class PypeIt(object):
"""
This class runs the primary calibration and extraction in PypeIt
Args:
pypeit_file (:obj:`str`): PypeIt filename
verbosity (:obj:`int`, optional):
Verbosity level of system output. Can be::
- 0: No output
- 1: Minimal output (default)
- 2: All output
overwrite (:obj:`bool`, optional):
Flag to overwrite any existing files/directories.
reuse_masters (bool, optional): Reuse any pre-existing calibration files
logname (:obj:`str`, optional):
The name of an ascii log file with the details of the
reduction.
redux_path (:obj:`str`, optional):
Over-ride reduction path in PypeIt file (e.g. Notebook usage)
show: (:obj:`bool`, optional):
Show reduction steps via plots (which will block further
execution until clicked on) and outputs to ginga. Requires
remote control ginga session via "ginga --modules=RC &"
Attributes:
pypeit_file (:obj:`str`):
Name of the pypeit file to read. PypeIt files have a specific
set of valid formats. A description can be found `here`_
(include doc link).
fitstbl (:obj:`pypit.metadata.PypeItMetaData`): holds the meta info
"""
# __metaclass__ = ABCMeta
def __init__(self, pypeit_file, verbosity=2, overwrite=True, reuse_masters=False, logname=None,
show=False, redux_path=None):
# Load
cfg_lines, data_files, frametype, usrdata, setups = parse_pypeit_file(pypeit_file, runtime=True)
self.pypeit_file = pypeit_file
# Spectrograph
cfg = ConfigObj(cfg_lines)
spectrograph_name = cfg['rdx']['spectrograph']
self.spectrograph = load_spectrograph(spectrograph_name)
# Par
# Defaults
spectrograph_def_par = self.spectrograph.default_pypeit_par()
# Grab a science file for configuration specific parameters
sci_file = None
for idx, row in enumerate(usrdata):
if 'science' in row['frametype']:
sci_file = data_files[idx]
break
# Set
spectrograph_cfg_lines = self.spectrograph.config_specific_par(spectrograph_def_par, sci_file).to_config()
self.par = PypeItPar.from_cfg_lines(cfg_lines=spectrograph_cfg_lines, merge_with=cfg_lines)
# Fitstbl
self.fitstbl = PypeItMetaData(self.spectrograph, self.par, file_list=data_files,
usrdata=usrdata, strict=True)
# The following could be put in a prepare_to_run() method in PypeItMetaData
if 'setup' not in self.fitstbl.keys():
self.fitstbl['setup'] = setups[0]
self.fitstbl.get_frame_types(user=frametype) # This sets them using the user inputs
self.fitstbl.set_defaults() # Only does something if values not set in PypeIt file
self.fitstbl._set_calib_group_bits()
self.fitstbl._check_calib_groups()
# Write .calib file (For QA naming amongst other things)
calib_file = pypeit_file.replace('.pypeit', '.calib')
self.fitstbl.write_calib(calib_file)
# Other Internals
self.logname = logname
self.overwrite = overwrite
# Currently the runtime argument determines the behavior for reuse_masters. There is also a reuse_masters
# parameter in the parset but it is currently ignored.
self.reuse_masters=reuse_masters
self.show = show
# Make the output directories
self.par['rdx']['redux_path'] = os.getcwd() if redux_path is None else redux_path
msgs.info("Setting reduction path to {:s}".format(self.par['rdx']['redux_path']))
paths.make_dirs(self.spectrograph.spectrograph, self.par['calibrations']['caldir'],
self.par['rdx']['scidir'], self.par['rdx']['qadir'],
overwrite=self.overwrite, redux_path=self.par['rdx']['redux_path'])
# Instantiate Calibrations class
self.caliBrate \
= calibrations.MultiSlitCalibrations(self.fitstbl, self.par['calibrations'], self.spectrograph,
redux_path=self.par['rdx']['redux_path'],
reuse_masters=self.reuse_masters,
save_masters=True, write_qa=True,
show=self.show)
# Init
self.verbosity = verbosity
# TODO: I don't think this ever used
self.frame = None
self.det = None
self.tstart = None
self.basename = None
self.sciI = None
self.obstime = None
def build_qa(self):
"""
Generate QA wrappers
"""
qa.gen_mf_html(self.pypeit_file)
qa.gen_exp_html()
def outfile_exists(self, frame):
"""
Check whether the 2D outfile of a given frame already exists
Args:
frame (int): Frame index from fitstbl
Returns:
bool: True if the 2d file exists
False if it does not exist
"""
# Check if the 2d output file exists
scidir = os.path.join(self.par['rdx']['redux_path'], self.par['rdx']['scidir'])
basename = self.fitstbl.construct_basename(frame)
outfile = scidir + '/spec2d_{:s}.fits'.format(basename)
return os.path.isfile(outfile)
def get_std_outfile(self, standard_frames):
"""
Grab the output filename from an input list of standard_frame indices
If more than one index is provided, the first is taken
Args:
standard_frames (list): List of indices corresponding to standard stars
Returns:
str: Full path to the standard spec1d output file
"""
# TODO: Need to decide how to associate standards with
# science frames in the case where there is more than one
# standard associated with a given science frame. Below, I
# just use the first standard
std_outfile = None
std_frame = None if len(standard_frames) == 0 else standard_frames[0]
# Prepare to load up standard?
if std_frame is not None:
std_outfile = os.path.join(self.par['rdx']['redux_path'], self.par['rdx']['scidir'],
'spec1d_{:s}.fits'.format(self.fitstbl.construct_basename(std_frame))) \
if isinstance(std_frame, (int,np.integer)) else None
if std_outfile is not None and not os.path.isfile(std_outfile):
msgs.error('Could not find standard file: {0}'.format(std_outfile))
return std_outfile
def reduce_all(self):
"""
Main driver of the entire reduction
Calibration and extraction via a series of calls to reduce_exposure()
"""
# Validate the parameter set
required = ['rdx', 'calibrations', 'scienceframe', 'scienceimage', 'flexure', 'fluxcalib']
can_be_None = ['flexure', 'fluxcalib']
self.par.validate_keys(required=required, can_be_None=can_be_None)
self.tstart = time.time()
# Find the standard frames
is_standard = self.fitstbl.find_frames('standard')
# Find the science frames
is_science = self.fitstbl.find_frames('science')
# Frame indices
frame_indx = np.arange(len(self.fitstbl))
# Iterate over each calibration group and reduce the standards
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the standard frames in this calibration group:
grp_standards = frame_indx[is_standard & in_grp]
# Reduce all the standard frames, loop on unique comb_id
u_combid_std= np.unique(self.fitstbl['comb_id'][grp_standards])
for j, comb_id in enumerate(u_combid_std):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
std_dict = self.reduce_exposure(frames, bg_frames=bg_frames)
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], std_dict, self.basename)
else:
msgs.info('Output file: {:s} already exists'.format(self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
# Iterate over each calibration group again and reduce the science frames
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the science frames in this calibration group:
grp_science = frame_indx[is_science & in_grp]
# Associate standards (previously reduced above) for this setup
std_outfile = self.get_std_outfile(frame_indx[is_standard])
# Reduce all the science frames; keep the basenames of the science frames for use in flux calibration
science_basename = [None]*len(grp_science)
# Loop on unique comb_id
u_combid = np.unique(self.fitstbl['comb_id'][grp_science])
for j, comb_id in enumerate(u_combid):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
sci_dict = self.reduce_exposure(frames, bg_frames=bg_frames, std_outfile=std_outfile)
science_basename[j] = self.basename
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], sci_dict, self.basename)
else:
msgs.warn('Output file: {:s} already exists'.format(self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
msgs.info('Finished calibration group {0}'.format(i))
# Finish
self.print_end_time()
def select_detectors(self):
"""
Return the 1-indexed list of detectors to reduce.
Returns:
list: List of detectors to be reduced
"""
if self.par['rdx']['detnum'] is None:
return np.arange(self.spectrograph.ndet)+1
return [self.par['rdx']['detnum']] if isinstance(self.par['rdx']['detnum'], int) \
else self.par['rdx']['detnum']
def reduce_exposure(self, frames, bg_frames=[], std_outfile=None):
"""
Reduce a single exposure
Args:
frame (:obj:`int`):
0-indexed row in :attr:`fitstbl` with the frame to
reduce
bgframes (:obj:`list`, optional):
List of frame indices for the background
std_outfile (:obj:`str`, optional):
the name of a file with a previously PypeIt-reduced standard spectrum.
Returns:
dict: The dictionary containing the primary outputs of extraction
"""
# if show is set, clear the ginga channels at the start of each new sci_ID
if self.show:
ginga.clear_all()
# Save the frame
self.frames = frames
self.bg_frames = bg_frames
# Is this an IR reduction?
self.ir_redux = True if len(bg_frames) > 0 else False
# JFH Why does this need to be ordered?
sci_dict = OrderedDict() # This needs to be ordered
sci_dict['meta'] = {}
sci_dict['meta']['vel_corr'] = 0.
sci_dict['meta']['ir_redux'] = self.ir_redux
# Print status message
msgs_string = 'Reducing target {:s}'.format(self.fitstbl['target'][self.frames[0]]) + msgs.newline()
msgs_string += 'Combining frames:' + msgs.newline()
for iframe in self.frames:
msgs_string += '{0:s}'.format(self.fitstbl['filename'][iframe]) + msgs.newline()
msgs.info(msgs_string)
if len(bg_frames) > 0:
bg_msgs_string = ''
for iframe in self.bg_frames:
bg_msgs_string += '{0:s}'.format(self.fitstbl['filename'][iframe]) + msgs.newline()
bg_msgs_string = msgs.newline() + 'Using background from frames:' + msgs.newline() + bg_msgs_string
msgs.info(bg_msgs_string)
# Find the detectors to reduce
detectors = self.select_detectors()
if len(detectors) != self.spectrograph.ndet:
msgs.warn('Not reducing detectors: {0}'.format(' '.join([ str(d) for d in
set(np.arange(self.spectrograph.ndet))-set(detectors)])))
# Loop on Detectors
for self.det in detectors:
msgs.info("Working on detector {0}".format(self.det))
sci_dict[self.det] = {}
# Calibrate
#TODO Is the right behavior to just use the first frame?
self.caliBrate.set_config(self.frames[0], self.det, self.par['calibrations'])
self.caliBrate.run_the_steps()
# Extract
# TODO: pass back the background frame, pass in background
# files as an argument. extract one takes a file list as an
# argument and instantiates science within
sci_dict[self.det]['sciimg'], sci_dict[self.det]['sciivar'], sci_dict[self.det]['skymodel'], \
sci_dict[self.det]['objmodel'], sci_dict[self.det]['ivarmodel'], sci_dict[self.det]['outmask'], \
sci_dict[self.det]['specobjs'], vel_corr \
= self.extract_one(self.frames, self.det, bg_frames = self.bg_frames, std_outfile = std_outfile)
if vel_corr is not None:
sci_dict['meta']['vel_corr'] = vel_corr
# JFH TODO write out the background frame?
# Return
return sci_dict
def flexure_correct(self, sobjs, maskslits):
"""
Correct for flexure
Spectra are modified in place (wavelengths are shifted)
Args:
sobjs (SpecObjs):
maskslits (ndarray): Mask of SpecObjs
"""
if self.par['flexure']['method'] != 'skip':
flex_list = wave.flexure_obj(sobjs, maskslits, self.par['flexure']['method'],
self.par['flexure']['spectrum'],
mxshft=self.par['flexure']['maxshift'])
# QA
wave.flexure_qa(sobjs, maskslits, self.basename, self.det, flex_list,
out_dir=self.par['rdx']['redux_path'])
else:
msgs.info('Skipping flexure correction.')
def helio_correct(self, sobjs, maskslits, frame, obstime):
"""
Perform a heliocentric correction on a set of spectra
Args:
sobjs (pypeit.specobjs.SpecObjs): Spectra
maskslits (ndarray): Slits that are masked
frame (int): Frame to use for meta info
obstime (astropy.time.Time):
Returns:
astropy.units.Quantity: Velocity correction in km/s
"""
# Helio, correct Earth's motion
if (self.caliBrate.par['wavelengths']['frame'] in ['heliocentric', 'barycentric']) \
and (self.caliBrate.par['wavelengths']['reference'] != 'pixel'):
# TODO change this keyword to refframe instead of frame
msgs.info("Performing a {0} correction".format(self.caliBrate.par['wavelengths']['frame']))
vel, vel_corr = wave.geomotion_correct(sobjs, maskslits, self.fitstbl, frame, obstime,
self.spectrograph.telescope['longitude'],
self.spectrograph.telescope['latitude'],
self.spectrograph.telescope['elevation'],
self.caliBrate.par['wavelengths']['frame'])
else:
msgs.info('A wavelength reference-frame correction will not be performed.')
vel_corr = None
return vel_corr
def get_sci_metadata(self, frame, det):
"""
Grab the meta data for a given science frame and specific detector
Args:
frame (int): Frame index
det (int): Detector index
Returns:
5 objects are returned::
- str: Object type; science or standard
- str: Setup string from master_key()
- astropy.time.Time: Time of observation
- str: Basename of the frame
- str: Binning of the detector
"""
# Set binning, obstime, basename, and objtype
binning = self.fitstbl['binning'][frame]
obstime = self.fitstbl.construct_obstime(frame)
basename = self.fitstbl.construct_basename(frame, obstime=obstime)
objtype = self.fitstbl['frametype'][frame]
if 'science' in objtype:
objtype_out = 'science'
elif 'standard' in objtype:
objtype_out = 'standard'
else:
msgs.error('Unrecognized objtype')
setup = self.fitstbl.master_key(frame, det=det)
return objtype_out, setup, obstime, basename, binning
def get_std_trace(self, std_redux, det, std_outfile):
"""
Returns the trace of the standard if it is applicable to the current reduction
Args:
std_redux (bool): If False, proceed
det (int): Detector index
std_outfile (str): Filename for the standard star spec1d file
Returns:
ndarray: Trace of the standard star on input detector
"""
if std_redux is False and std_outfile is not None:
sobjs, hdr_std = load.load_specobjs(std_outfile)
# Does the detector match?
# TODO Instrument specific logic here could be implemented with the parset. For example LRIS-B or LRIS-R we
# we would use the standard from another detector
this_det = sobjs.det == det
if np.any(this_det):
sobjs_det = sobjs[this_det]
sobjs_std = sobjs_det.get_std()
std_trace = sobjs_std.trace_spat
# flatten the array if this multislit
if 'MultiSlit' in self.spectrograph.pypeline:
std_trace = std_trace.flatten()
elif 'Echelle' in self.spectrograph.pypeline:
std_trace = std_trace.T
else:
msgs.error('Unrecognized pypeline')
else:
std_trace = None
else:
std_trace = None
return std_trace
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, optional): List of frames to use as the background
std_outfile (str, optional):
Returns:
eight 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
- astropy.units.Quantity: velocity correction
"""
# 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)
# Instantiate ScienceImage for the files we will reduce
sci_files = self.fitstbl.frame_paths(frames)
self.sciI = scienceimage.ScienceImage(self.spectrograph, sci_files,
bg_file_list=self.fitstbl.frame_paths(bg_frames),
ir_redux = self.ir_redux,
par=self.par['scienceframe'],
det=det,
binning=self.binning)
# For QA on crash.
msgs.sciexp = self.sciI
# Process images (includes inverse variance image, rn2 image, and CR mask)
self.sciimg, self.sciivar, self.rn2img, self.mask, self.crmask = \
self.sciI.proc(self.caliBrate.msbias, self.caliBrate.mspixflatnrm.copy(),
self.caliBrate.msbpm, illum_flat=self.caliBrate.msillumflat,
show=self.show)
# Object finding, first pass on frame without sky subtraction
self.maskslits = self.caliBrate.maskslits.copy()
self.redux = reduce.instantiate_me(self.spectrograph, self.caliBrate.tslits_dict,
self.mask, self.par,
ir_redux = self.ir_redux,
objtype=self.objtype, setup=self.setup,
det=det, binning=self.binning)
# Prep for manual extraction (if requested)
manual_extract_dict = self.fitstbl.get_manual_extract(frames, det)
# Do one iteration of object finding, and sky subtract to get initial sky model
self.sobjs_obj, self.nobj, skymask_init = \
self.redux.find_objects(self.sciimg, self.sciivar, std=self.std_redux, ir_redux=self.ir_redux,
std_trace=std_trace,maskslits=self.maskslits,
show=self.show & (not self.std_redux),
manual_extract_dict=manual_extract_dict)
# Global sky subtraction, first pass. Uses skymask from object finding step above
self.initial_sky = \
self.redux.global_skysub(self.sciimg, self.sciivar, self.caliBrate.tilts_dict['tilts'], skymask=skymask_init,
std=self.std_redux, maskslits=self.maskslits, show=self.show)
if not self.std_redux:
# Object finding, second pass on frame *with* sky subtraction. Show here if requested
self.sobjs_obj, self.nobj, self.skymask = \
self.redux.find_objects(self.sciimg - self.initial_sky, self.sciivar, std=self.std_redux, ir_redux=self.ir_redux,
std_trace=std_trace,maskslits=self.maskslits,show=self.show,
manual_extract_dict=manual_extract_dict)
# If there are objects, do 2nd round of global_skysub, local_skysub_extract, flexure, geo_motion
if self.nobj > 0:
# Global sky subtraction second pass. Uses skymask from object finding
self.global_sky = self.initial_sky if self.std_redux else \
self.redux.global_skysub(self.sciimg, self.sciivar, self.caliBrate.tilts_dict['tilts'],
skymask=self.skymask, maskslits=self.maskslits, show=self.show)
self.skymodel, self.objmodel, self.ivarmodel, self.outmask, self.sobjs = \
self.redux.local_skysub_extract(self.sciimg, self.sciivar, self.caliBrate.tilts_dict['tilts'], self.caliBrate.mswave,
self.global_sky, self.rn2img, self.sobjs_obj,
model_noise=(not self.ir_redux),std = self.std_redux,
maskslits=self.maskslits, show_profile=self.show,show=self.show)
# Purge out the negative objects if this was a near-IR reduction.
# TODO should we move this purge call to local_skysub_extract??
if self.ir_redux:
self.sobjs.purge_neg()
# Flexure correction if this is not a standard star
if not self.std_redux:
self.redux.flexure_correct(self.sobjs, self.basename)
# Grab coord
radec = ltu.radec_to_coord((self.fitstbl["ra"][frames[0]], self.fitstbl["dec"][frames[0]]))
self.vel_corr = self.redux.helio_correct(self.sobjs, radec, self.obstime)
else:
# Print status message
msgs_string = 'No objects to extract for target {:s}'.format(self.fitstbl['target'][frames[0]]) + msgs.newline()
msgs_string += 'On frames:' + msgs.newline()
for iframe in frames:
msgs_string += '{0:s}'.format(self.fitstbl['filename'][iframe]) + msgs.newline()
msgs.warn(msgs_string)
# set to first pass global sky
self.skymodel = self.initial_sky
self.objmodel = np.zeros_like(self.sciimg)
# Set to sciivar. Could create a model but what is the point?
self.ivarmodel = np.copy(self.sciivar)
# Set to the initial mask in case no objects were found
self.outmask = self.redux.mask
# empty specobjs object from object finding
if self.ir_redux:
self.sobjs_obj.purge_neg()
self.sobjs = self.sobjs_obj
self.vel_corr = None
return self.sciimg, self.sciivar, self.skymodel, self.objmodel, self.ivarmodel, self.outmask, self.sobjs, self.vel_corr
# TODO: Why not use self.frame?
def save_exposure(self, frame, sci_dict, basename):
"""
Save the outputs from extraction for a given exposure
Args:
frame (:obj:`int`):
0-indexed row in the metadata table with the frame that
has been reduced.
sci_dict (:obj:`dict`):
Dictionary containing the primary outputs of extraction
basename (:obj:`str`):
The root name for the output file.
Returns:
None or SpecObjs: All of the objects saved to disk
"""
# TODO: Need some checks here that the exposure has been reduced
# Determine the headers
head1d = self.fitstbl[frame]
# Need raw file header information
rawfile = self.fitstbl.frame_paths(frame)
head2d = fits.getheader(rawfile, ext=self.spectrograph.primary_hdrext,)
refframe = 'pixel' if self.caliBrate.par['wavelengths']['reference'] == 'pixel' else \
self.caliBrate.par['wavelengths']['frame']
# Determine the paths/filenames
scipath = os.path.join(self.par['rdx']['redux_path'], self.par['rdx']['scidir'])
save.save_all(sci_dict, self.caliBrate.master_key_dict, self.caliBrate.master_dir, self.spectrograph,
head1d, head2d, scipath, basename, refframe=refframe,
update_det=self.par['rdx']['detnum'], binning=self.fitstbl['binning'][frame])
return
def msgs_reset(self):
"""
Reset the msgs object
"""
# Reset the global logger
msgs.reset(log=self.logname, verbosity=self.verbosity)
msgs.pypeit_file = self.pypeit_file
def print_end_time(self):
"""
Print the elapsed time
"""
# Capture the end time and print it to user
tend = time.time()
codetime = tend-self.tstart
if codetime < 60.0:
msgs.info('Execution time: {0:.2f}s'.format(codetime))
elif codetime/60.0 < 60.0:
mns = int(codetime/60.0)
scs = codetime - 60.0*mns
msgs.info('Execution time: {0:d}m {1:.2f}s'.format(mns, scs))
else:
hrs = int(codetime/3600.0)
mns = int(60.0*(codetime/3600.0 - hrs))
scs = codetime - 60.0*mns - 3600.0*hrs
msgs.info('Execution time: {0:d}h {1:d}m {2:.2f}s'.format(hrs, mns, scs))
# TODO: Move this to fitstbl?
def show_science(self):
"""
Simple print of science frames
"""
indx = self.fitstbl.find_frames('science')
print(self.fitstbl[['target','ra','dec','exptime','dispname']][indx])
def __repr__(self):
# Generate sets string
return '<{:s}: pypeit_file={}>'.format(self.__class__.__name__, self.pypeit_file)
class PypeIt:
"""
This class runs the primary calibration and extraction in PypeIt
.. todo::
Fill in list of attributes!
Args:
pypeit_file (:obj:`str`):
PypeIt filename.
verbosity (:obj:`int`, optional):
Verbosity level of system output. Can be:
- 0: No output
- 1: Minimal output (default)
- 2: All output
overwrite (:obj:`bool`, optional):
Flag to overwrite any existing files/directories.
reuse_masters (:obj:`bool`, optional):
Reuse any pre-existing calibration files
logname (:obj:`str`, optional):
The name of an ascii log file with the details of the
reduction.
show: (:obj:`bool`, optional):
Show reduction steps via plots (which will block further
execution until clicked on) and outputs to ginga. Requires
remote control ginga session via ``ginga --modules=RC,SlitWavelength &``
redux_path (:obj:`str`, optional):
Over-ride reduction path in PypeIt file (e.g. Notebook usage)
calib_only: (:obj:`bool`, optional):
Only generate the calibration files that you can
Attributes:
pypeit_file (:obj:`str`):
Name of the pypeit file to read. PypeIt files have a
specific set of valid formats. A description can be found
:ref:`pypeit_file`.
fitstbl (:obj:`pypeit.metadata.PypeItMetaData`): holds the meta info
"""
def __init__(self,
pypeit_file,
verbosity=2,
overwrite=True,
reuse_masters=False,
logname=None,
show=False,
redux_path=None,
calib_only=False):
# Set up logging
self.logname = logname
self.verbosity = verbosity
self.pypeit_file = pypeit_file
self.msgs_reset()
# Load
cfg_lines, data_files, frametype, usrdata, setups, _ \
= parse_pypeit_file(pypeit_file, runtime=True)
self.calib_only = calib_only
# Spectrograph
cfg = ConfigObj(cfg_lines)
spectrograph_name = cfg['rdx']['spectrograph']
self.spectrograph = load_spectrograph(spectrograph_name)
msgs.info('Loaded spectrograph {0}'.format(self.spectrograph.name))
# --------------------------------------------------------------
# Get the full set of PypeIt parameters
# - Grab a science or standard file for configuration specific parameters
config_specific_file = None
for idx, row in enumerate(usrdata):
if ('science' in row['frametype']) or ('standard'
in row['frametype']):
config_specific_file = data_files[idx]
# search for arcs, trace if no scistd was there
if config_specific_file is None:
for idx, row in enumerate(usrdata):
if ('arc' in row['frametype']) or ('trace'
in row['frametype']):
config_specific_file = data_files[idx]
if config_specific_file is not None:
msgs.info(
'Setting configuration-specific parameters using {0}'.format(
os.path.split(config_specific_file)[1]))
spectrograph_cfg_lines = self.spectrograph.config_specific_par(
config_specific_file).to_config()
# - Build the full set, merging with any user-provided
# parameters
self.par = PypeItPar.from_cfg_lines(cfg_lines=spectrograph_cfg_lines,
merge_with=cfg_lines)
msgs.info('Built full PypeIt parameter set.')
# Check the output paths are ready
if redux_path is not None:
self.par['rdx']['redux_path'] = redux_path
# TODO: Write the full parameter set here?
# --------------------------------------------------------------
# --------------------------------------------------------------
# Build the meta data
# - Re-initilize based on the file data
msgs.info('Compiling metadata')
self.fitstbl = PypeItMetaData(self.spectrograph,
self.par,
files=data_files,
usrdata=usrdata,
strict=True)
# - Interpret automated or user-provided data from the PypeIt
# file
self.fitstbl.finalize_usr_build(frametype, setups[0])
# --------------------------------------------------------------
# - Write .calib file (For QA naming amongst other things)
calib_file = pypeit_file.replace('.pypeit', '.calib')
self.fitstbl.write_calib(calib_file)
# Other Internals
self.overwrite = overwrite
# Currently the runtime argument determines the behavior for
# reuse_masters.
self.reuse_masters = reuse_masters
self.show = show
# Set paths
self.calibrations_path = os.path.join(
self.par['rdx']['redux_path'],
self.par['calibrations']['master_dir'])
# Check for calibrations
if not self.calib_only:
calibrations.check_for_calibs(
self.par,
self.fitstbl,
raise_error=self.par['calibrations']['raise_chk_error'])
# Report paths
msgs.info('Setting reduction path to {0}'.format(
self.par['rdx']['redux_path']))
msgs.info('Master calibration data output to: {0}'.format(
self.calibrations_path))
msgs.info('Science data output to: {0}'.format(self.science_path))
msgs.info('Quality assessment plots output to: {0}'.format(
self.qa_path))
# TODO: Is anything written to the qa dir or only to qa/PNGs?
# Should we have separate calibration and science QA
# directories?
# An html file wrapping them all too
# Init
# TODO: I don't think this ever used
self.det = None
self.tstart = None
self.basename = None
self.sciI = None
self.obstime = None
@property
def science_path(self):
"""Return the path to the science directory."""
return os.path.join(self.par['rdx']['redux_path'],
self.par['rdx']['scidir'])
@property
def qa_path(self):
"""Return the path to the top-level QA directory."""
return os.path.join(self.par['rdx']['redux_path'],
self.par['rdx']['qadir'])
def build_qa(self):
"""
Generate QA wrappers
"""
msgs.qa_path = self.qa_path
qa.gen_qa_dir(self.qa_path)
qa.gen_mf_html(self.pypeit_file, self.qa_path)
qa.gen_exp_html()
# TODO: This should go in a more relevant place
def spec_output_file(self, frame, twod=False):
"""
Return the path to the spectral output data file.
Args:
frame (:obj:`int`):
Frame index from :attr:`fitstbl`.
twod (:obj:`bool`):
Name for the 2D output file; 1D file otherwise.
Returns:
:obj:`str`: The path for the output file
"""
return os.path.join(
self.science_path,
'spec{0}d_{1}.fits'.format('2' if twod else '1',
self.fitstbl.construct_basename(frame)))
def outfile_exists(self, frame):
"""
Check whether the 2D outfile of a given frame already exists
Args:
frame (int): Frame index from fitstbl
Returns:
bool: True if the 2d file exists, False if it does not exist
"""
return os.path.isfile(self.spec_output_file(frame, twod=True))
def get_std_outfile(self, standard_frames):
"""
Grab the output filename from an input list of standard_frame indices
If more than one index is provided, the first is taken
Args:
standard_frames (list): List of indices corresponding to standard stars
Returns:
str: Full path to the standard spec1d output file
"""
# TODO: Need to decide how to associate standards with
# science frames in the case where there is more than one
# standard associated with a given science frame. Below, I
# just use the first standard
std_outfile = None
std_frame = None if len(standard_frames) == 0 else standard_frames[0]
# Prepare to load up standard?
if std_frame is not None:
std_outfile = self.spec_output_file(std_frame) \
if isinstance(std_frame, (int,np.integer)) else None
if std_outfile is not None and not os.path.isfile(std_outfile):
msgs.error('Could not find standard file: {0}'.format(std_outfile))
return std_outfile
def calib_all(self, run=True):
"""
Create calibrations for all setups
This will not crash if not all of the standard set of files are not provided
Args:
run (bool, optional): If False, only print the calib names and do
not actually run. Only used with the pypeit_parse_calib_id script
Returns:
dict: A simple dict summarizing the calibration names
"""
calib_dict = {}
self.tstart = time.time()
# Frame indices
frame_indx = np.arange(len(self.fitstbl))
for i in range(self.fitstbl.n_calib_groups):
# 1-indexed calib number
calib_grp = str(i + 1)
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
grp_frames = frame_indx[in_grp]
# Find the detectors to reduce
# detectors = PypeIt.select_detectors(detnum=self.par['rdx']['detnum'],
# ndet=self.spectrograph.ndet)
detectors = self.spectrograph.select_detectors(
subset=self.par['rdx']['detnum'])
calib_dict[calib_grp] = {}
# Loop on Detectors
for self.det in detectors:
# Instantiate Calibrations class
self.caliBrate = calibrations.Calibrations.get_instance(
self.fitstbl,
self.par['calibrations'],
self.spectrograph,
self.calibrations_path,
qadir=self.qa_path,
reuse_masters=self.reuse_masters,
show=self.show,
user_slits=slittrace.merge_user_slit(
self.par['rdx']['slitspatnum'],
self.par['rdx']['maskIDs']))
# Do it
# TODO: Why isn't set_config part of the Calibrations.__init__ method?
self.caliBrate.set_config(grp_frames[0], self.det,
self.par['calibrations'])
# Allow skipping the run (e.g. parse_calib_id.py script)
if run:
self.caliBrate.run_the_steps()
key = self.caliBrate.master_key_dict['frame']
calib_dict[calib_grp][key] = {}
for step in self.caliBrate.steps:
if step in ['bpm', 'slits', 'wv_calib', 'tilts', 'flats']:
continue
elif step == 'tiltimg': # Annoying kludge
step = 'tilt'
# Prep
raw_files, self.caliBrate.master_key_dict[
step] = self.caliBrate._prep_calibrations(step)
masterframe_name = masterframe.construct_file_name(
buildimage.frame_image_classes[step],
self.caliBrate.master_key_dict[step],
master_dir=self.caliBrate.master_dir)
# Add to dict
if len(raw_files) > 0:
calib_dict[calib_grp][key][step] = {}
calib_dict[calib_grp][key][step][
'master_key'] = self.caliBrate.master_key_dict[
step]
calib_dict[calib_grp][key][step][
'master_name'] = os.path.basename(masterframe_name)
calib_dict[calib_grp][key][step]['raw_files'] = [
os.path.basename(ifile) for ifile in raw_files
]
# Print the results
print(json.dumps(calib_dict, sort_keys=True, indent=4))
# Write
msgs.info('Writing calib file')
calib_file = self.pypeit_file.replace('.pypeit', '.calib_ids')
ltu.savejson(calib_file, calib_dict, overwrite=True, easy_to_read=True)
# Finish
self.print_end_time()
# Return
return calib_dict
def reduce_all(self):
"""
Main driver of the entire reduction
Calibration and extraction via a series of calls to reduce_exposure()
"""
# Validate the parameter set
self.par.validate_keys(required=[
'rdx', 'calibrations', 'scienceframe', 'reduce', 'flexure'
])
self.tstart = time.time()
# Find the standard frames
is_standard = self.fitstbl.find_frames('standard')
# Find the science frames
is_science = self.fitstbl.find_frames('science')
# Frame indices
frame_indx = np.arange(len(self.fitstbl))
# Standard Star(s) Loop
# Iterate over each calibration group and reduce the standards
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the standard frames in this calibration group:
grp_standards = frame_indx[is_standard & in_grp]
# Reduce all the standard frames, loop on unique comb_id
u_combid_std = np.unique(self.fitstbl['comb_id'][grp_standards])
for j, comb_id in enumerate(u_combid_std):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
# Build history to document what contributd to the reduced
# exposure
history = History(self.fitstbl.frame_paths(frames[0]))
history.add_reduce(i, self.fitstbl, frames, bg_frames)
std_spec2d, std_sobjs = self.reduce_exposure(
frames, bg_frames=bg_frames)
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], std_spec2d, std_sobjs,
self.basename, history)
else:
msgs.info(
'Output file: {:s} already exists'.format(
self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
# Science Frame(s) Loop
# Iterate over each calibration group again and reduce the science frames
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the science frames in this calibration group:
grp_science = frame_indx[is_science & in_grp]
# Associate standards (previously reduced above) for this setup
std_outfile = self.get_std_outfile(frame_indx[is_standard])
# Reduce all the science frames; keep the basenames of the science frames for use in flux calibration
science_basename = [None] * len(grp_science)
# Loop on unique comb_id
u_combid = np.unique(self.fitstbl['comb_id'][grp_science])
for j, comb_id in enumerate(u_combid):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
# Find all frames whose comb_id matches the current frames bkg_id.
bg_frames = np.where((self.fitstbl['comb_id'] ==
self.fitstbl['bkg_id'][frames][0])
& (self.fitstbl['comb_id'] >= 0))[0]
# JFH changed the syntax below to that above, which allows frames to be used more than once
# as a background image. The syntax below would require that we could somehow list multiple
# numbers for the bkg_id which is impossible without a comma separated list
# bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
# Build history to document what contributd to the reduced
# exposure
history = History(self.fitstbl.frame_paths(frames[0]))
history.add_reduce(i, self.fitstbl, frames, bg_frames)
# TODO -- Should we reset/regenerate self.slits.mask for a new exposure
sci_spec2d, sci_sobjs = self.reduce_exposure(
frames, bg_frames=bg_frames, std_outfile=std_outfile)
science_basename[j] = self.basename
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], sci_spec2d, sci_sobjs,
self.basename, history)
else:
msgs.warn(
'Output file: {:s} already exists'.format(
self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
msgs.info('Finished calibration group {0}'.format(i))
# Finish
self.print_end_time()
def reduce_exposure(self, frames, bg_frames=None, std_outfile=None):
"""
Reduce a single exposure
Args:
frames (:obj:`list`):
List of 0-indexed rows in :attr:`fitstbl` with the frames to
reduce.
bg_frames (:obj:`list`, optional):
List of frame indices for the background.
std_outfile (:obj:`str`, optional):
File with a previously reduced standard spectrum from
PypeIt.
Returns:
dict: The dictionary containing the primary outputs of
extraction.
"""
# if show is set, clear the ginga channels at the start of each new sci_ID
if self.show:
# TODO: Put this in a try/except block?
display.clear_all(allow_new=True)
has_bg = True if bg_frames is not None and len(
bg_frames) > 0 else False
# Is this an b/g subtraction reduction?
if has_bg:
self.bkg_redux = True
# The default is to find_negative objects if the bg_frames are classified as "science", and to not find_negative
# objects if the bg_frames are classified as "sky". This can be explicitly overridden if
# par['reduce']['findobj']['find_negative'] is set to something other than the default of None.
self.find_negative = (('science' in self.fitstbl['frametype'][bg_frames[0]]) |
('standard' in self.fitstbl['frametype'][bg_frames[0]]))\
if self.par['reduce']['findobj']['find_negative'] is None else self.par['reduce']['findobj']['find_negative']
else:
self.bkg_redux = False
self.find_negative = False
# Container for all the Spec2DObj
all_spec2d = spec2dobj.AllSpec2DObj()
all_spec2d['meta']['bkg_redux'] = self.bkg_redux
all_spec2d['meta']['find_negative'] = self.find_negative
# TODO -- Should we reset/regenerate self.slits.mask for a new exposure
# container for specobjs during first loop (objfind)
all_specobjs_objfind = specobjs.SpecObjs()
# container for specobjs during second loop (extraction)
all_specobjs_extract = specobjs.SpecObjs()
# list of global_sky obtained during objfind and used in extraction
initial_sky_list = []
# list of sciImg
sciImg_list = []
# List of detectors with successful calibration
calibrated_det = []
# list of successful MasterSlits calibrations to be used in the extraction loop
calib_slits = []
# List of objFind objects
objFind_list = []
# Print status message
msgs_string = 'Reducing target {:s}'.format(
self.fitstbl['target'][frames[0]]) + msgs.newline()
# TODO: Print these when the frames are actually combined,
# backgrounds are used, etc?
msgs_string += 'Combining frames:' + msgs.newline()
for iframe in frames:
msgs_string += '{0:s}'.format(
self.fitstbl['filename'][iframe]) + msgs.newline()
msgs.info(msgs_string)
if has_bg:
bg_msgs_string = ''
for iframe in bg_frames:
bg_msgs_string += '{0:s}'.format(
self.fitstbl['filename'][iframe]) + msgs.newline()
bg_msgs_string = msgs.newline(
) + 'Using background from frames:' + msgs.newline(
) + bg_msgs_string
msgs.info(bg_msgs_string)
# Find the detectors to reduce
subset = self.par['rdx']['slitspatnum'] if self.par['rdx']['slitspatnum'] is not None \
else self.par['rdx']['detnum']
detectors = self.spectrograph.select_detectors(subset=subset)
msgs.info(f'Detectors to work on: {detectors}')
# Loop on Detectors
# TODO: Attempt to put in a multiprocessing call here?
# objfind
for self.det in detectors:
msgs.info("Working on detector {0}".format(self.det))
# run calibration
self.caliBrate = self.calib_one(frames, self.det)
if not self.caliBrate.success:
msgs.warn(
f'Calibrations for detector {self.det} were unsuccessful! The step '
f'that failed was {self.caliBrate.failed_step}. Continuing by '
f'skipping this detector.')
continue
# we save only the detectors that had a successful calibration,
# and we use only those in the extract loop below
calibrated_det.append(self.det)
# we also save the successful MasterSlits calibrations because they are used and modified
# in the slitmask stuff in between the two loops
calib_slits.append(self.caliBrate.slits)
# global_sky, skymask and sciImg are needed in the extract loop
initial_sky, sobjs_obj, sciImg, objFind = self.objfind_one(
frames, self.det, bg_frames, std_outfile=std_outfile)
if len(sobjs_obj) > 0:
all_specobjs_objfind.add_sobj(sobjs_obj)
initial_sky_list.append(initial_sky)
sciImg_list.append(sciImg)
objFind_list.append(objFind)
# slitmask stuff
if self.par['reduce']['slitmask']['assign_obj']:
# get object positions from slitmask design and slitmask offsets for all the detectors
spat_flexure = np.array([ss.spat_flexure for ss in sciImg_list])
# Grab platescale with binning
bin_spec, bin_spat = parse.parse_binning(self.binning)
platescale = np.array(
[ss.detector.platescale * bin_spat for ss in sciImg_list])
# get the dither offset if available
if self.par['reduce']['slitmask']['use_dither_offset']:
dither = self.spectrograph.parse_dither_pattern(
[self.fitstbl.frame_paths(frames[0])])
dither_off = dither[2][0] if dither is not None else None
else:
dither_off = None
calib_slits = slittrace.get_maskdef_objpos_offset_alldets(
all_specobjs_objfind,
calib_slits,
spat_flexure,
platescale,
self.par['calibrations']['slitedges']['det_buffer'],
self.par['reduce']['slitmask'],
dither_off=dither_off)
# determine if slitmask offsets exist and compute an average offsets over all the detectors
calib_slits = slittrace.average_maskdef_offset(
calib_slits, platescale[0],
self.spectrograph.list_detectors(
mosaic='MSC' in calib_slits[0].detname))
# slitmask design matching and add undetected objects
all_specobjs_objfind = slittrace.assign_addobjs_alldets(
all_specobjs_objfind, calib_slits, spat_flexure, platescale,
self.par['reduce']['slitmask'],
self.par['reduce']['findobj']['find_fwhm'])
# Extract
for i, self.det in enumerate(calibrated_det):
# re-run (i.e., load) calibrations
self.caliBrate = self.calib_one(frames, self.det)
self.caliBrate.slits = calib_slits[i]
detname = sciImg_list[i].detector.name
# TODO: pass back the background frame, pass in background
# files as an argument. extract one takes a file list as an
# argument and instantiates science within
if all_specobjs_objfind.nobj > 0:
all_specobjs_on_det = all_specobjs_objfind[
all_specobjs_objfind.DET == detname]
else:
all_specobjs_on_det = all_specobjs_objfind
# Extract
all_spec2d[detname], tmp_sobjs \
= self.extract_one(frames, self.det, sciImg_list[i], objFind_list[i],
initial_sky_list[i], all_specobjs_on_det)
# Hold em
if tmp_sobjs.nobj > 0:
all_specobjs_extract.add_sobj(tmp_sobjs)
# JFH TODO write out the background frame?
# TODO -- Save here? Seems like we should. Would probably need to use update_det=True
# Return
return all_spec2d, all_specobjs_extract
def get_sci_metadata(self, frame, det):
"""
Grab the meta data for a given science frame and specific detector
Args:
frame (int): Frame index
det (int): Detector index
Returns:
5 objects are returned::
- str: Object type; science or standard
- str: Setup string from master_key()
- astropy.time.Time: Time of observation
- str: Basename of the frame
- str: Binning of the detector
"""
# Set binning, obstime, basename, and objtype
binning = self.fitstbl['binning'][frame]
obstime = self.fitstbl.construct_obstime(frame)
basename = self.fitstbl.construct_basename(frame, obstime=obstime)
objtype = self.fitstbl['frametype'][frame]
if 'science' in objtype:
objtype_out = 'science'
elif 'standard' in objtype:
objtype_out = 'standard'
else:
msgs.error('Unrecognized objtype')
setup = self.fitstbl.master_key(frame, det=det)
return objtype_out, setup, obstime, basename, binning
def calib_one(self, frames, det):
"""
Run Calibration for a single exposure/detector pair
Args:
frames (:obj:`list`):
List of frames to extract; stacked if more than one
is provided
det (:obj:`int`):
Detector number (1-indexed)
Returns:
caliBrate (:class:`pypeit.calibrations.Calibrations`)
"""
msgs.info("Working on detector {0}".format(det))
# Instantiate Calibrations class
caliBrate = calibrations.Calibrations.get_instance(
self.fitstbl,
self.par['calibrations'],
self.spectrograph,
self.calibrations_path,
qadir=self.qa_path,
reuse_masters=self.reuse_masters,
show=self.show,
user_slits=slittrace.merge_user_slit(
self.par['rdx']['slitspatnum'], self.par['rdx']['maskIDs']))
#slitspat_num=self.par['rdx']['slitspatnum'])
# These need to be separate to accomodate COADD2D
caliBrate.set_config(frames[0], det, self.par['calibrations'])
caliBrate.run_the_steps()
return caliBrate
def objfind_one(self, frames, det, bg_frames, std_outfile=None):
"""
Reduce + Find Objects in a single exposure/detector pair
sci_ID and det need to have been set internally prior to calling this method
Parameters
----------
frames : :obj:`list`
List of frames to extract; stacked if more than one is provided
det : :obj:`int`
Detector number (1-indexed)
bg_frames : :obj:`list`
List of frames to use as the background. Can be empty.
std_outfile : :obj:`str`, optional
Filename for the standard star spec1d file. Passed directly to
:func:`get_std_trace`.
Returns
-------
global_sky : `numpy.ndarray`_
Initial global sky model
sobjs_obj : :class:`~pypeit.specobjs.SpecObjs`
List of objects found
sciImg : :class:`~pypeit.images.pypeitimage.PypeItImage`
Science image
objFind : :class:`~pypeit.find_objects.FindObjects`
Object finding speobject
"""
# 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)
msgs.info("Object finding begins for {} on det={}".format(
self.basename, det))
# Is this a standard star?
self.std_redux = 'standard' in self.objtype
frame_par = self.par['calibrations'][
'standardframe'] if self.std_redux else self.par['scienceframe']
# Get the standard trace if need be
if self.std_redux is False and std_outfile is not None:
std_trace = specobjs.get_std_trace(
self.spectrograph.get_det_name(det), std_outfile)
else:
std_trace = None
# Build Science image
sci_files = self.fitstbl.frame_paths(frames)
sciImg = buildimage.buildimage_fromlist(
self.spectrograph,
det,
frame_par,
sci_files,
bias=self.caliBrate.msbias,
bpm=self.caliBrate.msbpm,
dark=self.caliBrate.msdark,
flatimages=self.caliBrate.flatimages,
slits=self.caliBrate.slits, # For flexure correction
ignore_saturation=False)
# Background Image?
if len(bg_frames) > 0:
bg_file_list = self.fitstbl.frame_paths(bg_frames)
sciImg = sciImg.sub(
buildimage.buildimage_fromlist(
self.spectrograph,
det,
frame_par,
bg_file_list,
bpm=self.caliBrate.msbpm,
bias=self.caliBrate.msbias,
dark=self.caliBrate.msdark,
flatimages=self.caliBrate.flatimages,
slits=self.caliBrate.slits, # For flexure correction
ignore_saturation=False),
frame_par['process'])
# Deal with manual extraction
row = self.fitstbl[frames[0]]
manual_obj = ManualExtractionObj.by_fitstbl_input(
row['filename'], row['manual'],
self.spectrograph) if len(row['manual'].strip()) > 0 else None
# Instantiate Reduce object
# Required for pypeline specific object
# At instantiaton, the fullmask in self.sciImg is modified
objFind = find_objects.FindObjects.get_instance(
sciImg,
self.spectrograph,
self.par,
self.caliBrate,
self.objtype,
bkg_redux=self.bkg_redux,
manual=manual_obj,
find_negative=self.find_negative,
std_redux=self.std_redux,
show=self.show,
basename=self.basename)
# Do it
initial_sky, sobjs_obj = objFind.run(std_trace=std_trace,
show_peaks=self.show)
# Return
return initial_sky, sobjs_obj, sciImg, objFind
def extract_one(self, frames, det, sciImg, objFind, initial_sky,
sobjs_obj):
"""
Extract Objects in a single exposure/detector pair
sci_ID and det need to have been set internally prior to calling this method
Args:
frames (:obj:`list`):
List of frames to extract; stacked if more than one
is provided
det (:obj:`int`):
Detector number (1-indexed)
sciImg (:class:`PypeItImage`):
Data container that holds a single image from a
single detector its related images (e.g. ivar, mask)
objFind : :class:`~pypeit.find_objects.FindObjects`
Object finding object
initial_sky (`numpy.ndarray`_):
Initial global sky model
sobjs_obj (:class:`pypeit.specobjs.SpecObjs`):
List of objects found during `run_objfind`
Returns:
tuple: Returns six `numpy.ndarray`_ objects and a
:class:`pypeit.specobjs.SpecObjs` object with the
extracted spectra from this exposure/detector pair. The
six `numpy.ndarray`_ objects are (1) the science image,
(2) its inverse variance, (3) the sky model, (4) the
object model, (5) the model inverse variance, and (6) the
mask.
"""
# 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
# Update the skymask
skymask = objFind.create_skymask(sobjs_obj)
# Update the global sky
if 'standard' in self.fitstbl['frametype'][frames[0]] or \
self.par['reduce']['findobj']['skip_final_global'] or \
self.par['reduce']['skysub']['load_mask'] or \
self.par['reduce']['skysub']['user_regions'] is not None:
final_global_sky = initial_sky
else:
final_global_sky = objFind.global_skysub(previous_sky=initial_sky,
skymask=skymask,
show=self.show)
scaleImg = objFind.scaleimg
# update here slits.mask since global_skysub modify reduce_bpm and we need to propagate it into extraction
flagged_slits = np.where(objFind.reduce_bpm)[0]
if len(flagged_slits) > 0:
self.caliBrate.slits.mask[flagged_slits] = \
self.caliBrate.slits.bitmask.turn_on(self.caliBrate.slits.mask[flagged_slits], 'BADREDUCE')
msgs.info("Extraction begins for {} on det={}".format(
self.basename, det))
# Instantiate Reduce object
# Required for pypeline specific object
# At instantiaton, the fullmask in self.sciImg is modified
# TODO Are we repeating steps in the init for FindObjects and Extract??
self.exTract = extraction.Extract.get_instance(
sciImg,
sobjs_obj,
self.spectrograph,
self.par,
self.caliBrate,
self.objtype,
bkg_redux=self.bkg_redux,
return_negative=self.par['reduce']['extraction']
['return_negative'],
std_redux=self.std_redux,
show=self.show,
basename=self.basename)
if not self.par['reduce']['extraction']['skip_extraction']:
skymodel, objmodel, ivarmodel, outmask, sobjs, waveImg, \
tilts = self.exTract.run(final_global_sky, ra=self.fitstbl["ra"][frames[0]],
dec=self.fitstbl["dec"][frames[0]], obstime=self.obstime)
else:
# Although exrtaction is not performed, still need to prepare some masks and the tilts
self.exTract.prepare_extraction()
# Since the extraction was not performed, fill the arrays with the best available information
skymodel = final_global_sky
objmodel = np.zeros_like(self.exTract.sciImg.image)
ivarmodel = np.copy(self.exTract.sciImg.ivar)
outmask = self.exTract.sciImg.fullmask
waveImg = self.exTract.waveimg
tilts = self.exTract.tilts
sobjs = sobjs_obj
# TODO -- Do this upstream
# Tack on detector and wavelength RMS
for sobj in sobjs:
sobj.DETECTOR = sciImg.detector
iwv = np.where(
self.caliBrate.wv_calib.spat_ids == sobj.SLITID)[0][0]
sobj.WAVE_RMS = self.caliBrate.wv_calib.wv_fits[iwv].rms
# Construct table of spectral flexure
spec_flex_table = Table()
spec_flex_table['spat_id'] = self.caliBrate.slits.spat_id
spec_flex_table['sci_spec_flexure'] = self.exTract.slitshift
# pull out maskdef_designtab from caliBrate.slits
maskdef_designtab = self.caliBrate.slits.maskdef_designtab
slits = copy.deepcopy(self.caliBrate.slits)
slits.maskdef_designtab = None
# Construct the Spec2DObj
spec2DObj = spec2dobj.Spec2DObj(
sciimg=sciImg.image,
ivarraw=sciImg.ivar,
skymodel=skymodel,
objmodel=objmodel,
ivarmodel=ivarmodel,
scaleimg=scaleImg,
waveimg=waveImg,
bpmmask=outmask,
detector=sciImg.detector,
sci_spat_flexure=sciImg.spat_flexure,
sci_spec_flexure=spec_flex_table,
vel_corr=self.exTract.vel_corr,
vel_type=self.par['calibrations']['wavelengths']['refframe'],
tilts=tilts,
slits=slits,
maskdef_designtab=maskdef_designtab)
spec2DObj.process_steps = sciImg.process_steps
# QA
spec2DObj.gen_qa()
# Return
return spec2DObj, sobjs
def save_exposure(self,
frame,
all_spec2d,
all_specobjs,
basename,
history=None):
"""
Save the outputs from extraction for a given exposure
Args:
frame (:obj:`int`):
0-indexed row in the metadata table with the frame
that has been reduced.
all_spec2d(:class:`pypeit.spec2dobj.AllSpec2DObj`):
sci_dict (:obj:`dict`):
Dictionary containing the primary outputs of
extraction
basename (:obj:`str`):
The root name for the output file.
history (:obj:`pypeit.history.History`):
History entries to be added to fits header
Returns:
None or SpecObjs: All of the objects saved to disk
"""
# TODO: Need some checks here that the exposure has been reduced?
# Determine the headers
row_fitstbl = self.fitstbl[frame]
# Need raw file header information
rawfile = self.fitstbl.frame_paths(frame)
head2d = fits.getheader(rawfile, ext=self.spectrograph.primary_hdrext)
# Check for the directory
if not os.path.isdir(self.science_path):
os.makedirs(self.science_path)
# NOTE: There are some gymnastics here to keep from altering
# self.par['rdx']['detnum']. I.e., I can't just set update_det =
# self.par['rdx']['detnum'] because that can alter the latter if I don't
# deepcopy it...
if self.par['rdx']['detnum'] is None:
update_det = None
elif isinstance(self.par['rdx']['detnum'], list):
update_det = [
self.spectrograph.allowed_mosaics.index(d) +
1 if isinstance(d, tuple) else d
for d in self.par['rdx']['detnum']
]
else:
update_det = self.par['rdx']['detnum']
subheader = self.spectrograph.subheader_for_spec(row_fitstbl, head2d)
# 1D spectra
if all_specobjs.nobj > 0:
# Spectra
outfile1d = os.path.join(self.science_path,
'spec1d_{:s}.fits'.format(basename))
# TODO
#embed(header='deal with the following for maskIDs; 713 of pypeit')
all_specobjs.write_to_fits(
subheader,
outfile1d,
update_det=update_det,
slitspatnum=self.par['rdx']['slitspatnum'],
history=history)
# Info
outfiletxt = os.path.join(self.science_path,
'spec1d_{:s}.txt'.format(basename))
# TODO: Note we re-read in the specobjs from disk to deal with situations where
# only a single detector is run in a second pass but in the same reduction directory.
# Thiw was to address Issue #1116 in PR #1154. Slightly inefficient, but only other
# option is to re-work write_info to also "append"
sobjs = specobjs.SpecObjs.from_fitsfile(outfile1d,
chk_version=False)
sobjs.write_info(outfiletxt, self.spectrograph.pypeline)
#all_specobjs.write_info(outfiletxt, self.spectrograph.pypeline)
# 2D spectra
outfile2d = os.path.join(self.science_path,
'spec2d_{:s}.fits'.format(basename))
# Build header
pri_hdr = all_spec2d.build_primary_hdr(
head2d,
self.spectrograph,
redux_path=self.par['rdx']['redux_path'],
master_key_dict=self.caliBrate.master_key_dict,
master_dir=self.caliBrate.master_dir,
subheader=subheader,
history=history)
# Write
all_spec2d.write_to_fits(outfile2d,
pri_hdr=pri_hdr,
update_det=update_det,
slitspatnum=self.par['rdx']['slitspatnum'])
def msgs_reset(self):
"""
Reset the msgs object
"""
# Reset the global logger
msgs.reset(log=self.logname, verbosity=self.verbosity)
msgs.pypeit_file = self.pypeit_file
def print_end_time(self):
"""
Print the elapsed time
"""
# Capture the end time and print it to user
msgs.info(utils.get_time_string(time.time() - self.tstart))
# TODO: Move this to fitstbl?
def show_science(self):
"""
Simple print of science frames
"""
indx = self.fitstbl.find_frames('science')
print(self.fitstbl[['target', 'ra', 'dec', 'exptime',
'dispname']][indx])
def __repr__(self):
# Generate sets string
return '<{:s}: pypeit_file={}>'.format(self.__class__.__name__,
self.pypeit_file)
class PypeIt(object):
"""
This class runs the primary calibration and extraction in PypeIt
.. todo::
Fill in list of attributes!
Args:
pypeit_file (:obj:`str`):
PypeIt filename.
verbosity (:obj:`int`, optional):
Verbosity level of system output. Can be:
- 0: No output
- 1: Minimal output (default)
- 2: All output
overwrite (:obj:`bool`, optional):
Flag to overwrite any existing files/directories.
reuse_masters (:obj:`bool`, optional):
Reuse any pre-existing calibration files
logname (:obj:`str`, optional):
The name of an ascii log file with the details of the
reduction.
show: (:obj:`bool`, optional):
Show reduction steps via plots (which will block further
execution until clicked on) and outputs to ginga. Requires
remote control ginga session via ``ginga --modules=RC &``
redux_path (:obj:`str`, optional):
Over-ride reduction path in PypeIt file (e.g. Notebook usage)
Attributes:
pypeit_file (:obj:`str`):
Name of the pypeit file to read. PypeIt files have a
specific set of valid formats. A description can be found
:ref:`pypeit_file`.
fitstbl (:obj:`pypit.metadata.PypeItMetaData`): holds the meta info
"""
# __metaclass__ = ABCMeta
def __init__(self,
pypeit_file,
verbosity=2,
overwrite=True,
reuse_masters=False,
logname=None,
show=False,
redux_path=None):
# Load
cfg_lines, data_files, frametype, usrdata, setups \
= parse_pypeit_file(pypeit_file, runtime=True)
self.pypeit_file = pypeit_file
# Spectrograph
cfg = ConfigObj(cfg_lines)
spectrograph_name = cfg['rdx']['spectrograph']
self.spectrograph = load_spectrograph(spectrograph_name,
ifile=data_files[0])
msgs.info('Loaded spectrograph {0}'.format(
self.spectrograph.spectrograph))
# --------------------------------------------------------------
# Get the full set of PypeIt parameters
# - Grab a science or standard file for configuration specific parameters
scistd_file = None
for idx, row in enumerate(usrdata):
if ('science' in row['frametype']) or ('standard'
in row['frametype']):
scistd_file = data_files[idx]
break
# - Configuration specific parameters for the spectrograph
if scistd_file is not None:
msgs.info(
'Setting configuration-specific parameters using {0}'.format(
os.path.split(scistd_file)[1]))
spectrograph_cfg_lines = self.spectrograph.config_specific_par(
scistd_file).to_config()
# - Build the full set, merging with any user-provided
# parameters
self.par = PypeItPar.from_cfg_lines(cfg_lines=spectrograph_cfg_lines,
merge_with=cfg_lines)
msgs.info('Built full PypeIt parameter set.')
# Check the output paths are ready
if redux_path is not None:
self.par['rdx']['redux_path'] = redux_path
# TODO: Write the full parameter set here?
# --------------------------------------------------------------
# --------------------------------------------------------------
# Build the meta data
# - Re-initilize based on the file data
msgs.info('Compiling metadata')
self.fitstbl = PypeItMetaData(self.spectrograph,
self.par,
files=data_files,
usrdata=usrdata,
strict=True)
# - Interpret automated or user-provided data from the PypeIt
# file
self.fitstbl.finalize_usr_build(frametype, setups[0])
# --------------------------------------------------------------
# - Write .calib file (For QA naming amongst other things)
calib_file = pypeit_file.replace('.pypeit', '.calib')
self.fitstbl.write_calib(calib_file)
# Other Internals
self.logname = logname
self.overwrite = overwrite
# Currently the runtime argument determines the behavior for
# reuse_masters.
self.reuse_masters = reuse_masters
self.show = show
# Set paths
if self.par['calibrations']['caldir'] == 'default':
self.calibrations_path = os.path.join(
self.par['rdx']['redux_path'], 'Masters')
else:
self.calibrations_path = self.par['calibrations']['caldir']
# Report paths
msgs.info('Setting reduction path to {0}'.format(
self.par['rdx']['redux_path']))
msgs.info('Master calibration data output to: {0}'.format(
self.calibrations_path))
msgs.info('Science data output to: {0}'.format(self.science_path))
msgs.info('Quality assessment plots output to: {0}'.format(
self.qa_path))
# TODO: Is anything written to the qa dir or only to qa/PNGs?
# Should we have separate calibration and science QA
# directories?
# Instantiate Calibrations class
self.caliBrate \
= calibrations.MultiSlitCalibrations(self.fitstbl, self.par['calibrations'],
self.spectrograph,
caldir=self.calibrations_path,
qadir=self.qa_path,
reuse_masters=self.reuse_masters,
show=self.show)
# Init
self.verbosity = verbosity
# TODO: I don't think this ever used
self.frame = None
self.det = None
self.tstart = None
self.basename = None
self.sciI = None
self.obstime = None
@property
def science_path(self):
"""Return the path to the science directory."""
return os.path.join(self.par['rdx']['redux_path'],
self.par['rdx']['scidir'])
@property
def qa_path(self):
"""Return the path to the top-level QA directory."""
return os.path.join(self.par['rdx']['redux_path'],
self.par['rdx']['qadir'])
def build_qa(self):
"""
Generate QA wrappers
"""
qa.gen_mf_html(self.pypeit_file, self.qa_path)
qa.gen_exp_html()
# TODO: This should go in a more relevant place
def spec_output_file(self, frame, twod=False):
"""
Return the path to the spectral output data file.
Args:
frame (:obj:`int`):
Frame index from :attr:`fitstbl`.
twod (:obj:`bool`):
Name for the 2D output file; 1D file otherwise.
Returns:
:obj:`str`: The path for the output file
"""
return os.path.join(
self.science_path,
'spec{0}d_{1}.fits'.format('2' if twod else '1',
self.fitstbl.construct_basename(frame)))
def outfile_exists(self, frame):
"""
Check whether the 2D outfile of a given frame already exists
Args:
frame (int): Frame index from fitstbl
Returns:
bool: True if the 2d file exists, False if it does not exist
"""
return os.path.isfile(self.spec_output_file(frame, twod=True))
def get_std_outfile(self, standard_frames):
"""
Grab the output filename from an input list of standard_frame indices
If more than one index is provided, the first is taken
Args:
standard_frames (list): List of indices corresponding to standard stars
Returns:
str: Full path to the standard spec1d output file
"""
# TODO: Need to decide how to associate standards with
# science frames in the case where there is more than one
# standard associated with a given science frame. Below, I
# just use the first standard
std_outfile = None
std_frame = None if len(standard_frames) == 0 else standard_frames[0]
# Prepare to load up standard?
if std_frame is not None:
std_outfile = self.spec_output_file(std_frame) \
if isinstance(std_frame, (int,np.integer)) else None
if std_outfile is not None and not os.path.isfile(std_outfile):
msgs.error('Could not find standard file: {0}'.format(std_outfile))
return std_outfile
def reduce_all(self):
"""
Main driver of the entire reduction
Calibration and extraction via a series of calls to reduce_exposure()
"""
# Validate the parameter set
required = [
'rdx', 'calibrations', 'scienceframe', 'reduce', 'flexure',
'fluxcalib'
]
can_be_None = ['flexure', 'fluxcalib']
self.par.validate_keys(required=required, can_be_None=can_be_None)
self.tstart = time.time()
# Find the standard frames
is_standard = self.fitstbl.find_frames('standard')
# Find the science frames
is_science = self.fitstbl.find_frames('science')
# Frame indices
frame_indx = np.arange(len(self.fitstbl))
# Iterate over each calibration group and reduce the standards
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the standard frames in this calibration group:
grp_standards = frame_indx[is_standard & in_grp]
# Reduce all the standard frames, loop on unique comb_id
u_combid_std = np.unique(self.fitstbl['comb_id'][grp_standards])
for j, comb_id in enumerate(u_combid_std):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
std_dict = self.reduce_exposure(frames,
bg_frames=bg_frames)
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], std_dict, self.basename)
else:
msgs.info(
'Output file: {:s} already exists'.format(
self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
# Iterate over each calibration group again and reduce the science frames
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the science frames in this calibration group:
grp_science = frame_indx[is_science & in_grp]
# Associate standards (previously reduced above) for this setup
std_outfile = self.get_std_outfile(frame_indx[is_standard])
# Reduce all the science frames; keep the basenames of the science frames for use in flux calibration
science_basename = [None] * len(grp_science)
# Loop on unique comb_id
u_combid = np.unique(self.fitstbl['comb_id'][grp_science])
for j, comb_id in enumerate(u_combid):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
# Find all frames whose comb_id matches the current frames bkg_id.
bg_frames = np.where((self.fitstbl['comb_id'] ==
self.fitstbl['bkg_id'][frames][0])
& (self.fitstbl['comb_id'] >= 0))[0]
# JFH changed the syntax below to that above, which allows frames to be used more than once
# as a background image. The syntax below would require that we could somehow list multiple
# numbers for the bkg_id which is impossible without a comma separated list
# bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
sci_dict = self.reduce_exposure(frames,
bg_frames=bg_frames,
std_outfile=std_outfile)
science_basename[j] = self.basename
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], sci_dict, self.basename)
else:
msgs.warn(
'Output file: {:s} already exists'.format(
self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
msgs.info('Finished calibration group {0}'.format(i))
# Finish
self.print_end_time()
# This is a static method to allow for use in coadding script
@staticmethod
def select_detectors(detnum=None, ndet=1):
"""
Return the 1-indexed list of detectors to reduce.
Args:
detnum (:obj:`int`, :obj:`list`, optional):
One or more detectors to reduce. If None, return the
full list for the provided number of detectors (`ndet`).
ndet (:obj:`int`, optional):
The number of detectors for this instrument. Only used
if `detnum is None`.
Returns:
list: List of detectors to be reduced
"""
if detnum is None:
return np.arange(1, ndet + 1).tolist()
return [detnum] if isinstance(detnum, int) else detnum
def reduce_exposure(self, frames, bg_frames=None, std_outfile=None):
"""
Reduce a single exposure
Args:
frame (:obj:`int`):
0-indexed row in :attr:`fitstbl` with the frame to
reduce.
bg_frames (:obj:`list`, optional):
List of frame indices for the background.
std_outfile (:obj:`str`, optional):
File with a previously reduced standard spectrum from
PypeIt.
Returns:
dict: The dictionary containing the primary outputs of
extraction.
"""
# TODO:
# - bg_frames should be None by default
# - change doc string to reflect that more than one frame can be
# provided
# if show is set, clear the ginga channels at the start of each new sci_ID
if self.show:
# TODO: Put this in a try/except block?
ginga.clear_all()
has_bg = True if bg_frames is not None and len(
bg_frames) > 0 else False
# Is this an IR reduction?
# TODO: Why specific to IR?
self.ir_redux = True if has_bg else False
# TODO: JFH Why does this need to be ordered?
sci_dict = OrderedDict() # This needs to be ordered
sci_dict['meta'] = {}
sci_dict['meta']['ir_redux'] = self.ir_redux
# Print status message
msgs_string = 'Reducing target {:s}'.format(
self.fitstbl['target'][frames[0]]) + msgs.newline()
# TODO: Print these when the frames are actually combined,
# backgrounds are used, etc?
msgs_string += 'Combining frames:' + msgs.newline()
for iframe in frames:
msgs_string += '{0:s}'.format(
self.fitstbl['filename'][iframe]) + msgs.newline()
msgs.info(msgs_string)
if has_bg:
bg_msgs_string = ''
for iframe in bg_frames:
bg_msgs_string += '{0:s}'.format(
self.fitstbl['filename'][iframe]) + msgs.newline()
bg_msgs_string = msgs.newline(
) + 'Using background from frames:' + msgs.newline(
) + bg_msgs_string
msgs.info(bg_msgs_string)
# Find the detectors to reduce
detectors = PypeIt.select_detectors(detnum=self.par['rdx']['detnum'],
ndet=self.spectrograph.ndet)
if len(detectors) != self.spectrograph.ndet:
msgs.warn('Not reducing detectors: {0}'.format(' '.join([
str(d) for d in set(np.arange(self.spectrograph.ndet)) -
set(detectors)
])))
# Loop on Detectors
for self.det in detectors:
msgs.info("Working on detector {0}".format(self.det))
sci_dict[self.det] = {}
# Calibrate
#TODO Is the right behavior to just use the first frame?
self.caliBrate.set_config(frames[0], self.det,
self.par['calibrations'])
self.caliBrate.run_the_steps()
# Extract
# TODO: pass back the background frame, pass in background
# files as an argument. extract one takes a file list as an
# argument and instantiates science within
sci_dict[self.det]['sciimg'], sci_dict[self.det]['sciivar'], \
sci_dict[self.det]['skymodel'], sci_dict[self.det]['objmodel'], \
sci_dict[self.det]['ivarmodel'], sci_dict[self.det]['outmask'], \
sci_dict[self.det]['specobjs'], \
= self.extract_one(frames, self.det, bg_frames,
std_outfile=std_outfile)
# JFH TODO write out the background frame?
# Return
return sci_dict
def get_sci_metadata(self, frame, det):
"""
Grab the meta data for a given science frame and specific detector
Args:
frame (int): Frame index
det (int): Detector index
Returns:
5 objects are returned::
- str: Object type; science or standard
- str: Setup string from master_key()
- astropy.time.Time: Time of observation
- str: Basename of the frame
- str: Binning of the detector
"""
# Set binning, obstime, basename, and objtype
binning = self.fitstbl['binning'][frame]
obstime = self.fitstbl.construct_obstime(frame)
basename = self.fitstbl.construct_basename(frame, obstime=obstime)
objtype = self.fitstbl['frametype'][frame]
if 'science' in objtype:
objtype_out = 'science'
elif 'standard' in objtype:
objtype_out = 'standard'
else:
msgs.error('Unrecognized objtype')
setup = self.fitstbl.master_key(frame, det=det)
return objtype_out, setup, obstime, basename, binning
def get_std_trace(self, std_redux, det, std_outfile):
"""
Returns the trace of the standard if it is applicable to the current reduction
Args:
std_redux (bool): If False, proceed
det (int): Detector index
std_outfile (str): Filename for the standard star spec1d file
Returns:
ndarray or None: Trace of the standard star on input detector
"""
if std_redux is False and std_outfile is not None:
sobjs = specobjs.SpecObjs.from_fitsfile(std_outfile)
# Does the detector match?
# TODO Instrument specific logic here could be implemented with the parset. For example LRIS-B or LRIS-R we
# we would use the standard from another detector
this_det = sobjs.DET == det
if np.any(this_det):
sobjs_det = sobjs[this_det]
sobjs_std = sobjs_det.get_std()
# No standard extracted on this detector??
if sobjs_std is None:
return None
std_trace = sobjs_std.TRACE_SPAT
# flatten the array if this multislit
if 'MultiSlit' in self.spectrograph.pypeline:
std_trace = std_trace.flatten()
elif 'Echelle' in self.spectrograph.pypeline:
std_trace = std_trace.T
else:
msgs.error('Unrecognized pypeline')
else:
std_trace = None
else:
std_trace = None
return std_trace
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
# TODO: Why not use self.frame?
def save_exposure(self, frame, sci_dict, basename):
"""
Save the outputs from extraction for a given exposure
Args:
frame (:obj:`int`):
0-indexed row in the metadata table with the frame that
has been reduced.
sci_dict (:obj:`dict`):
Dictionary containing the primary outputs of extraction
basename (:obj:`str`):
The root name for the output file.
Returns:
None or SpecObjs: All of the objects saved to disk
"""
# TODO: Need some checks here that the exposure has been reduced
# Determine the headers
head1d = self.fitstbl[frame]
# Need raw file header information
rawfile = self.fitstbl.frame_paths(frame)
head2d = fits.getheader(rawfile, ext=self.spectrograph.primary_hdrext)
refframe = 'pixel' if self.caliBrate.par['wavelengths']['reference'] == 'pixel' else \
self.caliBrate.par['wavelengths']['frame']
# Determine the paths/filenames
save.save_all(sci_dict,
self.caliBrate.master_key_dict,
self.caliBrate.master_dir,
self.spectrograph,
head1d,
head2d,
self.science_path,
basename,
update_det=self.par['rdx']['detnum'],
binning=self.fitstbl['binning'][frame])
def msgs_reset(self):
"""
Reset the msgs object
"""
# Reset the global logger
msgs.reset(log=self.logname, verbosity=self.verbosity)
msgs.pypeit_file = self.pypeit_file
def print_end_time(self):
"""
Print the elapsed time
"""
# Capture the end time and print it to user
tend = time.time()
codetime = tend - self.tstart
if codetime < 60.0:
msgs.info('Execution time: {0:.2f}s'.format(codetime))
elif codetime / 60.0 < 60.0:
mns = int(codetime / 60.0)
scs = codetime - 60.0 * mns
msgs.info('Execution time: {0:d}m {1:.2f}s'.format(mns, scs))
else:
hrs = int(codetime / 3600.0)
mns = int(60.0 * (codetime / 3600.0 - hrs))
scs = codetime - 60.0 * mns - 3600.0 * hrs
msgs.info('Execution time: {0:d}h {1:d}m {2:.2f}s'.format(
hrs, mns, scs))
# TODO: Move this to fitstbl?
def show_science(self):
"""
Simple print of science frames
"""
indx = self.fitstbl.find_frames('science')
print(self.fitstbl[['target', 'ra', 'dec', 'exptime',
'dispname']][indx])
def __repr__(self):
# Generate sets string
return '<{:s}: pypeit_file={}>'.format(self.__class__.__name__,
self.pypeit_file)
class PypeIt(object):
"""
This class runs the primary calibration and extraction in PypeIt
.. todo::
Fill in list of attributes!
Args:
pypeit_file (:obj:`str`):
PypeIt filename.
verbosity (:obj:`int`, optional):
Verbosity level of system output. Can be:
- 0: No output
- 1: Minimal output (default)
- 2: All output
overwrite (:obj:`bool`, optional):
Flag to overwrite any existing files/directories.
reuse_masters (:obj:`bool`, optional):
Reuse any pre-existing calibration files
logname (:obj:`str`, optional):
The name of an ascii log file with the details of the
reduction.
show: (:obj:`bool`, optional):
Show reduction steps via plots (which will block further
execution until clicked on) and outputs to ginga. Requires
remote control ginga session via ``ginga --modules=RC &``
redux_path (:obj:`str`, optional):
Over-ride reduction path in PypeIt file (e.g. Notebook usage)
calib_only: (:obj:`bool`, optional):
Only generate the calibration files that you can
Attributes:
pypeit_file (:obj:`str`):
Name of the pypeit file to read. PypeIt files have a
specific set of valid formats. A description can be found
:ref:`pypeit_file`.
fitstbl (:obj:`pypeit.metadata.PypeItMetaData`): holds the meta info
"""
# __metaclass__ = ABCMeta
def __init__(self,
pypeit_file,
verbosity=2,
overwrite=True,
reuse_masters=False,
logname=None,
show=False,
redux_path=None,
calib_only=False):
# Load
cfg_lines, data_files, frametype, usrdata, setups \
= parse_pypeit_file(pypeit_file, runtime=True)
self.pypeit_file = pypeit_file
self.calib_only = calib_only
# Spectrograph
cfg = ConfigObj(cfg_lines)
spectrograph_name = cfg['rdx']['spectrograph']
self.spectrograph = load_spectrograph(spectrograph_name)
msgs.info('Loaded spectrograph {0}'.format(
self.spectrograph.spectrograph))
# --------------------------------------------------------------
# Get the full set of PypeIt parameters
# - Grab a science or standard file for configuration specific parameters
config_specific_file = None
for idx, row in enumerate(usrdata):
if ('science' in row['frametype']) or ('standard'
in row['frametype']):
config_specific_file = data_files[idx]
# search for arcs, trace if no scistd was there
if config_specific_file is None:
for idx, row in enumerate(usrdata):
if ('arc' in row['frametype']) or ('trace'
in row['frametype']):
config_specific_file = data_files[idx]
if config_specific_file is not None:
msgs.info(
'Setting configuration-specific parameters using {0}'.format(
os.path.split(config_specific_file)[1]))
spectrograph_cfg_lines = self.spectrograph.config_specific_par(
config_specific_file).to_config()
# - Build the full set, merging with any user-provided
# parameters
self.par = PypeItPar.from_cfg_lines(cfg_lines=spectrograph_cfg_lines,
merge_with=cfg_lines)
msgs.info('Built full PypeIt parameter set.')
# Check the output paths are ready
if redux_path is not None:
self.par['rdx']['redux_path'] = redux_path
# TODO: Write the full parameter set here?
# --------------------------------------------------------------
# --------------------------------------------------------------
# Build the meta data
# - Re-initilize based on the file data
msgs.info('Compiling metadata')
self.fitstbl = PypeItMetaData(self.spectrograph,
self.par,
files=data_files,
usrdata=usrdata,
strict=True)
# - Interpret automated or user-provided data from the PypeIt
# file
self.fitstbl.finalize_usr_build(frametype, setups[0])
# --------------------------------------------------------------
# - Write .calib file (For QA naming amongst other things)
calib_file = pypeit_file.replace('.pypeit', '.calib')
self.fitstbl.write_calib(calib_file)
# Other Internals
self.logname = logname
self.overwrite = overwrite
# Currently the runtime argument determines the behavior for
# reuse_masters.
self.reuse_masters = reuse_masters
self.show = show
# Set paths
self.calibrations_path = os.path.join(
self.par['rdx']['redux_path'],
self.par['calibrations']['master_dir'])
# Check for calibrations
if not self.calib_only:
calibrations.check_for_calibs(
self.par,
self.fitstbl,
raise_error=self.par['calibrations']['raise_chk_error'])
# Report paths
msgs.info('Setting reduction path to {0}'.format(
self.par['rdx']['redux_path']))
msgs.info('Master calibration data output to: {0}'.format(
self.calibrations_path))
msgs.info('Science data output to: {0}'.format(self.science_path))
msgs.info('Quality assessment plots output to: {0}'.format(
self.qa_path))
# TODO: Is anything written to the qa dir or only to qa/PNGs?
# Should we have separate calibration and science QA
# directories?
# An html file wrapping them all too
# # Instantiate Calibrations class
# if self.spectrograph.pypeline in ['MultiSlit', 'Echelle']:
# self.caliBrate \
# = calibrations.MultiSlitCalibrations(self.fitstbl, self.par['calibrations'],
# self.spectrograph, self.calibrations_path,
# qadir=self.qa_path,
# reuse_masters=self.reuse_masters,
# show=self.show,
# slitspat_num=self.par['rdx']['slitspatnum'])
# elif self.spectrograph.pypeline in ['IFU']:
# self.caliBrate \
# = calibrations.IFUCalibrations(self.fitstbl, self.par['calibrations'],
# self.spectrograph,
# self.calibrations_path,
# qadir=self.qa_path,
# reuse_masters=self.reuse_masters,
# show=self.show)
# else:
# msgs.error("No calibration available to support pypeline: {0:s}".format(self.spectrograph.pypeline))
# Init
self.verbosity = verbosity
# TODO: I don't think this ever used
self.det = None
self.tstart = None
self.basename = None
self.sciI = None
self.obstime = None
@property
def science_path(self):
"""Return the path to the science directory."""
return os.path.join(self.par['rdx']['redux_path'],
self.par['rdx']['scidir'])
@property
def qa_path(self):
"""Return the path to the top-level QA directory."""
return os.path.join(self.par['rdx']['redux_path'],
self.par['rdx']['qadir'])
def build_qa(self):
"""
Generate QA wrappers
"""
qa.gen_mf_html(self.pypeit_file, self.qa_path)
qa.gen_exp_html()
# TODO: This should go in a more relevant place
def spec_output_file(self, frame, twod=False):
"""
Return the path to the spectral output data file.
Args:
frame (:obj:`int`):
Frame index from :attr:`fitstbl`.
twod (:obj:`bool`):
Name for the 2D output file; 1D file otherwise.
Returns:
:obj:`str`: The path for the output file
"""
return os.path.join(
self.science_path,
'spec{0}d_{1}.fits'.format('2' if twod else '1',
self.fitstbl.construct_basename(frame)))
def outfile_exists(self, frame):
"""
Check whether the 2D outfile of a given frame already exists
Args:
frame (int): Frame index from fitstbl
Returns:
bool: True if the 2d file exists, False if it does not exist
"""
return os.path.isfile(self.spec_output_file(frame, twod=True))
def get_std_outfile(self, standard_frames):
"""
Grab the output filename from an input list of standard_frame indices
If more than one index is provided, the first is taken
Args:
standard_frames (list): List of indices corresponding to standard stars
Returns:
str: Full path to the standard spec1d output file
"""
# TODO: Need to decide how to associate standards with
# science frames in the case where there is more than one
# standard associated with a given science frame. Below, I
# just use the first standard
std_outfile = None
std_frame = None if len(standard_frames) == 0 else standard_frames[0]
# Prepare to load up standard?
if std_frame is not None:
std_outfile = self.spec_output_file(std_frame) \
if isinstance(std_frame, (int,np.integer)) else None
if std_outfile is not None and not os.path.isfile(std_outfile):
msgs.error('Could not find standard file: {0}'.format(std_outfile))
return std_outfile
def calib_all(self):
"""
Create calibrations for all setups
This will not crash if not all of the standard set of files are not provided
"""
self.tstart = time.time()
# Frame indices
frame_indx = np.arange(len(self.fitstbl))
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
grp_frames = frame_indx[in_grp]
# Find the detectors to reduce
detectors = PypeIt.select_detectors(
detnum=self.par['rdx']['detnum'], ndet=self.spectrograph.ndet)
# Loop on Detectors
for self.det in detectors:
# Instantiate Calibrations class
self.caliBrate = calibrations.Calibrations.get_instance(
self.fitstbl,
self.par['calibrations'],
self.spectrograph,
self.calibrations_path,
qadir=self.qa_path,
reuse_masters=self.reuse_masters,
show=self.show,
slitspat_num=self.par['rdx']['slitspatnum'])
# Do it
self.caliBrate.set_config(grp_frames[0], self.det,
self.par['calibrations'])
self.caliBrate.run_the_steps()
# Finish
self.print_end_time()
def reduce_all(self):
"""
Main driver of the entire reduction
Calibration and extraction via a series of calls to reduce_exposure()
"""
# Validate the parameter set
self.par.validate_keys(required=[
'rdx', 'calibrations', 'scienceframe', 'reduce', 'flexure'
])
self.tstart = time.time()
# Find the standard frames
is_standard = self.fitstbl.find_frames('standard')
# Find the science frames
is_science = self.fitstbl.find_frames('science')
# Frame indices
frame_indx = np.arange(len(self.fitstbl))
# Iterate over each calibration group and reduce the standards
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the standard frames in this calibration group:
grp_standards = frame_indx[is_standard & in_grp]
# Reduce all the standard frames, loop on unique comb_id
u_combid_std = np.unique(self.fitstbl['comb_id'][grp_standards])
for j, comb_id in enumerate(u_combid_std):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
std_spec2d, std_sobjs = self.reduce_exposure(
frames, bg_frames=bg_frames)
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], std_spec2d, std_sobjs,
self.basename)
else:
msgs.info(
'Output file: {:s} already exists'.format(
self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
# Iterate over each calibration group again and reduce the science frames
for i in range(self.fitstbl.n_calib_groups):
# Find all the frames in this calibration group
in_grp = self.fitstbl.find_calib_group(i)
# Find the indices of the science frames in this calibration group:
grp_science = frame_indx[is_science & in_grp]
# Associate standards (previously reduced above) for this setup
std_outfile = self.get_std_outfile(frame_indx[is_standard])
# Reduce all the science frames; keep the basenames of the science frames for use in flux calibration
science_basename = [None] * len(grp_science)
# Loop on unique comb_id
u_combid = np.unique(self.fitstbl['comb_id'][grp_science])
for j, comb_id in enumerate(u_combid):
frames = np.where(self.fitstbl['comb_id'] == comb_id)[0]
# Find all frames whose comb_id matches the current frames bkg_id.
bg_frames = np.where((self.fitstbl['comb_id'] ==
self.fitstbl['bkg_id'][frames][0])
& (self.fitstbl['comb_id'] >= 0))[0]
# JFH changed the syntax below to that above, which allows frames to be used more than once
# as a background image. The syntax below would require that we could somehow list multiple
# numbers for the bkg_id which is impossible without a comma separated list
# bg_frames = np.where(self.fitstbl['bkg_id'] == comb_id)[0]
if not self.outfile_exists(frames[0]) or self.overwrite:
# TODO -- Should we reset/regenerate self.slits.mask for a new exposure
sci_spec2d, sci_sobjs = self.reduce_exposure(
frames, bg_frames=bg_frames, std_outfile=std_outfile)
science_basename[j] = self.basename
# TODO come up with sensible naming convention for save_exposure for combined files
self.save_exposure(frames[0], sci_spec2d, sci_sobjs,
self.basename)
else:
msgs.warn(
'Output file: {:s} already exists'.format(
self.fitstbl.construct_basename(frames[0])) +
'. Set overwrite=True to recreate and overwrite.')
msgs.info('Finished calibration group {0}'.format(i))
# Check if this is an IFU reduction. If so, make a datacube
if self.spectrograph.pypeline == "IFU" and self.par['reduce']['cube'][
'make_cube']:
msgs.work("Generate datacube")
# Finish
self.print_end_time()
# This is a static method to allow for use in coadding script
@staticmethod
def select_detectors(detnum=None, ndet=1, slitspatnum=None):
"""
Return the 1-indexed list of detectors to reduce.
Args:
detnum (:obj:`int`, :obj:`list`, optional):
One or more detectors to reduce. If None, return the
full list for the provided number of detectors (`ndet`).
ndet (:obj:`int`, optional):
The number of detectors for this instrument. Only used
if `detnum is None`.
Returns:
list: List of detectors to be reduced
"""
if detnum is not None and slitspatnum is not None:
msgs.error(
"You cannot specify both detnum and slitspatnum. Too painful for over-writing SpecObjs"
)
if detnum is None and slitspatnum is None:
return np.arange(1, ndet + 1).tolist()
elif detnum is not None:
return np.atleast_1d(detnum).tolist()
else:
return slittrace.parse_slitspatnum(slitspatnum)[0].tolist()
def reduce_exposure(self, frames, bg_frames=None, std_outfile=None):
"""
Reduce a single exposure
Args:
frame (:obj:`int`):
0-indexed row in :attr:`fitstbl` with the frame to
reduce.
bg_frames (:obj:`list`, optional):
List of frame indices for the background.
std_outfile (:obj:`str`, optional):
File with a previously reduced standard spectrum from
PypeIt.
Returns:
dict: The dictionary containing the primary outputs of
extraction.
"""
# TODO:
# - change doc string to reflect that more than one frame can be
# provided
# if show is set, clear the ginga channels at the start of each new sci_ID
if self.show:
# TODO: Put this in a try/except block?
display.clear_all()
has_bg = True if bg_frames is not None and len(
bg_frames) > 0 else False
# Is this an IR reduction?
# TODO: Why specific to IR?
self.ir_redux = True if has_bg else False
# Container for all the Spec2DObj
all_spec2d = spec2dobj.AllSpec2DObj()
all_spec2d['meta']['ir_redux'] = self.ir_redux
# TODO -- Should we reset/regenerate self.slits.mask for a new exposure
all_specobjs = specobjs.SpecObjs()
# Print status message
msgs_string = 'Reducing target {:s}'.format(
self.fitstbl['target'][frames[0]]) + msgs.newline()
# TODO: Print these when the frames are actually combined,
# backgrounds are used, etc?
msgs_string += 'Combining frames:' + msgs.newline()
for iframe in frames:
msgs_string += '{0:s}'.format(
self.fitstbl['filename'][iframe]) + msgs.newline()
msgs.info(msgs_string)
if has_bg:
bg_msgs_string = ''
for iframe in bg_frames:
bg_msgs_string += '{0:s}'.format(
self.fitstbl['filename'][iframe]) + msgs.newline()
bg_msgs_string = msgs.newline(
) + 'Using background from frames:' + msgs.newline(
) + bg_msgs_string
msgs.info(bg_msgs_string)
# Find the detectors to reduce
detectors = PypeIt.select_detectors(
detnum=self.par['rdx']['detnum'],
slitspatnum=self.par['rdx']['slitspatnum'],
ndet=self.spectrograph.ndet)
if len(detectors) != self.spectrograph.ndet:
msgs.warn('Not reducing detectors: {0}'.format(' '.join([
str(d) for d in set(np.arange(self.spectrograph.ndet)) -
set(detectors)
])))
# Loop on Detectors
# TODO: Attempt to put in a multiprocessing call here?
for self.det in detectors:
msgs.info("Working on detector {0}".format(self.det))
# Instantiate Calibrations class
self.caliBrate = calibrations.Calibrations.get_instance(
self.fitstbl,
self.par['calibrations'],
self.spectrograph,
self.calibrations_path,
qadir=self.qa_path,
reuse_masters=self.reuse_masters,
show=self.show,
slitspat_num=self.par['rdx']['slitspatnum'])
# These need to be separate to accomodate COADD2D
self.caliBrate.set_config(frames[0], self.det,
self.par['calibrations'])
self.caliBrate.run_the_steps()
# Extract
# TODO: pass back the background frame, pass in background
# files as an argument. extract one takes a file list as an
# argument and instantiates science within
all_spec2d[self.det], tmp_sobjs \
= self.reduce_one(frames, self.det, bg_frames, std_outfile=std_outfile)
# Hold em
if tmp_sobjs.nobj > 0:
all_specobjs.add_sobj(tmp_sobjs)
# JFH TODO write out the background frame?
# TODO -- Save here? Seems like we should. Would probably need to use update_det=True
# Return
return all_spec2d, all_specobjs
def get_sci_metadata(self, frame, det):
"""
Grab the meta data for a given science frame and specific detector
Args:
frame (int): Frame index
det (int): Detector index
Returns:
5 objects are returned::
- str: Object type; science or standard
- str: Setup string from master_key()
- astropy.time.Time: Time of observation
- str: Basename of the frame
- str: Binning of the detector
"""
# Set binning, obstime, basename, and objtype
binning = self.fitstbl['binning'][frame]
obstime = self.fitstbl.construct_obstime(frame)
basename = self.fitstbl.construct_basename(frame, obstime=obstime)
objtype = self.fitstbl['frametype'][frame]
if 'science' in objtype:
objtype_out = 'science'
elif 'standard' in objtype:
objtype_out = 'standard'
else:
msgs.error('Unrecognized objtype')
setup = self.fitstbl.master_key(frame, det=det)
return objtype_out, setup, obstime, basename, binning
def get_std_trace(self, std_redux, det, std_outfile):
"""
Returns the trace of the standard if it is applicable to the current reduction
Args:
std_redux (bool): If False, proceed
det (int): Detector index
std_outfile (str): Filename for the standard star spec1d file
Returns:
ndarray or None: Trace of the standard star on input detector
"""
if std_redux is False and std_outfile is not None:
sobjs = specobjs.SpecObjs.from_fitsfile(std_outfile)
# Does the detector match?
# TODO Instrument specific logic here could be implemented with the parset. For example LRIS-B or LRIS-R we
# we would use the standard from another detector
this_det = sobjs.DET == det
if np.any(this_det):
sobjs_det = sobjs[this_det]
sobjs_std = sobjs_det.get_std()
# No standard extracted on this detector??
if sobjs_std is None:
return None
std_trace = sobjs_std.TRACE_SPAT
# flatten the array if this multislit
if 'MultiSlit' in self.spectrograph.pypeline:
std_trace = std_trace.flatten()
elif 'Echelle' in self.spectrograph.pypeline:
std_trace = std_trace.T
else:
msgs.error('Unrecognized pypeline')
else:
std_trace = None
else:
std_trace = None
return std_trace
def reduce_one(self, frames, det, bg_frames, std_outfile=None):
"""
Reduce + Extract a single exposure/detector pair
sci_ID and det need to have been set internally prior to calling this method
Args:
frames (:obj:`list`):
List of frames to extract; stacked if more than one
is provided
det (:obj:`int`):
Detector number (1-indexed)
bg_frames (:obj:`list`):
List of frames to use as the background. Can be
empty.
std_outfile (:obj:`str`, optional):
Filename for the standard star spec1d file. Passed
directly to :func:`get_std_trace`.
Returns:
tuple: Returns six `numpy.ndarray`_ objects and a
:class:`pypeit.specobjs.SpecObjs` object with the
extracted spectra from this exposure/detector pair. The
six `numpy.ndarray`_ objects are (1) the science image,
(2) its inverse variance, (3) the sky model, (4) the
object model, (5) the model inverse variance, and (6) the
mask.
"""
# 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)
msgs.info("Extraction begins for {} on det={}".format(
self.basename, det))
# Is this a standard star?
self.std_redux = 'standard' in self.objtype
if self.std_redux:
frame_par = self.par['calibrations']['standardframe']
else:
frame_par = self.par['scienceframe']
# 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)
sciImg = buildimage.buildimage_fromlist(
self.spectrograph,
det,
frame_par,
sci_files,
bias=self.caliBrate.msbias,
bpm=self.caliBrate.msbpm,
dark=self.caliBrate.msdark,
flatimages=self.caliBrate.flatimages,
slits=self.caliBrate.slits, # For flexure correction
ignore_saturation=False)
# Background Image?
if len(bg_frames) > 0:
bg_file_list = self.fitstbl.frame_paths(bg_frames)
sciImg = sciImg.sub(
buildimage.buildimage_fromlist(
self.spectrograph,
det,
frame_par,
bg_file_list,
bpm=self.caliBrate.msbpm,
bias=self.caliBrate.msbias,
dark=self.caliBrate.msdark,
flatimages=self.caliBrate.flatimages,
slits=self.caliBrate.slits, # For flexure correction
ignore_saturation=False),
frame_par['process'])
# Instantiate Reduce object
# Required for pypeline specific object
# At instantiaton, the fullmask in self.sciImg is modified
self.redux = reduce.Reduce.get_instance(sciImg,
self.spectrograph,
self.par,
self.caliBrate,
self.objtype,
ir_redux=self.ir_redux,
std_redux=self.std_redux,
setup=self.setup,
show=self.show,
det=det,
binning=self.binning,
std_outfile=std_outfile,
basename=self.basename)
# Show?
if self.show:
self.redux.show('image',
image=sciImg.image,
chname='processed',
slits=True,
clear=True)
# Do it
skymodel, objmodel, ivarmodel, outmask, sobjs, scaleImg, waveImg, tilts = self.redux.run(
std_trace=std_trace,
show_peaks=self.show,
ra=self.fitstbl["ra"][frames[0]],
dec=self.fitstbl["dec"][frames[0]],
obstime=self.obstime)
# TODO -- Save the slits yet again?
# TODO -- Do this upstream
# Tack on detector
for sobj in sobjs:
sobj.DETECTOR = sciImg.detector
# Construct the Spec2DObj
spec2DObj = spec2dobj.Spec2DObj(det=self.det,
sciimg=sciImg.image,
ivarraw=sciImg.ivar,
skymodel=skymodel,
objmodel=objmodel,
ivarmodel=ivarmodel,
scaleimg=scaleImg,
waveimg=waveImg,
bpmmask=outmask,
detector=sciImg.detector,
sci_spat_flexure=sciImg.spat_flexure,
tilts=tilts,
slits=copy.deepcopy(
self.caliBrate.slits))
spec2DObj.process_steps = sciImg.process_steps
# Return
return spec2DObj, sobjs
def save_exposure(self, frame, all_spec2d, all_specobjs, basename):
"""
Save the outputs from extraction for a given exposure
Args:
frame (:obj:`int`):
0-indexed row in the metadata table with the frame
that has been reduced.
all_spec2d(:class:`pypeit.spec2dobj.AllSpec2DObj`):
sci_dict (:obj:`dict`):
Dictionary containing the primary outputs of
extraction
basename (:obj:`str`):
The root name for the output file.
Returns:
None or SpecObjs: All of the objects saved to disk
"""
# TODO: Need some checks here that the exposure has been reduced?
# Determine the headers
row_fitstbl = self.fitstbl[frame]
# Need raw file header information
rawfile = self.fitstbl.frame_paths(frame)
head2d = fits.getheader(rawfile, ext=self.spectrograph.primary_hdrext)
# Check for the directory
if not os.path.isdir(self.science_path):
os.makedirs(self.science_path)
subheader = self.spectrograph.subheader_for_spec(row_fitstbl, head2d)
# 1D spectra
if all_specobjs.nobj > 0:
# Spectra
outfile1d = os.path.join(self.science_path,
'spec1d_{:s}.fits'.format(basename))
all_specobjs.write_to_fits(
subheader,
outfile1d,
update_det=self.par['rdx']['detnum'],
slitspatnum=self.par['rdx']['slitspatnum'])
# Info
outfiletxt = os.path.join(self.science_path,
'spec1d_{:s}.txt'.format(basename))
all_specobjs.write_info(outfiletxt, self.spectrograph.pypeline)
# 2D spectra
outfile2d = os.path.join(self.science_path,
'spec2d_{:s}.fits'.format(basename))
# Build header
pri_hdr = all_spec2d.build_primary_hdr(
head2d,
self.spectrograph,
redux_path=self.par['rdx']['redux_path'],
master_key_dict=self.caliBrate.master_key_dict,
master_dir=self.caliBrate.master_dir,
subheader=subheader)
# Write
all_spec2d.write_to_fits(outfile2d,
pri_hdr=pri_hdr,
update_det=self.par['rdx']['detnum'])
def msgs_reset(self):
"""
Reset the msgs object
"""
# Reset the global logger
msgs.reset(log=self.logname, verbosity=self.verbosity)
msgs.pypeit_file = self.pypeit_file
def print_end_time(self):
"""
Print the elapsed time
"""
# Capture the end time and print it to user
tend = time.time()
codetime = tend - self.tstart
if codetime < 60.0:
msgs.info('Execution time: {0:.2f}s'.format(codetime))
elif codetime / 60.0 < 60.0:
mns = int(codetime / 60.0)
scs = codetime - 60.0 * mns
msgs.info('Execution time: {0:d}m {1:.2f}s'.format(mns, scs))
else:
hrs = int(codetime / 3600.0)
mns = int(60.0 * (codetime / 3600.0 - hrs))
scs = codetime - 60.0 * mns - 3600.0 * hrs
msgs.info('Execution time: {0:d}h {1:d}m {2:.2f}s'.format(
hrs, mns, scs))
# TODO: Move this to fitstbl?
def show_science(self):
"""
Simple print of science frames
"""
indx = self.fitstbl.find_frames('science')
print(self.fitstbl[['target', 'ra', 'dec', 'exptime',
'dispname']][indx])
def __repr__(self):
# Generate sets string
return '<{:s}: pypeit_file={}>'.format(self.__class__.__name__,
self.pypeit_file)