def _inputs_for_term(term, workspace, graph):
"""
Compute inputs for the given term.
This is mostly complicated by the fact that for each input we store as
many rows as will be necessary to serve **any** computation requiring
that input.
"""
offsets = graph.offset
out = []
if term.windowed:
# If term is windowed, then all input data should be instances of
# AdjustedArray.
for input_ in term.inputs:
adjusted_array = ensure_adjusted_array(
workspace[input_], input_.missing_value,
)
out.append(
adjusted_array.traverse(
window_length=term.window_length,
offset=offsets[term, input_],
)
)
else:
# If term is not windowed, input_data may be an AdjustedArray or
# np.ndarray. Coerce the former to the latter.
for input_ in term.inputs:
input_data = ensure_ndarray(workspace[input_])
offset = offsets[term, input_]
# OPTIMIZATION: Don't make a copy by doing input_data[0:] if
# offset is zero.
if offset:
input_data = input_data[offset:]
out.append(input_data)
return out
def _inputs_for_term(term, workspace, graph, domain, refcounts):
"""
Compute inputs for the given term.
This is mostly complicated by the fact that for each input we store as
many rows as will be necessary to serve **any** computation requiring
that input.
"""
offsets = graph.offset
out = []
# We need to specialize here because we don't change ComputableTerm
# after resolving domains, so they can still contain generic terms as
# inputs.
specialized = [maybe_specialize(t, domain) for t in term.inputs]
if term.windowed:
# If term is windowed, then all input data should be instances of
# AdjustedArray.
for input_ in specialized:
adjusted_array = ensure_adjusted_array(
workspace[input_],
input_.missing_value,
)
out.append(
adjusted_array.traverse(
window_length=term.window_length,
offset=offsets[term, input_],
# If the refcount for the input is > 1, we will need
# to traverse this array again so we must copy.
# If the refcount for the input == 0, this is the last
# traversal that will happen so we can invalidate
# the AdjustedArray and mutate the data in place.
copy=refcounts[input_] > 1,
))
else:
# If term is not windowed, input_data may be an AdjustedArray or
# np.ndarray. Coerce the former to the latter.
for input_ in specialized:
input_data = ensure_ndarray(workspace[input_])
offset = offsets[term, input_]
# OPTIMIZATION: Don't make a copy by doing input_data[0:] if
# offset is zero.
if offset:
input_data = input_data[offset:]
out.append(input_data)
return out
def _inputs_for_term(term, workspace, graph, domain):
"""
Compute inputs for the given term.
This is mostly complicated by the fact that for each input we store as
many rows as will be necessary to serve **any** computation requiring
that input.
"""
offsets = graph.offset
out = []
# We need to specialize here because we don't change ComputableTerm
# after resolving domains, so they can still contain generic terms as
# inputs.
specialized = [maybe_specialize(t, domain) for t in term.inputs]
if term.windowed:
# If term is windowed, then all input data should be instances of
# AdjustedArray.
for input_ in specialized:
adjusted_array = ensure_adjusted_array(
workspace[input_], input_.missing_value,
)
out.append(
adjusted_array.traverse(
window_length=term.window_length,
offset=offsets[term, input_],
)
)
else:
# If term is not windowed, input_data may be an AdjustedArray or
# np.ndarray. Coerce the former to the latter.
for input_ in specialized:
input_data = ensure_ndarray(workspace[input_])
offset = offsets[term, input_]
# OPTIMIZATION: Don't make a copy by doing input_data[0:] if
# offset is zero.
if offset:
input_data = input_data[offset:]
out.append(input_data)
return out
