Numpy: slicing a volume using a matrix
I have a 3D numpy volume and a 2D numpy matrix:
foo = np.random.rand(20,20,10)
amin = np.argmin(foo, axis=2)
i would like to use amin variable to slice the volume in the same way np.min would do:
grid = np.indices(min.shape)
idcs = np.stack([grid[0], grid[1], min])
fmin = foo[idcs[0], idcs[1], idcs[2]]
problem is that i can't use np.min because i also need the amin neighbors for interpolation reasons, something that i would obtain doing:
pre = foo[idcs[0], idcs[1], np.clip(idcs[2]-1, 0, 9)]
post = foo[idcs[0], idcs[1], np.clip(idcs[2]+1, 0, 9)]
Is there a more pythonic (nupyic) way to do this without creating an np.grid? something like:
foo[:,:,amin-1:amin+1]
that actually works (i would care about margin handling with an early-padding)
python numpy
asked Nov 28 '18 at 13:20
LucaLuca
69711029
add a comment |
I have a 3D numpy volume and a 2D numpy matrix:
foo = np.random.rand(20,20,10)
amin = np.argmin(foo, axis=2)
i would like to use amin variable to slice the volume in the same way np.min would do:
grid = np.indices(min.shape)
idcs = np.stack([grid[0], grid[1], min])
fmin = foo[idcs[0], idcs[1], idcs[2]]
problem is that i can't use np.min because i also need the amin neighbors for interpolation reasons, something that i would obtain doing:
pre = foo[idcs[0], idcs[1], np.clip(idcs[2]-1, 0, 9)]
post = foo[idcs[0], idcs[1], np.clip(idcs[2]+1, 0, 9)]
Is there a more pythonic (nupyic) way to do this without creating an np.grid? something like:
foo[:,:,amin-1:amin+1]
that actually works (i would care about margin handling with an early-padding)
python numpy
asked Nov 28 '18 at 13:20
LucaLuca
69711029
add a comment |
I have a 3D numpy volume and a 2D numpy matrix:
foo = np.random.rand(20,20,10)
amin = np.argmin(foo, axis=2)
i would like to use amin variable to slice the volume in the same way np.min would do:
grid = np.indices(min.shape)
idcs = np.stack([grid[0], grid[1], min])
fmin = foo[idcs[0], idcs[1], idcs[2]]
problem is that i can't use np.min because i also need the amin neighbors for interpolation reasons, something that i would obtain doing:
pre = foo[idcs[0], idcs[1], np.clip(idcs[2]-1, 0, 9)]
post = foo[idcs[0], idcs[1], np.clip(idcs[2]+1, 0, 9)]
Is there a more pythonic (nupyic) way to do this without creating an np.grid? something like:
foo[:,:,amin-1:amin+1]
that actually works (i would care about margin handling with an early-padding)
python numpy
asked Nov 28 '18 at 13:20
LucaLuca
69711029
I have a 3D numpy volume and a 2D numpy matrix:
foo = np.random.rand(20,20,10)
amin = np.argmin(foo, axis=2)
i would like to use amin variable to slice the volume in the same way np.min would do:
grid = np.indices(min.shape)
idcs = np.stack([grid[0], grid[1], min])
fmin = foo[idcs[0], idcs[1], idcs[2]]
problem is that i can't use np.min because i also need the amin neighbors for interpolation reasons, something that i would obtain doing:
pre = foo[idcs[0], idcs[1], np.clip(idcs[2]-1, 0, 9)]
post = foo[idcs[0], idcs[1], np.clip(idcs[2]+1, 0, 9)]
Is there a more pythonic (nupyic) way to do this without creating an np.grid? something like:
foo[:,:,amin-1:amin+1]
that actually works (i would care about margin handling with an early-padding)
python numpy
python numpy
asked Nov 28 '18 at 13:20
LucaLuca
69711029
asked Nov 28 '18 at 13:20
LucaLuca
69711029
edited Nov 28 '18 at 13:34
Luca
asked Nov 28 '18 at 13:20
LucaLuca
69711029
asked Nov 28 '18 at 13:20
LucaLuca
69711029
asked Nov 28 '18 at 13:20
LucaLuca
69711029
69711029
add a comment |
add a comment |
1 Answer
1
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votes
You could use np.ogrid instead of np.indices to save memory.
np.ogrid returns an "open" meshgrid:
In [24]: np.ogrid[:5,:5]
Out[24]:
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
ogrid returns component arrays which can be used as indices
in the same way as one would use np.indices.
NumPy will automatically broadcast the values in the open mesh when they are used as indices:
In [49]: (np.indices((5,5)) == np.broadcast_arrays(*np.ogrid[:5, :5])).all()
Out[49]: True
import numpy as np
h, w, d = 20, 20, 10
foo = np.random.rand(h, w, d)
amin = np.argmin(foo, axis=2)
X, Y = np.ogrid[:h, :w]
amins = np.stack([np.clip(amin+i, 0, d-1) for i in [-1, 0, 1]])
fmins = foo[X, Y, amins]
It's better to store fmin, pre and post in one array, fmins,
since some NumPy/Scipy operations (like argmin or griddata) may need the values in one array. If, later, you need to operate on the 3 components individually, you can always access them using fmins[i] or define
pre, fmin, post = fmins
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
I like your answer, if nobody come up with anything better i will mark as answer
– Luca
Nov 28 '18 at 13:59
1
Shouldn't it benp.clip(amin+i, ..., so you getamin - 1, amin, amin + 1?
– jdehesa
Nov 28 '18 at 14:16
@jdehesa well, it depends on the definiton of pre and post, the way you say is the one i was searching for
– Luca
Nov 28 '18 at 14:19
@jdehesa: Thanks for the correction.
– unutbu
Nov 28 '18 at 14:24
add a comment |
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1 Answer
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1 Answer
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votes
You could use np.ogrid instead of np.indices to save memory.
np.ogrid returns an "open" meshgrid:
In [24]: np.ogrid[:5,:5]
Out[24]:
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
ogrid returns component arrays which can be used as indices
in the same way as one would use np.indices.
NumPy will automatically broadcast the values in the open mesh when they are used as indices:
In [49]: (np.indices((5,5)) == np.broadcast_arrays(*np.ogrid[:5, :5])).all()
Out[49]: True
import numpy as np
h, w, d = 20, 20, 10
foo = np.random.rand(h, w, d)
amin = np.argmin(foo, axis=2)
X, Y = np.ogrid[:h, :w]
amins = np.stack([np.clip(amin+i, 0, d-1) for i in [-1, 0, 1]])
fmins = foo[X, Y, amins]
It's better to store fmin, pre and post in one array, fmins,
since some NumPy/Scipy operations (like argmin or griddata) may need the values in one array. If, later, you need to operate on the 3 components individually, you can always access them using fmins[i] or define
pre, fmin, post = fmins
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
I like your answer, if nobody come up with anything better i will mark as answer
– Luca
Nov 28 '18 at 13:59
1
Shouldn't it benp.clip(amin+i, ..., so you getamin - 1, amin, amin + 1?
– jdehesa
Nov 28 '18 at 14:16
@jdehesa well, it depends on the definiton of pre and post, the way you say is the one i was searching for
– Luca
Nov 28 '18 at 14:19
@jdehesa: Thanks for the correction.
– unutbu
Nov 28 '18 at 14:24
add a comment |
You could use np.ogrid instead of np.indices to save memory.
np.ogrid returns an "open" meshgrid:
In [24]: np.ogrid[:5,:5]
Out[24]:
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
ogrid returns component arrays which can be used as indices
in the same way as one would use np.indices.
NumPy will automatically broadcast the values in the open mesh when they are used as indices:
In [49]: (np.indices((5,5)) == np.broadcast_arrays(*np.ogrid[:5, :5])).all()
Out[49]: True
import numpy as np
h, w, d = 20, 20, 10
foo = np.random.rand(h, w, d)
amin = np.argmin(foo, axis=2)
X, Y = np.ogrid[:h, :w]
amins = np.stack([np.clip(amin+i, 0, d-1) for i in [-1, 0, 1]])
fmins = foo[X, Y, amins]
It's better to store fmin, pre and post in one array, fmins,
since some NumPy/Scipy operations (like argmin or griddata) may need the values in one array. If, later, you need to operate on the 3 components individually, you can always access them using fmins[i] or define
pre, fmin, post = fmins
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
I like your answer, if nobody come up with anything better i will mark as answer
– Luca
Nov 28 '18 at 13:59
1
Shouldn't it benp.clip(amin+i, ..., so you getamin - 1, amin, amin + 1?
– jdehesa
Nov 28 '18 at 14:16
@jdehesa well, it depends on the definiton of pre and post, the way you say is the one i was searching for
– Luca
Nov 28 '18 at 14:19
@jdehesa: Thanks for the correction.
– unutbu
Nov 28 '18 at 14:24
add a comment |
You could use np.ogrid instead of np.indices to save memory.
np.ogrid returns an "open" meshgrid:
In [24]: np.ogrid[:5,:5]
Out[24]:
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
ogrid returns component arrays which can be used as indices
in the same way as one would use np.indices.
NumPy will automatically broadcast the values in the open mesh when they are used as indices:
In [49]: (np.indices((5,5)) == np.broadcast_arrays(*np.ogrid[:5, :5])).all()
Out[49]: True
import numpy as np
h, w, d = 20, 20, 10
foo = np.random.rand(h, w, d)
amin = np.argmin(foo, axis=2)
X, Y = np.ogrid[:h, :w]
amins = np.stack([np.clip(amin+i, 0, d-1) for i in [-1, 0, 1]])
fmins = foo[X, Y, amins]
It's better to store fmin, pre and post in one array, fmins,
since some NumPy/Scipy operations (like argmin or griddata) may need the values in one array. If, later, you need to operate on the 3 components individually, you can always access them using fmins[i] or define
pre, fmin, post = fmins
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
You could use np.ogrid instead of np.indices to save memory.
np.ogrid returns an "open" meshgrid:
In [24]: np.ogrid[:5,:5]
Out[24]:
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
ogrid returns component arrays which can be used as indices
in the same way as one would use np.indices.
NumPy will automatically broadcast the values in the open mesh when they are used as indices:
In [49]: (np.indices((5,5)) == np.broadcast_arrays(*np.ogrid[:5, :5])).all()
Out[49]: True
import numpy as np
h, w, d = 20, 20, 10
foo = np.random.rand(h, w, d)
amin = np.argmin(foo, axis=2)
X, Y = np.ogrid[:h, :w]
amins = np.stack([np.clip(amin+i, 0, d-1) for i in [-1, 0, 1]])
fmins = foo[X, Y, amins]
It's better to store fmin, pre and post in one array, fmins,
since some NumPy/Scipy operations (like argmin or griddata) may need the values in one array. If, later, you need to operate on the 3 components individually, you can always access them using fmins[i] or define
pre, fmin, post = fmins
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
edited Nov 28 '18 at 14:24
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
answered Nov 28 '18 at 13:55
unutbuunutbu
559k10512041257
559k10512041257
I like your answer, if nobody come up with anything better i will mark as answer
– Luca
Nov 28 '18 at 13:59
1
Shouldn't it benp.clip(amin+i, ..., so you getamin - 1, amin, amin + 1?
– jdehesa
Nov 28 '18 at 14:16
@jdehesa well, it depends on the definiton of pre and post, the way you say is the one i was searching for
– Luca
Nov 28 '18 at 14:19
@jdehesa: Thanks for the correction.
– unutbu
Nov 28 '18 at 14:24
add a comment |
I like your answer, if nobody come up with anything better i will mark as answer
– Luca
Nov 28 '18 at 13:59
1
Shouldn't it benp.clip(amin+i, ..., so you getamin - 1, amin, amin + 1?
– jdehesa
Nov 28 '18 at 14:16
@jdehesa well, it depends on the definiton of pre and post, the way you say is the one i was searching for
– Luca
Nov 28 '18 at 14:19
@jdehesa: Thanks for the correction.
– unutbu
Nov 28 '18 at 14:24
I like your answer, if nobody come up with anything better i will mark as answer
– Luca
Nov 28 '18 at 13:59
I like your answer, if nobody come up with anything better i will mark as answer
– Luca
Nov 28 '18 at 13:59
1
1
Shouldn't it be
np.clip(amin+i, ..., so you get amin - 1, amin, amin + 1?– jdehesa
Nov 28 '18 at 14:16
Shouldn't it be
np.clip(amin+i, ..., so you get amin - 1, amin, amin + 1?– jdehesa
Nov 28 '18 at 14:16
@jdehesa well, it depends on the definiton of pre and post, the way you say is the one i was searching for
– Luca
Nov 28 '18 at 14:19
@jdehesa well, it depends on the definiton of pre and post, the way you say is the one i was searching for
– Luca
Nov 28 '18 at 14:19
@jdehesa: Thanks for the correction.
– unutbu
Nov 28 '18 at 14:24
@jdehesa: Thanks for the correction.
– unutbu
Nov 28 '18 at 14:24
add a comment |
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