Mailman 3 July 2009 - NumPy-Discussion

Is anyone knowledgeable about dll deployment on windows ?
by David Cournapeau 01 Dec '09

01 Dec '09

Hi, My head hurt trying to understand dll management with windows. I wanted to find a sane way to use dll for numscons, but I can't see how to do this, so I was wondering if anyone on this ML had any deep knowledge on how to install dll, and reuse them with python extensions ? The problem is the following: - you have some dll (say MKL) in a path (say C:\program files\MKL). - how do you tell another dll (more exactly a python extension, a .pyd) to look for mkl dll into the MKL path ? It seems that there is no rpath-like capability on windows, and the only way to load dll is to put them into some windows directory, or to put the dll path into PATH. Is it really the only way ? cheers, David

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suggestion for generalizing numpy functions
by Darren Dale 13 Sep '09

13 Sep '09

I spent some time over the weekend fixing a few bugs in numpy that were exposed when attempting to use ufuncs with ndarray subclasses. It got me thinking that, with relatively little work, numpy's functions could be made to be more general. For example, the numpy.ma module redefines many of the standard ufuncs in order to do some preprocessing before the builtin ufunc is called. Likewise, in the units/quantities package I have been working on, I would like to perform a dimensional analysis to make sure an operation is allowed before I call a ufunc that might change data in place. Imagine an ndarray subclass with methods like __gfunc_pre__ and __gfunc_post__. __gfunc_pre__ could accept the context that is currently provided to __array_wrap__ (the inputs and the function called), perform whatever preprocessing is desired, and maybe return a dictionary containing metadata. Numpy functions could then be wrapped with a decorator that 1) calls __gfunc_pre__ and obtain any metadata that is returned 2) calls the wrapped functions, and then 3) calls __gfunc_post__, which might be very similar to __array_wrap__ except that it would also accept the metadata created by __gfunc_pre__. In cases where the routines to be called by __gfunc_pre__ and _post__ depend on what function is called, the the subclass could implement routines and store them in a dictionary-like object that is keyed using the function called. I have been exploring this approach with Quantities and it seems to work well. For example: def __gfunc_pre__(self, gfunc, *args): try: return gfunc_pre_registry[gfunc](*args) except KeyError: return {} I think such an approach for generalizing numpy's functions could be implemented without being disruptive to the existing __array_wrap__ framework. The decorator would attempt to identify an input or output array to use to call __gfunc_pre__ and _post__. If it finds them, it uses them. If it doesnt find them, no harm done, the existing __array_wrap__ mechanisms are still in place if the wrapped function is a ufunc. One other nice feature: the metadata that is returned by __gfunc_pre__ could contain an optional flag that the decorator attempts to pass to the wrapped function so that __gfunc_pre__ and _post are not called for any decorated internal functions. That way the subclass could specify that __gfunc_pre__ and _post should be called only for the outer-most function. Comments? Darren

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IndexExpression bug?
by Michael McNeil Forbes 17 Aug '09

17 Aug '09

>>> np.array([0,1,2,3])[1:-1] array([1, 2]) but >>> np.array([0,1,2,3])[np.s_[1:-1]] array([1, 2, 3]) >>> np.array([0,1,2,3])[np.index_exp[1:-1]] array([1, 2, 3]) Possible fix: class IndexExpression(object): ... def __len__(self): return 0 (Presently this returns sys.maxint) Does this break anything (I can't find any coverage tests)? If not, I will submit a ticket. Michael.

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Masking an array with another array
by Gökhan SEVER 12 Aug '09

12 Aug '09

Hello, Could you please give me some hints about how to mask an array using another arrays like in the following example. In [14]: a = arange(5) In [15]: a Out[15]: array([0, 1, 2, 3, 4]) and my secondary array is "b" In [16]: b = array([2,3]) What I want to do is to mask a with b values and get an array of: array([False, False, True, True, False], dtype=bool) That is just an manually created array. I still don't know how to do this programmatically in Pythonic fashion or numpy's masked array functions. Thank you. Gökhan

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(newbie) How can I use NumPy to wrap my C++ class with 2-dimensional arrays?
by Raymond de Vries 03 Aug '09

03 Aug '09

Hi everyone, (I sent this message yesterday as well but somehow it didn't come through...) I would like to ask your advice how I can use NumPy to wrap my existing C++ library with 2-dimensional arrays. I am wrapping the library with swig and I have the typemap function declaration. But now I am struggling a lot to find an implementation for getting access to the 2-dimensional arrays in my C++ classes. I have wrapped most of the library but the multi-dimensional arrays are problematic. From what I have read, I can use NumPy for this.. There is so much information about NumPy, Numeric etc that it is not clear anymore which example I can use best. It's clear that NumPy is probably the way to go. This is a small example of the class that I would like to wrap: struct MyClass { float array1[2][2]; std::vector<float> array2[2]; }; I've also read that I need to call import_array() but where should I put this? Is there a small example with NumPy c-api to wrap/convert my 2-dimensional arrays to python? Thanks a lot already Raymond

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Add/multiply reduction confusion
by Andrew Friedley 03 Aug '09

03 Aug '09

Hi, I'm trying to understand how integer types are upcast for add/multiply operations for my GSoC project (Implementing Ufuncs using CorePy). The documentation says that for reduction with add/multiply operations, integer types are 'upcast' to the int_ type (int64 on my system). What exactly does this mean, internally? Where/when does the upcasting occur? Is it a C-style cast, or a memory copy to a new temporary array? I'm a confused as to which low-level ufunc loop type is used (and why). This is what I see: >>> a = numpy.arange(131072, dtype=numpy.int32) >>> r = numpy.add.reduce(a) >>> print type(r) <type 'numpy.int64'> >>> print hex(r) 0x1ffff0000L Okay, fine. But I have my own ufunc, which defines only the following types right now (I stripped it down for debugging): >>> print corefunc.add.types ['ii->i', 'll->l'] NumPy has this, for comparison: >>> print numpy.add.types ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G', 'OO->O'] Also just to verify I did this: >>> print numpy.typeDict['i'] <type 'numpy.int32'> >>> print numpy.typeDict['l'] <type 'numpy.int64'> Yet when I call my own ufunc, this happens: >>> a = numpy.arange(131072, dtype=numpy.int32) >>> r = corefunc.add.reduce(a) >>> print type(r) <type 'numpy.int32'> >>> print hex(r) -0x10000 It looks like no upcasting is occurring here? My ii->i loop is being used, not the ll->l loop.. why? I'm guessing this is something I am doing wrong, any ideas what it is? Andrew

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** On entry to ILAENV parameter number 2 had an illegal value
by Steven Coutts 03 Aug '09

03 Aug '09

I am getting this error when trying to run a script using scipy. Python 2.5 atlas-3.9.0 lapack-3.2 numpy-1.3.0 scipy-0.7.1 Anyone any ideas how I can fix this? Regards

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Differences Between Arrays and Matrices in Numpy
by Nanime Puloski 02 Aug '09

02 Aug '09

What are some differences between arrays and matrices using the Numpy library? When would one want to use arrays instead of matrices and vice versa?

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Is this a bug?
by Chris Colbert 31 Jul '09

31 Jul '09

Numpy 1.3 In [1]: import numpy as np In [2]: a = np.zeros(5).fill(5) In [3]: a In [4]: type(a) Out[4]: <type 'NoneType'> In [5]: a = np.zeros(5) In [6]: a.fill(5) In [7]: a Out[7]: array([ 5., 5., 5., 5., 5.]) What i'm trying to do may not be the best way, but I think it should still have worked. Thoughts? Cheers, Chris

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Functions to pack/unpack bytes?
by Neal Becker 31 Jul '09

31 Jul '09

Does numpy have functions to convert between e.g. an array of uint32 and uint8, where the uint32 array is a packed version of the uint8 array (selecting little/big endian)?

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