REDUCE module¶
reduce module is still under development. In any case, users may find it better to put together their own reduction sequences that allow them to fully understand what they are doing to their data!
reduce is a module that implements basic data reduction for imaging and spectroscopic data. It works by reading a configuration file that contains blocks of information needed to reduce a set of data for a given instrument in a given night; multiple blocks can be specified in a single input configuration file if desired. It can be run in an interactive mode where the user sees the results of each step and can make some modification, and also in a batch mode where the processing proceeds automatically.
The reduction is run using the reduce.reduce() routine where a configuration file is a required input. Optional inputs control the amount of user interaction: the plot= option allows the user to specify a matplotlib figure in which some data are displayed, and the display= option allows the user to specify a pyvista.tv.TV() instance into which image data are displayed. A verbose= option allows the user to control the level of output.
The configuration file provides an instrument identification, which is used to read an instrument configuration file with basic information such as overscan region, gain, readout noise, normalization region, etc. It then contains blocks of information used for the calibration (superbias, superdark, superflat, wavelength calibration), which can either specify existing calibration products, or a list of frames to be used to construct new calibration products. Multiple calibration products of a given type can be constructed and each is given a label by which it can be referenced for use in reduction. Finally, blocks of information are given for the object frames to reduce.
The configuration file is formatted as a YAML file, which is a simply and natural way to provide the required information. There is some required information in the file, and some optional information. A complete list is provided below.
--- # required line for YAML file
date: "October 2019" #
groups : # required start list of reduction blocks
- name : "ARCTIC" # name of first block
skip : True
inst : "ARCTIC" # instrument, used to identify reduction configuration
rawdir : "UT191028/ARCTIC" # directory with raw input images, relative to run directory
reddir : "red/UT191028/ARCTIC" # directory for reduced output images, relative to run directory
biases : # optional specification(s) for superbias frame
- id : "bias" # identification name and name for output file
frames : [1,2,3,4,5,6,7,8,9,10] # list of frame IDs (number or character) to construct superbias if it doesn't exist
darks : # optional specification(s) for superbias frame
- id : "dark_1200" # identification name and name for output file
frames : [82,83,84,85,86,87,88,89,90] # list of frame IDs (number or character) to construct superbias if it doesn't exist
bias : "bias" # bias frame used to reduce individual darks before superdark construction
flats : # optional specification(s) for superflat frame
- id : "domeflat_z" # identification name and name for first flat
frames : [1,2,3,4,5] # list of frame IDs
bias : "bias" # bias frame used to reduce individual flats before superflat construction
- id : "domeflat_r" # identification name and name for second flat
frames : [6,7,8,9,10] # list of frame IDs
bias : "bias" # bias frame used to reduce individual flats before superflat construction
....
arcs : # frames to be used for wavelength calibration
- id : "pre" # ID for first wavecal set
frames : [19,20,21,22] # frames to use
bias : "bias" # bias to use
wref : "ARCES_wave" # reference file for template wavecals (pkl file)
wavecal_type : "echelle" # type: echelle or longslit
- id : "post" # ID for second wavecal set
frames : [32]
bias : "bias"
wref : "ARCES_wave"
wavecal_type : "echelle"
reduce functions¶
- pyvista.reduce.all(ymlfile, display=None, plot=None, verbose=True, clobber=True, wclobber=None, groups='all', solve=False, htmlfile='index.html', threads=0)[source]¶
Reduce full night(s) of data given input configuration file
- pyvista.reduce.mkcal(cals, caltype, reducer, reddir, sbias=None, sdark=None, clobber=False, html=None, **kwargs)[source]¶
Make calibration frames given input lists
- Args :
cals : list of different sets of given calibration type, as dictionaries caltype : gives caltype, of ‘bias’, ‘dark’, ‘flat’ reddir : directory for cal frames clobber= : set to True to force construction even if cal frame already exists