Python support.product函数代码示例

 def create_iter(self, shape=None, backward_broadcast=False):
     if shape is not None and \
             support.product(shape) > support.product(self.get_shape()):
         r = calculate_broadcast_strides(self.get_strides(),
                                         self.get_backstrides(),
                                         self.get_shape(), shape,
                                         backward_broadcast)
         i = ArrayIter(self, support.product(shape), shape, r[0], r[1])
     else:
         i = ArrayIter(self, self.get_size(), self.shape,
                       self.strides, self.backstrides)
     return i, i.reset()

 def from_shape_and_storage(space, shape, storage, dtype, storage_bytes=-1,
                            order='C', owning=False, w_subtype=None,
                            w_base=None, writable=True, strides=None, start=0):
     from pypy.module.micronumpy import concrete
     from pypy.module.micronumpy.strides import (calc_strides,
                                                 calc_backstrides)
     isize = dtype.elsize
     if storage_bytes > 0 :
         totalsize = support.product(shape) * isize
         if totalsize > storage_bytes:
             raise OperationError(space.w_TypeError, space.wrap(
                 "buffer is too small for requested array"))
     else:
         storage_bytes = support.product(shape) * isize
     if strides is None:
         strides, backstrides = calc_strides(shape, dtype, order)
     else:
         if len(strides) != len(shape):
             raise oefmt(space.w_ValueError,
                 'strides, if given, must be the same length as shape')
         for i in range(len(strides)):
             if strides[i] < 0 or strides[i]*shape[i] > storage_bytes:
                 raise oefmt(space.w_ValueError,
                     'strides is incompatible with shape of requested '
                     'array and size of buffer')
         backstrides = calc_backstrides(strides, shape)
     if w_base is not None:
         if owning:
             raise OperationError(space.w_ValueError,
                     space.wrap("Cannot have owning=True when specifying a buffer"))
         if writable:
             impl = concrete.ConcreteArrayWithBase(shape, dtype, order,
                                 strides, backstrides, storage, w_base,
                                 start=start)
         else:
             impl = concrete.ConcreteNonWritableArrayWithBase(shape, dtype, order,
                                                              strides, backstrides,
                                                              storage, w_base)
     elif owning:
         # Will free storage when GCd
         impl = concrete.ConcreteArray(shape, dtype, order, strides,
                                       backstrides, storage=storage)
     else:
         impl = concrete.ConcreteArrayNotOwning(shape, dtype, order, strides,
                                                backstrides, storage)
     if w_subtype:
         w_ret = space.allocate_instance(W_NDimArray, w_subtype)
         W_NDimArray.__init__(w_ret, impl)
         space.call_method(w_ret, '__array_finalize__', w_subtype)
         return w_ret
     return W_NDimArray(impl)

    def test_iterator_step(self):
        #iteration in C order with #contiguous layout => strides[-1] is 1
        #skip less than the shape
        shape = [3, 5]
        strides = [5, 1]
        backstrides = [x * (y - 1) for x,y in zip(strides, shape)]
        assert backstrides == [10, 4]
        i = ArrayIter(MockArray, support.product(shape), shape,
                      strides, backstrides)
        s = i.reset()
        s = i.next_skip_x(s, 2)
        s = i.next_skip_x(s, 2)
        s = i.next_skip_x(s, 2)
        assert s.offset == 6
        assert not i.done(s)
        assert s.indices == [1,1]
        #And for some big skips
        s = i.next_skip_x(s, 5)
        assert s.offset == 11
        assert s.indices == [2,1]
        s = i.next_skip_x(s, 5)
        # Note: the offset does not overflow but recycles,
        # this is good for broadcast
        assert s.offset == 1
        assert s.indices == [0,1]
        assert i.done(s)

        #Now what happens if the array is transposed? strides[-1] != 1
        # therefore layout is non-contiguous
        strides = [1, 3]
        backstrides = [x * (y - 1) for x,y in zip(strides, shape)]
        assert backstrides == [2, 12]
        i = ArrayIter(MockArray, support.product(shape), shape,
                      strides, backstrides)
        s = i.reset()
        s = i.next_skip_x(s, 2)
        s = i.next_skip_x(s, 2)
        s = i.next_skip_x(s, 2)
        assert s.offset == 4
        assert s.indices == [1,1]
        assert not i.done(s)
        s = i.next_skip_x(s, 5)
        assert s.offset == 5
        assert s.indices == [2,1]
        assert not i.done(s)
        s = i.next_skip_x(s, 5)
        assert s.indices == [0,1]
        assert s.offset == 3
        assert i.done(s)

 def create_iter(self, shape=None, backward_broadcast=False, require_index=False):
     if shape is not None and \
             support.product(shape) > support.product(self.get_shape()):
         r = calculate_broadcast_strides(self.get_strides(),
                                         self.get_backstrides(),
                                         self.get_shape(), shape,
                                         backward_broadcast)
         return iter.MultiDimViewIterator(self.parent, self.dtype,
                                          self.start, r[0], r[1], shape)
     if len(self.get_shape()) == 1:
         return iter.OneDimViewIterator(self.parent, self.dtype, self.start,
                 self.get_strides(), self.get_shape())
     return iter.MultiDimViewIterator(self.parent, self.dtype, self.start,
                                 self.get_strides(),
                                 self.get_backstrides(), self.get_shape())

 def __init__(self, shape, dtype, order, strides, backstrides,
              storage=lltype.nullptr(RAW_STORAGE), zero=True):
     if storage == lltype.nullptr(RAW_STORAGE):
         storage = dtype.itemtype.malloc(support.product(shape) *
                                         dtype.elsize, zero=zero)
     ConcreteArrayNotOwning.__init__(self, shape, dtype, order, strides, backstrides,
                                     storage)

 def get_iter(self, space, i):
     arr = self.seq[i]
     imp = arr.implementation
     if arr.is_scalar():
         return ConcreteIter(imp, 1, [], [], [], self.op_flags[i], self)
     shape = self.shape
     if self.external_loop and len(self.seq) < 2 and self.buffered:
         # Special case, always return a memory-ordered iterator
         stride = imp.dtype.elsize
         backstride = imp.size * stride - stride
         return ConcreteIter(
             imp, imp.get_size(), [support.product(shape)], [stride], [backstride], self.op_flags[i], self
         )
     backward = imp.order != self.order
     # XXX cleanup needed
     strides = imp.strides
     backstrides = imp.backstrides
     if self.allow_backward:
         if (abs(imp.strides[0]) < abs(imp.strides[-1]) and not backward) or (
             abs(imp.strides[0]) > abs(imp.strides[-1]) and backward
         ):
             # flip the strides. Is this always true for multidimension?
             strides = imp.strides[:]
             backstrides = imp.backstrides[:]
             shape = imp.shape[:]
             strides.reverse()
             backstrides.reverse()
             shape.reverse()
     r = calculate_broadcast_strides(strides, backstrides, imp.shape, shape, backward)
     iter_shape = shape
     if len(shape) != len(r[0]):
         # shape can be shorter when using an external loop, just return a view
         iter_shape = imp.shape
     return ConcreteIter(imp, imp.get_size(), iter_shape, r[0], r[1], self.op_flags[i], self)

def _zeros_or_empty(space, w_shape, w_dtype, w_order, zero):
    dtype = space.interp_w(descriptor.W_Dtype,
        space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
    if dtype.is_str_or_unicode() and dtype.elsize < 1:
        dtype = descriptor.variable_dtype(space, dtype.char + '1')
    shape = shape_converter(space, w_shape, dtype)
    for dim in shape:
        if dim < 0:
            raise OperationError(space.w_ValueError, space.wrap(
                "negative dimensions are not allowed"))
    try:
        support.product(shape)
    except OverflowError:
        raise OperationError(space.w_ValueError, space.wrap(
            "array is too big."))
    return W_NDimArray.from_shape(space, shape, dtype=dtype, zero=zero)

    def from_shape(space, shape, dtype, order="C", w_instance=None, zero=True):
        from pypy.module.micronumpy import concrete, descriptor, boxes
        from pypy.module.micronumpy.strides import calc_strides

        if len(shape) > NPY.MAXDIMS:
            raise oefmt(space.w_ValueError, "sequence too large; must be smaller than %d", NPY.MAXDIMS)
        try:
            support.product(shape) * dtype.elsize
        except OverflowError as e:
            raise oefmt(space.w_ValueError, "array is too big")
        strides, backstrides = calc_strides(shape, dtype.base, order)
        impl = concrete.ConcreteArray(shape, dtype.base, order, strides, backstrides, zero=zero)
        if dtype == descriptor.get_dtype_cache(space).w_objectdtype:
            impl.fill(space, boxes.W_ObjectBox(space.w_None))
        if w_instance:
            return wrap_impl(space, space.type(w_instance), w_instance, impl)
        return W_NDimArray(impl)

    def test_iterator_basic(self):
        #Let's get started, simple iteration in C order with
        #contiguous layout => strides[-1] is 1
        shape = [3, 5]
        strides = [5, 1]
        backstrides = [x * (y - 1) for x,y in zip(strides, shape)]
        assert backstrides == [10, 4]
        i = ArrayIter(MockArray(shape, strides), support.product(shape), shape,
                      strides, backstrides)
        assert i.contiguous
        s = i.reset()
        s = i.next(s)
        s = i.next(s)
        s = i.next(s)
        assert s.offset == 3
        assert not i.done(s)
        assert s._indices == [0,0]
        assert i.indices(s) == [0,3]
        #cause a dimension overflow
        s = i.next(s)
        s = i.next(s)
        assert s.offset == 5
        assert s._indices == [0,3]
        assert i.indices(s) == [1,0]

        #Now what happens if the array is transposed? strides[-1] != 1
        # therefore layout is non-contiguous
        strides = [1, 3]
        backstrides = [x * (y - 1) for x,y in zip(strides, shape)]
        assert backstrides == [2, 12]
        i = ArrayIter(MockArray(shape, strides), support.product(shape), shape,
                      strides, backstrides)
        assert not i.contiguous
        s = i.reset()
        s = i.next(s)
        s = i.next(s)
        s = i.next(s)
        assert s.offset == 9
        assert not i.done(s)
        assert s._indices == [0,3]
        #cause a dimension overflow
        s = i.next(s)
        s = i.next(s)
        assert s.offset == 1
        assert s._indices == [1,0]

 def set_shape(self, space, orig_array, new_shape):
     if not new_shape:
         return self
     if support.product(new_shape) == 1:
         arr = W_NDimArray.from_shape(space, new_shape, self.dtype)
         arr_iter = arr.create_iter(new_shape)
         arr_iter.setitem(self.value)
         return arr.implementation
     raise OperationError(space.w_ValueError, space.wrap("total size of the array must be unchanged"))

 def __init__(self, array, dtype, start, strides, backstrides, shape):
     self.indexes = [0] * len(shape)
     self.array = array
     self.dtype = dtype
     self.shape = shape
     self.offset = start
     self.shapelen = len(shape)
     self._done = self.shapelen == 0 or product(shape) == 0
     self.strides = strides
     self.backstrides = backstrides
     self.size = array.size

def AxisIter(array, shape, axis):
    strides = array.get_strides()
    backstrides = array.get_backstrides()
    if len(shape) == len(strides):
        # keepdims = True
        strides = strides[:axis] + [0] + strides[axis + 1:]
        backstrides = backstrides[:axis] + [0] + backstrides[axis + 1:]
    else:
        strides = strides[:axis] + [0] + strides[axis:]
        backstrides = backstrides[:axis] + [0] + backstrides[axis:]
    return ArrayIter(array, support.product(shape), shape, strides, backstrides)

def coalesce_iter(old_iter, op_flags, it, order, flat=True):
    """
    We usually iterate through an array one value at a time.
    But after coalesce(), getoperand() will return a slice by removing
    the fastest varying dimension(s) from the beginning or end of the shape.
    If flat is true, then the slice will be 1d, otherwise stack up the shape of
    the fastest varying dimension in the slice, so an iterator of a  'C' array
    of shape (2,4,3) after two calls to coalesce will iterate 2 times over a slice
    of shape (4,3) by setting the offset to the beginning of the data at each iteration
    """
    shape = [s + 1 for s in old_iter.shape_m1]
    if len(shape) < 1:
        return old_iter
    strides = old_iter.strides
    backstrides = old_iter.backstrides
    if order == NPY.FORTRANORDER:
        new_shape = shape[1:]
        new_strides = strides[1:]
        new_backstrides = backstrides[1:]
        _stride = old_iter.slice_stride + [strides[0]]
        _shape = old_iter.slice_shape + [shape[0]]
        _backstride = old_iter.slice_backstride + [strides[0] * (shape[0] - 1)]
        fastest = shape[0]
    else:
        new_shape = shape[:-1]
        new_strides = strides[:-1]
        new_backstrides = backstrides[:-1]
        # use the operand's iterator's rightmost stride,
        # even if it is not the fastest (for 'F' or swapped axis)
        _stride = [strides[-1]] + old_iter.slice_stride
        _shape = [shape[-1]] + old_iter.slice_shape
        _backstride = [(shape[-1] - 1) * strides[-1]] + old_iter.slice_backstride
        fastest = shape[-1]
    if fastest == 0:
        return old_iter
    if flat:
        _shape = [support.product(_shape)]
        if len(_stride) > 1:
            _stride = [min(_stride[0], _stride[1])]
        _backstride = [(shape[0] - 1) * _stride[0]]
    return SliceIter(
        old_iter.array,
        old_iter.size / fastest,
        new_shape,
        new_strides,
        new_backstrides,
        _shape,
        _stride,
        _backstride,
        op_flags,
        it,
    )

def split_iter(arr, axis_flags):
    """Prepare 2 iterators for nested iteration over `arr`.

    Arguments:
        arr: instance of BaseConcreteArray
        axis_flags: list of bools, one for each dimension of `arr`.The inner
        iterator operates over the dimensions for which the flag is True
    """
    shape = arr.get_shape()
    strides = arr.get_strides()
    backstrides = arr.get_backstrides()
    shapelen = len(shape)
    assert len(axis_flags) == shapelen
    inner_shape = [-1] * shapelen
    inner_strides = [-1] * shapelen
    inner_backstrides = [-1] * shapelen
    outer_shape = [-1] * shapelen
    outer_strides = [-1] * shapelen
    outer_backstrides = [-1] * shapelen
    for i in range(len(shape)):
        if axis_flags[i]:
            inner_shape[i] = shape[i]
            inner_strides[i] = strides[i]
            inner_backstrides[i] = backstrides[i]
            outer_shape[i] = 1
            outer_strides[i] = 0
            outer_backstrides[i] = 0
        else:
            outer_shape[i] = shape[i]
            outer_strides[i] = strides[i]
            outer_backstrides[i] = backstrides[i]
            inner_shape[i] = 1
            inner_strides[i] = 0
            inner_backstrides[i] = 0
    inner_iter = ArrayIter(arr, support.product(inner_shape),
                           inner_shape, inner_strides, inner_backstrides)
    outer_iter = ArrayIter(arr, support.product(outer_shape),
                           outer_shape, outer_strides, outer_backstrides)
    return inner_iter, outer_iter

 def __init__(self, shape, dtype, order, strides, backstrides, storage, start=0):
     make_sure_not_resized(shape)
     make_sure_not_resized(strides)
     make_sure_not_resized(backstrides)
     self.shape = shape
     # already tested for overflow in from_shape_and_storage
     self.size = support.product(shape) * dtype.elsize
     self.order = order
     self.dtype = dtype
     self.strides = strides
     self.backstrides = backstrides
     self.storage = storage
     self.start = start
     self.gcstruct = V_OBJECTSTORE