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 common view-based method:
reversed_arr = arr[::-1]
Is there any other way to do it more efficiently, or is it just an illusion from my obsession with unrealistic numpy performance?
The slice notation based analog to np.flip would be [::-1,::-1]
a = np.array([[1., 2.], [3., 4.], [5, 6]])
print(a)
out: [[1. 2.]
[3. 4.]
[5. 6.]]
b=a[::-1,::-1]
print(b)
out: [[1. 2.]
[3. 4.]
[5. 6.]]
Expanding on what others have said I will give a short example.
If you have a 1D array ...
>>> import numpy as np
>>> x = np.arange(4) # array([0, 1, 2, 3])
>>> x[::-1] # returns a view
Out[1]:
array([3, 2, 1, 0])
But if you are working with a 2D array ...
>>> x = np.arange(10).reshape(2, 5)
>>> x
Out[2]:
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> x[::-1] # returns a view:
Out[3]: array([[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4]])
This does not actually reverse the Matrix.
Should use np.flip to actually reverse the elements
>>> np.flip(x)
Out[4]: array([[9, 8, 7, 6, 5],
[4, 3, 2, 1, 0]])
If you want to print the elements of a matrix one-by-one use flat along with flip
>>> for el in np.flip(x).flat:
>>> print(el, end = ' ')
9 8 7 6 5 4 3 2 1 0