pypeit.core.wavecal.waveio module

Module for I/O in arclines

pypeit.core.wavecal.waveio.load_line_list(line_file, use_ion=False)

Parameters:

• line_file (str) – Full path to line_list or name of ion

• use_ion (bool, optional) – Interpret line_file as an ion, e.g. CuI

Returns:

line_list

Return type:

astropy.table.Table

pypeit.core.wavecal.waveio.load_line_lists(lamps, all=False, include_unknown: bool = False, restrict_on_instr=None)

Loads a series of line list files

Parameters:

• lamps (list) – List of arc lamps to be used for wavelength calibration. E.g., [‘ArI’,’NeI’,’KrI’,’XeI’]

• restrict_on_instr (str, optional) – Restrict according to the input spectrograph

• all (bool, optional) – Load all line lists, independent of the input lamps (not recommended)

• include_unknown (bool, optional) – If True, the tot_line_list includes the unknown lines

Returns:

• tot_line_list (astropy Table of line lists (including unknown lines, if requested))

• line_list (astropy Table of line lists)

• unkn_lines (astropy Table of unknown lines)

pypeit.core.wavecal.waveio.load_reid_arxiv(arxiv_file)

Load a REID arxiv file Now there are 2 possible formats. We need to consolidate

Parameters:

arxiv_file (str) –

Return type:

dict, dict-like

pypeit.core.wavecal.waveio.load_template(arxiv_file: str, det: int, wvrng: list | None = None) → tuple[numpy.ndarray, numpy.ndarray, int]

Load a full template file from disk

Parameters:

• arxiv_file (str) – File with archive spectrum

• det (int) – Detector number

• wvrng (list, optional) – min, max wavelength range for the arxiv

Returns:

• wave (ndarray) – Wavelength vector

• flux (ndarray) – Flux vector

• binning (int) – binning of the template arc spectrum

pypeit.core.wavecal.waveio.load_tree(polygon=4, numsearch=20)

Load a KDTree of ThAr patterns that is stored on disk

Parameters:

• polygon (int) –

  Number of sides to the polygon used in pattern matching:

  • polygon=3 –> trigon (two anchor lines and one floating line)

  • polygon=4 –> tetragon (two anchor lines and two floating lines)

  • polygon=5 –> pentagon (two anchor lines and three floating lines)

• numsearch (int) –

  Number of consecutive detected lines used to generate a pattern. For example, if numsearch is 4, then for a trigon, the following patterns will be generated (assuming line #1 is the left anchor):

  • 1 2 3 (in this case line #3 is the right anchor)

  • 1 2 4 (in this case line #4 is the right anchor)

  • 1 3 4 (in this case line #4 is the right anchor)

Returns:

• file_load (KDTree instance) – The KDTree containing the patterns

• index (ndarray) – For each pattern in the KDTree, this array stores the corresponding index in the linelist

pypeit.core.wavecal.waveio.load_unknown_list(lines, unknwn_file=None, all=False)

Parameters:

• lines (list) – Restricted lines; use all=True for all

• unknwn_file (str, optional) –

• all (bool, optional) –

Returns:

unknwn_lines

Return type:

astropy.table.Table

pypeit.core.wavecal.waveio.load_wavelength_calibration(filename: Path) → dict

Load the wavelength calibration data from a file.

Parameters:

filename (pathlib.Path) – Name of the json file.

Returns:

Returns the wavelength calibration dictionary. Lists read from the json file are returnes as numpy arrays.

Return type:

dict