What is the official way to zero out an array in numarray/Numeric?
While I can create a new array of all zeros and then assign it to the old variable, this is extremely wasteful of memory.
Currently, I am just using:
import numarray a = numarray.arange(6) a
array([0, 1, 2, 3, 4, 5])
a[:] = 0.0 a
array([0, 0, 0, 0, 0, 0])
I looked through the manual for a function or array mathod which would accomplish the same thing, but I didn't find an obvious one. Did I miss something obvious?
Thanks, -a
On Wed, 2003-05-14 at 16:21, Andrew P. Lentvorski, Jr. wrote:
What is the official way to zero out an array in numarray/Numeric?
I'm don't think there is one in numarray; which is to say, I'd do what you did below.
While I can create a new array of all zeros and then assign it to the old variable, this is extremely wasteful of memory.
Currently, I am just using:
import numarray a = numarray.arange(6) a
array([0, 1, 2, 3, 4, 5])
a[:] = 0.0 a
array([0, 0, 0, 0, 0, 0])
I looked through the manual for a function or array mathod which would accomplish the same thing, but I didn't find an obvious one. Did I miss something obvious?
I don't think so.
Todd
Currently, I am just using:
import numarray a = numarray.arange(6) a
array([0, 1, 2, 3, 4, 5])
a[:] = 0.0 a
array([0, 0, 0, 0, 0, 0])
Is there any reason for anything else if this does the job? This is how I would recommend the action be performed.
Perry Greenfield
On Wed, 14 May 2003, Perry Greenfield wrote:
import numarray a = numarray.arange(6) a
array([0, 1, 2, 3, 4, 5])
a[:] = 0.0 a
array([0, 0, 0, 0, 0, 0])
Is there any reason for anything else if this does the job? This is how I would recommend the action be performed.
It depends. How does the slicing code work?
If it iterates across the matrix indicies (requiring multiply and add operations to index specific memory locations), this will be much slower than doing a contiguous memory fill (1 add and 1 multiply per index vs. increment). This is not a small hit, we're talking factors of 2x, 4x, 6x.
If the slicing code special cases "array[:] = n" to be a memory fill, then probably not.
-a
Andrew P. Lentvorski, Jr. wrote:
On Wed, 14 May 2003, Perry Greenfield wrote:
import numarray a = numarray.arange(6) a
array([0, 1, 2, 3, 4, 5])
a[:] = 0.0 a
array([0, 0, 0, 0, 0, 0])
Is there any reason for anything else if this does the job? This is how I would recommend the action be performed.
It depends. How does the slicing code work?
If it iterates across the matrix indicies (requiring multiply and add operations to index specific memory locations), this will be much slower than doing a contiguous memory fill (1 add and 1 multiply per index vs. increment). This is not a small hit, we're talking factors of 2x, 4x, 6x.
If the slicing code special cases "array[:] = n" to be a memory fill, then probably not.
Well, there are two issues the question relates to:
1) what should the user interface be for filling arrays with a constant value. I'd argue that a slice assignment is both sufficent and idiomatic.
2) is it efficient.
As to 2, the current implementation handles scalar assignment by broadcasting a one-element array across the array being assigned to (that's the simplest way to code it). That isn't the fastest implementation when the array being assigned to is congtiguous. It could be made faster. But frankly, it isn't very high on our list of priorities. And I'm a little skeptical that this performance improvement is an important one. Is your program perfromance dominated by the time it takes to zero an array? I have trouble thinking of many realistic problems where that would be true (not to say that there aren't any).
If someone is willing to write special code to optimize this, that would be fine with us.
Perry
participants (3)
-
Andrew P. Lentvorski, Jr. -
Perry Greenfield -
Todd Miller