I have an old VM from 2008 (Mandrake 2008 Spring) which I use for testing. This has numpy 1.0.4, hence the minimum version at http://www.nmr-relax.com/download.html. This is a reasonable estimate for 'old' systems. If you wish to use a newer feature, we could always wrap it up in a function in lib.compat. See the norm() function for example. This adds a small overhead, but if it is called only once or twice per dispersion target function call, it should be reasonable.
Regards, Edward On 10 June 2014 18:02, Troels Emtekær Linnet <[email protected]> wrote: > Hi Ed. > > Well, I have before been limited to the old RHEL standard libraries > '1.4.1' from yum. > > Our server admin refuses to install any non-standard packages to our > computation server. > > This is very very logic, since you often end up in a big fight, if > start opening up for this. > > But, I had just a very big success with a Canopy installation. > http://wiki.nmr-relax.com/Epd_canopy > > This essential lets the user install a local python if wanted. > And let the admin make a shared installation. > > Our server admin can accept this solution, since it does not interfere > with the package manager. > > So numpy is now '1.8.0'. :-) > > best > Troels > > > > 2014-06-10 17:50 GMT+02:00 Edward d'Auvergne <[email protected]>: >> Hi, >> >> I haven't seen HBO's "Silicon Valley" yet. But you should soon reach >> tip-to-tip efficiency with both relax and your Python programming >> skills. Anyway, I'm thinking that the numpy version minimal number >> will need to be increased to 1.6.2. This really depends on how much >> users complain though. Most stay reasonably up to date so that the >> current 1.0.4 at http://www.nmr-relax.com/download.html is never >> encountered anyway. The test suite should pull out the relax users >> with too old versions. I am also using lib.compat for some of these >> version issues, as there is always a less efficient way of performing >> the operation using an older numpy version. Do you have any opinions >> for the minimum version number? Which versions do you have installed >> on your various systems? >> >> Regards, >> >> Edward >> >> >> >> On 10 June 2014 17:40, Troels Emtekær Linnet <[email protected]> wrote: >>> Hi Ed. >>> >>> I worry about, if I use some numpy things, which is of to high a version? >>> What is the "relax" limit on this? >>> >>> Best >>> Troels >>> >>> >>> 2014-06-10 17:27 GMT+02:00 Troels Emtekær Linnet <[email protected]>: >>>> I have learned SO much these days!!! >>>> >>>> Class programming. >>>> How numpy arrays work together, and broadcasts. >>>> How to profile and make efficiency. >>>> >>>> I feel like in HBO "Silicon Valley": >>>> http://en.wikipedia.org/wiki/Silicon_Valley_%28TV_series%29 >>>> >>>> Episode 8, "Optimal Tip-to-Tip Efficiency", is exactly what is >>>> happening right now. :-) >>>> >>>> best >>>> Troels >>>> >>>> 2014-06-10 17:04 GMT+02:00 Edward d'Auvergne <[email protected]>: >>>>> Hi Troels, >>>>> >>>>> Is it possible to shift the mask_replace part into __init__()? That >>>>> might give more speed ups. I'm not so familiar with numpy masks, so I >>>>> couldn't have helped you with that. Anyway, those 4 hours invested is >>>>> guaranteed to save you much more than 4 hours when you use this later. >>>>> >>>>> Regards, >>>>> >>>>> Edward >>>>> >>>>> >>>>> >>>>> On 10 June 2014 16:51, <[email protected]> wrote: >>>>>> Author: tlinnet >>>>>> Date: Tue Jun 10 16:51:33 2014 >>>>>> New Revision: 23788 >>>>>> >>>>>> URL: http://svn.gna.org/viewcvs/relax?rev=23788&view=rev >>>>>> Log: >>>>>> Implemented a masked array search for where "missing" array is equal 1. >>>>>> >>>>>> This makes it possible to replace all values with this mask, from the >>>>>> value array. >>>>>> >>>>>> This eliminates the last loops over the missing values. >>>>>> >>>>>> It took over 4 hours to figure out, that the mask should be called with >>>>>> mask.mask, >>>>>> to return the same fulls structure, >>>>>> >>>>>> Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion >>>>>> models for Clustered analysis. >>>>>> >>>>>> Modified: >>>>>> branches/disp_spin_speed/target_functions/relax_disp.py >>>>>> >>>>>> Modified: branches/disp_spin_speed/target_functions/relax_disp.py >>>>>> URL: >>>>>> http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/target_functions/relax_disp.py?rev=23788&r1=23787&r2=23788&view=diff >>>>>> ============================================================================== >>>>>> --- branches/disp_spin_speed/target_functions/relax_disp.py >>>>>> (original) >>>>>> +++ branches/disp_spin_speed/target_functions/relax_disp.py Tue Jun >>>>>> 10 16:51:33 2014 >>>>>> @@ -29,6 +29,7 @@ >>>>>> from math import pi >>>>>> from numpy import array, asarray, complex64, dot, float64, int16, max, >>>>>> ones, sqrt, sum, zeros >>>>>> import numpy as np >>>>>> +from numpy.ma import masked_equal >>>>>> >>>>>> # relax module imports. >>>>>> from lib.dispersion.b14 import r2eff_B14 >>>>>> @@ -418,6 +419,7 @@ >>>>>> # The number of disp point can change per spectrometer, so >>>>>> we make the maximum size. >>>>>> self.values_a = deepcopy(self.zeros_a) >>>>>> self.errors_a = deepcopy(self.ones_a) >>>>>> + self.missing_a = zeros(self.numpy_array_shape) >>>>>> >>>>>> self.cpmg_frqs_a = deepcopy(self.ones_a) >>>>>> self.num_disp_points_a = deepcopy(self.zeros_a) >>>>>> @@ -456,6 +458,7 @@ >>>>>> for di in >>>>>> range(self.num_disp_points[ei][si][mi][oi]): >>>>>> if self.missing[ei][si][mi][oi][di]: >>>>>> self.has_missing = True >>>>>> + self.missing_a[ei][si][mi][oi][di] >>>>>> = 1.0 >>>>>> >>>>>> # Make copy of values structure. >>>>>> self.back_calc_a = deepcopy(self.values_a) >>>>>> @@ -574,15 +577,11 @@ >>>>>> >>>>>> ## 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: >>>>>> - # Loop over the spins. >>>>>> - for si in range(self.num_spins): >>>>>> - # Loop over the spectrometer frequencies. >>>>>> - for mi in range(self.num_frq): >>>>>> - # Loop over the dispersion points. >>>>>> - for di in range(self.num_disp_points[0][si][mi][0]): >>>>>> - if self.missing[0][si][mi][0][di]: >>>>>> - #self.back_calc[0][si][mi][0][di] = >>>>>> self.values[0][si][mi][0][di] >>>>>> - self.back_calc_a[0][si][mi][0][di] = >>>>>> self.values[0][si][mi][0][di] >>>>>> + # 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] >>>>>> >>>>>> ## Calculate the chi-squared statistic. >>>>>> return sum((1.0 / self.errors_a * (self.values_a - >>>>>> self.back_calc_a))**2) >>>>>> >>>>>> >>>>>> _______________________________________________ >>>>>> 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
