-
Notifications
You must be signed in to change notification settings - Fork 4
/
new_reduction.py
146 lines (121 loc) · 4.86 KB
/
new_reduction.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
import numpy as np
import ccdproc,os
from astropy import units as u
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.time import Time
from glob import glob
#from astroquery.astrometry_net import AstrometryNet
from astropy.wcs import WCS
import astroalign as aa
data_path=''
result_path=''
def clean_the_images(path,filename):
#ast=AstrometryNet()
#ast.api_key= 'iqmqwvazpvolmjmn'
dir = path
gain = 2 * u.electron / u.adu
readnoise = 7.5 * u.electron
ra=input('Enter the RA of the source: ')
dec=input('Enter the DEC of the source: ')
'''
wcs_header=ast.solve_from_image(path+filename)
wcs=WCS(wcs_header)
ran,decn=wcs.all_pix2world(1024,1024,0)
print(ran,decn)
'''
file_name = os.path.join(dir,filename)
image=ccdproc.CCDData.read(file_name,unit='adu')
header=fits.getheader(file_name,0)
time=header['DATE']
t=Time(time,format='isot',scale='utc')
print(t.jd,t.mjd)
header.insert(15,('RA',ra))
header.insert(16,('DEC',dec))
a = sorted(glob(os.path.join(dir,'bias*.fits')))
biaslist = []
for i in range (0,len(a)):
data= ccdproc.CCDData.read(a[i],unit='adu')
#data = ccdproc.create_deviation(data, gain=gain, readnoise=readnoise)
#data= data-(data.uncertainty.array)
biaslist.append(data)
combiner = ccdproc.Combiner(biaslist)
masterbias = combiner.median_combine()
masterbias.write('masterbias.fit', overwrite=True)
mbias=ccdproc.CCDData.read('masterbias.fit',unit='adu')
#masterbias.meta=image.meta
print('master bias generated')
print(np.mean(masterbias), np.median(masterbias))
c=sorted(glob(os.path.join(dir,'flat*.fits')))
flatlist = []
for j in range(0,len(c)):
flat=ccdproc.CCDData.read(c[j],unit='adu')
#flat= ccdproc.create_deviation(flat, gain=gain, readnoise=readnoise)
flat=ccdproc.subtract_bias(flat,masterbias)
flatlist.append(flat)
combiner = ccdproc.Combiner(flatlist)
masterflat = combiner.median_combine()
masterflat.write('masterflat.fits', overwrite=True)
mflat=ccdproc.CCDData.read('masterflat.fits',unit='adu')
print('master flat generated')
print(np.mean(masterflat), np.median(masterflat))
#masterflat.meta=image.meta
bias_subtracted = ccdproc.subtract_bias(image, masterbias)
flat_corrected = ccdproc.flat_correct(bias_subtracted, masterflat)
cr_cleaned = ccdproc.cosmicray_lacosmic(flat_corrected,readnoise=7.5, sigclip=5)
print('cosmic ray removed')
fits.writeto(dir+'j_0947_i_1_clean.fits',cr_cleaned,header,overwrite=True)
print('image cleaned')
# To align multiple images with respect to one image we use the astroalign package.
def align_the_images(path,filename,ref_image):
nfiles=sorted(glob(path+filename))
image_data=fits.open(path+ref_image)
reference_image=image_data[0].data
for i in range(len(nfiles)):
image_data=fits.open(nfiles[i])
source_image=image_data[0].data
image_aligned,footprint=aa.register(source_image,reference_image)
fits.writeto(path+'j0947_corrected_%i.fits'%i,image_aligned,overwrite=True)
print('No. %i done'%i)
def time_to_jd(path,filename):
files=sorted(glob(os.path.join(dir,filename)))
nof=np.zeros(len(files))
for i in range(0,len(files)):
data=fits.open(files[i])
header=data[0].header
image=data[0].data
k=np.shape(image)
nof[i]=k[0]
check_header=header['ACQMODE']
if (check_header=='Single Scan'):
jd_up=image
time=header['DATE']
t=Time(time,format='isot',scale='utc')
time_jd=t.jd
header.insert(15,('JD',time_jd))
files[i]
mod_file_1=files[i].replace('.fits','')
fits.writeto(mod_file_1+'_sliced_'+'.fits',jd_up,header,overwrite=True)
#print(files[i],t.jd,t.mjd,'single scan image')
elif (check_header=='Kinetics'):
exposure=header['EXPOSURE']
print('kinetic mode image with no. of files:',files[i])
name_of_file=files[i]
mod_file=name_of_file.replace('.fits','')
time=header['DATE']
#print(time)
t=Time(time,format='isot',scale='utc')
tim=t.jd
temp=int(nof[i])
mod_jd=np.zeros(temp)
exp_time=header['EXPOSURE']
exp_time=exp_time/86400 # for the 'day' from seconds calculation.
mod_jd[0]=tim
for j in range(1,temp):
mod_jd[j]=mod_jd[j-1]+exp_time
for k in range(0,len(mod_jd)):
sliced_image=image[k]
time_jd=mod_jd[k]
header.insert(15,('JD',time_jd))
fits.writeto(mod_file+'_sliced_%g'%k+'.fits',sliced_image,header,overwrite=True)
print(mod_file+'_sliced_%g'%k+'.fits has been written')