Mailman 3 July 2010 - SciPy-User

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

3 7

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

3 2

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

2 6

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

5 6

[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/

2 2

Re: [SciPy-User] FreeImage <-> numpy IO wrappers
by Luis Pedro Coelho Oct. 25, 2010

Oct. 25, 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

4 12

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

6 9

64 bit on Mac?
by Keflavich Sept. 26, 2010

Sept. 26, 2010

Hi folks, I'd really like to work with some of the large images I have; I work on a galactic plane survey that has lots of images with ~10^8 floating point pixels. I'm also using a mac. Unfortunately I can't get a 64-bit version of python compiled and google has been unhelpful in resolving the problem. Is there a workaround to get 64 bit numpy working without 64 bit python installed? I doubt that, but if anyone has tips I'd appreciate them. On a related note, I also use a linux machine with 64 bit python installed, but it crashes with large arrays giving a segfault where my mac spits out the "ValueError: dimensions too large." error. Any ideas there? Thanks, Adam

10 17

Porting matlab's ksdensity to scipy
by Tadas Vilkeliskis Aug. 5, 2010

Aug. 5, 2010

Hi guys! I am trying to port the code written in matlab to python and I got stuck with matlab's ksdensity function. The matlab code I have looks something like this: [f, x] = ksdensity(values) I used scipy's gaussian_kde to achieve the same result; however, I modified the matlab's code to f = ksdensity(values, x) and where x are fixed values. When x are fixed gaussian_kde works fine, but this is not what I need. I want to select x based on the input values as in [f, x] = ksdensity(values) which returns x based on the input. Is there a way to do this in scipy? For instance, if I have input values [0, 1, 40, 2, 3, 2] how do I get the range of x values? Thank you very much. Tadas

2 2

Scipy.io .mat files are bigger than those made with Matlab
by Hua Wong Aug. 4, 2010

Aug. 4, 2010

Is there any option I should set to make these .mat smaller? One 2726*2726 matrix gives 10Mo with matlab and goes up to 57Mo when exported with scipy.io

2 1