def execute(self, interp):
w_lhs = self.lhs.execute(interp)
if isinstance(self.rhs, SliceConstant):
w_rhs = self.rhs.wrap(interp.space)
else:
w_rhs = self.rhs.execute(interp)
if not isinstance(w_lhs, W_NDimArray):
# scalar
dtype = get_dtype_cache(interp.space).w_float64dtype
w_lhs = W_NDimArray.new_scalar(interp.space, dtype, w_lhs)
assert isinstance(w_lhs, W_NDimArray)
if self.name == '+':
w_res = w_lhs.descr_add(interp.space, w_rhs)
elif self.name == '*':
w_res = w_lhs.descr_mul(interp.space, w_rhs)
elif self.name == '-':
w_res = w_lhs.descr_sub(interp.space, w_rhs)
elif self.name == '**':
w_res = w_lhs.descr_pow(interp.space, w_rhs)
elif self.name == '->':
if isinstance(w_rhs, FloatObject):
w_rhs = IntObject(int(w_rhs.floatval))
assert isinstance(w_lhs, W_NDimArray)
w_res = w_lhs.descr_getitem(interp.space, w_rhs)
else:
raise NotImplementedError
if (not isinstance(w_res, W_NDimArray) and
not isinstance(w_res, boxes.W_GenericBox)):
dtype = get_dtype_cache(interp.space).w_float64dtype
w_res = W_NDimArray.new_scalar(interp.space, dtype, w_res)
return w_res
def execute(self, interp):
w_lhs = self.lhs.execute(interp)
if isinstance(self.rhs, SliceConstant):
w_rhs = self.rhs.wrap(interp.space)
else:
w_rhs = self.rhs.execute(interp)
if not isinstance(w_lhs, W_NDimArray):
# scalar
dtype = get_dtype_cache(interp.space).w_float64dtype
w_lhs = W_NDimArray.new_scalar(interp.space, dtype, w_lhs)
assert isinstance(w_lhs, W_NDimArray)
if self.name == '+':
w_res = w_lhs.descr_add(interp.space, w_rhs)
elif self.name == '*':
w_res = w_lhs.descr_mul(interp.space, w_rhs)
elif self.name == '-':
w_res = w_lhs.descr_sub(interp.space, w_rhs)
elif self.name == '**':
w_res = w_lhs.descr_pow(interp.space, w_rhs)
elif self.name == '->':
if isinstance(w_rhs, FloatObject):
w_rhs = IntObject(int(w_rhs.floatval))
assert isinstance(w_lhs, W_NDimArray)
w_res = w_lhs.descr_getitem(interp.space, w_rhs)
else:
raise NotImplementedError
if (not isinstance(w_res, W_NDimArray) and
not isinstance(w_res, boxes.W_GenericBox)):
dtype = get_dtype_cache(interp.space).w_float64dtype
w_res = W_NDimArray.new_scalar(interp.space, dtype, w_res)
return w_res
def _PyArray_FromAny(space, w_obj, w_dtype, min_depth, max_depth, requirements,
context):
""" This is the main function used to obtain an array from any nested
sequence, or object that exposes the array interface, op. The
parameters allow specification of the required dtype, the
minimum (min_depth) and maximum (max_depth) number of dimensions
acceptable, and other requirements for the array.
The dtype argument needs to be a PyArray_Descr structure indicating
the desired data-type (including required byteorder). The dtype
argument may be NULL, indicating that any data-type (and byteorder)
is acceptable.
Unless FORCECAST is present in flags, this call will generate an error
if the data type cannot be safely obtained from the object. If you
want to use NULL for the dtype and ensure the array is notswapped then
use PyArray_CheckFromAny.
A value of 0 for either of the depth parameters causes the parameter
to be ignored.
Any of the following array flags can be added (e.g. using |) to get
the requirements argument. If your code can handle general (e.g.
strided, byte-swapped, or unaligned arrays) then requirements
may be 0. Also, if op is not already an array (or does not expose
the array interface), then a new array will be created (and filled
from op using the sequence protocol). The new array will have
NPY_DEFAULT as its flags member.
The context argument is passed to the __array__ method of op and is
only used if the array is constructed that way. Almost always this
parameter is NULL.
"""
if requirements not in (0, NPY_DEFAULT):
raise OperationError(
space.w_NotImplementedError,
space.wrap(
'_PyArray_FromAny called with not-implemented requirements argument'
))
w_array = array(space, w_obj, w_dtype=w_dtype, copy=False)
if min_depth != 0 and len(w_array.get_shape()) < min_depth:
raise OperationError(
space.w_ValueError,
space.wrap('object of too small depth for desired array'))
elif max_depth != 0 and len(w_array.get_shape()) > max_depth:
raise OperationError(
space.w_ValueError,
space.wrap('object of too deep for desired array'))
elif w_array.is_scalar():
# since PyArray_DATA() fails on scalars, create a 1D array and set empty
# shape. So the following combination works for *reading* scalars:
# PyObject *arr = PyArray_FromAny(obj);
# int nd = PyArray_NDIM(arr);
# void *data = PyArray_DATA(arr);
impl = w_array.implementation
w_array = W_NDimArray.from_shape(space, [1], impl.dtype)
w_array.implementation.setitem(0, impl.getitem(impl.start + 0))
w_array.implementation.shape = []
return w_array
def simple_new(space,
nd,
dims,
typenum,
order=CORDER,
owning=False,
w_subtype=None):
shape, dtype = get_shape_and_dtype(space, nd, dims, typenum)
return W_NDimArray.from_shape(space, shape, dtype)
def _PyArray_FromAny(space, w_obj, w_dtype, min_depth, max_depth, requirements, context):
""" This is the main function used to obtain an array from any nested
sequence, or object that exposes the array interface, op. The
parameters allow specification of the required dtype, the
minimum (min_depth) and maximum (max_depth) number of dimensions
acceptable, and other requirements for the array.
The dtype argument needs to be a PyArray_Descr structure indicating
the desired data-type (including required byteorder). The dtype
argument may be NULL, indicating that any data-type (and byteorder)
is acceptable.
Unless FORCECAST is present in flags, this call will generate an error
if the data type cannot be safely obtained from the object. If you
want to use NULL for the dtype and ensure the array is notswapped then
use PyArray_CheckFromAny.
A value of 0 for either of the depth parameters causes the parameter
to be ignored.
Any of the following array flags can be added (e.g. using |) to get
the requirements argument. If your code can handle general (e.g.
strided, byte-swapped, or unaligned arrays) then requirements
may be 0. Also, if op is not already an array (or does not expose
the array interface), then a new array will be created (and filled
from op using the sequence protocol). The new array will have
NPY_DEFAULT as its flags member.
The context argument is passed to the __array__ method of op and is
only used if the array is constructed that way. Almost always this
parameter is NULL.
"""
if requirements not in (0, NPY_DEFAULT):
raise OperationError(space.w_NotImplementedError, space.wrap(
'_PyArray_FromAny called with not-implemented requirements argument'))
w_array = array(space, w_obj, w_dtype=w_dtype, copy=False)
if min_depth !=0 and len(w_array.get_shape()) < min_depth:
raise OperationError(space.w_ValueError, space.wrap(
'object of too small depth for desired array'))
elif max_depth !=0 and len(w_array.get_shape()) > max_depth:
raise OperationError(space.w_ValueError, space.wrap(
'object of too deep for desired array'))
elif w_array.is_scalar():
# since PyArray_DATA() fails on scalars, create a 1D array and set empty
# shape. So the following combination works for *reading* scalars:
# PyObject *arr = PyArray_FromAny(obj);
# int nd = PyArray_NDIM(arr);
# void *data = PyArray_DATA(arr);
impl = w_array.implementation
w_array = W_NDimArray.from_shape(space, [1], impl.dtype)
w_array.implementation.setitem(0, impl.getitem(impl.start + 0))
w_array.implementation.shape = []
return w_array
def simple_new_from_data(space,
nd,
dims,
typenum,
data,
order=CORDER,
owning=False,
w_subtype=None):
shape, dtype = get_shape_and_dtype(space, nd, dims, typenum)
storage = rffi.cast(RAW_STORAGE_PTR, data)
return W_NDimArray.from_shape_and_storage(space,
shape,
storage,
dtype,
order=order,
owning=owning,
w_subtype=w_subtype)
def scalar(space):
dtype = get_dtype_cache(space).w_float64dtype
return W_NDimArray.new_scalar(space, dtype, space.wrap(10.))
def iarray(space, shape, order=NPY.CORDER):
dtype = get_dtype_cache(space).w_int64dtype
return W_NDimArray.from_shape(space, shape, dtype, order=order)
def simple_new_from_data(space, nd, dims, typenum, data,
order='C', owning=False, w_subtype=None):
shape, dtype = get_shape_and_dtype(space, nd, dims, typenum)
storage = rffi.cast(RAW_STORAGE_PTR, data)
return W_NDimArray.from_shape_and_storage(space, shape, storage, dtype,
order=order, owning=owning, w_subtype=w_subtype)
def simple_new(space, nd, dims, typenum,
order='C', owning=False, w_subtype=None):
shape, dtype = get_shape_and_dtype(space, nd, dims, typenum)
return W_NDimArray.from_shape(space, shape, dtype)
def execute(self, interp):
arr = self.args[0].execute(interp)
if not isinstance(arr, W_NDimArray):
raise ArgumentNotAnArray
if self.name in SINGLE_ARG_FUNCTIONS:
if len(self.args) != 1 and self.name != 'sum':
raise ArgumentMismatch
if self.name == "sum":
if len(self.args) > 1:
var = self.args[1]
if isinstance(var, DtypeClass):
w_res = arr.descr_sum(interp.space, None,
var.execute(interp))
else:
w_res = arr.descr_sum(interp.space,
self.args[1].execute(interp))
else:
w_res = arr.descr_sum(interp.space)
elif self.name == "prod":
w_res = arr.descr_prod(interp.space)
elif self.name == "max":
w_res = arr.descr_max(interp.space)
elif self.name == "min":
w_res = arr.descr_min(interp.space)
elif self.name == "any":
w_res = arr.descr_any(interp.space)
elif self.name == "all":
w_res = arr.descr_all(interp.space)
elif self.name == "cumsum":
w_res = arr.descr_cumsum(interp.space)
elif self.name == "logical_xor_reduce":
logical_xor = ufuncs.get(interp.space).logical_xor
w_res = logical_xor.reduce(interp.space, arr, None)
elif self.name == "unegative":
neg = ufuncs.get(interp.space).negative
w_res = neg.call(interp.space, [arr], None, 'unsafe', None)
elif self.name == "cos":
cos = ufuncs.get(interp.space).cos
w_res = cos.call(interp.space, [arr], None, 'unsafe', None)
elif self.name == "flat":
w_res = arr.descr_get_flatiter(interp.space)
elif self.name == "argsort":
w_res = arr.descr_argsort(interp.space)
elif self.name == "tostring":
arr.descr_tostring(interp.space)
w_res = None
else:
assert False # unreachable code
elif self.name in TWO_ARG_FUNCTIONS:
if len(self.args) != 2:
raise ArgumentMismatch
arg = self.args[1].execute(interp)
if not isinstance(arg, W_NDimArray):
raise ArgumentNotAnArray
if self.name == "dot":
w_res = arr.descr_dot(interp.space, arg)
elif self.name == 'multiply':
w_res = arr.descr_mul(interp.space, arg)
elif self.name == 'take':
w_res = arr.descr_take(interp.space, arg)
elif self.name == "searchsorted":
w_res = arr.descr_searchsorted(interp.space, arg,
interp.space.newtext('left'))
else:
assert False # unreachable code
elif self.name in THREE_ARG_FUNCTIONS:
if len(self.args) != 3:
raise ArgumentMismatch
arg1 = self.args[1].execute(interp)
arg2 = self.args[2].execute(interp)
if not isinstance(arg1, W_NDimArray):
raise ArgumentNotAnArray
if not isinstance(arg2, W_NDimArray):
raise ArgumentNotAnArray
if self.name == "where":
w_res = where(interp.space, arr, arg1, arg2)
else:
assert False # unreachable code
elif self.name in TWO_ARG_FUNCTIONS_OR_NONE:
if len(self.args) != 2:
raise ArgumentMismatch
arg = self.args[1].execute(interp)
if self.name == 'view':
w_res = arr.descr_view(interp.space, arg)
elif self.name == 'astype':
w_res = arr.descr_astype(interp.space, arg)
elif self.name == 'reshape':
w_arg = self.args[1]
assert isinstance(w_arg, ArrayConstant)
order = -1
w_res = arr.reshape(interp.space, w_arg.wrap(interp.space),
order)
else:
assert False
else:
raise WrongFunctionName
if isinstance(w_res, W_NDimArray):
return w_res
if isinstance(w_res, FloatObject):
dtype = get_dtype_cache(interp.space).w_float64dtype
elif isinstance(w_res, IntObject):
dtype = get_dtype_cache(interp.space).w_int64dtype
elif isinstance(w_res, BoolObject):
dtype = get_dtype_cache(interp.space).w_booldtype
elif isinstance(w_res, boxes.W_GenericBox):
dtype = w_res.get_dtype(interp.space)
else:
dtype = None
return W_NDimArray.new_scalar(interp.space, dtype, w_res)
def array(space, shape, order='C'):
dtype = get_dtype_cache(space).w_float64dtype
return W_NDimArray.from_shape(space, shape, dtype, order=order)
def execute(self, interp):
arr = self.args[0].execute(interp)
if not isinstance(arr, W_NDimArray):
raise ArgumentNotAnArray
if self.name in SINGLE_ARG_FUNCTIONS:
if len(self.args) != 1 and self.name != 'sum':
raise ArgumentMismatch
if self.name == "sum":
if len(self.args)>1:
w_res = arr.descr_sum(interp.space,
self.args[1].execute(interp))
else:
w_res = arr.descr_sum(interp.space)
elif self.name == "prod":
w_res = arr.descr_prod(interp.space)
elif self.name == "max":
w_res = arr.descr_max(interp.space)
elif self.name == "min":
w_res = arr.descr_min(interp.space)
elif self.name == "any":
w_res = arr.descr_any(interp.space)
elif self.name == "all":
w_res = arr.descr_all(interp.space)
elif self.name == "cumsum":
w_res = arr.descr_cumsum(interp.space)
elif self.name == "logical_xor_reduce":
logical_xor = ufuncs.get(interp.space).logical_xor
w_res = logical_xor.reduce(interp.space, arr, None)
elif self.name == "unegative":
neg = ufuncs.get(interp.space).negative
w_res = neg.call(interp.space, [arr], None, None, None)
elif self.name == "cos":
cos = ufuncs.get(interp.space).cos
w_res = cos.call(interp.space, [arr], None, None, None)
elif self.name == "flat":
w_res = arr.descr_get_flatiter(interp.space)
elif self.name == "argsort":
w_res = arr.descr_argsort(interp.space)
elif self.name == "tostring":
arr.descr_tostring(interp.space)
w_res = None
else:
assert False # unreachable code
elif self.name in TWO_ARG_FUNCTIONS:
if len(self.args) != 2:
raise ArgumentMismatch
arg = self.args[1].execute(interp)
if not isinstance(arg, W_NDimArray):
raise ArgumentNotAnArray
if self.name == "dot":
w_res = arr.descr_dot(interp.space, arg)
elif self.name == 'take':
w_res = arr.descr_take(interp.space, arg)
elif self.name == "searchsorted":
w_res = arr.descr_searchsorted(interp.space, arg,
interp.space.wrap('left'))
else:
assert False # unreachable code
elif self.name in THREE_ARG_FUNCTIONS:
if len(self.args) != 3:
raise ArgumentMismatch
arg1 = self.args[1].execute(interp)
arg2 = self.args[2].execute(interp)
if not isinstance(arg1, W_NDimArray):
raise ArgumentNotAnArray
if not isinstance(arg2, W_NDimArray):
raise ArgumentNotAnArray
if self.name == "where":
w_res = where(interp.space, arr, arg1, arg2)
else:
assert False
elif self.name in TWO_ARG_FUNCTIONS_OR_NONE:
if len(self.args) != 2:
raise ArgumentMismatch
arg = self.args[1].execute(interp)
if self.name == 'view':
w_res = arr.descr_view(interp.space, arg)
elif self.name == 'astype':
w_res = arr.descr_astype(interp.space, arg)
else:
assert False
else:
raise WrongFunctionName
if isinstance(w_res, W_NDimArray):
return w_res
if isinstance(w_res, FloatObject):
dtype = get_dtype_cache(interp.space).w_float64dtype
elif isinstance(w_res, IntObject):
dtype = get_dtype_cache(interp.space).w_int64dtype
elif isinstance(w_res, BoolObject):
dtype = get_dtype_cache(interp.space).w_booldtype
elif isinstance(w_res, boxes.W_GenericBox):
dtype = w_res.get_dtype(interp.space)
else:
dtype = None
return W_NDimArray.new_scalar(interp.space, dtype, w_res)
def iarray(space, shape, order=NPY.CORDER):
dtype = get_dtype_cache(space).w_int64dtype
return W_NDimArray.from_shape(space, shape, dtype, order=order)
def scalar(space):
dtype = get_dtype_cache(space).w_float64dtype
return W_NDimArray.new_scalar(space, dtype, space.wrap(10.))
def array(space, shape, order='C'):
dtype = get_dtype_cache(space).w_float64dtype
return W_NDimArray.from_shape(space, shape, dtype, order=order)
