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 _______________________________________________ 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
