Mixing
Adding different sized/shaped displaced NumPy matrices
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9
In short: I have two matrices (or arrays):
import numpy
block_1 = numpy.matrix([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]])
block_2 = numpy.matrix([[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]])
I have the displacement of block_2 in the block_1 element coordinate system.
pos = (1,1)
I want to be able to add them (quickly), to get:
[[0 0 0 0 0]
[0 1 1 1 0]
[0 1 1 1 0]
[0 1 1 1 0]]
In long: I would like a fast way to add two different shape matrices together, where one of the matrices can be displaced. The resulting matrix must have the shape of the first matrix, and the overlapping elements between the two matrices are summed. If there is no overlap, just the first matrix is returned unmutated.
I have a function that works fine, but it's kind of ugly, and elementwise:
def add_blocks(block_1, block_2, pos):
for i in xrange(0, block_2.shape[0]):
for j in xrange(0, block_2.shape[1]):
if (i + pos[1] >= 0) and (i + pos[1] < block_1.shape[0])
and (j + pos[0] >= 0) and (j + pos[0] < block_1.shape[1]):
block_1[pos[1] + i, pos[0] + j] += block_2[i,j]
return block_1
Can broadcasting or slicing perhaps do this?
I feel like maybe I'm missing something obvious.
python numpy
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
add a comment |
9
In short: I have two matrices (or arrays):
import numpy
block_1 = numpy.matrix([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]])
block_2 = numpy.matrix([[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]])
I have the displacement of block_2 in the block_1 element coordinate system.
pos = (1,1)
I want to be able to add them (quickly), to get:
[[0 0 0 0 0]
[0 1 1 1 0]
[0 1 1 1 0]
[0 1 1 1 0]]
In long: I would like a fast way to add two different shape matrices together, where one of the matrices can be displaced. The resulting matrix must have the shape of the first matrix, and the overlapping elements between the two matrices are summed. If there is no overlap, just the first matrix is returned unmutated.
I have a function that works fine, but it's kind of ugly, and elementwise:
def add_blocks(block_1, block_2, pos):
for i in xrange(0, block_2.shape[0]):
for j in xrange(0, block_2.shape[1]):
if (i + pos[1] >= 0) and (i + pos[1] < block_1.shape[0])
and (j + pos[0] >= 0) and (j + pos[0] < block_1.shape[1]):
block_1[pos[1] + i, pos[0] + j] += block_2[i,j]
return block_1
Can broadcasting or slicing perhaps do this?
I feel like maybe I'm missing something obvious.
python numpy
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
add a comment |
9
9
9
2
In short: I have two matrices (or arrays):
import numpy
block_1 = numpy.matrix([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]])
block_2 = numpy.matrix([[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]])
I have the displacement of block_2 in the block_1 element coordinate system.
pos = (1,1)
I want to be able to add them (quickly), to get:
[[0 0 0 0 0]
[0 1 1 1 0]
[0 1 1 1 0]
[0 1 1 1 0]]
In long: I would like a fast way to add two different shape matrices together, where one of the matrices can be displaced. The resulting matrix must have the shape of the first matrix, and the overlapping elements between the two matrices are summed. If there is no overlap, just the first matrix is returned unmutated.
I have a function that works fine, but it's kind of ugly, and elementwise:
def add_blocks(block_1, block_2, pos):
for i in xrange(0, block_2.shape[0]):
for j in xrange(0, block_2.shape[1]):
if (i + pos[1] >= 0) and (i + pos[1] < block_1.shape[0])
and (j + pos[0] >= 0) and (j + pos[0] < block_1.shape[1]):
block_1[pos[1] + i, pos[0] + j] += block_2[i,j]
return block_1
Can broadcasting or slicing perhaps do this?
I feel like maybe I'm missing something obvious.
python numpy
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
In short: I have two matrices (or arrays):
import numpy
block_1 = numpy.matrix([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]])
block_2 = numpy.matrix([[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]])
I have the displacement of block_2 in the block_1 element coordinate system.
pos = (1,1)
I want to be able to add them (quickly), to get:
[[0 0 0 0 0]
[0 1 1 1 0]
[0 1 1 1 0]
[0 1 1 1 0]]
In long: I would like a fast way to add two different shape matrices together, where one of the matrices can be displaced. The resulting matrix must have the shape of the first matrix, and the overlapping elements between the two matrices are summed. If there is no overlap, just the first matrix is returned unmutated.
I have a function that works fine, but it's kind of ugly, and elementwise:
def add_blocks(block_1, block_2, pos):
for i in xrange(0, block_2.shape[0]):
for j in xrange(0, block_2.shape[1]):
if (i + pos[1] >= 0) and (i + pos[1] < block_1.shape[0])
and (j + pos[0] >= 0) and (j + pos[0] < block_1.shape[1]):
block_1[pos[1] + i, pos[0] + j] += block_2[i,j]
return block_1
Can broadcasting or slicing perhaps do this?
I feel like maybe I'm missing something obvious.
python numpy
python numpy
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
edited Mar 27 '12 at 22:58
Peter Mortensen
13.9k1987114
13.9k1987114
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
asked Mar 27 '12 at 9:00
fraxelfraxel
26.2k67586
26.2k67586
add a comment |
add a comment |
5 Answers
5
active
oldest
votes
4
You just have to find the overlapping range, and then add the arrays using slicing.
b1 = np.zeros((4,5))
b2 = np.ones((4,3))
pos_v, pos_h = 2, 3 # offset
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
b1[v_range1, h_range1] += b2[v_range2, h_range2]
They're added in-place, but you could also create a new array. I might have missed some corner cases, though, but it seems to work fine.
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
I ended up doing something very similar to this. The ability to create slice objects is really great, thanks for that!
– fraxel
Mar 27 '12 at 12:06
I think the v_range1 and h_range1 code is missing a final closing ')'.
– David Poole
Mar 28 '12 at 15:03
Thanks! I just fixed that.
– jorgeca
Mar 28 '12 at 17:58
add a comment |
14
An easy solution that looks like MATLAB solution is:
import numpy as np
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
block1[1:4,2:4] += block2 # use array slicing
print(block1)
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
So package it as a reusable function:
import numpy as np
def addAtPos(mat1, mat2, xypos):
"""
Add two matrices of different sizes in place, offset by xy coordinates
Usage:
- mat1: base matrix
- mat2: add this matrix to mat1
- xypos: tuple (x,y) containing coordinates
"""
x, y = xypos
ysize, xsize = mat2.shape
xmax, ymax = (x + xsize), (y + ysize)
mat1[y:ymax, x:xmax] += mat2
return mat1
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
pos = (2,1)
print(addAtPos(block1, block2, pos))
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
edited May 31 '18 at 6:17
Brian Burns
7,12254646
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
1
This is looking good,and much more readable. But if some ofblock_2falls outsideblock_1it fails. Easy to fix of course.
– fraxel
Mar 27 '12 at 9:45
@fraxel Yeah, you can always add size checking if needed ;)
– EwyynTomato
Mar 27 '12 at 9:52
add a comment |
2
This is great, and here's how to extend the addition to a 3D matrix by adding a few lines to jorgeca's code:
import numpy as np
#two 3d arrays, of different size.
b1 = np.zeros((5,5,5), dtype=np.int) # a 5x5x5 matrix of zeroes
b2 = np.ones((3,3,3), dtype=np.int) # a 3x3x3 matrix of ones
pos_v, pos_h, pos_z = 2, 2, 2 # a 3d offset -> to plonk b2 in the corner of b1
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
z_range1 = slice(max(0, pos_z), max(min(pos_z + b2.shape[2], b1.shape[2]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
z_range2 = slice(max(0, -pos_z), min(-pos_z + b1.shape[2], b2.shape[2]))
b1[v_range1, h_range1, z_range1] += b2[v_range2, h_range2, z_range2]
This might help someone who wants to do the same in 3d (like me).
answered Sep 2 '14 at 22:46
kabammikabammi
10512
add a comment |
1
I'm sure there is a fast NumPy way to do this, but there is a more efficient way to do it even in normal Python:
block_1 = [ [ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]
block_2 = [ [ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]]
pos = (1, 1)
x, y = pos
# width of the rows in block_2
length = len(block_2[0])
# skip the first y rows
for row_1, row_2 in zip(block_1[y:], block_2):
# set length elements offset by x to the sum.
row_1[x:length + x] = map(sum, zip(row_2, row_1[x:length + x]))
print 'n'.join(' '.join(map(str, row)) for row in block_1)
"""
0 0 0 0 0
0 1 1 1 0
0 1 1 1 0
0 1 1 1 0
"""
answered Mar 27 '12 at 9:26
agfagf
117k28230209
add a comment |
1
Here's @jorgeca's great code as a function, with some tests - I expanded the slices to try to make it a little more readable:
import numpy as np
def addAtPos(matrix1, matrix2, xypos, inPlace=False):
"""
Add matrix2 into matrix1 at position xypos (x,y), in-place or in new matrix.
Handles matrix2 going off edges of matrix1.
"""
x, y = xypos
h1, w1 = matrix1.shape
h2, w2 = matrix2.shape
# get slice ranges for matrix1
x1min = max(0, x)
y1min = max(0, y)
x1max = max(min(x + w2, w1), 0)
y1max = max(min(y + h2, h1), 0)
# get slice ranges for matrix2
x2min = max(0, -x)
y2min = max(0, -y)
x2max = min(-x + w1, w2)
y2max = min(-y + h1, h2)
if inPlace:
# add matrix2 into matrix1, in place
matrix1[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
else:
# create and return a new matrix
matrix1copy = matrix1.copy()
matrix1copy[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
return matrix1copy
def test_addAtPos():
matrix1 = np.zeros((2,2))
matrix2 = np.ones((2,2))
test(addAtPos(matrix1, matrix2, ( 0, 0)), [[1,1],[1,1]])
test(addAtPos(matrix1, matrix2, ( 2, 2)), [[0,0],[0,0]])
test(addAtPos(matrix1, matrix2, (-1,-1)), [[1,0],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1,-1)), [[0,1],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1, 1)), [[0,0],[0,1]])
test(addAtPos(matrix1, matrix2, (-1, 1)), [[0,0],[1,0]])
def test(actual, expected, message=''):
"Compare actual and expected values and print OK or FAIL"
passed = (actual == expected)
if type(passed) == np.ndarray:
passed = passed.all()
actual = str(actual).replace('n', '')
expected = str(expected).replace('n', '')
if passed:
print('[OK] ', message, actual)
else:
print('[FAIL]', message, actual, ' != expected value of', expected)
test_addAtPos()
Output:
[OK] [[1. 1.] [1. 1.]]
[OK] [[0. 0.] [0. 0.]]
[OK] [[1. 0.] [0. 0.]]
[OK] [[0. 1.] [0. 0.]]
[OK] [[0. 0.] [0. 1.]]
[OK] [[0. 0.] [1. 0.]]
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
add a comment |
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5 Answers
5
active
oldest
votes
5 Answers
5
active
oldest
votes
active
oldest
votes
active
oldest
votes
4
You just have to find the overlapping range, and then add the arrays using slicing.
b1 = np.zeros((4,5))
b2 = np.ones((4,3))
pos_v, pos_h = 2, 3 # offset
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
b1[v_range1, h_range1] += b2[v_range2, h_range2]
They're added in-place, but you could also create a new array. I might have missed some corner cases, though, but it seems to work fine.
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
I ended up doing something very similar to this. The ability to create slice objects is really great, thanks for that!
– fraxel
Mar 27 '12 at 12:06
I think the v_range1 and h_range1 code is missing a final closing ')'.
– David Poole
Mar 28 '12 at 15:03
Thanks! I just fixed that.
– jorgeca
Mar 28 '12 at 17:58
add a comment |
4
You just have to find the overlapping range, and then add the arrays using slicing.
b1 = np.zeros((4,5))
b2 = np.ones((4,3))
pos_v, pos_h = 2, 3 # offset
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
b1[v_range1, h_range1] += b2[v_range2, h_range2]
They're added in-place, but you could also create a new array. I might have missed some corner cases, though, but it seems to work fine.
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
I ended up doing something very similar to this. The ability to create slice objects is really great, thanks for that!
– fraxel
Mar 27 '12 at 12:06
I think the v_range1 and h_range1 code is missing a final closing ')'.
– David Poole
Mar 28 '12 at 15:03
Thanks! I just fixed that.
– jorgeca
Mar 28 '12 at 17:58
add a comment |
4
4
4
You just have to find the overlapping range, and then add the arrays using slicing.
b1 = np.zeros((4,5))
b2 = np.ones((4,3))
pos_v, pos_h = 2, 3 # offset
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
b1[v_range1, h_range1] += b2[v_range2, h_range2]
They're added in-place, but you could also create a new array. I might have missed some corner cases, though, but it seems to work fine.
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
You just have to find the overlapping range, and then add the arrays using slicing.
b1 = np.zeros((4,5))
b2 = np.ones((4,3))
pos_v, pos_h = 2, 3 # offset
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
b1[v_range1, h_range1] += b2[v_range2, h_range2]
They're added in-place, but you could also create a new array. I might have missed some corner cases, though, but it seems to work fine.
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
edited Mar 28 '12 at 17:54
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
answered Mar 27 '12 at 10:12
jorgecajorgeca
4,37411834
4,37411834
I ended up doing something very similar to this. The ability to create slice objects is really great, thanks for that!
– fraxel
Mar 27 '12 at 12:06
I think the v_range1 and h_range1 code is missing a final closing ')'.
– David Poole
Mar 28 '12 at 15:03
Thanks! I just fixed that.
– jorgeca
Mar 28 '12 at 17:58
add a comment |
I ended up doing something very similar to this. The ability to create slice objects is really great, thanks for that!
– fraxel
Mar 27 '12 at 12:06
I think the v_range1 and h_range1 code is missing a final closing ')'.
– David Poole
Mar 28 '12 at 15:03
Thanks! I just fixed that.
– jorgeca
Mar 28 '12 at 17:58
I ended up doing something very similar to this. The ability to create slice objects is really great, thanks for that!
– fraxel
Mar 27 '12 at 12:06
I ended up doing something very similar to this. The ability to create slice objects is really great, thanks for that!
– fraxel
Mar 27 '12 at 12:06
I think the v_range1 and h_range1 code is missing a final closing ')'.
– David Poole
Mar 28 '12 at 15:03
I think the v_range1 and h_range1 code is missing a final closing ')'.
– David Poole
Mar 28 '12 at 15:03
Thanks! I just fixed that.
– jorgeca
Mar 28 '12 at 17:58
Thanks! I just fixed that.
– jorgeca
Mar 28 '12 at 17:58
add a comment |
14
An easy solution that looks like MATLAB solution is:
import numpy as np
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
block1[1:4,2:4] += block2 # use array slicing
print(block1)
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
So package it as a reusable function:
import numpy as np
def addAtPos(mat1, mat2, xypos):
"""
Add two matrices of different sizes in place, offset by xy coordinates
Usage:
- mat1: base matrix
- mat2: add this matrix to mat1
- xypos: tuple (x,y) containing coordinates
"""
x, y = xypos
ysize, xsize = mat2.shape
xmax, ymax = (x + xsize), (y + ysize)
mat1[y:ymax, x:xmax] += mat2
return mat1
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
pos = (2,1)
print(addAtPos(block1, block2, pos))
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
edited May 31 '18 at 6:17
Brian Burns
7,12254646
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
1
This is looking good,and much more readable. But if some ofblock_2falls outsideblock_1it fails. Easy to fix of course.
– fraxel
Mar 27 '12 at 9:45
@fraxel Yeah, you can always add size checking if needed ;)
– EwyynTomato
Mar 27 '12 at 9:52
add a comment |
14
An easy solution that looks like MATLAB solution is:
import numpy as np
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
block1[1:4,2:4] += block2 # use array slicing
print(block1)
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
So package it as a reusable function:
import numpy as np
def addAtPos(mat1, mat2, xypos):
"""
Add two matrices of different sizes in place, offset by xy coordinates
Usage:
- mat1: base matrix
- mat2: add this matrix to mat1
- xypos: tuple (x,y) containing coordinates
"""
x, y = xypos
ysize, xsize = mat2.shape
xmax, ymax = (x + xsize), (y + ysize)
mat1[y:ymax, x:xmax] += mat2
return mat1
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
pos = (2,1)
print(addAtPos(block1, block2, pos))
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
edited May 31 '18 at 6:17
Brian Burns
7,12254646
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
1
This is looking good,and much more readable. But if some ofblock_2falls outsideblock_1it fails. Easy to fix of course.
– fraxel
Mar 27 '12 at 9:45
@fraxel Yeah, you can always add size checking if needed ;)
– EwyynTomato
Mar 27 '12 at 9:52
add a comment |
14
14
14
An easy solution that looks like MATLAB solution is:
import numpy as np
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
block1[1:4,2:4] += block2 # use array slicing
print(block1)
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
So package it as a reusable function:
import numpy as np
def addAtPos(mat1, mat2, xypos):
"""
Add two matrices of different sizes in place, offset by xy coordinates
Usage:
- mat1: base matrix
- mat2: add this matrix to mat1
- xypos: tuple (x,y) containing coordinates
"""
x, y = xypos
ysize, xsize = mat2.shape
xmax, ymax = (x + xsize), (y + ysize)
mat1[y:ymax, x:xmax] += mat2
return mat1
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
pos = (2,1)
print(addAtPos(block1, block2, pos))
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
edited May 31 '18 at 6:17
Brian Burns
7,12254646
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
An easy solution that looks like MATLAB solution is:
import numpy as np
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
block1[1:4,2:4] += block2 # use array slicing
print(block1)
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
So package it as a reusable function:
import numpy as np
def addAtPos(mat1, mat2, xypos):
"""
Add two matrices of different sizes in place, offset by xy coordinates
Usage:
- mat1: base matrix
- mat2: add this matrix to mat1
- xypos: tuple (x,y) containing coordinates
"""
x, y = xypos
ysize, xsize = mat2.shape
xmax, ymax = (x + xsize), (y + ysize)
mat1[y:ymax, x:xmax] += mat2
return mat1
block1 = np.zeros((5,4))
block2 = np.ones((3,2))
pos = (2,1)
print(addAtPos(block1, block2, pos))
[[0. 0. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]
[0. 0. 0. 0.]]
edited May 31 '18 at 6:17
Brian Burns
7,12254646
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
edited May 31 '18 at 6:17
Brian Burns
7,12254646
edited May 31 '18 at 6:17
Brian Burns
7,12254646
edited May 31 '18 at 6:17
Brian Burns
7,12254646
7,12254646
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
answered Mar 27 '12 at 9:27
EwyynTomatoEwyynTomato
2,72412133
2,72412133
1
This is looking good,and much more readable. But if some ofblock_2falls outsideblock_1it fails. Easy to fix of course.
– fraxel
Mar 27 '12 at 9:45
@fraxel Yeah, you can always add size checking if needed ;)
– EwyynTomato
Mar 27 '12 at 9:52
add a comment |
1
This is looking good,and much more readable. But if some ofblock_2falls outsideblock_1it fails. Easy to fix of course.
– fraxel
Mar 27 '12 at 9:45
@fraxel Yeah, you can always add size checking if needed ;)
– EwyynTomato
Mar 27 '12 at 9:52
1
1
This is looking good,and much more readable. But if some of
block_2 falls outside block_1 it fails. Easy to fix of course.– fraxel
Mar 27 '12 at 9:45
This is looking good,and much more readable. But if some of
block_2 falls outside block_1 it fails. Easy to fix of course.– fraxel
Mar 27 '12 at 9:45
@fraxel Yeah, you can always add size checking if needed ;)
– EwyynTomato
Mar 27 '12 at 9:52
@fraxel Yeah, you can always add size checking if needed ;)
– EwyynTomato
Mar 27 '12 at 9:52
add a comment |
2
This is great, and here's how to extend the addition to a 3D matrix by adding a few lines to jorgeca's code:
import numpy as np
#two 3d arrays, of different size.
b1 = np.zeros((5,5,5), dtype=np.int) # a 5x5x5 matrix of zeroes
b2 = np.ones((3,3,3), dtype=np.int) # a 3x3x3 matrix of ones
pos_v, pos_h, pos_z = 2, 2, 2 # a 3d offset -> to plonk b2 in the corner of b1
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
z_range1 = slice(max(0, pos_z), max(min(pos_z + b2.shape[2], b1.shape[2]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
z_range2 = slice(max(0, -pos_z), min(-pos_z + b1.shape[2], b2.shape[2]))
b1[v_range1, h_range1, z_range1] += b2[v_range2, h_range2, z_range2]
This might help someone who wants to do the same in 3d (like me).
answered Sep 2 '14 at 22:46
kabammikabammi
10512
add a comment |
2
This is great, and here's how to extend the addition to a 3D matrix by adding a few lines to jorgeca's code:
import numpy as np
#two 3d arrays, of different size.
b1 = np.zeros((5,5,5), dtype=np.int) # a 5x5x5 matrix of zeroes
b2 = np.ones((3,3,3), dtype=np.int) # a 3x3x3 matrix of ones
pos_v, pos_h, pos_z = 2, 2, 2 # a 3d offset -> to plonk b2 in the corner of b1
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
z_range1 = slice(max(0, pos_z), max(min(pos_z + b2.shape[2], b1.shape[2]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
z_range2 = slice(max(0, -pos_z), min(-pos_z + b1.shape[2], b2.shape[2]))
b1[v_range1, h_range1, z_range1] += b2[v_range2, h_range2, z_range2]
This might help someone who wants to do the same in 3d (like me).
answered Sep 2 '14 at 22:46
kabammikabammi
10512
add a comment |
2
2
2
This is great, and here's how to extend the addition to a 3D matrix by adding a few lines to jorgeca's code:
import numpy as np
#two 3d arrays, of different size.
b1 = np.zeros((5,5,5), dtype=np.int) # a 5x5x5 matrix of zeroes
b2 = np.ones((3,3,3), dtype=np.int) # a 3x3x3 matrix of ones
pos_v, pos_h, pos_z = 2, 2, 2 # a 3d offset -> to plonk b2 in the corner of b1
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
z_range1 = slice(max(0, pos_z), max(min(pos_z + b2.shape[2], b1.shape[2]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
z_range2 = slice(max(0, -pos_z), min(-pos_z + b1.shape[2], b2.shape[2]))
b1[v_range1, h_range1, z_range1] += b2[v_range2, h_range2, z_range2]
This might help someone who wants to do the same in 3d (like me).
answered Sep 2 '14 at 22:46
kabammikabammi
10512
This is great, and here's how to extend the addition to a 3D matrix by adding a few lines to jorgeca's code:
import numpy as np
#two 3d arrays, of different size.
b1 = np.zeros((5,5,5), dtype=np.int) # a 5x5x5 matrix of zeroes
b2 = np.ones((3,3,3), dtype=np.int) # a 3x3x3 matrix of ones
pos_v, pos_h, pos_z = 2, 2, 2 # a 3d offset -> to plonk b2 in the corner of b1
v_range1 = slice(max(0, pos_v), max(min(pos_v + b2.shape[0], b1.shape[0]), 0))
h_range1 = slice(max(0, pos_h), max(min(pos_h + b2.shape[1], b1.shape[1]), 0))
z_range1 = slice(max(0, pos_z), max(min(pos_z + b2.shape[2], b1.shape[2]), 0))
v_range2 = slice(max(0, -pos_v), min(-pos_v + b1.shape[0], b2.shape[0]))
h_range2 = slice(max(0, -pos_h), min(-pos_h + b1.shape[1], b2.shape[1]))
z_range2 = slice(max(0, -pos_z), min(-pos_z + b1.shape[2], b2.shape[2]))
b1[v_range1, h_range1, z_range1] += b2[v_range2, h_range2, z_range2]
This might help someone who wants to do the same in 3d (like me).
answered Sep 2 '14 at 22:46
kabammikabammi
10512
answered Sep 2 '14 at 22:46
kabammikabammi
10512
answered Sep 2 '14 at 22:46
kabammikabammi
10512
answered Sep 2 '14 at 22:46
kabammikabammi
10512
10512
add a comment |
add a comment |
1
I'm sure there is a fast NumPy way to do this, but there is a more efficient way to do it even in normal Python:
block_1 = [ [ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]
block_2 = [ [ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]]
pos = (1, 1)
x, y = pos
# width of the rows in block_2
length = len(block_2[0])
# skip the first y rows
for row_1, row_2 in zip(block_1[y:], block_2):
# set length elements offset by x to the sum.
row_1[x:length + x] = map(sum, zip(row_2, row_1[x:length + x]))
print 'n'.join(' '.join(map(str, row)) for row in block_1)
"""
0 0 0 0 0
0 1 1 1 0
0 1 1 1 0
0 1 1 1 0
"""
answered Mar 27 '12 at 9:26
agfagf
117k28230209
add a comment |
1
I'm sure there is a fast NumPy way to do this, but there is a more efficient way to do it even in normal Python:
block_1 = [ [ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]
block_2 = [ [ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]]
pos = (1, 1)
x, y = pos
# width of the rows in block_2
length = len(block_2[0])
# skip the first y rows
for row_1, row_2 in zip(block_1[y:], block_2):
# set length elements offset by x to the sum.
row_1[x:length + x] = map(sum, zip(row_2, row_1[x:length + x]))
print 'n'.join(' '.join(map(str, row)) for row in block_1)
"""
0 0 0 0 0
0 1 1 1 0
0 1 1 1 0
0 1 1 1 0
"""
answered Mar 27 '12 at 9:26
agfagf
117k28230209
add a comment |
1
1
1
I'm sure there is a fast NumPy way to do this, but there is a more efficient way to do it even in normal Python:
block_1 = [ [ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]
block_2 = [ [ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]]
pos = (1, 1)
x, y = pos
# width of the rows in block_2
length = len(block_2[0])
# skip the first y rows
for row_1, row_2 in zip(block_1[y:], block_2):
# set length elements offset by x to the sum.
row_1[x:length + x] = map(sum, zip(row_2, row_1[x:length + x]))
print 'n'.join(' '.join(map(str, row)) for row in block_1)
"""
0 0 0 0 0
0 1 1 1 0
0 1 1 1 0
0 1 1 1 0
"""
answered Mar 27 '12 at 9:26
agfagf
117k28230209
I'm sure there is a fast NumPy way to do this, but there is a more efficient way to do it even in normal Python:
block_1 = [ [ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]
block_2 = [ [ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1],
[ 1, 1, 1]]
pos = (1, 1)
x, y = pos
# width of the rows in block_2
length = len(block_2[0])
# skip the first y rows
for row_1, row_2 in zip(block_1[y:], block_2):
# set length elements offset by x to the sum.
row_1[x:length + x] = map(sum, zip(row_2, row_1[x:length + x]))
print 'n'.join(' '.join(map(str, row)) for row in block_1)
"""
0 0 0 0 0
0 1 1 1 0
0 1 1 1 0
0 1 1 1 0
"""
answered Mar 27 '12 at 9:26
agfagf
117k28230209
answered Mar 27 '12 at 9:26
agfagf
117k28230209
answered Mar 27 '12 at 9:26
agfagf
117k28230209
answered Mar 27 '12 at 9:26
agfagf
117k28230209
117k28230209
add a comment |
add a comment |
1
Here's @jorgeca's great code as a function, with some tests - I expanded the slices to try to make it a little more readable:
import numpy as np
def addAtPos(matrix1, matrix2, xypos, inPlace=False):
"""
Add matrix2 into matrix1 at position xypos (x,y), in-place or in new matrix.
Handles matrix2 going off edges of matrix1.
"""
x, y = xypos
h1, w1 = matrix1.shape
h2, w2 = matrix2.shape
# get slice ranges for matrix1
x1min = max(0, x)
y1min = max(0, y)
x1max = max(min(x + w2, w1), 0)
y1max = max(min(y + h2, h1), 0)
# get slice ranges for matrix2
x2min = max(0, -x)
y2min = max(0, -y)
x2max = min(-x + w1, w2)
y2max = min(-y + h1, h2)
if inPlace:
# add matrix2 into matrix1, in place
matrix1[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
else:
# create and return a new matrix
matrix1copy = matrix1.copy()
matrix1copy[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
return matrix1copy
def test_addAtPos():
matrix1 = np.zeros((2,2))
matrix2 = np.ones((2,2))
test(addAtPos(matrix1, matrix2, ( 0, 0)), [[1,1],[1,1]])
test(addAtPos(matrix1, matrix2, ( 2, 2)), [[0,0],[0,0]])
test(addAtPos(matrix1, matrix2, (-1,-1)), [[1,0],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1,-1)), [[0,1],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1, 1)), [[0,0],[0,1]])
test(addAtPos(matrix1, matrix2, (-1, 1)), [[0,0],[1,0]])
def test(actual, expected, message=''):
"Compare actual and expected values and print OK or FAIL"
passed = (actual == expected)
if type(passed) == np.ndarray:
passed = passed.all()
actual = str(actual).replace('n', '')
expected = str(expected).replace('n', '')
if passed:
print('[OK] ', message, actual)
else:
print('[FAIL]', message, actual, ' != expected value of', expected)
test_addAtPos()
Output:
[OK] [[1. 1.] [1. 1.]]
[OK] [[0. 0.] [0. 0.]]
[OK] [[1. 0.] [0. 0.]]
[OK] [[0. 1.] [0. 0.]]
[OK] [[0. 0.] [0. 1.]]
[OK] [[0. 0.] [1. 0.]]
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
add a comment |
1
Here's @jorgeca's great code as a function, with some tests - I expanded the slices to try to make it a little more readable:
import numpy as np
def addAtPos(matrix1, matrix2, xypos, inPlace=False):
"""
Add matrix2 into matrix1 at position xypos (x,y), in-place or in new matrix.
Handles matrix2 going off edges of matrix1.
"""
x, y = xypos
h1, w1 = matrix1.shape
h2, w2 = matrix2.shape
# get slice ranges for matrix1
x1min = max(0, x)
y1min = max(0, y)
x1max = max(min(x + w2, w1), 0)
y1max = max(min(y + h2, h1), 0)
# get slice ranges for matrix2
x2min = max(0, -x)
y2min = max(0, -y)
x2max = min(-x + w1, w2)
y2max = min(-y + h1, h2)
if inPlace:
# add matrix2 into matrix1, in place
matrix1[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
else:
# create and return a new matrix
matrix1copy = matrix1.copy()
matrix1copy[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
return matrix1copy
def test_addAtPos():
matrix1 = np.zeros((2,2))
matrix2 = np.ones((2,2))
test(addAtPos(matrix1, matrix2, ( 0, 0)), [[1,1],[1,1]])
test(addAtPos(matrix1, matrix2, ( 2, 2)), [[0,0],[0,0]])
test(addAtPos(matrix1, matrix2, (-1,-1)), [[1,0],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1,-1)), [[0,1],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1, 1)), [[0,0],[0,1]])
test(addAtPos(matrix1, matrix2, (-1, 1)), [[0,0],[1,0]])
def test(actual, expected, message=''):
"Compare actual and expected values and print OK or FAIL"
passed = (actual == expected)
if type(passed) == np.ndarray:
passed = passed.all()
actual = str(actual).replace('n', '')
expected = str(expected).replace('n', '')
if passed:
print('[OK] ', message, actual)
else:
print('[FAIL]', message, actual, ' != expected value of', expected)
test_addAtPos()
Output:
[OK] [[1. 1.] [1. 1.]]
[OK] [[0. 0.] [0. 0.]]
[OK] [[1. 0.] [0. 0.]]
[OK] [[0. 1.] [0. 0.]]
[OK] [[0. 0.] [0. 1.]]
[OK] [[0. 0.] [1. 0.]]
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
add a comment |
1
1
1
Here's @jorgeca's great code as a function, with some tests - I expanded the slices to try to make it a little more readable:
import numpy as np
def addAtPos(matrix1, matrix2, xypos, inPlace=False):
"""
Add matrix2 into matrix1 at position xypos (x,y), in-place or in new matrix.
Handles matrix2 going off edges of matrix1.
"""
x, y = xypos
h1, w1 = matrix1.shape
h2, w2 = matrix2.shape
# get slice ranges for matrix1
x1min = max(0, x)
y1min = max(0, y)
x1max = max(min(x + w2, w1), 0)
y1max = max(min(y + h2, h1), 0)
# get slice ranges for matrix2
x2min = max(0, -x)
y2min = max(0, -y)
x2max = min(-x + w1, w2)
y2max = min(-y + h1, h2)
if inPlace:
# add matrix2 into matrix1, in place
matrix1[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
else:
# create and return a new matrix
matrix1copy = matrix1.copy()
matrix1copy[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
return matrix1copy
def test_addAtPos():
matrix1 = np.zeros((2,2))
matrix2 = np.ones((2,2))
test(addAtPos(matrix1, matrix2, ( 0, 0)), [[1,1],[1,1]])
test(addAtPos(matrix1, matrix2, ( 2, 2)), [[0,0],[0,0]])
test(addAtPos(matrix1, matrix2, (-1,-1)), [[1,0],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1,-1)), [[0,1],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1, 1)), [[0,0],[0,1]])
test(addAtPos(matrix1, matrix2, (-1, 1)), [[0,0],[1,0]])
def test(actual, expected, message=''):
"Compare actual and expected values and print OK or FAIL"
passed = (actual == expected)
if type(passed) == np.ndarray:
passed = passed.all()
actual = str(actual).replace('n', '')
expected = str(expected).replace('n', '')
if passed:
print('[OK] ', message, actual)
else:
print('[FAIL]', message, actual, ' != expected value of', expected)
test_addAtPos()
Output:
[OK] [[1. 1.] [1. 1.]]
[OK] [[0. 0.] [0. 0.]]
[OK] [[1. 0.] [0. 0.]]
[OK] [[0. 1.] [0. 0.]]
[OK] [[0. 0.] [0. 1.]]
[OK] [[0. 0.] [1. 0.]]
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
Here's @jorgeca's great code as a function, with some tests - I expanded the slices to try to make it a little more readable:
import numpy as np
def addAtPos(matrix1, matrix2, xypos, inPlace=False):
"""
Add matrix2 into matrix1 at position xypos (x,y), in-place or in new matrix.
Handles matrix2 going off edges of matrix1.
"""
x, y = xypos
h1, w1 = matrix1.shape
h2, w2 = matrix2.shape
# get slice ranges for matrix1
x1min = max(0, x)
y1min = max(0, y)
x1max = max(min(x + w2, w1), 0)
y1max = max(min(y + h2, h1), 0)
# get slice ranges for matrix2
x2min = max(0, -x)
y2min = max(0, -y)
x2max = min(-x + w1, w2)
y2max = min(-y + h1, h2)
if inPlace:
# add matrix2 into matrix1, in place
matrix1[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
else:
# create and return a new matrix
matrix1copy = matrix1.copy()
matrix1copy[y1min:y1max, x1min:x1max] += matrix2[y2min:y2max, x2min:x2max]
return matrix1copy
def test_addAtPos():
matrix1 = np.zeros((2,2))
matrix2 = np.ones((2,2))
test(addAtPos(matrix1, matrix2, ( 0, 0)), [[1,1],[1,1]])
test(addAtPos(matrix1, matrix2, ( 2, 2)), [[0,0],[0,0]])
test(addAtPos(matrix1, matrix2, (-1,-1)), [[1,0],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1,-1)), [[0,1],[0,0]])
test(addAtPos(matrix1, matrix2, ( 1, 1)), [[0,0],[0,1]])
test(addAtPos(matrix1, matrix2, (-1, 1)), [[0,0],[1,0]])
def test(actual, expected, message=''):
"Compare actual and expected values and print OK or FAIL"
passed = (actual == expected)
if type(passed) == np.ndarray:
passed = passed.all()
actual = str(actual).replace('n', '')
expected = str(expected).replace('n', '')
if passed:
print('[OK] ', message, actual)
else:
print('[FAIL]', message, actual, ' != expected value of', expected)
test_addAtPos()
Output:
[OK] [[1. 1.] [1. 1.]]
[OK] [[0. 0.] [0. 0.]]
[OK] [[1. 0.] [0. 0.]]
[OK] [[0. 1.] [0. 0.]]
[OK] [[0. 0.] [0. 1.]]
[OK] [[0. 0.] [1. 0.]]
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
answered May 31 '18 at 17:38
Brian BurnsBrian Burns
7,12254646
7,12254646
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