Python numpy.core.numeric.shape() Examples
The following are 30
code examples of numpy.core.numeric.shape().
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Example #1
| Source File: shape_base.py From pySINDy with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #2
| Source File: shape_base.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #3
| Source File: shape_base.py From lambda-packs with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #4
| Source File: shape_base.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #5
| Source File: shape_base.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if len(_nx.shape(sub_arys[i])) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #6
| Source File: shape_base.py From keras-lambda with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if len(_nx.shape(sub_arys[i])) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #7
| Source File: shape_base.py From Carnets with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #8
| Source File: shape_base.py From vnpy_crypto with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #9
| Source File: shape_base.py From coffeegrindsize with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #10
| Source File: shape_base.py From Computable with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if len(_nx.shape(sub_arys[i])) == 0:
sub_arys[i] = _nx.array([])
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.array([])
return sub_arys
Example #11
| Source File: shape_base.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #12
| Source File: shape_base.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #13
| Source File: shape_base.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #14
| Source File: shape_base.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #15
| Source File: shape_base.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #16
| Source File: shape_base.py From ImageFusion with MIT License | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if len(_nx.shape(sub_arys[i])) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #17
| Source File: shape_base.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if len(_nx.shape(sub_arys[i])) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #18
| Source File: shape_base.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #19
| Source File: shape_base.py From recruit with Apache License 2.0 | 5 votes |
|
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys
Example #20
| Source File: shape_base.py From ImageFusion with MIT License | 4 votes |
|
def expand_dims(a, axis):
"""
Expand the shape of an array.
Insert a new axis, corresponding to a given position in the array shape.
Parameters
----------
a : array_like
Input array.
axis : int
Position (amongst axes) where new axis is to be inserted.
Returns
-------
res : ndarray
Output array. The number of dimensions is one greater than that of
the input array.
See Also
--------
doc.indexing, atleast_1d, atleast_2d, atleast_3d
Examples
--------
>>> x = np.array([1,2])
>>> x.shape
(2,)
The following is equivalent to ``x[np.newaxis,:]`` or ``x[np.newaxis]``:
>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)
>>> y = np.expand_dims(x, axis=1) # Equivalent to x[:,newaxis]
>>> y
array([[1],
[2]])
>>> y.shape
(2, 1)
Note that some examples may use ``None`` instead of ``np.newaxis``. These
are the same objects:
>>> np.newaxis is None
True
"""
a = asarray(a)
shape = a.shape
if axis < 0:
axis = axis + len(shape) + 1
return a.reshape(shape[:axis] + (1,) + shape[axis:])
Example #21
| Source File: shape_base.py From keras-lambda with MIT License | 4 votes |
|
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
Example #22
| Source File: shape_base.py From recruit with Apache License 2.0 | 4 votes |
|
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D arrays
of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
`(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
`dsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of arrays
The arrays must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 3-D.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
_warn_for_nonsequence(tup)
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
Example #23
| Source File: shape_base.py From mxnet-lambda with Apache License 2.0 | 4 votes |
|
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
This function continues to be supported for backward compatibility, but
you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack``
function was added in NumPy 1.10.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)`` if `tup` contains arrays that
are at least 3-dimensional.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
Example #24
| Source File: shape_base.py From keras-lambda with MIT License | 4 votes |
|
def dsplit(ary, indices_or_sections):
"""
Split array into multiple sub-arrays along the 3rd axis (depth).
Please refer to the `split` documentation. `dsplit` is equivalent
to `split` with ``axis=2``, the array is always split along the third
axis provided the array dimension is greater than or equal to 3.
See Also
--------
split : Split an array into multiple sub-arrays of equal size.
Examples
--------
>>> x = np.arange(16.0).reshape(2, 2, 4)
>>> x
array([[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.]],
[[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]]])
>>> np.dsplit(x, 2)
[array([[[ 0., 1.],
[ 4., 5.]],
[[ 8., 9.],
[ 12., 13.]]]),
array([[[ 2., 3.],
[ 6., 7.]],
[[ 10., 11.],
[ 14., 15.]]])]
>>> np.dsplit(x, np.array([3, 6]))
[array([[[ 0., 1., 2.],
[ 4., 5., 6.]],
[[ 8., 9., 10.],
[ 12., 13., 14.]]]),
array([[[ 3.],
[ 7.]],
[[ 11.],
[ 15.]]]),
array([], dtype=float64)]
"""
if len(_nx.shape(ary)) < 3:
raise ValueError('dsplit only works on arrays of 3 or more dimensions')
return split(ary, indices_or_sections, 2)
Example #25
| Source File: shape_base.py From mxnet-lambda with Apache License 2.0 | 4 votes |
|
def array_split(ary, indices_or_sections, axis=0):
"""
Split an array into multiple sub-arrays.
Please refer to the ``split`` documentation. The only difference
between these functions is that ``array_split`` allows
`indices_or_sections` to be an integer that does *not* equally
divide the axis.
See Also
--------
split : Split array into multiple sub-arrays of equal size.
Examples
--------
>>> x = np.arange(8.0)
>>> np.array_split(x, 3)
[array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])]
"""
try:
Ntotal = ary.shape[axis]
except AttributeError:
Ntotal = len(ary)
try:
# handle scalar case.
Nsections = len(indices_or_sections) + 1
div_points = [0] + list(indices_or_sections) + [Ntotal]
except TypeError:
# indices_or_sections is a scalar, not an array.
Nsections = int(indices_or_sections)
if Nsections <= 0:
raise ValueError('number sections must be larger than 0.')
Neach_section, extras = divmod(Ntotal, Nsections)
section_sizes = ([0] +
extras * [Neach_section+1] +
(Nsections-extras) * [Neach_section])
div_points = _nx.array(section_sizes).cumsum()
sub_arys = []
sary = _nx.swapaxes(ary, axis, 0)
for i in range(Nsections):
st = div_points[i]
end = div_points[i + 1]
sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))
return sub_arys
Example #26
| Source File: shape_base.py From ImageFusion with MIT License | 4 votes |
|
def array_split(ary, indices_or_sections, axis=0):
"""
Split an array into multiple sub-arrays.
Please refer to the ``split`` documentation. The only difference
between these functions is that ``array_split`` allows
`indices_or_sections` to be an integer that does *not* equally
divide the axis.
See Also
--------
split : Split array into multiple sub-arrays of equal size.
Examples
--------
>>> x = np.arange(8.0)
>>> np.array_split(x, 3)
[array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])]
"""
try:
Ntotal = ary.shape[axis]
except AttributeError:
Ntotal = len(ary)
try:
# handle scalar case.
Nsections = len(indices_or_sections) + 1
div_points = [0] + list(indices_or_sections) + [Ntotal]
except TypeError:
# indices_or_sections is a scalar, not an array.
Nsections = int(indices_or_sections)
if Nsections <= 0:
raise ValueError('number sections must be larger than 0.')
Neach_section, extras = divmod(Ntotal, Nsections)
section_sizes = ([0] +
extras * [Neach_section+1] +
(Nsections-extras) * [Neach_section])
div_points = _nx.array(section_sizes).cumsum()
sub_arys = []
sary = _nx.swapaxes(ary, axis, 0)
for i in range(Nsections):
st = div_points[i]
end = div_points[i + 1]
sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))
# This "kludge" was introduced here to replace arrays shaped (0, 10)
# or similar with an array shaped (0,).
# There seems no need for this, so give a FutureWarning to remove later.
if sub_arys[-1].size == 0 and sub_arys[-1].ndim != 1:
warnings.warn("in the future np.array_split will retain the shape of "
"arrays with a zero size, instead of replacing them by "
"`array([])`, which always has a shape of (0,).",
FutureWarning)
sub_arys = _replace_zero_by_x_arrays(sub_arys)
return sub_arys
Example #27
| Source File: shape_base.py From keras-lambda with MIT License | 4 votes |
|
def array_split(ary, indices_or_sections, axis=0):
"""
Split an array into multiple sub-arrays.
Please refer to the ``split`` documentation. The only difference
between these functions is that ``array_split`` allows
`indices_or_sections` to be an integer that does *not* equally
divide the axis.
See Also
--------
split : Split array into multiple sub-arrays of equal size.
Examples
--------
>>> x = np.arange(8.0)
>>> np.array_split(x, 3)
[array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])]
"""
try:
Ntotal = ary.shape[axis]
except AttributeError:
Ntotal = len(ary)
try:
# handle scalar case.
Nsections = len(indices_or_sections) + 1
div_points = [0] + list(indices_or_sections) + [Ntotal]
except TypeError:
# indices_or_sections is a scalar, not an array.
Nsections = int(indices_or_sections)
if Nsections <= 0:
raise ValueError('number sections must be larger than 0.')
Neach_section, extras = divmod(Ntotal, Nsections)
section_sizes = ([0] +
extras * [Neach_section+1] +
(Nsections-extras) * [Neach_section])
div_points = _nx.array(section_sizes).cumsum()
sub_arys = []
sary = _nx.swapaxes(ary, axis, 0)
for i in range(Nsections):
st = div_points[i]
end = div_points[i + 1]
sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))
return sub_arys
Example #28
| Source File: shape_base.py From ImageFusion with MIT License | 4 votes |
|
def hsplit(ary, indices_or_sections):
"""
Split an array into multiple sub-arrays horizontally (column-wise).
Please refer to the `split` documentation. `hsplit` is equivalent
to `split` with ``axis=1``, the array is always split along the second
axis regardless of the array dimension.
See Also
--------
split : Split an array into multiple sub-arrays of equal size.
Examples
--------
>>> x = np.arange(16.0).reshape(4, 4)
>>> x
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]])
>>> np.hsplit(x, 2)
[array([[ 0., 1.],
[ 4., 5.],
[ 8., 9.],
[ 12., 13.]]),
array([[ 2., 3.],
[ 6., 7.],
[ 10., 11.],
[ 14., 15.]])]
>>> np.hsplit(x, np.array([3, 6]))
[array([[ 0., 1., 2.],
[ 4., 5., 6.],
[ 8., 9., 10.],
[ 12., 13., 14.]]),
array([[ 3.],
[ 7.],
[ 11.],
[ 15.]]),
array([], dtype=float64)]
With a higher dimensional array the split is still along the second axis.
>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
array([[[ 0., 1.],
[ 2., 3.]],
[[ 4., 5.],
[ 6., 7.]]])
>>> np.hsplit(x, 2)
[array([[[ 0., 1.]],
[[ 4., 5.]]]),
array([[[ 2., 3.]],
[[ 6., 7.]]])]
"""
if len(_nx.shape(ary)) == 0:
raise ValueError('hsplit only works on arrays of 1 or more dimensions')
if len(ary.shape) > 1:
return split(ary, indices_or_sections, 1)
else:
return split(ary, indices_or_sections, 0)
Example #29
| Source File: shape_base.py From ImageFusion with MIT License | 4 votes |
|
def dsplit(ary, indices_or_sections):
"""
Split array into multiple sub-arrays along the 3rd axis (depth).
Please refer to the `split` documentation. `dsplit` is equivalent
to `split` with ``axis=2``, the array is always split along the third
axis provided the array dimension is greater than or equal to 3.
See Also
--------
split : Split an array into multiple sub-arrays of equal size.
Examples
--------
>>> x = np.arange(16.0).reshape(2, 2, 4)
>>> x
array([[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.]],
[[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]]])
>>> np.dsplit(x, 2)
[array([[[ 0., 1.],
[ 4., 5.]],
[[ 8., 9.],
[ 12., 13.]]]),
array([[[ 2., 3.],
[ 6., 7.]],
[[ 10., 11.],
[ 14., 15.]]])]
>>> np.dsplit(x, np.array([3, 6]))
[array([[[ 0., 1., 2.],
[ 4., 5., 6.]],
[[ 8., 9., 10.],
[ 12., 13., 14.]]]),
array([[[ 3.],
[ 7.]],
[[ 11.],
[ 15.]]]),
array([], dtype=float64)]
"""
if len(_nx.shape(ary)) < 3:
raise ValueError('dsplit only works on arrays of 3 or more dimensions')
return split(ary, indices_or_sections, 2)
Example #30
| Source File: shape_base.py From ImageFusion with MIT License | 4 votes |
|
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join arrays.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
