Re: [Numpy-discussion] find appropriate dtype based on a set of values
Thanks Stéfan, your script works well. There's a small typo on line
12. I also discovered the functions 'np.iinfo' and 'np.finfo' for
machine limits on integer/float types (a note for myself, you might be
already familiar with them).
After having read the docstring, I was only curious why this feature
is not provided by the function itself, as returning the input array's
dtype seems not so useful (can't imagine such a use case).
Gregorio
2013/9/2 Stéfan van der Walt ste...@sun.ac.za:
On Mon, Sep 2, 2013 at 4:21 PM, Gregorio Bastardo
gregorio.basta...@gmail.com wrote:
np.min_scalar_type([-1,256]) # int16 expected
dtype('int32')
Am I missing something? Anyone knows how to achieve the desired operation?
The docstring states explicitly that this use case is not supported.
Here's one way of doing it: https://gist.github.com/stefanv/6413742
Stéfan
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Re: [Numpy-discussion] find appropriate dtype based on a set of values
On Mon, Sep 2, 2013 at 3:55 PM, Stéfan van der Walt ste...@sun.ac.za
wrote:
On Mon, Sep 2, 2013 at 4:21 PM, Gregorio Bastardo
gregorio.basta...@gmail.com wrote:
np.min_scalar_type([-1,256]) # int16 expected
dtype('int32')
Am I missing something? Anyone knows how to achieve the desired
operation?
The docstring states explicitly that this use case is not supported.
Here's one way of doing it: https://gist.github.com/stefanv/6413742
You can probably reduce the amount of work by only comparing a.min() and
a.max() instead of the whole array.
--
Robert Kern
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Re: [Numpy-discussion] find appropriate dtype based on a set of values
On Tue, Sep 3, 2013 at 2:47 PM, Robert Kern robert.k...@gmail.com wrote: Here's one way of doing it: https://gist.github.com/stefanv/6413742 You can probably reduce the amount of work by only comparing a.min() and a.max() instead of the whole array. Thanks, fixed. Stéfan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20130902-win-amd64-py2.7/. I have not looked in more detail or at other Python versions yet. Christoph
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/**projects/numpy/files/NumPy/1.**8.0b1/https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~** gohlke/pythonlibs/tests/**20130902-win-amd64-py2.7-** numpy-1.8.0.dev-86a6e6c/http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~**gohlke/pythonlibs/tests/** 20130902-win-amd64-py2.7/http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20130902-win-amd64-py2.7/. I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/**projects/numpy/files/NumPy/1.**8.0b1/https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~** gohlke/pythonlibs/tests/**20130902-win-amd64-py2.7-** numpy-1.8.0.dev-86a6e6c/http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~**gohlke/pythonlibs/tests/** 20130902-win-amd64-py2.7/http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20130902-win-amd64-py2.7/. I have not looked in more detail or at other Python versions yet. It's pretty clear that I will need a windows environment to debug this. I have windows 7 running in a virtual machine, and have downloaded the vsc9 express compiler and isos. Do I need to burn those guys to a disk in order to install or is there some windows magic that will let me install them directly from the isos? That done, I assume I can just download python 2.7 for windows, clone the repository, and do the usual python setup.py install thing. Anything I need to be wary about, any pointers? Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/. I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 Christoph ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke cgoh...@uci.edu wrote: On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/ . I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 My, that does look suspicious. That function is new in 1.8 I believe. Looks like it needs fixing whatever else it fixes. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On 9/3/2013 4:32 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/. I have not looked in more detail or at other Python versions yet. It's pretty clear that I will need a windows environment to debug this. I have windows 7 running in a virtual machine, and have downloaded the vsc9 express compiler and isos. Do I need to burn those guys to a disk in order to install or is there some windows magic that will let me install them directly from the isos? That done, I assume I can just download python 2.7 for windows, clone the repository, and do the usual python setup.py install thing. Anything I need to be wary about, any pointers? Chuck I would not recommend the VS Express version. Instead use the Microsoft Windows SDK for Windows 7 and .NET Framework 3.5 SP1 http://www.microsoft.com/en-us/download/details.aspx?id=3138, which contains compatible 32 and 64 bit compilers for Python 2.6 to 3.2. Use the web installer or mount the ISO with VirtualCloneDrive http://www.slysoft.com/en/virtual-clonedrive.html. Then, on a command prompt in the numpy source directory type (not tested, but should work for 64 bit Python 2.7): setlocal EnableDelayedExpansion call %ProgramFiles%\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.Cmd /Release /x64 /vista set DISTUTILS_USE_SDK=1 C:\Python27\python.exe setup.py build Christoph ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On Tue, Sep 3, 2013 at 5:45 PM, Charles R Harris charlesr.har...@gmail.comwrote: On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke cgoh...@uci.edu wrote: On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/ . I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 My, that does look suspicious. That function is new in 1.8 I believe. Looks like it needs fixing whatever else it fixes. BTW, do the tests pass with a 32 build? Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On 9/3/2013 4:45 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu mailto:cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/. I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 My, that does look suspicious. That function is new in 1.8 I believe. Looks like it needs fixing whatever else it fixes. Chuck In fact, using a npy_longlong instead of npy_long fixes all numpy test errors and failures. But it probably foils the recent optimizations. Christoph ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] (no subject)
Hello, I'm new to numpy, and I'm a stuck on my first real project with it. I am trying to take the rfft of a numpy array, like this: my_rfft = numpy.fft.rfft(my_numpy_array) and replace the amplitudes that can be obtained with: my_amplitudes = numpy.abs(my_rfft) with amplitudes from an arbitrary numpy array's rFFT, which is to then be converted back using numpy.fft.irfft . Alternately, some future plans will involve having to modify individual array element amplitudes directly based on other parameters. I would think that modifying and re-synthesizing signals using FFT is a fairly common use-case, but my attempts at Googling example code have been fruitless. I'm not sure if my rudimentary knowledge of FFT is failing me, or if I'm just not understanding how numpy represents and exposes the data, but I would really appreciate any help I can get :) /carl/ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On Tue, Sep 3, 2013 at 6:09 PM, Christoph Gohlke cgoh...@uci.edu wrote: On 9/3/2013 4:45 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edumailto: cgoh...@uci.edu mailto:cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/ . I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 My, that does look suspicious. That function is new in 1.8 I believe. Looks like it needs fixing whatever else it fixes. Chuck In fact, using a npy_longlong instead of npy_long fixes all numpy test errors and failures. But it probably foils the recent optimizations. Great! I think the function is not used for numeric things so I'm not sure what optimizations could be affected. I'll put up a PR and backport it. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
On 9/3/2013 4:52 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 5:45 PM, Charles R Harris charlesr.har...@gmail.com mailto:charlesr.har...@gmail.com wrote: On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu mailto:cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/. I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 My, that does look suspicious. That function is new in 1.8 I believe. Looks like it needs fixing whatever else it fixes. BTW, do the tests pass with a 32 build? Chuck The 32 bit build fails two tests (unrelated to the above 64 bit issue): == FAIL: test_invalid (test_errstate.TestErrstate) -- Traceback (most recent call last): File X:\Python27\lib\site-packages\numpy\testing\decorators.py, line 146, in skipper_func return f(*args, **kwargs) File X:\Python27\lib\site-packages\numpy\core\tests\test_errstate.py, line 23, in test_invalid self.fail(Did not raise an invalid error) AssertionError: Did not raise an invalid error == FAIL: simd tests on max/min -- Traceback (most recent call last): File X:\Python27\lib\site-packages\numpy\core\tests\test_umath.py, line 678, in test_minmax_blocked msg=repr(inp) + '\n' + msg) AssertionError: array([ 0., 1., nan, 3., 4., 5., 6., 7., 8., 9., 10.], dtype=float32) unary offset=(0, 0), size=11, dtype=type 'numpy.float32', out of place -- Christoph ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release
FWIW, You all may know this already, but a long is 64 bit on most 64 bit platforms, but 32 bit on Windows. Can we start using stdint.h and int32_t and friends? -CHB On Sep 3, 2013, at 5:18 PM, Charles R Harris charlesr.har...@gmail.com wrote: On Tue, Sep 3, 2013 at 6:09 PM, Christoph Gohlke cgoh...@uci.edu wrote: On 9/3/2013 4:45 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edumailto: cgoh...@uci.edu mailto:cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/ . I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 My, that does look suspicious. That function is new in 1.8 I believe. Looks like it needs fixing whatever else it fixes. Chuck In fact, using a npy_longlong instead of npy_long fixes all numpy test errors and failures. But it probably foils the recent optimizations. Great! I think the function is not used for numeric things so I'm not sure what optimizations could be affected. I'll put up a PR and backport it. Chuck ___ 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] ANN: Numpy 1.8.0 beta 1 release
On Tue, Sep 3, 2013 at 6:18 PM, Charles R Harris charlesr.har...@gmail.comwrote: On Tue, Sep 3, 2013 at 6:09 PM, Christoph Gohlke cgoh...@uci.edu wrote: On 9/3/2013 4:45 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/3/2013 2:51 PM, Charles R Harris wrote: On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke cgoh...@uci.edumailto: cgoh...@uci.edu mailto:cgoh...@uci.edu mailto:cgoh...@uci.edu wrote: On 9/1/2013 9:54 AM, Charles R Harris wrote: Hi all, I'm happy to announce the first beta release of Numpy 1.8.0. Please try this beta and report any issues on the numpy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/ https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/. The Windows and OS X installers will follow when the infrastructure issues are dealt with. Chuck Hello, I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on win-amd64-py2.7. It builds OK but there are 23 test errors and 6 failures (attached). Some 3rd party packages (e.g. scipy, numexpr, pytables, bottleneck, pandas and matplotlib) that were built against numpy-MKL 1.7 fail tests when used with numpy-MKL 1.8. Other packages test OK (e.g. skimage, sklearn, statsmodels, mahotas, pygame). See http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/ compared to http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/ http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/ . I have not looked in more detail or at other Python versions yet. Thanks Christoph, Looks like some work to do. I wonder what is different between windows and linux here? Chuck Looks like the fundamental PyArray_PyIntAsIntp function is broken on 64 bit Windows. 64 bit PyLong values are intermediately stored in a 32 bit C long variable. But maybe I am missing something... https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729 https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767 My, that does look suspicious. That function is new in 1.8 I believe. Looks like it needs fixing whatever else it fixes. Chuck In fact, using a npy_longlong instead of npy_long fixes all numpy test errors and failures. But it probably foils the recent optimizations. Great! I think the function is not used for numeric things so I'm not sure what optimizations could be affected. I'll put up a PR and backport it. Looks like there are several errors in that function. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] (no subject)
I am trying to take the rfft of a numpy array, like this:
my_rfft = numpy.fft.rfft(my_numpy_array)
and replace the amplitudes that can be obtained with:
my_amplitudes = numpy.abs(my_rfft)
with amplitudes from an arbitrary numpy array's rFFT, which is to then be
converted back using numpy.fft.irfft . Alternately, some future plans will
involve having to modify individual array element amplitudes directly based
on other parameters. I would think that modifying and re-synthesizing
signals using FFT is a fairly common use-case, but my attempts at Googling
example code have been fruitless.
I have FFT transform filter in my tidal analysis package. See
http://sourceforge.net/apps/mediawiki/tappy/index.php?title=CompareTidalFilters
for a comparison and short description.
See my function below. My earlier self made some poor variable name
choices. The 'low_bound' variable is actually where frequencies
greater are set to zero ('factor[freq low_bound] = 0.0'), then
factor is ramped from 0 at 'low_bound' to 1 at 'high_bound'. To
filter out tidal signals if your water elevations are hourly then
'low_bound' = 1/30.0 and 'high_bound' = 1/40.0. Having this gradual
change in the frequency domain rather than an abrupt change makes a
better filter.
def fft_lowpass(nelevation, low_bound, high_bound):
Performs a low pass filter on the nelevation series.
low_bound and high_bound specifies the boundary of the filter.
import numpy.fft as F
if len(nelevation) % 2:
result = F.rfft(nelevation, len(nelevation))
else:
result = F.rfft(nelevation)
freq = F.fftfreq(len(nelevation))[:len(nelevation)/2]
factor = np.ones_like(result)
factor[freq low_bound] = 0.0
sl = np.logical_and(high_bound freq, freq low_bound)
a = factor[sl]
# Create float array of required length and reverse
a = np.arange(len(a) + 2).astype(float)[::-1]
# Ramp from 1 to 0 exclusive
a = (a/a[0])[1:-1]
# Insert ramp into factor
factor[sl] = a
result = result * factor
print 'result=', len(result)
relevation = F.irfft(result, len(nelevation))
print 'result=', len(relevation)
return relevation
Kindest regards,
Tim
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Re: [Numpy-discussion] (no subject)
Hi Tim,
Brilliant! Many thanks... I think this is exactly what I need, I owe you
a beer (or other beverage of your choice).
I'm now going to lock myself in the basement until I can work out an
implementation of this for my use-case :)
/Carl
On Tue, Sep 3, 2013 at 9:05 PM, Cera, Tim t...@cerazone.net wrote:
I am trying to take the rfft of a numpy array, like this:
my_rfft = numpy.fft.rfft(my_numpy_array)
and replace the amplitudes that can be obtained with:
my_amplitudes = numpy.abs(my_rfft)
with amplitudes from an arbitrary numpy array's rFFT, which is to then be
converted back using numpy.fft.irfft . Alternately, some future plans
will
involve having to modify individual array element amplitudes directly
based
on other parameters. I would think that modifying and re-synthesizing
signals using FFT is a fairly common use-case, but my attempts at
Googling
example code have been fruitless.
I have FFT transform filter in my tidal analysis package. See
http://sourceforge.net/apps/mediawiki/tappy/index.php?title=CompareTidalFilters
for a comparison and short description.
See my function below. My earlier self made some poor variable name
choices. The 'low_bound' variable is actually where frequencies
greater are set to zero ('factor[freq low_bound] = 0.0'), then
factor is ramped from 0 at 'low_bound' to 1 at 'high_bound'. To
filter out tidal signals if your water elevations are hourly then
'low_bound' = 1/30.0 and 'high_bound' = 1/40.0. Having this gradual
change in the frequency domain rather than an abrupt change makes a
better filter.
def fft_lowpass(nelevation, low_bound, high_bound):
Performs a low pass filter on the nelevation series.
low_bound and high_bound specifies the boundary of the filter.
import numpy.fft as F
if len(nelevation) % 2:
result = F.rfft(nelevation, len(nelevation))
else:
result = F.rfft(nelevation)
freq = F.fftfreq(len(nelevation))[:len(nelevation)/2]
factor = np.ones_like(result)
factor[freq low_bound] = 0.0
sl = np.logical_and(high_bound freq, freq low_bound)
a = factor[sl]
# Create float array of required length and reverse
a = np.arange(len(a) + 2).astype(float)[::-1]
# Ramp from 1 to 0 exclusive
a = (a/a[0])[1:-1]
# Insert ramp into factor
factor[sl] = a
result = result * factor
print 'result=', len(result)
relevation = F.irfft(result, len(nelevation))
print 'result=', len(relevation)
return relevation
Kindest regards,
Tim
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