Mailman 3 October 2003 - NumPy-Discussion

Re: [Numpy-discussion] How can I make a Windows distribution like Numeric
by gvermeul＠grenoble.cnrs.fr Oct. 24, 2003

Oct. 24, 2003

> Hello - > > Believe it or not but I have written Python code that will mostly be used on > Windows. I want to make distribution easier by making a nice Window's install > just like Numeric. Can anbybody fill me in on what software they use to make the > Windows binary install? > > Thanks, Mark > Check out the documentation on distutils that comes with Python. Gerard ------------------------------------------------------------- This message was sent using HTTPS service from CNRS Grenoble. ---> https://grenoble.cnrs.fr <---

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by ADV; . Energy Systems & Electrical Design Oct. 24, 2003

Oct. 24, 2003

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How can I make a Windows distribution like Numeric
by Mark Bakker Oct. 24, 2003

Oct. 24, 2003

Hello - Believe it or not but I have written Python code that will mostly be used on Windows. I want to make distribution easier by making a nice Window's install just like Numeric. Can anbybody fill me in on what software they use to make the Windows binary install? Thanks, Mark

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Documentation using PyDoc
by Colin J. Williams Oct. 23, 2003

Oct. 23, 2003

[Message copied to comp.lang.python] I am building a module which is based on the numarray package and its sub-packages. I am proposing to use PyDoc to document this module. The problem is that PyDoc generates links to documentation on the imported modules and expects to find HTML files in the style: nameOfImportedModule.html. The numarray documentation is not in this style. Is there some way that I can guide PyDoc in the generation of names to the imported links? Supposing I create my own links, how can I ensure that these automatically call the appropriate node in the numarray tree? Is there any possibility that the numarray documentation could be generated by PyDoc? I would appreciate any advice. Colin W. PS I don't see any documentation on the generic module.

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Incorrect OverflowError?
by Edward C. Jones Oct. 23, 2003

Oct. 23, 2003

#! /usr/bin/env python import numarray from numarray.numerictypes import * # I run Gentoo 1.4 Linux with gcc 3.2.2. numarray.Error.setMode(all='ignore') arr = numarray.zeros((1,), Float64) arr[0] = 4000000000.0 print '%f' % arr[0] try: arr[0] = 4000000000 print "Doesn't get here." except OverflowError, s: print 'Python OverflowError raised' print s # OverflowError: long int too large to convert to int # The Python long "4000000000" can be represented as a C double. # If the OverflowError came from numarray, why was an exception raised? # Are these problems bugs?

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Symmetry of functions
by Colin J. Williams Oct. 23, 2003

Oct. 23, 2003

A suggestion: fromstring accepts a string - tostring returns a string fromfile accepts a file - tofile returns None This is to suggest that generic.tofile be modified to return the file object, which may be open or closed. Colin W.

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by Marketing Oct. 22, 2003

Oct. 22, 2003

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RE: [Numpy-discussion] Possible bug in scalar * array
by Nadav Horesh Oct. 22, 2003

Oct. 22, 2003

To my opinion state 2 is surplus: Consider a large loop where an integer array s are multiplied by a wide range of scalars, and, at some point an exception is raised; It is not easy to track down what happened, especially when the scalars are not ordered (say, read from a data file). I can not find any justification for state 2 singularity (A*n is ok, A*(n+1) is not). I suspect that state 3 is the fastest (it is up to you to judge), it is also consistent with the behavior of the __add__ operator. Why the __add__ operator should have the risk of being nonportable and __mul__ should not? Which state should be the default? There are two way to use the system: interactive and in a script. To my opinion the default should be the state that fits more the interactive mode --- slower and with a lot of checking. Maybe it is better to add a section to the documentation, how to tune the package for maximum performance: those who are interested in a high performance computing should be ready to do some extra work (read the manual, at least). Nadav. -----Original Message----- From: Todd Miller [mailto:jmiller@stsci.edu] Sent: Tue 21-Oct-03 17:12 To: Nadav Horesh Cc: Edward C. Jones; numpy-discussion Subject: RE: [Numpy-discussion] Possible bug in scalar * array On Tue, 2003-10-21 at 10:07, Nadav Horesh wrote: > 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 This sounds like an attempt to turn a bug fix into a coherent plan. :) We could implement what you're describing here a lot like we handle IEEE floating point, but I'm wondering if we should. I think state 3 is marginally portable, so I'm not sure we should support it, but if we did, what would we use it for? Similarly, if we support both states 1 and 2, is anyone going to be sufficiently on the ball to know the difference and set their error handling appropriately? Or, are 99.9% of the people going to just use whatever the default is? If the latter is the case, we should just implement "the default" and keep life simple. I'm not opposed to this if there are valid uses for it, but we should know those reasons before implementing, and I don't. Thanks for the ideas, Todd > > -----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 > > > > -- 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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Doc bug for NA_New...
by Edward C. Jones Oct. 21, 2003

Oct. 21, 2003

The source code for NA_vNewArray says "Call with buffer==NULL to allocate storage." This is handled in NA_NewAllFromBuffer and so applies to all the NA_New... functions. This needs to be mentioned in the documentation. Ed Jones

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Numeric odd tostring() behaviour
by Russell Valentine Oct. 21, 2003

Oct. 21, 2003

Hello, It may be my fault, but I think the following behaviour is odd. If I try to change a array to a string it seems like it adds a lot of extra zero characters. Take the following script attached as a example, it gives me this output. ta.tostring is not equal Zero characters - 25 255 characters - 3 tast is equal Zero characters - 4 255 characters - 3 tostring() is so much more faster than the second way, but it isn't giving me the desired results. Have I done something wrong? I'm using Numeric 23.1 Thanks for your help. Russell Valentine

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