def compute_superflat(self,
images_info,
segmask=None,
step=0,
method=None,
method_kwargs=None):
self.logger.info("Step %d, SF: combining the frames without offsets",
step)
base_imgs = [img.resized_base for img in images_info]
with nfcom.manage_fits(base_imgs) as imgs:
data = []
masks = []
for img, img_info in zip(imgs, images_info):
self.logger.debug('Step %d, opening resized frame %s', step,
img_info.resized_base)
data.append(img['primary'].data[img_info.valid_region])
scales = [numpy.median(d) for d in data]
if segmask is not None:
masks = [segmask[frame.valid_region] for frame in images_info]
else:
for frame in images_info:
self.logger.debug('Step %d, opening resized mask %s',
step, frame.resized_mask)
hdulist = fits.open(frame.resized_mask,
memmap=True,
mode='readonly')
# filelist.append(hdulist)
masks.append(hdulist['primary'].data[frame.valid_region])
masks = None
self.logger.debug("Step %d, combining %d frames using '%s'", step,
len(data), method.__name__)
sf_data, _sf_var, sf_num = method(data,
masks,
scales=scales,
dtype='float32',
**method_kwargs)
# Normalize, flat has mean = 1
sf_data[sf_data == 0] = 1e-5
sf_data /= sf_data.mean()
# sf_data[sf_data <= 0] = 1.0
# Auxiliary data
sfhdu = fits.PrimaryHDU(sf_data)
self.save_intermediate_img(sfhdu, name_skyflat('comb', step))
return sf_data
def compute_offset_xy_crosscor_regions(self, iinfo, regions, refine=False, tol=0.5):
names = [frame.lastname for frame in iinfo]
print(names)
print(regions)
with nfcom.manage_fits(names) as imgs:
arrs = [img[0].data for img in imgs]
offsets_xy = offsets_from_crosscor_regions(
arrs, regions,
refine=refine, order='xy', tol=tol
)
self.logger.debug("offsets_xy cross-corr %s", offsets_xy)
# Offsets in numpy order, swaping
return offsets_xy
def compute_offset_xy_crosscor_regions(self,
iinfo,
regions,
refine=False,
tol=0.5):
names = [frame.lastname for frame in iinfo]
with nfcom.manage_fits(names) as imgs:
arrs = [img[0].data for img in imgs]
offsets_xy = offsets_from_crosscor_regions(arrs,
regions,
refine=refine,
order='xy',
tol=tol)
self.logger.debug("offsets_xy cross-corr:\n%s", offsets_xy)
return offsets_xy
def compute_superflat(self, images_info, segmask=None, step=0):
self.logger.info("Step %d, SF: combining the frames without offsets", step)
base_imgs = [img.resized_base for img in images_info]
with nfcom.manage_fits(base_imgs) as imgs:
data = []
masks = []
for img, img_info in zip(imgs, images_info):
self.logger.debug('Step %d, opening resized frame %s',
step, img_info.resized_base)
data.append(img['primary'].data[img_info.valid_region])
scales = [numpy.median(d) for d in data]
if segmask is not None:
masks = [segmask[frame.valid_region] for frame in images_info]
else:
for frame in images_info:
self.logger.debug('Step %d, opening resized mask %s',
step, frame.resized_mask)
hdulist = fits.open(
frame.resized_mask, memmap=True, mode='readonly')
#filelist.append(hdulist)
masks.append(hdulist['primary'].data[frame.valid_region])
masks = None
self.logger.debug('Step %d, combining %d frames', step, len(data))
sf_data, _sf_var, sf_num = nacom.median(data, masks, scales=scales,
dtype='float32',
#blank=1.0 / scales[0]
)
# Normalize, flat has mean = 1
sf_data[sf_data == 0] = 1e-5
sf_data /= sf_data.mean()
#sf_data[sf_data <= 0] = 1.0
# Auxiliary data
sfhdu = fits.PrimaryHDU(sf_data)
self.save_intermediate_img(sfhdu, name_skyflat('comb', step))
return sf_data
def compute_size(self, images_info, baseshape, user_offsets=None):
# Reference pixel in the center of the frame
refpix = numpy.array([[baseshape[0] / 2.0, baseshape[1] / 2.0]])
target_info = [iinfo for iinfo in images_info if iinfo.valid_target]
if user_offsets is not None:
self.logger.info('Using offsets from parameters')
base_ref = numpy.asarray(user_offsets)
list_of_offsets = -(base_ref - base_ref[0])
else:
self.logger.info('Computing offsets from WCS information')
with nfcom.manage_fits(img.origin
for img in target_info) as images:
list_of_offsets = offsets_from_wcs_imgs(images, refpix)
# FIXME: I am 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 iinfo, off in zip(target_info, list_of_offsets):
# Insert pixel offsets between frames
iinfo.pix_offset = off
self.logger.debug('Frame %s, offset=%s', iinfo.label, off)
self.logger.info('Computing relative offsets')
offsets = [iinfo.pix_offset for iinfo in target_info]
offsets = numpy.round(offsets).astype('int')
finalshape, offsetsp = narray.combine_shape(baseshape, offsets)
self.logger.debug("Relative offsetsp:\n%s", offsetsp)
self.logger.info('Shape of resized array is (NAXIS2, NAXIS1) = %s',
finalshape)
return finalshape, offsetsp, refpix, list_of_offsets
def compute_size(self, images_info, baseshape, user_offsets=None):
# Reference pixel in the center of the frame
refpix = numpy.array([[baseshape[0] / 2.0, baseshape[1] / 2.0]])
target_info = [iinfo for iinfo in images_info if iinfo.valid_target]
if user_offsets is not None:
self.logger.info('Using offsets from parameters')
base_ref = numpy.asarray(user_offsets)
list_of_offsets = -(base_ref - base_ref[0])
else:
self.logger.debug('Computing offsets from WCS information')
with nfcom.manage_fits(img.origin for img in target_info) as images:
list_of_offsets = offsets_from_wcs_imgs(images, refpix)
# FIXME: I am 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 iinfo, off in zip(target_info, list_of_offsets):
# Insert pixel offsets between frames
iinfo.pix_offset = off
self.logger.debug('Frame %s, offset=%s',
iinfo.label, off)
self.logger.info('Computing relative offsets')
offsets = [iinfo.pix_offset for iinfo in target_info]
offsets = numpy.round(offsets).astype('int')
finalshape, offsetsp = narray.combine_shape(baseshape, offsets)
self.logger.debug("Relative offsetsp %s", offsetsp)
self.logger.info('Shape of resized array is %s', finalshape)
return finalshape, offsetsp, refpix, list_of_offsets