How would one (efficiently) do the following:
x = np.arange(49)
x2 = np.reshape(x, (7,7))
x2
array([[ 0, 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12, 13],
[14, 15, 16, 17, 18, 19, 20],
[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34],
[35, 36, 37, 38, 39, 40, 41],
[42, 43, 44, 45, 46, 47, 48]])
From here I want to roll a couple of things.
I want to roll 0,7,14,21 etc so 14 comes to top.
Then the same with 4,11,18,25 etc so 39 comes to top.
Result should be:
x2
array([[14, 1, 2, 3, 39, 5, 6],
[21, 8, 9, 10, 46, 12, 13],
[28, 15, 16, 17, 4, 19, 20],
[35, 22, 23, 24, 11, 26, 27],
[42, 29, 30, 31, 18, 33, 34],
[ 0, 36, 37, 38, 25, 40, 41],
[ 7, 43, 44, 45, 32, 47, 48]])
I looked up numpy.roll, here and google but couldn't find how one would do this.
For horizontal rolls, I could do:
np.roll(x2[0], 3, axis=0)
x3
array([4, 5, 6, 0, 1, 2, 3])
But how do I return the full array with this roll change as a new copy?
Roll with a negative shift:
x2[:, 0] = np.roll(x2[:, 0], -2)
Roll with a positive shift:
x2[:, 4] = np.roll(x2[:, 4], 2)
gives:
>>>x2
array([[14, 1, 2, 3, 39, 5, 6],
[21, 8, 9, 10, 46, 12, 13],
[28, 15, 16, 17, 4, 19, 20],
[35, 22, 23, 24, 11, 26, 27],
[42, 29, 30, 31, 18, 33, 34],
[ 0, 36, 37, 38, 25, 40, 41],
[ 7, 43, 44, 45, 32, 47, 48]])
Here's a way to roll multiple columns in one go with advanced-indexing
-
# Params
cols = [0,4] # Columns to be rolled
dirn = [2,-2] # Offset with direction as sign
n = x2.shape[0]
x2[:,cols] = x2[np.mod(np.arange(n)[:,None] + dirn,n),cols]
Sample run -
In [45]: x2
Out[45]:
array([[ 0, 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12, 13],
[14, 15, 16, 17, 18, 19, 20],
[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34],
[35, 36, 37, 38, 39, 40, 41],
[42, 43, 44, 45, 46, 47, 48]])
In [46]: cols = [0,4,5] # Columns to be rolled
...: dirn = [2,-2,4] # Offset with direction as sign
...: n = x2.shape[0]
...: x2[:,cols] = x2[np.mod(np.arange(n)[:,None] + dirn,n),cols]
...:
In [47]: x2 # Three columns rolled
Out[47]:
array([[14, 1, 2, 3, 39, 33, 6],
[21, 8, 9, 10, 46, 40, 13],
[28, 15, 16, 17, 4, 47, 20],
[35, 22, 23, 24, 11, 5, 27],
[42, 29, 30, 31, 18, 12, 34],
[ 0, 36, 37, 38, 25, 19, 41],
[ 7, 43, 44, 45, 32, 26, 48]])
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