unexpected behavior of na.where (no short-circuiting)
Hi, I was very surprised when I got this warning:
a = na.arange(4)-2 na.where(a != 0,1./a, 999) Warning: Encountered divide by zero(s) in divide [ -0.5 -1. 999. 1. ]
Then I realized that this is generally referred to as (not) "short circuiting" (e.g. in the case of the '?:'-C-operator, the middle part never gets evaluated at all if the first part evals to 0 ) Especially annoying was this because (for debugging) I had set this error-mode:
na.Error.setMode(dividebyzero="error")
My questions are: a) Did other people encounter this problem ? b) What is the general feeling about this actually being a "problem" ? c) Could this (at all possible) be implemented differently ? Thanks, Sebastian Haase
Hi, any thoughts on this ? I really think it's counter intuitive ? Thanks, Sebastian Haase On Tuesday 05 July 2005 17:34, Sebastian Haase wrote:
Hi,
I was very surprised when I got this warning:
a = na.arange(4)-2 na.where(a != 0,1./a, 999)
Warning: Encountered divide by zero(s) in divide [ -0.5 -1. 999. 1. ]
Then I realized that this is generally referred to as (not) "short circuiting" (e.g. in the case of the '?:'-C-operator, the middle part never gets evaluated at all if the first part evals to 0 )
Especially annoying was this because (for debugging) I had set this
error-mode:
na.Error.setMode(dividebyzero="error")
My questions are: a) Did other people encounter this problem ? b) What is the general feeling about this actually being a "problem" ? c) Could this (at all possible) be implemented differently ?
Thanks, Sebastian Haase
------------------------------------------------------- SF.Net email is sponsored by: Discover Easy Linux Migration Strategies from IBM. Find simple to follow Roadmaps, straightforward articles, informative Webcasts and more! Get everything you need to get up to speed, fast. http://ads.osdn.com/?ad_id=7477&alloc_id=16492&op=click _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Well, that's the way Python works - function arguments are evaluated before the function is called. Even if there were some way to pass the arguments in as expressions, and evaluate them on the fly, you'd need some sort of lazy evaluation, and some pretty deep parsing of the expression - in this case, you could calc 1./a[x,y,z,whatever] for the appropriate values of [x,y,z,whatever], but what if, instead of 1./a, you have divide.outer(b,c)? Or videoCamera.getNextFrame()? I don't think the general problem of figuring out how to iterate over the indices of subexpressions in the argument to get the appropriate index into the evaluated argument is soluble, even when there are indexable subexpressions. In this case, you can pre-mask the array:
a = na.arange(4)-2 a[a==0] = 1./999 na.where(a != 0,1./a, 999)
Warren Focke On Tue, 19 Jul 2005, Sebastian Haase wrote:
Hi, any thoughts on this ? I really think it's counter intuitive ?
Thanks, Sebastian Haase
On Tuesday 05 July 2005 17:34, Sebastian Haase wrote:
Hi,
I was very surprised when I got this warning:
a = na.arange(4)-2 na.where(a != 0,1./a, 999)
Warning: Encountered divide by zero(s) in divide [ -0.5 -1. 999. 1. ]
Then I realized that this is generally referred to as (not) "short circuiting" (e.g. in the case of the '?:'-C-operator, the middle part never gets evaluated at all if the first part evals to 0 )
Especially annoying was this because (for debugging) I had set this
error-mode:
na.Error.setMode(dividebyzero="error")
My questions are: a) Did other people encounter this problem ? b) What is the general feeling about this actually being a "problem" ? c) Could this (at all possible) be implemented differently ?
Thanks, Sebastian Haase
------------------------------------------------------- SF.Net email is sponsored by: Discover Easy Linux Migration Strategies from IBM. Find simple to follow Roadmaps, straightforward articles, informative Webcasts and more! Get everything you need to get up to speed, fast. http://ads.osdn.com/?ad_id=7477&alloc_id=16492&op=click _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
------------------------------------------------------- SF.Net email is sponsored by: Discover Easy Linux Migration Strategies from IBM. Find simple to follow Roadmaps, straightforward articles, informative Webcasts and more! Get everything you need to get up to speed, fast. http://ads.osdn.com/?ad_id=7477&alloc_id=16492&op=click _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
participants (2)
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Sebastian Haase -
Warren Focke