def update(self, data_and_metadata_list: typing.Sequence[DataAndMetadata.DataAndMetadata], state: str, view_id) -> None:
frames = self.__multi_acquire_parameters[self.__current_parameters_index]['frames']
sum_frames = self.__multi_acquire_settings['sum_frames']
for i, data_and_metadata in enumerate(data_and_metadata_list):
data_item = self.__data_items[i]
scan_shape = data_and_metadata.data_shape
data_shape_and_dtype = (tuple(scan_shape), data_and_metadata.data_dtype)
data_descriptor = DataAndMetadata.DataDescriptor(frames > 1 and not sum_frames, 0, len(tuple(scan_shape)))
dimensional_calibrations = data_and_metadata.dimensional_calibrations
if frames > 1 and not sum_frames:
dimensional_calibrations = (Calibration.Calibration(),) + tuple(dimensional_calibrations)
data_shape = data_shape_and_dtype[0]
data_shape = (frames,) + data_shape
data_shape_and_dtype = (data_shape, data_shape_and_dtype[1])
intensity_calibration = data_and_metadata.intensity_calibration
metadata = data_and_metadata.metadata
metadata["MultiAcquire.settings"] = copy.deepcopy(self.__multi_acquire_settings)
metadata["MultiAcquire.parameters"] = copy.deepcopy(self.__multi_acquire_parameters[self.__current_parameters_index])
data_metadata = DataAndMetadata.DataMetadata(data_shape_and_dtype,
intensity_calibration,
dimensional_calibrations,
metadata=data_and_metadata.metadata,
data_descriptor=data_descriptor)
src_slice = (Ellipsis,)
dst_slice = (Ellipsis,)
if frames > 1:
if sum_frames:
existing_data = data_item.data
if existing_data is not None:
summed_data = existing_data[dst_slice] + data_and_metadata.data[src_slice]
data_and_metadata._set_data(summed_data)
else:
dst_slice = (self.current_frames_index,) + dst_slice # type: ignore
self.__document_model.update_data_item_partial(data_item, data_metadata, data_and_metadata, src_slice, dst_slice)
def update(self, data_and_metadata: DataAndMetadata.DataAndMetadata, state: str, scan_shape: Geometry.IntSize, dest_sub_area: Geometry.IntRect, sub_area: Geometry.IntRect, view_id) -> None:
# This method is always called with a collection of 1d or 2d data. Re-order axes as required and remove length-1-axes.
axes_descriptor = self.__grab_sync_info.axes_descriptor
# Calibrations, data descriptor and shape estimates are updated accordingly.
dimensional_calibrations = data_and_metadata.dimensional_calibrations
data = data_and_metadata.data
axes_order, data_shape = self.__calculate_axes_order_and_data_shape(axes_descriptor, scan_shape, data.shape[len(tuple(scan_shape)):])
assert len(axes_order) == data.ndim
data = numpy.moveaxis(data, axes_order, list(range(data.ndim)))
dimensional_calibrations = numpy.array(dimensional_calibrations)[axes_order].tolist()
is_sequence = axes_descriptor.sequence_axes is not None
collection_dimension_count = len(axes_descriptor.collection_axes) if axes_descriptor.collection_axes is not None else 0
datum_dimension_count = len(axes_descriptor.data_axes) if axes_descriptor.data_axes is not None else 0
src_slice = tuple()
dst_slice = tuple()
for index in axes_order:
if index >= len(sub_area.slice):
src_slice += (slice(None),)
else:
src_slice += (sub_area.slice[index],)
if index >= len(dest_sub_area.slice):
dst_slice += (slice(None),)
else:
dst_slice += (dest_sub_area.slice[index],)
if is_sequence and data.shape[0] == 1:
data = numpy.squeeze(data, axis=0)
dimensional_calibrations = dimensional_calibrations[1:]
src_slice = src_slice[1:]
dst_slice = dst_slice[1:]
is_sequence = False
data_descriptor = DataAndMetadata.DataDescriptor(is_sequence, collection_dimension_count, datum_dimension_count)
data_and_metadata = DataAndMetadata.new_data_and_metadata(data, data_and_metadata.intensity_calibration,
dimensional_calibrations,
data_and_metadata.metadata, None,
data_descriptor, None,
None)
data_metadata = DataAndMetadata.DataMetadata((tuple(data_shape), data_and_metadata.data_dtype),
data_and_metadata.intensity_calibration,
data_and_metadata.dimensional_calibrations,
metadata=data_and_metadata.metadata,
data_descriptor=data_descriptor)
self.__document_model.update_data_item_partial(self.__data_item, data_metadata, data_and_metadata, src_slice, dst_slice)
def update(self, data_and_metadata: DataAndMetadata.DataAndMetadata, state: str, scan_shape: Geometry.IntSize, dest_sub_area: Geometry.IntRect, sub_area: Geometry.IntRect, view_id) -> None:
# there are a few techniques for getting data into a data item. this method prefers directly calling the
# document model method update_data_item_partial, which is thread safe. if that method is not available, it
# falls back to the data item method set_data_and_metadata, which must be called from the main thread.
# the hardware source also supplies a data channel which is thread safe and ends up calling set_data_and_metadata
# but we skip that so that the updates fit into this class instead.
frames = self.__multi_acquire_parameters[self.__current_parameters_index]['frames']
sum_frames = self.__multi_acquire_settings['sum_frames']
collection_rank = len(tuple(scan_shape))
data_shape_and_dtype = (tuple(scan_shape) + data_and_metadata.data_shape[collection_rank:], data_and_metadata.data_dtype)
data_descriptor = DataAndMetadata.DataDescriptor(frames > 1 and not sum_frames, collection_rank, len(data_and_metadata.data_shape) - collection_rank)
dimensional_calibrations = data_and_metadata.dimensional_calibrations
if frames > 1 and not sum_frames:
dimensional_calibrations = (Calibration.Calibration(),) + tuple(dimensional_calibrations)
data_shape = data_shape_and_dtype[0]
data_shape = (frames,) + data_shape
data_shape_and_dtype = (data_shape, data_shape_and_dtype[1])
intensity_calibration = data_and_metadata.intensity_calibration
if self.__multi_acquire_settings['use_multi_eels_calibration']:
metadata = data_and_metadata.metadata.get('hardware_source', {})
counts_per_electron = metadata.get('counts_per_electron', 1)
exposure_s = metadata.get('exposure', self.__multi_acquire_parameters[self.__current_parameters_index]['exposure_ms']*0.001)
_number_frames = 1 if not sum_frames else frames
intensity_scale = (data_and_metadata.intensity_calibration.scale / counts_per_electron /
data_and_metadata.dimensional_calibrations[-1].scale / exposure_s / _number_frames)
intensity_calibration = Calibration.Calibration(scale=intensity_scale)
metadata = data_and_metadata.metadata
metadata["MultiAcquire.settings"] = copy.deepcopy(self.__multi_acquire_settings)
metadata["MultiAcquire.parameters"] = copy.deepcopy(self.__multi_acquire_parameters[self.__current_parameters_index])
data_metadata = DataAndMetadata.DataMetadata(data_shape_and_dtype,
intensity_calibration,
dimensional_calibrations,
metadata=data_and_metadata.metadata,
data_descriptor=data_descriptor)
src_slice = sub_area.slice + (Ellipsis,)
dst_slice = dest_sub_area.slice + (Ellipsis,)
if frames > 1:
if sum_frames:
existing_data = self.__data_item.data
if existing_data is not None:
existing_data[dst_slice] += data_and_metadata.data[src_slice]
else:
dst_slice = (self.current_frames_index,) + dst_slice # type: ignore
self.__document_model.update_data_item_partial(self.__data_item, data_metadata, data_and_metadata, src_slice, dst_slice)
self.update_progress(dest_sub_area.bottom)
