Fix sparsing from eiger stream files

hexrd/imageseries/load/eiger_stream_v1.py

@@ -5,8 +5,11 @@
 from dectris.compression import decompress
 import h5py
 import numpy as np
+import yaml
 
+from hexrd.utils.compatibility import h5py_read_string
 from hexrd.utils.hdf5 import unwrap_h5_to_dict
+from hexrd.utils.yaml import NumpyToNativeDumper
 
 from . import ImageSeriesAdapter
 from ..imageseriesiter import ImageSeriesIterator
@@ -95,8 +98,22 @@
 
     def _get_metadata(self):
         d = {}
+        # First, unwrap the metadata from the HDF5 file into a dict
         unwrap_h5_to_dict(self.__h5file['/metadata'], d)
-        return d
+
+        # Because frame cache imageseries do not yet support nested
+        # metadata structures, we should not use them. The frame cache
+        # imageseries should, at some point, start allowing for nested
+        # metadata structures. At that point, we can return to the
+        # previous way.
+        # For now instead, we will serialize this nested structure to
+        # yaml and keep it as a string. This means the metadata is still
+        # saved and accessible.
+        metadata = {
+            'eiger_metadata_as_yaml': yaml.dump(d, Dumper=NumpyToNativeDumper)
+        }
+
+        return metadata
 
     @property
     def metadata(self):
@@ -116,7 +133,7 @@
 
     @property
     def dtype(self):
-        return self._first_data_entry['dtype'][()]
+        return h5py_read_string(self._first_data_entry['dtype'])
 
     @property
     def shape(self):

hexrd/utils/yaml.py

@@ -12,9 +12,13 @@ class NumpyToNativeDumper(yaml.SafeDumper):
     converted to a basic type.
     """
     def represent_data(self, data):
-        if isinstance(data, np.ndarray):
+        # Empty shape arrays should be treated as numbers, not arrays.
+        is_empty_shape_array = (
+            isinstance(data, np.ndarray) and data.shape == ()
+        )
+        if isinstance(data, np.ndarray) and not is_empty_shape_array:
             return self.represent_list(data.tolist())
-        elif isinstance(data, (np.generic, np.number)):
+        elif isinstance(data, (np.generic, np.number)) or is_empty_shape_array:
             item = data.item()
             if isinstance(item, (np.generic, np.number)):
                 # This means it was not converted successfully.