feat: Add NpyCodec for lazy-loading numpy arrays

Implement the `<npy@>` codec for schema-addressed numpy array storage:

- Add SchemaCodec base class for path-addressed storage codecs
- Add NpyRef class for lazy array references with metadata
- Add NpyCodec using .npy format with shape/dtype inspection
- Refactor ObjectCodec to inherit from SchemaCodec
- Rename is_external to is_store throughout codebase
- Export SchemaCodec and NpyRef from public API
- Bump version to 2.0.0a17

Key features:
- Lazy loading: inspect shape/dtype without downloading
- NumPy integration via __array__ protocol
- Safe bulk fetch: returns NpyRef objects, not arrays
- Schema-addressed paths: {schema}/{table}/{pk}/{attr}.npy

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: dimitri-yatsenko

src/datajoint/__init__.py

@@ -47,16 +47,19 @@
     "MatStruct",
     # Codec API
     "Codec",
+    "SchemaCodec",
     "list_codecs",
     "get_codec",
+    "ObjectRef",
+    "NpyRef",
+    # Other
     "errors",
     "migrate",
     "DataJointError",
     "key",
     "key_hash",
     "logger",
     "cli",
-    "ObjectRef",
     "ValidationResult",
 ]
 
@@ -70,6 +73,10 @@
     get_codec,
     list_codecs,
 )
+from .builtin_codecs import (
+    SchemaCodec,
+    NpyRef,
+)
 from .blob import MatCell, MatStruct
 from .connection import Connection, conn
 from .errors import DataJointError