async def map_new_edge(document_controller, model_data_item, edge) -> None:
document_model = document_controller.document_model
project = document_model.get_project_for_item(model_data_item)
map_data_item = DataItem.new_data_item()
map_data_item.title = "{} of {}".format(_("Map"), str(edge.electron_shell))
map_data_item.category = model_data_item.category
map_data_item.source = model_data_item
document_model.append_data_item(map_data_item, project=project)
computation = document_model.create_computation()
computation.source = map_data_item
computation.create_input_item(
"spectrum_image_xdata",
Symbolic.make_item(model_data_item, type="xdata"))
computation.create_input_item("fit_interval",
Symbolic.make_item(edge.data_structure),
property_name="fit_interval")
computation.create_input_item("signal_interval",
Symbolic.make_item(edge.data_structure),
property_name="signal_interval")
computation.processing_id = "eels.mapping"
computation.create_output_item("map", Symbolic.make_item(map_data_item))
document_model.append_computation(computation, project=project)
await document_model.compute_immediate(document_controller.event_loop,
computation)
map_display_item = document_model.get_display_item_for_data_item(
map_data_item)
document_controller.show_display_item(map_display_item)
def test_cursor_over_2d_data_sequence_displays_correct_ordering_of_indices(
self):
document_model = DocumentModel.DocumentModel()
document_controller = DocumentController.DocumentController(
self.app.ui, document_model, workspace_id="library")
with contextlib.closing(document_controller):
display_panel = document_controller.selected_display_panel
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((20, 100, 100), numpy.float64),
data_descriptor=DataAndMetadata.DataDescriptor(True, 0, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_item.display_data_channels[0].sequence_index = 4
display_item.calibration_style_id = "pixels-top-left"
display_panel.set_display_panel_display_item(display_item)
header_height = display_panel.header_canvas_item.header_height
info_panel = document_controller.find_dock_panel("info-panel")
display_panel.root_container.layout_immediate(
(1000 + header_height, 1000))
display_panel.display_canvas_item.mouse_entered()
document_controller.periodic()
display_panel.display_canvas_item.mouse_position_changed(
500, 500, Graphics.NullModifiers())
document_controller.periodic()
self.assertEqual(info_panel.label_row_1.text,
"Position: 50.0, 50.0, 4.0")
self.assertEqual(info_panel.label_row_2.text, "Value: 1")
self.assertIsNone(info_panel.label_row_3.text, None)
display_panel.display_canvas_item.mouse_exited()
def test_dimension_used_for_scale_marker_on_4d_diffraction_image_is_correct(
self):
document_model = DocumentModel.DocumentModel()
document_controller = DocumentController.DocumentController(
self.app.ui, document_model, workspace_id="library")
with contextlib.closing(document_controller):
display_panel = document_controller.selected_display_panel
calibrations = [
Calibration.Calibration(units="y"),
Calibration.Calibration(units="x"),
Calibration.Calibration(units="a"),
Calibration.Calibration(units="b")
]
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((10, 10, 50, 50)),
dimensional_calibrations=calibrations,
data_descriptor=DataAndMetadata.DataDescriptor(False, 2, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_panel.set_display_panel_display_item(display_item)
header_height = display_panel.header_canvas_item.header_height
display_panel.root_container.layout_immediate(
(1000 + header_height, 1000))
# run test
self.assertEqual(
display_panel.display_canvas_item.
_info_overlay_canvas_item_for_test.
_dimension_calibration_for_test.units, "b")
def test_changing_sequence_index_updates_display_range(self):
with TestContext.create_memory_context() as test_context:
document_model = test_context.create_document_model()
data = numpy.zeros((3, 8, 8))
data[1, ...] = 1
data[2, ...] = 2
xdata = DataAndMetadata.new_data_and_metadata(
data,
data_descriptor=DataAndMetadata.DataDescriptor(True, 0, 2))
data_item = DataItem.new_data_item(xdata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_range, (0, 0))
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).data_range, (0, 0))
display_data_channel.sequence_index = 1
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_range, (1, 1))
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).data_range, (1, 1))
def test_exception_during_calculate_display_values_recovers_gracefully(
self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
d = numpy.random.randn(4, 4, 3, 3)
data_and_metadata = DataAndMetadata.new_data_and_metadata(
d, data_descriptor=DataAndMetadata.DataDescriptor(False, 2, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
def next_calculated_display_values():
pass
display_data_channel = display_item.display_data_channels[0]
listener = display_data_channel.add_calculated_display_values_listener(
next_calculated_display_values)
with contextlib.closing(listener):
display_data = display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data
# now run the test
display_data_channel.collection_index = 2, 2 # should trigger the thread
display_data = display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data
display_data_channel.collection_index = 2, 2
display_data = display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data
self.assertTrue(numpy.array_equal(display_data, d[2, 2, ...]))
def test_mapping_widget_to_image_on_3d_spectrum_image_uses_collection_dimensions(
self):
document_model = DocumentModel.DocumentModel()
document_controller = DocumentController.DocumentController(
self.app.ui, document_model, workspace_id="library")
with contextlib.closing(document_controller):
display_panel = document_controller.selected_display_panel
document_controller.selected_display_panel.change_display_panel_content(
{"type": "image"})
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((10, 10, 50)),
data_descriptor=DataAndMetadata.DataDescriptor(False, 2, 1))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_panel.set_display_panel_display_item(display_item)
header_height = display_panel.header_canvas_item.header_height
display_panel.root_container.layout_immediate(
(100 + header_height, 100))
# run test
document_controller.tool_mode = "line-profile"
display_panel.display_canvas_item.simulate_drag((20, 25), (65, 85))
self.assertEqual(display_item.graphics[0].vector,
((0.2, 0.25), (0.65, 0.85)))
def test_line_plot_calculates_calibrated_vs_uncalibrated_display_y_values(
self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
data_item = DataItem.new_data_item(
DataAndMetadata.new_data_and_metadata(
numpy.ones((8, )),
intensity_calibration=Calibration.Calibration(offset=0,
scale=10,
units="nm")))
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_item.display_type = "line_plot"
display_panel = document_controller.selected_display_panel
display_panel.set_display_panel_display_item(display_item)
display_panel.display_canvas_item.layout_immediate((640, 480))
self.assertTrue(
numpy.array_equal(
display_panel.display_canvas_item.line_graph_canvas_item.
calibrated_xdata.data, numpy.full((8, ), 10)))
display_item.calibration_style_id = "pixels-top-left"
display_panel.display_canvas_item.layout_immediate((640, 480))
self.assertTrue(
numpy.array_equal(
display_panel.display_canvas_item.line_graph_canvas_item.
calibrated_xdata.data, numpy.ones((8, ))))
def test_line_plot_handles_calibrated_vs_uncalibrated_display(self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
data_item = DataItem.new_data_item(
DataAndMetadata.new_data_and_metadata(
numpy.ones((8, )),
dimensional_calibrations=[
Calibration.Calibration(offset=0, scale=10, units="nm")
]))
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_item.display_type = "line_plot"
display_panel = document_controller.selected_display_panel
display_panel.set_display_panel_display_item(display_item)
display_panel.display_canvas_item.layout_immediate((640, 480))
self.assertEqual(
display_panel.display_canvas_item.line_graph_canvas_item.
calibrated_xdata.dimensional_calibrations[-1].units, "nm")
display_item.calibration_style_id = "pixels-top-left"
display_panel.display_canvas_item.layout_immediate((640, 480))
self.assertFalse(
display_panel.display_canvas_item.line_graph_canvas_item.
calibrated_xdata.dimensional_calibrations[-1].units)
def test_dimension_used_for_scale_marker_on_2d_data_stack_is_correct(self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
display_panel = document_controller.selected_display_panel
calibrations = [
Calibration.Calibration(units="s"),
Calibration.Calibration(units="y"),
Calibration.Calibration(units="x")
]
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((50, 10, 10)),
dimensional_calibrations=calibrations,
data_descriptor=DataAndMetadata.DataDescriptor(True, 0, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_panel.set_display_panel_display_item(display_item)
header_height = display_panel.header_canvas_item.header_height
display_panel.root_container.layout_immediate(
(1000 + header_height, 1000))
# run test
self.assertEqual(
display_panel.display_canvas_item.
_info_overlay_canvas_item_for_test.
_dimension_calibration_for_test.units, "x")
def test_display_data_is_1d_for_collection_of_1d_datum(self):
# this tests the changes of 1d collections of 1d data. see #529.
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((2, 8), float),
data_descriptor=DataAndMetadata.DataDescriptor(False, 1, 1))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
self.assertEqual(
(8, ),
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_shape)
self.assertEqual(
float,
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_dtype)
self.assertEqual((8, ), display_data_channel.display_data_shape)
self.assertEqual(
1,
len(
display_data_channel.get_datum_calibrations(
data_item.dimensional_calibrations)))
def test_collection_index_validates_when_data_changes(self):
document_model = DocumentModel.DocumentModel()
document_controller = DocumentController.DocumentController(
self.app.ui, document_model, workspace_id="library")
with contextlib.closing(document_controller):
d = numpy.random.randn(4, 4, 3, 3)
data_and_metadata = DataAndMetadata.new_data_and_metadata(
d, data_descriptor=DataAndMetadata.DataDescriptor(False, 2, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
display_data_channel.collection_index = 3, 3
display_data = display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data
self.assertTrue(numpy.array_equal(display_data, d[3, 3, ...]))
d2 = numpy.random.randn(2, 2, 3, 3)
data_and_metadata2 = DataAndMetadata.new_data_and_metadata(
d2,
data_descriptor=DataAndMetadata.DataDescriptor(False, 2, 2))
display_item.data_item.set_xdata(data_and_metadata2)
display_data2 = display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data
self.assertTrue(numpy.array_equal(display_data2, d2[1, 1, ...]))
def test_cursor_over_1d_image_without_exception_x(self):
with TestContext.create_memory_context() as test_context:
document_model = test_context.create_document_model()
data_item = DataItem.new_data_item(DataAndMetadata.new_data_and_metadata(numpy.zeros((4, 25)), data_descriptor=DataAndMetadata.DataDescriptor(False, 1, 1)))
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(data_item)
display_item.display_type = "image"
p, v = display_item.get_value_and_position_text(display_item.display_data_channel, (2, 20))
self.assertEqual(p, "20.0, 2.0")
self.assertEqual(v, "0")
def test_cursor_over_1d_multiple_data_but_2_datum_dimensions_displays_without_exception(self):
document_model = DocumentModel.DocumentModel()
with contextlib.closing(document_model):
data_and_metadata = DataAndMetadata.new_data_and_metadata(numpy.zeros((4, 1000), numpy.float64), data_descriptor=DataAndMetadata.DataDescriptor(False, 0, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(data_item)
display_item.calibration_style_id = "pixels-top-left"
p, v = display_item.get_value_and_position_text(display_item.display_data_channel, (500,))
self.assertEqual(p, "500.0, 0.0")
self.assertEqual(v, "0")
def test_cursor_over_1d_sequence_data_displays_without_exception(self):
with TestContext.create_memory_context() as test_context:
document_model = test_context.create_document_model()
data_and_metadata = DataAndMetadata.new_data_and_metadata(numpy.zeros((4, 1000), numpy.float64), data_descriptor=DataAndMetadata.DataDescriptor(True, 0, 1))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(data_item)
display_item.calibration_style_id = "pixels-top-left"
p, v = display_item.get_value_and_position_text(display_item.display_data_channel, (500,))
self.assertEqual(p, "500.0, 0.0")
self.assertEqual(v, "0")
def __create_spectrum(self) -> DataItem.DataItem:
data = numpy.random.uniform(10, 1000, 1024).astype(numpy.float32)
intensity_calibration = Calibration.Calibration(units="~")
dimensional_calibrations = [
Calibration.Calibration(scale=2.0, units="eV")
]
data_descriptor = DataAndMetadata.DataDescriptor(
is_sequence=False,
collection_dimension_count=0,
datum_dimension_count=1)
xdata = DataAndMetadata.new_data_and_metadata(
data,
intensity_calibration=intensity_calibration,
dimensional_calibrations=dimensional_calibrations,
data_descriptor=data_descriptor)
return DataItem.new_data_item(xdata)
def test_display_data_is_2d_for_collection_of_1d_datum(self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((2, 8), numpy.float64),
data_descriptor=DataAndMetadata.DataDescriptor(False, 1, 1))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_shape, (2, 8))
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_dtype, numpy.float64)
self.assertEqual(display_data_channel.display_data_shape, (2, 8))
def test_create_rgba_sequence_should_work(self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
data = (numpy.random.rand(4, 64, 64, 3) * 255).astype(numpy.uint8)
data_item = DataItem.new_data_item(
DataAndMetadata.new_data_and_metadata(
data,
data_descriptor=DataAndMetadata.DataDescriptor(True, 0,
2)))
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_dtype, numpy.uint8)
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_shape[-1], 3)
def test_display_data_is_2d_for_collection_of_1d_datum(self):
document_model = DocumentModel.DocumentModel()
document_controller = DocumentController.DocumentController(
self.app.ui, document_model, workspace_id="library")
with contextlib.closing(document_controller):
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((2, 8), numpy.float64),
data_descriptor=DataAndMetadata.DataDescriptor(False, 1, 1))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_shape, (2, 8))
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_dtype, numpy.float64)
self.assertEqual(display_data_channel.display_data_shape, (2, 8))
def test_create_rgba_sequence_should_work(self):
document_model = DocumentModel.DocumentModel()
document_controller = DocumentController.DocumentController(
self.app.ui, document_model, workspace_id="library")
with contextlib.closing(document_controller):
data = (numpy.random.rand(4, 64, 64, 3) * 255).astype(numpy.uint8)
data_item = DataItem.new_data_item(
DataAndMetadata.new_data_and_metadata(
data,
data_descriptor=DataAndMetadata.DataDescriptor(True, 0,
2)))
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_dtype, numpy.uint8)
self.assertEqual(
display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data_shape[-1], 3)
async def map_new_edge(document_controller, model_data_item, edge) -> None:
document_model = document_controller.document_model
map_data_item = DataItem.new_data_item()
map_data_item.title = "{} of {}".format(_("Map"), str(edge.electron_shell))
map_data_item.category = model_data_item.category
map_data_item.source = model_data_item
document_model.append_data_item(map_data_item)
computation = document_model.create_computation()
computation.source = map_data_item
computation.create_object("spectrum_image_xdata", document_model.get_object_specifier(model_data_item, "xdata"))
computation.create_input("fit_interval", document_model.get_object_specifier(edge.data_structure), "fit_interval")
computation.create_input("signal_interval", document_model.get_object_specifier(edge.data_structure), "signal_interval")
computation.processing_id = "eels.mapping"
computation.create_result("map", document_model.get_object_specifier(map_data_item))
document_model.append_computation(computation)
await document_model.compute_immediate(document_controller.event_loop, computation)
map_display_item = document_model.get_display_item_for_data_item(map_data_item)
document_controller.show_display_item(map_display_item)
def test_sequence_index_validates_when_data_changes(self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
d = numpy.random.randn(4, 3, 3)
data_and_metadata = DataAndMetadata.new_data_and_metadata(
d, data_descriptor=DataAndMetadata.DataDescriptor(True, 0, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_data_channel = display_item.display_data_channels[0]
display_data_channel.sequence_index = 3
display_data = display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data
self.assertTrue(numpy.array_equal(display_data, d[3, ...]))
d2 = numpy.random.randn(2, 3, 3)
data_and_metadata2 = DataAndMetadata.new_data_and_metadata(
d2, data_descriptor=DataAndMetadata.DataDescriptor(True, 0, 2))
display_item.data_item.set_xdata(data_and_metadata2)
display_data2 = display_data_channel.get_calculated_display_values(
True).display_data_and_metadata.data
self.assertTrue(numpy.array_equal(display_data2, d2[1, ...]))
def test_mapping_widget_to_image_on_2d_data_stack_uses_signal_dimensions(
self):
with TestContext.create_memory_context() as test_context:
document_controller = test_context.create_document_controller()
document_model = document_controller.document_model
display_panel = document_controller.selected_display_panel
document_controller.selected_display_panel.change_display_panel_content(
{"type": "image"})
data_and_metadata = DataAndMetadata.new_data_and_metadata(
numpy.ones((50, 10, 10)),
data_descriptor=DataAndMetadata.DataDescriptor(True, 0, 2))
data_item = DataItem.new_data_item(data_and_metadata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
display_panel.set_display_panel_display_item(display_item)
header_height = display_panel.header_canvas_item.header_height
display_panel.root_container.layout_immediate(
(100 + header_height, 100))
# run test
document_controller.tool_mode = "line-profile"
display_panel.display_canvas_item.simulate_drag((20, 25), (65, 85))
self.assertEqual(display_item.graphics[0].vector,
((0.2, 0.25), (0.65, 0.85)))
def test_calibrate_spectrum_for_single_spectrum(self):
with TestContext.create_memory_context() as profile_context:
document_controller = profile_context.create_document_controller_with_application(
)
document_model = document_controller.document_model
data = numpy.zeros((100, ), dtype=numpy.float32)
data[10] = 1
intensity_calibration = Calibration.Calibration(units="~")
dimensional_calibrations = [
Calibration.Calibration(scale=1.0, units="eV")
]
data_descriptor = DataAndMetadata.DataDescriptor(
is_sequence=False,
collection_dimension_count=0,
datum_dimension_count=1)
xdata = DataAndMetadata.new_data_and_metadata(
data,
intensity_calibration=intensity_calibration,
dimensional_calibrations=dimensional_calibrations,
data_descriptor=data_descriptor)
data_item = DataItem.new_data_item(xdata)
document_model.append_data_item(data_item)
display_item = document_model.get_display_item_for_data_item(
data_item)
document_controller.select_display_items_in_data_panel(
[display_item])
document_controller.data_panel_focused()
api = Facade.get_api("~1.0", "~1.0")
dialog = AlignZLP.calibrate_spectrum(
api, api.application.document_windows[0])
self.assertEqual(1, len(document_model.data_items))
self.assertEqual(1, len(document_model.display_items))
self.assertEqual(1, len(api.library.data_items))
# calibrate_spectrum should create two graphics, offset and scale
self.assertEqual(2, len(api.library.data_items[0].graphics))
# It should find the peak at 10 and set the calibration offset accordingly
self.assertAlmostEqual(
data_item.dimensional_calibrations[0].offset, -10.5)
# Move the scale graphic and check that the calibration changed accordingly
offset_graphic = api.library.data_items[0].graphics[0]
scale_graphic = api.library.data_items[0].graphics[1]
position_diff = scale_graphic.position - offset_graphic.position
scale_graphic.position = offset_graphic.position + 0.5 * position_diff
self.assertAlmostEqual(data_item.dimensional_calibrations[0].scale,
2.0)
# Move the offset graphic and check that the calibration offset and the scale graphic have moved
position_diff = scale_graphic.position - offset_graphic.position
offset_graphic.position += 0.1
self.assertAlmostEqual(
position_diff,
scale_graphic.position - offset_graphic.position)
self.assertAlmostEqual(
data_item.dimensional_calibrations[0].offset, -41)
# Closing the dialog should remove the graphics
dialog.request_close()
self.assertEqual(0, len(api.library.data_items[0].graphics))
# Test cleanup
document_model.remove_data_item(data_item)
self.assertEqual(0, len(document_model.data_items))
self.assertEqual(0, len(document_model.display_items))
self.assertEqual(0, len(document_model.data_structures))
def __create_spectrum_image(self) -> DataItem.DataItem:
return DataItem.new_data_item(self.__create_spectrum_image_xdata())