Source

pypy / pypy / module / micronumpy / interp_numarray.py

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from pypy.interpreter.error import operationerrfmt, OperationError
from pypy.interpreter.typedef import TypeDef, GetSetProperty
from pypy.interpreter.gateway import interp2app, unwrap_spec
from pypy.module.micronumpy.base import W_NDimArray, convert_to_array,\
     ArrayArgumentException
from pypy.module.micronumpy import interp_dtype, interp_ufuncs, interp_boxes
from pypy.module.micronumpy.strides import find_shape_and_elems,\
     get_shape_from_iterable, to_coords
from pypy.module.micronumpy.interp_flatiter import W_FlatIterator
from pypy.module.micronumpy.interp_support import unwrap_axis_arg
from pypy.module.micronumpy.appbridge import get_appbridge_cache
from pypy.module.micronumpy import loop
from pypy.module.micronumpy.dot import match_dot_shapes
from pypy.module.micronumpy.interp_arrayops import repeat
from pypy.tool.sourcetools import func_with_new_name
from pypy.rlib import jit
from pypy.rlib.rstring import StringBuilder

def _find_shape(space, w_size):
    if space.isinstance_w(w_size, space.w_int):
        return [space.int_w(w_size)]
    shape = []
    for w_item in space.fixedview(w_size):
        shape.append(space.int_w(w_item))
    return shape

class __extend__(W_NDimArray):
    @jit.unroll_safe
    def descr_get_shape(self, space):
        shape = self.get_shape()
        return space.newtuple([space.wrap(i) for i in shape])

    def get_shape(self):
        return self.implementation.get_shape()

    def descr_set_shape(self, space, w_new_shape):
        self.implementation = self.implementation.set_shape(space,
            get_shape_from_iterable(space, self.get_size(), w_new_shape))

    def get_dtype(self):
        return self.implementation.dtype

    def get_order(self):
        return self.implementation.order

    def descr_get_dtype(self, space):
        return self.implementation.dtype

    def descr_get_ndim(self, space):
        return space.wrap(len(self.get_shape()))

    def descr_get_itemsize(self, space):
        return space.wrap(self.get_dtype().itemtype.get_element_size())

    def descr_get_nbytes(self, space):
        return space.wrap(self.get_size() * self.get_dtype().itemtype.get_element_size())

    def descr_fill(self, space, w_value):
        self.fill(self.get_dtype().coerce(space, w_value))

    def descr_tostring(self, space):
        return space.wrap(loop.tostring(space, self))

    def getitem_filter(self, space, arr):
        if arr.get_size() > self.get_size():
            raise OperationError(space.w_ValueError,
                                 space.wrap("index out of range for array"))
        size = loop.count_all_true(arr)
        res = W_NDimArray.from_shape([size], self.get_dtype())
        return loop.getitem_filter(res, self, arr)

    def setitem_filter(self, space, idx, val):
        if idx.get_size() > self.get_size():
            raise OperationError(space.w_ValueError,
                                 space.wrap("index out of range for array"))
        loop.setitem_filter(self, idx, val)

    def _prepare_array_index(self, space, w_index):
        if isinstance(w_index, W_NDimArray):
            return w_index.get_shape(), [w_index]
        w_lst = space.listview(w_index)
        for w_item in w_lst:
            if not space.isinstance_w(w_item, space.w_int):
                break
        else:
            arr = convert_to_array(space, w_index)
            return arr.get_shape(), [arr]
        xxx

    def getitem_array_int(self, space, w_index):
        iter_shape, indexes = self._prepare_array_index(space, w_index)
        shape = iter_shape + self.get_shape()[len(indexes):]
        res = W_NDimArray.from_shape(shape, self.get_dtype(), self.get_order())
        return loop.getitem_array_int(space, self, res, iter_shape, indexes)

    def descr_getitem(self, space, w_idx):
        if (isinstance(w_idx, W_NDimArray) and
            w_idx.get_dtype().is_bool_type()):
            return self.getitem_filter(space, w_idx)
        try:
            return self.implementation.descr_getitem(space, w_idx)
        except ArrayArgumentException:
            return self.getitem_array_int(space, w_idx)
        except OperationError:
            raise OperationError(space.w_IndexError, space.wrap("wrong index"))

    def getitem(self, space, index_list):
        return self.implementation.getitem_index(space, index_list)

    def setitem(self, space, index_list, w_value):
        self.implementation.setitem_index(space, index_list, w_value)

    def descr_setitem(self, space, w_idx, w_value):
        if (isinstance(w_idx, W_NDimArray) and
            w_idx.get_dtype().is_bool_type()):
            return self.setitem_filter(space, w_idx,
                                       convert_to_array(space, w_value))
        self.implementation.descr_setitem(space, w_idx, w_value)

    def descr_len(self, space):
        shape = self.get_shape()
        if len(shape):
            return space.wrap(shape[0])
        raise OperationError(space.w_TypeError, space.wrap(
            "len() of unsized object"))

    def descr_repr(self, space):
        cache = get_appbridge_cache(space)
        if cache.w_array_repr is None:
            return space.wrap(self.dump_data())
        return space.call_function(cache.w_array_repr, self)

    def descr_str(self, space):
        cache = get_appbridge_cache(space)
        if cache.w_array_str is None:
            return space.wrap(self.dump_data())
        return space.call_function(cache.w_array_str, self)

    def dump_data(self):
        i = self.create_iter(self.get_shape())
        first = True
        dtype = self.get_dtype()
        s = StringBuilder()
        s.append('array([')
        while not i.done():
            if first:
                first = False
            else:
                s.append(', ')
            s.append(dtype.itemtype.str_format(i.getitem()))
            i.next()
        s.append('])')
        return s.build()

    def create_iter(self, shape=None):
        if shape is None:
            shape = self.get_shape()
        return self.implementation.create_iter(shape)

    def create_axis_iter(self, shape, dim):
        return self.implementation.create_axis_iter(shape, dim)

    def create_dot_iter(self, shape, skip):
        return self.implementation.create_dot_iter(shape, skip)

    def is_scalar(self):
        return self.implementation.is_scalar()

    def set_scalar_value(self, w_val):
        self.implementation.set_scalar_value(w_val)

    def fill(self, box):
        self.implementation.fill(box)

    def descr_get_size(self, space):
        return space.wrap(self.get_size())

    def get_size(self):
        return self.implementation.get_size()

    def get_scalar_value(self):
        return self.implementation.get_scalar_value()

    def descr_copy(self, space):
        return W_NDimArray(self.implementation.copy())

    def descr_reshape(self, space, args_w):
        """reshape(...)
        a.reshape(shape)

        Returns an array containing the same data with a new shape.

        Refer to `numpypy.reshape` for full documentation.

        See Also
        --------
        numpypy.reshape : equivalent function
        """
        if len(args_w) == 1:
            w_shape = args_w[0]
        else:
            w_shape = space.newtuple(args_w)
        new_shape = get_shape_from_iterable(space, self.get_size(), w_shape)
        new_impl = self.implementation.reshape(space, new_shape)
        if new_impl is not None:
            return W_NDimArray(new_impl)
        # Create copy with contiguous data
        arr = self.descr_copy(space)
        if arr.get_size() > 0:
            arr.implementation = arr.implementation.reshape(space, new_shape)
            assert arr.implementation
        else:
            arr.implementation.shape = new_shape
        return arr

    def descr_get_transpose(self, space):
        return W_NDimArray(self.implementation.transpose())

    @unwrap_spec(axis1=int, axis2=int)
    def descr_swapaxes(self, space, axis1, axis2):
        """a.swapaxes(axis1, axis2)
    
        Return a view of the array with `axis1` and `axis2` interchanged.
    
        Refer to `numpy.swapaxes` for full documentation.
    
        See Also
        --------
        numpy.swapaxes : equivalent function
        """
        if self.is_scalar():
            return self
        return self.implementation.swapaxes(axis1, axis2)

    def descr_tolist(self, space):
        if len(self.get_shape()) == 0:
            return self.get_scalar_value().item(space)
        l_w = []
        for i in range(self.get_shape()[0]):
            l_w.append(space.call_method(self.descr_getitem(space,
                                         space.wrap(i)), "tolist"))
        return space.newlist(l_w)

    def descr_ravel(self, space, w_order=None):
        if w_order is None or space.is_w(w_order, space.w_None):
            order = 'C'
        else:
            order = space.str_w(w_order)
        if order != 'C':
            raise OperationError(space.w_NotImplementedError, space.wrap(
                "order not implemented"))
        return self.descr_reshape(space, [space.wrap(-1)])

    def descr_take(self, space, w_obj, w_axis=None, w_out=None):
        # if w_axis is None and w_out is Nont this is an equivalent to
        # fancy indexing
        raise Exception("unsupported for now")
        if not space.is_w(w_axis, space.w_None):
            raise OperationError(space.w_NotImplementedError,
                                 space.wrap("axis unsupported for take"))
        if not space.is_w(w_out, space.w_None):
            raise OperationError(space.w_NotImplementedError,
                                 space.wrap("out unsupported for take"))
        return self.getitem_int(space, convert_to_array(space, w_obj))

    def descr_compress(self, space, w_obj, w_axis=None):
        index = convert_to_array(space, w_obj)
        return self.getitem_filter(space, index)

    def descr_flatten(self, space, w_order=None):
        if self.is_scalar():
            # scalars have no storage
            return self.descr_reshape(space, [space.wrap(1)])
        w_res = self.descr_ravel(space, w_order)
        if w_res.implementation.storage == self.implementation.storage:
            return w_res.descr_copy(space)
        return w_res

    @unwrap_spec(repeats=int)
    def descr_repeat(self, space, repeats, w_axis=None):
        return repeat(space, self, repeats, w_axis)

    def descr_get_flatiter(self, space):
        return space.wrap(W_FlatIterator(self))

    def to_coords(self, space, w_index):
        coords, _, _ = to_coords(space, self.get_shape(),
                                 self.get_size(), self.get_order(),
                                 w_index)
        return coords

    def descr_item(self, space, w_arg=None):
        if space.is_w(w_arg, space.w_None):
            if self.is_scalar():
                return self.get_scalar_value().item(space)
            if self.get_size() == 1:
                w_obj = self.getitem(space,
                                     [0] * range(len(self.get_shape())))
                assert isinstance(w_obj, interp_boxes.W_GenericBox)
                return w_obj.item(space)
            raise OperationError(space.w_IndexError,
                                 space.wrap("index out of bounds"))
        if space.isinstance_w(w_arg, space.w_int):
            if self.is_scalar():
                raise OperationError(space.w_IndexError,
                                     space.wrap("index out of bounds"))
            i = self.to_coords(space, w_arg)
            item = self.getitem(space, i)
            assert isinstance(item, interp_boxes.W_GenericBox)
            return item.item(space)
        raise OperationError(space.w_NotImplementedError, space.wrap(
            "non-int arg not supported"))

    def descr_array_iface(self, space):
        addr = self.implementation.get_storage_as_int(space)
        # will explode if it can't
        w_d = space.newdict()
        space.setitem_str(w_d, 'data', space.newtuple([space.wrap(addr),
                                                       space.w_False]))
        return w_d


    # --------------------- operations ----------------------------

    def _unaryop_impl(ufunc_name):
        def impl(self, space, w_out=None):
            return getattr(interp_ufuncs.get(space), ufunc_name).call(space,
                                                                [self, w_out])
        return func_with_new_name(impl, "unaryop_%s_impl" % ufunc_name)

    descr_pos = _unaryop_impl("positive")
    descr_neg = _unaryop_impl("negative")
    descr_abs = _unaryop_impl("absolute")
    descr_invert = _unaryop_impl("invert")

    def descr_nonzero(self, space):
        if self.get_size() > 1:
            raise OperationError(space.w_ValueError, space.wrap(
                "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"))
        iter = self.create_iter(self.get_shape())
        return space.wrap(space.is_true(iter.getitem()))

    def _binop_impl(ufunc_name):
        def impl(self, space, w_other, w_out=None):
            return getattr(interp_ufuncs.get(space), ufunc_name).call(space,
                                                        [self, w_other, w_out])
        return func_with_new_name(impl, "binop_%s_impl" % ufunc_name)

    descr_add = _binop_impl("add")
    descr_sub = _binop_impl("subtract")
    descr_mul = _binop_impl("multiply")
    descr_div = _binop_impl("divide")
    descr_truediv = _binop_impl("true_divide")
    descr_floordiv = _binop_impl("floor_divide")
    descr_mod = _binop_impl("mod")
    descr_pow = _binop_impl("power")
    descr_lshift = _binop_impl("left_shift")
    descr_rshift = _binop_impl("right_shift")
    descr_and = _binop_impl("bitwise_and")
    descr_or = _binop_impl("bitwise_or")
    descr_xor = _binop_impl("bitwise_xor")

    def descr_divmod(self, space, w_other):
        w_quotient = self.descr_div(space, w_other)
        w_remainder = self.descr_mod(space, w_other)
        return space.newtuple([w_quotient, w_remainder])

    descr_eq = _binop_impl("equal")
    descr_ne = _binop_impl("not_equal")
    descr_lt = _binop_impl("less")
    descr_le = _binop_impl("less_equal")
    descr_gt = _binop_impl("greater")
    descr_ge = _binop_impl("greater_equal")

    def _binop_right_impl(ufunc_name):
        def impl(self, space, w_other, w_out=None):
            dtype = interp_ufuncs.find_dtype_for_scalar(space, w_other,
                                                        self.get_dtype())
            w_other = W_NDimArray.new_scalar(space, dtype, w_other)
            return getattr(interp_ufuncs.get(space), ufunc_name).call(space, [w_other, self, w_out])
        return func_with_new_name(impl, "binop_right_%s_impl" % ufunc_name)

    descr_radd = _binop_right_impl("add")
    descr_rsub = _binop_right_impl("subtract")
    descr_rmul = _binop_right_impl("multiply")
    descr_rdiv = _binop_right_impl("divide")
    descr_rtruediv = _binop_right_impl("true_divide")
    descr_rfloordiv = _binop_right_impl("floor_divide")
    descr_rmod = _binop_right_impl("mod")
    descr_rpow = _binop_right_impl("power")
    descr_rlshift = _binop_right_impl("left_shift")
    descr_rrshift = _binop_right_impl("right_shift")
    descr_rand = _binop_right_impl("bitwise_and")
    descr_ror = _binop_right_impl("bitwise_or")
    descr_rxor = _binop_right_impl("bitwise_xor")

    def descr_rdivmod(self, space, w_other):
        w_quotient = self.descr_rdiv(space, w_other)
        w_remainder = self.descr_rmod(space, w_other)
        return space.newtuple([w_quotient, w_remainder])

    def descr_dot(self, space, w_other):
        other = convert_to_array(space, w_other)
        if other.is_scalar():
            #Note: w_out is not modified, this is numpy compliant.
            return self.descr_mul(space, other)
        elif len(self.get_shape()) < 2 and len(other.get_shape()) < 2:
            w_res = self.descr_mul(space, other)
            assert isinstance(w_res, W_NDimArray)
            return w_res.descr_sum(space, space.wrap(-1))
        dtype = interp_ufuncs.find_binop_result_dtype(space,
                                     self.get_dtype(), other.get_dtype())
        if self.get_size() < 1 and other.get_size() < 1:
            # numpy compatability
            return W_NDimArray.new_scalar(space, dtype, space.wrap(0))
        # Do the dims match?
        out_shape, other_critical_dim = match_dot_shapes(space, self, other)
        result = W_NDimArray.from_shape(out_shape, dtype)
        # This is the place to add fpypy and blas
        return loop.multidim_dot(space, self, other,  result, dtype,
                                 other_critical_dim)

    def descr_var(self, space, w_axis=None):
        return get_appbridge_cache(space).call_method(space, '_var', self,
                                                      w_axis)

    def descr_std(self, space, w_axis=None):
        return get_appbridge_cache(space).call_method(space, '_std', self,
                                                      w_axis)

    # ----------------------- reduce -------------------------------

    def _reduce_ufunc_impl(ufunc_name, promote_to_largest=False):
        def impl(self, space, w_axis=None, w_out=None, w_dtype=None):
            if space.is_w(w_out, space.w_None) or not 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
            return getattr(interp_ufuncs.get(space), ufunc_name).reduce(space,
                                        self, True, promote_to_largest, w_axis,
                                                         False, out, w_dtype)
        return func_with_new_name(impl, "reduce_%s_impl" % ufunc_name)

    descr_sum = _reduce_ufunc_impl("add")
    descr_sum_promote = _reduce_ufunc_impl("add", True)
    descr_prod = _reduce_ufunc_impl("multiply", True)
    descr_max = _reduce_ufunc_impl("maximum")
    descr_min = _reduce_ufunc_impl("minimum")
    descr_all = _reduce_ufunc_impl('logical_and')
    descr_any = _reduce_ufunc_impl('logical_or')

    def descr_mean(self, space, w_axis=None, w_out=None):
        if space.is_w(w_axis, space.w_None):
            w_denom = space.wrap(self.get_size())
        else:
            axis = unwrap_axis_arg(space, len(self.get_shape()), w_axis)
            w_denom = space.wrap(self.get_shape()[axis])
        return space.div(self.descr_sum_promote(space, w_axis, w_out), w_denom)

    def _reduce_argmax_argmin_impl(op_name):
        def impl(self, space):
            if self.get_size() == 0:
                raise OperationError(space.w_ValueError,
                    space.wrap("Can't call %s on zero-size arrays" % op_name))
            return space.wrap(loop.argmin_argmax(op_name, self))
        return func_with_new_name(impl, "reduce_arg%s_impl" % op_name)

    descr_argmax = _reduce_argmax_argmin_impl("max")
    descr_argmin = _reduce_argmax_argmin_impl("min")


@unwrap_spec(offset=int)
def descr_new_array(space, w_subtype, w_shape, w_dtype=None, w_buffer=None,
                    offset=0, w_strides=None, w_order=None):
    if (offset != 0 or not space.is_w(w_strides, space.w_None) or
        not space.is_w(w_order, space.w_None) or
        not space.is_w(w_buffer, space.w_None)):
        raise OperationError(space.w_NotImplementedError,
                             space.wrap("unsupported param"))
    dtype = space.interp_w(interp_dtype.W_Dtype,
          space.call_function(space.gettypefor(interp_dtype.W_Dtype), w_dtype))
    shape = _find_shape(space, w_shape)
    if not shape:
        return W_NDimArray.new_scalar(space, dtype)
    return W_NDimArray.from_shape(shape, dtype)

W_NDimArray.typedef = TypeDef(
    "ndarray",
    __new__ = interp2app(descr_new_array),

    __len__ = interp2app(W_NDimArray.descr_len),
    __getitem__ = interp2app(W_NDimArray.descr_getitem),
    __setitem__ = interp2app(W_NDimArray.descr_setitem),

    __repr__ = interp2app(W_NDimArray.descr_repr),
    __str__ = interp2app(W_NDimArray.descr_str),

    __pos__ = interp2app(W_NDimArray.descr_pos),
    __neg__ = interp2app(W_NDimArray.descr_neg),
    __abs__ = interp2app(W_NDimArray.descr_abs),
    __invert__ = interp2app(W_NDimArray.descr_invert),
    __nonzero__ = interp2app(W_NDimArray.descr_nonzero),

    __add__ = interp2app(W_NDimArray.descr_add),
    __sub__ = interp2app(W_NDimArray.descr_sub),
    __mul__ = interp2app(W_NDimArray.descr_mul),
    __div__ = interp2app(W_NDimArray.descr_div),
    __truediv__ = interp2app(W_NDimArray.descr_truediv),
    __floordiv__ = interp2app(W_NDimArray.descr_floordiv),
    __mod__ = interp2app(W_NDimArray.descr_mod),
    __divmod__ = interp2app(W_NDimArray.descr_divmod),
    __pow__ = interp2app(W_NDimArray.descr_pow),
    __lshift__ = interp2app(W_NDimArray.descr_lshift),
    __rshift__ = interp2app(W_NDimArray.descr_rshift),
    __and__ = interp2app(W_NDimArray.descr_and),
    __or__ = interp2app(W_NDimArray.descr_or),
    __xor__ = interp2app(W_NDimArray.descr_xor),

    __radd__ = interp2app(W_NDimArray.descr_radd),
    __rsub__ = interp2app(W_NDimArray.descr_rsub),
    __rmul__ = interp2app(W_NDimArray.descr_rmul),
    __rdiv__ = interp2app(W_NDimArray.descr_rdiv),
    __rtruediv__ = interp2app(W_NDimArray.descr_rtruediv),
    __rfloordiv__ = interp2app(W_NDimArray.descr_rfloordiv),
    __rmod__ = interp2app(W_NDimArray.descr_rmod),
    __rdivmod__ = interp2app(W_NDimArray.descr_rdivmod),
    __rpow__ = interp2app(W_NDimArray.descr_rpow),
    __rlshift__ = interp2app(W_NDimArray.descr_rlshift),
    __rrshift__ = interp2app(W_NDimArray.descr_rrshift),
    __rand__ = interp2app(W_NDimArray.descr_rand),
    __ror__ = interp2app(W_NDimArray.descr_ror),
    __rxor__ = interp2app(W_NDimArray.descr_rxor),

    __eq__ = interp2app(W_NDimArray.descr_eq),
    __ne__ = interp2app(W_NDimArray.descr_ne),
    __lt__ = interp2app(W_NDimArray.descr_lt),
    __le__ = interp2app(W_NDimArray.descr_le),
    __gt__ = interp2app(W_NDimArray.descr_gt),
    __ge__ = interp2app(W_NDimArray.descr_ge),

    dtype = GetSetProperty(W_NDimArray.descr_get_dtype),
    shape = GetSetProperty(W_NDimArray.descr_get_shape,
                           W_NDimArray.descr_set_shape),
    ndim = GetSetProperty(W_NDimArray.descr_get_ndim),
    size = GetSetProperty(W_NDimArray.descr_get_size),
    itemsize = GetSetProperty(W_NDimArray.descr_get_itemsize),
    nbytes = GetSetProperty(W_NDimArray.descr_get_nbytes),

    fill = interp2app(W_NDimArray.descr_fill),
    tostring = interp2app(W_NDimArray.descr_tostring),

    mean = interp2app(W_NDimArray.descr_mean),
    sum = interp2app(W_NDimArray.descr_sum),
    prod = interp2app(W_NDimArray.descr_prod),
    max = interp2app(W_NDimArray.descr_max),
    min = interp2app(W_NDimArray.descr_min),
    argmax = interp2app(W_NDimArray.descr_argmax),
    argmin = interp2app(W_NDimArray.descr_argmin),
    all = interp2app(W_NDimArray.descr_all),
    any = interp2app(W_NDimArray.descr_any),
    dot = interp2app(W_NDimArray.descr_dot),
    var = interp2app(W_NDimArray.descr_var),
    std = interp2app(W_NDimArray.descr_std),

    copy = interp2app(W_NDimArray.descr_copy),
    reshape = interp2app(W_NDimArray.descr_reshape),
    T = GetSetProperty(W_NDimArray.descr_get_transpose),
    transpose = interp2app(W_NDimArray.descr_get_transpose),
    tolist = interp2app(W_NDimArray.descr_tolist),
    flatten = interp2app(W_NDimArray.descr_flatten),
    ravel = interp2app(W_NDimArray.descr_ravel),
    take = interp2app(W_NDimArray.descr_take),
    compress = interp2app(W_NDimArray.descr_compress),
    repeat = interp2app(W_NDimArray.descr_repeat),
    swapaxes = interp2app(W_NDimArray.descr_swapaxes),
    flat = GetSetProperty(W_NDimArray.descr_get_flatiter),
    item = interp2app(W_NDimArray.descr_item),

    __array_interface__ = GetSetProperty(W_NDimArray.descr_array_iface),
)

@unwrap_spec(ndmin=int, copy=bool, subok=bool)
def array(space, w_object, w_dtype=None, copy=True, w_order=None, subok=False,
          ndmin=0):
    if not space.issequence_w(w_object):
        if w_dtype is None or space.is_w(w_dtype, space.w_None):
            w_dtype = interp_ufuncs.find_dtype_for_scalar(space, w_object)
        dtype = space.interp_w(interp_dtype.W_Dtype,
          space.call_function(space.gettypefor(interp_dtype.W_Dtype), w_dtype))
        return W_NDimArray.new_scalar(space, dtype, w_object)
    if w_order is None or space.is_w(w_order, space.w_None):
        order = 'C'
    else:
        order = space.str_w(w_order)
        if order != 'C':  # or order != 'F':
            raise operationerrfmt(space.w_ValueError, "Unknown order: %s",
                                  order)
    if isinstance(w_object, W_NDimArray):
        if (not space.is_w(w_dtype, space.w_None) and
            w_object.get_dtype() is not w_dtype):
            raise OperationError(space.w_NotImplementedError, space.wrap(
                                  "copying over different dtypes unsupported"))
        if copy:
            return w_object.descr_copy(space)
        return w_object
    dtype = interp_dtype.decode_w_dtype(space, w_dtype)
    shape, elems_w = find_shape_and_elems(space, w_object, dtype)
    if dtype is None:
        for w_elem in elems_w:
            dtype = interp_ufuncs.find_dtype_for_scalar(space, w_elem,
                                                        dtype)
            if dtype is interp_dtype.get_dtype_cache(space).w_float64dtype:
                break
        if dtype is None:
            dtype = interp_dtype.get_dtype_cache(space).w_float64dtype
    if ndmin > len(shape):
        shape = [1] * (ndmin - len(shape)) + shape
    arr = W_NDimArray.from_shape(shape, dtype, order=order)
    arr_iter = arr.create_iter(arr.get_shape())
    for w_elem in elems_w:
        arr_iter.setitem(dtype.coerce(space, w_elem))
        arr_iter.next()
    return arr

@unwrap_spec(order=str)
def zeros(space, w_shape, w_dtype=None, order='C'):
    dtype = space.interp_w(interp_dtype.W_Dtype,
        space.call_function(space.gettypefor(interp_dtype.W_Dtype), w_dtype)
    )
    shape = _find_shape(space, w_shape)
    if not shape:
        return W_NDimArray.new_scalar(space, dtype, space.wrap(0))
    return space.wrap(W_NDimArray.from_shape(shape, dtype=dtype, order=order))

@unwrap_spec(order=str)
def ones(space, w_shape, w_dtype=None, order='C'):
    dtype = space.interp_w(interp_dtype.W_Dtype,
        space.call_function(space.gettypefor(interp_dtype.W_Dtype), w_dtype)
    )
    shape = _find_shape(space, w_shape)
    if not shape:
        return W_NDimArray.new_scalar(space, dtype, space.wrap(0))
    arr = W_NDimArray.from_shape(shape, dtype=dtype, order=order)
    one = dtype.box(1)
    arr.fill(one)
    return space.wrap(arr)

W_FlatIterator.typedef = TypeDef(
    'flatiter',
    __iter__ = interp2app(W_FlatIterator.descr_iter),
    __getitem__ = interp2app(W_FlatIterator.descr_getitem),
    __setitem__ = interp2app(W_FlatIterator.descr_setitem),
    __len__ = interp2app(W_FlatIterator.descr_len),

    __eq__ = interp2app(W_FlatIterator.descr_eq),
    __ne__ = interp2app(W_FlatIterator.descr_ne),
    __lt__ = interp2app(W_FlatIterator.descr_lt),
    __le__ = interp2app(W_FlatIterator.descr_le),
    __gt__ = interp2app(W_FlatIterator.descr_gt),
    __ge__ = interp2app(W_FlatIterator.descr_ge),

    next = interp2app(W_FlatIterator.descr_next),
    base = GetSetProperty(W_FlatIterator.descr_base),
    index = GetSetProperty(W_FlatIterator.descr_index),
    coords = GetSetProperty(W_FlatIterator.descr_coords),
)
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