def MasterTrace(self, fitsdict, det):
"""
Generate Master Trace frame for a given detector
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
det : int
Index of the detector
Returns
-------
boolean : bool
Should other ScienceExposure classes be updated?
"""
# If the master trace is already made, use it
if self._mstrace[det-1] is not None:
msgs.info("An identical master trace frame already exists")
return False
if self._argflag['reduce']['usetrace'] in ['trace', 'blzflat']:
if self._argflag['masters']['use']:
# Attempt to load the Master Frame
mstrace_name = armasters.master_name(self._argflag['run']['masterdir'],
'trace', self._argflag['masters']['setup'])
try:
mstrace, head = arload.load_master(mstrace_name, frametype="trace")
except IOError:
msgs.warn("No MasterTrace frame found {:s}".format(mstrace_name))
else:
# Extras
lordloc, _ = arload.load_master(mstrace_name, frametype="trace", exten=1)
rordloc, _ = arload.load_master(mstrace_name, frametype="trace", exten=2)
pixcen, _ = arload.load_master(mstrace_name, frametype="trace", exten=3)
pixwid, _ = arload.load_master(mstrace_name, frametype="trace", exten=4)
lordpix, _ = arload.load_master(mstrace_name, frametype="trace", exten=5)
rordpix, _ = arload.load_master(mstrace_name, frametype="trace", exten=6)
self.SetFrame(self._lordloc, lordloc, det)
self.SetFrame(self._rordloc, rordloc, det)
self.SetFrame(self._pixcen, pixcen.astype(np.int), det)
self.SetFrame(self._pixwid, pixwid.astype(np.int), det)
self.SetFrame(self._lordpix, lordpix.astype(np.int), det)
self.SetFrame(self._rordpix, rordpix.astype(np.int), det)
#
self._argflag['masters']['loaded'].append('trace'+self._argflag['masters']['setup'])
if 'trace'+self._argflag['masters']['setup'] not in self._argflag['masters']['loaded']:
msgs.info("Preparing a master trace frame with {0:s}".format(self._argflag['reduce']['usetrace']))
ind = self._idx_trace
# Load the frames for tracing
frames = arload.load_frames(self, fitsdict, ind, det, frametype='trace', msbias=self._msbias[det-1],
trim=self._argflag['reduce']['trim'], transpose=self._transpose)
if self._argflag['reduce']['flatmatch'] > 0.0:
sframes = arsort.match_frames(frames, self._argflag['reduce']['flatmatch'], msgs, frametype='trace', satlevel=self._spect['det'][det-1]['saturation']*self._spect['det'][det-1]['nonlinear'])
subframes = np.zeros((frames.shape[0], frames.shape[1], len(sframes)))
numarr = np.array([])
for i in xrange(len(sframes)):
numarr = np.append(numarr, sframes[i].shape[2])
mstrace = arcomb.comb_frames(sframes[i], det, spect=self._spect, frametype='trace', **self._argflag['trace']['comb'])
subframes[:,:,i] = mstrace.copy()
del sframes
# Combine all sub-frames
mstrace = arcomb.comb_frames(subframes, det, spect=self._spect, frametype='trace', weights=numarr, **self._argflag['trace']['comb'])
del subframes
else:
mstrace = arcomb.comb_frames(frames, det, spect=self._spect, frametype='trace', **self._argflag['trace']['comb'])
del frames
elif self._argflag['reduce']['usetrace'] == 'science':
msgs.error("Tracing with a science frame is not yet implemented")
else: # It must be the name of a file the user wishes to load
mstrace_name = self._argflag['run']['masterdir']+'/'+self._argflag['reduce']['usetrace']
mstrace, head = arload.load_master(mstrace_name, frametype=None)
debugger.set_trace() # NEED TO LOAD EXTRAS AS ABOVE
# Set and then delete the Master Trace frame
self.SetMasterFrame(mstrace, "trace", det)
del mstrace
return True
def MasterFlatField(self, fitsdict, det):
"""
Generate Master Flat-field frame for a given detector
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
det : int
Index of the detector
Returns
-------
boolean : bool
Should other ScienceExposure classes be updated?
"""
if self._argflag['reduce']['flatfield']: # Only do it if the user wants to flat field
# If the master pixflat is already made, use it
if self._mspixflat[det-1] is not None:
msgs.info("An identical master pixflat frame already exists")
if self._mspixflatnrm[det-1] is None:
# Normalize the flat field
msgs.info("Normalizing the pixel flat")
mspixflatnrm, msblaze = arproc.flatnorm(self, det, self.GetMasterFrame("pixflat", det),
overpix=0, plotdesc="Blaze function")
self.SetFrame(self._msblaze, msblaze, det)
self.SetMasterFrame(mspixflatnrm, "normpixflat", det)
return False
###############
# Generate a master pixel flat frame
if self._argflag['reduce']['useflat'] in ['pixflat', 'blzflat']:
if self._argflag['masters']['use']:
# Attempt to load the Master Frame
msflat_name = armasters.master_name(self._argflag['run']['masterdir'],
'normpixflat', self._argflag['masters']['setup'])
try:
mspixflatnrm, head = arload.load_master(msflat_name, frametype="normpixflat")
except IOError:
msgs.warn("No MasterFlatField frame found {:s}".format(msflat_name))
else:
self._argflag['masters']['loaded'].append('normpixflat'+self._argflag['masters']['setup'])
mspixflat = mspixflatnrm
if 'normpixflat'+self._argflag['masters']['setup'] not in self._argflag['masters']['loaded']:
msgs.info("Preparing a master pixel flat frame with {0:s}".format(self._argflag['reduce']['useflat']))
# Get all of the pixel flat frames for this science frame
ind = self._idx_flat
# Load the frames for tracing
frames = arload.load_frames(self, fitsdict, ind, det, frametype='pixel flat',
msbias=self._msbias[det-1], transpose=self._transpose)
if self._argflag['reduce']['flatmatch'] > 0.0:
sframes = arsort.match_frames(frames, self._argflag['reduce']['flatmatch'],
frametype='pixel flat', satlevel=self._nonlinear)
subframes = np.zeros((frames.shape[0], frames.shape[1], len(sframes)))
numarr = np.array([])
for i in xrange(len(sframes)):
numarr = np.append(numarr, sframes[i].shape[2])
mspixflat = arcomb.comb_frames(sframes[i], det, spect=self._spect, frametype='pixel flat',
**self._argflag['pixflat']['comb'])
subframes[:,:,i] = mspixflat.copy()
del sframes
# Combine all sub-frames
mspixflat = arcomb.comb_frames(subframes, det, spect=self._spect, frametype='pixel flat',
weights=numarr, **self._argflag['pixflat']['comb'])
del subframes
else:
mspixflat = arcomb.comb_frames(frames, det, spect=self._spect, frametype='pixel flat',
**self._argflag['pixflat']['comb'])
del frames
# Apply gain (instead of ampsec scale)
mspixflat *= arproc.gain_frame(self, det)
# Normalize the flat field
mspixflatnrm, msblaze = arproc.flatnorm(self, det, mspixflat, overpix=0, plotdesc="Blaze function")
self.SetFrame(self._msblaze, msblaze, det)
else: # It must be the name of a file the user wishes to load
mspixflat_name = armasters.user_master_name(self._argflag['run']['masterdir'],
self._argflag['reduce']['useflat'])
mspixflatnrm, head = arload.load_master(mspixflat_name, exten=det, frametype=None)
mspixflat = mspixflatnrm
# Now that the combined, master flat field frame is loaded...
else:
msgs.work("Pixel Flat arrays need to be generated when not flat fielding")
msgs.bug("Blaze is currently undefined")
mspixflat = np.ones_like(self._msarc)
mspixflatnrm = np.ones_like(self._msarc)
# Set Master Frames
self.SetMasterFrame(mspixflat, "pixflat", det)
self.SetMasterFrame(mspixflatnrm, "normpixflat", det)
return True
def MasterArc(self, fitsdict, det):
"""
Generate Master Arc frame for a given detector
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
det : int
Index of the detector
Returns
-------
boolean : bool
Should other ScienceExposure classes be updated?
"""
if self._msarc[det-1] is not None:
msgs.info("An identical master arc frame already exists")
return False
if self._argflag['reduce']['usearc'] in ['arc']:
# Attempt to load the Master Frame
if self._argflag['masters']['use']:
msarc_name = armasters.master_name(self._argflag['run']['masterdir'],
'arc', self._argflag['masters']['setup'])
try:
msarc, head = arload.load_master(msarc_name, frametype="arc")
except IOError:
msgs.warn("No MasterArc frame found {:s}".format(msarc_name))
else:
self._transpose = head['transp']
if self._transpose: # Need to setup for flipping
self._argflag['trace']['disp']['direction'] = 1
else:
self._argflag['trace']['disp']['direction'] = 0
# Append as loaded
self._argflag['masters']['loaded'].append('arc'+self._argflag['masters']['setup'])
if 'arc'+self._argflag['masters']['setup'] not in self._argflag['masters']['loaded']:
msgs.info("Preparing a master arc frame")
ind = self._idx_arcs
# Load the arc frames
frames = arload.load_frames(self, fitsdict, ind, det, frametype='arc',
msbias=self._msbias[det-1])
if self._argflag['reduce']['arcmatch'] > 0.0:
sframes = arsort.match_frames(frames, self._argflag['reduce']['arcmatch'], msgs, frametype='arc',
satlevel=self._spect['det']['saturation']*self._spect['det']['nonlinear'])
subframes = np.zeros((frames.shape[0], frames.shape[1], len(sframes)))
numarr = np.array([])
for i in xrange(len(sframes)):
numarr = np.append(numarr, sframes[i].shape[2])
msarc = arcomb.comb_frames(sframes[i], det, spect=self._spect,
frametype='arc', **self._argflag['arc']['comb'])
# Send the data away to be saved
subframes[:,:,i] = msarc.copy()
del sframes
# Combine all sub-frames
msarc = arcomb.comb_frames(subframes, det, spect=self._spect,
frametype='arc', weights=numarr, **self._argflag['arc']['comb'])
del subframes
else:
msarc = arcomb.comb_frames(frames, det, spect=self._spect,
frametype='arc', **self._argflag['arc']['comb'])
del frames
# # Derive a suitable name for the master arc frame
# msarc_name = "{0:s}/{1:s}/msarc{2:s}_{3:03d}.fits".format(os.getcwd(),self._argflag['run']['masterdir'],self._spect["det"][det-1]["suffix"],len(self._done_arcs))
# self._tltprefix = os.path.splitext(os.path.basename(msarc_name))[0]
# # Send the data away to be saved
# arsave.save_master(self, msarc, filename=msarc_name, frametype='arc', ind=ind)
# # Store the files used and the master bias name in case it can be used during the later reduction processes
# self._done_arcs.append(ind)
# self._name_arcs.append(msarc_name)
else:
msarc_name = self._argflag['run']['masterdir']+'/'+self._argflag['reduce']['usearc']
msarc, head = arload.load_master(msarc_name, frametype=None)
# Set and then delete the Master Arc frame
self.SetMasterFrame(msarc, "arc", det)
del msarc
return True
def MasterTrace(self, fitsdict, det):
"""
Generate Master Trace frame for a given detector
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
det : int
Index of the detector
Returns
-------
boolean : bool
Should other ScienceExposure classes be updated?
"""
dnum = settings.get_dnum(det)
# If the master trace is already made, use it
if self._mstrace[det - 1] is not None:
msgs.info("An identical master trace frame already exists")
return False
if settings.argflag['reduce']['trace']['useframe'] in ['trace']:
try:
mstrace = armasters.get_master_frame(self, "trace", det=det)
except IOError:
msgs.info("Preparing a master trace frame with {0:s}".format(
settings.argflag['reduce']['trace']['useframe']))
ind = self._idx_trace
# Load the frames for tracing
frames = arload.load_frames(
fitsdict,
ind,
det,
frametype='trace',
msbias=self._msbias[det - 1],
trim=settings.argflag['reduce']['trim'])
if settings.argflag['trace']['combine']['match'] > 0.0:
sframes = arsort.match_frames(
frames,
settings.argflag['trace']['combine']['match'],
frametype='trace',
satlevel=settings.spect[dnum]['saturation'] *
settings.spect['det'][det - 1]['nonlinear'])
subframes = np.zeros(
(frames.shape[0], frames.shape[1], len(sframes)))
numarr = np.array([])
for i in range(len(sframes)):
numarr = np.append(numarr, sframes[i].shape[2])
mstrace = arcomb.comb_frames(sframes[i], det, 'trace')
subframes[:, :, i] = mstrace.copy()
del sframes
# Combine all sub-frames
mstrace = arcomb.comb_frames(subframes,
det,
'trace',
weights=numarr)
del subframes
else:
mstrace = arcomb.comb_frames(frames, det, 'trace')
del frames
else: # It must be the name of a file the user wishes to load
mstrace_name = settings.argflag['run']['directory'][
'master'] + '/' + settings.argflag['reduce']['trace'][
'useframe']
mstrace, _ = arload.load_master(mstrace_name, frametype=None)
debugger.set_trace(
) # NEED TO LOAD EXTRAS AS ABOVE; SHOULD MODIFY get_master_frame()
# Set and then delete the Master Trace frame
self.SetMasterFrame(mstrace, "trace", det)
del mstrace
return True
def MasterFlatField(self, fitsdict, det):
"""
Generate Master Flat-field frame for a given detector
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
det : int
Index of the detector
Returns
-------
boolean : bool
Should other ScienceExposure classes be updated?
"""
from pypit import arqa
if settings.argflag['reduce']['flatfield'][
'perform']: # Only do it if the user wants to flat field
# If the master pixelflat is already made, use it
if self._mspixelflat[det - 1] is not None:
msgs.info("An identical master pixelflat frame already exists")
if self._mspixelflatnrm[det - 1] is None:
# Normalize the flat field
msgs.info("Normalizing the pixel flat")
slit_profiles, mstracenrm, msblaze, flat_ext1d, extrap_slit = \
arproc.slit_profile(self, self.GetMasterFrame("pixelflat", det),
det, ntcky=settings.argflag['reduce']['flatfield']['params'][0])
# If some slit profiles/blaze functions need to be extrapolated, do that now
if np.sum(extrap_slit) != 0.0:
slit_profiles, mstracenrm, msblaze = arproc.slit_profile_pca(
self, self.GetMasterFrame("pixelflat", det), det,
msblaze, extrap_slit, slit_profiles)
mspixelflatnrm = mstracenrm.copy()
winpp = np.where(slit_profiles != 0.0)
mspixelflatnrm[winpp] /= slit_profiles[winpp]
self.SetMasterFrame(mspixelflatnrm, "normpixelflat", det)
armasters.save_masters(self, det, mftype='normpixelflat')
if np.array_equal(self._idx_flat, self._idx_trace):
# The flat field frame is also being used to trace the slit edges and determine the slit
# profile. Avoid recalculating the slit profile and blaze function and save them here.
self.SetFrame(self._msblaze, msblaze, det)
self.SetFrame(self._slitprof, slit_profiles, det)
armasters.save_masters(self, det, mftype='slitprof')
if settings.argflag["reduce"]["slitprofile"][
"perform"]:
msgs.info("Preparing QA of each slit profile")
arqa.slit_profile(self,
mstracenrm,
slit_profiles,
self._lordloc[det - 1],
self._rordloc[det - 1],
self._slitpix[det - 1],
desc="Slit profile")
msgs.info("Saving blaze function QA")
arqa.plot_orderfits(self,
msblaze,
flat_ext1d,
desc="Blaze function")
return False
###############
# Generate/load a master pixel flat frame
if settings.argflag['reduce']['flatfield']['useframe'] in [
'pixelflat', 'trace'
]:
try:
mspixelflatnrm = armasters.get_master_frame(
self, "normpixelflat")
except IOError:
msgs.info("Preparing a master pixel flat frame with {0:s}".
format(settings.argflag['reduce']['flatfield']
['useframe']))
# Get all of the pixel flat frames for this science frame
ind = self._idx_flat
# Load the frames for tracing
frames = arload.load_frames(fitsdict,
ind,
det,
frametype='pixel flat',
msbias=self._msbias[det - 1])
if settings.argflag['pixelflat']['combine']['match'] > 0.0:
sframes = arsort.match_frames(
frames,
settings.argflag['pixelflat']['combine']['match'],
frametype='pixel flat',
satlevel=self._nonlinear)
subframes = np.zeros(
(frames.shape[0], frames.shape[1], len(sframes)))
numarr = np.array([])
for i in range(len(sframes)):
numarr = np.append(numarr, sframes[i].shape[2])
mspixelflat = arcomb.comb_frames(
sframes[i],
det,
'pixelflat',
printtype='pixel flat')
subframes[:, :, i] = mspixelflat.copy()
del sframes
# Combine all sub-frames
mspixelflat = arcomb.comb_frames(
subframes,
det,
'pixelflat',
weights=numarr,
printtype='pixel flat')
del subframes
else:
mspixelflat = arcomb.comb_frames(
frames, det, 'pixelflat', printtype='pixel flat')
del frames
# Apply gain (instead of ampsec scale)
mspixelflat *= arproc.gain_frame(self, det)
# Normalize the flat field
msgs.info("Normalizing the pixel flat")
slit_profiles, mstracenrm, msblaze, flat_ext1d, extrap_slit = \
arproc.slit_profile(self, mspixelflat, det, ntcky=settings.argflag['reduce']['flatfield']['params'][0])
# If some slit profiles/blaze functions need to be extrapolated, do that now
if np.sum(extrap_slit) != 0.0:
slit_profiles, mstracenrm, msblaze = arproc.slit_profile_pca(
self, mspixelflat, det, msblaze, extrap_slit,
slit_profiles)
mspixelflatnrm = mstracenrm.copy()
winpp = np.where(slit_profiles != 0.0)
mspixelflatnrm[winpp] /= slit_profiles[winpp]
if np.array_equal(self._idx_flat, self._idx_trace):
# The flat field frame is also being used to trace the slit edges and determine the slit
# profile. Avoid recalculating the slit profile and blaze function and save them here.
self.SetFrame(self._msblaze, msblaze, det)
self.SetFrame(self._slitprof, slit_profiles, det)
armasters.save_masters(self, det, mftype='slitprof')
if settings.argflag["reduce"]["slitprofile"][
"perform"]:
msgs.info("Preparing QA of each slit profile")
arqa.slit_profile(self,
mstracenrm,
slit_profiles,
self._lordloc[det - 1],
self._rordloc[det - 1],
self._slitpix[det - 1],
desc="Slit profile")
msgs.info("Saving blaze function QA")
arqa.plot_orderfits(self,
msblaze,
flat_ext1d,
desc="Blaze function")
else:
mspixelflat = mspixelflatnrm
else: # It must be the name of a file the user wishes to load
mspixelflat_name = armasters.user_master_name(
settings.argflag['run']['directory']['master'],
settings.argflag['reduce']['flatfield']['useframe'])
mspixelflatnrm, head = arload.load_master(mspixelflat_name,
exten=det,
frametype=None)
mspixelflat = mspixelflatnrm
# Now that the combined, master flat field frame is loaded...
else:
msgs.work(
"Pixel Flat arrays need to be generated when not flat fielding"
)
msgs.bug("Blaze is currently undefined")
mspixelflat = np.ones_like(self._msarc)
mspixelflatnrm = np.ones_like(self._msarc)
# Set Master Frames
self.SetMasterFrame(mspixelflat, "pixelflat", det)
self.SetMasterFrame(mspixelflatnrm, "normpixelflat", det)
armasters.save_masters(self, det, mftype='normpixelflat')
return True
def MasterArc(self, fitsdict, det):
"""
Generate Master Arc frame for a given detector
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
det : int
Index of the detector
Returns
-------
boolean : bool
Should other ScienceExposure classes be updated?
"""
dnum = settings.get_dnum(det)
if self._msarc[det - 1] is not None:
msgs.info("A master arc frame already exists for this frame")
return False
if settings.argflag['arc']['useframe'] in ['arc']:
# Master Frame
try:
msarc = armasters.get_master_frame(self, "arc")
except IOError:
msgs.info("Preparing a master arc frame")
ind = self._idx_arcs
# Load the arc frames
frames = arload.load_frames(fitsdict,
ind,
det,
frametype='arc',
msbias=self._msbias[det - 1])
if settings.argflag['arc']['combine']['match'] > 0.0:
sframes = arsort.match_frames(
frames,
settings.argflag['arc']['combine']['match'],
frametype='arc',
satlevel=settings.spect[dnum]['saturation'] *
settings.spect[dnum]['nonlinear'])
subframes = np.zeros(
(frames.shape[0], frames.shape[1], len(sframes)))
numarr = np.array([])
for i in range(len(sframes)):
numarr = np.append(numarr, sframes[i].shape[2])
msarc = arcomb.comb_frames(sframes[i], det, 'arc')
# Send the data away to be saved
subframes[:, :, i] = msarc.copy()
del sframes
# Combine all sub-frames
msarc = arcomb.comb_frames(subframes,
det,
'arc',
weights=numarr)
del subframes
else:
msarc = arcomb.comb_frames(frames, det, 'arc')
del frames
else: # Use input frame name located in MasterFrame directory
msarc_name = settings.argflag['run']['directory'][
'master'] + '/' + settings.argflag['arc']['useframe']
msarc, _ = arload.load_master(msarc_name, frametype=None)
# Set and then delete the Master Arc frame
self.SetMasterFrame(msarc, "arc", det)
armasters.save_masters(self, det, mftype='arc')
del msarc
return True
