Mailman 3 July 2014 - SciPy-User

Confusing behaviour of map_interpolate
by Hanno Klemm 27 Jul '14

27 Jul '14

Hi, I want to use scipy.ndimage.map_coordinates to interpolate a 3D cube of data. I am not sure if this is expected behaviour, or not what I am seeing. The example script below produces very “rugged” looking traces, when I look at the data along the z-axis. It appears as if the interpolation in the third direction is a nearest neighbour interpolation. If this is expected, it is not made very clear in the docs, I expected a spline interpolation in that direction, as well. Is that a bug, or are the docs unclear? #This is the script import numpy as np import scipy.ndimage as sn import pylab as plt x, y = np.ogrid[-3:3:20j, -3:3:20j] z = np.linspace(1, 2, 11) def ff(x,y,z): return np.exp(-(x[:,:,None]**2+y[:,:,None]**2)/(2*z[None,None,::-1]**2)) res = ff(x,y,z) x_index,y_index = np.mgrid[:20:,:20:] z_i=np.linspace(0,11, 23) coords = np.array((x_index, y_index, np.zeros_like(x_index))) spline_filter = sn.spline_filter(res, order=3, output=np.float64) interp_res = np.empty((20,20,23)) for k,i in enumerate(z_i): coords[2] = i sn.map_coordinates(res, coords, output=interp_res[:,:,k], mode='constant', cval=0., prefilter=True, order=3 ) plt.plot(interp_res[3,3,:], '.-') plt.show() #After running it, I get: In [326]: interp_res[3,3,:] Out[326]: array([ 0.34877645, 0.34877645, 0.31126267, 0.31126267, 0.27242277, 0.27242277, 0.23272727, 0.23272727, 0.19285457, 0.19285457, 0.15372712, 0.15372712, 0.11652598, 0.11652598, 0.08265543, 0.08265543, 0.0536164 , 0.0536164 , 0.03074392, 0.03074392, 0.01479751, 0.01479751, 0. ]) In [327]: scipy.__version__ Out[327]: '0.14.0' In [328]: Thanks, Hanno

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optimising python numpy snippet
by Sai Rajeshwar 25 Jul '14

25 Jul '14

hi all, I have written the following for loop statement inn my code.. when i profiled it.. i found that it is taking huge amount of time.. can any one suggest to make this statement faster ---------------------------------------------------------------------------- f *or i in xrange(self.pooled_shape[1]): for j in xrange(self.pooled_shape[2]): for k in xrange(self.pooled_shape[3]): for l in xrange(self.pooled_shape[4]): self.pooled[0][i][j][k][l]=math.tanh((numpy.sum(self.conv_out[0][i][j][k*3][l*3:(l+1)*3])+numpy.sum(self.conv_out[0][i][j][k*3+1][l*3:(l+1)*3])+numpy.sum(self.conv_out[0][i][j][k*3+2][l*3:(l+1)*3]))/9.0+self.b[i][j])* thanks a lot in advance *with regards..* *M. Sai Rajeswar* *M-tech Computer Technology* *IIT Delhi----------------------------------Cogito Ergo Sum---------*

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[OT] - Research position in the Brazilian Research Institute for Science and Neurotechnology – BRAINN
by Thiago Franco de Moraes 23 Jul '14

23 Jul '14

Research position in the Brazilian Research Institute for Science and Neurotechnology – BRAINN Postdoc researcher to work with software development for medical imaging The Brazilian Research Institute for Neuroscience and Neurotechnology (BRAINN) (www.brainn.org.br) focuses on the investigation of basic mechanisms leading to epilepsy and stroke, and the injury mechanisms that follow disease onset and progression. This research has important applications related to prevention, diagnosis, treatment and rehabilitation and will serve as a model for better understanding normal and abnormal brain function. The BRAINN Institute is composed of 10 institutions from Brazil and abroad and hosted by State University of Campinas (UNICAMP). Among the associated institutions is Renato Archer Information Technology Center (CTI) that has a specialized team in open-source software development for medical imaging (www.cti.gov.br/invesalius) and 3D printing applications for healthcare. CTI is located close the UNICAMP in the city of Campinas, State of São Paulo in a very technological region of Brazil and is looking for a postdoc researcher to work with software development for medical imaging related to the imaging analysis, diagnosis and treatment of brain diseases. The postdoc position is for two years with the possibility of being renovated for more two years. Education - PhD in computer science, computer engineering, mathematics, physics or related. Requirements - Digital image processing (Medical imaging) - Computer graphics (basic) Benefits 6.143,40 Reais per month free of taxes (about US$ 2.800,00); 15% technical reserve for conferences participation and specific materials acquisition; Interested Send curriculum to: jorge.silva(a)cti.gov.br with subject “Postdoc position” Applications reviews will begin August 1, 2014 and continue until the position is filled.

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Scipy.io.netcdf: does nc.close release memory?
by lists＠juliensalort.org 23 Jul '14

23 Jul '14

Hello, Consider the following simple code snipet: ---8<------8<------8<------8<------8<------8<------8<------8<------ import numpy as np from scipy.io.netcdf import netcdf_file ncfile = 'bos-00008.nc' nc = netcdf_file(ncfile) grid_X = np.reshape(nc.variables['vec2_patch_X'].data, (ny, nx)) grid_Y = np.reshape(nc.variables['vec2_patch_Y'].data, (ny, nx)) grid_U = np.reshape(nc.variables['vec2_patch_U'].data, (ny, nx)) grid_V = np.reshape(nc.variables['vec2_patch_V'].data, (ny, nx)) nc.close() ---8<------8<------8<------8<------8<------8<------8<------8<------ It is not clear to me whether or not it is safe to use grid_X, grid_Y, grid_U and grid_V beyond the nc.close(). Does nc.close() release any memory? I am asking because I have one system where I get a segmentation fault when I do the following code, *after* nc.close(). If I remove the nc.close() then I get the proper plot. ---8<------8<------8<------8<------8<------8<------8<------8<------ grid_Module = np.sqrt(grid_U**2 + grid_V**2) plt.streamplot(grid_X, grid_Y, grid_U, grid_V, density=2, color=grid_Module) ---8<------8<------8<------8<------8<------8<------8<------8<------ System where I get the segmentation fault: Mac OS X 10.9.4 running MacPorts Python 2.7.8 with Scipy 0.14.0. No such problem with this system: Debian 7 running Python 2.7.3 with Scipy 0.10.1 So, I wonder if I have a problem with my Mac OS X system, or if it was unsafe to call nc.close anyway. Thanks for any pointers, Julien -- http://www.juliensalort.org

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Maximum Lyapunov exponent of my previous system
by Barrett B 22 Jul '14

22 Jul '14

I need some help setting up the calculation of the maximum Lyapunov exponent of the system I was describing in my previous thread, "Heaviside function in vector form." Note that I list the actual function being integrated, f(X, t), in my first follow-up post on that thread. Here is a brief discussion of the Lyapunov exponent: https://en.wikipedia.org/wiki/Lyapunov_exponent A simple Google search may come up with a better description. I tried simply introducing a fourth differential variable and starting to track it after some arbitrary time t_crit, but because of the nature of the integrator, that doesn't work. So I need help trying to calculate the MLE.

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Three scipy.test() errors
by McCully, Dwayne (NIH/NIAMS) [C] 22 Jul '14

22 Jul '14

Trying to install scipy 0.14.0 under Python 3.3.4 but got test errors on three modules with the configuration listed below. Using LAPACK, ATLAS, and BLAS rpm's that comes with Red Hat 6. Any help would be appreciated. Dwayne ====================================================================== FAIL: test_basic.test_xlogy ---------------------------------------------------------------------- Traceback (most recent call last): File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/nose-1.3.0-py3.3.egg/nose/case.py", line 198, in runTest self.test(*self.arg) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/scipy/special/tests/test_basic.py", line 2878, in test_xlogy assert_func_equal(special.xlogy, w2, z2, rtol=1e-13, atol=1e-13) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/scipy/special/_testutils.py", line 87, in assert_func_equal fdata.check() File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/scipy/special/_testutils.py", line 292, in check assert_(False, "\n".join(msg)) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 44, in assert_ raise AssertionError(msg) AssertionError: Max |adiff|: 712.561 Max |rdiff|: 1028.01 Bad results (3 out of 6) for the following points (in output 0): 0j (nan+0j) => (-0+0j) != (nan+nanj) (rdiff 0.0) (1+0j) (2+0j) => (-711.8665072622568+1.5707963267948752j) != (0.6931471805599453+0j) (rdiff 1028.0087776302707) (1+0j) 1j => (-711.8665072622568+1.5707963267948752j) != 1.5707963267948966j (rdiff 453.18829380940315) ====================================================================== FAIL: test_lambertw.test_values ---------------------------------------------------------------------- Traceback (most recent call last): File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/nose-1.3.0-py3.3.egg/nose/case.py", line 198, in runTest self.test(*self.arg) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py", line 21, in test_values assert_equal(lambertw(inf,1).real, inf) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 304, in assert_equal raise AssertionError(msg) AssertionError: Items are not equal: ACTUAL: nan DESIRED: inf ====================================================================== FAIL: test_lambertw.test_ufunc ---------------------------------------------------------------------- Traceback (most recent call last): File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 581, in chk_same_position assert_array_equal(x_id, y_id) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 718, in assert_array_equal verbose=verbose, header='Arrays are not equal') File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not equal (mismatch 66.66666666666666%) x: array([False, True, True], dtype=bool) y: array([False, False, False], dtype=bool) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/nose-1.3.0-py3.3.egg/nose/case.py", line 198, in runTest self.test(*self.arg) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py", line 93, in test_ufunc lambertw(r_[0., e, 1.]), r_[0., 1., 0.567143290409783873]) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 811, in assert_array_almost_equal header=('Arrays are not almost equal to %d decimals' % decimal)) File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 607, in assert_array_compare chk_same_position(x_isnan, y_isnan, hasval='nan') File "/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/testing/utils.py", line 587, in chk_same_position raise AssertionError(msg) AssertionError: Arrays are not almost equal to 6 decimals x and y nan location mismatch: x: array([ 0.+0.j, nan+0.j, nan+0.j]) y: array([ 0. , 1. , 0.56714329]) ---------------------------------------------------------------------- Ran 16420 tests in 223.156s FAILED (KNOWNFAIL=277, SKIP=1178, failures=3) <nose.result.TextTestResult run=16420 errors=0 failures=3> [root ~]# python -c 'from numpy.f2py.diagnose import run; run()' ------ os.name='posix' ------ sys.platform='linux' ------ sys.version: 3.3.4 (default, Feb 27 2014, 17:05:47) [GCC 4.4.7 20120313 (Red Hat 4.4.7-4)] ------ sys.prefix: /cm/shared/apps/python/3.3.4 ------ sys.path=':/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/setuptools-2.2-py3.3.egg:/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/snakemake-2.5-py3.3.egg:/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/nose-1.3.0-py3.3.egg:/cm/shared/apps/python/3.3.4/lib/python33.zip:/cm/shared/apps/python/3.3.4/lib/python3.3:/cm/shared/apps/python/3.3.4/lib/python3.3/plat-linux:/cm/shared/apps/python/3.3.4/lib/python3.3/lib-dynload:/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages' ------ Found new numpy version '1.8.1' in /cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/__init__.py Found f2py2e version '2' in /cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/f2py/f2py2e.py Found numpy.distutils version '0.4.0' in '/cm/shared/apps/python/3.3.4/lib/python3.3/site-packages/numpy/distutils/__init__.py' ------ Importing numpy.distutils.fcompiler ... ok ------ Checking availability of supported Fortran compilers: Gnu95FCompiler instance properties: archiver = ['/usr/bin/gfortran', '-cr'] compile_switch = '-c' compiler_f77 = ['/usr/bin/gfortran', '-Wall', '-ffixed-form', '-fno- second-underscore', '-fPIC', '-O3', '-funroll-loops'] compiler_f90 = ['/usr/bin/gfortran', '-Wall', '-fno-second-underscore', '-fPIC', '-O3', '-funroll-loops'] compiler_fix = ['/usr/bin/gfortran', '-Wall', '-ffixed-form', '-fno- second-underscore', '-Wall', '-fno-second-underscore', '- fPIC', '-O3', '-funroll-loops'] libraries = ['gfortran'] library_dirs = [] linker_exe = ['/usr/bin/gfortran', '-Wall', '-Wall'] linker_so = ['/usr/bin/gfortran', '-Wall', '-Wall', '-shared'] object_switch = '-o ' ranlib = ['/usr/bin/gfortran'] version = LooseVersion ('4.4.7') version_cmd = ['/usr/bin/gfortran', '--version'] GnuFCompiler instance properties: archiver = ['/usr/bin/g77', '-cr'] compile_switch = '-c' compiler_f77 = ['/usr/bin/g77', '-g', '-Wall', '-fno-second- underscore', '-fPIC', '-O3', '-funroll-loops'] compiler_f90 = None compiler_fix = None libraries = ['g2c'] library_dirs = [] linker_exe = ['/usr/bin/g77', '-g', '-Wall', '-g', '-Wall'] linker_so = ['/usr/bin/g77', '-g', '-Wall', '-g', '-Wall', '- shared'] object_switch = '-o ' ranlib = ['/usr/bin/g77'] version = LooseVersion ('3.4.6') version_cmd = ['/usr/bin/g77', '--version'] Fortran compilers found: --fcompiler=gnu GNU Fortran 77 compiler (3.4.6) --fcompiler=gnu95 GNU Fortran 95 compiler (4.4.7) Compilers available for this platform, but not found: --fcompiler=absoft Absoft Corp Fortran Compiler --fcompiler=compaq Compaq Fortran Compiler --fcompiler=g95 G95 Fortran Compiler --fcompiler=intel Intel Fortran Compiler for 32-bit apps --fcompiler=intele Intel Fortran Compiler for Itanium apps --fcompiler=intelem Intel Fortran Compiler for 64-bit apps --fcompiler=lahey Lahey/Fujitsu Fortran 95 Compiler --fcompiler=nag NAGWare Fortran 95 Compiler --fcompiler=pathf95 PathScale Fortran Compiler --fcompiler=pg Portland Group Fortran Compiler --fcompiler=vast Pacific-Sierra Research Fortran 90 Compiler Compilers not available on this platform: --fcompiler=hpux HP Fortran 90 Compiler --fcompiler=ibm IBM XL Fortran Compiler --fcompiler=intelev Intel Visual Fortran Compiler for Itanium apps --fcompiler=intelv Intel Visual Fortran Compiler for 32-bit apps --fcompiler=intelvem Intel Visual Fortran Compiler for 64-bit apps --fcompiler=mips MIPSpro Fortran Compiler --fcompiler=none Fake Fortran compiler --fcompiler=sun Sun or Forte Fortran 95 Compiler For compiler details, run 'config_fc --verbose' setup command. ------ Importing numpy.distutils.cpuinfo ... ok ------ CPU information: CPUInfoBase__get_nbits getNCPUs has_mmx has_sse has_sse2 has_sse3 has_ssse3 is_64bit is_Intel is_XEON is_Xeon is_i686 ------ [root@niamsirpapp01 ~]# gcc -v Using built-in specs. Target: x86_64-redhat-linux Configured with: ../configure --prefix=/usr --mandir=/usr/share/man --infodir=/usr/share/info --with-bugurl=http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared --enable-threads=posix --enable-checking=release --with-system-zlib --enable-__cxa_atexit --disable-libunwind-exceptions --enable-gnu-unique-object --enable-languages=c,c++,objc,obj-c++,java,fortran,ada --enable-java-awt=gtk --disable-dssi --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre --enable-libgcj-multifile --enable-java-maintainer-mode --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 --build=x86_64-redhat-linux Thread model: posix gcc version 4.4.7 20120313 (Red Hat 4.4.7-4) (GCC) [root@niamsirpapp01 ~]# g77 --version GNU Fortran (GCC) 3.4.6 20060404 (Red Hat 3.4.6-19.el6) Copyright (C) 2006 Free Software Foundation, Inc. GNU Fortran comes with NO WARRANTY, to the extent permitted by law. You may redistribute copies of GNU Fortran under the terms of the GNU General Public License. For more information about these matters, see the file named COPYING or type the command `info -f g77 Copying'.

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ipywidgets
by Antonelli Maria Rosaria 22 Jul '14

22 Jul '14

Hi, I would like to get the ipywidgets to make interactive plot in my notebooks. What should I do ? Here is the answer that I got to the command line "sys.version" : '2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun 2 2014, 12:48:16) \n[GCC 4.0.1 (Apple Inc. build 5493)]' Best, Rosa

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[ANN] Submissions open for Python symposium at AMS2015
by Jonathan Rocher 21 Jul '14

21 Jul '14

[Sorry for cross-posts] Dear all, Abstract submission has been open for the Fifth Symposium on *Modeling and Analysis Using Python* at the American Meteorological Society annual meeting in Phoenix, AZ, January 3-8, 2015. We are soliciting papers related to all areas of the use of Python in the atmospheric and oceanic sciences. The call for papers and link to submit an abstract is located at: http://annual.ametsoc.org/2015/index.cfm/programs-and-events/conferences-an… The abstract submission deadline is coming up quickly: *August 1st*. We encourage students to submit papers. There is a prize for the best paper. Information is here <http://annual.ametsoc.org/2015/index.cfm/programs-and-events/conferences-an…>. The abstract deadline for students is later, August 21. The conference will be preceded by three or four 2-days of tutorials, called "short courses". They will cover a wide range of topics relevant to scientific analysis and modeling in Python for beginners and more advanced programmers (HPC). They will be announced shortly at: http://annual.ametsoc.org/2015/index.cfm/programs-and-events/short-courses/ (Last year's short courses are listed here <http://annual.ametsoc.org/2014/index.cfm/programs-and-events/short-courses/> and this year's offering will be a superset of what we offered last year.) Please pass along this announcement to your friends and colleagues! Thanks! Regards, Jonathan -- Jonathan Rocher Austin TX, USA Cell : +1 512 501 0865 http://www.linkedin.com/in/jonathanrocher -------------------------------------------------------------

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ANN: Spyder v2.3 is released!
by Carlos Córdoba 20 Jul '14

20 Jul '14

Hi all, On the behalf of Spyder's development team (http://code.google.com/p/spyderlib/people/list), I'm pleased to announce that Spyder 2.3 has been released and is available for Windows XP/Vista/7/8, GNU/Linux and MacOS X: https://bitbucket.org/spyder-ide/spyderlib/downloads This release represents 14 months of development since version 2.2 and introduces major enhancements and new features: * Python 3 support (versions 3.2, 3.3 and 3.4 are supported) * Drop Python 2.5 support * Various Editor improvements: - Use the Tab key to do code completions - Highlight cells - First-class support for Enaml files - Syntax highlighting for Julia files - Use Shift+Tab to show calltips - Improve how calltips are shown - Do code completions using the tokens found in a file * Better looking Object Inspector * Several refinements to the user interface to make it easier and more intuitive * And many other changes: http://code.google.com/p/spyderlib/wiki/ChangeLog Spyder 2.2 has been a huge success (being downloaded more than 400,000 times!) and we hope 2.3 will be as successful as it. For that we fixed 70 important bugs, merged 30 pull requests from 11 authors and added almost 1000 commits between these two releases. Spyder is a free, open-source (MIT license) interactive development environment for the Python language with advanced editing, interactive testing, debugging and introspection features. Originally designed to provide MATLAB-like features (integrated help, interactive console, variable explorer with GUI-based editors for dictionaries, NumPy arrays, ...), it is strongly oriented towards scientific computing and software development. Thanks to the `spyderlib` library, Spyder also provides powerful ready-to-use widgets: embedded Python console (example: http://packages.python.org/guiqwt/_images/sift3.png), NumPy array editor (example: http://packages.python.org/guiqwt/_images/sift2.png), dictionary editor, source code editor, etc. Description of key features with tasty screenshots can be found at: http://code.google.com/p/spyderlib/wiki/Features Don't forget to follow Spyder updates/news: * on the project website: http://code.google.com/p/spyderlib/ * and on our official blog: http://spyder-ide.blogspot.com/ Last, but not least, we welcome any contribution that helps making Spyder an efficient scientific development/computing environment. Join us to help creating your favorite environment! (http://code.google.com/p/spyderlib/wiki/NoteForContributors) Enjoy! -Carlos

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Linking problems MKL + numpy 1.8.1 + scipy 0.14.0
by Paul Mayer 19 Jul '14

19 Jul '14

Dear Scipy-Users, I am running into problems trying to get scipy running on a machine that uses ICC (composer xe 2013 sp1.3.174 suite). As for my site.cfg, I followed this tutorial: https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl?languag… and further have AMD / UMFPACK / FFTW installed. Unfortunately, my Intel suite does not come with ifort, so I am stuck with gfortran for compiling scipy. The numpy build works just fine, passes all the tests and shows the correct configuration. scipy however does not pass the tests, because I get several cases of: * ImportError: /usr/local/lib/python2.7/dist-packages/scipy/linalg/_fblas.so: undefined symbol: _intel_fast_memcpy * ImportError: /usr/local/lib/python2.7/dist-packages/scipy/special/_ufuncs.so: undefined symbol: __libm_sse2_sincos I am quite sure this is due to me not using ifort but gfortran. Could anyone maybe provide some pointers on how to fix that? I am happy to provide more information, however am not sure what exactly would be required. Thanks & Kind Regards, Paul

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