Mailman 3 May 2011 - NumPy-Discussion

How to fix my data for visualize with Mayavi2 and to use TVTK?
by Aradenatorix Veckhom Vacelaevus 28 May '11

28 May '11

Hi again: Well, finally I solved my troubles with the importation of data from the Fortran files I've mentioned here few weeks ago... but the importation using the VTK legacy formats doesn't works... I have not idea how to build the LUT (Look up table) for my case and... I'm not sure if that's the only way for can fix my data and visualize them inside mayavi2. Well... although I don't know if this is the appropriate list for ask about mayavi2 and TVTK. Well sorry if it's the wrong place. I need to build and visualize an structured grid object, I have the data but I don't know how to insert the data in mayavi for visualize. I was checking the documentation, the cookbook but I can't find a clear answer. I know I can create an script in python and use the nyumpy arrays for it, but not more. I hope someone can help me. Regards: Aradnix

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problem with installation of numpy+scipy on intel platform
by akshar bhosale 27 May '11

27 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. -akshar

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Adding the arrays in an array iterator
by srean 27 May '11

27 May '11

Hi List, I have to sum up an unknown number of ndarrays of the same size. These arrays, possibly thousands in number, are provided by an iterator. Right now I use python reduce with operator.add. Does that invoke the corresponding ufunc internally, I want to avoid creating temporaries. With ufunc I know how to do it, but not sure how to make use of that in reduce. It is not essential that I use reduce though, so I would welcome idiomatic and efficient way of executing this. So far I have stayed away from building an ndarray object and summing across the relevant dimension. Is that what I should be doing ? Different invocations of this function has different number of arrays, so I cannot pre-compile away this into a numexpr. Thanks and regards srean

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Numpy steering group?
by Matthew Brett 27 May '11

27 May '11

Hi, This is just to follow up on a dead thread of mine a little while back. I was asking about letters for Clint Whaley's tenure case, from numpy, but I realized that I don't know who 'numpy' is :) Is there in fact a numpy steering group? Who is best to write letters representing the 'numpy community'? Thanks a lot, Matthew

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Extending dimensions of a matrix
by santhu kumar 27 May '11

27 May '11

Hello All, I am trying to extend a piece of matlab code into numpy. And I am stuck at converting, ( x is a nX3 matrix) Matlab : dim = size(x,1) % would be n nx = x(:,:,ones(1,dim)) % would be nX3Xn matrix .. Now the same in Numpy seems to be tricky to me .. Can somebody help me in writing this piece of code. Thanks Santhosh

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Py3: numpy.rec.array fails with open file
by Christoph Gohlke 26 May '11

26 May '11

Hello, the following code raises a ValueError on Python 3, but works as expected with Python 2: >>> import sys, numpy >>> fd = open(sys.executable, 'rb') >>> numpy.rec.array(fd, dtype=[('_', 'b')]) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Python32\lib\site-packages\numpy\core\records.py", line 799, in array raise ValueError("Unknown input type") ValueError: Unknown input type The problem seems to be the test for file objects in numpy/compat/py3k.py: def isfileobj(f): return isinstance(f, io.FileIO) If this is updated to include _io.BufferedReader, the type of `fd`, numpy.rec.array works with file objects opened in 'rb' mode: def isfileobj(f): return isinstance(f, (io.FileIO, io._io.BufferedReader)) Tested with numpy 1.6.0 on Windows. Christoph

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weird TypeError: only length-1 arrays can be converted to Python scalars
by Wolfgang Kerzendorf 26 May '11

26 May '11

Dear all, I have come across a weird error, in which the traceback doesn't really reflect the problem (ah osx 10.6.7, python2.6, np 1.6, sp 0.9, ipython 0.10.1, mpl 1.0.1): ---- File "weird_error.py", line 8, in <module> fmin.simplex() File "/Users/wkerzend/scripts/python/pyspecgrid/specgrid.py", line 134, in __call__ gridSpec = self.specGrid.getSpec(*args) File "/Users/wkerzend/scripts/python/pyspecgrid/specgrid.py", line 235, in getSpec return oned.onedspec(self.wave, self.interpGrid(args), mode='waveflux') File "interpnd.pyx", line 120, in interpnd.NDInterpolatorBase.__call__ (scipy/interpolate/interpnd.c:1896) File "interpnd.pyx", line 142, in interpnd._ndim_coords_from_arrays (scipy/interpolate/interpnd.c:2145) File "/Library/Python/2.6/site-packages/numpy/lib/stride_tricks.py", line 69, in broadcast_arrays args = map(np.asarray, args) File "/Library/Python/2.6/site-packages/numpy/core/numeric.py", line 235, in asarray return array(a, dtype, copy=False, order=order) TypeError: only length-1 arrays can be converted to Python scalars ------ I have debugged it here: --> 235 return array(a, dtype, copy=False, order=order) 236 ipdb> a a = 0.1 dtype = None order = None ipdb> type(a) <type 'float'> ---------------- It just doesn't make sense to me, please help, Wolfgang

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Re: [Numpy-discussion] [SciPy-User] [ANN] Guaranteed solution of nonlinear equation(s)
by Dmitrey 25 May '11

25 May '11

--- Исходное сообщение --- От кого: "Yosef Meller" <yosefmel(a)post.tau.ac.il> Кому: scipy-user(a)scipy.org Дата: 25 мая 2011, 08:54:16 Тема: Re: [SciPy-User] [ANN] Guaranteed solution of nonlinear equation(s) On ??? ????? 24 ??? 2011 13:22:47 Dmitrey wrote: > Hi all, > I have made my free solver interalg ( http://openopt.org/interalg ) be > capable of solving nonlinear equations and systems of them. Unlike > scipy optimize fsolve it doesn't matter which functions are involved - > convex, nonconvex, multiextremum etc. Even some discontinuous funcs > can be handled. If no solution exists, interalg determines it rather > quickly. > > For more info see http://forum.openopt.org/viewtopic.php?id=423 Interesting. Is there any description of the actual algorithm? I tried looking for it in the link and the openopt site, but couldn't find it. Algorithm belongs to family of interval methods. Those ones, along with Lipschitz methods, are capable of doing the task (searching extremum with guaranteed precision), but require extremely much computational time and memory, thus are very rarely used. Some ideas created and programmed by me increased speed in many orders (see that benchmark vs Direct, intsolver and commercial BARON), memory consumption also isn't very huge. As for solving equations system, currently |f1| + |f2| + |f3| + ... + |f_k| is minimized (| . | means abs( . )). I know better way of handling nonlinear systems by interalg, but it would take much time to write those enhancements (maybe some weeks or even more), my current work doesn't allow me to spend so much time for interalg and other openopt items development. Regards, D.

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Runtime error in numpy development version
by Kathleen M Tacina 25 May '11

25 May '11

I get the following runtime error when I import numpy: "module compiled against ABI version 2000000 but this version of numpy is 1000009" (see below). Python 2.7.1 (r271:86832, May 20 2011, 14:41:42) [GCC 4.4.0 20090514 (Red Hat 4.4.0-6)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as np RuntimeError: module compiled against ABI version 2000000 but this version of numpy is 1000009 >>> np.version.version '2.0.0.dev-3071eab' >>> This error doesn't seem to effect the operation of numpy --- most tests in np.test('full') pass and most of the numpy functions I used regularly work --- but it causes the import of opencv to fail. >>> import cv RuntimeError: module compiled against ABI version 2000000 but this version of numpy is 1000009 Traceback (most recent call last): File "<stdin>", line 1, in <module> ImportError: numpy.core.multiarray failed to import >>> import numpy.core.multiarray >>> numpy.core.multiarray.arange(5) array([0, 1, 2, 3, 4]) >>> Thanks for any help you can give me with this. Kathy

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2D Fourier Transform with 1D functions
by Goo Creations 24 May '11

24 May '11

Hi all I know there exists a function for Fourier transforming 2D : fft2 But I'm trying to understand how this function works. As far as I know, first a column based transform is done and then a row based transform. This is what I've created for a test: *test = np.array([[1+0j,2+0j], [3+0j, 4+0j]]) own = np.array([[1+0j, 2+0j, 3+0j, 4+0j]]) testRes = np.fft.fft2(test, (2,2)) print testRes ownRes = np.fft.fft(own,4) * At this point I'm stuck, since I'm not sure how to do the row based transform on ownRes. Is there anyone out there who knows how to do this? Chris

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