Eric Firing wrote:
> John Hunter wrote:
>> On Fri, Jul 18, 2008 at 4:42 PM, Andrew Straw <[EMAIL PROTECTED]>
>> wrote:
>>
>>> If you're happy with that extra cost, I'll modify
>>> axes.delete_masked_points() so that hexbin and scatter are automatically
>>> filtered in this way. Given the speed complaints we sometimes get, I was
>>> hesitant to add another pass through the data.
>
> masks.extend([~np.isfinite(x) for x in args])
>
> may be the quickest and most general way to do it. I believe
> ~np.isfinite is both more general and significantly faster than np.isnan.
Clever, but it won't work as-is. np.isfinite('b') returns a
NotImplementedType, and a default argument to scatter is c='b', which
gets passed to this function. Anyhow, I implemented your idea with a
check for NotImplementedType and some unit tests in r5791.
> A kwarg could be used to enable or disable this checking.
Yes, but since we're grabbing *args, that would mean parsing **kwargs --
so I just implemented it without the option of disabling it. I'm happy
to add this if desired.
>> Happy is the wrong word -- we certainly should make these things as
>> efficient as reasonable so if your optimization worked it would be
>> preferable numpy passes are rarely the bottleneck but they are good
>> to be aware of. We could provide some helper function in nxutils to
>> do more stuff in a single pass for common use cases (eg a minmax
>> function to get both the min and the max...)
Yes, good idea. (In fact I wonder why numpy doesn't have minmax.) And
apologies for suggesting that you might be "happy" with a (minor)
slowdown -- not the best choice of words.
-Andrew
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