def make_blaze(value):
if not isinstance(value, blaze.Array):
dshape = T.typeof(value)
if not dshape.shape:
value = blaze.array([value], dshape)
else:
value = blaze.array(value, dshape)
return value
def construct(bfunc, ctx, overload, args):
"""
Blaze expression graph construction for deferred evaluation.
Parameters
----------
bfunc : Blaze Function
(Overloaded) blaze function representing the operation
ctx: ExprContext
Context of the expression
overload: blaze.overload.Overload
Instance representing the overloaded function
args: list
bfunc parameters
"""
assert isinstance(bfunc, BlazeFunc), bfunc
params = [] # [(graph_term, ExprContext)]
# -------------------------------------------------
# Build type unification parameters
for i, arg in enumerate(args):
if isinstance(arg, blaze.Array) and arg.expr:
# Compose new expression using previously constructed expression
term, context = arg.expr
if not arg.deferred:
ctx.add_input(term, arg)
elif isinstance(arg, blaze.Array):
term = ArrayOp(arg.dshape)
ctx.add_input(term, arg)
empty = ExprContext()
arg.expr = (term, empty)
elif not isinstance(arg, blaze.Array):
term = ArrayOp(T.typeof(arg))
ctx.terms[term] = arg
params.append(term)
# -------------------------------------------------
assert isinstance(overload.resolved_sig, T.Function)
restype = blaze.dshape(overload.resolved_sig.parameters[-1])
# -------------------------------------------------
return KernelOp(restype, *params, kernel=bfunc, overload=overload,
**bfunc.metadata)
def from_value(value):
return ArrayOp(T.typeof(value), value)
