diff --git a/docs/meta/todo.md b/docs/meta/todo.md
index 92355cf..d2bf9ac 100644
--- a/docs/meta/todo.md
+++ b/docs/meta/todo.md
@@ -23,7 +23,7 @@ Cleanup
                 
 - [ ] Make a minimal pydanticgen-only package to slim linkml deps?
 - [ ] Disambiguate "maps" terminology - split out simple maps from the eg. dataset mapping classes
-- [ ] Remove unnecessary imports
+- [x] Remove unnecessary imports
   - dask
   - nptyping
 - [ ] Adapt the split generation to the new split generator style 
diff --git a/docs/pdm.lock b/docs/pdm.lock
index db4920e..0dcaca2 100644
--- a/docs/pdm.lock
+++ b/docs/pdm.lock
@@ -3,9 +3,12 @@
 
 [metadata]
 groups = ["default"]
-strategy = ["cross_platform", "inherit_metadata"]
-lock_version = "4.4.2"
-content_hash = "sha256:42dbf7249d28b6d1ad06ccd4593eac866228ad47d884ea8f4b118d5331da2aa0"
+strategy = ["inherit_metadata"]
+lock_version = "4.5.0"
+content_hash = "sha256:6d950420dfea0b5a96435adc77fb4ee411fcada547a1c55e6f1dc74f6ba6b199"
+
+[[metadata.targets]]
+requires_python = ">=3.10,<3.13"
 
 [[package]]
 name = "alabaster"
@@ -24,6 +27,9 @@ version = "0.7.0"
 requires_python = ">=3.8"
 summary = "Reusable constraint types to use with typing.Annotated"
 groups = ["default"]
+dependencies = [
+    "typing-extensions>=4.0.0; python_version < \"3.9\"",
+]
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -34,6 +40,9 @@ name = "antlr4-python3-runtime"
 version = "4.9.3"
 summary = "ANTLR 4.9.3 runtime for Python 3.7"
 groups = ["default"]
+dependencies = [
+    "typing; python_version < \"3.5\"",
+]
 files = [
     {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
 ]
@@ -89,6 +98,7 @@ summary = "Annotate AST trees with source code positions"
 groups = ["default"]
 dependencies = [
     "six>=1.12.0",
+    "typing; python_version < \"3.5\"",
 ]
 files = [
     {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
@@ -97,13 +107,16 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "24.2.0"
 requires_python = ">=3.7"
 summary = "Classes Without Boilerplate"
 groups = ["default"]
+dependencies = [
+    "importlib-metadata; python_version < \"3.8\"",
+]
 files = [
-    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
-    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
+    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
 ]
 
 [[package]]
@@ -114,6 +127,7 @@ summary = "Seamlessly integrate pydantic models in your Sphinx documentation."
 groups = ["default"]
 dependencies = [
     "Sphinx>=4.0",
+    "importlib-metadata>1; python_version <= \"3.8\"",
     "pydantic-settings<3.0.0,>=2.0",
     "pydantic<3.0.0,>=2.0",
 ]
@@ -127,6 +141,9 @@ version = "2.15.0"
 requires_python = ">=3.8"
 summary = "Internationalization utilities"
 groups = ["default"]
+dependencies = [
+    "pytz>=2015.7; python_version < \"3.9\"",
+]
 files = [
     {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"},
     {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"},
@@ -147,41 +164,35 @@ files = [
 ]
 
 [[package]]
-name = "blosc2"
-version = "2.7.0"
-requires_python = "<4,>=3.10"
-summary = "Python wrapper for the C-Blosc2 library"
+name = "black"
+version = "24.8.0"
+requires_python = ">=3.8"
+summary = "The uncompromising code formatter."
 groups = ["default"]
 dependencies = [
-    "msgpack",
-    "ndindex>=1.4",
-    "numexpr",
-    "numpy>=1.20.3",
-    "py-cpuinfo",
+    "click>=8.0.0",
+    "mypy-extensions>=0.4.3",
+    "packaging>=22.0",
+    "pathspec>=0.9.0",
+    "platformdirs>=2",
+    "tomli>=1.1.0; python_version < \"3.11\"",
+    "typing-extensions>=4.0.1; python_version < \"3.11\"",
 ]
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 ]
 
 [[package]]
@@ -197,7 +208,7 @@ files = [
 
 [[package]]
 name = "cffi"
-version = "1.16.0"
+version = "1.17.0"
 requires_python = ">=3.8"
 summary = "Foreign Function Interface for Python calling C code."
 groups = ["default"]
@@ -206,39 +217,42 @@ dependencies = [
     "pycparser",
 ]
 files = [
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 [[package]]
@@ -328,23 +342,13 @@ summary = "Composable command line interface toolkit"
 groups = ["default"]
 dependencies = [
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 summary = "Idiomatic conversion between URIs and compact URIs (CURIEs)."
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 [[package]]
@@ -479,14 +462,14 @@ files = [
 
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+version = "1.2.2"
 requires_python = ">=3.7"
 summary = "Backport of PEP 654 (exception groups)"
 groups = ["default"]
 marker = "python_version < \"3.11\""
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 summary = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
 groups = ["default"]
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 requires_python = ">=3.8"
 summary = "A clean customisable Sphinx documentation theme."
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 requires_python = ">=3.7"
 summary = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 groups = ["default"]
+dependencies = [
+    "typing-extensions; python_version < \"3.8\"",
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+version = "8.2.0"
 requires_python = ">=3.8"
 summary = "Read metadata from Python packages"
 groups = ["default"]
 dependencies = [
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 [[package]]
@@ -900,7 +879,9 @@ summary = "An implementation of JSON Schema validation for Python"
 groups = ["default"]
 dependencies = [
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+    "importlib-resources>=1.4.0; python_version < \"3.9\"",
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@@ -916,6 +897,7 @@ requires_python = ">=3.8"
 summary = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 groups = ["default"]
 dependencies = [
+    "importlib-resources>=1.4.0; python_version < \"3.9\"",
     "referencing>=0.31.0",
 ]
 files = [
@@ -974,6 +956,7 @@ requires_python = ">=3.8"
 summary = "Jupyter protocol implementation and client libraries"
 groups = ["default"]
 dependencies = [
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+revision = "0a6578bff4713688260f64b3076b197bd6decce9"
 summary = "Linked Open Data Modeling Language"
 groups = ["default"]
 dependencies = [
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     "requests>=2.22",
     "sqlalchemy>=1.4.31",
+    "typing-extensions>=4.4.0; python_version < \"3.9\"",
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 [[package]]
@@ -1262,21 +1203,21 @@ dependencies = [
 
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 name = "myst-parser"
-version = "3.0.1"
-requires_python = ">=3.8"
+version = "4.0.0"
+requires_python = ">=3.10"
 summary = "An extended [CommonMark](https://spec.commonmark.org/) compliant parser,"
 groups = ["default"]
 dependencies = [
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+    "docutils<0.22,>=0.19",
     "jinja2",
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 groups = ["default"]
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 groups = ["default"]
 dependencies = [
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 [[package]]
 name = "pyzmq"
-version = "26.0.3"
+version = "26.1.0"
 requires_python = ">=3.7"
 summary = "Python bindings for 0MQ"
 groups = ["default"]
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     "cffi; implementation_name == \"pypy\"",
 ]
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@@ -2868,14 +2747,6 @@ files = [
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     {file = "watchfiles-0.22.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:030bc4e68d14bcad2294ff68c1ed87215fbd9a10d9dea74e7cfe8a17869785ab"},
     {file = "watchfiles-0.22.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ace7d060432acde5532e26863e897ee684780337afb775107c0a90ae8dbccfd2"},
-    {file = "watchfiles-0.22.0-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:5834e1f8b71476a26df97d121c0c0ed3549d869124ed2433e02491553cb468c2"},
-    {file = "watchfiles-0.22.0-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:0bc3b2f93a140df6806c8467c7f51ed5e55a931b031b5c2d7ff6132292e803d6"},
-    {file = "watchfiles-0.22.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8fdebb655bb1ba0122402352b0a4254812717a017d2dc49372a1d47e24073795"},
-    {file = "watchfiles-0.22.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0c8e0aa0e8cc2a43561e0184c0513e291ca891db13a269d8d47cb9841ced7c71"},
-    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:2f350cbaa4bb812314af5dab0eb8d538481e2e2279472890864547f3fe2281ed"},
-    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:7a74436c415843af2a769b36bf043b6ccbc0f8d784814ba3d42fc961cdb0a9dc"},
-    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:00ad0bcd399503a84cc688590cdffbe7a991691314dde5b57b3ed50a41319a31"},
-    {file = "watchfiles-0.22.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:72a44e9481afc7a5ee3291b09c419abab93b7e9c306c9ef9108cb76728ca58d2"},
     {file = "watchfiles-0.22.0.tar.gz", hash = "sha256:988e981aaab4f3955209e7e28c7794acdb690be1efa7f16f8ea5aba7ffdadacb"},
 ]
 
@@ -2884,6 +2755,9 @@ name = "wcwidth"
 version = "0.2.13"
 summary = "Measures the displayed width of unicode strings in a terminal"
 groups = ["default"]
+dependencies = [
+    "backports-functools-lru-cache>=1.2.1; python_version < \"3.2\"",
+]
 files = [
     {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
     {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
@@ -2945,29 +2819,19 @@ files = [
     {file = "websockets-12.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c4e37d36f0d19f0a4413d3e18c0d03d0c268ada2061868c1e6f5ab1a6d575077"},
     {file = "websockets-12.0-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d829f975fc2e527a3ef2f9c8f25e553eb7bc779c6665e8e1d52aa22800bb38b"},
     {file = "websockets-12.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:2c71bd45a777433dd9113847af751aae36e448bc6b8c361a566cb043eda6ec30"},
-    {file = "websockets-12.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0bee75f400895aef54157b36ed6d3b308fcab62e5260703add87f44cee9c82a6"},
-    {file = "websockets-12.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:423fc1ed29f7512fceb727e2d2aecb952c46aa34895e9ed96071821309951123"},
-    {file = "websockets-12.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:27a5e9964ef509016759f2ef3f2c1e13f403725a5e6a1775555994966a66e931"},
-    {file = "websockets-12.0-pp38-pypy38_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c3181df4583c4d3994d31fb235dc681d2aaad744fbdbf94c4802485ececdecf2"},
-    {file = "websockets-12.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:b067cb952ce8bf40115f6c19f478dc71c5e719b7fbaa511359795dfd9d1a6468"},
-    {file = "websockets-12.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:00700340c6c7ab788f176d118775202aadea7602c5cc6be6ae127761c16d6b0b"},
-    {file = "websockets-12.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e469d01137942849cff40517c97a30a93ae79917752b34029f0ec72df6b46399"},
-    {file = "websockets-12.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ffefa1374cd508d633646d51a8e9277763a9b78ae71324183693959cf94635a7"},
-    {file = "websockets-12.0-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba0cab91b3956dfa9f512147860783a1829a8d905ee218a9837c18f683239611"},
-    {file = "websockets-12.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:2cb388a5bfb56df4d9a406783b7f9dbefb888c09b71629351cc6b036e9259370"},
     {file = "websockets-12.0-py3-none-any.whl", hash = "sha256:dc284bbc8d7c78a6c69e0c7325ab46ee5e40bb4d50e494d8131a07ef47500e9e"},
     {file = "websockets-12.0.tar.gz", hash = "sha256:81df9cbcbb6c260de1e007e58c011bfebe2dafc8435107b0537f393dd38c8b1b"},
 ]
 
 [[package]]
 name = "wheel"
-version = "0.43.0"
+version = "0.44.0"
 requires_python = ">=3.8"
 summary = "A built-package format for Python"
 groups = ["default"]
 files = [
-    {file = "wheel-0.43.0-py3-none-any.whl", hash = "sha256:55c570405f142630c6b9f72fe09d9b67cf1477fcf543ae5b8dcb1f5b7377da81"},
-    {file = "wheel-0.43.0.tar.gz", hash = "sha256:465ef92c69fa5c5da2d1cf8ac40559a8c940886afcef87dcf14b9470862f1d85"},
+    {file = "wheel-0.44.0-py3-none-any.whl", hash = "sha256:2376a90c98cc337d18623527a97c31797bd02bad0033d41547043a1cbfbe448f"},
+    {file = "wheel-0.44.0.tar.gz", hash = "sha256:a29c3f2817e95ab89aa4660681ad547c0e9547f20e75b0562fe7723c9a2a9d49"},
 ]
 
 [[package]]
diff --git a/docs/pyproject.toml b/docs/pyproject.toml
index 2a4b0a5..73f84a0 100644
--- a/docs/pyproject.toml
+++ b/docs/pyproject.toml
@@ -14,7 +14,6 @@ dependencies = [
     "furo>=2023.8.19",
     "myst-parser>=2.0.0",
     "autodoc-pydantic>=2.0.1",
-    "nptyping>=2.5.0",
     "sphinx-autobuild>=2021.3.14",
     "sphinx-design>=0.5.0",
     "sphinx-togglebutton>=0.3.2",
diff --git a/nwb_linkml/src/nwb_linkml/adapters/namespaces.py b/nwb_linkml/src/nwb_linkml/adapters/namespaces.py
index ca354a5..f8ea857 100644
--- a/nwb_linkml/src/nwb_linkml/adapters/namespaces.py
+++ b/nwb_linkml/src/nwb_linkml/adapters/namespaces.py
@@ -266,10 +266,7 @@ class NamespacesAdapter(Adapter):
         else:
             ns = ns[0]
 
-        schema_names = []
-        for sch in ns.schema_:
-            if sch.source is not None:
-                schema_names.append(sch.source)
+        schema_names = [sch.source for sch in ns.schema_ if sch.source is not None]
         return schema_names
 
     def schema_namespace(self, name: str) -> Optional[str]:
diff --git a/nwb_linkml/src/nwb_linkml/includes/hdmf.py b/nwb_linkml/src/nwb_linkml/includes/hdmf.py
index faf9107..d080d03 100644
--- a/nwb_linkml/src/nwb_linkml/includes/hdmf.py
+++ b/nwb_linkml/src/nwb_linkml/includes/hdmf.py
@@ -133,7 +133,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
@@ -382,6 +382,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 DYNAMIC_TABLE_IMPORTS = Imports(
     imports=[
         Import(
@@ -417,7 +431,9 @@ Imports required for the dynamic table mixin
 VectorData is purposefully excluded as an import or an inject so that it will be
 resolved to the VectorData definition in the generated module
 """
-DYNAMIC_TABLE_INJECTS = [VectorDataMixin, VectorIndexMixin, DynamicTableMixin]
-
-# class VectorDataMixin(BaseModel):
-#     index: Optional[BaseModel] = None
+DYNAMIC_TABLE_INJECTS = [
+    VectorDataMixin,
+    VectorIndexMixin,
+    DynamicTableRegionMixin,
+    DynamicTableMixin,
+]
diff --git a/nwb_linkml/src/nwb_linkml/io/hdf5.py b/nwb_linkml/src/nwb_linkml/io/hdf5.py
index d902bd9..387e4a6 100644
--- a/nwb_linkml/src/nwb_linkml/io/hdf5.py
+++ b/nwb_linkml/src/nwb_linkml/io/hdf5.py
@@ -242,10 +242,7 @@ def find_references(h5f: h5py.File, path: str) -> List[str]:
 
     def _find_references(name: str, obj: h5py.Group | h5py.Dataset) -> None:
         pbar.update()
-        refs = []
-        for attr in obj.attrs.values():
-            if isinstance(attr, h5py.h5r.Reference):
-                refs.append(attr)
+        refs = [attr for attr in obj.attrs.values() if isinstance(attr, h5py.h5r.Reference)]
 
         if isinstance(obj, h5py.Dataset):
             # dataset is all references
diff --git a/nwb_linkml/src/nwb_linkml/maps/dtype.py b/nwb_linkml/src/nwb_linkml/maps/dtype.py
index 1c90e0d..d618dbe 100644
--- a/nwb_linkml/src/nwb_linkml/maps/dtype.py
+++ b/nwb_linkml/src/nwb_linkml/maps/dtype.py
@@ -100,10 +100,9 @@ np_to_python = {
             np.float64,
             np.single,
             np.double,
-            np.float_,
         )
     },
-    **{n: str for n in (np.character, np.str_, np.string_, np.unicode_)},
+    **{n: str for n in (np.character, np.str_)},
 }
 
 allowed_precisions = {
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_0/hdmf_common_table.py
index ab05d9d..8d3f9e9 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_0/hdmf_common_table.py
@@ -171,6 +171,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -281,7 +295,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_2/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_2/hdmf_common_table.py
index 7da6b05..2be1fbe 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_2/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_2/hdmf_common_table.py
@@ -171,6 +171,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -281,7 +295,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_3/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_3/hdmf_common_table.py
index a1ba281..6cf1bf2 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_3/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_1_3/hdmf_common_table.py
@@ -171,6 +171,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -281,7 +295,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_0/hdmf_common_table.py
index bd4f1aa..8e79364 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_0/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_1/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_1/hdmf_common_table.py
index 2d676af..b78ba5c 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_1/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_2_1/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_3_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_3_0/hdmf_common_table.py
index 19f855b..b17a853 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_3_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_3_0/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_4_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_4_0/hdmf_common_table.py
index 1d1cbf4..536d5af 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_4_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_4_0/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_0/hdmf_common_table.py
index a911159..80d1d6d 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_0/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_1/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_1/hdmf_common_table.py
index 266c8d4..7ddc7ec 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_1/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_5_1/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_6_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_6_0/hdmf_common_table.py
index b1a9b5a..a8315f3 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_6_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_6_0/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_7_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_7_0/hdmf_common_table.py
index ca52a0b..91e25e7 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_7_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_7_0/hdmf_common_table.py
@@ -172,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -282,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and len(val) > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_8_0/hdmf_common_table.py b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_8_0/hdmf_common_table.py
index e6e364b..8e9d681 100644
--- a/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_8_0/hdmf_common_table.py
+++ b/nwb_linkml/src/nwb_linkml/models/pydantic/hdmf_common/v1_8_0/hdmf_common_table.py
@@ -1,7 +1,12 @@
 from __future__ import annotations
-from ...hdmf_common.v1_8_0.hdmf_common_base import Data
+from datetime import datetime, date
+from decimal import Decimal
+from enum import Enum
+import re
+import sys
+from ...hdmf_common.v1_8_0.hdmf_common_base import Data, Container
 from pandas import DataFrame, Series
-from typing import Any, ClassVar, List, Dict, Optional, Union, overload, Tuple
+from typing import Any, ClassVar, List, Literal, Dict, Optional, Union, overload, Tuple
 from pydantic import (
     BaseModel,
     ConfigDict,
@@ -167,6 +172,20 @@ class VectorIndexMixin(BaseModel):
         return len(self.value)
 
 
+class DynamicTableRegionMixin(BaseModel):
+    """
+    Mixin to allow indexing references to regions of dynamictables
+    """
+
+    table: "DynamicTableMixin"
+
+    def __getitem__(self, item: Union[str, int, slice, Tuple[Union[str, int, slice], ...]]) -> Any:
+        return self.table[item]
+
+    def __setitem__(self, key: Union[int, str, slice], value: Any) -> None:
+        self.table[key] = value
+
+
 class DynamicTableMixin(BaseModel):
     """
     Mixin to make DynamicTable subclasses behave like tables/dataframes
@@ -277,7 +296,7 @@ class DynamicTableMixin(BaseModel):
                 # special case where pandas will unpack a pydantic model
                 # into {n_fields} rows, rather than keeping it in a dict
                 val = Series([val])
-            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and val.shape[0] > 1:
+            elif isinstance(rows, int) and hasattr(val, "shape") and val.shape and len(val) > 1:
                 # special case where we are returning a row in a ragged array,
                 # same as above - prevent pandas pivoting to long
                 val = Series([val])
diff --git a/nwb_linkml/src/nwb_linkml/types/df.py b/nwb_linkml/src/nwb_linkml/types/df.py
index 2fcea88..19d36a5 100644
--- a/nwb_linkml/src/nwb_linkml/types/df.py
+++ b/nwb_linkml/src/nwb_linkml/types/df.py
@@ -14,180 +14,163 @@ Pydantic models that behave like pandas dataframes
     left in this module since it is necessary for it to make sense.
 """
 
-import ast
-from typing import Any, Dict, Optional, Type
+#
+# class DataFrame(BaseModel, pd.DataFrame):
+#     """
+#     Pydantic model root class that mimics a pandas dataframe.
+#
+#     Notes:
+#
+#         The synchronization between the underlying lists in the pydantic model
+#         and the derived dataframe is partial, and at the moment unidirectional.
+#         This class is primarily intended for reading from tables stored in
+#         NWB files rather than being able to manipulate them.
+#
+#         The dataframe IS updated when new values are *assigned* to a field.
+#
+#         eg.::
+#
+#             MyModel.fieldval = [1,2,3]
+#
+#         But the dataframe is NOT updated when existing values are updated.
+#
+#         eg.::
+#
+#             MyModel.fieldval.append(4)
+#
+#         In that case you need to call :meth:`.update_df` manually.
+#
+#         Additionally, if the dataframe is modified, the underlying lists are NOT updated,
+#         but when the model is dumped to a dictionary or serialized, the dataframe IS used,
+#         so changes will be reflected then.
+#
+#         Fields that shadow pandas methods WILL prevent them from being usable, except
+#         by directly accessing the dataframe like ``mymodel._df``
+#
+#     """
+#
+#     _df: pd.DataFrame = None
+#     model_config = ConfigDict(validate_assignment=True)
+#
+#     def __init__(self, **kwargs):
+#         # pdb.set_trace()
+#         super().__init__(**kwargs)
+#
+#         self._df = self.__make_df()
+#
+#     def __make_df(self) -> pd.DataFrame:
+#         # make dict that can handle ragged arrays and NoneTypes
+#         items = {k: v for k, v in self.__dict__.items() if k in self.model_fields}
+#
+#         df_dict = {
+#             k: (pd.Series(v) if isinstance(v, list) else pd.Series([v])) for k, v in items.items()
+#         }
+#         df = pd.DataFrame(df_dict)
+#         # replace Nans with None
+#         df = df.fillna(np.nan).replace([np.nan], [None])
+#         return df
+#
+#     def update_df(self) -> None:
+#         """
+#         Update the internal dataframe in the case that the model values are changed
+#         in a way that we can't detect, like appending to one of the lists.
+#
+#         """
+#         self._df = self.__make_df()
+#
+#     def __getattr__(self, item: str):
+#         """
+#         Mimic pandas dataframe and pydantic model behavior
+#         """
+#         if item in ("df", "_df"):
+#             return self.__pydantic_private__["_df"]
+#         elif item in self.model_fields:
+#             return self._df[item]
+#         else:
+#             try:
+#                 return object.__getattribute__(self._df, item)
+#             except AttributeError:
+#                 return object.__getattribute__(self, item)
+#
+#     @model_validator(mode="after")
+#     def recreate_df(self) -> None:
+#         """
+#         Remake DF when validating (eg. when updating values on assignment)
+#         """
+#         self.update_df()
+#
+#     @model_serializer(mode="wrap", when_used="always")
+#     def serialize_model(self, nxt: SerializerFunctionWrapHandler) -> Dict[str, Any]:
+#         """
+#         We don't handle values that are changed on the dataframe by directly
+#         updating the underlying model lists, but we implicitly handle them
+#         by using the dataframe as the source when serializing
+#         """
+#         if self._df is None:
+#             return nxt(self)
+#         else:
+#             out = self._df.to_dict("list")
+#             # remove Nones
+#             out = {k: [inner_v for inner_v in v if inner_v is not None] for k, v in out.items()}
+#             return nxt(self.__class__(**out))
 
-import h5py
-import numpy as np
-import pandas as pd
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    SerializerFunctionWrapHandler,
-    model_serializer,
-    model_validator,
-)
-
-from nwb_linkml.maps.hdmf import dereference_reference_vector, model_from_dynamictable
-from nwb_linkml.types.hdf5 import HDF5_Path
-
-
-class DataFrame(BaseModel, pd.DataFrame):
-    """
-    Pydantic model root class that mimics a pandas dataframe.
-
-    Notes:
-
-        The synchronization between the underlying lists in the pydantic model
-        and the derived dataframe is partial, and at the moment unidirectional.
-        This class is primarily intended for reading from tables stored in
-        NWB files rather than being able to manipulate them.
-
-        The dataframe IS updated when new values are *assigned* to a field.
-
-        eg.::
-
-            MyModel.fieldval = [1,2,3]
-
-        But the dataframe is NOT updated when existing values are updated.
-
-        eg.::
-
-            MyModel.fieldval.append(4)
-
-        In that case you need to call :meth:`.update_df` manually.
-
-        Additionally, if the dataframe is modified, the underlying lists are NOT updated,
-        but when the model is dumped to a dictionary or serialized, the dataframe IS used,
-        so changes will be reflected then.
-
-        Fields that shadow pandas methods WILL prevent them from being usable, except
-        by directly accessing the dataframe like ``mymodel._df``
-
-    """
-
-    _df: pd.DataFrame = None
-    model_config = ConfigDict(validate_assignment=True)
-
-    def __init__(self, **kwargs):
-        # pdb.set_trace()
-        super().__init__(**kwargs)
-
-        self._df = self.__make_df()
-
-    def __make_df(self) -> pd.DataFrame:
-        # make dict that can handle ragged arrays and NoneTypes
-        items = {k: v for k, v in self.__dict__.items() if k in self.model_fields}
-
-        df_dict = {
-            k: (pd.Series(v) if isinstance(v, list) else pd.Series([v])) for k, v in items.items()
-        }
-        df = pd.DataFrame(df_dict)
-        # replace Nans with None
-        df = df.fillna(np.nan).replace([np.nan], [None])
-        return df
-
-    def update_df(self) -> None:
-        """
-        Update the internal dataframe in the case that the model values are changed
-        in a way that we can't detect, like appending to one of the lists.
-
-        """
-        self._df = self.__make_df()
-
-    def __getattr__(self, item: str):
-        """
-        Mimic pandas dataframe and pydantic model behavior
-        """
-        if item in ("df", "_df"):
-            return self.__pydantic_private__["_df"]
-        elif item in self.model_fields:
-            return self._df[item]
-        else:
-            try:
-                return object.__getattribute__(self._df, item)
-            except AttributeError:
-                return object.__getattribute__(self, item)
-
-    @model_validator(mode="after")
-    def recreate_df(self) -> None:
-        """
-        Remake DF when validating (eg. when updating values on assignment)
-        """
-        self.update_df()
-
-    @model_serializer(mode="wrap", when_used="always")
-    def serialize_model(self, nxt: SerializerFunctionWrapHandler) -> Dict[str, Any]:
-        """
-        We don't handle values that are changed on the dataframe by directly
-        updating the underlying model lists, but we implicitly handle them
-        by using the dataframe as the source when serializing
-        """
-        if self._df is None:
-            return nxt(self)
-        else:
-            out = self._df.to_dict("list")
-            # remove Nones
-            out = {k: [inner_v for inner_v in v if inner_v is not None] for k, v in out.items()}
-            return nxt(self.__class__(**out))
-
-
-def dynamictable_to_df(
-    group: h5py.Group, model: Optional[Type[DataFrame]] = None, base: Optional[BaseModel] = None
-) -> DataFrame:
-    """Generate a dataframe from an NDB DynamicTable"""
-    if model is None:
-        model = model_from_dynamictable(group, base)
-
-    items = {}
-    for col, _col_type in model.model_fields.items():
-        if col not in group:
-            continue
-        idxname = col + "_index"
-        if idxname in group:
-            idx = group.get(idxname)[:]
-            data = group.get(col)[idx - 1]
-        else:
-            data = group.get(col)[:]
-
-        # Handle typing inside of list
-        if isinstance(data[0], bytes):
-            data = data.astype("unicode")
-        if isinstance(data[0], str):
-            # lists and other compound data types can get flattened out to strings when stored
-            # so we try and literal eval and recover them
-            try:
-                eval_type = type(ast.literal_eval(data[0]))
-            except (ValueError, SyntaxError):
-                eval_type = str
-
-            # if we've found one of those, get the data type within it.
-            if eval_type is not str:
-                eval_list = []
-                for item in data.tolist():
-                    try:
-                        eval_list.append(ast.literal_eval(item))
-                    except ValueError:
-                        eval_list.append(None)
-                data = eval_list
-        elif isinstance(data[0], h5py.h5r.Reference):
-            data = [HDF5_Path(group[d].name) for d in data]
-        elif isinstance(data[0], tuple) and any(
-            [isinstance(d, h5py.h5r.Reference) for d in data[0]]
-        ):
-            # references stored inside a tuple, reference + location.
-            # dereference them!?
-            dset = group.get(col)
-            names = dset.dtype.names
-            if names is not None and names[0] == "idx_start" and names[1] == "count":
-                data = dereference_reference_vector(dset, data)
-
-        else:
-            data = data.tolist()
-
-        # After list, check if we need to put this thing inside of
-        # another class, as indicated by the enclosing model
-
-        items[col] = data
-
-    return model(hdf5_path=group.name, name=group.name.split("/")[-1], **items)
+#
+# def dynamictable_to_df(
+#     group: h5py.Group, model: Optional[Type[DataFrame]] = None, base: Optional[BaseModel] = None
+# ) -> DataFrame:
+#     """Generate a dataframe from an NDB DynamicTable"""
+#     if model is None:
+#         model = model_from_dynamictable(group, base)
+#
+#     items = {}
+#     for col, _col_type in model.model_fields.items():
+#         if col not in group:
+#             continue
+#         idxname = col + "_index"
+#         if idxname in group:
+#             idx = group.get(idxname)[:]
+#             data = group.get(col)[idx - 1]
+#         else:
+#             data = group.get(col)[:]
+#
+#         # Handle typing inside of list
+#         if isinstance(data[0], bytes):
+#             data = data.astype("unicode")
+#         if isinstance(data[0], str):
+#             # lists and other compound data types can get flattened out to strings when stored
+#             # so we try and literal eval and recover them
+#             try:
+#                 eval_type = type(ast.literal_eval(data[0]))
+#             except (ValueError, SyntaxError):
+#                 eval_type = str
+#
+#             # if we've found one of those, get the data type within it.
+#             if eval_type is not str:
+#                 eval_list = []
+#                 for item in data.tolist():
+#                     try:
+#                         eval_list.append(ast.literal_eval(item))
+#                     except ValueError:
+#                         eval_list.append(None)
+#                 data = eval_list
+#         elif isinstance(data[0], h5py.h5r.Reference):
+#             data = [HDF5_Path(group[d].name) for d in data]
+#         elif isinstance(data[0], tuple) and any(
+#             [isinstance(d, h5py.h5r.Reference) for d in data[0]]
+#         ):
+#             # references stored inside a tuple, reference + location.
+#             # dereference them!?
+#             dset = group.get(col)
+#             names = dset.dtype.names
+#             if names is not None and names[0] == "idx_start" and names[1] == "count":
+#                 data = dereference_reference_vector(dset, data)
+#
+#         else:
+#             data = data.tolist()
+#
+#         # After list, check if we need to put this thing inside of
+#         # another class, as indicated by the enclosing model
+#
+#         items[col] = data
+#
+#     return model(hdf5_path=group.name, name=group.name.split("/")[-1], **items)
diff --git a/nwb_linkml/tests/test_includes/test_hdmf.py b/nwb_linkml/tests/test_includes/test_hdmf.py
index 557e7db..8e1bd64 100644
--- a/nwb_linkml/tests/test_includes/test_hdmf.py
+++ b/nwb_linkml/tests/test_includes/test_hdmf.py
@@ -67,8 +67,9 @@ def units(request) -> Tuple[Units, list[np.ndarray], np.ndarray]:
     """
 
     n_units = 24
+    generator = np.random.default_rng()
     spike_times = [
-        np.full(shape=np.random.randint(10, 50), fill_value=i, dtype=float) for i in range(n_units)
+        np.full(shape=generator.integers(10, 50), fill_value=i, dtype=float) for i in range(n_units)
     ]
     spike_idx = []
     for i in range(n_units):
@@ -141,6 +142,19 @@ def test_dynamictable_indexing(electrical_series):
     assert subsection.dtypes.values.tolist() == dtypes[0:3]
 
 
+def test_dynamictable_region(electrical_series):
+    """
+    Dynamictableregion should
+    Args:
+        electrical_series:
+
+    Returns:
+
+    """
+    series, electrodes = electrical_series
+    
+
+
 def test_dynamictable_ragged_arrays(units):
     """
     Should be able to index ragged arrays using an implicit _index column
diff --git a/nwb_linkml/tests/test_providers/test_provider_schema.py b/nwb_linkml/tests/test_providers/test_provider_schema.py
index e92e466..9da6296 100644
--- a/nwb_linkml/tests/test_providers/test_provider_schema.py
+++ b/nwb_linkml/tests/test_providers/test_provider_schema.py
@@ -4,8 +4,9 @@ from pathlib import Path
 from typing import Optional
 
 import pytest
-from nptyping import Shape, UByte
-from numpydantic import NDArray
+from numpydantic import NDArray, Shape
+import numpy as np
+
 
 import nwb_linkml
 from nwb_linkml.maps.naming import version_module_case
@@ -77,7 +78,7 @@ def test_linkml_build_from_yaml(tmp_output_dir):
                 "comments": Optional[str],
                 "data": "TimeSeriesData",
                 "timestamps": "Optional",  # __name__ just gets the first part of Optional[TimeSeriesTimestamps]
-                "control": Optional[NDArray[Shape["* num_times"], UByte]],
+                "control": Optional[NDArray[Shape["* num_times"], np.uint8]],
             },
         )
     ],
diff --git a/pyproject.toml b/pyproject.toml
index eae490f..b8723db 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,6 +58,10 @@ select = [
     "D210", "D211",
     # emptiness
     "D419",
+    # perf
+    "PERF",
+    # numpy
+    "NPY",
 ]
 ignore = [
     # annotations for *args and **kwargs
diff --git a/scripts/generate_core.py b/scripts/generate_core.py
index 6cbb83f..35faf43 100644
--- a/scripts/generate_core.py
+++ b/scripts/generate_core.py
@@ -3,6 +3,7 @@ import os
 import sys
 import traceback
 from pdb import post_mortem
+import subprocess
 
 from argparse import ArgumentParser
 from pathlib import Path
@@ -179,6 +180,8 @@ def generate_versions(
             with open(pydantic_path / "__init__.py", "w") as initfile:
                 initfile.write(f"from .pydantic.core.{latest_version.name}.namespace import *")
 
+            subprocess.run(["black", "."])
+
     finally:
         if len(failed_versions) > 0:
             print("Failed Building Versions:")