Mailman 3 August 2013 - SciPy-User

ANN: Scipy 0.13.0 beta 1 release
by Ralf Gommers Sept. 2, 2013

Sept. 2, 2013

Hi all, I'm happy to announce the availability of the first beta release of Scipy 0.13.0. Please try this beta and report any issues on the scipy-dev mailing list. Source tarballs and release notes can be found at https://sourceforge.net/projects/scipy/files/scipy/0.13.0b1/. Windows and OS X installers will follow later (we have a minor infrastructure issue to solve, and I'm at EuroScipy now). Cheers, Ralf

2 2

Scipy installation seemingly went ok, but some modules missing
by Dino Bektešević Aug. 30, 2013

Aug. 30, 2013

Hello, After a little mishap from ubuntu 12.04 after which I reinstalled the OS I installed the entire scipy-stack: >sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose as per instructions on scipy.org. Running numpy.test() returns OK (knownfail=3 skip=4) but running scipy.test() returns 'failed' and it seems that ndimage module wasn't even installed, or at the very least doesn't appear as a module in the IDLEs autocomplete and I kind of need it. Using apt-get again doesn't detect any new updates >sudo apt-get install python-scipy 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. neither does pip manage to update the library: (I understand this is a lot of text, but I don't know what is important and what isn't. The results of scipy.test() are given below so if this is not info of importance just scroll down below the > signs). >Downloading/unpacking scipy > Downloading scipy-0.12.0.zip (10.2Mb): 10.2Mb downloaded > Running setup.py egg_info for package scipy > Running from scipy source directory. > blas_opt_info: > blas_mkl_info: > libraries mkl,vml,guide not found in /usr/local/lib > libraries mkl,vml,guide not found in /usr/lib > libraries mkl,vml,guide not found in /usr/lib/x86_64-linux-gnu > NOT AVAILABLE > > atlas_blas_threads_info: > Setting PTATLAS=ATLAS > libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib > libraries ptf77blas,ptcblas,atlas not found in /usr/lib > libraries ptf77blas,ptcblas,atlas not found in /usr/lib/x86_64-linux-gnu > NOT AVAILABLE > > atlas_blas_info: > libraries f77blas,cblas,atlas not found in /usr/local/lib > libraries f77blas,cblas,atlas not found in /usr/lib > libraries f77blas,cblas,atlas not found in /usr/lib/x86_64-linux-gnu > NOT AVAILABLE > > /usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py:1423: >UserWarning: > Atlas (http://math-atlas.sourceforge.net/) libraries not found. > Directories to search for the libraries can be specified in the > numpy/distutils/site.cfg file (section [atlas]) or by setting > the ATLAS environment variable. > warnings.warn(AtlasNotFoundError.__doc__) > blas_info: > libraries blas not found in /usr/local/lib > libraries blas not found in /usr/lib > libraries blas not found in /usr/lib/x86_64-linux-gnu > NOT AVAILABLE > > /usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py:1432: >UserWarning: > Blas (http://www.netlib.org/blas/) libraries not found. > Directories to search for the libraries can be specified in the > numpy/distutils/site.cfg file (section [blas]) or by setting > the BLAS environment variable. > warnings.warn(BlasNotFoundError.__doc__) > blas_src_info: > NOT AVAILABLE > > /usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py:1435: >UserWarning: > Blas (http://www.netlib.org/blas/) sources not found. > Directories to search for the sources can be specified in the > numpy/distutils/site.cfg file (section [blas_src]) or by setting > the BLAS_SRC environment variable. > warnings.warn(BlasSrcNotFoundError.__doc__) > Traceback (most recent call last): > File "<string>", line 14, in <module> > File "/home/dino/build/scipy/setup.py", line 165, in <module> > setup_package() > File "/home/dino/build/scipy/setup.py", line 161, in setup_package > configuration=configuration) > File "/usr/lib/python2.7/dist-packages/numpy/distutils/core.py", line 152, >in setup > config = configuration() > File "/home/dino/build/scipy/setup.py", line 136, in configuration > config.add_subpackage('scipy') > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >1002, in add_subpackage > caller_level = 2) > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >971, in get_subpackage > caller_level = caller_level + 1) > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >908, in _get_configuration_from_setup_py > config = setup_module.configuration(*args) > File "scipy/setup.py", line 9, in configuration > config.add_subpackage('integrate') > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >1002, in add_subpackage > caller_level = 2) > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >971, in get_subpackage > caller_level = caller_level + 1) > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >908, in _get_configuration_from_setup_py > config = setup_module.configuration(*args) > File "scipy/integrate/setup.py", line 11, in configuration > blas_opt = get_info('blas_opt',notfound_action=2) > File "/usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py", >line 320, in get_info > return cl().get_info(notfound_action) > File "/usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py", >line 471, in get_info > raise self.notfounderror(self.notfounderror.__doc__) > numpy.distutils.system_info.BlasNotFoundError: > Blas (http://www.netlib.org/blas/) libraries not found. > Directories to search for the libraries can be specified in the > numpy/distutils/site.cfg file (section [blas]) or by setting > the BLAS environment variable. > Complete output from command python setup.py egg_info: > Running from scipy source directory. > >blas_opt_info: > >blas_mkl_info: > > libraries mkl,vml,guide not found in /usr/local/lib > > libraries mkl,vml,guide not found in /usr/lib > > libraries mkl,vml,guide not found in /usr/lib/x86_64-linux-gnu > > NOT AVAILABLE > > > >atlas_blas_threads_info: > >Setting PTATLAS=ATLAS > > libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib > > libraries ptf77blas,ptcblas,atlas not found in /usr/lib > > libraries ptf77blas,ptcblas,atlas not found in /usr/lib/x86_64-linux-gnu > > NOT AVAILABLE > > > >atlas_blas_info: > > libraries f77blas,cblas,atlas not found in /usr/local/lib > > libraries f77blas,cblas,atlas not found in /usr/lib > > libraries f77blas,cblas,atlas not found in /usr/lib/x86_64-linux-gnu > > NOT AVAILABLE > > > >/usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py:1423: >UserWarning: > > Atlas (http://math-atlas.sourceforge.net/) libraries not found. > > Directories to search for the libraries can be specified in the > > numpy/distutils/site.cfg file (section [atlas]) or by setting > > the ATLAS environment variable. > > warnings.warn(AtlasNotFoundError.__doc__) > >blas_info: > > libraries blas not found in /usr/local/lib > > libraries blas not found in /usr/lib > > libraries blas not found in /usr/lib/x86_64-linux-gnu > > NOT AVAILABLE > > > >/usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py:1432: >UserWarning: > > Blas (http://www.netlib.org/blas/) libraries not found. > > Directories to search for the libraries can be specified in the > > numpy/distutils/site.cfg file (section [blas]) or by setting > > the BLAS environment variable. > > warnings.warn(BlasNotFoundError.__doc__) > >blas_src_info: > > NOT AVAILABLE > > > >/usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py:1435: >UserWarning: > > Blas (http://www.netlib.org/blas/) sources not found. > > Directories to search for the sources can be specified in the > > numpy/distutils/site.cfg file (section [blas_src]) or by setting > > the BLAS_SRC environment variable. > > warnings.warn(BlasSrcNotFoundError.__doc__) > >Traceback (most recent call last): > > File "<string>", line 14, in <module> > > File "/home/dino/build/scipy/setup.py", line 165, in <module> > > setup_package() > > File "/home/dino/build/scipy/setup.py", line 161, in setup_package > > configuration=configuration) > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/core.py", line 152, in >setup > > config = configuration() > > File "/home/dino/build/scipy/setup.py", line 136, in configuration > > config.add_subpackage('scipy') > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >1002, in add_subpackage > > caller_level = 2) > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >971, in get_subpackage > > caller_level = caller_level + 1) > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >908, in _get_configuration_from_setup_py > > config = setup_module.configuration(*args) > > File "scipy/setup.py", line 9, in configuration > > config.add_subpackage('integrate') > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >1002, in add_subpackage > > caller_level = 2) > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >971, in get_subpackage > > caller_level = caller_level + 1) > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/misc_util.py", line >908, in _get_configuration_from_setup_py > > config = setup_module.configuration(*args) > > File "scipy/integrate/setup.py", line 11, in configuration > > blas_opt = get_info('blas_opt',notfound_action=2) > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py", line >320, in get_info > > return cl().get_info(notfound_action) > > File "/usr/lib/python2.7/dist-packages/numpy/distutils/system_info.py", line >471, in get_info > > raise self.notfounderror(self.notfounderror.__doc__) > >numpy.distutils.system_info.BlasNotFoundError: > > Blas (http://www.netlib.org/blas/) libraries not found. > > Directories to search for the libraries can be specified in the > > numpy/distutils/site.cfg file (section [blas]) or by setting > > the BLAS environment variable. > >---------------------------------------- >Command python setup.py egg_info failed with error code 1 >Storing complete log in /home/dino/.pip/pip.log HERE ARE THE RESULTS OF SCIPY.TEST(): ________________________________________________________________ ________________________________________________________________ Python 2.7.3 (default, Apr 10 2013, 06:20:15) [GCC 4.6.3] on linux2 Type "copyright", "credits" or "license()" for more information. >>> import scipy >>> scipy.test() Running unit tests for scipy NumPy version 1.6.1 NumPy is installed in /usr/lib/python2.7/dist-packages/numpy SciPy version 0.9.0 SciPy is installed in /usr/lib/python2.7/dist-packages/scipy Python version 2.7.3 (default, Apr 10 2013, 06:20:15) [GCC 4.6.3] nose version 1.1.2 ........................................K..K............................................................................................................................................................................................................................................................................................................................................................................................................................................................................SSSSSS......SSSSSS.....................................................................................................................................................................................................F......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K..................................................................................................................................................................................K...............................................................K...........................................................................................................................................................KK...............................................................................................................................................................................................................................................................K.K........................................................................................................................................................................................................................................................................................................................................................................................K........K........... Warning (from warnings module): File "/usr/lib/python2.7/dist-packages/scipy/special/tests/testutils.py", line 196 diff = np.absolute(x - y) RuntimeWarning: invalid value encountered in subtract .... Warning (from warnings module): File "/usr/lib/python2.7/dist-packages/scipy/special/tests/testutils.py", line 199 rdiff = diff / np.absolute(y) RuntimeWarning: invalid value encountered in divide .................................................................................................................................................................................................................................................................................................................................................................................................................................... Warning (from warnings module): File "/usr/lib/python2.7/dist-packages/scipy/stats/distributions.py", line 3955 vals = 2*(bt+1.0)*sqrt(b-2.0)/((b-3.0)*sqrt(b)) RuntimeWarning: invalid value encountered in sqrt .........................................................................................................................................................................................................................................................................................................S..............................................................................................................................................................................................F............................................................................................................................................................................................................................................................................................................................................................................................................. ====================================================================== FAIL: test_io.test_imread ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest self.test(*self.arg) File "/usr/lib/python2.7/dist-packages/numpy/testing/decorators.py", line 146, in skipper_func return f(*args, **kwargs) File "/usr/lib/python2.7/dist-packages/scipy/ndimage/tests/test_io.py", line 16, in test_imread assert_array_equal(img.shape, (300, 420, 3)) File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 707, in assert_array_equal verbose=verbose, header='Arrays are not equal') File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 600, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not equal (shapes (2,), (3,) mismatch) x: array([300, 420]) y: array([300, 420, 3]) ====================================================================== FAIL: test_expon (test_morestats.TestAnderson) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/scipy/stats/tests/test_morestats.py", line 72, in test_expon assert_array_less(crit[:-1], A) File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 869, in assert_array_less header='Arrays are not less-ordered') File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 613, in assert_array_compare chk_same_position(x_id, y_id, hasval='inf') File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 588, in chk_same_position raise AssertionError(msg) AssertionError: Arrays are not less-ordered x and y inf location mismatch: x: array([ 0.911, 1.065, 1.325, 1.587]) y: array(inf) ---------------------------------------------------------------------- Ran 3780 tests in 57.542s FAILED (KNOWNFAIL=11, SKIP=13, failures=2) <nose.result.TextTestResult run=3780 errors=0 failures=2> >>> help is greatly appreciated, Dino Bektešević

1 0

Bug? in scipy.interpolate.splprep
by scott rifkin Aug. 30, 2013

Aug. 30, 2013

I have been getting an error running splprep in the interpolate module with task=-1. This is reproducible across different distributions of python (enthought, regular) although all 2.7.x I have pasted the error message below. In all cases it ends up Aborting or killing the kernel (for ipython notebook). The call is: tck,u=spi.splprep(xyz, k=deg,u=uu,task=-1,t=knots) where spi is scipy.interpolate, xyz is the data (3x216 numpy array), deg is 3 here, uu is a 1x216 numpy array, and knots is also a numpy array. In this case, knots started with four 0s, ended with 4 1s and had numbers about every 0.05. I don't get this error when task=1. Sometimes (although not very often) task=-1 doesn't give me an error, but I need to explore this more to see if there are any patterns (like a particular set of data). Thanks much for any help, Scott *** glibc detected *** python: free(): invalid next size (fast): 0x0bbd9bb8 *** ======= Backtrace: ========= /lib/i386-linux-gnu/libc.so.6(+0x75ee2)[0xb73e4ee2] /usr/lib/python2.7/dist-packages/numpy/core/multiarray.so(+0x8f729)[0xb6348729] python[0x81669e3] python[0x816653b] python(PyEval_EvalCodeEx+0x16a)[0x81a3bca] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalCode+0x57)[0x81a4b57] python(PyEval_EvalFrameEx+0x2b1e)[0x819fe5e] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyEval_EvalFrameEx+0x7c4)[0x819db04] python(PyEval_EvalCodeEx+0x150)[0x81a3bb0] python(PyRun_FileExFlags+0xe1)[0x80db871] python(PyRun_SimpleFileExFlags+0x21a)[0x80dbb2a] python(Py_Main+0x84c)[0x80dcf0c] python(main+0x1b)[0x805e9fb] /lib/i386-linux-gnu/libc.so.6(__libc_start_main+0xf3)[0xb73884d3] python[0x805ea21] ======= Memory map: ======== 08048000-0829e000 r-xp 00000000 08:01 4850418 /usr/bin/python2.7 0829e000-0829f000 r--p 00255000 08:01 4850418 /usr/bin/python2.7 0829f000-082f4000 rw-p 00256000 08:01 4850418 /usr/bin/python2.7 082f4000-08300000 rw-p 00000000 00:00 0 09da2000-0c6ac000 rw-p 00000000 00:00 0 [heap] b3e02000-b3e66000 r-xp 00000000 08:01 7736484 /usr/lib/pyshared/python2.7/matplotlib/backends/_backend_agg.so b3e66000-b3e67000 r--p 00064000 08:01 7736484 /usr/lib/pyshared/python2.7/matplotlib/backends/_backend_agg.so b3e67000-b3e68000 rw-p 00065000 08:01 7736484 /usr/lib/pyshared/python2.7/matplotlib/backends/_backend_agg.so b3e68000-b3e87000 r-xp 00000000 08:01 7736485 /usr/lib/pyshared/python2.7/matplotlib/backends/_tkagg.so b3e87000-b3e88000 r--p 0001e000 08:01 7736485 /usr/lib/pyshared/python2.7/matplotlib/backends/_tkagg.so b3e88000-b3e89000 rw-p 0001f000 08:01 7736485 /usr/lib/pyshared/python2.7/matplotlib/backends/_tkagg.so b3e89000-b3e8a000 ---p 00000000 00:00 0 b3e8a000-b468a000 rw-p 00000000 00:00 0 b468a000-b468f000 r-xp 00000000 08:01 4854393 /usr/lib/i386-linux-gnu/libXdmcp.so.6.0.0 b468f000-b4690000 r--p 00004000 08:01 4854393 /usr/lib/i386-linux-gnu/libXdmcp.so.6.0.0 b4690000-b4691000 rw-p 00005000 08:01 4854393 /usr/lib/i386-linux-gnu/libXdmcp.so.6.0.0 b4691000-b4699000 r-xp 00000000 08:01 4854415 /usr/lib/i386-linux-gnu/libXrender.so.1.3.0 b4699000-b469a000 r--p 00007000 08:01 4854415 /usr/lib/i386-linux-gnu/libXrender.so.1.3.0 b469a000-b469b000 rw-p 00008000 08:01 4854415 /usr/lib/i386-linux-gnu/libXrender.so.1.3.0 b469b000-b46ab000 r-xp 00000000 08:01 4854395 /usr/lib/i386-linux-gnu/libXext.so.6.4.0 b46ab000-b46ac000 r--p 0000f000 08:01 4854395 /usr/lib/i386-linux-gnu/libXext.so.6.4.0 b46ac000-b46ad000 rw-p 00010000 08:01 4854395 /usr/lib/i386-linux-gnu/libXext.so.6.4.0 b46ad000-b46cc000 r-xp 00000000 08:01 4854968 /usr/lib/i386-linux-gnu/libxcb.so.1.1.0 b46cc000-b46cd000 r--p 0001f000 08:01 4854968 /usr/lib/i386-linux-gnu/libxcb.so.1.1.0 b46cd000-b46ce000 rw-p 00020000 08:01 4854968 /usr/lib/i386-linux-gnu/libxcb.so.1.1.0 b46ce000-b4700000 r-xp 00000000 08:01 4854555 /usr/lib/i386-linux-gnu/libfontconfig.so.1.4.4 b4700000-b4701000 r--p 00032000 08:01 4854555 /usr/lib/i386-linux-gnu/libfontconfig.so.1.4.4 b4701000-b4702000 rw-p 00033000 08:01 4854555 /usr/lib/i386-linux-gnu/libfontconfig.so.1.4.4 b4702000-b4716000 r-xp 00000000 08:01 4854399 /usr/lib/i386-linux-gnu/libXft.so.2.2.0 b4716000-b4717000 r--p 00013000 08:01 4854399 /usr/lib/i386-linux-gnu/libXft.so.2.2.0 b4717000-b4718000 rw-p 00014000 08:01 4854399 /usr/lib/i386-linux-gnu/libXft.so.2.2.0 b4718000-b4848000 r-xp 00000000 08:01 4854380 /usr/lib/i386-linux-gnu/libX11.so.6.3.0 b4848000-b4849000 r--p 0012f000 08:01 4854380 /usr/lib/i386-linux-gnu/libX11.so.6.3.0 b4849000-b484b000 rw-p 00130000 08:01 4854380 /usr/lib/i386-linux-gnu/libX11.so.6.3.0 b484b000-b484c000 rw-p 00000000 00:00 0 b484c000-b495f000 r-xp 00000000 08:01 4852727 /usr/lib/libtcl8.5.so.0 b495f000-b4961000 r--p 00113000 08:01 4852727 /usr/lib/libtcl8.5.so.0 b4961000-b4965000 rw-p 00115000 08:01 4852727 /usr/lib/libtcl8.5.so.0 b4965000-b4966000 rw-p 00000000 00:00 0 b4966000-b4a87000 r-xp 00000000 08:01 4859164 /usr/lib/libtk8.5.so.0 b4a87000-b4a88000 ---p 00121000 08:01 4859164 /usr/lib/libtk8.5.so.0 b4a88000-b4a8e000 r--p 00121000 08:01 4859164 /usr/lib/libtk8.5.so.0 b4a8e000-b4a99000 rw-p 00127000 08:01 4859164 /usr/lib/libtk8.5.so.0 b4a99000-b4b73000 r-xp 00000000 08:01 4859166 /usr/lib/libBLT.2.4.so.8.5 b4b73000-b4b74000 r--p 000d9000 08:01 4859166 /usr/lib/libBLT.2.4.so.8.5 b4b74000-b4b85000 rw-p 000da000 08:01 4859166 /usr/lib/libBLT.2.4.so.8.5 b4b85000-b4b86000 rw-p 00000000 00:00 0 b4b9a000-b4ba5000 r-xp 00000000 08:01 4989616 /usr/lib/python2.7/lib-dynload/_tkinter.so b4ba5000-b4ba6000 r--p 0000a000 08:01 4989616 /usr/lib/python2.7/lib-dynload/_tkinter.so b4ba6000-b4ba7000 rw-p 0000b000 08:01 4989616 /usr/lib/python2.7/lib-dynload/_tkinter.so b4ba7000-b4bdc000 r-xp 00000000 08:01 7736488 /usr/lib/pyshared/python2.7/matplotlib/_tri.so b4bdc000-b4bdd000 r--p 00035000 08:01 7736488 /usr/lib/pyshared/python2.7/matplotlib/_tri.so b4bdd000-b4bde000 rw-p 00036000 08:01 7736488 /usr/lib/pyshared/python2.7/matplotlib/_tri.so b4bde000-b4c1e000 r-xp 00000000 08:01 7736489 /usr/lib/pyshared/python2.7/matplotlib/_image.so b4c1e000-b4c1f000 r--p 00040000 08:01 7736489 /usr/lib/pyshared/python2.7/matplotlib/_image.so b4c1f000-b4c21000 rw-p 00041000 08:01 7736489 /usr/lib/pyshared/python2.7/matplotlib/_image.so b4c21000-b4c49000 r-xp 00000000 08:01 9438192 /lib/i386-linux-gnu/libpng12.so.0.46.0 b4c49000-b4c4a000 r--p 00027000 08:01 9438192 /lib/i386-linux-gnu/libpng12.so.0.46.0 b4c4a000-b4c4b000 rw-p 00028000 08:01 9438192 /lib/i386-linux-gnu/libpng12.so.0.46.0 b4c4f000-b4c5d000 r-xp 00000000 08:01 7736490 /usr/lib/pyshared/python2.7/matplotlib/_delaunay.so b4c5d000-b4c5e000 r--p 0000d000 08:01 7736490 /usr/lib/pyshared/python2.7/matplotlib/_delaunay.so b4c5e000-b4c5f000 rw-p 0000e000 08:01 7736490 /usr/lib/pyshared/python2.7/matplotlib/_delaunay.so b4c5f000-b4c83000 r-xp 00000000 08:01 7736486 /usr/lib/pyshared/python2.7/matplotlib/_png.so b4c83000-b4c84000 r--p 00024000 08:01 7736486 /usr/lib/pyshared/python2.7/matplotlib/_png.so b4c84000-b4c85000 rw-p 00025000 08:01 7736486 /usr/lib/pyshared/python2.7/matplotlib/_png.so b4c85000-b4d1a000 r-xp 00000000 08:01 4854563 /usr/lib/i386-linux-gnu/libfreetype.so.6.8.0 b4d1a000-b4d1e000 r--p 00094000 08:01 4854563 /usr/lib/i386-linux-gnu/libfreetype.so.6.8.0 b4d1e000-b4d1f000 rw-p 00098000 08:01 4854563 /usr/lib/i386-linux-gnu/libfreetype.so.6.8.0 b4d1f000-b4d5f000 r-xp 00000000 08:01 7736491 /usr/lib/pyshared/python2.7/matplotlib/ft2font.so b4d5f000-b4d61000 r--p 0003f000 08:01 7736491 /usr/lib/pyshared/python2.7/matplotlib/ft2font.so b4d61000-b4d63000 rw-p 00041000 08:01 7736491 /usr/lib/pyshared/python2.7/matplotlib/ft2font.so b4d63000-b4ee4000 rw-p 00000000 00:00 0 b4ee4000-b4eed000 r-xp 00000000 08:01 4985383 /usr/lib/python2.7/dist-packages/scipy/spatial/_distance_wrap.so b4eed000-b4eee000 r--p 00009000 08:01 4985383 /usr/lib/python2.7/dist-packages/scipy/spatial/_distance_wrap.so b4eee000-b4eef000 rw-p 0000a000 08:01 4985383 /usr/lib/python2.7/dist-packages/scipy/spatial/_distance_wrap.so b4eef000-b4f6a000 r-xp 00000000 08:01 4985382 /usr/lib/python2.7/dist-packages/scipy/spatial/qhull.so b4f6a000-b4f6b000 r--p 0007a000 08:01 4985382 /usr/lib/python2.7/dist-packages/scipy/spatial/qhull.so b4f6b000-b4f6f000 rw-p 0007b000 08:01 4985382 /usr/lib/python2.7/dist-packages/scipy/spatial/qhull.so b4f6f000-b4f71000 rw-p 00000000 00:00 0 b4f71000-b4f84000 r-xp 00000000 08:01 4985381 /usr/lib/python2.7/dist-packages/scipy/spatial/ckdtree.so b4f84000-b4f85000 r--p 00012000 08:01 4985381 /usr/lib/python2.7/dist-packages/scipy/spatial/ckdtree.so b4f85000-b4f87000 rw-p 00013000 08:01 4985381 /usr/lib/python2.7/dist-packages/scipy/spatial/ckdtree.so b4f87000-b4f8d000 r-xp 00000000 08:01 7214810 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csgraph.so b4f8d000-b4f8e000 r--p 00005000 08:01 7214810 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csgraph.so b4f8e000-b4f8f000 rw-p 00006000 08:01 7214810 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csgraph.so b4f8f000-b5083000 r-xp 00000000 08:01 7214806 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_bsr.so b5083000-b5084000 r--p 000f3000 08:01 7214806 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_bsr.so b5084000-b5085000 rw-p 000f4000 08:01 7214806 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_bsr.so b5085000-b50a9000 r-xp 00000000 08:01 7214807 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_coo.so b50a9000-b50aa000 r--p 00023000 08:01 7214807 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_coo.so b50aa000-b50ab000 rw-p 00024000 08:01 7214807 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_coo.so b50ab000-b5131000 r-xp 00000000 08:01 7214808 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csc.so b5131000-b5132000 r--p 00085000 08:01 7214808 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csc.so b5132000-b5133000 rw-p 00086000 08:01 7214808 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csc.so b5133000-b520b000 r-xp 00000000 08:01 4854892 /usr/lib/i386-linux-gnu/libstdc++.so.6.0.16 b520b000-b520c000 ---p 000d8000 08:01 4854892 /usr/lib/i386-linux-gnu/libstdc++.so.6.0.16 b520c000-b5210000 r--p 000d8000 08:01 4854892 /usr/lib/i386-linux-gnu/libstdc++.so.6.0.16 b5210000-b5211000 rw-p 000dc000 08:01 4854892 /usr/lib/i386-linux-gnu/libstdc++.so.6.0.16 b5211000-b5218000 rw-p 00000000 00:00 0 b521c000-b522a000 r-xp 00000000 08:01 7214809 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_dia.so b522a000-b522b000 r--p 0000d000 08:01 7214809 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_dia.so b522b000-b522c000 rw-p 0000e000 08:01 7214809 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_dia.so b522c000-b5301000 r-xp 00000000 08:01 7214811 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csr.so b5301000-b5302000 r--p 000d4000 08:01 7214811 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csr.so b5302000-b5303000 rw-p 000d5000 08:01 7214811 /usr/lib/python2.7/dist-packages/scipy/sparse/sparsetools/_csr.so b5303000-b532a000 r-xp 00000000 08:01 8132175 /usr/lib/python2.7/dist-packages/scipy/interpolate/interpnd.so b532a000-b532b000 r--p 00026000 08:01 8132175 /usr/lib/python2.7/dist-packages/scipy/interpolate/interpnd.so b532b000-b532e000 rw-p 00027000 08:01 8132175 /usr/lib/python2.7/dist-packages/scipy/interpolate/interpnd.so b532e000-b5333000 r-xp 00000000 08:01 8132213 /usr/lib/python2.7/dist-packages/scipy/linalg/cblas.so b5333000-b5334000 r--p 00004000 08:01 8132213 /usr/lib/python2.7/dist-packages/scipy/linalg/cblas.so b5334000-b5335000 rw-p 00005000 08:01 8132213 /usr/lib/python2.7/dist-packages/scipy/linalg/cblas.so b5335000-b5361000 r-xp 00000000 08:01 8132207 /usr/lib/python2.7/dist-packages/scipy/linalg/fblas.so b5361000-b5362000 r--p 0002b000 08:01 8132207 /usr/lib/python2.7/dist-packages/scipy/linalg/fblas.so b5362000-b536d000 rw-p 0002c000 08:01 8132207 /usr/lib/python2.7/dist-packages/scipy/linalg/fblas.so b536d000-b5377000 r-xp 00000000 08:01 8132210 /usr/lib/python2.7/dist-packages/scipy/linalg/calc_lwork.so b5377000-b5378000 r--p 00009000 08:01 8132210 /usr/lib/python2.7/dist-packages/scipy/linalg/calc_lwork.so b5378000-b537a000 rw-p 0000a000 08:01 8132210 /usr/lib/python2.7/dist-packages/scipy/linalg/calc_lwork.so b537a000-b537f000 r-xp 00000000 08:01 8132208 /usr/lib/python2.7/dist-packages/scipy/linalg/clapack.so b537f000-b5380000 r--p 00004000 08:01 8132208 /usr/lib/python2.7/dist-packages/scipy/linalg/clapack.so b5380000-b5381000 rw-p 00005000 08:01 8132208 /usr/lib/python2.7/dist-packages/scipy/linalg/clapack.so b5381000-b53db000 r-xp 00000000 08:01 8132211 /usr/lib/python2.7/dist-packages/scipy/linalg/flapack.so b53db000-b53dc000 r--p 00059000 08:01 8132211 /usr/lib/python2.7/dist-packages/scipy/linalg/flapack.so[1]+ Killed idle -n Aborted (core dumped)

1 0

__sizeof__ disagrees with .nbytes
by Gabriel Gaster Aug. 29, 2013

Aug. 29, 2013

Run the following code: from scipy import sparse x = sparse.rand(100,10,.2) print x.__sizeof__() ## 32 or 16, depending print x.data.__sizeof__() ## 80 or 40, depending print x.data.nbytes ## 1600 import numpy print numpy.arange(200).__sizeof__() ## 80 or 40, depending print numpy.arange(200).nbytes ## 1600 1. Why is nbytes different than __sizeof__ ? I believe nbytes and do not believe __sizeof__. __sizeof__ should just be rewritten to default to nbytes. 2. It seems that the __sizeof__ methods need to be updated for numpy.array AND for scipy.sparse. I'm happy to do this -- but before I do, I wanted to sanity check with the list. Thanks. Gabe -- gabe

1 0

Re: [SciPy-User] Populate an array with integers (randomly, weighted)
by Johannes Radinger Aug. 29, 2013

Aug. 29, 2013

Hi, > > Thank you Robert, that sounds like a very straight forward solution to > that task. > > The working example following the given data can look like: > > > > np.random.multinomial(300, y.flat, size=1).reshape(y.shape) > > > > > > However, I had a similar discussion a while ago already in stackoverflow > where a different solution has been proposed: > > > > np.bincount(np.searchsorted(np.cumsum(y), np.random.random(300)), > minlength=y.size).reshape(y.size) > > > > respectively for numpy > 1.7 > > np.bincount(np.searchsorted(np.random.choice(y.size, 300, p=y.flat), > minlength=y.size).reshape(y.size) > > > > So what I am wondering is what is the actual difference in the meaning > behind both approaches? > > Do they actually provide results based on a totally different meaning of > "weighted random"? And what are the consequences then? As am not really > familiar with statistics maybe someone can clarify that? > > First, if you would like to participate in threads, we would really > appreciate it if you subscribe to the mailing list normally instead of > using the digest. If you feel you must use the digest, please trim your > replies and adjust the Subject: line. Thanks. > Oh sorry, I totally forgot that the digest was included in the mail, I was just faster writing email than I was thinking ;), sorry for that. Now the mail shoud be trimmed and the subject is adjusted. > > As for the different approaches, they only minor benefit of the second > approach is that it does not need normalized weights. Otherwise, all of the > approaches are sampling the same thing, just rather less efficiently and > clearly than multinomial() does. > Thank you for that answer, and as normalizing weights is an easy step and the multinomial() approach is much clearer to me I'll stay with that. /Johannes > > -- > Robert Kern > http://mail.scipy.org/mailman/listinfo/scipy-user > >

1 0

Re: [SciPy-User] SciPy-User Digest, Vol 120, Issue 19
by Johannes Radinger Aug. 28, 2013

Aug. 28, 2013

Thank you Robert, that sounds like a very straight forward solution to that task. The working example following the given data can look like: np.random.multinomial(300, y.flat, size=1).reshape(y.shape) However, I had a similar discussion a while ago already in stackoverflow where a different solution has been proposed: np.bincount(np.searchsorted(np.cumsum(y), np.random.random(300)), minlength=y.size).reshape(y.size) respectively for numpy > 1.7 np.bincount(np.searchsorted(np.random.choice(y.size, 300, p=y.flat), minlength=y.size).reshape(y.size) So what I am wondering is what is the actual difference in the meaning behind both approaches? Do they actually provide results based on a totally different meaning of "weighted random"? And what are the consequences then? As am not really familiar with statistics maybe someone can clarify that? Thanks a lot! cheers, Johannes On Tue, Aug 27, 2013 at 7:00 PM, <scipy-user-request(a)scipy.org> wrote: > Send SciPy-User mailing list submissions to > scipy-user(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://mail.scipy.org/mailman/listinfo/scipy-user > or, via email, send a message with subject or body 'help' to > scipy-user-request(a)scipy.org > > You can reach the person managing the list at > scipy-user-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of SciPy-User digest..." > > > Today's Topics: > > 1. Populate an array with integers (randomly, weighted) > (Johannes Radinger) > 2. Re: Populate an array with integers (randomly, weighted) > (Robert Kern) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 27 Aug 2013 09:54:13 +0200 > From: Johannes Radinger <johannesradinger(a)gmail.com> > Subject: [SciPy-User] Populate an array with integers (randomly, > weighted) > To: scipy-user(a)scipy.org > Message-ID: > <CABsGe_yqbiZG-N9Wzm4-kJSp-9wSXxSAgcV-x7c= > JU_OCVic3Q(a)mail.gmail.com> > Content-Type: text/plain; charset="iso-8859-1" > > Hi, > > I'd like to randomly populate a numpy array (100,100) so that the > numpy.sum() of the array equals exactly 300 > (e.g. distribute 300 rice grains on a chess board). The final array should > only contain integers (so no floats) from > 0 to X (max 300). Furthermore I'd like to use a weighing factor, so that > some of my array-cells are more probably to > be populated than others. Therefore I have an array of the same size > (100,100) and that is in numpy.sum = 1: > > x = numpy.random.random_sample((10,10)) > y = x/numpy.sum(x) > > Some side conditions: Of course if one cell has exactly 0 as weighing > factor, also the result has to be 0 for that > cell. If only one cell has a weighing factor at all (all others are 0) the > entire 300 has to be assigned to that cell. > > Can that easily be achieved with Scipy/Numpy? > > Thank you so much for your help, > > Johannes >

2 1

Small bug? in matplotlib.pyplot.plot_date()
by Karl Kappler Aug. 28, 2013

Aug. 28, 2013

Hello, Wanted to share some unexpected behaviour encountered with matplotlib.pyplot.plot_date(). This method allows easy plotting of data with datetime abscissa values. A code snippet for usage is here: import matplotlib as mpl import numpy as np #<Make some data and metadata> N = 899 #nObservations data = np.random.rand(N) dt = 86400./N #set timeStep in seconds t0 = datetime.datetime.now() #t0 = datetime.datetime(1,1,1,0,0,0) #t0 = datetime.datetime(2000,1,1,0,0,0) #<\Make some data and metadata> #<Reformat x-axis consistant for plot_date> list_of_datetimes = [t0 + datetime.timedelta(seconds=i*dt) for i in range(N)] dates = mpl.dates.date2num(list_of_datetimes) #<\Reformat x-axis consistant for plot_date> mpl.pyplot.plot_date(dates, np.real(gData), tz=None, fmt = 'b-', hold=None) mpl.pyplot.show() The above snippet creates a plot with time-axis that I want. Uncommentting the second definition of t0 however results in a plot with no x-axis labels. Output to console (I am working in Spyder) is pasted below. Uncommentting the third definition of t0 results in all working fine again. So I am speculating that there is a problem in plot_date() having to do with string casting the year, perhaps an assumption that it has four digits? For my use cases I can work around this, but is it a (small) bug? Karl ________________________________ CONSOLE OUTPUT: Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_qt4.py", line 220, in resizeEvent self.draw() File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_qt4agg.py", line 125, in draw FigureCanvasAgg.draw(self) File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_agg.py", line 421, in draw self.figure.draw(self.renderer) File "/usr/lib/pymodules/python2.7/matplotlib/artist.py", line 55, in draw_wrapper draw(artist, renderer, *args, **kwargs) File "/usr/lib/pymodules/python2.7/matplotlib/figure.py", line 898, in draw func(*args) File "/usr/lib/pymodules/python2.7/matplotlib/artist.py", line 55, in draw_wrapper draw(artist, renderer, *args, **kwargs) File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 1997, in draw a.draw(renderer) File "/usr/lib/pymodules/python2.7/matplotlib/artist.py", line 55, in draw_wrapper draw(artist, renderer, *args, **kwargs) File "/usr/lib/pymodules/python2.7/matplotlib/axis.py", line 1041, in draw ticks_to_draw = self._update_ticks(renderer) File "/usr/lib/pymodules/python2.7/matplotlib/axis.py", line 931, in _update_ticks tick_tups = [ t for t in self.iter_ticks()] File "/usr/lib/pymodules/python2.7/matplotlib/axis.py", line 878, in iter_ticks majorLocs = self.major.locator() File "/usr/lib/pymodules/python2.7/matplotlib/dates.py", line 750, in __call__ return self._locator() File "/usr/lib/pymodules/python2.7/matplotlib/dates.py", line 571, in __call__ start = dmin - delta File "/usr/lib/python2.7/dist-packages/dateutil/relativedelta.py", line 289, in __rsub__ return self.__neg__().__radd__(other) File "/usr/lib/python2.7/dist-packages/dateutil/relativedelta.py", line 276, in __radd__ microseconds=self.microseconds)) OverflowError: date value out of range

1 0

Populate an array with integers (randomly, weighted)
by Johannes Radinger Aug. 27, 2013

Aug. 27, 2013

Hi, I'd like to randomly populate a numpy array (100,100) so that the numpy.sum() of the array equals exactly 300 (e.g. distribute 300 rice grains on a chess board). The final array should only contain integers (so no floats) from 0 to X (max 300). Furthermore I'd like to use a weighing factor, so that some of my array-cells are more probably to be populated than others. Therefore I have an array of the same size (100,100) and that is in numpy.sum = 1: x = numpy.random.random_sample((10,10)) y = x/numpy.sum(x) Some side conditions: Of course if one cell has exactly 0 as weighing factor, also the result has to be 0 for that cell. If only one cell has a weighing factor at all (all others are 0) the entire 300 has to be assigned to that cell. Can that easily be achieved with Scipy/Numpy? Thank you so much for your help, Johannes

4 3

sprint mini-report
by Ralf Gommers Aug. 25, 2013

Aug. 25, 2013

Hi all, Today we had 18 people in the room at the EuroScipy sprint - many of which made their first contribution to scipy today - plus Josef joining remotely and David C hiding out in the scikit-learn room. Some statistics on what we managed to do: - 21 scipy PRs - 9 scipy PRs merged - 5 scipy issues closed, and many more discussed - 2 numpy commits - 2 scipy.org PRs Thanks everyone for joining (keep those PRs coming...:) ), and Pierre for the coffee runs and pizza! Cheers, Ralf

1 0

Optimization on the subspace of a domain
by Cy. T. Aug. 25, 2013

Aug. 25, 2013

Hello there, As I am learning how to do minimization using SciPy, I wonder if I can do the minimization just on a (proper) subspace of the domain of a function. For instance, if I have a multivariate function f(x,y,z), can I ask SciPy to find a minimum by changing only x and y and keeping z fixed? It would work if I define a new function: f(x,y,z_0) = h(x,y) for a given z_0, but I wonder if there is an option for doing that without defining another function. Thanks.

3 3