goodman_pipeline.images package#

Submodules#

goodman_pipeline.images.data_classifier module#

class goodman_pipeline.images.data_classifier.DataClassifier#

Bases: object

Classifies the data being presented to the pipeline.

Data classifier is intended to define the camera that is being used and the technique in use. This will be used later to make important decisions regarding the process to be used.

goodman_pipeline.images.goodman_ccd module#

class goodman_pipeline.images.goodman_ccd.ReduceCCD#

Bases: object

goodman_pipeline.images.goodman_ccd.get_args(arguments=None)#

Get command line arguments.

The list of arguments can be obtained by using the argument --help. All the arguments start with two dashes and single-character arguments where avoided in order to eliminate confusion.

Parameters:

arguments (list) – A list containing the arguments as elements.

Returns:

argparse instance. Contains all the arguments as

attributes

Return type:

args (object)

goodman_pipeline.images.image_processor module#

class goodman_pipeline.images.image_processor.ImageProcessor(args, data_container)#

Bases: object

Image processing class

This class contains methods for performing CCD image reduction for spectroscopy and imaging.

process_imaging_science(imaging_group)#

Does image reduction for science imaging data.

Parameters:

imaging_group (object) – DataFrame instance that contains a list of science data that are compatible with a given instrument configuration and can be reduced together.

process_spectroscopy_science(science_group, save_all=False)#

Process Spectroscopy science images.

This function handles the full image reduction process for science files. if save_all is set to True, all intermediate steps are saved.

Parameters:

  • science_group (object) – DataFrame instance that contains a list of science images that where observed at the same pointing and time. It also contains a set of selected keywords from the image’s header.

  • save_all (bool) – If True the pipeline will save all the intermediate files such as after overscan correction or bias corrected and etc.

goodman_pipeline.images.night_organizer module#

class goodman_pipeline.images.night_organizer.NightOrganizer(full_path, instrument, technique, ignore_bias=False, ignore_flats=False)#

Bases: object

check_header_cards()#

Check if the header contains all the keywords (cards) expected.

This is critical for old goodman data.

Raises:

ValueError – If any of the cards does not exists in the image’s header

imaging_night()#

Organizes data for imaging

For imaging there is no discrimination regarding night data since the process is simpler. It is a three stage process classifying BIAS, FLAT and OBJECT data type. The data is packed in groups that are pandas.DataFrame objects.

spectroscopy_night(file_collection, data_container)#

Organizes data for spectroscopy

This method identifies all combinations of nine key keywords that can set apart different objects with their respective calibration data or not. The keywords used are:

  • GAIN

  • RDNOISE

  • GRATING

  • FILTER2

  • CAM_TARG

  • GRT_TARG

  • SLIT

  • OBSRA

  • OBSDEC

This method populates the data_container class attribute which is an instance of the goodman_pipeline.core.core.NightDataContainer. A data group is an instance of a pandas.DataFrame.

Module contents#

Goodman CCD Reduction Tool