def test_compressed_image_data_float32(self):
n = np.arange(100, dtype='float32')
hdu = fits.ImageHDU(n)
comp_hdu = fits.CompImageHDU(hdu.data, hdu.header)
comp_hdu.writeto(self.temp('tmp.fits'), checksum=True)
hdu.writeto(self.temp('uncomp.fits'), checksum=True)
with fits.open(self.temp('tmp.fits'), checksum=True) as hdul:
assert np.all(hdul[1].data == comp_hdu.data)
assert np.all(hdul[1].data == hdu.data)
assert 'CHECKSUM' in hdul[0].header
assert hdul[0].header['CHECKSUM'] == 'D8iBD6ZAD6fAD6ZA'
assert 'DATASUM' in hdul[0].header
assert hdul[0].header['DATASUM'] == '0'
assert 'CHECKSUM' in hdul[1].header
assert 'DATASUM' in hdul[1].header
if not sys.platform.startswith('win32'):
assert hdul[1]._header['CHECKSUM'] == 'eATIf3SHe9SHe9SH'
assert hdul[1]._header['DATASUM'] == '1277667818'
with fits.open(self.temp('uncomp.fits'), checksum=True) as hdul2:
header_comp = hdul[1]._header
header_uncomp = hdul2[1].header
assert 'ZHECKSUM' in header_comp
assert 'CHECKSUM' in header_uncomp
assert header_uncomp['CHECKSUM'] == 'Cgr5FZo2Cdo2CZo2'
assert header_comp['ZHECKSUM'] == header_uncomp['CHECKSUM']
assert 'ZDATASUM' in header_comp
assert 'DATASUM' in header_uncomp
assert header_uncomp['DATASUM'] == '2393636889'
assert header_comp['ZDATASUM'] == header_uncomp['DATASUM']
def test_compressed_image_data_int16(self):
n = np.arange(100, dtype='int16')
hdu = fits.ImageHDU(n)
comp_hdu = fits.CompImageHDU(hdu.data, hdu.header)
comp_hdu.writeto(self.temp('tmp.fits'), checksum=True)
hdu.writeto(self.temp('uncomp.fits'), checksum=True)
with fits.open(self.temp('tmp.fits'), checksum=True) as hdul:
assert np.all(hdul[1].data == comp_hdu.data)
assert np.all(hdul[1].data == hdu.data)
assert 'CHECKSUM' in hdul[0].header
assert hdul[0].header['CHECKSUM'] == 'D8iBD6ZAD6fAD6ZA'
assert 'DATASUM' in hdul[0].header
assert hdul[0].header['DATASUM'] == '0'
assert 'CHECKSUM' in hdul[1].header
assert hdul[1]._header['CHECKSUM'] == 'J5cCJ5c9J5cAJ5c9'
assert 'DATASUM' in hdul[1].header
assert hdul[1]._header['DATASUM'] == '2453673070'
assert 'CHECKSUM' in hdul[1].header
with fits.open(self.temp('uncomp.fits'), checksum=True) as hdul2:
header_comp = hdul[1]._header
header_uncomp = hdul2[1].header
assert 'ZHECKSUM' in header_comp
assert 'CHECKSUM' in header_uncomp
assert header_uncomp['CHECKSUM'] == 'ZE94eE91ZE91bE91'
assert header_comp['ZHECKSUM'] == header_uncomp['CHECKSUM']
assert 'ZDATASUM' in header_comp
assert 'DATASUM' in header_uncomp
assert header_uncomp['DATASUM'] == '160565700'
assert header_comp['ZDATASUM'] == header_uncomp['DATASUM']
def test_compressed_image_data_float32(self):
n = np.arange(100, dtype='float32')
hdu = fits.ImageHDU(n)
comp_hdu = fits.CompImageHDU(hdu.data, hdu.header)
comp_hdu.writeto(self.temp('tmp.fits'), checksum=True)
hdu.writeto(self.temp('uncomp.fits'), checksum=True)
with fits.open(self.temp('tmp.fits'), checksum=True) as hdul:
assert np.all(hdul[1].data == comp_hdu.data)
assert np.all(hdul[1].data == hdu.data)
assert 'CHECKSUM' in hdul[0].header
assert hdul[0].header['CHECKSUM'] == 'D8iBD6ZAD6fAD6ZA'
assert 'DATASUM' in hdul[0].header
assert hdul[0].header['DATASUM'] == '0'
assert 'CHECKSUM' in hdul[1].header
assert 'DATASUM' in hdul[1].header
assert 'CHECKSUM' in hdul[1].header
if sys.platform != 'win32':
# The checksum ends up being different on Windows, possibly due
# to slight floating point differences
# TODO: In Astropy mark these properly as known fail
assert hdul[1]._header['CHECKSUM'] == 'eATIf3SHe9SHe9SH'
assert hdul[1]._header['DATASUM'] == '1277667818'
with fits.open(self.temp('uncomp.fits'), checksum=True) as hdul2:
header_comp = hdul[1]._header
header_uncomp = hdul2[1].header
assert 'ZHECKSUM' in header_comp
assert 'CHECKSUM' in header_uncomp
assert header_uncomp['CHECKSUM'] == 'Cgr5FZo2Cdo2CZo2'
assert header_comp['ZHECKSUM'] == header_uncomp['CHECKSUM']
assert 'ZDATASUM' in header_comp
assert 'DATASUM' in header_uncomp
assert header_uncomp['DATASUM'] == '2393636889'
assert header_comp['ZDATASUM'] == header_uncomp['DATASUM']
def _test_uint(self, utype, compressed):
bits = 8 * int(utype[1])
if platform.architecture()[0] == '64bit' or bits != 64:
if compressed:
hdu = fits.CompImageHDU(np.array([-3, -2, -1, 0, 1, 2, 3]))
hdu_number = 1
else:
hdu = fits.PrimaryHDU(np.array([-3, -2, -1, 0, 1, 2, 3]))
hdu_number = 0
hdu.scale('int%s' % bits, '', bzero=2**(bits - 1))
with ignore_warnings():
hdu.writeto(self.temp('tempfile.fits'), clobber=True)
with fits.open(self.temp('tempfile.fits'), uint=True) as hdul:
assert hdul[hdu_number].data.dtype == self.utype_map[utype]
assert (hdul[hdu_number].data == np.array(
[(2**bits) - 3, (2**bits) - 2, (2**bits) - 1, 0, 1, 2, 3],
dtype=self.utype_map[utype])).all()
with ignore_warnings():
hdul.writeto(self.temp('tempfile1.fits'), clobber=True)
with fits.open(self.temp('tempfile1.fits'),
uint16=True) as hdul1:
d1 = hdul[hdu_number].data
d2 = hdul1[hdu_number].data
assert (d1 == d2).all()
if not compressed:
# TODO: Enable these lines if CompImageHDUs ever grow
# .section support
sec = hdul[hdu_number].section[:1]
assert sec.dtype.name == 'uint%s' % bits
assert (sec == d1[:1]).all()
def _add_hdu(hdus, data, pyfits_compress):
import pyfits
if pyfits_compress:
if len(hdus) == 0:
hdus.append(pyfits.PrimaryHDU()) # Need a blank PrimaryHDU
hdu = pyfits.CompImageHDU(data, compressionType=pyfits_compress)
else:
if len(hdus) == 0:
hdu = pyfits.PrimaryHDU(data)
else:
hdu = pyfits.ImageHDU(data)
hdus.append(hdu)
return hdu
def test_identical_comp_image_hdus(self):
"""Regression test for https://aeon.stsci.edu/ssb/trac/pyfits/ticket/189
For this test we mostly just care that comparing to compressed images
does not crash, and returns the correct results. Two compressed images
will be considered identical if the decompressed data is the same.
Obviously we test whether or not the same compression was used by
looking for (or ignoring) header differences.
"""
data = np.arange(100.0).reshape((10, 10))
hdu = fits.CompImageHDU(data=data)
hdu.writeto(self.temp('test.fits'))
hdula = fits.open(self.temp('test.fits'))
hdulb = fits.open(self.temp('test.fits'))
diff = FITSDiff(hdula, hdulb)
assert diff.identical
def esi_reduce(date):
#Get edge masks from file
orders_mask = pickle.load(
open(str(date) + '/Calibs/orders_mask_' + str(date) + '.p', 'rb'))
background_mask = pickle.load(
open(str(date) + '/Calibs/background_mask_' + str(date) + '.p', 'rb'))
#Bias
bias = pyfits.getdata(str(date) + '/Calibs/bias_' + str(date) +
'.fits')[:, 25:2070]
#Normalized Flat
flat = pyfits.getdata(
str(date) + '/Calibs/norm_flat_' + str(date) + '.fits')
#READ LOG
im1 = open(str(date) + '/Logs/esi_info_' + str(date) + '.dat', 'r')
data1 = im1.readlines()
im1.close()
filename = []
dateobs = []
objname = []
imgtype = []
ra = []
dec = []
exptime = []
usable = []
for line in data1:
p = line.split()
filename.append(p[0])
dateobs.append(p[1])
objname.append(p[2])
imgtype.append(p[3])
ra.append(p[4])
dec.append(p[5])
exptime.append(p[6])
usable.append(p[7])
#Rewrite in a more convenient array with format array[line][element]
alldata = []
for line in range(len(usable)):
alldata.append([
filename[line], dateobs[line], objname[line], imgtype[line],
ra[line], dec[line], exptime[line], usable[line]
])
#Find good files:
good = []
for line in range(len(alldata)):
if "yes" in alldata[line][7]:
good.append(alldata[line])
#Find list of objects
names = []
for line in range(len(alldata)):
if ("Object" in alldata[line][3] and float(alldata[line][6]) > 600):
names.append(alldata[line][2])
objects = np.array(list(set(names)))
objects.sort() #ascending order, modify in place
#Make directory to hold decosmicified files
if not os.path.exists(str(date) + '/Calibs/reduced/'):
os.makedirs(str(date) + '/Calibs/reduced/')
#Reduce for each object
for obj_id in objects:
print "reducing " + str(obj_id) + '...'
#Find files related to obj_id:
aobj_id = []
for line in range(len(good)):
if str(obj_id) in good[line][2]:
aobj_id.append(good[line])
print str(len(aobj_id)), "files for " + str(obj_id)
if len(aobj_id) == 0:
continue
#Write Path to each object's files
obj_locs = [
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits' for line in range(len(aobj_id))
]
#Read in Data
all_sci = []
for line in obj_locs:
sci = pyfits.getdata(line)[:, 25:2070]
all_sci.append(sci)
#Get a mask for remaining cosmic rays; mean combine
print "averaging with rejection..."
filtered = sigma_clip(all_sci,
axis=0,
copy=False,
varfunc=np.median,
sig=15)
filtered = (filtered - bias) / flat
mean = np.mean(filtered, axis=0)
mean.data[background_mask] = 0
hdu = pyfits.CompImageHDU(mean.data)
hdu.writeto(str(date) + '/Calibs/reduced/' + str(obj_id) +
'_mean.fits',
clobber=True)
#pdb.set_trace()
#Reduce lines
names = []
for line in range(len(alldata)):
if ("Line" in alldata[line][3]) or ("*" in alldata[line][2]):
names.append(alldata[line][2])
objects = np.array(list(set(names)))
objects.sort() #ascending order, modify in place
#Reduce for each line or star (because not decosmfied)
for obj_id in objects:
print "reducing " + str(obj_id) + '...'
#Find files related to obj_id:
aobj_id = []
for line in range(len(good)):
if str(obj_id) in good[line][2]:
aobj_id.append(good[line])
print str(len(aobj_id)), "files for " + str(obj_id)
if len(aobj_id) == 0:
continue
#Write Path to each object's files
obj_locs = [
str(date) + '/Raw/' + str(aobj_id[line][0])
for line in range(len(aobj_id))
]
#Read in Data
all_sci = []
for line in obj_locs:
sci = pyfits.getdata(line)[:, 25:2070]
sci = (sci - bias) / flat
all_sci.append(sci)
#median, not mean, for lines
mean = np.median(all_sci, axis=0)
mean[background_mask] = 0
hdu = pyfits.CompImageHDU(mean)
hdu.writeto(str(date) + '/Calibs/reduced/' + str(obj_id) +
'_mean.fits',
clobber=True)
#Make variance image for each science image:
#The break in the amplifiers is at 1022-1023. Everything increases at 1023.
names = []
for line in range(len(alldata)):
if ("Object" in alldata[line][3]
and float(alldata[line][6]) > 600) or ("*"
in alldata[line][2]):
names.append(alldata[line][2])
objects = np.array(list(set(names)))
objects.sort() #ascending order, modify in place
for obj_id in objects:
print "making variance for " + str(obj_id) + '...'
#Find files related to obj_id:
aobj_id = []
for line in range(len(good)):
if str(obj_id) in good[line][2]:
aobj_id.append(good[line])
print str(len(aobj_id)), "files for " + str(obj_id)
if len(aobj_id) == 0:
continue
#Write Path to each object's files
obj_locs = [
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits' for line in range(len(aobj_id))
]
#CALCULATE EACH NOISE CONTRIBUTION SEPARATELY
#Read in Data
all_var = []
for line in range(len(obj_locs)):
im = pyfits.getdata(obj_locs[line])[:, 25:2070]
mask = bias > im #to avoid taking sqrt(negative)
im = im - bias
rn = np.zeros((4096, 2045))
#make gain mask:
right_mask = np.empty(flat.shape, dtype=bool)
right_mask[:, :1022] = 0
right_mask[:, 1023:] = 1
left_mask = -right_mask
#MAKE A READ NOISE FRAME
#random bias image
biases = []
for line in range(len(good)):
if "Bias" in good[line][3] and "Bias" in good[line][2]:
biases.append(good[line])
rand_path = str(date) + '/Raw/' + str(
biases[0][0]) #pick the first bias.
rand_bias = pyfits.getdata(rand_path)[:, 25:2070]
read_noise = rand_bias - bias
#Big enough square to be representative
left_rn = np.std(read_noise[2000:2100, 920:1020])
right_rn = np.std(read_noise[2000:2100, 1030:1130])
rn[left_mask] = left_rn
rn[right_mask] = right_rn
#as expected, the read nose is a bit higher on the right side.
#GAIN AND POISSON NOISE
#gain on right side == 1.29 e-/DN
poisson = np.zeros((4096, 2045))
left_bias = np.mean(bias[2000:2100, 920:1020])
right_bias = np.mean(bias[2000:2100, 1030:1130])
left_gain = 1.29
right_gain = left_gain * left_bias / right_bias
#as expected, gain a little lower on right side
poisson[left_mask] = np.sqrt(im[left_mask] / left_gain)
poisson[right_mask] = np.sqrt(im[right_mask] / right_gain)
poisson[mask] = np.median(poisson)
noise = np.sqrt(rn**2 + poisson**2)
noise = noise / flat
#variance = 1/noise**2
#noise = (noise - bias)/flat
#noise[background_mask] = 0
all_var.append(noise)
#write to file
#fits = pyfits.PrimaryHDU(variance)
#fits.writeto('Calibs/variance/'+str(obj_id)+'_'+str(line + 1)+'_var.fits', clobber = True)
#Make directory to hold decosmicified files
if not os.path.exists(str(date) + '/Calibs/variance/'):
os.makedirs(str(date) + '/Calibs/variance/')
#Combine variance images for each object
tot_noise = np.sqrt(
np.sum([(siggma / len(all_var))**2 for siggma in all_var], axis=0))
tot_noise[background_mask] = 0
fits = pyfits.CompImageHDU(tot_noise)
fits.writeto(str(date) + '/Calibs/variance/' + str(obj_id) +
'_noise.fits',
clobber=True)
def generate_fits(self, out_dir='', filename=None, ldcoeffs=None):
""" Saves the exposure as a HST style fits file.
:param out_dir: director to save output
:type out_dir: str
:param filename: filename to save the fits
:type filename: str
:return:
"""
assert (len(self.reads) == (self.exp_info['NSAMP'])), \
'Reads {} != NSAMP {}'.format(len(self.reads),
self.exp_info['NSAMP'])
if filename is None:
filename = self.exp_info['filename']
out_path = os.path.join(out_dir, filename)
science_header = self.generate_science_header(ldcoeffs=ldcoeffs)
hdulist = fits.HDUList([science_header])
compression = 'RICE_1'
for i, (data, header) in enumerate(reversed(self.reads)):
# compression currently disabled as its producing stripey data
header.set('SAMPNUM', len(self.reads) - 1 - i)
read_HDU = fits.ImageHDU(data, header, name='SCI')
error_array = fits.CompImageHDU(compression_type=compression,
name='ERR')
""" This array contains 16 independent flags indicating various
status and problem conditions associated with each corresponding
pixel in the science image. Each flag has a true (set) or false
(unset) state and is encoded as a bit in a 16-bit integer word.
Users are advised that this word should not be interpreted as as
simple integer, but must be converted to base-2 and each bit
interpreted as a flag. Table 2.5 lists the WFC3 data quality flags.
"""
data_quality_array = fits.CompImageHDU(
compression_type=compression, name='DQ')
""" This array is present only for IR data. It is a 16-bit integer
array and contains the number of samples used to derive the
corresponding pixel values in the science image. For raw and
intermediate data files, the sample values are set to the number of
readouts that contributed to the science image. For calibrated
files, the SAMP array contains the total number of valid samples
used to compute the final science image pixel value, obtained by
combining the data from all the readouts and rejecting cosmic
ray hits and saturated pixels. Similarly, when multiple
exposures (i.e., REPEAT-OBS) are combined to produce a single
image, the SAMP array contains the total number of samples
retained at each pixel for all the exposures.
"""
samples_HDU = fits.CompImageHDU(compression_type=compression,
name='SAMP')
""" This array is present only for IR data. This is a
floating-point array that contains the effective integration
time associated with each corresponding science image pixel value.
For raw and intermediate data files, the time value is the total
integration time of data that contributed to the science image.
For calibrated datasets, the TIME array contains the combined
exposure time of the valid readouts or exposures that were used
to compute the final science image pixel value, after rejection of
cosmic rays and saturated pixels from the intermediate data.
"""
integration_time_HDU = fits.CompImageHDU(
compression_type=compression, name='TIME')
hdulist.extend([
read_HDU, error_array, data_quality_array, samples_HDU,
integration_time_HDU
])
if os.path.isfile(out_path):
os.system("rm {0}".format(out_path))
hdulist.writeto(out_path)
def esi_cosmic(date):
#Get edge masks from file
orders_mask = pickle.load(
open(str(date) + '/Calibs/orders_mask_' + str(date) + '.p', 'rb'))
background_mask = pickle.load(
open(str(date) + '/Calibs/background_mask_' + str(date) + '.p', 'rb'))
#Bias
bias = pyfits.getdata(str(date) + '/Calibs/bias_' + str(date) + '.fits')
#Normalized Flat
flat = pyfits.getdata(
str(date) + '/Calibs/norm_flat_' + str(date) + '.fits')
#READ LOG
im1 = open(str(date) + '/Logs/esi_info_' + str(date) + '.dat', 'r')
data1 = im1.readlines()
im1.close()
filename = []
dateobs = []
objname = []
imgtype = []
ra = []
dec = []
exptime = []
usable = []
for line in data1:
p = line.split()
filename.append(p[0])
dateobs.append(p[1])
objname.append(p[2])
imgtype.append(p[3])
ra.append(p[4])
dec.append(p[5])
exptime.append(p[6])
usable.append(p[7])
#Rewrite in a more convenient array with format array[line][element]
alldata = []
for line in range(len(usable)):
alldata.append([
filename[line], dateobs[line], objname[line], imgtype[line],
ra[line], dec[line], exptime[line], usable[line]
])
#Find good files:
good = []
for line in range(len(alldata)):
if "yes" in alldata[line][7]:
good.append(alldata[line])
#Find list of objects:
names = []
for line in range(len(alldata)):
if ("Object" in alldata[line][3]
and float(alldata[line][6]) > 600) or "*" in alldata[line][2]:
names.append(alldata[line][2])
objects = np.array(list(set(names)))
objects.sort() #ascending order, modify in place
#Find list of lines
names = []
for line in range(len(alldata)):
if "Line" in alldata[line][3]:
names.append(alldata[line][2])
lines = np.array(list(set(names)))
lines.sort() #ascending order, modify in place
#Find list of stars:
names = []
for line in range(len(alldata)):
if "*" in alldata[line][2]:
names.append(alldata[line][2])
stars = np.array(list(set(names)))
stars.sort() #ascending order, modify in place
#Make directory to hold decosmicified files
if not os.path.exists(str(date) + '/Calibs/cosmicless/'):
os.makedirs(str(date) + '/Calibs/cosmicless/')
#Remove cosmics from each lamp
for obj_id in lines:
print "reducing " + str(obj_id) + '...'
#Find files related to this object
aobj_id = []
for line in range(len(good)):
if str(obj_id) in good[line][2]:
aobj_id.append(good[line])
print str(len(aobj_id)), "files for " + str(obj_id)
#Write path to each objects files:
obj_locs = [
str(date) + '/Raw/' + str(aobj_id[line][0])
for line in range(len(aobj_id))
]
#Read in and de-cosmify
for line in range(len(obj_locs)):
array, header = cosmics.fromfits(obj_locs[line])
#array = array - bias #backwards for some reason
c = cosmics.cosmicsimage(array,
gain=1.29,
readnoise=2.2,
sigclip=6,
objlim=5.0,
sigfrac=0.7,
satlevel=1e4)
c.run(
maxiter=3
) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits(
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits', c.cleanarray, header)
#now zip it (to save space)
f = pyfits.getdata(
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits')
hdu = pyfits.CompImageHDU(f)
hdu.writeto(str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits',
clobber=True)
#Remove cosmics from each object
for obj_id in objects:
print "reducing " + str(obj_id) + '...'
#Find files related to this object
aobj_id = []
for line in range(len(good)):
if str(obj_id) in good[line][2]:
aobj_id.append(good[line])
print str(len(aobj_id)), "files for " + str(obj_id)
#Write path to each objects files:
obj_locs = [
str(date) + '/Raw/' + str(aobj_id[line][0])
for line in range(len(aobj_id))
]
#Read in and de-cosmify
for line in range(len(obj_locs)):
array, header = cosmics.fromfits(obj_locs[line])
#array = array - bias #backwards for some reason
c = cosmics.cosmicsimage(array,
gain=1.29,
readnoise=2.2,
sigclip=3.8,
objlim=3.0,
sigfrac=0.7,
satlevel=-1)
c.run(
maxiter=3
) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits(
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits', c.cleanarray, header)
#now zip it (to save space)
f = pyfits.getdata(
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits')
hdu = pyfits.CompImageHDU(f)
hdu.writeto(str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits',
clobber=True)
#Remove cosmics from each star
for obj_id in stars:
print "reducing " + str(obj_id) + '...'
#Find files related to this object
aobj_id = []
for line in range(len(good)):
if str(obj_id) == good[line][2]:
aobj_id.append(good[line])
print str(len(aobj_id)), "files for " + str(obj_id)
#Write path to each objects files:
obj_locs = [
str(date) + '/Raw/' + str(aobj_id[line][0])
for line in range(len(aobj_id))
]
#Read in and de-cosmify
for line in range(len(obj_locs)):
array, header = cosmics.fromfits(obj_locs[line])
#array = array - bias #backwards for some reason
c = cosmics.cosmicsimage(array,
gain=1.29,
readnoise=2.2,
sigclip=10,
objlim=7.0,
sigfrac=0.7,
satlevel=15000)
c.run(
maxiter=3
) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits(
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits', c.cleanarray, header)
#now zip it (to save space)
f = pyfits.getdata(
str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits')
hdu = pyfits.CompImageHDU(f)
hdu.writeto(str(date) + '/Calibs/cosmicless/' + str(obj_id) + '_' +
str(line + 1) + 'decos.fits',
clobber=True)