def call(self, space, args_w):
w_obj = args_w[0]
out = None
if len(args_w) > 1:
out = args_w[1]
if space.is_w(out, space.w_None):
out = None
w_obj = convert_to_array(space, w_obj)
dtype = w_obj.get_dtype()
if dtype.is_flexible_type():
raise OperationError(space.w_TypeError,
space.wrap('Not implemented for this type'))
if (self.int_only and not dtype.is_int_type()
or not self.allow_bool and dtype.is_bool_type()
or not self.allow_complex and dtype.is_complex_type()):
raise OperationError(
space.w_TypeError,
space.wrap("ufunc %s not supported for the input type" %
self.name))
calc_dtype = find_unaryop_result_dtype(
space,
w_obj.get_dtype(),
promote_to_float=self.promote_to_float,
promote_bools=self.promote_bools)
if out is not None:
if not isinstance(out, W_NDimArray):
raise OperationError(space.w_TypeError,
space.wrap('output must be an array'))
res_dtype = out.get_dtype()
#if not w_obj.get_dtype().can_cast_to(res_dtype):
# raise operationerrfmt(space.w_TypeError,
# "Cannot cast ufunc %s output from dtype('%s') to dtype('%s') with casting rule 'same_kind'", self.name, w_obj.get_dtype().name, res_dtype.name)
elif self.bool_result:
res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype
else:
res_dtype = calc_dtype
if self.complex_to_float and calc_dtype.is_complex_type():
if calc_dtype.name == 'complex64':
res_dtype = interp_dtype.get_dtype_cache(
space).w_float32dtype
else:
res_dtype = interp_dtype.get_dtype_cache(
space).w_float64dtype
if w_obj.is_scalar():
w_val = self.func(calc_dtype,
w_obj.get_scalar_value().convert_to(calc_dtype))
if out is None:
return w_val
if out.is_scalar():
out.set_scalar_value(w_val)
else:
out.fill(res_dtype.coerce(space, w_val))
return out
shape = shape_agreement(space,
w_obj.get_shape(),
out,
broadcast_down=False)
return loop.call1(space, shape, self.func, calc_dtype, res_dtype,
w_obj, out)
def setslice(self, space, arr):
impl = arr.implementation
if impl.is_scalar():
self.fill(impl.get_scalar_value())
return
shape = shape_agreement(space, self.get_shape(), arr)
if impl.storage == self.storage:
impl = impl.copy(space)
loop.setslice(space, shape, self, impl)
def setslice(self, space, arr):
impl = arr.implementation
if impl.is_scalar():
self.fill(impl.get_scalar_value())
return
shape = shape_agreement(space, self.get_shape(), arr)
if impl.storage == self.storage:
impl = impl.copy(space)
loop.setslice(space, shape, self, impl)
def setslice(self, space, arr):
if len(arr.get_shape()) > 0 and len(self.get_shape()) == 0:
raise oefmt(space.w_ValueError,
"could not broadcast input array from shape "
"(%s) into shape ()",
','.join([str(x) for x in arr.get_shape()]))
shape = shape_agreement(space, self.get_shape(), arr)
impl = arr.implementation
if impl.storage == self.storage:
impl = impl.copy(space)
loop.setslice(space, shape, self, impl)
def setslice(self, space, arr):
if len(arr.get_shape()) > 0 and len(self.get_shape()) == 0:
raise oefmt(
space.w_ValueError,
"could not broadcast input array from shape "
"(%s) into shape ()",
','.join([str(x) for x in arr.get_shape()]))
shape = shape_agreement(space, self.get_shape(), arr)
impl = arr.implementation
if impl.storage == self.storage:
impl = impl.copy(space)
loop.setslice(space, shape, self, impl)
def call(self, space, args_w):
w_obj = args_w[0]
out = None
if len(args_w) > 1:
out = args_w[1]
if space.is_w(out, space.w_None):
out = None
w_obj = convert_to_array(space, w_obj)
dtype = w_obj.get_dtype()
if dtype.is_flexible_type():
raise OperationError(space.w_TypeError,
space.wrap('Not implemented for this type'))
if (self.int_only and not dtype.is_int_type() or
not self.allow_bool and dtype.is_bool_type() or
not self.allow_complex and dtype.is_complex_type()):
raise OperationError(space.w_TypeError, space.wrap(
"ufunc %s not supported for the input type" % self.name))
calc_dtype = find_unaryop_result_dtype(space,
w_obj.get_dtype(),
promote_to_float=self.promote_to_float,
promote_bools=self.promote_bools)
if out is not None:
if not isinstance(out, W_NDimArray):
raise OperationError(space.w_TypeError, space.wrap(
'output must be an array'))
res_dtype = out.get_dtype()
#if not w_obj.get_dtype().can_cast_to(res_dtype):
# raise operationerrfmt(space.w_TypeError,
# "Cannot cast ufunc %s output from dtype('%s') to dtype('%s') with casting rule 'same_kind'", self.name, w_obj.get_dtype().name, res_dtype.name)
elif self.bool_result:
res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype
else:
res_dtype = calc_dtype
if self.complex_to_float and calc_dtype.is_complex_type():
if calc_dtype.name == 'complex64':
res_dtype = interp_dtype.get_dtype_cache(space).w_float32dtype
else:
res_dtype = interp_dtype.get_dtype_cache(space).w_float64dtype
if w_obj.is_scalar():
w_val = self.func(calc_dtype,
w_obj.get_scalar_value().convert_to(calc_dtype))
if out is None:
return w_val
if out.is_scalar():
out.set_scalar_value(w_val)
else:
out.fill(res_dtype.coerce(space, w_val))
return out
shape = shape_agreement(space, w_obj.get_shape(), out,
broadcast_down=False)
return loop.call1(space, shape, self.func, calc_dtype, res_dtype,
w_obj, out)
def setslice(self, space, arr):
if len(arr.get_shape()) > len(self.get_shape()):
# record arrays get one extra dimension
if not self.dtype.is_record() or \
len(arr.get_shape()) > len(self.get_shape()) + 1:
raise oefmt(space.w_ValueError,
"could not broadcast input array from shape "
"(%s) into shape (%s)",
','.join([str(x) for x in arr.get_shape()]),
','.join([str(x) for x in self.get_shape()]),
)
shape = shape_agreement(space, self.get_shape(), arr)
impl = arr.implementation
if impl.storage == self.storage:
impl = impl.copy(space)
loop.setslice(space, shape, self, impl)
def setslice(self, space, arr):
if len(arr.get_shape()) > len(self.get_shape()):
# record arrays get one extra dimension
if not self.dtype.is_record() or \
len(arr.get_shape()) > len(self.get_shape()) + 1:
raise oefmt(
space.w_ValueError,
"could not broadcast input array from shape "
"(%s) into shape (%s)",
','.join([str(x) for x in arr.get_shape()]),
','.join([str(x) for x in self.get_shape()]),
)
shape = shape_agreement(space, self.get_shape(), arr)
impl = arr.implementation
if impl.storage == self.storage:
impl = impl.copy(space)
loop.setslice(space, shape, self, impl)
def where(space, w_arr, w_x=None, w_y=None):
"""where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x` and `y` need to have the same
shape as `condition`.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
NOTE: support for not passing x and y is unsupported
"""
if space.is_none(w_y):
if space.is_none(w_x):
raise OperationError(
space.w_NotImplementedError,
space.wrap("1-arg where unsupported right now"))
raise OperationError(
space.w_ValueError,
space.wrap("Where should be called with either 1 or 3 arguments"))
if space.is_none(w_x):
raise OperationError(
space.w_ValueError,
space.wrap("Where should be called with either 1 or 3 arguments"))
arr = convert_to_array(space, w_arr)
x = convert_to_array(space, w_x)
y = convert_to_array(space, w_y)
if x.is_scalar() and y.is_scalar() and arr.is_scalar():
if arr.get_dtype().itemtype.bool(arr.get_scalar_value()):
return x
return y
dtype = ufuncs.find_binop_result_dtype(space, x.get_dtype(), y.get_dtype())
shape = shape_agreement(space, arr.get_shape(), x)
shape = shape_agreement(space, shape, y)
out = W_NDimArray.from_shape(space, shape, dtype)
return loop.where(space, out, shape, arr, x, y, dtype)
def where(space, w_arr, w_x=None, w_y=None):
"""where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x` and `y` need to have the same
shape as `condition`.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
NOTE: support for not passing x and y is unsupported
"""
if space.is_none(w_y):
if space.is_none(w_x):
raise OperationError(space.w_NotImplementedError, space.wrap("1-arg where unsupported right now"))
raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments"))
if space.is_none(w_x):
raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments"))
arr = convert_to_array(space, w_arr)
x = convert_to_array(space, w_x)
y = convert_to_array(space, w_y)
if x.is_scalar() and y.is_scalar() and arr.is_scalar():
if arr.get_dtype().itemtype.bool(arr.get_scalar_value()):
return x
return y
dtype = interp_ufuncs.find_binop_result_dtype(space, x.get_dtype(), y.get_dtype())
shape = shape_agreement(space, arr.get_shape(), x)
shape = shape_agreement(space, shape, y)
out = W_NDimArray.from_shape(space, shape, dtype)
return loop.where(out, shape, arr, x, y, dtype)
def call(self, space, args_w):
if len(args_w) > 2:
[w_lhs, w_rhs, w_out] = args_w
else:
[w_lhs, w_rhs] = args_w
w_out = None
w_lhs = convert_to_array(space, w_lhs)
w_rhs = convert_to_array(space, w_rhs)
w_ldtype = w_lhs.get_dtype()
w_rdtype = w_rhs.get_dtype()
if w_ldtype.is_str_type() and w_rdtype.is_str_type() and \
self.comparison_func:
pass
elif (w_ldtype.is_str_type() or w_rdtype.is_str_type()) and \
self.comparison_func and w_out is None:
return space.wrap(False)
elif (w_ldtype.is_flexible_type() or \
w_rdtype.is_flexible_type()):
raise OperationError(space.w_TypeError, space.wrap(
'unsupported operand dtypes %s and %s for "%s"' % \
(w_rdtype.get_name(), w_ldtype.get_name(),
self.name)))
if self.are_common_types(w_ldtype, w_rdtype):
if not w_lhs.is_scalar() and w_rhs.is_scalar():
w_rdtype = w_ldtype
elif w_lhs.is_scalar() and not w_rhs.is_scalar():
w_ldtype = w_rdtype
if (self.int_only and (not w_ldtype.is_int_type() or not w_rdtype.is_int_type()) or
not self.allow_bool and (w_ldtype.is_bool_type() or w_rdtype.is_bool_type()) or
not self.allow_complex and (w_ldtype.is_complex_type() or w_rdtype.is_complex_type())):
raise OperationError(space.w_TypeError, space.wrap("Unsupported types"))
calc_dtype = find_binop_result_dtype(space,
w_ldtype, w_rdtype,
promote_to_float=self.promote_to_float,
promote_bools=self.promote_bools)
if space.is_none(w_out):
out = None
elif not isinstance(w_out, W_NDimArray):
raise OperationError(space.w_TypeError, space.wrap(
'output must be an array'))
else:
out = w_out
calc_dtype = out.get_dtype()
if self.comparison_func:
res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype
else:
res_dtype = calc_dtype
if w_lhs.is_scalar() and w_rhs.is_scalar():
arr = self.func(calc_dtype,
w_lhs.get_scalar_value().convert_to(calc_dtype),
w_rhs.get_scalar_value().convert_to(calc_dtype)
)
if isinstance(out, W_NDimArray):
if out.is_scalar():
out.set_scalar_value(arr)
else:
out.fill(arr)
else:
out = arr
return out
new_shape = shape_agreement(space, w_lhs.get_shape(), w_rhs)
new_shape = shape_agreement(space, new_shape, out, broadcast_down=False)
return loop.call2(space, new_shape, self.func, calc_dtype,
res_dtype, w_lhs, w_rhs, out)
def call(self, space, args_w):
if len(args_w) > 2:
[w_lhs, w_rhs, w_out] = args_w
else:
[w_lhs, w_rhs] = args_w
w_out = None
w_lhs = convert_to_array(space, w_lhs)
w_rhs = convert_to_array(space, w_rhs)
w_ldtype = w_lhs.get_dtype()
w_rdtype = w_rhs.get_dtype()
if w_ldtype.is_str_type() and w_rdtype.is_str_type() and \
self.comparison_func:
pass
elif (w_ldtype.is_str_type() or w_rdtype.is_str_type()) and \
self.comparison_func and w_out is None:
return space.wrap(False)
elif (w_ldtype.is_flexible_type() or \
w_rdtype.is_flexible_type()):
raise OperationError(space.w_TypeError, space.wrap(
'unsupported operand dtypes %s and %s for "%s"' % \
(w_rdtype.get_name(), w_ldtype.get_name(),
self.name)))
if self.are_common_types(w_ldtype, w_rdtype):
if not w_lhs.is_scalar() and w_rhs.is_scalar():
w_rdtype = w_ldtype
elif w_lhs.is_scalar() and not w_rhs.is_scalar():
w_ldtype = w_rdtype
if (self.int_only and
(not w_ldtype.is_int_type() or not w_rdtype.is_int_type())
or not self.allow_bool and
(w_ldtype.is_bool_type() or w_rdtype.is_bool_type())
or not self.allow_complex and
(w_ldtype.is_complex_type() or w_rdtype.is_complex_type())):
raise OperationError(space.w_TypeError,
space.wrap("Unsupported types"))
calc_dtype = find_binop_result_dtype(
space,
w_ldtype,
w_rdtype,
promote_to_float=self.promote_to_float,
promote_bools=self.promote_bools)
if space.is_none(w_out):
out = None
elif not isinstance(w_out, W_NDimArray):
raise OperationError(space.w_TypeError,
space.wrap('output must be an array'))
else:
out = w_out
calc_dtype = out.get_dtype()
if self.comparison_func:
res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype
else:
res_dtype = calc_dtype
if w_lhs.is_scalar() and w_rhs.is_scalar():
arr = self.func(calc_dtype,
w_lhs.get_scalar_value().convert_to(calc_dtype),
w_rhs.get_scalar_value().convert_to(calc_dtype))
if isinstance(out, W_NDimArray):
if out.is_scalar():
out.set_scalar_value(arr)
else:
out.fill(arr)
else:
out = arr
return out
new_shape = shape_agreement(space, w_lhs.get_shape(), w_rhs)
new_shape = shape_agreement(space,
new_shape,
out,
broadcast_down=False)
return loop.call2(space, new_shape, self.func, calc_dtype, res_dtype,
w_lhs, w_rhs, out)
def call(self, space, args_w):
if len(args_w) > 2:
[w_lhs, w_rhs, w_out] = args_w
else:
[w_lhs, w_rhs] = args_w
w_out = None
w_lhs = convert_to_array(space, w_lhs)
w_rhs = convert_to_array(space, w_rhs)
w_ldtype = w_lhs.get_dtype()
w_rdtype = w_rhs.get_dtype()
if w_ldtype.is_str() and w_rdtype.is_str() and \
self.comparison_func:
pass
elif (w_ldtype.is_str() or w_rdtype.is_str()) and \
self.comparison_func and w_out is None:
return space.wrap(False)
elif w_ldtype.is_flexible() or w_rdtype.is_flexible():
if self.comparison_func:
if self.name == 'equal' or self.name == 'not_equal':
res = w_ldtype.eq(space, w_rdtype)
if not res:
return space.wrap(self.name == 'not_equal')
else:
return space.w_NotImplemented
else:
raise oefmt(space.w_TypeError,
'unsupported operand dtypes %s and %s for "%s"',
w_rdtype.get_name(), w_ldtype.get_name(),
self.name)
if self.are_common_types(w_ldtype, w_rdtype):
if not w_lhs.is_scalar() and w_rhs.is_scalar():
w_rdtype = w_ldtype
elif w_lhs.is_scalar() and not w_rhs.is_scalar():
w_ldtype = w_rdtype
calc_dtype = find_binop_result_dtype(space,
w_ldtype, w_rdtype,
promote_to_float=self.promote_to_float,
promote_bools=self.promote_bools)
if (self.int_only and (not w_ldtype.is_int() or
not w_rdtype.is_int() or
not calc_dtype.is_int()) or
not self.allow_bool and (w_ldtype.is_bool() or
w_rdtype.is_bool()) or
not self.allow_complex and (w_ldtype.is_complex() or
w_rdtype.is_complex())):
raise oefmt(space.w_TypeError,
"ufunc '%s' not supported for the input types", self.name)
if space.is_none(w_out):
out = None
elif not isinstance(w_out, W_NDimArray):
raise oefmt(space.w_TypeError, 'output must be an array')
else:
out = w_out
calc_dtype = out.get_dtype()
if self.comparison_func:
res_dtype = descriptor.get_dtype_cache(space).w_booldtype
else:
res_dtype = calc_dtype
if w_lhs.is_scalar() and w_rhs.is_scalar():
arr = self.func(calc_dtype,
w_lhs.get_scalar_value().convert_to(space, calc_dtype),
w_rhs.get_scalar_value().convert_to(space, calc_dtype)
)
if isinstance(out, W_NDimArray):
if out.is_scalar():
out.set_scalar_value(arr)
else:
out.fill(space, arr)
else:
out = arr
return out
new_shape = shape_agreement(space, w_lhs.get_shape(), w_rhs)
new_shape = shape_agreement(space, new_shape, out, broadcast_down=False)
return loop.call2(space, new_shape, self.func, calc_dtype,
res_dtype, w_lhs, w_rhs, out)
def __init__(self,
space,
w_seq,
w_flags,
w_op_flags,
w_op_dtypes,
w_casting,
w_op_axes,
w_itershape,
buffersize=0,
order=NPY.KEEPORDER,
allow_backward=True):
self.external_loop = False
self.buffered = False
self.tracked_index = ''
self.common_dtype = False
self.delay_bufalloc = False
self.grow_inner = False
self.ranged = False
self.refs_ok = False
self.reduce_ok = False
self.zerosize_ok = False
self.index_iter = None
self.done = False
self.first_next = True
self.op_axes = []
self.allow_backward = allow_backward
if not space.is_w(w_casting, space.w_None):
self.casting = space.str_w(w_casting)
else:
self.casting = 'safe'
# convert w_seq operands to a list of W_NDimArray
if space.isinstance_w(w_seq, space.w_tuple) or \
space.isinstance_w(w_seq, space.w_list):
w_seq_as_list = space.listview(w_seq)
self.seq = [
convert_to_array(space, w_elem)
if not space.is_none(w_elem) else None
for w_elem in w_seq_as_list
]
else:
self.seq = [convert_to_array(space, w_seq)]
if order == NPY.ANYORDER:
# 'A' means "'F' order if all the arrays are Fortran contiguous,
# 'C' order otherwise"
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_flags() & NPY.ARRAY_F_CONTIGUOUS):
break
else:
order = NPY.FORTRANORDER
elif order == NPY.KEEPORDER:
# 'K' means "as close to the order the array elements appear in
# memory as possible", so match self.order to seq.order
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_order() == NPY.FORTRANORDER):
break
else:
order = NPY.FORTRANORDER
self.order = order
parse_func_flags(space, self, w_flags)
self.op_flags = parse_op_arg(space, 'op_flags', w_op_flags,
len(self.seq), parse_op_flag)
# handle w_op_axes
oa_ndim = -1
if not space.is_none(w_op_axes):
oa_ndim = self.set_op_axes(space, w_op_axes)
self.ndim = calculate_ndim(self.seq, oa_ndim)
# handle w_op_dtypes part 1: creating self.dtypes list from input
if not space.is_none(w_op_dtypes):
w_seq_as_list = space.listview(w_op_dtypes)
self.dtypes = [
decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list
]
if len(self.dtypes) != len(self.seq):
raise oefmt(
space.w_ValueError,
"op_dtypes must be a tuple/list matching the number of ops"
)
else:
self.dtypes = []
# handle None or writable operands, calculate my shape
outargs = [
i for i in range(len(self.seq))
if self.seq[i] is None or self.op_flags[i].rw == 'w'
]
if len(outargs) > 0:
out_shape = shape_agreement_multiple(
space, [self.seq[i] for i in outargs])
else:
out_shape = None
if space.isinstance_w(w_itershape, space.w_tuple) or \
space.isinstance_w(w_itershape, space.w_list):
self.shape = [space.int_w(i) for i in space.listview(w_itershape)]
else:
self.shape = shape_agreement_multiple(space,
self.seq,
shape=out_shape)
if len(outargs) > 0:
# Make None operands writeonly and flagged for allocation
if len(self.dtypes) > 0:
out_dtype = self.dtypes[outargs[0]]
else:
out_dtype = None
for i in range(len(self.seq)):
if self.seq[i] is None:
self.op_flags[i].allocate = True
continue
if self.op_flags[i].rw == 'w':
continue
out_dtype = find_binop_result_dtype(
space, self.seq[i].get_dtype(), out_dtype)
for i in outargs:
if self.seq[i] is None:
# XXX can we postpone allocation to later?
self.seq[i] = W_NDimArray.from_shape(
space, self.shape, out_dtype)
else:
if not self.op_flags[i].broadcast:
# Raises if output cannot be broadcast
try:
shape_agreement(space, self.shape, self.seq[i],
False)
except OperationError as e:
raise oefmt(
space.w_ValueError, "non-broadcastable"
" output operand with shape %s doesn't match "
"the broadcast shape %s",
str(self.seq[i].get_shape()), str(self.shape))
if self.tracked_index != "":
order = self.order
if order == NPY.KEEPORDER:
order = self.seq[0].implementation.order
if self.tracked_index == "multi":
backward = False
else:
backward = ((order == NPY.CORDER and self.tracked_index != 'C')
or (order == NPY.FORTRANORDER
and self.tracked_index != 'F'))
self.index_iter = IndexIterator(self.shape, backward=backward)
# handle w_op_dtypes part 2: copy where needed if possible
if len(self.dtypes) > 0:
for i in range(len(self.seq)):
self_d = self.dtypes[i]
seq_d = self.seq[i].get_dtype()
if not self_d:
self.dtypes[i] = seq_d
elif self_d != seq_d:
impl = self.seq[i].implementation
if self.buffered or 'r' in self.op_flags[i].tmp_copy:
if not can_cast_array(space, self.seq[i], self_d,
self.casting):
raise oefmt(
space.w_TypeError, "Iterator operand %d"
" dtype could not be cast from %s to %s"
" according to the rule '%s'", i,
space.str_w(seq_d.descr_repr(space)),
space.str_w(self_d.descr_repr(space)),
self.casting)
order = support.get_order_as_CF(impl.order, self.order)
new_impl = impl.astype(space, self_d,
order).copy(space)
self.seq[i] = W_NDimArray(new_impl)
else:
raise oefmt(
space.w_TypeError, "Iterator "
"operand required copying or buffering, "
"but neither copying nor buffering was "
"enabled")
if 'w' in self.op_flags[i].rw:
if not can_cast_type(space, self_d, seq_d,
self.casting):
raise oefmt(
space.w_TypeError, "Iterator"
" requested dtype could not be cast from "
" %s to %s, the operand %d dtype, accord"
"ing to the rule '%s'",
space.str_w(self_d.descr_repr(space)),
space.str_w(seq_d.descr_repr(space)), i,
self.casting)
elif self.buffered and not (self.external_loop and len(self.seq) < 2):
for i in range(len(self.seq)):
if i not in outargs:
self.seq[i] = self.seq[i].descr_copy(space,
w_order=space.wrap(
self.order))
self.dtypes = [s.get_dtype() for s in self.seq]
else:
#copy them from seq
self.dtypes = [s.get_dtype() for s in self.seq]
# create an iterator for each operand
self.iters = []
for i in range(len(self.seq)):
it = self.get_iter(space, i)
it.contiguous = False
self.iters.append((it, it.reset()))
if self.external_loop:
coalesce_axes(self, space)
def __init__(self, space, w_seq, w_flags, w_op_flags, w_op_dtypes,
w_casting, w_op_axes, w_itershape, buffersize=0, order='K'):
from pypy.module.micronumpy.ufuncs import find_binop_result_dtype
self.order = order
self.external_loop = False
self.buffered = False
self.tracked_index = ''
self.common_dtype = False
self.delay_bufalloc = False
self.grow_inner = False
self.ranged = False
self.refs_ok = False
self.reduce_ok = False
self.zerosize_ok = False
self.index_iter = None
self.done = False
self.first_next = True
self.op_axes = []
# convert w_seq operands to a list of W_NDimArray
if space.isinstance_w(w_seq, space.w_tuple) or \
space.isinstance_w(w_seq, space.w_list):
w_seq_as_list = space.listview(w_seq)
self.seq = [convert_to_array(space, w_elem)
if not space.is_none(w_elem) else None
for w_elem in w_seq_as_list]
else:
self.seq = [convert_to_array(space, w_seq)]
parse_func_flags(space, self, w_flags)
self.op_flags = parse_op_arg(space, 'op_flags', w_op_flags,
len(self.seq), parse_op_flag)
# handle w_op_axes
oa_ndim = -1
if not space.is_none(w_op_axes):
oa_ndim = self.set_op_axes(space, w_op_axes)
self.ndim = calculate_ndim(self.seq, oa_ndim)
# handle w_op_dtypes part 1: creating self.dtypes list from input
if not space.is_none(w_op_dtypes):
w_seq_as_list = space.listview(w_op_dtypes)
self.dtypes = [decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list]
if len(self.dtypes) != len(self.seq):
raise oefmt(space.w_ValueError,
"op_dtypes must be a tuple/list matching the number of ops")
else:
self.dtypes = []
# handle None or writable operands, calculate my shape
outargs = [i for i in range(len(self.seq))
if self.seq[i] is None or self.op_flags[i].rw == 'w']
if len(outargs) > 0:
out_shape = shape_agreement_multiple(space, [self.seq[i] for i in outargs])
else:
out_shape = None
if space.isinstance_w(w_itershape, space.w_tuple) or \
space.isinstance_w(w_itershape, space.w_list):
self.shape = [space.int_w(i) for i in space.listview(w_itershape)]
else:
self.shape = shape_agreement_multiple(space, self.seq,
shape=out_shape)
if len(outargs) > 0:
# Make None operands writeonly and flagged for allocation
if len(self.dtypes) > 0:
out_dtype = self.dtypes[outargs[0]]
else:
out_dtype = None
for i in range(len(self.seq)):
if self.seq[i] is None:
self.op_flags[i].allocate = True
continue
if self.op_flags[i].rw == 'w':
continue
out_dtype = find_binop_result_dtype(
space, self.seq[i].get_dtype(), out_dtype)
for i in outargs:
if self.seq[i] is None:
# XXX can we postpone allocation to later?
self.seq[i] = W_NDimArray.from_shape(space, self.shape, out_dtype)
else:
if not self.op_flags[i].broadcast:
# Raises if ooutput cannot be broadcast
shape_agreement(space, self.shape, self.seq[i], False)
if self.tracked_index != "":
if self.order == "K":
self.order = self.seq[0].implementation.order
if self.tracked_index == "multi":
backward = False
else:
backward = self.order != self.tracked_index
self.index_iter = IndexIterator(self.shape, backward=backward)
# handle w_op_dtypes part 2: copy where needed if possible
if len(self.dtypes) > 0:
for i in range(len(self.seq)):
selfd = self.dtypes[i]
seq_d = self.seq[i].get_dtype()
if not selfd:
self.dtypes[i] = seq_d
elif selfd != seq_d:
if not 'r' in self.op_flags[i].tmp_copy:
raise oefmt(space.w_TypeError,
"Iterator operand required copying or "
"buffering for operand %d", i)
impl = self.seq[i].implementation
new_impl = impl.astype(space, selfd)
self.seq[i] = W_NDimArray(new_impl)
else:
#copy them from seq
self.dtypes = [s.get_dtype() for s in self.seq]
# create an iterator for each operand
self.iters = []
for i in range(len(self.seq)):
it = get_iter(space, self.order, self.seq[i], self.shape,
self.dtypes[i], self.op_flags[i], self)
it.contiguous = False
self.iters.append((it, it.reset()))
if self.external_loop:
coalesce_axes(self, space)
def __init__(self,
space,
w_seq,
w_flags,
w_op_flags,
w_op_dtypes,
w_casting,
w_op_axes,
w_itershape,
buffersize=0,
order='K'):
self.order = order
self.external_loop = False
self.buffered = False
self.tracked_index = ''
self.common_dtype = False
self.delay_bufalloc = False
self.grow_inner = False
self.ranged = False
self.refs_ok = False
self.reduce_ok = False
self.zerosize_ok = False
self.index_iter = None
self.done = False
self.first_next = True
self.op_axes = []
# convert w_seq operands to a list of W_NDimArray
if space.isinstance_w(w_seq, space.w_tuple) or \
space.isinstance_w(w_seq, space.w_list):
w_seq_as_list = space.listview(w_seq)
self.seq = [
convert_to_array(space, w_elem)
if not space.is_none(w_elem) else None
for w_elem in w_seq_as_list
]
else:
self.seq = [convert_to_array(space, w_seq)]
parse_func_flags(space, self, w_flags)
self.op_flags = parse_op_arg(space, 'op_flags', w_op_flags,
len(self.seq), parse_op_flag)
# handle w_op_axes
oa_ndim = -1
if not space.is_none(w_op_axes):
oa_ndim = self.set_op_axes(space, w_op_axes)
self.ndim = calculate_ndim(self.seq, oa_ndim)
# handle w_op_dtypes part 1: creating self.dtypes list from input
if not space.is_none(w_op_dtypes):
w_seq_as_list = space.listview(w_op_dtypes)
self.dtypes = [
decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list
]
if len(self.dtypes) != len(self.seq):
raise oefmt(
space.w_ValueError,
"op_dtypes must be a tuple/list matching the number of ops"
)
else:
self.dtypes = []
# handle None or writable operands, calculate my shape
outargs = [
i for i in range(len(self.seq))
if self.seq[i] is None or self.op_flags[i].rw == 'w'
]
if len(outargs) > 0:
out_shape = shape_agreement_multiple(
space, [self.seq[i] for i in outargs])
else:
out_shape = None
if space.isinstance_w(w_itershape, space.w_tuple) or \
space.isinstance_w(w_itershape, space.w_list):
self.shape = [space.int_w(i) for i in space.listview(w_itershape)]
else:
self.shape = shape_agreement_multiple(space,
self.seq,
shape=out_shape)
if len(outargs) > 0:
# Make None operands writeonly and flagged for allocation
if len(self.dtypes) > 0:
out_dtype = self.dtypes[outargs[0]]
else:
out_dtype = None
for i in range(len(self.seq)):
if self.seq[i] is None:
self.op_flags[i].allocate = True
continue
if self.op_flags[i].rw == 'w':
continue
out_dtype = find_binop_result_dtype(
space, self.seq[i].get_dtype(), out_dtype)
for i in outargs:
if self.seq[i] is None:
# XXX can we postpone allocation to later?
self.seq[i] = W_NDimArray.from_shape(
space, self.shape, out_dtype)
else:
if not self.op_flags[i].broadcast:
# Raises if ooutput cannot be broadcast
shape_agreement(space, self.shape, self.seq[i], False)
if self.tracked_index != "":
if self.order == "K":
self.order = self.seq[0].implementation.order
if self.tracked_index == "multi":
backward = False
else:
backward = self.order != self.tracked_index
self.index_iter = IndexIterator(self.shape, backward=backward)
# handle w_op_dtypes part 2: copy where needed if possible
if len(self.dtypes) > 0:
for i in range(len(self.seq)):
selfd = self.dtypes[i]
seq_d = self.seq[i].get_dtype()
if not selfd:
self.dtypes[i] = seq_d
elif selfd != seq_d:
if not 'r' in self.op_flags[i].tmp_copy:
raise oefmt(
space.w_TypeError,
"Iterator operand required copying or "
"buffering for operand %d", i)
impl = self.seq[i].implementation
new_impl = impl.astype(space, selfd)
self.seq[i] = W_NDimArray(new_impl)
else:
#copy them from seq
self.dtypes = [s.get_dtype() for s in self.seq]
# create an iterator for each operand
self.iters = []
for i in range(len(self.seq)):
it = get_iter(space, self.order, self.seq[i], self.shape,
self.dtypes[i], self.op_flags[i], self)
it.contiguous = False
self.iters.append((it, it.reset()))
if self.external_loop:
coalesce_axes(self, space)
def __init__(
self,
space,
w_seq,
w_flags,
w_op_flags,
w_op_dtypes,
w_casting,
w_op_axes,
w_itershape,
buffersize=0,
order=NPY.KEEPORDER,
allow_backward=True,
):
self.external_loop = False
self.buffered = False
self.tracked_index = ""
self.common_dtype = False
self.delay_bufalloc = False
self.grow_inner = False
self.ranged = False
self.refs_ok = False
self.reduce_ok = False
self.zerosize_ok = False
self.index_iter = None
self.done = False
self.first_next = True
self.op_axes = []
self.allow_backward = allow_backward
if not space.is_w(w_casting, space.w_None):
self.casting = space.str_w(w_casting)
else:
self.casting = "safe"
# convert w_seq operands to a list of W_NDimArray
if space.isinstance_w(w_seq, space.w_tuple) or space.isinstance_w(w_seq, space.w_list):
w_seq_as_list = space.listview(w_seq)
self.seq = [
convert_to_array(space, w_elem) if not space.is_none(w_elem) else None for w_elem in w_seq_as_list
]
else:
self.seq = [convert_to_array(space, w_seq)]
if order == NPY.ANYORDER:
# 'A' means "'F' order if all the arrays are Fortran contiguous,
# 'C' order otherwise"
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_flags() & NPY.ARRAY_F_CONTIGUOUS):
break
else:
order = NPY.FORTRANORDER
elif order == NPY.KEEPORDER:
# 'K' means "as close to the order the array elements appear in
# memory as possible", so match self.order to seq.order
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_order() == NPY.FORTRANORDER):
break
else:
order = NPY.FORTRANORDER
self.order = order
parse_func_flags(space, self, w_flags)
self.op_flags = parse_op_arg(space, "op_flags", w_op_flags, len(self.seq), parse_op_flag)
# handle w_op_axes
oa_ndim = -1
if not space.is_none(w_op_axes):
oa_ndim = self.set_op_axes(space, w_op_axes)
self.ndim = calculate_ndim(self.seq, oa_ndim)
# handle w_op_dtypes part 1: creating self.dtypes list from input
if not space.is_none(w_op_dtypes):
w_seq_as_list = space.listview(w_op_dtypes)
self.dtypes = [decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list]
if len(self.dtypes) != len(self.seq):
raise oefmt(space.w_ValueError, "op_dtypes must be a tuple/list matching the number of ops")
else:
self.dtypes = []
# handle None or writable operands, calculate my shape
outargs = [i for i in range(len(self.seq)) if self.seq[i] is None or self.op_flags[i].rw == "w"]
if len(outargs) > 0:
out_shape = shape_agreement_multiple(space, [self.seq[i] for i in outargs])
else:
out_shape = None
if space.isinstance_w(w_itershape, space.w_tuple) or space.isinstance_w(w_itershape, space.w_list):
self.shape = [space.int_w(i) for i in space.listview(w_itershape)]
else:
self.shape = shape_agreement_multiple(space, self.seq, shape=out_shape)
if len(outargs) > 0:
# Make None operands writeonly and flagged for allocation
if len(self.dtypes) > 0:
out_dtype = self.dtypes[outargs[0]]
else:
out_dtype = None
for i in range(len(self.seq)):
if self.seq[i] is None:
self.op_flags[i].allocate = True
continue
if self.op_flags[i].rw == "w":
continue
out_dtype = find_binop_result_dtype(space, self.seq[i].get_dtype(), out_dtype)
for i in outargs:
if self.seq[i] is None:
# XXX can we postpone allocation to later?
self.seq[i] = W_NDimArray.from_shape(space, self.shape, out_dtype)
else:
if not self.op_flags[i].broadcast:
# Raises if output cannot be broadcast
try:
shape_agreement(space, self.shape, self.seq[i], False)
except OperationError as e:
raise oefmt(
space.w_ValueError,
"non-broadcastable"
" output operand with shape %s doesn't match "
"the broadcast shape %s",
str(self.seq[i].get_shape()),
str(self.shape),
)
if self.tracked_index != "":
order = self.order
if order == NPY.KEEPORDER:
order = self.seq[0].implementation.order
if self.tracked_index == "multi":
backward = False
else:
backward = (order == NPY.CORDER and self.tracked_index != "C") or (
order == NPY.FORTRANORDER and self.tracked_index != "F"
)
self.index_iter = IndexIterator(self.shape, backward=backward)
# handle w_op_dtypes part 2: copy where needed if possible
if len(self.dtypes) > 0:
for i in range(len(self.seq)):
self_d = self.dtypes[i]
seq_d = self.seq[i].get_dtype()
if not self_d:
self.dtypes[i] = seq_d
elif self_d != seq_d:
impl = self.seq[i].implementation
if self.buffered or "r" in self.op_flags[i].tmp_copy:
if not can_cast_array(space, self.seq[i], self_d, self.casting):
raise oefmt(
space.w_TypeError,
"Iterator operand %d"
" dtype could not be cast from %s to %s"
" according to the rule '%s'",
i,
space.str_w(seq_d.descr_repr(space)),
space.str_w(self_d.descr_repr(space)),
self.casting,
)
order = support.get_order_as_CF(impl.order, self.order)
new_impl = impl.astype(space, self_d, order).copy(space)
self.seq[i] = W_NDimArray(new_impl)
else:
raise oefmt(
space.w_TypeError,
"Iterator "
"operand required copying or buffering, "
"but neither copying nor buffering was "
"enabled",
)
if "w" in self.op_flags[i].rw:
if not can_cast_type(space, self_d, seq_d, self.casting):
raise oefmt(
space.w_TypeError,
"Iterator"
" requested dtype could not be cast from "
" %s to %s, the operand %d dtype, accord"
"ing to the rule '%s'",
space.str_w(self_d.descr_repr(space)),
space.str_w(seq_d.descr_repr(space)),
i,
self.casting,
)
elif self.buffered and not (self.external_loop and len(self.seq) < 2):
for i in range(len(self.seq)):
if i not in outargs:
self.seq[i] = self.seq[i].descr_copy(space, w_order=space.wrap(self.order))
self.dtypes = [s.get_dtype() for s in self.seq]
else:
# copy them from seq
self.dtypes = [s.get_dtype() for s in self.seq]
# create an iterator for each operand
self.iters = []
for i in range(len(self.seq)):
it = self.get_iter(space, i)
it.contiguous = False
self.iters.append((it, it.reset()))
if self.external_loop:
coalesce_axes(self, space)
