I have the following numpy array
u = np.array([a1,b1,a2,b2...,an,bn])
where I would like to subtract the a and b elements from each other and end up with a numpy array:
u_result = np.array([(a2-a1),(b2-b1),(a3-a2),(b3-b2),....,(an-a_(n-1)),(an-a_(n-1))])
How can I do this without too much array splitting and for loops? I'm using this in a larger loop so ideally, I would like to do this efficiently (and learn something new)
(I hope the indexing of the resulting array is clear)
Or simply, perform a substraction :
u = np.array([3, 2, 5, 3, 7, 8, 12, 28])
u[2:] - u[:-2]
Output:
array([ 2, 1, 2, 5, 5, 20])
you can use ravel torearrange as your original vector.
Short answer:
u_r = np.ravel([np.diff(u[::2]),
np.diff(u[1::2])], 'F')
Here a long and moore detailed explanation:
a from b in u this can be achieved indexinga and b you can use np.diff for easiness of code.#------- Create u---------------
import numpy as np
a_aux = np.array([50,49,47,43,39,34,28])
b_aux = np.array([1,2,3,4,5,6,7])
u = np.ravel([a_aux,b_aux],'F')
print(u)
#-------------------------------
#1)
# get a as elements with index 0, 2, 4 ....
a = u[::2]
b = u[1::2] #get b as 1,3,5,....
#2)
#differentiate
ad = np.diff(a)
bd = np.diff(b)
#3)
#ravel putting one of everyone
u_result = np.ravel([ad,bd],'F')
print(u_result)
You can try in this way. Firstly, split all a and b elements using array[::2], array[1::2]. Finally, subtract from b to a (np.array(array[1::2] - array[::2])).
import numpy as np
array = np.array([7,8,9,6,5,2])
u_result = np.array(array[1::2] - array[::2] )
print(u_result)
Looks like you need to use np.roll:
shift = 2
u = np.array([1, 11, 2, 12, 3, 13, 4, 14])
shifted_u = np.roll(u, -shift)
(shifted_u - u)[:-shift]
Returns:
array([1, 1, 1, 1, 1, 1])
