def cosmicray_lacosmic(im,sigclip=5,sigfrac=0.3,readnoise=1,gain=1,objlim=5,satlevel=65535.0,niter=5,verbose=False):
if isinstance(im,CCDData):
cosmic = cosmicsimage(im.data,gain=gain,readnoise=readnoise,sigclip=sigclip,sigfrac=sigfrac,objlim=objlim,satlevel=satlevel,verbose=verbose)
cosmic.run(maxiter=niter,verbose=verbose)
im.data = cosmic.cleanarray
im.mask = cosmic.mask
im = im.to_hdu()
else:
cosmic = cosmicsimage(im,gain=gain,readnoise=readnoise,sigclip=sigclip,sigfrac=sigfrac,objlim=objlim,satlevel=satlevel,verbose=verbose)
cosmic.run(maxiter=niter,verbose=verbose)
im = cosmic.cleanarray
return im
def run_lacosmic(flc='j9cv13pcq_flc.fits', split=1024, pssl=0.0, sigclip=4.0, objlim=3.8, sigfrac=0.3):
"""
drizzlepac.astrodrizzle.AstroDrizzle('ESO550-IG02-13-083-F814W_asn.fits', skysub=True, clean=True, final_wcs=True, final_scale=0.05, final_pixfrac=0.8, context=False, resetbits=0, final_bits=576, preserve=False)
"""
import cosmics
import threedhst
im = pyfits.open(flc, mode='update')
h = im[0].header
nx, ny = 4096/split, 2048/split
for ext in [1,2]:
if flc.startswith('i'):
dq = np.zeros((2051, 4096), dtype=int)
else:
dq = np.zeros((2048, 4096), dtype=int)
for i in range(nx):
for j in range(ny):
threedhst.showMessage('ext: %d, (i,j)=%d,%d' %(ext, i,j))
subim = im['sci',ext].data[j*split:(j+1)*split, i*split:(i+1)*split]
c = cosmics.cosmicsimage(subim, pssl=pssl, gain=1, readnoise=h['READNSEA'], sigclip=objlim, sigfrac=sigfrac, objlim=objlim, satlevel=84700.0, verbose=True)
c.run(maxiter=4)
dq[j*split:(j+1)*split, i*split:(i+1)*split] += c.mask*1
im['dq',ext].data |= dq*4096
pyfits.writeto(im.filename().replace('.fits', '_lac_%d.fits' %(ext)), data=dq*4096, header=im['dq', ext].header, clobber=True)
im.flush()
def clean_cosmic(f):
'''
From lacosmic.
'''
import cosmics
out = f.replace('.fits', '_clean.fits')
#If it does already exist, just return the name.
if (os.path.isfile(out)):
return out
#Otherwise, run the cosmic ray rejection based on LA Cosmic.
g = fitsutils.get_par(f, "GAIN")
if (fitsutils.has_par(f, "RDNOISE")):
rn = fitsutils.get_par(f, "RDNOISE")
else:
rn = 20
array, header = cosmics.fromfits(f)
try:
c = cosmics.cosmicsimage(array, gain=g, readnoise=rn, sigclip = 8.0, sigfrac = 0.3, satlevel = 64000.0)
c.run(maxiter = 3)
out = f.replace('.fits', '_clean.fits')
cosmics.tofits(out, c.cleanarray, header)
fitsutils.update_par(out, "CRREJ", 1)
#os.remove(f)
except:
pass
return out
def remove_cosmic(self, img):
self.update_deque()
test = cosmicsimage(img, sigclip=3.0, objlim=3.0)
num = 2
for i in xrange(num):
test.lacosmiciteration(True)
test.clean()
self._mask_data = np.logical_or(self._mask_data, np.array(test.mask, dtype='bool'))
print test.mask
def clean_data(data,maxiter=5,gain=2.2,readnoise=10.0,sigclip=5,
sigfrac=0.3,objlim=5.0,satlevel=50000,getmask=False,verbose=False):
# Perform Laplacian cosmic ray subtraction
cosmic = cosmics.cosmicsimage(data,gain=gain,readnoise=readnoise,sigclip=sigclip,sigfrac=sigfrac,objlim=objlim,satlevel=satlevel,verbose=verbose)
cosmic.run(maxiter=maxiter,verbose=verbose)
if getmask:
return cosmic.cleanarray,cosmic.mask
else:
return cosmic.cleanarray
def clean_cosmicrays( image, params ):
imdat, imhead = cosmics.fromfits(image, verbose=False)
# DEFAULT HEADER PARAMETERS FOR VIRUS-P AS OF 2012
try: gain = imhead['GAIN1']
except: gain = 2.0
try: rdnoise = imhead['RDNOISE1']
except: rdnoise = 2.5
c = cosmics.cosmicsimage( imdat, gain=gain, readnoise=rdnoise, \
sigclip=5., sigfrac=0.3, objlim=5.0 )
c.run( maxiter=4 )
cosmics.tofits(image[:-5]+'c.fits', c.cleanarray, imhead)
cosmics.tofits(image[:-5]+'_mask.fits', c.mask, imhead)
def clean_cosmic(f, name):
'''
From lacosmic.
'''
import cosmics
g = fitsutils.get_par(f, "GAIN")
rn = fitsutils.get_par(f, "RDNOISE")
array, header = cosmics.fromfits(f)
try:
c = cosmics.cosmicsimage(array, gain=g, readnoise=rn, sigclip = 8.0, sigfrac = 0.3, satlevel = 64000.0)
c.run(maxiter = 10)
out = f.replace('.fits', '_clean.fits')
cosmics.tofits(out, c.cleanarray, header)
os.remove(f)
except:
pass
def origRemoval(fitsFile):
"""
Runs the original cosmics.py script on the input FITS file.
Outputs: origClean.fits, origMask.Fits
"""
# Read the FITS :
array, header = origCosmics.fromfits(fitsFile)
# array is a 2D numpy array
# Build the object :
c = origCosmics.cosmicsimage(array, gain=2.2, readnoise=10.0, sigclip = 5.0,
sigfrac = 0.3, objlim = 5.0)
# There are other options, check the manual...
# Run the full artillery :
c.run(maxiter = 4)
# Write the cleaned image into a new FITS file, conserving the original header :
origCosmics.tofits("origClean.fits", c.cleanarray, header)
# If you want the mask, here it is :
origCosmics.tofits("origMask.fits", c.mask, header)
def cosmiczap(filename, outname, sigclip=6.0, maxiter=3, verbose=True):
"""
NAME:
cosmiczap
PURPOSE:
Removes cosmic rays using Laplacian cosmic ray identification written in cosmics.py
INPUTS:
filename - file or list of files to be cosmic ray zapped
outfile - name of output file
OPTIONAL KEYWORDS:
sigclip - sigma to clip
maxiter - maximum number of times to iterate loop
verbose - quiet?
EXAMPLE:
cosmiczap(filename, outname)
DEPENDENCIES:
cosmic.py (described in http://arxiv.org/pdf/1506.07791v3.pdf)
FUTURE IMPROVEMENTS:
Read readnoise from header?
"""
data, head = cosmics.fromfits(filename, verbose=False)
gain = head['GAIN']
c = cosmics.cosmicsimage(data, gain=gain, readnoise=18, sigclip=sigclip,
sigfrac = 0.5, objlim = 5.0, verbose=False)
tot = c.run(maxiter=maxiter, verbose=False)
head['NPZAP'] = (tot, "Num. of pixels zapped by cosmiczap")
date = datetime.datetime.now().isoformat()
head.add_history('Processed by cosmiczap ' + date)
if verbose: print ' Zapped %d total affected pixels (%.3f%% of total)' \
%(tot,tot*100.0/np.size(data))
cosmics.tofits(outname, c.cleanarray, head, verbose=False)
def process_single_image(self, infn, nlacosmic=2):
""" Do all the basic clean up steps for a single image
Imfix
Bias subtract.
Clean up cosmic rays
Generate a noise map
nlacosmic -- number of LA Cosmic iterations to do
-- A small number means that a smaller number of cosmics get removed, but the code
runs faster.
"""
# Read in the data
ff = pyfits.open(infn)
arr = ff[0].data
ff.close()
# Image fix and bias subtract
arr = self._imfix(arr) - self.get_bias()
# Cosmic ray cleanup
# The parameters here are hardcoded... in general, we should clean these up somewhat
cc = cosmics.cosmicsimage(arr, gain=self.gain, readnoise=self.readnoise, objlim=1.5)
cc.findsatstars() # Mark the saturated stars
cc.run(nlacosmic)
arr = cc.cleanarray.copy()
# Now generate a median filtered image
m5 = ndimage.filters.median_filter(arr, size=5, mode='mirror')
m5clipped = m5.clip(min=0.00001) # As we will take the sqrt
ivar = self.gain**2/(self.gain*m5clipped + self.readnoise*self.readnoise)
# Now set the bad pixels to zero ivar
ivar[cc.mask == 1] = 0.0
return arr, ivar
def run_cosmics(filename):
'''
The main controller.
'''
output = filename.split('_')[0] + "_cr_" + filename.split('_')[1]
# Assert the input file exists
error = filename + ' input for run_cosmics in '
error += 'run_cosmics.py does not exist.'
assert os.access(filename, os.F_OK), error
# Define the output name and delete if exists.
query = os.access(output, os.F_OK)
if query == True:
os.remove(output)
# Find the number of extentions.
header = pyfits.open(filename)
header_count = len(header)
header.close()
for ext in range(0, header_count):
print filename + '[' + str(ext) + ']'
if ext in [0, 5]:
hdu = pyfits.open(filename)
array = hdu[ext].data
header = hdu[ext].header
pyfits.append(
output,
array,
header)
hdu.close()
else:
# Read the FITS :
array, header = cosmics.fromfits(filename, hdu = ext)
# array is a 2D numpy array
# Build the object
# WFPC2 PC Chip Settings
if ext == 1:
c = cosmics.cosmicsimage(
array,
pssl = 0.0,
gain = 1.0,
readnoise = 5.0,
sigclip = 3.0,
sigfrac = 0.01,
objlim = 4.0,
satlevel = 4095.0,
verbose = True)
# Build the object
# WFPC2 WF Chip Settings
elif ext in [2, 3, 4]:
c = cosmics.cosmicsimage(
array,
pssl = 0.0,
gain=1.0,
readnoise=5.0,
sigclip = 2.5,
sigfrac = 0.001,
objlim = 5.0,
satlevel = 4095.0,
verbose = True)
# Run the full artillery
c.run(maxiter = 7)
# Write the cleaned image into a new FITS file
# conserving the original header :
#cosmics.tofits(output, c.cleanarray, header)
pyfits.append(
output,
c.cleanarray.transpose(),
header,
ext = 'SCI')
# If you want the mask, here it is :
# cosmics.tofits("mask.fits", c.mask, header)
# (c.mask is a boolean numpy array, that gets
# converted here to an integer array)
assert os.path.isfile(output) == True, 'run_cosmics did not create: ' + output
# Create a symbolic link to the original c1m files that matches the
# Output filename.
make_c1m_link(os.path.abspath(filename))
import cosmics
import pyfits as pf
import sys
fn = sys.argv[1]
FF = pf.open(fn)
if FF[0].header['ADCSPEED'] == 2: RN = 21
else: RN = 5
c = cosmics.cosmicsimage(FF[0].data, gain=1, readnoise=RN, sigclip = 5,
sigfrac=0.5, objlim=4.0)
c.run(maxiter=4, verbose=True)
FF[0].data = c.cleanarray
FF[0].header["CRR"] = ("Processed", "With cosmics.py 0.4")
import os
outn = os.path.join(os.path.dirname(fn), "crr_" + os.path.basename(fn))
FF.writeto(outn)
# We read array and header of that fits file :
(a, h) = cosmics.fromfits("g.fits", verbose=False)
# We gather some parameters :
pssl = image['skylevel'] # The Previously Subtracted Sky Level
print "PSSL (TM): %.2f" % pssl
gain = image['gain']
readnoise = image['readnoise']
sigclip = cosmicssigclip
sigfrac = 0.3
objlim = 3.0
# Creating the object :
c = cosmics.cosmicsimage(a, pssl=pssl, gain=gain, readnoise=readnoise, sigclip=sigclip, sigfrac=sigfrac, objlim=objlim, satlevel=-1.0, verbose=False)
# negative satlevel : we do not look for saturated stars
# Ok let's try a full run :
c.run(maxiter=3)
ncosmics = np.sum(c.mask)
if ncosmics != 0:
print "--- %i pixels ---" % ncosmics
# We do the rest anyway (easier):
# We write the mask :
cosmics.tofits("cosmicsmask.fits", c.getdilatedmask(size=5), verbose=False)
# The following two lines are only needed as cosmic.py is not in this directory nor in the python path.
# They would not be required if you copy cosmics.py in this directory.
import sys
import os
import pyfits
sys.path.append("../.") # The directory that contains cosmic.py
import cosmics
# File information
path = '/home/bquint/Data/20151203/012'
name = 'xjfp_sami_C001.602.fits'
filename = os.path.join(path, name)
# Read the FITS :
array = pyfits.getdata(filename)
# Build the object :
c = cosmics.cosmicsimage(array, gain=2.1, readnoise=10.0, sigclip = 3.0, sigfrac = 0.3, objlim = 5.0)
# There are other options, check the manual...
# Run the full artillery :
c.run(maxiter = 4)
array = c.cleanarray
pyfits.writeto(filename.replace('.fits', '_crclean.fits'), array, clobber=True)
def cr_clean(fits_fn):
det = pyfits.getval(fits_fn, 'DETECTOR')
if det == 'IR':
print('CR-cleaning IR FLT file: %s' % fits_fn)
readnoise = 20.0 # for WFC3/IR
#sigclip = 5.0
sigclip = 5.0
sigfrac = 0.3 # small means crs are grown more 0.3 is good!
#objlim = 5.0
objlim = 12.0
#maxiter = 4
maxiter = 3
exptime = pyfits.getval(fits_fn, 'EXPTIME')
gain = exptime
(image, header) = cosmics.fromfits(fits_fn)
c = cosmics.cosmicsimage(image, gain=gain, readnoise=readnoise, sigclip=sigclip, sigfrac=sigfrac, objlim=objlim)
c.run(maxiter=maxiter)
clean_fn = fits_fn.replace('.fits', '_crclean.fits')
mask_fn = fits_fn.replace('.fits', '_crmask.fits')
fo = pyfits.open(fits_fn)
fo[1].data = c.cleanarray
fo.writeto(clean_fn)
fo.close()
#cosmics.tofits(clean_fn, c.cleanarray, header)
cosmics.tofits(mask_fn, c.mask, header)
if det == 'UVIS':
print('CR-cleaning UVIS FLT file: %s' % fits_fn)
readnoise = 3.105 # for WFC3/UVIS
# good
#sigclip = 5.0
#sigfrac = 0.1 # small means crs are grown more 0.3 is good!
#objlim = 5.0
# some residuals
#sigclip = 4.5
#sigfrac = 0.1 # small means crs are grown more 0.3 is good!
#objlim = 4.0
sigclip = 4.5
sigfrac = 0.1 # small means crs are grown more 0.3 is good!
objlim = 2.0
maxiter = 4
exptime = pyfits.getval(fits_fn, 'EXPTIME')
gain = 1.0
ff = pyfits.open(fits_fn)
header = ff[0].header
image = ff[1].data
#(image, header) = cosmics.fromfits(fits_fn)
c = cosmics.cosmicsimage(image, gain=gain, readnoise=readnoise, sigclip=sigclip, sigfrac=sigfrac, objlim=objlim)
c.run(maxiter=maxiter)
image2 = ff[4].data
d = cosmics.cosmicsimage(image2, gain=gain, readnoise=readnoise, sigclip=sigclip, sigfrac=sigfrac, objlim=objlim)
d.run(maxiter=maxiter)
clean_fn = fits_fn.replace('.fits', '_crclean.fits')
mask_fn = fits_fn.replace('.fits', '_crmask.fits')
fo = pyfits.open(fits_fn)
fo[1].data = c.cleanarray
fo[4].data = d.cleanarray
fo.writeto(clean_fn)
fo.close()
#cosmics.tofits(clean_fn, c.cleanarray, header)
cosmics.tofits(mask_fn, c.mask, header)
def whirc_reduce(obj_id):
import pyfits
import numpy as np
import matplotlib.pyplot as plt
import cosmics
import astropy.coordinates as coord
import astropy.units as u
#READ IN DATA AND OBSERVING LOG
#Read in the bias file to be subtracted from flats
bias = pyfits.getdata("Calibs/bias.fits")
bias = bias[0:2048,0:2048]
#Read in the dark file to be subtracted from flats
dark = pyfits.getdata("Calibs/dark.fits")
dark = dark[0:2048,0:2048]
#Read in flats
J_flat = pyfits.getdata('Calibs/New_J_Flat.fits')
K_flat = pyfits.getdata('Calibs/New_K_Flat.fits')
#Read in data from "whirc_info.dat" (the observation log)
im1 = open('whirc_info.dat','r')
data1 = im1.readlines()
im1.close()
filename = []
dateobs = []
objname = []
imgtype = []
ra = []
dec = []
exptime = []
usable = []
rotangle = []
raoffset = []
decoffset = []
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])
rotangle.append(p[8])
raoffset.append(p[9])
decoffset.append(p[10])
#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],rotangle[line],raoffset[line],decoffset[line]])
#Find junk files and good files:
junk = []
good = []
for line in range(len(alldata)):
if "no" in alldata[line][7]:
junk.append(alldata[line])
if "yes" in alldata[line][7]:
good.append(alldata[line])
#Find files related to obj_id in good files
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)
#Find files through J_filter
aobj_idJ = []
for line in range(len(aobj_id)):
if "J" in aobj_id[line][2]:
aobj_idJ.append(aobj_id[line])
print str(len(aobj_idJ)), "J files for "+str(obj_id)
#Find files through K_filter
aobj_idK = []
for line in range(len(aobj_id)):
if "K" in aobj_id[line][2]:
aobj_idK.append(aobj_id[line])
print str(len(aobj_idK)), "K files for "+str(obj_id)
#Find J files from Night 1
aobj_idJN1 = []
for line in range(len(aobj_idJ)):
if "N1" in aobj_idJ[line][0]:
aobj_idJN1.append(aobj_idJ[line])
print str(len(aobj_idJN1)), "J files in N1"
#Fink K files from Night 1
aobj_idKN1 = []
for line in range(len(aobj_idK)):
if "N1" in aobj_idK[line][0]:
aobj_idKN1.append(aobj_idK[line])
print str(len(aobj_idKN1)), "K files in N1"
#Find J files from Night 2
aobj_idJN2 = []
for line in range(len(aobj_idJ)):
if "N2" in aobj_idJ[line][0]:
aobj_idJN2.append(aobj_idJ[line])
print str(len(aobj_idJN2)), "J files in N2"
#Find K files from Night 2
aobj_idKN2 = []
for line in range(len(aobj_idK)):
if "N2" in aobj_idK[line][0]:
aobj_idKN2.append(aobj_idK[line])
print str(len(aobj_idKN2)), "K files in N2"
#Find J files from Night 3
aobj_idJN3 = []
for line in range(len(aobj_idJ)):
if "N3" in aobj_idJ[line][0]:
aobj_idJN3.append(aobj_idJ[line])
print str(len(aobj_idJN3)), "J files in N3"
#Find K files from Night 3
aobj_idKN3 = []
for line in range(len(aobj_idK)):
if "N3" in aobj_idK[line][0]:
aobj_idKN3.append(aobj_idK[line])
print str(len(aobj_idKN3)), "K files in N3"
#WRITE PATH TO J_FILES_N1
Jobj_idN1_locs = []
for line in range(len(aobj_idJN1)):
Jobj_idN1_locs.append("Raw/"+str(aobj_idJN1[line][0]))
#WRITE PATH TO K_FILES_N1
Kobj_idN1_locs = []
for line in range(len(aobj_idKN1)):
Kobj_idN1_locs.append("Raw/"+str(aobj_idKN1[line][0]))
#WRITE PATH OF J_FILES_N2
Jobj_idN2_locs = []
for line in range(len(aobj_idJN2)):
Jobj_idN2_locs.append("Raw/"+str(aobj_idJN2[line][0]))
#WRITE PATH OF K_FILES_N2
Kobj_idN2_locs = []
for line in range(len(aobj_idKN2)):
Kobj_idN2_locs.append("Raw/"+str(aobj_idKN2[line][0]))
#WRITE PATH OF J_FILES_N3
Jobj_idN3_locs = []
for line in range(len(aobj_idJN3)):
Jobj_idN3_locs.append("Raw/"+str(aobj_idJN3[line][0]))
#WRITE PATH OF K_FILES_N3
Kobj_idN3_locs = []
for line in range(len(aobj_idKN3)):
Kobj_idN3_locs.append("Raw/"+str(aobj_idKN3[line][0]))
#READ IN SKY FILES
#READ IN SCIENCE EXPOSURES
#J_filter, Night 1
print "reading in JN1..."
if len(aobj_idJN1) > 0:
skyJN1 = pyfits.getdata('Calibs/sky/sky_'+str(obj_id)+'J_N1.fits')
for line in range(len(aobj_idJN1)):
sci = pyfits.getdata(Jobj_idN1_locs[line])[0:2048,0:2048]
reduced_sci = (sci - bias - dark - skyJN1)/J_flat
file = pyfits.PrimaryHDU(reduced_sci)
file.writeto('Calibs/reduced/'+str(obj_id)+'_'+'JN1_'+str(line + 1)+'.fits', clobber = True)
array, header = cosmics.fromfits('Calibs/reduced/'+str(obj_id)+'_'+'JN1_'+str(line + 1)+'.fits')
c = cosmics.cosmicsimage(array)
c.run(maxiter = 1) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits('Calibs/reduced/'+str(obj_id)+'_'+'JN1_cos'+str(line + 1)+'.fits', c.cleanarray, header)
#K_filter, Night 1
print "reading in KN1..."
if len(aobj_idKN1) > 0:
skyKN1 = pyfits.getdata('Calibs/sky/sky_'+str(obj_id)+'K_N1.fits')
for line in range(len(aobj_idKN1)):
sci = pyfits.getdata(Kobj_idN1_locs[line])[0:2048,0:2048]
reduced_sci = (sci - bias - dark - skyKN1)/K_flat
file = pyfits.PrimaryHDU(reduced_sci)
file.writeto('Calibs/reduced/'+str(obj_id)+'_'+'KN1_'+str(line + 1)+'.fits', clobber = True)
array, header = cosmics.fromfits('Calibs/reduced/'+str(obj_id)+'_'+'KN1_'+str(line + 1)+'.fits')
c = cosmics.cosmicsimage(array)
c.run(maxiter = 1) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits('Calibs/reduced/'+str(obj_id)+'_'+'KN1_cos'+str(line + 1)+'.fits', c.cleanarray, header)
#J_filter, Night 2
print "reading in JN2..."
if len(aobj_idJN2) > 0:
skyJN2 = pyfits.getdata('Calibs/sky/sky_'+str(obj_id)+'J_N2.fits')
for line in range(len(aobj_idJN2)):
sci = pyfits.getdata(Jobj_idN2_locs[line])[0:2048,0:2048]
reduced_sci = (sci - bias - dark - skyJN2)/J_flat
file = pyfits.PrimaryHDU(reduced_sci)
file.writeto('Calibs/reduced/'+str(obj_id)+'_'+'JN2_'+str(line + 1)+'.fits', clobber = True)
array, header = cosmics.fromfits('Calibs/reduced/'+str(obj_id)+'_'+'JN2_'+str(line + 1)+'.fits')
c = cosmics.cosmicsimage(array)
c.run(maxiter = 1) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits('Calibs/reduced/'+str(obj_id)+'_'+'JN2_cos'+str(line + 1)+'.fits', c.cleanarray, header)
#K_filter, Night 2
print "reading in KN2..."
if len(aobj_idKN2) > 0:
skyKN2 = pyfits.getdata('Calibs/sky/sky_'+str(obj_id)+'K_N2.fits')
for line in range(len(aobj_idKN2)):
sci = pyfits.getdata(Kobj_idN2_locs[line])[0:2048,0:2048]
reduced_sci = (sci - bias - dark - skyKN2)/K_flat
file = pyfits.PrimaryHDU(reduced_sci)
file.writeto('Calibs/reduced/'+str(obj_id)+'_'+'KN2_'+str(line + 1)+'.fits', clobber = True)
array, header = cosmics.fromfits('Calibs/reduced/'+str(obj_id)+'_'+'KN2_'+str(line + 1)+'.fits')
c = cosmics.cosmicsimage(array)
c.run(maxiter = 1) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits('Calibs/reduced/'+str(obj_id)+'_'+'KN2_cos'+str(line + 1)+'.fits', c.cleanarray, header)
#J_filter, Night 3
print "reading in JN3..."
if len(aobj_idJN3) > 0:
skyJN3 = pyfits.getdata('Calibs/sky/sky_'+str(obj_id)+'J_N3.fits')
for line in range(len(aobj_idJN3)):
sci = pyfits.getdata(Jobj_idN3_locs[line])[0:2048,0:2048]
reduced_sci = (sci - bias - dark - skyJN3)/J_flat
file = pyfits.PrimaryHDU(reduced_sci)
file.writeto('Calibs/reduced/'+str(obj_id)+'_'+'JN3_'+str(line + 1)+'.fits', clobber = True)
array, header = cosmics.fromfits('Calibs/reduced/'+str(obj_id)+'_'+'JN3_'+str(line + 1)+'.fits')
c = cosmics.cosmicsimage(array)
c.run(maxiter = 1) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits('Calibs/reduced/'+str(obj_id)+'_'+'JN3_cos'+str(line + 1)+'.fits', c.cleanarray, header)
if len(aobj_idKN3) > 0:
skyKN3 = pyfits.getdata('Calibs/sky/sky_'+str(obj_id)+'K_N3.fits')
for line in range(len(aobj_idKN3)):
sci = pyfits.getdata(Kobj_idN3_locs[line])[0:2048,0:2048]
reduced_sci = (sci - bias - dark - skyKN3)/K_flat
file = pyfits.PrimaryHDU(reduced_sci)
file.writeto('Calibs/reduced/'+str(obj_id)+'_'+'KN3_'+str(line + 1)+'.fits', clobber = True)
array, header = cosmics.fromfits('Calibs/reduced/'+str(obj_id)+'_'+'KN3_'+str(line + 1)+'.fits')
c = cosmics.cosmicsimage(array)
c.run(maxiter = 1) # can increase up to 4 to improve precision, but takes longer
cosmics.tofits('Calibs/reduced/'+str(obj_id)+'_'+'KN3_cos'+str(line + 1)+'.fits', c.cleanarray, header)
#!/usr/bin/python
from dbauth import *
import cosmics
import os
file_name = 'a.fits'
# some comments on params are here http://obswww.unige.ch/~tewes/cosmics_dot_py/
pssl = 0.0
ccd_gain = 2.687
ccd_readnoise = 8.14
sigclip = 5.0
sigfrac = 0.2
objlim = 3.0
satlevel = 30000
maxiter = 10
verbose = True
(array, header) = cosmics.fromfits(file_name, verbose=verbose)
c = cosmics.cosmicsimage(array, pssl=pssl, gain=ccd_gain, readnoise=ccd_readnoise,
sigclip=sigclip, sigfrac=sigfrac, objlim=objlim,
satlevel=satlevel, verbose=verbose)
c.run(maxiter=maxiter, verbose=verbose)
#cleaned_file_name = "a_crcl.fits"
#cosmics.tofits(cleaned_file_name, c.cleanarray, header, verbose=verbose)
#c.make_plots('/disk/Work/Cosmic_rays/Code/CosmicRayExtractor/RP','a_crcl.png')
def prep():
import glob
import os
import threedhst
import threedhst.prep_flt_astrodrizzle as init
from threedhst import catIO
import unicorn
import research.hawkiff
### Make ACS associations
unicorn.candels.make_asn_files(uniquename=True, translate={'-ROT': ''})
threedhst.options['FLT_PERSISTENCE_PATH'] = '../Persistence/'
#### IR
files = glob.glob('*-F1*asn.fits')
radec = None
for asn_file in files:
init.prep_direct_grism_pair(
direct_asn=asn_file,
grism_asn=None,
radec=radec,
ACS=False,
align_threshold=5,
raw_path='../RAW/',
order=0)
###
for asn_file in files:
drizzlepac.astrodrizzle.AstroDrizzle(
asn_file,
static=False,
skysub=False,
driz_separate=False,
driz_sep_wcs=False,
median=False,
blot=False,
driz_cr=False,
driz_combine=True,
final_wht_type='IVM',
clean=True,
final_wcs=True,
final_refimage='IRAS23436+5257-87-304-F814W_drc_sci.fits',
final_pixfrac=1,
context=False,
resetbits=0,
final_bits=576,
preserve=False)
### Match WCS of NB observations
init.copy_adriz_headerlets(
glob.glob('*-F1*[MW]*asn.fits')[0],
glob.glob('*-F1*[N]*asn.fits')[0],
order=[1, 0])
### Flag negative pixels
for asn_file in files:
asn = threedhst.utils.ASNFile(asn_file)
for exp in asn.exposures:
im = pyfits.open('%s_flt.fits' % (exp), mode='update')
neg = im['SCI'].data < -3 * im['ERR'].data
print '%s #negative = %d' % (exp, neg.sum())
im['DQ'].data[neg] |= 4096
im.flush()
#
drizzlepac.astrodrizzle.AstroDrizzle(
asn_file,
skysub=True,
static=True,
driz_separate=True,
driz_sep_wcs=True,
median=True,
blot=True,
driz_cr=True,
driz_combine=True,
clean=True,
final_wcs=True,
final_scale=0.1,
final_rot=0,
final_pixfrac=1,
context=False,
resetbits=0,
final_bits=64,
preserve=False)
sci = pyfits.open(asn_file.replace('_asn', '_drz_sci'), mode='update')
wht = pyfits.open(asn_file.replace('_asn', '_drz_wht'))
sci[0].data[wht[0].data == 0] = 0
sci.flush()
#### ACS / UVIS
files = glob.glob('*-F6*asn.fits')
files = glob.glob('*-F[48]*asn.fits')
files.sort()
for asn_file in files[::-1]:
asn = threedhst.utils.ASNFile(asn_file)
for i in range(len(asn.exposures)):
asn.exposures[i] = asn.exposures[i].split('_flc')[0]
print asn_file, asn.exposures
asn.write(asn_file)
if ('814' in asn_file) | ('625' in asn_file):
radec = None
else:
root = glob.glob('*-F[86]*W*sci.fits')[0].split('_sci')[0]
if not os.path.exists('%s_sci.cat' % (root)):
os.system(
'cp /user/brammer/software/share/gauss_5.0_9x9.conv .')
research.hawkiff.make_catalog(
root=root,
sci_ext='sci',
threshold=3,
use_rms=False,
subtract_background=True)
cat = catIO.Table('%s_sci.cat' % (root))
cat['X_WORLD', 'Y_WORLD'].write(
'ref_opt.radec', format='ascii.commented_header')
radec = 'ref_opt.radec'
radec = None
init.prep_direct_grism_pair(
direct_asn=asn_file,
grism_asn=None,
radec=radec,
ACS=True,
align_threshold=5,
raw_path='../RAW/')
#
for asn_file in files:
radec = None
init.prep_direct_grism_pair(
direct_asn=asn_file,
grism_asn=None,
radec=radec,
ACS=True,
align_threshold=5,
raw_path='../RAW/')
files = glob.glob('*sci.fits')
ims = {}
for file in files:
im = pyfits.open(file)
filter = file.split('-')[-1][:5]
pix = np.sqrt(im[0].header['CD1_1']**2 +
im[0].header['CD1_2']**2) * 3600
scl = im[0].header['PHOTFLAM'] / 1.e-19 * (0.1 / pix)**2
im[0].data *= scl
ims[filter] = im
### Try Multidrizzle CR with combined image
import drizzlepac
asn_files = glob.glob('*-F[4-8]*asn.fits')
exposures = []
for file in asn_files:
asn = threedhst.utils.ASNFile(file)
exposures.extend(asn.exposures)
target = '-'.join(file.split('-')[:-1])
asn.exposures = exposures
asn.product = target
asn_file = '%s_asn.fits' % (asn.product)
asn.write(asn_file, clobber=True)
### modify: make copy of FLCs, make new mosaic and then copy FLCs back
##xxx
drizzlepac.astrodrizzle.AstroDrizzle(
asn_file,
skysub=False,
clean=True,
final_wcs=True,
final_scale=0.05,
final_pixfrac=0.8,
context=False,
resetbits=4096,
final_bits=576,
preserve=False)
#
bits = 576
drizzlepac.astrodrizzle.AstroDrizzle(
asn_file,
static=False,
skysub=False,
driz_separate=False,
driz_sep_wcs=False,
median=False,
blot=False,
driz_cr=False,
driz_combine=True,
final_wht_type='IVM',
clean=True,
final_wcs=True,
final_rot=0,
final_scale=0.1,
final_pixfrac=1,
context=False,
resetbits=0,
final_bits=bits,
preserve=False)
#### rerun final mosaics
refimage = '%s_drz_sci.fits' % (target)
for file in asn_files:
drizzlepac.astrodrizzle.AstroDrizzle(
file,
static=False,
skysub=False,
driz_separate=False,
driz_sep_wcs=False,
median=False,
blot=False,
driz_cr=False,
driz_combine=True,
final_wht_type='IVM',
clean=True,
final_wcs=True,
final_refimage=refimage,
final_pixfrac=1,
context=False,
resetbits=0,
final_bits=bits,
preserve=False)
for file in asn_files:
print file
sci = pyfits.open(
glob.glob(file.replace('_asn', '_dr?_sci'))[0], mode='update')
wht = pyfits.open(sci.filename().replace('_sci', '_wht'))
sci[0].data[wht[0].data == 0] = 0
sci.flush()
#### Try own driz CR
import stwcs
from drizzlepac import astrodrizzle, quickDeriv
import scipy.ndimage as nd
ref_file = 'ESO550-IG02-13-083-F435W_drc_sci.fits'
ref_file = 'ESO550-IG02-13-083-F814W_drc_sci.fits'
ref_file = 'ESO550-IG02-13-083_drc_sci.fits'
ref_ext = 0
ref = pyfits.open(ref_file)
ref_wcs = stwcs.wcsutil.HSTWCS(ref, ext=ref_ext)
asn_file = 'ESO550-IG02-13-083-F814W_asn.fits'
asn = threedhst.utils.ASNFile(asn_file)
for exp in asn.exposures:
flt = pyfits.open('%s_flc.fits' % (exp)) #, mode='update')
for ext in [1, 2]:
flt_wcs = stwcs.wcsutil.HSTWCS(flt, ext=('sci', ext))
blotted_ref = astrodrizzle.ablot.do_blot(
ref[ref_ext].data,
ref_wcs,
flt_wcs,
1,
coeffs=True,
interp='nearest',
sinscl=1.0,
stepsize=10,
wcsmap=None)
#
blotDeriv = quickDeriv.qderiv(blotted_ref)
scl = flt['sci', ext].header['PHOTFLAM'] / ref[0].header[
'PHOTFLAM'] / flt[0].header['EXPTIME']
noise = flt['err', ext].data
driz_scale = 1.2
driz_snr = 3.5
driz_scale = 1.0
driz_snr = 6
driz_f = 2
# |data_image - blotted_image| > scale x deriv + SNR x noise
abs = np.abs(flt['sci', ext].data * scl - blotted_ref * driz_f)
xderiv = driz_scale * blotDeriv
xsnr = driz_snr * scl * noise
mask = abs > (xderiv + xsnr)
new_dq = mask & ((flt['dq', ext].data & 4096) == 0)
### Try "cosmics" LA Cosmic
im = pyfits.open('ESO550-IG02-13-083-F814W_drc_sci.fits')
slx, sly = slice(1500, 2600), slice(1800, 2800)
ext = 0
subim = im[ext].data[sly, slx]
h = im[ext].header
subim *= im[0].header['EXPTIME']
#### FLT
im = pyfits.open('j9cv13pcq_flc.fits')
ext = 0
slx, sly = slice(1500, 2600), slice(0, 400)
subim = im[4].data[sly, slx]
h = im[0].header
import cosmics
c = cosmics.cosmicsimage(
subim,
pssl=20.0,
gain=1,
readnoise=h['READNSEA'],
sigclip=4.0,
sigfrac=0.3,
objlim=3.8,
satlevel=84700.0,
verbose=True)
c.run(maxiter=4)
crflux = subim * c.mask
plt.hist(np.log10(crflux[c.mask]), range=[1, 6], bins=100, alpha=0.5)
####
import research.pab.pab
files = glob.glob('*-F*asn.fits')
#files=glob.glob('IC*-F814*asn.fits')
for file in files:
asn = threedhst.utils.ASNFile(file)
for exp in asn.exposures:
flc = pyfits.open('%s_flc.fits' % (exp))
research.pab.pab.run_lacosmic(
'%s_flc.fits' % (exp),
split=2048,
sigclip=5,
pssl=flc[1].header['MDRIZSK0'])
#
drizzlepac.updatehdr.update_from_shiftfile(
file.replace('asn.fits', 'shifts.txt'))
drizzlepac.astrodrizzle.AstroDrizzle(
file,
skysub=True,
clean=True,
final_wcs=True,
final_scale=0.05,
final_pixfrac=0.8,
context=False,
resetbits=0,
final_bits=576,
preserve=False)
drizzlepac.astrodrizzle.AstroDrizzle(
file,
output='sub_' + file.split('_asn')[0],
skysub=True,
static=True,
driz_separate=False,
driz_sep_wcs=False,
median=False,
blot=False,
driz_cr=False,
driz_combine=True,
clean=True,
final_wcs=True,
final_scale=0.08,
final_ra=60.635233,
final_dec=60.344597,
final_outnx=800,
final_outny=800,
final_rot=0,
final_pixfrac=0.8,
context=False,
resetbits=0,
final_bits=576,
preserve=False)
### Final mosaics
f110w = glob.glob('*110W*asn.fits')[0]
drizzlepac.astrodrizzle.AstroDrizzle(
f110w,
output='sub_' + f110w.split('_asn')[0],
skysub=True,
static=True,
driz_separate=False,
driz_sep_wcs=False,
median=False,
blot=False,
driz_cr=False,
driz_combine=True,
clean=True,
final_wcs=True,
final_scale=0.1,
final_rot=0,
final_pixfrac=1,
context=False,
resetbits=0,
final_bits=576 - 512,
preserve=False)
files = glob.glob('*-F*asn.fits')
for file in files:
if 'F110W' in file:
continue
#
drizzlepac.astrodrizzle.AstroDrizzle(
file,
output='sub_' + file.split('_asn')[0],
skysub=True,
static=True,
driz_separate=False,
driz_sep_wcs=False,
median=False,
blot=False,
driz_cr=False,
driz_combine=True,
clean=True,
final_wcs=True,
final_refimage='sub_' + f110w.replace('_asn', '_drz_sci'),
final_pixfrac=1,
context=False,
resetbits=0,
final_bits=576 - 512 * ('-F1' in file),
preserve=False)
for sci_im in glob.glob('sub*sci.fits'):
print sci_im
sci = pyfits.open(sci_im, mode='update')
wht = pyfits.open(sci_im.replace('_sci', '_wht'))
sci[0].data[wht[0].data == 0] = 0
sci.flush()
#### Test alignment
import align
ra_list, de_list = np.loadtxt('f814w.radec', unpack=True)
for geom in ['shift', 'shift', 'rxyscale', 'shift', 'shift']:
align.get_align_to_subaru(
sci='sub_ESO550-IG025-41-115-F673N_drc_sci.fits',
wht='sub_ESO550-IG025-41-115-F673N_drc_wht.fits',
verbose=False,
fitgeometry=geom,
align_data=(ra_list, de_list, ra_list * 0),
THRESH=1.2)
#drizzlepac.tweakback.tweakback('ESO550-IG025-41-115-F673N_drc_sci.fits', force=True, verbose=True) #, origwcs='DRZWCS')
ra_list, de_list = np.loadtxt('f110w.radec', unpack=True)
for geom in ['shift', 'shift', 'rxyscale', 'shift', 'shift']:
align.get_align_to_subaru(
sci='sub_ESO550-IG025-41-115-F132N_drz_sci.fits',
wht='sub_ESO550-IG025-41-115-F132N_drz_wht.fits',
verbose=False,
fitgeometry=geom,
align_data=(ra_list, de_list, ra_list * 0),
THRESH=1.2)
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)
def reduce(self, overwrite=False):
""" Reduce data from this pointing.
"""
# solve for wavelength at every pixel
science_wvln = self.wavelength_image
ccd = self.night.observing_run.ccd
n_flat = self.night.make_normalized_flat()
path = self.night.redux_path
for fn in self.file_paths:
_data_fn = os.path.splitext(os.path.split(fn)[1])[0]
data_fn = os.path.join(path, "{0}_2d.fit".format(_data_fn))
self._data_file_paths[fn] = data_fn
if os.path.exists(data_fn) and overwrite:
os.remove(data_fn)
if os.path.exists(data_fn):
continue
# subtract bias
hdu = fits.open(fn)[0]
frame_data = hdu.data
hdr = hdu.header
frame_data = ccd.zero_correct_frame(frame_data,
self.night.master_zero)
#TODO: frame.inverse_variance # automatically created?
variance_image = frame_data*ccd.gain + ccd.read_noise**2
inv_var = 1./variance_image
# divide by master normalized flat
frame_data /= n_flat
inv_var *= n_flat**2
# if the exposure was more than 60 seconds, run this cosmic
# ray flagger on the CCD data
if hdr['EXPTIME'] > 60:
c = cosmics.cosmicsimage(frame_data, gain=ccd.gain,
readnoise=ccd.read_noise,
sigclip=8.0, sigfrac=0.5,
objlim=10.0)
#c.run(maxiter=6)
#frame_data = c.cleanarray
# if exposure time > 60 seconds, do sky subtraction
if hdr['EXPTIME'] > 60:
pass
# extract only the science data region
science_data = frame_data[ccd.regions["science"]]
science_inv_var = inv_var[ccd.regions["science"]]
# write this back out as a 2D image
new_hdu0 = fits.PrimaryHDU(science_data, header=hdr)
new_hdu1 = fits.ImageHDU(science_wvln,
header=fits.Header([("NAME","wavelength")]))
new_hdu2 = fits.ImageHDU(science_inv_var,
header=fits.Header([("NAME","inverse variance")]))
hdul = fits.HDUList([new_hdu0,new_hdu1,new_hdu2])
hdul.writeto(data_fn)