Add support for 3D detectors

Author: coretl

src/ophyd_async/epics/adcore/_data_logic.py

@@ -70,6 +70,8 @@ async def get_ndarray_resource_info(
         description.data_type_signal.get_value(),
         description.color_mode_signal.get_value(),
     )
+    # Remove entries in shape that are zero
+    shape = [x for x in shape if x > 0]
     if datatype is ADBaseDataType.UNDEFINED:
         raise ValueError(
             f"{description.data_type_signal.source} is blank, this is not supported"
@@ -309,7 +311,7 @@ def make_writer_data_logic(
 ) -> tuple[NDPluginFileIO, DetectorDataLogic]:
     plugins = plugins or {}
     description = NDArrayDescription(
-        shape_signals=[driver.array_size_y, driver.array_size_x],
+        shape_signals=[driver.array_size_z, driver.array_size_y, driver.array_size_x],
         data_type_signal=driver.data_type,
         color_mode_signal=driver.color_mode,
     )

src/ophyd_async/epics/adcore/_io.py

@@ -55,10 +55,11 @@ class NDArrayBaseIO(EpicsDevice):
     nd_attributes_file: A[SignalRW[str], PvSuffix("NDAttributesFile")]
     acquire: A[SignalRW[bool], PvSuffix.rbv("Acquire"), EpicsOptions(wait=non_zero)]
 
-    # The array_size_x/y signals are used when outputting an NDArray
+    # The array_size_x/y/z signals are used when outputting an NDArray
     # (i.e. from a driver)
     array_size_x: A[SignalR[int], PvSuffix("ArraySizeX_RBV")]
     array_size_y: A[SignalR[int], PvSuffix("ArraySizeY_RBV")]
+    array_size_z: A[SignalR[int], PvSuffix("ArraySizeZ_RBV")]
 
     # The array_size0/1/2 signals are used when receiving an NDArray
     # (i.e. in a plugin)

tests/unit_tests/epics/adcore/test_data_logic.py

@@ -228,3 +228,20 @@ async def test_describe_different_color_modes(
                 "source": ANY,
             },
         }
+
+
+async def test_3d_dataset_shape(hdf_det: adcore.AreaDetector[adcore.ADBaseIO]):
+    writer = hdf_det.get_plugin("writer", adcore.NDPluginFileIO)
+    set_mock_value(writer.file_path_exists, True)
+    set_mock_value(hdf_det.driver.array_size_z, 10)
+    await hdf_det.prepare(TriggerInfo())
+    describe = await hdf_det.describe()
+    assert describe == {
+        "detector": {
+            "dtype": "array",
+            "dtype_numpy": "<u2",
+            "external": "STREAM:",
+            "shape": [1, 10, 768, 1024],
+            "source": ANY,
+        },
+    }