Mailman 3 February 2007 - NumPy-Discussion

Re: [Numpy-discussion] Numpy and iterative procedures
by Geoffrey Zhu 15 Feb '07

15 Feb '07

Thanks Chuck. I am trying to use Successive Over-relaxation to solve linear equations defined by M*v=3Dq. There are several goals: 1. Eventually (in production) I need it to be fast. 2. I am playing with the guts of the algorithm for now, to see how it works. that means i need some control for now. 3. Even in production, there is a chance i'd like to have the ability to tinker with the algorithm. _____ From: numpy-discussion-bounces(a)scipy.org [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Charles R Harris Sent: Thursday, February 15, 2007 10:11 AM To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] Numpy and iterative procedures On 2/15/07, Geoffrey Zhu <gzhu(a)peak6.com> wrote: Hi, I am new to numpy. I'd like to know if it is possible to code efficient iterative procedures with numpy. Specifically, I have the following problem. M is an N*N matrix. Q is a N*1 vector. V is an N*1 vector I am trying to find iteratively from the initial value V_0. The procedure is simply to calculate V_{n+1}[i]=3D3D1/M[I,i]*(q[i]- (M[i,1]*v_{n+1}[1]+M[I,2]*v_{n+1}[2]+..+M[i,i-1]*v_{n+1}[i-1]) - (M[I,i+1]*v_{n}[i+1]+M[I,i+2]*v_{n}[i+2]+..+M[I,N]*v_{n}[N])) I do not see that this is something that can esaily be vectorized, is it? I think it would be better if you stated what the actual problem is. Is it a differential equation, for instance. That way we can determine what the problem class is and what algorithms are available to solve it. Chuck _______________________________________________________=0A= =0A= The information in this email or in any file attached=0A= hereto is intended only for the personal and confiden-=0A= tial use of the individual or entity to which it is=0A= addressed and may contain information that is propri-=0A= etary and confidential. If you are not the intended=0A= recipient of this message you are hereby notified that=0A= any review, dissemination, distribution or copying of=0A= this message is strictly prohibited. This communica-=0A= tion is for information purposes only and should not=0A= be regarded as an offer to sell or as a solicitation=0A= of an offer to buy any financial product. Email trans-=0A= mission cannot be guaranteed to be secure or error-=0A= free. P6070214=0A=

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Numpy and iterative procedures
by Geoffrey Zhu 15 Feb '07

15 Feb '07

Hi, I am new to numpy. I'd like to know if it is possible to code efficient iterative procedures with numpy. Specifically, I have the following problem. M is an N*N matrix. Q is a N*1 vector. V is an N*1 vector I am trying to find iteratively from the initial value V_0. The procedure is simply to calculate V_{n+1}[i]=3D1/M[I,i]*(q[i]- (M[i,1]*v_{n+1}[1]+M[I,2]*v_{n+1}[2]+..+M[i,i-1]*v_{n+1}[i-1]) - (M[I,i+1]*v_{n}[i+1]+M[I,i+2]*v_{n}[i+2]+..+M[I,N]*v_{n}[N])) I do not see that this is something that can esaily be vectorized, is it? Thanks, Geoffrey _______________________________________________________=0A= =0A= The information in this email or in any file attached=0A= hereto is intended only for the personal and confiden-=0A= tial use of the individual or entity to which it is=0A= addressed and may contain information that is propri-=0A= etary and confidential. If you are not the intended=0A= recipient of this message you are hereby notified that=0A= any review, dissemination, distribution or copying of=0A= this message is strictly prohibited. This communica-=0A= tion is for information purposes only and should not=0A= be regarded as an offer to sell or as a solicitation=0A= of an offer to buy any financial product. Email trans-=0A= mission cannot be guaranteed to be secure or error-=0A= free. P6070214=0A=

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lsq problem
by Tommy Grav 14 Feb '07

14 Feb '07

I need to fit a gaussian profile to a set of points and would like to use scipy (or numpy) to do the least square fitting if possible. I am however unsure if the proper routines are available, so I thought I would ask to get some hints to get going in the right direction. The input are two 1-dimensional arrays x and flux, together with a function def Gaussian(a,b,c,x1): return a*exp(-(pow(x1,2)/pow(c,2))) - c I would like to find the values of (a,b,c), such that the difference between the gaussian and fluxes are minimalized. Would scipy.linalg.lstsq be the right function to use, or is this problem not linear? (I know I could find out this particular problem with a little research, but I am under a little time pressure and I can not for the life of me remember my old math classes). If the problem is not linear, is there another function that can be used or do I have to code up my own lstsq function to solve the problem? Thanks in advance for any hints to the answers. Cheers Tommy

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Vectorizing a class method
by Wojciech Śmigaj 14 Feb '07

14 Feb '07

Hi, I have a question about the vectorize function. I'd like to use it to create a vectorized version of a class method. I've tried the following code: from numpy import * class X: def func(self, n): return 2 * n # example func = vectorize(func) Now, when I declare an instance of the class X and invoke func() as an unbound method, it works: x = X() print X.func(x, [1, 2]) # output: [2 4] But an attempt to invoke it "normally", i.e. like print x.func([1, 2]) fails with the message Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib/python2.4/site-packages/numpy/lib/function_base.py", line 823, in __call__ raise ValueError, "mismatch between python function inputs"\ ValueError: mismatch between python function inputs and received arguments It seems that in this case the class instance (x) isn't passed to the vectorize.__call__() method, and as a result the number of arguments does not agree with what this method expects. Does anybody have an idea of how to do it correctly? As a workaround, I can write a wrapper function on the module level, which can be vectorized without problems, and call it from inside the class---but it looks ugly and is tedious given that I have multiple functions to be handled in this way. Thanks in advance for any help, Wojciech Smigaj

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fromstring, tostring slow?
by Mark Janikas 13 Feb '07

13 Feb '07

Hello all, I am finding that directly packing numpy arrays into binary using the tostring and fromstring methods do not provide a speed improvement over writing the same arrays to ascii files. Obviously, the size of the resulting files is far smaller, but I was hoping to get an improvement in the speed of writing. I got that speed improvement using the struct module directly, or by using generic python arrays. Let me further describe my methodological issue as it may directly relate to any solution you might have. My output file is heterogeneous. Each line is either an array of integers or floats. Each record is made up of three entries. They serve as a sparse representation of a large matrix. 1) row, n (both integers) 2) array of integers of length n, representing columns 3) array of floats of length n, representing values Here, "n" is not constant across the records, so many of the database structures I have looked at do not apply. Any suggestions would be greatly appreciated. Mark Janikas

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Docstring formatting
by Pierre GM 13 Feb '07

13 Feb '07

Dear All, Did we come to some kind of agreement about the formatting of docstring ? In any case, where could I find the 'official' format that Travis recommended ? Thanks a lot in advance P.

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Request for porting pycdf to NumPy
by Daran L. Rife 13 Feb '07

13 Feb '07

Hi Travis, If you're still offering NumPy "patches" to third party packages that rely upon Numeric, I would really like for pycdf to be ported to NumPy. This would allow me to completely transition to NumPy. Thanks very much for considering my request. Daran Rife

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concatenate and different numbers of dimensions
by Evan Mason 12 Feb '07

12 Feb '07

Hi, I am trying to use concatenate to join together a 40x50x45 array and a 50x45 array. The shape of the resulting array should be 41x50x45. In [132]: tempLevel.shape Out[132]: (40, 50, 45) In [133]: temp.shape Out[133]: (50, 45) I've tried various ways to do this using concatenate but always get the following: In [142]: concatenate((tempLevel, temp), axis=0) --------------------------------------------------------------------------- exceptions.ValueError Traceback (most recent call last) /home/emason/python/tools/<ipython console> ValueError: arrays must have same number of dimensions How do I do this with concatenate, or is there another way to do it? Many thanks, Evan

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pickling ufuncs?
by eric jones 12 Feb '07

12 Feb '07

I recently noticed that we can't pickle ufuncs (like sin, ...). Is there any technical reason this doesn't work, or is it in the category of 'just needs to be done...' FYI, I noticed that it didn't work on the old Numeric either. thanks, eric Python 2.4.3 - Enthought Edition 1.0.0 (#69, Aug 2 2006, 12:09:59) [MSC v.1310 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import pickle >>> import numpy >>> import math >>> pickle.dumps(numpy.arange) 'cnumpy.core.multiarray\narange\np0\n.' >>> pickle.dumps(math.sin) 'cmath\nsin\np0\n.' >>> pickle.dumps(numpy.sin) Traceback (most recent call last): File "<stdin>", line 1, in ? File "C:\Python24\lib\pickle.py", line 1386, in dumps Pickler(file, protocol, bin).dump(obj) File "C:\Python24\lib\pickle.py", line 231, in dump self.save(obj) File "C:\Python24\lib\pickle.py", line 313, in save rv = reduce(self.proto) File "C:\Python24\lib\copy_reg.py", line 69, in _reduce_ex raise TypeError, "can't pickle %s objects" % base.__name__ TypeError: can't pickle ufunc objects >>>

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using a vector of values in masked array's filled() function
by Gregor Leban 12 Feb '07

12 Feb '07

Hi, I'm converting my Numeric code to numpy and I ran into a problem. In Numeric the MA.filled() function was able to accept a vector of fill values and it used the first value in the vector to fill the missing values in the first row of the matrix, the second value for the second row, etc. The filled() function in numpy now throws an exception "ValueError: array is not broadcastable to correct shape" if I give it a vector of fill values. Is there any other way or function in numpy that would perform the thing as Numeric did? I know that I could write a loop but I often have data with a huge number of rows and I would prefer some optimized way of doing this. Best regards, Gregor

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