Mailman 3 October 2007 - NumPy-Discussion

Re: [Numpy-discussion] Iterate over all 1-dim views
by Pauli Virtanen Oct. 7, 2007

Oct. 7, 2007

Neal Becker <ndbecker2(a)gmail.com> kirjoitti: > Suppose I have a function F(), which is defined for 1-dim arguments. If the > user passes an n>1 dim array, I want to apply F to each 1-dim view. > > For example, for a 2-d array, apply F to each row and return a 2-d result. > > For a 3-d array, select each 2-d subarray and see above. Return 3-d result. > > Any suggestions on how to code something like this in numpy? You may be looking for numpy.apply_along_axis: >>> from numpy import * >>> x=arange(2*3*4); x.shape=(2,3,4) >>> x array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) >>> apply_along_axis(cumsum, 0, x) array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 14, 16, 18], [20, 22, 24, 26], [28, 30, 32, 34]]]) >>> apply_along_axis(cumsum, 1, x) array([[[ 0, 1, 2, 3], [ 4, 6, 8, 10], [12, 15, 18, 21]], [[12, 13, 14, 15], [28, 30, 32, 34], [48, 51, 54, 57]]]) >>> apply_along_axis(cumsum, 2, x) array([[[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]], [[12, 25, 39, 54], [16, 33, 51, 70], [20, 41, 63, 86]]]) --

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Casting a float array into a string array
by Matthieu Brucher Oct. 6, 2007

Oct. 6, 2007

Hi, I'm trying to cast a float array into a string array (for instance transforming [[2., 3.], [4., 5.]] into [['2.', '3.'], ['4.', '5.']]), I tried with astype(str) and every variation (str_, string, string_, string0), but not luck. Is there a function or a method of the array class that can fulfill my needs ? And if there is a way to add a formatting option ('1.1f' for instance), it would be even better. Matthieu

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bug in vectorize? (was: Re: Casting a float array into a string array)
by Anne Archibald Oct. 5, 2007

Oct. 5, 2007

On 05/10/2007, Christopher Barker <Chris.Barker(a)noaa.gov> wrote: > I don't know how to generalize this to n-d though -- maybe numpy.vectorize? Oops! Looks like there's a big somewhere: In [1]: from numpy import * In [2]: vectorize(lambda x: "%5.3g" % x)(ones((2,2,2))) Out[2]: array([[[' ', '\xc1'], [' ', '\xc1']], [[' ', '\xc1'], [' ', '\xc1']]], dtype='|S1') In [3]: vectorize? Segmentation fault (core dumped) In particular, vectorize creates a string array (rather than an object array), and some unsafe operation is done on that array. Anne

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howto convert float array to array of integers
by dmitrey Oct. 5, 2007

Oct. 5, 2007

hi all, I have an array like array([ 1., 0., 2., -10.]) what is most efficient (i.e. fastest) way to convert the one to array of integers? array([ 1, 0, 2, -10]) Thx in advance, D.

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adding field to rec array
by John Hunter Oct. 5, 2007

Oct. 5, 2007

I have a record array r and I want to add a new field to it. I have been looking at setfield but I am not sure how to use it for this purpose. Eg # r is some npy record array N = len(r) x = npy.zeros(N) # add array of floats x to r with dtype name 'jdh' and type '<f8' Any suggestions? JDH

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More complex data types
by Neal Becker Oct. 5, 2007

Oct. 5, 2007

I'm thinking (again) about using numpy for signal processing applications. One issue is that there are more data types that are commonly used in signal processing that are not available in numpy (or python). Specifically, it is frequently required to convert floating point algorithms into integer algorithms. numpy is fine for arrays of integers (of various sizes), but it is also very useful to have arrays of complex<integers>. While numpy has complex<double,float>, it doesn't have complex<int,int_64...> Has anyone thought about this?

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Re: [Numpy-discussion] Numpy-discussion Digest, Vol 13, Issue 11
by Vincent Broman Oct. 5, 2007

Oct. 5, 2007

On Friday, 05 October 2007, Neal Becker wrote: > I'm thinking (again) about using numpy for signal processing applications. > One issue is that there are more data types that are commonly used in > signal processing that are not available in numpy (or python). > Specifically, it is frequently required to convert floating point > algorithms into integer algorithms. numpy is fine for arrays of integers > (of various sizes), but it is also very useful to have arrays of > complex<integers>. While numpy has complex<double,float>, it doesn't have > complex<int,int_64...> Has anyone thought about this? Complex ints (or Gaussian integers) are algebraically interesting but they're just a Ring, not a Field. Before you know it, you're going to want to divide something. In signal processing, int measurements are usually not counters, they're just fixed point numbers with an implicit scale, and you only want to do integer arithmetic to save time/space, not because it is a good conceptual fit to the problem. In an interpreted language like Python, fixed point seems like a strange bird. I just use complex floats and a fast library. Vincent Broman

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ERROR in installation of NumPy
by Simone Marras Oct. 5, 2007

Oct. 5, 2007

Hello everyone, I am trying to install numpy on my Suse 10.2 using Python 2.5 Python is correctly installed and when I launch > python setup.py install, I get the following error: numpy/core/src/multiarraymodule.c:7604: fatal error: error writing to /tmp/ccNImg9Q.s: No space left on devicetee: /tmp/tmpLmEe5Y: No space left on device compilation terminated. _exec_command_posix failed (status=256) Can anyone help me, pelase? thanks in advance, simone

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problem converting to matrix from Unicode input string
by Basilisk96 Oct. 5, 2007

Oct. 5, 2007

Hello all, I have the following function, with print statements inserted for debugging: import numpy def file2mat(inFile, sep=None, T=True): try: input = inFile.readlines() print "input=%s" % input except: raise finally: inFile.close() data = [line.split(sep) for line in input] print "data=%s" % data if T==True: return numpy.mat(data).astype(numpy.float64).T else: return numpy.mat(data).astype(numpy.float64) which is then tested as follows: >>> s = "-0.500 -0.500\n0.500 -0.500\n-0.500 0.500\n0.500 0.500" >>> u = unicode(s) >>> from cStringIO import StringIO >>> file2mat(StringIO(s)) input=['-0.500 -0.500\n', '0.500 -0.500\n', '-0.500 0.500\n', '0.500 0.500'] data=[['-0.500', '-0.500'], ['0.500', '-0.500'], ['-0.500', '0.500'], ['0.500', '0.500']] matrix([[-0.5, 0.5, -0.5, 0.5], [-0.5, -0.5, 0.5, 0.5]]) This is the expected result matrix... But now: >>> file2mat(StringIO(u)) input=['-\x000\x00.\x005\x000\x000\x00 \x00- \x000\x00.\x005\x000\x000\x00\n', '\x000\x00.\x005\x000\x000\x00 \x00- \x000\x00.\x005\x000\x000\x00\n', '\x00-\x000\x00.\x005\x000\x000\x00 \x000\x00.\x005\x000\x000\x00\n', '\x000\x00.\x005\x000\x000\x00 \x000\x00.\x005\x000\x000\x00'] data=[['-\x000\x00.\x005\x000\x000\x00', '\x00- \x000\x00.\x005\x000\x000\x00'], ['\x000\x00.\x005\x000\x000\x00', '\x00-\x000\x00.\x005\x000\x000\x00'], ['\x00- \x000\x00.\x005\x000\x000\x00', '\x000\x00.\x005\x000\x000\x00'], ['\x000\x00.\x005\x000\x000\x00', '\x000\x00.\x005\x000\x000\x00']] Traceback (most recent call last): ... ValueError: invalid literal for float(): - When I explicitly cast the input to 'string', I get the expected result: >>> file2mat(StringIO(str(u))) input=['-0.500 -0.500\n', '0.500 -0.500\n', '-0.500 0.500\n', '0.500 0.500'] data=[['-0.500', '-0.500'], ['0.500', '-0.500'], ['-0.500', '0.500'], ['0.500', '0.500']] matrix([[-0.5, 0.5, -0.5, 0.5], [-0.5, -0.5, 0.5, 0.5]]) Any suggestions on how to improve my code? Is this a Unicode issue, numpy issue, or both? The input string can come from an ASCII file or a GUI text control. In the case of a GUI, the control returns a Unicode string, so for now I am casting it to str(), but it seems like a hack.. BTW, the reason that I am using the astype() method of numpy.matrix is that I get a "ValueError: setting an array element with a sequence" when trying to use return numpy.mat(data, numpy.float64) in the above function. Thank you, -Basilisk96

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Default value in documentation
by Pierre GM Oct. 5, 2007

Oct. 5, 2007

All, I'm starting to update the documentation of maskedarray to the latest standard. How should I represent the default value of an optional parameter ? I was thinking something like def function(a, value=None) """Does something *Parameters*: a : {ndarray} Input array. value : {float} *[None]* Some value. If None, a default based on the dtype of a is used. """" Suggestions/ideas are welcome. Thanks a lot in advance P.

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