Mailman 3 July 2008 - NumPy-Discussion

SciPy 2008 Registration Deadline Extended
by Travis Vaught 12 Jul '08

12 Jul '08

Greetings, The merchant account processor that we use for the SciPy Conference online registration has been experiencing some inexplicable problems authorizing some registrations. Apologies to those who have struggled to register and have not been successful. Because of the problems, we're extending the early-bird rates through Monday at midnight Central Time. If you experience any problems registering, please give us a call during business hours Monday (9:00am - 5:00pm Central - 512.536.1057). http://conference.scipy.org/ For those of you who have set up an account on the conference site, but have not yet registered, I encourage you to do so in time to take advantage of the lower rates. I also encourage everyone to make sure you've specified which tutorial track, T-shirt size, whether you'll attend the sprint, and meal preferences in your profile (http://conference.scipy.org/profile ). Please send me an email if you have any questions. Best, Travis

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Intel Math Kernel Library "FATAL ERROR"
by Jan Tore Korneliussen 12 Jul '08

12 Jul '08

I found that the NumPy regression test error for MKL that was reported a while ago happens with MKL 10.0.3.020, but not with MKL 10.0.1.014 (everything else equal) Here is a dump of the regression test on my machine with 10.0.3.020 >>> import numpy >>> numpy.test() Numpy is installed in /usr/lib/python2.5/site-packages/numpy Numpy version 1.1.0 Python version 2.5.2 (r252:60911, Apr 21 2008, 11:12:42) [GCC 4.2.3 (Ubuntu 4.2.3-2ubuntu7)] Found 36/36 tests for numpy.core.tests.test_numerictypes Found 16/16 tests for numpy.core.tests.test_umath Found 3/3 tests for numpy.core.tests.test_memmap Found 7/7 tests for numpy.core.tests.test_scalarmath Found 12/12 tests for numpy.core.tests.test_records Found 286/286 tests for numpy.core.tests.test_multiarray Found 143/143 tests for numpy.core.tests.test_regression Found 2/2 tests for numpy.core.tests.test_ufunc Found 18/18 tests for numpy.core.tests.test_defmatrix Found 3/3 tests for numpy.core.tests.test_errstate Found 70/70 tests for numpy.core.tests.test_numeric Found 63/63 tests for numpy.core.tests.test_unicode Found 4/4 tests for numpy.distutils.tests.test_fcompiler_gnu Found 5/5 tests for numpy.distutils.tests.test_misc_util Found 3/3 tests for numpy.fft.tests.test_helper Found 2/2 tests for numpy.fft.tests.test_fftpack Found 5/5 tests for numpy.lib.tests.test_getlimits Found 15/15 tests for numpy.lib.tests.test_io Found 6/6 tests for numpy.lib.tests.test_index_tricks Found 49/49 tests for numpy.lib.tests.test_shape_base Found 53/53 tests for numpy.lib.tests.test_function_base Found 24/24 tests for numpy.lib.tests.test__datasource Found 4/4 tests for numpy.lib.tests.test_polynomial Found 15/15 tests for numpy.lib.tests.test_twodim_base Found 1/1 tests for numpy.lib.tests.test_regression Found 1/1 tests for numpy.lib.tests.test_financial Found 43/43 tests for numpy.lib.tests.test_type_check Found 1/1 tests for numpy.lib.tests.test_machar Found 1/1 tests for numpy.lib.tests.test_ufunclike Found 10/10 tests for numpy.lib.tests.test_arraysetops Found 89/89 tests for numpy.linalg.tests.test_linalg Found 3/3 tests for numpy.linalg.tests.test_regression Found 36/36 tests for numpy.ma.tests.test_old_ma Found 94/94 tests for numpy.ma.tests.test_core Found 15/15 tests for numpy.ma.tests.test_extras Found 4/4 tests for numpy.ma.tests.test_subclassing Found 17/17 tests for numpy.ma.tests.test_mrecords Found 7/7 tests for numpy.tests.test_random Found 16/16 tests for numpy.testing.tests.test_utils Found 5/5 tests for numpy.tests.test_ctypeslib .......................................................................................................................................................................................................MKL FATAL ERROR: /opt/intel/mkl/10.0.3.020/lib/32//: cannot read file data: Is a directory

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python user defined type
by Nicolas Rougier 11 Jul '08

11 Jul '08

Hi all, I'm rather new to the list so maybe the question is well known but from my researches on the web and list archive, I did not find a clear explanation. I would like to create numpy array with my own (python) datatype, so I tried the obvious solution: from numpy import * class Unit(object): def __init__(self,value=0.0): self.value = value def __float__(self): return self.value def __repr__(self): return str(self.value) a = array([[Unit(1), Unit(2)], [Unit(3), Unit(4)]]) print a print a.dtype.name print a[0,0].value #a[0,0] = 0 #print a[0,0].value a = array (array([[1,2],[3,4]]), dtype=Unit) print a print a.dtype print a[0,0].value but the last print make python to raise an AttributeError stating that int object do not have a 'value' attribute. I guess I missed something concerning numpy object data type. Do I need to implement some special methods in Unit to make numpy happy ? Also, the commented line (a[0,0] = 0) makes the item to become an int while I need to set the value of the item instead, is that possible ? Nicolas

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doctests failing in ipython
by Charles R Harris 11 Jul '08

11 Jul '08

The problem is the Out[#] appended to the output. ................................................Out[4]: poly1d([ 1., 2., 3.]) ********************************************************************** File "/usr/lib/python2.5/site-packages/numpy/lib/tests/test_polynomial.py", line 6, in test_polynomial Failed example: p Expected: poly1d([ 1., 2., 3.]) Got nothing Tons of these. Chuck

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new chararray test fails on Mac OS X
by Christopher Hanley 11 Jul '08

11 Jul '08

From the svn log it looks like the tests are intended to fail? However I would prefer tests that are designed only to fail when problems occur. Otherwise we end up with problems with our automatic build and test scripts. Thanks, Chris ====================================================================== FAIL: test_mul (test_defchararray.TestOperations) ---------------------------------------------------------------------- Traceback (most recent call last): File "/Users/chanley/dev/site-packages/lib/python/numpy/core/tests/test_defcha rarray.py", line 49, in test_mul assert all(Ar == (self.A * r)) AssertionError ====================================================================== FAIL: test_rmul (test_defchararray.TestOperations) ---------------------------------------------------------------------- Traceback (most recent call last): File "/Users/chanley/dev/site-packages/lib/python/numpy/core/tests/test_defcha rarray.py", line 65, in test_rmul assert all(Ar == (r * self.A)) AssertionError ---------------------------------------------------------------------- Ran 1930 tests in 13.130s FAILED (failures=2) <nose.result.TextTestResult run=1930 errors=0 failures=2> >>> n.__version__ '1.2.0.dev5385' -- Christopher Hanley Systems Software Engineer Space Telescope Science Institute 3700 San Martin Drive Baltimore MD, 21218 (410) 338-4338

4 8

"expected a single-segment buffer object"
by Anne Archibald 11 Jul '08

11 Jul '08

Hi, When trying to construct an ndarray, I sometimes run into the more-or-less mystifying error "expected a single-segment buffer object": Out[54]: (0, 16, 8) In [55]: A=np.zeros(2); A=A[np.newaxis,...]; np.ndarray(strides=A.strides,shape=A.shape,buffer=A,dtype=A.dtype) --------------------------------------------------------------------------- <type 'exceptions.TypeError'> Traceback (most recent call last) /home/peridot/<ipython console> in <module>() <type 'exceptions.TypeError'>: expected a single-segment buffer object In [56]: A.strides Out[56]: (0, 8) That is, when I try to construct an ndarray based on an array with a zero stride, I get this mystifying error. Zero-strided arrays appear naturally when one uses newaxis, but they are valuable in their own right (for example for broadcasting purposes). So it's a bit awkward to have this error appearing when one tries to feed them to ndarray.__new__ as a buffer. I can, I think, work around it by removing all axes with stride zero: def bufferize(A): idx = [] for v in A.strides: if v==0: idx.append(0) else: idx.append(slice(None,None,None)) return A[tuple(idx)] Is there any reason for this restriction? Thanks, Anne

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Multiplying every 3 elements by a vector?
by Marlin Rowley 09 Jul '08

09 Jul '08

All: I'm trying to take a constant vector: v = (0.122169, 0.61516, 0.262671) and multiply those values by every 3 components in an array of length N: A = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ....] So what I want is: v[0]*A[0] v[1]*A[1] v[2]*A[2] v[0]*A[3] v[1]*A[4] v[2]*A[5] v[0]*A[6] ... How do I do this with one command in numPy? -M _________________________________________________________________ Need to know now? Get instant answers with Windows Live Messenger. http://www.windowslive.com/messenger/connect_your_way.html?ocid=TXT_TAGLM_W…

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chararray constructor change
by Alan McIntyre 09 Jul '08

09 Jul '08

I'd like to make the following change to the chararray constructor. This is motivated by some of chararray's methods constructing new chararrays with NumPy integer arguments to itemsize, and it just seemed easier to fix this in the constructor. Index: numpy/numpy/core/defchararray.py =================================================================== --- numpy/numpy/core/defchararray.py (revision 5378) +++ numpy/numpy/core/defchararray.py (working copy) @@ -25,6 +25,11 @@ else: dtype = string_ + # force itemsize to be a Python long, since using Numpy integer + # types results in itemsize.itemsize being used as the size of + # strings in the new array. + itemsize = long(itemsize) + _globalvar = 1 if buffer is None: self = ndarray.__new__(subtype, shape, (dtype, itemsize),

1 0

Unused matrix method
by Alan McIntyre 09 Jul '08

09 Jul '08

There's a _get_truendim method on matrix that isn't referenced anywhere in NumPy, SciPy, or matplotlib. Should this get deprecated or removed in 1.2?

2 2

alterdot and restoredot
by Keith Goodman 09 Jul '08

09 Jul '08

I don't know what to write for a doc string for alterdot and restoredot. Any ideas?

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NumPy-Discussion July 2008