Route array representation for HTML

src/hdmf/backends/hdf5/h5tools.py

@@ -1541,7 +1541,7 @@
 
         array_info_dict = get_basic_array_info(dataset)
         if isinstance(dataset, h5py.Dataset):
-
+            dataset_type = "HDF5 dataset"
             # get info from hdf5 dataset
             compressed_size = dataset.id.get_storage_size()
             if hasattr(dataset, "nbytes"):  # TODO: Remove this after h5py minimal version is larger than 3.0
@@ -1556,10 +1556,13 @@
                             "Compression opts": dataset.compression_opts,
                             "Compression ratio": compression_ratio,
                             }
-
             array_info_dict.update(hdf5_info_dict)
 
-        # generate html repr
-        repr_html = generate_array_html_repr(array_info_dict, dataset, "HDF5 dataset")
+        elif isinstance(dataset, np.ndarray):
+            dataset_type = "NumPy array"
+        else:
+            dataset_type = dataset.__class__.__name__
+
+        repr_html = generate_array_html_repr(array_info_dict, dataset, dataset_type)
 
         return repr_html

src/hdmf/container.py

@@ -707,8 +707,11 @@
             return f'<div style="margin-left: {level * 20}px;" class="container-fields"><span class="field-key"' \
                    f' title="{access_code}">{key}: </span><span class="field-value">{value}</span></div>'
 
-        is_array_data = isinstance(value, (np.ndarray, h5py.Dataset, DataIO)) or \
-            (hasattr(value, "store") and hasattr(value, "shape"))  # Duck typing for zarr array
+        # Detects array-like objects that conform to the Array Interface specification
+        # (e.g., NumPy arrays, HDF5 datasets, DataIO objects). Objects must have both
+        # 'shape' and 'dtype' attributes. Iterators are excluded as they lack 'shape'.
+        # This approach keeps the implementation generic without coupling to specific backends methods
+        is_array_data = hasattr(value, "shape") and hasattr(value, "dtype")
 
         if is_array_data:
             html_content = self._generate_array_html(value, level + 1)
@@ -735,14 +738,29 @@
 
 
     def _generate_array_html(self, array, level):
-        """Generates HTML for array data"""
+        """Generates HTML for array data (e.g., NumPy arrays, HDF5 datasets, Zarr datasets and DataIO objects)."""
 
-        read_io = self.get_read_io()  # if the Container was read from file, get IO object
-        if read_io is not None: # Note that sometimes numpy array have a read_io attribute
-            repr_html = read_io.generate_dataset_html(array)
-        else:
+        is_numpy_array = isinstance(array, np.ndarray)
+        it_was_read_with_io = self.get_read_io() is not None
+        is_data_io = isinstance(array, DataIO)
+
+        if is_numpy_array:
             array_info_dict = get_basic_array_info(array)
             repr_html = generate_array_html_repr(array_info_dict, array, "NumPy array")
+        elif is_data_io:
+            array_info_dict = get_basic_array_info(array.data)
+            repr_html = generate_array_html_repr(array_info_dict, array.data, "DataIO")
+        elif it_was_read_with_io:
+            # The backend handles the representation here. Two special cases worth noting:
+            # 1. Array-type attributes (e.g., start_frame in ImageSeries) remain NumPy arrays
+            #    even when their parent container has an IO
+            # 2. Data may have been modified after being read from storage
+            read_io = self.get_read_io()
+            repr_html = read_io.generate_dataset_html(array)
+        else:  # Not sure which object could get here
+            object_class = array.__class__.__name__
+            array_info_dict = get_basic_array_info(array.data)
+            repr_html = generate_array_html_repr(array_info_dict, array.data, object_class)
 
         return f'<div style="margin-left: {level * 20}px;" class="container-fields">{repr_html}</div>'
 

src/hdmf/utils.py

@@ -894,7 +894,7 @@
 
     return basic_array_info_dict
 
-def generate_array_html_repr(backend_info_dict, array, dataset_type=None):
+def generate_array_html_repr(array_info_dict, array, dataset_type=None):
     def html_table(item_dicts) -> str:
         """
         Generates an html table from a dictionary
@@ -912,14 +912,22 @@
         report += "</table>"
         return report
 
-    array_info_html = html_table(backend_info_dict)
+    array_info_html = html_table(array_info_dict)
     repr_html = dataset_type + "<br>" + array_info_html if dataset_type is not None else array_info_html
 
-    if hasattr(array, "nbytes"):  # TODO: Remove this after h5py minimal version is larger than 3.0
-        array_size = array.nbytes
+    # Array like might lack nbytes (h5py < 3.0) or size (DataIO object)
+    if hasattr(array, "nbytes"):
+        array_size_bytes = array.nbytes
     else:
-        array_size = array.size * array.dtype.itemsize
-    array_is_small = array_size < 1024 * 0.1 # 10 % a kilobyte to display the array
+        if hasattr(array, "size"):
+            array_size = array.size
+        else:
+            import math
+            array_size = math.prod(array.shape)
+        array_size_bytes = array_size * array.dtype.itemsize
+
+    # Heuristic for displaying data
+    array_is_small = array_size_bytes < 1024 * 0.1 # 10 % a kilobyte to display the array
     if array_is_small:
         repr_html += "<br>" + str(np.asarray(array))