continuing manual ruff fixes

nwb_linkml/src/nwb_linkml/adapters/adapter.py

@@ -135,12 +135,15 @@ class Adapter(BaseModel):
             # do nothing, is a string or whatever
             pass
 
-    def walk_fields(self, input: BaseModel | list | dict, field: str | Tuple[str, ...]):
+    def walk_fields(
+        self, input: BaseModel | list | dict, field: str | Tuple[str, ...]
+    ) -> Generator[Any, None, None]:
         """
         Recursively walk input for fields that match ``field``
 
         Args:
-            input (:class:`pydantic.BaseModel`) : Model to walk (or a list or dictionary to walk too)
+            input (:class:`pydantic.BaseModel`) : Model to walk (or a list or dictionary
+                to walk too)
             field (str, Tuple[str, ...]):
 
         Returns:
@@ -156,19 +159,20 @@ class Adapter(BaseModel):
         self, input: BaseModel | list | dict, field: str, value: Optional[Any] = None
     ) -> Generator[BaseModel, None, None]:
         """
-        Recursively walk input for **models** that contain a ``field`` as a direct child with a value matching ``value``
+        Recursively walk input for **models** that contain a ``field`` as a direct child
+        with a value matching ``value``
 
         Args:
             input (:class:`pydantic.BaseModel`): Model to walk
             field (str): Name of field - unlike :meth:`.walk_fields`, only one field can be given
-            value (Any): Value to match for given field. If ``None`` , return models that have the field
+            value (Any): Value to match for given field. If ``None`` ,
+                return models that have the field
 
         Returns:
             :class:`pydantic.BaseModel` the matching model
         """
         for item in self.walk(input):
-            if isinstance(item, BaseModel):
+            if isinstance(item, BaseModel) and field in item.model_fields:
-                if field in item.model_fields:
                 if value is None:
                     yield item
                 field_value = item.model_dump().get(field, None)
@@ -178,6 +182,13 @@ class Adapter(BaseModel):
     def walk_types(
         self, input: BaseModel | list | dict, get_type: Type[T] | Tuple[Type[T], Type[Unpack[Ts]]]
     ) -> Generator[T | Ts, None, None]:
+        """
+        Walk a model, yielding items that are the same type as the given type
+
+        Args:
+            input (:class:`pydantic.BaseModel`, list, dict): Object to yield from
+            get_type (:class:`~typing.Type`, tuple[:class:`~typing.Type`]): Type to match
+        """
         if not isinstance(get_type, (list, tuple)):
             get_type = [get_type]
 

nwb_linkml/src/nwb_linkml/adapters/classes.py

@@ -40,15 +40,19 @@ class ClassAdapter(Adapter):
 
         If the class has no parent, then...
 
-        * Its name is inferred from its `neurodata_type_def`,  fixed name, or `neurodata_type_inc` in that order
+        * Its name is inferred from its `neurodata_type_def`,  fixed name, or
+          `neurodata_type_inc` in that order
         * It is just built as normal class!
-        * It will be indicated as a ``tree_root`` (which will primarily be used to invert the translation for write operations)
+        * It will be indicated as a ``tree_root`` (which will primarily be used to invert the
+          translation for write operations)
 
         If the class has a parent, then...
 
-        * If it has a `neurodata_type_def` or `inc`,  that will be used as its name, otherwise concatenate `parent__child`,
+        * If it has a `neurodata_type_def` or `inc`,  that will be used as its name,
+          otherwise concatenate `parent__child`,
           eg. ``TimeSeries__TimeSeriesData``
-        * A slot will also be made and returned with the BuildResult, which the parent will then have as one of its attributes.
+        * A slot will also be made and returned with the BuildResult,
+          which the parent will then have as one of its attributes.
         """
 
         # Build this class
@@ -83,6 +87,15 @@ class ClassAdapter(Adapter):
         return res
 
     def build_attrs(self, cls: Dataset | Group) -> List[SlotDefinition]:
+        """
+        Pack the class attributes into a list of SlotDefinitions
+
+        Args:
+            cls: (:class:`.Dataset` | :class:`.Group`): Class to pack
+
+        Returns:
+            list[:class:`.SlotDefinition`]
+        """
         attrs = [
             SlotDefinition(
                 name=attr.name,
@@ -153,6 +166,15 @@ class ClassAdapter(Adapter):
 
     @classmethod
     def handle_dtype(cls, dtype: DTypeType | None) -> str:
+        """
+        Get the string form of a dtype
+
+        Args:
+            dtype (:class:`.DTypeType`): Dtype to stringify
+
+        Returns:
+            str
+        """
         if isinstance(dtype, ReferenceDtype):
             return dtype.target_type
         elif dtype is None or dtype == []:

nwb_linkml/src/nwb_linkml/adapters/dataset.py

@@ -16,10 +16,16 @@ from nwb_schema_language import Dataset
 
 
 class DatasetMap(Map):
+    """
+    Abstract builder class for dataset elements
+    """
 
     @classmethod
     @abstractmethod
     def check(c, cls: Dataset) -> bool:
+        """
+        Check if this map applies
+        """
         pass  # pragma: no cover
 
     @classmethod
@@ -27,6 +33,9 @@ class DatasetMap(Map):
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Apply this mapping
+        """
         pass  # pragma: no cover
 
 
@@ -94,22 +103,22 @@ class MapScalar(DatasetMap):
               - ``str``
 
         """
-        if (
+        return (
             cls.neurodata_type_inc != "VectorData"
             and not cls.neurodata_type_inc
             and not cls.attributes
             and not cls.dims
             and not cls.shape
             and cls.name
-        ):
+        )
-            return True
-        else:
-            return False
 
     @classmethod
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Map to a scalar value
+        """
         this_slot = SlotDefinition(
             name=cls.name,
             description=cls.doc,
@@ -147,22 +156,22 @@ class MapScalarAttributes(DatasetMap):
               - ``str``
 
         """
-        if (
+        return (
             cls.neurodata_type_inc != "VectorData"
             and not cls.neurodata_type_inc
             and cls.attributes
             and not cls.dims
             and not cls.shape
             and cls.name
-        ):
+        )
-            return True
-        else:
-            return False
 
     @classmethod
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Map to a scalar attribute with an adjoining "value" slot
+        """
         value_slot = SlotDefinition(
             name="value", range=ClassAdapter.handle_dtype(cls.dtype), required=True
         )
@@ -177,23 +186,26 @@ class MapListlike(DatasetMap):
 
     @classmethod
     def check(c, cls: Dataset) -> bool:
+        """
+        Check if we are a 1D dataset that isn't a normal datatype
+        """
         dtype = ClassAdapter.handle_dtype(cls.dtype)
-        if is_1d(cls) and dtype != "AnyType" and dtype not in flat_to_linkml.keys():
+        return is_1d(cls) and dtype != "AnyType" and dtype not in flat_to_linkml
-            return True
-        else:
-            return False
 
     @classmethod
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Map to a list of the given class
+        """
         dtype = camel_to_snake(ClassAdapter.handle_dtype(cls.dtype))
         slot = SlotDefinition(
             name=dtype,
             multivalued=True,
             range=ClassAdapter.handle_dtype(cls.dtype),
             description=cls.doc,
-            required=False if cls.quantity in ("*", "?") else True,
+            required=cls.quantity not in ("*", "?"),
         )
         res.classes[0].attributes[dtype] = slot
         return res
@@ -209,15 +221,18 @@ class MapArraylike(DatasetMap):
 
     @classmethod
     def check(c, cls: Dataset) -> bool:
-        if cls.name and all([cls.dims, cls.shape]) and not has_attrs(cls):
+        """
-            return True
+        Check if we're a plain array
-        else:
+        """
-            return False
+        return cls.name and all([cls.dims, cls.shape]) and not has_attrs(cls)
 
     @classmethod
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Map to an array class and the adjoining slot
+        """
         array_class = make_arraylike(cls, name)
         name = camel_to_snake(cls.name)
         res = BuildResult(
@@ -227,7 +242,7 @@ class MapArraylike(DatasetMap):
                     multivalued=False,
                     range=array_class.name,
                     description=cls.doc,
-                    required=False if cls.quantity in ("*", "?") else True,
+                    required=cls.quantity not in ("*", "?"),
                 )
             ],
             classes=[array_class],
@@ -254,22 +269,24 @@ class MapArrayLikeAttributes(DatasetMap):
 
     @classmethod
     def check(c, cls: Dataset) -> bool:
+        """
+        Check that we're an array with some additional metadata
+        """
         dtype = ClassAdapter.handle_dtype(cls.dtype)
-        if (
+        return (
             all([cls.dims, cls.shape])
             and cls.neurodata_type_inc != "VectorData"
             and has_attrs(cls)
             and (dtype == "AnyType" or dtype in flat_to_linkml)
-        ):
+        )
-            return True
-
-        else:
-            return False
 
     @classmethod
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Map to an arraylike class
+        """
         array_class = make_arraylike(cls, name)
         # make a slot for the arraylike class
         array_slot = SlotDefinition(name="array", range=array_class.name)
@@ -286,27 +303,30 @@ class MapArrayLikeAttributes(DatasetMap):
 
 class Map1DVector(DatasetMap):
     """
-    ``VectorData`` is subclassed with a name but without dims or attributes, treat this as a normal 1D array
+    ``VectorData`` is subclassed with a name but without dims or attributes,
-    slot that replaces any class that would be built for this
+    treat this as a normal 1D array slot that replaces any class that would be built for this
     """
 
     @classmethod
     def check(c, cls: Dataset) -> bool:
-        if (
+        """
+        Check that we're a 1d VectorData class
+        """
+        return (
             cls.neurodata_type_inc == "VectorData"
             and not cls.dims
             and not cls.shape
             and not cls.attributes
             and cls.name
-        ):
+        )
-            return True
-        else:
-            return False
 
     @classmethod
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Return a simple multivalued slot
+        """
         this_slot = SlotDefinition(
             name=cls.name,
             description=cls.doc,
@@ -328,21 +348,23 @@ class MapNVectors(DatasetMap):
 
     @classmethod
     def check(c, cls: Dataset) -> bool:
-        if (
+        """
+        Check for being an unnamed multivalued vector class
+        """
+        return (
             cls.name is None
             and cls.neurodata_type_def is None
             and cls.neurodata_type_inc
             and cls.quantity in ("*", "+")
-        ):
+        )
-            # cls.neurodata_type_inc in ('VectorIndex', 'VectorData') and \
-            return True
-        else:
-            return False
 
     @classmethod
     def apply(
         c, cls: Dataset, res: Optional[BuildResult] = None, name: Optional[str] = None
     ) -> BuildResult:
+        """
+        Return a slot mapping to multiple values of the type
+        """
         this_slot = SlotDefinition(
             name=camel_to_snake(cls.neurodata_type_inc),
             description=cls.doc,
@@ -355,9 +377,15 @@ class MapNVectors(DatasetMap):
 
 
 class DatasetAdapter(ClassAdapter):
+    """
+    Orchestrator class for datasets - calls the set of applicable mapping classes
+    """
     cls: Dataset
 
     def build(self) -> BuildResult:
+        """
+        Build the base result, and then apply the applicable mappings.
+        """
         res = self.build_base()
 
         # find a map to use
@@ -377,6 +405,11 @@ class DatasetAdapter(ClassAdapter):
 
 
 def make_arraylike(cls: Dataset, name: Optional[str] = None) -> ClassDefinition:
+    """
+    Create a containing arraylike class
+
+    This is likely deprecated so this docstring is a placeholder to satisfy the linter...
+    """
     # The schema language doesn't have a way of specifying a dataset/group is "abstract"
     # and yet hdmf-common says you don't need a dtype if the dataset is "abstract"
     # so....
@@ -421,10 +454,7 @@ def make_arraylike(cls: Dataset, name: Optional[str] = None) -> ClassDefinition:
             required = False
 
         # use cardinality to do shape
-        if shape == "null":
+        cardinality = None if shape == "null" else shape
-            cardinality = None
-        else:
-            cardinality = shape
 
         slots.append(
             SlotDefinition(
@@ -436,7 +466,8 @@ def make_arraylike(cls: Dataset, name: Optional[str] = None) -> ClassDefinition:
             )
         )
 
-    # and then the class is just a subclass of `Arraylist` (which is imported by default from `nwb.language.yaml`)
+    # and then the class is just a subclass of `Arraylist`
+    # (which is imported by default from `nwb.language.yaml`)
     if name:
         pass
     elif cls.neurodata_type_def:
@@ -453,20 +484,20 @@ def make_arraylike(cls: Dataset, name: Optional[str] = None) -> ClassDefinition:
 
 
 def is_1d(cls: Dataset) -> bool:
-    if (
+    """
+    Check if the values of a dataset are 1-dimensional
+    """
+    return (
         not any([isinstance(dim, list) for dim in cls.dims]) and len(cls.dims) == 1
     ) or (  # nested list
         all([isinstance(dim, list) for dim in cls.dims])
         and len(cls.dims) == 1
         and len(cls.dims[0]) == 1
-    ):
+    )
-        return True
-    else:
-        return False
 
 
 def has_attrs(cls: Dataset) -> bool:
-    if len(cls.attributes) > 0 and all([not a.value for a in cls.attributes]):
+    """
-        return True
+    Check if a dataset has any attributes at all without defaults
-    else:
+    """
-        return False
+    return len(cls.attributes) > 0 and all([not a.value for a in cls.attributes])

nwb_linkml/src/nwb_linkml/adapters/group.py

@@ -13,9 +13,15 @@ from nwb_schema_language import Group
 
 
 class GroupAdapter(ClassAdapter):
+    """
+    Adapt NWB Groups to LinkML Classes
+    """
     cls: Group
 
     def build(self) -> BuildResult:
+        """
+        Do the translation, yielding the BuildResult
+        """
         # Handle container groups with only * quantity unnamed groups
         if len(self.cls.groups) > 0 and all(
             [self._check_if_container(g) for g in self.cls.groups]
@@ -80,12 +86,7 @@ class GroupAdapter(ClassAdapter):
 
         # don't build subgroups as their own classes, just make a slot
         # that can contain them
-        if self.cls.name:
+        name = cls.name if self.cls.name else "children"
-            name = cls.name
-        # elif len(cls.groups) == 1:
-        #     name = camel_to_snake(cls.groups[0].neurodata_type_inc)
-        else:
-            name = "children"
 
         slot = SlotDefinition(
             name=name,
@@ -126,10 +127,7 @@ class GroupAdapter(ClassAdapter):
               doc: Optional additional table(s) for describing other experimental time intervals.
               quantity: '*'
         """
-        if not self.cls.name:
+        name = camel_to_snake(self.cls.neurodata_type_inc) if not self.cls.name else cls.name
-            name = camel_to_snake(self.cls.neurodata_type_inc)
-        else:
-            name = cls.name
 
         return BuildResult(
             slots=[
@@ -163,7 +161,8 @@ class GroupAdapter(ClassAdapter):
 
         # Groups are a bit more complicated because they can also behave like
         # range declarations:
-        # eg. a group can have multiple groups with `neurodata_type_inc`, no name, and quantity of *,
+        # eg. a group can have multiple groups with `neurodata_type_inc`, no name,
+        # and quantity of *,
         # the group can then contain any number of groups of those included types as direct children
 
         group_res = BuildResult()
@@ -191,7 +190,4 @@ class GroupAdapter(ClassAdapter):
             doc: Images objects containing images of presented stimuli.
             quantity: '*'
         """
-        if not group.name and group.quantity in ("*", "+") and group.neurodata_type_inc:
+        return not group.name and group.quantity in ("*", "+") and group.neurodata_type_inc
-            return True
-        else:
-            return False

nwb_linkml/src/nwb_linkml/adapters/namespaces.py

@@ -4,7 +4,7 @@ Namespaces adapter
 Wraps the :class:`nwb_schema_language.Namespaces` and other objects with convenience methods
 for extracting information and generating translated schema
 """
-
+import contextlib
 from copy import copy
 from pathlib import Path
 from pprint import pformat
@@ -22,14 +22,17 @@ from nwb_schema_language import Namespaces
 
 
 class NamespacesAdapter(Adapter):
+    """
+    Translate a NWB Namespace to a LinkML Schema
+    """
     namespaces: Namespaces
     schemas: List[SchemaAdapter]
     imported: List["NamespacesAdapter"] = Field(default_factory=list)
 
     _imports_populated: bool = PrivateAttr(False)
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: dict):
-        super(NamespacesAdapter, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         self._populate_schema_namespaces()
 
     @classmethod
@@ -37,8 +40,8 @@ class NamespacesAdapter(Adapter):
         """
         Create a NamespacesAdapter from a nwb schema language namespaces yaml file.
 
-        Also attempts to provide imported implicitly imported schema (using the namespace key, rather than source, eg.
+        Also attempts to provide imported implicitly imported schema (using the namespace key,
-        with hdmf-common)
+        rather than source, eg. with hdmf-common)
         """
         from nwb_linkml.io import schema as schema_io
         from nwb_linkml.providers.git import DEFAULT_REPOS
@@ -49,10 +52,10 @@ class NamespacesAdapter(Adapter):
 
         need_imports = []
         for needed in ns_adapter.needed_imports.values():
-            need_imports.extend([n for n in needed if n not in ns_adapter.needed_imports.keys()])
+            need_imports.extend([n for n in needed if n not in ns_adapter.needed_imports])
 
         for needed in need_imports:
-            if needed in DEFAULT_REPOS.keys():
+            if needed in DEFAULT_REPOS:
                 needed_source_ns = DEFAULT_REPOS[needed].provide_from_git()
                 needed_adapter = NamespacesAdapter.from_yaml(needed_source_ns)
                 ns_adapter.imported.append(needed_adapter)
@@ -62,24 +65,23 @@ class NamespacesAdapter(Adapter):
     def build(
         self, skip_imports: bool = False, progress: Optional[AdapterProgress] = None
     ) -> BuildResult:
+        """
+        Build the NWB namespace to the LinkML Schema
+        """
         if not self._imports_populated and not skip_imports:
             self.populate_imports()
 
         sch_result = BuildResult()
         for sch in self.schemas:
             if progress is not None:
-                try:
+                with contextlib.suppress(KeyError):
-                    progress.update(sch.namespace, action=sch.name)
-                except KeyError:  # pragma: no cover
                     # happens when we skip builds due to caching
-                    pass
+                    progress.update(sch.namespace, action=sch.name)
             sch_result += sch.build()
             if progress is not None:
-                try:
+                with contextlib.suppress(KeyError):
-                    progress.update(sch.namespace, advance=1)
-                except KeyError:  # pragma: no cover
                     # happens when we skip builds due to caching
-                    pass
+                    progress.update(sch.namespace, advance=1)
 
         # recursive step
         if not skip_imports:
@@ -125,8 +127,10 @@ class NamespacesAdapter(Adapter):
 
         return sch_result
 
-    def _populate_schema_namespaces(self):
+    def _populate_schema_namespaces(self) -> None:
-        # annotate for each schema which namespace imports it
+        """
+        annotate for each schema which namespace imports it
+        """
         for sch in self.schemas:
             # imports seem to always be from same folder, so we can just use name part
             sch_name = sch.path.name
@@ -154,7 +158,7 @@ class NamespacesAdapter(Adapter):
         if len(internal_matches) > 1:
             raise KeyError(
                 f"Found multiple schemas in namespace that define {name}:\ninternal:"
-                f" {pformat(internal_matches)}\nimported:{pformat(import_matches)}"
+                f" {pformat(internal_matches)}\nimported:{pformat(internal_matches)}"
             )
         elif len(internal_matches) == 1:
             return internal_matches[0]
@@ -176,7 +180,7 @@ class NamespacesAdapter(Adapter):
         else:
             raise KeyError(f"No schema found that define {name}")
 
-    def populate_imports(self):
+    def populate_imports(self) -> None:
         """
         Populate the imports that are needed for each schema file
 
@@ -199,7 +203,14 @@ class NamespacesAdapter(Adapter):
 
         self._imports_populated = True
 
-    def to_yaml(self, base_dir: Path):
+    def to_yaml(self, base_dir: Path) -> None:
+        """
+        Build the schemas, saving them to ``yaml`` files according to
+        their ``name``
+
+        Args:
+            base_dir (:class:`.Path`): Directory to save ``yaml`` files
+        """
         schemas = self.build().schemas
         base_dir = Path(base_dir)
 

nwb_linkml/src/nwb_linkml/adapters/schema.py

@@ -4,7 +4,7 @@ to call them "schema" objects
 """
 
 from pathlib import Path
-from typing import List, NamedTuple, Optional, Type
+from typing import List, Optional, Type
 
 from linkml_runtime.linkml_model import SchemaDefinition
 from pydantic import Field, PrivateAttr
@@ -15,11 +15,6 @@ from nwb_linkml.adapters.group import GroupAdapter
 from nwb_schema_language import Dataset, Group
 
 
-class SplitSchema(NamedTuple):
-    main: BuildResult
-    split: Optional[BuildResult]
-
-
 class SchemaAdapter(Adapter):
     """
     An individual schema file in nwb_schema_language
@@ -43,6 +38,9 @@ class SchemaAdapter(Adapter):
 
     @property
     def name(self) -> str:
+        """
+        The namespace.schema name for a single schema
+        """
         return ".".join([self.namespace, self.path.with_suffix("").name])
 
     def __repr__(self):
@@ -82,7 +80,7 @@ class SchemaAdapter(Adapter):
 
         if (
             len(res.slots) > 0
-        ):  # pragma: no cover - hard to induce this error because the child classes don't fuck up like this
+        ):  # pragma: no cover - hard to induce this because child classes don't fuck up like this
             raise RuntimeError(
                 "Generated schema in this translation can only have classes, all slots should be"
                 " attributes within a class"
@@ -107,6 +105,9 @@ class SchemaAdapter(Adapter):
 
     @property
     def created_classes(self) -> List[Type[Group | Dataset]]:
+        """
+        All the group and datasets created in this schema
+        """
         if len(self._created_classes) == 0:
             self._created_classes = [
                 t

nwb_linkml/src/nwb_linkml/annotations.py

@@ -3,10 +3,15 @@ Utility functions for introspection on python annotations
 """
 
 import typing
-from typing import Any, List
+from typing import Any, List, Optional, Type, TypeVar
+
+T = TypeVar('T')
 
 
-def unwrap_optional(annotation):
+def unwrap_optional(annotation: Type[Optional[T]]) -> Type[T]:
+    """
+    Get the inner type of an `Optional[T]` type
+    """
     if typing.get_origin(annotation) == typing.Union:
         args = typing.get_args(annotation)
 
@@ -15,7 +20,10 @@ def unwrap_optional(annotation):
     return annotation
 
 
-def get_inner_types(annotation) -> List[Any]:
+def get_inner_types(annotation: Type) -> List[Any]:
+    """
+    Get the inner types in some nested type, recursively
+    """
     types = []
     args = typing.get_args(annotation)
     for arg in args:

nwb_linkml/src/nwb_linkml/config.py

@@ -54,6 +54,9 @@ class Config(BaseSettings):
     @field_validator("cache_dir", mode="before")
     @classmethod
     def folder_exists(cls, v: Path, info: FieldValidationInfo) -> Path:
+        """
+        The base cache dir should exist before validating other paths
+        """
         v = Path(v)
         v.mkdir(exist_ok=True)
         assert v.exists()
@@ -61,7 +64,10 @@ class Config(BaseSettings):
 
     @model_validator(mode="after")
     def folders_exist(self) -> "Config":
-        for field, path in self.model_dump().items():
+        """
+        All folders, including computed folders, should exist.
+        """
+        for path in self.model_dump().values():
             if isinstance(path, Path):
                 path.mkdir(exist_ok=True, parents=True)
                 assert path.exists()

nwb_linkml/src/nwb_linkml/generators/__init__.py

@@ -1 +1,12 @@
+"""
+Generate downstream output from LinkML schema
+
+Mostly for monkeypatching the pydantic generator from linkml with
+changes that are unlikely to be useful upstream
+"""
+
 from nwb_linkml.generators.pydantic import PydanticGenerator
+
+__all__ = [
+    'PydanticGenerator'
+]

nwb_linkml/src/nwb_linkml/generators/pydantic.py

@@ -25,6 +25,9 @@ The `serialize` method:
 
 """
 
+# FIXME: Remove this after we refactor this generator
+# ruff: noqa
+
 import inspect
 import sys
 import warnings

nwb_linkml/src/nwb_linkml/io/__init__.py

@@ -1,2 +1,11 @@
+"""
+Loading and dumping data from and to files
+"""
+
 from nwb_linkml.io import schema
 from nwb_linkml.io.hdf5 import HDF5IO
+
+__all__ = [
+    "HDF5IO",
+    "schema"
+]

nwb_linkml/src/nwb_linkml/io/hdf5.py

@@ -1,5 +1,6 @@
 """
-This is a sandbox file that should be split out to its own pydantic-hdf5 package, but just experimenting here to get our bearings
+This is a sandbox file that should be split out to its own pydantic-hdf5 package,
+but just experimenting here to get our bearings
 
 Notes:
 
@@ -42,6 +43,9 @@ from nwb_linkml.providers.schema import SchemaProvider
 
 
 class HDF5IO:
+    """
+    Read (and eventually write) from an NWB HDF5 file.
+    """
 
     def __init__(self, path: Path):
         self.path = Path(path)
@@ -59,7 +63,8 @@ class HDF5IO:
 
         The read process is in several stages:
 
-        * Use :meth:`.make_provider` to generate any needed LinkML Schema or Pydantic Classes using a :class:`.SchemaProvider`
+        * Use :meth:`.make_provider` to generate any needed LinkML Schema or Pydantic Classes
+          using a :class:`.SchemaProvider`
         * :func:`flatten_hdf` file into a :class:`.ReadQueue` of nodes.
         * Apply the queue's :class:`ReadPhases` :
 
@@ -67,24 +72,29 @@ class HDF5IO:
             * ``read`` - load the actual data into temporary holding objects
             * ``construct`` - cast the read data into models.
 
-        Read is split into stages like this to handle references between objects, where the read result of one node
+        Read is split into stages like this to handle references between objects,
-        might depend on another having already been completed. It also allows us to parallelize the operations
+        where the read result of one node
+        might depend on another having already been completed.
+        It also allows us to parallelize the operations
         since each mapping operation is independent of the results of all the others in that pass.
 
         .. todo::
 
             Implement reading, skipping arrays - they are fast to read with the ArrayProxy class
             and dask, but there are times when we might want to leave them out of the read entirely.
-            This might be better implemented as a filter on ``model_dump`` , but to investigate further
+            This might be better implemented as a filter on ``model_dump`` ,
-            how best to support reading just metadata, or even some specific field value, or if
+            but to investigate further how best to support reading just metadata,
+            or even some specific field value, or if
             we should leave that to other implementations like eg. after we do SQL export then
             not rig up a whole query system ourselves.
 
         Args:
-            path (Optional[str]): If ``None`` (default), read whole file. Otherwise, read from specific (hdf5) path and its children
+            path (Optional[str]): If ``None`` (default), read whole file.
+                Otherwise, read from specific (hdf5) path and its children
 
         Returns:
-            ``NWBFile`` if ``path`` is ``None``, otherwise whatever Model or dictionary of models applies to the requested ``path``
+            ``NWBFile`` if ``path`` is ``None``,
+            otherwise whatever Model or dictionary of models applies to the requested ``path``
         """
 
         provider = self.make_provider()

nwb_linkml/src/nwb_linkml/maps/__init__.py

@@ -1,5 +1,18 @@
-# Import everything so it's defined, but shoudlnt' necessarily be used from here
+"""
+Mapping from one domain to another
+"""
+
 from nwb_linkml.maps.dtype import flat_to_linkml, flat_to_nptyping
 from nwb_linkml.maps.map import Map
 from nwb_linkml.maps.postload import MAP_HDMF_DATATYPE_DEF, MAP_HDMF_DATATYPE_INC
 from nwb_linkml.maps.quantity import QUANTITY_MAP
+
+__all__ = [
+    "MAP_HDMF_DATATYPE_DEF",
+    "MAP_HDMF_DATATYPE_INC",
+    "QUANTITY_MAP",
+    "Map",
+    "flat_to_linkml",
+    "flat_to_nptyping"
+]
+

nwb_schema_language/src/nwb_schema_language/__init__.py

@@ -16,8 +16,24 @@ try:
     )
 
     DTypeType = Union[List[CompoundDtype], FlatDtype, ReferenceDtype]
+
+
 except (NameError, RecursionError):
     warnings.warn(
         "Error importing pydantic classes, passing because we might be in the process of patching"
         " them, but it is likely they are broken and you will be unable to use them!"
     )
+
+__all__ = [
+    "Attribute",
+    "CompoundDtype",
+    "Dataset",
+    "DTypeType",
+    "FlatDtype",
+    "Group",
+    "Link",
+    "Namespace",
+    "Namespaces",
+    "ReferenceDtype",
+    "Schema",
+]

pyproject.toml

@@ -70,7 +70,7 @@ ignore = [
 fixable = ["ALL"]
 
 [tool.ruff.lint.per-file-ignores]
-"**/tests/**.py" = ["D", "ANN"]
+"**/tests/**.py" = ["D", "ANN", "E501", "F841", "F722"]
 
 [tool.mypy]
 plugins = [