def find_objects_pypeline(self, image, ivar, std=False, std_trace = None, maskslits=None,
show=False, show_peaks=False, show_fits=False, show_trace = False, debug=False,
manual_extract_dict=None):
# create the ouptut image for skymask
skymask = np.zeros_like(image, dtype=bool)
plate_scale = self.spectrograph.order_platescale(binning=self.binning)
inmask = self.mask == 0
# Find objects
specobj_dict = {'setup': self.setup, 'slitid': 999, 'orderindx': 999,
'det': self.det, 'objtype': self.objtype, 'pypeline': self.pypeline}
# ToDO implement parsets here!
sig_thresh = 30.0 if std else self.redux_par['sig_thresh']
sobjs_ech, skymask[self.slitmask > -1] = \
extract.ech_objfind(image, ivar, self.slitmask, self.tslits_dict['slit_left'], self.tslits_dict['slit_righ'],
inmask=inmask, ncoeff=self.redux_par['trace_npoly'],
hand_extract_dict=manual_extract_dict,
plate_scale=plate_scale, std_trace=std_trace,
specobj_dict=specobj_dict,sig_thresh=sig_thresh,
show_peaks=show_peaks, show_fits=show_fits,
show_trace=show_trace, debug=debug)
# Steps
self.steps.append(inspect.stack()[0][3])
if show:
self.show('image', image=image*(self.mask == 0), chname = 'ech_objfind',sobjs=sobjs_ech, slits=False)
return sobjs_ech, len(sobjs_ech), skymask
def find_objects_pypeline(self,
image,
std_trace=None,
show=False,
show_peaks=False,
show_fits=False,
show_trace=False,
debug=False,
manual_extract_dict=None):
"""
Pipeline specific find objects routine
Args:
image (np.ndarray):
std_trace (np.ndarray, optional):
manual_extract_dict (dict, optional):
show_peaks (bool, optional):
Generate QA showing peaks identified by object finding
show_fits (bool, optional):
Generate QA showing fits to traces
show_trace (bool, optional):
Generate QA showing traces identified. Requires an open ginga RC modules window
show (bool, optional):
debug (bool, optional):
Returns:
tuple:
specobjs : Specobjs object
Container holding Specobj objects
nobj (int):
Number of objects identified
skymask : ndarray
Boolean image indicating which pixels are useful for global sky subtraction
"""
# create the ouptut image for skymask
skymask = np.zeros_like(image, dtype=bool)
plate_scale = self.spectrograph.order_platescale(self.order_vec,
binning=self.binning)
inmask = self.sciImg.fullmask == 0
# Find objects
specobj_dict = {
'SLITID': 999, #'orderindx': 999,
'DET': self.det,
'OBJTYPE': self.objtype,
'PYPELINE': self.pypeline
}
sobjs_ech, skymask[self.slitmask > -1] = extract.ech_objfind(
image,
self.sciImg.ivar,
self.slitmask,
self.slits_left,
self.slits_right,
self.order_vec,
self.reduce_bpm,
spec_min_max=np.vstack((self.slits.specmin, self.slits.specmax)),
inmask=inmask,
ir_redux=self.ir_redux,
ncoeff=self.par['reduce']['findobj']['trace_npoly'],
hand_extract_dict=manual_extract_dict,
plate_scale=plate_scale,
std_trace=std_trace,
specobj_dict=specobj_dict,
sig_thresh=self.par['reduce']['findobj']['sig_thresh'],
show_peaks=show_peaks,
show_fits=show_fits,
trim_edg=self.par['reduce']['findobj']['find_trim_edge'],
cont_fit=self.par['reduce']['findobj']['find_cont_fit'],
npoly_cont=self.par['reduce']['findobj']['find_npoly_cont'],
fwhm=self.par['reduce']['findobj']['find_fwhm'],
maxdev=self.par['reduce']['findobj']['find_maxdev'],
max_snr=self.par['reduce']['findobj']['ech_find_max_snr'],
min_snr=self.par['reduce']['findobj']['ech_find_min_snr'],
nabove_min_snr=self.par['reduce']['findobj']
['ech_find_nabove_min_snr'],
show_trace=show_trace,
debug=debug)
# Steps
self.steps.append(inspect.stack()[0][3])
if show:
self.show('image',
image=image * (self.sciImg.fullmask == 0),
chname='ech_objfind',
sobjs=sobjs_ech,
slits=False)
return sobjs_ech, len(sobjs_ech), skymask
#usepca = np.zeros(slit_mid.shape[1],dtype=bool)
#usepca[0:8] = True
#pca_out = pca_trace(slit_mid, npca = 4, npoly_cen = 3)
#sys.exit(-1)
#viewer, ch = ginga.show_image(ordermask > 0)
#ginga.show_slits(viewer,ch, slit_mid, pca_out)
# create the ouptut images skymask and objmask
#skymask = np.zeros_like(objminsky, dtype=bool)
#objmask = np.zeros_like(objminsky, dtype=bool)
image = objminsky.copy()
inmask = mask.copy()
#Routine starts here.
#------
ncoeff = 5
box_radius = 2.0 # arcseconds
min_snr = 0.2
nabove_min_snr = 2
npca = None
pca_explained_var = 99.8
sig_thresh = 3.0
box_radius = 2.0
ncoeff = 5
trim_edg = (5,5)
sobjs_final = extract.ech_objfind(image, ivar, ordermask, slit_left, slit_righ,inmask=inmask,plate_scale=plate_scale,
std_trace = std_trace, pca_explained_var=pca_explained_var, min_snr=min_snr,
nabove_min_snr=nabove_min_snr,box_radius=box_radius,sig_thresh=sig_thresh,
show_peaks=False,show_fits=False,show_trace=True,debug=True)
#Routine starts here.
#------
ncoeff = 5
box_radius = 2.0 # arcseconds
min_snr = 0.2
nabove_min_snr = 2
npca = None
pca_explained_var = 99.8
sig_thresh = 3.0
box_radius = 2.0
ncoeff = 5
trim_edg = (5, 5)
sobjs_final = extract.ech_objfind(image,
ivar,
ordermask,
slit_left,
slit_righ,
inmask=inmask,
plate_scale=plate_scale,
std_trace=std_trace,
pca_explained_var=pca_explained_var,
min_snr=min_snr,
nabove_min_snr=nabove_min_snr,
box_radius=box_radius,
sig_thresh=sig_thresh,
show_peaks=False,
show_fits=False,
show_trace=True,
debug=True)
def find_objects_pypeline(self,
image,
std_trace=None,
show=False,
show_peaks=False,
show_fits=False,
show_trace=False,
debug=False,
manual_extract_dict=None):
"""
Pipeline specific find objects routine
Args:
image (np.ndarray):
std_trace (np.ndarray, optional):
manual_extract_dict (dict, optional):
show_peaks (bool, optional):
Generate QA showing peaks identified by object finding
show_fits (bool, optional):
Generate QA showing fits to traces
show_trace (bool, optional):
Generate QA showing traces identified. Requires an open ginga RC modules window
show (bool, optional):
debug (bool, optional):
Returns:
tuple:
specobjs : Specobjs object
Container holding Specobj objects
nobj (int):
Number of objects identified
skymask : ndarray
Boolean image indicating which pixels are useful for global sky subtraction
"""
# create the ouptut image for skymask
skymask = np.zeros_like(image, dtype=bool)
# TODO: Figure out where to do this properly
spec_min = np.zeros(self.tslits_dict['nslits'], dtype=int) \
if self.tslits_dict['spec_min'] is None else self.tslits_dict['spec_min']
spec_max = np.full(self.tslits_dict['nslits'], self.tslits_dict['nspec']-1, dtype=int) \
if self.tslits_dict['spec_max'] is None else self.tslits_dict['spec_max']
plate_scale = self.spectrograph.order_platescale(self.order_vec,
binning=self.binning)
inmask = self.sciImg.mask == 0
# Find objects
specobj_dict = {
'setup': self.setup,
'slitid': 999, #'orderindx': 999,
'det': self.det,
'objtype': self.objtype,
'pypeline': self.pypeline
}
# TODO This is a bad idea -- we want to find everything for standards
#sig_thresh = 30.0 if std else self.redux_par['sig_thresh']
sobjs_ech, skymask[self.slitmask > -1] = extract.ech_objfind(
image,
self.sciImg.ivar,
self.slitmask,
self.tslits_dict['slit_left'],
self.tslits_dict['slit_righ'],
self.order_vec,
self.maskslits,
spec_min_max=np.vstack((spec_min, spec_max)),
inmask=inmask,
ir_redux=self.ir_redux,
ncoeff=self.par['reduce']['findobj']['trace_npoly'],
hand_extract_dict=manual_extract_dict,
plate_scale=plate_scale,
std_trace=std_trace,
specobj_dict=specobj_dict,
sig_thresh=self.par['reduce']['findobj']['sig_thresh'],
show_peaks=show_peaks,
show_fits=show_fits,
trim_edg=self.par['reduce']['findobj']['find_trim_edge'],
cont_fit=self.par['reduce']['findobj']['find_cont_fit'],
npoly_cont=self.par['reduce']['findobj']['find_npoly_cont'],
fwhm=self.par['reduce']['findobj']['find_fwhm'],
maxdev=self.par['reduce']['findobj']['find_maxdev'],
max_snr=self.par['reduce']['findobj']['ech_find_max_snr'],
min_snr=self.par['reduce']['findobj']['ech_find_min_snr'],
nabove_min_snr=self.par['reduce']['findobj']
['ech_find_nabove_min_snr'],
show_trace=show_trace,
debug=debug)
# Steps
self.steps.append(inspect.stack()[0][3])
if show:
self.show('image',
image=image * (self.sciImg.mask == 0),
chname='ech_objfind',
sobjs=sobjs_ech,
slits=False)
return sobjs_ech, len(sobjs_ech), skymask