Allow None in Shapes

Author: Tom-Willemsen

pyproject.toml

@@ -15,7 +15,8 @@ description = "Asynchronous Bluesky hardware abstraction code, compatible with c
 dependencies = [
     "numpy",
     "bluesky>=1.13.1rc2",
-    "event-model>=1.22.1",
+    # Unpin once an event-model release is made containing https://github.com/bluesky/event-model/pull/352
+    "event-model @ git+https://github.com/bluesky/event-model@18e1168c7518401ca691d86f6c5cf5030413251f",
     "pyyaml",
     "colorlog",
     "pydantic>=2.0",

src/ophyd_async/core/_hdf_dataset.py

@@ -23,7 +23,7 @@ class HDFDatasetDescription(BaseModel):
     """The dataset name within the HDF file,
     e.g. /entry/data/data or /entry/instrument/NDAttributes/sum"""
 
-    shape: tuple[int, ...] = Field(default_factory=tuple)
+    shape: tuple[int | None, ...] = Field(default_factory=tuple)
     """The shape of a single event's data in the HDF file,
     e.g. (1, 768, 1024) for arrays or () for scalars"""
 

src/ophyd_async/core/_providers.py

@@ -228,5 +228,5 @@ async def np_datatype(self) -> str:
         """Return the numpy datatype for this dataset."""
 
     @abstractmethod
-    async def shape(self) -> tuple[int, ...]:
+    async def shape(self) -> tuple[int | None, ...]:
         """Get the shape of the data collection."""

src/ophyd_async/core/_signal_backend.py

@@ -176,7 +176,7 @@ def _datakey_dtype_numpy(
         raise TypeError(f"Can't make dtype_numpy for {datatype}")
 
 
-def _datakey_shape(value: SignalDatatype) -> list[int]:
+def _datakey_shape(value: SignalDatatype) -> list[int | None]:
     if type(value) in _primitive_dtype or isinstance(value, EnumTypes):
         return []
     elif isinstance(value, np.ndarray):

src/ophyd_async/epics/adcore/_hdf_writer.py

@@ -1,6 +1,7 @@
 import asyncio
 from collections.abc import AsyncIterator
 from pathlib import Path
+from typing import cast
 from xml.etree import ElementTree as ET
 
 from bluesky.protocols import StreamAsset
@@ -75,6 +76,19 @@ async def open(
         # Determine number of frames that will be saved per HDF chunk
         frames_per_chunk = await self.fileio.num_frames_chunks.get_value()
 
+        if None in detector_shape:
+            # Questions:
+            # - Can AreaDetector support this?
+            # - How to deal with chunking?
+            # Don't support for now - leave option open to support it later
+            raise ValueError(
+                "Datasets with partially unknown dimensionality "
+                "are not currently supported by ADHDFWriter."
+            )
+
+        # Pyright doesn't understand the above check as a type-narrowing expression
+        detector_shape = cast(tuple[int, ...], detector_shape)
+
         # Add the main data
         self._datasets = [
             HDFDatasetDescription(