def deduce_fix(self, ds_id, atypical_content, typical_content):
dicts = []
atypical = atypical_content["data.dim_names"]
typical = typical_content["data.dim_names"]
# length of atypical should be longer if extra coord
# Will this always be the case?
if len(atypical) <= len(typical):
return None
extra_coords = get_extra_items_in_larger_sequence(typical, atypical)
if extra_coords:
if len(extra_coords) > 0:
fix_cls = get_fix(self.associated_fix)
for coord in extra_coords:
index = atypical.index(coord)
if atypical_content["data.shape"][index] != 1:
extra_coords.remove(coord)
operands = {"dims": extra_coords}
fix = fix_cls(ds_id, **operands)
d = fix.to_dict()
dicts.append(d)
return dicts
# fix isn't suitable - no extra coords
else:
return None
def test_get_items_larger_sequence_6():
large = ["i", "j", "latitude", "longitude", "time", "type"]
small = ["latitude", "longitude", "time", "x", "y"]
res = get_extra_items_in_larger_sequence(small, large)
assert res is None
def test_get_items_larger_sequence_5():
large = [("time", 3600), ("level", 1)]
small = [("realisation", 1)]
res = get_extra_items_in_larger_sequence(small, large)
assert res is None
def test_get_items_larger_sequence_3():
large = [3540, 1]
small = [3540]
res = get_extra_items_in_larger_sequence(small, large)
assert res == [large[1]]
def test_get_items_larger_sequence_2():
large = ["lev", "time"]
small = ["time"]
res = get_extra_items_in_larger_sequence(small, large)
assert res == [large[0]]
def test_get_extra_items_in_larger_sequence():
large = [("time", 12), ("level", 1)]
small = [("time", 12)]
res = get_extra_items_in_larger_sequence(small, large)
assert res == [large[1]]
def deduce_fix(self, ds_id, atypical_content, typical_content):
dicts = []
atypical = atypical_content["coordinates.*.id"]
typical = typical_content["coordinates.*.id"]
# length of atypical should be shorter if missing coord
# Will this always be the case?
if len(atypical) >= len(typical):
return None
# # use mappings to get equivalent coords
# for coord in atypical:
# if coord not in typical:
# equivalent_coord = coord_mappings[coord]
# atypical = [equivalent_coord if i == coord else i for i in atypical]
missing_coords = get_extra_items_in_larger_sequence(atypical, typical)
if missing_coords:
if len(missing_coords) > 0:
fix_cls = get_fix(self.associated_fix)
for coord in missing_coords:
typical_coord_attrs = []
typical_ds_ids = self.sample.copy()
typical_ds_ids.remove(ds_id[0])
for ds in typical_ds_ids:
coord_attrs = nested_lookup(f"coordinates.{coord}",
self._cache[ds],
must_exist=True)
coord_attrs = namedtuple(
"coord_attrs", coord_attrs.keys())(**coord_attrs)
typical_coord_attrs.append(coord_attrs)
frequency = Counter(d for d in typical_coord_attrs)
typical_coord = frequency.most_common(1)[0][0]
typical_length = typical_coord.length
# check missing coord is scalar
if typical_length == 1:
operands = {}
operand_dict = dict(typical_coord._asdict())
for k in [
"dtype", "value", "id", "length", "coord_type"
]:
v = operand_dict.pop(k)
operands[k] = v
operands["attrs"] = operand_dict
fix = fix_cls(ds_id, **operands)
d = fix.to_dict()
# coordinate isn't scalar - fix isn't suitable
else:
d = None
dicts.append(d)
return dicts
# fix isn't suitable - no missing coords
else:
return None
