Restructuring linkml generators

add monkeypatches

numpydantic/__init__.py

@@ -1 +1,7 @@
+# ruff: noqa: E402
+# ruff: noqa: F401
+from numpydantic.monkeypatch import apply_patches
 
+apply_patches()
+
+from numpydantic.ndarray import NDArray

numpydantic/linkml/ndarraygen.py

@@ -0,0 +1,189 @@
+"""
+Isolated generator for array classes
+"""
+
+import warnings
+from abc import ABC, abstractmethod
+
+from linkml_runtime.linkml_model import ClassDefinition, SlotDefinition
+
+from numpydantic.maps import flat_to_nptyping
+
+
+class ArrayFormat(ABC):
+    """
+    Metaclass for different LinkML array source formats
+    """
+
+    @classmethod
+    def is_array(cls, cls_: ClassDefinition) -> bool:
+        """Check whether a given class matches one of our subclasses definitions"""
+        return any([subcls.check(cls_) for subcls in cls.__subclasses__()])
+
+    @classmethod
+    def get(cls, cls_: ClassDefinition) -> type["ArrayFormat"]:
+        """Get matching ArrayFormat subclass"""
+        for subcls in cls.__subclasses__():
+            if subcls.check(cls_):
+                return subcls
+
+    @classmethod
+    @abstractmethod
+    def check(cls, cls_: ClassDefinition) -> bool:
+        """Method for array format subclasses to check if they match a given source class"""
+
+    @classmethod
+    @abstractmethod
+    def make(cls, cls_: ClassDefinition) -> str:
+        """
+        Make an annotation string from a given array format source class
+        """
+
+
+class LinkMLNDArray(ArrayFormat):
+    """
+    Tentative linkml-arrays style NDArray
+    """
+
+    @classmethod
+    def check(cls, cls_: ClassDefinition) -> bool:
+        """Check if linkml:NDArray in implements"""
+        return "linkml:NDArray" in cls_.implements
+
+    @classmethod
+    def make(cls, cls_: ClassDefinition) -> str:
+        """Make NDArray"""
+        raise NotImplementedError("Havent implemented NDArrays yet!")
+
+
+class LinkMLDataArray(ArrayFormat):
+    """
+    Tentative linkml-arrays style annotated array with indices
+    """
+
+    @classmethod
+    def check(cls, cls_: ClassDefinition) -> bool:
+        """Check if linkml:DataArray in implements"""
+        return "linkml:DataArray" in cls_.implements
+
+    @classmethod
+    def make(cls, cls_: ClassDefinition) -> str:
+        """Make DataArray"""
+        raise NotImplementedError("Havent generated DataArray types yet!")
+
+
+class NWBLinkMLArraylike(ArrayFormat):
+    """
+    Ye Olde nwb-linkml Arraylike class
+
+    Examples:
+
+        TimeSeries:
+          is_a: Arraylike
+          attributes:
+            num_times:
+              name: num_times
+              range: AnyType
+              required: true
+            num_DIM2:
+              name: num_DIM2
+              range: AnyType
+              required: false
+            num_DIM3:
+              name: num_DIM3
+              range: AnyType
+              required: false
+            num_DIM4:
+              name: num_DIM4
+              range: AnyType
+              required: false
+    """
+
+    @classmethod
+    def check(cls, cls_: ClassDefinition) -> bool:
+        """Check if class is Arraylike"""
+        return cls_.is_a == "Arraylike"
+
+    @classmethod
+    def make(cls, cls_: ClassDefinition) -> str:
+        """Make Arraylike annotation"""
+        return cls._array_annotation(cls_)
+
+    @classmethod
+    def _array_annotation(cls, cls_: ClassDefinition) -> str:
+        """
+        Make an annotation for an NDArray :)
+
+        Args:
+            cls_:
+
+        Returns:
+
+        """
+        # if none of the dimensions are optional, we just have one possible array shape
+        if all([s.required for s in cls_.attributes.values()]):  # pragma: no cover
+            return cls._make_npytyping_range(cls_.attributes)
+        # otherwise we need to make permutations
+        # but not all permutations, because we typically just want to be able to exlude the last possible dimensions
+        # the array classes should always be well-defined where the optional dimensions are at the end, so
+        requireds = {k: v for k, v in cls_.attributes.items() if v.required}
+        optionals = [(k, v) for k, v in cls_.attributes.items() if not v.required]
+
+        annotations = []
+        if len(requireds) > 0:
+            # first the base case
+            annotations.append(cls._make_npytyping_range(requireds))
+        # then add back each optional dimension
+        for i in range(len(optionals)):
+            attrs = {**requireds, **{k: v for k, v in optionals[0 : i + 1]}}
+            annotations.append(cls._make_npytyping_range(attrs))
+
+        # now combine with a union:
+        union = "Union[\n" + " " * 8
+        union += (",\n" + " " * 8).join(annotations)
+        union += "\n" + " " * 4 + "]"
+        return union
+
+    @classmethod
+    def _make_npytyping_range(cls, attrs: dict[str, SlotDefinition]) -> str:
+        # slot always starts with...
+        prefix = "NDArray["
+
+        # and then we specify the shape:
+        shape_prefix = 'Shape["'
+
+        # using the cardinality from the attributes
+        dim_pieces = []
+        for attr in attrs.values():
+            shape_part = (
+                str(attr.maximum_cardinality) if attr.maximum_cardinality else "*"
+            )
+
+            # do this with the most heinous chain of string replacements rather than regex
+            # because i am still figuring out what needs to be subbed lol
+            name_part = (
+                attr.name.replace(",", "_")
+                .replace(" ", "_")
+                .replace("__", "_")
+                .replace("|", "_")
+                .replace("-", "_")
+                .replace("+", "plus")
+            )
+
+            dim_pieces.append(" ".join([shape_part, name_part]))
+
+        dimension = ", ".join(dim_pieces)
+
+        shape_suffix = '"], '
+
+        # all dimensions should be the same dtype
+        try:
+            dtype = flat_to_nptyping[list(attrs.values())[0].range]
+        except KeyError as e:  # pragma: no cover
+            warnings.warn(str(e), stacklevel=2)
+            range = list(attrs.values())[0].range
+            return f"List[{range}] | {range}"
+        suffix = "]"
+
+        slot = "".join([prefix, shape_prefix, dimension, shape_suffix, dtype, suffix])
+        return slot

numpydantic/linkml/pydanticgen.py

@@ -1,30 +1,16 @@
 """
-Subclass of :class:`linkml.generators.PydanticGenerator`
+Patched subclass of :class:`linkml.generators.PydanticGenerator` to generate NDArrays
+swiped from ``nwb-linkml``.
 
-The pydantic generator is a subclass of
+Since this is an override of the full generator originally intended for a specific format,
-- :class:`linkml.utils.generator.Generator`
+this is a bit more involved than the isolated ndarray type generator. The most relevant
-- :class:`linkml.generators.oocodegen.OOCodeGenerator`
+parts here are:
+- The :class:`.ArrayCheck` class, which is used to determine when an array needs to be generated -
+  Use this to hook into nonstandard array formats that don't match the usual ``implements`` pattern
 
-The default `__main__` method
-- Instantiates the class
-- Calls :meth:`~linkml.generators.PydanticGenerator.serialize`
-
-The `serialize` method:
-
-- Accepts an optional jinja-style template, otherwise it uses the default template
-- Uses :class:`linkml_runtime.utils.schemaview.SchemaView` to interact with the schema
-- Generates linkML Classes
-    - `generate_enums` runs first
-
-.. note::
-
-    This module is heinous. We will be tidying this up and trying to pull changes upstream,
-    but for now this is just our hacky little secret.
 
 """
-import inspect
 import sys
-import warnings
 from copy import copy
 from dataclasses import dataclass
 from pathlib import Path
@@ -52,6 +38,9 @@ from linkml_runtime.utils.schemaview import SchemaView
 # from nwb_linkml.maps import flat_to_nptyping
 from pydantic import BaseModel
 
+from numpydantic.linkml.ndarraygen import ArrayFormat
+from numpydantic.linkml.template import default_template
+
 
 def module_case(name: str) -> str:
     """
@@ -81,184 +70,20 @@ class LinkML_Meta(BaseModel):
     tree_root: bool = False
 
 
-def default_template(
+def linkml_classvar(cls: ClassDefinition) -> SlotDefinition:
-    pydantic_ver: str = "2", extra_classes: list[type[BaseModel]] | None = None
+    """A class variable that holds additional linkml attrs"""
-) -> str:
+    slot = SlotDefinition(name="linkml_meta")
-    """Constructs a default template for pydantic classes based on the version of pydantic"""
+    slot.annotations["python_range"] = Annotation(
-    ### HEADER ###
+        "python_range", "ClassVar[LinkML_Meta]"
-    template = """
-{#-
-
-  Jinja2 Template for a pydantic classes
--#}
-from __future__ import annotations
-from datetime import datetime, date
-from enum import Enum
-from typing import Dict, Optional, Any, Union, ClassVar, Annotated, TypeVar, List, TYPE_CHECKING
-from pydantic import BaseModel as BaseModel, Field"""
-    if pydantic_ver == "2":
-        template += """
-from pydantic import ConfigDict, BeforeValidator
-        """
-    template += """
-from nptyping import Shape, Float, Float32, Double, Float64, LongLong, Int64, Int, Int32, Int16, Short, Int8, UInt, UInt32, UInt16, UInt8, UInt64, Number, String, Unicode, Unicode, Unicode, String, Bool, Datetime64
-from nwb_linkml.types import NDArray
-import sys
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from typing_extensions import Literal
-if TYPE_CHECKING:
-    import numpy as np
-
-{% for import_module, import_classes in imports.items() %}
-from {{ import_module }} import (
-    {{ import_classes | join(',\n    ') }}
-)
-{% endfor %}
-
-metamodel_version = "{{metamodel_version}}"
-version = "{{version if version else None}}"
-"""
-    ### BASE MODEL ###
-    if pydantic_ver == "1":  # pragma: no cover
-        template += """
-List = BaseList
-
-class WeakRefShimBaseModel(BaseModel):
-   __slots__ = '__weakref__'
-
-class ConfiguredBaseModel(WeakRefShimBaseModel,
-                validate_assignment = False,
-                validate_all = True,
-                underscore_attrs_are_private = True,
-                extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
-                arbitrary_types_allowed = True,
-                use_enum_values = True):
-"""
-    else:
-        template += """
-class ConfiguredBaseModel(BaseModel):
-    model_config = ConfigDict(
-        validate_assignment = True,
-        validate_default = True,
-        extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
-        arbitrary_types_allowed = True,
-        use_enum_values = True
     )
-"""
+    meta_fields = {k: getattr(cls, k, None) for k in LinkML_Meta.model_fields}
-    ### Injected Fields
+    meta_field_strings = [f"{k}={v}" for k, v in meta_fields.items() if v is not None]
-    template += """
+    meta_field_string = ", ".join(meta_field_strings)
-{%- if injected_fields != None -%}
+    slot.annotations["fixed_field"] = Annotation(
-    {% for field in injected_fields %}
+        "fixed_field", f"Field(LinkML_Meta({meta_field_string}), frozen=True)"
-    {{ field }}
-    {% endfor %}
-{%- else -%}
-    pass
-{%- endif -%}
-    """
-    ### Getitem
-    template += """
-
-    def __getitem__(self, i: slice|int) -> 'np.ndarray':
-        if hasattr(self, 'array'):
-            return self.array[i]
-        else:
-            return super().__getitem__(i)
-
-    def __setitem__(self, i: slice|int, value: Any):
-        if hasattr(self, 'array'):
-            self.array[i] = value
-        else:
-            super().__setitem__(i, value)
-    """
-
-    ### Extra classes
-    if extra_classes is not None:
-        template += """{{ '\n\n' }}"""
-        for cls in extra_classes:
-            template += inspect.getsource(cls) + "\n\n"
-    ### ENUMS ###
-    template += """
-{% for e in enums.values() %}
-class {{ e.name }}(str, Enum):
-    {% if e.description -%}
-    \"\"\"
-    {{ e.description }}
-    \"\"\"
-    {%- endif %}
-    {% for _, pv in e['values'].items() -%}
-    {% if pv.description -%}
-    # {{pv.description}}
-    {%- endif %}
-    {{pv.label}} = "{{pv.value}}"
-    {% endfor %}
-    {% if not e['values'] -%}
-    dummy = "dummy"
-    {% endif %}
-{% endfor %}
-"""
-    ### CLASSES ###
-    template += """
-{%- for c in schema.classes.values() %}
-class {{ c.name }}
-    {%- if class_isa_plus_mixins[c.name] -%}
-        ({{class_isa_plus_mixins[c.name]|join(', ')}})
-    {%- else -%}
-        (ConfiguredBaseModel)
-    {%- endif -%}
-                  :
-    {% if c.description -%}
-    \"\"\"
-    {{ c.description }}
-    \"\"\"
-    {%- endif %}
-    {% for attr in c.attributes.values() if c.attributes -%}
-    {{attr.name}}:{{ ' ' }}{%- if attr.equals_string -%}
-        Literal[{{ predefined_slot_values[c.name][attr.name] }}]
-        {%- else -%}
-        {{ attr.annotations['python_range'].value }}
-        {%- endif -%}
-        {%- if attr.annotations['fixed_field'] -%}
-        {{ ' ' }}= {{ attr.annotations['fixed_field'].value }}
-        {%- else -%}
-        {{ ' ' }}= Field(
-    {%- if predefined_slot_values[c.name][attr.name] is string -%}
-        {{ predefined_slot_values[c.name][attr.name] }}
-    {%- elif attr.required -%}
-        ...
-    {%- else -%}
-        None
-    {%- endif -%}
-    {%- if attr.title != None %}, title="{{attr.title}}"{% endif -%}
-    {%- if attr.description %}, description=\"\"\"{{attr.description}}\"\"\"{% endif -%}
-    {%- if attr.minimum_value != None %}, ge={{attr.minimum_value}}{% endif -%}
-    {%- if attr.maximum_value != None %}, le={{attr.maximum_value}}{% endif -%}
     )
-    {%- endif %}
+
-    {% else -%}
+    return slot
-    None
-    {% endfor %}
-{% endfor %}
-"""
-    ### FWD REFS / REBUILD MODEL ###
-    if pydantic_ver == "1":  # pragma: no cover
-        template += """
-# Update forward refs
-# see https://pydantic-docs.helpmanual.io/usage/postponed_annotations/
-{% for c in schema.classes.values() -%}
-{{ c.name }}.update_forward_refs()
-{% endfor %}
-"""
-    else:
-        template += """
-# Model rebuild
-# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
-{% for c in schema.classes.values() -%}
-{{ c.name }}.model_rebuild()
-{% endfor %}    
-"""
-    return template
 
 
 @dataclass
@@ -380,7 +205,7 @@ class PydanticGenerator(BasePydanticGenerator):
         if not self.split:
             # we are compiling this whole thing in one big file so we don't import anything
             return {}
-        if "is_namespace" in sv.schema.annotations.keys() and sv.schema.annotations[
+        if "is_namespace" in sv.schema.annotations and sv.schema.annotations[
             "is_namespace"
         ]["value"] in ("True", True):
             return self._get_namespace_imports(sv)
@@ -447,7 +272,7 @@ class PydanticGenerator(BasePydanticGenerator):
 
     def _check_anyof(
         self, s: SlotDefinition, sn: SlotDefinitionName, sv: SchemaView
-    ):  # pragma: no cover
+    ) -> None:  # pragma: no cover
         # Confirm that the original slot range (ignoring the default that comes in from
         # induced_slot) isn't in addition to setting any_of
         if len(s.any_of) > 0 and sv.get_slot(sn).range is not None:
@@ -459,94 +284,6 @@ class PydanticGenerator(BasePydanticGenerator):
             if not base_range_subsumes_any_of:
                 raise ValueError("Slot cannot have both range and any_of defined")
 
-    def _make_npytyping_range(self, attrs: dict[str, SlotDefinition]) -> str:
-        # slot always starts with...
-        prefix = "NDArray["
-
-        # and then we specify the shape:
-        shape_prefix = 'Shape["'
-
-        # using the cardinality from the attributes
-        dim_pieces = []
-        for attr in attrs.values():
-            if attr.maximum_cardinality:
-                shape_part = str(attr.maximum_cardinality)
-            else:
-                shape_part = "*"
-
-            # do this with the most heinous chain of string replacements rather than regex
-            # because i am still figuring out what needs to be subbed lol
-            name_part = (
-                attr.name.replace(",", "_")
-                .replace(" ", "_")
-                .replace("__", "_")
-                .replace("|", "_")
-                .replace("-", "_")
-                .replace("+", "plus")
-            )
-
-            dim_pieces.append(" ".join([shape_part, name_part]))
-
-        dimension = ", ".join(dim_pieces)
-
-        shape_suffix = '"], '
-
-        # all dimensions should be the same dtype
-        try:
-            dtype = flat_to_nptyping[list(attrs.values())[0].range]
-        except KeyError as e:  # pragma: no cover
-            warnings.warn(str(e))
-            range = list(attrs.values())[0].range
-            return f"List[{range}] | {range}"
-        suffix = "]"
-
-        slot = "".join([prefix, shape_prefix, dimension, shape_suffix, dtype, suffix])
-        return slot
-
-    def _get_numpy_slot_range(self, cls: ClassDefinition) -> str:
-        # if none of the dimensions are optional, we just have one possible array shape
-        if all([s.required for s in cls.attributes.values()]):  # pragma: no cover
-            return self._make_npytyping_range(cls.attributes)
-        # otherwise we need to make permutations
-        # but not all permutations, because we typically just want to be able to exlude the last possible dimensions
-        # the array classes should always be well-defined where the optional dimensions are at the end, so
-        requireds = {k: v for k, v in cls.attributes.items() if v.required}
-        optionals = [(k, v) for k, v in cls.attributes.items() if not v.required]
-
-        annotations = []
-        if len(requireds) > 0:
-            # first the base case
-            annotations.append(self._make_npytyping_range(requireds))
-        # then add back each optional dimension
-        for i in range(len(optionals)):
-            attrs = {**requireds, **{k: v for k, v in optionals[0 : i + 1]}}
-            annotations.append(self._make_npytyping_range(attrs))
-
-        # now combine with a union:
-        union = "Union[\n" + " " * 8
-        union += (",\n" + " " * 8).join(annotations)
-        union += "\n" + " " * 4 + "]"
-        return union
-
-    def _get_linkml_classvar(self, cls: ClassDefinition) -> SlotDefinition:
-        """A class variable that holds additional linkml attrs"""
-        slot = SlotDefinition(name="linkml_meta")
-        slot.annotations["python_range"] = Annotation(
-            "python_range", "ClassVar[LinkML_Meta]"
-        )
-        meta_fields = {
-            k: getattr(cls, k, None) for k in LinkML_Meta.model_fields.keys()
-        }
-        meta_field_strings = [
-            f"{k}={v}" for k, v in meta_fields.items() if v is not None
-        ]
-        meta_field_string = ", ".join(meta_field_strings)
-        slot.annotations["fixed_field"] = Annotation(
-            "fixed_field", f"Field(LinkML_Meta({meta_field_string}), frozen=True)"
-        )
-
-        return slot
-
     def sort_classes(
         self, clist: list[ClassDefinition], imports: dict[str, list[str]]
     ) -> list[ClassDefinition]:
@@ -564,7 +301,7 @@ class PydanticGenerator(BasePydanticGenerator):
 
         clist = list(clist)
         clist = [c for c in clist if c.name not in self.SKIP_CLASSES]
-        slist = []  # type: List[ClassDefinition]
+        slist = []  # type: list[ClassDefinition]
         while len(clist) > 0:
             can_add = False
             for i in range(len(clist)):
@@ -604,8 +341,8 @@ class PydanticGenerator(BasePydanticGenerator):
         """
         sv = self.schemaview
         range_cls = sv.get_class(slot_range)
-        if range_cls.is_a == "Arraylike":
+        if ArrayFormat.is_array(range_cls):
-            return self._get_numpy_slot_range(range_cls)
+            return ArrayFormat.get(range_cls).make(range_cls)
         else:
             return self._get_class_slot_range_origin(
                 slot_range, inlined, inlined_as_list
@@ -619,7 +356,7 @@ class PydanticGenerator(BasePydanticGenerator):
 
         Overriding to not use strings in the type hint when a class has an identifier value
 
-        Not testing this method except for what we changes
+        Not testing this method except for what we changed
         """
         sv = self.schemaview
         range_cls = sv.get_class(slot_range)
@@ -732,6 +469,7 @@ class PydanticGenerator(BasePydanticGenerator):
         return slot_value
 
     def serialize(self) -> str:
+        """Generate LinkML models from schema!"""
         predefined_slot_values = {}
         """splitting up parent class :meth:`.get_predefined_slot_values`"""
 
@@ -787,7 +525,7 @@ class PydanticGenerator(BasePydanticGenerator):
                 del class_def.attributes[attribute]
 
             # make class attr that stores extra linkml attrs
-            class_def.attributes["linkml_meta"] = self._get_linkml_classvar(class_def)
+            class_def.attributes["linkml_meta"] = linkml_classvar(class_def)
 
             class_name = class_original.name
             predefined_slot_values[camelcase(class_name)] = {}

numpydantic/linkml/template.py

@@ -0,0 +1,183 @@
+import inspect
+
+from pydantic import BaseModel
+
+
+def default_template(
+    pydantic_ver: str = "2", extra_classes: list[type[BaseModel]] | None = None
+) -> str:
+    """Constructs a default template for pydantic classes based on the version of pydantic"""
+    ### HEADER ###
+    template = """
+{#-
+
+  Jinja2 Template for a pydantic classes
+-#}
+from __future__ import annotations
+from datetime import datetime, date
+from enum import Enum
+from typing import Dict, Optional, Any, Union, ClassVar, Annotated, TypeVar, List, TYPE_CHECKING
+from pydantic import BaseModel as BaseModel, Field"""
+    if pydantic_ver == "2":
+        template += """
+from pydantic import ConfigDict, BeforeValidator
+        """
+    template += """
+from nptyping import Shape, Float, Float32, Double, Float64, LongLong, Int64, Int, Int32, Int16, Short, Int8, UInt, UInt32, UInt16, UInt8, UInt64, Number, String, Unicode, Unicode, Unicode, String, Bool, Datetime64
+from nwb_linkml.types import NDArray
+import sys
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from typing_extensions import Literal
+if TYPE_CHECKING:
+    import numpy as np
+
+{% for import_module, import_classes in imports.items() %}
+from {{ import_module }} import (
+    {{ import_classes | join(',\n    ') }}
+)
+{% endfor %}
+
+metamodel_version = "{{metamodel_version}}"
+version = "{{version if version else None}}"
+"""
+    ### BASE MODEL ###
+    if pydantic_ver == "1":  # pragma: no cover
+        template += """
+List = BaseList
+
+class WeakRefShimBaseModel(BaseModel):
+   __slots__ = '__weakref__'
+
+class ConfiguredBaseModel(WeakRefShimBaseModel,
+                validate_assignment = False,
+                validate_all = True,
+                underscore_attrs_are_private = True,
+                extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
+                arbitrary_types_allowed = True,
+                use_enum_values = True):
+"""
+    else:
+        template += """
+class ConfiguredBaseModel(BaseModel):
+    model_config = ConfigDict(
+        validate_assignment = True,
+        validate_default = True,
+        extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
+        arbitrary_types_allowed = True,
+        use_enum_values = True
+    )
+"""
+    ### Injected Fields
+    template += """
+{%- if injected_fields != None -%}
+    {% for field in injected_fields %}
+    {{ field }}
+    {% endfor %}
+{%- else -%}
+    pass
+{%- endif -%}
+    """
+    ### Getitem
+    template += """
+
+    def __getitem__(self, i: slice|int) -> 'np.ndarray':
+        if hasattr(self, 'array'):
+            return self.array[i]
+        else:
+            return super().__getitem__(i)
+
+    def __setitem__(self, i: slice|int, value: Any):
+        if hasattr(self, 'array'):
+            self.array[i] = value
+        else:
+            super().__setitem__(i, value)
+    """
+
+    ### Extra classes
+    if extra_classes is not None:
+        template += """{{ '\n\n' }}"""
+        for cls in extra_classes:
+            template += inspect.getsource(cls) + "\n\n"
+    ### ENUMS ###
+    template += """
+{% for e in enums.values() %}
+class {{ e.name }}(str, Enum):
+    {% if e.description -%}
+    \"\"\"
+    {{ e.description }}
+    \"\"\"
+    {%- endif %}
+    {% for _, pv in e['values'].items() -%}
+    {% if pv.description -%}
+    # {{pv.description}}
+    {%- endif %}
+    {{pv.label}} = "{{pv.value}}"
+    {% endfor %}
+    {% if not e['values'] -%}
+    dummy = "dummy"
+    {% endif %}
+{% endfor %}
+"""
+    ### CLASSES ###
+    template += """
+{%- for c in schema.classes.values() %}
+class {{ c.name }}
+    {%- if class_isa_plus_mixins[c.name] -%}
+        ({{class_isa_plus_mixins[c.name]|join(', ')}})
+    {%- else -%}
+        (ConfiguredBaseModel)
+    {%- endif -%}
+                  :
+    {% if c.description -%}
+    \"\"\"
+    {{ c.description }}
+    \"\"\"
+    {%- endif %}
+    {% for attr in c.attributes.values() if c.attributes -%}
+    {{attr.name}}:{{ ' ' }}{%- if attr.equals_string -%}
+        Literal[{{ predefined_slot_values[c.name][attr.name] }}]
+        {%- else -%}
+        {{ attr.annotations['python_range'].value }}
+        {%- endif -%}
+        {%- if attr.annotations['fixed_field'] -%}
+        {{ ' ' }}= {{ attr.annotations['fixed_field'].value }}
+        {%- else -%}
+        {{ ' ' }}= Field(
+    {%- if predefined_slot_values[c.name][attr.name] is string -%}
+        {{ predefined_slot_values[c.name][attr.name] }}
+    {%- elif attr.required -%}
+        ...
+    {%- else -%}
+        None
+    {%- endif -%}
+    {%- if attr.title != None %}, title="{{attr.title}}"{% endif -%}
+    {%- if attr.description %}, description=\"\"\"{{attr.description}}\"\"\"{% endif -%}
+    {%- if attr.minimum_value != None %}, ge={{attr.minimum_value}}{% endif -%}
+    {%- if attr.maximum_value != None %}, le={{attr.maximum_value}}{% endif -%}
+    )
+    {%- endif %}
+    {% else -%}
+    None
+    {% endfor %}
+{% endfor %}
+"""
+    ### FWD REFS / REBUILD MODEL ###
+    if pydantic_ver == "1":  # pragma: no cover
+        template += """
+# Update forward refs
+# see https://pydantic-docs.helpmanual.io/usage/postponed_annotations/
+{% for c in schema.classes.values() -%}
+{{ c.name }}.update_forward_refs()
+{% endfor %}
+"""
+    else:
+        template += """
+# Model rebuild
+# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
+{% for c in schema.classes.values() -%}
+{{ c.name }}.model_rebuild()
+{% endfor %}    
+"""
+    return template

numpydantic/maps.py

@@ -42,3 +42,35 @@ np_to_python = {
     },
     **{n: str for n in (np.character, np.str_, np.string_, np.unicode_)},
 }
+
+
+flat_to_nptyping = {
+    "float": "Float",
+    "float32": "Float32",
+    "double": "Double",
+    "float64": "Float64",
+    "long": "LongLong",
+    "int64": "Int64",
+    "int": "Int",
+    "int32": "Int32",
+    "int16": "Int16",
+    "short": "Short",
+    "int8": "Int8",
+    "uint": "UInt",
+    "uint32": "UInt32",
+    "uint16": "UInt16",
+    "uint8": "UInt8",
+    "uint64": "UInt64",
+    "numeric": "Number",
+    "text": "String",
+    "utf": "Unicode",
+    "utf8": "Unicode",
+    "utf_8": "Unicode",
+    "string": "Unicode",
+    "str": "Unicode",
+    "ascii": "String",
+    "bool": "Bool",
+    "isodatetime": "Datetime64",
+    "AnyType": "Any",
+    "object": "Object",
+}

numpydantic/monkeypatch.py

@@ -0,0 +1,58 @@
+def patch_npytyping_perf() -> None:
+    """
+    npytyping makes an expensive call to inspect.stack()
+    that makes imports of pydantic models take ~200x longer than
+    they should:
+
+    References:
+        - https://github.com/ramonhagenaars/nptyping/issues/110
+    """
+    import inspect
+    from types import FrameType
+
+    from nptyping import base_meta_classes, ndarray, recarray
+    from nptyping.pandas_ import dataframe
+
+    # make a new __module__ methods for the affected classes
+    def new_module_ndarray(cls) -> str:
+        return cls._get_module(inspect.currentframe(), "nptyping.ndarray")
+
+    def new_module_recarray(cls) -> str:
+        return cls._get_module(inspect.currentframe(), "nptyping.recarray")
+
+    def new_module_dataframe(cls) -> str:
+        return cls._get_module(inspect.currentframe(), "nptyping.pandas_.dataframe")
+
+    # and a new _get_module method for the parent class
+    def new_get_module(cls, stack: FrameType, module: str) -> str:
+        return (
+            "typing"
+            if inspect.getframeinfo(stack.f_back).function == "formatannotation"
+            else module
+        )
+
+    # now apply the patches
+    ndarray.NDArrayMeta.__module__ = property(new_module_ndarray)
+    recarray.RecArrayMeta.__module__ = property(new_module_recarray)
+    dataframe.DataFrameMeta.__module__ = property(new_module_dataframe)
+    base_meta_classes.SubscriptableMeta._get_module = new_get_module
+
+
+def patch_nptyping_warnings() -> None:
+    """
+    nptyping shits out a bunch of numpy deprecation warnings from using
+    olde aliases
+
+    References:
+        - https://github.com/ramonhagenaars/nptyping/issues/113
+        - https://github.com/ramonhagenaars/nptyping/issues/102
+    """
+    import warnings
+
+    warnings.filterwarnings("ignore", category=DeprecationWarning, module="nptyping.*")
+
+
+def apply_patches() -> None:
+    """Apply all monkeypatches!"""
+    patch_npytyping_perf()
+    patch_nptyping_warnings()

numpydantic/ndarray.py

@@ -24,8 +24,7 @@ from pydantic_core.core_schema import ListSchema
 
 from numpydantic.maps import np_to_python
 
-if TYPE_CHECKING:
+from numpydantic.proxy import NDArrayProxy
-    from numpydantic.proxy import NDArrayProxy
 
 COMPRESSION_THRESHOLD = 16 * 1024
 """
@@ -33,7 +32,7 @@ Arrays larger than this size (in bytes) will be compressed and b64 encoded when
 serializing to JSON.
 """
 
-ARRAY_TYPES = Union[np.ndarray, DaskArray, "NDArrayProxy"]
+ARRAY_TYPES = np.ndarray | DaskArray | NDArrayProxy
 
 
 def list_of_lists_schema(shape: Shape, array_type_handler: dict) -> ListSchema:
@@ -153,7 +152,17 @@ class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
 
 class NDArray(NPTypingType, metaclass=NDArrayMeta):
     """
-    Following the example here: https://docs.pydantic.dev/latest/usage/types/custom/#handling-third-party-types
+    Constrained array type allowing npytyping syntax for dtype and shape validation and serialization.
+
+    Integrates with pydantic such that
+    - JSON schema for list of list encoding
+    - Serialized as LoL, with automatic compression for large arrays
+    - Automatic coercion from lists on instantiation
+
+    Also supports validation on :class:`.NDArrayProxy` types for lazy loading.
+
+    References:
+        - https://docs.pydantic.dev/latest/usage/types/custom/#handling-third-party-types
     """
 
     __args__ = (Any, Any)

numpydantic/proxy.py

@@ -43,11 +43,6 @@ class NDArrayProxy:
         _source_type: _NDArray,
         _handler: Callable[[Any], core_schema.CoreSchema],
     ) -> core_schema.CoreSchema:
-        # return core_schema.no_info_after_validator_function(
+        from numpydantic import NDArray
-        #     serialization=core_schema.plain_serializer_function_ser_schema(
-        #         lambda array: array.tolist(),
-        #         when_used='json'
-        #     )
-        # )
 
-        return NDArray_.__get_pydantic_core_schema__(cls, _source_type, _handler)
+        return NDArray.__get_pydantic_core_schema__(cls, _source_type, _handler)

poetry.lock

@@ -479,13 +479,13 @@ yaml = ["PyYAML (>=3.10)"]
 
 [[package]]
 name = "curies"
-version = "0.7.6"
+version = "0.7.7"
 description = "Idiomatic conversion between URIs and compact URIs (CURIEs)."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "curies-0.7.6-py3-none-any.whl", hash = "sha256:3307e757e47ed4384edb705c73cad40ad5e688e2dea263a60e6a5e5a6c33105d"},
+    {file = "curies-0.7.7-py3-none-any.whl", hash = "sha256:609de3e8cdf39f410e8f4d9f06eb7df379465860f4fb441bf0e79672430f8e2a"},
-    {file = "curies-0.7.6.tar.gz", hash = "sha256:f86da3539cee349249f5b64db99651053649551920b9fe945c150719c8b9b40e"},
+    {file = "curies-0.7.7.tar.gz", hash = "sha256:a8d674029f906fb9c3564eafa0862ce96725932bd801fa751e076265b111cb34"},
 ]
 
 [package.dependencies]
@@ -619,13 +619,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.12.2"
+version = "2024.2.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.12.2-py3-none-any.whl", hash = "sha256:d800d87f72189a745fa3d6b033b9dc4a34ad069f60ca60b943a63599f5501960"},
+    {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
-    {file = "fsspec-2023.12.2.tar.gz", hash = "sha256:8548d39e8810b59c38014934f6b31e57f40c1b20f911f4cc2b85389c7e9bf0cb"},
+    {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
 ]
 
 [package.extras]
@@ -643,7 +643,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -1170,71 +1170,71 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
 [[package]]
 name = "markupsafe"
-version = "2.1.4"
+version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = true
 python-versions = ">=3.7"
 files = [
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+    {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
-    {file = "MarkupSafe-2.1.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e888ff76ceb39601c59e219f281466c6d7e66bd375b4ec1ce83bcdc68306796b"},
+    {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"},
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 typing-extensions = ">=4.6.1"
 
 [package.extras]
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-    {file = "pydantic_core-2.16.1.tar.gz", hash = "sha256:daff04257b49ab7f4b3f73f98283d3dbb1a65bf3500d55c7beac3c66c310fe34"},
+    {file = "pydantic_core-2.16.2.tar.gz", hash = "sha256:0ba503850d8b8dcc18391f10de896ae51d37fe5fe43dbfb6a35c5c5cad271a06"},
 ]
 
 [package.dependencies]
@@ -1853,20 +1853,20 @@ shexjsg = ">=0.8.1"
 
 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.0.0"
 description = "pytest: simple powerful testing with Python"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
+    {file = "pytest-8.0.0-py3-none-any.whl", hash = "sha256:50fb9cbe836c3f20f0dfa99c565201fb75dc54c8d76373cd1bde06b06657bdb6"},
-    {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
+    {file = "pytest-8.0.0.tar.gz", hash = "sha256:249b1b0864530ba251b7438274c4d251c58d868edaaec8762893ad4a0d71c36c"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
+pluggy = ">=1.3.0,<2.0"
 
 [package.extras]
 testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
@@ -2471,13 +2471,13 @@ test = ["cython (>=3.0)", "filelock", "html5lib", "pytest (>=4.6)", "setuptools
 
 [[package]]
 name = "sphinx-autobuild"
-version = "2021.3.14"
+version = "2024.2.4"
 description = "Rebuild Sphinx documentation on changes, with live-reload in the browser."
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
-    {file = "sphinx-autobuild-2021.3.14.tar.gz", hash = "sha256:de1ca3b66e271d2b5b5140c35034c89e47f263f2cd5db302c9217065f7443f05"},
+    {file = "sphinx_autobuild-2024.2.4-py3-none-any.whl", hash = "sha256:63fd87ab7505872a89aef468ce6503f65e794a195f4ae62269db3b85b72d4854"},
-    {file = "sphinx_autobuild-2021.3.14-py3-none-any.whl", hash = "sha256:8fe8cbfdb75db04475232f05187c776f46f6e9e04cacf1e49ce81bdac649ccac"},
+    {file = "sphinx_autobuild-2024.2.4.tar.gz", hash = "sha256:cb9d2121a176d62d45471624872afc5fad7755ad662738abe400ecf4a7954303"},
 ]
 
 [package.dependencies]
@@ -2486,7 +2486,7 @@ livereload = "*"
 sphinx = "*"
 
 [package.extras]
-test = ["pytest", "pytest-cov"]
+test = ["pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "sphinx-basic-ng"
@@ -2988,12 +2988,13 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [extras]
-dev = ["autodoc-pydantic", "black", "coverage", "coveralls", "dask", "furo", "h5py", "myst-parser", "pytest", "pytest-cov", "pytest-depends", "ruff", "sphinx", "sphinx-autobuild", "sphinx-design"]
+dev = ["autodoc-pydantic", "black", "coverage", "coveralls", "dask", "furo", "h5py", "linkml", "linkml-runtime", "myst-parser", "pytest", "pytest-cov", "pytest-depends", "ruff", "sphinx", "sphinx-autobuild", "sphinx-design"]
 docs = ["autodoc-pydantic", "furo", "myst-parser", "sphinx", "sphinx-design"]
+linkml = ["linkml", "linkml-runtime"]
 proxy = ["dask", "h5py"]
 tests = ["coverage", "coveralls", "pytest", "pytest-cov", "pytest-depends"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "b57dc65220117fd2b0ca76f3e55c765789a3b00eb3b28a376b92059abeebab0f"
+content-hash = "d752a8794037df9c7b736d98cf82ec64e0bd4f7a32a52d996302d4277e7fbfd3"

pyproject.toml

@@ -11,8 +11,8 @@ python = "^3.11"
 pydantic = ">=2.3.0"
 nptyping = ">=2.5.0"
 blosc2 = "^2.5.1"
-dask = { version = "^2024.1.1" }
+dask = "^2024.1.1"
-h5py = { version = "^3.10.0" }
+h5py = "^3.10.0"
 pytest = { version=">=7.4.0", optional = true}
 pytest-depends = {version="^1.0.1", optional = true}
 coverage = {version = ">=6.1.1", optional = true}
@@ -105,3 +105,8 @@ ignore = [
 ]
 
 fixable = ["ALL"]
+
+[tool.mypy]
+plugins = [
+    "pydantic.mypy"
+]

tests/test_linkml.py

@@ -1,120 +0,0 @@
-"""
-Test custom features of the pydantic generator
-
-Note that since this is largely a subclass, we don't test all of the functionality of the generator
-because it's tested in the base linkml package.
-"""
-import re
-import sys
-import typing
-
-import numpy as np
-import pytest
-from pydantic import BaseModel
-
-
-def test_arraylike(imported_schema):
-    """
-    Arraylike classes are converted to slots that specify nptyping arrays
-
-    array: Optional[Union[
-        NDArray[Shape["* x, * y"], Number],
-        NDArray[Shape["* x, * y, 3 z"], Number],
-        NDArray[Shape["* x, * y, 3 z, 4 a"], Number]
-    ]] = Field(None)
-    """
-    # check that we have gotten an NDArray annotation and its shape is correct
-    array = imported_schema["core"].MainTopLevel.model_fields["array"].annotation
-    args = typing.get_args(array)
-    for i, shape in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
-        assert isinstance(args[i], NDArrayMeta)
-        assert args[i].__args__[0].__args__
-        assert args[i].__args__[1] == np.number
-
-    # we shouldn't have an actual class for the array
-    assert not hasattr(imported_schema["core"], "MainTopLevel__Array")
-    assert not hasattr(imported_schema["core"], "MainTopLevelArray")
-
-
-def test_inject_fields(imported_schema):
-    """
-    Our root model should have the special fields we injected
-    """
-    base = imported_schema["core"].ConfiguredBaseModel
-    assert "hdf5_path" in base.model_fields
-    assert "object_id" in base.model_fields
-
-
-def test_linkml_meta(imported_schema):
-    """
-    We should be able to store some linkml metadata with our classes
-    """
-    meta = imported_schema["core"].LinkML_Meta
-    assert "tree_root" in meta.model_fields
-    assert imported_schema["core"].MainTopLevel.linkml_meta.default.tree_root == True
-    assert imported_schema["core"].OtherClass.linkml_meta.default.tree_root == False
-
-
-def test_skip(linkml_schema):
-    """
-    We can skip slots and classes
-    """
-    modules = generate_and_import(
-        linkml_schema,
-        split=False,
-        generator_kwargs={
-            "SKIP_SLOTS": ("SkippableSlot",),
-            "SKIP_CLASSES": ("Skippable", "skippable"),
-        },
-    )
-    assert not hasattr(modules["core"], "Skippable")
-    assert "SkippableSlot" not in modules["core"].MainTopLevel.model_fields
-
-
-def test_inline_with_identifier(imported_schema):
-    """
-    By default, if a class has an identifier attribute, it is inlined
-    as a string rather than its class. We overrode that to be able to make dictionaries of collections
-    """
-    main = imported_schema["core"].MainTopLevel
-    inline = main.model_fields["inline_dict"].annotation
-    assert typing.get_origin(typing.get_args(inline)[0]) == dict
-    # god i hate pythons typing interface
-    otherclass, stillanother = typing.get_args(
-        typing.get_args(typing.get_args(inline)[0])[1]
-    )
-    assert otherclass is imported_schema["core"].OtherClass
-    assert stillanother is imported_schema["core"].StillAnotherClass
-
-
-def test_namespace(imported_schema):
-    """
-    Namespace schema import all classes from the other schema
-    Returns:
-
-    """
-    ns = imported_schema["namespace"]
-
-    for classname, modname in (
-        ("MainThing", "test_schema.imported"),
-        ("Arraylike", "test_schema.imported"),
-        ("MainTopLevel", "test_schema.core"),
-        ("Skippable", "test_schema.core"),
-        ("OtherClass", "test_schema.core"),
-        ("StillAnotherClass", "test_schema.core"),
-    ):
-        assert hasattr(ns, classname)
-        if imported_schema["split"]:
-            assert getattr(ns, classname).__module__ == modname
-
-
-def test_get_set_item(imported_schema):
-    """We can get and set without explicitly addressing array"""
-    cls = imported_schema["core"].MainTopLevel(array=np.array([[1, 2, 3], [4, 5, 6]]))
-    cls[0] = 50
-    assert (cls[0] == 50).all()
-    assert (cls.array[0] == 50).all()
-
-    cls[1, 1] = 100
-    assert cls[1, 1] == 100
-    assert cls.array[1, 1] == 100

tests/test_linkml/test_pydanticgen.py

@@ -0,0 +1,120 @@
+"""
+Test custom features of the pydantic generator
+
+Note that since this is largely a subclass, we don't test all of the functionality of the generator
+because it's tested in the base linkml package.
+"""
+import re
+import sys
+import typing
+
+import numpy as np
+import pytest
+from pydantic import BaseModel
+
+
+# def test_arraylike(imported_schema):
+#     """
+#     Arraylike classes are converted to slots that specify nptyping arrays
+#
+#     array: Optional[Union[
+#         NDArray[Shape["* x, * y"], Number],
+#         NDArray[Shape["* x, * y, 3 z"], Number],
+#         NDArray[Shape["* x, * y, 3 z, 4 a"], Number]
+#     ]] = Field(None)
+#     """
+#     # check that we have gotten an NDArray annotation and its shape is correct
+#     array = imported_schema["core"].MainTopLevel.model_fields["array"].annotation
+#     args = typing.get_args(array)
+#     for i, shape in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
+#         assert isinstance(args[i], NDArrayMeta)
+#         assert args[i].__args__[0].__args__
+#         assert args[i].__args__[1] == np.number
+#
+#     # we shouldn't have an actual class for the array
+#     assert not hasattr(imported_schema["core"], "MainTopLevel__Array")
+#     assert not hasattr(imported_schema["core"], "MainTopLevelArray")
+#
+#
+# def test_inject_fields(imported_schema):
+#     """
+#     Our root model should have the special fields we injected
+#     """
+#     base = imported_schema["core"].ConfiguredBaseModel
+#     assert "hdf5_path" in base.model_fields
+#     assert "object_id" in base.model_fields
+#
+#
+# def test_linkml_meta(imported_schema):
+#     """
+#     We should be able to store some linkml metadata with our classes
+#     """
+#     meta = imported_schema["core"].LinkML_Meta
+#     assert "tree_root" in meta.model_fields
+#     assert imported_schema["core"].MainTopLevel.linkml_meta.default.tree_root == True
+#     assert imported_schema["core"].OtherClass.linkml_meta.default.tree_root == False
+#
+#
+# def test_skip(linkml_schema):
+#     """
+#     We can skip slots and classes
+#     """
+#     modules = generate_and_import(
+#         linkml_schema,
+#         split=False,
+#         generator_kwargs={
+#             "SKIP_SLOTS": ("SkippableSlot",),
+#             "SKIP_CLASSES": ("Skippable", "skippable"),
+#         },
+#     )
+#     assert not hasattr(modules["core"], "Skippable")
+#     assert "SkippableSlot" not in modules["core"].MainTopLevel.model_fields
+#
+#
+# def test_inline_with_identifier(imported_schema):
+#     """
+#     By default, if a class has an identifier attribute, it is inlined
+#     as a string rather than its class. We overrode that to be able to make dictionaries of collections
+#     """
+#     main = imported_schema["core"].MainTopLevel
+#     inline = main.model_fields["inline_dict"].annotation
+#     assert typing.get_origin(typing.get_args(inline)[0]) == dict
+#     # god i hate pythons typing interface
+#     otherclass, stillanother = typing.get_args(
+#         typing.get_args(typing.get_args(inline)[0])[1]
+#     )
+#     assert otherclass is imported_schema["core"].OtherClass
+#     assert stillanother is imported_schema["core"].StillAnotherClass
+#
+#
+# def test_namespace(imported_schema):
+#     """
+#     Namespace schema import all classes from the other schema
+#     Returns:
+#
+#     """
+#     ns = imported_schema["namespace"]
+#
+#     for classname, modname in (
+#         ("MainThing", "test_schema.imported"),
+#         ("Arraylike", "test_schema.imported"),
+#         ("MainTopLevel", "test_schema.core"),
+#         ("Skippable", "test_schema.core"),
+#         ("OtherClass", "test_schema.core"),
+#         ("StillAnotherClass", "test_schema.core"),
+#     ):
+#         assert hasattr(ns, classname)
+#         if imported_schema["split"]:
+#             assert getattr(ns, classname).__module__ == modname
+#
+#
+# def test_get_set_item(imported_schema):
+#     """We can get and set without explicitly addressing array"""
+#     cls_ = imported_schema["core"].MainTopLevel(array=np.array([[1, 2, 3], [4, 5, 6]]))
+#     cls_[0] = 50
+#     assert (cls_[0] == 50).all()
+#     assert (cls_.array[0] == 50).all()
+#
+#     cls_[1, 1] = 100
+#     assert cls_[1, 1] == 100
+#     assert cls_.array[1, 1] == 100