# Modify element and neighbor in numpy 2d array

I am trying find a more efficient way to modify every element of an array, and its lowest neighbor, by some amount. In the example code below, I modify them by their difference, but the `change()` function could be anything.

After searching, `scipy.ndimage.generic_filter()` seemed to be the ideal method to use, since it allows for easy comparison between elements and their neighbors. After getting the offset from `ndimage.generic_filter()`, I am feeding that to `numpy.roll()` to modify each element's chosen neighbor.

The problem is that, with very large arrays and multiple iterations, the inefficiency of looping through np.roll() `ndimage.generic_filter()` eats into performance. With a 10000x10000 array, my execution time of the code below is 5m42s. Is there a more efficient way of doing this using scipy or numpy?

``````import numpy as np
from scipy import ndimage

dic = {0: (-1, -1), 1: (-1, 0), 2: (-1, 1),
3: (0, -1),  4: (0, 0),  5: (0, 1),
6: (1, -1),  7: (1, 0),  8: (1, 1)}

def change(a):
return (a[4] - min(a)) / 2

def movement(a):
return np.argmin(a)

P = np.random.uniform(10, size=(5,5))

# determine element change and direction
chng = np.zeros((5, 5))
ndimage.generic_filter(P, change, size=3, output=chng)
move = np.random.randint(10, size=(5, 5))
ndimage.generic_filter(P, movement, size=3, output=move)
P -= chng

# determine neighbor change
chng2 = np.zeros((5, 5))
for j in range(9):
if j == 4:
continue
p = np.where(move == j, chng, 0)
p = np.roll(p, dic[j], axis=(0, 1))
chng2 += p
P += chng2
``````

EDIT: Below is a more efficient solution. Many thanks @Paul Panzer.

``````import numpy as np

P = np.random.uniform(10, size=(1000, 1000))

# determine element change and direction
PP = np.bmat([[P[:, -1:], P, P[:, :1]]])
PP = np.bmat([[PP[-1:]], [PP], [PP[:1]]])
PPP = np.lib.stride_tricks.as_strided(PP, (1000, 1000, 3, 3), 2 * PP.strides)
am1 = np.argmin(PPP, axis=3)
i, j, k = np.ogrid[(*map(slice, PPP.shape[:-1]),)]
am0 = np.argmin(PPP[i, j, k, am1], axis=2)
i, j = np.ogrid[(*map(slice, PPP.shape[:-2]),)]
am1 = am1[i, j, am0]
mn = PPP[i, j, am0, am1]
change = (P - mn) / 2
P -= change

# determine neighbor change
am1 -= 1
am0 -= 1
i, j = np.ogrid[(*map(slice, P.shape),)]
np.add.at(P, ((am0 + i) % P.shape[0], (am1 + j) % P.shape[1]), change)
``````

This might be what you are looking for https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.convolve2d.html

in2 (Cf documentation) would be a matrix corresponding to what you wrote as a dict

``````dic = {0: (-1, -1), 1: (-1, 0), 2: (-1, 1),
3: (0, -1),  4: (0, 0),  5: (0, 1),
6: (1, -1),  7: (1, 0),  8: (1, 1)}
``````

Hope it helps

You could use `np.add.at`. The following snippet picks up after your `P -= chng`:

``````>>> P_pp = P.copy()
>>> dic_i, dic_j = np.indices((3, 3)).reshape(2, 9) - 1
>>> i, j = np.ogrid[(*map(slice, P_pp.shape),)]
>>> np.add.at(P_pp, ((dic_i[move] + i) % P_pp.shape[0], (dic_j[move] + j) % P_pp.shape[1]), chng)
``````

Since we worked on a copy of `P` we can now execute the rest of your code and then:

``````# Tada!
>>> np.allclose(P_pp, P)
True
``````

Update: Here is a method to calculate local argmin without using `ndimage`. One potential advantage is that we get the corresponding minima cheaply once we have the argminima. Note that the argmin is already 2D first component is in `am0`, second in `am1`. Each ranges between `0` and `2`, so the center is at `1,1`, The minima are in `mn`

``````>>> P = np.random.uniform(10, size=(5,5))
>>> PP = np.bmat([[P[:,-1:], P, P[:, :1]]])
>>> PP = np.bmat([[PP[-1:]], [PP], [PP[:1]]])
>>> PPP = np.lib.stride_tricks.as_strided(PP, (5, 5, 3, 3), 2 * PP.strides)
>>> am1 = np.argmin(PPP, axis=3)
>>> i, j, k = np.ogrid[(*map(slice, PPP.shape[:-1]),)]
>>> am0 = np.argmin(PPP[i, j, k, am1], axis=2)
>>> i, j = np.ogrid[(*map(slice, PPP.shape[:-2]),)]
>>> am1 = am1[i, j, am0]
>>> mn = PPP[i, j, am0, am1]
``````