def test_artists_normalization_state_labeled_correctly_for_non_dist_workspace_with_uneven_spectra(self):
non_dist_ws = CreateWorkspace(DataX=[10, 20, 25, 30],
DataY=[2, 3, 4, 5],
DataE=[1, 2, 1, 2],
NSpec=1,
Distribution=False,
OutputWorkspace='non_dist_workpace')
self.ax.plot(non_dist_ws, specNum=1)
config['graph1d.autodistribution'] = 'Off'
self.assertTrue(self.ax.tracked_workspaces[non_dist_ws.name()][0].is_normalized)
del self.ax.tracked_workspaces[non_dist_ws.name()]
def test_artists_normalization_labeled_correctly_for_2d_plots_of_non_dist_workspace_and_dist_argument_true(self):
plot_funcs = ['imshow', 'pcolor', 'pcolormesh', 'pcolorfast', 'tripcolor',
'contour', 'contourf', 'tricontour', 'tricontourf']
non_dist_2d_ws = CreateWorkspace(DataX=[10, 20, 10, 20],
DataY=[2, 3, 2, 3],
DataE=[1, 2, 1, 2],
NSpec=2,
Distribution=False,
OutputWorkspace='non_dist_workpace')
for plot_func in plot_funcs:
func = getattr(self.ax, plot_func)
func(non_dist_2d_ws, distribution=True)
self.assertFalse(self.ax.tracked_workspaces[non_dist_2d_ws.name()][0].is_normalized)
del self.ax.tracked_workspaces[non_dist_2d_ws.name()]
def test_artists_normalization_state_labeled_correctly_for_2d_plots_of_non_dist_workspace_with_uneven_spectra(self):
plot_funcs = ['imshow', 'pcolor', 'pcolormesh', 'pcolorfast', 'tripcolor',
'contour', 'contourf', 'tricontour', 'tricontourf']
non_dist_2d_ws = CreateWorkspace(DataX=[10, 20, 25, 30, 10, 20, 25, 30],
DataY=[2, 3, 4, 5, 2, 3, 4, 5],
DataE=[1, 2, 1, 2, 1, 2, 1, 2],
NSpec=2,
Distribution=False,
OutputWorkspace='non_dist_workpace')
for plot_func in plot_funcs:
func = getattr(self.ax, plot_func)
func(non_dist_2d_ws)
config['graph1d.autodistribution'] = 'Off'
self.assertTrue(self.ax.tracked_workspaces[non_dist_2d_ws.name()][0].is_normalized)
del self.ax.tracked_workspaces[non_dist_2d_ws.name()]
def test_artists_normalization_labeled_correctly_for_2d_plots_of_non_dist_workspace_and_global_setting_on(self):
plot_funcs = ['imshow', 'pcolor', 'pcolormesh', 'pcolorfast', 'tripcolor',
'contour', 'contourf', 'tricontour', 'tricontourf']
non_dist_2d_ws = CreateWorkspace(DataX=[10, 20, 10, 20],
DataY=[2, 3, 2, 3],
DataE=[1, 2, 1, 2],
NSpec=2,
Distribution=False,
OutputWorkspace='non_dist_workpace')
for plot_func in plot_funcs:
func = getattr(self.ax, plot_func)
func(non_dist_2d_ws)
auto_dist = (config['graph1d.autodistribution'] == 'On')
self.assertEqual(auto_dist, self.ax.tracked_workspaces[non_dist_2d_ws.name()][0].is_normalized)
del self.ax.tracked_workspaces[non_dist_2d_ws.name()]
def test_do_simultaneous_fit_adds_multi_input_workspace_to_fit_context(
self):
# create function
single_func = ';name=FlatBackground,$domains=i,A0=0'
multi_func = 'composite=MultiDomainFunction,NumDeriv=1' + single_func + single_func + ";"
trial_function = FunctionFactory.createInitialized(multi_func)
x_data = range(0, 100)
y_data = [5 + x * x for x in x_data]
workspace1 = CreateWorkspace(x_data, y_data)
workspace2 = CreateWorkspace(x_data, y_data)
parameter_dict = {
'Function': trial_function,
'InputWorkspace': [workspace1.name(),
workspace2.name()],
'Minimizer': 'Levenberg-Marquardt',
'StartX': [0.0] * 2,
'EndX': [100.0] * 2,
'EvaluationType': 'CentrePoint'
}
self.model.do_simultaneous_fit(parameter_dict, global_parameters=[])
fit_context = self.model.context.fitting_context
self.assertEqual(1, len(fit_context))
def test_artists_normalization_state_labeled_correctly_for_dist_workspace(self):
dist_ws = CreateWorkspace(DataX=[10, 20],
DataY=[2, 3],
DataE=[1, 2],
NSpec=1,
Distribution=True,
OutputWorkspace='dist_workpace')
self.ax.plot(dist_ws, specNum=1, distribution=False)
self.ax.plot(dist_ws, specNum=1, distribution=True)
self.ax.plot(dist_ws, specNum=1)
ws_artists = self.ax.tracked_workspaces[dist_ws.name()]
self.assertTrue(ws_artists[0].is_normalized)
self.assertTrue(ws_artists[1].is_normalized)
self.assertTrue(ws_artists[2].is_normalized)
def test_plot_is_not_normalized_for_bin_plots(self):
workspace = CreateWorkspace(DataX=[10, 20],
DataY=[2, 3, 4, 5, 6],
DataE=[1, 2, 1, 2, 1],
NSpec=5,
Distribution=False,
OutputWorkspace='workspace')
self.ax.plot(workspace, specNum=1, axis=MantidAxType.BIN, distribution=False)
self.ax.plot(workspace, specNum=1, axis=MantidAxType.BIN, distribution=True)
self.ax.plot(workspace, specNum=1, axis=MantidAxType.BIN)
ws_artists = self.ax.tracked_workspaces[workspace.name()]
self.assertFalse(ws_artists[0].is_normalized)
self.assertFalse(ws_artists[1].is_normalized)
self.assertFalse(ws_artists[2].is_normalized)
def test_artists_normalization_state_labeled_correctly_for_non_dist_workspace(
self):
non_dist_ws = CreateWorkspace(DataX=[10, 20],
DataY=[2, 3],
DataE=[1, 2],
NSpec=1,
Distribution=False,
OutputWorkspace='non_dist_workpace')
self.ax.plot(non_dist_ws, specNum=1, distribution=False)
self.assertTrue(
self.ax.tracked_workspaces[non_dist_ws.name()][0].is_normalized)
del self.ax.tracked_workspaces[non_dist_ws.name()]
self.ax.errorbar(non_dist_ws, specNum=1, distribution=True)
self.assertFalse(
self.ax.tracked_workspaces[non_dist_ws.name()][0].is_normalized)
del self.ax.tracked_workspaces[non_dist_ws.name()]
auto_dist = (config['graph1d.autodistribution'] == 'On')
self.ax.plot(non_dist_ws, specNum=1)
self.assertEqual(
auto_dist,
self.ax.tracked_workspaces[non_dist_ws.name()][0].is_normalized)
del self.ax.tracked_workspaces[non_dist_ws.name()]
def test_do_simultaneous_fit_adds_single_input_workspace_to_fit_context_with_globals(self):
trial_function = FunctionFactory.createInitialized('name = Quadratic, A0 = 0, A1 = 0, A2 = 0')
x_data = range(0, 100)
y_data = [5 + x * x for x in x_data]
workspace = CreateWorkspace(x_data, y_data)
parameter_dict = {'Function': trial_function, 'InputWorkspace': [workspace.name()],
'Minimizer': 'Levenberg-Marquardt',
'StartX': [0.0], 'EndX': [100.0], 'EvaluationType': 'CentrePoint',
'FitGroupName': 'SimulFit'}
global_parameters = ['A0']
self.model.do_simultaneous_fit(parameter_dict, global_parameters)
fit_context = self.model.context.fitting_context
self.assertEqual(1, len(fit_context))
self.assertEqual(global_parameters, fit_context.fit_list[0].parameters.global_parameters)
def setUpClass(cls):
cls.ws_widget = WorkspaceWidget(QMainWindow())
mat_ws = CreateSampleWorkspace()
table_ws = CreateEmptyTableWorkspace()
group_ws = GroupWorkspaces([mat_ws, table_ws])
single_val_ws = CreateSingleValuedWorkspace(5, 6)
# Create ragged workspace
ws2d_ragged = CreateWorkspace(DataX=[10, 20, 30],
DataY=[1, 2, 3],
NSpec=1,
OutputWorkspace='Ragged')
temp = CreateWorkspace(DataX=[15, 25, 35, 45],
DataY=[1, 2, 3, 4],
NSpec=1)
ConjoinWorkspaces(ws2d_ragged, temp, CheckOverlapping=False)
ws2d_ragged = AnalysisDataService.retrieve('Ragged')
cls.w_spaces = [mat_ws, table_ws, group_ws, single_val_ws]
cls.ws_names = ['MatWS', 'TableWS', 'GroupWS', 'SingleValWS']
# create md workspace
md_ws = CreateMDHistoWorkspace(SignalInput='1,2,3,4,2,1',
ErrorInput='1,1,1,1,1,1',
Dimensionality=3,
Extents='-1,1,-1,1,0.5,6.5',
NumberOfBins='1,1,6',
Names='x,y,|Q|',
Units='mm,km,AA^-1',
OutputWorkspace='MDHistoWS1D')
# self.w_spaces = [mat_ws, table_ws, group_ws, single_val_ws, md_ws]
# self.ws_names = ['MatWS', 'TableWS', 'GroupWS', 'SingleValWS', 'MDHistoWS1D']
for ws_name, ws in zip(cls.ws_names, cls.w_spaces):
cls.ws_widget._ads.add(ws_name, ws)
cls.ws_names.append(md_ws.name())
cls.w_spaces.append(md_ws)
cls.ws_names.append(ws2d_ragged.name())
cls.w_spaces.append(ws2d_ragged)
