Merge pull request #20 from p2p-ld/dump_json

Roundtrip JSON serialization/deserialization

docs/api/monkeypatch.md

@@ -1,6 +0,0 @@
-# monkeypatch
-
-```{eval-rst}
-.. automodule:: numpydantic.monkeypatch
-    :members:
-```

docs/api/serialization.md

@@ -0,0 +1,7 @@
+# serialization
+
+```{eval-rst}
+.. automodule:: numpydantic.serialization
+    :members:
+    :undoc-members:
+```

docs/changelog.md

@@ -2,6 +2,73 @@
 
 ## 1.*
 
+### 1.6.*
+
+#### 1.6.0 - 24-09-23 - Roundtrip JSON Serialization
+
+Roundtrip JSON serialization is here - with serialization to list of lists,
+as well as file references that don't require copying the whole array if 
+used in data modeling, control over path relativization, and stamping of
+interface version for the extra provenance conscious.
+
+Please see [serialization](./serialization.md) for narrative documentation :)
+
+**Potentially Breaking Changes**
+- See [development](./development.md) for a statement about API stability
+- An additional {meth}`.Interface.deserialize` method has been added to
+  {meth}`.Interface.validate` - downstream users are not intended to override the
+  `validate method`, but if they have, then JSON deserialization will not work for them.
+- `Interface` subclasses now require a `name` attribute, a short string identifier for that interface,
+  and a `json_model` that inherits from {class}`.interface.JsonDict`. Interfaces without
+  these attributes will not be able to be instantiated.
+- {meth}`.Interface.to_json` is now an abstract method that all interfaces must define.
+
+**Features**
+- Roundtrip JSON serialization - by default dump to a list of list arrays, but
+  support the `round_trip` keyword in `model_dump_json` for provenance-preserving dumps
+- JSON Schema generation has been separated from `core_schema` generation in {class}`.NDArray`.
+  Downstream interfaces can customize json schema generation without compromising ability to validate.
+- All proxy classes must have an `__eq__` dunder method to compare equality -
+  in proxy classes, these compare equality of arguments, since the arrays that
+  are referenced on disk should be equal by definition. Direct array comparison
+  should use {func}`numpy.array_equal`
+- Interfaces previously couldn't be instantiated without explicit shape and dtype arguments,
+  these have been given `Any` defaults.
+- New {mod}`numpydantic.serialization` module to contain serialization logic.
+
+**New Classes**
+See the docstrings for descriptions of each class
+- `MarkMismatchError` for when an array serialized with `mark_interface` doesn't match
+  the interface that's deserializing it
+- {class}`.interface.InterfaceMark`
+- {class}`.interface.MarkedJson`
+- {class}`.interface.JsonDict`
+  - {class}`.dask.DaskJsonDict`
+  - {class}`.hdf5.H5JsonDict`
+  - {class}`.numpy.NumpyJsonDict`
+  - {class}`.video.VideoJsonDict`
+  - {class}`.zarr.ZarrJsonDict`
+
+**Bugfix**
+- [`#17`](https://github.com/p2p-ld/numpydantic/issues/17) - Arrays are re-validated as lists, rather than arrays
+- Some proxy classes would fail to be serialized becauase they lacked an `__array__` method.
+  `__array__` methods have been added, and tests for coercing to an array to prevent regression.
+- Some proxy classes lacked a `__name__` attribute, which caused failures to serialize
+  when the `__getattr__` methods attempted to pass it through. These have been added where needed.
+
+**Docs**
+- Add statement about versioning and API stability to [development](./development.md)
+- Add docs for serialization!
+- Remove stranded docs from hooks and monkeypatch
+- Added `myst_nb` to docs dependencies for direct rendering of code and output
+
+**Tests**
+- Marks have been added for running subsets of the tests for a given interface,
+  package feature, etc.
+- Tests for all the above functionality
+
+
+
 ### 1.5.*
 
 #### 1.5.3 - 24-09-03 - Bugfix, type checking for empty HDF5 datasets

docs/conf.py

@@ -25,7 +25,7 @@ extensions = [
     "sphinx.ext.doctest",
     "sphinx_design",
     "sphinxcontrib.mermaid",
-    "myst_parser",
+    "myst_nb",
     "sphinx.ext.todo",
 ]
 
@@ -77,3 +77,8 @@ napoleon_attr_annotations = True
 # todo
 todo_include_todos = True
 todo_link_only = True
+
+# myst
+# myst-nb
+nb_render_markdown_format = "myst"
+nb_execution_show_tb = True

docs/data/test.zarr/.zarray

@@ -0,0 +1,22 @@
+{
+    "chunks": [
+        2,
+        2
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<i8",
+    "fill_value": 0,
+    "filters": null,
+    "order": "C",
+    "shape": [
+        2,
+        2
+    ],
+    "zarr_format": 2
+}

docs/development.md

@@ -0,0 +1,84 @@
+# Development
+
+## Versioning
+
+This package uses a colloquial form of [semantic versioning 2](https://semver.org/).
+
+Specifically:
+
+- Major version `2.*.*` is reserved for the transition from nptyping to using
+  `TypeVarTuple`, `Generic`, and `Protocol`. Until `2.*.*`...
+  - breaking changes will be indicated with an advance in `MINOR`
+      version, taking the place of `MAJOR` in semver
+  - backwards-compatible bugfixes **and** additions in functionality
+    will be indicated by a `PATCH` release, taking the place of `MINOR` and
+    `PATCH` in semver.
+- After `2.*.*`, semver as usual will resume
+
+You are encouraged to set an upper bound on your version dependencies until
+we pass `2.*.*`, as the major function of numpydantic is stable,
+but there is still a decent amount of jostling things around to be expected.
+
+
+### API Stability
+
+- All breaking changes to the **public API** will be signaled by a major
+  version's worth of deprecation warnings
+- All breaking changes to the **development API** will be signaled by a
+  minor version's worth of deprecation warnings.
+- Changes to the remainder of the package, whether marked as private with a
+  leading underscore or not, including the import structure of the package,
+  are not considered part of the API and should not be relied on as stable 
+  until explicitly marked otherwise.
+
+#### Public API
+
+**Only the {class}`.NDArray` and {class}`.Shape` classes should be considered
+part of the stable public API.**
+
+All associated functionality for validation should also be considered
+a stable part of the `NDArray` and `Shape` classes - functionality
+will only be added here, and the departure for the string-form of the 
+shape specifications (and its removal) will take place in `v3.*.*`
+
+End-users of numpydantic should pin an upper bound for the `MAJOR` version
+until after `v2.*.*`, after which time it is up to your discretion - 
+no breaking changes are planned, but they would be signaled by a major version change.
+
+#### Development API
+
+**Only the {class}`.Interface` class and its subclasses, 
+along with the Public API,
+should be considered part of the stable development API.**
+
+The `Interface` class is the primary point of external development expected
+for numpydantic. It is still somewhat in flux, but it is prioritized for stability
+and deprecation warnings above the rest of the package. 
+
+Dependent packages that define their own `Interface`s should pin an upper
+bound for the `PATCH` version until `2.*.*`, and afterwards likely pin a `MINOR` version.
+Tests are designed such that it should be easy to test major features against
+each interface, and that work is also ongoing. Once the test suite reaches
+maturity, it should be possible for any downstream interfaces to simply use those to
+ensure they are compatible with the latest version.
+
+## Release Schedule
+
+There is no release schedule. Versions are released according to need and available labor.
+
+## Contributing
+
+### Dev environment
+
+```{todo}
+Document dev environment
+
+Really it's very simple, you just clone a fork and install
+the `dev` environment like `pip install '.[dev]'`
+```
+
+### Pull Requests
+
+```{todo}
+Document pull requests if we ever receive one
+```

docs/hooks.md

@@ -1,11 +0,0 @@
-# Hooks
-
-What hooks do we want to expose to downstream users so they can use this without needing
-to override everything?
-
-```{todo} 
-**NWB Compatibility**
-
-**Precision:** NWB allows for a sort of hierarchy of type specification - 
-a less precise type also allows the data to be specified in a more precise type
-```

docs/index.md

@@ -86,8 +86,8 @@ isinstance(np.zeros((1,2,3), dtype=float), array_type)
   and a simple extension system to make it work with whatever else you want! Provides
   a uniform and transparent interface so you can both use common indexing operations
   and also access any special features of a given array library.
-- **Serialization** - Dump an array as a JSON-compatible array-of-arrays with enough metadata to be able to 
+- [**Serialization**](./serialization.md) - Dump an array as a JSON-compatible array-of-arrays with enough metadata to be able to 
-  recreate the model in the native format
+  recreate the model in the native format. Full roundtripping is supported :)
 - **Schema Generation** - Correct JSON Schema for arrays, complete with shape and dtype constraints, to
   make your models interoperable 
 
@@ -473,9 +473,8 @@ dumped = instance.model_dump_json(context={'zarr_dump_array': True})
 
 design
 syntax
+serialization
 interfaces
-todo
-changelog
 ```
 
 ```{toctree}
@@ -489,13 +488,23 @@ api/dtype
 api/ndarray
 api/maps
 api/meta
-api/monkeypatch
 api/schema
+api/serialization
 api/shape
 api/types
 
 ```
 
+```{toctree}
+:maxdepth: 2
+:caption: Meta
+:hidden: true
+
+changelog
+development
+todo
+```
+
 ## See Also
 
 - [`jaxtyping`](https://docs.kidger.site/jaxtyping/)

docs/interfaces.md

@@ -46,6 +46,11 @@ for interfaces to implement custom behavior that matches the array format.
 
 {meth}`.Interface.validate` calls the following methods, in order:
 
+A method to deserialize the array dumped with a {func}`~pydantic.BaseModel.model_dump_json`
+with `round_trip = True` (see [serialization](./serialization.md))
+ 
+- {meth}`.Interface.deserialize`
+
 An initial hook for modifying the input data before validation, eg.
 if it needs to be coerced or wrapped in some proxy class. This method
 should accept all and only the types specified in that interface's

docs/serialization.md

@@ -0,0 +1,332 @@
+---
+file_format: mystnb
+mystnb:
+    output_stderr: remove
+    render_text_lexer: python
+    render_markdown_format: myst
+myst:
+    enable_extensions: ["colon_fence"]
+---
+
+# Serialization
+
+## Python
+
+In most cases, dumping to python should work as expected.
+
+When a given array framework doesn't provide a tidy means of interacting
+with it from python, we substitute a proxy class like {class}`.hdf5.H5Proxy`,
+but aside from that numpydantic {class}`.NDArray` annotations
+should be passthrough when using {func}`~pydantic.BaseModel.model_dump` .
+
+## JSON
+
+JSON is the ~ ♥ fun one ♥ ~
+
+There isn't necessarily a single optimal way to represent all possible
+arrays in JSON. The standard way that n-dimensional arrays are rendered
+in json is as a list-of-lists (or array of arrays, in JSON parlance),
+but that's almost never what is desirable, especially for large arrays.
+
+### Normal Style[^normalstyle]
+
+Lists-of-lists are the standard, however, so it is the default behavior
+for all interfaces, and all interfaces must support it.
+
+```{code-cell}
+---
+tags: [hide-cell]
+---
+
+from pathlib import Path
+from pydantic import BaseModel
+from numpydantic import NDArray, Shape
+from numpydantic.interface.dask import DaskJsonDict
+from numpydantic.interface.numpy import NumpyJsonDict
+import numpy as np
+import dask.array as da
+import zarr
+import json
+from rich import print
+from rich.console import Console
+
+def print_json(string:str):
+    data = json.loads(string)
+    console = Console(width=74)
+    console.print(data)
+```
+
+For our humble model:
+
+```{code-cell}
+class MyModel(BaseModel):
+    array: NDArray
+```
+
+We should get the same thing for each interface:
+
+```{code-cell}
+model = MyModel(array=[[1,2],[3,4]])
+print(model.model_dump_json())
+```
+
+```{code-cell}
+model = MyModel(array=da.array([[1,2],[3,4]], dtype=int))
+print(model.model_dump_json())
+```
+
+```{code-cell}
+model = MyModel(array=zarr.array([[1,2],[3,4]], dtype=int))
+print(model.model_dump_json())
+```
+
+```{code-cell}
+model = MyModel(array="data/test.avi")
+print(model.model_dump_json())
+```
+
+(ok maybe not that last one, since the video reader still incorrectly
+reads grayscale videos as BGR values for now, but you get the idea)
+
+Since by default arrays are dumped into unadorned JSON arrays,
+when they are re-validated, they will always be handled by the
+{class}`.NumpyInterface`
+
+```{code-cell}
+dask_array = da.array([[1,2],[3,4]], dtype=int)
+model = MyModel(array=dask_array)
+type(model.array)
+```
+
+```{code-cell}
+model_json = model.model_dump_json()
+deserialized_model = MyModel.model_validate_json(model_json)
+type(deserialized_model.array)
+```
+
+All information about `dtype` will be lost, and numbers will be either parsed
+as `int` ({class}`numpy.int64`) or `float` ({class}`numpy.float64`)
+
+## Roundtripping
+
+To roundtrip make arrays round-trippable, use the `round_trip` argument
+to {func}`~pydantic.BaseModel.model_dump_json`.
+
+All the following should return an equivalent array from the same
+file/etc. as the source array when using 
+`{func}`~pydantic.BaseModel.model_validate_json`` .
+
+```{code-cell}
+print_json(model.model_dump_json(round_trip=True))
+```
+
+Each interface must implement a dataclass that describes a
+json-able roundtrip form (see {class}`.interface.JsonDict`).
+
+That dataclass then has a {meth}`JsonDict.is_valid` method that checks
+whether an incoming dict matches its schema
+
+```{code-cell}
+roundtrip_json = json.loads(model.model_dump_json(round_trip=True))['array']
+DaskJsonDict.is_valid(roundtrip_json)
+```
+
+```{code-cell}
+NumpyJsonDict.is_valid(roundtrip_json)
+```
+
+#### Controlling paths
+
+When possible, the full content of the array is omitted in favor
+of the path to the file that provided it.
+
+```{code-cell}
+model = MyModel(array="data/test.avi")
+print_json(model.model_dump_json(round_trip=True))
+```
+
+```{code-cell}
+model = MyModel(array=("data/test.h5", "/data"))
+print_json(model.model_dump_json(round_trip=True))
+```
+
+You may notice the relative, rather than absolute paths.
+
+
+We expect that when people are dumping data to json in this roundtripped
+form that they are either working locally 
+(e.g. transmitting an array specification across a socket in multiprocessing
+or in a computing cluster),
+or exporting to some directory structure of data, 
+where they are making an index file that refers to datasets in a directory
+as part of a data standard or vernacular format.
+
+By default, numpydantic uses the current working directory as the root to find
+paths relative to, but this can be controlled by the [`relative_to`](#relative_to)
+context parameter:
+
+For example if you're working on data in many subdirectories,
+you might want to serialize relative to each of them:
+
+```{code-cell}
+print_json(
+  model.model_dump_json(
+    round_trip=True, 
+    context={"relative_to": Path('./data')}
+  ))
+```
+
+Or in the other direction:
+
+```{code-cell}
+print_json(
+  model.model_dump_json(
+    round_trip=True, 
+    context={"relative_to": Path('../')}
+  ))
+```
+
+Or you might be working in some completely different place,
+numpydantic will try and find the way from here to there as long as it exists,
+even if it means traversing to the root of the readthedocs filesystem
+
+```{code-cell}
+print_json(
+  model.model_dump_json(
+    round_trip=True, 
+    context={"relative_to": Path('/a/long/distance/directory')}
+    ))
+```
+
+You can force absolute paths with the `absolute_paths` context parameter
+
+```{code-cell}
+print_json(
+  model.model_dump_json(
+    round_trip=True, 
+    context={"absolute_paths": True}
+    ))
+```
+
+#### Durable Interface Metadata
+
+Numpydantic tries to be [stable](./development.md#api-stability),
+but we're not perfect. To preserve the full information about the
+interface that's needed to load the data referred to by the value,
+use the `mark_interface` contest parameter:
+
+```{code-cell}
+print_json(
+  model.model_dump_json(
+    round_trip=True, 
+    context={"mark_interface": True}
+    ))
+```
+
+When an array marked with the interface is deserialized,
+it short-circuits the {meth}`.Interface.match` method,
+attempting to directly return the indicated interface as long as the
+array dumped in `value` still satisfies that interface's {meth}`.Interface.check`
+method. Arrays dumped *without* `round_trip=True` might *not* validate with
+the originating model, even when marked -- eg. an array dumped without `round_trip`
+will be revalidated as a numpy array for the same reasons it is everywhere else,
+since all connection to the source file is lost.
+
+```{todo}
+Currently, the version of the package the interface is from (usually `numpydantic`)
+will be stored, but there is no means of resolving it on the fly. 
+If there is a mismatch between the marked interface description and the interface
+that was matched on revalidation, a warning is emitted, but validation
+attempts to proceed as normal.
+
+This feature is for extra-verbose provenance, rather than airtight serialization
+and deserialization, but PRs welcome if you would like to make it be that way.
+```
+
+```{todo}
+We will also add a separate `mark_version` parameter for marking
+the specific version of the relevant interface package, like `zarr`, or `numpy`,
+patience.
+```
+
+
+
+## Context parameters
+
+A reference listing of all the things that can be passed to
+{func}`~pydantic.BaseModel.model_dump_json`
+
+
+### `mark_interface`
+
+Nest an additional layer of metadata for unambigous serialization that
+can be absolutely resolved across numpydantic versions 
+(for now for downstream metadata purposes only, 
+automatically resolving to a numpydantic version is not yet possible.)
+
+Supported interfaces:
+
+- (all)
+
+```{code-cell}
+model = MyModel(array=[[1,2],[3,4]])
+data = model.model_dump_json(
+  round_trip=True,
+  context={"mark_interface": True}
+)
+print_json(data)
+```
+
+### `absolute_paths`
+
+Make all paths (that exist) absolute.
+
+Supported interfaces:
+
+- (all)
+
+```{code-cell}
+model = MyModel(array=("data/test.h5", "/data"))
+data = model.model_dump_json(
+    round_trip=True, 
+    context={"absolute_paths": True}
+    )
+print_json(data)
+```
+
+### `relative_to`
+
+Make all paths (that exist) relative to the given path
+
+Supported interfaces:
+
+- (all)
+
+```{code-cell}
+model = MyModel(array=("data/test.h5", "/data"))
+data = model.model_dump_json(
+    round_trip=True, 
+    context={"relative_to": Path('../')}
+    )
+print_json(data)
+```
+
+### `dump_array`
+
+Dump the raw array contents when serializing to json inside an `array` field
+
+Supported interfaces:
+- {class}`.ZarrInterface`
+
+```{code-cell}
+model = MyModel(array=("data/test.zarr",))
+data = model.model_dump_json(
+    round_trip=True, 
+    context={"dump_array": True}
+    )
+print_json(data)
+```
+
+
+
+[^normalstyle]: o ya we're posting JSON [normal style](https://normal.style)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "numpydantic"
-version = "1.5.3"
+version = "1.6.0"
 description = "Type and shape validation and serialization for arbitrary array types in pydantic models"
 authors = [
     {name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"},
@@ -73,12 +73,15 @@ tests = [
     "coveralls<4.0.0,>=3.3.1",
 ]
 docs = [
+    "numpydantic[arrays]",
     "sphinx<8.0.0,>=7.2.6",
     "furo>=2024.1.29",
     "myst-parser<3.0.0,>=2.0.0",
     "autodoc-pydantic<3.0.0,>=2.0.1",
     "sphinx-design<1.0.0,>=0.5.0",
     "sphinxcontrib-mermaid>=0.9.2",
+    "myst-nb>=1.1.1",
+    "rich>=13.8.1",
 ]
 dev = [
     "numpydantic[tests,docs]",
@@ -109,6 +112,18 @@ filterwarnings = [
     # nptyping's alias warnings
     'ignore:.*deprecated alias.*Deprecated NumPy 1\.24.*'
 ]
+markers = [
+    "dtype: mark test related to dtype validation",
+    "shape: mark test related to shape validation",
+    "json_schema: mark test related to json schema generation",
+    "serialization: mark test related to serialization",
+    "proxy: test for proxy class in any interface",
+    "dask: dask interface",
+    "hdf5: hdf5 interface",
+    "numpy: numpy interface",
+    "video: video interface",
+    "zarr: zarr interface",
+]
 
 [tool.ruff]
 target-version = "py311"

src/numpydantic/exceptions.py

@@ -25,3 +25,7 @@ class NoMatchError(MatchError):
 
 class TooManyMatchesError(MatchError):
     """Too many matches found by :class:`.Interface.match`"""
+
+
+class MarkMismatchError(MatchError):
+    """A serialized :class:`.InterfaceMark` doesn't match the receiving interface"""

src/numpydantic/interface/__init__.py

@@ -4,15 +4,23 @@ Interfaces between nptyping types and array backends
 
 from numpydantic.interface.dask import DaskInterface
 from numpydantic.interface.hdf5 import H5Interface
-from numpydantic.interface.interface import Interface
+from numpydantic.interface.interface import (
+    Interface,
+    InterfaceMark,
+    JsonDict,
+    MarkedJson,
+)
 from numpydantic.interface.numpy import NumpyInterface
 from numpydantic.interface.video import VideoInterface
 from numpydantic.interface.zarr import ZarrInterface
 
 __all__ = [
-    "Interface",
     "DaskInterface",
     "H5Interface",
+    "Interface",
+    "InterfaceMark",
+    "JsonDict",
+    "MarkedJson",
     "NumpyInterface",
     "VideoInterface",
     "ZarrInterface",

src/numpydantic/interface/dask.py

@@ -2,34 +2,73 @@
 Interface for Dask arrays
 """
 
-from typing import Any, Optional
+from typing import Any, Iterable, List, Literal, Optional, Union
 
 import numpy as np
 from pydantic import SerializationInfo
 
-from numpydantic.interface.interface import Interface
+from numpydantic.interface.interface import Interface, JsonDict
 from numpydantic.types import DtypeType, NDArrayType
 
 try:
+    from dask.array import from_array
     from dask.array.core import Array as DaskArray
 except ImportError:  # pragma: no cover
     DaskArray = None
 
 
+def _as_tuple(a_list: Any) -> tuple:
+    """Make a list of list into a tuple of tuples"""
+    return tuple(
+        [_as_tuple(item) if isinstance(item, list) else item for item in a_list]
+    )
+
+
+class DaskJsonDict(JsonDict):
+    """
+    Round-trip json serialized form of a dask array
+    """
+
+    type: Literal["dask"]
+    name: str
+    chunks: Iterable[tuple[int, ...]]
+    dtype: str
+    array: list
+
+    def to_array_input(self) -> DaskArray:
+        """Construct a dask array"""
+        np_array = np.array(self.array, dtype=self.dtype)
+        array = from_array(
+            np_array,
+            name=self.name,
+            chunks=_as_tuple(self.chunks),
+        )
+        return array
+
+
 class DaskInterface(Interface):
     """
     Interface for Dask :class:`~dask.array.core.Array`
     """
 
-    input_types = (DaskArray,)
+    name = "dask"
+    input_types = (DaskArray, dict)
     return_type = DaskArray
+    json_model = DaskJsonDict
 
     @classmethod
     def check(cls, array: Any) -> bool:
         """
         check if array is a dask array
         """
-        return DaskArray is not None and isinstance(array, DaskArray)
+        if DaskArray is None:  # pragma: no cover - no tests for interface deps atm
+            return False
+        elif isinstance(array, DaskArray):
+            return True
+        elif isinstance(array, dict):
+            return DaskJsonDict.is_valid(array)
+        else:
+            return False
 
     def get_object_dtype(self, array: NDArrayType) -> DtypeType:
         """Dask arrays require a compute() call to retrieve a single value"""
@@ -43,7 +82,7 @@ class DaskInterface(Interface):
     @classmethod
     def to_json(
         cls, array: DaskArray, info: Optional[SerializationInfo] = None
-    ) -> list:
+    ) -> Union[List, DaskJsonDict]:
         """
         Convert an array to a JSON serializable array by first converting to a numpy
         array and then to a list.
@@ -56,4 +95,14 @@ class DaskInterface(Interface):
             method of serialization here using the python object itself rather than
             its JSON representation.
         """
-        return np.array(array).tolist()
+        np_array = np.array(array)
+        as_json = np_array.tolist()
+        if info.round_trip:
+            as_json = DaskJsonDict(
+                type=cls.name,
+                array=as_json,
+                name=array.name,
+                chunks=array.chunks,
+                dtype=str(np_array.dtype),
+            )
+        return as_json

src/numpydantic/interface/hdf5.py

@@ -47,7 +47,7 @@ from typing import Any, Iterable, List, NamedTuple, Optional, Tuple, TypeVar, Un
 import numpy as np
 from pydantic import SerializationInfo
 
-from numpydantic.interface.interface import Interface
+from numpydantic.interface.interface import Interface, JsonDict
 from numpydantic.types import DtypeType, NDArrayType
 
 try:
@@ -76,6 +76,20 @@ class H5ArrayPath(NamedTuple):
     """Refer to a specific field within a compound dtype"""
 
 
+class H5JsonDict(JsonDict):
+    """Round-trip Json-able version of an HDF5 dataset"""
+
+    file: str
+    path: str
+    field: Optional[str] = None
+
+    def to_array_input(self) -> H5ArrayPath:
+        """Construct an :class:`.H5ArrayPath`"""
+        return H5ArrayPath(
+            **{k: v for k, v in self.model_dump().items() if k in H5ArrayPath._fields}
+        )
+
+
 class H5Proxy:
     """
     Proxy class to mimic numpy-like array behavior with an HDF5 array
@@ -106,10 +120,11 @@ class H5Proxy:
         annotation_dtype: Optional[DtypeType] = None,
     ):
         self._h5f = None
-        self.file = Path(file)
+        self.file = Path(file).resolve()
         self.path = path
         self.field = field
         self._annotation_dtype = annotation_dtype
+        self._h5arraypath = H5ArrayPath(self.file, self.path, self.field)
 
     def array_exists(self) -> bool:
         """Check that there is in fact an array at :attr:`.path` within :attr:`.file`"""
@@ -134,10 +149,20 @@ class H5Proxy:
             else:
                 return obj.dtype[self.field]
 
-    def __getattr__(self, item: str):
+    def __array__(self) -> np.ndarray:
+        """To a numpy array"""
         with h5py.File(self.file, "r") as h5f:
             obj = h5f.get(self.path)
-            return getattr(obj, item)
+            return obj[:]
+
+    def __getattr__(self, item: str):
+        if item == "__name__":
+            # special case for H5Proxies that don't refer to a real file during testing
+            return "H5Proxy"
+        with h5py.File(self.file, "r") as h5f:
+            obj = h5f.get(self.path)
+            val = getattr(obj, item)
+            return val
 
     def __getitem__(
         self, item: Union[int, slice, Tuple[Union[int, slice], ...]]
@@ -205,6 +230,15 @@ class H5Proxy:
         """self.shape[0]"""
         return self.shape[0]
 
+    def __eq__(self, other: "H5Proxy") -> bool:
+        """
+        Check that we are referring to the same hdf5 array
+        """
+        if isinstance(other, H5Proxy):
+            return self._h5arraypath == other._h5arraypath
+        else:
+            raise ValueError("Can only compare equality of two H5Proxies")
+
     def open(self, mode: str = "r") -> "h5py.Dataset":
         """
         Return the opened :class:`h5py.Dataset` object
@@ -244,8 +278,10 @@ class H5Interface(Interface):
     passthrough numpy-like interface to the dataset.
     """
 
+    name = "hdf5"
     input_types = (H5ArrayPath, H5Arraylike, H5Proxy)
     return_type = H5Proxy
+    json_model = H5JsonDict
 
     @classmethod
     def enabled(cls) -> bool:
@@ -261,6 +297,13 @@ class H5Interface(Interface):
         if isinstance(array, (H5ArrayPath, H5Proxy)):
             return True
 
+        if isinstance(array, dict):
+            if array.get("type", False) == cls.name:
+                return True
+            # continue checking if dict contains an hdf5 file
+            file = array.get("file", "")
+            array = (file, "")
+
         if isinstance(array, (tuple, list)) and len(array) in (2, 3):
             # check that the first arg is an hdf5 file
             try:
@@ -342,21 +385,27 @@ class H5Interface(Interface):
     @classmethod
     def to_json(cls, array: H5Proxy, info: Optional[SerializationInfo] = None) -> dict:
         """
-        Dump to a dictionary containing
+        Render HDF5 array as JSON
+
+        If ``round_trip == True``, we dump just the proxy info, a dictionary like:
 
         * ``file``: :attr:`.file`
         * ``path``: :attr:`.path`
         * ``attrs``: Any HDF5 attributes on the dataset
         * ``array``: The array as a list of lists
+
+        Otherwise, we dump the array as a list of lists
         """
+        if info.round_trip:
+            as_json = {
+                "type": cls.name,
+            }
+            as_json.update(array._h5arraypath._asdict())
+        else:
             try:
                 dset = array.open()
-            meta = {
+                as_json = dset[:].tolist()
-                "file": array.file,
-                "path": array.path,
-                "attrs": dict(dset.attrs),
-                "array": dset[:].tolist(),
-            }
-            return meta
             finally:
                 array.close()
+
+        return as_json

src/numpydantic/interface/interface.py

@@ -2,15 +2,20 @@
 Base Interface metaclass
 """
 
+import inspect
+import warnings
 from abc import ABC, abstractmethod
+from functools import lru_cache
+from importlib.metadata import PackageNotFoundError, version
 from operator import attrgetter
 from typing import Any, Generic, Optional, Tuple, Type, TypeVar, Union
 
 import numpy as np
-from pydantic import SerializationInfo
+from pydantic import BaseModel, SerializationInfo, ValidationError
 
 from numpydantic.exceptions import (
     DtypeError,
+    MarkMismatchError,
     NoMatchError,
     ShapeError,
     TooManyMatchesError,
@@ -19,6 +24,130 @@ from numpydantic.shape import check_shape
 from numpydantic.types import DtypeType, NDArrayType, ShapeType
 
 T = TypeVar("T", bound=NDArrayType)
+U = TypeVar("U", bound="JsonDict")
+V = TypeVar("V")  # input type
+W = TypeVar("W")  # Any type in handle_input
+
+
+class InterfaceMark(BaseModel):
+    """JSON-able mark to be able to round-trip json dumps"""
+
+    module: str
+    cls: str
+    name: str
+    version: str
+
+    def is_valid(self, cls: Type["Interface"], raise_on_error: bool = False) -> bool:
+        """
+        Check that a given interface matches the mark.
+
+        Args:
+            cls (Type): Interface type to check
+            raise_on_error (bool): Raise an ``MarkMismatchError`` when the match
+                is incorrect
+
+        Returns:
+            bool
+
+        Raises:
+            :class:`.MarkMismatchError` if requested by ``raise_on_error``
+            for an invalid match
+        """
+        mark = cls.mark_interface()
+        valid = self == mark
+        if not valid and raise_on_error:
+            raise MarkMismatchError(
+                "Mismatch between serialized mark and current interface, "
+                f"Serialized: {self}; current: {cls}"
+            )
+        return valid
+
+    def match_by_name(self) -> Optional[Type["Interface"]]:
+        """
+        Try to find a matching interface by its name, returning it if found,
+        or None if not found.
+        """
+        for i in Interface.interfaces(sort=False):
+            if i.name == self.name:
+                return i
+        return None
+
+
+class JsonDict(BaseModel):
+    """
+    Representation of array when dumped with round_trip == True.
+    """
+
+    type: str
+
+    @abstractmethod
+    def to_array_input(self) -> V:
+        """
+        Convert this roundtrip specifier to the relevant input class
+        (one of the ``input_types`` of an interface).
+        """
+
+    @classmethod
+    def is_valid(cls, val: dict, raise_on_error: bool = False) -> bool:
+        """
+        Check whether a given dictionary matches this JsonDict specification
+
+        Args:
+            val (dict): The dictionary to check for validity
+            raise_on_error (bool): If ``True``, raise the validation error
+                rather than returning a bool. (default: ``False``)
+
+        Returns:
+            bool - true if valid, false if not
+        """
+        try:
+            _ = cls.model_validate(val)
+            return True
+        except ValidationError as e:
+            if raise_on_error:
+                raise e
+            return False
+
+    @classmethod
+    def handle_input(cls: Type[U], value: Union[dict, U, W]) -> Union[V, W]:
+        """
+        Handle input that is the json serialized roundtrip version
+        (from :func:`~pydantic.BaseModel.model_dump` with ``round_trip=True``)
+        converting it to the input format with :meth:`.JsonDict.to_array_input`
+        or passing it through if not applicable
+        """
+        if isinstance(value, dict):
+            value = cls(**value).to_array_input()
+        elif isinstance(value, cls):
+            value = value.to_array_input()
+        return value
+
+
+class MarkedJson(BaseModel):
+    """
+    Model of JSON dumped with an additional interface mark
+    with ``model_dump_json({'mark_interface': True})``
+    """
+
+    interface: InterfaceMark
+    value: Union[list, dict]
+    """
+    Inner value of the array, we don't validate for JsonDict here, 
+    that should be downstream from us for performance reasons 
+    """
+
+    @classmethod
+    def try_cast(cls, value: Union[V, dict]) -> Union[V, "MarkedJson"]:
+        """
+        Try to cast to MarkedJson if applicable, otherwise return input
+        """
+        if isinstance(value, dict) and "interface" in value and "value" in value:
+            try:
+                value = MarkedJson(**value)
+            except ValidationError:
+                # fine, just not a MarkedJson dict even if it looks like one
+                return value
+        return value
 
 
 class Interface(ABC, Generic[T]):
@@ -30,7 +159,7 @@ class Interface(ABC, Generic[T]):
     return_type: Type[T]
     priority: int = 0
 
-    def __init__(self, shape: ShapeType, dtype: DtypeType) -> None:
+    def __init__(self, shape: ShapeType = Any, dtype: DtypeType = Any) -> None:
         self.shape = shape
         self.dtype = dtype
 
@@ -40,6 +169,7 @@ class Interface(ABC, Generic[T]):
 
         Calls the methods, in order:
 
+        * array = :meth:`.deserialize` (array)
         * array = :meth:`.before_validation` (array)
         * dtype = :meth:`.get_dtype` (array) - get the dtype from the array,
             override if eg. the dtype is not contained in ``array.dtype``
@@ -74,6 +204,8 @@ class Interface(ABC, Generic[T]):
             :class:`.DtypeError` and :class:`.ShapeError` (both of which are children
             of :class:`.InterfaceError` )
         """
+        array = self.deserialize(array)
+
         array = self.before_validation(array)
 
         dtype = self.get_dtype(array)
@@ -86,8 +218,32 @@ class Interface(ABC, Generic[T]):
         self.raise_for_shape(shape_valid, shape)
 
         array = self.after_validation(array)
+
         return array
 
+    def deserialize(self, array: Any) -> Union[V, Any]:
+        """
+        If given a JSON serialized version of the array,
+        deserialize it first.
+
+        If a roundtrip-serialized :class:`.JsonDict`,
+        pass to :meth:`.JsonDict.handle_input`.
+
+        If a roundtrip-serialized :class:`.MarkedJson`,
+        unpack mark, check for validity, warn if not,
+        and try to continue with validation
+        """
+        if isinstance(marked_array := MarkedJson.try_cast(array), MarkedJson):
+            try:
+                marked_array.interface.is_valid(self.__class__, raise_on_error=True)
+            except MarkMismatchError as e:
+                warnings.warn(
+                    str(e) + "\nAttempting to continue validation...", stacklevel=2
+                )
+            array = marked_array.value
+
+        return self.json_model.handle_input(array)
+
     def before_validation(self, array: Any) -> NDArrayType:
         """
         Optional step pre-validation that coerces the input into a type that can be
@@ -117,8 +273,6 @@ class Interface(ABC, Generic[T]):
         """
         Validate the dtype of the given array, returning
         ``True`` if valid, ``False`` if not.
-
-
         """
         if self.dtype is Any:
             return True
@@ -211,17 +365,48 @@ class Interface(ABC, Generic[T]):
         installed, etc.)
         """
 
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """
+        Short name for this interface
+        """
+
+    @property
+    @abstractmethod
+    def json_model(self) -> JsonDict:
+        """
+        The :class:`.JsonDict` model used for roundtripping
+        JSON serialization
+        """
+
     @classmethod
-    def to_json(
+    @abstractmethod
-        cls, array: Type[T], info: Optional[SerializationInfo] = None
+    def to_json(cls, array: Type[T], info: SerializationInfo) -> Union[list, JsonDict]:
-    ) -> Union[list, dict]:
         """
         Convert an array of :attr:`.return_type` to a JSON-compatible format using
         base python types
         """
-        if not isinstance(array, np.ndarray):  # pragma: no cover
+
-            array = np.array(array)
+    @classmethod
-        return array.tolist()
+    def mark_json(cls, array: Union[list, dict]) -> dict:
+        """
+        When using ``model_dump_json`` with ``mark_interface: True`` in the ``context``,
+        add additional annotations that would allow the serialized array to be
+        roundtripped.
+
+        Default is just to add an :class:`.InterfaceMark`
+
+        Examples:
+
+            >>> from pprint import pprint
+            >>> pprint(Interface.mark_json([1.0, 2.0]))
+            {'interface': {'cls': 'Interface',
+                           'module': 'numpydantic.interface.interface',
+                           'version': '1.2.2'},
+             'value': [1.0, 2.0]}
+        """
+        return {"interface": cls.mark_interface(), "value": array}
 
     @classmethod
     def interfaces(
@@ -274,6 +459,28 @@ class Interface(ABC, Generic[T]):
 
         return tuple(in_types)
 
+    @classmethod
+    def match_mark(cls, array: Any) -> Optional[Type["Interface"]]:
+        """
+        Match a marked JSON dump of this array to the interface that it indicates.
+
+        First find an interface that matches by name, and then run its
+        ``check`` method, because arrays can be dumped with a mark
+        but without ``round_trip == True`` (and thus can't necessarily
+        use the same interface that they were dumped with)
+
+        Returns:
+            Interface if match found, None otherwise
+        """
+        mark = MarkedJson.try_cast(array)
+        if not isinstance(mark, MarkedJson):
+            return None
+
+        interface = mark.interface.match_by_name()
+        if interface is not None and interface.check(mark.value):
+            return interface
+        return None
+
     @classmethod
     def match(cls, array: Any, fast: bool = False) -> Type["Interface"]:
         """
@@ -291,11 +498,18 @@ class Interface(ABC, Generic[T]):
               check each interface (as ordered by its ``priority`` , decreasing),
               and return on the first match.
         """
+        # Shortcircuit match if this is a marked json dump
+        array = MarkedJson.try_cast(array)
+        if (match := cls.match_mark(array)) is not None:
+            return match
+        elif isinstance(array, MarkedJson):
+            array = array.value
+
         # first try and find a non-numpy interface, since the numpy interface
         # will try and load the array into memory in its check method
         interfaces = cls.interfaces()
-        non_np_interfaces = [i for i in interfaces if i.__name__ != "NumpyInterface"]
+        non_np_interfaces = [i for i in interfaces if i.name != "numpy"]
-        np_interface = [i for i in interfaces if i.__name__ == "NumpyInterface"][0]
+        np_interface = [i for i in interfaces if i.name == "numpy"][0]
 
         if fast:
             matches = []
@@ -335,3 +549,29 @@ class Interface(ABC, Generic[T]):
             raise NoMatchError(f"No matching interfaces found for output {array}")
         else:
             return matches[0]
+
+    @classmethod
+    @lru_cache(maxsize=32)
+    def mark_interface(cls) -> InterfaceMark:
+        """
+        Create an interface mark indicating this interface for validation after
+        JSON serialization with ``round_trip==True``
+        """
+        interface_module = inspect.getmodule(cls)
+        interface_module = (
+            None if interface_module is None else interface_module.__name__
+        )
+        try:
+            v = (
+                None
+                if interface_module is None
+                else version(interface_module.split(".")[0])
+            )
+        except (
+            PackageNotFoundError
+        ):  # pragma: no cover - no tests for missing interface deps
+            v = None
+
+        return InterfaceMark(
+            module=interface_module, cls=cls.__name__, name=cls.name, version=v
+        )

src/numpydantic/interface/numpy.py

@@ -2,9 +2,11 @@
 Interface to numpy arrays
 """
 
-from typing import Any
+from typing import Any, Literal, Union
 
-from numpydantic.interface.interface import Interface
+from pydantic import SerializationInfo
+
+from numpydantic.interface.interface import Interface, JsonDict
 
 try:
     import numpy as np
@@ -18,13 +20,31 @@ except ImportError:  # pragma: no cover
     np = None
 
 
+class NumpyJsonDict(JsonDict):
+    """
+    JSON-able roundtrip representation of numpy array
+    """
+
+    type: Literal["numpy"]
+    dtype: str
+    array: list
+
+    def to_array_input(self) -> ndarray:
+        """
+        Construct a numpy array
+        """
+        return np.array(self.array, dtype=self.dtype)
+
+
 class NumpyInterface(Interface):
     """
     Numpy :class:`~numpy.ndarray` s!
     """
 
+    name = "numpy"
     input_types = (ndarray, list)
     return_type = ndarray
+    json_model = NumpyJsonDict
     priority = -999
     """
     The numpy interface is usually the interface of last resort.
@@ -41,6 +61,8 @@ class NumpyInterface(Interface):
         """
         if isinstance(array, ndarray):
             return True
+        elif isinstance(array, dict):
+            return NumpyJsonDict.is_valid(array)
         else:
             try:
                 _ = np.array(array)
@@ -61,3 +83,22 @@ class NumpyInterface(Interface):
     def enabled(cls) -> bool:
         """Check that numpy is present in the environment"""
         return ENABLED
+
+    @classmethod
+    def to_json(
+        cls, array: ndarray, info: SerializationInfo = None
+    ) -> Union[list, JsonDict]:
+        """
+        Convert an array of :attr:`.return_type` to a JSON-compatible format using
+        base python types
+        """
+        if not isinstance(array, np.ndarray):  # pragma: no cover
+            array = np.array(array)
+
+        json_array = array.tolist()
+
+        if info.round_trip:
+            json_array = NumpyJsonDict(
+                type=cls.name, dtype=str(array.dtype), array=json_array
+            )
+        return json_array

src/numpydantic/interface/video.py

@@ -3,10 +3,12 @@ Interface to support treating videos like arrays using OpenCV
 """
 
 from pathlib import Path
-from typing import Any, Optional, Tuple, Union
+from typing import Any, Literal, Optional, Tuple, Union
 
 import numpy as np
+from pydantic_core.core_schema import SerializationInfo
 
+from numpydantic.interface import JsonDict
 from numpydantic.interface.interface import Interface
 
 try:
@@ -19,6 +21,19 @@ except ImportError:  # pragma: no cover
 VIDEO_EXTENSIONS = (".mp4", ".avi", ".mov", ".mkv")
 
 
+class VideoJsonDict(JsonDict):
+    """Json-able roundtrip representation of a video file"""
+
+    type: Literal["video"]
+    file: str
+
+    def to_array_input(self) -> "VideoProxy":
+        """
+        Construct a :class:`.VideoProxy`
+        """
+        return VideoProxy(path=Path(self.file))
+
+
 class VideoProxy:
     """
     Passthrough proxy class to interact with videos as arrays
@@ -33,7 +48,7 @@ class VideoProxy:
             )
 
         if path is not None:
-            path = Path(path)
+            path = Path(path).resolve()
         self.path = path
 
         self._video = video  # type: Optional[VideoCapture]
@@ -52,6 +67,9 @@ class VideoProxy:
                     "and it cant be reopened since source path cant be gotten "
                     "from VideoCapture objects"
                 )
+            if not self.path.exists():
+                raise FileNotFoundError(f"Video file {self.path} does not exist!")
+
             self._video = VideoCapture(str(self.path))
         return self._video
 
@@ -137,6 +155,10 @@ class VideoProxy:
             slice_ = slice(0, slice_.stop, slice_.step)
         return slice_
 
+    def __array__(self) -> np.ndarray:
+        """Whole video as a numpy array"""
+        return self[:]
+
     def __getitem__(self, item: Union[int, slice, tuple]) -> np.ndarray:
         if isinstance(item, int):
             # want a single frame
@@ -178,8 +200,16 @@ class VideoProxy:
         raise NotImplementedError("Setting pixel values on videos is not supported!")
 
     def __getattr__(self, item: str):
+        if item == "__name__":
+            return "VideoProxy"
         return getattr(self.video, item)
 
+    def __eq__(self, other: "VideoProxy") -> bool:
+        """Check if this is a proxy to the same video file"""
+        if not isinstance(other, VideoProxy):
+            raise TypeError("Can only compare equality of two VideoProxies")
+        return self.path == other.path
+
     def __len__(self) -> int:
         """Number of frames in the video"""
         return self.shape[0]
@@ -190,8 +220,10 @@ class VideoInterface(Interface):
     OpenCV interface to treat videos as arrays.
     """
 
+    name = "video"
     input_types = (str, Path, VideoCapture, VideoProxy)
     return_type = VideoProxy
+    json_model = VideoJsonDict
 
     @classmethod
     def enabled(cls) -> bool:
@@ -209,6 +241,9 @@ class VideoInterface(Interface):
         ):
             return True
 
+        if isinstance(array, dict):
+            array = array.get("file", "")
+
         if isinstance(array, str):
             try:
                 array = Path(array)
@@ -227,3 +262,13 @@ class VideoInterface(Interface):
         else:
             proxy = VideoProxy(path=array)
         return proxy
+
+    @classmethod
+    def to_json(
+        cls, array: VideoProxy, info: SerializationInfo
+    ) -> Union[list, VideoJsonDict]:
+        """Return a json-representation of a video"""
+        if info.round_trip:
+            return VideoJsonDict(type=cls.name, file=str(array.path))
+        else:
+            return np.array(array).tolist()

src/numpydantic/interface/zarr.py

@@ -5,12 +5,12 @@ Interface to zarr arrays
 import contextlib
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Optional, Sequence, Union
+from typing import Any, Literal, Optional, Sequence, Union
 
 import numpy as np
 from pydantic import SerializationInfo
 
-from numpydantic.interface.interface import Interface
+from numpydantic.interface.interface import Interface, JsonDict
 from numpydantic.types import DtypeType
 
 try:
@@ -56,13 +56,36 @@ class ZarrArrayPath:
             raise ValueError("Only len 1-2 iterables can be used for a ZarrArrayPath")
 
 
+class ZarrJsonDict(JsonDict):
+    """Round-trip Json-able version of a Zarr Array"""
+
+    info: dict[str, str]
+    type: Literal["zarr"]
+    file: Optional[str] = None
+    path: Optional[str] = None
+    array: Optional[list] = None
+
+    def to_array_input(self) -> Union[ZarrArray, ZarrArrayPath]:
+        """
+        Construct a ZarrArrayPath if file and path are present,
+        otherwise a ZarrArray
+        """
+        if self.file:
+            array = ZarrArrayPath(file=self.file, path=self.path)
+        else:
+            array = zarr.array(self.array)
+        return array
+
+
 class ZarrInterface(Interface):
     """
     Interface to in-memory or on-disk zarr arrays
     """
 
+    name = "zarr"
     input_types = (Path, ZarrArray, ZarrArrayPath)
     return_type = ZarrArray
+    json_model = ZarrJsonDict
 
     @classmethod
     def enabled(cls) -> bool:
@@ -71,7 +94,7 @@ 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, ZarrArray):
             return array
@@ -92,6 +115,12 @@ class ZarrInterface(Interface):
         if isinstance(array, ZarrArray):
             return True
 
+        if isinstance(array, dict):
+            if array.get("type", False) == cls.name:
+                return True
+            # continue checking if dict contains a zarr file
+            array = array.get("file", "")
+
         # See if can be coerced to ZarrArrayPath
         if isinstance(array, (Path, str)):
             array = ZarrArrayPath(file=array)
@@ -135,26 +164,48 @@ class ZarrInterface(Interface):
         cls,
         array: Union[ZarrArray, str, Path, ZarrArrayPath, Sequence],
         info: Optional[SerializationInfo] = None,
-    ) -> dict:
+    ) -> Union[list, ZarrJsonDict]:
         """
-        Dump just the metadata for an array from :meth:`zarr.core.Array.info_items`
+        Dump a Zarr Array to JSON
-        plus the :meth:`zarr.core.Array.hexdigest`.
 
-        The full array can be returned by passing ``'zarr_dump_array': True`` to the
+        If ``info.round_trip == False``, dump the array as a list of lists.
-        serialization ``context`` ::
+        This may be a memory-intensive operation.
+
+        Otherwise, dump the metadata for an array from
+        :meth:`zarr.core.Array.info_items`
+        plus the :meth:`zarr.core.Array.hexdigest` as a :class:`.ZarrJsonDict`
+
+        If either the ``dump_array`` value in the context dictionary is ``True``
+        or the zarr array is an in-memory array, dump the array as well
+        (since without a persistent array it would be impossible to roundtrip and
+        dumping to JSON would be meaningless)
+
+        Passing ```dump_array': True`` to the serialization ``context``
+        looks like this::
 
             model.model_dump_json(context={'zarr_dump_array': True})
         """
+        array = cls._get_array(array)
+
+        if info.round_trip:
             dump_array = False
             if info is not None and info.context is not None:
-            dump_array = info.context.get("zarr_dump_array", False)
+                dump_array = info.context.get("dump_array", False)
+            is_file = False
 
-        array = cls._get_array(array)
+            as_json = {"type": cls.name}
-        info = array.info_items()
+            if hasattr(array.store, "dir_path"):
-        info_dict = {i[0]: i[1] for i in info}
+                is_file = True
-        info_dict["hexdigest"] = array.hexdigest()
+                as_json["file"] = array.store.dir_path()
+                as_json["path"] = array.name
+            as_json["info"] = {i[0]: i[1] for i in array.info_items()}
+            as_json["info"]["hexdigest"] = array.hexdigest()
 
-        if dump_array:
+            if dump_array or not is_file:
-            info_dict["array"] = array[:].tolist()
+                as_json["array"] = array[:].tolist()
 
-        return info_dict
+            as_json = ZarrJsonDict(**as_json)
+        else:
+            as_json = array[:].tolist()
+
+        return as_json

src/numpydantic/ndarray.py

@@ -24,11 +24,10 @@ from numpydantic.exceptions import InterfaceError
 from numpydantic.interface import Interface
 from numpydantic.maps import python_to_nptyping
 from numpydantic.schema import (
-    _handler_type,
-    _jsonize_array,
     get_validate_interface,
     make_json_schema,
 )
+from numpydantic.serialization import jsonize_array
 from numpydantic.types import DtypeType, NDArrayType, ShapeType
 from numpydantic.vendor.nptyping.error import InvalidArgumentsError
 from numpydantic.vendor.nptyping.ndarray import NDArrayMeta as _NDArrayMeta
@@ -41,6 +40,9 @@ from numpydantic.vendor.nptyping.typing_ import (
 
 if TYPE_CHECKING:  # pragma: no cover
     from nptyping.base_meta_classes import SubscriptableMeta
+    from pydantic._internal._schema_generation_shared import (
+        CallbackGetCoreSchemaHandler,
+    )
 
     from numpydantic import Shape
 
@@ -164,33 +166,34 @@ class NDArray(NPTypingType, metaclass=NDArrayMeta):
     def __get_pydantic_core_schema__(
         cls,
         _source_type: "NDArray",
-        _handler: _handler_type,
+        _handler: "CallbackGetCoreSchemaHandler",
     ) -> core_schema.CoreSchema:
         shape, dtype = _source_type.__args__
         shape: ShapeType
         dtype: DtypeType
 
-        # get pydantic core schema as a list of lists for JSON schema
+        # make core schema for json schema, store it and any model definitions
-        list_schema = make_json_schema(shape, dtype, _handler)
+        # note that there is a big of fragility in this function,
+        # as we need to access a private method of _handler to
+        # flatten out the json schema. See help(make_json_schema)
+        json_schema = make_json_schema(shape, dtype, _handler)
 
-        return core_schema.json_or_python_schema(
+        return core_schema.with_info_plain_validator_function(
-            json_schema=list_schema,
+            get_validate_interface(shape, dtype),
-            python_schema=core_schema.with_info_plain_validator_function(
-                get_validate_interface(shape, dtype)
-            ),
             serialization=core_schema.plain_serializer_function_ser_schema(
-                _jsonize_array, when_used="json", info_arg=True
+                jsonize_array, when_used="json", info_arg=True
             ),
+            metadata=json_schema,
         )
 
     @classmethod
     def __get_pydantic_json_schema__(
         cls, schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
     ) -> core_schema.JsonSchema:
-        json_schema = handler(schema)
+        shape, dtype = cls.__args__
+        json_schema = handler(schema["metadata"])
         json_schema = handler.resolve_ref_schema(json_schema)
 
-        dtype = cls.__args__[1]
         if not isinstance(dtype, tuple) and dtype.__module__ not in (
             "builtins",
             "typing",

src/numpydantic/schema.py

@@ -5,10 +5,10 @@ Helper functions for use with :class:`~numpydantic.NDArray` - see the note in
 
 import hashlib
 import json
-from typing import TYPE_CHECKING, Any, Callable, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional
 
 import numpy as np
-from pydantic import BaseModel, SerializationInfo
+from pydantic import BaseModel
 from pydantic_core import CoreSchema, core_schema
 from pydantic_core.core_schema import ListSchema, ValidationInfo
 
@@ -19,13 +19,16 @@ from numpydantic.types import DtypeType, NDArrayType, ShapeType
 from numpydantic.vendor.nptyping.structure import StructureMeta
 
 if TYPE_CHECKING:  # pragma: no cover
+    from pydantic._internal._schema_generation_shared import (
+        CallbackGetCoreSchemaHandler,
+    )
+
     from numpydantic import Shape
 
-_handler_type = Callable[[Any], core_schema.CoreSchema]
-_UNSUPPORTED_TYPES = (complex,)
 
-
+def _numeric_dtype(
-def _numeric_dtype(dtype: DtypeType, _handler: _handler_type) -> CoreSchema:
+    dtype: DtypeType, _handler: "CallbackGetCoreSchemaHandler"
+) -> CoreSchema:
     """Make a numeric dtype that respects min/max values from extended numpy types"""
     if dtype in (np.number,):
         dtype = float
@@ -36,14 +39,15 @@ def _numeric_dtype(dtype: DtypeType, _handler: _handler_type) -> CoreSchema:
     elif issubclass(dtype, np.integer):
         info = np.iinfo(dtype)
         schema = core_schema.int_schema(le=int(info.max), ge=int(info.min))
-
     else:
         schema = _handler.generate_schema(dtype)
 
     return schema
 
 
-def _lol_dtype(dtype: DtypeType, _handler: _handler_type) -> CoreSchema:
+def _lol_dtype(
+    dtype: DtypeType, _handler: "CallbackGetCoreSchemaHandler"
+) -> CoreSchema:
     """Get the innermost dtype schema to use in the generated pydantic schema"""
     if isinstance(dtype, StructureMeta):  # pragma: no cover
         raise NotImplementedError("Structured dtypes are currently unsupported")
@@ -79,11 +83,12 @@ def _lol_dtype(dtype: DtypeType, _handler: _handler_type) -> CoreSchema:
                 # does this need a warning?
                 python_type = Any
 
-        if python_type in _UNSUPPORTED_TYPES:
+        if python_type in (float, int):
-            array_type = core_schema.any_schema()
-            # TODO: warn and log here
-        elif python_type in (float, int):
             array_type = _numeric_dtype(dtype, _handler)
+        elif python_type is bool:
+            array_type = core_schema.bool_schema()
+        elif python_type is Any:
+            array_type = core_schema.any_schema()
         else:
             array_type = _handler.generate_schema(python_type)
 
@@ -208,14 +213,24 @@ def _unbounded_shape(
 
 
 def make_json_schema(
-    shape: ShapeType, dtype: DtypeType, _handler: _handler_type
+    shape: ShapeType, dtype: DtypeType, _handler: "CallbackGetCoreSchemaHandler"
 ) -> ListSchema:
     """
-    Make a list of list JSON schema from a shape and a dtype.
+    Make a list of list pydantic core schema for an array from a shape and a dtype.
+    Used to generate JSON schema in the containing model, but not for validation,
+    which is handled by interfaces.
 
     First resolves the dtype into a pydantic ``CoreSchema`` ,
     and then uses that with :func:`.list_of_lists_schema` .
 
+    .. admonition:: Potentially Fragile
+
+        Uses a private method from the handler to flatten out nested definitions
+        (e.g. when dtype is a pydantic model)
+        so that they are present in the generated schema directly rather than
+        as references. Otherwise, at the time __get_pydantic_json_schema__ is called,
+        the definition references are lost.
+
     Args:
         shape ( ShapeType ): Specification of a shape, as a tuple or
             an nptyping ``Shape``
@@ -234,6 +249,8 @@ def make_json_schema(
     else:
         list_schema = list_of_lists_schema(shape, dtype_schema)
 
+    list_schema = _handler._generate_schema.clean_schema(list_schema)
+
     return list_schema
 
 
@@ -252,9 +269,3 @@ def get_validate_interface(shape: ShapeType, dtype: DtypeType) -> Callable:
         return value
 
     return validate_interface
-
-
-def _jsonize_array(value: Any, info: SerializationInfo) -> Union[list, dict]:
-    """Use an interface class to render an array as JSON"""
-    interface_cls = Interface.match_output(value)
-    return interface_cls.to_json(value, info)

src/numpydantic/serialization.py

@@ -0,0 +1,128 @@
+"""
+Serialization helpers for :func:`pydantic.BaseModel.model_dump`
+and :func:`pydantic.BaseModel.model_dump_json` .
+"""
+
+from pathlib import Path
+from typing import Any, Callable, TypeVar, Union
+
+from pydantic_core.core_schema import SerializationInfo
+
+from numpydantic.interface import Interface, JsonDict
+
+T = TypeVar("T")
+U = TypeVar("U")
+
+
+def jsonize_array(value: Any, info: SerializationInfo) -> Union[list, dict]:
+    """Use an interface class to render an array as JSON"""
+    interface_cls = Interface.match_output(value)
+    array = interface_cls.to_json(value, info)
+    if isinstance(array, JsonDict):
+        array = array.model_dump(exclude_none=True)
+
+    if info.context:
+        if info.context.get("mark_interface", False):
+            array = interface_cls.mark_json(array)
+
+        if info.context.get("absolute_paths", False):
+            array = _absolutize_paths(array)
+        else:
+            relative_to = info.context.get("relative_to", ".")
+            array = _relativize_paths(array, relative_to)
+    else:
+        # relativize paths by default
+        array = _relativize_paths(array, ".")
+
+    return array
+
+
+def _relativize_paths(value: dict, relative_to: str = ".") -> dict:
+    """
+    Make paths relative to either the current directory or the provided
+    ``relative_to`` directory, if provided in the context
+    """
+    relative_to = Path(relative_to).resolve()
+    # pdb.set_trace()
+
+    def _r_path(v: Any) -> Any:
+        try:
+            path = Path(v)
+            if not path.exists():
+                return v
+            return str(relative_path(path, relative_to))
+        except (TypeError, ValueError):
+            return v
+
+    return _walk_and_apply(value, _r_path)
+
+
+def _absolutize_paths(value: dict) -> dict:
+    def _a_path(v: Any) -> Any:
+        try:
+            path = Path(v)
+            if not path.exists():
+                return v
+            return str(path.resolve())
+        except (TypeError, ValueError):
+            return v
+
+    return _walk_and_apply(value, _a_path)
+
+
+def _walk_and_apply(value: T, f: Callable[[U], U]) -> T:
+    """
+    Walk an object, applying a function
+    """
+    if isinstance(value, dict):
+        for k, v in value.items():
+            if isinstance(v, dict):
+                _walk_and_apply(v, f)
+            elif isinstance(v, list):
+                value[k] = [_walk_and_apply(sub_v, f) for sub_v in v]
+            else:
+                value[k] = f(v)
+    elif isinstance(value, list):
+        value = [_walk_and_apply(v, f) for v in value]
+    else:
+        value = f(value)
+    return value
+
+
+def relative_path(self: Path, other: Path, walk_up: bool = True) -> Path:
+    """
+    "Backport" of :meth:`pathlib.Path.relative_to` with ``walk_up=True``
+    that's not available pre 3.12.
+
+    Return the relative path to another path identified by the passed
+    arguments.  If the operation is not possible (because this is not
+    related to the other path), raise ValueError.
+
+    The *walk_up* parameter controls whether `..` may be used to resolve
+    the path.
+
+    References:
+        https://github.com/python/cpython/blob/8a2baedc4bcb606da937e4e066b4b3a18961cace/Lib/pathlib/_abc.py#L244-L270
+    """
+    # pdb.set_trace()
+    if not isinstance(other, Path):  # pragma: no cover - ripped from cpython
+        other = Path(other)
+    self_parts = self.parts
+    other_parts = other.parts
+    anchor0, parts0 = self_parts[0], list(reversed(self_parts[1:]))
+    anchor1, parts1 = other_parts[0], list(reversed(other_parts[1:]))
+    if anchor0 != anchor1:
+        raise ValueError(f"{self!r} and {other!r} have different anchors")
+    while parts0 and parts1 and parts0[-1] == parts1[-1]:
+        parts0.pop()
+        parts1.pop()
+    for part in parts1:  # pragma: no cover - not testing, ripped off from cpython
+        if not part or part == ".":
+            pass
+        elif not walk_up:
+            raise ValueError(f"{self!r} is not in the subpath of {other!r}")
+        elif part == "..":
+            raise ValueError(f"'..' segment in {other!r} cannot be walked")
+        else:
+            parts0.append("..")
+    return Path(*reversed(parts0))

tests/conftest.py

@@ -1,4 +1,3 @@
-import pdb
 import sys
 
 import pytest

tests/test_interface/conftest.py

@@ -1,6 +1,6 @@
 import pytest
 
-from typing import Tuple, Callable
+from typing import Callable, Tuple, Type
 import numpy as np
 import dask.array as da
 import zarr
@@ -12,27 +12,47 @@ from numpydantic import interface, NDArray
 @pytest.fixture(
     scope="function",
     params=[
+        pytest.param(
             ([[1, 2], [3, 4]], interface.NumpyInterface),
+            marks=pytest.mark.numpy,
+            id="numpy-list",
+        ),
+        pytest.param(
             (np.zeros((3, 4)), interface.NumpyInterface),
+            marks=pytest.mark.numpy,
+            id="numpy",
+        ),
+        pytest.param(
             ("hdf5_array", interface.H5Interface),
+            marks=pytest.mark.hdf5,
+            id="h5-array-path",
+        ),
+        pytest.param(
             (da.random.random((10, 10)), interface.DaskInterface),
+            marks=pytest.mark.dask,
+            id="dask",
+        ),
+        pytest.param(
             (zarr.ones((10, 10)), interface.ZarrInterface),
+            marks=pytest.mark.zarr,
+            id="zarr-memory",
+        ),
+        pytest.param(
             ("zarr_nested_array", interface.ZarrInterface),
+            marks=pytest.mark.zarr,
+            id="zarr-nested",
+        ),
+        pytest.param(
             ("zarr_array", interface.ZarrInterface),
-        ("avi_video", interface.VideoInterface),
+            marks=pytest.mark.zarr,
-    ],
+            id="zarr-array",
-    ids=[
+        ),
-        "numpy_list",
+        pytest.param(
-        "numpy",
+            ("avi_video", interface.VideoInterface), marks=pytest.mark.video, id="video"
-        "H5ArrayPath",
+        ),
-        "dask",
-        "zarr_memory",
-        "zarr_nested",
-        "zarr_array",
-        "video",
     ],
 )
-def interface_type(request) -> Tuple[NDArray, interface.Interface]:
+def interface_type(request) -> Tuple[NDArray, Type[interface.Interface]]:
     """
     Test cases for each interface's ``check`` method - each input should match the
     provided interface and that interface only

tests/test_interface/test_dask.py

@@ -1,5 +1,3 @@
-import pdb
-
 import pytest
 import json
 
@@ -11,6 +9,8 @@ from numpydantic.exceptions import DtypeError, ShapeError
 
 from tests.conftest import ValidationCase
 
+pytestmark = pytest.mark.dask
+
 
 def dask_array(case: ValidationCase) -> da.Array:
     if issubclass(case.dtype, BaseModel):
@@ -42,14 +42,17 @@ def test_dask_check(interface_type):
         assert not DaskInterface.check(interface_type[0])
 
 
+@pytest.mark.shape
 def test_dask_shape(shape_cases):
     _test_dask_case(shape_cases)
 
 
+@pytest.mark.dtype
 def test_dask_dtype(dtype_cases):
     _test_dask_case(dtype_cases)
 
 
+@pytest.mark.serialization
 def test_dask_to_json(array_model):
     array_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
     array = da.array(array_list)

tests/test_interface/test_dunder.py

@@ -1,10 +0,0 @@
-"""
-Tests for dunder methods on all interfaces
-"""
-
-
-def test_dunder_len(all_interfaces):
-    """
-    Each interface or proxy type should support __len__
-    """
-    assert len(all_interfaces.array) == all_interfaces.array.shape[0]

tests/test_interface/test_hdf5.py

@@ -14,6 +14,8 @@ from numpydantic.exceptions import DtypeError, ShapeError
 
 from tests.conftest import ValidationCase
 
+pytestmark = pytest.mark.hdf5
+
 
 def hdf5_array_case(
     case: ValidationCase, array_func, compound: bool = False
@@ -72,11 +74,13 @@ def test_hdf5_check_not_hdf5(tmp_path):
     assert not H5Interface.check(spec)
 
 
+@pytest.mark.shape
 @pytest.mark.parametrize("compound", [True, False])
 def test_hdf5_shape(shape_cases, hdf5_array, compound):
     _test_hdf5_case(shape_cases, hdf5_array, compound)
 
 
+@pytest.mark.dtype
 @pytest.mark.parametrize("compound", [True, False])
 def test_hdf5_dtype(dtype_cases, hdf5_array, compound):
     _test_hdf5_case(dtype_cases, hdf5_array, compound)
@@ -90,6 +94,7 @@ def test_hdf5_dataset_not_exists(hdf5_array, model_blank):
         assert "no array found" in e
 
 
+@pytest.mark.proxy
 def test_assignment(hdf5_array, model_blank):
     array = hdf5_array()
 
@@ -101,7 +106,9 @@ def test_assignment(hdf5_array, model_blank):
     assert (model.array[1:3, 2:4] == 10).all()
 
 
-def test_to_json(hdf5_array, array_model):
+@pytest.mark.serialization
+@pytest.mark.parametrize("round_trip", (True, False))
+def test_to_json(hdf5_array, array_model, round_trip):
     """
     Test serialization of HDF5 arrays to JSON
     Args:
@@ -115,15 +122,19 @@ def test_to_json(hdf5_array, array_model):
 
     instance = model(array=array)  # type: BaseModel
 
-    json_str = instance.model_dump_json()
+    json_str = instance.model_dump_json(
-    json_dict = json.loads(json_str)["array"]
+        round_trip=round_trip, context={"absolute_paths": True}
-
+    )
-    assert json_dict["file"] == str(array.file)
+    json_dumped = json.loads(json_str)["array"]
-    assert json_dict["path"] == str(array.path)
+    if round_trip:
-    assert json_dict["attrs"] == {}
+        assert json_dumped["file"] == str(array.file)
-    assert json_dict["array"] == instance.array[:].tolist()
+        assert json_dumped["path"] == str(array.path)
+    else:
+        assert json_dumped == instance.array[:].tolist()
 
 
+@pytest.mark.dtype
+@pytest.mark.proxy
 def test_compound_dtype(tmp_path):
     """
     hdf5 proxy indexes compound dtypes as single fields when field is given
@@ -158,6 +169,8 @@ def test_compound_dtype(tmp_path):
     assert all(instance.array[1] == 2)
 
 
+@pytest.mark.dtype
+@pytest.mark.proxy
 @pytest.mark.parametrize("compound", [True, False])
 def test_strings(hdf5_array, compound):
     """
@@ -177,6 +190,8 @@ def test_strings(hdf5_array, compound):
     assert all(instance.array[1] == "sup")
 
 
+@pytest.mark.dtype
+@pytest.mark.proxy
 @pytest.mark.parametrize("compound", [True, False])
 def test_datetime(hdf5_array, compound):
     """
@@ -218,3 +233,29 @@ def test_empty_dataset(dtype, tmp_path):
         array: NDArray[Any, dtype]
 
     _ = MyModel(array=(array_path, "/data"))
+
+
+@pytest.mark.proxy
+@pytest.mark.parametrize(
+    "comparison,valid",
+    [
+        (H5Proxy(file="test_file.h5", path="/subpath", field="sup"), True),
+        (H5Proxy(file="test_file.h5", path="/subpath"), False),
+        (H5Proxy(file="different_file.h5", path="/subpath"), False),
+        (("different_file.h5", "/subpath", "sup"), ValueError),
+        ("not even a proxy-like thing", ValueError),
+    ],
+)
+def test_proxy_eq(comparison, valid):
+    """
+    test the __eq__ method of H5ArrayProxy matches proxies to the same
+    dataset (and path), or raises a ValueError
+    """
+    proxy_a = H5Proxy(file="test_file.h5", path="/subpath", field="sup")
+    if valid is True:
+        assert proxy_a == comparison
+    elif valid is False:
+        assert proxy_a != comparison
+    else:
+        with pytest.raises(valid):
+            assert proxy_a == comparison

tests/test_interface/test_interface_base.py

@@ -4,11 +4,32 @@ for tests that should apply to all interfaces, use ``test_interfaces.py``
 """
 
 import gc
+from typing import Literal
 
 import pytest
 import numpy as np
 
-from numpydantic.interface import Interface
+from numpydantic.interface import (
+    Interface,
+    JsonDict,
+    InterfaceMark,
+    NumpyInterface,
+    MarkedJson,
+)
+from pydantic import ValidationError
+
+from numpydantic.interface.interface import V
+
+
+class MyJsonDict(JsonDict):
+    type: Literal["my_json_dict"]
+    field: str
+    number: int
+
+    def to_array_input(self) -> V:
+        dumped = self.model_dump()
+        dumped["extra_input_param"] = True
+        return dumped
 
 
 @pytest.fixture(scope="module")
@@ -162,3 +183,66 @@ def test_interface_recursive(interfaces):
     assert issubclass(interfaces.interface3, interfaces.interface1)
     assert issubclass(interfaces.interface1, Interface)
     assert interfaces.interface4 in ifaces
+
+
+@pytest.mark.serialization
+def test_jsondict_is_valid():
+    """
+    A JsonDict should return a bool true/false if it is valid or not,
+    and raise an error when requested
+    """
+    invalid = {"doesnt": "have", "the": "props"}
+    valid = {"type": "my_json_dict", "field": "a_field", "number": 1}
+    assert MyJsonDict.is_valid(valid)
+    assert not MyJsonDict.is_valid(invalid)
+    with pytest.raises(ValidationError):
+        assert not MyJsonDict.is_valid(invalid, raise_on_error=True)
+
+
+@pytest.mark.serialization
+def test_jsondict_handle_input():
+    """
+    JsonDict should be able to parse a valid dict and return it to the input format
+    """
+    valid = {"type": "my_json_dict", "field": "a_field", "number": 1}
+    instantiated = MyJsonDict(**valid)
+    expected = {
+        "type": "my_json_dict",
+        "field": "a_field",
+        "number": 1,
+        "extra_input_param": True,
+    }
+
+    for item in (valid, instantiated):
+        result = MyJsonDict.handle_input(item)
+        assert result == expected
+
+
+@pytest.mark.serialization
+@pytest.mark.parametrize("interface", Interface.interfaces())
+def test_interface_mark_match_by_name(interface):
+    """
+    Interface mark should match an interface by its name
+    """
+    # other parts don't matter
+    mark = InterfaceMark(module="fake", cls="fake", version="fake", name=interface.name)
+    fake_mark = InterfaceMark(
+        module="fake", cls="fake", version="fake", name="also_fake"
+    )
+    assert mark.match_by_name() is interface
+    assert fake_mark.match_by_name() is None
+
+
+@pytest.mark.serialization
+def test_marked_json_try_cast():
+    """
+    MarkedJson.try_cast should try and cast to a markedjson!
+    returning the value unchanged if it's not a match
+    """
+    valid = {"interface": NumpyInterface.mark_interface(), "value": [[1, 2], [3, 4]]}
+    invalid = [1, 2, 3, 4, 5]
+    mimic = {"interface": "not really", "value": "still not really"}
+
+    assert isinstance(MarkedJson.try_cast(valid), MarkedJson)
+    assert MarkedJson.try_cast(invalid) is invalid
+    assert MarkedJson.try_cast(mimic) is mimic

tests/test_interface/test_interfaces.py

@@ -2,6 +2,41 @@
 Tests that should be applied to all interfaces
 """
 
+import pytest
+from typing import Callable
+from importlib.metadata import version
+import json
+
+import numpy as np
+import dask.array as da
+from zarr.core import Array as ZarrArray
+from pydantic import BaseModel
+
+from numpydantic.interface import Interface, InterfaceMark, MarkedJson
+
+
+def _test_roundtrip(source: BaseModel, target: BaseModel, round_trip: bool):
+    """Test model equality for roundtrip tests"""
+    if round_trip:
+        assert type(target.array) is type(source.array)
+        if isinstance(source.array, (np.ndarray, ZarrArray)):
+            assert np.array_equal(target.array, np.array(source.array))
+        elif isinstance(source.array, da.Array):
+            assert np.all(da.equal(target.array, source.array))
+        else:
+            assert target.array == source.array
+
+        assert target.array.dtype == source.array.dtype
+    else:
+        assert np.array_equal(target.array, np.array(source.array))
+
+
+def test_dunder_len(all_interfaces):
+    """
+    Each interface or proxy type should support __len__
+    """
+    assert len(all_interfaces.array) == all_interfaces.array.shape[0]
+
 
 def test_interface_revalidate(all_interfaces):
     """
@@ -10,3 +45,86 @@ def test_interface_revalidate(all_interfaces):
     See: https://github.com/p2p-ld/numpydantic/pull/14
     """
     _ = type(all_interfaces)(array=all_interfaces.array)
+
+
+def test_interface_rematch(interface_type):
+    """
+    All interfaces should match the results of the object they return after validation
+    """
+    array, interface = interface_type
+    if isinstance(array, Callable):
+        array = array()
+
+    assert Interface.match(interface().validate(array)) is interface
+
+
+def test_interface_to_numpy_array(all_interfaces):
+    """
+    All interfaces should be able to have the output of their validation stage
+    coerced to a numpy array with np.array()
+    """
+    _ = np.array(all_interfaces.array)
+
+
+@pytest.mark.serialization
+def test_interface_dump_json(all_interfaces):
+    """
+    All interfaces should be able to dump to json
+    """
+    all_interfaces.model_dump_json()
+
+
+@pytest.mark.serialization
+@pytest.mark.parametrize("round_trip", [True, False])
+def test_interface_roundtrip_json(all_interfaces, round_trip):
+    """
+    All interfaces should be able to roundtrip to and from json
+    """
+    dumped_json = all_interfaces.model_dump_json(round_trip=round_trip)
+    model = all_interfaces.model_validate_json(dumped_json)
+    _test_roundtrip(all_interfaces, model, round_trip)
+
+
+@pytest.mark.serialization
+@pytest.mark.parametrize("an_interface", Interface.interfaces())
+def test_interface_mark_interface(an_interface):
+    """
+    All interfaces should be able to mark the current version and interface info
+    """
+    mark = an_interface.mark_interface()
+    assert isinstance(mark, InterfaceMark)
+    assert mark.name == an_interface.name
+    assert mark.cls == an_interface.__name__
+    assert mark.module == an_interface.__module__
+    assert mark.version == version(mark.module.split(".")[0])
+
+
+@pytest.mark.serialization
+@pytest.mark.parametrize("valid", [True, False])
+@pytest.mark.parametrize("round_trip", [True, False])
+@pytest.mark.filterwarnings("ignore:Mismatch between serialized mark")
+def test_interface_mark_roundtrip(all_interfaces, valid, round_trip):
+    """
+    All interfaces should be able to roundtrip with the marked interface,
+    and a mismatch should raise a warning and attempt to proceed
+    """
+    dumped_json = all_interfaces.model_dump_json(
+        round_trip=round_trip, context={"mark_interface": True}
+    )
+
+    data = json.loads(dumped_json)
+
+    # ensure that we are a MarkedJson
+    _ = MarkedJson.model_validate_json(json.dumps(data["array"]))
+
+    if not valid:
+        # ruin the version
+        data["array"]["interface"]["version"] = "v99999999"
+        dumped_json = json.dumps(data)
+
+        with pytest.warns(match="Mismatch.*"):
+            model = all_interfaces.model_validate_json(dumped_json)
+    else:
+        model = all_interfaces.model_validate_json(dumped_json)
+
+    _test_roundtrip(all_interfaces, model, round_trip)

tests/test_interface/test_numpy.py

@@ -5,6 +5,8 @@ from numpydantic.exceptions import DtypeError, ShapeError
 
 from tests.conftest import ValidationCase
 
+pytestmark = pytest.mark.numpy
+
 
 def numpy_array(case: ValidationCase) -> np.ndarray:
     if issubclass(case.dtype, BaseModel):
@@ -22,10 +24,12 @@ def _test_np_case(case: ValidationCase):
             case.model(array=array)
 
 
+@pytest.mark.shape
 def test_numpy_shape(shape_cases):
     _test_np_case(shape_cases)
 
 
+@pytest.mark.dtype
 def test_numpy_dtype(dtype_cases):
     _test_np_case(dtype_cases)
 

tests/test_interface/test_video.py

@@ -14,6 +14,8 @@ from numpydantic import NDArray, Shape
 from numpydantic import dtype as dt
 from numpydantic.interface.video import VideoProxy
 
+pytestmark = pytest.mark.video
+
 
 @pytest.mark.parametrize("input_type", [str, Path])
 def test_video_validation(avi_video, input_type):
@@ -49,6 +51,7 @@ def test_video_from_videocapture(avi_video):
         opened_vid.release()
 
 
+@pytest.mark.shape
 def test_video_wrong_shape(avi_video):
     shape = (100, 50)
 
@@ -65,6 +68,7 @@ def test_video_wrong_shape(avi_video):
         instance = MyModel(array=vid)
 
 
+@pytest.mark.proxy
 def test_video_getitem(avi_video):
     """
     Should be able to get individual frames and slices as if it were a normal array
@@ -127,6 +131,7 @@ def test_video_getitem(avi_video):
         instance.array[5] = 10
 
 
+@pytest.mark.proxy
 def test_video_attrs(avi_video):
     """Should be able to access opencv properties"""
     shape = (100, 50)
@@ -142,6 +147,7 @@ def test_video_attrs(avi_video):
     assert int(instance.array.get(cv2.CAP_PROP_POS_FRAMES)) == 5
 
 
+@pytest.mark.proxy
 def test_video_close(avi_video):
     """Should close and reopen video file if needed"""
     shape = (100, 50)
@@ -158,3 +164,42 @@ def test_video_close(avi_video):
     assert instance.array._video is None
     # reopen
     assert isinstance(instance.array.video, cv2.VideoCapture)
+
+
+@pytest.mark.proxy
+def test_video_not_exists(tmp_path):
+    """
+    A video file that doesn't exist should raise an error
+    """
+    video = VideoProxy(tmp_path / "not_real.avi")
+    with pytest.raises(FileNotFoundError):
+        _ = video.video
+
+
+@pytest.mark.proxy
+@pytest.mark.parametrize(
+    "comparison,valid",
+    [
+        (VideoProxy("test_video.avi"), True),
+        (VideoProxy("not_real_video.avi"), False),
+        ("not even a video proxy", TypeError),
+    ],
+)
+def test_video_proxy_eq(comparison, valid):
+    """
+    Comparing a video proxy's equality should be valid if the path matches
+    Args:
+        comparison:
+        valid:
+
+    Returns:
+
+    """
+    proxy_a = VideoProxy("test_video.avi")
+    if valid is True:
+        assert proxy_a == comparison
+    elif valid is False:
+        assert proxy_a != comparison
+    else:
+        with pytest.raises(valid):
+            assert proxy_a == comparison

tests/test_interface/test_zarr.py

@@ -6,13 +6,14 @@ import zarr
 from pydantic import BaseModel, ValidationError
 from numcodecs import Pickle
 
-
 from numpydantic.interface import ZarrInterface
 from numpydantic.interface.zarr import ZarrArrayPath
 from numpydantic.exceptions import DtypeError, ShapeError
 
 from tests.conftest import ValidationCase
 
+pytestmark = pytest.mark.zarr
+
 
 @pytest.fixture()
 def dir_array(tmp_output_dir_func) -> zarr.DirectoryStore:
@@ -87,10 +88,12 @@ def test_zarr_check(interface_type):
         assert not ZarrInterface.check(interface_type[0])
 
 
+@pytest.mark.shape
 def test_zarr_shape(store, shape_cases):
     _test_zarr_case(shape_cases, store)
 
 
+@pytest.mark.dtype
 def test_zarr_dtype(dtype_cases, store):
     _test_zarr_case(dtype_cases, store)
 
@@ -123,7 +126,10 @@ def test_zarr_array_path_from_iterable(zarr_array):
     assert apath.path == inner_path
 
 
-def test_zarr_to_json(store, model_blank):
+@pytest.mark.serialization
+@pytest.mark.parametrize("dump_array", [True, False])
+@pytest.mark.parametrize("roundtrip", [True, False])
+def test_zarr_to_json(store, model_blank, roundtrip, dump_array):
     expected_fields = (
         "Type",
         "Data type",
@@ -137,17 +143,22 @@ def test_zarr_to_json(store, model_blank):
 
     array = zarr.array(lol_array, store=store)
     instance = model_blank(array=array)
-    as_json = json.loads(instance.model_dump_json())["array"]
-    assert "array" not in as_json
-    for field in expected_fields:
-        assert field in as_json
-    assert len(as_json["hexdigest"]) == 40
 
-    # dump the array itself too
+    context = {"dump_array": dump_array}
-    as_json = json.loads(instance.model_dump_json(context={"zarr_dump_array": True}))[
+    as_json = json.loads(
-        "array"
+        instance.model_dump_json(round_trip=roundtrip, context=context)
-    ]
+    )["array"]
-    for field in expected_fields:
+
-        assert field in as_json
+    if roundtrip:
-    assert len(as_json["hexdigest"]) == 40
+        if dump_array:
             assert as_json["array"] == lol_array
+        else:
+            if as_json.get("file", False):
+                assert "array" not in as_json
+
+        for field in expected_fields:
+            assert field in as_json["info"]
+        assert len(as_json["info"]["hexdigest"]) == 40
+
+    else:
+        assert as_json == lol_array

tests/test_ndarray.py

@@ -1,5 +1,3 @@
-import pdb
-
 import pytest
 
 from typing import Union, Optional, Any
@@ -15,6 +13,7 @@ from numpydantic import dtype
 from numpydantic.dtype import Number
 
 
+@pytest.mark.json_schema
 def test_ndarray_type():
     class Model(BaseModel):
         array: NDArray[Shape["2 x, * y"], Number]
@@ -40,6 +39,8 @@ def test_ndarray_type():
     instance = Model(array=np.zeros((2, 3)), array_any=np.ones((3, 4, 5)))
 
 
+@pytest.mark.dtype
+@pytest.mark.json_schema
 def test_schema_unsupported_type():
     """
     Complex numbers should just be made with an `any` schema
@@ -55,9 +56,11 @@ def test_schema_unsupported_type():
     }
 
 
+@pytest.mark.dtype
+@pytest.mark.json_schema
 def test_schema_tuple():
     """
-    Types specified as tupled should have their schemas as a union
+    Types specified as tuples should have their schemas as a union
     """
 
     class Model(BaseModel):
@@ -72,6 +75,8 @@ def test_schema_tuple():
     assert all([i["minimum"] == 0 for i in conditions])
 
 
+@pytest.mark.dtype
+@pytest.mark.json_schema
 def test_schema_number():
     """
     np.numeric should just be the float schema
@@ -115,12 +120,12 @@ def test_ndarray_union():
         instance = Model(array=np.random.random((5, 10, 4, 6)))
 
 
+@pytest.mark.shape
+@pytest.mark.dtype
 @pytest.mark.parametrize("dtype", dtype.Number)
 def test_ndarray_unparameterized(dtype):
     """
     NDArray without any parameters is any shape, any type
-    Returns:
-
     """
 
     class Model(BaseModel):
@@ -134,6 +139,7 @@ def test_ndarray_unparameterized(dtype):
         _ = Model(array=np.zeros(dim_sizes, dtype=dtype))
 
 
+@pytest.mark.shape
 def test_ndarray_any():
     """
     using :class:`typing.Any` in for the shape means any shape
@@ -164,6 +170,19 @@ def test_ndarray_coercion():
         amod = Model(array=["a", "b", "c"])
 
 
+@pytest.mark.shape
+def test_shape_ellipsis():
+    """
+    Test that ellipsis is a wildcard, rather than "repeat the last index"
+    """
+
+    class MyModel(BaseModel):
+        array: NDArray[Shape["1, 2, ..."], Number]
+
+    _ = MyModel(array=np.zeros((1, 2, 3, 4, 5)))
+
+
+@pytest.mark.serialization
 def test_ndarray_serialize():
     """
     Arrays should be dumped to a list when using json, but kept as ndarray otherwise
@@ -188,6 +207,7 @@ _json_schema_types = [
 ]
 
 
+@pytest.mark.json_schema
 def test_json_schema_basic(array_model):
     """
     NDArray types should correctly generate a list of lists JSON schema
@@ -210,6 +230,8 @@ def test_json_schema_basic(array_model):
     assert inner["items"]["type"] == "number"
 
 
+@pytest.mark.dtype
+@pytest.mark.json_schema
 @pytest.mark.parametrize("dtype", [*dtype.Integer, *dtype.Float])
 def test_json_schema_dtype_single(dtype, array_model):
     """
@@ -240,6 +262,8 @@ def test_json_schema_dtype_single(dtype, array_model):
     )
 
 
+@pytest.mark.dtype
+@pytest.mark.json_schema
 @pytest.mark.parametrize(
     "dtype,expected",
     [
@@ -266,6 +290,8 @@ def test_json_schema_dtype_builtin(dtype, expected, array_model):
         assert inner_type["type"] == expected
 
 
+@pytest.mark.dtype
+@pytest.mark.json_schema
 def test_json_schema_dtype_model():
     """
     Pydantic models can be used in arrays as dtypes
@@ -314,6 +340,8 @@ def _recursive_array(schema):
     assert any_of[1]["minimum"] == 0
 
 
+@pytest.mark.shape
+@pytest.mark.json_schema
 def test_json_schema_ellipsis():
     """
     NDArray types should create a recursive JSON schema for any-shaped arrays

tests/test_serialization.py

@@ -0,0 +1,95 @@
+"""
+Test serialization-specific functionality that doesn't need to be
+applied across every interface (use test_interface/test_interfaces for that
+"""
+
+import h5py
+import pytest
+from pathlib import Path
+from typing import Callable
+import numpy as np
+import json
+
+pytestmark = pytest.mark.serialization
+
+
+@pytest.fixture(scope="module")
+def hdf5_at_path() -> Callable[[Path], None]:
+    _path = ""
+
+    def _hdf5_at_path(path: Path) -> None:
+        nonlocal _path
+        _path = path
+        h5f = h5py.File(path, "w")
+        _ = h5f.create_dataset("/data", data=np.array([[1, 2], [3, 4]]))
+        _ = h5f.create_dataset("subpath/to/dataset", data=np.array([[1, 2], [4, 5]]))
+        h5f.close()
+
+    yield _hdf5_at_path
+
+    Path(_path).unlink(missing_ok=True)
+
+
+def test_relative_path(hdf5_at_path, tmp_output_dir, model_blank):
+    """
+    By default, we should make all paths relative to the cwd
+    """
+    out_path = tmp_output_dir / "relative.h5"
+    hdf5_at_path(out_path)
+    model = model_blank(array=(out_path, "/data"))
+    rt = model.model_dump_json(round_trip=True)
+    file = json.loads(rt)["array"]["file"]
+
+    # should not be absolute
+    assert not Path(file).is_absolute()
+    # should be relative to cwd
+    out_file = (Path.cwd() / file).resolve()
+    assert out_file == out_path.resolve()
+
+
+def test_relative_to_path(hdf5_at_path, tmp_output_dir, model_blank):
+    """
+    When explicitly passed a path to be ``relative_to`` ,
+    relative to that instead of cwd
+    """
+    out_path = tmp_output_dir / "relative.h5"
+    relative_to_path = Path(__file__) / "fake_dir" / "sub_fake_dir"
+    expected_path = "../../../__tmp__/relative.h5"
+
+    hdf5_at_path(out_path)
+    model = model_blank(array=(out_path, "/data"))
+    rt = model.model_dump_json(
+        round_trip=True, context={"relative_to": str(relative_to_path)}
+    )
+    data = json.loads(rt)["array"]
+    file = data["file"]
+
+    # should not be absolute
+    assert not Path(file).is_absolute()
+    # should be expected path and reach the file
+    assert file == expected_path
+    assert (relative_to_path / file).resolve() == out_path.resolve()
+
+    # we shouldn't have touched `/data` even though it is pathlike
+    assert data["path"] == "/data"
+
+
+def test_relative_to_path(hdf5_at_path, tmp_output_dir, model_blank):
+    """
+    When told, we make paths absolute
+    """
+    out_path = tmp_output_dir / "relative.h5"
+    expected_dataset = "subpath/to/dataset"
+
+    hdf5_at_path(out_path)
+    model = model_blank(array=(out_path, expected_dataset))
+    rt = model.model_dump_json(round_trip=True, context={"absolute_paths": True})
+    data = json.loads(rt)["array"]
+    file = data["file"]
+
+    # should be absolute and equal to out_path
+    assert Path(file).is_absolute()
+    assert Path(file) == out_path.resolve()
+
+    # shouldn't have absolutized subpath even if it's pathlike
+    assert data["path"] == expected_dataset

tests/test_shape.py

@@ -1,5 +1,3 @@
-import pdb
-
 import pytest
 
 from typing import Any
@@ -9,6 +7,8 @@ import numpy as np
 
 from numpydantic import NDArray, Shape
 
+pytestmark = pytest.mark.shape
+
 
 @pytest.mark.parametrize(
     "shape,valid",