def checkfile(filename):
'''Print statistics and run open imexamine task'''
iraf.imstatistics.unlearn()
iraf.imexamine.unlearn()
print 'Check output file:'
iraf.imstatistics(filename)
print ' Running "imexamine" task..'
iraf.imexamine(filename, 1)
def checkfile(filename):
'''Print statistics and run open imexamine task'''
iraf.imstatistics.unlearn()
iraf.imexamine.unlearn()
print 'Check output file:'
iraf.imstatistics(filename)
print ' Running "imexamine" task..'
iraf.imexamine(filename, 1)
def makeNirc2mask(dark, flat, outDir):
"""Make the static bad pixel mask for NIRC2. This only needs to be
run once. This creates a file called nirc2mask.fits which is
subsequently used throughout the pipeline. The dark should be a long
integration dark.
@param dark: The full absolute path to a medianed dark file. This is
used to construct a hot pixel mask (4 sigma detection thresh).
@type dark: str
@param flat: The full absolute path to a medianed flat file. This is
used to construct a dead pixel mask.
@type flat: str
@param outDir: full path to output directory with '/' at the end.
@type outDir: str
"""
_out = outDir + 'nirc2mask.fits'
_dark = dark
_flat = flat
util.rmall([_out])
# Make hot pixel mask
text_output = ir.imstatistics(_dark, fields="mean,stddev",
nclip=10, format=0, Stdout=1)
values = text_output[0].split()
hi = float(values[0]) + (15.0 * float(values[1]))
img_dk = pyfits.getdata(_dark)
hot = img_dk > hi
print 'Found %d hot pixels' % (hot.sum())
# Make dead pixel mask
text_output = ir.imstatistics(_flat, fields="mean,stddev",
nclip=10, format=0, Stdout=1)
values = text_output[0].split()
# Assuming flat is normalized, we don't want pixels with less
# than 0.5 sensitivity
#lo = float(values[0]) - (15.0 * float(values[1]))
lo = 0.5 #mask = hot
hi = float(values[0]) + (15.0 * float(values[1]))
img_fl = pyfits.getdata(_flat)
dead = logical_or(img_fl > hi, img_fl < lo)
print 'Found %d dead pixels' % (dead.sum())
# Combine into a final supermask
file = pyfits.open(_flat)
mask = hot + dead
mask = (mask != 0)
unmask = (mask == 0)
file[0].data[unmask] = 0
file[0].data[mask] = 1
file[0].writeto(_out, output_verify='silentfix')
def run_daofind(image, wht='NA', extension=0, outfile='default',dthreshold=3.0, fwhmpsf=2.5, backsigma=-1.0,rdnoise=-1.0):
'''RUN DAOFIND ON INPUT IMAGE'''
# Parse input parameters
if outfile == 'default': outfile = image+'0.coo.1'
# Read in fits header
f = pyfits.open(image)
fheader = f[0].header
# Extract relevant info from the header (exposure, filter, input/output pixel scale)
exptime = fheader['exptime']
instr = fheader['INSTRUME']
if instr == 'WFC3':
filter = fheader['FILTER']
else: #assuming ACS
filter = fheader['FILTER1']
if filter[0] == 'C': filter == fheader['FILTER2']
opxscl=0.03962
ipxscl=0.03962
f.close()
# Assign number of flt images (IR/calibration images only have 1 chip; NDRIZIM keyword includes both chips from single FLT)
if (fheader['detector'] == 'IR'): nchips = 1.0 # IR
elif (fheader['subarray'] == True) and (len(fheader['CCDAMP']) == 1): nchips = 1.0 # UVIS sub-array
elif (fheader['detector'] == 'UVIS') and (fheader['subarray'] == False): nchips = 2.0 # UVIS full-frame
else: raise exception('Image type is not defined.')
num_flts = 1.0
# Perform read noise correction
if rdnoise < 0.0:
amps = fheader['CCDAMP']
rdnoise = np.zeros(len(amps))
for namp in xrange(len(amps)): rdnoise[namp] = fheader['READNSE'+amps[namp]]
rdnoise_corr = np.sqrt(num_flts * (np.average(rdnoise) * opxscl/ipxscl)**2)
# Perform background noise calculation
if backsigma < 0.0:
backstats=iraf.imstatistics(image+'[1]', fields='stddev', lower = -100, upper = 100, nclip=5, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backsigma=float(backstats[0])
# remove old daofind files
file_query = os.access(outfile, os.R_OK)
if file_query == True: os.remove(outfile)
iraf.daofind.unlearn()
iraf.daofind(image=image+'[1]', interactive='no', verify='no',output=outfile, fwhmpsf=fwhmpsf, sigma=backsigma, \
readnoise=rdnoise_corr, itime=exptime, threshold=dthreshold, datamin=-10, datamax=100000)
# Display results of daofind (***WORK IN PROGRESS***)
#os.system('ds9&')
#tmp=image.split('_cnts')
#iraf.display(tmp[0]+'.fits',1, zscale='no', zrange='no', z1=0, z2=100,ztrans='log')
#iraf.tvmark(1,outfile,mark = 'circle', radii = 8, color = 205)
return outfile # return name of coordinate file
def run_daofind(image, wht='NA', extension=0, outfile='default',dthreshold=3.0, fwhmpsf=2.5, backsigma=-1.0,rdnoise=-1.0):
'''RUN DAOFIND ON INPUT IMAGE'''
# Parse input parameters
if outfile == 'default': outfile = image+'0.coo.1'
# Read in fits header
f = pyfits.open(image)
fheader = f[0].header
# Extract relevant info from the header (exposure, filter, input/output pixel scale)
exptime = fheader['exptime']
instr = fheader['INSTRUME']
if instr == 'WFC3':
filter = fheader['FILTER']
else: #assuming ACS
filter = fheader['FILTER1']
if filter[0] == 'C': filter == fheader['FILTER2']
opxscl=0.03962
ipxscl=0.03962
f.close()
# Assign number of flt images (IR/calibration images only have 1 chip; NDRIZIM keyword includes both chips from single FLT)
if (fheader['detector'] == 'IR'): nchips = 1.0 # IR
elif (fheader['subarray'] == True) and (len(fheader['CCDAMP']) == 1): nchips = 1.0 # UVIS sub-array
elif (fheader['detector'] == 'UVIS') and (fheader['subarray'] == False): nchips = 2.0 # UVIS full-frame
else: raise exception('Image type is not defined.')
num_flts = 1.0
# Perform read noise correction
if rdnoise < 0.0:
amps = fheader['CCDAMP']
rdnoise = np.zeros(len(amps))
for namp in xrange(len(amps)): rdnoise[namp] = fheader['READNSE'+amps[namp]]
rdnoise_corr = np.sqrt(num_flts * (np.average(rdnoise) * opxscl/ipxscl)**2)
# Perform background noise calculation
if backsigma < 0.0:
backstats=iraf.imstatistics(image+'[0]', fields='stddev', lower = -100, upper = 100, nclip=5, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backsigma=float(backstats[0])
# remove old daofind files
file_query = os.access(outfile, os.R_OK)
if file_query == True: os.remove(outfile)
iraf.daofind.unlearn()
iraf.daofind(image=image+'[0]', interactive='no', verify='no',output=outfile, fwhmpsf=fwhmpsf, sigma=backsigma, \
readnoise=rdnoise_corr, itime=exptime, threshold=dthreshold, datamin=-10, datamax=100000)
# Display results of daofind (***WORK IN PROGRESS***)
#os.system('ds9&')
#tmp=image.split('_cnts')
#iraf.display(tmp[0]+'.fits',1, zscale='no', zrange='no', z1=0, z2=100,ztrans='log')
#iraf.tvmark(1,outfile,mark = 'circle', radii = 8, color = 205)
return outfile # return name of coordinate file
def makeflats():
filters = ['J', '1113', '1184']
flatfiles = ['@FlatfilesJ', '@Flatfiles1113', '@Flatfiles1184']
for i in range(len(filters)):
filt = filters[i]
files = flatfiles[i]
#files='*'+filt+'*.fits'
flat = 'flat' + filt
iraf.imcombine(files,
output=flat,
combine="median",
reject="minmax",
scale='median',
weight='exposure',
statsec="[400:600,400:600]",
nlow=2,
nhigh=4)
input = flat + '.fits[300:800,300:800]'
stats = iraf.imstatistics(input,
fields="mean",
lower=1,
format=0,
Stdout=1)
print 'stats = ', stats, stats[0]
ave = float(stats[0])
iraf.imarith(flat, "/", ave, flat) #normalize flat
def getMaxValue(self, fn):
result = iraf.imstatistics(fn, usigma=2.5, lsigma=2.5, nclip=3, \
format="no", field="max", Stdout=1)
try:
maxval=float(result[0])
except:
maxval=None
return(maxval)
def mask_image(imageFile, resolvedSources, maskSize=150):
img = pyfits.getdata(imageFile)
# Calculate the sky level for this image.
text_output = ir.imstatistics(imageFile,
fields='mean',
nclip=10,
lsigma=10,
usigma=2,
format=0,
Stdout=1)
backLevel = float(text_output[0])
backImage = img * 0.0 + backLevel
imgNoBack = img - backImage
# Make a mask image.
for rr in range(len(resolvedSources)):
coords = resolvedSources[rr]
if type(maskSize) == int:
msize = maskSize
elif len(maskSize) > 1:
msize = maskSize[rr]
mask1D = np.hanning(msize)
mask = -1.0 * np.outer(mask1D, mask1D) + 1.0
# Find the image borders
m_xlo = int(coords[1] - msize / 2)
m_xhi = m_xlo + msize
m_ylo = int(coords[0] - msize / 2)
m_yhi = m_ylo + msize
i_xhi = imgNoBack.shape[0]
i_yhi = imgNoBack.shape[1]
if m_xlo < 0:
mask = mask[abs(m_xlo):, :]
m_xlo = 0
if m_ylo < 0:
mask = mask[:, abs(m_ylo):]
m_ylo = 0
if m_xhi > i_xhi:
mask = mask[:i_xhi - m_xhi, :]
m_xhi = i_xhi
if m_yhi > i_yhi:
mask = mask[:, :i_yhi - m_yhi]
m_yhi = i_yhi
imgNoBack[m_xlo:m_xhi, m_ylo:m_yhi] *= mask
img = imgNoBack + backImage
return img
def statisticsAmongImages(self, fn_list):
vals=[]
for fn in fn_list:
print('%s%s' % (fn, self.statarea))
result = iraf.imstatistics('%s%s' % (fn, self.statarea), format="no", field="midpt", Stdout=1)
print(SacraFile.str2float_iraf(result[0]))
vals.append(SacraFile.str2float_iraf(result[0]))
c_max = numpy.amax(vals)
c_min = numpy.amin(vals)
c_ave = numpy.average(vals)
c_std = numpy.std(vals)
print("debug:", c_max, c_min, c_ave, c_std)
return(c_max, c_min, c_ave, c_std)
def printStats(frame, tint, sampmode, reads):
files = range(frame, frame + 3)
fileName = "dark_%ds_1ca_%d_%dsm.fits" % (tint, sampmode, reads)
if skipcombo == False:
makedark(files, fileName)
text_output = ir.imstatistics("darks/" + fileName, fields="mean,stddev", nclip=10, format=0, Stdout=1)
values = text_output[0].split()
darkMean = float(values[0])
darkStdv = float(values[1])
return darkMean, darkStdv
def imgAddHeaderSkyLevelEstimate(self, fn_in, skyarea):
#skyarea = '[x1:x1, y1:y2]' ## iraf region fromat
try:
skylevel = iraf.imstatistics('%s%s' % (fn_in, skyarea),
format='no', field='midpt',
Stdout=1)
skycor = (-1.0) * float(skylevel[0])
self.addFitsHeader(fn_in, 'SKYLVCOR', skycor,
'Sky correction estimation in %s' % (skyarea))
print('%s: SKYLVCOR, %f' % (fn_in, skycor))
except:
sys.stderr.write('(SKYLVCOR measureing error in %s' % (fn_in))
retrun(-1)
return(0)
def mask_image(imageFile, resolvedSources, maskSize=150):
img = pyfits.getdata(imageFile)
# Calculate the sky level for this image.
text_output = ir.imstatistics(imageFile, fields='mean', nclip=10,
lsigma=10, usigma=2, format=0, Stdout=1)
backLevel = float(text_output[0])
backImage = img * 0.0 + backLevel
imgNoBack = img - backImage
# Make a mask image.
for rr in range(len(resolvedSources)):
coords = resolvedSources[rr]
if type(maskSize) == int:
msize = maskSize
elif len(maskSize) > 1:
msize = maskSize[rr]
mask1D = np.hanning(msize)
mask = -1.0 * np.outer(mask1D, mask1D) + 1.0
# Find the image borders
m_xlo = coords[1] - msize/2
m_xhi = m_xlo + msize
m_ylo = coords[0] - msize/2
m_yhi = m_ylo + msize
i_xhi = imgNoBack.shape[0]
i_yhi = imgNoBack.shape[1]
if m_xlo < 0:
mask = mask[abs(m_xlo):,:]
m_xlo = 0
if m_ylo < 0:
mask = mask[:,abs(m_ylo):]
m_ylo = 0
if m_xhi > i_xhi:
mask = mask[:i_xhi - m_xhi,:]
m_xhi = i_xhi
if m_yhi > i_yhi:
mask = mask[:,:i_yhi - m_yhi]
m_yhi = i_yhi
imgNoBack[m_xlo:m_xhi, m_ylo:m_yhi] *= mask
img = imgNoBack + backImage
return img
def run_daofind(image, extension=0,outfile='default',dthreshold=3.0, fwhmpsf=2.5, backsigma=-1.0,rdnoise=5.2): '''THIS PROCEDURE RUNS DAOFIND ON INPUT IMAGE''' # Parse input parameters if outfile == 'default': outfile = image+'0.coo.1' # Read in fits header f = pyfits.open(image) fheader = f[0].header f.close() # Extract relevant info from the header exptime = fheader['texptime'] instr = fheader['INSTRUME'] if instr == 'WFC3': filter = fheader['FILTER'] else: #assuming ACS filter = fheader['FILTER1'] if filter[0] == 'C': filter == fheader['FILTER2'] ipxscl = fheader['D001ISCL'] opxscl = fheader['D001SCAL'] num_flts = float(fheader['NDRIZIM'])/2.0 #df_max = 10000000. # upper limit for "good" pixels (sometimes used to ID bad pixels)---Not used here. # Perform read noise correction rdnoise_corr = np.sqrt(num_flts * (rdnoise * opxscl/ipxscl)**2) # Perform background noise calculation if backsigma < 0.0: backrms=iraf.imstatistics(image+'[0]', fields='stddev', nclip=10, lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1) backsigma=float(backrms[0]) '''Run daofind''' # remove old daofind files file_query = os.access(outfile, os.R_OK) if file_query == True: os.remove(outfile) iraf.daofind.unlearn() iraf.daofind(image=image+'[0]', interactive='no', verify='no',output=outfile, fwhmpsf=fwhmpsf, sigma=backsigma, \ readnoise=rdnoise_corr, itime=exptime, threshold=dthreshold) # Display results of daofind (***WORK IN PROGRESS***) #!ds9 & #iraf.display(file+'['+exten+']',1) #iraf.tvmark(1,ofile+'0.coo.1',mark = 'circle', radii = 8, color = 205) return outfile # return name of coordinate file
def printStats(frame, tint, sampmode, reads):
files = range(frame, frame + 3)
fileName = 'dark_%ds_1ca_%d_%dsm.fits' % (tint, sampmode, reads)
if (skipcombo == False):
makedark(files, fileName)
text_output = ir.imstatistics('darks/' + fileName,
fields="mean,stddev",
nclip=10,
format=0,
Stdout=1)
values = text_output[0].split()
darkMean = float(values[0])
darkStdv = float(values[1])
return darkMean, darkStdv
def domask(files, invmask):
current = files
#for current in files:
mfile = "m" + current
#iraf.mask(current,mask=invmask,stat="[400:600,400:600]")
mode = iraf.imstatistics(current,
fields="mode",
lower=1,
format=0,
Stdout=1)
mode = float(mode[0])
print "masking ", current, " with mode = ", mode
iraf.imarith(1, "-", invmask, "tempmask")
iraf.imarith(current, "*", "tempmask", mfile)
iraf.imarith(invmask, "*", mode, "tempmask2")
iraf.imarith(mfile, "+", "tempmask2", mfile)
iraf.imdel("tempmask")
iraf.imdel("tempmask2")
def run_daophot(image, outfile='default', coordfile='NA', apertures='5.0,16.66', annulus=17.0, dannulus=3.0, calgorithm='centroid', salgorithm='median', fwhmpsf=2.5, backsigma=-1.0,rdnoise=5.2):
'''THIS PROCEDURE RUNS DAOPHOT ON INPUT IMAGE'''
# Parse input parameters
if outfile == 'default': outfile = image + '0.mag.1'
if coordfile == 'NA': coordfile = image + '0.coo.1'
# Read in fits header
f = pyfits.open(image)
fheader = f[0].header
f.close()
# Extract relevant info from the header
exptime = fheader['texptime']
filter = fheader['FILTER2']
ipxscl = fheader['D001ISCL']
opxscl = fheader['D001SCAL']
num_flts = float(fheader['NDRIZIM'])/2.0
dp_zmag = -2.5 * np.log10(float(fheader['PHOTFLAM'])) + float(fheader['PHOTZPT']) # zero-pt for each filter
#df_max = 10000000. # upper limit for "good" pixels (sometimes used to ID bad pixels). Not using this parameter right now.
# Perform read noise correction
rdnoise_corr = np.sqrt(num_flts * (rdnoise * opxscl/ipxscl)**2)
# Perform background noise calculation
if backsigma < 0.0:
backrms=iraf.imstatistics(image+'[0]', fields='stddev', nclip=10, lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backsigma=float(backrms[0])
'''Run daophot'''
# Remove old phot output files
file_query = os.access(outfile, os.R_OK)
if file_query == True: os.remove(outfile)
# Run phot
iraf.phot.unlearn() # reset daophot parameters to default values
iraf.phot(image=image+'[0]', interactive='no', verify='no', coords=coordfile, output=outfile, fwhmpsf=fwhmpsf, \
sigma=backsigma, readnoise=rdnoise_corr, itime=exptime, calgorithm=calgorithm, salgorithm=salgorithm, \
annulus=annulus, dannulus=dannulus, apertures=apertures,zmag=dp_zmag)
return outfile # return name of output catalog
iraf.ccdred.combine.unlearn()
iraf.ccdred.zerocombine.unlearn()
iraf.ccdred.flatcombine.unlearn()
iraf.specred.response.unlearn()
# regular expression of files (e.g bias_00*.fits, flat-2000jan01_?.*)
theflat = str(raw_input('Enter flat image: '))
iraf.specred.extinction = ''
iraf.specred.caldir = ''
iraf.specred.observatory = 'lna'
print 'Create a response for flat...'
iraf.specred.response.interactive = True
iraf.specred.response.high_reject = 3.0
iraf.specred.response.low_reject = 3.0
iraf.specred.response(calibration=theflat,
normalization=theflat,
response='nFlat')
# check output flat image
print 'openning a ds9 window if not already openned...'
ds9.ds9()
print 'Check output file:'
iraf.imstatistics('nFlat')
print ' Running "imexamine" task..'
iraf.imexamine('nFlat', 1)
print '--- DONE ---'
combine="median",
scale="median",
stats="[400:600,400:600]",
reject="ccdclip",
lsigma=5.5,
hsig=4.5,
rdnoise=17.5,
gain=4.35,
maskt="goodval",
lthres=500,
hthres=32000,
nkeep=2,
blank=-999,
grow=3)
stats = iraf.imstatistics(flat, fields="mean", lower=1, format=0, Stdout=1)
ave = float(stats[0])
iraf.imarith(flat, "/", ave, flat) #normalize flat
for file in files:
dfile = 'd' + file
mfile = 'md' + file
ffile = 'fmd' + file
m999file = 'mfmd' + file
gfile = 'gmfmd' + file
if npass == 1:
iraf.imarith(mfile, "/", flat, ffile)
diminput = fname
if npass == 2:
iraf.imdelete(ffile)
iraf.imarith(mfile, "/", flat, ffile)
def __init__(self,imagen,outfile):
temp=sys.stdout # Store original stdout object for later
sys.stdout =open(outfile,'w') # redirect all output to the file
self.test=iraf.imstatistics(images=imagen,fields="stddev,min,max,mode",lower=low,upper=up,lsigma=1,usigma=1)
sys.stdout.close() # closing the file
sys.stdout=temp # back to the normal print command
import pprint
from pyraf import iraf
stats = iraf.imstatistics('../VIS.fits[1]', Stdout=1)
pprint.pprint(stats)
def makemask(dark, flat, output):
"""Make bad pixel mask for NIRC2 data. Makes a calib/ directory
and stores all output there. All output and temporary files
will be created in a masks/ subdirectory.
@param dark: The full relative path to a dark file. This is used to
construct a hot pixel mask. Use a long (t>20sec) exposure dark.
@type dark: str
@param flat: The full relative path to a flat file. This is used to
construct a dead pixel mask. The flat should be normalized.
@type flat: str
@param output: output file name. This will be created in the masks/
subdirectory.
@type output: str
"""
redDir = os.getcwd() + '/'
calDir = redDir + 'calib/'
maskDir = util.trimdir(calDir + 'masks/')
flatDir = util.trimdir(calDir + 'flats/')
darkDir = util.trimdir(calDir + 'darks/')
rawDir = util.trimdir(os.path.abspath(redDir + '../raw') + '/')
dataDir = util.trimdir(os.path.abspath(redDir + '../..') + '/')
util.mkdir(calDir)
util.mkdir(maskDir)
_out = maskDir + output
_dark = darkDir + dark
_flat = flatDir + flat
_nirc2mask = module_dir + '/masks/nirc2mask.fits'
util.rmall([_out])
# Make hot pixel mask
whatDir = redDir + dark
print(whatDir)
text_output = ir.imstatistics(_dark,
fields="mean,stddev",
nclip=10,
format=0,
Stdout=1)
print text_output
values = text_output[0].split()
hi = float(values[0]) + (10.0 * float(values[1]))
img_dk = pyfits.getdata(_dark)
hot = img_dk > hi
# Make dead pixel mask
text_output = ir.imstatistics(_flat,
fields="mean,stddev",
nclip=10,
format=0,
Stdout=1)
values = text_output[0].split()
#lo = float(values[0]) - (15.0 * float(values[1]))
# If flat is normalized, then lo should be set to 0.5
lo = 0.5
hi = float(values[0]) + (15.0 * float(values[1]))
img_fl = pyfits.getdata(_flat)
dead = np.logical_or(img_fl > hi, img_fl < lo)
# We also need the original NIRC2 mask (with cracks and such)
nirc2mask = pyfits.getdata(_nirc2mask)
# Combine into a final supermask. Use the flat file just as a template
# to get the header from.
ofile = pyfits.open(_flat)
if ((hot.shape)[0] == (nirc2mask.shape)[0]):
mask = hot + dead + nirc2mask
else:
mask = hot + dead
mask = (mask != 0)
unmask = (mask == 0)
ofile[0].data[unmask] = 0
ofile[0].data[mask] = 1
ofile[0].writeto(_out, output_verify='silentfix')
def makeNirc2mask(dark, flat, outDir):
"""Make the static bad pixel mask for NIRC2. This only needs to be
run once. This creates a file called nirc2mask.fits which is
subsequently used throughout the pipeline. The dark should be a long
integration dark.
@param dark: The full absolute path to a medianed dark file. This is
used to construct a hot pixel mask (4 sigma detection thresh).
@type dark: str
@param flat: The full absolute path to a medianed flat file. This is
used to construct a dead pixel mask.
@type flat: str
@param outDir: full path to output directory with '/' at the end.
@type outDir: str
"""
_out = outDir + 'nirc2mask.fits'
_dark = dark
_flat = flat
util.rmall([_out])
# Make hot pixel mask
text_output = ir.imstatistics(_dark,
fields="mean,stddev",
nclip=10,
format=0,
Stdout=1)
values = text_output[0].split()
hi = float(values[0]) + (15.0 * float(values[1]))
img_dk = pyfits.getdata(_dark)
hot = img_dk > hi
print 'Found %d hot pixels' % (hot.sum())
# Make dead pixel mask
text_output = ir.imstatistics(_flat,
fields="mean,stddev",
nclip=10,
format=0,
Stdout=1)
values = text_output[0].split()
# Assuming flat is normalized, we don't want pixels with less
# than 0.5 sensitivity
#lo = float(values[0]) - (15.0 * float(values[1]))
lo = 0.5 #mask = hot
hi = float(values[0]) + (15.0 * float(values[1]))
img_fl = pyfits.getdata(_flat)
dead = logical_or(img_fl > hi, img_fl < lo)
print 'Found %d dead pixels' % (dead.sum())
# Combine into a final supermask
file = pyfits.open(_flat)
mask = hot + dead
mask = (mask != 0)
unmask = (mask == 0)
file[0].data[unmask] = 0
file[0].data[mask] = 1
file[0].writeto(_out, output_verify='silentfix')
if not os.path.isdir('flat_files'):
os.mkdir('flat_files')
allfiles = flatlist
for file in allfiles:
shutil.copy(file, 'flat_files/')
# combine flat images
print 'Combining flat images ...'
iraf.ccdred.flatcombine.ccdtype = ''
iraf.ccdred.flatcombine.process = 'no'
iraf.ccdred.flatcombine.reject = 'ccdclip'
iraf.ccdred.flatcombine.rdnoise = 'rdnoise'
iraf.ccdred.flatcombine.gain = 'gain'
iraf.ccdred.flatcombine.output = 'Flat'
iraf.ccdred.flatcombine(input=flatin)
for file in flatlist:
os.remove(file)
print 'openning a ds9 window if not already openned...'
ds9.ds9()
# check output flat image
print 'Check output file:'
iraf.imstatistics('Flat')
print ' Running "imexamine" task..'
iraf.imexamine('Flat', 1)
print '--- DONE ---'
print '\ncreating a zero_files/ dir to store original data'
if not os.path.isdir('zero_files'):
os.mkdir('zero_files')
allfiles = zerolist
for file in allfiles:
shutil.copy(file, 'zero_files/')
# combine bias images
print 'Combining zero level images...'
iraf.ccdred.zerocombine.ccdtype = ''
iraf.ccdred.zerocombine.reject = 'ccdclip'
iraf.ccdred.zerocombine.rdnoise = 'rdnoise'
iraf.ccdred.zerocombine.gain = 'gain'
iraf.ccdred.zerocombine(input=zeroin)
for zero in zerolist:
os.remove(zero)
print 'openning a ds9 window if not already openned...'
ds9.ds9()
# check output image
print 'Check output file:'
iraf.imstatistics('Zero')
print ' Running "imexamine" task..'
iraf.imexamine('Zero', 1)
print '--- DONE ---'
import pprint
from pyraf import iraf
stats = iraf.imstatistics("../VIS.fits[1]", Stdout=1)
pprint.pprint(stats)
def makemask(dark, flat, output):
"""Make bad pixel mask for NIRC2 data. Makes a calib/ directory
and stores all output there. All output and temporary files
will be created in a masks/ subdirectory.
@param dark: The full relative path to a dark file. This is used to
construct a hot pixel mask. Use a long (t>20sec) exposure dark.
@type dark: str
@param flat: The full relative path to a flat file. This is used to
construct a dead pixel mask. The flat should be normalized.
@type flat: str
@param output: output file name. This will be created in the masks/
subdirectory.
@type output: str
"""
redDir = os.getcwd() + '/'
calDir = redDir + 'calib/'
maskDir = util.trimdir(calDir + 'masks/')
flatDir = util.trimdir(calDir + 'flats/')
darkDir = util.trimdir(calDir + 'darks/')
rawDir = util.trimdir(os.path.abspath(redDir + '../raw') + '/')
dataDir = util.trimdir(os.path.abspath(redDir + '../..') + '/')
util.mkdir(calDir)
util.mkdir(maskDir)
_out = maskDir + output
_dark = darkDir + dark
_flat = flatDir + flat
_nirc2mask = module_dir + '/masks/nirc2mask.fits'
util.rmall([_out])
# Make hot pixel mask
whatDir = redDir + dark
print(whatDir)
text_output = ir.imstatistics(_dark, fields="mean,stddev",
nclip=10, format=0, Stdout=1)
print text_output
values = text_output[0].split()
hi = float(values[0]) + (10.0 * float(values[1]))
img_dk = pyfits.getdata(_dark)
hot = img_dk > hi
# Make dead pixel mask
text_output = ir.imstatistics(_flat, fields="mean,stddev",
nclip=10, format=0, Stdout=1)
values = text_output[0].split()
#lo = float(values[0]) - (15.0 * float(values[1]))
# If flat is normalized, then lo should be set to 0.5
lo = 0.5
hi = float(values[0]) + (15.0 * float(values[1]))
img_fl = pyfits.getdata(_flat)
dead = np.logical_or(img_fl > hi, img_fl < lo)
# We also need the original NIRC2 mask (with cracks and such)
nirc2mask = pyfits.getdata(_nirc2mask)
# Combine into a final supermask. Use the flat file just as a template
# to get the header from.
ofile = pyfits.open(_flat)
if ((hot.shape)[0] == (nirc2mask.shape)[0]):
mask = hot + dead + nirc2mask
else:
mask = hot + dead
mask = (mask != 0)
unmask = (mask == 0)
ofile[0].data[unmask] = 0
ofile[0].data[mask] = 1
ofile[0].writeto(_out, output_verify='silentfix')
def run_daophot(image, outfile='default', coordfile='NA', backmethod='mean',apertures='1,2,3,4,5,6,7,8,9,10,12,14,16,18,20,24,28,32,36,40,45,50,55,60,65,70', cbox=10.0, \
backmean=-9999.0,annulus=17.0, dannulus=3.0, calgorithm='centroid', salgorithm='median', fwhmpsf=2.5, backsigma=-1.0,rdnoise=-1.0, epadu=1.0):
'''THIS PROCEDURE RUNS DAOPHOT ON INPUT IMAGE'''
# Parse input parameters
if outfile == 'default': outfile = image + '0.mag.1'
if coordfile == 'NA': coordfile = image + '0.coo.1'
# Read in fits header
f = pyfits.open(image)
fheader = f[0].header
# Extract relevant info from the header
naxis1 = fheader['NAXIS1']
naxis2 = fheader['NAXIS2']
exptime = fheader['exptime']
instr = fheader['INSTRUME']
if instr == 'WFC3':
filter = fheader['FILTER']
else: #assuming ACS
filter = fheader['FILTER1']
if filter[0] == 'C': filter == fheader['FILTER2']
ipxscl = 0.03962
opxscl = 0.03962
f.close()
dp_zmag = get_uvis_zeropoint(filter)
# Number of flt images (tricky: IR/calibration images may have only 1 chip--NDRIZIM keyword adds both chips)
if (fheader['detector'] == 'IR'): nchips = 1.0 # IR
elif (fheader['subarray'] == True) and (len(fheader['CCDAMP']) == 1): nchips = 1.0 # UVIS sub-array
elif (fheader['detector'] == 'UVIS') and (fheader['subarray'] == False): nchips = 2.0 # UVIS full-frame
else: raise exception('Image type is not defined.')
#num_flts = fheader['NDRIZIM']/nchips
num_flts = 1.0
# Perform read noise correction
if rdnoise < 0.0:
amps = fheader['CCDAMP']
rdnoise = np.zeros(len(amps))
for namp in xrange(len(amps)): rdnoise[namp] = fheader['READNSE'+amps[namp]]
rdnoise_corr = np.sqrt(num_flts * (np.average(rdnoise) * opxscl/ipxscl)**2)
# Measure the background and noise
if (backmean < -1000.0) or (backsigma < 0.0):
# read in the x/y center of the source
xc, yc = np.loadtxt(coordfile, unpack=True, usecols = (0,1))
#create temporary image for bckgrd measurement that masks sources out to 60 pixels (assign a very low number)
tmp_image = image+'.back.fits'
shutil.copy(image, tmp_image)
ff=pyfits.open(tmp_image, mode='update')
maskim = ff[0].data
maskim[circular_mask(maskim.shape,60, x_offset=(xc-naxis1/2.0), y_offset=(yc-naxis2/2.0))] = -99999.0
# Also mask out sources with zero effective exposure [WE ELIMINATE PIXELS WITHIN 20 OF IMAGE BORDER]
maskim[:,0:20] = -99999.0
maskim[:,-20:] = -99999.0
maskim[0:20,:] = -99999.0
maskim[-20:,:] = -99999.0
ff.close()
# generate initial guess for lower/upper limits (use 10 sigma)
if (backmean < -1000.0) | (backsigma < 0.0):
initback = iraf.imstatistics(tmp_image+'[0]', fields='mode,stddev', lower = -100, upper = 10000, nclip=7, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
if len(initback[0].split(' ')) != 2:
raise Exception('Could not parse output from imstatistics.')
else:
llim = float(initback[0].split(' ')[0]) - 10.0*float(initback[0].split(' ')[1])
ulim = float(initback[0].split(' ')[0]) + 10.0*float(initback[0].split(' ')[1])
# measure mode and std dev of background using initial guess to constrain dynamic range
if backmean < -1000.0:
backstats=iraf.imstatistics(tmp_image+'[0]', fields=backmethod, lower = llim, upper = ulim, nclip=7, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backmean=float(backstats[0])
if backsigma < 0.0:
backstats=iraf.imstatistics(tmp_image+'[0]', fields='stddev', lower = llim, upper = ulim, nclip=7, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backsigma=float(backstats[0])
#remove temporary image
#os.remove(tmp_image)
print ' BACKGROUND = '+str(backmean)
print ' BACKGROUND RMS = '+str(backsigma)
# Case of no aperture size given (we select aperture sizes of: WFC3= 0.27 and 0.4" && ACS=0.25 and 0.5")
if apertures == '0.0':
if instr == 'WFC3' and filter[1] == '1': apertures=str(0.27/opxscl)+','+str(0.4/opxscl) # case of IR filters
elif instr == 'WFC3' and filter[1] != '1': apertures='5,'+str(0.4/opxscl) # case of UVIS filters
elif instr == 'WFC': apertures = '5,16.66'
else: raise exception('UNKNOWN INSTRUMENT/FILTER')
# Remove old phot output files
file_query = os.access(outfile, os.R_OK)
if file_query == True: os.remove(outfile)
# Run phot
iraf.phot.unlearn() # reset daophot parameters to default values
iraf.phot(image=image+'[0]', interactive='no', verify='no', coords=coordfile, output=outfile, fwhmpsf=fwhmpsf, \
sigma=backsigma, readnoise=rdnoise_corr, itime=exptime, calgorithm=calgorithm, cbox=cbox, skyvalue=backmean, \
apertures=apertures,zmag=dp_zmag, salgorithm='constant') #annulus=annulus, dannulus=dannulus
# Display results of daophot
#iraf.display(tmp[0]+'.fits',1, zscale='no', zrange='no', z1=0, z2=100,ztrans='log')
#iraf.tvmark(1,outfile,mark = 'circle', radii = 10, color = 206)
#return outfile # return name of output catalog
return backmean,backsigma # return computed background stats for image
x1=np.round(xc)+sz/2.
y0=np.round(yc)-sz/2.
y1=np.round(yc)+sz/2.
#fig = pylab.figure()
#fig.subplots_adjust(wspace=0.4)
ax1 = pylab.subplot(1,2,1)
ax1.imshow(np.log10(im[y0:y1,x0:x1]),interpolation='nearest')
ax1.autoscale(axis='both',enable=False)
ax1.scatter([xc-x0-1.0], [yc-y0-1.0], marker='x', s=200., color='w')
pylab.title('X = '+str(xc)+' Y = '+str(yc))
# plot #2 - background histogram
tmp_image=glob.glob('*back.fits')[0]
backim = pyfits.getdata(tmp_image)
#--measure back statistics (mean and mode via IRAF)
initback = iraf.imstatistics(tmp_image+'[0]', fields='mode,stddev', lower = -100, upper = 10000, nclip=7,lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
llim = float(initback[0].split(' ')[0]) - 10.0*float(initback[0].split(' ')[1])
ulim = float(initback[0].split(' ')[0]) + 10.0*float(initback[0].split(' ')[1])
backstats=iraf.imstatistics(tmp_image+'[0]', fields='mean,mode', lower = llim, upper = ulim, nclip=7,lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backmean=float(backstats[0].split(' ')[0])
backmode=float(backstats[0].split(' ')[1])
fbackim= np.ndarray.flatten(backim)
gd=np.where((fbackim > llim) & (fbackim < ulim))[0]
backmedian=meanclip(fbackim[gd],maxiter=7,return_median=1)[0]
ax2 = pylab.subplot(1,2,2)
pylab.hist(fbackim[gd],log=True)
pylab.ylim(0.5,600000)
pylab.xlim(-20,20)
pylab.plot([backmode,backmode],[0.5,600000],ls='-',color='red',label='mode')
pylab.plot([backmedian,backmedian],[0.5,600000],ls='--',color='aqua',label='median')
def run_daofind(image, outfile='default', dthreshold=3.0, fwhmpsf=2.5, backsigma=None,rdnoise=None):
'''RUN DAOFIND ON INPUT IMAGE'''
# -- parse output filename
if outfile == 'default': outfile = image+'.coo'
# -- extract header info
prihdr = pyfits.getheader(image)
exptime = prihdr['exptime']
instrum = prihdr['INSTRUME']
detector = prihdr['DETECTOR']
SUBARRAY = prihdr['SUBARRAY']
ccdamp = prihdr['CCDAMP']
# -- record filter name, native pixel scale, and no. of chips
if instrum == 'WFC3':
if detector == 'UVIS':
pscale_nat = 0.03962
if ((SUBARRAY == True) & (len(ccdamp) == 1)): nchips = 1.0
elif SUBARRAY == False: nchips = 2.0
else: raise Exception('Image type is not defined.')
elif detector == 'IR':
pscale_nat = 0.12825
nchips = 1.0
else: raise Exception('Detector '+detector+' not covered in our case list.')
elif instrum == 'ACS':
if detector == 'WFC':
pscale_nat = 0.049
if ((SUBARRAY == True) & (len(ccdamp) == 1)): nchips = 1.0
elif SUBARRAY == False: nchips = 2.0
else: raise Exception('Image type is not defined.')
else: raise Exception('Detector '+detector+' not covered in our case list.')
else: raise Exception('Instrument '+instrum+' not covered in our case list.')
# -- record pixel scale of current image, image type, and number of flts
sciext = []
pscale_img = prihdr.get('D001SCAL',default='NA')
if pscale_img == 'NA':
imtype = 'flt' # we dont distinguish between flt/crclean, i.e., assume pscales are equal
pscale_img = pscale_nat
num_flts = 1.0
# -- record location of science extension
hdulist = pyfits.open(image)
for ext in xrange(len(hdulist)):
if hdulist[ext].name == 'SCI': sciext.append(ext)
hdulist.close()
if len(sciext) != 1: raise Exception('We do not handle images with '+str(len(sciext))+' SCI extensions.')
else:
imtype ='drz'
num_flts = prihdr['NDRIZIM']/nchips
sciext.append(0)
# -- estimate read noise
if rdnoise == None:
rdnoise = np.zeros(len(ccdamp))
for namp in xrange(len(ccdamp)): rdnoise[namp] = prihdr['READNSE'+ccdamp[namp]]
rdnoise_corr = np.sqrt(num_flts * (np.average(rdnoise) * pixscale_img/pixscale_nat)**2)
# -- perform rough background noise calculation
if backsigma == None:
backstats=iraf.imstatistics(image+'['+str(sciext[0])+']', fields='stddev', lower = -100, upper = 100, nclip=5, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backsigma=float(backstats[0])
# -- remove old daofind files/run daofind
file_query = os.access(outfile, os.R_OK)
if file_query == True: os.remove(outfile)
iraf.daofind.unlearn()
iraf.daofind(image=image+'['+str(sciext[0])+']', interactive='no', verify='no',output=outfile, fwhmpsf=fwhmpsf, \
sigma=backsigma, readnoise=rdnoise_corr, itime=exptime, threshold=dthreshold, datamin=-10, datamax=100000)
return outfile
ax2.scatter([xc - x0 - 1.0], [yc - y0 - 1.0],
marker='x',
s=200.,
color='w',
alpha=0.1)
pylab.title('DQ ARRAY', fontsize='small')
# plot #3 - background histogram
tmp_image = glob.glob('*back.fits')[0]
backim = pyfits.getdata(tmp_image)
#--measure back statistics (mean and mode via IRAF)
initback = iraf.imstatistics(tmp_image + '[0]',
fields='mode,stddev',
lower=-100,
upper=10000,
nclip=7,
lsigma=3.0,
usigma=3.0,
cache='yes',
format='no',
Stdout=1)
llim = float(initback[0].split(' ')[0]) - 10.0 * float(
initback[0].split(' ')[1])
ulim = float(initback[0].split(' ')[0]) + 10.0 * float(
initback[0].split(' ')[1])
backstats = iraf.imstatistics(tmp_image + '[0]',
fields='mean,mode,stddev',
lower=llim,
upper=ulim,
nclip=7,
lsigma=3.0,
usigma=3.0,
print '\ncreating a zero_files/ dir to store original data'
if not os.path.isdir('zero_files'):
os.mkdir('zero_files')
allfiles = zerolist
for file in allfiles:
shutil.copy(file, 'zero_files/')
# combine bias images
print 'Combining zero level images...'
iraf.ccdred.zerocombine.ccdtype = ''
iraf.ccdred.zerocombine.reject = 'ccdclip'
iraf.ccdred.zerocombine.rdnoise = 'rdnoise'
iraf.ccdred.zerocombine.gain = 'gain'
iraf.ccdred.zerocombine(input=zeroin)
for zero in zerolist:
os.remove(zero)
print 'openning a ds9 window if not already openned...'
ds9.ds9()
# check output image
print 'Check output file:'
iraf.imstatistics('Zero')
print ' Running "imexamine" task..'
iraf.imexamine('Zero', 1)
print '--- DONE ---'
def run_daofind(image,
outfile='default',
dthreshold=3.0,
fwhmpsf=2.5,
backsigma=None,
rdnoise=None):
'''RUN DAOFIND ON INPUT IMAGE'''
# -- parse output filename
if outfile == 'default': outfile = image + '.coo'
# -- extract header info
prihdr = pyfits.getheader(image)
exptime = prihdr['exptime']
instrum = prihdr['INSTRUME']
detector = prihdr['DETECTOR']
SUBARRAY = prihdr['SUBARRAY']
ccdamp = prihdr['CCDAMP']
# -- record filter name, native pixel scale, and no. of chips
if instrum == 'WFC3':
if detector == 'UVIS':
pscale_nat = 0.03962
if ((SUBARRAY == True) & (len(ccdamp) == 1)): nchips = 1.0
elif SUBARRAY == False: nchips = 2.0
else: raise Exception('Image type is not defined.')
elif detector == 'IR':
pscale_nat = 0.12825
nchips = 1.0
else:
raise Exception('Detector ' + detector +
' not covered in our case list.')
elif instrum == 'ACS':
if detector == 'WFC':
pscale_nat = 0.049
if ((SUBARRAY == True) & (len(ccdamp) == 1)): nchips = 1.0
elif SUBARRAY == False: nchips = 2.0
else: raise Exception('Image type is not defined.')
else:
raise Exception('Detector ' + detector +
' not covered in our case list.')
else:
raise Exception('Instrument ' + instrum +
' not covered in our case list.')
# -- record pixel scale of current image, image type, and number of flts
sciext = []
pscale_img = prihdr.get('D001SCAL', default='NA')
if pscale_img == 'NA':
imtype = 'flt' # we dont distinguish between flt/crclean, i.e., assume pscales are equal
pscale_img = pscale_nat
num_flts = 1.0
# -- record location of science extension
hdulist = pyfits.open(image)
for ext in xrange(len(hdulist)):
if hdulist[ext].name == 'SCI': sciext.append(ext)
hdulist.close()
if len(sciext) != 1:
raise Exception('We do not handle images with ' +
str(len(sciext)) + ' SCI extensions.')
else:
imtype = 'drz'
num_flts = prihdr['NDRIZIM'] / nchips
sciext.append(0)
# -- estimate read noise
if rdnoise == None:
rdnoise = np.zeros(len(ccdamp))
for namp in xrange(len(ccdamp)):
rdnoise[namp] = prihdr['READNSE' + ccdamp[namp]]
rdnoise_corr = np.sqrt(
num_flts * (np.average(rdnoise) * pixscale_img / pixscale_nat)**2)
# -- perform rough background noise calculation
if backsigma == None:
backstats=iraf.imstatistics(image+'['+str(sciext[0])+']', fields='stddev', lower = -100, upper = 100, nclip=5, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backsigma = float(backstats[0])
# -- remove old daofind files/run daofind
file_query = os.access(outfile, os.R_OK)
if file_query == True: os.remove(outfile)
iraf.daofind.unlearn()
iraf.daofind(image=image+'['+str(sciext[0])+']', interactive='no', verify='no',output=outfile, fwhmpsf=fwhmpsf, \
sigma=backsigma, readnoise=rdnoise_corr, itime=exptime, threshold=dthreshold, datamin=-10, datamax=100000)
return outfile
def run_daophot(image, outfile='default', coordfile='NA', backmethod='mean',apertures='1,2,3,4,5,6,7,8,9,10,12,14,16,18,20,24,28,32,36,40,45,50,55,60,65,70', cbox=10.0, \
backmean=-9999.0,annulus=17.0, dannulus=3.0, calgorithm='centroid', salgorithm='median', fwhmpsf=2.5, backsigma=-1.0,rdnoise=-1.0, epadu=1.0):
'''THIS PROCEDURE RUNS DAOPHOT ON INPUT IMAGE'''
# Parse input parameters
if outfile == 'default': outfile = image + '0.mag.1'
if coordfile == 'NA': coordfile = image + '0.coo.1'
# Read in fits header
f = pyfits.open(image)
fheader = f[0].header
# Extract relevant info from the header
naxis1 = fheader['NAXIS1']
naxis2 = fheader['NAXIS2']
exptime = fheader['exptime']
instr = fheader['INSTRUME']
if instr == 'WFC3':
filter = fheader['FILTER']
else: #assuming ACS
filter = fheader['FILTER1']
if filter[0] == 'C': filter == fheader['FILTER2']
ipxscl = 0.03962
opxscl = 0.03962
f.close()
dp_zmag = get_uvis_zeropoint(filter)
# Number of flt images (tricky: IR/calibration images may have only 1 chip--NDRIZIM keyword adds both chips)
if (fheader['detector'] == 'IR'): nchips = 1.0 # IR
elif (fheader['subarray'] == True) and (len(fheader['CCDAMP']) == 1):
nchips = 1.0 # UVIS sub-array
elif (fheader['detector'] == 'UVIS') and (fheader['subarray'] == False):
nchips = 2.0 # UVIS full-frame
else:
raise exception('Image type is not defined.')
#num_flts = fheader['NDRIZIM']/nchips
num_flts = 1.0
# Perform read noise correction
if rdnoise < 0.0:
amps = fheader['CCDAMP']
rdnoise = np.zeros(len(amps))
for namp in xrange(len(amps)):
rdnoise[namp] = fheader['READNSE' + amps[namp]]
rdnoise_corr = np.sqrt(num_flts *
(np.average(rdnoise) * opxscl / ipxscl)**2)
# Measure the background and noise
if (backmean < -1000.0) or (backsigma < 0.0):
# read in the x/y center of the source
xc, yc = np.loadtxt(coordfile, unpack=True, usecols=(0, 1))
#create temporary image for bckgrd measurement that masks sources out to 60 pixels (assign a very low number)
tmp_image = image + '.back.fits'
shutil.copy(image, tmp_image)
ff = pyfits.open(tmp_image, mode='update')
maskim = ff[0].data
maskim[circular_mask(maskim.shape,
60,
x_offset=(xc - naxis1 / 2.0),
y_offset=(yc - naxis2 / 2.0))] = -99999.0
# Also mask out sources with zero effective exposure [WE ELIMINATE PIXELS WITHIN 20 OF IMAGE BORDER]
maskim[:, 0:20] = -99999.0
maskim[:, -20:] = -99999.0
maskim[0:20, :] = -99999.0
maskim[-20:, :] = -99999.0
ff.close()
# generate initial guess for lower/upper limits (use 10 sigma)
if (backmean < -1000.0) | (backsigma < 0.0):
initback = iraf.imstatistics(tmp_image+'[0]', fields='mode,stddev', lower = -100, upper = 10000, nclip=7, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
if len(initback[0].split(' ')) != 2:
raise Exception('Could not parse output from imstatistics.')
else:
llim = float(initback[0].split(' ')[0]) - 10.0 * float(
initback[0].split(' ')[1])
ulim = float(initback[0].split(' ')[0]) + 10.0 * float(
initback[0].split(' ')[1])
# measure mode and std dev of background using initial guess to constrain dynamic range
if backmean < -1000.0:
backstats=iraf.imstatistics(tmp_image+'[0]', fields=backmethod, lower = llim, upper = ulim, nclip=7, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backmean = float(backstats[0])
if backsigma < 0.0:
backstats=iraf.imstatistics(tmp_image+'[0]', fields='stddev', lower = llim, upper = ulim, nclip=7, \
lsigma=3.0, usigma=3.0, cache='yes', format='no',Stdout=1)
backsigma = float(backstats[0])
#remove temporary image
#os.remove(tmp_image)
print ' BACKGROUND = ' + str(backmean)
print ' BACKGROUND RMS = ' + str(backsigma)
# Case of no aperture size given (we select aperture sizes of: WFC3= 0.27 and 0.4" && ACS=0.25 and 0.5")
if apertures == '0.0':
if instr == 'WFC3' and filter[1] == '1':
apertures = str(0.27 / opxscl) + ',' + str(
0.4 / opxscl) # case of IR filters
elif instr == 'WFC3' and filter[1] != '1':
apertures = '5,' + str(0.4 / opxscl) # case of UVIS filters
elif instr == 'WFC':
apertures = '5,16.66'
else:
raise exception('UNKNOWN INSTRUMENT/FILTER')
# Remove old phot output files
file_query = os.access(outfile, os.R_OK)
if file_query == True: os.remove(outfile)
# Run phot
iraf.phot.unlearn() # reset daophot parameters to default values
iraf.phot(image=image+'[0]', interactive='no', verify='no', coords=coordfile, output=outfile, fwhmpsf=fwhmpsf, \
sigma=backsigma, readnoise=rdnoise_corr, itime=exptime, calgorithm=calgorithm, cbox=cbox, skyvalue=backmean, \
apertures=apertures,zmag=dp_zmag, salgorithm='constant') #annulus=annulus, dannulus=dannulus
# Display results of daophot
#iraf.display(tmp[0]+'.fits',1, zscale='no', zrange='no', z1=0, z2=100,ztrans='log')
#iraf.tvmark(1,outfile,mark = 'circle', radii = 10, color = 206)
#return outfile # return name of output catalog
return backmean, backsigma # return computed background stats for image
