>>>>> "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()
print 'numarray %1.2f elapsed seconds'%(tdone - tnow)
print numarray.__version__
-------------------------------------------------------------------------
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