NumPy-Discussion June 2006

numpy-discussion@python.org

168 participants
209 discussions

by Christopher Barker

I'm trying to get the (x,y) coords for all the points in a grid, bound by xmin, xmax, ymin, ymax. This list comprehension does it fine: Points = [(x,y) for x in xrange(minx, maxx) for y in xrange(miny, maxy)] But I can't think at the moment how to do it with numpy. Any ideas? Thanks, -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/OR&R/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

15 years, 7 months

numpy vs numeric benchmarks

by Eric Jonas

Hello! I've been using numeric for a while, and the recent list traffic prompted me to finally migrate all my old code. On a whim, we were benchmarking numpy vs numeric and have been lead to the conclusion that numpy is at least 50x slower; a 1000x1000 matmul takes 16 sec in numpy but 300 ms in numeric. Now, of course, I don't believe this, but I can't figure out what we're doing wrong; I'm not the only person who has looked at this code, so can anyone tell me what we're doing wrong? We run both benchmarks twice to try and mitigate any start-up and cache effects. This is with debian-amd64's packaged numeric 24.2-2 and a locally built numpy-0.9.8. /usr/bin/python import time import numpy import random import Numeric def numpytest(): N = 1000 x = numpy.zeros((N,N),'f') y = numpy.zeros((N,N),'f') for i in range(N): for j in range(N): x[i, j] = random.random() y[i, j] = random.random() t1 = time.clock() z = numpy.matrixmultiply(x, y) t2 = time.clock() print (((t2 - t1)*1000)) def numerictest(): N = 1000 x = Numeric.zeros((N,N),'f') y = Numeric.zeros((N,N),'f') for i in range(N): for j in range(N): x[i, j] = random.random() y[i, j] = random.random() t1 = time.clock() z = Numeric.matrixmultiply(x, y) t2 = time.clock() print (((t2 - t1)*1000)) numerictest() numpytest() numpytest() numerictest() on our hardware a call to numerictest() takes 340 ms and a numpytest takes around 13 sec (!). Any advice on what we're doing wrong would be very helpful. ...Eric

15 years, 7 months

Re: [Numpy-discussion] Free SciPy 2006 porting service

by r.demaria＠tiscali.it

Hi, maybe is not what you meant, but presently I'm looking for a sparse eigenvalue solver. As far as I've understood the ARPACK bindings are still missing. This library is one of the most used, so I think it would be very useful to have integrated in numpy. Riccardo La gara più entusiasmante dell'anno! Gioca e corri alla velocità della luce sui 18 circuiti di Intel Speed Contest 2006! I più bravi vincono Notebook Sony VAIO, iPod da 60 GB e altro ancora... Sfida gli amici! http://intelspeedcontest2006.tiscali.it/

15 years, 7 months

Re: [Numpy-discussion] numpy vs numeric benchmarks

by RayS

favorable numpy creates arrays much faster, fft seems a tad faster a useful metric, I think, for O-scope and ADC apps I get 0.0039054614015815738 0.0019759541205486885 0.023268623246481726 0.0023570392204637913 from the below on a PIII 600... from time import * n=4096 r = range(n) #numpy import numpy arr = numpy.array # array creation t0 = clock() for i in r: a = arr(r) (clock()-t0)/float(n) #fft of n fftn = numpy.fft t0 = clock() for i in r: f = fftn(a) (clock()-t0)/float(n) #Numeric import Numeric arr = Numeric.array # array creation t0 = clock() for i in r: a = arr(r) (clock()-t0)/float(n) #fft of n from FFT import * t0 = clock() for i in r: f = fft(a) (clock()-t0)/float(n)

15 years, 7 months

lexsort

by Tom Denniston

This function is really useful but it seems to only take tuples not ndarrays. This seems kinda strange. Does one have to convert the ndarray into a tuple to use it? This seems extremely inefficient. Is there an efficient way to argsort a 2d array based upon multiple columns if lexsort is not the correct way to do this? The only way I have found to do this is to construct a list of tuples and sort them using python's list sort. This is inefficient and convoluted so I was hoping lexsort would provide a simple solution. --Tom

15 years, 7 months

Free SciPy 2006 porting service

by Travis Oliphant

I will be available during the SciPy 2006 conference to help port open-source applications to NumPy for no charge. (I'm always available for porting commercial code for a reasonable fee). Others who want to assist will be welcome. Conference attendees will get first priority, but others who want to email their request can do so. Offer will be on a first come, first serve basis but I will reserve the liberty to rearrange the order to serve as many projects as possible. I'll place a note on the Wiki Coding Sprint page to this effect. -Travis O.

15 years, 7 months

Re: Any Numeric or numarray users on this list?

by Travis Oliphant

Berthold Höllmann wrote: > Travis Oliphant <oliphant.travis(a)ieee.org> writes: > > >> 2) Will you transition within the next 6 months? (if you answered No to #1) >> > > Unlikely > > >> 3) Please, explain your reason(s) for not making the switch. (if you >> answered No to #2) >> > > Lack of resources (Numeric is used in hand coded extensions; are > arrays of type PyObject supported in NumPy, they were not in numarray) > Yes, NumPy is actually quite similar to Numeric. Most C-extensions are easily ported simply by replacing #include Numeric/arrayobject.h with #include numpy/arrayobject.h (and making sure you get the right location for the headers). -Travis

15 years, 7 months

What am I missing about concatenate?

by Christopher Barker

I want to take two (2,) arrays and put them together into one (2,2) array. I thought one of these would work: >>> N.concatenate(((1,2),(3,4)),0) array([1, 2, 3, 4]) >>> N.concatenate(((1,2),(3,4)),1) array([1, 2, 3, 4]) Is this the best I can do? >>> N.concatenate(((1,2),(3,4))).reshape(2,2) array([[1, 2], [3, 4]]) Is it because the arrays I'm putting together are rank-1? >>> N.__version__ '0.9.6' -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/OR&R/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

15 years, 7 months

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15 years, 7 months

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NumPy-Discussion June 2006