def compute_up(expr, data, **kwargs):
leaf = expr._leaves()[0]
chunk = symbol('chunk', DataShape(*(tuple(map(first, data.chunks)) +
(leaf.dshape.measure,))))
(chunk, chunk_expr), (agg, agg_expr) = split(expr._child, expr,
chunk=chunk)
inds = tuple(range(ndim(leaf)))
dtype = expr.dshape.measure.to_numpy_dtype()
tmp = atop(
curry(compute_it, chunk_expr, [chunk], **kwargs),
inds,
data,
inds,
dtype=dtype,
)
return atop(
compose(
curry(compute_it, agg_expr, [agg], **kwargs),
curry(_concatenate2, axes=expr.axis),
),
tuple(i for i in inds if i not in expr.axis),
tmp,
inds,
dtype=dtype,
)
def compute_up(expr, data, **kwargs):
leaf = expr._leaves()[0]
chunk = symbol(
'chunk',
DataShape(*(tuple(map(first, data.chunks)) + (leaf.dshape.measure, ))))
(chunk, chunk_expr), (agg, agg_expr) = split(expr._child,
expr,
chunk=chunk)
inds = tuple(range(ndim(leaf)))
dtype = expr.dshape.measure.to_numpy_dtype()
tmp = atop(
curry(compute_it, chunk_expr, [chunk], **kwargs),
inds,
data,
inds,
dtype=dtype,
)
return atop(
compose(
curry(compute_it, agg_expr, [agg], **kwargs),
curry(_concatenate2, axes=expr.axis),
),
tuple(i for i in inds if i not in expr.axis),
tmp,
inds,
dtype=dtype,
)
def elemwise_array(expr, *data, **kwargs):
leaves = expr._inputs
expr_inds = tuple(range(ndim(expr)))[::-1]
return atop(curry(compute_it, expr, leaves, **kwargs),
expr_inds,
*concat((dat, tuple(range(ndim(dat))[::-1])) for dat in data),
dtype=expr.dshape.measure.to_numpy_dtype())
def elemwise_array(expr, *data, **kwargs):
leaves = expr._inputs
expr_inds = tuple(range(ndim(expr)))[::-1]
return atop(
curry(compute_it, expr, leaves, **kwargs),
expr_inds,
*concat((dat, tuple(range(ndim(dat))[::-1])) for dat in data)
)
def compute_broadcast(expr, *data, **kwargs):
expr_inds = tuple(range(ndim(expr)))[::-1]
func = get_numba_ufunc(expr)
return atop(func,
expr_inds,
*concat(
(dat, tuple(range(ndim(dat))[::-1])) for dat in data),
dtype=data[-1].dtype)
def elemwise_array(expr, *data, **kwargs):
leaves = expr._inputs
expr_inds = tuple(range(ndim(expr)))[::-1]
return atop(
curry(compute_it, expr, leaves, **kwargs),
expr_inds,
*concat((dat, tuple(range(ndim(dat))[::-1])) for dat in data),
dtype=expr.dshape.measure.to_numpy_dtype()
)
def compute_broadcast(expr, *data, **kwargs):
expr_inds = tuple(range(ndim(expr)))[::-1]
func = get_numba_ufunc(expr)
return atop(func, expr_inds, *concat((dat, tuple(range(ndim(dat))[::-1])) for dat in data))
def elemwise_array(expr, *data, **kwargs):
leaves = expr._inputs
expr_inds = tuple(range(ndim(expr)))[::-1]
return atop(curry(compute_it, expr, leaves, **kwargs), next(names),
expr_inds,
*concat((dat, tuple(range(ndim(dat))[::-1])) for dat in data))
