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 (any([
sub_arys[i].size == 0 and sub_arys[i].ndim != 1
for i in range(len(sub_arys))
])):
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
def array_split(ary, indices_or_sections, axis=0):
""" Divide an array into a list of sub-arrays.
Description:
Divide ary into a list of sub-arrays along the
specified axis. If indices_or_sections is an integer,
ary is divided into that many equally sized arrays.
If it is impossible to make an equal split, each of the
leading arrays in the list have one additional member. If
indices_or_sections is a list of sorted integers, its
entries define the indexes where ary is split.
Arguments:
ary -- N-D array.
Array to be divided into sub-arrays.
indices_or_sections -- integer or 1D array.
If integer, defines the number of (close to) equal sized
sub-arrays. If it is a 1D array of sorted indices, it
defines the indexes at which ary is divided. Any empty
list results in a single sub-array equal to the original
array.
axis -- integer. default=0.
Specifies the axis along which to split ary.
Caveats:
Currently, the default for axis is 0. This
means a 2D array is divided into multiple groups
of rows. This seems like the appropriate default,
"""
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))
# there is a wierd issue with array slicing that allows
# 0x10 arrays and other such things. The following cluge is needed
# to get around this issue.
sub_arys = _replace_zero_by_x_arrays(sub_arys)
# end cluge.
return sub_arys
def array_split(ary,indices_or_sections,axis = 0):
""" Divide an array into a list of sub-arrays.
Description:
Divide ary into a list of sub-arrays along the
specified axis. If indices_or_sections is an integer,
ary is divided into that many equally sized arrays.
If it is impossible to make an equal split, each of the
leading arrays in the list have one additional member. If
indices_or_sections is a list of sorted integers, its
entries define the indexes where ary is split.
Arguments:
ary -- N-D array.
Array to be divided into sub-arrays.
indices_or_sections -- integer or 1D array.
If integer, defines the number of (close to) equal sized
sub-arrays. If it is a 1D array of sorted indices, it
defines the indexes at which ary is divided. Any empty
list results in a single sub-array equal to the original
array.
axis -- integer. default=0.
Specifies the axis along which to split ary.
Caveats:
Currently, the default for axis is 0. This
means a 2D array is divided into multiple groups
of rows. This seems like the appropriate default,
"""
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))
# there is a wierd issue with array slicing that allows
# 0x10 arrays and other such things. The following cluge is needed
# to get around this issue.
sub_arys = _replace_zero_by_x_arrays(sub_arys)
# end cluge.
return sub_arys
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 (any([sub_arys[i].size == 0 and
sub_arys[i].ndim != 1 for i in range(len(sub_arys))])):
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
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. For an array of length l that should be split
into n sections, it returns l % n sub-arrays of size l//n + 1
and the rest of size l//n.
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.])]
>>> x = np.arange(9)
>>> np.array_split(x, 4)
[array([0, 1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]
"""
try:
Ntotal = ary.shape[axis]
except AttributeError:
Ntotal = len(ary)
try:
# handle array 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, dtype=_nx.intp).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
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. For an array of length l that should be split
into n sections, it returns l % n sub-arrays of size l//n + 1
and the rest of size l//n.
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.])]
>>> x = np.arange(7.0)
>>> np.array_split(x, 3)
[array([0., 1., 2.]), array([3., 4.]), array([5., 6.])]
"""
try:
Ntotal = ary.shape[axis]
except AttributeError:
Ntotal = len(ary)
try:
# handle array 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, dtype=_nx.intp).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
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))
# there is a weird issue with array slicing that allows
# 0x10 arrays and other such things. The following kludge is needed
# to get around this issue.
sub_arys = _replace_zero_by_x_arrays(sub_arys)
# end kludge.
return sub_arys
def array_split(ary, chunks=None, 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
`chunks` 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(chunks) + 1
div_points = [0] + list(chunks) + [Ntotal]
except TypeError:
# chunks is not iterable
try:
Nsections = int(chunks)
except TypeError:
# chunks cannot be converted to int
Nsections = Ntotal
finally:
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
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))
# there is a weird issue with array slicing that allows
# 0x10 arrays and other such things. The following kludge is needed
# to get around this issue.
sub_arys = _replace_zero_by_x_arrays(sub_arys)
# end kludge.
return sub_arys
def _array_split(idx, batch_size, axis=0):
N = len(idx) + 1
div_points = [0]
for i in range(1, N):
if divmod(i, batch_size)[1] == 0:
div_points.append(i)
sub_arys = []
sary = _nx.swapaxes(idx, axis, 0)
for i in range(len(div_points) - 1):
st = div_points[i]
end = div_points[i + 1]
sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))
sub_arys.append(_nx.swapaxes(sary[-batch_size:], axis, 0))
return sub_arys
def array_split(ary, indices_or_sections, axis=0, two_dimensional=False):
"""
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. For an array of length l that should be split
into n sections, it returns l % n sub-arrays of size l//n + 1
and the rest of size l//n. In the case where two_dimensional is set
to True, this holds for both elements of `indices_or_sections`.
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.])]
>>> x = np.arange(7.0)
>>> np.array_split(x, 3)
[array([0., 1., 2.]), array([3., 4.]), array([5., 6.])]
>>> x = np.reshape(np.arange(16), (4, 4))
>>> np.array_split(x, [3, 3], 0, True)
[array([[0, 1]),
array([4, 5]]),
array([[2], [6]]),
array([[3], [7]]),
array([[8, 9]]),
array([[10]]),
array([[11]]),
array([[12, 13]]),
array([[14]]),
array([[15]])]
"""
if two_dimensional:
try:
indices_or_sections[1]
except (IndexError, TypeError):
raise ValueError('indices_or_sections must be an array of length 2.')
subarrays = array_split(ary, indices_or_sections[0], axis=0, two_dimensional=False)
res = []
for subarray in subarrays:
res.extend(array_split(subarray, indices_or_sections[1], axis=1, two_dimensional=False))
return res
try:
Ntotal = ary.shape[axis]
except AttributeError:
Ntotal = len(ary)
try:
# handle array 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, dtype=_nx.intp).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
