706.205797724669765, -706.205797724669765,
>>>>>>>> -706.205797724669765, -706.205797724669765,
>>>>>>>> -706.205797724669765, -706.205797724669765,
>>>>>>>> -706.205797724669765, -706.205797724669765]],
>>>>>>>>
>>>>>>>> [[-588.483418069912318, -588.483418069912318,
>>>>>>>> -588.483418069912318, -588.483418069912318,
>>>>>>>> -588.483418069912318, -588.483418069912318,
>>>>>>>> -588.483418069912318, -588.483418069912318]]],
>>>>>>>>
>>>>>>>>
>>>>>>>> [[[ -74.03738885349469 , -74.03738885349469 ,
>>>>>>>> -74.03738885349469 , -74.03738885349469 ,
>>>>>>>> -74.03738885349469 , -74.03738885349469 ,
>>>>>>>> -74.03738885349469 , -74.03738885349469 ]],
>>>>>>>>
>>>>>>>> [[ -61.69557910435401 , -61.69557910435401 ,
>>>>>>>> -61.69557910435401 , -61.69557910435401 ,
>>>>>>>> -61.69557910435401 , -61.69557910435401 ,
>>>>>>>> -61.69557910435401 , -61.69557910435401 ]]]]])
>>>>>>>>
>>>>>>>>
>>>>>>>> # Target function.
>>>>>>>> ##################
>>>>>>>>
>>>>>>>> # Loop over the dw elements (one per spin).
>>>>>>>> for si in range(NS):
>>>>>>>> # First multiply the spin specific dw with the spin specific
>>>>>>>> frequency mask, using temporary storage.
>>>>>>>> multiply(dw[si], dw_mask[si], dw_temp[si])
>>>>>>>>
>>>>>>>> # The add to the total.
>>>>>>>> add(dw_struct, dw_temp[si], dw_struct)
>>>>>>>>
>>>>>>>> # Show that the structure is reproduced perfectly.
>>>>>>>> print(dw_struct - dw_final)
>>>>>>>> """
>>>>>>>>
>>>>>>>> As mentioned in the comments, the structures come from the
>>>>>>>> Relax_disp.test_cpmg_synthetic_ns3d_to_cr72_noise_cluster. I just
>>>>>>>> added a check of "if len(dw) > 1: asdfasd" to kill the test, and added
>>>>>>>> printouts to obtain dw, frq_a, dw_frq_a, etc. This is exactly the
>>>>>>>> implementation I described. Although there might be an even faster
>>>>>>>> way, this will eliminate all numpy array creation and deletion via
>>>>>>>> Python garbage collection in the target functions (when used for R20
>>>>>>>> as well).
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>>
>>>>>>>> Edward
>>>>>>>>
>>>>>>>> On 10 June 2014 21:09, Edward d'Auvergne <[email protected]> wrote:
>>>>>>>>> If you have a really complicated example of your current 'dw_frq_a'
>>>>>>>>> data structure for multiple spins and multiple fields, that could help
>>>>>>>>> to construct an example.
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>>
>>>>>>>>> Edward
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 10 June 2014 20:57, Edward d'Auvergne <[email protected]> wrote:
>>>>>>>>>> Hi,
>>>>>>>>>>
>>>>>>>>>> I'll have a look tomorrow but, as you've probably seen, some of the
>>>>>>>>>> fine details such as indices to be used need to be sorted out when
>>>>>>>>>> implementing this.
>>>>>>>>>>
>>>>>>>>>> Regards,
>>>>>>>>>>
>>>>>>>>>> Edward
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 10 June 2014 20:49, Troels Emtekær Linnet <[email protected]>
>>>>>>>>>> wrote:
>>>>>>>>>>> What ever I do, I cannot get this to work?
>>>>>>>>>>>
>>>>>>>>>>> Can you show an example ?
>>>>>>>>>>>
>>>>>>>>>>> 2014-06-10 16:29 GMT+02:00 Edward d'Auvergne <[email protected]>:
>>>>>>>>>>>> Here is an example of avoiding automatic numpy data structure
>>>>>>>>>>>> creation
>>>>>>>>>>>> and then garbage collection:
>>>>>>>>>>>>
>>>>>>>>>>>> """
>>>>>>>>>>>> from numpy import add, ones, zeros
>>>>>>>>>>>>
>>>>>>>>>>>> a = zeros((5, 4))
>>>>>>>>>>>> a[1] = 1
>>>>>>>>>>>> a[:,1] = 2
>>>>>>>>>>>>
>>>>>>>>>>>> b = ones((5, 4))
>>>>>>>>>>>>
>>>>>>>>>>>> add(a, b, a)
>>>>>>>>>>>> print(a)
>>>>>>>>>>>> """
>>>>>>>>>>>>
>>>>>>>>>>>> The result is:
>>>>>>>>>>>>
>>>>>>>>>>>> [[ 1. 3. 1. 1.]
>>>>>>>>>>>> [ 2. 3. 2. 2.]
>>>>>>>>>>>> [ 1. 3. 1. 1.]
>>>>>>>>>>>> [ 1. 3. 1. 1.]
>>>>>>>>>>>> [ 1. 3. 1. 1.]]
>>>>>>>>>>>>
>>>>>>>>>>>> The out argument for numpy.add() is used here to operate in a
>>>>>>>>>>>> similar
>>>>>>>>>>>> way to the Python "+=" operation. But it avoids the temporary
>>>>>>>>>>>> numpy
>>>>>>>>>>>> data structures that the Python "+=" operation will create. This
>>>>>>>>>>>> will
>>>>>>>>>>>> save a lot of time in the dispersion code.
>>>>>>>>>>>>
>>>>>>>>>>>> Regards,
>>>>>>>>>>>>
>>>>>>>>>>>> Edward
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 10 June 2014 15:56, Edward d'Auvergne <[email protected]>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>> Hi Troels,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Here is one suggestion, of many that I have, for significantly
>>>>>>>>>>>>> improving the speed of the analytic dispersion models in your
>>>>>>>>>>>>> 'disp_spin_speed' branch. The speed ups you have currently
>>>>>>>>>>>>> achieved
>>>>>>>>>>>>> for spin clusters are huge and very impressive. But now that you
>>>>>>>>>>>>> have
>>>>>>>>>>>>> the infrastructure in place, you can advance this much more!
>>>>>>>>>>>>>
>>>>>>>>>>>>> The suggestion has to do with the R20, R20A, and R20B numpy data
>>>>>>>>>>>>> structures. They way they are currently handled is relatively
>>>>>>>>>>>>> inefficient, in that they are created de novo for each function
>>>>>>>>>>>>> call.
>>>>>>>>>>>>> This means that memory allocation and Python garbage collection
>>>>>>>>>>>>> happens for every single function call - something which should be
>>>>>>>>>>>>> avoided at almost all costs.
>>>>>>>>>>>>>
>>>>>>>>>>>>> A better way to do this would be to have a self.R20_struct,
>>>>>>>>>>>>> self.R20A_struct, and self.R20B_struct created in __init__(), and
>>>>>>>>>>>>> then
>>>>>>>>>>>>> to pack in the values from the parameter vector into these
>>>>>>>>>>>>> structures.
>>>>>>>>>>>>> You could create a special structure in __init__() for this. It
>>>>>>>>>>>>> would
>>>>>>>>>>>>> have the dimensions [r20_index][ei][si][mi][oi], where the first
>>>>>>>>>>>>> dimension corresponds to the different R20 parameters. And for
>>>>>>>>>>>>> each
>>>>>>>>>>>>> r20_index element, you would have ones at the [ei][si][mi][oi]
>>>>>>>>>>>>> positions where you would like R20 to be, and zeros elsewhere.
>>>>>>>>>>>>> The
>>>>>>>>>>>>> key is that this is created at the target function start up, and
>>>>>>>>>>>>> not
>>>>>>>>>>>>> for each function call.
>>>>>>>>>>>>>
>>>>>>>>>>>>> This would be combined with the very powerful 'out' argument set
>>>>>>>>>>>>> to
>>>>>>>>>>>>> self.R20_struct with the numpy.add() and numpy.multiply()
>>>>>>>>>>>>> functions to
>>>>>>>>>>>>> prevent all memory allocations and garbage collection. Masks
>>>>>>>>>>>>> could be
>>>>>>>>>>>>> used, but I think that that would be much slower than having
>>>>>>>>>>>>> special
>>>>>>>>>>>>> numpy structures with ones where R20 should be and zeros
>>>>>>>>>>>>> elsewhere.
>>>>>>>>>>>>> For just creating these structures, looping over a single
>>>>>>>>>>>>> r20_index
>>>>>>>>>>>>> loop and multiplying by the special [r20_index][ei][si][mi][oi]
>>>>>>>>>>>>> one/zero structure and using numpy.add() and numpy.multiply()
>>>>>>>>>>>>> with out
>>>>>>>>>>>>> arguments would be much, much faster than masks or the current
>>>>>>>>>>>>> R20_axis logic. It will also simplify the code.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Edward
>>>>>>>>>>>>
>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>> 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
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