Mailman 3 January 2006 - NumPy-Discussion

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by eBay 12 Jan '06

12 Jan '06

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Who will use numpy.ma?
by Sasha 12 Jan '06

12 Jan '06

MA is intended to be a drop-in replacement for Numeric arrays that can explicitely handle missing observations. With the recent improvements to the array object in NumPy, the MA library has fallen behind. There are more than 50 methods in the ndarray object that are not present in ma.array. I would like to hear from people who work with datasets with missing observations? Do you use MA? Do you think with the support for nan's and replaceable mathematical operations, should missing observations be handled in numpy using special values rather than an array of masks? Thanks. -- sasha

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Conversion of heterogeneous arrays in numpy<-->numarray
by Francesc Altet 10 Jan '06

10 Jan '06

Hi, Is there any plan to provide a conversion between recarrays in numpy and numarray?. For the moment it does not seem to work: In [83]: r=numarray.records.array([(1,11,'a'),(2,22,'b')], formats='u1,f4,a1') In [84]: a=numpy.array(r, dtype='u1,f4,a1') In [85]: numarray.records.array(a) --------------------------------------------------------------------------- exceptions.ValueError Traceback (most recent call last) /tmp/<console> /usr/lib/python2.3/site-packages/numarray-1.5.0-py2.3-linux-i686.egg/numarray/records.py in array(buffer, formats, shape, names, byteorder, aligned) 406 byteorder=byteorder, aligned=aligned) 407 else: --> 408 raise ValueError("Unknown input type") 409 410 def _RecGetType(name): ValueError: Unknown input type In [86]: numpy.array(r) Out[86]: array([0Aa, °Ab], dtype=(void,6)) However, if one specifies the format in for the numarray-->numpy it does seem to work: In [87]: numpy.array(r, dtype='u1,f4,a1') Out[87]: array([(1, 11.0, 'a'), (2, 22.0, 'b')], dtype=(void,6)) But not in the sense numpy-->numarray: In [88]: numarray.records.array(a, formats='u1,f4,a1') --------------------------------------------------------------------------- exceptions.ValueError Traceback (most recent call last) /tmp/<console> /usr/lib/python2.3/site-packages/numarray-1.5.0-py2.3-linux-i686.egg/numarray/records.py in array(buffer, formats, shape, names, byteorder, aligned) 406 byteorder=byteorder, aligned=aligned) 407 else: --> 408 raise ValueError("Unknown input type") 409 410 def _RecGetType(name): ValueError: Unknown input type Having this would be great for supporting heterogeneous arrays in PyTables. Regards, -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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initialise an array
by Humufr 10 Jan '06

10 Jan '06

Hello, I have a function like this: def test(x,xall=None): if xall == None: xall =x I obtain this error message when I'm doing this with numpy (I don't have this problem with numarray and Numeric). ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all How can I do something similar? Do I have to use the function suggest in the error message? The function is something inside a long script with a lot of condition similar to this one so it's why I ask if it's normal to not have the special None parameter take in count. Thanks, N.

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Re: [SciPy-dev] How to handle a[...] in numpy?
by Francesc Altet 10 Jan '06

10 Jan '06

A Diumenge 08 Gener 2006 05:10, Sasha va escriure: > In Numeric a[...] would return an array unless a was 0-rank and a > python type othewise. What is the right way to do the same in numpy? [snip] > Proposal: Although I like a lot that 0-rank arrays and numpy scalar > types non-iterable, it may be reasonable to allow a[...]. This way > ellipsis can be interpereted as any number of ":"s including zero. > Another subscript operation that makes sense for scalars would be > a[...,newaxis] or even a[{newaxis, }* ..., {newaxis,}*], where > {newaxis,}* stands for any number of comma-separated newaxis tokens. > This will allow one to use ellipsis in generic code that would work on > any numpy type. I will contribute code if there is any interest. +1 More specifically, provided that: In [65]: type(numpy.array([0])[...]) Out[65]: <type 'numpy.ndarray'> In [66]: type(numpy.array([0])[0]) Out[66]: <type 'int32_arrtype'> In [67]: type(numpy.array([0]).item()) Out[67]: <type 'int'> I'd propose the next behaviour for 0-rank arrays: In [65]: type(numpy.array(0)[...]) Out[65]: <type 'numpy.ndarray'> In [66]: type(numpy.array(0)[()]) # Indexing a la numarray Out[66]: <type 'int32_arrtype'> In [67]: type(numpy.array(0).item()) # already works Out[67]: <type 'int'> -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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Why I cannot import NumPy?
by lefeil＠email.arizona.edu 08 Jan '06

08 Jan '06

After installation, I cannot import NumPy. I tried to run setup.py in the numpy directory. But the following error occurs: IDLE 1.1.2 >>> ================================ RESTART ================================ >>> Assuming default configuration (distutils\command/{setup_command,setup}.py was not found) Appending numpy.distutils.command configuration to numpy.distutils Assuming default configuration (distutils\fcompiler/{setup_fcompiler,setup}.py was not found) Appending numpy.distutils.fcompiler configuration to numpy.distutils Appending numpy.distutils configuration to numpy Appending numpy.testing configuration to numpy F2PY Version 2_1830 Appending numpy.f2py configuration to numpy Traceback (most recent call last): File "C:\Python24\lib\site-packages\numpy\setup.py", line 27, in -toplevel- setup(**configuration(top_path='')) File "C:\Python24\lib\site-packages\numpy\setup.py", line 10, in configuration config.add_subpackage('core') File "C:\Python24\Lib\site-packages\numpy\distutils\misc_util.py", line 399, in add_subpackage config = self.get_subpackage(subpackage_name,subpackage_path) File "C:\Python24\Lib\site-packages\numpy\distutils\misc_util.py", line 389, in get_subpackage config = setup_module.configuration(*args) File "C:\Python24\Lib\site-packages\numpy\core\setup.py", line 19, in configuration open(generate_umath_py,'U'),generate_umath_py, IOError: [Errno 2] No such file or directory: 'core\\code_generators\\generate_umath.py' Can someone tell me how to solve this problem? There is no directory names code_generators in the code directory. Thanks Lefei

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wanted info
by Adkins Sherrie 08 Jan '06

08 Jan '06

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Properties of ma.masked singleton
by Alexander Belopolsky 06 Jan '06

06 Jan '06

In the current numpy version ma.masked singleton has the following properties: >>> len(ma.masked) 0 which is a change from old MA, where >>> len(MA.masked) 1 Similarly: >>> ma.masked.shape (0,) >>> MA.masked.shape () It changes shape when filled: >>> ma.masked.filled() array([999999]) and so on. The code contains numerous "if x is masked" statements to support all this logic. I would like to suggest a somewhat radical change for your review. There are two main uses of ma.masked: 1. To set mask: >>> x = ma.zeros(5) >>> x[2:4] = ma.masked >>> print x [0 0 -- -- 0] 2. To check if an element is masked: >>> x[2] is ma.masked True The second property allows a cute looking idiom "x[i] is masked", but only works for single element access: >>> x[2:4] is ma.masked False It also leads to strange results: >>> x[2].shape (0,) My proposal is to eliminate the property #2 from ma and redefine masked as None. Single element getitem will return a rank zero MaskedArray. We can also add "is_masked" property, which will allow a convenient check in the form "x[i].is_masked". The main benefit of this proposal is that x[i] will be duck typing compatible with numpy scalars. -- sasha

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Installation of numpy header files
by Alexander Belopolsky 06 Jan '06

06 Jan '06

Numpy include files get installed in <python prefix>/lib/pythonX.X/site- packages/numpy/base/include/numpy instead of <python prefix>/include/ pythonX.X/numpy. The attached patch fixes the problem. Does anyone rely on the current behavior? -- sasha PS: Using scipy core svn r1847 on a Linux system.

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ANN: Release of NumPy 0.9.2
by Travis Oliphant 06 Jan '06

06 Jan '06

Numpy 0.9.2 is the successor to both Numeric and Numarray and builds and uses code from both. This release marks the first release using the new (but historical) Numpy name. The release notes are included below: Best regards, -Travis Oliphant Release Notes ================== NumPy 0.9.2 marks the first release of the new array package under its new name. This new name should reflect that the new package is a hybrid of the Numeric and Numarray packages. This release adds many more features and speed-enhancements from Numarray. Changes from (SciPy Core) 0.8.4: - Namespace and Python package name is now "numpy" and "numpy" instead of "scipy" and "scipy_core" respectively. This should help packagers and egg-builders. - The NumPy arrayobject now both exports and consumes the full array_descr protocol (including field information). - Removed NOTSWAPPED flag. The byteswapping information is handled by the data-type descriptor. - The faster sorting functions were brought over from numarray leading to a factor of 2-3 speed increase in sorting. Also changed .sort() method to be in-place like numarray and lists. - Polynomial division has been fixed. - basic.fft, basic.linalg, basic.random have been moved to dft, linalg, and random respectively (no extra basic sub-package layer). - Introduced numpy.dual to allow calling of functions that are both in SciPy and NumPy when it is desired to use the SciPy function if the user has it but otherwise use the NumPy function. - The "rtype" keyword used in a couple of places has been changed to "dtype" for consistency. - Fixes so that the standard array constructor can be used to construct record-arrays with fields. - Changed method .toscalar() to .item() (added to convertcode.py) - Added numpy.lib.mlab to be fully compatible with old MLab including the addition of a kaiser window even when full SciPy is not installed. - Arrays of nested records should behave better. - Fixed masked arrays buglets. - Added code so that strings can be converted to numbers using .astype() - Added a lexsort (lexigraphic) function so that sorting on multiple keys can be done -- very useful for record-arrays - Speed ups and bug-fixes for 1-d "fancy" indexing by going through the flattened array iterator when possible. - Added the ability to add docstrings to builtin objects "on-the-fly". Allows adding docstrings without re-compiling C-code. - Moved the weave subpackage to SciPy. - Changed the fields attribute of the dtypedescr object to return a "read-only" dictionary when accessed from Python. - Added a typeNA dictionary for the numarray types and added a compare function for dtypedescr objects so that equivalent types can be detected. Please not that all modules are imported using lower-case letters (so don't let the NumPy marketing name confuse you, the package to import is "numpy").

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