Mailman 3 October 2003 - NumPy-Discussion

RE: [Numpy-discussion] Possible bug in scalar * array
by Nadav Horesh Oct. 21, 2003

Oct. 21, 2003

As I underoustand the range checking (from the results --- not from the source code), it checks if the range of the scalar exceeds the range of the array elements type. Don't see any significant execution time penalty with that. However there might be a place for a flag-controlled behavior: * State 1: stay with the current "saturated" over/underflow whatever the scalar is. This is consistent with what numarray does now with scalars in range. * State 2: Raise exception as suggested. * State 3: Use the normal wrap-around on integer overflow, thus make a*2 give the same results as a+a in the following example: >>> a = array((100,200,128), type=UInt8) >>> a+a array([200, 144, 0], type=UInt8) >>> a*2 array([200, 255, 255], type=UInt8) Nadav -----Original Message----- From: Todd Miller [mailto:jmiller@stsci.edu] Sent: Mon 20-Oct-03 21:36 To: Edward C. Jones Cc: numpy-discussion Subject: Re: [Numpy-discussion] Possible bug in scalar * array I tracked down the problem to some (relatively) new overflow checking code which detects the overflow of the scalar -1 as it is assigned to an array pseudo buffer of type UInt8. This error was mishandled, and hence was transformed into an invalid shape tuple (you gotta smile :-)). The *2nd* call is where the exception shows up because of caching logic. I talked this over with Perry and we concluded that it's probably a good thing to trap the out of range scalar values before using them. Thus, we're proposing to fix the error handling, but to make the calls in question raise an overflow exception on the first call. We are interested in hearing other opinions however. Comments? Regards, Todd On Sat, 2003-10-18 at 18:18, Edward C. Jones wrote: > #! /usr/bin/env python > > # Python 2.3.2, numarray 0.7 > import numarray > > def fun2(code, scale): > arr = numarray.ones((4,4), code) > arr2 = scale * arr > # Bug appears at second multiply. > arr3 = scale * arr > > # These calls fail when "scale" is too big for "code": > > # File > "/usr/local/lib/python2.3/site-packages/numarray/numarraycore.py", line > 653, in __rmul__ > # def __rmul__(self, operand): return ufunc.multiply(operand, self) > # ValueError: invalid shape tuple > > #fun2('Int16', 100000) > fun2('UInt8' , -1) > > > > ------------------------------------------------------- > This SF.net email sponsored by: Enterprise Linux Forum Conference & Expo > The Event For Linux Datacenter Solutions & Strategies in The Enterprise > Linux in the Boardroom; in the Front Office; & in the Server Room > http://www.enterpriselinuxforum.com > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/numpy-discussion -- Todd Miller Space Telescope Science Institute 3700 San Martin Drive Baltimore MD, 21030 (410) 338 - 4576 ------------------------------------------------------- This SF.net email is sponsored by OSDN developer relations Here's your chance to show off your extensive product knowledge We want to know what you know. Tell us and you have a chance to win $100 http://www.zoomerang.com/survey.zgi?HRPT1X3RYQNC5V4MLNSV3E54 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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RE: [Numpy-discussion] Possible bug in scalar * array
by Nadav Horesh Oct. 21, 2003

Oct. 21, 2003

You have to convert ta to an (Unsigned)Int8 in order to make it work: ta = N.array([0, 255, 255, 255,0,0,0], N.UnsignedInt8) .......................................^^^^^^^^^^^^^^ The default integer type is 32 bits signed. Nadav. -----Original Message----- From: Francesc Alted [mailto:falted@openlc.org] Sent: Tue 21-Oct-03 09:53 To: numpy-discussion Cc: Subject: Re: [Numpy-discussion] Possible bug in scalar * array A Dilluns 20 Octubre 2003 21:36, Todd Miller va escriure: > I talked this over with Perry and we concluded that it's probably a good > thing to trap the out of range scalar values before using them. Thus, > we're proposing to fix the error handling, but to make the calls in > question raise an overflow exception on the first call. We are > interested in hearing other opinions however. Comments? You mean implementing range checking in numarray objects? In my opinion, that would be a very nice feature, although I don't know how that would affect the assignment performance. -- Francesc Alted ------------------------------------------------------- This SF.net email is sponsored by OSDN developer relations Here's your chance to show off your extensive product knowledge We want to know what you know. Tell us and you have a chance to win $100 http://www.zoomerang.com/survey.zgi?HRPT1X3RYQNC5V4MLNSV3E54 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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Possible bug in scalar * array
by Edward C. Jones Oct. 21, 2003

Oct. 21, 2003

#! /usr/bin/env python # Python 2.3.2, numarray 0.7 import numarray def fun1(code, scale): arr1 = numarray.ones((4,4), code) arr2 = scale * arr1 arr3 = numarray.ones((4,4), code) # Bug appears at second multiply. arr4 = scale * arr3 def fun2(code, scale): arr = numarray.ones((4,4), code) arr2 = scale * arr # Bug appears at second multiply. arr3 = scale * arr # These calls fail when "scale" is too big for "code": # File "/usr/local/lib/python2.3/site-packages/numarray/numarraycore.py", line 653, in __rmul__ # def __rmul__(self, operand): return ufunc.multiply(operand, self) # ValueError: invalid shape tuple #fun2('Int16', 100000) fun2('UInt8' , -1)

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numarray.convolve bug and a fix
by Nadav Horesh Oct. 20, 2003

Oct. 20, 2003

numarray.convolve.correlate2d (and probably some other functions in the convolve module) doesn't work with the option fft=1. Fix: in iraf_frame.py line 28 and Convolve.py line 136 replace "type=" by "typecode=" Nadav.

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by Director Oct. 19, 2003

Oct. 19, 2003

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by Branden Figueroa Oct. 18, 2003

Oct. 18, 2003

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numpy welcomes Peter Verveer as a new developer
by Todd Miller Oct. 16, 2003

Oct. 16, 2003

By vote of the existing numpy developers, Peter Verveer has been added to the project as a developer. Peter has been working on a multi-dimensional image processing package soon to be called numarray.nd_image. This is a great day for numarray and numpy! Welcome Peter. Todd -- Todd Miller <jmiller(a)stsci.edu>

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How to access to the start of a mmaped numarray object?
by Francesc Alted Oct. 15, 2003

Oct. 15, 2003

Hi, While dealing with memory mapped numarray objects, I'm having some problems to access to the address where the data starts. For normal (i.e. no memory-mapped) objects, I normally used this: PyObject_AsReadBuffer(recarr._data, &self.rbuf, &buflen) that works fine, but after creating a RecArray associated to a memory mapped file in the normal way, i.e.: self.mmfile = memmap.Memmap("prova.out",mode="r") recarr = records.RecArray(self.mmfile[:], formats=self.description._v_recarrfmt, shape=self.totalrecords) then, I'm unable to use the PyObject_AsWriteBuffer anymore. I've tried the following: PyObject_AsReadBuffer(recarr._data, &self.mmrbuf, &buflen) (this returns a -1, failed, value) PyObject_AsReadBuffer(recarr._data._buffer, &self.mmrbuf, &buflen) (this produces a segmentation fault) PyObject_AsReadBuffer(recarr._data.__buffer__(), &self.mmrbuf, &buflen) (another seg fault) Of course, I've checked out that the recarr object is sane (i.e. it has the same data than the file). Any hint? -- Francesc Alted

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