Mailman 3 May 2011 - NumPy-Discussion

Changing milestones on tickets
by Pierre GM 31 May '11

31 May '11

All, Could somebody tell me the advantage of changing the milestone of tickets that have been closed for more than 10 months ? I'm genuinely curious. P.

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local variable referenced before assignment
by Edoardo Pasca 31 May '11

31 May '11

Dear all, sometimes I encounter the problem that calling many times a function it happens that some local variables are not defined and the procedure crashes. For example I have a function defined as def procedure(tt, Ctissue, WeinmannFit, bodyWeight): for maxFastSampling in range(1,len(tt)): if tt[maxFastSampling] - tt[maxFastSampling-1] > 10: break if numpy.equal(Ctissue[:maxFastSampling] , 0 ).all(): # do something Now the inputs are all numpy.array except bodyWeight that is a number. The program calls the function procedure many times but at some point fails at the evaluation of numpy.equal with UnboundLocalError: local variable 'maxFastSampling' referenced before assignment I wonder whether you see any way that the maxFastSampling variable might not be set. Python 2.6.4 numpy 1.5.0 Thanks Edo -- Edo weblog http://edo.imanetti.net/wordpress podcast http://sieegiueeaccaso.blogspot.com cooking http://edo.imanetti.net/cucina :wq

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Numpy question
by Talla 30 May '11

30 May '11

Hello I installed python2.7 and numpy on windows XP and the installation goes very smooth, now when I am trying to run a python script to get the band structure I got the following message: C:\Python27>python AbinitBandStructureMaker.py Traceback (most recent call last): File "AbinitBandStructureMaker.py", line 16, in <module> from Numeric import * ImportError: No module named Numeric C:\Python27> In addition when I run import command I got ('import' is not recognized as an internal or external command, operable program or batch file.) Therefore, It seems to me it is not getting the correct path for numpy and therefore "AbinitBandStructureMaker.py" can not be executed properly , I tried to set the path variables with no luck. I need to get over this as it consumes all my time. Your help is really appreciated. Talla

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distutils and SWIG problem
by Branimir Sesar 30 May '11

30 May '11

Dear Numpy users, I've been trying to compile Scikits ANN (http://projects.scipy.org/scikits/wiki/AnnWrapper) with Python 2.7.1, numpy 1.6.0, and SWIG 2.0.3 but the compilation breaks down down with this error: running install running bdist_egg running egg_info running build_src build_src building extension "scikits.ann._ANN" sources Traceback (most recent call last): File "setup.py", line 139, in <module> test_suite = 'nose.collector', File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/core.py", line 186, in setup return old_setup(**new_attr) File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/core.py", line 152, in setup dist.run_commands() File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py", line 953, in run_commands self.run_command(cmd) File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py", line 972, in run_command cmd_obj.run() File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/install.py", line 57, in run r = self.setuptools_run() File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/install.py", line 51, in setuptools_run self.do_egg_install() File "build/bdist.linux-x86_64/egg/setuptools/command/install.py", line 96, in do_egg_install File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/cmd.py", line 326, in run_command self.distribution.run_command(command) File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py", line 972, in run_command cmd_obj.run() File "build/bdist.linux-x86_64/egg/setuptools/command/bdist_egg.py", line 167, in run File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/cmd.py", line 326, in run_command self.distribution.run_command(command) File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py", line 972, in run_command cmd_obj.run() File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/egg_info.py", line 8, in run self.run_command("build_src") File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/cmd.py", line 326, in run_command self.distribution.run_command(command) File "/home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py", line 972, in run_command cmd_obj.run() File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py", line 152, in run self.build_sources() File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py", line 169, in build_sources self.build_extension_sources(ext) File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py", line 332, in build_extension_sources sources = self.swig_sources(sources, ext) File "/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py", line 646, in swig_sources extension.swig_opts.remove('-c++') AttributeError: 'str' object has no attribute 'remove' Is this a distutils bug or am I doing something wrong? Thank you very much for your help, Brani

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numpy,scipy installation using mkl
by akshar bhosale 29 May '11

29 May '11

Hi, Is this right forum for doubts abt numpy/scipy installation? Please find our issue below .. we have machine having intel xeon x7350 processors(8 nos) and RHEL 5.2 x86_64 with kernel 2.6.18-92.el5. We have following configuration : /opt/intel/Compiler/11.0/069/ mkl/lib/em64t Now we want to install numpy and scipy as an user in my home directory. Following are the libraries build inside MKL. libfftw2x_cdft_DOUBLE.a libmkl_blacs_sgimpt_ilp64.a libmkl_intel_ilp64.a libmkl_pgi_thread.so libmkl_vml_mc2.so libfftw2xc_intel.a libmkl_blacs_sgimpt_lp64.a libmkl_intel_ilp64.so libmkl_scalapack.a libmkl_vml_mc3.so libfftw2xf_intel.a libmkl_blas95.a libmkl_intel_lp64.a libmkl_scalapack_ilp64.a libmkl_vml_mc.so libfftw3xc_intel.a libmkl_cdft.a libmkl_intel_lp64.so libmkl_scalapack_ilp64.so libmkl_vml_p4n.so libfftw3xf_intel.a libmkl_cdft_core.a libmkl_intel_sp2dp.a libmkl_scalapack_lp64.a locale libmkl_blacs_ilp64.a libmkl_core.a libmkl_intel_sp2dp.so libmkl_scalapack_lp64.so mkl77_blas.mod libmkl_blacs_intelmpi20_ilp64.a libmkl_core.so libmkl_intel_thread.a libmkl_sequential.a mkl77_lapack1.mod libmkl_blacs_intelmpi20_lp64.a libmkl_def.so libmkl_intel_thread.so libmkl_sequential.so mkl77_lapack.mod libmkl_blacs_intelmpi_ilp64.a libmkl_em64t.a libmkl_lapack95.a libmkl.so mkl95_blas.mod libmkl_blacs_intelmpi_ilp64.so libmkl_gf_ilp64.a libmkl_lapack.a libmkl_solver.a mkl95_lapack.mod libmkl_blacs_intelmpi_lp64.a libmkl_gf_ilp64.so libmkl_lapack.so libmkl_solver_ilp64.a mkl95_precision.mod libmkl_blacs_intelmpi_lp64.so libmkl_gf_lp64.a libmkl_mc3.so libmkl_solver_ilp64_sequential.a libmkl_blacs_lp64.a libmkl_gf_lp64.so libmkl_mc.so libmkl_solver_lp64.a libmkl_blacs_openmpi_ilp64.a libmkl_gnu_thread.a libmkl_p4n.so libmkl_solver_lp64_sequential.a libmkl_blacs_openmpi_lp64.a libmkl_gnu_thread.so libmkl_pgi_thread.a libmkl_vml_def.so Version we are trying to build of numpy and scipy are : numpy : 1.6.0b2 and scipy : 0.9.0 We have configured python as a user in my home directory with version : 2.6.6 our machine has python : 2.4.3 We want to know the exact procedure for installing it using MKL as we are facing lot of issues while installing the same. Please help us.

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Contribution
by Dmitriy Rybalkin 29 May '11

29 May '11

Greeting the community of Numpy project My name is Dmitriy and I'am a computer science master student of Australian National University and, to be honest, not a good programmer, though I found that Numpy/Scipy project has a good Cookbook and I thought I could contribute to the Numpy project by showing some examples of simple classification techniques (Fisher discriminant function, Naive Bayes) which use numpy functions. So, I'am inquiring about such contribution and some details: can I do that and, if yes, then how can I submit such example or should I only modify the Wiki page and request the permission? With best regards Dmitriy Rybalkin

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Is this right forum for doubts abt numpy/scipy installation
by akshar bhosale 28 May '11

28 May '11

Hi, Is this right forum for doubts abt numpy/scipy installation? Please find our issue below .. we have machine having intel xeon x7350 processors(8 nos) and RHEL 5.2 x86_64 with kernel 2.6.18-92.el5. We have following configuration : /opt/intel/Compiler/11.0/069/ mkl/lib/em64t Now we want to install numpy and scipy as an user in my home directory. Following are the libraries build inside MKL. libfftw2x_cdft_DOUBLE.a libmkl_blacs_sgimpt_ilp64.a libmkl_intel_ilp64.a libmkl_pgi_thread.so libmkl_vml_mc2.so libfftw2xc_intel.a libmkl_blacs_sgimpt_lp64.a libmkl_intel_ilp64.so libmkl_scalapack.a libmkl_vml_mc3.so libfftw2xf_intel.a libmkl_blas95.a libmkl_intel_lp64.a libmkl_scalapack_ilp64.a libmkl_vml_mc.so libfftw3xc_intel.a libmkl_cdft.a libmkl_intel_lp64.so libmkl_scalapack_ilp64.so libmkl_vml_p4n.so libfftw3xf_intel.a libmkl_cdft_core.a libmkl_intel_sp2dp.a libmkl_scalapack_lp64.a locale libmkl_blacs_ilp64.a libmkl_core.a libmkl_intel_sp2dp.so libmkl_scalapack_lp64.so mkl77_blas.mod libmkl_blacs_intelmpi20_ilp64.a libmkl_core.so libmkl_intel_thread.a libmkl_sequential.a mkl77_lapack1.mod libmkl_blacs_intelmpi20_lp64.a libmkl_def.so libmkl_intel_thread.so libmkl_sequential.so mkl77_lapack.mod libmkl_blacs_intelmpi_ilp64.a libmkl_em64t.a libmkl_lapack95.a libmkl.so mkl95_blas.mod libmkl_blacs_intelmpi_ilp64.so libmkl_gf_ilp64.a libmkl_lapack.a libmkl_solver.a mkl95_lapack.mod libmkl_blacs_intelmpi_lp64.a libmkl_gf_ilp64.so libmkl_lapack.so libmkl_solver_ilp64.a mkl95_precision.mod libmkl_blacs_intelmpi_lp64.so libmkl_gf_lp64.a libmkl_mc3.so libmkl_solver_ilp64_sequential.a libmkl_blacs_lp64.a libmkl_gf_lp64.so libmkl_mc.so libmkl_solver_lp64.a libmkl_blacs_openmpi_ilp64.a libmkl_gnu_thread.a libmkl_p4n.so libmkl_solver_lp64_sequential.a libmkl_blacs_openmpi_lp64.a libmkl_gnu_thread.so libmkl_pgi_thread.a libmkl_vml_def.so Version we are trying to build of numpy and scipy are : numpy : 1.6.0b2 and scipy : 0.9.0 We have configured python as a user in my home directory with version : 2.6.6 our machine has python : 2.4.3 We want to know the exact procedure for installing it using MKL as we are facing lot of issues while installing the same. Please help us.

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[ANN] PyPhy - Python in Physics - Satellite of Euroscipy 2011
by Emmanuelle Gouillart 28 May '11

28 May '11

On behalf of the organizers, it is my pleasure to announce PyPhy, a satellite meeting of Euroscipy 2011 on **Python in Physics**. Date and venue -------------- August 29th, 2011 (full day) Department of Physics, Ecole normale supérieure, Paris Main topics ------------ * Python for teaching physics * Python for research in physics * developing Python modules for applications in physics Organizers ---------- * Alberto Rosso (LPTMS, Université Paris-Sud - alberto.rosso(a)u-psud.fr) * Werner Krauth (Physics Department, ENS Paris - werner.krauth(a)ens.fr) In physics, Python is widely used as a scripting and programming language for data processing, but also for numerical computing where it is a viable alternative to compiled languages, with the advantage of being easy to learn, to write, to read and to modify. For this reason, Python, which is entirely free, is an ideal tool for teaching algorithmic contents in physics. A number of initiatives integrating Python into curricula have been developed in the last few years, largely independently in Physics departments all over the world. At the same time, Python libraries for numerical analysis, combinatorics, graphics, interfacing other languages, etc. have either reached maturity, or are developing very rapidly. This makes Python an exciting option for research applications. A growing number of research programs now write code directly in Python. This informal workshop on teaching and research using Python in Physics will be a forum for coordinating these initiatives, for sharing different experiences, and for exchanging with the key developers of the scientific Python modules present at the EuroSciPy 2011 conference. Beginners and Students are welcome to attend. People interested in presenting a contribution should contact the organizers and send a short abstract before June 30, 2011. We welcome contributions on your experience in teaching Python to physicists and engineers, developing a Python module for applications in physics, or using Python for your everyday research work. Participation in this workshop will be free of charge but participants should also make themselves known by email (alberto.rosso(a)u-psud.fr) before August 28, 2011. Important dates --------------- * June 30: deadline for contributions * August 28: deadline for (free) registration Invited Speakers ---------------- * Samuel Bottani (MSC, Paris Diderot) * Gianfranco Durin (ISI, Turin) * Emmanuelle Gouillart (Saint-Gobain Research) * Konrad Hinsen (CBM Université d'Orleans) * Vivien Lecomte (LPMA Paris Diderot) * Chris Myers (Department of Physics, Cornell University) * Michael Schindler (LPCT, EPSCI, Paris) * Georg von Hippel (INP Universität Mainz) How to get there ---------------- See the instructions on http://www.phys.ens.fr/spip.php?rubrique42&lang=en Website ------- http://www.euroscipy.org/card/pyphy2011 --------- Apologies if you receive this announcement more than once! Emmanuelle

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Problem with installation of numpy and scipy using MKL
by akshar bhosale 28 May '11

28 May '11

Hi, we have machine having intel xeon x7350 processors(8 nos) and RHEL 5.2 x86_64 with kernel 2.6.18-92.el5. We have following configuration : /opt/intel/Compiler/11.0/069/ mkl/lib/em64t Now we want to install numpy and scipy as an user in my home directory. Following are the libraries build inside MKL. libfftw2x_cdft_DOUBLE.a libmkl_blacs_sgimpt_ilp64.a libmkl_intel_ilp64.a libmkl_pgi_thread.so libmkl_vml_mc2.so libfftw2xc_intel.a libmkl_blacs_sgimpt_lp64.a libmkl_intel_ilp64.so libmkl_scalapack.a libmkl_vml_mc3.so libfftw2xf_intel.a libmkl_blas95.a libmkl_intel_lp64.a libmkl_scalapack_ilp64.a libmkl_vml_mc.so libfftw3xc_intel.a libmkl_cdft.a libmkl_intel_lp64.so libmkl_scalapack_ilp64.so libmkl_vml_p4n.so libfftw3xf_intel.a libmkl_cdft_core.a libmkl_intel_sp2dp.a libmkl_scalapack_lp64.a locale libmkl_blacs_ilp64.a libmkl_core.a libmkl_intel_sp2dp.so libmkl_scalapack_lp64.so mkl77_blas.mod libmkl_blacs_intelmpi20_ilp64.a libmkl_core.so libmkl_intel_thread.a libmkl_sequential.a mkl77_lapack1.mod libmkl_blacs_intelmpi20_lp64.a libmkl_def.so libmkl_intel_thread.so libmkl_sequential.so mkl77_lapack.mod libmkl_blacs_intelmpi_ilp64.a libmkl_em64t.a libmkl_lapack95.a libmkl.so mkl95_blas.mod libmkl_blacs_intelmpi_ilp64.so libmkl_gf_ilp64.a libmkl_lapack.a libmkl_solver.a mkl95_lapack.mod libmkl_blacs_intelmpi_lp64.a libmkl_gf_ilp64.so libmkl_lapack.so libmkl_solver_ilp64.a mkl95_precision.mod libmkl_blacs_intelmpi_lp64.so libmkl_gf_lp64.a libmkl_mc3.so libmkl_solver_ilp64_sequential.a libmkl_blacs_lp64.a libmkl_gf_lp64.so libmkl_mc.so libmkl_solver_lp64.a libmkl_blacs_openmpi_ilp64.a libmkl_gnu_thread.a libmkl_p4n.so libmkl_solver_lp64_sequential.a libmkl_blacs_openmpi_lp64.a libmkl_gnu_thread.so libmkl_pgi_thread.a libmkl_vml_def.so Version we are trying to build of numpy and scipy are : numpy : 1.6.0b2 and scipy : 0.9.0 We have configured python as a user in my home directory with version : 2.6.6 our machine has python : 2.4.3 We want to know the exact procedure for installing it using MKL as we are facing lot of issues while installing the same. Please help us.

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labeled axes
by Craig Yoshioka 28 May '11

28 May '11

Hi all, I've read some discussions about adding labeled axes, and even ticks, to numpy arrays (such as in Luis' dataarray). I have recently found that the ability to label axes would be very helpful to me, but I'd like to keep the implementation as lightweight as possible. The reason I would find this useful is because I am writing a ndarray subclass that loads image/volume file formats into numpy arrays. Some of these files might have multiple images/volumes, I'll call them channels, and also may have an additional dimension for vectors associated with each pixel/voxel, like color. The max dims of the array would then be 5. Example: data = ndarray([1023,128,128,128,3]) might mean (channels,z,y,x,rgb) for one array. Now I want to keep as much of the fancy indexing capabilities of numpy as I can, but I am finding it difficult to track the removal of axes that can occur from indexing. For example data[2,2] would return an array of shape (128,128,3), or the third slice through the third volume in the dataset, but the returned array has lost the meaning associated with its axes, so saving it back out would require manual relabeling of the axes. I'd like to be able to track the axes as metadata and retain all the fancy numpy indexing. There are two ways I could accomplish this with minimal code on the python side: One would be if indexing of the array always returned an array of the same dimensionality, that is data[2,2] returned an array of shape (1,1,128,128,3). I could then delete the degenerate axes labels from the metadata, and return the compressed array, resulting in the same output: class Data(np.ndarray): def __getitem__(self,indices): data = np.ndarray.__getitem__(self,indices,donotcompress=True) # as an example data.axeslabels = [label for label,dim in zip(self.axeslabels,data.shape) if dim > 1] return data.compress() def __getslice__(self,s1,s2,step): # trivial case Another approach would be if there is some function in the numpy internals that I could use to get the needed information before calling the ndarray's __getitem__ function: class Data(np.ndarray): def __getitem__(self,indices): unique = np.uniqueIndicesPerDimension(indices) data = np.ndarray.__getitem__(self,indices) data.axeslabels = [label for label,dim in zip(self.axeslabels, unique) if dim > 1] return data Finally, I could implement my own parser for the passed indices to figure this out myself. This would be bad since I'd have to recreate a lot of the same code that must go on inside numpy, and it would be slower, error-prone, etc. : class Data(np.ndarray): def __getitem__(self,indices): indices = self.uniqueDimensionIndices(indices) data = np.ndarray.__getitem__(self,indices) data.axeslabels = [label for label,dim in zip(self.axeslabels,indices) if dim > 1] return data def uniqueDimensionIndices(self,indices): if isinstance(indices,int): indices = (indices,) if isinstance(indices,tuple): .... elif isinstance(indices,list): ... Is there anything in the numpy internals already that would allow me to do #1 or #2?, I don't think #3 is a very good option. Thanks!

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