def test_cosmetics_mask_2():
size = 200
flat1 = numpy.ones((size, size))
flat1[0:100, 0] = 0
flat2 = flat1
mask = numpy.zeros((size, size), dtype="int")
mask[0:10, 0] = 1
mascara = cosmetics(flat1, flat2, mask)
expected_mask = numpy.zeros((size, size), dtype="int")
expected_mask[0:100, 0] = 1
expected_mask = expected_mask.astype("bool")
assert mascara.all() == expected_mask.all()
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):
# 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 test_cosmetics_mask():
flat1 = numpy.ones((2, 2))
flat2 = flat1
mask = numpy.zeros((2, 2), dtype="int")
mascara = cosmetics(flat1, flat2, mask)
assert mascara.all() == False
def test_cosmetics_no_mask():
flat1 = numpy.ones((20, 20))
flat2 = flat1
mascara = cosmetics(flat1, flat2)
assert mascara.all() == False
def test_cosmetics_flat2_None():
flat1 = numpy.ones((20, 20))
mascara = cosmetics(flat1)
assert mascara.all() == False
