def test_parser_speed(string, n):
start_time = time.time()
for i in range(n):
parse(string)
elapsed_time = time.time() - start_time
print(
f"\nelapsed time: {elapsed_time}; time per parse: {elapsed_time / n}")
def is_rp5_streamflow_ensemble_percentile(p, cell_methods):
cell_methods = parse_if_str(cell_methods)
return (
len(cell_methods) == 4
and is_rp5_streamflow_climatology_single_model(cell_methods[0:3])
and cell_methods[4] == parse(f"models: percentile[{p}]")[0]
)
def check_final_cell_method(cell_methods, target_method, default_to_mean=True):
"""Determines whether the final method in a cell methods string
(corresponding to a statistical aggregation) matches the target.
If default_to_mean is true, treats errors and unrecognized methods
as though they are climatological means. This compensates for some
noisy cell_methods attributes in our data, all of which are
climatological means.
"""
parsed = parse(cell_methods)
if target_method == "mean" and default_to_mean:
# determine means by process of elimination
nonmeans = [m for m in VALID_CELL_METHODS_PARAMETERS if m != "mean"]
return parsed is None or parsed[-1].method.name not in nonmeans
elif parsed is not None:
return parsed[-1].method.name == target_method
else:
# unparsable cell methods string
return False
def test_is_conventional(cell_method_str, expected):
cell_methods = parse(cell_method_str)
assert is_conventional(cell_methods) is expected
def test_single(cell_method_str, conventional, extended):
cell_methods = parse(cell_method_str)
assert is_conventional_1(cell_methods[0]) is conventional
assert is_extended_1(cell_methods[0]) is extended
def parse_if_str(cell_methods):
return parse(cell_methods) if isinstance(cell_methods, str) \
else cell_methods