def validate_file_dimensions(cls, descriptors: [FileDescriptor],
data_model: DataModel) -> bool:
# Get list of paths of selected files
if len(descriptors) > 0:
# If precalibration file is in use, add that name to the list
if data_model.get_precalibration_type(
) == Constants.CALIBRATION_FIXED_FILE:
calibration_descriptor = \
RmFitsUtil.make_file_descriptor(data_model.get_precalibration_fixed_path())
descriptors.append(calibration_descriptor)
# Get binning and dimension of first to use as a reference
assert len(descriptors) > 0
reference_file: FileDescriptor = descriptors[0]
reference_binning = reference_file.get_binning()
reference_x_size = reference_file.get_x_dimension()
reference_y_size = reference_file.get_y_dimension()
# Check all files in the list against these specifications
descriptor: FileDescriptor
for descriptor in descriptors:
if descriptor.get_binning() != reference_binning:
return False
if descriptor.get_x_dimension() != reference_x_size:
return False
if descriptor.get_y_dimension() != reference_y_size:
return False
return True
def validate_file_dimensions(cls, descriptors: [FileDescriptor],
data_model: DataModel) -> bool:
"""
Determine if the dimensions of all the supplied files are the same.
All selected files must be the same size and the same binning.
Include the precalibration bias or dark file in this test if that method is selected.
:param descriptors: Files to be checked for compatibility
:param data_model: Data model gives precalibration type and file if needed
:return: True if all files are the same size and binning, so compatible
"""
# Get list of paths of selected files
if len(descriptors) > 0:
# If precalibration file is in use, add that name to the list
if data_model.get_precalibration_type(
) == Constants.CALIBRATION_FIXED_FILE:
calibration_descriptor = \
RmFitsUtil.make_file_descriptor(data_model.get_precalibration_fixed_path())
descriptors.append(calibration_descriptor)
# Get binning and dimension of first to use as a reference
assert len(descriptors) > 0
reference_file: FileDescriptor = descriptors[0]
reference_binning = reference_file.get_binning()
reference_x_size = reference_file.get_x_dimension()
reference_y_size = reference_file.get_y_dimension()
# Check all files in the list against these specifications
descriptor: FileDescriptor
for descriptor in descriptors:
if descriptor.get_binning() != reference_binning:
return False
if descriptor.get_x_dimension() != reference_x_size:
return False
if descriptor.get_y_dimension() != reference_y_size:
return False
return True
def __init__(self, preferences: Preferences, data_model: DataModel):
"""Initialize MainWindow class"""
self._preferences = preferences
self._data_model = data_model
QMainWindow.__init__(self)
self.ui = uic.loadUi(
MultiOsUtil.path_for_file_in_program_directory("MainWindow.ui"))
self._field_validity: {object, bool} = {}
self._table_model: FitsFileTableModel
self._indent_level = 0
# Load algorithm from preferences
algorithm = data_model.get_master_combine_method()
if algorithm == Constants.COMBINE_MEAN:
self.ui.combineMeanRB.setChecked(True)
elif algorithm == Constants.COMBINE_MEDIAN:
self.ui.combineMedianRB.setChecked(True)
elif algorithm == Constants.COMBINE_MINMAX:
self.ui.combineMinMaxRB.setChecked(True)
else:
assert (algorithm == Constants.COMBINE_SIGMA_CLIP)
self.ui.combineSigmaRB.setChecked(True)
self.ui.minMaxNumDropped.setText(
str(data_model.get_min_max_number_clipped_per_end()))
self.ui.sigmaThreshold.setText(
str(data_model.get_sigma_clip_threshold()))
# Load disposition from preferences
disposition = data_model.get_input_file_disposition()
if disposition == Constants.INPUT_DISPOSITION_SUBFOLDER:
self.ui.dispositionSubFolderRB.setChecked(True)
else:
assert (disposition == Constants.INPUT_DISPOSITION_NOTHING)
self.ui.dispositionNothingRB.setChecked(True)
self.ui.subFolderName.setText(
data_model.get_disposition_subfolder_name())
# Pre-calibration options
precalibration_option = data_model.get_precalibration_type()
if precalibration_option == Constants.CALIBRATION_FIXED_FILE:
self.ui.fixedPreCalFileRB.setChecked(True)
elif precalibration_option == Constants.CALIBRATION_NONE:
self.ui.noPreClalibrationRB.setChecked(True)
elif precalibration_option == Constants.CALIBRATION_AUTO_DIRECTORY:
self.ui.autoPreCalibrationRB.setChecked(True)
else:
assert precalibration_option == Constants.CALIBRATION_PEDESTAL
self.ui.fixedPedestalRB.setChecked(True)
self.ui.fixedPedestalAmount.setText(
str(data_model.get_precalibration_pedestal()))
self.ui.precalibrationPathDisplay.setText(
os.path.basename(data_model.get_precalibration_fixed_path()))
self.ui.autoDirectoryName.setText(
os.path.basename(data_model.get_precalibration_auto_directory()))
self.ui.autoRecursive.setChecked(
data_model.get_auto_directory_recursive())
self.ui.autoBiasOnly.setChecked(
data_model.get_auto_directory_bias_only())
# Grouping boxes and parameters
self.ui.groupBySizeCB.setChecked(data_model.get_group_by_size())
self.ui.groupByExposureCB.setChecked(
data_model.get_group_by_exposure())
self.ui.groupByTemperatureCB.setChecked(
data_model.get_group_by_temperature())
self.ui.ignoreSmallGroupsCB.setChecked(
data_model.get_ignore_groups_fewer_than())
self.ui.exposureGroupBandwidth.setText(
f"{data_model.get_exposure_group_bandwidth()}")
self.ui.temperatureGroupBandwidth.setText(
f"{data_model.get_temperature_group_bandwidth()}")
self.ui.minimumGroupSize.setText(
str(data_model.get_minimum_group_size()))
# Set up the file table
self._table_model = FitsFileTableModel(
self.ui.filesTable, data_model.get_ignore_file_type())
self.ui.filesTable.setModel(self._table_model)
# Columns should resize to best fit their contents
self.ui.filesTable.horizontalHeader().setSectionResizeMode(
QHeaderView.ResizeToContents)
# Write a summary, in the main tab, of the settings from the options tab (and data model)
self.fill_options_readout()
self.connect_responders()
# If a window size is saved, set the window size
window_size = self._preferences.get_main_window_size()
if window_size is not None:
self.ui.resize(window_size)
self.enable_fields()
self.enable_buttons()
