[Numpy-discussion] Indexing in Numpy vs. IDL?
Sun Nov 16 16:15:51 CST 2008
I'm fairly new to Numpy and I've been trying to port over some IDL code
to become more familiar. I've been moderately successful with
numpy.where and numpy.compress to do some of things that were pretty
easy to do in IDL. I'm a bit confused about how the indexing of arrays
This is pretty straightforward:
data = [50.00, 100.00, 150.00, 200.00, 250.00, 300.00, 350.00]
index = WHERE((data GT 100.00) AND (data LT 300.00))
new_data = data[index]
150.000 200.000 250.000
>>> import numpy
>>> from numpy import *
>>> data = [50.00, 100.00, 150.00, 200.00, 250.00, 300.00, 350.00]
>>> data = array(data, dtype=float32) #Convert list to array
>>> index_mask = numpy.where((data > 100.00) & (data < 300.00), 1,0)
#Test for the condition.
>>> index_one = numpy.compress(index_mask, data)
>>> print index_one
[ 150. 200. 250.]
But I'm having a bit of trouble with the Python equivalent of this:
index_two = WHERE ((data[index_one] GT bottom) AND (data[index_one] LE
and also this:
result = MAX(data[index_one[index_two]])
From what I've read it looks like numpy.take() might work to do the
indexing. I've tried to test this but I'm not getting the answers I'd
expect. Am I overlooking something obvious here?
Thanks in advance for any responses.
More information about the Numpy-discussion