CODEWITHSUNDEEP
pythonperformancenumpy
Roman
Roman

Reputation: 3241

Finding indices that fall between given ranges

I want to find the elements in dataX and dataY which are between boundaries (B4X1, B4X2) and (B4Y1, B4Y2) 

import numpy as np 

B4X1 = 50.
B4X2 = 60.


B4Y1 = 150.
B4Y2 = 160.

dataX = np.array([40, 25, 50, 60, 55])
dataY = np.array([140, 125, 150, 160, 155])

Expected result is:

result = array([False, False, False, False, True], dtype=bool)

How would be the faster way of doing it?

I did as:

OK = (B4X1 < dataX < B4X2) & (B4Y1 < dataY < B4Y2)

print OK

But the error:

Traceback (most recent call last):
  File "C:\Users\je\Desktop\test.py", line 14, in <module>
    OK = (B4X1 < dataX < B4X2) & (B4Y1 < dataY < B4Y2)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Upvotes: 2

Views: 76

Answers (2)

Josephine Moeller
Josephine Moeller

Reputation: 944

The shortcut notation B4X1 < dataX < B4X2 doesn't work.

You need to do the following: 

OK = (B4X1 < dataX) & (dataX < B4X2) & (B4Y1 < dataY) & (dataY < B4Y2)

EDIT:

Since timing was brought up:

In [23]: dataX = np.random.randint(200, size=100)

In [24]: dataY = np.random.randint(200, size=100)

In [25]: %timeit OK = (B4X1 < dataX) & (dataX < B4X2) & (B4Y1 < dataY) & (dataY < B4Y2)
10000 loops, best of 3: 23.6 µs per loop

In [26]: %timeit OK = np.logical_and.reduce([B4X1<dataX,dataX<B4X2,B4Y1<dataY,dataY<B4Y2])
10000 loops, best of 3: 26.7 µs per loop

In [27]: %timeit for i in dataX: OK = (B4X1 < i and i < B4X2) and (B4Y1 < i and i < B4Y2)
1000 loops, best of 3: 449 µs per loop

In [28]: %timeit for i in dataX: OK = (B4X1 < i and i < B4X2) and (B4Y1 < i and i < B4Y2)
1000 loops, best of 3: 329 µs per loop

Upvotes: 2

Kasravnd
Kasravnd

Reputation: 107347

Since you want to apply a logical_and operation on all of your conditions as an elegant way you can use np.logical_and.reduce:

>>> np.logical_and.reduce([B4X1<dataX,dataX<B4X2,B4Y1<dataY,dataY<B4Y2])
array([False, False, False, False,  True], dtype=bool)

Upvotes: 1

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