Epoch classes¶

frame_class(craftutils.observation.image.ESOImagingImage)

alias of FORS2Image

coadded_class(craftutils.observation.epoch.ImagingEpoch.coadded_class)

alias of FORS2CoaddedImage

Public members¶

instrument_name = 'vlt-fors2'

source_extraction(output_dir: str, do_diagnostics: bool = True, **)

reduce(output_dir: str | None = None)

photometric_calibration(output_path: str | None = None, ...)

zeropoint(image_dict: dict, output_path: str, ...)

stage_params : dict

exclude_frames : list

FORS2StandardEpoch(centre_coords: SkyCoord, instrument: str, ...)

Initialize self. See help(type(self)) for accurate signature.

classmethod select_child_class(instrument: str | Instrument)

mode = 'imaging'

frames_for_combined = 'astrometry'

skip_for_combined = ['download', 'initial_setup', 'sort_reduced', 'trim_reduced', 'convert_to_cs', 'correct_astrometry_frames']

validation_stages = ['insert_synthetic_frames']

classmethod stages()

n_frames(fil: str)

proc_download(output_dir: str, **kwargs)

proc_insert_synthetic_frames(output_dir: str, **kwargs)

generate_validation_catalogue(force=False, ...)

insert_synthetic_frames(frame_type: str, output_dir: str, **kwargs)

proc_defringe(output_dir: str, **kwargs)

generate_master_biases(output_dir: str | None = None, ...)

This does nothing, but will eventually do what the function name says.

generate_master_flats(output_dir: str | None = None, ...)

This does nothing, but will eventually do what the function name says.

generate_fringe_map(fil: str, output_dir: str | None = None, ...)

Returns¶

proc_subtract_background_frames(output_dir: str, **kwargs)

subtract_background_frames(output_dir: str, ...)

proc_register(output_dir: str, **kwargs)

register(output_dir: str, frames: dict | None = None, ...)

Parameters¶

output_dir: str

frames: dict | None = None

template

There are three options for this parameter: int: An integer specifying the position of the image in the list to use as the template for alignment (ie, each filter will use the same list position) dict: a dictionary with keys reflecting the filter names, with values specifying the list position as above ImagingImage: an image from outside this epoch to use as template. You can also pass the path to the image

as a string.

kwargs

Returns¶

proc_correct_astrometry_frames(output_dir: str, **kwargs)

correct_astrometry_frames(output_dir: str, ...)

proc_frame_diagnostics(output_dir: str, **kwargs)

frame_psf_diagnostics(output_dir: str, frame_dict: dict, ...)

proc_coadd(output_dir: str, **kwargs)

coadd(output_dir: str, frames: str = 'astrometry', ...)

Use Montage and ccdproc to coadd individual frames.

proc_correct_astrometry_coadded(output_dir: str, **kwargs)

correct_astrometry_coadded(output_dir: str, ...)

proc_trim_coadded(output_dir: str, **kwargs)

trim_coadded(output_dir: str, images: dict | None = None, ...)

proc_source_extraction(output_dir: str, **kwargs)

proc_photometric_calibration(output_dir: str, **kwargs)

proc_dual_mode_source_extraction(output_dir: str, **kwargs)

dual_mode_source_extraction(path: str, ...)

proc_finalise(output_dir: str, **kwargs)

finalise(image_type: str = 'final', ...)

Performs a number of wrap-up actions: - Ensures final fits files have correct header information - Renames and copies final files to appropriate paths - Updates epoch tables

proc_get_photometry(output_dir: str, **kwargs)

did_local_background_subtraction()

best_for_path(image_type: str = 'final', exclude: list = ())

probabilistic_association(image_type: str = 'final', **path_kwargs)

get_photometry(path: str, image_type: str = 'finalised', ...)

Retrieve photometric properties of key objects and write to disk.

validation_checks(...)

astrometry_diagnostics(images: ~typing.Optional[dict] = None, ...)

psf_diagnostics(images: dict | None = None)

guess_data_path()

load_output_file(**kwargs)

Loads the output .yaml file, which contains various values derived from this Epoch, using the object’s output_file attribute (which is a path to the file).

generate_astrometry_indices(cat_name='gaia', ...)

Generates astrometry indices using astrometry.net and the specified catalogue, unless they have been generated before; in which case it simply copies them to the main index directory (overwriting those of other epochs there).

epoch_gaia_catalogue(correct_to_epoch: bool = True)

add_frame_raw(raw_frame: ImagingImage | str)

add_frame_reduced(frame: str | ImagingImage)

add_frame_trimmed(frame: ImagingImage)

add_frame_subtracted(frame: str | ImagingImage)

add_frame_registered(frame: str | ImagingImage)

add_frame_astrometry(frame: str | ImagingImage)

add_frame_diagnosed(frame: str | ImagingImage)

add_frame_normalised(frame: str | ImagingImage)

add_coadded_trimmed_image(img: str | Image, key: str, **kwargs)

add_coadded_unprojected_image(img: str | Image, key: str, **kwargs)

add_coadded_subtracted_image(img: str | Image, key: str, **kwargs)

add_coadded_subtracted_trimmed_image(img: str | Image, key: str, **)

add_coadded_subtracted_patch_image(img: str | Image, key: str, **)

add_coadded_astrometry_image(img: str | Image, key: str, **kwargs)

check_filter(fil: str)

If a filter name is not present in the various lists and dictionaries that use it, adds it. :param fil: :return: False if None, True if not.

plot_object(img: str, fil: str, fig: ~matplotlib.figure.Figure, ...)

push_to_table()

classmethod from_params(name: str, instrument: str, ...)

classmethod build_param_path(instrument_name: str, field_name, ...)

classmethod build_data_path_absolute(field: Field, ...)

classmethod from_file(param_file: str | dict, ...)

classmethod default_params()

is_excluded(frame: Image | str)

__str__()

Return str(self).

__repr__()

Return repr(self).

date_str(include_time: bool = False)

mjd()

proc_initial_setup(output_dir: str, **kwargs)

set_program_id(program_id: str)

set_date(date: str | Time)

set_target(target: str)

get_binning()

set_binning(binning: str)

get_binning_std()

set_binning_std(binning: str)

get_path(key: str)

get_master_bias(chip: int)

get_master_flat(chip: int, fil: str)

classmethod sort_by_chip(images: list)

add_coadded_image(img: str | Image, key: str, **kwargs)

sort_frame(frame: Image, sort_key: str | None = None)

classmethod new_yaml(name: str, path: str | None = None, **kwargs)

stage_output_dirs = True

add_log(action: str, method=None, method_args=None, ...)

set_path(key: str, value: str)

classmethod enumerate_stages(show: bool = True)

query_stage(message: str, stage_name: str, n: float, ...)

Helper method for asking the user if we need to do this stage of processing. If self.do is True, skips the query and returns True.

check_done(stage: str)

pipeline(no_query: bool = False, **kwargs)

Performs the pipeline methods given in stages() for this instance.

update_output_file()

update_param_file(param: str)