Python imputils.impl_ret_new_ref函数代码示例

def get_attr_impl(context, builder, typ, value, attr):
    """
    Generic getattr() for @jitclass instances.
    """
    if attr in typ.struct:
        # It's a struct field
        inst = context.make_helper(builder, typ, value=value)
        data_pointer = inst.data
        data = context.make_data_helper(builder, typ.get_data_type(),
                                        ref=data_pointer)
        return imputils.impl_ret_borrowed(context, builder,
                                          typ.struct[attr],
                                          getattr(data, _mangle_attr(attr)))
    elif attr in typ.jitprops:
        # It's a jitted property
        getter = typ.jitprops[attr]['get']
        sig = templates.signature(None, typ)
        dispatcher = types.Dispatcher(getter)
        sig = dispatcher.get_call_type(context.typing_context, [typ], {})
        call = context.get_function(dispatcher, sig)
        out = call(builder, [value])
        _add_linking_libs(context, call)
        return imputils.impl_ret_new_ref(context, builder, sig.return_type, out)

    raise NotImplementedError('attribute {0!r} not implemented'.format(attr))

 def imp(context, builder, sig, args):
     instance_type = sig.args[0]
     method = instance_type.jitmethods[attr]
     disp_type = types.Dispatcher(method)
     call = context.get_function(disp_type, sig)
     out = call(builder, args)
     return imputils.impl_ret_new_ref(context, builder,
                                      sig.return_type, out)

def array_sinc(context, builder, sig, args):
    def array_sinc_impl(arr):
        out = numpy.zeros_like(arr)
        for index, val in numpy.ndenumerate(arr):
            out[index] = numpy.sinc(val)
        return out
    res = context.compile_internal(builder, array_sinc_impl, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)

 def codegen(context, builder, signature, args):
     # check that the return type is now defined
     arrty = signature.return_type
     assert arrty.is_precise()
     shapes = unpack_tuple(builder, args[0])
     # redirect implementation to np.empty
     res = _empty_nd_impl(context, builder, arrty, shapes)
     return impl_ret_new_ref(context, builder, arrty, res._getvalue())

def array_round(context, builder, sig, args):
    def array_round_impl(arr, decimals, out):
        if arr.shape != out.shape:
            raise ValueError("invalid output shape")
        for index, val in numpy.ndenumerate(arr):
            out[index] = numpy.round(val, decimals)
        return out

    res = context.compile_internal(builder, array_round_impl, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)

def array_nonzero(context, builder, sig, args):
    aryty = sig.args[0]
    # Return type is a N-tuple of 1D C-contiguous arrays
    retty = sig.return_type
    outaryty = retty.dtype
    ndim = aryty.ndim
    nouts = retty.count

    ary = make_array(aryty)(context, builder, args[0])
    shape = cgutils.unpack_tuple(builder, ary.shape)
    strides = cgutils.unpack_tuple(builder, ary.strides)
    data = ary.data
    layout = aryty.layout

    # First count the number of non-zero elements
    zero = context.get_constant(types.intp, 0)
    one = context.get_constant(types.intp, 1)
    count = cgutils.alloca_once_value(builder, zero)
    with cgutils.loop_nest(builder, shape, zero.type) as indices:
        ptr = cgutils.get_item_pointer2(builder, data, shape, strides,
                                        layout, indices)
        val = load_item(context, builder, aryty, ptr)
        nz = context.is_true(builder, aryty.dtype, val)
        with builder.if_then(nz):
            builder.store(builder.add(builder.load(count), one), count)

    # Then allocate output arrays of the right size
    out_shape = (builder.load(count),)
    outs = [_empty_nd_impl(context, builder, outaryty, out_shape)._getvalue()
            for i in range(nouts)]
    outarys = [make_array(outaryty)(context, builder, out) for out in outs]
    out_datas = [out.data for out in outarys]

    # And fill them up
    index = cgutils.alloca_once_value(builder, zero)
    with cgutils.loop_nest(builder, shape, zero.type) as indices:
        ptr = cgutils.get_item_pointer2(builder, data, shape, strides,
                                        layout, indices)
        val = load_item(context, builder, aryty, ptr)
        nz = context.is_true(builder, aryty.dtype, val)
        with builder.if_then(nz):
            # Store element indices in output arrays
            if not indices:
                # For a 0-d array, store 0 in the unique output array
                indices = (zero,)
            cur = builder.load(index)
            for i in range(nouts):
                ptr = cgutils.get_item_pointer2(builder, out_datas[i],
                                                out_shape, (),
                                                'C', [cur])
                store_item(context, builder, outaryty, indices[i], ptr)
            builder.store(builder.add(cur, one), index)

    tup = context.make_tuple(builder, sig.return_type, outs)
    return impl_ret_new_ref(context, builder, sig.return_type, tup)

def dot_2_mv(context, builder, sig, args):
    """
    np.dot(matrix, vector)
    """
    def dot_impl(a, b):
        m, n = a.shape
        _n, = b.shape
        out = np.empty((m, ), a.dtype)
        return np.dot(a, b, out)

    res = context.compile_internal(builder, dot_impl, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)

def make_zip_object(context, builder, sig, args):
    zip_type = sig.return_type

    assert len(args) == len(zip_type.source_types)

    zipobj = context.make_helper(builder, zip_type)

    for i, (arg, srcty) in enumerate(zip(args, sig.args)):
        zipobj[i] = call_getiter(context, builder, srcty, arg)

    res = zipobj._getvalue()
    return impl_ret_new_ref(context, builder, sig.return_type, res)

def build_list(context, builder, list_type, items):
    """
    Build a list of the given type, containing the given items.
    """
    nitems = len(items)
    inst = ListInstance.allocate(context, builder, list_type, nitems)
    # Populate list
    inst.size = context.get_constant(types.intp, nitems)
    for i, val in enumerate(items):
        inst.setitem(context.get_constant(types.intp, i), val)

    return impl_ret_new_ref(context, builder, list_type, inst.value)

def dot_2_vm(context, builder, sig, args):
    """
    np.dot(vector, matrix)
    """
    def dot_impl(a, b):
        m, = a.shape
        _m, n = b.shape
        out = numpy.empty((n, ), a.dtype)
        return numpy.dot(a, b, out)

    res = context.compile_internal(builder, dot_impl, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)

def list_pop(context, builder, sig, args):
    inst = ListInstance(context, builder, sig.args[0], args[0])

    n = inst.size
    cgutils.guard_zero(context, builder, n,
                       (IndexError, "pop from empty list"))
    n = builder.sub(n, ir.Constant(n.type, 1))
    res = inst.getitem(n)
    inst.incref_value(res)  # incref the pop'ed element
    inst.clear_value(n)     # clear the storage space
    inst.resize(n)
    return impl_ret_new_ref(context, builder, sig.return_type, res)

def set_constructor(context, builder, sig, args):
    set_type = sig.return_type
    items_type, = sig.args
    items, = args

    # If the argument has a len(), preallocate the set so as to
    # avoid resizes.
    n = call_len(context, builder, items_type, items)
    inst = SetInstance.allocate(context, builder, set_type, n)
    with for_iter(context, builder, items_type, items) as loop:
        inst.add(loop.value)

    return impl_ret_new_ref(context, builder, set_type, inst.value)

def array_angle_kwarg(context, builder, sig, args):
    arg = sig.args[0]
    if isinstance(arg.dtype, types.Complex):
        retty = arg.dtype.underlying_float
    else:
        retty = arg.dtype
    def array_angle_impl(arr, deg=False):
        out = numpy.zeros_like(arr, dtype=retty)
        for index, val in numpy.ndenumerate(arr):
            out[index] = numpy.angle(val, deg)
        return out
    res = context.compile_internal(builder, array_angle_impl, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)

def array_angle_kwarg(context, builder, sig, args):
    arg = sig.args[0]
    ret_dtype = sig.return_type.dtype

    def array_angle_impl(arr, deg):
        out = numpy.zeros_like(arr, dtype=ret_dtype)
        for index, val in numpy.ndenumerate(arr):
            out[index] = numpy.angle(val, deg)
        return out

    if len(args) == 1:
        args = args + (cgutils.false_bit,)
        sig = signature(sig.return_type, *(sig.args + (types.boolean,)))

    res = context.compile_internal(builder, array_angle_impl, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)

def array_cumprod(context, builder, sig, args):
    scalar_dtype = sig.return_type.dtype
    dtype = as_dtype(scalar_dtype)

    def array_cumprod_impl(arr):
        size = 1
        for i in arr.shape:
            size = size * i
        out = numpy.empty(size, dtype)
        c = 1
        for idx, v in enumerate(arr.flat):
            c *= v
            out[idx] = c
        return out

    res = context.compile_internal(builder, array_cumprod_impl, sig, args, locals=dict(c=scalar_dtype))
    return impl_ret_new_ref(context, builder, sig.return_type, res)