Mailman 3 September 2010 - SciPy-User

Using Pyinstaller or cx_freeze with scipy
by Craig April 12, 2011

April 12, 2011

Hello: I was attempting to use cx_freeze to create a stand-alone application that uses scipy, but ran into a problem. When I attempt to run the frozen application, I get the error: scipy\interpolate\polyint.py Cannot import name factorial I looked at the scipy package and the factorial function is in scipy.misc.common.py but the file scipy.interpolate.polyint.py has the line: from scipy import factorial So the polyint script is looking for the factorial function in some other place than its actual physical location. I guess scipy somehow inserts the factorial function in the correct namespace so that it runs correctly. Is there a way to get scipy and cx_freeze to get along? Thanks for your help in advance. Regards, Craig Howard

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scipy.stats.invnorm - rename to invgauss?
by Matthew Brett Feb. 23, 2011

Feb. 23, 2011

Hi, I was caught in the same trap as another innocent, in thinking that scipy.stats.invnorm was the inverse of the normal distribution: http://bytes.com/topic/python/answers/478142-scipy-numpy-inverse-cumulative… In my desire to improve the docstring, I started looking; after a while I found this: http://en.wikipedia.org/wiki/Inverse_Gaussian_distribution - and that seems to fit the invnorm code. A Google search for 'inverse normal distribution' does not include the page above in the first 20 hits, but does find the inverse of the normal distribution, and this: http://en.wikipedia.org/wiki/Normal-inverse_Gaussian_distribution I wonder whether it would be good to rename scipy.stats.invnorm to scipy.stats.invgauss ? Maybe with a deprecation warning to invnorm? Best, Matthew

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scipy/numpy on CentOS 5/ Dependency problem
by Matthias Blaicher Dec. 8, 2010

Dec. 8, 2010

Hello, I want to install SciPy and numpy on a CentOS 5 system. It contains a fresh install with up-to-date packages. I use the "official" Suse Build repos by DavidCournapeau. It fails with a conflict between refblas3 and blas. [root@localhost yum.repos.d]# yum install python-numpy python-scipy Loading "fastestmirror" plugin Loading mirror speeds from cached hostfile * home_ashigabou: download.opensuse.org * extras: mirrors.tummy.com * updates: dds.gina.alaska.edu * base: mirror.hmc.edu * addons: mirrors.tummy.com extras 100% |=========================| 1.1 kB 00:00 updates 100% |=========================| 951 B 00:00 base 100% |=========================| 1.1 kB 00:00 addons 100% |=========================| 951 B 00:00 Setting up Install Process Parsing package install arguments Resolving Dependencies --> Running transaction check ---> Package python-scipy.i386 0:0.6.0-2.1 set to be updated --> Processing Dependency: libblas.so.3 for package: python-scipy --> Processing Dependency: libblas.so.3 for package: python-scipy --> Processing Dependency: liblapack.so.3 for package: python-scipy --> Processing Dependency: libgfortran.so.1 for package: python-scipy --> Processing Dependency: liblapack.so.3 for package: python-scipy ---> Package python-numpy.i386 0:1.2.0-1.1 set to be updated --> Processing Dependency: gcc-gfortran for package: python-numpy --> Processing Dependency: refblas3 for package: python-numpy --> Processing Dependency: lapack3 < 3.1 for package: python-numpy --> Running transaction check ---> Package gcc-gfortran.i386 0:4.1.2-42.el5 set to be updated --> Processing Dependency: gcc = 4.1.2-42.el5 for package: gcc-gfortran ---> Package blas.i386 0:3.0-37.el5 set to be updated ---> Package lapack3.i386 0:3.0-19.1 set to be updated ---> Package lapack.i386 0:3.0-37.el5 set to be updated ---> Package refblas3.i386 0:3.0-11.1 set to be updated ---> Package libgfortran.i386 0:4.1.2-42.el5 set to be updated --> Running transaction check ---> Package gcc.i386 0:4.1.2-42.el5 set to be updated --> Processing Dependency: libgomp.so.1 for package: gcc --> Processing Dependency: glibc-devel >= 2.2.90-12 for package: gcc --> Processing Dependency: libgomp = 4.1.2-42.el5 for package: gcc --> Running transaction check ---> Package glibc-devel.i386 0:2.5-24 set to be updated --> Processing Dependency: glibc-headers for package: glibc-devel --> Processing Dependency: glibc-headers = 2.5-24 for package: glibc-devel ---> Package libgomp.i386 0:4.1.2-42.el5 set to be updated --> Running transaction check ---> Package glibc-headers.i386 0:2.5-24 set to be updated --> Processing Dependency: kernel-headers for package: glibc-headers --> Processing Dependency: kernel-headers >= 2.2.1 for package: glibc-headers --> Running transaction check ---> Package kernel-headers.i386 0:2.6.18-92.1.13.el5 set to be updated --> Processing Conflict: refblas3 conflicts blas --> Finished Dependency Resolution Error: refblas3 conflicts with blas Is there anything I'm doing wrong here, as I don't want to compile all dependencies by myself.. Sincerly, Matthias Blaicher

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Reading / writing sparse matrices
by Lutz Maibaum Nov. 12, 2010

Nov. 12, 2010

How can I write a sparse matrix with elements of type uint64 to a file, and recover it while preserving the data type? For example: >>> import numpy as np >>> import scipy.sparse >>> a=scipy.sparse.lil_matrix((5,5), dtype=np.uint64) >>> a[0,0]=9876543210 Now I save this matrix to a file: >>> import scipy.io >>> scipy.io.mmwrite("test.mtx", a, field='integer') If I do not specify the field argument of mmwrite, I get a "unexpected dtype of kind u" exception. The generated file test.mtx looks as expected. But when I try to read this matrix, it is converted to int32: >>> b=scipy.io.mmread("test.mtx") >>> b.dtype dtype('int32') >>> b.data array([-2147483648], dtype=int32) As far as I can tell, it is not possible to specify a dtype when calling mmread. Is there a better way to go about this? Any help is much appreciated. Lutz

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trouble installing scipy on Mac10.6.4 with Python2.7
by Eric Schow Nov. 2, 2010

Nov. 2, 2010

Hi all, I am new to Python and NumPy/SciPy, and I'm having trouble getting scipy installed on my machine. I am working on an Intel Mac running OS 10.6.4, and I've recently updated the python installation to 2.7. I have installed NumPy 1.5.0, and I am trying to install scipy 0.8.0, which I downloaded as a tarball from sourceforge. I have gcc 4.2.1 installed (through Xcode), although the build process seems to be calling gcc4.0. I have gfortran, which I just re-installed from http://r.research.att.com/tools/, as suggested in the installation notes that came with scipy. The problem seems to be with gfortran, although I admit I'm a little out of my league as I try to troubleshoot this one. The log file is attached. Finally, I initiated the build with this command: LDFLAGS="-arch x86_64" FFLAGS="-arch x86_64" python setup.py build Any help on this issue will be appreciated. BTW, I would prefer to go with Python 2.7 (as opposed to 2.6, for which there is a .dmg available) because of its purported increased compatibility with Python 3.x. Thanks a mole, Eric

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SciPy ODE integrator
by David Pine Oct. 27, 2010

Oct. 27, 2010

Is there a SciPy ODE integrator that does adaptive stepsize integration AND produces output with the adaptive time steps intact? The standard SciPy ODE integrator seems to be scipy.integrate.odeint and its simpler cousin scipy.integrate.ode. These work just fine but both take a user-specified time series and returns the solution at those points only. Often, I prefer to have a more classic adaptive stepsize integrator that returns the solution at time steps determined by the integrator (and the degree of desired precision input by the user). This is often the most useful kind of solution because it tends to produce more points where the solution is varying rapidly and fewer where it is not varying much. A classic Runge-Kugga adaptive stepsize ODE solver does this as to many others, but I can't find a nice implementation in SciPy or NumPy. Please advise. Thanks. David

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[newbie] how to compare two datasets
by ben h Oct. 26, 2010

Oct. 26, 2010

The datasets are borehole data - so they have borehole name, depth, and a value. Each borehole has two datasets - one real, one modelled. I want to compare the values between them for each depth in modelled dataset (lower resolution / fewer samples). If there is no matching depth in real dataset I want to linearly interpolate between nearest values. Comparison to be quite simple at first, difference between values, and stats for entire set of differences. example data (depth, value): model: 0 15.5 -10 17.0 -20 18.5 -30 20.0 real: 0 16.5 -1 16.6 -2 16.6 ... -655 55.3 Not having used python much, i don't know best data structure (dictionary? sequence? list?), or if there are helpful things in SciPy to help this come together (stats, methods for comparing datasets like these, linear interp methods?). Looking for inspiration and pointers! ben. _________________________________________________________________ New, Used, Demo, Dealer or Private? Find it at CarPoint.com.au http://clk.atdmt.com/NMN/go/206222968/direct/01/

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Re: [SciPy-User] FreeImage <-> numpy IO wrappers
by Luis Pedro Coelho Oct. 26, 2010

Oct. 26, 2010

On Wednesday, Sebastian Haase wrote: > this sounds exciting and I might find some time to try it out ... > BTW, the Python image-sig should not be a "PIL only" mailing list. So > (eventually) I feel, this issue could be brought up there, too. I have created a mailing list for python computer vision topics (things that are images but not PIL related): http://groups.google.com/group/pythonvision?pli=1 It is currently very low traffic since it just started (this is my first public announcement). * Btw, for the same sort of issues (opening 16-bit TIFFs in particular), I once wrote a wrapper around imagemagick's C++ image opening functions: http://github.com/luispedro/readmagick I works nicely on linux, but some people were trying to use it on Mac or Windows and got really stuck b/c they didn't know how to compile it and I couldn't help them, so I gave up on trying to make this more widely used. HTH -- Luis Pedro Coelho | Carnegie Mellon University | http://luispedro.org

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scikits.timeseries: How to define frequency of 15minutes
by Martin Felder Oct. 12, 2010

Oct. 12, 2010

Hi *, just for the record, I'm having the exact same problem as Georges. I read through your discussion from three weeks ago, but I also don't feel up to modifying the C code myself (being a Fortran kind of guy...). I understand implementing custom user-defined frequencies is probably a lot of effort, but maybe it's less troublesome to just add some frequencies often used (=by Georges and me, and hopefully others?) to the currently implemented ones? I'd be extremely happy to have 12h, 6h, 3h, 15min and 10min intervals in addition to the existing ones. If you could point me to the part of the code that would have to be modified for that, maybe I can find someone more apt in C who can implement it. Thanks, Martin

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scipy.sparse.linalg.cg statistics?
by Nico Schlömer Oct. 10, 2010

Oct. 10, 2010

Hi all, I was just running some test problems with scipy.sparse.linalg.cg and thing seem work work out fine. To compare preconditioners, I'd like to get more detailed information about the convergence history, but I haven't been able to figure out how to, e.g., get the number of performed iterations or the (relative) residual for each of those steps. Compare this with http://www.mathworks.com/help/techdoc/ref/pcg.html. Any hints? Cheers, Nico

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