finish replacing interface tests with new helper system

src/numpydantic/testing/cases.py

@@ -31,6 +31,53 @@
     YES_PIPE = False
 
 
+def merged_product(
+    *args: Sequence[ValidationCase],
+) -> Generator[ValidationCase, None, None]:
+    """
+    Generator for the product of the iterators of validation cases,
+    merging each tuple, and respecting if they should be :meth:`.ValidationCase.skip`
+    or not.
+
+    Examples:
+
+        .. code-block:: python
+
+            shape_cases = [
+                ValidationCase(shape=(10, 10, 10), passes=True, id="valid shape"),
+                ValidationCase(shape=(10, 10), passes=False, id="missing dimension"),
+            ]
+            dtype_cases = [
+                ValidationCase(dtype=float, passes=True, id="float"),
+                ValidationCase(dtype=int, passes=False, id="int"),
+            ]
+
+            iterator = merged_product(shape_cases, dtype_cases))
+            next(iterator)
+            # ValidationCase(
+            #     shape=(10, 10, 10),
+            #     dtype=float,
+            #     passes=True,
+            #     id="valid shape-float"
+            # )
+            next(iterator)
+            # ValidationCase(
+            #     shape=(10, 10, 10),
+            #     dtype=int,
+            #     passes=False,
+            #     id="valid shape-int"
+            # )
+
+
+    """
+    iterator = product(*args)
+    for case_tuple in iterator:
+        case = merge_cases(case_tuple)
+        if case.skip():
+            continue
+        yield case
+
+
 class BasicModel(BaseModel):
     x: int
 
@@ -58,7 +105,6 @@ class SubClass(BasicModel):
 STRING: TypeAlias = NDArray[Shape["*, *, *"], str]
 MODEL: TypeAlias = NDArray[Shape["*, *, *"], BasicModel]
 UNION_TYPE: TypeAlias = NDArray[Shape["*, *, *"], Union[np.uint32, np.float32]]
-UNION_PIPE: TypeAlias = NDArray[Shape["*, *, *"], np.uint32 | np.float32]
 
 SHAPE_CASES = (
     ValidationCase(shape=(10, 10, 10), passes=True, id="valid shape"),
@@ -135,6 +181,8 @@ class SubClass(BasicModel):
 
 
 if YES_PIPE:
+    UNION_PIPE: TypeAlias = NDArray[Shape["*, *, *"], np.uint32 | np.float32]
+
     DTYPE_CASES.extend(
         [
             ValidationCase(
@@ -178,50 +226,3 @@ class SubClass(BasicModel):
     ZarrNestedCase,
     VideoCase,
 ]
-
-
-def merged_product(
-    *args: Sequence[ValidationCase],
-) -> Generator[ValidationCase, None, None]:
-    """
-    Generator for the product of the iterators of validation cases,
-    merging each tuple, and respecting if they should be :meth:`.ValidationCase.skip`
-    or not.
-
-    Examples:
-
-        .. code-block:: python
-
-            shape_cases = [
-                ValidationCase(shape=(10, 10, 10), passes=True, id="valid shape"),
-                ValidationCase(shape=(10, 10), passes=False, id="missing dimension"),
-            ]
-            dtype_cases = [
-                ValidationCase(dtype=float, passes=True, id="float"),
-                ValidationCase(dtype=int, passes=False, id="int"),
-            ]
-
-            iterator = merged_product(shape_cases, dtype_cases))
-            next(iterator)
-            # ValidationCase(
-            #     shape=(10, 10, 10),
-            #     dtype=float,
-            #     passes=True,
-            #     id="valid shape-float"
-            # )
-            next(iterator)
-            # ValidationCase(
-            #     shape=(10, 10, 10),
-            #     dtype=int,
-            #     passes=False,
-            #     id="valid shape-int"
-            # )
-
-
-    """
-    iterator = product(*args)
-    for case_tuple in iterator:
-        case = merge_cases(case_tuple)
-        if case.skip():
-            continue
-        yield case

src/numpydantic/testing/helpers.py

@@ -46,9 +46,16 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> Optional[NDArrayType]:
         """
         Make an array from a shape and dtype, and a path if needed
+
+        Args:
+            shape: shape of the array
+            dtype: dtype of the array
+            path: Path, if needed to generate on disk
+            array: Rather than passing shape and dtype, pass a literal arraylike thing
         """
 
     @classmethod

src/numpydantic/testing/interfaces.py

@@ -19,7 +19,7 @@
     ZarrInterface,
 )
 from numpydantic.testing.helpers import InterfaceCase
-from numpydantic.types import DtypeType
+from numpydantic.types import DtypeType, NDArrayType
 
 
 class NumpyCase(InterfaceCase):
@@ -33,8 +33,11 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> np.ndarray:
-        if issubclass(dtype, BaseModel):
+        if array is not None:
+            return np.array(array, dtype=dtype)
+        elif issubclass(dtype, BaseModel):
             return np.full(shape=shape, fill_value=dtype(x=1))
         else:
             return np.zeros(shape=shape, dtype=dtype)
@@ -59,6 +62,7 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> Optional[H5ArrayPath]:
         if cls.skip(shape, dtype):
             return None
@@ -67,7 +71,9 @@ def make_array(
         array_path = "/" + "_".join([str(s) for s in shape]) + "__" + dtype.__name__
         generator = np.random.default_rng()
 
-        if dtype is str:
+        if array is not None:
+            data = np.array(array, dtype=dtype)
+        elif dtype is str:
             data = generator.random(shape).astype(bytes)
         elif dtype is datetime:
             data = np.empty(shape, dtype="S32")
@@ -91,13 +97,16 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> Optional[H5ArrayPath]:
         if cls.skip(shape, dtype):
             return None
 
         hdf5_file = path / "h5f.h5"
         array_path = "/" + "_".join([str(s) for s in shape]) + "__" + dtype.__name__
-        if dtype is str:
+        if array is not None:
+            data = np.array(array, dtype=dtype)
+        elif dtype is str:
             dt = np.dtype([("data", np.dtype("S10")), ("extra", "i8")])
             data = np.array([("hey", 0)] * np.prod(shape), dtype=dt).reshape(shape)
         elif dtype is datetime:
@@ -128,7 +137,10 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> da.Array:
+        if array is not None:
+            return da.array(array, dtype=dtype, chunks=-1)
         if issubclass(dtype, BaseModel):
             return da.full(shape=shape, fill_value=dtype(x=1), chunks=-1)
         else:
@@ -142,7 +154,7 @@ class _ZarrMetaCase(InterfaceCase):
 
     @classmethod
     def skip(cls, shape: Tuple[int, ...], dtype: DtypeType) -> bool:
-        return not issubclass(dtype, BaseModel)
+        return issubclass(dtype, BaseModel)
 
 
 class ZarrCase(_ZarrMetaCase):
@@ -154,8 +166,12 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> Optional[zarr.Array]:
-        return zarr.zeros(shape=shape, dtype=dtype)
+        if array is not None:
+            return zarr.array(array, dtype=dtype, chunks=-1)
+        else:
+            return zarr.zeros(shape=shape, dtype=dtype)
 
 
 class ZarrDirCase(_ZarrMetaCase):
@@ -167,9 +183,13 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> Optional[zarr.Array]:
         store = zarr.DirectoryStore(str(path / "array.zarr"))
-        return zarr.zeros(shape=shape, dtype=dtype, store=store)
+        if array is not None:
+            return zarr.array(array, dtype=dtype, store=store, chunks=-1)
+        else:
+            return zarr.zeros(shape=shape, dtype=dtype, store=store)
 
 
 class ZarrZipCase(_ZarrMetaCase):
@@ -181,9 +201,13 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> Optional[zarr.Array]:
         store = zarr.ZipStore(str(path / "array.zarr"), mode="w")
-        return zarr.zeros(shape=shape, dtype=dtype, store=store)
+        if array is not None:
+            return zarr.array(array, dtype=dtype, store=store, chunks=-1)
+        else:
+            return zarr.zeros(shape=shape, dtype=dtype, store=store)
 
 
 class ZarrNestedCase(_ZarrMetaCase):
@@ -195,11 +219,15 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> ZarrArrayPath:
         file = str(path / "nested.zarr")
         root = zarr.open(file, mode="w")
         subpath = "a/b/c"
-        _ = root.zeros(subpath, shape=shape, dtype=dtype)
+        if array is not None:
+            _ = root.array(subpath, array, dtype=dtype)
+        else:
+            _ = root.zeros(subpath, shape=shape, dtype=dtype)
         return ZarrArrayPath(file=file, path=subpath)
 
 
@@ -214,10 +242,15 @@ def make_array(
         shape: Tuple[int, ...] = (10, 10),
         dtype: DtypeType = float,
         path: Optional[Path] = None,
+        array: Optional[NDArrayType] = None,
     ) -> Optional[Path]:
         if cls.skip(shape, dtype):
             return None
 
+        if array is not None:
+            array = np.ndarray(shape, dtype=np.uint8)
+            shape = array.shape
+
         is_color = len(shape) == 4
         frames = shape[0]
         frame_shape = shape[1:]
@@ -232,13 +265,16 @@ def make_array(
         )
 
         for i in range(frames):
-            # make fresh array every time bc opencv eats them
-            array = np.zeros(frame_shape, dtype=np.uint8)
-            if not is_color:
-                array[i, i] = i
+            if array is not None:
+                frame = array[i]
             else:
-                array[i, i, :] = i
-            writer.write(array)
+                # make fresh array every time bc opencv eats them
+                frame = np.zeros(frame_shape, dtype=np.uint8)
+                if not is_color:
+                    frame[i, i] = i
+                else:
+                    frame[i, i, :] = i
+            writer.write(frame)
         writer.release()
         return video_path
 

tests/fixtures/generation.py

@@ -7,7 +7,6 @@
 
 from numpydantic.interface.hdf5 import H5ArrayPath
 from numpydantic.interface.zarr import ZarrArrayPath
-from numpydantic.testing import ValidationCase
 from numpydantic.testing.interfaces import HDF5Case, HDF5CompoundCase, VideoCase
 
 
@@ -57,8 +56,8 @@ def _make_video(shape=(100, 50), frames=10, is_color=True) -> Path:
         shape = (frames, *shape)
         if is_color:
             shape = (*shape, 3)
-        return VideoCase.array_from_case(
-            ValidationCase(shape=shape, dtype=np.uint8), tmp_output_dir_func
+        return VideoCase.make_array(
+            shape=shape, dtype=np.uint8, path=tmp_output_dir_func
         )
 
     return _make_video