def run(self, rinput):
self.logger.info('starting processing for bars detection')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
self.save_intermediate_img(hdulist, 'reduced_image.fits')
try:
rotang = hdr['ROTANG']
tsutc1 = hdr['TSUTC1']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
csupos = datamodel.get_csup_from_header(hdr)
if len(csupos) != 2 * EMIR_NBARS:
raise RecipeError('Number of CSUPOS != 2 * NBARS')
csusens = datamodel.get_cs_from_header(hdr)
except KeyError as error:
self.logger.error(error)
raise RecipeError(error)
self.logger.debug('start finding bars')
allpos, slits = find_bars(
hdulist,
rinput.bars_nominal_positions,
csupos,
dtur,
average_box_row_size=rinput.average_box_row_size,
average_box_col_size=rinput.average_box_col_size,
fit_peak_npoints=rinput.fit_peak_npoints,
median_filter_size=rinput.median_filter_size,
logger=self.logger)
self.logger.debug('end finding bars')
if self.intermediate_results:
with open('ds9.reg', 'w') as ds9reg:
slits_to_ds9_reg(ds9reg, slits)
result = self.create_result(
frame=hdulist,
slits=slits,
positions9=allpos[9],
positions7=allpos[7],
positions5=allpos[5],
positions3=allpos[3],
DTU=dtub,
ROTANG=rotang,
TSUTC1=tsutc1,
csupos=csupos,
csusens=csusens,
)
return result
def run(self, rinput):
_logger.info('starting bias reduction')
iinfo = gather_info_frames(rinput.obresult.frames)
if iinfo:
mode = iinfo[0]['readmode']
if mode.lower() not in EMIR_BIAS_MODES:
msg = 'readmode %s, is not a bias mode' % mode
_logger.error(msg)
raise RecipeError(msg)
flow = lambda x: x
hdulist = basic_processing_with_combination(rinput,
flow,
method=median,
errors=False)
pdata = hdulist[0].data
# update hdu header with
# reduction keywords
hdr = hdulist[0].header
self.set_base_headers(hdr)
hdr['CCDMEAN'] = pdata.mean()
_logger.info('bias reduction ended')
result = self.create_result(biasframe=DataFrame(hdulist))
return result
def run(self, rinput):
_logger.info('starting dark reduction')
flow = self.init_filters(rinput)
iinfo = gather_info_frames(rinput.obresult.frames)
ref_exptime = 0.0
for el in iinfo[1:]:
if abs(el['texp'] - ref_exptime) > 1e-4:
_logger.error('image with wrong exposure time')
raise RecipeError('image with wrong exposure time')
hdulist = basic_processing_with_combination(rinput,
flow,
method=median,
errors=True)
pdata = hdulist[0].data
# update hdu header with
# reduction keywords
hdr = hdulist[0].header
self.set_base_headers(hdr)
hdr['CCDMEAN'] = pdata.mean()
_logger.info('dark reduction ended')
result = self.create_result(darkframe=hdulist)
return result
def aggregate_result(self, partial_result, rinput):
obresult = rinput.obresult
# Check if this is our first run
naccum = getattr(obresult, 'naccum', 0)
accum = getattr(obresult, 'accum', None)
# result to accumulate
result_key = 'reduced_mos_abba'
field_to_accum = getattr(partial_result, result_key)
if naccum == 0:
self.logger.debug('naccum is not set, do not accumulate')
return partial_result
elif naccum == 1:
self.logger.debug('round %d initialize accumulator', naccum)
newaccum = field_to_accum
elif naccum > 1:
self.logger.debug('round %d of accumulation', naccum)
newaccum = self.aggregate_frames(accum, field_to_accum, naccum)
else:
msg = 'naccum set to %d, invalid' % (naccum, )
self.logger.error(msg)
raise RecipeError(msg)
# Update partial result
partial_result.accum = newaccum
return partial_result
def run(self, rinput):
resets = []
ramps = []
for frame in rinput.obresult.frames:
if frame.itype == 'RESET':
resets.append(frame.label)
_logger.debug('%s is RESET', frame.label)
elif frame.itype == 'RAMP':
ramps.append(frame.label)
_logger.debug('%s is RAMP', frame.label)
else:
raise RecipeError('frame is neither a RAMP nor a RESET')
channels = self.region(rinput)
result_gain = numpy.zeros((len(channels), ))
result_ron = numpy.zeros_like(result_gain)
counts = numpy.zeros((len(ramps), len(channels)))
variance = numpy.zeros_like(counts)
last_reset = resets[-1]
_logger.debug('opening last reset image %s', last_reset)
last_reset_data = fits.getdata(last_reset)
for i, di in enumerate(ramps):
with fits.open(di, mode='readonly') as fd:
restdata = fd[0].data - last_reset_data
for j, channel in enumerate(channels):
c = restdata[channel].mean()
_logger.debug('%f counts in channel', c)
counts[i, j] = c
v = restdata[channel].var(ddof=1)
_logger.debug('%f variance in channel', v)
variance[i, j] = v
for j, _ in enumerate(channels):
res = scipy.stats.linregress(counts[:, j], variance[:, j])
slope, intercept, _r_value, _p_value, _std_err = res
result_gain[j] = 1.0 / slope
result_ron[j] = math.sqrt(intercept)
cube = numpy.zeros((2, 2048, 2048))
for gain, var, channel in zip(result_gain, result_ron, channels):
cube[0][channel] = gain
cube[1][channel] = var
hdu = fits.PrimaryHDU(cube[0])
hduvar = fits.ImageHDU(cube[1])
hdulist = fits.HDUList([hdu, hduvar])
gain = MasterGainMap(mean=result_gain,
var=numpy.array([]),
frame=DataFrame(hdulist))
ron = MasterRONMap(mean=result_ron, var=numpy.array([]))
return self.create_result(gain=gain, ron=ron)
def run(self, rinput):
# FIXME:
# We need 2 flats
# Of different exposure times
#
# And their calibrations
#
if len(rinput.obresult.frames) < 2:
raise RecipeError('The recipe requires 2 flat frames')
iinfo = []
for frame in rinput.obresult.frames:
with frame.open() as hdulist:
iinfo.append(gather_info(hdulist))
# Loading calibrations
with rinput.master_bias.open() as hdul:
readmode = hdul[0].header.get('READMODE', 'undefined')
if readmode.lower() in ['simple', 'bias']:
self.logger.debug('loading bias')
mbias = hdul[0].data
bias_corrector = proc.BiasCorrector(mbias)
else:
self.logger.debug('ignoring bias')
bias_corrector = numina.util.node.IdNode()
with rinput.master_dark.open() as mdark_hdul:
self.logger.debug('loading dark')
mdark = mdark_hdul[0].data
dark_corrector = proc.DarkCorrector(mdark)
flow = numina.util.flow.SerialFlow([bias_corrector, dark_corrector])
self.logger.info('processing flat #1')
with rinput.obresult.frames[0].open() as hdul:
other = flow(hdul)
f1 = other[0].data.copy() * iinfo[0]['texp'] * 1e-3
self.logger.info('processing flat #2')
with rinput.obresult.frames[1].open() as hdul:
other = flow(hdul)
f2 = other[0].data.copy() * iinfo[1]['texp'] * 1e-3
# Preprocess...
maxiter = rinput.maxiter
lowercut = rinput.lowercut
uppercut = rinput.uppercut
ninvalid = 0
mask = None
if mask:
m = fits.getdata(mask)
ninvalid = numpy.count_nonzero(m)
else:
m = numpy.zeros_like(f1, dtype='int')
for niter in range(1, maxiter + 1):
self.logger.debug('iter %d', niter)
ratio, m, sigma = cosmetics(f1,
f2,
m,
lowercut=lowercut,
uppercut=uppercut)
if self.intermediate_results:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
fits.writeto('numina-cosmetics-i%02d.fits' % niter,
ratio,
overwrite=True)
fits.writeto('numina-mask-i%02d.fits' % niter,
m,
overwrite=True)
fits.writeto('numina-sigma-i%02d.fits' % niter,
m * 0.0 + sigma,
overwrite=True)
self.logger.debug('iter %d, invalid points in input mask: %d',
niter, ninvalid)
self.logger.debug('iter %d, estimated sigma is %f', niter, sigma)
n_ninvalid = numpy.count_nonzero(m)
# Probably there is something wrong here
# too much defective pixels
if ninvalid / m.size >= 0.10:
# This should set a flag in the output
msg = 'defective pixels are greater than 10%'
self.logger.warning(msg)
if n_ninvalid == ninvalid:
self.logger.info('convergence reached after %d iterations',
niter)
break
self.logger.info('new invalid points: %d', n_ninvalid - ninvalid)
ninvalid = n_ninvalid
else:
# This should set a flag in the output
msg = 'convergence not reached after %d iterations' % maxiter
self.logger.warning(msg)
self.logger.info('number of dead pixels %d',
numpy.count_nonzero(m == PIXEL_DEAD))
self.logger.info('number of hot pixels %d',
numpy.count_nonzero(m == PIXEL_HOT))
if self.intermediate_results:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
fits.writeto('numina-cosmetics.fits', ratio, overwrite=True)
fits.writeto('numina-mask.fits', m, overwrite=True)
fits.writeto('numina-sigma.fits',
sigma * numpy.ones_like(m),
overwrite=True)
hdu = fits.PrimaryHDU(ratio)
hdr = hdu.header
hdr['NUMXVER'] = (__version__, 'Numina package version')
hdr['NUMRNAM'] = (self.__class__.__name__, 'Numina recipe name')
hdr['NUMRVER'] = (self.__version__, 'Numina recipe version')
ratiohdl = fits.HDUList([hdu])
maskhdu = fits.PrimaryHDU(m)
hdr = maskhdu.header
hdr['NUMXVER'] = (__version__, 'Numina package version')
hdr['NUMRNAM'] = (self.__class__.__name__, 'Numina recipe name')
hdr['NUMRVER'] = (self.__version__, 'Numina recipe version')
maskhdl = fits.HDUList([maskhdu])
res = self.create_result(ratioframe=ratiohdl, maskframe=maskhdl)
return res
def run(self, rinput):
self.logger.info('starting processing for object detection')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
self.logger.debug('finding point sources')
try:
filtername = hdr['FILTER']
readmode = hdr['READMODE']
rotang = hdr['ROTANG']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
self.logger.error(error)
raise RecipeError(error)
data = hdulist[0].data
# Copy needed in numpy 1.7
# This seems already bitswapped??
# FIXME: check this works offline/online
# ndata = data.byteswap().newbyteorder()
# data = data.byteswap(inplace=True).newbyteorder()
snr_detect = 5.0
fwhm = 4.0
npixels = 15
box_shape = [64, 64]
self.logger.info('point source detection2')
self.logger.info('using internal mask to remove corners')
# Corners
mask = numpy.zeros_like(data, dtype='int32')
mask[2000:, 0:80] = 1
mask[2028:, 2000:] = 1
mask[:50, 1950:] = 1
mask[:100, :50] = 1
# Remove corner regions
self.logger.info('compute background map, %s', box_shape)
bkg = sep.Background(data)
self.logger.info('reference fwhm is %5.1f pixels', fwhm)
self.logger.info('detect threshold, %3.1f over background', snr_detect)
self.logger.info('convolve with gaussian kernel, FWHM %3.1f pixels',
fwhm)
sigma = fwhm * gaussian_fwhm_to_sigma
#
kernel = Gaussian2DKernel(sigma)
kernel.normalize()
thresh = snr_detect * bkg.globalrms
data_s = data - bkg.back()
objects, segmap = sep.extract(data - bkg.back(),
thresh,
minarea=npixels,
filter_kernel=kernel.array,
segmentation_map=True,
mask=mask)
fits.writeto('segmap.fits', segmap)
self.logger.info('detected %d objects', len(objects))
# Hardcoded values
rs2 = 15.0
fit_rad = 10.0
flux_min = 1000.0
flux_max = 30000.0
self.logger.debug('Flux limit is %6.1f %6.1f', flux_min, flux_max)
# FIXME: this should be a view, not a copy
xall = objects['x']
yall = objects['y']
mm = numpy.array([xall, yall]).T
self.logger.info('computing FWHM')
# Find objects with pairs inside fit_rad
kdtree = KDTree(mm)
nearobjs = (kdtree.query_ball_tree(kdtree, r=fit_rad))
positions = []
for idx, obj in enumerate(objects):
x0 = obj['x']
y0 = obj['y']
sl = image_box2d(x0, y0, data.shape, (fit_rad, fit_rad))
# sl_sky = image_box2d(x0, y0, data.shape, (rs2, rs2))
part_s = data_s[sl]
# Logical coordinates
xx0 = x0 - sl[1].start
yy0 = y0 - sl[0].start
_, fwhm_x, fwhm_y = compute_fwhm_2d_simple(part_s, xx0, yy0)
if min(fwhm_x, fwhm_x) < 3:
continue
if flux_min > obj['peak'] or flux_max < obj['peak']:
continue
# nobjs is the number of object inside fit_rad
nobjs = len(nearobjs[idx])
flag = 0 if nobjs == 1 else 1
positions.append([idx, x0, y0, obj['peak'], fwhm_x, fwhm_y, flag])
self.logger.info('saving photometry')
positions = numpy.array(positions)
positions_alt = positions
self.logger.info('end processing for object detection')
result = self.create_result(
frame=hdulist,
positions=positions_alt,
positions_alt=positions_alt,
filter=filtername,
DTU=dtub,
readmode=readmode,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa,
param_recenter=rinput.recenter,
param_max_recenter_radius=rinput.max_recenter_radius,
param_box_half_size=rinput.box_half_size)
return result
def run(self, rinput):
self.logger.info('starting slit processing')
self.logger.info('basic image reduction')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
try:
rotang = hdr['ROTANG']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
self.logger.error(error)
raise RecipeError(error)
self.logger.debug('finding slits')
# Filter values below 0.0
self.logger.debug('Filter values below 0')
data1 = hdulist[0].data[:]
data1[data1 < 0.0] = 0.0
# First, prefilter with median
median_filter_size = rinput.median_filter_size
canny_sigma = rinput.canny_sigma
self.logger.debug('Median filter with box %d', median_filter_size)
data2 = median_filter(data1, size=median_filter_size)
# Grey level image
img_grey = normalize_raw(data2)
# Find edges with Canny
self.logger.debug('Find edges, Canny sigma %f', canny_sigma)
# These thresholds corespond roughly with
# value x (2**16 - 1)
high_threshold = rinput.canny_high_threshold
low_threshold = rinput.canny_low_threshold
self.logger.debug('Find edges, Canny high threshold %f', high_threshold)
self.logger.debug('Find edges, Canny low threshold %f', low_threshold)
edges = canny(img_grey, sigma=canny_sigma,
high_threshold=high_threshold,
low_threshold=low_threshold)
# Fill edges
self.logger.debug('Fill holes')
# I do a dilation and erosion to fill
# possible holes in 'edges'
fill = ndimage.binary_dilation(edges)
fill2 = ndimage.binary_fill_holes(fill)
fill_slits = ndimage.binary_erosion(fill2)
self.logger.debug('Label objects')
label_objects, nb_labels = ndimage.label(fill_slits)
self.logger.debug('%d objects found', nb_labels)
ids = list(six.moves.range(1, nb_labels + 1))
self.logger.debug('Find regions and centers')
regions = ndimage.find_objects(label_objects)
centers = ndimage.center_of_mass(data2, labels=label_objects,
index=ids
)
table = char_slit(data2, regions,
slit_size_ratio=-1.0
)
result = self.create_result(frame=hdulist,
slitstable=table,
DTU=dtub,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa
)
return result
def run(self, rinput):
self.logger.info('starting slit processing')
self.logger.info('basic image reduction')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
try:
rotang = hdr['ROTANG']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
self.logger.error(error)
raise RecipeError(error)
self.logger.debug('finding slits')
# First, prefilter with median
median_filter_size = rinput.median_filter_size
canny_sigma = rinput.canny_sigma
obj_min_size = rinput.obj_min_size
obj_max_size = rinput.obj_max_size
data1 = hdulist[0].data
self.logger.debug('Median filter with box %d', median_filter_size)
data2 = median_filter(data1, size=median_filter_size)
# Grey level image
img_grey = normalize_raw(data2)
# Find edges with Canny
self.logger.debug('Find edges with Canny, sigma %f', canny_sigma)
# These thresholds corespond roughly with
# value x (2**16 - 1)
high_threshold = rinput.canny_high_threshold
low_threshold = rinput.canny_low_threshold
self.logger.debug('Find edges, Canny high threshold %f', high_threshold)
self.logger.debug('Find edges, Canny low threshold %f', low_threshold)
edges = canny(img_grey, sigma=canny_sigma,
high_threshold=high_threshold,
low_threshold=low_threshold)
# Fill edges
self.logger.debug('Fill holes')
fill_slits = ndimage.binary_fill_holes(edges)
self.logger.debug('Label objects')
label_objects, nb_labels = ndimage.label(fill_slits)
self.logger.debug('%d objects found', nb_labels)
# Filter on the area of the labeled region
# Perhaps we could ignore this filtering and
# do it later?
self.logger.debug('Filter objects by size')
# Sizes of regions
sizes = numpy.bincount(label_objects.ravel())
self.logger.debug('Min size is %d', obj_min_size)
self.logger.debug('Max size is %d', obj_max_size)
mask_sizes = (sizes > obj_min_size) & (sizes < obj_max_size)
# Filter out regions
nids, = numpy.where(mask_sizes)
mm = numpy.in1d(label_objects, nids)
mm.shape = label_objects.shape
fill_slits_clean = numpy.where(mm, 1, 0)
#plt.imshow(fill_slits_clean)
# and relabel
self.logger.debug('Label filtered objects')
relabel_objects, nb_labels = ndimage.label(fill_slits_clean)
self.logger.debug('%d objects found after filtering', nb_labels)
ids = list(six.moves.range(1, nb_labels + 1))
self.logger.debug('Find regions and centers')
regions = ndimage.find_objects(relabel_objects)
centers = ndimage.center_of_mass(data2, labels=relabel_objects,
index=ids
)
table = char_slit(data2, regions,
slit_size_ratio=rinput.slit_size_ratio
)
result = self.create_result(frame=hdulist, slitstable=table,
DTU=dtub,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa
)
return result
def process(self, ri,
window=None, subpix=1,
store_intermediate=True,
target_is_sky=True, stop_after=PRERED):
numpy.seterr(divide='raise')
recipe_input = ri
# FIXME: hardcoded instrument information
keywords = {'airmass': 'AIRMASS',
'exposure': 'EXPTIME',
'imagetype': 'IMGTYP',
'juliandate': 'MJD-OBS',
'tstamp': 'TSTAMP'
}
baseshape = [2048, 2048]
channels = FULL
if window is None:
window = tuple((0, siz) for siz in baseshape)
if store_intermediate:
pass
# States
sf_data = None
state = self.BASIC
step = 0
try:
niteration = ri.iterations
except KeyError:
niteration = 1
while True:
if state == self.BASIC:
_logger.info('Basic processing')
# Basic processing
basicflow = self.init_filters(recipe_input)
for frame in ri.obresult.frames:
with frame.open() as hdulist:
hdulist = basicflow(hdulist)
if stop_after == state:
break
else:
state = self.PRERED
elif state == self.PRERED:
# Shape of the window
windowshape = tuple((i[1] - i[0]) for i in window)
_logger.debug('Shape of window is %s', windowshape)
# Shape of the scaled window
subpixshape = tuple((side * subpix) for side in windowshape)
# Scaled window region
scalewindow = tuple(
slice(*(subpix * i for i in p)) for p in window)
# Window region
window = tuple(slice(*p) for p in window)
scaled_chan = clip_slices(channels, window, scale=subpix)
# Reference pixel in the center of the frame
refpix = numpy.divide(
numpy.array([baseshape], dtype='int'), 2).astype('float')
# lists of targets and sky frames
targetframes = []
skyframes = []
for frame in ri.obresult.frames:
# Getting some metadata from FITS header
hdr = fits.getheader(frame.label)
try:
frame.exposure = hdr[str(keywords['exposure'])]
# frame.baseshape = get_image_shape(hdr)
frame.airmass = hdr[str(keywords['airmass'])]
frame.mjd = hdr[str(keywords['tstamp'])]
except KeyError as e:
raise KeyError("%s in frame %s" %
(str(e), frame.label))
frame.baselabel = os.path.splitext(frame.label)[0]
frame.mask = ri.master_bpm
# Insert pixel offsets between frames
frame.objmask_data = None
frame.valid_target = False
frame.valid_sky = False
frame.valid_region = scalewindow
# FIXME: hardcode itype for the moment
frame.itype = 'TARGET'
if frame.itype == 'TARGET':
frame.valid_target = True
targetframes.append(frame)
if target_is_sky:
frame.valid_sky = True
skyframes.append(frame)
if frame.itype == 'SKY':
frame.valid_sky = True
skyframes.append(frame)
# labels = [frame.label for frame in targetframes]
if ri.offsets is not None:
_logger.info('Using offsets from parameters')
base_ref = numpy.asarray(ri.offsets)
list_of_offsets= -(base_ref - base_ref[0])
else:
_logger.info('Computing offsets from WCS information')
list_of_offsets = offsets_from_wcs(targetframes, refpix)
# FIXME: Im using offsets in row/columns
# the values are provided in XY so flip-lr
list_of_offsets = numpy.fliplr(list_of_offsets)
# Insert pixel offsets between frames
for frame, off in zip(targetframes, list_of_offsets):
# Insert pixel offsets between frames
frame.pix_offset = off
frame.scaled_pix_offset = subpix * off
_logger.debug('Frame %s, offset=%s, scaled=%s',
frame.label, off, subpix * off)
_logger.info('Computing relative offsets')
offsets = [(frame.scaled_pix_offset)
for frame in targetframes]
offsets = numpy.round(offsets).astype('int')
finalshape, offsetsp = combine_shape(subpixshape, offsets)
_logger.info('Shape of resized array is %s', finalshape)
# Resizing target frames
self.resize(targetframes, subpixshape, offsetsp, finalshape,
window=window, scale=subpix)
if not target_is_sky:
for frame in skyframes:
frame.resized_base = frame.label
frame.resized_mask = frame.mask
# superflat
_logger.info('Step %d, superflat correction (SF)', step)
# Compute scale factors (median)
self.update_scale_factors(ri.obresult.frames)
# Create superflat
superflat = self.compute_superflat(skyframes,
channels=scaled_chan,
step=step)
# Apply superflat
self.figure_init(subpixshape)
self.apply_superflat(ri.obresult.frames, superflat)
_logger.info('Simple sky correction')
if target_is_sky:
# Each frame is the closest sky frame available
for frame in ri.obresult.frames:
self.compute_simple_sky_for_frame(frame, frame)
else:
self.compute_simple_sky(targetframes, skyframes)
# Combining the frames
_logger.info("Step %d, Combining target frames", step)
sf_data = self.combine_frames(
targetframes, extinction=ri.extinction)
self.figures_after_combine(sf_data)
_logger.info('Step %d, finished', step)
if stop_after == state:
break
else:
state = self.CHECKRED
elif state == self.CHECKRED:
seeing_fwhm = None
# self.check_position(images_info, sf_data, seeing_fwhm)
recompute = False
if recompute:
_logger.info('Recentering is needed')
state = self.PRERED
else:
_logger.info('Recentering is not needed')
_logger.info('Checking photometry')
check_photometry(targetframes, sf_data,
seeing_fwhm, figure=self._figure)
if stop_after == state:
break
else:
state = self.FULLRED
elif state == self.FULLRED:
# Generating segmentation image
_logger.info('Step %d, generating segmentation image', step)
objmask, seeing_fwhm = self.create_mask(
sf_data, seeing_fwhm, step=step)
step += 1
# Update objects mask
# For all images
# FIXME:
for frame in targetframes:
frame.objmask = name_object_mask(frame.baselabel, step)
_logger.info(
'Step %d, create object mask %s', step, frame.objmask)
frame.objmask_data = objmask[frame.valid_region]
fits.writeto(
frame.objmask, frame.objmask_data, overwrite=True)
if not target_is_sky:
# Empty object mask for sky frames
bogus_objmask = numpy.zeros(windowshape, dtype='int')
for frame in skyframes:
frame.objmask_data = bogus_objmask
_logger.info('Step %d, superflat correction (SF)', step)
# Compute scale factors (median)
self.update_scale_factors(ri.obresult.frames, step)
# Create superflat
superflat = self.compute_superflat(skyframes, scaled_chan,
segmask=objmask, step=step)
# Apply superflat
self.figure_init(subpixshape)
self.apply_superflat(
ri.obresult.frames, superflat, step=step, save=True)
_logger.info('Step %d, advanced sky correction (SC)', step)
self.compute_advanced_sky(targetframes, objmask,
skyframes=skyframes,
target_is_sky=target_is_sky,
step=step)
# Combining the images
_logger.info("Step %d, Combining the images", step)
# FIXME: only for science
sf_data = self.combine_frames(
targetframes, ri.extinction, step=step)
self.figures_after_combine(sf_data)
if step >= niteration:
state = self.COMPLETE
else:
break
if sf_data is None:
raise RecipeError(
'no combined image has been generated at step %d', state)
hdu = fits.PrimaryHDU(sf_data[0])
hdr = hdu.header
hdr.update('NUMXVER', __version__, 'Numina package version')
hdr.update('NUMRNAM', self.__class__.__name__, 'Numina recipe name')
hdr.update('NUMRVER', self.__version__, 'Numina recipe version')
hdr.update('FILENAME', 'result.fits')
hdr.update('IMGTYP', 'TARGET', 'Image type')
hdr.update('NUMTYP', 'TARGET', 'Data product type')
varhdu = fits.ImageHDU(sf_data[1], name='VARIANCE')
num = fits.ImageHDU(sf_data[2], name='MAP')
result = fits.HDUList([hdu, varhdu, num])
_logger.info("Final frame created")
return DataFrame(result), SourcesCatalog()
