def test_tuple_binding_pads_to_index_if_necessary(self) -> None:
# this allows the source to more easily go from None to a partialy tuple None -> (3, None) -> (3, 4)
source = typing.cast(typing.Any, TupleModel())
self.assertEqual(None, source.tuple)
binding0 = Binding.TuplePropertyBinding(source, "tuple", 0)
binding2 = Binding.TuplePropertyBinding(source, "tuple", 2)
binding0.update_source("abc")
self.assertEqual(("abc", ), source.tuple)
binding2.update_source("ghi")
self.assertEqual(("abc", None, "ghi"), source.tuple)
def __connect_pick_graphic(self, src, target, pick_graphic, computation):
def _update_collection_index(axis, value):
libertem_metadata = copy.deepcopy(src.metadata.get('libertem-io'))
if not libertem_metadata:
return
file_parameters = libertem_metadata['file_parameters']
file_type = file_parameters.pop('type')
current_index = libertem_metadata['display_slice']['start']
current_index = np.unravel_index(current_index, target.data.shape)
if value == current_index[axis]:
return
executor = Registry.get_component('libertem_executor')
if not executor:
return
executor = executor.ensure_sync()
ds = dataset.load(file_type, executor, **file_parameters)
roi = np.zeros(ds.shape.nav, dtype=bool)
if axis == 0:
roi[value, current_index[1]] = True
current_index = (value, current_index[1])
else:
roi[current_index[0], value] = True
current_index = (current_index[0], value)
result = UDFRunner(PickUDF()).run_for_dataset(ds,
executor,
roi=roi)
result_array = np.squeeze(np.array(result['intensity']))
new_metadata = copy.deepcopy(src.metadata)
new_display_slice = np.ravel_multi_index(current_index,
target.data.shape)
new_metadata['libertem-io']['display_slice'][
'start'] = new_display_slice
new_xdata = self.__api.create_data_and_metadata(
result_array, metadata=new_metadata)
src.set_data_and_metadata(new_xdata)
computation.pick_graphic_binding_0 = Binding.TuplePropertyBinding(
pick_graphic._graphic,
'position',
0,
converter=FloatTupleToIntTupleConverter(target.data.shape[0], 0))
computation.pick_graphic_binding_1 = Binding.TuplePropertyBinding(
pick_graphic._graphic,
'position',
1,
converter=FloatTupleToIntTupleConverter(target.data.shape[1], 1))
computation.pick_graphic_binding_0.target_setter = functools.partial(
_update_collection_index, 0)
computation.pick_graphic_binding_1.target_setter = functools.partial(
_update_collection_index, 1)
def test_tuple_property_binding_refcount(self) -> None:
binding = Binding.TuplePropertyBinding(Model.PropertyModel((1, 2, 3)),
"value", 1)
binding_ref = weakref.ref(binding)
del binding
self.assertIsNone(binding_ref())
def __connect_pick_graphic(self,
src,
target,
pick_graphic,
computation,
do_wait=-1):
def _update_collection_index(axis, value):
if src.xdata.is_collection or src.xdata.is_sequence:
display_item = self.__api.application._application.document_model.get_display_item_for_data_item(
src._data_item)
collection_index = display_item.display_data_channel.collection_index
if axis == 0:
if value != collection_index[0]:
display_item.display_data_channel.collection_index = (
value, collection_index[1], 0)
else:
if value != collection_index[1]:
display_item.display_data_channel.collection_index = (
collection_index[0], value, 0)
else:
libertem_metadata = copy.deepcopy(
src.metadata.get('libertem-io'))
if not libertem_metadata:
return
file_parameters = libertem_metadata['file_parameters']
file_type = file_parameters.pop('type')
current_index = libertem_metadata['display_slice']['start']
current_index = np.unravel_index(current_index,
target.data.shape)
if value == current_index[axis]:
return
executor = Registry.get_component('libertem_executor')
if not executor:
return
executor = executor.ensure_sync()
ds = dataset.load(file_type, executor, **file_parameters)
roi = np.zeros(ds.shape.nav, dtype=bool)
if axis == 0:
roi[value, current_index[1]] = True
current_index = (value, current_index[1])
else:
roi[current_index[0], value] = True
current_index = (current_index[0], value)
result = UDFRunner(PickUDF()).run_for_dataset(ds,
executor,
roi=roi)
result_array = np.squeeze(np.array(result['intensity']))
new_metadata = copy.deepcopy(src.metadata)
new_display_slice = np.ravel_multi_index(
current_index, target.data.shape)
new_metadata['libertem-io']['display_slice'][
'start'] = new_display_slice
new_xdata = self.__api.create_data_and_metadata(
result_array, metadata=new_metadata)
src.set_data_and_metadata(new_xdata)
if do_wait > 0:
starttime = time.time()
while target.data is None:
if time.time() - starttime > do_wait:
break
time.sleep(0.1)
if target.data is None:
return
shape = target.data.shape
computation.pick_graphic_binding_0 = Binding.TuplePropertyBinding(
pick_graphic._graphic,
'position',
0,
converter=FloatTupleToIntTupleConverter(shape[0], 0))
computation.pick_graphic_binding_1 = Binding.TuplePropertyBinding(
pick_graphic._graphic,
'position',
1,
converter=FloatTupleToIntTupleConverter(shape[1], 1))
computation.pick_graphic_binding_0.target_setter = functools.partial(
_update_collection_index, 0)
computation.pick_graphic_binding_1.target_setter = functools.partial(
_update_collection_index, 1)
def collection_index_changed(key):
if src.xdata.is_collection:
display_item = self.__api.application._application.document_model.get_display_item_for_data_item(
src._data_item)
if key == 'collection_index':
collection_index = display_item.display_data_channel.collection_index
if int(pick_graphic.position[0] *
shape[0]) != collection_index[0]:
computation.pick_graphic_binding_0.update_source(
collection_index)
if int(pick_graphic.position[1] *
shape[1]) != collection_index[1]:
computation.pick_graphic_binding_1.update_source(
collection_index)
if src.xdata.is_collection:
display_item = self.__api.application._application.document_model.get_display_item_for_data_item(
src._data_item)
computation.collection_index_changed_event_listener = display_item.display_data_channel.property_changed_event.listen(
collection_index_changed)
