def main(pargs):
import time
from pypeit import ginga
from pypeit import flatfield
import subprocess
# Load up
flatField = flatfield.FlatField.from_master_file(pargs.master_file)
try:
ginga.connect_to_ginga(raise_err=True)
except ValueError:
subprocess.Popen(['ginga', '--modules=RC'])
time.sleep(3)
# Show RawFlatImage
viewer, ch = ginga.show_image(flatField.rawflatimg.image,
chname='Raw Flat')
# PixelFlat
if flatField.mspixelflat is not None:
viewer, ch = ginga.show_image(flatField.mspixelflat,
chname='Pixel Flat')
# Illumination flat
if flatField.msillumflat is not None:
viewer, ch = ginga.show_image(flatField.msillumflat,
chname='Illumination Flat')
# Illumination flat
if flatField.flat_model is not None:
viewer, ch = ginga.show_image(flatField.flat_model,
chname='Flat Model')
print("Check your Ginga viewer")
def visual_inspect(camera='b3', idx=0, old=True):
"""
View an image bias-subtracted by the old or new fig
Args:
camera:
idx:
old:
Returns:
"""
dpath = '/home/xavier/DESI/DESI_SCRATCH/minisv2/data/20200304/'
bias_files = glob.glob(dpath + '000*/desi-*.fz')
bias_files.sort()
bias_file = bias_files[idx]
print("Inspecting: {}".format(bias_file))
# Load
hdul = fits.open(bias_file)
hdu = hdul[camera.upper()]
bias = hdu.data
stack_bias, _ = grab_stack_bias_img(camera, old=old)
# View
ginga.show_image(bias - stack_bias)
def show(self, slits=True, wcs_match=True):
"""
Show all of the flat field products
Args:
slits (bool, optional):
wcs_match (bool, optional):
"""
viewer, ch = ginga.show_image(self.mspixelflat,
chname='pixeflat',
cuts=(0.9, 1.1),
wcs_match=wcs_match,
clear=True)
viewer, ch = ginga.show_image(self.msillumflat,
chname='illumflat',
cuts=(0.9, 1.1),
wcs_match=wcs_match)
viewer, ch = ginga.show_image(self.rawflatimg,
chname='flat',
wcs_match=wcs_match)
viewer, ch = ginga.show_image(self.flat_model,
chname='flat_model',
wcs_match=wcs_match)
if slits:
if self.tslits_dict is not None:
slit_ids = [
edgetrace.get_slitid(self.rawflatimg.shape,
self.tslits_dict['slit_left'],
self.tslits_dict['slit_righ'], ii)[0]
for ii in range(self.tslits_dict['slit_left'].shape[1])
]
ginga.show_slits(viewer, ch, self.tslits_dict['slit_left'],
self.tslits_dict['slit_righ'], slit_ids)
def show(self):
"""
Simple show method
"""
if self.image is None:
msgs.warn("No image to show!")
return
ginga.show_image(self.image, chname='image')
def show(self):
"""
Show the image in a ginga viewer.
"""
if self.image is None:
# TODO: This should fault.
msgs.warn("No image to show!")
return
ginga.show_image(self.image, chname='image')
def get_tslits_nires(flat_files,
user_settings=par,
gingashow=True,
tilt_root='tilt_nires'):
"""Precess flat files and get titlts for NIRES
"""
# Process flat images
tImage = traceimage.TraceImage(spectrograph,
file_list=flat_files,
par=par['calibrations']['traceframe'])
tflat = tImage.process(bias_subtract='overscan',
trim=False)
mstrace = tflat.copy()
# Define pixlocn and bpm
pixlocn = pixels.gen_pixloc(tImage.stack.shape)
bpm = spectrograph.bpm(shape=tflat.shape, det=1)
# Instantiate Trace
tSlits = traceslits.TraceSlits(mstrace,
pixlocn,
par=par['calibrations']['slits'],
binbpx=bpm)
tslits_dict = tSlits.run(plate_scale = 0.123)
if gingashow:
# Look at what TraceSlits was actually trying to trace
viewer, ch = ginga.show_image(tSlits.edgearr)
# Look at the sawtooth convolved image
viewer, ch = ginga.show_image(tSlits.siglev)
tmp = tSlits.edgearr * 100.
tmp[np.where(tmp == 0.)] = 1.
ginga.show_image(tSlits.mstrace * tmp)
ginga.show_slits(viewer,
ch,
tSlits.lcen,
tSlits.rcen,
slit_ids=np.arange(tSlits.lcen.shape[1]) + 1,
pstep=50)
if tilt_root is not None:
# Write dict on a json file
jdict = ltu.jsonify(tslits_dict.copy())
ltu.savejson(tilt_root + '.json', jdict, overwrite=True, indent=None, easy_to_read=True)
print("Wrote: {:s}".format(tilt_root + '.json'))
return tslits_dict
def main(args):
import subprocess
from astropy.io import fits
from pypeit import msgs
from pypeit.spectrographs import keck_lris
from pypeit.spectrographs import keck_deimos
from pypeit.spectrographs import gemini_gmos
from pypeit import msgs
from pypeit import ginga
# List only?
if args.list:
hdu = fits.open(args.file)
print(hdu.info())
return
# Setup for PYPIT imports
msgs.reset(verbosity=2)
# RAW_LRIS??
if 'keck_lris' in args.spectrograph:
#
if args.spectrograph == 'keck_lris_red_orig':
gen_lris = keck_lris.KeckLRISROrigSpectrograph()
img = gen_lris.get_rawimage(args.file, 1)[1]
else:
gen_lris = keck_lris.KeckLRISRSpectrograph() # Using LRISr, but this will work for LRISb too
img = gen_lris.get_rawimage(args.file, None)[1]
# RAW_DEIMOS??
elif args.spectrograph == 'keck_deimos':
#
gen_deimos = keck_deimos.KeckDEIMOSSpectrograph()
img = gen_deimos.get_rawimage(args.file, None)[1]
# RAW_GEMINI??
elif 'gemini_gmos' in args.spectrograph:
# TODO this routine should show the whole mosaic if no detector number is passed in!
# Need to figure out the number of amps
gen_gmos = gemini_gmos.GeminiGMOSSpectrograph()
img = gen_gmos.get_rawimage(args.file, args.det)[1]
else:
hdu = fits.open(args.file)
img = hdu[args.exten].data
# Write
ginga.show_image(img)
def show(self, item='wave'):
"""
Show the image
Args:
item (str, optional):
Returns:
"""
if item == 'wave':
if self.mswave is not None:
ginga.show_image(self.mswave)
else:
msgs.warn("Not able to show this type of image")
def show(self, attr, slit=None, display='ginga', cname=None):
"""
Display an image or spectrum in TraceSlits
Parameters
----------
attr : str
'fweight' -- Show the msarc image and the tilts traced by fweight
'model' -- Show the msarc image and the poylynomial model fits to the individual arc lines that
were traced by fweight.
'arcmodel -- This illustrates the global final 2-d model fit to the indivdiaul models of each traced fweight arc line
tilts evaluated at the location of the specific arclines that wered use for the fit.
'final_tilts' -- Show the final 2-d tilt model for all the slits that were fit.
slit : int, optional
-- The slit to plot. This needs to be an integer between 1 and nslit
display : str (optional)
'ginga' -- Display to an RC Ginga
"""
viewer, ch = ginga.show_image(self.arcimg * (self.slitmask == slit),
chname='Tilts')
ginga.show_tilts(viewer,
ch,
self.trace_dict,
sedges=(self.tslits_dict['slit_left'][:, slit],
self.tslits_dict['slit_righ'][:, slit]),
points=True,
clear_canvas=True)
def show(self, attr='stack', idx=None, display='ginga'):
"""
Show an image
Parameters
----------
attr : str, optional
Internal name of the image to show
proc_image, raw_image, stack
idx : int, optional
Specifies the index of the raw or processed image
Required if proc_image or raw_image is called
display : str
Returns
-------
"""
if 'proc_image' in attr:
img = self.proc_images[:, :, idx]
elif 'raw_image' in attr:
img = self.raw_images[idx]
elif 'stack' in attr:
img = self.stack
else:
msgs.warn("Options: proc_image, raw_image, stack")
return
# Show
viewer, ch = ginga.show_image(img)
def main(args):
import subprocess
from astropy.io import fits
from pypeit import msgs
from pypeit.spectrographs import keck_lris
from pypeit.spectrographs import keck_deimos
from pypeit.spectrographs import gemini_gmos
from pypeit import msgs
from pypeit import ginga
# List only?
if args.list:
hdu = fits.open(args.file)
print(hdu.info())
return
# Setup for PYPIT imports
msgs.reset(verbosity=2)
# RAW_LRIS??
if args.raw_lris:
#
img, _, _ = keck_lris.read_lris(args.file)
# RAW_DEIMOS??
elif args.raw_deimos:
#
img, _, _ = keck_deimos.read_deimos(args.file)
# RAW_GEMINI??
elif args.raw_gmos:
# TODO this routine should show the whole mosaic if no detector number is passed in!
# Need to figure out the number of amps
img, _, _ = gemini_gmos.read_gmos(args.file, det=args.det)
else:
hdu = fits.open(args.file)
img = hdu[args.exten].data
# Write
ginga.show_image(img)
def show(self, image, chname=None):
"""
Show one of the internal images
Args:
image : ndarray, optional
User supplied image to display
"""
ch_name = chname if chname is not None else 'image'
viewer, ch = ginga.show_image(image, chname=ch_name)
def qck_bias(cameras=['z3'], debug=False):
"""
Generate and write to disk a Bias image
Args:
cameras (list):
"""
dpath = '/home/xavier/DESI/DESI_SCRATCH/minisv2/data/20200304/'
bias_files = glob.glob(dpath + '000*/desi-*.fz')
bias_files.sort()
# Load em
for camera in cameras:
bias_imgs = None
for ss, bias_file in enumerate(bias_files):
print("Loading {}".format(bias_file))
hdul = fits.open(bias_file)
#- Check if this file uses amp names 1,2,3,4 (old) or A,B,C,D (new)
hdu = hdul[camera.upper()]
header = hdu.header
if bias_imgs is None:
bias_imgs = np.zeros(
(len(bias_files), hdu.data.shape[0], hdu.data.shape[1]),
dtype=float)
bias_imgs[ss, :, :] = hdu.data.astype(float)
# Stack em
bias_img, _, _ = stats.sigma_clipped_stats(bias_imgs, axis=0)
if debug:
ginga.show_image(bias_img)
embed(header='51 of qa_preproc')
# Write
hdu0 = fits.PrimaryHDU(bias_img)
hdul = fits.HDUList([hdu0])
outfile = os.path.join(dpath, 'bias_{}.fits'.format(camera))
hdul.writeto(outfile, overwrite=True)
print("Wrote: {}".format(outfile))
def main(pargs):
import pdb as debugger
import time
from pypeit import ginga
from pypeit import traceslits
from pypeit.core.trace_slits import get_slitid
import subprocess
# Load up
tslits_dict, mstrace = traceslits.load_tslits(pargs.root)
import pdb
pdb.set_trace()
try:
ginga.connect_to_ginga(raise_err=True)
except ValueError:
subprocess.Popen(['ginga', '--modules=RC'])
time.sleep(3)
# This is deprecated since the class is not stored to disk anymore. You can just debug to achieve this functionality
#if pargs.show is not None:
# Tslits.show(pargs.show)
# print("Check your Ginga viewer")
# return
# Show Image
viewer, ch = ginga.show_image(mstrace, chname=pargs.chname)
nslits = tslits_dict['nslits']
# Get slit ids
stup = (mstrace.shape, tslits_dict['slit_left'], tslits_dict['slit_righ'])
if pargs.dumb_ids:
slit_ids = range(nslits)
else:
slit_ids = [
get_slitid(stup[0], stup[1], stup[2], ii)[0]
for ii in range(nslits)
]
ginga.show_slits(viewer,
ch,
tslits_dict['slit_left'],
tslits_dict['slit_righ'],
slit_ids,
pstep=50)
print("Check your Ginga viewer")
def show_alignment(alignframe, align_traces=None, slits=None, clear=False):
"""
Show one of the class internals
Parameters
----------
alignframe : `numpy.ndarray`_
Image to be plotted (i.e. the master align frame)
align_traces : list, optional
The align traces
slits : :class:`pypeit.slittrace.SlitTraceSet`, optional
properties of the slits, including traces.
clear : bool, optional
Clear the plotting window in ginga?
Returns
-------
"""
ginga.connect_to_ginga(raise_err=True, allow_new=True)
ch_name = 'alignment'
viewer, channel = ginga.show_image(alignframe,
chname=ch_name,
clear=clear,
wcs_match=False)
# Display the slit edges
if slits is not None and viewer is not None:
left, right, mask = slits.select_edges()
ginga.show_slits(viewer, channel, left, right)
# Display the alignment traces
if align_traces is not None and viewer is not None:
for bar in range(align_traces.shape[1]):
for slt in range(align_traces.shape[2]):
# Alternate the colors of the slits
color = 'orange'
if slt % 2 == 0:
color = 'magenta'
# Display the trace
ginga.show_trace(viewer,
channel,
align_traces[:, bar, slt],
trc_name="",
color=color)
def show_flats(pixelflat, illumflat, procflat, flat_model, wcs_match=True, slits=None):
"""
Interface to ginga to show a set of flat images
Args:
pixelflat (`numpy.ndarray`_):
illumflat (`numpy.ndarray`_ or None):
procflat (`numpy.ndarray`_):
flat_model (`numpy.ndarray`_):
wcs_match (bool, optional):
slits (:class:`pypeit.slittrace.SlitTraceSet`, optional):
Returns:
"""
ginga.connect_to_ginga(raise_err=True, allow_new=True)
if slits is not None:
left, right, mask = slits.select_edges()
gpm = mask == 0
# Loop me
clear = True
for img, name, cut in zip([pixelflat, illumflat, procflat, flat_model],
['pixelflat', 'illumflat', 'flat', 'flat_model'],
[(0.9, 1.1), (0.9, 1.1), None, None]):
if img is None:
continue
# TODO: Add an option that shows the relevant stuff in a
# matplotlib window.
viewer, ch = ginga.show_image(img, chname=name, cuts=cut,
wcs_match=wcs_match, clear=clear)
if slits is not None:
ginga.show_slits(viewer, ch, left[:, gpm], right[:, gpm],
slit_ids=slits.spat_id[gpm])
# Turn off clear
if clear:
clear = False
def main(args):
# List only?
hdu = fits.open(args.file)
head0 = hdu[0].header
if args.list:
hdu.info()
return
# Setup for PYPIT imports
msgs.reset(verbosity=2)
# Init
sdet = get_dnum(args.det, prefix=False)
# One detector, sky sub for now
names = [hdu[i].name for i in range(len(hdu))]
try:
exten = names.index('DET{:s}-PROCESSED'.format(sdet))
except: # Backwards compatability
msgs.error(
'Requested detector {:s} was not processed.\n'
'Maybe you chose the wrong one to view?\n'
'Set with --det= or check file contents with --list'.format(sdet))
sciimg = hdu[exten].data
try:
exten = names.index('DET{:s}-SKY'.format(sdet))
except: # Backwards compatability
msgs.error(
'Requested detector {:s} has no sky model.\n'
'Maybe you chose the wrong one to view?\n'
'Set with --det= or check file contents with --list'.format(sdet))
skymodel = hdu[exten].data
try:
exten = names.index('DET{:s}-MASK'.format(sdet))
except ValueError: # Backwards compatability
msgs.error(
'Requested detector {:s} has no bit mask.\n'
'Maybe you chose the wrong one to view?\n'
'Set with --det= or check file contents with --list'.format(sdet))
mask = hdu[exten].data
try:
exten = names.index('DET{:s}-IVARMODEL'.format(sdet))
except ValueError: # Backwards compatability
msgs.error(
'Requested detector {:s} has no IVARMODEL.\n'
'Maybe you chose the wrong one to view?\n' +
'Set with --det= or check file contents with --list'.format(sdet))
ivarmodel = hdu[exten].data
# Read in the object model for residual map
try:
exten = names.index('DET{:s}-OBJ'.format(sdet))
except ValueError: # Backwards compatability
msgs.error(
'Requested detector {:s} has no object model.\n'
'Maybe you chose the wrong one to view?\n' +
'Set with --det= or check file contents with --list'.format(sdet))
objmodel = hdu[exten].data
# Get waveimg
mdir = head0['PYPMFDIR'] + '/'
if not os.path.exists(mdir):
mdir_base = os.path.basename(os.path.dirname(mdir)) + '/'
msgs.warn('Master file dir: {0} does not exist. Using ./{1}'.format(
mdir, mdir_base))
mdir = mdir_base
trace_key = '{0}_{1:02d}'.format(head0['TRACMKEY'], args.det)
trc_file = os.path.join(
mdir, MasterFrame.construct_file_name('Trace', trace_key))
wave_key = '{0}_{1:02d}'.format(head0['ARCMKEY'], args.det)
waveimg = os.path.join(mdir,
MasterFrame.construct_file_name('Wave', wave_key))
tslits_dict = TraceSlits.load_from_file(trc_file)[0]
slitmask = pixels.tslits2mask(tslits_dict)
shape = (tslits_dict['nspec'], tslits_dict['nspat'])
slit_ids = [
trace_slits.get_slitid(shape, tslits_dict['slit_left'],
tslits_dict['slit_righ'], ii)[0]
for ii in range(tslits_dict['slit_left'].shape[1])
]
# Show the bitmask?
mask_in = mask if args.showmask else None
# Object traces
spec1d_file = args.file.replace('spec2d', 'spec1d')
det_nm = 'DET{:s}'.format(sdet)
if os.path.isfile(spec1d_file):
hdulist_1d = fits.open(spec1d_file)
else:
hdulist_1d = []
msgs.warn('Could not find spec1d file: {:s}'.format(spec1d_file) +
msgs.newline() +
' No objects were extracted.')
# Unpack the bitmask
bitMask = bitmask()
bpm, crmask, satmask, minmask, offslitmask, nanmask, ivar0mask, ivarnanmask, extractmask \
= bitMask.unpack(mask)
# Now show each image to a separate channel
# SCIIMG
image = sciimg # Raw science image
(mean, med, sigma) = sigma_clipped_stats(image[mask == 0],
sigma_lower=5.0,
sigma_upper=5.0)
cut_min = mean - 1.0 * sigma
cut_max = mean + 4.0 * sigma
chname_skysub = 'sciimg-det{:s}'.format(sdet)
# Clear all channels at the beginning
viewer, ch = ginga.show_image(image,
chname=chname_skysub,
waveimg=waveimg,
bitmask=mask_in,
clear=True)
#, cuts=(cut_min, cut_max), wcs_match=True)
ginga.show_slits(viewer, ch, tslits_dict['slit_left'],
tslits_dict['slit_righ'], slit_ids)
#, args.det)
# SKYSUB
image = (sciimg - skymodel) * (mask == 0) # sky subtracted image
(mean, med, sigma) = sigma_clipped_stats(image[mask == 0],
sigma_lower=5.0,
sigma_upper=5.0)
cut_min = mean - 1.0 * sigma
cut_max = mean + 4.0 * sigma
chname_skysub = 'skysub-det{:s}'.format(sdet)
# Clear all channels at the beginning
# TODO: JFH For some reason Ginga crashes when I try to put cuts in here.
viewer, ch = ginga.show_image(
image, chname=chname_skysub, waveimg=waveimg,
bitmask=mask_in) #, cuts=(cut_min, cut_max),wcs_match=True)
show_trace(hdulist_1d, det_nm, viewer, ch)
ginga.show_slits(viewer, ch, tslits_dict['slit_left'],
tslits_dict['slit_righ'], slit_ids)
#, args.det)
# SKRESIDS
chname_skyresids = 'sky_resid-det{:s}'.format(sdet)
image = (sciimg - skymodel) * np.sqrt(ivarmodel) * (mask == 0
) # sky residual map
viewer, ch = ginga.show_image(image,
chname_skyresids,
waveimg=waveimg,
cuts=(-5.0, 5.0),
bitmask=mask_in) #,wcs_match=True)
show_trace(hdulist_1d, det_nm, viewer, ch)
ginga.show_slits(viewer, ch, tslits_dict['slit_left'],
tslits_dict['slit_righ'], slit_ids)
#, args.det)
# RESIDS
chname_resids = 'resid-det{:s}'.format(sdet)
# full model residual map
image = (sciimg - skymodel - objmodel) * np.sqrt(ivarmodel) * (mask == 0)
viewer, ch = ginga.show_image(image,
chname=chname_resids,
waveimg=waveimg,
cuts=(-5.0, 5.0),
bitmask=mask_in) #,wcs_match=True)
show_trace(hdulist_1d, det_nm, viewer, ch)
ginga.show_slits(viewer, ch, tslits_dict['slit_left'],
tslits_dict['slit_righ'], slit_ids)
#, args.det)
# After displaying all the images sync up the images with WCS_MATCH
shell = viewer.shell()
out = shell.start_global_plugin('WCSMatch')
out = shell.call_global_plugin_method('WCSMatch', 'set_reference_channel',
[chname_resids], {})
if args.embed:
IPython.embed()
slit_righ = tslits_dict['rcen'][:, slit]
thismask = (tslits_dict['slitpix'] == slit + 1)
inmask = None # in the future set this to the bpm
sys.exit(-1)
pixelflat[thismask], illumflat[thismask], flat_model[
thismask] = flat.fit_flat(flatimg,
mstilts,
thismask,
slit_left,
slit_righ,
inmask=inmask,
debug=debug)
ginga.show_image(pixelflat,
cuts=(0.9, 1.1),
chname='pixeflat',
wcs_match=True,
clear=True)
ginga.show_image(illumflat,
cuts=(0.9, 1.1),
chname='illumflat',
wcs_match=True)
ginga.show_image(flatimg, chname='flat', wcs_match=True)
ginga.show_image(flat_model, chname='flat_model', wcs_match=True)
'''
bkspace = 1.0/nsamp # This is the spatial sampling interval in units of fractional slit width
fit_spat = thismask & inmask
isrt_spat = np.argsort(ximg[fit_spat])
ximg_fit = ximg[fit_spat][isrt_spat]
norm_spec_fit = norm_spec[fit_spat][isrt_spat]
def show(self,
attr,
image=None,
showmask=False,
sobjs=None,
chname=None,
slits=False,
clear=False):
"""
Show one of the internal images
.. todo::
Should probably put some of these in ProcessImages
Parameters
----------
attr : str
global -- Sky model (global)
sci -- Processed science image
rawvar -- Raw variance image
modelvar -- Model variance image
crmasked -- Science image with CRs set to 0
skysub -- Science image with global sky subtracted
image -- Input image
display : str, optional
image : ndarray, optional
User supplied image to display
Returns
-------
"""
if showmask:
mask_in = self.sciImg.fullmask
bitmask_in = self.sciImg.bitmask
else:
mask_in = None
bitmask_in = None
if attr == 'global':
# global sky subtraction
if self.sciImg.image is not None and self.global_sky is not None and self.sciImg.fullmask is not None:
# sky subtracted image
image = (self.sciImg.image -
self.global_sky) * (self.sciImg.fullmask == 0)
mean, med, sigma = stats.sigma_clipped_stats(
image[self.sciImg.fullmask == 0],
sigma_lower=5.0,
sigma_upper=5.0)
cut_min = mean - 1.0 * sigma
cut_max = mean + 4.0 * sigma
ch_name = chname if chname is not None else 'global_sky_{}'.format(
self.det)
viewer, ch = ginga.show_image(image,
chname=ch_name,
bitmask=bitmask_in,
mask=mask_in,
clear=clear,
wcs_match=True)
#, cuts=(cut_min, cut_max))
elif attr == 'local':
# local sky subtraction
if self.sciImg.image is not None and self.skymodel is not None and self.sciImg.fullmask is not None:
# sky subtracted image
image = (self.sciImg.image -
self.skymodel) * (self.sciImg.fullmask == 0)
mean, med, sigma = stats.sigma_clipped_stats(
image[self.sciImg.fullmask == 0],
sigma_lower=5.0,
sigma_upper=5.0)
cut_min = mean - 1.0 * sigma
cut_max = mean + 4.0 * sigma
ch_name = chname if chname is not None else 'local_sky_{}'.format(
self.det)
viewer, ch = ginga.show_image(image,
chname=ch_name,
bitmask=bitmask_in,
mask=mask_in,
clear=clear,
wcs_match=True)
#, cuts=(cut_min, cut_max))
elif attr == 'sky_resid':
# sky residual map with object included
if self.sciImg.image is not None and self.skymodel is not None \
and self.objmodel is not None and self.ivarmodel is not None \
and self.sciImg.fullmask is not None:
image = (self.sciImg.image - self.skymodel) * np.sqrt(
self.ivarmodel)
image *= (self.sciImg.fullmask == 0)
ch_name = chname if chname is not None else 'sky_resid_{}'.format(
self.det)
viewer, ch = ginga.show_image(image,
chname=ch_name,
cuts=(-5.0, 5.0),
bitmask=bitmask_in,
mask=mask_in,
clear=clear,
wcs_match=True)
elif attr == 'resid':
# full residual map with object model subtractede
if self.sciImg.image is not None and self.skymodel is not None \
and self.objmodel is not None and self.ivarmodel is not None \
and self.sciImg.fullmask is not None:
# full model residual map
image = (self.sciImg.image - self.skymodel -
self.objmodel) * np.sqrt(self.ivarmodel)
image *= (self.sciImg.fullmask == 0)
ch_name = chname if chname is not None else 'resid_{}'.format(
self.det)
viewer, ch = ginga.show_image(image,
chname=ch_name,
cuts=(-5.0, 5.0),
bitmask=bitmask_in,
mask=mask_in,
clear=clear,
wcs_match=True)
elif attr == 'image':
ch_name = chname if chname is not None else 'image'
viewer, ch = ginga.show_image(image,
chname=ch_name,
clear=clear,
wcs_match=True)
else:
msgs.warn("Not an option for show")
if sobjs is not None:
for spec in sobjs:
color = 'magenta' if spec.hand_extract_flag else 'orange'
ginga.show_trace(viewer,
ch,
spec.TRACE_SPAT,
spec.NAME,
color=color)
if slits and self.slits_left is not None:
ginga.show_slits(viewer, ch, self.slits_left, self.slits_right)
from pypeit import ginga from astropy.io import fits from pypeit.spectrographs import keck_lris lowrdx_path = '/Users/joe/pypeit_vs_lowredux/c17_60L/Red400/' pypeit_path = '/Users/joe/python/PypeIt-development-suite/REDUX_OUT/Keck_LRIS_red/multi_400_8500_d560/' rawpath = '/Users/joe/python/PypeIt-development-suite/RAW_DATA/Keck_LRIS_red/multi_400_8500_d560/' flatfile = rawpath + 'r170320_2057.fits' flatfile_lowrdx = lowrdx_path + 'pixflat-r170320_2057.fits' flatfile_pypeit = pypeit_path + 'MF_keck_lris_red/MasterFlatField_A_01_aa.fits' flat, _, _ = keck_lris.read_lris(flatfile, det=1, TRIM=True) hdu = fits.open(flatfile_lowrdx) flat_lowrdx = hdu[0].data flat_lowrdx = flat_lowrdx[:, 0:1024] hdu = fits.open(flatfile_pypeit) flat_pypeit = hdu[0].data cuts = (0.8, 1.5) viewer, ch = ginga.show_image(flat_lowrdx, chname='LOWREDUX', cuts=cuts) viewer, ch = ginga.show_image(flat_pypeit, chname='PYPEIT', cuts=cuts) viewer, ch = ginga.show_image(flat, chname='FLAT')
def run(self, doqa=True, debug=False, show=False):
"""
Main driver for tracing arc lines
Code flow:
#. Extract an arc spectrum down the center of each slit/order
#. Loop on slits/orders
#. Trace and fit the arc lines (This is done twice, once
with trace_crude as the tracing crutch, then again
with a PCA model fit as the crutch).
#. Repeat trace.
#. 2D Fit to the offset from slitcen
#. Save
Args:
doqa (bool):
debug (bool):
show (bool):
Returns:
:class:`WaveTilts`:
"""
# Extract the arc spectra for all slits
self.arccen, self.arccen_bpm = self.extract_arcs()
# TODO: Leave for now. Used for debugging
# self.par['rm_continuum'] = True
# debug = True
# show = True
# Subtract arc continuum
_mstilt = self.mstilt.image.copy()
if self.par['rm_continuum']:
continuum = self.model_arc_continuum(debug=debug)
_mstilt -= continuum
if debug:
# TODO: Put this into a function
vmin, vmax = visualization.ZScaleInterval().get_limits(_mstilt)
w,h = plt.figaspect(1)
fig = plt.figure(figsize=(3*w,h))
ax = fig.add_axes([0.15/3, 0.1, 0.8/3, 0.8])
ax.imshow(self.mstilt.image, origin='lower', interpolation='nearest',
aspect='auto', vmin=vmin, vmax=vmax)
ax.set_title('MasterArc')
ax = fig.add_axes([1.15/3, 0.1, 0.8/3, 0.8])
ax.imshow(continuum, origin='lower', interpolation='nearest',
aspect='auto', vmin=vmin, vmax=vmax)
ax.set_title('Continuum')
ax = fig.add_axes([2.15/3, 0.1, 0.8/3, 0.8])
ax.imshow(_mstilt, origin='lower', interpolation='nearest',
aspect='auto', vmin=vmin, vmax=vmax)
ax.set_title('MasterArc - Continuum')
plt.show()
# Final tilts image
self.final_tilts = np.zeros(self.shape_science,dtype=float)
max_spat_dim = (np.asarray(self.par['spat_order']) + 1).max()
max_spec_dim = (np.asarray(self.par['spec_order']) + 1).max()
self.coeffs = np.zeros((max_spec_dim, max_spat_dim,self.slits.nslits))
self.spat_order = np.zeros(self.slits.nslits, dtype=int)
self.spec_order = np.zeros(self.slits.nslits, dtype=int)
# TODO sort out show methods for debugging
if show:
viewer,ch = ginga.show_image(self.mstilt.image*(self.slitmask > -1),chname='tilts')
# Loop on all slits
for slit_idx, slit_spat in enumerate(self.slits.spat_id):
if self.tilt_bpm[slit_idx]:
continue
#msgs.info('Computing tilts for slit {0}/{1}'.format(slit, self.slits.nslits-1))
msgs.info('Computing tilts for slit {0}/{1}'.format(slit_idx, self.slits.nslits))
# Identify lines for tracing tilts
msgs.info('Finding lines for tilt analysis')
self.lines_spec, self.lines_spat \
= self.find_lines(self.arccen[:,slit_idx], self.slitcen[:,slit_idx],
slit_idx,
bpm=self.arccen_bpm[:,slit_idx], debug=debug)
if self.lines_spec is None:
self.slits.mask[slit_idx] = self.slits.bitmask.turn_on(self.slits.mask[slit_idx], 'BADTILTCALIB')
continue
thismask = self.slitmask == slit_spat
# Performs the initial tracing of the line centroids as a
# function of spatial position resulting in 1D traces for
# each line.
msgs.info('Trace the tilts')
self.trace_dict = self.trace_tilts(_mstilt, self.lines_spec, self.lines_spat,
thismask, self.slitcen[:, slit_idx])
# TODO: Show the traces before running the 2D fit
if show:
ginga.show_tilts(viewer, ch, self.trace_dict)
self.spat_order[slit_idx] = self._parse_param(self.par, 'spat_order', slit_idx)
self.spec_order[slit_idx] = self._parse_param(self.par, 'spec_order', slit_idx)
# 2D model of the tilts, includes construction of QA
# NOTE: This also fills in self.all_fit_dict and self.all_trace_dict
coeff_out = self.fit_tilts(self.trace_dict, thismask, self.slitcen[:,slit_idx],
self.spat_order[slit_idx], self.spec_order[slit_idx],
slit_idx,
doqa=doqa, show_QA=show, debug=show)
self.coeffs[:self.spec_order[slit_idx]+1,:self.spat_order[slit_idx]+1,slit_idx] = coeff_out
# TODO: Need a way to assess the success of fit_tilts and
# flag the slit if it fails
# Tilts are created with the size of the original slitmask,
# which corresonds to the same binning as the science
# images, trace images, and pixelflats etc.
self.tilts = tracewave.fit2tilts(self.slitmask_science.shape, coeff_out,
self.par['func2d'])
# Save to final image
thismask_science = self.slitmask_science == slit_spat
self.final_tilts[thismask_science] = self.tilts[thismask_science]
if debug:
# TODO: Add this to the show method?
vmin, vmax = visualization.ZScaleInterval().get_limits(_mstilt)
plt.imshow(_mstilt, origin='lower', interpolation='nearest', aspect='auto',
vmin=vmin, vmax=vmax)
for slit in self.slit_idx:
spat = self.all_trace_dict[slit]['tilts_spat']
spec = self.all_trace_dict[slit]['tilts']
spec_fit = self.all_trace_dict[slit]['tilts_fit']
in_fit = self.all_trace_dict[slit]['tot_mask']
not_fit = np.invert(in_fit) & (spec > 0)
fit_rej = in_fit & np.invert(self.all_trace_dict[slit]['fit_mask'])
fit_keep = in_fit & self.all_trace_dict[slit]['fit_mask']
plt.scatter(spat[not_fit], spec[not_fit], color='C1', marker='.', s=30, lw=0)
plt.scatter(spat[fit_rej], spec[fit_rej], color='C3', marker='.', s=30, lw=0)
plt.scatter(spat[fit_keep], spec[fit_keep], color='k', marker='.', s=30, lw=0)
with_fit = np.invert(np.all(np.invert(fit_keep), axis=0))
for t in range(in_fit.shape[1]):
if not with_fit[t]:
continue
l, r = np.nonzero(in_fit[:,t])[0][[0,-1]]
plt.plot(spat[l:r+1,t], spec_fit[l:r+1,t], color='k')
plt.show()
# Record the Mask
bpmtilts = np.zeros_like(self.slits.mask, dtype=self.slits.bitmask.minimum_dtype())
for flag in ['BADTILTCALIB']:
bpm = self.slits.bitmask.flagged(self.slits.mask, flag)
if np.any(bpm):
bpmtilts[bpm] = self.slits.bitmask.turn_on(bpmtilts[bpm], flag)
# Build and return DataContainer
tilts_dict = {'coeffs':self.coeffs,
'func2d':self.par['func2d'], 'nslit':self.slits.nslits,
'spat_order':self.spat_order, 'spec_order':self.spec_order,
'spat_id':self.slits.spat_id, 'bpmtilts': bpmtilts,
'spat_flexure': self.spat_flexure,
'PYP_SPEC': self.spectrograph.spectrograph}
return WaveTilts(**tilts_dict)
def ech_objfind(image, ivar, ordermask, slit_left, slit_righ,inmask=None,plate_scale=0.2,npca=2,ncoeff = 5,min_snr=0.0,nabove_min_snr=0,
pca_percentile=20.0,snr_pca=3.0,box_radius=2.0,show_peaks=False,show_fits=False,show_trace=False):
if inmask is None:
inmask = (ordermask > 0)
frameshape = image.shape
nspec = frameshape[0]
norders = slit_left.shape[1]
if isinstance(plate_scale,(float, int)):
plate_scale_ord = np.full(norders, plate_scale) # 0.12 binned by 3 spatially for HIRES
elif isinstance(plate_scale,(np.ndarray, list, tuple)):
if len(plate_scale) == norders:
plate_scale_ord = plate_scale
elif len(plate_scale) == 1:
plate_scale_ord = np.full(norders, plate_scale[0])
else:
msgs.error('Invalid size for plate_scale. It must either have one element or norders elements')
else:
msgs.error('Invalid type for plate scale')
specmid = nspec // 2
slit_width = slit_righ - slit_left
spec_vec = np.arange(nspec)
slit_spec_pos = nspec/2.0
slit_spat_pos = np.zeros((norders, 2))
for iord in range(norders):
slit_spat_pos[iord, :] = (np.interp(slit_spec_pos, spec_vec, slit_left[:,iord]), np.interp(slit_spec_pos, spec_vec, slit_righ[:,iord]))
# Loop over orders and find objects
sobjs = specobjs.SpecObjs()
show_peaks=True
show_fits=True
# ToDo replace orderindx with the true order number here? Maybe not. Clean up slitid and orderindx!
for iord in range(norders):
msgs.info('Finding objects on slit # {:d}'.format(iord + 1))
thismask = ordermask == (iord + 1)
inmask_iord = inmask & thismask
specobj_dict = {'setup': 'HIRES', 'slitid': iord + 1, 'scidx': 0,'det': 1, 'objtype': 'science'}
sobjs_slit, skymask[thismask], objmask[thismask], proc_list = \
extract.objfind(image, thismask, slit_left[:,iord], slit_righ[:,iord], inmask=inmask_iord,show_peaks=show_peaks,
show_fits=show_fits, show_trace=False, specobj_dict = specobj_dict)#, sig_thresh = 3.0)
# ToDO make the specobjs _set_item_ work with expressions like this spec[:].orderindx = iord
for spec in sobjs_slit:
spec.ech_orderindx = iord
sobjs.add_sobj(sobjs_slit)
nfound = len(sobjs)
# Compute the FOF linking length based on the instrument place scale and matching length FOFSEP = 1.0"
FOFSEP = 1.0 # separation of FOF algorithm in arcseconds
FOF_frac = FOFSEP/(np.median(slit_width)*np.median(plate_scale_ord))
# Feige: made the code also works for only one object found in one order
# Run the FOF. We use fake coordinaes
fracpos = sobjs.spat_fracpos
ra_fake = fracpos/1000.0 # Divide all angles by 1000 to make geometry euclidian
dec_fake = 0.0*fracpos
if nfound>1:
(ingroup, multgroup, firstgroup, nextgroup) = spheregroup(ra_fake, dec_fake, FOF_frac/1000.0)
group = ingroup.copy()
uni_group, uni_ind = np.unique(group, return_index=True)
nobj = len(uni_group)
msgs.info('FOF matching found {:d}'.format(nobj) + ' unique objects')
elif nfound==1:
group = np.zeros(1,dtype='int')
uni_group, uni_ind = np.unique(group, return_index=True)
nobj = len(group)
msgs.warn('Only find one object no FOF matching is needed')
gfrac = np.zeros(nfound)
for jj in range(nobj):
this_group = group == uni_group[jj]
gfrac[this_group] = np.median(fracpos[this_group])
uni_frac = gfrac[uni_ind]
sobjs_align = sobjs.copy()
# Now fill in the missing objects and their traces
for iobj in range(nobj):
for iord in range(norders):
# Is there an object on this order that grouped into the current group in question?
on_slit = (group == uni_group[iobj]) & (sobjs_align.ech_orderindx == iord)
if not np.any(on_slit):
# Add this to the sobjs_align, and assign required tags
thisobj = specobjs.SpecObj(frameshape, slit_spat_pos[iord,:], slit_spec_pos, det = sobjs_align[0].det,
setup = sobjs_align[0].setup, slitid = (iord + 1),
scidx = sobjs_align[0].scidx, objtype=sobjs_align[0].objtype)
thisobj.ech_orderindx = iord
thisobj.spat_fracpos = uni_frac[iobj]
thisobj.trace_spat = slit_left[:,iord] + slit_width[:,iord]*uni_frac[iobj] # new trace
thisobj.trace_spec = spec_vec
thisobj.spat_pixpos = thisobj.trace_spat[specmid]
thisobj.set_idx()
# Use the real detections of this objects for the FWHM
this_group = group == uni_group[iobj]
# Assign to the fwhm of the nearest detected order
imin = np.argmin(np.abs(sobjs_align[this_group].ech_orderindx - iord))
thisobj.fwhm = sobjs_align[imin].fwhm
thisobj.maskwidth = sobjs_align[imin].maskwidth
thisobj.ech_fracpos = uni_frac[iobj]
thisobj.ech_group = uni_group[iobj]
thisobj.ech_usepca = True
sobjs_align.add_sobj(thisobj)
group = np.append(group, uni_group[iobj])
gfrac = np.append(gfrac, uni_frac[iobj])
else:
# ToDo fix specobjs to get rid of these crappy loops!
for spec in sobjs_align[on_slit]:
spec.ech_fracpos = uni_frac[iobj]
spec.ech_group = uni_group[iobj]
spec.ech_usepca = False
# Some code to ensure that the objects are sorted in the sobjs_align by fractional position on the order and by order
# respectively
sobjs_sort = specobjs.SpecObjs()
for iobj in range(nobj):
this_group = group == uni_group[iobj]
this_sobj = sobjs_align[this_group]
sobjs_sort.add_sobj(this_sobj[np.argsort(this_sobj.ech_orderindx)])
# Loop over the objects and perform a quick and dirty extraction to assess S/N.
varimg = utils.calc_ivar(ivar)
flux_box = np.zeros((nspec, norders, nobj))
ivar_box = np.zeros((nspec, norders, nobj))
mask_box = np.zeros((nspec, norders, nobj))
SNR_arr = np.zeros((norders, nobj))
for iobj in range(nobj):
for iord in range(norders):
indx = (sobjs_sort.ech_group == uni_group[iobj]) & (sobjs_sort.ech_orderindx == iord)
spec = sobjs_sort[indx]
thismask = ordermask == (iord + 1)
inmask_iord = inmask & thismask
box_rad_pix = box_radius/plate_scale_ord[iord]
flux_tmp = extract.extract_boxcar(image*inmask_iord, spec.trace_spat,box_rad_pix, ycen = spec.trace_spec)
var_tmp = extract.extract_boxcar(varimg*inmask_iord, spec.trace_spat,box_rad_pix, ycen = spec.trace_spec)
ivar_tmp = utils.calc_ivar(var_tmp)
pixtot = extract.extract_boxcar(ivar*0 + 1.0, spec.trace_spat,box_rad_pix, ycen = spec.trace_spec)
mask_tmp = (extract.extract_boxcar(ivar*inmask_iord == 0.0, spec.trace_spat,box_rad_pix, ycen = spec.trace_spec) != pixtot)
flux_box[:,iord,iobj] = flux_tmp*mask_tmp
ivar_box[:,iord,iobj] = np.fmax(ivar_tmp*mask_tmp,0.0)
mask_box[:,iord,iobj] = mask_tmp
(mean, med_sn, stddev) = sigma_clipped_stats(flux_box[mask_tmp,iord,iobj]*np.sqrt(ivar_box[mask_tmp,iord,iobj]),
sigma_lower=5.0,sigma_upper=5.0)
SNR_arr[iord,iobj] = med_sn
# Purge objects with low SNR and that don't show up in enough orders
keep_obj = np.zeros(nobj,dtype=bool)
sobjs_trim = specobjs.SpecObjs()
uni_group_trim = np.array([],dtype=int)
uni_frac_trim = np.array([],dtype=float)
for iobj in range(nobj):
if (np.sum(SNR_arr[:,iobj] > min_snr) >= nabove_min_snr):
keep_obj[iobj] = True
ikeep = sobjs_sort.ech_group == uni_group[iobj]
sobjs_trim.add_sobj(sobjs_sort[ikeep])
uni_group_trim = np.append(uni_group_trim, uni_group[iobj])
uni_frac_trim = np.append(uni_frac_trim, uni_frac[iobj])
else:
msgs.info('Purging object #{:d}'.format(iobj) + ' which does not satisfy min_snr > {:5.2f}'.format(min_snr) +
' on at least nabove_min_snr >= {:d}'.format(nabove_min_snr) + ' orders')
nobj_trim = np.sum(keep_obj)
if nobj_trim == 0:
return specobjs.SpecObjs()
SNR_arr_trim = SNR_arr[:,keep_obj]
# Do a final loop over objects and make the final decision about which orders will be interpolated/extrapolated by the PCA
for iobj in range(nobj_trim):
SNR_now = SNR_arr_trim[:,iobj]
indx = (sobjs_trim.ech_group == uni_group_trim[iobj])
# PCA interp/extrap if:
# (SNR is below pca_percentile of the total SNRs) AND (SNR < snr_pca)
# OR
# (if this order was not originally traced by the object finding, see above)
usepca = ((SNR_now < np.percentile(SNR_now, pca_percentile)) & (SNR_now < snr_pca)) | sobjs_trim[indx].ech_usepca
# ToDo fix specobjs to get rid of these crappy loops!
for iord, spec in enumerate(sobjs_trim[indx]):
spec.ech_usepca = usepca[iord]
if usepca[iord]:
msgs.info('Using PCA to predict trace for object #{:d}'.format(iobj) + ' on order #{:d}'.format(iord))
sobjs_final = sobjs_trim.copy()
# Loop over the objects one by one and adjust/predict the traces
npoly_cen = 3
pca_fits = np.zeros((nspec, norders, nobj_trim))
for iobj in range(nobj_trim):
igroup = sobjs_final.ech_group == uni_group_trim[iobj]
# PCA predict the masked orders which were not traced
pca_fits[:,:,iobj] = pca_trace((sobjs_final[igroup].trace_spat).T, usepca = None, npca = npca, npoly_cen = npoly_cen)
# usepca = sobjs_final[igroup].ech_usepca,
# Perform iterative flux weighted centroiding using new PCA predictions
xinit_fweight = pca_fits[:,:,iobj].copy()
inmask_now = inmask & (ordermask > 0)
xfit_fweight = extract.iter_tracefit(image, xinit_fweight, ncoeff, inmask = inmask_now, show_fits=show_fits)
# Perform iterative Gaussian weighted centroiding
xinit_gweight = xfit_fweight.copy()
xfit_gweight = extract.iter_tracefit(image, xinit_gweight, ncoeff, inmask = inmask_now, gweight=True,show_fits=show_fits)
# Assign the new traces
for iord, spec in enumerate(sobjs_final[igroup]):
spec.trace_spat = xfit_gweight[:,iord]
spec.spat_pixpos = spec.trace_spat[specmid]
# Set the IDs
sobjs_final.set_idx()
if show_trace:
viewer, ch = ginga.show_image(objminsky*(ordermask > 0))
for iobj in range(nobj_trim):
for iord in range(norders):
ginga.show_trace(viewer, ch, pca_fits[:,iord, iobj], str(uni_frac[iobj]), color='yellow')
for spec in sobjs_trim:
color = 'green' if spec.ech_usepca else 'magenta'
ginga.show_trace(viewer, ch, spec.trace_spat, spec.idx, color=color)
#for spec in sobjs_final:
# color = 'red' if spec.ech_usepca else 'green'
# ginga.show_trace(viewer, ch, spec.trace_spat, spec.idx, color=color)
return sobjs_final
debug = True
# Loop on slits
for slit in gdslits:
msgs.info("Computing flat field image for slit: {:d}".format(slit + 1))
slit_left = tslits_dict['lcen'][:, slit]
slit_righ = tslits_dict['rcen'][:, slit]
thismask = (tslits_dict['slitpix'] == slit + 1)
inmask = None # in the future set this to the bpm
sys.exit(-1)
pixelflat[thismask], illumflat[thismask], flat_model[thismask] = flat.fit_flat(flatimg, mstilts, thismask,
slit_left, slit_righ,inmask=inmask,
debug = debug)
ginga.show_image(pixelflat,cuts = (0.9,1.1),chname='pixeflat', wcs_match=True, clear=True)
ginga.show_image(illumflat,cuts = (0.9,1.1), chname='illumflat', wcs_match=True)
ginga.show_image(flatimg,chname='flat', wcs_match=True)
ginga.show_image(flat_model,chname='flat_model',wcs_match = True)
'''
bkspace = 1.0/nsamp # This is the spatial sampling interval in units of fractional slit width
fit_spat = thismask & inmask
isrt_spat = np.argsort(ximg[fit_spat])
ximg_fit = ximg[fit_spat][isrt_spat]
norm_spec_fit = norm_spec[fit_spat][isrt_spat]
norm_spec_ivar = np.ones_like(norm_spec_fit)/(0.03**2)
sigrej_illum = 3.0
nfit_spat = np.sum(fit_spat)
msgs.info('Fit to flatfield slit illumination function {:}'.format(nfit_spat) + ' pixels')
def spat_flexure_shift(sciimg, slits, debug=False, maxlag=20):
"""
Calculate a rigid flexure shift in the spatial dimension
between the slitmask and the science image.
It is *important* to use original=True when defining the
slitmask as everything should be relative to the initial slits
Otherwise, the WaveTilts could get out of sync with science images
Args:
sciimg (`numpy.ndarray`_):
slits (:class:`pypeit.slittrace.SlitTraceSet`):
maxlag (:obj:`int`, optional):
Maximum flexure searched for
Returns:
float: The spatial flexure shift relative to the initial slits
"""
# Mask -- Includes short slits and those excluded by the user (e.g. ['rdx']['slitspatnum'])
slitmask = slits.slit_img(initial=True,
exclude_flag=slits.bitmask.exclude_for_flexure)
_sciimg = sciimg if slitmask.shape == sciimg.shape \
else arc.resize_mask2arc(slitmask.shape, sciimg)
onslits = slitmask > -1
corr_slits = onslits.astype(float).flatten()
# Compute
mean_sci, med_sci, stddev_sci = stats.sigma_clipped_stats(_sciimg[onslits])
thresh = med_sci + 5.0 * stddev_sci
corr_sci = np.fmin(_sciimg.flatten(), thresh)
lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, maxlag)
xcorr_denom = np.sqrt(
np.sum(corr_sci * corr_sci) * np.sum(corr_slits * corr_slits))
xcorr_norm = xcorr / xcorr_denom
# TODO -- Generate a QA plot
tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig \
= arc.detect_lines(xcorr_norm, sigdetect=3.0, fit_frac_fwhm=1.5, fwhm=5.0,
cont_frac_fwhm=1.0, cont_samp=30, nfind=1, debug=debug)
# No peak? -- e.g. data fills the entire detector
if len(tampl) == 0:
msgs.warn(
'No peak found in spatial flexure. Assuming there is none..')
if debug:
embed(header='68 of flexure')
return 0.
# Find the peak
xcorr_max = np.interp(pix_max, np.arange(lags.shape[0]), xcorr_norm)
lag_max = np.interp(pix_max, np.arange(lags.shape[0]), lags)
msgs.info('Spatial flexure measured: {}'.format(lag_max[0]))
if debug:
plt.figure(figsize=(14, 6))
plt.plot(lags,
xcorr_norm,
color='black',
drawstyle='steps-mid',
lw=3,
label='x-corr',
linewidth=1.0)
plt.plot(lag_max[0], xcorr_max[0], 'g+', markersize=6.0, label='peak')
plt.title('Best shift = {:5.3f}'.format(lag_max[0]) +
', corr_max = {:5.3f}'.format(xcorr_max[0]))
plt.legend()
plt.show()
#tslits_shift = trace_slits.shift_slits(tslits_dict, lag_max)
# Now translate the tilts
#slitmask_shift = pixels.tslits2mask(tslits_shift)
#slitmask_shift = slits.slit_img(flexure=lag_max[0])
if debug:
# Now translate the slits in the tslits_dict
all_left_flexure, all_right_flexure, mask = slits.select_edges(
flexure=lag_max[0])
gpm = mask == 0
viewer, ch = ginga.show_image(_sciimg)
ginga.show_slits(viewer, ch, left_flexure[:, gpm],
right_flexure)[:, gpm] #, slits.id) #, args.det)
embed(header='83 of flexure.py')
#ginga.show_slits(viewer, ch, tslits_shift['slit_left'], tslits_shift['slit_righ'])
#ginga.show_slits(viewer, ch, tslits_dict['slit_left'], tslits_dict['slit_righ'])
return lag_max[0]
sig_thresh=3.0,
inmask=mask_AB,
FWHM=FWHM,
nperslit=1,
trim_edg=(3, 3),
show_trace=False,
show_peaks=False,
show_fits =False)
if specobj_slit_pos is not None:
specobjs_pos.add_sobj(specobj_slit_pos.specobjs.tolist())
skymask[thismask] = (skymask_pos & skymask_neg)
# Show results on ginga
if gingashow:
# Plot the chi image
chi = (diff_AB - residual_img) * np.sqrt(ivar_AB) * (slitpix > 0) * ((edgmask == False) & (mask_AB == True))
viewer, ch = ginga.show_image(chi)
ginga.show_slits(viewer, ch, lcen, rcen, slit_ids=None)
for islit in range(0, nslits):
ginga.show_trace(viewer, ch, specobjs_pos[islit].trace_spat, trc_name=specobjs_pos[islit].idx, color='blue')
ginga.show_trace(viewer, ch, specobjs_neg[islit].trace_spat, trc_name=specobjs_neg[islit].idx,
color='orange')
# Boxcar extraction
from pypeit.core.extract import extract_boxcar
outmask = (slitpix > 0) * ((edgmask == False) & (mask_AB == True))
box_rad = 8.0
# ToDo -- Check for indexes in islit [0-based or 1-based?]
for islit in range(1, nslits + 1):
# Positive trace
def main(args):
# List only?
if args.list:
hdu = fits.open(args.file)
hdu.info()
return
# Load it up
spec2DObj = spec2dobj.Spec2DObj.from_file(args.file, args.det)
# Setup for PYPIT imports
msgs.reset(verbosity=2)
# Init
# TODO: get_dnum needs to be deprecated...
sdet = get_dnum(args.det, prefix=False)
# if not os.path.exists(mdir):
# mdir_base = os.path.join(os.getcwd(), os.path.basename(mdir))
# msgs.warn('Master file dir: {0} does not exist. Using {1}'.format(mdir, mdir_base))
# mdir=mdir_base
# Slits
# slits_key = '{0}_{1:02d}'.format(spec2DObj.head0['TRACMKEY'], args.det)
# slit_file = os.path.join(mdir, masterframe.construct_file_name(slittrace.SlitTraceSet, slits_key))
# slits = slittrace.SlitTraceSet.from_file(slit_file)
# Grab the slit edges
slits = spec2DObj.slits
if spec2DObj.sci_spat_flexure is not None:
msgs.info("Offseting slits by {}".format(spec2DObj.sci_spat_flexure))
all_left, all_right, mask = slits.select_edges(
flexure=spec2DObj.sci_spat_flexure)
# TODO -- This may be too restrictive, i.e. ignore BADFLTCALIB??
gpm = mask == 0
left = all_left[:, gpm]
right = all_right[:, gpm]
slid_IDs = spec2DObj.slits.slitord_id[gpm]
bitMask = ImageBitMask()
# Object traces from spec1d file
spec1d_file = args.file.replace('spec2d', 'spec1d')
if os.path.isfile(spec1d_file):
sobjs = specobjs.SpecObjs.from_fitsfile(spec1d_file)
else:
sobjs = None
msgs.warn('Could not find spec1d file: {:s}'.format(spec1d_file) +
msgs.newline() +
' No objects were extracted.')
ginga.connect_to_ginga(raise_err=True, allow_new=True)
# Now show each image to a separate channel
# Show the bitmask?
mask_in = None
if args.showmask:
viewer, ch = ginga.show_image(spec2DObj.bpmmask,
chname="BPM",
waveimg=spec2DObj.waveimg,
clear=True)
#bpm, crmask, satmask, minmask, offslitmask, nanmask, ivar0mask, ivarnanmask, extractmask \
# SCIIMG
image = spec2DObj.sciimg # Processed science image
mean, med, sigma = sigma_clipped_stats(image[spec2DObj.bpmmask == 0],
sigma_lower=5.0,
sigma_upper=5.0)
cut_min = mean - 1.0 * sigma
cut_max = mean + 4.0 * sigma
chname_skysub = 'sciimg-det{:s}'.format(sdet)
# Clear all channels at the beginning
viewer, ch = ginga.show_image(image,
chname=chname_skysub,
waveimg=spec2DObj.waveimg,
clear=True)
if sobjs is not None:
show_trace(sobjs, args.det, viewer, ch)
ginga.show_slits(viewer, ch, left, right, slit_ids=slid_IDs)
# SKYSUB
if args.ignore_extract_mask:
# TODO -- Is there a cleaner way to do this?
gpm = (spec2DObj.bpmmask == 0) | (spec2DObj.bpmmask == 2**
bitMask.bits['EXTRACT'])
else:
gpm = spec2DObj.bpmmask == 0
image = (spec2DObj.sciimg - spec2DObj.skymodel
) * gpm #(spec2DObj.mask == 0) # sky subtracted image
mean, med, sigma = sigma_clipped_stats(image[spec2DObj.bpmmask == 0],
sigma_lower=5.0,
sigma_upper=5.0)
cut_min = mean - 1.0 * sigma
cut_max = mean + 4.0 * sigma
chname_skysub = 'skysub-det{:s}'.format(sdet)
# Clear all channels at the beginning
# TODO: JFH For some reason Ginga crashes when I try to put cuts in here.
viewer, ch = ginga.show_image(
image,
chname=chname_skysub,
waveimg=spec2DObj.waveimg,
bitmask=bitMask,
mask=mask_in) #, cuts=(cut_min, cut_max),wcs_match=True)
if not args.removetrace and sobjs is not None:
show_trace(sobjs, args.det, viewer, ch)
ginga.show_slits(viewer, ch, left, right, slit_ids=slid_IDs)
# SKRESIDS
chname_skyresids = 'sky_resid-det{:s}'.format(sdet)
image = (spec2DObj.sciimg - spec2DObj.skymodel) * np.sqrt(
spec2DObj.ivarmodel) * (spec2DObj.bpmmask == 0) # sky residual map
viewer, ch = ginga.show_image(image,
chname_skyresids,
waveimg=spec2DObj.waveimg,
cuts=(-5.0, 5.0),
bitmask=bitMask,
mask=mask_in)
if not args.removetrace and sobjs is not None:
show_trace(sobjs, args.det, viewer, ch)
ginga.show_slits(viewer, ch, left, right, slit_ids=slid_IDs)
# RESIDS
chname_resids = 'resid-det{:s}'.format(sdet)
# full model residual map
image = (spec2DObj.sciimg - spec2DObj.skymodel - spec2DObj.objmodel
) * np.sqrt(spec2DObj.ivarmodel) * (spec2DObj.bpmmask == 0)
viewer, ch = ginga.show_image(image,
chname=chname_resids,
waveimg=spec2DObj.waveimg,
cuts=(-5.0, 5.0),
bitmask=bitMask,
mask=mask_in)
if not args.removetrace and sobjs is not None:
show_trace(sobjs, args.det, viewer, ch)
ginga.show_slits(viewer, ch, left, right, slit_ids=slid_IDs)
# After displaying all the images sync up the images with WCS_MATCH
shell = viewer.shell()
out = shell.start_global_plugin('WCSMatch')
out = shell.call_global_plugin_method('WCSMatch', 'set_reference_channel',
[chname_resids], {})
if args.embed:
embed()
def ech_objfind(image,
ivar,
ordermask,
slit_left,
slit_righ,
inmask=None,
plate_scale=0.2,
npca=2,
ncoeff=5,
min_snr=0.0,
nabove_min_snr=0,
pca_percentile=20.0,
snr_pca=3.0,
box_radius=2.0,
show_peaks=False,
show_fits=False,
show_trace=False):
if inmask is None:
inmask = (ordermask > 0)
frameshape = image.shape
nspec = frameshape[0]
norders = slit_left.shape[1]
if isinstance(plate_scale, (float, int)):
plate_scale_ord = np.full(
norders, plate_scale) # 0.12 binned by 3 spatially for HIRES
elif isinstance(plate_scale, (np.ndarray, list, tuple)):
if len(plate_scale) == norders:
plate_scale_ord = plate_scale
elif len(plate_scale) == 1:
plate_scale_ord = np.full(norders, plate_scale[0])
else:
msgs.error(
'Invalid size for plate_scale. It must either have one element or norders elements'
)
else:
msgs.error('Invalid type for plate scale')
specmid = nspec // 2
slit_width = slit_righ - slit_left
spec_vec = np.arange(nspec)
slit_spec_pos = nspec / 2.0
slit_spat_pos = np.zeros((norders, 2))
for iord in range(norders):
slit_spat_pos[iord, :] = (np.interp(slit_spec_pos, spec_vec,
slit_left[:, iord]),
np.interp(slit_spec_pos, spec_vec,
slit_righ[:, iord]))
# Loop over orders and find objects
sobjs = specobjs.SpecObjs()
show_peaks = True
show_fits = True
# ToDo replace orderindx with the true order number here? Maybe not. Clean up slitid and orderindx!
for iord in range(norders):
msgs.info('Finding objects on slit # {:d}'.format(iord + 1))
thismask = ordermask == (iord + 1)
inmask_iord = inmask & thismask
specobj_dict = {
'setup': 'HIRES',
'slitid': iord + 1,
'scidx': 0,
'det': 1,
'objtype': 'science'
}
sobjs_slit, skymask[thismask], objmask[thismask], proc_list = \
extract.objfind(image, thismask, slit_left[:,iord], slit_righ[:,iord], inmask=inmask_iord,show_peaks=show_peaks,
show_fits=show_fits, show_trace=False, specobj_dict = specobj_dict)#, sig_thresh = 3.0)
# ToDO make the specobjs _set_item_ work with expressions like this spec[:].orderindx = iord
for spec in sobjs_slit:
spec.ech_orderindx = iord
sobjs.add_sobj(sobjs_slit)
nfound = len(sobjs)
# Compute the FOF linking length based on the instrument place scale and matching length FOFSEP = 1.0"
FOFSEP = 1.0 # separation of FOF algorithm in arcseconds
FOF_frac = FOFSEP / (np.median(slit_width) * np.median(plate_scale_ord))
# Feige: made the code also works for only one object found in one order
# Run the FOF. We use fake coordinaes
fracpos = sobjs.spat_fracpos
ra_fake = fracpos / 1000.0 # Divide all angles by 1000 to make geometry euclidian
dec_fake = 0.0 * fracpos
if nfound > 1:
(ingroup, multgroup, firstgroup,
nextgroup) = spheregroup(ra_fake, dec_fake, FOF_frac / 1000.0)
group = ingroup.copy()
uni_group, uni_ind = np.unique(group, return_index=True)
nobj = len(uni_group)
msgs.info('FOF matching found {:d}'.format(nobj) + ' unique objects')
elif nfound == 1:
group = np.zeros(1, dtype='int')
uni_group, uni_ind = np.unique(group, return_index=True)
nobj = len(group)
msgs.warn('Only find one object no FOF matching is needed')
gfrac = np.zeros(nfound)
for jj in range(nobj):
this_group = group == uni_group[jj]
gfrac[this_group] = np.median(fracpos[this_group])
uni_frac = gfrac[uni_ind]
sobjs_align = sobjs.copy()
# Now fill in the missing objects and their traces
for iobj in range(nobj):
for iord in range(norders):
# Is there an object on this order that grouped into the current group in question?
on_slit = (group == uni_group[iobj]) & (sobjs_align.ech_orderindx
== iord)
if not np.any(on_slit):
# Add this to the sobjs_align, and assign required tags
thisobj = specobjs.SpecObj(frameshape,
slit_spat_pos[iord, :],
slit_spec_pos,
det=sobjs_align[0].det,
setup=sobjs_align[0].setup,
slitid=(iord + 1),
scidx=sobjs_align[0].scidx,
objtype=sobjs_align[0].objtype)
thisobj.ech_orderindx = iord
thisobj.spat_fracpos = uni_frac[iobj]
thisobj.trace_spat = slit_left[:,
iord] + slit_width[:, iord] * uni_frac[
iobj] # new trace
thisobj.trace_spec = spec_vec
thisobj.spat_pixpos = thisobj.trace_spat[specmid]
thisobj.set_idx()
# Use the real detections of this objects for the FWHM
this_group = group == uni_group[iobj]
# Assign to the fwhm of the nearest detected order
imin = np.argmin(
np.abs(sobjs_align[this_group].ech_orderindx - iord))
thisobj.fwhm = sobjs_align[imin].fwhm
thisobj.maskwidth = sobjs_align[imin].maskwidth
thisobj.ech_fracpos = uni_frac[iobj]
thisobj.ech_group = uni_group[iobj]
thisobj.ech_usepca = True
sobjs_align.add_sobj(thisobj)
group = np.append(group, uni_group[iobj])
gfrac = np.append(gfrac, uni_frac[iobj])
else:
# ToDo fix specobjs to get rid of these crappy loops!
for spec in sobjs_align[on_slit]:
spec.ech_fracpos = uni_frac[iobj]
spec.ech_group = uni_group[iobj]
spec.ech_usepca = False
# Some code to ensure that the objects are sorted in the sobjs_align by fractional position on the order and by order
# respectively
sobjs_sort = specobjs.SpecObjs()
for iobj in range(nobj):
this_group = group == uni_group[iobj]
this_sobj = sobjs_align[this_group]
sobjs_sort.add_sobj(this_sobj[np.argsort(this_sobj.ech_orderindx)])
# Loop over the objects and perform a quick and dirty extraction to assess S/N.
varimg = utils.calc_ivar(ivar)
flux_box = np.zeros((nspec, norders, nobj))
ivar_box = np.zeros((nspec, norders, nobj))
mask_box = np.zeros((nspec, norders, nobj))
SNR_arr = np.zeros((norders, nobj))
for iobj in range(nobj):
for iord in range(norders):
indx = (sobjs_sort.ech_group
== uni_group[iobj]) & (sobjs_sort.ech_orderindx == iord)
spec = sobjs_sort[indx]
thismask = ordermask == (iord + 1)
inmask_iord = inmask & thismask
box_rad_pix = box_radius / plate_scale_ord[iord]
flux_tmp = extract.extract_boxcar(image * inmask_iord,
spec.trace_spat,
box_rad_pix,
ycen=spec.trace_spec)
var_tmp = extract.extract_boxcar(varimg * inmask_iord,
spec.trace_spat,
box_rad_pix,
ycen=spec.trace_spec)
ivar_tmp = utils.calc_ivar(var_tmp)
pixtot = extract.extract_boxcar(ivar * 0 + 1.0,
spec.trace_spat,
box_rad_pix,
ycen=spec.trace_spec)
mask_tmp = (extract.extract_boxcar(ivar * inmask_iord == 0.0,
spec.trace_spat,
box_rad_pix,
ycen=spec.trace_spec) != pixtot)
flux_box[:, iord, iobj] = flux_tmp * mask_tmp
ivar_box[:, iord, iobj] = np.fmax(ivar_tmp * mask_tmp, 0.0)
mask_box[:, iord, iobj] = mask_tmp
(mean, med_sn, stddev) = sigma_clipped_stats(
flux_box[mask_tmp, iord, iobj] *
np.sqrt(ivar_box[mask_tmp, iord, iobj]),
sigma_lower=5.0,
sigma_upper=5.0)
SNR_arr[iord, iobj] = med_sn
# Purge objects with low SNR and that don't show up in enough orders
keep_obj = np.zeros(nobj, dtype=bool)
sobjs_trim = specobjs.SpecObjs()
uni_group_trim = np.array([], dtype=int)
uni_frac_trim = np.array([], dtype=float)
for iobj in range(nobj):
if (np.sum(SNR_arr[:, iobj] > min_snr) >= nabove_min_snr):
keep_obj[iobj] = True
ikeep = sobjs_sort.ech_group == uni_group[iobj]
sobjs_trim.add_sobj(sobjs_sort[ikeep])
uni_group_trim = np.append(uni_group_trim, uni_group[iobj])
uni_frac_trim = np.append(uni_frac_trim, uni_frac[iobj])
else:
msgs.info(
'Purging object #{:d}'.format(iobj) +
' which does not satisfy min_snr > {:5.2f}'.format(min_snr) +
' on at least nabove_min_snr >= {:d}'.format(nabove_min_snr) +
' orders')
nobj_trim = np.sum(keep_obj)
if nobj_trim == 0:
return specobjs.SpecObjs()
SNR_arr_trim = SNR_arr[:, keep_obj]
# Do a final loop over objects and make the final decision about which orders will be interpolated/extrapolated by the PCA
for iobj in range(nobj_trim):
SNR_now = SNR_arr_trim[:, iobj]
indx = (sobjs_trim.ech_group == uni_group_trim[iobj])
# PCA interp/extrap if:
# (SNR is below pca_percentile of the total SNRs) AND (SNR < snr_pca)
# OR
# (if this order was not originally traced by the object finding, see above)
usepca = ((SNR_now < np.percentile(SNR_now, pca_percentile)) &
(SNR_now < snr_pca)) | sobjs_trim[indx].ech_usepca
# ToDo fix specobjs to get rid of these crappy loops!
for iord, spec in enumerate(sobjs_trim[indx]):
spec.ech_usepca = usepca[iord]
if usepca[iord]:
msgs.info('Using PCA to predict trace for object #{:d}'.format(
iobj) + ' on order #{:d}'.format(iord))
sobjs_final = sobjs_trim.copy()
# Loop over the objects one by one and adjust/predict the traces
npoly_cen = 3
pca_fits = np.zeros((nspec, norders, nobj_trim))
for iobj in range(nobj_trim):
igroup = sobjs_final.ech_group == uni_group_trim[iobj]
# PCA predict the masked orders which were not traced
pca_fits[:, :, iobj] = pca_trace((sobjs_final[igroup].trace_spat).T,
usepca=None,
npca=npca,
npoly_cen=npoly_cen)
# usepca = sobjs_final[igroup].ech_usepca,
# Perform iterative flux weighted centroiding using new PCA predictions
xinit_fweight = pca_fits[:, :, iobj].copy()
inmask_now = inmask & (ordermask > 0)
xfit_fweight = extract.iter_tracefit(image,
xinit_fweight,
ncoeff,
inmask=inmask_now,
show_fits=show_fits)
# Perform iterative Gaussian weighted centroiding
xinit_gweight = xfit_fweight.copy()
xfit_gweight = extract.iter_tracefit(image,
xinit_gweight,
ncoeff,
inmask=inmask_now,
gweight=True,
show_fits=show_fits)
# Assign the new traces
for iord, spec in enumerate(sobjs_final[igroup]):
spec.trace_spat = xfit_gweight[:, iord]
spec.spat_pixpos = spec.trace_spat[specmid]
# Set the IDs
sobjs_final.set_idx()
if show_trace:
viewer, ch = ginga.show_image(objminsky * (ordermask > 0))
for iobj in range(nobj_trim):
for iord in range(norders):
ginga.show_trace(viewer,
ch,
pca_fits[:, iord, iobj],
str(uni_frac[iobj]),
color='yellow')
for spec in sobjs_trim:
color = 'green' if spec.ech_usepca else 'magenta'
ginga.show_trace(viewer,
ch,
spec.trace_spat,
spec.idx,
color=color)
#for spec in sobjs_final:
# color = 'red' if spec.ech_usepca else 'green'
# ginga.show_trace(viewer, ch, spec.trace_spat, spec.idx, color=color)
return sobjs_final
def process(self,
par,
bpm=bpm,
flatimages=None,
bias=None,
slits=None,
debug=False,
dark=None):
"""
Process the image
Note: The processing step order is currently 'frozen' as is.
We may choose to allow optional ordering
Here are the allowed steps, in the order they will be applied:
subtract_overscan -- Analyze the overscan region and subtract from the image
trim -- Trim the image down to the data (i.e. remove the overscan)
orient -- Orient the image in the PypeIt orientation (spec, spat) with blue to red going down to up
subtract_bias -- Subtract a bias image
apply_gain -- Convert to counts, amp by amp
flatten -- Divide by the pixel flat and (if provided) the illumination flat
extras -- Generate the RN2 and IVAR images
crmask -- Generate a CR mask
Args:
par (:class:`pypeit.par.pypeitpar.ProcessImagesPar`):
Parameters that dictate the processing of the images. See
:class:`pypeit.par.pypeitpar.ProcessImagesPar` for the
defaults.
bpm (`numpy.ndarray`_, optional):
flatimages (:class:`pypeit.flatfield.FlatImages`):
bias (`numpy.ndarray`_, optional):
Bias image
slits (:class:`pypeit.slittrace.SlitTraceSet`, optional):
Used to calculate spatial flexure between the image and the slits
Returns:
:class:`pypeit.images.pypeitimage.PypeItImage`:
"""
self.par = par
self._bpm = bpm
# Get started
# Standard order
# -- May need to allow for other order some day..
if par['use_overscan']:
self.subtract_overscan()
if par['trim']:
self.trim()
if par['orient']:
self.orient()
if par['use_biasimage']: # Bias frame, if it exists, is *not* trimmed nor oriented
self.subtract_bias(bias)
if par['use_darkimage']: # Dark frame, if it exists, is TODO:: check: trimmed, oriented (and oscan/bias subtracted?)
self.subtract_dark(dark)
if par['apply_gain']:
self.apply_gain()
# This needs to come after trim, orient
# Calculate flexure -- May not be used, but always calculated when slits are provided
if slits is not None and self.par['spat_flexure_correct']:
self.spat_flexure_shift = flexure.spat_flexure_shift(
self.image, slits)
# Generate the illumination flat, as needed
illum_flat = None
if self.par['use_illumflat']:
if flatimages is None:
msgs.error(
"Cannot illumflatten, no such image generated. Add one or more illumflat images to your PypeIt file!!"
)
if slits is None:
msgs.error("Need to provide slits to create illumination flat")
illum_flat = flatimages.fit2illumflat(
slits, flexure_shift=self.spat_flexure_shift)
if debug:
from pypeit import ginga
left, right = slits.select_edges(
flexure=self.spat_flexure_shift)
viewer, ch = ginga.show_image(illum_flat, chname='illum_flat')
ginga.show_slits(viewer, ch, left, right) # , slits.id)
#
orig_image = self.image.copy()
viewer, ch = ginga.show_image(orig_image, chname='orig_image')
ginga.show_slits(viewer, ch, left, right) # , slits.id)
# Flat field -- We cannot do illumination flat without a pixel flat (yet)
if self.par['use_pixelflat'] or self.par['use_illumflat']:
if flatimages is None or flatimages.pixelflat is None:
msgs.error("Flat fielding desired but not generated/provided.")
else:
self.flatten(flatimages.pixelflat,
illum_flat=illum_flat,
bpm=self.bpm)
# Fresh BPM
bpm = self.spectrograph.bpm(self.filename,
self.det,
shape=self.image.shape)
# Extras
self.build_rn2img()
self.build_ivar()
# Generate a PypeItImage
pypeitImage = pypeitimage.PypeItImage(
self.image,
ivar=self.ivar,
rn2img=self.rn2img,
bpm=bpm,
detector=self.detector,
spat_flexure=self.spat_flexure_shift,
PYP_SPEC=self.spectrograph.spectrograph)
pypeitImage.rawheadlist = self.headarr
pypeitImage.process_steps = [
key for key in self.steps.keys() if self.steps[key]
]
# Mask(s)
if par['mask_cr']:
pypeitImage.build_crmask(self.par)
#
nonlinear_counts = self.spectrograph.nonlinear_counts(
self.detector, apply_gain=self.par['apply_gain'])
# Build
pypeitImage.build_mask(saturation=nonlinear_counts)
# Return
return pypeitImage
from pypeit import ginga from astropy.io import fits from pypeit.spectrographs import keck_lris lowrdx_path = '/Users/joe/pypeit_vs_lowredux/c17_60L/Red400/' pypeit_path = '/Users/joe/python/PypeIt-development-suite/REDUX_OUT/Keck_LRIS_red/multi_400_8500_d560/' rawpath = '/Users/joe/python/PypeIt-development-suite/RAW_DATA/Keck_LRIS_red/multi_400_8500_d560/' flatfile = rawpath + 'r170320_2057.fits' flatfile_lowrdx = lowrdx_path + 'pixflat-r170320_2057.fits' flatfile_pypeit = pypeit_path + 'MF_keck_lris_red/MasterFlatField_A_01_aa.fits' flat, _, _ = keck_lris.read_lris(flatfile, det = 1, TRIM=True) hdu = fits.open(flatfile_lowrdx) flat_lowrdx = hdu[0].data flat_lowrdx = flat_lowrdx[:,0:1024] hdu = fits.open(flatfile_pypeit) flat_pypeit = hdu[0].data cuts = (0.8,1.5) viewer, ch = ginga.show_image(flat_lowrdx, chname='LOWREDUX',cuts = cuts) viewer, ch = ginga.show_image(flat_pypeit, chname='PYPEIT',cuts = cuts) viewer, ch = ginga.show_image(flat, chname='FLAT')
def show(self, attr='edges', pstep=50, extras=None):
"""
Display an image or spectrum in TraceSlits
Args:
attr (str, optional):
'edges' -- Show the mstrace image and the edges
'binarr' -- Show the binarr image
'edgearr' -- Show the edgearr image
'siglev' -- Show the Sobolev image
'traces' -- Show the traces at an intermediate stage
'refined_edges' -- Show the traces at an intermediate stage in _pca_refine()
'xset' -- Check the output from the trace crude
extras: anything
Extra bits and pieces needed for plotting
"""
if attr == 'edges':
viewer, ch = ginga.show_image(self.mstrace, chname='edges')
if self.slit_left is not None:
ginga.show_slits(viewer, ch, self.slit_left, self.slit_righ, slit_ids=(np.arange(self.slit_left.shape[1]).astype(int) + 1).tolist(),
pstep=pstep)
elif attr == 'binarr':
ginga.show_image(self.binarr, chname='binarr')
elif attr == 'xset':
viewer, ch = ginga.show_image(self.mstrace, chname='slit_xset')
color = dict(left='green', right='red')
viewer, ch = ginga.show_image(self.mstrace)
for side in ['left', 'right']:
for kk in range(self.tc_dict[side]['xset'].shape[1]):
ginga.show_trace(viewer, ch, self.tc_dict[side]['xset'][:, kk], trc_name=side+ str(kk),color=color[side])
elif attr == 'refined_edges':
# Used in _pca_refine()
edges_dict = extras
#
color = dict(left='green', right='red')
viewer, ch = ginga.show_image(self.mstrace, chname='refined_edges')
if edges_dict['show'] == 'both':
for side in ['left', 'right']:
for kk in range(edges_dict[side]['nstart']):
ginga.show_trace(viewer, ch, edges_dict[side]['trace'][:, kk], trc_name=side + str(kk),
color=color[side])
else:
for side in [edges_dict['show']]:
for kk in range(edges_dict[side]['nstart']):
ginga.show_trace(viewer, ch, edges_dict[side]['trace'][:, kk], trc_name='left_' + str(kk), color=color[side])
elif attr == 'traces':
viewer, ch = ginga.show_image(self.mstrace, chname='slit_traces')
color = dict(left='green', right='red')
viewer, ch = ginga.show_image(self.mstrace)
for side in ['left', 'right']:
for kk in range(self.tc_dict[side]['traces'].shape[1]):
ginga.show_trace(viewer, ch, self.tc_dict[side]['traces'][:, kk], trc_name=side+ str(kk),color=color[side], pstep=pstep)
elif attr == 'edgearr':
if np.min(self.edgearr) == -1: # Ungrouped
tmp = self.mstrace.copy()
# Left edges
left = self.edgearr == -1
tmp[left] = -99999.
# Right edges
right = self.edgearr == 1
tmp[right] = 99999.
viewer, ch = ginga.show_image(tmp, chname='edgearr')
else: # Grouped
viewer, ch = ginga.show_image(self.siglev, chname='edgearr')
# Traces
all_uni = np.unique(self.edgearr[self.edgearr != 0])
for uni in all_uni:
# Color
clr = 'green' if uni < 0 else 'red'
# Do it
tidx = np.where(self.edgearr == uni)
ginga.show_trace(viewer, ch, tidx[1], trc_name=str(uni), yval=tidx[0], color=clr)
elif attr == 'siglev':
ginga.show_image(self.siglev, chname='siglev')
def show(self,
attr,
image=None,
align_traces=None,
chname=None,
slits=False,
clear=False):
"""
Show one of the class internals
Parameters
----------
attr : str
image - plot the master align frame
image : ndarray
Image to be plotted (i.e. the master align frame)
align_traces : list
The align traces
chname : str
The channel name sent to ginga
slits : bool
Overplot the slit edges?
clear : bool
Clear the plotting window in ginga?
Returns:
"""
if attr == 'image':
ch_name = chname if chname is not None else 'align_traces'
self.viewer, self.channel = ginga.show_image(image,
chname=ch_name,
clear=clear,
wcs_match=False)
elif attr == 'overplot':
pass
else:
msgs.warn("Not an option for show")
if align_traces is not None and self.viewer is not None:
for spec in align_traces:
color = 'magenta' if spec.hand_extract_flag else 'orange'
ginga.show_trace(self.viewer,
self.channel,
spec.TRACE_SPAT,
trc_name="",
color=color)
if slits:
if self.tslits_dict is not None and self.viewer is not None:
slit_ids = [
edgetrace.get_slitid(image.shape,
self.tslits_dict['slit_left'],
self.tslits_dict['slit_righ'], ii)[0]
for ii in range(self.tslits_dict['slit_left'].shape[1])
]
ginga.show_slits(self.viewer, self.channel,
self.tslits_dict['slit_left'],
self.tslits_dict['slit_righ'], slit_ids)
return
