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 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()
