fix generics with defaults, use typing_extensions

nwb_linkml/pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
     "h5py>=3.9.0",
     "pydantic-settings>=2.0.3",
     "tqdm>=4.66.1",
-    'typing-extensions>=4.12.2;python_version<"3.11"',
+    'typing-extensions>=4.12.2;python_version<"3.13"',
     "numpydantic>=1.6.0",
     "black>=24.4.2",
     "pandas>=2.2.2",

nwb_linkml/src/nwb_linkml/generators/pydantic.py

@@ -284,9 +284,9 @@ class AfterGenerateClass:
             cls.cls.bases = ["AlignedDynamicTableMixin", "DynamicTable"]
         elif cls.cls.name == "ElementIdentifiers":
             cls.cls.bases = ["ElementIdentifiersMixin", "Data"]
-            # make ``value`` generic on T
+            # Formerly make this generic, but that breaks json serialization
             if "value" in cls.cls.attributes:
-                cls.cls.attributes["value"].range = "Optional[T]"
+                cls.cls.attributes["value"].range = "Optional[NDArray]"
         elif cls.cls.name == "TimeSeriesReferenceVectorData":
             # in core.nwb.base, so need to inject and import again
             cls.cls.bases = ["TimeSeriesReferenceVectorDataMixin", "VectorData"]

nwb_linkml/src/nwb_linkml/includes/hdmf.py

@@ -13,10 +13,10 @@ from typing import (
     List,
     Optional,
     Tuple,
-    TypeVar,
     Union,
     overload,
 )
+from typing_extensions import TypeVar
 
 import numpy as np
 import pandas as pd
@@ -36,8 +36,8 @@ from pydantic import (
 if TYPE_CHECKING:  # pragma: no cover
     from nwb_models.models import VectorData, VectorIndex
 
-T = TypeVar("T", bound=NDArray)
-T_INJECT = 'T = TypeVar("T", bound=NDArray)'
+T = TypeVar("T", default=NDArray)
+T_INJECT = 'T = TypeVar("T", default=NDArray)'
 
 if "pytest" in sys.modules:
     from nwb_models.models import ConfiguredBaseModel
@@ -71,7 +71,7 @@ class DynamicTableMixin(ConfiguredBaseModel):
     """
 
     model_config = ConfigDict(extra="allow", validate_assignment=True)
-    __pydantic_extra__: Dict[str, Union["VectorDataMixin", "VectorIndexMixin", "NDArray", list]]
+    __pydantic_extra__: Dict[str, Union["VectorDataMixin", "VectorIndexMixin"]]
     NON_COLUMN_FIELDS: ClassVar[tuple[str]] = (
         "id",
         "name",
@@ -899,10 +899,10 @@ DYNAMIC_TABLE_IMPORTS = Imports(
                 ObjectImport(name="Generic"),
                 ObjectImport(name="Iterable"),
                 ObjectImport(name="Tuple"),
-                ObjectImport(name="TypeVar"),
                 ObjectImport(name="overload"),
             ],
         ),
+        Import(module="typing_extensions", objects=[ObjectImport(name="TypeVar")]),
         Import(
             module="numpydantic", objects=[ObjectImport(name="NDArray"), ObjectImport(name="Shape")]
         ),

nwb_linkml/src/nwb_linkml/maps/map.py

@@ -1,26 +0,0 @@
-"""
-Abstract base classes for Map types
-
-.. todo::
-    Make this consistent or don't call them all maps lmao
-"""
-
-from abc import ABC, abstractmethod
-from typing import Any, Mapping, Sequence
-
-
-class Map(ABC):
-    """
-    The generic top-level mapping class is just a classmethod for checking if the map applies and a
-    method for applying the check if it does
-    """
-
-    @classmethod
-    @abstractmethod
-    def check(cls, *args: Sequence, **kwargs: Mapping) -> bool:
-        """Check if this map applies to the given item to read"""
-
-    @classmethod
-    @abstractmethod
-    def apply(cls, *args: Sequence, **kwargs: Mapping) -> Any:
-        """Actually apply the map!"""

nwb_linkml/tests/test_io/test_io_hdf5.py

@@ -3,8 +3,13 @@ import networkx as nx
 import numpy as np
 import pytest
 
-from nwb_linkml.io.hdf5 import HDF5IO, filter_dependency_graph, hdf_dependency_graph, truncate_file
-from nwb_linkml.maps.hdf5 import resolve_hardlink
+from nwb_linkml.io.hdf5 import (
+    HDF5IO,
+    filter_dependency_graph,
+    hdf_dependency_graph,
+    truncate_file,
+    resolve_hardlink,
+)
 
 
 @pytest.mark.skip()

nwb_linkml/tests/test_io/test_io_nwb.py

@@ -66,6 +66,17 @@ def test_nwbfile_base(read_nwbfile, read_pynwb):
     _compare_attrs(read_nwbfile, read_pynwb)
 
 
+def test_nwbfile_dump(read_nwbfile):
+    electrode_id = read_nwbfile.general.extracellular_ephys.electrodes.id.model_dump_json(
+        round_trip=True
+    )
+    electrodes = read_nwbfile.general.extracellular_ephys.electrodes.model_dump_json(
+        round_trip=True
+    )
+    # data = read_nwbfile.model_dump_json(round_trip=True, serialize_as_any=True)
+    # pdb.set_trace()
+
+
 def test_timeseries(read_nwbfile, read_pynwb):
     py_acq = read_pynwb.get_acquisition("test_timeseries")
     acq = read_nwbfile.acquisition["test_timeseries"]

nwb_models/pyproject.toml

@@ -8,7 +8,8 @@ authors = [
 dependencies = [
     "pydantic>=2.3.0",
     "numpydantic>=1.3.3",
-    "pandas>=2.2.2"
+    "pandas>=2.2.2",
+    'typing-extensions>=4.12.2;python_version<"3.13"', # for default in TypeVar
 ]
 requires-python = ">=3.10"
 readme = "README.md"

nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_8_0/hdmf_common_table.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import pdb
 import re
 import sys
 from datetime import date, datetime, time
@@ -15,10 +16,10 @@ from typing import (
     Literal,
     Optional,
     Tuple,
-    TypeVar,
     Union,
     overload,
 )
+from typing_extensions import TypeVar
 
 import numpy as np
 import pandas as pd
@@ -153,7 +154,8 @@ class LinkMLMeta(RootModel):
 
 NUMPYDANTIC_VERSION = "1.2.1"
 
-T = TypeVar("T", bound=NDArray)
+T = TypeVar("T", default=NDArray)
+U = TypeVar("U", default=NDArray)
 
 
 class VectorDataMixin(ConfiguredBaseModel, Generic[T]):
@@ -364,7 +366,7 @@ class DynamicTableMixin(ConfiguredBaseModel):
     """
 
     model_config = ConfigDict(extra="allow", validate_assignment=True)
-    __pydantic_extra__: Dict[str, Union["VectorDataMixin", "VectorIndexMixin", "NDArray", list]]
+    __pydantic_extra__: Dict[str, Union["VectorDataMixin", "VectorIndexMixin"]]
     NON_COLUMN_FIELDS: ClassVar[tuple[str]] = (
         "id",
         "name",
@@ -657,10 +659,14 @@ class DynamicTableMixin(ConfiguredBaseModel):
         Ensure that all columns are equal length
         """
         lengths = [len(v) for v in self._columns.values() if v is not None] + [len(self.id)]
-        assert all([length == lengths[0] for length in lengths]), (
-            "DynamicTable columns are not of equal length! "
-            f"Got colnames:\n{self.colnames}\nand lengths: {lengths}"
-        )
+
+        try:
+            assert all([length == lengths[0] for length in lengths]), (
+                "DynamicTable columns are not of equal length! "
+                f"Got colnames:\n{self.colnames}\nand lengths: {lengths}"
+            )
+        except AssertionError:
+            pdb.set_trace()
         return self
 
     @field_validator("*", mode="wrap")
@@ -958,7 +964,7 @@ class ElementIdentifiers(ElementIdentifiersMixin, Data):
     name: str = Field(
         "element_id", json_schema_extra={"linkml_meta": {"ifabsent": "string(element_id)"}}
     )
-    value: Optional[T] = Field(
+    value: Optional[NDArray] = Field(
         None,
         json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_elements"}]}}},
     )