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
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
def forward_fill(marr, maxgap=None):
"""forward_fill(marr, maxgap=None)
Forward fills masked values in a 1-d array when there are less maxgap
consecutive masked values. If maxgap is None, then forward fill all
masked values."""
# Initialization ..................
if numeric.ndim(marr) > 1:
raise ValueError,"The input array should be 1D only!"
marr = marray(marr, copy=True)
if getmask(marr) is nomask or marr.size == 0:
return marr
#
currGap = 0
if maxgap is not None:
for i in range(1, marr.size):
if marr._mask[i]:
currGap += 1
if currGap <= maxgap and not marr._mask[i-1]:
marr._data[i] = marr._data[i-1]
marr._mask[i] = False
elif currGap == maxgap + 1:
marr._mask[i-maxgap:i] = True
else:
currGap = 0
else:
for i in range(1, marr.size):
# CHECK: We should probable be able to speed things up here
if marr._mask[i] and not marr._mask[i-1]:
marr._data[i] = marr._data[i-1]
marr._mask[i] = False
return marr
def vsplit(ary, indices_or_sections):
"""
Split an array into multiple sub-arrays vertically (row-wise).
Please refer to the ``split`` documentation. ``vsplit`` is equivalent
to ``split`` with `axis=0` (default), the array is always split along the
first 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.vsplit(x, 2)
[array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.]]),
array([[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]])]
>>> np.vsplit(x, np.array([3, 6]))
[array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]]),
array([[ 12., 13., 14., 15.]]),
array([], dtype=float64)]
With a higher dimensional array the split is still along the first axis.
>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
array([[[ 0., 1.],
[ 2., 3.]],
[[ 4., 5.],
[ 6., 7.]]])
>>> np.vsplit(x, 2)
[array([[[ 0., 1.],
[ 2., 3.]]]),
array([[[ 4., 5.],
[ 6., 7.]]])]
"""
if _nx.ndim(ary) < 2:
raise ValueError('vsplit only works on arrays of 2 or more dimensions')
return split(ary, indices_or_sections, 0)
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 _nx.ndim(ary) < 3:
raise ValueError('dsplit only works on arrays of 3 or more dimensions')
return split(ary, indices_or_sections, 2)
def _block_check_depths_match(arrays, parent_index=[]):
"""
Copied from NumPy 1.14.1.
"""
def format_index(index):
idx_str = ''.join('[{}]'.format(i) for i in index if i is not None)
return 'arrays' + idx_str
if type(arrays) is tuple:
# not strictly necessary, but saves us from:
# - more than one way to do things - no point treating tuples like
# lists
# - horribly confusing behaviour that results when tuples are
# treated like ndarray
raise TypeError(
'{} is a tuple. '
'Only lists can be used to arrange blocks, and np.block does '
'not allow implicit conversion from tuple to ndarray.'.format(
format_index(parent_index)
)
)
elif type(arrays) is list and len(arrays) > 0:
idxs_ndims = (_block_check_depths_match(arr, parent_index + [i])
for i, arr in enumerate(arrays))
first_index, max_arr_ndim = next(idxs_ndims)
for index, ndim in idxs_ndims:
if ndim > max_arr_ndim:
max_arr_ndim = ndim
if len(index) != len(first_index):
raise ValueError(
"List depths are mismatched. First element was at depth "
"{}, but there is an element at depth {} ({})".format(
len(first_index),
len(index),
format_index(index)
)
)
return first_index, max_arr_ndim
elif type(arrays) is list and len(arrays) == 0:
# We've 'bottomed out' on an empty list
return parent_index + [None], 0
else:
# We've 'bottomed out' - arrays is either a scalar or an array
return parent_index, _nx.ndim(arrays)
def _block_check_depths_match(arrays, parent_index=[]):
"""
Copied from NumPy 1.14.1.
"""
def format_index(index):
idx_str = ''.join('[{}]'.format(i) for i in index if i is not None)
return 'arrays' + idx_str
if type(arrays) is tuple:
# not strictly necessary, but saves us from:
# - more than one way to do things - no point treating tuples like
# lists
# - horribly confusing behaviour that results when tuples are
# treated like ndarray
raise TypeError(
'{} is a tuple. '
'Only lists can be used to arrange blocks, and np.block does '
'not allow implicit conversion from tuple to ndarray.'.format(
format_index(parent_index)
)
)
elif type(arrays) is list and len(arrays) > 0:
idxs_ndims = (_block_check_depths_match(arr, parent_index + [i])
for i, arr in enumerate(arrays))
first_index, max_arr_ndim = next(idxs_ndims)
for index, ndim in idxs_ndims:
if ndim > max_arr_ndim:
max_arr_ndim = ndim
if len(index) != len(first_index):
raise ValueError(
"List depths are mismatched. First element was at depth "
"{}, but there is an element at depth {} ({})".format(
len(first_index),
len(index),
format_index(index)
)
)
return first_index, max_arr_ndim
elif type(arrays) is list and len(arrays) == 0:
# We've 'bottomed out' on an empty list
return parent_index + [None], 0
else:
# We've 'bottomed out' - arrays is either a scalar or an array
return parent_index, _nx.ndim(arrays)
def interp_masked1d(marr, kind='linear'):
"""interp_masked1d(marr, king='linear')
Interpolates masked values in marr according to method kind.
kind must be one of 'constant', 'linear', 'cubic', quintic'
"""
if numeric.ndim(marr) > 1:
raise ValueError("array must be 1 dimensional!")
#
marr = marray(marr, copy=True)
if getmask(marr) is nomask:
return marr
#
unmaskedIndices = (~marr._mask).nonzero()[0]
if unmaskedIndices.size < 2:
return marr
#
kind = kind.lower()
if kind == 'constant':
return forward_fill(marr)
try:
k = {'linear' : 1,
'cubic' : 3,
'quintic' : 5}[kind.lower()]
except KeyError:
raise ValueError("Unsupported interpolation type.")
first_unmasked, last_unmasked = flatnotmasked_edges(marr)
vals = marr.data[unmaskedIndices]
tck = fitpack.splrep(unmaskedIndices, vals, k=k)
maskedIndices = marr._mask.nonzero()[0]
interpIndices = maskedIndices[(maskedIndices > first_unmasked) & \
(maskedIndices < last_unmasked)]
marr[interpIndices] = fitpack.splev(interpIndices, tck).astype(marr.dtype)
return marr
def __getitem__(self, key):
# handle matrix builder syntax
if isinstance(key, str):
frame = sys._getframe().f_back
mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals)
return mymat
if not isinstance(key, tuple):
key = (key, )
# copy attributes, since they can be overridden in the first argument
trans1d = self.trans1d
ndmin = self.ndmin
matrix = self.matrix
axis = self.axis
objs = []
scalars = []
arraytypes = []
scalartypes = []
for k, item in enumerate(key):
scalar = False
if isinstance(item, slice):
step = item.step
start = item.start
stop = item.stop
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size = int(abs(step))
newobj = function_base.linspace(start, stop, num=size)
else:
newobj = _nx.arange(start, stop, step)
if ndmin > 1:
newobj = array(newobj, copy=False, ndmin=ndmin)
if trans1d != -1:
newobj = newobj.swapaxes(-1, trans1d)
elif isinstance(item, str):
if k != 0:
raise ValueError("special directives must be the "
"first entry.")
if item in ('r', 'c'):
matrix = True
col = (item == 'c')
continue
if ',' in item:
vec = item.split(',')
try:
axis, ndmin = [int(x) for x in vec[:2]]
if len(vec) == 3:
trans1d = int(vec[2])
continue
except Exception:
raise ValueError("unknown special directive")
try:
axis = int(item)
continue
except (ValueError, TypeError):
raise ValueError("unknown special directive")
elif type(item) in ScalarType:
newobj = array(item, ndmin=ndmin)
scalars.append(len(objs))
scalar = True
scalartypes.append(newobj.dtype)
else:
item_ndim = ndim(item)
newobj = array(item, copy=False, subok=True, ndmin=ndmin)
if trans1d != -1 and item_ndim < ndmin:
k2 = ndmin - item_ndim
k1 = trans1d
if k1 < 0:
k1 += k2 + 1
defaxes = list(range(ndmin))
axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2]
newobj = newobj.transpose(axes)
objs.append(newobj)
if not scalar and isinstance(newobj, _nx.ndarray):
arraytypes.append(newobj.dtype)
# Ensure that scalars won't up-cast unless warranted
final_dtype = find_common_type(arraytypes, scalartypes)
if final_dtype is not None:
for k in scalars:
objs[k] = objs[k].astype(final_dtype)
res = self.concatenate(tuple(objs), axis=axis)
if matrix:
oldndim = res.ndim
res = self.makemat(res)
if oldndim == 1 and col:
res = res.T
return res
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([], shape=(4, 0), 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 _nx.ndim(ary) == 0:
raise ValueError("hsplit only works on arrays of 1 or more dimensions")
if ary.ndim > 1:
return split(ary, indices_or_sections, 1)
else:
return split(ary, indices_or_sections, 0)
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 _nx.ndim(ary) == 0:
raise ValueError('hsplit only works on arrays of 1 or more dimensions')
if ary.ndim > 1:
return split(ary, indices_or_sections, 1)
else:
return split(ary, indices_or_sections, 0)
def __getitem__(self, key):
# handle matrix builder syntax
if isinstance(key, str):
frame = sys._getframe().f_back
mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals)
return mymat
if not isinstance(key, tuple):
key = (key,)
# copy attributes, since they can be overridden in the first argument
trans1d = self.trans1d
ndmin = self.ndmin
matrix = self.matrix
axis = self.axis
objs = []
scalars = []
arraytypes = []
scalartypes = []
for k, item in enumerate(key):
scalar = False
if isinstance(item, slice):
step = item.step
start = item.start
stop = item.stop
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size = int(abs(step))
newobj = function_base.linspace(start, stop, num=size)
else:
newobj = _nx.arange(start, stop, step)
if ndmin > 1:
newobj = array(newobj, copy=False, ndmin=ndmin)
if trans1d != -1:
newobj = newobj.swapaxes(-1, trans1d)
elif isinstance(item, str):
if k != 0:
raise ValueError("special directives must be the "
"first entry.")
if item in ('r', 'c'):
matrix = True
col = (item == 'c')
continue
if ',' in item:
vec = item.split(',')
try:
axis, ndmin = [int(x) for x in vec[:2]]
if len(vec) == 3:
trans1d = int(vec[2])
continue
except Exception:
raise ValueError("unknown special directive")
try:
axis = int(item)
continue
except (ValueError, TypeError):
raise ValueError("unknown special directive")
elif type(item) in ScalarType:
newobj = array(item, ndmin=ndmin)
scalars.append(len(objs))
scalar = True
scalartypes.append(newobj.dtype)
else:
item_ndim = ndim(item)
newobj = array(item, copy=False, subok=True, ndmin=ndmin)
if trans1d != -1 and item_ndim < ndmin:
k2 = ndmin - item_ndim
k1 = trans1d
if k1 < 0:
k1 += k2 + 1
defaxes = list(range(ndmin))
axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2]
newobj = newobj.transpose(axes)
objs.append(newobj)
if not scalar and isinstance(newobj, _nx.ndarray):
arraytypes.append(newobj.dtype)
# Ensure that scalars won't up-cast unless warranted
final_dtype = find_common_type(arraytypes, scalartypes)
if final_dtype is not None:
for k in scalars:
objs[k] = objs[k].astype(final_dtype)
res = self.concatenate(tuple(objs), axis=axis)
if matrix:
oldndim = res.ndim
res = self.makemat(res)
if oldndim == 1 and col:
res = res.T
return res
