def test_proc_diff(nires_sci_files, nires_bg_files):
"""
Run on near-IR frames
"""
# Setup
det = 1
bpm = np.zeros((2048,1024))
pixelflat = np.ones_like(bpm)
# Sci image
sciImg = scienceimage.build_from_file_list(keck_nires, det,
nires_par['scienceframe']['process'], bpm,
nires_sci_files, None, pixelflat)
# Bg image
bgImg = scienceimage.build_from_file_list(keck_nires, det,
nires_par['scienceframe']['process'], bpm,
nires_bg_files, None, pixelflat)
# Difference
sciImg = sciImg - bgImg
# Test
assert isinstance(sciImg, scienceimage.ScienceImage)
def test_from_list(shane_kast_blue_sci_files):
"""
Run on two frames
"""
det = 1
# Load calibrations
pixelflat = load_kast_blue_masters(pixflat=True)[0]
bpm = kast_blue.empty_bpm(shane_kast_blue_sci_files[0], det)
# Do it
sciImg = scienceimage.build_from_file_list(
kast_blue, det, kast_par['scienceframe']['process'], bpm,
shane_kast_blue_sci_files, None, pixelflat)
# Test
assert isinstance(sciImg, scienceimage.ScienceImage)
def extract_one(self, frames, det, bg_frames, std_outfile=None):
"""
Extract a single exposure/detector pair
sci_ID and det need to have been set internally prior to calling this method
Args:
frames (list):
List of frames to extract; stacked if more than one is provided
det (int):
bg_frames (list):
List of frames to use as the background
Can be empty
std_outfile (str, optional):
Returns:
seven objects are returned::
- ndarray: Science image
- ndarray: Science inverse variance image
- ndarray: Model of the sky
- ndarray: Model of the object
- ndarray: Model of inverse variance
- ndarray: Mask
- :obj:`pypeit.specobjs.SpecObjs`: spectra
"""
# Grab some meta-data needed for the reduction from the fitstbl
self.objtype, self.setup, self.obstime, self.basename, self.binning = self.get_sci_metadata(
frames[0], det)
# Is this a standard star?
self.std_redux = 'standard' in self.objtype
# Get the standard trace if need be
std_trace = self.get_std_trace(self.std_redux, det, std_outfile)
# Build Science image
sci_files = self.fitstbl.frame_paths(frames)
self.sciImg = scienceimage.build_from_file_list(
self.spectrograph,
det,
self.par['scienceframe']['process'],
self.caliBrate.msbpm,
sci_files,
self.caliBrate.msbias,
self.caliBrate.mspixelflat,
illum_flat=self.caliBrate.msillumflat)
# Background Image?
if len(bg_frames) > 0:
bg_file_list = self.fitstbl.frame_paths(bg_frames)
self.sciImg = self.sciImg - scienceimage.build_from_file_list(
self.spectrograph,
det,
self.par['scienceframe']['process'],
self.caliBrate.msbpm,
bg_file_list,
self.caliBrate.msbias,
self.caliBrate.mspixelflat,
illum_flat=self.caliBrate.msillumflat)
# Update mask for slitmask
slitmask = pixels.tslits2mask(self.caliBrate.tslits_dict)
self.sciImg.update_mask_slitmask(slitmask)
# For QA on crash
msgs.sciexp = self.sciImg
# Instantiate Reduce object
self.maskslits = self.caliBrate.tslits_dict['maskslits'].copy()
# Required for pypeline specific object
# TODO -- caliBrate should be replaced by the ~3 primary Objects needed
# once we have the data models in place.
self.redux = reduce.instantiate_me(self.sciImg,
self.spectrograph,
self.par,
self.caliBrate,
maskslits=self.maskslits,
ir_redux=self.ir_redux,
std_redux=self.std_redux,
objtype=self.objtype,
setup=self.setup,
show=self.show,
det=det,
binning=self.binning)
# Show?
if self.show:
self.redux.show('image',
image=self.sciImg.image,
chname='processed',
slits=True,
clear=True)
# Prep for manual extraction (if requested)
manual_extract_dict = self.fitstbl.get_manual_extract(frames, det)
self.skymodel, self.objmodel, self.ivarmodel, self.outmask, self.sobjs = self.redux.run(
std_trace=std_trace,
manual_extract_dict=manual_extract_dict,
show_peaks=self.show,
basename=self.basename,
ra=self.fitstbl["ra"][frames[0]],
dec=self.fitstbl["dec"][frames[0]],
obstime=self.obstime)
# Return
return self.sciImg.image, self.sciImg.ivar, self.skymodel, self.objmodel, self.ivarmodel, self.outmask, self.sobjs