Mailman 3 February 2007 - NumPy-Discussion

Re: [Numpy-discussion] Numpy and iterative procedures
by Geoffrey Zhu 16 Feb '07

16 Feb '07

Hi Nadav, The code is attached at the end. There is probably still bugs in there but it does not prevent me from showing the difficulity. If you look at the inner loop below, you will see that vector v is updated element by element. The new value of v[i] depends on the new value of v[i-1] and the old value of v[i+1]. Updating an element involves the new values of the already updated elements and the old values of the rest of the elements that we have yet to update. This makes vectorization difficult. for i in range(1,N-1): temp[i]=3D(1-w)*v[i]+w/D[i]*(q[i]-L[i-1]*v[i-1]-U[i]*v[i+1]) err +=3D (temp[i]-v[i])**2 v[i]=3Dtemp[i] Thanks, Geoffrey Complete code here; def sor(v, L, D, U ,q, tol, w): '''solve M*v=3Dq. return v. L, D, U are the sub-diagonal, diagonal, and super-diagonal of the matrix M. ''' err=3D9999999 N=3DD.shape[0] #number of elements temp=3Dempty(N) while err> tol : err=3D0 temp[0]=3D(1-w)*v[0]+w/D[0]*(q[0]-U[0]*v[1]) err +=3D (temp[0]-v[0])**2 v[0]=3Dtemp[0] for i in range(1,N-1): temp[i]=3D(1-w)*v[i]+w/D[i]*(q[i]-L[i-1]*v[i-1]-U[i]*v[i+1]) err +=3D (temp[i]-v[i])**2 v[i]=3Dtemp[i] temp[N-1]=3D(1-w)*v[N-1]+w/D[N-1]*(q[N-1]-L[N-2]*v[N-2]) err +=3D (temp[N-1]-v[N-1])**2 v[N-1]=3Dtemp[N-1] return v -----Original Message----- From: numpy-discussion-bounces(a)scipy.org [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Nadav Horesh Sent: Friday, February 16, 2007 8:52 AM To: Discussion of Numerical Python Subject: RE: [Numpy-discussion] Numpy and iterative procedures At first glance it doesn't look hard to, at least, avoid looping over i, by replacing [i] by [:-2], [i+1] by [1:-1] and [i+2] by [2:]. But I might be wrong. Can you submit the piece of code with at least the most internal loop? Nadav. -----Original Message----- From: numpy-discussion-bounces(a)scipy.org on behalf of Geoffrey Zhu Sent: Thu 15-Feb-07 18:32 To: Discussion of Numerical Python Cc: Subject: Re: [Numpy-discussion] Numpy and iterative procedures 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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Re: [Numpy-discussion] Numpy and iterative procedures
by Geoffrey Zhu 16 Feb '07

16 Feb '07

In fact I am not looking for an implementation but a method to update a vector iteratively when the value of an item of the vector depend partly on the already updated items and partly depend on the old items. -----Original Message----- From: numpy-discussion-bounces(a)scipy.org [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Nils Wagner Sent: Friday, February 16, 2007 9:19 AM To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] Numpy and iterative procedures Nadav Horesh wrote: > At first glance it doesn't look hard to, at least, avoid looping over i, by replacing [i] by [:-2], [i+1] by [1:-1] and [i+2] by [2:]. But I might be wrong. Can you submit the piece of code with at least the most internal loop? > > Nadav. > I guess he is looking for an implementation of http://en.wikipedia.org/wiki/Successive_over-relaxation Nils > -----Original Message----- > From: numpy-discussion-bounces(a)scipy.org on behalf of Geoffrey Zhu > Sent: Thu 15-Feb-07 18:32 > To: Discussion of Numerical Python > Cc: > Subject: Re: [Numpy-discussion] Numpy and iterative procedures > > Thanks Chuck. > > I am trying to use Successive Over-relaxation to solve linear > equations defined by M*v=q. > > 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]=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? > > > 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 > > > > > _______________________________________________________ > > The information in this email or in any file attached > hereto is intended only for the personal and confiden- > tial use of the individual or entity to which it is > addressed and may contain information that is propri- > etary and confidential. If you are not the intended > recipient of this message you are hereby notified that > any review, dissemination, distribution or copying of > this message is strictly prohibited. This communica- > tion is for information purposes only and should not > be regarded as an offer to sell or as a solicitation > of an offer to buy any financial product. Email trans- > mission cannot be guaranteed to be secure or error- > free. P6070214 > > > > > > ------------------------------------------------------------------------ > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion(a)scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion

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(no subject)
by gzhu＠peak6.com 16 Feb '07

16 Feb '07

Hi Nadav, The code is attached at the end. There is probably still bugs in there but it does not prevent me from showing the difficulity. If you look at the inner loop below, you will see that vector v is updated element by element. The new value of v[i] depends on the new value of v[i-1] and the old value of v[i+1]. Updating an element involves the new values of the already updated elements and the old values of the rest of the elements that we have yet to update. This makes vectorization difficult. for i in range(1,N-1): temp[i]=(1-w)*v[i]+w/D[i]*(q[i]-L[i-1]*v[i-1]-U[i]*v[i+1]) err += (temp[i]-v[i])**2 v[i]=temp[i] Thanks, Geoffrey Complete code here; def sor(v, L, D, U ,q, tol, w): '''solve M*v=q. return v. L, D, U are the sub-diagonal, diagonal, and super-diagonal of the matrix M. ''' err=9999999 N=D.shape[0] #number of elements temp=empty(N) while err> tol : err=0 temp[0]=(1-w)*v[0]+w/D[0]*(q[0]-U[0]*v[1]) err += (temp[0]-v[0])**2 v[0]=temp[0] for i in range(1,N-1): temp[i]=(1-w)*v[i]+w/D[i]*(q[i]-L[i-1]*v[i-1]-U[i]*v[i+1]) err += (temp[i]-v[i])**2 v[i]=temp[i] temp[N-1]=(1-w)*v[N-1]+w/D[N-1]*(q[N-1]-L[N-2]*v[N-2]) err += (temp[N-1]-v[N-1])**2 v[N-1]=temp[N-1] return v -----Original Message----- From: numpy-discussion-bounces(a)scipy.org [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Nadav Horesh Sent: Friday, February 16, 2007 8:52 AM To: Discussion of Numerical Python Subject: RE: [Numpy-discussion] Numpy and iterative procedures At first glance it doesn't look hard to, at least, avoid looping over i, by replacing [i] by [:-2], [i+1] by [1:-1] and [i+2] by [2:]. But I might be wrong. Can you submit the piece of code with at least the most internal loop? Nadav. -----Original Message----- From: numpy-discussion-bounces(a)scipy.org on behalf of Geoffrey Zhu Sent: Thu 15-Feb-07 18:32 To: Discussion of Numerical Python Cc: Subject: Re: [Numpy-discussion] Numpy and iterative procedures Thanks Chuck. I am trying to use Successive Over-relaxation to solve linear equations defined by M*v=q. 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]=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? 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

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problem installing numpy on OS X
by joris＠ster.kuleuven.ac.be 16 Feb '07

16 Feb '07

Hi, I'm trying to install numpy 1.0.2 (trunk) on my intel mac with Tiger 10.4.8, but I experience some problems, which I think may be compiler problems. From the beginning: I installed MacPython 2.5: Python 2.5 (r25:51918, Sep 19 2006, 08:49:13) [GCC 4.0.1 (Apple Computer, Inc. build 5341)] on darwin Type "help", "copyright", "credits" or "license" for more information. I installed the complete Apple developer tools from my retail CD which give me gcc-3.3 and gcc-4.0. I used gcc_select to ensure that gcc-4.0 is the default compiler, although this appeared unnecessary, as it already was. However, there was no 'gcc' in /usr/bin/ so I made a link from gcc to gcc-4.0. In /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/ I downloaded the latest numpy from the trunk with svn. "python setup.py install" runs (with errors though, see later), and leads to the following error when trying to import numpy: ~>python Python 2.5 (r25:51918, Sep 19 2006, 08:49:13) [GCC 4.0.1 (Apple Computer, Inc. build 5341)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from numpy import * Running from numpy source directory. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/__init__.py", line 43, in <module> import ctypeslib File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/ctypeslib.py", line 5, in <module> from numpy import integer, ndarray, dtype as _dtype, deprecate ImportError: cannot import name integer The errors I was talking about seem to relate to the compiler, and are listed below. Does anyone have a clue how I can resolve them? Cheers, Joris compile options: '-Inumpy/core/src -Inumpy/core/include -I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5 -c' gcc: _configtest.c _configtest.c: In function 'main': _configtest.c:4: error: 'isnan' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) _configtest.c: In function 'main': _configtest.c:4: error: 'isnan' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) lipo: can't figure out the architecture type of: /var/tmp//ccIuW81W.out _configtest.c: In function 'main': _configtest.c:4: error: 'isnan' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) _configtest.c: In function 'main': _configtest.c:4: error: 'isnan' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) lipo: can't figure out the architecture type of: /var/tmp//ccIuW81W.out failure. removing: _configtest.c _configtest.o C compiler: gcc -arch ppc -arch i386 -isysroot /Developer/SDKs/MacOSX10.4u.sdk -fno-strict-aliasing -Wno-long-double -no-cpp-precomp -mno-fused-madd -fno-common -dynamic -DNDEBUG -g -O3 compile options: '-Inumpy/core/src -Inumpy/core/include -I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5 -c' gcc: _configtest.c _configtest.c: In function 'main': _configtest.c:4: error: 'isinf' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) _configtest.c: In function 'main': _configtest.c:4: error: 'isinf' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) lipo: can't figure out the architecture type of: /var/tmp//ccbLrVkU.out _configtest.c: In function 'main': _configtest.c:4: error: 'isinf' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) _configtest.c: In function 'main': _configtest.c:4: error: 'isinf' undeclared (first use in this function) _configtest.c:4: error: (Each undeclared identifier is reported only once _configtest.c:4: error: for each function it appears in.) lipo: can't figure out the architecture type of: /var/tmp//ccbLrVkU.out failure. removing: _configtest.c _configtest.o C compiler: gcc -arch ppc -arch i386 -isysroot /Developer/SDKs/MacOSX10.4u.sdk -fno-strict-aliasing -Wno-long-double -no-cpp-precomp -mno-fused-madd -fno-common -dynamic -DNDEBUG -g -O3 compile options: '-Inumpy/core/src -Inumpy/core/include -I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5 -c' gcc: _configtest.c _configtest.c:7:2: error: #error No _WIN32 _configtest.c:7:2: error: #error No _WIN32 lipo: can't figure out the architecture type of: /var/tmp//cc10iUsH.out _configtest.c:7:2: error: #error No _WIN32 _configtest.c:7:2: error: #error No _WIN32 lipo: can't figure out the architecture type of: /var/tmp//cc10iUsH.out failure. Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

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Re: [Numpy-discussion] Numpy and iterative procedures
by Nadav Horesh 16 Feb '07

16 Feb '07

At first glance it doesn't look hard to, at least, avoid looping over i, by replacing [i] by [:-2], [i+1] by [1:-1] and [i+2] by [2:]. But I might be wrong. Can you submit the piece of code with at least the most internal loop? Nadav. -----Original Message----- From: numpy-discussion-bounces(a)scipy.org on behalf of Geoffrey Zhu Sent: Thu 15-Feb-07 18:32 To: Discussion of Numerical Python Cc: Subject: Re: [Numpy-discussion] Numpy and iterative procedures Thanks Chuck. I am trying to use Successive Over-relaxation to solve linear equations defined by M*v=q. 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]=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? 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 _______________________________________________________ The information in this email or in any file attached hereto is intended only for the personal and confiden- tial use of the individual or entity to which it is addressed and may contain information that is propri- etary and confidential. If you are not the intended recipient of this message you are hereby notified that any review, dissemination, distribution or copying of this message is strictly prohibited. This communica- tion is for information purposes only and should not be regarded as an offer to sell or as a solicitation of an offer to buy any financial product. Email trans- mission cannot be guaranteed to be secure or error- free. P6070214

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Possible solution to binary distributionproblems for numpy on linux?
by David Cournapeau 16 Feb '07

16 Feb '07

Hi there, I came across an interesting post on Miguel De Icaza's blog this week-end: http://tirania.org/blog/archive/2007/Jan-26.html The build system used in opensuse is open sourced for a few weeks. The basic idea is that it provides a build farm to build packages for most major distributions, automatically, with automatic dependency tracking for rebuilding a package when its dependencies changed, etc... [1] My questions are: - does it seem interesting to numpy developers ? My impression is that binary distribution of numpy is a big problem for many linux users, and that is entry barrier for many users (I may be wrong, that's just an impression from the ML). - the registration requires agreement from the open build system's team for now. I would be interesting in trying this out, but I didn't want to "proclaim" myself as a numpy developer without consent from the numpy dev team. cheers, David [1]I have not studied throughly, but the idea is: - you submit the sources of your package + a description file - you upload it to the build system - the build systeme consists in a build farm to build binary packages automatically for many distribution (including opensuse, Suse, fedora, ubuntu and debian; the biggest distribution in term of marketshare which are not there are slackware + gentoo, but I guess users of those distribution would know enough to compile packages themselves). Besides the build farm, some advantages are: - automatic rebuilding when one of the dependency changed (let's say the fortran compiler changed in debian -> numpy which depends on it would be rebuilt automatically) - a system for mirroring. The system is still in beta, and requires registration for trying it as a developer.

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problems with auto detection and broadcasting for userdefined numpy types
by Tom Denniston 15 Feb '07

15 Feb '07

I have a userdefined numpy type for mx.DateTime and one for mx.DateTimeDelta. Info on these packages is available here (http://www.egenix.com/files/python/eGenix-mx-Extensions.html). In any case most things work. What I am left with is three problems: 1. I cannot figure out how to cleanly or even at all, get type detection to work So this works: In [35]: numpy.array([D('2002-01-02 00:00:00.00'), D('2002-01-03 00:00:00.00')], dtype=numpy.mxDateTimeType) Out[35]: array([2002-01-02 00:00:00.00, 2002-01-03 00:00:00.00], dtype=mxNumpyDateTime) But this doesn't: In [36]: numpy.array([D('2002-01-02 00:00:00.00'), D('2002-01-03 00:00:00.00')]) Out[36]: array([2002-01-02 00:00:00.00, 2002-01-03 00:00:00.00], dtype=object) I.e. if I don't specify the type it gives me an object dtype. This is unlike the behavior for strings, ints, etc. I think I know where the C code is that controls this but it is not obvious to me how to change it without making it dependent on mx.DateTime at compile time or having to put a bunch of #define #ifdef magic around it. It would be preferable to figure out a way to specify autodetection, from the outside, so to speak. 2. I can get printing to work but only using a quasi hack in the numpy arrayprint.py code. It works with or without presense of the mx libraries but doesn't seem to me entirely clean. 3. Broadcasting has bugs. See example below: The basic case (2,) - (2,) arrays works. In [37]: numpy.array([D('2002-01-02 00:00:00.00'), D('2002-01-03 00:00:00.00')], dtype=numpy.mxDateTimeType) - numpy.array([DateTime.DateTimeDeltaFromDays(1)], dtype=numpy.mxDateTimeDeltaType) Out[37]: array([2002-01-01 00:00:00.00, 2002-01-02 00:00:00.00], dtype=mxNumpyDateTime) The broadcasting case of (2,) - (1,) works: In [39]: numpy.array([D('2002-01-02 00:00:00.00'), D('2002-01-03 00:00:00.00')], dtype=numpy.mxDateTimeType) - numpy.array([DateTime.DateTimeDeltaFromDays(20)], dtype=numpy.mxDateTimeDeltaType) Out[39]: array([2001-12-13 00:00:00.00, 2001-12-14 00:00:00.00], dtype=mxNumpyDateTime) The extremely similar (2,) - () array: In [40]: numpy.array([D('2002-01-02 00:00:00.00'), D('2002-01-03 00:00:00.00')], dtype=numpy.mxDateTimeType) - numpy.array(DateTime.DateTimeDeltaFromDays(20), dtype=numpy.mxDateTimeDeltaType) --------------------------------------------------------------------------- <type 'exceptions.TypeError'> Traceback (most recent call last) /home/tom/src/<ipython console> in <module>() <type 'exceptions.TypeError'>: function not supported for these types, and can't coerce safely to supported types Finally the (2,) - scalar doesn't work either: In [41]: numpy.array([D('2002-01-02 00:00:00.00'), D('2002-01-03 00:00:00.00')], dtype=numpy.mxDateTimeType) - DateTime.DateTimeDeltaFromDays(20) --------------------------------------------------------------------------- <type 'exceptions.TypeError'> Traceback (most recent call last) /home/tom/src/<ipython console> in <module>() <type 'exceptions.TypeError'>: function not supported for these types, and can't coerce safely to supported types The final one might not work even if broadcasting worked properly simply because of the autodetection problem listed in 2. The (2,) - (), however, should work, I would think, because it has the type explicitly set. --Tom

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Unifying numpy, scipy, and matplotlib docstring formats
by Keir Mierle 15 Feb '07

15 Feb '07

I'd like to help the docstring formats of numpy, scipy. and matplotlib converge on a high-quality standard (hence the cross-posting). However, before that can happen all maintainers from all three packages need to agree on a format. In the interest of speeding things along, I've taken the liberty of extending [1] the docstring format proposed by Travis on January 10th on the scipy-dev mailing list [2]. I also volunteer to convert a non-trivial portion of the numpy, scipy, and matplotlib docstrings. If all goes well on the consensus front, I'll try to coordinate an IRC docstring sprint or two. The notable differences from the format Travis posted is that I have capitalized headers for readability; the other changes are mostly modest, or additions to handle the more demanding requirements of matplotlib docstrings. On the DocstringStandard page I have also put a completely re-done docstring for the 'contour' function from matplotlib. I think it is far more readable than the original [3]. JDH and other matplotlibheads, what do you think? Travis, do you find my additions reasonable? Scipy maintainers, would you consider adopting this format (especially if someone helps with the gruntwork)? Thanks in advance, Keir [1] http://scipy.org/DocstringStandard [2] http://article.gmane.org/gmane.comp.python.scientific.devel/5572 [3] http://matplotlib.sourceforge.net/matplotlib.pylab.html#-contour p.s. This is part of my plan to kick off http://scipy.org/PyLab

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Update on sparse matrix in binary
by Mark Janikas 15 Feb '07

15 Feb '07

I wanted to thank all of you who helped me with my making my sparse matrix representation cross-platform in binary format! I ended up writing and reading everything explicitly in little endian. To recap, each row in the matrix is represented by three records: 1) row#, nn (number of non-zero elements) 2) col (indices of non-zero elements in row #) 3) values (doubles corresponding to indices provided in the previous array) I created the arrays using: a = array([values], '<l or d').tostring(); f.write(a) And read using: a = f.read(# of bytes, 8 for first record, nn*4 for longs, nn*8 for doubles); a = fromstring(a, '<l or d') The tofile() and fromfile() version work as well, but it wasn't any faster. So I went with what I thought was more intuitive in the context of the algorithm. So far, it has worked on the Windows, Linux, Solaris and Mac OSX boxes I have tried. I know it is hardware specific as opposed to platform, so I will continue to seek out the rarer vintages of machines that can run the software we provide. Again, thanks to all; and if there are any more suggestions as to how this process could be more optimized, please feel free to respond. Best wishes, Mark Janikas Product Engineer ESRI, Geoprocessing 380 New York St. Redlands, CA 92373 909-793-2853 (2563) mjanikas(a)esri.com

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Confusing selector?
by Geoffrey Zhu 15 Feb '07

15 Feb '07

I am really new to numpy but I just found that v[2:4] means selecting v[2] and v[3]. V[4] is not included! This is quite different from the conventions of matlab and R. _______________________________________________________=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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