Believe it or not, after profiling my current code, the repetitive operation of numpy array reversion ate a giant chunk of the running time. What I have right now is the com
I will expand on the earlier answer about np.fliplr(). Here is some code that demonstrates constructing a 1d array, transforming it into a 2d array, flipping it, then converting back into a 1d array. time.clock() will be used to keep time, which is presented in terms of seconds.
import time
import numpy as np
start = time.clock()
x = np.array(range(3))
#transform to 2d
x = np.atleast_2d(x)
#flip array
x = np.fliplr(x)
#take first (and only) element
x = x[0]
#print x
end = time.clock()
print end-start
With print statement uncommented:
[2 1 0]
0.00203907123594
With print statement commented out:
5.59799927506e-05
So, in terms of efficiency, I think that's decent. For those of you that love to do it in one line, here is that form.
np.fliplr(np.atleast_2d(np.array(range(3))))[0]