Re: [Numpy-discussion] Numpy 1.4.0 rc1 released
David Cournapeau wrote: Hi, The first release candidate for 1.4.0 has been released. The sources, as well as mac and windows installers may be found here: https://sourceforge.net/projects/numpy/files/ The main improvements compared to 1.3.0 are: * Faster import time * Extended array wrapping mechanism for ufuncs * New Neighborhood iterator (C-level only) * C99-like complex functions in npymath As well as more than 50 bug fixes. The detailed list of changes may be found on trac: http://projects.scipy.org/numpy/roadmap cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Thanks for your hard work David! :-) --V. Stokes ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Python 3K merge
On Tue, Dec 1, 2009 at 5:04 AM, Charles R Harris charlesr.har...@gmail.com wrote: Hi Pauli, It looks like you doing great stuff with the py3k transition. Do you and David have any sort of merge schedule in mind? I have updated my py3k branch for numpy.distutils, and it is ready to merge: http://github.com/cournape/numpy/tree/py3k_bootstrap_take3 I have not thoroughly tested it, but it can run on both 2.4 and 3.1 on Linux at least. The patch is much smaller than my previous attempts as well, so I would just push it to the trunk, and deal with the issues as they come. cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.4.0 rc1 released
On Tue, Dec 1, 2009 at 9:17 AM, Virgil Stokes v...@it.uu.se wrote: David Cournapeau wrote: Hi, The first release candidate for 1.4.0 has been released. The sources, as well as mac and windows installers may be found here: https://sourceforge.net/projects/numpy/files/ The main improvements compared to 1.3.0 are: * Faster import time * Extended array wrapping mechanism for ufuncs * New Neighborhood iterator (C-level only) * C99-like complex functions in npymath As well as more than 50 bug fixes. The detailed list of changes may be found on trac: http://projects.scipy.org/numpy/roadmap cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Thanks for your hard work David! :-) --V. Stokes I can only agree - great work ! Where can one find out about the * New Neighborhood iterator (C-level only) ? I would problably like to use it in some SWIGged code of mine - even though I have never before used numpy-C code... Thanks, Sebastian ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.4.0 rc1 released
On Tue, Dec 1, 2009 at 6:00 PM, Sebastian Haase seb.ha...@gmail.com wrote: I can only agree - great work ! Thanks. Where can one find out about the * New Neighborhood iterator (C-level only) ? Here: http://docs.scipy.org/doc/numpy/reference/c-api.array.html#functions You can find some examples in the multiarray_tests.c in numpy/core (which test stacked iterators), as well as in scipy.signal (the nd-correlate function uses the neighborhood iterator). Note that optimizations such as used in VTK to separate the zones where boundaries handling is needed from the ones without is not implemented yet. cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convert strides/shape/offset into nd index?
Anne Archibald wrote: 2009/11/30 James Bergstra bergs...@iro.umontreal.ca: Your question involves a few concepts: - an integer vector describing the position of an element - the logical shape (another int vector) - the physical strides (another int vector) Ignoring the case of negative offsets, a physical offset is the inner product of the physical strides with the position vector. In these terms, you are asking how to solve the inner-product equation for the position vector. There can be many possible solutions (like, if there is a stride of 1, then you can make that dimension account for the entire offset. This is often not the solution you want.). For valid ndarrays though, there is at most one solution though with the property that every position element is less than the shape. You will also need to take into account that for certain stride vectors, there is no way to get certain offsets. Imagine all the strides were even, and you needed to get at an odd offset... it would be impossible. It would even be impossible if there were a dimension with stride 1 but it had shape of 1 too. I can't think of an algorithm off the top of my head that would do this in a quick and elegant way. Not to be a downer, but this problem is technically NP-complete. The so-called knapsack problem is to find a subset of a collection of numbers that adds up to the specified number, and it is NP-complete. Unfortunately, it is exactly what you need to do to find the indices to a particular memory location in an array of shape (2,2,...,2). What that means in practice is that either you have to allow potentially very slow algorithms (though you know that there will never be more than 32 different values in the knapsack, which might or might not be enough to keep things tractable) or use heuristic algorithms that don't always work. There are probably fairly good heuristics, particularly if the array elements are all at distinct memory locations (arrays with overlapping elements can arise from broadcasting and other slightly more arcane operations). Not that this should be done, but getting a chance to discuss NP is always fun: I think this particular problem can be solved in O(d*n^2) or better, where n is the offset in question and d the number of dimensions of the array, by using dynamic programming on the buffer offset in question (so first try for offset 1, then 2, and so on up to n). Which doesn't contradict the fact that the problem is exponential (n is exponential in terms of the length of the input to the problem), but it is still not *too* bad in many cases, because the exponential term is always smaller than the size of the array. Dag Sverre ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convert strides/shape/offset into nd index?
Dag Sverre Seljebotn wrote: Anne Archibald wrote: 2009/11/30 James Bergstra bergs...@iro.umontreal.ca: Your question involves a few concepts: - an integer vector describing the position of an element - the logical shape (another int vector) - the physical strides (another int vector) Ignoring the case of negative offsets, a physical offset is the inner product of the physical strides with the position vector. In these terms, you are asking how to solve the inner-product equation for the position vector. There can be many possible solutions (like, if there is a stride of 1, then you can make that dimension account for the entire offset. This is often not the solution you want.). For valid ndarrays though, there is at most one solution though with the property that every position element is less than the shape. You will also need to take into account that for certain stride vectors, there is no way to get certain offsets. Imagine all the strides were even, and you needed to get at an odd offset... it would be impossible. It would even be impossible if there were a dimension with stride 1 but it had shape of 1 too. I can't think of an algorithm off the top of my head that would do this in a quick and elegant way. Not to be a downer, but this problem is technically NP-complete. The so-called knapsack problem is to find a subset of a collection of numbers that adds up to the specified number, and it is NP-complete. Unfortunately, it is exactly what you need to do to find the indices to a particular memory location in an array of shape (2,2,...,2). What that means in practice is that either you have to allow potentially very slow algorithms (though you know that there will never be more than 32 different values in the knapsack, which might or might not be enough to keep things tractable) or use heuristic algorithms that don't always work. There are probably fairly good heuristics, particularly if the array elements are all at distinct memory locations (arrays with overlapping elements can arise from broadcasting and other slightly more arcane operations). Not that this should be done, but getting a chance to discuss NP is always fun: I think this particular problem can be solved in O(d*n^2) or better, Hmm, I guess that should be O(d*n). http://en.wikipedia.org/wiki/Knapsack_problem has the exact algorithm (though it needs some customization). Dag Sverre where n is the offset in question and d the number of dimensions of the array, by using dynamic programming on the buffer offset in question (so first try for offset 1, then 2, and so on up to n). Which doesn't contradict the fact that the problem is exponential (n is exponential in terms of the length of the input to the problem), but it is still not *too* bad in many cases, because the exponential term is always smaller than the size of the array. Dag Sverre ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Is anyone knowledgeable about dll deployment on windows ?
Thanks for these references (that's a pity we currently can't find anything related to runtime libraries versioning on the msdn database). Eloi David Cournapeau wrote: On Mon, Nov 30, 2009 at 8:52 PM, Eloi Gaudry e...@fft.be wrote: Well, I wasn't aware of Microsoft willing to giving up the whole SxS/manifest thing. Is there any MSDN information available? I have seen this mentioned for the first time on the python-dev ML: http://aspn.activestate.com/ASPN/Mail/Message/python-dev/3764855 The mention of including the version in the dll file, if true, is tragically comic. Maybe in 20 years windows will be able to have a system which exists for more than a decade on conventional unix... The link given by M.A Lemburg has changed since, though, as the description is nowhere to be found in the link. I think I have read that VS 2010 will never install the runtime in the SxS configuration, but I of course cannot find this information anymore. Maybe it is not true anymore, VS 2010 has not yet been released. You can also find useful manifest troubleshooting information there: http://blogs.msdn.com/junfeng/archive/2006/04/14/576314.aspx cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Eloi Gaudry Free Field Technologies Axis Park Louvain-la-Neuve Rue Emile Francqui, 1 B-1435 Mont-Saint Guibert BELGIUM Company Phone: +32 10 487 959 Company Fax: +32 10 454 626 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Is anyone knowledgeable about dll deployment on windows ?
I've done so, thanks for pointing the discussion. In the meantime, I've just patched distutils/msvc9compiler.py so that it neither embed nor create a manifest assembly. This way, I'll be sure that the assembly information would be fetched from the main python (or python-based) binaries (i.e. pythonX.dll). That may be a very strong prerequisites in some cases, but never in my very particular case. Eloi Christoph Gohlke wrote: The most popular/simple way to deal with the VC90.CRT dependency issue is to have the user install the runtime redistributable on their system. If you don't want to put that burden on the user, which I understand, you have to make adjustments to the assembly manifests. This is not unofficial or unsupported. It is a bug in Python that it embeds the assemblyIdentity for VC90.CRT in all extensions build with distutils/msvc9compiler.py. In fact, the *.pyd distributed with Python 2.6.3+ don't have that problem. Maybe you can raise your concerns about future compatibility at http://bugs.python.org/issue4120. Christoph On 11/30/2009 1:11 AM, Eloi Gaudry wrote: Christoph, thanks for pointing this discussion. That's a perfect match. If the workaround provided offers a solution to the current redistribution issue, I'm wondering if it will still be the case when an update to the assembly check function will be activated/implemented (within Windows). The manifest edition (removing the assemblyIdentity tag) doesn't seem to be a popular/official/supported way of dealing with the whole runtime libraries issue. Don't you think ? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Eloi Gaudry Free Field Technologies Axis Park Louvain-la-Neuve Rue Emile Francqui, 1 B-1435 Mont-Saint Guibert BELGIUM Company Phone: +32 10 487 959 Company Fax: +32 10 454 626 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convert strides/shape/offset into nd index?
Not to be a downer, but this problem is technically NP-complete. The so-called knapsack problem is to find a subset of a collection of numbers that adds up to the specified number, and it is NP-complete. Unfortunately, it is exactly what you need to do to find the indices to a particular memory location in an array of shape (2,2,...,2). Ha ha, right -- that is the knapsack problem isn't it. Oh well... I'll just require fortran- or C-style strided arrays, for which case it is easy to unravel offsets into indices. Thanks everyone! Zach ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] a simple examplr showing numpy and matplotlib failing
Click on Hello World twice and get a memory error. Comment out the ax.plot call and get no error. import numpy import sys import gtk from matplotlib.figure import Figure from matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg as FigureCanvas ax=None fig=None canvas=None def doplot(widget,box1): global ax,fig,canvas data=numpy.zeros(shape=(3508,125,129)) plot_data=data[0,0:,0] if canvas: box1.remove(canvas) canvas=None if ax: ax.cla() ax=None if fig: fig=None fig = Figure(figsize=(5,5), dpi=100) ax = fig.add_subplot(111) mif=numpy.arange(plot_data.shape[0]) #if the next line is commented out, all is good ax.plot(plot_data,mif) canvas = FigureCanvas(fig) box1.pack_start(canvas, True, True, 0) canvas.show() def delete_event(widget, event, data=None): return False window = gtk.Window(gtk.WINDOW_TOPLEVEL) window.connect(destroy, lambda x: gtk.main_quit()) box1 = gtk.HBox(False, 0) window.add(box1) button = gtk.Button(Hello World) box1.pack_start(button, True, True, 0) #window.add(box1) button.show() button.connect(clicked, doplot, box1) box1.show() window.set_default_size(500,400) window.show() gtk.main() ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] a simple examplr showing numpy and matplotlib failing
Hmm... works for me. What platform, with how much physical and virtual RAM? One thing you may want to try is to completely destroy the figure each time: if fig: fig.clf() fig=None Mike Yeates, Mathew C (388D) wrote: Click on “Hello World” twice and get a memory error. Comment out the ax.plot call and get no error. import numpy import sys import gtk from matplotlib.figure import Figure from matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg as FigureCanvas ax=None fig=None canvas=None def doplot(widget,box1): global ax,fig,canvas data=numpy.zeros(shape=(3508,125,129)) plot_data=data[0,0:,0] if canvas: box1.remove(canvas) canvas=None if ax: ax.cla() ax=None if fig: fig=None fig = Figure(figsize=(5,5), dpi=100) ax = fig.add_subplot(111) mif=numpy.arange(plot_data.shape[0]) #if the next line is commented out, all is good ax.plot(plot_data,mif) canvas = FigureCanvas(fig) box1.pack_start(canvas, True, True, 0) canvas.show() def delete_event(widget, event, data=None): return False window = gtk.Window(gtk.WINDOW_TOPLEVEL) window.connect(destroy, lambda x: gtk.main_quit()) box1 = gtk.HBox(False, 0) window.add(box1) button = gtk.Button(Hello World) box1.pack_start(button, True, True, 0) #window.add(box1) button.show() button.connect(clicked, doplot, box1) box1.show() window.set_default_size(500,400) window.show() gtk.main() ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Michael Droettboom Science Software Branch Operations and Engineering Division Space Telescope Science Institute Operated by AURA for NASA ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] a simple examplr showing numpy and matplotlib failing
Hi Mathew, I saw your email and I was curious about it. I tried your code and it does work for me without any problem. Santanu On Tue, Dec 1, 2009 at 2:58 PM, Michael Droettboom md...@stsci.edu wrote: Hmm... works for me. What platform, with how much physical and virtual RAM? One thing you may want to try is to completely destroy the figure each time: if fig: fig.clf() fig=None Mike Yeates, Mathew C (388D) wrote: Click on “Hello World” twice and get a memory error. Comment out the ax.plot call and get no error. import numpy import sys import gtk from matplotlib.figure import Figure from matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg as FigureCanvas ax=None fig=None canvas=None def doplot(widget,box1): global ax,fig,canvas data=numpy.zeros(shape=(3508,125,129)) plot_data=data[0,0:,0] if canvas: box1.remove(canvas) canvas=None if ax: ax.cla() ax=None if fig: fig=None fig = Figure(figsize=(5,5), dpi=100) ax = fig.add_subplot(111) mif=numpy.arange(plot_data.shape[0]) #if the next line is commented out, all is good ax.plot(plot_data,mif) canvas = FigureCanvas(fig) box1.pack_start(canvas, True, True, 0) canvas.show() def delete_event(widget, event, data=None): return False window = gtk.Window(gtk.WINDOW_TOPLEVEL) window.connect(destroy, lambda x: gtk.main_quit()) box1 = gtk.HBox(False, 0) window.add(box1) button = gtk.Button(Hello World) box1.pack_start(button, True, True, 0) #window.add(box1) button.show() button.connect(clicked, doplot, box1) box1.show() window.set_default_size(500,400) window.show() gtk.main() ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Michael Droettboom Science Software Branch Operations and Engineering Division Space Telescope Science Institute Operated by AURA for NASA ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.4.0 rc1 released
On Tue, Dec 1, 2009 at 4:47 AM, David Cournapeau courn...@gmail.com wrote: The first release candidate for 1.4.0 has been released. Excellent! Thanks for all your effort, Jarrod ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.4.0 rc1 released
On 30-Nov-09, at 10:47 PM, David Cournapeau wrote: Hi, The first release candidate for 1.4.0 has been released. The sources, as well as mac and windows installers may be found here: https://sourceforge.net/projects/numpy/files/ Hi David, All clear on my Intel Atom and Core i5 boxes, though problem on a PowerPC machine (I assume it's just more 'long double' weirdness that's platform-specific): == FAIL: test_umath.test_nextafterl -- Traceback (most recent call last): File /Users/dwf/numpyrc/lib/python2.5/site-packages/nose-0.11.1- py2.5.egg/nose/case.py, line 183, in runTest self.test(*self.arg) File /Users/dwf/numpyrc/lib/python2.5/site-packages/numpy/testing/ decorators.py, line 215, in knownfailer return f(*args, **kwargs) File /Users/dwf/numpyrc/lib/python2.5/site-packages/numpy/core/ tests/test_umath.py, line 866, in test_nextafterl return _test_nextafter(np.longdouble) File /Users/dwf/numpyrc/lib/python2.5/site-packages/numpy/core/ tests/test_umath.py, line 852, in _test_nextafter assert np.nextafter(one, two) - one == eps AssertionError == FAIL: test_umath.test_spacingl -- Traceback (most recent call last): File /Users/dwf/numpyrc/lib/python2.5/site-packages/nose-0.11.1- py2.5.egg/nose/case.py, line 183, in runTest self.test(*self.arg) File /Users/dwf/numpyrc/lib/python2.5/site-packages/numpy/testing/ decorators.py, line 215, in knownfailer return f(*args, **kwargs) File /Users/dwf/numpyrc/lib/python2.5/site-packages/numpy/core/ tests/test_umath.py, line 886, in test_spacingl return _test_spacing(np.longdouble) File /Users/dwf/numpyrc/lib/python2.5/site-packages/numpy/core/ tests/test_umath.py, line 873, in _test_spacing assert np.spacing(one) == eps AssertionError -- Ran 2484 tests in 12.445s FAILED (KNOWNFAIL=4, SKIP=1, failures=2) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] a simple examplr showing numpy and matplotlib failing
I found a workaround. If I replace plot_data=data[0,0:,0] With plot_data=numpy.copy(data[0,0:,0]) Everything is okay. I am on Windows XP 64 with 4 Gigs ram. (Note: the data array is greater than 4 Gigs since my datatype is float64. If I decrease the size so that the array is around 3 Gigs, all is good) Mathew From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-boun...@scipy.org] On Behalf Of Santanu Chatterjee Sent: Tuesday, December 01, 2009 12:15 PM To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] a simple examplr showing numpy and matplotlib failing Hi Mathew, I saw your email and I was curious about it. I tried your code and it does work for me without any problem. Santanu On Tue, Dec 1, 2009 at 2:58 PM, Michael Droettboom md...@stsci.edumailto:md...@stsci.edu wrote: Hmm... works for me. What platform, with how much physical and virtual RAM? One thing you may want to try is to completely destroy the figure each time: if fig: fig.clf() fig=None Mike Yeates, Mathew C (388D) wrote: Click on Hello World twice and get a memory error. Comment out the ax.plot call and get no error. import numpy import sys import gtk from matplotlib.figure import Figure from matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg as FigureCanvas ax=None fig=None canvas=None def doplot(widget,box1): global ax,fig,canvas data=numpy.zeros(shape=(3508,125,129)) plot_data=data[0,0:,0] if canvas: box1.remove(canvas) canvas=None if ax: ax.cla() ax=None if fig: fig=None fig = Figure(figsize=(5,5), dpi=100) ax = fig.add_subplot(111) mif=numpy.arange(plot_data.shape[0]) #if the next line is commented out, all is good ax.plot(plot_data,mif) canvas = FigureCanvas(fig) box1.pack_start(canvas, True, True, 0) canvas.show() def delete_event(widget, event, data=None): return False window = gtk.Window(gtk.WINDOW_TOPLEVEL) window.connect(destroy, lambda x: gtk.main_quit()) box1 = gtk.HBox(False, 0) window.add(box1) button = gtk.Button(Hello World) box1.pack_start(button, True, True, 0) #window.add(box1) button.show() button.connect(clicked, doplot, box1) box1.show() window.set_default_size(500,400) window.show() gtk.main() ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.orgmailto:NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Michael Droettboom Science Software Branch Operations and Engineering Division Space Telescope Science Institute Operated by AURA for NASA ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.orgmailto:NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.4.0 rc1 released
On Mon, Nov 30, 2009 at 9:47 PM, David Cournapeau courn...@gmail.com wrote: Hi, The first release candidate for 1.4.0 has been released. The sources, as well as mac and windows installers may be found here: https://sourceforge.net/projects/numpy/files/ I installed 32-bit Python 2.6.3 and 23-bit Python numpy on a Win7 Pro 64-bit system. I get the following failure: Python 2.6.3 (r263rc1:75186, Oct 2 2009, 20:40:30) [MSC v.1500 32 bit (Intel)] on win32 Type copyright, credits or license() for more information. Personal firewall software may warn about the connection IDLE makes to its subprocess using this computer's internal loopback interface. This connection is not visible on any external interface and no data is sent to or received from the Internet. IDLE 2.6.3 import numpy as np np.__version__ '1.4.0rc1' np.test() Running unit tests for numpy NumPy version 1.4.0rc1 NumPy is installed in E:\Python26\lib\site-packages\numpy Python version 2.6.3 (r263rc1:75186, Oct 2 2009, 20:40:30) [MSC v.1500 32 bit (Intel)] nose version 0.11.1 K...K..K.K..K.F... ...KK..S. . == FAIL: test_special_values (test_umath_complex.TestClog) -- Traceback (most recent call last): File E:\Python26\lib\site-packages\numpy\core\tests\test_umath_complex.py, line 179, in test_special_values assert_almost_equal(np.log(x), y) File E:\Python26\lib\site-packages\numpy\testing\utils.py, line 437, in assert_almost_equal DESIRED: %s\n % (str(actual), str(desired))) AssertionError: Items are not equal: ACTUAL: [ NaN+2.35619449j] DESIRED: (inf+2.35619449019j) Bruce -- Ran 2336 tests in 23.571s FAILED (KNOWNFAIL=7, SKIP=1, failures=1) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.4.0 rc1 released
On Wed, Dec 2, 2009 at 12:17 PM, Bruce Southey bsout...@gmail.com wrote: Traceback (most recent call last): File E:\Python26\lib\site-packages\numpy\core\tests\test_umath_complex.py, line 179, in test_special_values assert_almost_equal(np.log(x), y) File E:\Python26\lib\site-packages\numpy\testing\utils.py, line 437, in assert_almost_equal DESIRED: %s\n % (str(actual), str(desired))) AssertionError: Items are not equal: ACTUAL: [ NaN+2.35619449j] DESIRED: (inf+2.35619449019j) That's a known failure on windows (which has not been marked as such, though). Unless you rely on C99 semantics for nan/inf for complex handling, it should not be a big problem, cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
