def find_item_suggestions(
name, modaliases, schema, *, item_types=None, limit=3,
collection=None, condition=None):
from . import modules as s_mod
if isinstance(name, sn.Name):
orig_modname = name.module
short_name = name.name
else:
orig_modname = None
short_name = name
modname = modaliases.get(orig_modname, orig_modname)
suggestions = []
if collection is not None:
suggestions.extend(collection)
else:
if modname:
module = schema.get_global(s_mod.Module, modname, None)
if module:
suggestions.extend(schema.get_objects(modules=[modname]))
if not orig_modname:
suggestions.extend(schema.get_objects(modules=['std']))
if item_types:
suggestions = list(
filter(lambda s: isinstance(s, item_types), suggestions))
if condition is not None:
suggestions = list(filter(condition, suggestions))
# Compute Levenshtein distance for each suggestion.
with_distance = [
(s, levenshtein.distance(short_name, get_nq_name(schema, s)))
for s in suggestions
]
# Filter out suggestions that are too dissimilar.
max_distance = 3
closest = list(filter(lambda s: s[1] < max_distance, with_distance))
# Sort by proximity, then by whether the suggestion is contains
# the source string at the beginning, then by suggestion name.
closest.sort(
key=lambda s: (
s[1],
not get_nq_name(schema, s[0]).startswith(short_name),
s[0].get_displayname(schema)
)
)
return [s[0] for s in closest[:limit]]
def find_item_suggestions(
name: Optional[str],
modaliases: Mapping[Optional[str], str],
schema: s_schema.Schema,
*,
item_types: Tuple[so.ObjectMeta, ...] = (),
limit: int = 3,
collection: Optional[Iterable[so.Object]] = None,
condition: Optional[Callable[[so.Object],
bool]] = None) -> List[so.Object]:
from . import functions as s_func
from . import modules as s_mod
if isinstance(name, sn.Name):
orig_modname: Optional[str] = name.module
short_name: str = name.name
else:
assert name is not None, ("A name must be provided either "
"as string or SchemaName")
orig_modname = None
short_name = name
modname = modaliases.get(orig_modname, orig_modname)
suggestions: List[so.Object] = []
if collection is not None:
suggestions.extend(collection)
else:
if modname:
module = schema.get_global(s_mod.Module, modname, None)
if module:
suggestions.extend(
schema.get_objects(included_modules=[modname]))
if not orig_modname:
suggestions.extend(schema.get_objects(included_modules=['std']))
filters = []
if item_types:
filters.append(lambda s: isinstance(s, item_types))
if condition is not None:
filters.append(condition)
if not item_types:
# When schema class is not specified, only suggest generic objects.
filters.append(lambda s: not sn.is_fullname(s.get_name(schema)))
filters.append(lambda s: not isinstance(s, s_func.CallableObject))
# Never suggest object fragments.
filters.append(lambda s: not isinstance(s, so.ObjectFragment))
filtered_suggestions = filter(lambda s: all(f(s) for f in filters),
suggestions)
# Compute Levenshtein distance for each suggestion.
with_distance: List[Tuple[so.Object, int]] = [
(s, levenshtein.distance(short_name, get_nq_name(schema, s)))
for s in filtered_suggestions
]
# Filter out suggestions that are too dissimilar.
max_distance = 3
closest = list(filter(lambda s: s[1] < max_distance, with_distance))
# Sort by proximity, then by whether the suggestion is contains
# the source string at the beginning, then by suggestion name.
closest.sort(key=lambda s: (s[1], not get_nq_name(schema, s[0]).startswith(
short_name), s[0].get_displayname(schema)))
return [s[0] for s in closest[:limit]]
