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 descr__new__(space, w_subtype, w_value=None):
from pypy.module.micronumpy.ctors import array
dtype = _get_dtype(space)
if not space.is_none(w_value):
w_arr = array(space, w_value, dtype, copy=False)
if len(w_arr.get_shape()) != 0:
return w_arr
w_value = w_arr.get_scalar_value().item(space)
return dtype.itemtype.coerce_subtype(space, w_subtype, w_value)
def descr__new__(space, w_subtype, w_value=None):
from pypy.module.micronumpy.ctors import array
dtype = _get_dtype(space)
if not space.is_none(w_value):
w_arr = array(space, w_value, dtype, copy=False)
if len(w_arr.get_shape()) != 0:
return w_arr
w_value = w_arr.get_scalar_value().item(space)
return dtype.itemtype.coerce_subtype(space, w_subtype, w_value)
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 convert_to_array(space, w_obj):
from pypy.module.micronumpy.ctors import array
if isinstance(w_obj, W_NDimArray):
return w_obj
return array(space, w_obj)
def execute(self, interp):
w_list = self.wrap(interp.space)
return array(interp.space, w_list)
def execute(self, interp):
w_list = interp.space.newlist(
[interp.space.newfloat(float(i)) for i in range(self.v)])
dtype = get_dtype_cache(interp.space).w_float64dtype
return array(interp.space, w_list, w_dtype=dtype, w_order=None)
def convert_to_array(space, w_obj):
from pypy.module.micronumpy.ctors import array
if isinstance(w_obj, W_NDimArray):
return w_obj
return array(space, w_obj)
def execute(self, interp):
w_list = self.wrap(interp.space)
return array(interp.space, w_list)
def execute(self, interp):
w_list = interp.space.newlist(
[interp.space.wrap(float(i)) for i in range(self.v)]
)
dtype = get_dtype_cache(interp.space).w_float64dtype
return array(interp.space, w_list, w_dtype=dtype, w_order=None)
