Re: [Numpy-discussion] round
Charles R Harris wrote: On 10/11/06, *Greg Willden* [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: Hi All, I've read discussions in the archives about how round() rounds to even and how that is supposedly better. But what I haven't been able to find is What do I use if I want the regular old round that you learn in school? Perhaps you could explain *why* you want the schoolbook round? Given that floating point is inherently inaccurate you would have to expect to produce a lot of numbers exactly of the form x.5 *without errors*, which means you probably don't need round to deal with it. Anyway, absent a flag somewhere, you can do something like (x + sign(x)*.5).astype(int). Chuck Also, where did you go to school? In Fairfax County, VA in the 80's at least, they were teaching round to even; since that time, I've taught math in a variety of locations and settings and with a variety of texts, and where I have seen the issue addressed, it's always round to even (or, as I learned it, the rule of fives). DG - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Travis Oliphant wrote: [EMAIL PROTECTED] wrote: Could sqrt(-1) made to return 1j again? Not in NumPy. But, in scipy it could. Ohmigod!!! You are definitely going to scare away many, many potential users - if I wasn't obliged to use open source at work, you'd be scaring me away. I was thinking about translating all my personal fractal-generating Matlab code into Python, but I certainly won't be doing that now! - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Sven Schreiber wrote: Travis Oliphant schrieb: If not, shouldn't numpy.sqrt(-1) raise a ValueError instead of returning silently nan? This is user adjustable. You change the error mode to raise on 'invalid' instead of pass silently which is now the default. -Travis Could you please explain how this adjustment is done, or point to the relevant documentation. Thank you, Sven I'm glad you asked this, Sven, 'cause I was thinking that if making this user adjustment is this advanced (I too have no idea what you're talking about, Travis), then this would be another significant strike against numpy (but I was holding my tongue, since I'd just let fly in my previous email). DG - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
David Goldsmith wrote: Travis Oliphant wrote: [EMAIL PROTECTED] wrote: Could sqrt(-1) made to return 1j again? Not in NumPy. But, in scipy it could. Ohmigod!!! You are definitely going to scare away many, many potential users - if I wasn't obliged to use open source at work, you'd be scaring me away. Why in the world does it scare you away. This makes no sense to me. If you don't like the scipy version don't use it. NumPy and SciPy are not the same thing. The problem we have is that the scipy version (0.3.2) already had this feature (and Numeric didn't). What is so new here that is so scary ? -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Travis Oliphant wrote: This is the one that concerns me. Slowing everybody down who knows they have positive values just for people that don't seems problematic. Then have a sqrtp function for those users who are fortunate enough to know ahead of time that they'll only be talking square roots of nonnegatives. DG - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
David Goldsmith wrote: Travis Oliphant wrote: What could be simpler? ;-) Having sqrt(-1) return 1j (without having to remember that in order to get this, you have to write sqrt(-1+0j) instead). But this can sometimes lead to confusing errors hard to track when you don't want to treat complex numbers. That's one of the thing I hated in matlab, actually: if you don't want to handle complex numbers, you have to check regularly for it. So I don't think this is simpler. David - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
David Goldsmith wrote: Travis Oliphant wrote: What could be simpler? ;-) Having sqrt(-1) return 1j (without having to remember that in order to get this, you have to write sqrt(-1+0j) instead). That's exactly what scipy.sqrt(-1) does. That was my point. -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] incrementing along a diagonal
Hi, I absolutely do not know perl, so I do not know what the expression you posted does. However, the key is just to understand indexing in numpy: if you have a matrix mat and index arrays index1, index2 with, lets say, index1 = array([ 17, 19, 29]) index2 = array([ 12, 3, 9]) then the entries of the index arrays are used as row and column indices respectively, and the result will be an array shaped like the index arrays. So doing mat[index1, index2] will give you -- array([ mat[17, 12], mat[19, 3], mat[29, 9]]). Now if you want the diagonal of a 3x3-mat, you need index1=index2=array([ 0, 1, 2]). mat[index1, index2] -- array([ mat[0,0], mat[1,1], mat[2,2]]) That is what my code does. If you need other, arbitrary subsets of mat, you just have to fill the index arrays accordingly. Johannes - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
On 10/12/06, Travis Oliphant [EMAIL PROTECTED] wrote:
Why in the world does it scare you away. This makes no sense to me.
If you don't like the scipy version don't use it. NumPy and SciPy are
not the same thing.
I'd like to pitch in (again) on this issue, but I'll try to make sure
that it's clear that I'm NOT arguing about sqrt() in particular, one
way or another.
It's perfectly clear that numpy != scipy to all of us. And yet, I
think it is equally clear that the two are /very/ tightly related.
Scipy builds on top of numpy and it directly exposes a LOT of the
numpy API as scipy functions:
In [21]: import numpy as n, scipy as s
In [22]: common_names = set(dir(n)) set(dir(s))
In [23]: [getattr(n,x) is getattr(s,x) for x in common_names ].count(True)
Out[23]: 450
In [24]: len(common_names)
Out[24]: 462
That's 450 objects from numpy which are directly exposed in Scipy,
while only 12 names are in both top-level namespaces and yet are
different objects. Put another way, scipy is a direct wrap of 97% of
the numpy top-level namespace. While /we/ know they are distinct
entities, to the casual user a 97% match looks pretty close to being
the same, especially when the non-identical things are all
non-numerical:
In [27]: [x for x in common_names if getattr(n,x) is not getattr(s,x)]
Out[27]:
['pkgload',
'version',
'__config__',
'__file__',
'__all__',
'__doc__',
'show_config',
'__version__',
'__path__',
'__name__',
'info',
'test']
In [32]: n.__version__,s.__version__
Out[32]: ('1.0.dev3306', '0.5.2.dev2252')
Basically, at least for these versions, the top-level API of scipy is
a strict superset of the numpy one for all practical purposes.
I think it's fair to say that if we start sprinkling special cases
where certain objects happen to have the same name but produce
different results for the same inputs, confusion will arise.
Please note that I see a valid reason for scipy.foo != numpy.foo when
the scipy version uses code with extra features, is faster, has
additional options, etc. But as I said in a previous message, I think
that /for the same input/, we should really try to satisfy that
numpy.foo(x) == scipy.foo(x) (which is NOT the same as 'numpy.foo is scipy.foo')
within reason. Obviously the scipy version may succeed where the
numpy one fails due to better algorithms, or be faster, etc. I'm
talking about a general principle here.
I doubt I'll be able to state my point with any more clarity, so I'll
stop now. But I really believe that this particular aspect of
consistency between numpy and scipy is a /very/ important one for its
adoption in wider communities.
Best,
f
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Re: [Numpy-discussion] Please test the SVN branch
Travis Oliphant wrote: I made some fixes to the asbuffer code which let me feel better about exposing it in NumPy (where it is now named int_asbuffer). This code takes a Python integer and a size and returns a buffer object that points to that memory. A little test is performed by trying to read (and possibly write if a writeable buffer is requested) to the first and last elements of the buffer. Any segfault is trapped and used to raise a Python error indicating you can't use that area of memory. It doesn't guarantee you won't shoot yourself, but it does make it more difficult to segfault Python. Previously a simple int_asbuffer(3423423, 5) would have segfaulted (unless by chance you the memory area 3423423 happens to be owned by Python). I have not tested the code on other platforms to make sure it works as expected, so please try and compiled it. -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion Numpy version 1.0.dev3315 Scipy version 0.5.2.dev2254 Works fine here. All tests passed ! x86_64 x86_64 x86_64 GNU/Linux Nils - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Fernando Perez wrote: Please note that I see a valid reason for scipy.foo != numpy.foo when the scipy version uses code with extra features, is faster, has additional options, etc. But as I said in a previous message, I think that /for the same input/, we should really try to satisfy that numpy.foo(x) == scipy.foo(x) (which is NOT the same as 'numpy.foo is scipy.foo') within reason. As far as I can tell this is exactly what happens. Consider the issue under discussion... -- import numpy as np np.sqrt(-1) -1.#IND np.sqrt(-1+0j) 1j a = complex(-1) np.sqrt(a) 1j import scipy as sp sp.sqrt(-1) -1.#IND np.sqrt(-1+0j) 1j sp.sqrt(a) 1j np.__version__ '1.0rc1' sp.__version__ '0.5.1' -- I'm sure that this hasn't changed in the development versions. Surely the point is that when your algorithm can potentially produce a complex result, the logical thing to do is to use a complex data type. In this case Numpy and Scipy behave in a way which is intuitive. If complex results are surprising and unexpected then the algorithm is probably in error or poorly understood ;-) Cheers, Scott - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Travis Oliphant wrote: David Goldsmith wrote: Travis Oliphant wrote: [EMAIL PROTECTED] wrote: Could sqrt(-1) made to return 1j again? Not in NumPy. But, in scipy it could. Ohmigod!!! You are definitely going to scare away many, many potential users - if I wasn't obliged to use open source at work, you'd be scaring me away. Why in the world does it scare you away. This makes no sense to me. If you don't like the scipy version don't use it. NumPy and SciPy are not the same thing. I don't use scipy (and don't want to because of the overhead) but it sounds like I should because if I'm taking the square root of a variable whose value at run time happens to be real but less than zero, I *want* the language I'm using to return an imaginary; in other words, it's not the scipy behavior which scares me, its the numpy (which I do/have been using) behavior. To which you might say, Well, if that's what you want, and you have Matlab (as you've said you do), then just use that. But that's precisely the point: people who don't want to be bothered with having to be a bit more care[ful] (as Chuck put it) - and who can afford it - are going to be inclined to choose Matlab over numpy. Perhaps one doesn't care - in the grand scheme of things, it certainly doesn't matter - but I think that you all should be aware that this numpy feature will be seen by many as more than just a nuisance. DG - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Travis Oliphant wrote: David Goldsmith wrote: Travis Oliphant wrote: What could be simpler? ;-) Having sqrt(-1) return 1j (without having to remember that in order to get this, you have to write sqrt(-1+0j) instead). That's exactly what scipy.sqrt(-1) does. That was my point. But I don't want to have to use scipy (which so far I haven't needed for any other reason) just to get this one behavior. But my personal preferences aside, I say again: if numpy doesn't behave this way, you *will* be scaring away many potential users. If you can live with that, so be it. DG DG -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
(Very) well said, Fernando. Thanks!
DG
Fernando Perez wrote:
On 10/12/06, Travis Oliphant [EMAIL PROTECTED] wrote:
Why in the world does it scare you away. This makes no sense to me.
If you don't like the scipy version don't use it. NumPy and SciPy are
not the same thing.
I'd like to pitch in (again) on this issue, but I'll try to make sure
that it's clear that I'm NOT arguing about sqrt() in particular, one
way or another.
It's perfectly clear that numpy != scipy to all of us. And yet, I
think it is equally clear that the two are /very/ tightly related.
Scipy builds on top of numpy and it directly exposes a LOT of the
numpy API as scipy functions:
In [21]: import numpy as n, scipy as s
In [22]: common_names = set(dir(n)) set(dir(s))
In [23]: [getattr(n,x) is getattr(s,x) for x in common_names ].count(True)
Out[23]: 450
In [24]: len(common_names)
Out[24]: 462
That's 450 objects from numpy which are directly exposed in Scipy,
while only 12 names are in both top-level namespaces and yet are
different objects. Put another way, scipy is a direct wrap of 97% of
the numpy top-level namespace. While /we/ know they are distinct
entities, to the casual user a 97% match looks pretty close to being
the same, especially when the non-identical things are all
non-numerical:
In [27]: [x for x in common_names if getattr(n,x) is not getattr(s,x)]
Out[27]:
['pkgload',
'version',
'__config__',
'__file__',
'__all__',
'__doc__',
'show_config',
'__version__',
'__path__',
'__name__',
'info',
'test']
In [32]: n.__version__,s.__version__
Out[32]: ('1.0.dev3306', '0.5.2.dev2252')
Basically, at least for these versions, the top-level API of scipy is
a strict superset of the numpy one for all practical purposes.
I think it's fair to say that if we start sprinkling special cases
where certain objects happen to have the same name but produce
different results for the same inputs, confusion will arise.
Please note that I see a valid reason for scipy.foo != numpy.foo when
the scipy version uses code with extra features, is faster, has
additional options, etc. But as I said in a previous message, I think
that /for the same input/, we should really try to satisfy that
numpy.foo(x) == scipy.foo(x) (which is NOT the same as 'numpy.foo is
scipy.foo')
within reason. Obviously the scipy version may succeed where the
numpy one fails due to better algorithms, or be faster, etc. I'm
talking about a general principle here.
I doubt I'll be able to state my point with any more clarity, so I'll
stop now. But I really believe that this particular aspect of
consistency between numpy and scipy is a /very/ important one for its
adoption in wider communities.
Best,
f
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Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
I'd like to pitch in (again) on this issue, but I'll try to make sure that it's clear that I'm NOT arguing about sqrt() in particular, one way or another. Fernando, I don't disagree with you in principle. I don't think anybody does. I think we should try to keep the interfaces and expectations of scipy and numpy the same. Unfortunately, we have competing issues in this particular case (in the case of the functions in numpy.lib.scimath). Nobody has suggested an alternative to the current situation in SVN that is satsifying to enough users. Here is the situation. 1) NumPy ufuncs never up-cast to complex numbers without the user explicitly requesting it so sqrt(-1) creates a floating-point error condition which is either caught or ignored according to the user's desires.To get complex results from sqrt you have put in complex numbers to begin with. That's inherent in the way ufuncs work. This is long-standing behavior that has good reasons for it's existence. I don't see this changing.That's why I suggested to move the discussion over to scipy (we have the fancy functions in NumPy, they are just not in the top-level name-space). Now, it would be possible to give ufuncs a dtype keyword argument that allowed you to specify which underlying loop was to be used for the calculation. That way you wouldn't have to convert inputs to complex numbers before calling the ufunc, but could let the ufunc do it in chunks during the loop. That is certainly a reasonable enhancement: sqrt(a, dtype=complex). This no-doubt has a library-ish-feeling, but that is what NumPy is. If such a change is desireable, I don't think it would be much to implement it. 2) In SciPy 0.3.2 the top-level math functions were overloaded with these fancy argument-checking versions. So that scipy.sqrt(-1) would return 1j. This was done precisely to attract users like David G. who don't mind data-type conversions on the fly, but prefer automatic conversion (funny being called non-sensical when I was the one who wrote those original scipy functions --- I guess I'm schizophrenic). We changed this in SciPy 0.5.1 by accident without any discussion. It was simply a by-product of moving scipy_base (including those special-handling functions) into NumPy and forgetting to import those functions again into top-level SciPy.It was an oversight that caused backwards compatibilty issues. So, I simply changed it back to what SciPy used to be in SVN. If we want to change SciPy, then fine, but let's move that discussion over to scipy-dev. In short, I appreciate all the comments and the differences of opinion they point out, but they are ultimately non-productive. We can't just change top-level sqrt to be the fancy function willy-nilly. Paul says I'm nice (he's not talked to my children recently), but I'm not that much of a push-over. There are very good reasons that NumPy has the behavior it does. In addition, the fancy functions are already there in numpy in numpy.lib.scimath. So, use them from there if you like them. Create your own little mynumpy module that does from numpy import * from numpy.lib.scimath import * and have a ball. Python is flexible enough that the sky is not going to fall if the library doesn't do things exactly the way you would do it. We can still cooperate in areas that we agree on. Again. Put this to rest on NumPy and move the discussion over to scipy-dev. -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
David Goldsmith wrote: I don't use scipy (and don't want to because of the overhead) but it sounds like I should because if I'm taking the square root of a variable whose value at run time happens to be real but less than zero, I *want* the language I'm using to return an imaginary; in other words, it's not the scipy behavior which scares me, its the numpy (which I do/have been using) behavior. O.K. Well the functions you want are in numpy.lib.scimath. I should have directed you there. You actually don't need scipy installed at all. Just import sqrt from numpy.lib.scimath. I'm sorry I misunderstood the issue. -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
PS: I am still sending this message to numpy list only because the proposal below affects numpy code, not scipy one. I think Fernando points make sense, numpy.foo(x) != scipy.foo(x) can cause confusion and frustration both among new numpy/scipy users and developers (who need to find explanations for the choises made). So, let me propose the following solution so that all parties will get the same results without sacrifying numpy.sqrt speed on non-negative input and scipy.sqrt backward compatibility: Define numpy.sqrt as follows: def sqrt(x): r = nx.sqrt(x) if nx.nan in r: i = nx.where(nx.isnan(r)) r = _tocomplex(r) r[i] = nx.sqrt(_tocomplex(x[i])) return r and define numpy.sqrtp that takes only non-negative input, this is for those users who expect sqrt to fail on negative input (as Numeric.sqrt and math.sqrt do). Pearu - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Compile with atlas 3.7.17 fails
Travis, thank you, setting the -fPIC option indeed solved my problem. Just for future reference (if anybody else needs it), I added the -fPIC flag to the compiler flags in Make.inc in the atlas build directory, as setting CCFLAGS somehwo didn't seem to be successful. This might not be elegant but it worked. Best regards, Hanno Travis Oliphant [EMAIL PROTECTED] said: Hanno Klemm wrote: Hi, I don't know if this is a bug or just me doing something wrong (I suspect the latter). I try to compile numpy-1.0rc1 with python2.5 and atlas 3.7.17. I have build the atlas library myself, it doesn't give any errors under make test or make pttest, so it seems to be okay. if I try to build numpy I get the following error: creating build/temp.linux-x86_64-2.5/numpy/core/blasdot compile options: '-DATLAS_INFO=\3.7.17\ -Inumpy/core/blasdot -I/scratch/python2.5/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.5/numpy/core -Inumpy/core/src -Inumpy/core/include -I/scratch/python2.5/include/python2.5 -c' gcc: numpy/core/blasdot/_dotblas.c gcc -pthread -shared build/temp.linux-x86_64-2.5/numpy/core/blasdot/_dotblas.o -L/scratch/python2.5/lib -lcblas -latlas -o build/lib.linux-x86_64-2.5/numpy/core/_dotblas.so /usr/bin/ld: /scratch/python2.5/lib/libcblas.a(cblas_dgemm.o): relocation R_X86_64_32 can not be used when making a shared object; recompile with -fPIC This may be part of your problem. It's looks like the linker is having a hard time making use of your compiled extension in a shared library. Perhaps you should make sure -fPIC is on when you compile atlas (I'm not sure how to do that --- perhaps setting CCFLAGS environment variable to include -fPIC would help). -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion -- Hanno Klemm [EMAIL PROTECTED] - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
My vote is for consistency in numpy.But it is unclear what consistency is.What is truly confusing for newbie Python users (and a source for error even after 5 years of Python programming) is that 2/3 0In that respect, I would think numpy.sqrt(2)should give 1, but it gives 1.4142135623730951So numpy does typechecking anyway (it gets an integer and makes it a float). If that is the consistent behavior, then by all means sqrt(-1)should return 1j.Wouldn't that be the consistent thing to doMark - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Casting
Hello list, I got some old code that uses Scientific and Numeric. Recently, these two packages were updated to the latest versions together with python and my code started failing. The first major problem I have is when downcasting. Code that used to work now fails because the array cannot be safely cast anymore to the required type. If anyone could help me please and direct me to some documentation about when this change happened (version), why it happened, is there any other solution but using astype, can numpy help me in anyway in this situation, because I got really lost ... Thank you in advance,Cristian - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
I've summarised this thread at http://www.scipy.org/NegativeSquareRoot Feel free to make adjustments, in case I missed something. Regards Stefan - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Please test the SVN branch
On Thu, Oct 12, 2006 at 12:38:51AM -0600, Travis Oliphant wrote: I made some fixes to the asbuffer code which let me feel better about exposing it in NumPy (where it is now named int_asbuffer). This code takes a Python integer and a size and returns a buffer object that points to that memory. A little test is performed by trying to read (and possibly write if a writeable buffer is requested) to the first and last elements of the buffer. Any segfault is trapped and used to raise a Python error indicating you can't use that area of memory. It doesn't guarantee you won't shoot yourself, but it does make it more difficult to segfault Python. Previously a simple int_asbuffer(3423423, 5) would have segfaulted (unless by chance you the memory area 3423423 happens to be owned by Python). Some argument checking and a docstring would be helpful. I guessed wrong the first time: In [1]: x = N.array([1,2,3]) In [4]: y = N.int_asbuffer(x.size,x.__array_interface__['data'][0]) In [5]: y[0] Segmentation fault Out of curiosity, why does the following outputs differ: In [39]: y = N.int_asbuffer(0L,x.size) --- exceptions.SystemError Traceback (most recent call last) /home/stefan/ipython console SystemError: error return without exception set In [40]: y = N.int_asbuffer(1L,x.size) --- exceptions.ValueErrorTraceback (most recent call last) /home/stefan/ipython console ValueError: cannot use memory location as a buffer. Thanks for your work on this. Cheers Stéfan - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Klaipeda Narva also comes
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[Numpy-discussion] arraysetops update
I have just added 'See also' to all docstrings in arraysetops functions, replaced old numpy idioms with new ones (aka modernizing) and improved speed of numpy.unique1d, which now reflects numpy.unique. hoping no bugs were introduced, regards, r. - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Compiling Numpy with py2exe
Hello, Ive a program using Numpy and I would like to compile it with Py2exe but Ive some problems. When I try to compile my program, some modules generated by numpy are not loaded. Ive already asked to the py2exe community if they have a solution for that and the answer was that it was possible to compile numpy modules with py2exe but I should ask to the Numpy community J So my question is, is there something special to do in order to compile a program using numpy module? This is the setup.py file that I use from distutils.core import setup import py2exe, sys sys.path.append(tools) sys.path.append(report) sys.path.append(spirent) sys.path.append(numpydir) setup( console = ['checkfile.py'], ) If I use this setup.py, lots of numpy file cannot be loaded. I get the following message copying c:\python24\lib\site-packages\py2exe\run.exe - Y:\svn\copy\dist\checkfile.exe The following modules appear to be missing ['DateTime', 'FFT', 'Ft.Lib', 'Ft.Lib.DumpBgTuple', 'Ft.__init__', 'LinearAlgebra', 'MA', 'MLab', 'Matrix', 'Numeric', 'Pyrex', 'Pyrex.Compiler', 'XPathParserc' , '_curses', 'backends.draw_if_interactive', 'backends.new_figure_manager', 'backends.pylab_setup', 'backends.show', 'core.abs', 'core.max', 'core.min', 'core.r ound', 'ctypes', 'ext.IsDOMString', 'ext.SplitQName', 'fcompiler.FCompiler', 'fcompiler.show_fcompilers', 'lib.add_newdoc', 'mlab.amax', 'mlab.amin', 'numarray' , 'numarray.convolve', 'numarray.fft', 'numarray.ieeespecial', 'numarray.linear_algebra', 'numarray.linear_algebra.mlab', 'numarray.ma', 'numarray.numeric', 'nu merix.ArrayType', 'numerix.Complex', 'numerix.Complex32', 'numerix.Complex64', 'numerix.Float', 'numerix.Float32', 'numerix.Float64', 'numerix.Int', 'numerix.In t16', 'numerix.Int32', 'numerix.Int8', 'numerix.NewAxis', 'numerix.UInt16', 'numerix.UInt32', 'numerix.UInt8', 'numerix.absolute', 'numerix.add', 'numerix.all', 'numerix.allclose', 'numerix.alltrue', 'numerix.any', 'numerix.arange', 'numerix.arccos', 'numerix.arccosh', 'numerix.arcsin', 'numerix.arcsinh', 'numerix.arct an', 'numerix.arctan2', 'numerix.arctanh', 'numerix.argmax', 'numerix.argmin', 'numerix.argsort', 'numerix.around', 'numerix.array', 'numerix.arrayrange', 'nume rix.asarray', 'numerix.asum', 'numerix.bitwise_and', 'numerix.bitwise_or', 'numerix.bitwise_xor', 'numerix.ceil', 'numerix.choose', 'numerix.clip', 'numerix.com press', 'numerix.concatenate', 'numerix.conjugate', 'numerix.convolve', 'numerix.cos', 'numerix.cosh', 'numerix.cross_correlate', 'numerix.cumproduct', 'numerix .cumsum', 'numerix.diagonal', 'numerix.divide', 'numerix.dot', 'numerix.equal', 'numerix.exp', 'numerix.fabs', 'numerix.fft.fft', 'numerix.fft.inverse_fft', 'nu merix.floor', 'numerix.fmod', 'numerix.fromfunction', 'numerix.fromstring', 'numerix.greater', 'numerix.greater_equal', 'numerix.hypot', 'numerix.identity', 'nu merix.indices', 'numerix.innerproduct', 'numerix.iscontiguous', 'numerix.isnan', 'numerix.less', 'numerix.less_equal', 'numerix.log', 'numerix.log10', 'numerix. logical_and', 'numerix.logical_not', 'numerix.logical_or', 'numerix.logical_xor', 'numerix.matrixmultiply', 'numerix.maximum', 'numerix.minimum', 'numerix.mlab. amax', 'numerix.mlab.amin', 'numerix.mlab.cov', 'numerix.mlab.diff', 'numerix.mlab.hanning', 'numerix.mlab.rand', 'numerix.mlab.std', 'numerix.mlab.svd', 'numer ix.multiply', 'numerix.negative', 'numerix.newaxis', 'numerix.nonzero', 'numerix.not_equal', 'numerix.nx', 'numerix.ones', 'numerix.outerproduct', 'numerix.pi', 'numerix.power', 'numerix.product', 'numerix.put', 'numerix.putmask', 'numerix.rank', 'numerix.ravel', 'numerix.repeat', 'numerix.reshape', 'numerix.resize', ' numerix.searchsorted', 'numerix.shape', 'numerix.sin', 'numerix.sinh', 'numerix.size', 'numerix.sometrue', 'numerix.sort', 'numerix.sqrt', 'numerix.subtract', ' numerix.swapaxes', 'numerix.take', 'numerix.tan', 'numerix.tanh', 'numerix.trace', 'numerix.transpose', 'numerix.typecode', 'numerix.typecodes', 'numerix.where' , 'numerix.which', 'numerix.zeros', 'pre', 'serial', 'setuptools', 'setuptools.command', 'setuptools.command.egg_info', 'setuptools.command.install', 'testing.S cipyTest', 'win32api', 'win32con', 'win32pdh', 'xml.xslt', 'xml.xslt.ParsedPattern', 'numpy.Complex', 'numpy.Complex32', 'numpy.Complex64', 'numpy.Float', 'nump y.Float32', 'numpy.Float64', 'numpy.Int', 'numpy.Int16', 'numpy.Int32', 'numpy.Int8', 'numpy.UInt16', 'numpy.UInt32', 'numpy.UInt8', 'numpy.arccos', 'numpy.arcc osh', 'numpy.arcsin', 'numpy.arcsinh', 'numpy.arctan', 'numpy.arctanh', 'numpy.bitwise_and', 'numpy.bitwise_or', 'numpy.bitwise_xor', 'numpy.ceil', 'numpy.conju gate', 'numpy.core.add', 'numpy.core.bitwise_and', 'numpy.core.bitwise_or', 'numpy.core.bitwise_xor', 'numpy.core.cdouble', 'numpy.core.complexfloating', 'numpy .core.conjugate', 'numpy.core.csingle', 'numpy.core.divide', 'numpy.core.double', 'numpy.core.equal', 'numpy.core.float64',
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
On 10/11/06, Bill Baxter [EMAIL PROTECTED] wrote: On 10/12/06, Greg Willden [EMAIL PROTECTED] wrote: Speed should not take precedence over correctness.Unless your goal is speed.Then speed should take precedence over correctness. Huh?Person 1: Hey you should use function X.Person 2: No, it doesn't give me the correct answer.Person 1: Who cares? It's fast!What kind of logic is that? I'm having serious doubts about my conversion to Numpy.Greg - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) ret urn 1j rather than nan?
On Thu, 12 Oct 2006, Greg Willden apparently wrote: I'm having serious doubts about my conversion to Numpy. That's fine, but be sure to treat the explanations you are receiving with the same seriousness that you treat your doubts. For example, in the speed vs. correctness discussion, be sure to understand the problems with the word correct. Which have been explained. correct != expected when expectations lack context Cheers, Alan Isaac (just a happy user) - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Mark Bakker wrote: My vote is for consistency in numpy. But it is unclear what consistency is. What is truly confusing for newbie Python users (and a source for error even after 5 years of Python programming) is that 2/3 0 I recommend that you slap from __future__ import division into site.py or the top of your program: from __future__ import division import numpy print 3/2 a = numpy.arange(3) print a / (a+5) print a // a+5 1.5 [ 0. 0.1667 0.28571429] [5 6 6] In that respect, I would think numpy.sqrt(2) should give 1, but it gives 1.4142135623730951 Is there any practical reason to return 1? If not, isn't this argument sort of silly? So numpy does typechecking anyway (it gets an integer and makes it a float). If that is the consistent behavior, then by all means sqrt(-1) should return 1j. Well, it could also return: -1j or as someone mentioned one of six (I think) different quaternion values. It all depends on what the domain/range of the problem is. Wouldn't that be the consistent thing to do No, there's a difference. In order to do the former, all that is required is that sqrt switches on the *type* of it argument. sqrt return a float for integer and floating point args and a complex for complex args. In order to the latter, sqrt needs to switch on the *value* of its argument, which is an entirely different beast both in theory and in practice. In particular: sqrt(some_big_array) would have to scan all the values in the array to determine if any were negative to decide whether to return a float or an imaginary number. It also means that the memory usage is unpredictable -- the returned array is double in size if any values are negative. In addition to the significant slowdown this introduces, the former approach (keeping the input and output domains the same) is somewhat more robust against error, particularly if one tightens up the error mode. I would guess that people are working the complex plane at most half the time, in the former case a negative square root signals a problem and promoting to complex is at best a nuisance and may result in a painful to track down error. In the latter case, it's easy enough for me to toss in a astype in situations where I'm mixing domains and need complex outputs. Ideally numpy and scipy would have chosen different names for forgiving and strict powers and square roots, square_root and power versus sqrt and pow for example, but it's probably too late to do anything about that now. Since it sounds like Travis is going to tighten up the default error mode, I think that this is a non issue. No ones going to run into NANs unexpectedly and the error when doing sqrt([1,2,3,-1]) should be confusing and most once. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Travis Oliphant wrote: Travis Oliphant wrote: Now, it would be possible to give ufuncs a dtype keyword argument that allowed you to specify which underlying loop was to be used for the calculation. That way you wouldn't have to convert inputs to complex numbers before calling the ufunc, but could let the ufunc do it in chunks during the loop. That is certainly a reasonable enhancement: sqrt(a, dtype=complex). This no-doubt has a library-ish-feeling, but that is what NumPy is. If such a change is desireable, I don't think it would be much to implement it. This could be implemented, but only with a version number increase in the C-API (we would have to change the c-signature of the ufunc tp_call. This would mean that the next release of NumPy would be binary incompatible with packages built to previous NumPy releases. I've really been trying to avoid doing that. So, unless there are strong requests for this feature that outweigh the negatives of re-building dependent packages, then this feature will have to wait. OTOH: I suppose it could be implemented in a different way (using indexing or a method call): sqrt[complex](a) --- I remember Tim suggesting some use for indexing on ufuncs earlier though. It wouldn't surprise me if I did -- it sounds like the kind of thing I'd propose -- but I certainly can't remember what I was proposing. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
On Thu, Oct 12, 2006 at 08:58:21AM -0500, Greg Willden wrote: On 10/11/06, Bill Baxter [EMAIL PROTECTED] wrote: On 10/12/06, Greg Willden [EMAIL PROTECTED] wrote: Speed should not take precedence over correctness. Unless your goal is speed. Then speed should take precedence over correctness. Huh? Person 1: Hey you should use function X. Person 2: No, it doesn't give me the correct answer. Person 1: Who cares? It's fast! What kind of logic is that? I tried to explain the argument at http://www.scipy.org/NegativeSquareRoot Cheers Stéfan - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Travis Oliphant wrote: Personally I think that the default error mode should be tightened up. Then people would only see these sort of things if they really care about them. Using Python 2.5 and the errstate class I posted earlier: # This is what I like for the default error state numpy.seterr (invalid='raise', divide='raise', over='raise', under='ignore') I like these choices too. Overflow, division by zero, and sqrt(-x) are usually errors, indicating bad data or programming bugs. Underflowed floats, OTOH, are just really, really small numbers and can be treated as zero. An exception might be if the result is used in division and no error is raised, resulting in a loss of accuracy. I'm fine with this. I've hesitated because error checking is by default slower. But, I can agree that it is less surprising to new-comers. People that don't mind no-checking can simply set their defaults back to 'ignore' Great. One thing we may want to do (numarray had this), was add a pseudo argument 'all', that allows you to set all of the values at once. Then if you want the full-bore, ignore-all-errors computation (and your using 2.5 and from __future__ import with_statement) you can just do: with errstate(all='ignore'): # computation here # back to being picky -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Please test the SVN branch
Travis Oliphant wrote: I made some fixes to the asbuffer code which let me feel better about exposing it in NumPy (where it is now named int_asbuffer). This code takes a Python integer and a size and returns a buffer object that points to that memory. A little test is performed by trying to read (and possibly write if a writeable buffer is requested) to the first and last elements of the buffer. Any segfault is trapped and used to raise a Python error indicating you can't use that area of memory. It doesn't guarantee you won't shoot yourself, but it does make it more difficult to segfault Python. Previously a simple int_asbuffer(3423423, 5) would have segfaulted (unless by chance you the memory area 3423423 happens to be owned by Python). I have not tested the code on other platforms to make sure it works as expected, so please try and compiled it. FWIW, it compiles fine and passes all tests under Visual Studio and Python 2.4.3. I haven't tried to use it though. -tim -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) ret urn 1j rather than nan?
On Thu, 12 Oct 2006, Stefan van der Walt apparently wrote: I tried to explain the argument at http://www.scipy.org/NegativeSquareRoot Helpful. But you start off by saying: In mathematics, the above assumption is true -- that the square root of -1 is 1j. Since square root is (here) a function, and part of the function definition is the domain and codomain, this statement is not correct. If the codomain is real numbers, the domain must correspondingly be (a subset of) the nonnegative reals. The nan output is the result of an **invalid** input. So the question is really: Why is a negative real number an invalid input in this implementation, or Why in this implementation is the type of the output restricted by the type of the input? You get a good start on answering that. Cheers, Alan Isaac - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
I'll try to quit being a pain and just work around it.Those wiki pages are really good to capture this information. I need to read all of them to know what other gotchas are out there.Thanks and Sorry,Greg On 10/12/06, Alan G Isaac [EMAIL PROTECTED] wrote: That's fine, but be sure to treat the explanations you arereceiving with the same seriousness that you treat yourdoubts.For example, in the speed vs. correctness discussion, besure to understand the problems with the word correct. Which have been explained.correct != expectedwhen expectations lack context - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
May I suggest the following change to generate_umath.py?completely untested string change'sqrt' : Ufunc(1, 1, None, 'square-root elementwise. For real x, the domain is restricted to x=0.\n\ For complex results for x0 see numpy.scimath.sqrt', TD(inexact, f='sqrt'), TD(M, f='sqrt'), ),'When sqrt throws a ValueError would it be possible/appropriate for the error message to mention numpy.scimath.sqrt?I'm just trying to think of other ways to help make the transition as smooth as possible for new users.Greg - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Tim Hochberg wrote: Travis Oliphant wrote: Tim Hochberg wrote: With python 2.5 out now, perhaps it's time to come up with a with statement context manager. Something like: a = numpy.arange(10) a/a # ignores divide by zero with errstate(divide='raise'): a/a # raise exception on divide by zer # Would ignore divide by zero again if we got here. -tim This looks great. I think most people aren't aware of the with statement and what it can do (I'm only aware because of your posts, for example). So, what needs to be added to your example in order to just add it to numpy? As far as I know, just testing and documentation -- however testing was so minimal that I may find some other stuff. I'll try to clean it up tomorrow so that I'm a little more confident that it works correctly and I'll send another note out then. For this particular application, why not a context manager which just substitutes in the appropriately-optimized version of sqrt? That is, don't change the error state, but actually change the value of the object pointed at by the name sqrt? Andrew - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Things to address for Py3K
On Oct 11, 2006, at 1:41 PM, Travis Oliphant wrote: a) an extra multiplication operator to distinguish between element-by-element and dot One of the things I like about numpy is that element-by-element multiplication is default. Really, most of what I do is element by element, punctuated by a few matrix multiplications (or whatever other matrix operations) at the end. If I had a penny for every '.' I put in front of an operator in MATLAB, I would buy us all new computers -- expensive ones even. I hope that, if there is another multiplication operator like .*, that this will be shorthand for dot(A, B), and not for elem-by-elem multiplication. -Rob Rob Hetland, Associate Professor Dept. of Oceanography, Texas AM University http://pong.tamu.edu/~rob phone: 979-458-0096, fax: 979-845-6331 - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than?nan?
On Thu, Oct 12, 2006 at 10:53:12AM -0400, Alan G Isaac wrote: On Thu, 12 Oct 2006, Stefan van der Walt apparently wrote: I tried to explain the argument at http://www.scipy.org/NegativeSquareRoot Helpful. But you start off by saying: In mathematics, the above assumption is true -- that the square root of -1 is 1j. Since square root is (here) a function, and part of the function definition is the domain and codomain, this statement is not correct. If the codomain is real numbers, the domain must correspondingly be (a subset of) the nonnegative reals. The nan output is the result of an **invalid** input. So the question is really: Why is a negative real number an invalid input in this implementation, or Why in this implementation is the type of the output restricted by the type of the input? You get a good start on answering that. You are more than welcome to change the wiki if you can think of a simpler way to explain the problem... In fact, I would encourage that -- I just put it there as a starting point. Cheers Stéfan - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Greg Willden wrote: May I suggest the following change to generate_umath.py? completely untested string change 'sqrt' : Ufunc(1, 1, None, 'square-root elementwise. For real x, the domain is restricted to x=0.\n\ For complex results for x0 see numpy.scimath.sqrt', TD(inexact, f='sqrt'), TD(M, f='sqrt'), ), ' When sqrt throws a ValueError would it be possible/appropriate for the error message to mention numpy.scimath.sqrt? I'm just trying to think of other ways to help make the transition as smooth as possible for new users. And helpful error messages are one of Python's best features - in general, numpy doesn't have enough of 'em, IMO. DG Greg - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Please test the SVN branch
Travis Oliphant wrote: I made some fixes to the asbuffer code which let me feel better about exposing it in NumPy (where it is now named int_asbuffer). This code takes a Python integer and a size and returns a buffer object that points to that memory. If I understand this correctly, maybe it should be called something like: int_asbuffer_pointer or something with pointer in it. I sure didn't expect that meaning from asbuffer or in-asbuffer Otherwise -- nice idea! -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/ORR/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Providing array interface from SWIG-wrapped C struct
Does anybody here have experience about offering the array interface
from a SWIG-wrapped C struct?
I have tried the following, borrowing code from numpy's arrayobject.c:
%extend real_vec_t {
PyObject *__array_struct__() {
/* From numpy/arrayobject.c/array_struct_get */
PyArrayInterface *inter;
inter = (PyArrayInterface *)_pya_malloc(sizeof(PyArrayInterface));
inter-two = 2;
inter-nd = 1;
inter-typekind = 'f';
inter-itemsize = sizeof(float);
inter-flags = (NPY_CONTIGUOUS | NPY_OWNDATA | NPY_ALIGNED |
NPY_NOTSWAPPED);
inter-shape = (intp *)_pya_malloc(2*sizeof(intp));
inter-strides = inter-shape + 1;
*(inter-shape) = self-n;
*(inter-strides) = 1;
inter-data = (void *)(self-d);
inter-descr = 0;
return PyCObject_FromVoidPtr(inter,0);
}
%}
When passing such a SWIG real_vec_t object to numpy.array, numpy gets
a PyArrayInterface that fails to check correctly as a Python C
object. Three comments:
- The original numpy/arrayobject.c code actually gets its return
value from PyCObject_FromVoidPtrAndDesc(inter,self,...); but in this
context self does not point to a PyObject, but rather to the C
struct, so I don't think I can use that form.
- The code above does not deal with properly destructing the
PyArrayInterface, setting refcounts, etc. But I was going to worry
about those later.
- My problem is probably with SWIG rather than numpy... but I thought
the people in this forum (rather than those at the SWIG forum) would
be most likely to have encountered a situation similar to these.
Thanks a lot for any help, or just for reading this post.
Michele
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Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
As someone on this list (sorry to quick with the delete button) said: numpy is closer to the metal than MATLAB MATLAB aside, numpy is somewhat close to the metal. I think this is clearly part of its design philosophy, and also something I personally like about out. Getting back to MATLAB (at least version 4, the last one I used regularly) -- everything in MATLAB is a matrix of double precision Floating points. (or complex - that does get weird!). This makes things simpler for a lot of use, but restricts the power and flexibility substantially. Everything in numpy is an array of arbitrary shape and many possible data types -- this is more to think about, but give you a lot of power and flexibility. In any case, that is what numpy is. Period. Given that, arrays of different types behave differently -- that's something you'd darn well better understand pretty soon in your numpy career: a = N.array((1,2,3), dtype=N.uint8) a *= 200 a array([200, 144, 88], dtype=uint8) Oh my gosh! 2 * 200 isn't 144! what happened? This isn't any different than sqrt(-1) resulting in NaN or an error. In fact, there are all sorts of places where upcasting is not happening automagically -- in fact, I think that's become more consistent in the new numpy. So, in numpy you need to choose the datatype appropriate for your problem at hand. I know I always know whether I want to work in the complex plane or not. Another change recently made is to make doubles the default in more places -- I like that change. So, given the entire philosophy and current status of how numpy works, the ONLY question that is legitimate here is: Should the default datatype be complex or double? I vote double. Travis Oliphant wrote: Now, it would be possible to give ufuncs a dtype keyword argument that allowed you to specify which underlying loop was to be used for the calculation. sqrt(a, dtype=complex). I like this OK, and isn't is similar to the dtype argument in the accumulating methods? (sum, etc.). However, it's probably not worth the C-api change -- is it that different than calling: sqrt(a.astype(complex)) ? As for scipy, I heartily agree that scipy should be a superset of numpy, and NOT change the behavior of numpy functions and methods. We're taking the opportunity at this point for numpy to break backward compatibility with Numeric/numarray -- this is probably the best time to do the same for scipy. couldn't we at least introduce new names? numpy.scimath.csqrt() for instance? -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/ORR/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] multiarray tests skipped under py2.5
Is any one else seeing the multiarray tests all get skipped because of an import error when compiling under python 2.5. Everything else seems to work and all the tests go fine under 2.4. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] multiarray tests skipped under py2.5
On Thu, 12 Oct 2006 11:04:55 -0700 Tim Hochberg [EMAIL PROTECTED] wrote: Is any one else seeing the multiarray tests all get skipped because of an import error when compiling under python 2.5. Everything else seems to work and all the tests go fine under 2.4. Everything looks fine here: sys.version '2.5 (r25:51908, Oct 8 2006, 07:23:15) \n[GCC 4.1.1 (Gentoo 4.1.1)]' np.version.version '1.0.dev3317' np.test() Found 13 tests for numpy.core.umath Found 9 tests for numpy.lib.arraysetops Found 36 tests for numpy.core.ma Found 3 tests for numpy.fft.helper Found 4 tests for numpy.ctypeslib Found 2 tests for numpy.lib.polynomial Found 8 tests for numpy.core.records Found 26 tests for numpy.core.numeric Found 5 tests for numpy.distutils.misc_util Found 3 tests for numpy.lib.getlimits Found 31 tests for numpy.core.numerictypes Found 4 tests for numpy.core.scalarmath Found 1 tests for numpy.lib.ufunclike Found 12 tests for numpy.lib.twodim_base Found 47 tests for numpy.lib.shape_base Found 4 tests for numpy.lib.index_tricks Found 32 tests for numpy.linalg.linalg Found 42 tests for numpy.lib.type_check Found 176 tests for numpy.core.multiarray Found 10 tests for numpy.core.defmatrix Found 41 tests for numpy.lib.function_base Found 0 tests for __main__ . -- Ran 509 tests in 0.265s OK unittest.TextTestRunner object at 0x2ed6a650 Gerard - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] multiarray tests skipped under py2.5
Gerard Vermeulen wrote: On Thu, 12 Oct 2006 11:04:55 -0700 Tim Hochberg [EMAIL PROTECTED] wrote: Is any one else seeing the multiarray tests all get skipped because of an import error when compiling under python 2.5. Everything else seems to work and all the tests go fine under 2.4. Everything looks fine here: Thanks. I suppose that means it's either Windows specific or I gummed up my build tree somehow. I'll make a clean checkout and see if it goes away. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] gcc/fortran compilers for building extensions with Tiger/MacPython
On Thu, 12 Oct 2006 14:03:31 -0400 Tom Loredo [EMAIL PROTECTED] wrote: Hi folks, I've just upgraded my G4 (PPC) from Pather to Tiger, and just installed MacPython. I now need to build a slew of numerical extensions. I'm building them from source (I have lots of custom extensions to build from source in any case). I see the binaries available for Tiger are built with gcc 3.3 for PPC and gcc 4 for Intel; I think they may be tied to ActivePython but I'm not sure. When I start MacPython, I see it is built with gcc 4. Is this what I should be using? I'll also be compiling fortran extensions; I presume I should use gfortran rather than g77 for this (g77 is what worked with gcc 3.3). Is this correct? For Intel, yes, you need to use gfortran, and it has to be the prelease version (4.2): 4.1 and earlier don't build on Intel Macs. g77 (and gcc 3.x) won't build either, unfortunately. If you're using MacPorts (previously DarwinPorts), you'll want the gcc42 port. Or, you can grab binaries at http://hpc.sourceforge.net/ -- ||\/| /--\ |David M. Cooke http://arbutus.physics.mcmaster.ca/dmc/ |[EMAIL PROTECTED] - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] numpy-1.0 problem with install --skip-build
It appears the problem with getting a numpy-1.0 built with RPM is caused by using 2 step process: python setup.py build python setup.py install --skip-build --root $RPM_BUILD_ROOT This doesn't seem to work correctly! If I change it to just do: python setup.py install it works OK. - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Providing array interface from SWIG-wrapped C struct
On 10/12/06, Michele Vallisneri [EMAIL PROTECTED] wrote:
Does anybody here have experience about offering the array interface
from a SWIG-wrapped C struct?
I have.
I have tried the following, borrowing code from numpy's arrayobject.c:
%extend real_vec_t {
PyObject *__array_struct__() {
/* From numpy/arrayobject.c/array_struct_get */
You are extending real_vec_t with a new METHOD, but what numpy
requests is an ATTRIBUTE. So, numpy simply queries your vec like:
arrstr = vec.__array_struct__
and not with a method call like this
arrstr = vec.__array_struct__()
So here is what I would do (can fail with some SWIG optimizations)
%extend Vec {
PyObject* __array_struct__ () { /* ... */ }
%pythoncode {
__array_struct__ = property(__array_struct__,
doc='Array protocol')
}
}
Hope you got the idea.
--
Lisandro Dalcín
---
Centro Internacional de Métodos Computacionales en Ingeniería (CIMEC)
Instituto de Desarrollo Tecnológico para la Industria Química (INTEC)
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PTLC - Güemes 3450, (3000) Santa Fe, Argentina
Tel/Fax: +54-(0)342-451.1594
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Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Hi, On Thursday 12 October 2006 10:49, Christopher Barker wrote: As someone on this list (sorry to quick with the delete button) said: snip I think I might agree with everything (!) you that - very well written . Travis Oliphant wrote: Now, it would be possible to give ufuncs a dtype keyword argument that allowed you to specify which underlying loop was to be used for the calculation. sqrt(a, dtype=complex). I like this OK, and isn't is similar to the dtype argument in the accumulating methods? (sum, etc.). However, it's probably not worth the C-api change -- is it that different than calling: sqrt(a.astype(complex)) ? It is very late in the game - obvious ! (one week before the announced final release). But I think adding the dtype argument to ufuncs would be a very useful addition !! It is very nice to look at ! And it saves (temporary) memory and should be noticeably faster than sqrt(a.astype(complex)). This dtype addition might address many of the issues I have raised before on this list. Travis, be courageous and change the C-API one last time before putting it in stone for 1.0 final !!! As for scipy, I heartily agree that scipy should be a superset of numpy, and NOT change the behavior of numpy functions and methods. We're taking the opportunity at this point for numpy to break backward compatibility with Numeric/numarray -- this is probably the best time to do the same for scipy. couldn't we at least introduce new names? numpy.scimath.csqrt() for instance? -Chris It has to be very well documented what the 12 functions (the last 3%) are were scipy and numpy differ. --- if at all possible I would prefer a name change like csqrt (or better sqrtc - I always like seen things together when sorted alphabetically ;-) ) [by the way: I like sqrt(-1) == nan just fine] Thanks for the great work. - Sebastian - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
Historical note. When Cleve Moler first created 'matlab' as a tool for teaching at the University of New Mexico, all numbers were complex. And, in fact, there was a limit of 10,000 numbers. There was a fixed array where these 10,000 numbers were stored. I got pretty excited about his project and wanted to use it to get eigenvalues etc. for matrices of interest from our codes. But I had a Cray 1 with an entire megaword of memory and my matrices wouldn't fit. So I set out to hack the code to add a memory manager so that we could use the entire memory. This was a big success. Then I decided it was sad that I had to write the matrices out of the code onto a disk and then read them into matlab, so I got the idea of making something similar as the front end of the code, only with data types (because I hated only being able to use half my Cray since my data was real, and our codes were often memory bound) and a full programming language like Fortran with array syntax for speed. This idea resulted in Basis ( basis.llnl.gov). My interest in Python (and hence numpy) evolved from that as OOP arrived. - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
David Goldsmith wrote: Got it. And if I understand correctly, the import order you specify in the little mynumpy example you included in your latest response to Fernando will result in any overlap between numpy and numpy.lib.scimath to call the latter's version of things rather than the former's, yes? Right. The last import will be used for any common-names (variables get re-bound to the new functions...) -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Compiling Numpy with py2exe
Charles Hanot wrote: Hello, I’ve a program using Numpy and I would like to compile it with Py2exe but I’ve some problems. When I try to compile my program, some modules generated by numpy are not loaded. I’ve already asked to the py2exe community if they have a solution for that and the answer was that it was possible to compile numpy modules with py2exe but I should ask to the Numpy community J So my question is, is there something special to do in order to compile a program using numpy module? The only thing I remember is that you have to explicitly make sure _internal.py file gets copied over as it is not possible to discern that by looking at the import statements in .py files. -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] More SVN testing
I just checked in a couple of changes to SVN. I was going to check in errstate, but it looks like Travis beat me to it, so I contented myself with adding a docstring and some tests. These tests are only run under 2.5; things seem to work fine here, but if someone on a Linux box whose running Python 2.5 could try it out I'd appreciate it. I'm suspicous of Linux/Windows differences right now because of my run in with them earlier today in the tests for test_multiarray (which I believe it now fixed -- at least everything is working here OK). -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] multiarray tests skipped under py2.5
Tim Hochberg wrote: Still there with a clean checkout. Blech! I hate weird import problems. I'll try to look into it, but if anyone has any good ideas, let me know Show us the errors, I may be able to help. It's possible some recent changes messed things up. -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] More SVN testing
Hello all -Original Message- From: [EMAIL PROTECTED] [mailto:numpy- [EMAIL PROTECTED] On Behalf Of Tim Hochberg Sent: 12 October 2006 21:24 To: numpy-discussion Subject: [Numpy-discussion] More SVN testing I just checked in a couple of changes to SVN. I was going to check in errstate, but it looks like Travis beat me to it, so I contented myself with adding a docstring and some tests. These tests are only run under 2.5; things seem to work fine here, but if someone on a Linux box whose running Python 2.5 could try it out I'd appreciate it. I'm suspicous of Linux/Windows differences right now because of my run in with them earlier today in the tests for test_multiarray (which I believe it now fixed -- at least everything is working here OK). This probably fixes the issue I reported here: http://projects.scipy.org/scipy/numpy/ticket/286 Maybe you can check that the test counts match up on Python 2.4 and 2.5 on Windows on your system and close that ticket. Cheers, Albert - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] More SVN testing
Albert Strasheim wrote: Hello all -Original Message- From: [EMAIL PROTECTED] [mailto:numpy- [EMAIL PROTECTED] On Behalf Of Tim Hochberg Sent: 12 October 2006 21:24 To: numpy-discussion Subject: [Numpy-discussion] More SVN testing I just checked in a couple of changes to SVN. I was going to check in errstate, but it looks like Travis beat me to it, so I contented myself with adding a docstring and some tests. These tests are only run under 2.5; things seem to work fine here, but if someone on a Linux box whose running Python 2.5 could try it out I'd appreciate it. I'm suspicous of Linux/Windows differences right now because of my run in with them earlier today in the tests for test_multiarray (which I believe it now fixed -- at least everything is working here OK). This probably fixes the issue I reported here: http://projects.scipy.org/scipy/numpy/ticket/286 Maybe you can check that the test counts match up on Python 2.4 and 2.5 on Windows on your system and close that ticket. Yep, that's the same one I ran into and it's fixed now on windows. It sounds like it didn't occur at all on Linux. Their are now actually two more tests under 2.5 than under 2.4, but that's on purpose -- I can't test 'with errstate' under 2.4, so I skip those tests. I went ahead and closed the ticket. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Polyfit
On 10/12/06, Greg Willden [EMAIL PROTECTED] wrote: Hi All,I'm using numpy.polyfit and it is giving me some really strange results when evaluated near 0. So I compared it with polyfit in matplotlib and the code and docstrings are nearly identical. However the slight differences in the code make a huge difference on my data. Here is the Numpy version with line numbers added:1 x = NX.asarray(x)+0.2 y = NX.asarray(y)+0.3 y = NX.reshape(y, (len(y), 1))4 X = vander(x, N+1)5 c, resids, rank, s = _lstsq(X, y) 6 c.shape = (N+1,)7 return cAnd here is the MPL version with line numbers added:1 x = asarray(x)+0.2 y = asarray(y)+0.3 y = reshape(y, (len(y),1))4 X = Matrix(vander(x, N+1)) 5 Xt = Matrix(transpose(X))6 c = array(linear_algebra.inverse(Xt*X)*Xt*y) # convert back to array7 c.shape = (N+1,)8 return c So lines 1-4 are basically the same.The MPL version produces a much more stable representation of my data Can someone please comment on the differences?Should these two be exactly the same?If not, what are the advantages to each algorithm?I'm guessing that the rcond number in the lstsq version (default 1e-10) is the difference. Generally the lstsq version should work better than the MPL version because at*a is not as well conditioned and vandermonde matrices are notoriously ill conditioned anyway for higher degree fits. It would help if you attached the data files in ascii form unless they happen to contain thousands of data items. Just the x will suffice, zip it up if you have to. Chuck - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] More SVN testing
numpy.test(10,10) : : : Ran 512 tests in 0.995s OK unittest.TextTestRunner object at 0x344ded0 numpy.__version__ '1.0.dev3320' on Intel Mac OS X 10.4.8, macpython 2.5 [Python 2.5 (r25:51918, Sep 19 2006, 08:49:13)] -Rob On Oct 12, 2006, at 2:24 PM, Tim Hochberg wrote: I just checked in a couple of changes to SVN. I was going to check in errstate, but it looks like Travis beat me to it, so I contented myself with adding a docstring and some tests. These tests are only run under 2.5; things seem to work fine here, but if someone on a Linux box whose running Python 2.5 could try it out I'd appreciate it. I'm suspicous of Linux/Windows differences right now because of my run in with them earlier today in the tests for test_multiarray (which I believe it now fixed -- at least everything is working here OK). -tim -- --- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel? cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion Rob Hetland, Associate Professor Dept. of Oceanography, Texas AM University http://pong.tamu.edu/~rob phone: 979-458-0096, fax: 979-845-6331 - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Polyfit
On 10/12/06, Charles R Harris [EMAIL PROTECTED] wrote: I'm guessing that the rcond number in the lstsq version (default 1e-10) is the difference. Generally the lstsq version should work better than the MPL version because at*a is not as well conditioned and vandermonde matrices are notoriously ill conditioned anyway for higher degree fits. It would help if you attached the data files in ascii form unless they happen to contain thousands of data items. Just the x will suffice, zip it up if you have to. Here are the files.Since the two algorithms behave differently and each has it place then can both be included in numpy?i.e. numpy.polyfit(x,y,N, mode='alg1')numpy.polyfit (x,y,N, mode='alg2')replacing alg1 and alg2 with meaningful names.ThanksGreg 2 4 0 16 1 3 14 17 12 13 8 7 5 11 23 19 24 9 18 15 44 22 20 25 27 10 41 45 26 40 21 50 29 38 28 37 53 48 57 78 54 61 39 64 65 58 43 35 55 47 60 66 46 68 52 51 42 75 93 56 87 74 59 71 84 49 72 81 73 63 94 67 36 80 30 70 106 69 62 77 76 90 98 103 6 172 79 83 82 31 85 97 105 91 121 89 95 101 116 135 92 102 96 88 120 137 136 34 389 499 489 565 479 555 412 390 443 442 456 365 404 593 457 364 375 441 367 360 453 376 482 576 550 445 409 658 363 435 381 477 549 396 460 544 601 466 384 557 391 461 511 642 547 558 542 610 608 639 438 591 551 378 599 552 397 569 578 437 643 361 385 399 538 600 395 561 602 649 454 464 447 386 535 554 577 596 686 635 459 514 628 448 583 407 408 603 379 626 410 500 570 449 582 434 401 417 501 462 566 366 394 631 485 382 563 369 784 781 824 838 783 826 825 854 867 763 801 799 879 795 759 852 872 831 802 769 873 773 762 779 772 789 836 881 839 827 796 882 851 786 797 755 726 841 896 865 890 837 857 775 853 767 785 756 750 754 887 861 894 770 749 771 840 844 782 780 877 859 774 832 871 730 722 901 794 884 846 760 848 729 876 724 720 757 830 798 850 818 764 875 787 829 899 885 874 960 815 864 777 723 820 891 747 751 725 821 776 823 866 800 833 788 758 878 1411 1350 1358 1353 1362 1378 1326 1348 1321 1355 1357 1349 1352 1346 1373 1430 1408 1351 1310 1418 1281 1336 1416 1428 1423 1436 1364 1432 1419 1377 1421 1359 1279 1401 1424 1323 1340 1322 1339 1407 1438 1427 1247 1435 1290 1311 1262 1343 1307 1437 1369 1315 1288 1356 1412 1309 1327 1276 1367 1325 1425 1420 1344 1361 1337 1368 1314 1415 1335 1278 1320 1280 1382 1282 1434 1328 1273 1332 1410 1347 1284 1338 1417 1354 1360 1329 1270 1305 1375 1414 1287 1291 1429 1379 1256 1372 1370 1289 1306 1275 1342 1274 1286 1313 1271 1248 1319 1268 1479 1440 1452 1449 1441 1542 1444 1447 1645 1569 1485 1537 1527 1522 1462 1461 1554 1651 1448 1652 1538 1529 1510 1451 1639 1570 1486 1737 1481 1623 1725 1453 1458 1459 1680 1484 1445 1671 1460 1524 1673 1666 1627 1540 1564 1626 1672 1499 1674 1561 1555 1519 1472 1632 1534 1548 1650 1470 1656 1637 1648 1717 1661 1521 1724 1755 1631 1735 1517 1667 1551 1728 1566 1575 1515 1539 1531 1541 1563 1558 1643 1454 1549 1758 1483 1761 1714 1638 1505 1654 1664 1475 1556 1473 1528 1516 1552 1443 1709 1553 1794 1571 1644 1520 1718 1742 1498 1736 2149 2052 2128 2141 2019 2108 2125 1998 2055 2087 2106 2123 2029 2036 1960 2121 2049 1945 2132 2058 2035 2144 2053 2109 1963 1949 1966 2137 2060 2153 2147 1956 2126 2115 2130 2129 1808 2021 2038 1804 2064 2104 1952 2107 1911 1958 2116 1810 2031 1940 2148 2117 2150 1814 2059 1916 2010 2026 1809 1964 2103 2032 2039 2118 2151 1973 2152 1951 2084 2011 2065 2105 2028 2112 2044 2040 2122 2033 1812 1941 1946 1987 1996 2127 2113 2048 1995 1950 2155 1959 2138 1803 1970 1961 2020 2008 1811 1968 1965 1947 1819 2037 2063 1817 2133 1967 2140 2047 2185 2161 2217 2241 2163 2164 2212 2220 2247 2162 2208 2248 2225 2255 2206 2191 2218 2165 2238 2237 2166 2231 2211 2192 2258 2233 2201 2221 2239 2251 2249 2219 2186 2197 2246 2210 2199 2184 2226 2243 2188 2240 2250 2204 2205 2193 2260 2160 2236 2187 2228 2182 2223 2215 2224 2235 2213 2198 2168 2202 2195 2183 2253 2209 2234 2288 2261 2256 2254 2200 2257 2242 2167 2280 2252 2245 2207 2263 2216 2277 2284 2244 2190 2259 2266 2267 2180 2227 2196 2229 2297 2189 2169 2296 2181 2279 2281 2179 2178 2194 2287 2285 2232 2301 2264 2214 2289 2787 2714 2823 2591 2640 2717 2650 2612 2838 2630 2827 2588 2685 2684 2677 2720 2613 2872 2729 2804 2622 2841 2642 2694 2867 2693 2865 2601 2808 2870 2691 2607 2877 2620 2836 2835 2576 2596 2636 2805 2802 2603 2579 2797 2610 2577 2716 2832 2626 2676 2678 2873 2785 2632 2608 2692 2643 2726 2813 2614 2675 2791 2803 2644 2701 2801 2594 2737 2625 2826 2839 2649 2700 2799 2634 2695 2730 2592 2874 2667 2647 2683 2586 2722 2679 2669 2629 2871 2549 2733 2582 2631 2807 2606 2641 2727 2724 2652 2623 2783 2646 2821 2602 2702 2708 2589 2815 2842 3222 3230 3229 2898 3228 3214 3232 3208 3213 2931 3202 2886 2922 3205 3221 3235 3212 3204 2887 2893 3209 2907 2921 3231 2891 2914 3223 3224 2890 2911 3189 2896 3233 2909 2917 3220 3198 3207 3226 3227 2900 2889 3225 2923 2884 3203 3192 2910 2926 3210 2885 2905 2913 2895 2897 2918 2924 3200 2927 2934 3234 2912 3206 3187 3236 3196 2881 3238 3190 3219
Re: [Numpy-discussion] Polyfit
On 10/12/06, Greg Willden [EMAIL PROTECTED] wrote: On 10/12/06, Charles R Harris [EMAIL PROTECTED] wrote: I'm guessing that the rcond number in the lstsq version (default 1e-10) is the difference. Generally the lstsq version should work better than the MPL version because at*a is not as well conditioned and vandermonde matrices are notoriously ill conditioned anyway for higher degree fits. It would help if you attached the data files in ascii form unless they happen to contain thousands of data items. Just the x will suffice, zip it up if you have to. Here are the files.Since the two algorithms behave differently and each has it place then can both be included in numpy?i.e. numpy.polyfit(x,y,N, mode='alg1')numpy.polyfit (x,y,N, mode='alg2')replacing alg1 and alg2 with meaningful names.The polyfit function looks seriously busted. If I do the fits by hand I get the same results using the (not so hot) MPL version or lstsq. I don't know what the problem is. The docstring is also incorrect for the method. Hmmm... Chuck - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Polyfit
On 10/12/06, Charles R Harris [EMAIL PROTECTED] wrote: On 10/12/06, Greg Willden [EMAIL PROTECTED] wrote: On 10/12/06, Charles R Harris [EMAIL PROTECTED] wrote: I'm guessing that the rcond number in the lstsq version (default 1e-10) is the difference. Generally the lstsq version should work better than the MPL version because at*a is not as well conditioned and vandermonde matrices are notoriously ill conditioned anyway for higher degree fits. It would help if you attached the data files in ascii form unless they happen to contain thousands of data items. Just the x will suffice, zip it up if you have to. Here are the files.Since the two algorithms behave differently and each has it place then can both be included in numpy?i.e. numpy.polyfit(x,y,N, mode='alg1')numpy.polyfit (x,y,N, mode='alg2')replacing alg1 and alg2 with meaningful names.The polyfit function looks seriously busted. If I do the fits by hand I get the same results using the (not so hot) MPL version or lstsq. I don't know what the problem is. The docstring is also incorrect for the method. Hmmm... Polyfit seems overly conservative in its choice of rcond. In [101]: lin.lstsq(v,y,1e-10)[0]Out[101]: array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01, 3.05631361e-02])In [107]: polyfit(x,y,3)Out[108]: array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01, 3.05631361e-02])CompareIn [112]: lin.lstsq(v,y,1e-12)[0]Out[112]: array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00, 6.51889107e+03])In [113]: dot(lin.inv(vtv),dot(v.T,y))Out[113]: array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00, 6.51889107e+03])So the default needs to be changed somewhere. Probably polyfit shoud accept rcond as a keyword. Where the actual problem lies is a bit obscure as the normal rcond default for lin.lstsq is 1e-12. Maybe some sort of import error somewhere down the line.Chuck - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Polyfit
On 10/12/06, Charles R Harris [EMAIL PROTECTED] wrote: On 10/12/06, Charles R Harris [EMAIL PROTECTED] wrote: On 10/12/06, Greg Willden [EMAIL PROTECTED] wrote: On 10/12/06, Charles R Harris [EMAIL PROTECTED] wrote: I'm guessing that the rcond number in the lstsq version (default 1e-10) is the difference. Generally the lstsq version should work better than the MPL version because at*a is not as well conditioned and vandermonde matrices are notoriously ill conditioned anyway for higher degree fits. It would help if you attached the data files in ascii form unless they happen to contain thousands of data items. Just the x will suffice, zip it up if you have to. Here are the files.Since the two algorithms behave differently and each has it place then can both be included in numpy?i.e. numpy.polyfit(x,y,N, mode='alg1')numpy.polyfit (x,y,N, mode='alg2')replacing alg1 and alg2 with meaningful names.The polyfit function looks seriously busted. If I do the fits by hand I get the same results using the (not so hot) MPL version or lstsq. I don't know what the problem is. The docstring is also incorrect for the method. Hmmm... Polyfit seems overly conservative in its choice of rcond. In [101]: lin.lstsq(v,y,1e-10)[0]Out[101]: array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01 , 3.05631361e-02])In [107]: polyfit(x,y,3)Out[108]: array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01, 3.05631361e-02])CompareIn [112]: lin.lstsq(v,y,1e-12)[0]Out[112]: array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00, 6.51889107e+03])In [113]: dot(lin.inv(vtv),dot(v.T,y))Out[113]: array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00, 6.51889107e+03])So the default needs to be changed somewhere. Probably polyfit shoud accept rcond as a keyword. Where the actual problem lies is a bit obscure as the normal rcond default for lin.lstsq is 1e-12. Maybe some sort of import error somewhere down the line.And here is the location of the problem in numpy/linalg/linalg.py :def lstsq(a, b, rcond=1.e-10):The 1e-10 is a bit conservative. On the other hand, I will note that the condition number of the dot(V^T ,V) matrix is somewhere around 1e22, which means in general terms that you need around 22 digits of accuracy. Inverting it only works sorta by accident in the current case. Generally, using Vandermonde matrices and polynomial fits it a bad idea when the dynamic range of the interval gets large and the degree gets up around 4-5 as it leads to ill conditioned sets of equations. When you really need the best start with chebychev polynomials or, bestest, compute a set of polynomials orthogonal over the sample points. Anyway, I think rcond should be something like 1e-12 or 1e-13 by default and be available as a keyword in the polyfit function. If no one complains I will make this change, although it is just a bandaid and things will fall apart again as soon as you call polyfit(x,y,4). Chuck - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] numpy on centos4.4?
Has anyone built numpy-1.0rc2/scipy-0.5.1 on centos4.4? It seems that a fortran90+ compiler is required, so I installed gcc4-gfortran-4.1.0-18.EL4 libgfortran-4.1.0-18.EL4 Didn't quite work though. Built numpy+scipy OK, but then: Warning: FAILURE importing tests for module 'scipy.linalg.blas' from '...packages/scipy/linalg/blas.pyc' /usr/lib/python2.3/site-packages/scipy/linalg/tests/test_blas.py:22: ImportError: /usr/lib/python2.3/site-pac\ kages/scipy/linalg/fblas.so: undefined symbol: srotmg_ (in ?) Not sure yet what's causing this. I suspect it is because of a mixture of different compilers/libraries? Like, maybe blas was compiled with f77 from gcc3, while fblas was compiled with gfortran from gcc4? - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Providing array interface from SWIG-wrapped C struct
Wonderful, it works, thanks!
Michele
--- Discussion of Numerical Python
numpy-discussion@lists.sourceforge.net wrote:
On 10/12/06, Michele Vallisneri
[EMAIL PROTECTED] wrote:
Does anybody here have experience about offering
the array interface
from a SWIG-wrapped C struct?
I have.
I have tried the following, borrowing code from numpy's arrayobject.c:
%extend real_vec_t {
PyObject *__array_struct__() {
/* From numpy/arrayobject.c/array_struct_get */
You are extending
real_vec_t with a new METHOD, but what numpy
requests is an ATTRIBUTE.
So, numpy simply queries your vec like:
arrstr = vec.__array_struct__
and not with a method call like this
arrstr = vec.__array_struct__()
So here is what I would do (can fail with some SWIG optimizations)
%extend Vec {
PyObject* __array_struct__ () { /* ... */ }
%pythoncode {
__array_struct__ = property(__array_struct__,
doc='Array protocol')
}
}
Hope you got the idea.
--
Lisandro Dalc�n
---
Centro Internacional de M�todos Computacionales en
Ingenier�a (CIMEC)
Instituto de Desarrollo Tecnol�gico para la Industria
Qu�mica (INTEC)
Consejo Nacional de Investigaciones Cient�ficas y T�cnicas
(CONICET)
PTLC - G�emes 3450, (3000) Santa Fe, Argentina
Tel/Fax:
+54-(0)342-451.1594
-
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[Numpy-discussion] rcond in polyfit
Hi all,I note that polyfit looks like it should work for single and double, real and complex, polynomials. On the otherhand, the default rcond needs to depend on the underlying precision. On the other, other hand, all the svd computations are done with dgelsd or zgelsd, i.e., double precision. Even so problems can arise from inherent errors of the input data if it is single precision to start with. I also think the final degree of the fit should be available somewhere if wanted, as it is an indication of what is going on. Sooo, any suggestions as to what to do? My initial impulse would be to set rcond=1e-6 for single, 1e-14 for double, make rcond a keyword, and kick the can down the road on returning the actual degree of the fit. ChuckPS, what is the best way of converting arbitrary arrays to the appropriate c data types float and double? - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Should numpy.sqrt(-1) return 1j rather than nan?
I find that acceptable for my purposes, but is there some way we can minimize the surprise(s) for newbies? (I know some suggestions have been put forward in this thread, but I don't know enough to cast a vote one way or another for any of those, just a vote for please do it.) And, in closing, I too would like to: thank you very much for all your work - despite my emotional outburst, I am generally very happy using numpy and regard it as an excellent product. DG Travis Oliphant wrote: David Goldsmith wrote: Got it. And if I understand correctly, the import order you specify in the little mynumpy example you included in your latest response to Fernando will result in any overlap between numpy and numpy.lib.scimath to call the latter's version of things rather than the former's, yes? Right. The last import will be used for any common-names (variables get re-bound to the new functions...) -Travis - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] How to make sqrt(-1) be 1j
On 10/12/06, Stefan van der Walt [EMAIL PROTECTED] wrote: On Thu, Oct 12, 2006 at 08:58:21AM -0500, Greg Willden wrote: On 10/11/06, Bill Baxter [EMAIL PROTECTED] wrote: I tried to explain the argument at http://www.scipy.org/NegativeSquareRoot The proposed fix for those who want sqrt(-1) to return 1j is: from numpy.lib import scimath as SM SM.sqrt(-1) But that creates a new namespace alias, different from numpy. So I'll call numpy.array() to create a new array, but SM.sqrt() when I want a square root. Am I wrong to want some simple way to change the behavior of numpy.sqrt itself? Seems like you can get that effect via something like: for n in numpy.lib.scimath.__all__: numpy.__dict__[n] = numpy.lib.scimath.__dict__[n] If that sort of function were available as numpy.use_scimath(), then folks who want numpy to be like scipy can achieve that with just one line at the top of their files. The import under a different name doesn't quite achieve the goal of making that behavior numpy's default. I guess I'm thinking mostly of the educational uses of numpy, where you may have users that haven't learned about much about numerical computing yet. I can just imagine the instructor starting off by saying ok everyone we're going to learn numpy today! First everyone type this: 'import numpy, from numpy.lib import scimath as SM' -- Don't worry about all the things there you don't understand. Whereas import numpy, numpy.use_scimath() seems easier to explain and much less intimidating as your first two lines of numpy to learn. Or is that just a bad idea for some reason? --bb - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] How to make sqrt(-1) be 1j
On 10/12/06, Bill Baxter [EMAIL PROTECTED] wrote: On 10/12/06, Stefan van der Walt [EMAIL PROTECTED] wrote: On Thu, Oct 12, 2006 at 08:58:21AM -0500, Greg Willden wrote: On 10/11/06, Bill Baxter [EMAIL PROTECTED] wrote: I tried to explain the argument at http://www.scipy.org/NegativeSquareRootThe proposed fix for those who want sqrt(-1) to return 1j is: from numpy.lib import scimath as SM SM.sqrt(-1)But that creates a new namespace alias, different from numpy.So I'llcall numpy.array() to create a new array, but SM.sqrt() when I want a square root.Am I wrong to want some simple way to change the behavior ofnumpy.sqrt itself?Seems like you can get that effect via something like:for n in numpy.lib.scimath.__all__:numpy.__dict__[n] = numpy.lib.scimath.__dict__[n]I don't like either of those ideas, although the second seems preferable. I think it better to make an efficient way of calling a sqrt routine that accepts negative floats and returns complex numbers. The behaviour could be chosen either by key word or by specially named routines, or maybe even some global flag, but I don't think it asking too much for the students to learn that sqrt(-1) doesn't exist as a real number and that efficient computation uses real whenever possible because it is a) smaller, and b) faster. That way we also avoid having software that only works for scimath but not for numpy. Chuck. - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] How to make sqrt(-1) be 1j
On 10/13/06, Charles R Harris [EMAIL PROTECTED] wrote: On 10/12/06, Bill Baxter [EMAIL PROTECTED] wrote: On 10/12/06, Stefan van der Walt [EMAIL PROTECTED] wrote: On Thu, Oct 12, 2006 at 08:58:21AM -0500, Greg Willden wrote: On 10/11/06, Bill Baxter [EMAIL PROTECTED] wrote: I tried to explain the argument at http://www.scipy.org/NegativeSquareRoot The proposed fix for those who want sqrt(-1) to return 1j is: from numpy.lib import scimath as SM SM.sqrt(-1) But that creates a new namespace alias, different from numpy. So I'll call numpy.array() to create a new array, but SM.sqrt() when I want a square root. Am I wrong to want some simple way to change the behavior of numpy.sqrt itself? Seems like you can get that effect via something like: for n in numpy.lib.scimath.__all__: numpy.__dict__[n] = numpy.lib.scimath.__dict__[n] I don't like either of those ideas, although the second seems preferable. I think it better to make an efficient way of calling a sqrt routine that accepts negative floats and returns complex numbers. The behaviour could be chosen either by key word or by specially named routines, or maybe even some global flag, We have the specially named routines way already. numpy.lib.scimath.sqrt but I don't think it asking too much for the students to learn that sqrt(-1) doesn't exist as a real number and that efficient computation uses real whenever possible because it is a) smaller, and b) faster. That way we also avoid having software that only works for scimath but not for numpy. I think efficiency is not a very good argument for the default behavior here, because -- lets face it -- if efficient execution was high on your priority list, you wouldn't be using python. And even if you do care about efficiency, one of the top rules of optimization is to first get it working, then get it working fast. Really, I'm just playing the devils advocate here, because I don't work with complex numbers (I see quaternions more often than complex numbers). But I would be willing to do something like numpy.use_realmath() in my code if it would make numpy more palatable to a wider audience. I wouldn't like it, however, if I had to do some import thing where I have to remember forever after that I should type 'numpy.tanh()' but 'realmath.arctanh()'. Anyway it seems like the folks who care about performance are the ones who will generally be more willing to make tweaks like that. But that's about all I have to say about this, since the status quo works fine for me. So I'll be quiet. Just it seems like the non-status-quo'ers here have some good points. I taught intro to computer science to non-majors one semester. I know I would not want to have to confront all the issues with numerical computing right off the bat if I was just trying to teach people how to do some math. --bb - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem buliding rc2 on OS X 10.3.9/Python2.5
I hate bumping my own messages, but does no one have any insight into this?
On 10/9/06, Jay Parlar [EMAIL PROTECTED] wrote:
In the process of finally switching over to Python 2.5, and am trying
to build numpy. Unfortunately, it dies during the build:
Jay-Computer:~/Desktop/numpy-1.0rc2 jayparlar$ python setup.py build
Running from numpy source directory.
F2PY Version 2_3296
blas_opt_info:
FOUND:
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3)]
extra_compile_args = ['-faltivec',
'-I/System/Library/Frameworks/vecLib.framework/Headers']
lapack_opt_info:
FOUND:
extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
define_macros = [('NO_ATLAS_INFO', 3)]
extra_compile_args = ['-faltivec']
running build
running config_fc
running build_src
building py_modules sources
building extension numpy.core.multiarray sources
Generating build/src.macosx-10.3-fat-2.5/numpy/core/config.h
customize NAGFCompiler
customize AbsoftFCompiler
customize IbmFCompiler
Could not locate executable g77
Could not locate executable f77
Could not locate executable gfortran
Could not locate executable f95
customize GnuFCompiler
customize Gnu95FCompiler
customize G95FCompiler
customize GnuFCompiler
customize Gnu95FCompiler
customize NAGFCompiler
customize NAGFCompiler using config
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:
'-I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5
-Inumpy/core/src -Inumpy/core/include
-I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5
-c'
gcc: _configtest.c
gcc: cannot specify -o with -c or -S and multiple compilations
gcc: cannot specify -o with -c or -S and multiple compilations
failure.
removing: _configtest.c _configtest.o
numpy/core/setup.py:50: DeprecationWarning: raising a string exception
is deprecated
raise ERROR: Failed to test configuration
Traceback (most recent call last):
File setup.py, line 89, in module
setup_package()
File setup.py, line 82, in setup_package
configuration=configuration )
File /Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/core.py,
line 174, in setup
return old_setup(**new_attr)
File
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/core.py,
line 151, in setup
dist.run_commands()
File
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py,
line 974, in run_commands
self.run_command(cmd)
File
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py,
line 994, in run_command
cmd_obj.run()
File
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/command/build.py,
line 112, in run
self.run_command(cmd_name)
File
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/cmd.py,
line 333, in run_command
self.distribution.run_command(command)
File
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py,
line 994, in run_command
cmd_obj.run()
File
/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py,
line 87, in run
self.build_sources()
File
/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py,
line 106, in build_sources
self.build_extension_sources(ext)
File
/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py,
line 212, in build_extension_sources
sources = self.generate_sources(sources, ext)
File
/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py,
line 270, in generate_sources
source = func(extension, build_dir)
File numpy/core/setup.py, line 50, in generate_config_h
raise ERROR: Failed to test configuration
ERROR: Failed to test configuration
This is with the Universal 2.5 binary, and OS X 10.3.9.
Any ideas? Sorry if this one has been asked before, but I can't seem
to find a solution anywhere.
Jay P.
-
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Get stuff done quickly with pre-integrated technology to make your job easier
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Re: [Numpy-discussion] How to make sqrt(-1) be 1j
Bill Baxter wrote: On 10/12/06, Stefan van der Walt [EMAIL PROTECTED] wrote: On Thu, Oct 12, 2006 at 08:58:21AM -0500, Greg Willden wrote: On 10/11/06, Bill Baxter [EMAIL PROTECTED] wrote: I tried to explain the argument at http://www.scipy.org/NegativeSquareRoot The proposed fix for those who want sqrt(-1) to return 1j is: from numpy.lib import scimath as SM SM.sqrt(-1) But that creates a new namespace alias, different from numpy. So I'll call numpy.array() to create a new array, but SM.sqrt() when I want a square root. Am I wrong to want some simple way to change the behavior of numpy.sqrt itself? Seems like you can get that effect via something like: for n in numpy.lib.scimath.__all__: numpy.__dict__[n] = numpy.lib.scimath.__dict__[n] If that sort of function were available as numpy.use_scimath(), then folks who want numpy to be like scipy can achieve that with just one line at the top of their files. The import under a different name doesn't quite achieve the goal of making that behavior numpy's default. I guess I'm thinking mostly of the educational uses of numpy, where you may have users that haven't learned about much about numerical computing yet. I can just imagine the instructor starting off by saying ok everyone we're going to learn numpy today! First everyone type this: 'import numpy, from numpy.lib import scimath as SM' -- Don't worry about all the things there you don't understand. Whereas import numpy, numpy.use_scimath() seems easier to explain and much less intimidating as your first two lines of numpy to learn. Or is that just a bad idea for some reason? Isn't that just going to make your students *more* confused later when then run into the standard behavior of numpy? For this sort of thing, I would just make a new module to pull together the function I want and use that instead. It's then easy to explain that this new module bbeconf (Bill Baxter's Excellent Collection Of Numeric Functions) is actually an amalgamation of stuff from multiple sources. # bbeconf.py from numpy import * fromnumpy.scimath import sqrt # possibly some other stuff to correctly handle subpackages... -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] How to make sqrt(-1) be 1j
On 10/13/06, Tim Hochberg [EMAIL PROTECTED] wrote: For this sort of thing, I would just make a new module to pull together the function I want and use that instead. It's then easy to explain that this new module bbeconf (Bill Baxter's Excellent Collection Of Numeric Functions) is actually an amalgamation of stuff from multiple sources. # bbeconf.py from numpy import * fromnumpy.scimath import sqrt # possibly some other stuff to correctly handle subpackages... That does sound like a good way to do it. Then you just tell your users to import 'eduNumpy' rather than numpy, and you're good to go. Added that suggestion to http://www.scipy.org/NegativeSquareRoot I'd like to ask one basic Python question related my previous suggestion of doing things like numpy.sqrt = numpy.lib.scimath.sqrt: In python does that make it so that any module importing numpy in the same program will now see the altered sqrt function? E.g. in my program I do import A,B. Module A alters numpy.sqrt. Does that also modify how module B sees numpy.sqrt? If so then that's a very good reason not to do it that way. I've heard people using the term monkey-patch before. Is that what that is? --bb - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
