John Hunter wrote:
>>>>>>"John" == John Hunter <[EMAIL PROTECTED]> writes:
>>>>>>
>>>>>>
>
>
>
>>>>>>"Erin" == Erin Sheldon <[EMAIL PROTECTED]> writes:
>>>>>>
>>>>>>
> Erin> The question I have been asking myself is "what is the
> Erin> advantage of such an approach?". It would be faster, but by
>
> John> In the use case that prompted this message, the pull from
> John> mysql took almost 3 seconds, and the conversion from lists
> John> to numpy arrays took more that 4 seconds. We have a list of
> John> about 500000 2 tuples of floats.
>
> John> Digging in a little bit, we found that numpy is about 3x
> John> slower than Numeric here
>
> John> peds-pc311:~> python test.py with dtype: 4.25 elapsed
> John> seconds w/o dtype 5.79 elapsed seconds Numeric 1.58 elapsed
> John> seconds 24.0b2 1.0.1.dev3432
>
> John> Hmm... So maybe the question is -- is there some low hanging
> John> fruit here to get numpy speeds up?
>
>And for reference, numarray is 5 times faster than Numeric here and 15
>times faster than numpy
>
> peds-pc311:~> python test.py
> with dtype: 4.20 elapsed seconds
> w/o dtype 5.71 elapsed seconds
> Numeric 1.60 elapsed seconds
> numarray 0.30 elapsed seconds
> 24.0b2
> 1.0.1.dev3432
> 1.5.1
>
>import numarray
>tnow = time.time()
>y = numarray.array(x, numarray.Float)
>tdone = time.time()
>
>
This sounds like it could be definitely be sped up, then.
Assign_Array is the relevant code (it then calls PySequence_SetItem) so
that basically
for k in a.shape[0]:
a[k] = x[k]
is what is being done. Thus, it might be the indexing code that is
causing this to be a slower operation. We should look at what numarray
is doing --- it has provided important speed-ups in the past.
I still don't have time to look at this, but please file a ticket as we
should fix this one. Reference the faster numarray implementation.
-Travis
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