Correct schema for hdf5 path type

dont double MRO for basemodels in generated dynamictable

nwb_linkml/src/nwb_linkml/io/hdf5.py

@@ -20,7 +20,7 @@ Other TODO:
 """
 import pdb
 import warnings
-from typing import Optional, Dict, overload, Type
+from typing import Optional, Dict, overload, Type, Union
 from pathlib import Path
 from types import ModuleType
 from typing import TYPE_CHECKING, NamedTuple
@@ -58,7 +58,7 @@ class HDF5IO():
     @overload
     def read(self, path:str) -> BaseModel | Dict[str, BaseModel]: ...
 
-    def read(self, path:Optional[str] = None):
+    def read(self, path:Optional[str] = None) -> Union['NWBFile', BaseModel, Dict[str, BaseModel]]:
         print('starting read')
         provider = self.make_provider()
         print('provider made')
@@ -95,6 +95,8 @@ class HDF5IO():
 
 
         queue.apply_phase(ReadPhases.construct)
+
+        pdb.set_trace()
         # --------------------------------------------------
         # FIXME: Hardcoding top-level file reading just for the win
         # --------------------------------------------------

nwb_linkml/src/nwb_linkml/maps/hdf5.py

@@ -4,7 +4,6 @@ Maps for reading and writing from HDF5
 We have sort of diverged from the initial idea of a generalized map as in :class:`linkml.map.Map` ,
 so we will make our own mapping class here and re-evaluate whether they should be unified later
 """
-import pdb
 from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Literal, List, Dict, Optional, Type, Union
@@ -12,8 +11,7 @@ from typing import Literal, List, Dict, Optional, Type, Union
 import h5py
 from enum import StrEnum
 
-from pydantic import BaseModel, Field, ConfigDict, ValidationError
-import dask.array as da
+from pydantic import BaseModel, Field, ConfigDict
 
 from nwb_linkml.providers.schema import SchemaProvider
 from nwb_linkml.maps.hdmf import dynamictable_to_model
@@ -30,7 +28,11 @@ class ReadPhases(StrEnum):
     """After reading, casting the results of the read into their models"""
 
 class H5SourceItem(BaseModel):
-    """Tuple of items for each element when flattening an hdf5 file"""
+    """
+    Descriptor of items for each element when :func:`.flatten_hdf` flattens an hdf5 file.
+
+    Consumed by :class:`.HDF5Map` classes, orchestrated by :class:`.ReadQueue`
+    """
     path: str
     """Absolute hdf5 path of element"""
     h5f_path: str
@@ -55,7 +57,11 @@ class H5SourceItem(BaseModel):
         return self.path.split('/')
 
 class H5ReadResult(BaseModel):
-    """Result returned by each of our mapping operations"""
+    """
+    Result returned by each of our mapping operations.
+
+    Also used as the source for operations in the ``construct`` :class:`.ReadPhases`
+    """
     path: str
     """absolute hdf5 path of element"""
     source: Union[H5SourceItem, 'H5ReadResult']
@@ -78,9 +84,9 @@ class H5ReadResult(BaseModel):
     """
     The model that this item should be cast into
     """
-    completes: List[str] = Field(default_factory=list)
+    completes: List[HDF5_Path] = Field(default_factory=list)
     """
-    If this result completes any other fields, we remove them from the build queue
+    If this result completes any other fields, we remove them from the build queue.
     """
     namespace: Optional[str] = None
     """
@@ -88,11 +94,11 @@ class H5ReadResult(BaseModel):
     """
     neurodata_type: Optional[str] = None
     """
-    Optional: The neurodata type to use for this object 
+    Optional: The neurodata type to use for this object
     """
     applied: List[str] = Field(default_factory=list)
     """
-    Which stages were applied to this item
+    Which map operations were applied to this item
     """
     errors: List[str] = Field(default_factory=list)
     """
@@ -109,19 +115,20 @@ FlatH5 = Dict[str, H5SourceItem]
 
 class HDF5Map(ABC):
     phase: ReadPhases
-    """
-    If ``True``, if the check is fulfilled, no other maps can be applied this phase
-    """
     priority: int = 0
+    """
+    Within a phase, sort mapping operations from low to high priority
+    (maybe this should be renamed because highest priority last doesnt make a lot of sense)
+    """
 
     @classmethod
     @abstractmethod
-    def check(cls, src: H5SourceItem, provider:SchemaProvider, completed: Dict[str, H5ReadResult]) -> bool:
+    def check(cls, src: H5SourceItem|H5ReadResult, provider:SchemaProvider, completed: Dict[str, H5ReadResult]) -> bool:
         """Check if this map applies to the given item to read"""
 
     @classmethod
     @abstractmethod
-    def apply(cls, src: H5SourceItem, provider:SchemaProvider, completed: Dict[str, H5ReadResult]) -> H5ReadResult:
+    def apply(cls, src: H5SourceItem|H5ReadResult, provider:SchemaProvider, completed: Dict[str, H5ReadResult]) -> H5ReadResult:
         """Actually apply the map!"""
 
 
@@ -157,11 +164,10 @@ class ResolveDynamicTable(HDF5Map):
     and then we include the datasets as :class:`~.nwb_linkml.types.ndarray.NDArrayProxy` objects which
     lazy load the arrays in a thread/process safe way.
 
-    This map also resolves
+    This map also resolves the child elements, indicating so by the ``completes`` field in the :class:`.ReadResult`
     """
     phase = ReadPhases.read
     priority = 1
-    exclusive = True
     @classmethod
     def check(cls, src: H5SourceItem, provider:SchemaProvider, completed: Dict[str, H5ReadResult]) -> bool:
         if src.h5_type == 'dataset':
@@ -191,7 +197,7 @@ class ResolveDynamicTable(HDF5Map):
             base_model = provider.get_class(src.namespace, src.neurodata_type)
             model = dynamictable_to_model(obj, base=base_model)
 
-            completes = ['/'.join([src.path, child]) for child in obj.keys()]
+            completes = [HDF5_Path(child.name) for child in obj.values()]
 
         return H5ReadResult(
             path=src.path,

nwb_linkml/src/nwb_linkml/maps/hdmf.py

@@ -69,12 +69,12 @@ def model_from_dynamictable(group:h5py.Group, base:Optional[BaseModel] = None) -
         #types[col] = (List[type_ | None], ...)
         types[col] = (type_, None)
 
-    if base is None:
-        #base = DataFrame
-        base = BaseModel
-    else:
-        base = (BaseModel, base)
-        #base = (DataFrame, base)
+    # if base is None:
+    #     #base = DataFrame
+    #     base = BaseModel
+    # else:
+    #     base = (BaseModel, base)
+    #     #base = (DataFrame, base)
 
 
     model = create_model(group.name.split('/')[-1], **types, __base__=base)
@@ -83,12 +83,12 @@ def model_from_dynamictable(group:h5py.Group, base:Optional[BaseModel] = None) -
 
 def dynamictable_to_model(
     group:h5py.Group,
-    model:Optional[Type[DataFrame]]=None,
-    base:Optional[BaseModel] = None) -> BaseModel:
+    model:Optional[Type[BaseModel]]=None,
+    base:Optional[Type[BaseModel]] = None) -> BaseModel:
     """
     Instantiate a dynamictable model
 
-    Calls :func:`.model_from_dynamictable` if model is not provided.
+    Calls :func:`.model_from_dynamictable` if ``model`` is not provided.
     """
     if model is None:
         model = model_from_dynamictable(group, base)

nwb_linkml/src/nwb_linkml/types/hdf5.py

@@ -1,5 +1,11 @@
-from typing import Annotated
+from typing import Any
+from pydantic_core import CoreSchema, core_schema
+from pydantic import GetCoreSchemaHandler
 
 class HDF5_Path(str):
     """Trivial subclass of string to indicate that it is a reference to a location within an HDF5 file"""
-    pass
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, source_type: Any, handler: GetCoreSchemaHandler
+    ) -> CoreSchema:
+        return core_schema.no_info_after_validator_function(cls, handler(str))