class RunTabPresenter(object):
class ConcreteRunTabListener(SANSDataProcessorGui.RunTabListener):
def __init__(self, presenter):
super(RunTabPresenter.ConcreteRunTabListener, self).__init__()
self._presenter = presenter
def on_user_file_load(self):
self._presenter.on_user_file_load()
def on_mask_file_add(self):
self._presenter.on_mask_file_add()
def on_batch_file_load(self):
self._presenter.on_batch_file_load()
def on_processed_clicked(self):
self._presenter.on_processed_clicked()
def on_multi_period_selection(self, show_periods):
self._presenter.on_multiperiod_changed(show_periods)
def on_data_changed(self, row, column, new_value, old_value):
self._presenter.on_data_changed(row, column, new_value, old_value)
def on_manage_directories(self):
self._presenter.on_manage_directories()
def on_instrument_changed(self):
self._presenter.on_instrument_changed()
def on_row_inserted(self, index, row):
self._presenter.on_row_inserted(index, row)
def on_rows_removed(self, rows):
self._presenter.on_rows_removed(rows)
def on_copy_rows_requested(self):
self._presenter.on_copy_rows_requested()
def on_paste_rows_requested(self):
self._presenter.on_paste_rows_requested()
def on_insert_row(self):
self._presenter.on_insert_row()
def on_erase_rows(self):
self._presenter.on_erase_rows()
def on_cut_rows(self):
self._presenter.on_cut_rows_requested()
class ProcessListener(WorkHandler.WorkListener):
def __init__(self, presenter):
super(RunTabPresenter.ProcessListener, self).__init__()
self._presenter = presenter
def on_processing_finished(self, result):
self._presenter.on_processing_finished(result)
def on_processing_error(self, error):
self._presenter.on_processing_error(error)
def __init__(self, facility, view=None):
super(RunTabPresenter, self).__init__()
self._facility = facility
# Logger
self.sans_logger = Logger("SANS")
# Name of grpah to output to
self.output_graph = 'SANS-Latest'
self.progress = 0
# Models that are being used by the presenter
self._state_model = None
self._table_model = TableModel()
# Presenter needs to have a handle on the view since it delegates it
self._view = None
self.set_view(view)
self._processing = False
self.work_handler = WorkHandler()
self.batch_process_runner = BatchProcessRunner(
self.notify_progress, self.on_processing_finished,
self.on_processing_error)
# File information for the first input
self._file_information = None
self._clipboard = []
# Settings diagnostic tab presenter
self._settings_diagnostic_tab_presenter = SettingsDiagnosticPresenter(
self)
# Masking table presenter
self._masking_table_presenter = MaskingTablePresenter(self)
# Beam centre presenter
self._beam_centre_presenter = BeamCentrePresenter(
self, WorkHandler, BeamCentreModel, SANSCentreFinder)
# Workspace Diagnostic page presenter
self._workspace_diagnostic_presenter = DiagnosticsPagePresenter(
self, WorkHandler, run_integral, create_state, self._facility)
def _default_gui_setup(self):
"""
Provides a default setup of the GUI. This is important for the initial start up, when the view is being set.
"""
# Set the possible reduction modes
reduction_mode_list = get_reduction_mode_strings_for_gui()
self._view.set_reduction_modes(reduction_mode_list)
# Set the possible instruments
instrument_list = get_instrument_strings_for_gui()
self._view.set_instruments(instrument_list)
# Set the step type options for wavelength
range_step_types = [
RangeStepType.to_string(RangeStepType.Lin),
RangeStepType.to_string(RangeStepType.Log),
RangeStepType.to_string(RangeStepType.RangeLog),
RangeStepType.to_string(RangeStepType.RangeLin)
]
self._view.wavelength_step_type = range_step_types
# Set the geometry options. This needs to include the option to read the sample shape from file.
sample_shape = [
"Read from file", SampleShape.Cylinder, SampleShape.FlatPlate,
SampleShape.Disc
]
self._view.sample_shape = sample_shape
# Set the q range
self._view.q_1d_step_type = [
RangeStepType.to_string(RangeStepType.Lin),
RangeStepType.to_string(RangeStepType.Log)
]
self._view.q_xy_step_type = [
RangeStepType.to_string(RangeStepType.Lin)
]
# Set the fit options
fit_types = [
FitType.to_string(FitType.Linear),
FitType.to_string(FitType.Logarithmic),
FitType.to_string(FitType.Polynomial)
]
self._view.transmission_sample_fit_type = fit_types
self._view.transmission_can_fit_type = fit_types
# ------------------------------------------------------------------------------------------------------------------
# Table + Actions
# ------------------------------------------------------------------------------------------------------------------
def set_view(self, view):
"""
Sets the view
:param view: the view is the SANSDataProcessorGui. The presenter needs to access some of the API
"""
if view is not None:
self._view = view
# Add a listener to the view
listener = RunTabPresenter.ConcreteRunTabListener(self)
self._view.add_listener(listener)
# Default gui setup
self._default_gui_setup()
# Set appropriate view for the state diagnostic tab presenter
self._settings_diagnostic_tab_presenter.set_view(
self._view.settings_diagnostic_tab)
# Set appropriate view for the masking table presenter
self._masking_table_presenter.set_view(self._view.masking_table)
# Set the appropriate view for the beam centre presenter
self._beam_centre_presenter.set_view(self._view.beam_centre)
# Set the appropriate view for the diagnostic page
self._workspace_diagnostic_presenter.set_view(
self._view.diagnostic_page, self._view.instrument)
self._view.setup_layout()
self._view.set_hinting_line_edit_for_column(
15, self._table_model.get_options_hint_strategy())
def on_user_file_load(self):
"""
Loads the user file. Populates the models and the view.
"""
try:
# 1. Get the user file path from the view
user_file_path = self._view.get_user_file_path()
if not user_file_path:
return
# 2. Get the full file path
user_file_path = FileFinder.getFullPath(user_file_path)
if not os.path.exists(user_file_path):
raise RuntimeError(
"The user path {} does not exist. Make sure a valid user file path"
" has been specified.".format(user_file_path))
self._table_model.user_file = user_file_path
# Clear out the current view
self._view.reset_all_fields_to_default()
# 3. Read and parse the user file
user_file_reader = UserFileReader(user_file_path)
user_file_items = user_file_reader.read_user_file()
# 4. Populate the model
self._state_model = StateGuiModel(user_file_items)
# 5. Update the views.
self._update_view_from_state_model()
self._beam_centre_presenter.update_centre_positions(
self._state_model)
self._beam_centre_presenter.on_update_rows()
self._masking_table_presenter.on_update_rows()
self._workspace_diagnostic_presenter.on_user_file_load(
user_file_path)
except Exception as e:
self.sans_logger.error(
"Loading of the user file failed. {}".format(str(e)))
self.display_warning_box('Warning',
'Loading of the user file failed.',
str(e))
def on_batch_file_load(self):
"""
Loads a batch file and populates the batch table based on that.
"""
try:
# 1. Get the batch file from the view
batch_file_path = self._view.get_batch_file_path()
if not batch_file_path:
return
if not os.path.exists(batch_file_path):
raise RuntimeError(
"The batch file path {} does not exist. Make sure a valid batch file path"
" has been specified.".format(batch_file_path))
self._table_model.batch_file = batch_file_path
# 2. Read the batch file
batch_file_parser = BatchCsvParser(batch_file_path)
parsed_rows = batch_file_parser.parse_batch_file()
# 3. Populate the table
self._table_model.clear_table_entries()
for index, row in enumerate(parsed_rows):
self._add_row_to_table_model(row, index)
self._table_model.remove_table_entries([len(parsed_rows)])
self.update_view_from_table_model()
self._beam_centre_presenter.on_update_rows()
self._masking_table_presenter.on_update_rows()
except RuntimeError as e:
self.sans_logger.error(
"Loading of the batch file failed. {}".format(str(e)))
self.display_warning_box('Warning',
'Loading of the batch file failed',
str(e))
def _add_row_to_table_model(self, row, index):
"""
Adds a row to the table
"""
def get_string_entry(_tag, _row):
_element = ""
if _tag in _row:
_element = _row[_tag]
return _element
def get_string_period(_tag):
return "" if _tag == ALL_PERIODS else str(_tag)
# 1. Pull out the entries
sample_scatter = get_string_entry(BatchReductionEntry.SampleScatter,
row)
sample_scatter_period = get_string_period(
get_string_entry(BatchReductionEntry.SampleScatterPeriod, row))
sample_transmission = get_string_entry(
BatchReductionEntry.SampleTransmission, row)
sample_transmission_period = \
get_string_period(get_string_entry(BatchReductionEntry.SampleTransmissionPeriod, row))
sample_direct = get_string_entry(BatchReductionEntry.SampleDirect, row)
sample_direct_period = get_string_period(
get_string_entry(BatchReductionEntry.SampleDirectPeriod, row))
can_scatter = get_string_entry(BatchReductionEntry.CanScatter, row)
can_scatter_period = get_string_period(
get_string_entry(BatchReductionEntry.CanScatterPeriod, row))
can_transmission = get_string_entry(
BatchReductionEntry.CanTransmission, row)
can_transmission_period = get_string_period(
get_string_entry(BatchReductionEntry.CanScatterPeriod, row))
can_direct = get_string_entry(BatchReductionEntry.CanDirect, row)
can_direct_period = get_string_period(
get_string_entry(BatchReductionEntry.CanDirectPeriod, row))
output_name = get_string_entry(BatchReductionEntry.Output, row)
file_information_factory = SANSFileInformationFactory()
file_information = file_information_factory.create_sans_file_information(
sample_scatter)
sample_thickness = file_information._thickness
user_file = get_string_entry(BatchReductionEntry.UserFile, row)
row_entry = [
sample_scatter, sample_scatter_period, sample_transmission,
sample_transmission_period, sample_direct, sample_direct_period,
can_scatter, can_scatter_period, can_transmission,
can_transmission_period, can_direct, can_direct_period,
output_name, user_file, sample_thickness, ''
]
table_index_model = TableIndexModel(*row_entry)
self._table_model.add_table_entry(index, table_index_model)
def update_view_from_table_model(self):
self._view.clear_table()
self._view.hide_period_columns()
for row_index, row in enumerate(self._table_model._table_entries):
row_entry = [str(x) for x in row.to_list()]
self._view.add_row(row_entry)
self._view.change_row_color(
row_state_to_colour_mapping[row.row_state], row_index + 1)
self._view.set_row_tooltip(row.tool_tip, row_index + 1)
if row.isMultiPeriod():
self._view.show_period_columns()
self._view.remove_rows([0])
self._view.clear_selection()
def on_data_changed(self, row, column, new_value, old_value):
self._table_model.update_table_entry(row, column, new_value)
self._view.change_row_color(
row_state_to_colour_mapping[RowState.Unprocessed], row)
self._view.set_row_tooltip('', row)
self._beam_centre_presenter.on_update_rows()
self._masking_table_presenter.on_update_rows()
def on_instrument_changed(self):
self._setup_instrument_specific_settings()
def on_processed_clicked(self):
"""
Prepares the batch reduction.
0. Validate rows and create dummy workspace if it does not exist
1. Sets up the states
2. Adds a dummy input workspace
3. Adds row index information
"""
try:
self._view.disable_buttons()
self._processing = True
self.sans_logger.information("Starting processing of batch table.")
# 1. Set up the states and convert them into property managers
selected_rows = self._view.get_selected_rows()
selected_rows = selected_rows if selected_rows else range(
self._table_model.get_number_of_rows())
for row in selected_rows:
self._table_model.reset_row_state(row)
self.update_view_from_table_model()
states, errors = self.get_states(row_index=selected_rows)
for row, error in errors.items():
self.on_processing_error(row, error)
if not states:
self.on_processing_finished(None)
return
# 4. Create the graph if continuous output is specified
if mantidplot:
if self._view.plot_results and not mantidplot.graph(
self.output_graph):
mantidplot.newGraph(self.output_graph)
# Check if optimizations should be used
use_optimizations = self._view.use_optimizations
# Get the output mode
output_mode = self._view.output_mode
# Check if results should be plotted
plot_results = self._view.plot_results
# Get the name of the graph to output to
output_graph = self.output_graph
self.progress = 0
setattr(self._view, 'progress_bar_value', self.progress)
setattr(self._view, 'progress_bar_maximum', len(states))
self.batch_process_runner.process_states(states, use_optimizations,
output_mode, plot_results,
output_graph)
except Exception as e:
self._view.enable_buttons()
self.sans_logger.error("Process halted due to: {}".format(str(e)))
self.display_warning_box('Warning', 'Process halted', str(e))
def on_multiperiod_changed(self, show_periods):
if show_periods:
self._view.show_period_columns()
else:
self._view.hide_period_columns()
def display_warning_box(self, title, text, detailed_text):
self._view.display_message_box(title, text, detailed_text)
def notify_progress(self, row):
self.increment_progress()
message = ''
self._table_model.set_row_to_processed(row, message)
self.update_view_from_table_model()
def on_processing_finished(self, result):
self._view.enable_buttons()
self._processing = False
def on_processing_error(self, row, error_msg):
self.increment_progress()
self._table_model.set_row_to_error(row, error_msg)
self.update_view_from_table_model()
def increment_progress(self):
self.progress = self.progress + 1
setattr(self._view, 'progress_bar_value', self.progress)
def on_row_inserted(self, index, row):
row_table_index = TableIndexModel(*row)
self._table_model.add_table_entry(index, row_table_index)
def on_insert_row(self):
selected_rows = self._view.get_selected_rows()
selected_row = selected_rows[
0] + 1 if selected_rows else self._table_model.get_number_of_rows(
)
table_entry_row = self._table_model.create_empty_row()
self._table_model.add_table_entry(selected_row, table_entry_row)
self.update_view_from_table_model()
def on_erase_rows(self):
selected_rows = self._view.get_selected_rows()
empty_row = TableModel.create_empty_row()
for row in selected_rows:
self._table_model.replace_table_entries([row], [empty_row])
self.update_view_from_table_model()
def on_rows_removed(self, rows):
self._table_model.remove_table_entries(rows)
self.update_view_from_table_model()
def on_copy_rows_requested(self):
selected_rows = self._view.get_selected_rows()
self._clipboard = []
for row in selected_rows:
data_from_table_model = self._table_model.get_table_entry(
row).to_list()
self._clipboard.append(data_from_table_model)
def on_cut_rows_requested(self):
self.on_copy_rows_requested()
rows = self._view.get_selected_rows()
self.on_rows_removed(rows)
def on_paste_rows_requested(self):
if self._clipboard:
selected_rows = self._view.get_selected_rows()
selected_rows = selected_rows if selected_rows else [
self._table_model.get_number_of_rows()
]
replacement_table_index_models = [
TableIndexModel(*x) for x in self._clipboard
]
self._table_model.replace_table_entries(
selected_rows, replacement_table_index_models)
self.update_view_from_table_model()
def on_manage_directories(self):
self._view.show_directory_manager()
def get_row_indices(self):
"""
Gets the indices of row which are not empty.
:return: a list of row indices.
"""
row_indices_which_are_not_empty = []
number_of_rows = self._table_model.get_number_of_rows()
for row in range(number_of_rows):
if not self.is_empty_row(row):
row_indices_which_are_not_empty.append(row)
return row_indices_which_are_not_empty
def on_mask_file_add(self):
"""
We get the added mask file name and add it to the list of masks
"""
new_mask_file = self._view.get_mask_file()
if not new_mask_file:
return
new_mask_file_full_path = FileFinder.getFullPath(new_mask_file)
if not new_mask_file_full_path:
return
# Add the new mask file to state model
mask_files = self._state_model.mask_files
mask_files.append(new_mask_file)
self._state_model.mask_files = mask_files
# Make sure that the sub-presenters are up to date with this change
self._masking_table_presenter.on_update_rows()
self._settings_diagnostic_tab_presenter.on_update_rows()
self._beam_centre_presenter.on_update_rows()
def is_empty_row(self, row):
"""
Checks if a row has no entries. These rows will be ignored.
:param row: the row index
:return: True if the row is empty.
"""
return self._table_model.is_empty_row(row)
# def _validate_rows(self):
# """
# Validation of the rows. A minimal setup requires that ScatterSample is set.
# """
# # If SampleScatter is empty, then don't run the reduction.
# # We allow empty rows for now, since we cannot remove them from Python.
# number_of_rows = self._table_model.get_number_of_rows()
# for row in range(number_of_rows):
# if not self.is_empty_row(row):
# sample_scatter = self._view.get_cell(row, 0)
# if not sample_scatter:
# raise RuntimeError("Row {} has not SampleScatter specified. Please correct this.".format(row))
# ------------------------------------------------------------------------------------------------------------------
# Controls
# ------------------------------------------------------------------------------------------------------------------
def disable_controls(self):
"""
Disable all input fields and buttons during the execution of the reduction.
"""
# TODO: think about enabling and disable some controls during reduction
pass
def enable_controls(self):
"""
Enable all input fields and buttons after the execution has completed.
"""
# TODO: think about enabling and disable some controls during reduction
pass
# ------------------------------------------------------------------------------------------------------------------
# Table Model and state population
# ------------------------------------------------------------------------------------------------------------------
def get_states(self, row_index=None, file_lookup=True):
"""
Gathers the state information for all rows.
:param row_index: if a single row is selected, then only this row is returned, else all the state for all
rows is returned
:return: a list of states
"""
start_time_state_generation = time.time()
# 1. Update the state model
state_model_with_view_update = self._get_state_model_with_view_update()
# 2. Update the table model
table_model = self._table_model
# 3. Go through each row and construct a state object
if table_model and state_model_with_view_update:
states, errors = create_states(state_model_with_view_update,
table_model,
self._view.instrument,
self._facility,
row_index=row_index,
file_lookup=file_lookup)
else:
states = None
errors = None
stop_time_state_generation = time.time()
time_taken = stop_time_state_generation - start_time_state_generation
self.sans_logger.information(
"The generation of all states took {}s".format(time_taken))
return states, errors
def get_state_for_row(self, row_index, file_lookup=True):
"""
Creates the state for a particular row.
:param row_index: the row index
:return: a state if the index is valid and there is a state else None
"""
states, errors = self.get_states(row_index=[row_index],
file_lookup=file_lookup)
if states is None:
self.sans_logger.warning(
"There does not seem to be data for a row {}.".format(
row_index))
return None
if row_index in list(states.keys()):
if states:
return states[row_index]
return None
def _update_view_from_state_model(self):
# Front tab view
self._set_on_view("zero_error_free")
self._set_on_view("save_types")
self._set_on_view("compatibility_mode")
self._set_on_view("merge_scale")
self._set_on_view("merge_shift")
self._set_on_view("merge_scale_fit")
self._set_on_view("merge_shift_fit")
self._set_on_view("merge_q_range_start")
self._set_on_view("merge_q_range_stop")
self._set_on_view("merge_max")
self._set_on_view("merge_min")
# Settings tab view
self._set_on_view("reduction_dimensionality")
self._set_on_view("reduction_mode")
self._set_on_view("event_slices")
self._set_on_view("event_binning")
self._set_on_view("merge_mask")
self._set_on_view("wavelength_step_type")
self._set_on_view("wavelength_min")
self._set_on_view("wavelength_max")
self._set_on_view("wavelength_step")
self._set_on_view("absolute_scale")
self._set_on_view("sample_shape")
self._set_on_view("sample_height")
self._set_on_view("sample_width")
self._set_on_view("sample_thickness")
self._set_on_view("z_offset")
# Adjustment tab
self._set_on_view("normalization_incident_monitor")
self._set_on_view("normalization_interpolate")
self._set_on_view("transmission_incident_monitor")
self._set_on_view("transmission_interpolate")
self._set_on_view("transmission_roi_files")
self._set_on_view("transmission_mask_files")
self._set_on_view("transmission_radius")
self._set_on_view("transmission_monitor")
self._set_on_view("transmission_mn_shift")
self._set_on_view("show_transmission")
self._set_on_view_transmission_fit()
self._set_on_view("pixel_adjustment_det_1")
self._set_on_view("pixel_adjustment_det_2")
self._set_on_view("wavelength_adjustment_det_1")
self._set_on_view("wavelength_adjustment_det_2")
# Q tab
self._set_on_view_q_rebin_string()
self._set_on_view("q_xy_max")
self._set_on_view("q_xy_step")
self._set_on_view("q_xy_step_type")
self._set_on_view("gravity_on_off")
self._set_on_view("gravity_extra_length")
self._set_on_view("use_q_resolution")
self._set_on_view_q_resolution_aperture()
self._set_on_view("q_resolution_delta_r")
self._set_on_view("q_resolution_collimation_length")
self._set_on_view("q_resolution_moderator_file")
self._set_on_view("r_cut")
self._set_on_view("w_cut")
# Mask
self._set_on_view("phi_limit_min")
self._set_on_view("phi_limit_max")
self._set_on_view("phi_limit_use_mirror")
self._set_on_view("radius_limit_min")
self._set_on_view("radius_limit_max")
def _set_on_view_transmission_fit_sample_settings(self):
# Set transmission_sample_use_fit
fit_type = self._state_model.transmission_sample_fit_type
use_fit = fit_type is not FitType.NoFit
self._view.transmission_sample_use_fit = use_fit
# Set the polynomial order for sample
polynomial_order = self._state_model.transmission_sample_polynomial_order if fit_type is FitType.Polynomial else 2 # noqa
self._view.transmission_sample_polynomial_order = polynomial_order
# Set the fit type for the sample
fit_type = fit_type if fit_type is not FitType.NoFit else FitType.Linear
self._view.transmission_sample_fit_type = fit_type
# Set the wavelength
wavelength_min = self._state_model.transmission_sample_wavelength_min
wavelength_max = self._state_model.transmission_sample_wavelength_max
if wavelength_min and wavelength_max:
self._view.transmission_sample_use_wavelength = True
self._view.transmission_sample_wavelength_min = wavelength_min
self._view.transmission_sample_wavelength_max = wavelength_max
def _set_on_view_transmission_fit(self):
# Steps for adding the transmission fit to the view
# 1. Check if individual settings exist. If so then set the view to separate, else set them to both
# 2. Apply the settings
separate_settings = self._state_model.has_transmission_fit_got_separate_settings_for_sample_and_can(
)
self._view.set_fit_selection(use_separate=separate_settings)
if separate_settings:
self._set_on_view_transmission_fit_sample_settings()
# Set transmission_sample_can_fit
fit_type_can = self._state_model.transmission_can_fit_type()
use_can_fit = fit_type_can is FitType.NoFit
self._view.transmission_can_use_fit = use_can_fit
# Set the polynomial order for can
polynomial_order_can = self._state_model.transmission_can_polynomial_order if fit_type_can is FitType.Polynomial else 2 # noqa
self._view.transmission_can_polynomial_order = polynomial_order_can
# Set the fit type for the can
fit_type_can = fit_type_can if fit_type_can is not FitType.NoFit else FitType.Linear
self.transmission_can_fit_type = fit_type_can
# Set the wavelength
wavelength_min = self._state_model.transmission_can_wavelength_min
wavelength_max = self._state_model.transmission_can_wavelength_max
if wavelength_min and wavelength_max:
self._view.transmission_can_use_wavelength = True
self._view.transmission_can_wavelength_min = wavelength_min
self._view.transmission_can_wavelength_max = wavelength_max
else:
self._set_on_view_transmission_fit_sample_settings()
def _set_on_view_q_resolution_aperture(self):
self._set_on_view("q_resolution_source_a")
self._set_on_view("q_resolution_sample_a")
self._set_on_view("q_resolution_source_h")
self._set_on_view("q_resolution_sample_h")
self._set_on_view("q_resolution_source_w")
self._set_on_view("q_resolution_sample_w")
# If we have h1, h2, w1, and w2 selected then we want to select the rectangular aperture.
is_rectangular = self._state_model.q_resolution_source_h and self._state_model.q_resolution_sample_h and \
self._state_model.q_resolution_source_w and self._state_model.q_resolution_sample_w # noqa
self._view.set_q_resolution_shape_to_rectangular(is_rectangular)
def _set_on_view_q_rebin_string(self):
"""
Maps the q_1d_rebin_string of the model to the q_1d_step and q_1d_step_type property of the view.
"""
rebin_string = self._state_model.q_1d_rebin_string
# Extract the min, max and step and step type from the rebin string
elements = rebin_string.split(",")
# If we have three elements then we want to set only the
if len(elements) == 3:
step_element = float(elements[1])
step = abs(step_element)
step_type = RangeStepType.Lin if step_element >= 0 else RangeStepType.Log
# Set on the view
self._view.q_1d_min_or_rebin_string = float(elements[0])
self._view.q_1d_max = float(elements[2])
self._view.q_1d_step = step
self._view.q_1d_step_type = step_type
else:
# Set the rebin string
self._view.q_1d_min_or_rebin_string = rebin_string
self._view.q_1d_step_type = self._view.VARIABLE
def _set_on_view(self, attribute_name):
attribute = getattr(self._state_model, attribute_name)
if attribute or isinstance(
attribute, bool
): # We need to be careful here. We don't want to set empty strings, or None, but we want to set boolean values. # noqa
setattr(self._view, attribute_name, attribute)
def _set_on_view_with_view(self, attribute_name, view):
attribute = getattr(self._state_model, attribute_name)
if attribute or isinstance(
attribute, bool
): # We need to be careful here. We don't want to set empty strings, or None, but we want to set boolean values. # noqa
setattr(view, attribute_name, attribute)
def _get_state_model_with_view_update(self):
"""
Goes through all sub presenters and update the state model based on the views.
Note that at the moment we have set up the view and the model such that the name of a property must be the same
in the view and the model. This can be easily changed, but it also provides a good cohesion.
"""
state_model = copy.deepcopy(self._state_model)
# If we don't have a state model then return None
if state_model is None:
return state_model
# Run tab view
self._set_on_state_model("zero_error_free", state_model)
self._set_on_state_model("save_types", state_model)
self._set_on_state_model("compatibility_mode", state_model)
self._set_on_state_model("merge_scale", state_model)
self._set_on_state_model("merge_shift", state_model)
self._set_on_state_model("merge_scale_fit", state_model)
self._set_on_state_model("merge_shift_fit", state_model)
self._set_on_state_model("merge_q_range_start", state_model)
self._set_on_state_model("merge_q_range_stop", state_model)
self._set_on_state_model("merge_mask", state_model)
self._set_on_state_model("merge_max", state_model)
self._set_on_state_model("merge_min", state_model)
# Settings tab
self._set_on_state_model("reduction_dimensionality", state_model)
self._set_on_state_model("reduction_mode", state_model)
self._set_on_state_model("event_slices", state_model)
self._set_on_state_model("event_binning", state_model)
self._set_on_state_model("wavelength_step_type", state_model)
self._set_on_state_model("wavelength_min", state_model)
self._set_on_state_model("wavelength_max", state_model)
self._set_on_state_model("wavelength_step", state_model)
self._set_on_state_model("wavelength_range", state_model)
self._set_on_state_model("absolute_scale", state_model)
self._set_on_state_model("sample_shape", state_model)
self._set_on_state_model("sample_height", state_model)
self._set_on_state_model("sample_width", state_model)
self._set_on_state_model("sample_thickness", state_model)
self._set_on_state_model("z_offset", state_model)
# Adjustment tab
self._set_on_state_model("normalization_incident_monitor", state_model)
self._set_on_state_model("normalization_interpolate", state_model)
self._set_on_state_model("transmission_incident_monitor", state_model)
self._set_on_state_model("transmission_interpolate", state_model)
self._set_on_state_model("transmission_roi_files", state_model)
self._set_on_state_model("transmission_mask_files", state_model)
self._set_on_state_model("transmission_radius", state_model)
self._set_on_state_model("transmission_monitor", state_model)
self._set_on_state_model("transmission_mn_shift", state_model)
self._set_on_state_model("show_transmission", state_model)
self._set_on_state_model_transmission_fit(state_model)
self._set_on_state_model("pixel_adjustment_det_1", state_model)
self._set_on_state_model("pixel_adjustment_det_2", state_model)
self._set_on_state_model("wavelength_adjustment_det_1", state_model)
self._set_on_state_model("wavelength_adjustment_det_2", state_model)
# Q tab
self._set_on_state_model_q_1d_rebin_string(state_model)
self._set_on_state_model("q_xy_max", state_model)
self._set_on_state_model("q_xy_step", state_model)
self._set_on_state_model("q_xy_step_type", state_model)
self._set_on_state_model("gravity_on_off", state_model)
self._set_on_state_model("gravity_extra_length", state_model)
self._set_on_state_model("use_q_resolution", state_model)
self._set_on_state_model("q_resolution_source_a", state_model)
self._set_on_state_model("q_resolution_sample_a", state_model)
self._set_on_state_model("q_resolution_source_h", state_model)
self._set_on_state_model("q_resolution_sample_h", state_model)
self._set_on_state_model("q_resolution_source_w", state_model)
self._set_on_state_model("q_resolution_sample_w", state_model)
self._set_on_state_model("q_resolution_delta_r", state_model)
self._set_on_state_model("q_resolution_collimation_length",
state_model)
self._set_on_state_model("q_resolution_moderator_file", state_model)
self._set_on_state_model("r_cut", state_model)
self._set_on_state_model("w_cut", state_model)
# Mask
self._set_on_state_model("phi_limit_min", state_model)
self._set_on_state_model("phi_limit_max", state_model)
self._set_on_state_model("phi_limit_use_mirror", state_model)
self._set_on_state_model("radius_limit_min", state_model)
self._set_on_state_model("radius_limit_max", state_model)
# Beam Centre
self._beam_centre_presenter.set_on_state_model("lab_pos_1",
state_model)
self._beam_centre_presenter.set_on_state_model("lab_pos_2",
state_model)
return state_model
def _set_on_state_model_transmission_fit(self, state_model):
# Behaviour depends on the selection of the fit
if self._view.use_same_transmission_fit_setting_for_sample_and_can():
use_fit = self._view.transmission_sample_use_fit
fit_type = self._view.transmission_sample_fit_type
polynomial_order = self._view.transmission_sample_polynomial_order
state_model.transmission_sample_fit_type = fit_type if use_fit else FitType.NoFit
state_model.transmission_can_fit_type = fit_type if use_fit else FitType.NoFit
state_model.transmission_sample_polynomial_order = polynomial_order
state_model.transmission_can_polynomial_order = polynomial_order
# Wavelength settings
if self._view.transmission_sample_use_wavelength:
wavelength_min = self._view.transmission_sample_wavelength_min
wavelength_max = self._view.transmission_sample_wavelength_max
state_model.transmission_sample_wavelength_min = wavelength_min
state_model.transmission_sample_wavelength_max = wavelength_max
state_model.transmission_can_wavelength_min = wavelength_min
state_model.transmission_can_wavelength_max = wavelength_max
else:
# Sample
use_fit_sample = self._view.transmission_sample_use_fit
fit_type_sample = self._view.transmission_sample_fit_type
polynomial_order_sample = self._view.transmission_sample_polynomial_order
state_model.transmission_sample_fit_type = fit_type_sample if use_fit_sample else FitType.NoFit
state_model.transmission_sample_polynomial_order = polynomial_order_sample
# Wavelength settings
if self._view.transmission_sample_use_wavelength:
wavelength_min = self._view.transmission_sample_wavelength_min
wavelength_max = self._view.transmission_sample_wavelength_max
state_model.transmission_sample_wavelength_min = wavelength_min
state_model.transmission_sample_wavelength_max = wavelength_max
# Can
use_fit_can = self._view.transmission_can_use_fit
fit_type_can = self._view.transmission_can_fit_type
polynomial_order_can = self._view.transmission_can_polynomial_order
state_model.transmission_can_fit_type = fit_type_can if use_fit_can else FitType.NoFit
state_model.transmission_can_polynomial_order = polynomial_order_can
# Wavelength settings
if self._view.transmission_can_use_wavelength:
wavelength_min = self._view.transmission_can_wavelength_min
wavelength_max = self._view.transmission_can_wavelength_max
state_model.transmission_can_wavelength_min = wavelength_min
state_model.transmission_can_wavelength_max = wavelength_max
def _set_on_state_model_q_1d_rebin_string(self, state_model):
q_1d_step_type = self._view.q_1d_step_type
# If we are dealing with a simple rebin string then the step type is None
if self._view.q_1d_step_type is None:
state_model.q_1d_rebin_string = self._view.q_1d_min_or_rebin_string
else:
q_1d_min = self._view.q_1d_min_or_rebin_string
q_1d_max = self._view.q_1d_max
q_1d_step = self._view.q_1d_step
if q_1d_min and q_1d_max and q_1d_step and q_1d_step_type:
q_1d_rebin_string = str(q_1d_min) + ","
q_1d_step_type_factor = -1. if q_1d_step_type is RangeStepType.Log else 1.
q_1d_rebin_string += str(
q_1d_step_type_factor * q_1d_step) + ","
q_1d_rebin_string += str(q_1d_max)
state_model.q_1d_rebin_string = q_1d_rebin_string
def _set_on_state_model(self, attribute_name, state_model):
attribute = getattr(self._view, attribute_name)
if attribute is not None and attribute != '':
setattr(state_model, attribute_name, attribute)
def get_cell_value(self, row, column):
return self._view.get_cell(row=row,
column=self.table_index[column],
convert_to=str)
# ------------------------------------------------------------------------------------------------------------------
# Settings
# ------------------------------------------------------------------------------------------------------------------
def _setup_instrument_specific_settings(self, instrument=None):
if not instrument:
instrument = self._view.instrument
self._view.set_instrument_settings(instrument)
self._beam_centre_presenter.on_update_instrument(instrument)
self._workspace_diagnostic_presenter.set_instrument_settings(
instrument)
class RunTabPresenter(object):
class ConcreteRunTabListener(SANSDataProcessorGui.RunTabListener):
def __init__(self, presenter):
super(RunTabPresenter.ConcreteRunTabListener, self).__init__()
self._presenter = presenter
def on_user_file_load(self):
self._presenter.on_user_file_load()
def on_mask_file_add(self):
self._presenter.on_mask_file_add()
def on_batch_file_load(self):
self._presenter.on_batch_file_load()
def on_processed_clicked(self):
self._presenter.on_processed_clicked()
def on_processing_finished(self):
self._presenter.on_processing_finished()
def __init__(self, facility, view=None):
super(RunTabPresenter, self).__init__()
self._facility = facility
# Logger
self.sans_logger = Logger("SANS")
# Presenter needs to have a handle on the view since it delegates it
self._view = None
self.set_view(view)
# Models that are being used by the presenter
self._state_model = None
self._table_model = None
# Due to the nature of the DataProcessorWidget we need to provide an algorithm with at least one input
# workspace and at least one output workspace. Our SANS state approach is not compatible with this. Hence
# we provide a dummy workspace which is not used. We keep it invisible on the ADS and delete it when the
# main_presenter is deleted.
# This is not a nice solution but in line with the SANS dummy algorithm approach that we have provided
# for the
self._create_dummy_input_workspace()
# File information for the first input
self._file_information = None
# Settings diagnostic tab presenter
self._settings_diagnostic_tab_presenter = SettingsDiagnosticPresenter(
self)
# Masking table presenter
self._masking_table_presenter = MaskingTablePresenter(self)
def __del__(self):
self._delete_dummy_input_workspace()
def _default_gui_setup(self):
"""
Provides a default setup of the GUI. This is important for the initial start up, when the view is being set.
"""
# Set the possible reduction modes
reduction_mode_list = get_reduction_mode_strings_for_gui()
self._view.set_reduction_modes(reduction_mode_list)
# Set the step type options for wavelength
range_step_types = [
RangeStepType.to_string(RangeStepType.Lin),
RangeStepType.to_string(RangeStepType.Log)
]
self._view.wavelength_step_type = range_step_types
# Set the geometry options. This needs to include the option to read the sample shape from file.
sample_shape = [
"Read from file",
SampleShape.to_string(SampleShape.CylinderAxisUp),
SampleShape.to_string(SampleShape.Cuboid),
SampleShape.to_string(SampleShape.CylinderAxisAlong)
]
self._view.sample_shape = sample_shape
# Set the q range
self._view.q_1d_step_type = range_step_types
self._view.q_xy_step_type = range_step_types
# Set the fit options
fit_types = [
FitType.to_string(FitType.Linear),
FitType.to_string(FitType.Logarithmic),
FitType.to_string(FitType.Polynomial)
]
self._view.transmission_sample_fit_type = fit_types
self._view.transmission_can_fit_type = fit_types
# ------------------------------------------------------------------------------------------------------------------
# Table + Actions
# ------------------------------------------------------------------------------------------------------------------
def set_view(self, view):
"""
Sets the view
:param view: the view is the SANSDataProcessorGui. The presenter needs to access some of the API
"""
if view is not None:
self._view = view
# Add a listener to the view
listener = RunTabPresenter.ConcreteRunTabListener(self)
self._view.add_listener(listener)
# Default gui setup
self._default_gui_setup()
# Set appropriate view for the state diagnostic tab presenter
self._settings_diagnostic_tab_presenter.set_view(
self._view.settings_diagnostic_tab)
# Set appropriate view for the masking table presenter
self._masking_table_presenter.set_view(self._view.masking_table)
def on_user_file_load(self):
"""
Loads the user file. Populates the models and the view.
"""
try:
# 1. Get the user file path from the view
user_file_path = self._view.get_user_file_path()
if not user_file_path:
return
# 2. Get the full file path
user_file_path = FileFinder.getFullPath(user_file_path)
if not os.path.exists(user_file_path):
raise RuntimeError(
"The user path {} does not exist. Make sure a valid user file path"
" has been specified.".format(user_file_path))
# Clear out the current view
self._view.reset_all_fields_to_default()
# 3. Read and parse the user file
user_file_reader = UserFileReader(user_file_path)
user_file_items = user_file_reader.read_user_file()
# 4. Populate the model
self._state_model = StateGuiModel(user_file_items)
# 5. Update the views.
self._update_view_from_state_model()
# 6. Perform calls on child presenters
self._masking_table_presenter.on_update_rows()
self._settings_diagnostic_tab_presenter.on_update_rows()
except Exception as e:
self.sans_logger.error(
"Loading of the user file failed. Ensure that the path to your files has been added "
"to the Mantid search directories! See here for more details: {}"
.format(str(e)))
def on_batch_file_load(self):
"""
Loads a batch file and populates the batch table based on that.
"""
try:
# 1. Get the batch file from the view
batch_file_path = self._view.get_batch_file_path()
if not batch_file_path:
return
if not os.path.exists(batch_file_path):
raise RuntimeError(
"The batch file path {} does not exist. Make sure a valid batch file path"
" has been specified.".format(batch_file_path))
# 2. Read the batch file
batch_file_parser = BatchCsvParser(batch_file_path)
parsed_rows = batch_file_parser.parse_batch_file()
# 3. Clear the table
self._view.clear_table()
# 4. Populate the table
for row in parsed_rows:
self._populate_row_in_table(row)
# 5. Populate the selected instrument and the correct detector selection
self._setup_instrument_specific_settings()
# 6. Perform calls on child presenters
self._masking_table_presenter.on_update_rows()
self._settings_diagnostic_tab_presenter.on_update_rows()
except RuntimeError as e:
self.sans_logger.error(
"Loading of the batch file failed. Ensure that the path to your files has been added"
" to the Mantid search directories! See here for more details: {}"
.format(str(e)))
def on_processed_clicked(self):
"""
Prepares the batch reduction.
0. Validate rows and create dummy workspace if it does not exist
1. Sets up the states
2. Adds a dummy input workspace
3. Adds row index information
"""
try:
self.sans_logger.information("Starting processing of batch table.")
# 0. Validate rows
self._create_dummy_input_workspace()
self._validate_rows()
# 1. Set up the states and convert them into property managers
states = self.get_states()
if not states:
raise RuntimeError(
"There seems to have been an issue with setting the states. Make sure that a user file"
"has been loaded")
property_manager_service = PropertyManagerService()
property_manager_service.add_states_to_pmds(states)
# 2. Add dummy input workspace to Options column
self._remove_dummy_workspaces_and_row_index()
self._set_dummy_workspace()
# 3. Add dummy row index to Options column
self._set_indices()
except:
self._view.halt_process_flag()
raise
def on_processing_finished(self):
self._remove_dummy_workspaces_and_row_index()
def on_mask_file_add(self):
"""
We get the added mask file name and add it to the list of masks
"""
new_mask_file = self._view.get_mask_file()
if not new_mask_file:
return
new_mask_file_full_path = FileFinder.getFullPath(new_mask_file)
if not new_mask_file_full_path:
return
# Add the new mask file to state model
mask_files = self._state_model.mask_files
mask_files.append(new_mask_file)
self._state_model.mask_files = mask_files
# Make sure that the sub-presenters are up to date with this change
self._masking_table_presenter.on_update_rows()
self._settings_diagnostic_tab_presenter.on_update_rows()
def _add_to_hidden_options(self, row, property_name, property_value):
"""
Adds a new property to the Hidden Options column
@param row: The row where the Options column is being altered
@param property_name: The property name on the GUI algorithm.
@param property_value: The value which is being set for the property.
"""
entry = property_name + OPTIONS_EQUAL + str(property_value)
options = self._get_hidden_options(row)
if options:
options += OPTIONS_SEPARATOR + entry
else:
options = entry
self._set_hidden_options(options, row)
def _set_hidden_options(self, value, row):
self._view.set_cell(value, row, HIDDEN_OPTIONS_INDEX)
def _get_options(self, row):
return self._view.get_cell(row, OPTIONS_INDEX, convert_to=str)
def _get_hidden_options(self, row):
return self._view.get_cell(row, HIDDEN_OPTIONS_INDEX, convert_to=str)
def is_empty_row(self, row):
"""
Checks if a row has no entries. These rows will be ignored.
:param row: the row index
:return: True if the row is empty.
"""
indices = range(OPTIONS_INDEX + 1)
for index in indices:
cell_value = self._view.get_cell(row, index, convert_to=str)
if cell_value:
return False
return True
def _remove_from_hidden_options(self, row, property_name):
"""
Remove the entries in the hidden options column
:param row: the row index
:param property_name: the property name which is to be removed
"""
options = self._get_hidden_options(row)
# Remove the property entry and the value
individual_options = options.split(",")
clean_options = []
for individual_option in individual_options:
if property_name not in individual_option:
clean_options.append(individual_option)
clean_options = ",".join(clean_options)
self._set_hidden_options(clean_options, row)
def _validate_rows(self):
"""
Validation of the rows. A minimal setup requires that ScatterSample is set.
"""
# If SampleScatter is empty, then don't run the reduction.
# We allow empty rows for now, since we cannot remove them from Python.
number_of_rows = self._view.get_number_of_rows()
for row in range(number_of_rows):
if not self.is_empty_row(row):
sample_scatter = self._view.get_cell(row, 0)
if not sample_scatter:
raise RuntimeError(
"Row {} has not SampleScatter specified. Please correct this."
.format(row))
def get_processing_options(self):
"""
Creates a processing string for the data processor widget
:return: A processing string for the data processor widget
"""
global_options = ""
# Check if optimizations should be used
optimization_selection = "UseOptimizations=1" if self._view.use_optimizations else "UseOptimizations=0"
global_options += optimization_selection
# Get the output mode
output_mode = self._view.output_mode
output_mode_selection = "OutputMode=" + OutputMode.to_string(
output_mode)
global_options += ","
global_options += output_mode_selection
return global_options
# ------------------------------------------------------------------------------------------------------------------
# Controls
# ------------------------------------------------------------------------------------------------------------------
def disable_controls(self):
"""
Disable all input fields and buttons during the execution of the reduction.
"""
# TODO: think about enabling and disable some controls during reduction
pass
def enable_controls(self):
"""
Enable all input fields and buttons after the execution has completed.
"""
# TODO: think about enabling and disable some controls during reduction
pass
# ------------------------------------------------------------------------------------------------------------------
# Table Model and state population
# ------------------------------------------------------------------------------------------------------------------
def get_states(self, row_index=None):
"""
Gathers the state information for all rows.
:param row_index: if a single row is selected, then only this row is returned, else all the state for all
rows is returned
:return: a list of states
"""
start_time_state_generation = time.time()
# 1. Update the state model
state_model_with_view_update = self._get_state_model_with_view_update()
# 2. Update the table model
table_model = self._get_table_model()
# 3. Go through each row and construct a state object
if table_model and state_model_with_view_update:
states = self._create_states(state_model_with_view_update,
table_model, row_index)
else:
states = None
stop_time_state_generation = time.time()
time_taken = stop_time_state_generation - start_time_state_generation
self.sans_logger.information(
"The generation of all states took {}s".format(time_taken))
return states
def get_row_indices(self):
"""
Gets the indices of row which are not empty.
:return: a list of row indices.
"""
row_indices_which_are_not_empty = []
number_of_rows = self._view.get_number_of_rows()
for row in range(number_of_rows):
if not self.is_empty_row(row):
row_indices_which_are_not_empty.append(row)
return row_indices_which_are_not_empty
def get_state_for_row(self, row_index):
"""
Creates the state for a particular row.
:param row_index: the row index
:return: a state if the index is valid and there is a state else None
"""
states = self.get_states(row_index=row_index)
if states is None:
self.sans_logger.warning(
"There does not seem to be data for a row {}.".format(
row_index))
return None
if row_index in list(states.keys()):
if states:
return states[row_index]
return None
def _update_view_from_state_model(self):
# Front tab view
self._set_on_view("zero_error_free")
self._set_on_view("save_types")
self._set_on_view("compatibility_mode")
self._set_on_view("merge_scale")
self._set_on_view("merge_shift")
self._set_on_view("merge_scale_fit")
self._set_on_view("merge_shift_fit")
self._set_on_view("merge_q_range_start")
self._set_on_view("merge_q_range_stop")
# Settings tab view
self._set_on_view("reduction_dimensionality")
self._set_on_view("reduction_mode")
self._set_on_view("event_slices")
self._set_on_view("event_binning")
self._set_on_view("wavelength_step_type")
self._set_on_view("wavelength_min")
self._set_on_view("wavelength_max")
self._set_on_view("wavelength_step")
self._set_on_view("absolute_scale")
self._set_on_view("sample_shape")
self._set_on_view("sample_height")
self._set_on_view("sample_width")
self._set_on_view("sample_thickness")
self._set_on_view("z_offset")
# Adjustment tab
self._set_on_view("normalization_incident_monitor")
self._set_on_view("normalization_interpolate")
self._set_on_view("transmission_incident_monitor")
self._set_on_view("transmission_interpolate")
self._set_on_view("transmission_roi_files")
self._set_on_view("transmission_mask_files")
self._set_on_view("transmission_radius")
self._set_on_view("transmission_monitor")
self._set_on_view("transmission_mn_shift")
self._set_on_view_transmission_fit()
self._set_on_view("pixel_adjustment_det_1")
self._set_on_view("pixel_adjustment_det_2")
self._set_on_view("wavelength_adjustment_det_1")
self._set_on_view("wavelength_adjustment_det_2")
# Q tab
self._set_on_view_q_rebin_string()
self._set_on_view("q_xy_max")
self._set_on_view("q_xy_step")
self._set_on_view("q_xy_step_type")
self._set_on_view("gravity_on_off")
self._set_on_view("gravity_extra_length")
self._set_on_view("use_q_resolution")
self._set_on_view_q_resolution_aperture()
self._set_on_view("q_resolution_delta_r")
self._set_on_view("q_resolution_collimation_length")
self._set_on_view("q_resolution_moderator_file")
# Mask
self._set_on_view("phi_limit_min")
self._set_on_view("phi_limit_max")
self._set_on_view("phi_limit_use_mirror")
self._set_on_view("radius_limit_min")
self._set_on_view("radius_limit_max")
def _set_on_view_transmission_fit_sample_settings(self):
# Set transmission_sample_use_fit
fit_type = self._state_model.transmission_sample_fit_type
use_fit = fit_type is not FitType.NoFit
self._view.transmission_sample_use_fit = use_fit
# Set the polynomial order for sample
polynomial_order = self._state_model.transmission_sample_polynomial_order if fit_type is FitType.Polynomial else 2 # noqa
self._view.transmission_sample_polynomial_order = polynomial_order
# Set the fit type for the sample
fit_type = fit_type if fit_type is not FitType.NoFit else FitType.Linear
self._view.transmission_sample_fit_type = fit_type
# Set the wavelength
wavelength_min = self._state_model.transmission_sample_wavelength_min
wavelength_max = self._state_model.transmission_sample_wavelength_max
if wavelength_min and wavelength_max:
self._view.transmission_sample_use_wavelength = True
self._view.transmission_sample_wavelength_min = wavelength_min
self._view.transmission_sample_wavelength_max = wavelength_max
def _set_on_view_transmission_fit(self):
# Steps for adding the transmission fit to the view
# 1. Check if individual settings exist. If so then set the view to separate, else set them to both
# 2. Apply the settings
separate_settings = self._state_model.has_transmission_fit_got_separate_settings_for_sample_and_can(
)
self._view.set_fit_selection(use_separate=separate_settings)
if separate_settings:
self._set_on_view_transmission_fit_sample_settings()
# Set transmission_sample_can_fit
fit_type_can = self._state_model.transmission_can_fit_type()
use_can_fit = fit_type_can is FitType.NoFit
self._view.transmission_can_use_fit = use_can_fit
# Set the polynomial order for can
polynomial_order_can = self._state_model.transmission_can_polynomial_order if fit_type_can is FitType.Polynomial else 2 # noqa
self._view.transmission_can_polynomial_order = polynomial_order_can
# Set the fit type for the can
fit_type_can = fit_type_can if fit_type_can is not FitType.NoFit else FitType.Linear
self.transmission_can_fit_type = fit_type_can
# Set the wavelength
wavelength_min = self._state_model.transmission_can_wavelength_min
wavelength_max = self._state_model.transmission_can_wavelength_max
if wavelength_min and wavelength_max:
self._view.transmission_can_use_wavelength = True
self._view.transmission_can_wavelength_min = wavelength_min
self._view.transmission_can_wavelength_max = wavelength_max
else:
self._set_on_view_transmission_fit_sample_settings()
def _set_on_view_q_resolution_aperture(self):
self._set_on_view("q_resolution_source_a")
self._set_on_view("q_resolution_sample_a")
self._set_on_view("q_resolution_source_h")
self._set_on_view("q_resolution_sample_h")
self._set_on_view("q_resolution_source_w")
self._set_on_view("q_resolution_sample_w")
# If we have h1, h2, w1, and w2 selected then we want to select the rectangular aperture.
is_rectangular = self._state_model.q_resolution_source_h and self._state_model.q_resolution_sample_h and \
self._state_model.q_resolution_source_w and self._state_model.q_resolution_sample_w # noqa
self._view.set_q_resolution_shape_to_rectangular(is_rectangular)
def _set_on_view_q_rebin_string(self):
"""
Maps the q_1d_rebin_string of the model to the q_1d_step and q_1d_step_type property of the view.
"""
rebin_string = self._state_model.q_1d_rebin_string
# Extract the min, max and step and step type from the rebin string
elements = rebin_string.split(",")
# If we have three elements then we want to set only the
if len(elements) == 3:
step_element = float(elements[1])
step = abs(step_element)
step_type = RangeStepType.Lin if step_element >= 0 else RangeStepType.Log
# Set on the view
self._view.q_1d_min_or_rebin_string = float(elements[0])
self._view.q_1d_max = float(elements[2])
self._view.q_1d_step = step
self._view.q_1d_step_type = step_type
else:
# Set the rebin string
self._view.q_1d_min_or_rebin_string = rebin_string
self._view.q_1d_step_type = self._view.VARIABLE
def _set_on_view(self, attribute_name):
attribute = getattr(self._state_model, attribute_name)
if attribute or isinstance(
attribute, bool
): # We need to be careful here. We don't want to set empty strings, or None, but we want to set boolean values. # noqa
setattr(self._view, attribute_name, attribute)
def _get_state_model_with_view_update(self):
"""
Goes through all sub presenters and update the state model based on the views.
Note that at the moment we have set up the view and the model such that the name of a property must be the same
in the view and the model. This can be easily changed, but it also provides a good cohesion.
"""
state_model = copy.deepcopy(self._state_model)
# If we don't have a state model then return None
if state_model is None:
return state_model
# Run tab view
self._set_on_state_model("zero_error_free", state_model)
self._set_on_state_model("save_types", state_model)
self._set_on_state_model("compatibility_mode", state_model)
self._set_on_state_model("merge_scale", state_model)
self._set_on_state_model("merge_shift", state_model)
self._set_on_state_model("merge_scale_fit", state_model)
self._set_on_state_model("merge_shift_fit", state_model)
self._set_on_state_model("merge_q_range_start", state_model)
self._set_on_state_model("merge_q_range_stop", state_model)
# Settings tab
self._set_on_state_model("reduction_dimensionality", state_model)
self._set_on_state_model("reduction_mode", state_model)
self._set_on_state_model("event_slices", state_model)
self._set_on_state_model("event_binning", state_model)
self._set_on_state_model("wavelength_step_type", state_model)
self._set_on_state_model("wavelength_min", state_model)
self._set_on_state_model("wavelength_max", state_model)
self._set_on_state_model("wavelength_step", state_model)
self._set_on_state_model("absolute_scale", state_model)
self._set_on_state_model("sample_shape", state_model)
self._set_on_state_model("sample_height", state_model)
self._set_on_state_model("sample_width", state_model)
self._set_on_state_model("sample_thickness", state_model)
self._set_on_state_model("z_offset", state_model)
# Adjustment tab
self._set_on_state_model("normalization_incident_monitor", state_model)
self._set_on_state_model("normalization_interpolate", state_model)
self._set_on_state_model("transmission_incident_monitor", state_model)
self._set_on_state_model("transmission_interpolate", state_model)
self._set_on_state_model("transmission_roi_files", state_model)
self._set_on_state_model("transmission_mask_files", state_model)
self._set_on_state_model("transmission_radius", state_model)
self._set_on_state_model("transmission_monitor", state_model)
self._set_on_state_model("transmission_mn_shift", state_model)
self._set_on_state_model_transmission_fit(state_model)
self._set_on_state_model("pixel_adjustment_det_1", state_model)
self._set_on_state_model("pixel_adjustment_det_2", state_model)
self._set_on_state_model("wavelength_adjustment_det_1", state_model)
self._set_on_state_model("wavelength_adjustment_det_2", state_model)
# Q tab
self._set_on_state_model_q_1d_rebin_string(state_model)
self._set_on_state_model("q_xy_max", state_model)
self._set_on_state_model("q_xy_step", state_model)
self._set_on_state_model("q_xy_step_type", state_model)
self._set_on_state_model("gravity_on_off", state_model)
self._set_on_state_model("gravity_extra_length", state_model)
self._set_on_state_model("use_q_resolution", state_model)
self._set_on_state_model("q_resolution_source_a", state_model)
self._set_on_state_model("q_resolution_sample_a", state_model)
self._set_on_state_model("q_resolution_source_h", state_model)
self._set_on_state_model("q_resolution_sample_h", state_model)
self._set_on_state_model("q_resolution_source_w", state_model)
self._set_on_state_model("q_resolution_sample_w", state_model)
self._set_on_state_model("q_resolution_delta_r", state_model)
self._set_on_state_model("q_resolution_collimation_length",
state_model)
self._set_on_state_model("q_resolution_moderator_file", state_model)
# Mask
self._set_on_state_model("phi_limit_min", state_model)
self._set_on_state_model("phi_limit_max", state_model)
self._set_on_state_model("phi_limit_use_mirror", state_model)
self._set_on_state_model("radius_limit_min", state_model)
self._set_on_state_model("radius_limit_max", state_model)
return state_model
def _set_on_state_model_transmission_fit(self, state_model):
# Behaviour depends on the selection of the fit
if self._view.use_same_transmission_fit_setting_for_sample_and_can():
use_fit = self._view.transmission_sample_use_fit
fit_type = self._view.transmission_sample_fit_type
polynomial_order = self._view.transmission_sample_polynomial_order
state_model.transmission_sample_fit_type = fit_type if use_fit else FitType.NoFit
state_model.transmission_can_fit_type = fit_type if use_fit else FitType.NoFit
state_model.transmission_sample_polynomial_order = polynomial_order
state_model.transmission_can_polynomial_order = polynomial_order
# Wavelength settings
if self._view.transmission_sample_use_wavelength:
wavelength_min = self._view.transmission_sample_wavelength_min
wavelength_max = self._view.transmission_sample_wavelength_max
state_model.transmission_sample_wavelength_min = wavelength_min
state_model.transmission_sample_wavelength_max = wavelength_max
state_model.transmission_can_wavelength_min = wavelength_min
state_model.transmission_can_wavelength_max = wavelength_max
else:
# Sample
use_fit_sample = self._view.transmission_sample_use_fit
fit_type_sample = self._view.transmission_sample_fit_type
polynomial_order_sample = self._view.transmission_sample_polynomial_order
state_model.transmission_sample_fit_type = fit_type_sample if use_fit_sample else FitType.NoFit
state_model.transmission_sample_polynomial_order = polynomial_order_sample
# Wavelength settings
if self._view.transmission_sample_use_wavelength:
wavelength_min = self._view.transmission_sample_wavelength_min
wavelength_max = self._view.transmission_sample_wavelength_max
state_model.transmission_sample_wavelength_min = wavelength_min
state_model.transmission_sample_wavelength_max = wavelength_max
# Can
use_fit_can = self._view.transmission_can_use_fit
fit_type_can = self._view.transmission_can_fit_type
polynomial_order_can = self._view.transmission_can_polynomial_order
state_model.transmission_can_fit_type = fit_type_can if use_fit_can else FitType.NoFit
state_model.transmission_can_polynomial_order = polynomial_order_can
# Wavelength settings
if self._view.transmission_can_use_wavelength:
wavelength_min = self._view.transmission_can_wavelength_min
wavelength_max = self._view.transmission_can_wavelength_max
state_model.transmission_can_wavelength_min = wavelength_min
state_model.transmission_can_wavelength_max = wavelength_max
def _set_on_state_model_q_1d_rebin_string(self, state_model):
q_1d_step_type = self._view.q_1d_step_type
# If we are dealing with a simple rebin string then the step type is None
if self._view.q_1d_step_type is None:
state_model.q_1d_rebin_string = self._view.q_1d_min_or_rebin_string
else:
q_1d_min = self._view.q_1d_min_or_rebin_string
q_1d_max = self._view.q_1d_max
q_1d_step = self._view.q_1d_step
if q_1d_min and q_1d_max and q_1d_step and q_1d_step_type:
q_1d_rebin_string = str(q_1d_min) + ","
q_1d_step_type_factor = -1. if q_1d_step_type is RangeStepType.Log else 1.
q_1d_rebin_string += str(
q_1d_step_type_factor * q_1d_step) + ","
q_1d_rebin_string += str(q_1d_max)
state_model.q_1d_rebin_string = q_1d_rebin_string
def _set_on_state_model(self, attribute_name, state_model):
attribute = getattr(self._view, attribute_name)
if attribute or isinstance(attribute, bool):
setattr(state_model, attribute_name, attribute)
def _get_table_model(self):
# 1. Create a new table model
user_file = self._view.get_user_file_path()
batch_file = self._view.get_batch_file_path()
table_model = TableModel()
table_model.user_file = user_file
self.batch_file = batch_file
# 2. Iterate over each row, create a table row model and insert it
number_of_rows = self._view.get_number_of_rows()
for row in range(number_of_rows):
sample_scatter = self._view.get_cell(row=row,
column=SAMPLE_SCATTER_INDEX,
convert_to=str)
sample_scatter_period = self._view.get_cell(
row=row, column=SAMPLE_SCATTER_PERIOD_INDEX, convert_to=str)
sample_transmission = self._view.get_cell(
row=row, column=SAMPLE_TRANSMISSION_INDEX, convert_to=str)
sample_transmission_period = self._view.get_cell(
row=row,
column=SAMPLE_TRANSMISSION_PERIOD_INDEX,
convert_to=str) # noqa
sample_direct = self._view.get_cell(row=row,
column=SAMPLE_DIRECT_INDEX,
convert_to=str)
sample_direct_period = self._view.get_cell(
row=row, column=SAMPLE_DIRECT_PERIOD_INDEX, convert_to=str)
can_scatter = self._view.get_cell(row=row,
column=CAN_SCATTER_INDEX,
convert_to=str)
can_scatter_period = self._view.get_cell(
row=row, column=CAN_SCATTER_PERIOD_INDEX, convert_to=str)
can_transmission = self._view.get_cell(
row=row, column=CAN_TRANSMISSION_INDEX, convert_to=str)
can_transmission_period = self._view.get_cell(
row=row, column=CAN_TRANSMISSION_PERIOD_INDEX, convert_to=str)
can_direct = self._view.get_cell(row=row,
column=CAN_DIRECT_INDEX,
convert_to=str)
can_direct_period = self._view.get_cell(
row=row, column=CAN_DIRECT_PERIOD_INDEX, convert_to=str)
output_name = self._view.get_cell(row=row,
column=OUTPUT_NAME_INDEX,
convert_to=str)
# Get the options string
# We don't have to add the hidden column here, since it only contains information for the SANS
# workflow to operate properly. It however does not contain information for the
options_string = self._get_options(row)
table_index_model = TableIndexModel(
index=row,
sample_scatter=sample_scatter,
sample_scatter_period=sample_scatter_period,
sample_transmission=sample_transmission,
sample_transmission_period=sample_transmission_period,
sample_direct=sample_direct,
sample_direct_period=sample_direct_period,
can_scatter=can_scatter,
can_scatter_period=can_scatter_period,
can_transmission=can_transmission,
can_transmission_period=can_transmission_period,
can_direct=can_direct,
can_direct_period=can_direct_period,
output_name=output_name,
options_column_string=options_string)
table_model.add_table_entry(row, table_index_model)
return table_model
def _create_states(self, state_model, table_model, row_index=None):
"""
Here we create the states based on the settings in the models
:param state_model: the state model object
:param table_model: the table model object
:param row_index: the selected row, if None then all rows are generated
"""
number_of_rows = self._view.get_number_of_rows()
if row_index is not None:
# Check if the selected index is valid
if row_index >= number_of_rows:
return None
rows = [row_index]
else:
rows = range(number_of_rows)
states = {}
gui_state_director = GuiStateDirector(table_model, state_model,
self._facility)
for row in rows:
self.sans_logger.information(
"Generating state for row {}".format(row))
if not self.is_empty_row(row):
try:
state = gui_state_director.create_state(row)
states.update({row: state})
except ValueError as e:
self.sans_logger.error(
"There was a bad entry for row {}. Ensure that the path to your files has "
"been added to the Mantid search directories! See here for more "
"details: {}".format(row, str(e)))
raise RuntimeError(
"There was a bad entry for row {}. Ensure that the path to your files has "
"been added to the Mantid search directories! See here for more "
"details: {}".format(row, str(e)))
return states
def _populate_row_in_table(self, row):
"""
Adds a row to the table
"""
def get_string_entry(_tag, _row):
_element = ""
if _tag in _row:
_element = _row[_tag]
return _element
def get_string_period(_tag):
return "" if _tag == ALL_PERIODS else str(_tag)
# 1. Pull out the entries
sample_scatter = get_string_entry(BatchReductionEntry.SampleScatter,
row)
sample_scatter_period = get_string_entry(
BatchReductionEntry.SampleScatterPeriod, row)
sample_transmission = get_string_entry(
BatchReductionEntry.SampleTransmission, row)
sample_transmission_period = get_string_entry(
BatchReductionEntry.SampleTransmissionPeriod, row)
sample_direct = get_string_entry(BatchReductionEntry.SampleDirect, row)
sample_direct_period = get_string_entry(
BatchReductionEntry.SampleDirectPeriod, row)
can_scatter = get_string_entry(BatchReductionEntry.CanScatter, row)
can_scatter_period = get_string_entry(
BatchReductionEntry.CanScatterPeriod, row)
can_transmission = get_string_entry(
BatchReductionEntry.CanTransmission, row)
can_transmission_period = get_string_entry(
BatchReductionEntry.CanScatterPeriod, row)
can_direct = get_string_entry(BatchReductionEntry.CanDirect, row)
can_direct_period = get_string_entry(
BatchReductionEntry.CanDirectPeriod, row)
output_name = get_string_entry(BatchReductionEntry.Output, row)
# 2. Create entry that can be understood by table
row_entry = "SampleScatter:{},ssp:{},SampleTrans:{},stp:{},SampleDirect:{},sdp:{}," \
"CanScatter:{},csp:{},CanTrans:{},ctp:{}," \
"CanDirect:{},cdp:{},OutputName:{}".format(sample_scatter,
get_string_period(sample_scatter_period),
sample_transmission,
get_string_period(sample_transmission_period),
sample_direct,
get_string_period(sample_direct_period),
can_scatter,
get_string_period(can_scatter_period),
can_transmission,
get_string_period(can_transmission_period),
can_direct,
get_string_period(can_direct_period),
output_name)
self._view.add_row(row_entry)
# ------------------------------------------------------------------------------------------------------------------
# Settings
# ------------------------------------------------------------------------------------------------------------------
def _setup_instrument_specific_settings(self):
# Get the first run number of the scatter data for the first table
sample_scatter = self._view.get_cell(row=0, column=0, convert_to=str)
# Check if it exists at all
if not sample_scatter:
return
# Get the file information from
file_information_factory = SANSFileInformationFactory()
try:
self._file_information = file_information_factory.create_sans_file_information(
sample_scatter)
except NotImplementedError:
self.sans_logger.warning(
"Could not get file information from {}.".format(
sample_scatter))
self._file_information = None
# Provide the instrument specific settings
if self._file_information:
# Set the instrument on the table
instrument = self._file_information.get_instrument()
self._view.set_instrument_settings(instrument)
# Set the reduction mode
reduction_mode_list = get_reduction_mode_strings_for_gui(
instrument=instrument)
self._view.set_reduction_modes(reduction_mode_list)
else:
self._view.set_instrument_settings(SANSInstrument.NoInstrument)
reduction_mode_list = get_reduction_mode_strings_for_gui()
self._view.set_reduction_modes(reduction_mode_list)
# ------------------------------------------------------------------------------------------------------------------
# Setting workaround for state in DataProcessorWidget
# ------------------------------------------------------------------------------------------------------------------
def _remove_dummy_workspaces_and_row_index(self):
number_of_rows = self._view.get_number_of_rows()
for row in range(number_of_rows):
self._remove_from_hidden_options(row, "InputWorkspace")
self._remove_from_hidden_options(row, "RowIndex")
def _set_indices(self):
number_of_rows = self._view.get_number_of_rows()
for row in range(number_of_rows):
to_set = Property.EMPTY_INT if self.is_empty_row(row) else row
self._add_to_hidden_options(row, "RowIndex", to_set)
def _set_dummy_workspace(self):
number_of_rows = self._view.get_number_of_rows()
for row in range(number_of_rows):
self._add_to_hidden_options(
row, "InputWorkspace",
SANS_DUMMY_INPUT_ALGORITHM_PROPERTY_NAME)
@staticmethod
def _create_dummy_input_workspace():
if not AnalysisDataService.doesExist(
SANS_DUMMY_INPUT_ALGORITHM_PROPERTY_NAME):
workspace = WorkspaceFactory.create("Workspace2D", 1, 1, 1)
AnalysisDataService.addOrReplace(
SANS_DUMMY_INPUT_ALGORITHM_PROPERTY_NAME, workspace)
@staticmethod
def _delete_dummy_input_workspace():
if AnalysisDataService.doesExist(
SANS_DUMMY_INPUT_ALGORITHM_PROPERTY_NAME):
AnalysisDataService.remove(
SANS_DUMMY_INPUT_ALGORITHM_PROPERTY_NAME)
