try:
from blaze.compute.numba import get_numba_ufunc, broadcast_collect, Broadcastable
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 optimize_array(expr, *data):
return broadcast_collect(expr, Broadcastable=Broadcastable, WantToBroadcast=Broadcastable)
for i in range(5):
compute_up.register(Broadcast, *([(Array, Number)] * i))(compute_broadcast)
optimize.register(Expr, *([(Array, Number)] * i))(optimize_array)
except ImportError:
pass
for i in range(5):
compute_up.register(ElemWise, *([Array] * i))(elemwise_array)
@dispatch(Reduction, Array)
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)
def compute_broadcast(expr, *data, **kwargs):
expr_inds = tuple(range(ndim(expr)))[::-1]
func = get_numba_ufunc(expr)
return atop(
func, next(names), expr_inds,
*concat((dat, tuple(range(ndim(dat))[::-1])) for dat in data))
def optimize_array(expr, *data):
return broadcast_collect(expr,
Broadcastable=Broadcastable,
WantToBroadcast=Broadcastable)
for i in range(5):
compute_up.register(Broadcast,
*([(Array, Number)] * i))(compute_broadcast)
optimize.register(Expr, *([(Array, Number)] * i))(optimize_array)
except ImportError:
pass
for i in range(5):
compute_up.register(ElemWise, *([Array] * i))(elemwise_array)
@dispatch(Reduction, Array)
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,