NXmx: handle multidimensional arrays

newsfragments/612.bugfix

@@ -0,0 +1 @@
+NeXus: handle dataset with multiple modules stored as multidimensional arrays

src/dxtbx/nexus/__init__.py

@@ -2,7 +2,7 @@
 
 import itertools
 import logging
-from typing import Literal, Optional, Tuple, cast
+from typing import Literal, Optional
 
 import h5py
 import numpy as np
@@ -404,9 +404,13 @@ def equipment_component_key(dependency):
                 origin -= nxdetector.beam_center_y.magnitude * pixel_size[1] * slow_axis
 
         # dxtbx requires image size in the order fast, slow - which is the reverse of what
-        # is stored in module.data_size
-        image_size = cast(Tuple[int, int], tuple(map(int, module.data_size[::-1])))
-        assert len(image_size) == 2
+        # is stored in module.data_size. Additionally, data_size can have more than 2
+        # dimensions, for multi-module detectors. So take the last two dimensions and reverse
+        # them.  Examples:
+        # [1,2,3,4] --> (4, 3)
+        # [1,2,3]   --> (3, 2)
+        # [1,2]     --> (2, 1)
+        image_size = (int(module.data_size[-1]), int(module.data_size[-2]))
         underload = (
             float(nxdetector.underload_value)
             if nxdetector.underload_value is not None
@@ -475,13 +479,17 @@ def get_static_mask(nxdetector: nxmx.NXdetector) -> tuple[flex.bool, ...] | None
         pixel_mask = nxdetector.pixel_mask
     except KeyError:
         return None
-    if pixel_mask is None or not pixel_mask.size or pixel_mask.ndim != 2:
+    if pixel_mask is None or not pixel_mask.size:
         return None
     all_slices = get_detector_module_slices(nxdetector)
-    return tuple(
-        flumpy.from_numpy(np.ascontiguousarray(pixel_mask[slices])) == 0
-        for slices in all_slices
-    )
+    all_mask_slices = []
+    for slices in all_slices:
+        mask_slice = flumpy.from_numpy(np.ascontiguousarray(pixel_mask[slices])) == 0
+        mask_slice.reshape(
+            flex.grid(mask_slice.all()[-2:])
+        )  # handle 3 or 4 dimension arrays
+        all_mask_slices.append(mask_slice)
+    return tuple(all_mask_slices)
 
 
 def _dataset_as_flex(
@@ -562,5 +570,8 @@ def get_raw_data(
         data_as_flex = _dataset_as_flex(
             sliced_outer, tuple(module_slices), bit_depth=bit_depth
         )
+        data_as_flex.reshape(
+            flex.grid(data_as_flex.all()[-2:])
+        )  # handle 3 or 4 dimension arrays
         all_data.append(data_as_flex)
     return tuple(all_data)