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Fixed bugs in OptExtract; good progress in making repository Python 3 compatible #20

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Fixed bugs in OptExtract; good progress in making repository Python 3 compatible #20

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3f07de5
Making Marsh.c Python 3 compatible
Mar 24, 2019
e362167
Auto-indenting Marsh.c
Mar 24, 2019
27cb4e2
Fixed bugs in code & documentation of Marsh.c
Mar 24, 2019
68cadf0
Running reindent on all Python files to eliminate mixing between tabs…
Mar 24, 2019
a95d3a2
Using 2to3 to make print statements compatible with both Python 2 and 3
Mar 24, 2019
42775c2
Better OptExtract instructions
Mar 24, 2019
0dc2cfc
Continuum C files are now Python 3 compatible
Mar 25, 2019
813ad55
StringIO is now compatible with Python 3
Mar 25, 2019
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451 changes: 226 additions & 225 deletions arces/arcespipe.py
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147 changes: 74 additions & 73 deletions arces/arcesutils.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
from __future__ import print_function
from astropy.io import fits as pyfits
import scipy
import glob
Expand All @@ -12,7 +13,7 @@

def FileClassify(dir, log,binning):
"""

Classifies all files in a directory and writes a night log of science images

"""
Expand All @@ -31,46 +32,46 @@ def FileClassify(dir, log,binning):

bad_files = []
if os.access(dir+'bad_files.txt',os.F_OK):
bf = open(dir+'bad_files.txt')
linesbf = bf.readlines()
for line in linesbf:
bad_files.append(dir+line[:-1])
bf.close()
bf = open(dir+'bad_files.txt')
linesbf = bf.readlines()
for line in linesbf:
bad_files.append(dir+line[:-1])
bf.close()

all_files = glob.glob(dir+"/*fits")
for archivo in all_files:
dump = False
for bf in bad_files:
if archivo == bf:
dump = True
break
h = pyfits.open(archivo)
if dump == False and h[0].header['INSTRUME'] == 'echelle':
if h[0].header['IMAGETYP'] == 'object':
science.append(archivo)
obname = h[0].header['OBJNAME']
ra = h[0].header['RA']
delta = h[0].header['DEC']
airmass = h[0].header['AIRMASS']
texp = h[0].header['EXPTIME']
date = h[0].header['DATE-OBS'][:10]
hour = h[0].header['DATE-OBS'][11:]
obnames.append( obname )
exptimes.append( texp )
line = "%-15s %10s %10s %8.2f %4.2f %8s %8s %s\n" % (obname, ra, delta, texp, airmass, date, hour, archivo)
f.write(line)
elif h[0].header['IMAGETYP'] == 'zero':
biases.append(archivo)
elif h[0].header['IMAGETYP'] == 'flat':
if h[0].header['FILTER'] == 'Blue':
quartzB.append(archivo)
elif h[0].header['FILTER'] == 'Open':
quartzR.append(archivo)
elif h[0].header['IMAGETYP'] == 'comp':
thars.append(archivo)
mjd, mjd0 = mjd_fromheader(h)
thar_dates.append( mjd )
dump = False
for bf in bad_files:
if archivo == bf:
dump = True
break
h = pyfits.open(archivo)
if dump == False and h[0].header['INSTRUME'] == 'echelle':
if h[0].header['IMAGETYP'] == 'object':
science.append(archivo)
obname = h[0].header['OBJNAME']
ra = h[0].header['RA']
delta = h[0].header['DEC']
airmass = h[0].header['AIRMASS']
texp = h[0].header['EXPTIME']
date = h[0].header['DATE-OBS'][:10]
hour = h[0].header['DATE-OBS'][11:]
obnames.append( obname )
exptimes.append( texp )
line = "%-15s %10s %10s %8.2f %4.2f %8s %8s %s\n" % (obname, ra, delta, texp, airmass, date, hour, archivo)
f.write(line)
elif h[0].header['IMAGETYP'] == 'zero':
biases.append(archivo)
elif h[0].header['IMAGETYP'] == 'flat':
if h[0].header['FILTER'] == 'Blue':
quartzB.append(archivo)
elif h[0].header['FILTER'] == 'Open':
quartzR.append(archivo)
elif h[0].header['IMAGETYP'] == 'comp':
thars.append(archivo)
mjd, mjd0 = mjd_fromheader(h)
thar_dates.append( mjd )

f.close()

return biases, quartzB, quartzR, science, thars, thar_dates, obnames, exptimes
Expand All @@ -79,7 +80,7 @@ def mjd_fromheader(h):
"""
return modified Julian date from header
"""

datetu = h[0].header['DATE-OBS'][:10]
ut = h[0].header['DATE-OBS'][11:]
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[0:4]),int(datetu[5:7]),int(datetu[8:]))
Expand All @@ -93,62 +94,62 @@ def mjd_fromheader(h):
return mjd, mjd0

def bad_col_corr(dat):
mat = np.array([[80 ,81 ,199 ,2048],\
[240 ,241 ,530 ,2048],\
[469 ,470 ,1555,1666],\
[547 ,548 ,1690,1703],\
[632 ,634 ,1985,1962],\
[766 ,767 ,782 ,1980],\
[832 ,833 ,1850,1906],\
[960 ,961, 1590,1871],\
[1263,1264,1772,1816],\
[1334,1335,1454,2048],\
[1361,1362,1885,1924],\
[1395,1396,1906,1955],\
[1460,1461,488 ,2048],\
[1477,1478,758 ,2048],\
[1510,1511,771 ,2048],\
[1581,1582,1398,1763],\
[1629,1630,420 ,2048],\
[1661,1662,643 ,1980]])
for bd in mat:
new = .5*( dat[bd[2]:bd[3], bd[0]-1] + dat[bd[2]:bd[3],bd[1]] )
bci = bd[0]
while bci < bd[1]:
dat[ bd[2]:bd[3], bci ] = new
bci += 1
return dat
mat = np.array([[80 ,81 ,199 ,2048],\
[240 ,241 ,530 ,2048],\
[469 ,470 ,1555,1666],\
[547 ,548 ,1690,1703],\
[632 ,634 ,1985,1962],\
[766 ,767 ,782 ,1980],\
[832 ,833 ,1850,1906],\
[960 ,961, 1590,1871],\
[1263,1264,1772,1816],\
[1334,1335,1454,2048],\
[1361,1362,1885,1924],\
[1395,1396,1906,1955],\
[1460,1461,488 ,2048],\
[1477,1478,758 ,2048],\
[1510,1511,771 ,2048],\
[1581,1582,1398,1763],\
[1629,1630,420 ,2048],\
[1661,1662,643 ,1980]])
for bd in mat:
new = .5*( dat[bd[2]:bd[3], bd[0]-1] + dat[bd[2]:bd[3],bd[1]] )
bci = bd[0]
while bci < bd[1]:
dat[ bd[2]:bd[3], bci ] = new
bci += 1
return dat

def MedianCombine(ImgList, bias = 0.):
"""
Median combine a list of images
"""
n = len(ImgList)
if n == 0:
print "\t\tWarning: 0 biases"
return 0, 0, 0
print("\t\tWarning: 0 biases")
return 0, 0, 0
h = pyfits.open(ImgList[0])[0]
d = h.data
d = OverscanTrim(d)
d = bad_col_corr(d)
d -= bias

factor = 1.25
if (n < 3):
factor = 1

ronoise = factor * h.header['RDNOISE'] / np.sqrt(n)
gain = h.header['GAIN']


if (n == 1):
return d, ronoise, gain
else:
for i in range(n-1):
h = pyfits.open(ImgList[i+1])[0]
#print ImgList[i+1], h.data.shape
d2 = OverscanTrim(h.data)
d2 = bad_col_corr(d2)
#print ImgList[i+1], h.data.shape
d2 = OverscanTrim(h.data)
d2 = bad_col_corr(d2)
d = np.dstack( (d ,d2 - bias ))
return np.median(d,axis=2), ronoise, gain

Expand All @@ -161,7 +162,7 @@ def OverscanTrim(d):
newdata = d[20:,21:-59]
overscan = np.zeros(newdata.shape)
for i in range(overscan.shape[1]):
overscan[:,i] = ov
overscan[:,i] = ov

newdata = newdata - overscan
return newdata
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