def describe_group(data_model: DataModel, number_files: int,
sample_file: FileDescriptor, console: Console):
binning = sample_file.get_binning()
exposure = sample_file.get_exposure()
temperature = sample_file.get_temperature()
processing_message = ""
if data_model.get_group_by_size():
processing_message += f"binned {binning} x {binning}"
if data_model.get_group_by_exposure():
if len(processing_message) > 0:
processing_message += ","
processing_message += f" exposed {exposure} seconds"
if data_model.get_group_by_temperature():
if len(processing_message) > 0:
processing_message += ","
processing_message += f" at {temperature} degrees."
console.message(
f"Processing {number_files} files {processing_message}", +1)
def describe_group(data_model: DataModel, number_files: int,
sample_file: FileDescriptor, console: Console):
"""
Display, on the console, a descriptive text string for the group being processed, using a given sample file
:param data_model: Data model giving the processing options
:param number_files: Number of files in the group being processed
:param sample_file: Sample file, representative of the characterstics of files in the group
:param console: Redirectable output console
"""
binning = sample_file.get_binning()
temperature = sample_file.get_temperature()
message_parts: [str] = []
if data_model.get_group_by_size():
message_parts.append(f"binned {binning} x {binning}")
if data_model.get_group_by_filter():
message_parts.append(
f"with {sample_file.get_filter_name()} filter")
if data_model.get_group_by_temperature():
message_parts.append(f"at {temperature} degrees")
processing_message = ", ".join(message_parts)
console.message(
f"Processing {number_files} files {processing_message}.", +1)
def process_groups(self, data_model: DataModel,
selected_files: [FileDescriptor], output_directory: str,
console: Console):
"""
Process the given selected files in groups by size, exposure, or temperature (or any combination)
Exceptions thrown:
NoGroupOutputDirectory Output directory does not exist and unable to create it
:param data_model: Data model specifying options for the current run
:param selected_files: List of descriptions of files to be grouped then processed
:param output_directory: Directory to contain output files from processed groups
:param console: Re-directable console output object
"""
console.push_level()
temperature_bandwidth = data_model.get_temperature_group_bandwidth()
disposition_folder = data_model.get_disposition_subfolder_name()
substituted_folder_name = SharedUtils.substitute_date_time_filter_in_string(
disposition_folder)
console.message(
"Process groups into output directory: " + output_directory, +1)
if not SharedUtils.ensure_directory_exists(output_directory):
raise MasterMakerExceptions.NoGroupOutputDirectory(
output_directory)
minimum_group_size = data_model.get_minimum_group_size() \
if data_model.get_ignore_groups_fewer_than() else 0
# Process size groups, or all sizes if not grouping
groups_by_size = self.get_groups_by_size(
selected_files, data_model.get_group_by_size())
group_by_size = data_model.get_group_by_size()
group_by_temperature = data_model.get_group_by_temperature()
group_by_filter = data_model.get_group_by_filter()
for size_group in groups_by_size:
self.check_cancellation()
console.push_level()
# Message about this group only if this grouping was requested
if len(size_group) < minimum_group_size:
if group_by_size:
console.message(
f"Ignoring one size group: {len(size_group)} "
f"files {size_group[0].get_size_key()}", +1)
else:
if group_by_size:
console.message(
f"Processing one size group: {len(size_group)} "
f"files {size_group[0].get_size_key()}", +1)
# Within this size group, process temperature groups, or all temperatures if not grouping
groups_by_temperature = \
self.get_groups_by_temperature(size_group,
data_model.get_group_by_temperature(),
temperature_bandwidth)
for temperature_group in groups_by_temperature:
self.check_cancellation()
console.push_level()
(_, mean_temperature
) = ImageMath.mean_exposure_and_temperature(
temperature_group)
if len(temperature_group) < minimum_group_size:
if group_by_temperature:
console.message(
f"Ignoring one temperature group: {len(temperature_group)} "
f"files with mean temperature {mean_temperature:.1f}",
+1)
else:
if group_by_temperature:
console.message(
f"Processing one temperature group: {len(temperature_group)} "
f"files with mean temperature {mean_temperature:.1f}",
+1)
# Within this temperature group, process filter groups, or all filters if not grouping
groups_by_filter = \
self.get_groups_by_filter(temperature_group,
data_model.get_group_by_filter())
for filter_group in groups_by_filter:
self.check_cancellation()
console.push_level()
filter_name = filter_group[0].get_filter_name()
if len(filter_group) < minimum_group_size:
if group_by_filter:
console.message(
f"Ignoring one filter group: {len(filter_group)} "
f"files with {filter_name} filter ",
+1)
else:
if group_by_filter:
console.message(
f"Processing one filter group: {len(filter_group)} "
f"files with {filter_name} filter ",
+1)
self.process_one_group(
data_model, filter_group, output_directory,
data_model.get_master_combine_method(),
substituted_folder_name, console)
console.pop_level()
self.check_cancellation()
console.pop_level()
console.pop_level()
console.message("Group combining complete", 0)
console.pop_level()
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()
def process_groups(self, data_model: DataModel,
selected_files: [FileDescriptor], output_directory: str,
console: Console):
console.push_level()
exposure_bandwidth = data_model.get_exposure_group_bandwidth()
temperature_bandwidth = data_model.get_temperature_group_bandwidth()
disposition_folder = data_model.get_disposition_subfolder_name()
substituted_folder_name = SharedUtils.substitute_date_time_filter_in_string(
disposition_folder)
console.message(
"Process groups into output directory: " + output_directory, +1)
if not SharedUtils.ensure_directory_exists(output_directory):
raise MasterMakerExceptions.NoGroupOutputDirectory(
output_directory)
minimum_group_size = data_model.get_minimum_group_size() \
if data_model.get_ignore_groups_fewer_than() else 0
# Process size groups, or all sizes if not grouping
groups_by_size = self.get_groups_by_size(
selected_files, data_model.get_group_by_size())
group_by_size = data_model.get_group_by_size()
group_by_exposure = data_model.get_group_by_exposure()
group_by_temperature = data_model.get_group_by_temperature()
for size_group in groups_by_size:
self.check_cancellation()
console.push_level()
# Message about this group only if this grouping was requested
if len(size_group) < minimum_group_size:
if group_by_size:
console.message(
f"Ignoring one size group: {len(size_group)} "
f"files {size_group[0].get_size_key()}", +1)
else:
if group_by_size:
console.message(
f"Processing one size group: {len(size_group)} "
f"files {size_group[0].get_size_key()}", +1)
# Within this size group, process exposure groups, or all exposures if not grouping
groups_by_exposure = self.get_groups_by_exposure(
size_group, data_model.get_group_by_exposure(),
exposure_bandwidth)
for exposure_group in groups_by_exposure:
self.check_cancellation()
(mean_exposure,
_) = ImageMath.mean_exposure_and_temperature(
exposure_group)
console.push_level()
if len(exposure_group) < minimum_group_size:
if group_by_exposure:
console.message(
f"Ignoring one exposure group: {len(exposure_group)} "
f"files exposed at mean {mean_exposure:.2f} seconds",
+1)
else:
if group_by_exposure:
console.message(
f"Processing one exposure group: {len(exposure_group)} "
f"files exposed at mean {mean_exposure:.2f} seconds",
+1)
# Within this exposure group, process temperature groups, or all temperatures if not grouping
groups_by_temperature = \
self.get_groups_by_temperature(exposure_group,
data_model.get_group_by_temperature(),
temperature_bandwidth)
for temperature_group in groups_by_temperature:
self.check_cancellation()
console.push_level()
(_, mean_temperature
) = ImageMath.mean_exposure_and_temperature(
temperature_group)
if len(temperature_group) < minimum_group_size:
if group_by_temperature:
console.message(
f"Ignoring one temperature group: {len(temperature_group)} "
f"files with mean temperature {mean_temperature:.1f}",
+1)
else:
if group_by_temperature:
console.message(
f"Processing one temperature group: {len(temperature_group)} "
f"files with mean temperature {mean_temperature:.1f}",
+1)
# Now we have a list of descriptors, grouped as appropriate, to process
self.process_one_group(
data_model, temperature_group,
output_directory,
data_model.get_master_combine_method(),
substituted_folder_name, console)
self.check_cancellation()
console.pop_level()
console.pop_level()
console.pop_level()
console.message("Group combining complete", 0)
console.pop_level()