2.3. Flat Recipes
2.3.1. makeProcessedFlat
Convert raw MAROON-X flat frames into a single processed flat.
The input flats are separated into two streams based on their fiber illumination pattern: FDDDF flats (fibers 1 and 5 illuminated) in the main stream and DFFFD flats (fibers 2, 3 and 4 illuminated) in a second stream. Each stream is stacked, its fiber stripes are traced and identified, and its stray light is removed. The two streams are then combined into a fully illuminated FFFFF flat by taking the pixel-by-pixel maximum, the stripe tracing is re-run on the combined frame, and 1D spectra are extracted for the illuminated fibers. The result is stored on disk by storeProcessedFlat under the name of the first input flat with “_FFFFF_flat.fits” appended.
A processed flatfield is required to perform optimal flux extraction and to determine the blaze function. Due to the stability of the spectrograph, one processed flatfield is typically valid for at least a two-week period. Cross-comparisons between processed flatfields taken months apart have not been conducted so far.
Parameters
----------
p : Primitives object
A primitive set matching the recipe_tags.
def makeProcessedFlat(p):
p.prepare()
p.checkArm()
p.checkND()
p.addDQ()
p.subtractOverscan()
p.trimOverscan()
p.correctImageOrientation()
p.addVAR(read_noise=True, poisson_noise=True)
# Creates 'DFFFD_flats' stream and leaves FDDDF flats in main stream
p.separateFlatStreams()
p.stackFlats(suffix='FDDDF_flats')
p.stackFlats(stream='DFFFD_flats', suffix='DFFFD')
# Define stripe info to ultimately remove stray light in each stream
p.findStripes()
p.findStripes(stream='DFFFD_flats')
# Identify stripes based on MX architecture files
p.identifyStripes(selected_fibers=[1, 5])
p.identifyStripes(stream='DFFFD_flats', selected_fibers=[2, 3, 4])
# Defines pixel inclusion for each flat region based on stripe ids
p.defineFlatStripes()
p.defineFlatStripes(stream='DFFFD_flats')
# Remove straylight (requires 2 partial illumination flat sets)
p.removeStrayLight(stream='main', filter_size=19, box_size=20)
p.removeStrayLight(stream='DFFFD_flats', filter_size=19, box_size=20)
# Legacy patch for removeStrayLight
# p.removeStrayLight_legacyPatch(stream='main', filter_size=19, box_size=20)
# p.removeStrayLight_legacyPatch(stream='DFFFD_flats', filter_size=19, box_size=20)
# Combine straylight-removed images
p.combineFlatStreams(stream='main', stream_2='DFFFD_flats')
# Remove second stream
p.clearStream(stream='DFFFD_flats')
# Re-run find/identify/define routine on combined frame
p.findStripes()
p.identifyStripes(selected_fibers=[1, 2, 3, 4, 5])
p.defineFlatStripes(extract=True)
# Perform optimal extraction on flat field to create 1D spectra
p.extractStripes()
p.optimalExtraction(optimal_extraction_fibers=[2, 3, 4, 5])
p.storeProcessedFlat(suffix='_FFFFF_flat')
2.3.2. makeProcessedFlatDFFFF
Convert raw MAROON-X flat frames into a processed DFFFF flat.
Variant of makeProcessedFlat for datasets without FDDDF flats: the DDDDF flats (only fiber 5 illuminated) are kept in the main stream and the DFFFD flats (fibers 2, 3 and 4 illuminated) in a second stream. Following the legacy processing order, each stream is stacked first; the stacked frames are then overscan subtracted again, trimmed and orientation corrected. The fiber stripes of each stream are traced and identified, its stray light is removed, and the two streams are combined into a DFFFF flat (fiber 1 dark) by taking the pixel-by-pixel maximum. The stripe tracing is re-run on the combined frame and the result is stored on disk by storeProcessedFlat with a “_DFFFF_flat” suffix.
Parameters
----------
p : Primitives object
A primitive set matching the recipe_tags.
def makeProcessedFlatDFFFF(p):
p.prepare()
p.checkArm()
# p.checkND()
p.addDQ()
p.subtractOverscan()
# p.trimOverscan() # noqa: ERA001
# p.correctImageOrientation() # noqa: ERA001
p.addVAR(read_noise=True, poisson_noise=True)
# Creates 'DFFFD_flats' stream and leaves FDDDF flats in main stream
p.separateFlatStreams()
p.stackFlats(stream='main', scale_mode='mean_frame', suffix='DDDDF')
p.stackFlats(stream='DFFFD_flats', scale_mode='mean_frame', suffix='DFFFD')
# Subtract overscan is run again in backgroundfit.py on the stacked flats
p.subtractOverscan(stream='main')
p.subtractOverscan(stream='DFFFD_flats')
p.trimOverscan(stream='main')
p.trimOverscan(stream='DFFFD_flats')
p.correctImageOrientation(stream='main')
p.correctImageOrientation(stream='DFFFD_flats')
# ================================================
# Define stripe info to ultimately remove stray light in each stream
p.findStripes(stream='main')
p.findStripes(stream='DFFFD_flats')
# Identify stripes based on MX architecture files
p.identifyStripes(stream='main', selected_fibers=[5])
p.identifyStripes(stream='DFFFD_flats', selected_fibers=[2, 3, 4])
# Defines pixel inclusion for each flat region based on stripe ids
p.defineFlatStripes(stream='main')
p.defineFlatStripes(stream='DFFFD_flats')
# Remove straylight (requires 2 partial illumination flat sets)
p.removeStrayLight(stream='main', filter_size=19, box_size=20)
p.removeStrayLight(stream='DFFFD_flats', filter_size=19, box_size=20)
# Legacy patch for removeStrayLight
# p.removeStrayLight_legacyPatch(stream='main', filter_size=19, box_size=20)
# p.removeStrayLight_legacyPatch(stream='DFFFD_flats', filter_size=19, box_size=20)
# Combine straylight-removed images
p.combineFlatStreams(stream='main', stream_2='DFFFD_flats')
# Remove second stream
p.clearStream(stream='DFFFD_flats')
# Re-run find/identify/define routine on combined frame
p.findStripes()
p.identifyStripes(selected_fibers=[2, 3, 4, 5])
p.defineFlatStripes(extract=True)
p.storeProcessedFlat(suffix='_DFFFF_flat')
2.3.3. makeBlaze
Measure the blaze function for each fiber of a processed masterflat.
This recipe fits a spline to the box-extracted flat spectrum of each fiber to model the blaze curve, then normalises each order so the peak equals 1. The result is stored as a BLAZE_FIBER_N extension for each fiber N and written to disk with a “_blaze” suffix.
Parameters
----------
p : Primitives object
A primitive set matching the recipe_tags.
def makeBlaze(p):
p.checkMaster()
p.extractStripes()
# p.boxExtraction()
p.optimalExtraction()
p.measureBlaze(n_knots=50)
p.writeOutputs(suffix='_blaze')