Re: [Numpy-discussion] Modulus (remainder) function corner cases

2016-02-14 Thread Nils Becker
2016-02-13 17:42 GMT+01:00 Charles R Harris : > The Fortran modulo function, which is the same basic function as in my >> branch, does not specify any bounds on the result for floating numbers, but >> gives only the formula, modulus(a, b) = a - b*floor(a/b), which has

Re: [Numpy-discussion] Linking other libm-Implementation

2016-02-10 Thread Nils Becker
2016-02-09 18:02 GMT+01:00 Gregor Thalhammer : >> It is not suitable as a standard for numpy. > > Why should numpy not provide fast transcendental math functions? For linear algebra it supports fast implementations, even non-free (MKL). Wouldn’t it be nice if numpy

Re: [Numpy-discussion] Linking other libm-Implementation

2016-02-09 Thread Nils Becker
2016-02-08 18:54 GMT+01:00 Julian Taylor : > which version of glibm was used here? There are significant difference > in performance between versions. > Also the input ranges are very important for these functions, depending > on input the speed of these functions

Re: [Numpy-discussion] Linking other libm-Implementation

2016-02-08 Thread Nils Becker
> The npy_math functions are used if otherwise unavailable OR if someone > has at some point noticed that say glibc 2.4-2.10 has a bad quality > tan (or whatever) and added a special case hack that checks for those > particular library versions and uses our built-in version instead. > It's not the

[Numpy-discussion] Linking other libm-Implementation

2016-02-07 Thread Nils Becker
Hi all, I wanted to know if there is any sane way to build numpy while linking to a different implementation of libm? A drop-in replacement for libm (e.g. openlibm) should in principle work, I guess, but I did not manage to actually make it work. As far as I understand the build code, setting

Re: [Numpy-discussion] method to calculate the magnitude squared

2015-10-11 Thread Nils Becker
Hey, I use complex numbers a lot and obviously need the modulus a lot. However, I am not sure if we need a special function for _performance_ reasons. At 10:01 AM 9/20/2015, you wrote: It is, but since that involves taking sqrt, it is *much* slower. Even now, ``` In [32]: r =

Re: [Numpy-discussion] FFTS for numpy's FFTs (was: Re: Choosing between NumPy and SciPy functions)

2014-10-30 Thread Nils Becker
I think that numpy.fft should be left there in its current state (although perhaps as deprecated). Now scipy.fft should have a good generic algorithm as default, and easily allow for different implementations to be accessed through the same interface. I also agree with the above. But I want to

Re: [Numpy-discussion] Dropping support for, Accelerate/veclib?

2013-06-11 Thread Nils Becker
I think for Scipy homebrew uses the Gfortran ABI: https://trac.macports.org/browser/trunk/dports/python/py-scipy/Portfile fwiw, homebrew is not macports. it's a more recent replacement that seems to be taking over gradually. ___ NumPy-Discussion

Re: [Numpy-discussion] histogram2d and histogramdd return counts as floats while histogram returns ints

2012-05-14 Thread Nils Becker
is this intended? np.histogramdd([[1,2],[3,4]],bins=2) (array([[ 1., 0.], [ 0., 1.]]), [array([ 1. , 1.5, 2. ]), array([ 3. , 3.5, 4. ])]) np.histogram2d([1,2],[3,4],bins=2) (array([[ 1., 0.], [ 0., 1.]]), array([ 1. , 1.5, 2. ]), array([ 3. , 3.5, 4.

[Numpy-discussion] histogram2d and histogramdd return counts as floats while histogram returns ints

2012-05-11 Thread Nils Becker
hi, is this intended? np.histogramdd([[1,2],[3,4]],bins=2) (array([[ 1., 0.], [ 0., 1.]]), [array([ 1. , 1.5, 2. ]), array([ 3. , 3.5, 4. ])]) np.histogram2d([1,2],[3,4],bins=2) (array([[ 1., 0.], [ 0., 1.]]), array([ 1. , 1.5, 2. ]), array([ 3. , 3.5, 4. ]))

Re: [Numpy-discussion] fast numpy i/o

2011-06-27 Thread Nils Becker
Hi, Finally, the former Scientific.IO NetCDF interface is now part of scipy.io, but I assume it only supports netCDF 3 (the documentation is not specific about that). This might be the easiest option for a portable data format (if Matlab supports it). Yes, it is NetCDF 3. In recent

Re: [Numpy-discussion] np.histogramdd of empty data

2011-03-31 Thread Nils Becker
Hi Ralf, I cloned numpy/master and played around a little. when giving the bins explicitely, now histogram2d and histogramdd work as expected in all tests i tried. However, some of the cases with missing bin specification appear somewhat inconsistent. The first question is if creating

[Numpy-discussion] np.histogramdd of empty data

2011-03-22 Thread Nils Becker
Hi, I was wondering why histogram2d and histogramdd raise a ValueError when fed with empty data of the correct dimensions. I came across this as a corner case when calling histogram2d from my own specialized histogram function. In comparison, histogram does handle this case correctly when bins

[Numpy-discussion] indexing of rank-0 structured arrays: why not?

2011-01-10 Thread Nils Becker
Hi, I noticed that I can index into a dtype when I take an element of a rank-1 array but not if I make a rank-0 array directly. This seems inconsistent. A bug? Nils In [76]: np.version.version Out[76]: '1.5.1' In [78]: dt = np.dtype([('x', 'f8'), ('y', 'f8')]) In [80]: a_rank_1 =

Re: [Numpy-discussion] indexing of rank-0 structured arrays: why not?

2011-01-10 Thread Nils Becker
Robert, your answer does work: after indexing with () I can then further index into the datatype. In [115]: a_rank_0[()][0] Out[115]: 0.0 I guess I just found the fact confusing that a_rank_1[0] and a_rank_0 compare and print equal but behave differently under indexing. More precisely if I do

[Numpy-discussion] truth value of dtypes

2010-12-10 Thread Nils Becker
Hi, why is bool(np.dtype(np.float)) False ? I came across this when using this python idiom: def f(dtype=None): if not dtype: print 'using default dtype' If there is no good reason to have a False truth value, I would vote for making it True since that is what one would expect

Re: [Numpy-discussion] Schedule for 1.5.1?

2010-10-07 Thread Nils Becker
Hi, what about the normed=True bug in numpy.histogram? It was discussed here a while ago, and fixed (although i did not find it on the tracker), but the message aanlktikwbsrxq0ynf3u3jo3ekrikszmwqy30pnsfg...@mail.gmail.com suggests it just missed 1.5.0? I don't have 1.5 installed, so I can't

Re: [Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)

2010-08-30 Thread Nils Becker
I think (a corrected) density histogram is core functionality for unequal bin lengths. The graph with raw count in the case of unequal bin sizes would be quite misleading when plotted and interpreted on the real line and not on discrete points (shaded areas instead of vertical lines). And

Re: [Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)

2010-08-29 Thread Nils Becker
On Sat, Aug 28, 2010 at 04:12, Zbyszek Szmek zbys...@in.waw.pl wrote: Hi, On Fri, Aug 27, 2010 at 06:43:26PM -0600, Charles R Harris wrote: ? ?On Fri, Aug 27, 2010 at 2:47 PM, Robert Kern robert.k...@gmail.com ? ?wrote: ? ? ?On Fri, Aug 27, 2010 at 15:32, David Huard david.hu...@gmail.com

[Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)

2010-08-06 Thread Nils Becker
Hi, I found what looks like a bug in histogram, when the option normed=True is used together with non-uniform bins. Consider this example: import numpy as np data = np.array([1, 2, 3, 4]) bins = np.array([.5, 1.5, 4.5]) bin_widths = np.diff(bins) (counts, dummy) = np.histogram(data, bins)

Re: [Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)

2010-08-06 Thread Nils Becker
Hi again, first a correction: I posted I believe np.histogram(data, bins, normed=True) effectively does : np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]). However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths but there is a normalization missing; it