def combine_files(self, input_files: [FileDescriptor],
data_model: DataModel, filter_name: str,
output_path: str, console: Console):
"""
Combine the given files, output to the given output file using the combination
method defined in the data model.
:param input_files: List of files to be combined
:param data_model: Data model with options for this run
:param filter_name: Human-readable filter name (for output file name and FITS comment)
:param output_path: Path for output fiel to be created
:param console: Redirectable console output object
"""
console.push_level(
) # Stack console indentation level to easily restore when done
substituted_file_name = SharedUtils.substitute_date_time_filter_in_string(
output_path)
file_names = [d.get_absolute_path() for d in input_files]
combine_method = data_model.get_master_combine_method()
# Get info about any precalibration that is to be done
calibrator = Calibrator(data_model)
calibration_tag = calibrator.fits_comment_tag()
assert len(input_files) > 0
binning: int = input_files[0].get_binning()
(mean_exposure, mean_temperature
) = ImageMath.mean_exposure_and_temperature(input_files)
if combine_method == Constants.COMBINE_MEAN:
mean_data = ImageMath.combine_mean(file_names, calibrator, console,
self._session_controller)
self.check_cancellation()
RmFitsUtil.create_combined_fits_file(
substituted_file_name, mean_data,
FileDescriptor.FILE_TYPE_FLAT, "Flat Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Flat MEAN combined {calibration_tag}")
elif combine_method == Constants.COMBINE_MEDIAN:
median_data = ImageMath.combine_median(file_names, calibrator,
console,
self._session_controller)
self.check_cancellation()
RmFitsUtil.create_combined_fits_file(
substituted_file_name, median_data,
FileDescriptor.FILE_TYPE_FLAT, "Flat Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Flat MEDIAN combined {calibration_tag}")
elif combine_method == Constants.COMBINE_MINMAX:
number_dropped_points = data_model.get_min_max_number_clipped_per_end(
)
min_max_clipped_mean = ImageMath.combine_min_max_clip(
file_names, number_dropped_points, calibrator, console,
self._session_controller)
self.check_cancellation()
assert min_max_clipped_mean is not None
RmFitsUtil.create_combined_fits_file(
substituted_file_name, min_max_clipped_mean,
FileDescriptor.FILE_TYPE_FLAT, "Flat Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Flat Min/Max Clipped "
f"(drop {number_dropped_points}) Mean combined"
f" {calibration_tag}")
else:
assert combine_method == Constants.COMBINE_SIGMA_CLIP
sigma_threshold = data_model.get_sigma_clip_threshold()
sigma_clipped_mean = ImageMath.combine_sigma_clip(
file_names, sigma_threshold, calibrator, console,
self._session_controller)
self.check_cancellation()
assert sigma_clipped_mean is not None
RmFitsUtil.create_combined_fits_file(
substituted_file_name, sigma_clipped_mean,
FileDescriptor.FILE_TYPE_FLAT, "Flat Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Flat Sigma Clipped "
f"(threshold {sigma_threshold}) Mean combined"
f" {calibration_tag}")
console.pop_level()
def combine_files(self, input_files: [FileDescriptor],
data_model: DataModel, filter_name: str,
output_path: str, console: Console):
console.push_level()
substituted_file_name = SharedUtils.substitute_date_time_filter_in_string(
output_path)
file_names = [d.get_absolute_path() for d in input_files]
combine_method = data_model.get_master_combine_method()
# Get info about any precalibration that is to be done
calibrator = Calibrator(data_model)
calibration_tag = calibrator.fits_comment_tag()
assert len(input_files) > 0
binning: int = input_files[0].get_binning()
(mean_exposure, mean_temperature
) = ImageMath.mean_exposure_and_temperature(input_files)
if combine_method == Constants.COMBINE_MEAN:
mean_data = ImageMath.combine_mean(file_names, calibrator, console,
self._session_controller)
self.check_cancellation()
RmFitsUtil.create_combined_fits_file(
substituted_file_name, mean_data,
FileDescriptor.FILE_TYPE_DARK, "Dark Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Dark MEAN combined {calibration_tag}")
elif combine_method == Constants.COMBINE_MEDIAN:
median_data = ImageMath.combine_median(file_names, calibrator,
console,
self._session_controller)
self.check_cancellation()
RmFitsUtil.create_combined_fits_file(
substituted_file_name, median_data,
FileDescriptor.FILE_TYPE_DARK, "Dark Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Dark MEDIAN combined {calibration_tag}")
elif combine_method == Constants.COMBINE_MINMAX:
number_dropped_points = data_model.get_min_max_number_clipped_per_end(
)
min_max_clipped_mean = ImageMath.combine_min_max_clip(
file_names, number_dropped_points, calibrator, console,
self._session_controller)
self.check_cancellation()
assert min_max_clipped_mean is not None
RmFitsUtil.create_combined_fits_file(
substituted_file_name, min_max_clipped_mean,
FileDescriptor.FILE_TYPE_DARK, "Dark Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Dark Min/Max Clipped "
f"(drop {number_dropped_points}) Mean combined"
f" {calibration_tag}")
else:
assert combine_method == Constants.COMBINE_SIGMA_CLIP
sigma_threshold = data_model.get_sigma_clip_threshold()
sigma_clipped_mean = ImageMath.combine_sigma_clip(
file_names, sigma_threshold, calibrator, console,
self._session_controller)
self.check_cancellation()
assert sigma_clipped_mean is not None
RmFitsUtil.create_combined_fits_file(
substituted_file_name, sigma_clipped_mean,
FileDescriptor.FILE_TYPE_DARK, "Dark Frame", mean_exposure,
mean_temperature, filter_name, binning,
f"Master Dark Sigma Clipped "
f"(threshold {sigma_threshold}) Mean combined"
f" {calibration_tag}")
console.pop_level()
