>>>>> "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
In the use case that prompted this message, the pull from mysql took
almost 3 seconds, and the conversion from lists to numpy arrays took
more that 4 seconds. We have a list of about 500000 2 tuples of
floats.
Digging in a little bit, we found that numpy is about 3x slower than
Numeric here
peds-pc311:~> python test.py
with dtype: 4.25 elapsed seconds
w/o dtype 5.79 elapsed seconds
Numeric 1.58 elapsed seconds
24.0b2
1.0.1.dev3432
Hmm... So maybe the question is -- is there some low hanging fruit
here to get numpy speeds up?
import time
import numpy
import numpy.random
rand = numpy.random.rand
x = [(rand(), rand()) for i in xrange(500000)]
tnow = time.time()
y = numpy.array(x, dtype=numpy.float_)
tdone = time.time()
print 'with dtype: %1.2f elapsed seconds'%(tdone - tnow)
tnow = time.time()
y = numpy.array(x)
tdone = time.time()
print 'w/o dtype %1.2f elapsed seconds'%(tdone - tnow)
import Numeric
tnow = time.time()
y = Numeric.array(x, Numeric.Float)
tdone = time.time()
print 'Numeric %1.2f elapsed seconds'%(tdone - tnow)
print Numeric.__version__
print numpy.__version__
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