Mailman 3 July 2010 - NumPy-Discussion

comparing floating point numbers
by Ondrej Certik July 21, 2010

July 21, 2010

Hi, I was always using something like abs(x-y) < eps or (abs(x-y) < eps).all() but today I needed to also make sure this works for larger numbers, where I need to compare relative errors, so I found this: http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm and wrote this: def feq(a, b, max_relative_error=1e-12, max_absolute_error=1e-12): a = float(a) b = float(b) # if the numbers are close enough (absolutely), then they are equal if abs(a-b) < max_absolute_error: return True # if not, they can still be equal if their relative error is small if abs(b) > abs(a): relative_error = abs((a-b)/b) else: relative_error = abs((a-b)/a) return relative_error <= max_relative_error Is there any function in numpy, that implements this? Or maybe even the better, integer based version, as referenced in the link above? I need this in tests, where I calculate something on some mesh, then compare to the correct solution projected on some other mesh, so I have to deal with accuracy issues. Ondrej

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inverse of compress
by Vincent Schut July 21, 2010

July 21, 2010

Hi, I find myself often looking for a numpy function that would behave like an inverse compress. E.g. one that satisfies a.compress(mask) = b, given b and mask, creating a. Or like a createFromMask function, which would create an array with shape of a given boolean mask, and filling all elements that are True with values from b, b being a flat array the length of sum(mask). Currently I of course just first create a, then do a[mask] = b. Which works, but is ugly :-) So if anyone has a oneliner which I missed, please step forward... regards, Vincent Schut.

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docstring for numpy.fft
by David Goldsmith July 21, 2010

July 21, 2010

I believe (hope) I've satisfactorily "finished" the docstring for numpy.fft; concerned parties are encouraged to take a look at it in the Wiki and provide comments there in its Discussion section. DG

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numpy.fft, yet again
by David Goldsmith July 21, 2010

July 21, 2010

>From the docstring: "A[0] contains the zero-frequency term (the mean of the signal)" And yet, consistent w/ the definition given in the docstring (and included w/ an earlier email), the code gives, e.g.: >>> import numpy as np >>> x = np.ones((16,)); x array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]) >>> y = np.fft.fft(x); y array([ 16.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]) i.e., the zero-th term is the sum, not the mean (which, again, is consistent w/ the stated defining formula). So, same ol', same ol': bug in the doc (presumably) or bug in the code? DG

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numpy records in C
by Caius Howcroft July 21, 2010

July 21, 2010

Hi all I'm playing with writing some C code to speed up an inner loop in my python code. This loop operates on a numpy record, e.g. soemthing like this: a = numpy.zeros((10,), dtype=[("myfvalue" ,"float"), ("myc", "int8"), ("anotheri", "uint64")]) which is then passed into c code like so: myCFunc(a, "blah") I was wondering if someone had an example of how to access particular columns of "a" in the C func. Clearly, this is going to involve a PyArray_Descr.fields somewhere, but an example would really help make things clearer for me. Thanks Regards Caius

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Stacking arrays along new dimension in front
by Gael Varoquaux July 20, 2010

July 20, 2010

Is there in numpy a function that does: np.concatenate([a_[np.newaxis] for a_ in a]) ? ie: add a dimension in front and stack along this dimension, just like np.array(a) would do, but more efficient. This is something that do all the time. Am I the only one? Cheers, Gaël

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Complex128
by Ross Harder July 20, 2010

July 20, 2010

I don't understand the following. >> a=np.zeros( (3,3,3), dtype='Complex128') >>> a array([[[0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j]], [[0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j]], [[0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j], [0.0+0.0j, 0.0+0.0j, 0.0+0.0j]]], dtype=complex256) i ask for Complex128 and get complex256? if i ask for complex128 (little c) I get complex128 if i ask for complex (little c) i get complex128 if i ask for Complex (cap C) i get unrecognized type

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numpy Trac emails
by Ralf Gommers July 20, 2010

July 20, 2010

Scipy Trac seems to work very well now, I get notification emails for comments on tickets etc. For numpy Trac, nothing right now. Can this be fixed? Thanks, Ralf

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Re: [Numpy-discussion] Crosstabulation
by Ionut Sandric July 19, 2010

July 19, 2010

Thank you Vincent: I will try with histogram Ionut ----- Original Message ----- From: "Vincent Schut" <schut(a)sarvision.nl> To: numpy-discussion(a)scipy.org Sent: Monday, July 19, 2010 12:00:38 PM GMT +02:00 Athens, Beirut, Bucharest, Istanbul Subject: Re: [Numpy-discussion] Crosstabulation On 07/19/2010 09:55 AM, sandric ionut wrote: > > Hi Friedrich: > > For land-use a class would be for example forest, other would be orchard > etc. For Slope gradient I would have values which <3 and between 3 and 7 > etc. So, I will have 2 raster data with, let's say, 3 classes each: > forest, orchards and built-up area and for slope gradient: 0-3, 3-15, > 15-35. The cross-tabulation analysis should give me a table like: > > forest orchards built-up > 0-3 10 &n bsp; 20 15 > 3-15 5 10 20 > 15-35 5 15 15 > > where the numbers represents all the common cells, for example: 10 cells > with forest correspond to 10 cells with 0-3 slope gradient interval and > so on > (by cells I mean the pixel from a raster data) > > The analysis is better illustrated here: > http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=tabulate_area > > Ionut Ha, we're messing up lingo's here :-) Need to switch to GIS (geographic information systems) dialect. - DEM = digital elevation map, usually a 2d array ('raster') with elevation values (for a certain area on earth) - slope gradient = the slope (literally, not as in math speak) of the surface depicted by the elevation map. Mostly defined as the maximum slope within a certain moving window; several competing methods to estimate/calculate slope exist. - land use/cover class: raster (array) where each cell ('pixel') has an integer value, which maps to some well defined land use at that location (e.g. 0 means sea, 1 means forest, 2 means agriculture, etc) - crosstabulation usually means some kind of 2d histogram, where the total number of raster cells with a certain value (e.g. depicting 'land use class') 'within' a range of values of another raster with the same shape (and matching locations). Like: how many cells of forest lie withing a slope range of 0-10 degrees? Right. On to the answers. I think you should look into numpy.histogram2d, where you can do exactly what you want. Your land use array is x, your slope gradient array = y, then you define the bins as your class numbers (for x) and your slope gradient ranges (for y), and you will get a pixel count for each bin combination. see: http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram2d.html Regards, Vincent Schut. > > ------------------------------------------------------------------------ > *From:* Friedrich Romstedt <friedrichromstedt(a)gmail.com> > *To:* Discussion of Numerical Python <numpy-discussion(a)scipy.org> > *Sent:* Sun, July 18, 2010 12:09:04 AM > *Subject:* Re: [Numpy-discussion] Crosstabulation > > 2010/7/17 Robert Kern <robert.kern(a)gmail.com > <mailto:robert.kern@gmail.com>>: > > On Sat, Jul 17, 2010 at 13:11, Friedrich Romstedt > > <friedrichromstedt(a)gmail.com <mailto:friedrichromstedt@gmail.com>> wrote: > >> 2010/7/14 Ionut Sandric <sandricionut(a)yahoo.com > <mailto:sandricionut@yahoo.com>>: > >> I'm afraid also Zach does not understand what you are talking about > >> ... So my first question (please bear with me) would be: What's a dem? > > > > Digital Elevation Map. > > > >> (n/a in my dictionary) And sorry for the cross-talk on the other > >> first post by you ... > >> > >> And by slope gradient you mean second derivative? > > > > No, the first derivative. "Slope gradient" is a reasonably common, > > albeit somewhat redundant, idiom meaning the gradient of an elevation > > map. > > Thanks Robert, that clarifies a lot. > > But still I don't understand how the crosstabulation shall work. What > are the "classes"? > > Friedrich > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org <mailto:NumPy-Discussion@scipy.org> > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion(a)scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

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numpy for Python 3?
by Richard D. Moores July 19, 2010

July 19, 2010

If not now, when? Thanks, Dick Moores

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