Mailman 3 December 2013 - NumPy-Discussion

Loading large NIfTI file -> MemoryError
by Amira Chekir Jan. 3, 2014

Jan. 3, 2014

Hello together, I try to load a (large) NIfTI file (DMRI from Human Connectome Project, about 1 GB) with NiBabel. import nibabel as nib img = nib.load("dmri.nii.gz") data = img.get_data() The program crashes during "img.get_data()" with an "MemoryError" (having 4 GB of RAM in my machine). Any suggestions? Best regards, AMIRA

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Re: [Numpy-discussion] proposal: min, max of complex should give warning (Ralf Gommers)
by David Goldsmith Dec. 31, 2013

Dec. 31, 2013

> > As for your proposal, it would be good to know if adding a warning would > actually catch any bugs. For the truncation warning it caught several in > scipy and other libs IIRC. > > Ralf > In light of this, perhaps the pertinent unit tests should be modified (even if the warning suggestion isn't adopted, about which I'm neutral...but I'm a little surprised that there isn't a generic way to globally turn off specific warnings). DG

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ANN: NumPy 1.7.2 release
by Julian Taylor Dec. 31, 2013

Dec. 31, 2013

Hello, I'm happy to announce the of Numpy 1.7.2. This is a bugfix only release supporting Python 2.4 - 2.7 and 3.1 - 3.3. More than 42 issues were fixed, the most important issues are listed in the release notes: https://github.com/numpy/numpy/blob/v1.7.2/doc/release/1.7.2-notes.rst Compared to the last release candidate four additional minor issues have been fixed and compatibility with python 3.4b1 improved. Source tarballs, installers and release notes can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.2 Cheers, Julian Taylor

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Re: [Numpy-discussion] Need help with np.ma.median and np.apply_along_axis
by alex Dec. 27, 2013

Dec. 27, 2013

median is faster in version 1.8

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Regression test
by janani padmanabhan Dec. 24, 2013

Dec. 24, 2013

Hello folks! I am a newbie, and I want to know how to add a regression test. Intuitively, I simply executed the corresponding test.py and got errors, even before having made any changes in the source code. How do I proceed? Thanking you in anticipation Janani (jennystone)

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Segmentation fault with pickled numpy float64 arrays since 1.8
by Hugo Gagnon Dec. 22, 2013

Dec. 22, 2013

Hi, Since I've updated numpy from 1.7 to 1.8 with EPD I get segmentation faults whenever I load back pickled float64 arrays. Here's a minimal example: """ import numpy import cPickle a = numpy.arange(5, dtype='float64') with open('test.p', 'wb') as fh: cPickle.dump(a, fh) with open('test.p') as fh: a2 = cPickle.load(fh) print a2 """ However the above works fine with int32 arrays, i.e. with a = numpy.arange(5). Does anyone else experience this problem? Thanks, -- Hugo Gagnon

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request for help
by janani padmanabhan Dec. 20, 2013

Dec. 20, 2013

Hello Hearty greetings I am a novice who is extremely interested in contributing to NumPy opensource community. How and where do I start? Thanking you

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git tag for version 1.8?
by Warren Weckesser Dec. 20, 2013

Dec. 20, 2013

Is version 1.8.0 tagged in git? I see tags up to 1.7.1. I suspect the tagging convention has changed in the git repo. How do I checkout v1.8.0? Warren

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Array search considering order
by rootspin Dec. 20, 2013

Dec. 20, 2013

Hello, Need some help in searching arrays (Im new to numpy) Is it possible to search a array, using another array considering order/sequence? x = np.array([1,2,3,4,5,6], np.int32) y = np.array([1,4,3,2,6,5], np.int32) query= np.array([1,2,3],np.int32) x versus query True y versus query False Tried with: np.searchsorted(x,query) -------> array([0, 1, 2]) np.searchsorted(y,query) -------> array([0, 1, 4]) Thanks -- View this message in context: http://numpy-discussion.10968.n7.nabble.com/Array-search-considering-order-… Sent from the Numpy-discussion mailing list archive at Nabble.com.

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Need help with np.ma.median and np.apply_along_axis
by Siegfried Gonzi Dec. 20, 2013

Dec. 20, 2013

Please have a look at version1 and version2. What are my other options here? Do I need to go the cython route here? Thanks, Siegfried == My array is as follows (shown here for dummy values; and yes this kind of arrays do exist: 150 observations x 8 years x 366 days x 24 hours x 7 model levels): data = np.random.random((150,8,366,24,7)) My function "np.apply_along_axis(my_moment,4,data)" takes 15 minutes. I thought making use of masked arrays "my_moment_fast(data,axis=4)" will speed up things but 1. It blows up the memory consumption to 6 GB at times and 2. It also takes... I do not know as I killed it after 20 minutes (it hangs at the median print statement). The calculation of the median is the bottleneck here. == import numpy as np def my_moment(data_in,nan_val=-999.0): tmp = data_in[np.where(data_in<>nan_val)] erg = np.array([np.min(tmp),np.mean(tmp),np.median(tmp),\ np.max(tmp),np.std(tmp),np.size(tmp)]) return erg def my_moment_fast(data_in,nan_val=-999.0,axis=4): import numpy as np print 'min max',np.min(data_in),np.max(data_in) mx = np.ma.masked_where((data_in<=0.0)&(data_in<=nan_val), data_in) print 'shape mx',np.shape(mx),np.min(mx),np.max(mx) print 'min' tmp_min = np.ma.min(mx,axis=axis) print 'max' tmp_max = np.ma.max(mx,axis=axis) print 'mean' tmp_mean = np.ma.mean(mx,axis=axis) print 'median' #tmp_median = np.ma.sort(mx,axis=axis) tmp_median = np.ma.median(mx,axis=axis) print 'std' tmp_std = np.ma.std(mx,axis=axis) print 'N' tmp_N = np.ones(np.shape(mx)) tmp_N[mx.mask] = 0.0e0 tmp_N = np.ma.sum(tmp_N,axis=axis) print 'shape min',np.shape(tmp_min),np.min(tmp_min),np.max(tmp_min) print 'shape max',np.shape(tmp_max),np.min(tmp_max),np.max(tmp_min) print 'shape mean',np.shape(tmp_mean),np.min(tmp_mean),np.max(tmp_mean) print 'shape median', np.shape(tmp_median), np.min(tmp_median), np.max(tmp_median) print 'shape std',np.shape(tmp_std),np.min(tmp_std),np.max(tmp_std) print 'shape N', np.shape(tmp_N), np.min(tmp_N), np.max(tmp_N), np.shape(mx.mask) return np.array([tmp_min,tmp_mean,tmp_median,tmp_max,tmp_std,tmp_N]) data = np.random.random((150,8,366,24,7)) data[134,5,300,:,2] = -999.0 data[14,3,300,:,0] = -999.0 version1 = my_moment_fast(data,axis=4) exit() version2 = np.apply_along_axis(my_moment,4,data) == What am I doing wrong here? I haven't tested it againts Fortran and have got no idea if sorting for fetching the median would be faster. Thanks, Siegfried == -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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