Mailman 3 August 2005 - NumPy-Discussion

V�agrra Works Very Good
by Ilmari Mendez 22 Aug '05

22 Aug '05

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Question about external libraries and ATLAS
by Darren Dale 21 Aug '05

21 Aug '05

We are having a discussion on the gentoo-science mailing list about linking Numeric and SciPy to external BLAS, LAPACK and ATLAS libraries. Can anyone tell me, is it necessary to include atlas in the library list at compile time in order to reap the rewards of atlas? Does Numeric use the atlas libraries directly, or indirectly through the blas and lapack libraries? Thanks, Darren

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reading data from file
by Hari Sundar 19 Aug '05

19 Aug '05

Hi, I have a small doubt and it is very difficult for me to word this for google. So maybe someone here can help. I use the 'fromfile' function to read data directly into arrays. This works pretty well. I have a special case, where I am reading in 2 files. The first one is a full 3D file, (x,y,z). Each location tells me whether data is available at that location in the other file or not. I call this the mask.The other file contains information which should sit in x*y*z*D space, i.e., I have a vector of length D at any given location (x,y,z) where the mask is not zero. How do I read this effectively ? I don't want to run a loop and test for the mask being true and reading in the vector. Also, I am currently thinking of keeping the information in memory as an array of shape (x,y,z,D), even though the data is sparse. Is there a more efficient way to do this ? thanks, ~Hari

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freeing memory
by Daniel Sheltraw 18 Aug '05

18 Aug '05

Hello All Is their a reliable way to free memory allocated for an array in Numeric? My application uses a lot of memory and I need to free some as it goes along. Thanks, Daniel

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RE: [Numpy-discussion] still no cross product for new array type
by Nadav Horesh 15 Aug '05

15 Aug '05

Usually this is within the scope of higher level packages such as ScientificPython. Nadav -----Original Message----- From: numpy-discussion-admin(a)lists.sourceforge.net on behalf of Gary Ruben Sent: Fri 12-Aug-05 14:44 To: numpy-discussion(a)lists.sourceforge.net Cc: Subject: [Numpy-discussion] still no cross product for new array type Just browsing through the new array PEP to see if there's support for 3-vector operations, such as cross product, norm, length functions. Sadly I don't see any. It's something I think is lacking from Numeric and numarray and would like to see implemented. Is it a deliberate choice not to include any? I understand that vectors are sufficiently different animals that you could argue that they shouldn't be supported. I use them enough to think that they should be. Gary Ruben ------------------------------------------------------- SF.Net email is Sponsored by the Better Software Conference & EXPO September 19-22, 2005 * San Francisco, CA * Development Lifecycle Practices Agile & Plan-Driven Development * Managing Projects & Teams * Testing & QA Security * Process Improvement & Measurement * http://www.sqe.com/bsce5sf _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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storing magnitude data
by Daniel Sheltraw 15 Aug '05

15 Aug '05

Hello All Would someone please tell me how to store magnitude data in the first half of a previously allocated complex array (I need to save memory). Memory saving things like this are so simple and intuitive in C but less so in NumPy. Thanks, Daniel

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RE: [Numpy-discussion] new __wrap_array__ magic method
by Sebastien.deMentendeHorne＠electrabel.com 15 Aug '05

15 Aug '05

> > >Hi, > > > >Currently, we have an __array__ magic method that can be > used to transform any object that implements it into an array. > >It think that a more useful magic method would be, for > ufuncs, a __wrap_array__ method that would return an array > object and a function to use after having applied the ufunc. > > > > > There is an interest in doing this but it is a bit more > complicated for > ufuncs with more than one input argument and more than one > output argument. > > I have not seen a proposal that really works. Your __wrap_array__ > method sounds interesting though. I think, however, that the > __wrap_array__ method would need to provide some kind of > array_interfaceable object to be really useful. > > In the new scipy.base, I've been trying to handle some of > this, but it > is more complicated than it might at first appear. > If we use the same convention as __add__ and __radd__ & co for binary functions this would make a __wrap_array__(self, ufunc): # all unary operators like __neg__, __abs__, __invert__ __lwrap_array__(self, other, ufunc): # all binary operators called with self on the lhs like __add__, __mul__, __pow__ __rwrap_array__(self, other, ufunc): # all binary operators called with self on the rhs like __radd__, __rmul__, __rpow__ with first a check on __lwrap_array__ and then __rwrap_array__ for binary functions. Those functions should then return the result of the operation (and not anymore an array object and a "constructor" function. My previous example would read: class TimeSerie: def __init__(self, data, times): self.data = data # an array self.times = times # anything, could be any metadata def __wrap_array__(self, ufunc): return TimeSerie( ufunc(self.data) , self.times) def __lwrap_array__(self, other, ufunc): return TimeSerie( ufunc(self.data, other) , self.times) def __rwrap_array__(self, other, ufunc): return TimeSerie( ufunc(other, self.data) , self.times) propagating this way the operation to the other object in binary operations. Better attempt ? Seb ======================================================= This message is confidential. It may also be privileged or otherwise protected by work product immunity or other legal rules. If you have received it by mistake please let us know by reply and then delete it from your system; you should not copy it or disclose its contents to anyone. All messages sent to and from Electrabel may be monitored to ensure compliance with internal policies and to protect our business. Emails are not secure and cannot be guaranteed to be error free as they can be intercepted, amended, lost or destroyed, or contain viruses. Anyone who communicates with us by email is taken to accept these risks. http://www.electrabel.be/homepage/general/disclaimer_EN.asp =======================================================

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Numeric Python EM site back up
by rob 15 Aug '05

15 Aug '05

Its at http://home.comcast.net/~europa100 I hope to morph it later with the latest numpy and/or numarray and/or scipy stuff. I would also like a more "glamorous" location for the site. Haha. Rob

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Suggested Addition to the new array PEP
by Chris Barker 12 Aug '05

12 Aug '05

Hi all, but particularly Travis, I just took the time to read the new array PEP: http://numeric.scipy.org/PEP.txt It looks great. I do have one suggestion for an addition method: change_type(NewType) # or some other name: Change the type that the bytes in the array represent, without changing the data at. Dimensions will change if the new type has a different itemsize than the old. This would be the equivalent of: A = Numeric.fromstring(A.tostring(), NewType) but without the extra copies of the data. When would anyone want this? I've written a function that does exactly this with Numeric arrays in order to efficiently slice, dice and re-arange a bunch of binary data files. The files consist of a bunch of C structs written sequentially to a file. In this case the structs are: (long, long, char) I have a file which is N of these structs written sequentially to a file, I can do: NumBytes = 9 # number of bytes in a record # Read the file data = fromstring(file.read(NumBytes*NumTimesteps*NumLEs),UnsignedInt8) file.close() data.shape = (NumTimesteps,NumLEs,NumBytes) # extract LE data: LEs = data[:,:,:8].copy() # extract the flags flags = data[:,:,8].copy() del data # here I can create the Int32 array without making a copy: # changetype returns a rank-1 array changetype(LEs,Int32) # It now gets reshaped to fit what it represents. LEs.shape = (NumTimesteps,NumLEs,2) I think it would only work in the general case with a contiguous array. All this really requires is to re-set the attributes of the array. with Numeric, the code is (for contiguous arrays): NewDescription = PyArray_DescrFromType(typecode); NewElementSize = NewDescription->elsize; if (TotalBytes % NewElementSize > 0){ PyErr_SetString (PyExc_ValueError, "The input array is the wrong size for the requested type"); return NULL; } Array->descr = NewDescription; // does the old descr need to be freed?? how?? Array->nd = 1; Array->strides[0] = NewElementSize; Array->dimensions[0] = TotalBytes / NewElementSize; The core By the way, I'm also not seeing tostring() or tobuffer() listed in the PEP -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/OR&R/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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RE: [Numpy-discussion] new __wrap_array__ magic method
by Sebastien.deMentendeHorne＠electrabel.com 12 Aug '05

12 Aug '05

> > > __wrap_array__(self, ufunc): # all unary operators like > __neg__, __abs__, __invert__ > > __lwrap_array__(self, other, ufunc): # all binary operators > called with self on the lhs like __add__, __mul__, __pow__ > > __rwrap_array__(self, other, ufunc): # all binary operators > called with self on the rhs like __radd__, __rmul__, __rpow__ > > The term "wrap_array" doesn't seem very descriptive of > what these are for. Wrapping of arrays may come into the > implementations of them, but it's not the main point. > The point is to apply a ufunc to the object and get > an appropriate result. > > Maybe just __ufunc__, __lufunc__, __rufunc__? Definitely ! I came first with a function that was more similar to __array__ and so __wrap_array__ seemed a plausible name for this function. Afterward, I changed a bit the role of this function to propose the alternative way and your names are way more adequate (it may be useful to add the unary/binary word in the function like: __unary_ufunc__ __binary_lufunc__ __binary_rufunc__ as it does not map exactly to __add__, __radd__ and people could mix __ufunc__ and __rufunc__ without understanding the __lufunc__... > > -- > Greg Ewing, Computer Science Dept, > +--------------------------------------+ > University of Canterbury, | A citizen of > NewZealandCorp, a | > Christchurch, New Zealand | wholly-owned subsidiary of > USA Inc. | > greg.ewing(a)canterbury.ac.nz +--------------------------------------+ > > > ------------------------------------------------------- > SF.Net email is Sponsored by the Better Software Conference & EXPO > September 19-22, 2005 * San Francisco, CA * Development > Lifecycle Practices > Agile & Plan-Driven Development * Managing Projects & Teams * > Testing & QA > Security * Process Improvement & Measurement * > http://www.sqe.com/bsce5sf > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > ======================================================= This message is confidential. It may also be privileged or otherwise protected by work product immunity or other legal rules. If you have received it by mistake please let us know by reply and then delete it from your system; you should not copy it or disclose its contents to anyone. All messages sent to and from Electrabel may be monitored to ensure compliance with internal policies and to protect our business. Emails are not secure and cannot be guaranteed to be error free as they can be intercepted, amended, lost or destroyed, or contain viruses. Anyone who communicates with us by email is taken to accept these risks. http://www.electrabel.be/homepage/general/disclaimer_EN.asp =======================================================

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