use pydantic model not dataclass

src/numpydantic/interface/dask.py

@@ -2,7 +2,6 @@
 Interface for Dask arrays
 """
 
-from dataclasses import dataclass
 from typing import Any, Iterable, Literal, Optional
 
 import numpy as np
@@ -25,7 +24,6 @@ def _as_tuple(a_list: list | Any) -> tuple:
     )
 
 
-@dataclass(kw_only=True)
 class DaskJsonDict(JsonDict):
     """
     Round-trip json serialized form of a dask array
@@ -78,6 +76,8 @@ class DaskInterface(Interface):
         """
         if isinstance(array, dict):
             array = DaskJsonDict(**array).to_array_input()
+        elif isinstance(array, DaskJsonDict):
+            array = array.to_array_input()
 
         return array
 

src/numpydantic/interface/hdf5.py

@@ -40,7 +40,6 @@ as ``S32`` isoformatted byte strings (timezones optional) like:
 """
 
 import sys
-from dataclasses import dataclass
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Iterable, List, NamedTuple, Optional, Tuple, TypeVar, Union
@@ -77,7 +76,6 @@ class H5ArrayPath(NamedTuple):
     """Refer to a specific field within a compound dtype"""
 
 
-@dataclass
 class H5JsonDict(JsonDict):
     """Round-trip Json-able version of an HDF5 dataset"""
 
@@ -88,7 +86,7 @@ class H5JsonDict(JsonDict):
     def to_array_input(self) -> H5ArrayPath:
         """Construct an :class:`.H5ArrayPath`"""
         return H5ArrayPath(
-            **{k: v for k, v in self.to_dict().items() if k in H5ArrayPath._fields}
+            **{k: v for k, v in self.model_dump().items() if k in H5ArrayPath._fields}
         )
 
 
@@ -330,6 +328,8 @@ class H5Interface(Interface):
         """Create an :class:`.H5Proxy` to use throughout validation"""
         if isinstance(array, dict):
             array = H5JsonDict(**array).to_array_input()
+        elif isinstance(array, H5JsonDict):
+            array = array.to_array_input()
 
         if isinstance(array, H5ArrayPath):
             array = H5Proxy.from_h5array(h5array=array)

src/numpydantic/interface/interface.py

@@ -4,13 +4,12 @@ Base Interface metaclass
 
 import inspect
 from abc import ABC, abstractmethod
-from dataclasses import asdict, dataclass
 from importlib.metadata import PackageNotFoundError, version
 from operator import attrgetter
 from typing import Any, Generic, Tuple, Type, TypedDict, TypeVar, Union
 
 import numpy as np
-from pydantic import SerializationInfo, TypeAdapter, ValidationError
+from pydantic import BaseModel, SerializationInfo, ValidationError
 
 from numpydantic.exceptions import (
     DtypeError,
@@ -32,13 +31,9 @@ class InterfaceMark(TypedDict):
     version: str
 
 
-@dataclass(kw_only=True)
-class JsonDict:
+class JsonDict(BaseModel):
     """
     Representation of array when dumped with round_trip == True.
-
-    Using a dataclass rather than a pydantic model to not tempt
-    us to use more sophisticated types than can be serialized to json.
     """
 
     type: str
@@ -50,18 +45,6 @@ class JsonDict:
         (one of the ``input_types`` of an interface).
         """
 
-    def to_dict(self) -> dict:
-        """
-        Convenience method for casting dataclass to dict,
-        removing None-valued items
-        """
-        return {k: v for k, v in asdict(self).items() if v is not None}
-
-    @classmethod
-    def get_adapter(cls) -> TypeAdapter:
-        """Convenience method to get a typeadapter for this class"""
-        return TypeAdapter(cls)
-
     @classmethod
     def is_valid(cls, val: dict, raise_on_error: bool = False) -> bool:
         """
@@ -75,9 +58,8 @@ class JsonDict:
         Returns:
             bool - true if valid, false if not
         """
-        adapter = cls.get_adapter()
         try:
-            _ = adapter.validate_python(val)
+            _ = cls.model_validate(val)
             return True
         except ValidationError as e:
             if raise_on_error:

src/numpydantic/interface/numpy.py

@@ -2,7 +2,6 @@
 Interface to numpy arrays
 """
 
-from dataclasses import dataclass
 from typing import Any, Literal, Union
 
 from pydantic import SerializationInfo
@@ -21,7 +20,6 @@ except ImportError:  # pragma: no cover
     np = None
 
 
-@dataclass
 class NumpyJsonDict(JsonDict):
     """
     JSON-able roundtrip representation of numpy array
@@ -78,6 +76,8 @@ class NumpyInterface(Interface):
         """
         if isinstance(array, dict):
             array = NumpyJsonDict(**array).to_array_input()
+        elif isinstance(array, NumpyJsonDict):
+            array = array.to_array_input()
 
         if not isinstance(array, ndarray):
             array = np.array(array)

src/numpydantic/interface/video.py

@@ -2,7 +2,6 @@
 Interface to support treating videos like arrays using OpenCV
 """
 
-from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Literal, Optional, Tuple, Union
 
@@ -22,7 +21,6 @@ except ImportError:  # pragma: no cover
 VIDEO_EXTENSIONS = (".mp4", ".avi", ".mov", ".mkv")
 
 
-@dataclass(kw_only=True)
 class VideoJsonDict(JsonDict):
     """Json-able roundtrip representation of a video file"""
 
@@ -256,6 +254,8 @@ class VideoInterface(Interface):
         """Get a :class:`.VideoProxy` object for this video"""
         if isinstance(array, dict):
             proxy = VideoJsonDict(**array).to_array_input()
+        elif isinstance(array, VideoJsonDict):
+            proxy = array.to_array_input()
         elif isinstance(array, VideoCapture):
             proxy = VideoProxy(video=array)
         elif isinstance(array, VideoProxy):

src/numpydantic/interface/zarr.py

@@ -56,7 +56,6 @@ class ZarrArrayPath:
             raise ValueError("Only len 1-2 iterables can be used for a ZarrArrayPath")
 
 
-@dataclass(kw_only=True)
 class ZarrJsonDict(JsonDict):
     """Round-trip Json-able version of a Zarr Array"""
 
@@ -94,10 +93,12 @@ class ZarrInterface(Interface):
 
     @staticmethod
     def _get_array(
-        array: Union[ZarrArray, str, Path, ZarrArrayPath, Sequence]
+        array: Union[ZarrArray, str, dict, ZarrJsonDict, Path, ZarrArrayPath, Sequence]
     ) -> ZarrArray:
         if isinstance(array, dict):
             array = ZarrJsonDict(**array).to_array_input()
+        elif isinstance(array, ZarrJsonDict):
+            array = array.to_array_input()
 
         if isinstance(array, ZarrArray):
             return array

src/numpydantic/serialization.py

@@ -19,7 +19,7 @@ def jsonize_array(value: Any, info: SerializationInfo) -> Union[list, dict]:
     interface_cls = Interface.match_output(value)
     array = interface_cls.to_json(value, info)
     if isinstance(array, JsonDict):
-        array = array.to_dict()
+        array = array.model_dump(exclude_none=True)
 
     if info.context:
         if info.context.get("mark_interface", False):