Add support for using dtype_numpy as in ophyd-async

databroker/mongo_normalized.py

@@ -135,15 +135,15 @@ def structure_from_descriptor(descriptor, sub_dict, max_seq_num, unicode_columns
  shape = tuple((max_seq_num - 1, *field_metadata["shape"]))
  # if we have a descr, then this is a
  dtype = _try_descr(field_metadata)
- dt_str = field_metadata.get("dtype_str")
+ dt_np = field_metadata.get("dtype_numpy") or field_metadata.get("dtype_str")
  if dtype is not None:
  if len(shape) > 2:
  raise RuntimeError(
  "We do not yet support general structured arrays, only 1D ones."
  )
  # if we have a detailed string, trust that
- elif dt_str is not None:
- dtype = BuiltinDtype.from_numpy_dtype(numpy.dtype(dt_str))
+ elif dt_np is not None:
+ dtype = BuiltinDtype.from_numpy_dtype(numpy.dtype(dt_np))
  # otherwise guess!
  else:
  dtype = JSON_DTYPE_TO_MACHINE_DATA_TYPE[field_metadata["dtype"]]
@@ -622,10 +622,10 @@ def __init__(
  unicode_keys = []
  for key, field_metadata in descriptor["data_keys"].items():
  if field_metadata["dtype"] == "string":
- # Skip this if it has a dtype_str with an itemsize.
- dtype_str = field_metadata.get("dtype_str")
- if dtype_str is not None:
- if numpy.dtype(dtype_str).itemsize != 0:
+ # Skip this if it has a dtype_numpy with an itemsize.
+ dt_np = field_metadata.get("dtype_numpy") or field_metadata.get("dtype_str")
+ if dt_np is not None:
+ if numpy.dtype(dt_np).itemsize != 0:
  continue
  unicode_keys.append(key)
  # Load the all the data for unicode columns to figure out the itemsize.
@@ -719,7 +719,7 @@ def read(self, fields=None):
  f"{key!r} actually has dtype {raw_array.dtype.str!r} "
  f"but was reported as having dtype {dtype.str!r}. "
  "It will be converted to the reported type, "
- "but this should be fixed by setting 'dtype_str' "
+ "but this should be fixed by setting 'dtype_numpy' "
  "in the data_key of the EventDescriptor. "
  f"RunStart UID: {self._run.metadata()['start']['uid']!r}"
  )
@@ -765,7 +765,7 @@ def read_block(self, variable, block, slice=None):
  f"{variable!r} actually has dtype {raw_array.dtype.str!r} "
  f"but was reported as having dtype {dtype.str!r}. "
  "It will be converted to the reported type, "
- "but this should be fixed by setting 'dtype_str' "
+ "but this should be fixed by setting 'dtype_numpy' "
  "in the data_key of the EventDescriptor. "
  f"RunStart UID: {self._run.metadata()['start']['uid']!r}"
  )
@@ -1057,16 +1057,16 @@ def build_config_xarray(
  for key, column in raw_columns.items():
  field_metadata = data_keys[key]
  dtype = _try_descr(field_metadata)
- dt_str = field_metadata.get("dtype_str")
+ dt_np = field_metadata.get("dtype_numpy") or field_metadata.get("dtype_str")
  if dtype is not None:
  if len(getattr(column[0], "shape", ())) > 2:
  raise RuntimeError(
  "We do not yet support general structured arrays, only 1D ones."
  )
  numpy_dtype = dtype.to_numpy_dtype()
  # if we have a detailed string, trust that
- elif dt_str is not None:
- numpy_dtype = numpy.dtype(dt_str)
+ elif dt_np is not None:
+ numpy_dtype = numpy.dtype(dt_np)
  # otherwise guess!
  else:
  numpy_dtype = JSON_DTYPE_TO_MACHINE_DATA_TYPE[
@@ -2056,7 +2056,7 @@ def parse_transforms(transforms):
 
 
 # These are fallback guesses when all we have is a general jsonschema "dtype"
-# like "array" no specific "dtype_str" like "<u2".
+# like "array" no specific "dtype_numpy" like "<u2".
 BOOLEAN_DTYPE = BuiltinDtype.from_numpy_dtype(numpy.dtype("bool"))
 FLOAT_DTYPE = BuiltinDtype.from_numpy_dtype(numpy.dtype("float64"))
 INT_DTYPE = BuiltinDtype.from_numpy_dtype(numpy.dtype("int64"))
@@ -2066,7 +2066,7 @@ def parse_transforms(transforms):
  "number": FLOAT_DTYPE,
  "integer": INT_DTYPE,
  "string": STRING_DTYPE,
- "array": FLOAT_DTYPE, # If this is wrong, set 'dtype_str' in data_key to override.
+ "array": FLOAT_DTYPE, # If this is wrong, set 'dtype_numpy' in data_key to override.
 }