def backup(args, fits_list):
""" Save a list of files into a
:param args:
:return:
"""
backup_dir = os.path.join(args.in_dir, "original_data")
utils.if_dir_not_exists_create(backup_dir)
for im in fits_list:
shutil.copy(im, backup_dir)
def combine(args):
# Create the folders that do not already exist for the output file
outdir, outfile = os.path.split(os.path.abspath(args.output))
if outdir == "":
outdir = "."
utils.if_dir_not_exists_create(outdir)
# Build a list of the filter of each image
#filter_list, images_filters = build_filter_list(args)
images_filters = utils.collect_from_images(args.input, args.filterk)
# If user wants all images to be combined together, regardless of filter:
if args.all_together:
images_filters = ["AllFilters"] * len(images_filters)
# Create a default dictionary for the resulting images
result = collections.defaultdict(str)
# For each of the filters present combine the images (and write)
for filt in set(images_filters):
# list of objects with current filter (exception: allfilters is true)
list1 = [args.input[p] for p,f in enumerate(images_filters) if f == filt ]
# Check that all images have same dimension. If not, exit program
if not utils.check_dimensions(list1):
sys.exit("Dimensions of images to combine are different!")
# Calculate scale of images
scales = compute_scales(list1, args.scale, args.mask_key)
# Get the sizes of the images
lx, ly = utils.get_from_header(list1[0], "NAXIS2", "NAXIS1")
# Now, in order to avoid loading many images in memory, we need to slice the images in pieces and combine a slice
# of all the images at a time
n_slices = 32 # divide the slow axis in this many pieces
# Define the whole image and set all elements of mask to False
whole_image = numpy.ma.zeros([lx,ly])
whole_image.mask = numpy.zeros_like(whole_image.data)
for xmin in range(0, lx, lx/n_slices):
xmax = min(xmin + lx/n_slices, lx)
# Now we can build and sort a section of the cube with all the images
cube = cube_images(list1, args.mask_key, scales, limits=[xmin, 0, xmax, ly])
cube.sort(axis=0)
# Finally, average! Remember that the cube is sorted so that
# cube[0,ii,jj] < cube[1,ii,jj] and that the highest values of all
# are the masked elements. We will take advantage of it if the median
# is selected, because nowadays the masked median is absurdly slow:
# https://github.com/numpy/numpy/issues/1811
map_cube = numpy.ma.count(cube, axis=0) # number non-masked values per pixel
if args.average == "mean":
image = numpy.ma.mean(cube, axis=0)
non_masked_equivalent = numpy.mean(cube.data, axis=0)
elif args.average == "median":
image = home_made_median(map_cube, cube)
non_masked_equivalent = numpy.median(cube.data, axis=0)
# Image is a masked array, we need to fill in masked values with the
# args.fill_val if user provided it. Also, values with less than
# args.nmin valid values should be masked out. If user did not provide
# a fill_val argument, we will substitute masked values with the
# unmasked equivalent operation.
image.mask[map_cube < args.nmin] = 1
mask = image.mask
if args.fill_val != '':
image = image.filled(args.fill_val)
else:
image.data[mask == True] = non_masked_equivalent[mask == True]
image = image.data
whole_image.data[xmin:xmax, 0:ly] = image[:,:]
whole_image.mask[xmin:xmax, 0:ly] = mask[:,:]
# And save images. If all_together is activated, use the file name given by user. If not, we need
# to separate by filter, so compose a new name with the one given by the user adding the filter
if args.all_together:
newfile = args.output
else:
newfile = os.path.join(outdir, utils.add_suffix_prefix(outfile, suffix="_" + filt) )
if args.out_mask != "":
name_mask = args.out_mask
else:
name_mask = newfile + ".msk"
if os.path.isfile(newfile):
os.remove(newfile)
if os.path.isfile(name_mask):
os.remove(name_mask)
fits.writeto(newfile, whole_image.data)
fits.writeto(name_mask, whole_image.mask.astype(numpy.int))
result[filt] = newfile
# Add comments to the headers
string1 = " - Image built from the combination of the images: "+\
", ".join(list1)
string2 = " combine = " + args.average + ", scale = " + args.scale
utils.add_history_line(newfile, string1 + string2 )
utils.add_history_line(name_mask, " - Mask of image: " + newfile)
if args.mask_key != "":
utils.header_update_keyword(newfile, args.mask_key, name_mask,
"Mask for this image")
# To normalize calculate median and call arith_images to divide by it.
if args.norm == True:
median = compute_scales([newfile], args.scale, args.mask_key)[0]
msg = "- NORMALIZED USING MEDIAN VALUE:"
arith_images.main(arguments=["--message", msg, "--output", newfile,
"--mask_key", args.mask_key, "--fill_val",
args.fill_val, newfile, "/", str(median)])
return result
def rename(args):
# List of fit and fits images in the directory
fits_list1 = glob.glob(os.path.join(args.in_dir, args.in_pattern + "*.fits"))
fits_list2 = glob.glob(os.path.join(args.in_dir, args.in_pattern + "*.fit"))
fits_list = fits_list1+fits_list2
# Sort all images chronologically
fits_list = sort_by_date(fits_list)
# If --copy was selected, copy all those files into a directory called original_data
if args.copy == True:
backup(args, fits_list)
# The output of the whole code will be this dictionary, in which the images
# are sorted in groups (bias, skyflats, domeflats, cigXXXX, ...)
empty_array = numpy.asarray([], dtype=object)
final_dict = {"filename":numpy.asarray([], dtype="S150"),
"type":empty_array,
"objname":empty_array,
"time":empty_array}
# Run through all images
for im_name in fits_list:
# Read image and header, extract name of object and filter.
im = astroim.Astroim(im_name)
object_name = re.sub('[\s\-_\(\)]', "", im.target.objname.lower())
object_type = im.target.objtype
object_filter = im.filter.__str__()
object_date = dateutil.parser.parse(im.primary_header.get(im.primary_header.datek)).date()
object_date = re.sub('[\s\-\_\:]', "", object_date.__str__())
# If the subfolder out_dir/object_type does not exist, create it,
# because we will create/move the new file there.
newdir = os.path.join(args.out_dir, object_type)
utils.if_dir_not_exists_create(newdir)
# Create the new name of the file
new_name = os.path.join(newdir, "{0}_{1}".format(object_name, object_date))
# New name for the file will be determined by the object type (for the
# subfolder), object, date and filter. For bias frames, the filter would
# have no meaning, so we don't put it, just add the "_" to separate the next thing (numbers)
if object_type != "bias":
new_name = "{0}_{1}_".format(new_name, object_filter)
else:
new_name = "{0}_".format(new_name)
# Now we need to find out which sequential number the image should have
ans = True
jj=1
while ans == True: # until file does not exist
newfile = os.path.join(new_name + str(jj).zfill(3)+'.fits')
ans = os.path.isfile(newfile)
jj += 1
oldname_base = os.path.basename(im_name)
newname_base = os.path.basename(newfile)
# Add history comment into the header. If image is to be overwritten,
# just update the image with the changes in the header and move it to
# its new name. Otherwise, save it to the new file immediately.
im = fits.open(im_name, 'update')
hdr = im[0].header
hdr.add_history("- Image "+oldname_base+" renamed "+newname_base)
if args.overwrite == True:
im.flush()
im.close()
os.rename(im_name, newfile)
else:
im.writeto(newfile)
# Add image to the dictionary for the output.
final_dict["filename"] = numpy.append(final_dict["filename"], newfile)
final_dict["objname"] = numpy.append(final_dict["objname"], object_name)
final_dict["type"] = numpy.append(final_dict["type"], object_type)
# Return the dictionary with the images sorted in groups.
return final_dict
