diff --git a/nwb_linkml/src/nwb_linkml/generators/pydantic.py b/nwb_linkml/src/nwb_linkml/generators/pydantic.py
index 1ead9f7..96b5b85 100644
--- a/nwb_linkml/src/nwb_linkml/generators/pydantic.py
+++ b/nwb_linkml/src/nwb_linkml/generators/pydantic.py
@@ -780,7 +780,7 @@ class NWBPydanticGenerator(PydanticGenerator):
         except NameError as e:
             raise e
 
-def compile_python(text_or_fn: str, package_path: Path = None, module_name:str='test') -> ModuleType:  
+def compile_python(text_or_fn: str, package_path: Path = None, module_name:str='test') -> ModuleType:
     """
     Compile the text or file and return the resulting module
     @param text_or_fn: Python text or file name that references python file
diff --git a/nwb_linkml/tests/fixtures.py b/nwb_linkml/tests/fixtures.py
index 68be6e2..bc1c81c 100644
--- a/nwb_linkml/tests/fixtures.py
+++ b/nwb_linkml/tests/fixtures.py
@@ -25,7 +25,7 @@ def tmp_output_dir_func(tmp_output_dir) -> Path:
     tmp output dir that gets cleared between every function
     cleans at the start rather than at cleanup in case the output is to be inspected
     """
-    subpath = tmp_output_dir / '__tmp__'
+    subpath = tmp_output_dir / '__tmpfunc__'
     if subpath.exists():
         shutil.rmtree(str(subpath))
     subpath.mkdir()
@@ -37,7 +37,7 @@ def tmp_output_dir_mod(tmp_output_dir) -> Path:
     tmp output dir that gets cleared between every function
     cleans at the start rather than at cleanup in case the output is to be inspected
     """
-    subpath = tmp_output_dir / '__tmp__'
+    subpath = tmp_output_dir / '__tmpmod__'
     if subpath.exists():
         shutil.rmtree(str(subpath))
     subpath.mkdir()
diff --git a/nwb_linkml/tests/test_types/test_ndarray.py b/nwb_linkml/tests/test_types/test_ndarray.py
index d7d0506..9b81f17 100644
--- a/nwb_linkml/tests/test_types/test_ndarray.py
+++ b/nwb_linkml/tests/test_types/test_ndarray.py
@@ -1,19 +1,22 @@
 import pdb
 from typing import Union, Optional, Any
+import json
 
 import pytest
 
 import numpy as np
+import h5py
 
 from pydantic import BaseModel, ValidationError, Field
-from nwb_linkml.types.ndarray import NDArray
+from nwb_linkml.types.ndarray import NDArray, NDArrayProxy
 from nptyping import Shape, Number
 
-from ..fixtures import data_dir
+from ..fixtures import data_dir, tmp_output_dir, tmp_output_dir_func
 def test_ndarray_type():
 
     class Model(BaseModel):
         array: NDArray[Shape["2 x, * y"], Number]
+        array_any: Optional[NDArray[Any, Any]] = None
 
     schema = Model.model_json_schema()
     assert schema['properties']['array']['items'] == {'items': {'type': 'number'}, 'type': 'array'}
@@ -29,6 +32,8 @@ def test_ndarray_type():
     with pytest.raises(ValidationError):
         instance = Model(array=np.ones((2,3), dtype=bool))
 
+    instance = Model(array=np.zeros((2,3)), array_any = np.ones((3,4,5)))
+
 
 def test_ndarray_union():
     class Model(BaseModel):
@@ -55,6 +60,60 @@ def test_ndarray_union():
     with pytest.raises(ValidationError):
         instance = Model(array=np.random.random((5,10,4,6)))
 
-@pytest.mark.skip()
-def test_ndarray_proxy(data_dir):
-    h5f_source = data_dir / 'aibs_ecephys.nwb'
+def test_ndarray_coercion():
+    """
+    Coerce lists to arrays
+    """
+
+    class Model(BaseModel):
+        array: NDArray[Shape["* x"], Number]
+
+    amod = Model(array=[1,2,3,4.5])
+    assert np.allclose(amod.array, np.array([1,2,3,4.5]))
+    with pytest.raises(ValidationError):
+        amod = Model(array=['a', 'b', 'c'])
+
+def test_ndarray_serialize():
+    """
+    Large arrays should get compressed with blosc, otherwise just to list
+    """
+    class Model(BaseModel):
+        large_array: NDArray[Any, Number]
+        small_array: NDArray[Any, Number]
+
+    mod = Model(large_array=np.random.random((1024,1024)), small_array=np.random.random((3,3)))
+    mod_str = mod.model_dump_json()
+    mod_json = json.loads(mod_str)
+    for a in ('array', 'shape', 'dtype', 'unpack_fns'):
+        assert a in mod_json['large_array'].keys()
+    assert isinstance(mod_json['large_array']['array'], str)
+    assert isinstance(mod_json['small_array'], list)
+
+    # but when we just dump to a dict we don't compress
+    mod_dict = mod.model_dump()
+    assert isinstance(mod_dict['large_array'], np.ndarray)
+
+
+
+def test_ndarray_proxy(tmp_output_dir_func):
+    h5f_source = tmp_output_dir_func / 'test.h5'
+    with h5py.File(h5f_source, 'w') as h5f:
+        dset_good = h5f.create_dataset('/data', data=np.random.random((1024,1024,3)))
+        dset_bad = h5f.create_dataset('/data_bad', data=np.random.random((1024, 1024, 4)))
+
+    class Model(BaseModel):
+        array: NDArray[Shape["* x, * y, 3 z"], Number]
+
+    mod = Model(array=NDArrayProxy(h5f_file=h5f_source, path='/data'))
+    subarray = mod.array[0:5, 0:5, :]
+    assert isinstance(subarray, np.ndarray)
+    assert isinstance(subarray.sum(), float)
+    assert mod.array.name == '/data'
+
+    with pytest.raises(NotImplementedError):
+        mod.array[0] = 5
+
+    with pytest.raises(ValidationError):
+        mod = Model(array=NDArrayProxy(h5f_file=h5f_source, path='/data_bad'))
+
+