On 2019-01-10 17:05, Madhavan Bomidi wrote:
Sorry for re-posting with a correction.
I have an array (numpy.ndarray) with shape (1500L,) as below:
x = array([ 3.00000000e+01, 6.00000000e+01, 9.00000000e+01, ...,
4.49400000e+04, 4.49700000e+04, 4.50000000e+04])
Now, I wanted to determine the indices of the x values between 0.0 and 15000.0. While this is simple in MATLAB or IDL by using find or where functions, I was unable to find a best way to find the indices of all elements between 0.0 and 15000.0 in the x array. Can you please suggest me a solution for the same?
I don't know if there's a better way, but:
>>> import numpy as np
>>> x = np.array([ 3.00000000e+01, 6.00000000e+01, 9.00000000e+01,
4.49400000e+04, 4.49700000e+04, 4.50000000e+04])
Unfortunately, chained comparisons aren't supported by numpy, so:
>>> np.logical_and(0.0 <= x, x <= 15000.0)
array([ True, True, True, False, False, False])
Now for the indices:
>>> np.arange(x.shape[0])
array([0, 1, 2, 3, 4, 5])
Extract the indices where the Boolean value is True:
>>> np.extract(np.logical_and(0.0 <= x, x <= 15000.0),
np.arange(x.shape[0]))
array([0, 1, 2])
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