def align_combine(fitsdir, myfilter, examine=True):
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
os.chdir(fitsdir)
listfiles = glob.glob(myfilter)
listfiles.sort()
if (examine):
print "Opening ",listfiles[0]," to examine."
iraf.imexamine(input=listfiles[0], \
logfile="coords.dat", \
keeplog="yes")
with open("align.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Aligning with reference:",listfiles[0]
iraf.imalign( input = "@align.list", referenc= listfiles[0], coords = "coords.dat", output = "*****@*****.**")
listfiles = glob.glob("a_"+myfilter)
listfiles.sort()
with open("comb.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Combining"
iraf.imcombine(input = "@comb.list",\
output = "out.fits",\
combine= "median")
def align_combine(fitsdir, myfilter, examine=True):
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
os.chdir(fitsdir)
listfiles = glob.glob(myfilter)
listfiles.sort()
if (examine):
print "Opening ",listfiles[0]," to examine."
iraf.imexamine(input=listfiles[0], \
logfile="coords.dat", \
keeplog="yes")
with open("align.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Aligning with reference:",listfiles[0]
iraf.imalign( input = "@align.list", referenc= listfiles[0], coords = "coords.dat", output = "*****@*****.**")
listfiles = glob.glob("a_"+myfilter)
listfiles.sort()
with open("comb.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Combining"
iraf.imcombine(input = "@comb.list",\
output = "out.fits",\
combine= "median")
def identify_objects(fnlist,skysiglist,fwhmlist,suffix=".coo"):
"""Runs the IRAF routine 'daofind' to locate objects in a series of images,
creating coordinate files.
Inputs:
fnlist -> List of strings, each the path to a fits image.
skysiglist -> List of floats, each the sky background sigma for an image.
fwhmlist -> List of floats, each the FWHM of objects in an image.
suffix -> Suffix for the coordinate files. '.coo' by default.
Outputs:
coolist -> List of strings, each the path to the coordinate files created.
"""
print "Identifying objects in images..."
coolist = []
#Open IRAF packages
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
for i in range(len(fnlist)):
coolist.append(fnlist[i]+suffix)
iraf.daofind(image=fnlist[i],
output=fnlist[i]+suffix,
fwhmpsf=fwhmlist[i],
sigma=skysiglist[i],
threshold=4.0,
datamin='INDEF',
datamax='INDEF',
verify='N')
return coolist
def _get_photometry(self):
""" Get the photometry for the target.
If the target is a standard star, aperture photometry will be performed. For the moment nothing is done with
the others, but in due time (TODO) photometry.py will be included here. """
basename = "standards"
fd, coords_file = tempfile.mkstemp(prefix=basename, suffix=".coords")
os.write(fd, "{0} {1} \n".format(self.RA, self.DEC))
os.close(fd)
if self.objtype == "standard":
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
seeing = self.header.hdr[self.header.seeingk]
photfile_name = self.header.im_name + ".mag.1"
utilities.if_exists_remove(photfile_name)
kwargs = dict(output=photfile_name, coords=coords_file,
wcsin='world', fwhm=seeing, gain=self.header.gaink, exposure=self.header.exptimek,
airmass=self.header.airmassk, annulus=6*seeing, dannulus=3*seeing,
apert=2*seeing, verbose="no", verify="no", interac="no")
iraf.phot(self.header.im_name, **kwargs)
[counts] = iraf.module.txdump(photfile_name, 'FLUX', 'yes', Stdout=subprocess.PIPE)
utilities.if_exists_remove(coords_file)
return float(counts)
def getSkyMeanSDinAnnulus(ann,delta=5):
iraf.noao()
iraf.digiphot()
iraf.apphot()
iraf.photpars.setParam('apertures','1')
iraf.phot.setParam('interactive','no')
iraf.phot.setParam('image',fitsDir+fitsFile)
iraf.phot.setParam('coords',fitsDir+fitsFile+".coo")
outmag=".maglim"
try:
os.remove(fitsDir+fitsFile+outmag)
except:
print "File does not exist BEFORE running phot, so no need to delete."
iraf.phot.setParam('output',fitsDir+fitsFile+outmag)
iraf.phot.setParam('interac','no')
iraf.fitskypars.setParam('annulus',str(ann))
iraf.fitskypars.setParam('dannulus',str(delta))
## NO SIGMA CLIPPING! JUST TO BE SAFE: (6/2013)
iraf.fitskypars.setParam('sloclip',"0")
iraf.fitskypars.setParam('shiclip',"0")
iraf.phot(fitsDir+fitsFile,Stdin=cr)
aa, nn, xx, ss = ao.readPhotMagFile(fitsDir,fitsFile,outmag)
try:
os.remove(fitsDir+fitsFile+outmag)
except:
print "File not found to delete AFTER running phot; that's odd."
return xx
def set_default(base):
# Loading necessary IRAF packages
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.reset(min_lenuserarea='200000')
iraf.datapars.setParam('datamin','INDEF')
iraf.datapars.setParam('datamax','60000')
iraf.datapars.setParam('exposure','EXPTIME')
iraf.datapars.setParam('airmass', 'AIRMASS')
iraf.datapars.setParam('filter', 'FILTER')
iraf.findpars.setParam('threshold', 3.0)
iraf.findpars.setParam('sharphi', 1)
iraf.findpars.setParam('roundhi', 1.3)
iraf.findpars.setParam('roundlo', -1.3)
iraf.daofind.setParam('verify','no')
iraf.daofind.setParam('interactive','no')
iraf.photpars.setParam('zmag',25.0)
iraf.photpars.setParam('weighti','constant')
iraf.photpars.setParam('apertur',3.0)
iraf.phot.setParam('output',base+'default')
iraf.phot.setParam('coords',base+'default')
iraf.phot.setParam('verify','no')
iraf.phot.setParam('interactive','no')
iraf.fitpsf.setParam('box',10.0)
iraf.fitpsf.setParam('verify','no')
iraf.fitpsf.setParam('interactive','no')
iraf.centerpars.setParam('calgori','none')
iraf.fitskypars.setParam('salgorithm','mode')
iraf.daopars.setParam('functio','moffat15')
iraf.daopars.setParam('varorde','0')
iraf.daopars.setParam('nclean','0')
iraf.daopars.setParam('saturat','no')
iraf.daopars.setParam('fitsky','yes')
iraf.daopars.setParam('recenter','yes')
iraf.daopars.setParam('groupsk','yes')
iraf.daopars.setParam('maxnsta','40000')
iraf.psf.setParam('photfile',base+'default')
iraf.psf.setParam('pstfile',base+'default')
iraf.psf.setParam('psfimage',base+'default')
iraf.psf.setParam('opstfile',base+'default')
iraf.psf.setParam('groupfil',base+'default')
iraf.psf.setParam('interac','no')
iraf.psf.setParam('matchby','yes')
iraf.psf.setParam('verify','no')
iraf.psf.setParam('showplo','no')
iraf.allstar.setParam('verify','no')
def get_fake_centroid(filename,x,y,instrument,filt):
"""
Locate the center of a fake psf
INPUTS: The fake-SN psf image in filename, the expected x,y position
of the center of the psf, the instrument and filter being modeled.
RETURNS: xcentroid, ycentroid, fluxcorr
"""
from pyraf import iraf
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn(iraf.apphot.phot)
iraf.unlearn(iraf.datapars)
iraf.unlearn(iraf.centerpars)
#Use the centroid algorithm right now as it seems more robust to geometric distortion.
iraf.centerpars.calgorithm = 'centroid'
iraf.centerpars.cbox = 5.0
iraf.unlearn(iraf.fitskypars)
iraf.unlearn(iraf.photpars)
photparams = {
'interac':False,
'radplot':False,
}
iraf.datapars.readnoise = 0.0
iraf.datapars.itime = 1.0
iraf.datapars.epadu = 1.0
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn(iraf.fitskypars)
iraf.fitskypars.salgorithm = 'constant'
iraf.fitskypars.skyvalue = 0.0
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = 20 # TODO : set this more intelligently !
iraf.photpars.zmag = 25
iraf.photpars.mkapert = False
#Write the coordinate file starting as position x and y
coxyfile = 'centroid.xycoo'
coxy = open(coxyfile, 'w')
print >> coxy, "%10.2f %10.2f" % (x,y)
coxy.close()
if os.path.exists('centroid.mag'): os.remove('centroid.mag')
iraf.phot(image=filename, skyfile='', coords=coxyfile, output='centroid.mag',
verify=False, verbose=True, Stdout=1, **photparams)
f = open('centroid.mag', 'r')
maglines = f.readlines()
f.close()
xcentroid = float(maglines[76].split()[0])
ycentroid = float(maglines[76].split()[1])
return xcentroid,ycentroid
def set_Pairitel_params(): '''Set some daophot parameters, which are typical for my Pairitel images ''' iraf.digiphot(_doprint=0) iraf.daophot(_doprint=0) iraf.apphot(_doprint=0) iraf.datapars.fwhmpsf = 4.5 iraf.datapars.datamax=15000. iraf.centerpars.cbox = 9.0 iraf.centerpars.calgorithm = "none" iraf.datapars.datamin = "INDEF" iraf.datapars.ccdread="RDNOISE" # 30. in 2MASS? iraf.datapars.gain = "GAIN" iraf.datapars.exposure = "EXPTIME" iraf.datapars.airmass = "AIRMASS" iraf.datapars.filter = "FILTER" iraf.datapars.obstime="DATE-OBS"
def createMagFile(inputDir, inputFile, useCoordFile, outputFile, fullyAutomatic=False, aperturesString='5,10,15,20', bestSkyAperture='100'):
cr=['\n']
iraf.cd(inputDir)
iraf.noao()
iraf.digiphot()
iraf.apphot()
iraf.phot.setParam('interactive','no')
iraf.centerpars.setParam('cbox','5')
iraf.photpars.setParam('apertures',aperturesString)
iraf.fitskypars.setParam('annulus',bestSkyAperture)
iraf.fitskypars.setParam('dannulus','10')
iraf.phot.setParam('image',inputFile)
iraf.phot.setParam('coords',useCoordFile)
iraf.phot.setParam('output',outputFile)
if fullyAutomatic: ## If you use this option, it will not prompt you for any of the aperture/sky settings
iraf.phot(mode='h',Stdin=cr)
else: # Otherwise, you can change them at will, and get to press enter a bunch
iraf.phot(mode='h')
def init_iraf():
"""Initializes the pyraf environment. """
# The display of graphics is not used, so skips Pyraf graphics
# initialization and run in terminal-only mode to avoid warning messages.
os.environ['PYRAF_NO_DISPLAY'] = '1'
# Set PyRAF process caching off to avoid errors if spawning multiple
# processes.
iraf.prcacheOff()
# Load iraf packages and does not show any output of the tasks.
iraf.digiphot(_doprint = 0)
iraf.apphot(_doprint = 0)
iraf.images(_doprint = 0)
# Set the iraf.phot routine to scripting mode.
iraf.phot.interactive = "no"
iraf.phot.verify = "no"
def init_iraf():
"""Initializes the pyraf environment. """
# The display of graphics is not used, so skips Pyraf graphics
# initialization and run in terminal-only mode to avoid warning messages.
os.environ['PYRAF_NO_DISPLAY'] = '1'
# Set PyRAF process caching off to avoid errors if spawning multiple
# processes.
iraf.prcacheOff()
# Load iraf packages and does not show any output of the tasks.
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.images(_doprint=0)
# Set the iraf.phot routine to scripting mode.
iraf.phot.interactive = "no"
iraf.phot.verify = "no"
def doPhotometryACS(self, file, inputcoords, apertures, zeropoint, bgsigma, skyann, skydann): ''' This function can be used to do photometry from a given image. Input parameters can be varied, however, the task assumes that photometry is done from an ACS image and the exposure time is found from the header of the file. Object recentering is done with centroid algorithm and shifts up to 6 pixels are possible. For skyfitting algorithm mode is adopted. ''' #load packages, should supress the output I.digiphot() I.apphot() #setting up for ACS I.datapar(sigma = bgsigma, exposure = 'exptime', gain = 'ccdgain') I.centerpars(calgorithm = 'centroid', cbox = 10., maxshift = 6.) I.fitskypars(salgorithm = 'mode', annulus = skyann, \ dannulus = skydann, skyvalue = 0.) I.photpars(apertures = apertures, zmag = zeropoint) I.phot(file, coords=inputcoords, verify='no', verbose = 'no')
def doPhotometryACS(self, file, inputcoords, apertures, zeropoint, bgsigma,
skyann, skydann):
'''
This function can be used to do photometry from a given image. Input parameters
can be varied, however, the task assumes that photometry is done from an ACS
image and the exposure time is found from the header of the file.
Object recentering is done with centroid algorithm and shifts up to 6 pixels are
possible. For skyfitting algorithm mode is adopted.
'''
#load packages, should supress the output
I.digiphot()
I.apphot()
#setting up for ACS
I.datapar(sigma=bgsigma, exposure='exptime', gain='ccdgain')
I.centerpars(calgorithm='centroid', cbox=10., maxshift=6.)
I.fitskypars(salgorithm = 'mode', annulus = skyann, \
dannulus = skydann, skyvalue = 0.)
I.photpars(apertures=apertures, zmag=zeropoint)
I.phot(file, coords=inputcoords, verify='no', verbose='no')
def do_phot(fnlist,coofile,fwhmlist,skysiglist):
"""Runs the IRAF 'phot' routine on a series of images to determine their
centroid positions and magnitudes. It is very likely you don't want to call
this routine on its own, but rather have align_norm do it for you.
Inputs:
fnlist -> List of strings, each the path to a fits image
coofile -> Path to the file containing the coordinate list of stars
fwhmlist -> List of the PSF FWHM's for each image
skysiglist -> List of the sky background sigmas for each image
Outputs:
photlist -> List of strings, the paths to the photometry outputs
"""
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
photlist = []
for i in range(len(fnlist)):
print "Performing photometry on the stars in image "+fnlist[i]
photlist.append(fnlist[i]+".mag")
iraf.phot(image=fnlist[i],
skyfile="",
coords=coofile,
output=photlist[i],
interactive="N",
sigma=skysiglist[i],
datamin='INDEF',
datamax='INDEF',
calgorithm='centroid',
cbox=2*fwhmlist[i],
salgorithm='centroid',
annulus=4*fwhmlist[i],
dannulus=6*fwhmlist[i],
apertures=4*fwhmlist[i],
verify='N')
return photlist
def calcFluxMulti(imageName, coords, rMin, rMax, step=1, centerBox=5.0):
nCoords = coords.shape[0]
results = []
coordsFN = tempfile.NamedTemporaryFile(delete=False)
for xx, yy in coords: print >>coordsFN, xx, yy
coordsFN.close()
digiphot = iraf.digiphot(_doprint=0)
apphot = iraf.apphot(_doprint=0)
qphot = iraf.qphot
pdump = iraf.pdump
qphot.coords = coordsFN.name
#if (iraf.hedit(image=imageName,fields='EXPTIME',value='.')):
#^this line prints the value of EXPTIME which is not needed
#need to check for EXPTIME or qphot will raise an error
try:
pyfits.open(imageName)[0].header['EXPTIME']
qphot.exposure = 'EXPTIME'
except:
pass
qphot.airmass = ''
qphot.interactive = False
qphot.radplots = False
qphot.verbose = False
apertures = ','.join([str(nn) for nn in range(int(rMin), int(rMax+1))])
annulus = rMax
dannulus = rMax + 1
qphot.output = 'qPhotOutput.tmp'
if os.path.exists(qphot.output): os.remove(qphot.output)
qphot(imageName, centerBox, annulus, dannulus, apertures, Stdout=1)
for nn in range(nCoords):
areas = np.array(map(float, pdump(qphot.output, 'AREA', 'yes', Stdout=1)[nn].split()))
fluxes = np.array(map(float, pdump(qphot.output, 'SUM', 'yes', Stdout=1)[nn].split()))
newXX = np.array(map(float, pdump(qphot.output, 'XCENTER', 'yes', Stdout=1)[nn].split()))[0]
newYY = np.array(map(float, pdump(qphot.output, 'YCENTER', 'yes', Stdout=1)[nn].split()))[0]
results.append([nn, coords[nn][0], coords[nn][1], newXX, newYY, areas, fluxes])
#if os.path.exists(qphot.output): os.remove(qphot.output)
return results
def get_star_data(asteroid_id, mag, expnum, header):
"""
From ossos psf fitted image, calculate mean of the flux of each row of the rotated PSF
"""
# calculate mean psf
uri = storage.get_uri(expnum.strip('p'), header[_CCD].split('d')[1])
ossos_psf = '{}.psf.fits'.format(uri.strip('.fits'))
local_psf = '{}{}.psf.fits'.format(expnum, header[_CCD].split('d')[1])
local_file_path = '{}/{}'.format(_STAMPS_DIR, local_psf)
storage.copy(ossos_psf, local_file_path)
# pvwcs = wcs.WCS(header)
# x, y = pvwcs.sky2xy(asteroid_id['ra'].values, asteroid_id['dec'].values)
x = asteroid_id[_XMID_HEADER].values[0]
y = asteroid_id[_YMID_HEADER].values[0]
# run seepsf on the mean psf image
iraf.set(uparm="./")
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.seepsf(local_file_path, local_psf, xpsf=x, ypsf=y, magnitude=mag)
with fits.open(local_psf) as hdulist:
data = hdulist[0].data
th = math.degrees(asteroid_id[_THETA_HEADER].values[0])
data_rot = rotate(data, th)
data_rot = np.ma.masked_where(data_rot == 0, data_rot)
data_mean = np.ma.mean(data_rot, axis=1)
os.unlink(local_psf)
os.unlink(local_file_path)
return data_mean[np.nonzero(np.ma.fix_invalid(data_mean, fill_value=0))[0]]
def measurephot(xpos, ypos, image):
coords = open("noisecoords.dat", 'w')
for i in range(len(xpos)):
coords.write("%8.1f %8.1f \n" % (xpos[i], ypos[i]))
sky = open("sky", 'w')
for i in range(npoints):
sky.write("0.0 \n")
sky.close()
aps = open("apertures", 'w')
aps.write("1,2,3,4,5,6,7,8,9,10,11,12,13,14,15")
aps.close()
os.system("rm noise.dat")
iraf.digiphot()
iraf.daophot()
print image
image = "j-sky.fits"
image = "n-sky.fits"
iraf.digiphot.daophot.phot(image,
coords="noisecoords.dat",
output="noise.dat",
skyfile="sky",
salgori="file",
aperture="apertures",
interactive="no",
wcsin='logical')
iraf.apphot()
iraf.phot('check.fits',
coords='noisecoords.dat',
output='noise.dat',
skyfile='sky',
salgori='file',
aperture='apertures',
interactive='no')
test = raw_input("enter anything")
def calcFlux(imageName, xx, yy, rr):
coordsFN = tempfile.NamedTemporaryFile(delete=False)
print >>coordsFN, xx, yy
coordsFN.close()
digiphot = iraf.digiphot(_doprint=0)
apphot = iraf.apphot(_doprint=0)
qphot = iraf.qphot
pdump = iraf.pdump
qphot.coords = coordsFN.name
#if (iraf.hedit(image=imageName,fields='EXPTIME',value='.')):
#^this line prints the value of EXPTIME which is not needed
#need to check for EXPTIME or qphot will raise an error
try:
pyfits.open(imageName)[0].header['EXPTIME']
qphot.exposure = 'EXPTIME'
except:
pass
qphot.airmass = ''
qphot.interactive = False
qphot.radplots = False
qphot.verbose = False
annulus = rr
dannulus = 1.0
qphot.output = 'qPhotOutput.tmp'
if os.path.exists(qphot.output): os.remove(qphot.output)
qphot(imageName, 1.0, annulus, dannulus, annulus, Stdout=1)
areas = np.array(map(float, pdump(qphot.output, 'AREA', 'yes', Stdout=1)[0].split()))
fluxes = np.array(map(float, pdump(qphot.output, 'SUM', 'yes', Stdout=1)[0].split()))
if os.path.exists(qphot.output): os.remove(qphot.output)
return [np.float64(areas[0]), np.float64(fluxes[0])]
def skySub(imageName):
#Set up iraf packages and variables for filenames
skyIm = imageName+'.sky'
outfile = 'mSky'+imageName+'.file'
avgValLog = 'mSkyLog'+imageName+'.file'
stDevLog = 'stDevLog'+imageName+'.file'
outputIm = imageName+'.sky'
iraf.apphot(_doprint=0)
#Delete exisiting files
for f in (skyIm,outfile,'mSky.file',outputIm+'.fits',avgValLog):
silentDelete(f)
#Configure fitsky parameters
iraf.fitsky.coords="coordslist"
iraf.fitsky.interactive='no'
iraf.fitsky.verify='no'
iraf.datapars.datamax=150000
iraf.datapars.datamin=-200
iraf.fitskypars.salgorithm="mode"
iraf.fitskypars.annulus=0
iraf.fitskypars.dannulus=40
iraf.fitskypars.shireject=2
iraf.fitskypars.sloreject=2
iraf.fitsky.verbose='no'
#Call fitsky to compute the mean of the sky
iraf.fitsky(imageName,output=outfile)
#Use 'PDUMP" to extract only the mean sky values
sys.stdout=open(avgValLog,"w")
iraf.pdump(outfile,'mSky',"yes")
sys.stdout=sys.__stdout__
#Use pdump to get the standard deviation from the sky
sys.stdout=open(stDevLog,"w")
iraf.pdump(outfile,'stDev',"yes")
sys.stdout=sys.__stdout__
#Read in the mSky values file
skyValues = open(avgValLog,'r')
valueList = skyValues.readlines()
#convert the value array to a numpy array
valueListNp = np.asfarray(valueList)
#compute average
average = np.mean(valueListNp)
#Print output
print(str(average)+" is the average")
#Read in the stDev log, take the average
stDevs = open(stDevLog,'r')
stDevList = stDevs.readlines()
stDevListNp = np.asfarray(stDevList)
stDevAvg = np.mean(stDevListNp)
#Configure imarith
iraf.imarith.pixtype="real"
iraf.imarith.calctype="real"
#Use imarith to subtract the average sky value from the image
iraf.imarith(imageName,"-",average,outputIm)
#Add the average sky value and stDev to the image header
iraf.hedit(outputIm,"SKY",average,add='yes',verify='no')
iraf.hedit(outputIm,"SKYSIGMA",stDevAvg,add='yes',verify='no')
#Print an output image
print("Subtracted "+ str(average)+ " to create a new image called "+outputIm)
#Delete acillary output files
silentDelete(outfile)
def escut(image, pos_file, fwhm, peak):
# input image file name, file name with matched source positions, **np.array of fwhm measurements for each source
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from pyraf import iraf
# import sewpy
import os
from matplotlib.path import Path
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
iraf.unlearn(iraf.phot, iraf.datapars, iraf.photpars, iraf.centerpars,
iraf.fitskypars)
iraf.apphot.phot.setParam('interactive', "no")
iraf.apphot.phot.setParam('verify', "no")
iraf.datapars.setParam('datamax', 50000.)
iraf.datapars.setParam('gain', "gain")
iraf.datapars.setParam('ccdread', "rdnoise")
iraf.datapars.setParam('exposure', "exptime")
iraf.datapars.setParam('airmass', "airmass")
iraf.datapars.setParam('filter', "filter")
iraf.datapars.setParam('obstime', "time-obs")
# iraf.datapars.setParam('obstime',"date-obs")
iraf.datapars.setParam('sigma', "INDEF")
iraf.photpars.setParam('zmag', 0.)
iraf.centerpars.setParam('cbox', 9.)
iraf.centerpars.setParam('maxshift', 3.)
iraf.fitskypars.setParam('salgorithm', "median")
iraf.fitskypars.setParam('dannulus', 10.)
# clean up the indefs so we can actually do stats, but reassign them to 99999 so we don't lose track of things
# keep a separate list without them to do the median (we need floats)
indefs = np.where(fwhm == 'INDEF')
good = np.where(fwhm != 'INDEF')
fwhm[indefs] = 99.999
fwhm = fwhm.astype(float)
fwhm_good = fwhm[good].astype(float)
indefs = np.where(peak == 'INDEF')
peak[indefs] = -999.999
peak = peak.astype(float)
peak_good = peak[good].astype(float)
if not os.path.isfile(image[0:-5] + '.txdump'):
# findavgfwhm = sewpy.SEW(
# params = ["X_IMAGE", "Y_IMAGE", "FWHM_IMAGE", "FLAGS"],
# config = {"DETECT_THRESH":200.0},
# sexpath = "sex"
# )
#
# out = findavgfwhm(image)["table"]
#
# fwhms = out['FWHM_IMAGE'] # This is an astropy table.
# flags = out['FLAGS']
# get a really rough estimate of the stellar FWHM in the image to set apertures
# use the input fwhm measurement
# ap1x = fwhm_est
# xpos = datatable['X_IMAGE']
# ypos = datatable['Y_IMAGE']
# fwhm = datatable['FWHM_IMAGE']
# flags = datatable['FLAGS']
# idno = datatable['NUMBER']
ap1x = np.median(
fwhm_good
) # only use isolated detections of stars, this is the 1x aperture
# print ap1x
ap2x = 2.0 * ap1x
# these = [ i for i,id in enumerate(idno) if (flags[i] == 0)]
# with open(image[0:-5]+'.escut.pos','w+') as f:
# for j in range(len(xpos)):
# print >> f, xpos[j], ypos[j], fwhm[j], idno[j]
iraf.datapars.setParam('fwhmpsf', ap1x)
iraf.photpars.setParam('apertures', repr(ap1x) + ', ' + repr(ap2x))
iraf.fitskypars.setParam('annulus', 4. * ap1x)
iraf.apphot.phot(image=image,
coords=pos_file,
output=image[0:-5] + '.phot')
with open(image[0:-5] + '.txdump', 'w+') as txdump_out:
iraf.ptools.txdump(
textfiles=image[0:-5] + '.phot',
fields=
"id,mag,merr,msky,stdev,rapert,xcen,ycen,ifilter,xairmass,image",
expr=
'MAG[1] != INDEF && MERR[1] != INDEF && MAG[2] != INDEF && MERR[2] != INDEF',
headers='no',
Stdout=txdump_out)
mag1x, mag2x = np.loadtxt(image[0:-5] + '.txdump',
usecols=(1, 2),
unpack=True)
iraf_id = np.loadtxt(image[0:-5] + '.txdump',
usecols=(0, ),
dtype=int,
unpack=True)
# idno = np.loadtxt(image[0:-5]+'.escut.pos', usecols=(3,), dtype=int, unpack=True)
xpos, ypos = np.loadtxt(pos_file, usecols=(0, 1), unpack=True)
keepIndex = iraf_id - 1
xpos, ypos, fwhm, peak = xpos[keepIndex], ypos[keepIndex], fwhm[
keepIndex], peak[keepIndex]
# print idno.size, iraf_id.size, xpos.size
diff = mag2x - mag1x
diffCut = diff
magCut = mag2x
xCut = xpos #[good]
yCut = ypos #[good]
idCut = iraf_id
fwhmCut = fwhm #_good
peakCut = peak
print(peakCut.size, magCut.size, diffCut.size)
print(diffCut.size, 0, np.median(diffCut), diffCut.std())
nRemoved = 1
# plt.clf()
# plt.scatter(peakCut, magCut, edgecolor='none')
# plt.savefig('peaktest.pdf')
plt.clf()
# plt.hlines(bin_edges, -2, 1, colors='red', linestyle='dashed')
plt.scatter(diff, mag2x, edgecolor='none', facecolor='black', s=4)
# plt.scatter(diffCut, magCut, edgecolor='none', facecolor='blue', s=4)
magdiff = list(
zip(magCut.tolist(), diffCut.tolist(), peakCut.tolist(),
idCut.tolist()))
dtype = [('mag', float), ('diff', float), ('peak', float), ('id', int)]
magdiff = np.array(magdiff, dtype=dtype)
magSort = np.sort(magdiff, order='peak')
peakRange = (magSort['peak'] > 20000.0) & (magSort['peak'] < 40000.0)
peakVal = np.median((magSort['diff'])[np.where(peakRange)])
# peakVal = np.median(diffCut)
print(peakVal)
plt.scatter((magSort['diff'])[np.where(peakRange)],
(magSort['mag'])[np.where(peakRange)],
edgecolor='none',
facecolor='blue',
s=4)
while nRemoved != 0:
nBefore = diffCut.size
diffCheck = np.where(
abs(peakVal - diffCut) < 2.0 *
diffCut.std()) #[i for i,d in enumerate(diff) if (-0.5 < d < 0.0)]
#
diffCut = diffCut[diffCheck]
nRemoved = nBefore - diffCut.size
magCut = magCut[diffCheck]
xCut = xCut[diffCheck]
yCut = yCut[diffCheck]
idCut = idCut[diffCheck]
fwhmCut = fwhmCut[diffCheck]
print(diffCut.size, nRemoved, np.median(diffCut), diffCut.std())
if 0.05 < diffCut.std() < 0.06:
nRemoved = 0
# plt.fill_betweenx(bin_centers, bin_meds+3.0*bin_stds, bin_meds-3.0*bin_stds, facecolor='red', edgecolor='none', alpha=0.4, label='2x RMS sigma clipping region')
# with open('escutSTD_i.pos','w+') as f:
# for i,blah in enumerate(xCut):
# print >> f, xCut[i], yCut[i], diffCut[i]
bin_meds, bin_edges, binnumber = stats.binned_statistic(magCut,
diffCut,
statistic='median',
bins=24,
range=(-12, 0))
bin_stds, bin_edges, binnumber = stats.binned_statistic(magCut,
diffCut,
statistic=np.std,
bins=24,
range=(-12, 0))
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width / 2
# print bin_meds, bin_stds
bin_hw = np.zeros_like(bin_stds)
for i, bin_std in enumerate(bin_stds):
if bin_std > 0.025:
bin_hw[i] = 3.0 * bin_std
else:
bin_hw[i] = 0.075
# print len(binnumber)
# for i,bin_hwi in enumerate(bin_hw):
left_edge = np.array(list(zip(peakVal - bin_hw, bin_centers)))
right_edge = np.flipud(np.array(list(zip(peakVal + bin_hw, bin_centers))))
# print left_edge, right_edge
verts = np.vstack((left_edge, right_edge))
# print verts
# verts = np.delete(verts, np.array([0,1,2,22,23,24,25,45,46,47]), axis=0)
# DON'T USE A PATH BECAUSE APPARENTLY IT CAN SELECT THE INVERSE SET!! WTF
# print verts
esRegion = Path(verts)
sources = esRegion.contains_points(list(zip(diff, mag2x)))
# print sources
with open('escutREG_i.pos', 'w+') as f:
for i, blah in enumerate(xpos[sources]):
print((xpos[sources])[i], (ypos[sources])[i], (diff[sources])[i],
file=f)
magCut2 = mag2x[sources]
magCut1 = mag1x[sources]
fwhmCut = fwhm[sources]
xCut = xpos[sources]
yCut = ypos[sources]
diffCut = diff[sources]
# find the sources that are in the std method but not the region method
# print idCut, idno[sources]
# extrasSTD = np.setdiff1d(idno[sources], idCut)
# print extrasSTD.size
# print extrasSTD
# with open('escutUNIQUE.pos','w+') as f:
# for i,blah in enumerate(extrasSTD):
# print >> f, xpos[blah-1], ypos[blah-1]
# fwhmcheck = np.loadtxt('testfwhmREG.log', usecols=(10,), unpack=True)
fwhmchk2 = np.where((magCut2 < -4) & (fwhmCut < 90.0))
print(np.median(fwhmCut[fwhmchk2]), np.std(fwhmCut[fwhmchk2]))
fwchk = np.where(
np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) > 10.0 *
np.std(fwhmCut[fwhmchk2]))
drop = np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) > 10.0 * np.std(
fwhmCut[fwhmchk2])
keep = np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) <= 10.0 * np.std(
fwhmCut[fwhmchk2])
with open('escutVBAD_i.pos', 'w+') as f:
for i, blah in enumerate(xCut[fwchk]):
print((xCut[fwchk])[i], (yCut[fwchk])[i], file=f)
with open('escut_i.pos', 'w+') as f:
for i, blah in enumerate(xCut):
if not drop[i]:
print(xCut[i],
yCut[i],
magCut2[i],
fwhmCut[i],
magCut1[i],
file=f)
with open('escut_g.pos', 'w+') as f:
for i, blah in enumerate(xCut):
if not drop[i]:
print(xCut[i],
yCut[i],
magCut2[i],
fwhmCut[i],
magCut1[i],
file=f)
plt.fill_betweenx(bin_centers,
peakVal + bin_hw,
peakVal - bin_hw,
facecolor='red',
edgecolor='none',
alpha=0.4,
label='2x RMS sigma clipping region')
plt.scatter(diffCut[fwchk],
magCut2[fwchk],
edgecolor='none',
facecolor='red',
s=4)
plt.ylim(0, -12)
plt.xlabel('$m_{2x} - m_{1x}$')
plt.ylabel('$m_{2x}$')
plt.xlim(-2, 1)
plt.savefig('testmagiraf.pdf')
plt.clf()
plt.scatter(magCut2, fwhmCut, edgecolor='none', facecolor='black')
plt.scatter(magCut2[fwchk],
fwhmCut[fwchk],
edgecolor='none',
facecolor='red')
plt.hlines([np.median(fwhmCut)], -12, 0, colors='red', linestyle='dashed')
plt.hlines([
np.median(fwhmCut) + fwhmCut.std(),
np.median(fwhmCut) - fwhmCut.std()
],
-12,
0,
colors='red',
linestyle='dotted')
plt.ylim(0, 20)
plt.xlim(-12, 0)
plt.ylabel('fwhm')
plt.xlabel('$m_{2x}$')
plt.savefig('fwhmcheck.pdf')
return fwhmCut[keep]
def get_app_phot(coords, image, plot_only=False, store=True, wcsin="world", fwhm=2, plotdir=".", box=15):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
if (not plot_only):
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".app.mag")
# Read values from .ec file
ecfile= image+".ec"
filter_value=''.join(ecfile).split('.',1)[0]
fwhm_value = fwhm
if (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
try:
with open(''.join(ecfile),'r') as f:
for line in f:
if "airmass" in line:
airmass_value = line.split('=',1)[1]
else:
airmass_value = 1
if "FWHM" in line:
print line
fwhm_value = line.split('FWHM=',1)[1]
fwhm_value = fwhm_value.rsplit("aperture")[0]
except:
pass
print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*2) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad)*5
print aperture_rad, sky_rad
if (not plot_only):
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
# Check if files in list, otherwise exit
if not ecfile:
print "No .ec files in directory, exiting"
sys.exit()
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 15. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filters" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = wcsin,
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("ph_mag","<f4"), ("stdev","<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
hdulist = pf.open(image)
prihdr = hdulist[0].header
img = hdulist[0].data * 1.
nx, ny = img.shape
dimX = int(4)
dimY = int(np.ceil(len(m)*1./4))
outerrad = sky_rad+10
cutrad = outerrad + 15
plt.suptitle("FWHM="+str(fwhm_value))
k = 0
for i in np.arange(dimX):
for j in np.arange(dimY):
if ( k < len(m)):
ax = plt.subplot2grid((dimX,dimY),(i, j))
y1, y2, x1, x2 = m[k]["X"]-cutrad, m[k]["X"]+cutrad, m[k]["Y"]-cutrad, m[k]["Y"]+cutrad
y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
try:
zmin, zmax = zscale.zscale(img[x1:x2,y1:y2], nsamples=1000, contrast=0.25)
except:
sh= img[x1:x2,y1:y2].shape
if sh[0]>0 and sh[1]>0:
zmin = np.nanmin(img[x1:x2,y1:y2])
zmax = np.nanmax(img[x1:x2,y1:y2])
continue
else:
continue
ax.imshow(img[x1:x2,y1:y2], aspect="equal", extent=(-cutrad, cutrad, -cutrad, cutrad), origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
c1 = plt.Circle( (0, 0), edgecolor="r", facecolor="none", radius=5.)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
c3 = plt.Circle( (0, 0), edgecolor="yellow", facecolor="none", radius=sky_rad+10)
plt.gca().add_artist(c1)
plt.gca().add_artist(c2)
plt.gca().add_artist(c3)
ax.set_xticks([])
ax.set_yticks([])
plt.text(+5, +5, "%d"%m[k]["id"])
plt.text(-cutrad, -cutrad, "%.2f$\pm$%.2f"%(m[k]["fit_mag"], m[k]["fiterr"]), color="b")
k = k+1
plt.savefig(os.path.join(plotdir, imname + "plot.png"))
plt.clf()
if list_images["type"][index] in ["cig","standards","clusters"]:
find_sky.main(arguments=[list_images["filename"][index]])
print "Detecting objects for images of CIG(s), standard(s) and cluster(s)"
# This part follows the example in the webpage:
#http://www.lancesimms.com/programs/Python/pyraf/daofind.py
for index, image in enumerate(list_images["filename"]):
if list_images["type"][index] in ["cig","standards","clusters"]:
outfile = utilities.replace_extension(image, ".cat")
if os.path.isfile(outfile):
os.remove(outfile)
# Prepare and run daofind
hdr = fits.getheader(image)
iraf.noao(_doprint=0) # Load noao
iraf.digiphot(_doprint=0) # Load digiphot
iraf.apphot(_doprint=0) # Load apphot
iraf.daofind.setParam('image',image)
FWHM = min(max_FWHM,float(hdr["LEMON FWHM"]))
iraf.daofind.setParam('fwhmpsf', FWHM)
iraf.daofind.setParam('output', outfile)
iraf.daofind.setParam('sigma', float(hdr["SKY_STD"]))
iraf.daofind.setParam('gain', gaink)
iraf.daofind.setParam('readnoise', float(hdr[read_noisek]))
iraf.daofind.setParam('roundlo', -0.3) # Minimal roundness (0 is round)
iraf.daofind.setParam('roundhi', 0.3 )
iraf.daofind.setParam('airmass', "airmass")
iraf.daofind.setParam('filter', filterk)
iraf.daofind.setParam('exposure', exptimek)
iraf.daofind.setParam('obstime', "date-obs")
iraf.daofind.setParam('verify', "no")
iraf.daofind.setParam('datamin', 500)
def run(imfile,
coordlist,
WCS=False,
outroot=None,
poisson=True,
verbose=True,
debug=False,
abmags=False,
decliner=False,
smallskyann=False,
calgorithm='gauss',
upperlim=None,
snanadat=False,
cbox=5.0):
"""
Use iraf.digiphot.apphot to collect aperture photometry in Vega magnitudes.
Required Arguments:
imfile (str): fits image file name (cannot be fpacked)
coord : the coordinate list for photometry
as a str, this is the name of a text file in two columns giving x,y coordinates.
as a python list or numpy array, this gives coordinate pairs for each target
e.g. coord=[ [1024,440], [502,680] ]
Optional arguments:
WCS : If input coordinates are in RA and DEC (degrees), user must set WCS=True.
outroot : root of the output file names (outroot.mag, outroot.full, outroot.out)
if unspecified, the root of imfile is used.
poisson: Use a poisson noise model or not. If set to false, a constant noise model is
used. Default is True
calgorithm: Centering algorithm to pass to iraf.apphot. Choices are "gauss","centroid",
"none", or "ofilter". Default is "gauss"
cbox: Size of centering box to pass to iraf.apphot. Default is 5.0
upperlim: None = when flux<3-sigma then report 3-sig upper limit
True = force computation of 3-sigma upper limit
False = disallow upper limits (i.e. always report measured flux)
snanadat: True = Report the mjd, flux and mag in a SNANA-style OBS: line
False = report the filename, mjd, source position, mag and errors.
verbose: Default is True.
debug: Start pdb. Default is False.
Output products : phot.out, phot.mag, phot.full
phot.out : the raw output from apphot
phot.full : a detailed photometry file
phot.mag : header+one line summary output file :
#image name, filter, xpos, ypos, magnitude, and errors
Requires : numpy, pyfits (astLib is required if WCS=True)
"""
if debug:
import pdb
pdb.set_trace()
from math import sqrt
from numpy import nan, log10
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
if outroot == None: outroot = os.path.splitext(os.path.basename(imfile))[0]
# Get info from header
hdr = pyfits.getheader(imfile)
exptime = hdr['EXPTIME']
if 'EXPSTART' in hdr:
mjdobs = "%.1f" % hdr['EXPSTART']
else:
mjdobs = '0.0'
if 'DATE-OBS' in hdr:
dateobs = hdr['DATE-OBS']
elif 'DATE' in hdr:
dateobs = hdr['DATE']
else:
dateobs = 'unknown'
if 'D001OUUN' in hdr:
if hdr['D001OUUN'].upper() == 'CPS':
gain = exptime
exptime = 1.0
elif hdr['D001OUUN'].upper() == 'COUNTS':
gain = 1.0
elif hdr['BUNIT'].upper() == 'ELECTRONS/S':
gain = exptime
exptime = 1.0
elif hdr['BUNIT'].upper() == 'ELECTRONS':
gain = 1.0
else:
print 'Problem determining units, check image'
return
if 'FILTER' in hdr:
filt = hdr['FILTER']
elif 'FILTER1' in hdr:
filt = hdr['FILTER1']
if filt.startswith('CLEAR'):
filt = hdr['FILTER2']
instrument = hdr['INSTRUME']
if instrument == 'WFC3':
instrument = instrument + '_' + hdr['DETECTOR']
if abmags:
if instrument == 'WFC3_IR':
ZPT = ZPT_WFC3_IR_AB[filt]
elif instrument == 'WFC3_UVIS':
ZPT = ZPT_WFC3_UVIS_AB[filt]
elif instrument == 'ACS':
ZPT = ZPT_ACS_AB[filt]
else:
print "Can't handle instrument: %s; check the fits header." % instrument
#Give up and die
return
else:
if instrument == 'WFC3_IR':
ZPT = ZPT_WFC3_IR_VEGA[filt]
elif instrument == 'WFC3_UVIS':
ZPT = ZPT_WFC3_UVIS_VEGA[filt]
elif instrument == 'ACS':
ZPT = ZPT_ACS_VEGA[filt]
else:
print "Can't handle instrument: %s; check the fits header." % instrument
#Give up and die
return
# format for coord list:
# coordlist= [ [x0,y0], [x1,y1], ... ]
# or a two-column text file
if type(coordlist) == str:
# user provided name of a coordinate file
fin = open(coordlist, 'r')
coordlines = fin.readlines()
fin.close()
coordvals = array([cline.split() for cline in coordlines], dtype=float)
elif len(shape(coordlist)) == 1:
# user provided something like coord=[x,y]
coordvals = array([coordlist])
else:
# user provided something like coord= [[x1,y1],[x2,y2],[x3,y3]]
coordvals = array(coordlist)
# how many objects do we have ?
numcoo = len(coordvals)
if WCS:
# If coords in wcs instead of x,y,
# get wcs information from imfile for converting to xy.
from astLib import astWCS
wcsfits = astWCS.WCS(imfile)
# (re)write a list of x,y positions
coxyfile = '%s.xycoo' % outroot
coxy = open(coxyfile, 'w')
for coord in coordvals:
if WCS:
#Convert from RA and Dec to xy
# NOTE: wcsfits returns values based on a 0,0 origin, but the iraf phot
# packages expect a 1,1 origin. So we add 1 to each value
xy = wcsfits.wcs2pix(coord[0], coord[1])
xy[0] += 1
xy[1] += 1
else:
xy = coord
print >> coxy, "%10.2f %10.2f" % (float(xy[0]), float(xy[1]))
coxy.close()
if verbose > 1: print("XY coords written to %s" % coxyfile)
""" iraf.digiphot.apphot.datapars :
2013.09.06 SR: updated to use Poisson noise model (which includes sky noise) as
the default, instead of 'constant' """
iraf.unlearn(iraf.apphot.phot)
iraf.unlearn(iraf.datapars)
iraf.datapars.scale = 1.0
iraf.datapars.fwhmpsf = psffwhm[instrument]
iraf.datapars.emission = not decliner
iraf.datapars.sigma = 'INDEF'
iraf.datapars.datamin = 'INDEF'
iraf.datapars.datamax = 'INDEF'
if poisson:
iraf.datapars.noise = 'poisson'
else:
iraf.datapars.noise = 'constant'
iraf.datapars.ccdread = ''
#iraf.datapars.gain = ''
iraf.datapars.readnoise = 0.0
#iraf.datapars.exposure = ' '
#iraf.datapars.airmass = ''
#iraf.datapars.obstime = ''
iraf.datapars.itime = exptime
iraf.datapars.epadu = gain
iraf.datapars.xairmass = 'INDEF'
iraf.datapars.ifilter = 'INDEF'
iraf.datapars.otime = 'INDEF'
# iraf.digiphot.apphot.centerpars :
iraf.unlearn(iraf.centerpars)
iraf.centerpars.calgorithm = calgorithm
iraf.centerpars.cbox = cbox
iraf.centerpars.cthreshold = 0.0
iraf.centerpars.minsnratio = 1.0
iraf.centerpars.cmaxiter = 10.0
iraf.centerpars.maxshift = 1.0
iraf.centerpars.clean = False
iraf.centerpars.rclean = 1.0
iraf.centerpars.rclip = 2.0
iraf.centerpars.kclean = 3.0
iraf.centerpars.mkcenter = False
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn(iraf.fitskypars)
iraf.fitskypars.salgorithm = 'median'
if smallskyann:
iraf.fitskypars.annulus = 8.0
iraf.fitskypars.dannulus = 12.0
else:
iraf.fitskypars.annulus = 25.0
iraf.fitskypars.dannulus = 40.0
iraf.fitskypars.skyvalue = 0.0
iraf.fitskypars.smaxiter = 10.0
iraf.fitskypars.sloclip = 0.0
iraf.fitskypars.shiclip = 0.0
iraf.fitskypars.snreject = 50.0
iraf.fitskypars.sloreject = 3.0
iraf.fitskypars.shireject = 3.0
iraf.fitskypars.khist = 3.0
iraf.fitskypars.binsize = 0.1
iraf.fitskypars.smooth = False
iraf.fitskypars.rgrow = 0.0
iraf.fitskypars.mksky = False
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = APLIST
iraf.photpars.zmag = ZPT
iraf.photpars.mkapert = False
photparams = {
'interac': False,
'radplot': False,
}
magfile_out = outroot + '.out'
magfile_full = outroot + '.full'
magfile_phot = outroot + '.mag'
if os.path.exists(magfile_out):
os.remove(magfile_out)
# run photometry using the newly created coxyfile for providing input coordinates
try:
iraf.phot(image=imfile,
skyfile='',
coords=coxyfile,
output=magfile_out,
verify=False,
verbose=True,
Stdout=1,
**photparams)
except iraf.IrafError, e:
print("phot failed on %s with IRAF error :\n%s" % (imfile, e))
from types import *
from numpy import matrix
from numpy import linalg
from iqpkg import *
import p60photutils
# Necessary packages
iraf.images()
iraf.immatch()
iraf.imfilter()
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.digiphot()
iraf.apphot()
yes = iraf.yes
no = iraf.no
INDEF = iraf.INDEF
hedit = iraf.hedit
imgets = iraf.imgets
imcombine = iraf.imcombine
pyrafdir = "python/pyraf/"
pyrafdir_key = 'PYRAFPARS'
if os.environ.has_key(pyrafdir_key):
pardir = os.environ[pyrafdir_key]
else:
pardir = os.environ['HOME'] + '/' + pyrafdir
def runApperturePhotometryOnObject(self, photObject, write=False):
# Aperture sizes
#fap = 1.0
#fdan = 2.0
#fan = 3.0
# To take photometry of a given object using DAOPHOT/APPHOT.PHOT
# Check we have an object list
coordfilename = self._Name.replace(".fits", "objectID_%s.coo" % photObject._id)
coordfilename2 = self._Name.replace(".fits", "_apphot_%s.reg" % photObject._Name.replace(" ",""))
try:
regionOut = open(coordfilename2, "w")
regionOut.write("image; circle(%s, %s, %s) # color = red\n"% (photObject._pixelx, photObject._pixely, photObject._fap))
regionOut.write("image; circle(%s, %s, %s) # color = blue\n" % (photObject._pixelx, photObject._pixely, photObject._fdan))
regionOut.write("image; circle(%s, %s, %s) # color = green\n" % (photObject._pixelx, photObject._pixely, photObject._fan))
regionOut.close()
except:
print "ERROR opening region file to write"
print sys.exc_info()[0]
try:
objectList = open(coordfilename,"r")
objectList.close()
except:
objectList = open(coordfilename,"w")
objectList.write("%s %s" % (photObject._pixelx, photObject._pixely))
objectList.close()
# Load as in IRAF and neglect output:
# NOAO
# DIGIPHOT
# APPHOT
# DAOFIND
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
# DAOPHOT/APPHOT - PHOT Package
# Parameters:
#
# image
# skyfile???
# coords - input file
# output - output file
# datapars
# centerpars
# fitskypars
# photpars
cwd = os.getcwd()
# datapars:
datapars = "%s/uparm/datapars.par" % cwd
#datapars = "uparm/datapars.par"
#iraf.datapars.saveParList(filename=datapars)
# centerpars:
centerpars = "%s/uparm/centerpars.par" % cwd
#centerpars = "uparm/centerpars.par"
# iraf.centerpars.saveParList(filename=centerpars)
# fitskypars:
fitskypars = "%s/uparm/fitskypars.par" % cwd
#fitskypars = "uparm/fitskypars.par"
# iraf.fitskypars.saveParList(filename=fitskypars)
# photpars:
photpars = "%s/uparm/photpars.par" % cwd
#photpars = "uparm/photpars.par"
# iraf.photpar.saveParList(filename=photpars)
#iraf.datapar(sigma=1.5, exposure='IMGEXP', gain='GAIN', ccdread='RON')
# standard
#iraf.phot.setParam('image', self._Name)
#iraf.phot.setParam("output", self._Name.replace(".fits", "_phot.mag"))
#iraf.phot.setParam("datapar", datapars)
#iraf.phot.setParam("centerp", centerpars)
#iraf.phot.setParam("fitskyp", fitskypars)
#iraf.phot.setParam("photpar", photpars)
#iraf.phot.setParam("interac", "no")
#iraf.phot.setParam("verify", "yes")
# co-ordinate file
iraf.phot.setParam("coords", coordfilename)
# run phot
apphotpar = "%s/uparm/phot.par" % cwd
# iraf.phot.saveParList(filename=apphotpar)
output = self._Name.replace(".fits", "_objectID%s_phot.mag" % photObject._id)
try:
self.cleanOutputFiles(output)
except:
print "Error cleaning failed"
print sys.exc_info()[0]
#apsizes= (.4*fap*self._MEDFWHM,.5*fap*self._MEDFWHM,.6*fap*self._MEDFWHM,.8*fap*self._MEDFWHM,
# 1.*fap*self._MEDFWHM,1.2*fap*self._MEDFWHM, 1.5*fap*self._MEDFWHM,2.*fap*self._MEDFWHM,
# 2.5*fap*self._MEDFWHM,3.*fap*self._MEDFWHM
# )
#irafapsizes = '%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f' % apsizes
#irafapsizes = '%.2f' % (5*apsizes[4])
self.loadHeader()
if self._Band in ["J", "H", "K"]:
ifilter = self.getHeader("FILTER")
else:
ifilter = self.getHeader("SUBSET")
print "FILTER: %s" % ifilter
print "RON: %s" % self.getHeader("RON")
print "GAIN: %s" % self.getHeader("GAIN")
print "EXPTIME: %s" % self.getHeader("EXPTIME")
print "AIRMASS: %s" % self.getHeader("AIRMASS")
print "DATE: %s" % self.getHeader("DATE-OBS")
print "ZP: %s" % self._ZPDICT[self.getHeader("FILTER")]
try:
EPADU = (2/3)*self.getHeader("NIMGS")*self.getHeader("GAIN")
print "Keyword used: NIMGS"
except:
EPADU = (2/3)**self.getHeader("NCOMBINE")*self.getHeader("GAIN")
print "Keyword uparmsed: NCOMBINE"
iraf.phot(image=self._Name,
coords=coordfilename,
output=output,
verify=0,
verbose=1,
interac="no",
scale=1,
fwhmpsf=self._MEDFWHM,
sigma=self._skySTDEV,
wcsin="logical",
wcsout="logical",
datamin=-100,
datamax="INDEF",
zmag=self._ZPDICT[self.getHeader("FILTER")], # to do: make function to calculate
annulus=photObject._fan,
dannulus=photObject._fdan,
calgorithm="none",
aperture=photObject._fap,
cbox=1.5*self._MEDFWHM,
maxshift=15,
mode="ql",
Stdout=1,
readnoi=self.getHeader("RON"),
epadu=EPADU,
itime=self.getHeader("EXPTIME"),
xairmass=self.getHeader("AIRMASS"),
ifilter=ifilter,
otime=self.getHeader("DATE-OBS"),
salgori="mode",
#skyvalu=0,
smaxite=1
)
# Parse the output
photout = open(output, "r")
photoObjectProperties = photout.readlines()
photout.close()
propList = []
for Properties in photoObjectProperties:
if Properties[0] != "#":
Property = [i for i in Properties.replace("\n","").replace("\\","").split(" ") if i != ""]
propList.append(Property)
try:
photObject._skyFlux = float(propList[3][0])
except:
photObject._skyFlux = (propList[3][0])
try:
photObject._flux = float(propList[4][1])
except:
photObject._flux = (propList[4][1])
try:
photObject._appMag = float(propList[4][4])
except:
photObject._appMag = (propList[4][4])
try:
photObject._appMagErr = float(propList[4][5])
except:
photObject._appMagErr = (propList[4][5])
photObject._midMJD, photObject._midMJDErr = self.getMidMJD()#seconds=True,zero=55822.89371528)
def build(f):
### is this an MEF file
current_ext = 0
NEXTEND = 0
EXTEND = f[0].header["EXTEND"]
if EXTEND == "T":
NEXTEND = f[0].header["NEXTEND"]
current_ext = 1
### create the name of the output MEF psf file
if not f[0].header.has_key("FILENAME"):
os.unlink(opt.filename)
sys.exit("The fits file " + opt.filename + " has no EXPNUM keyword\n")
sexp = f[0].header["FILENAME"]
mef_psf = sexp + "p_psf_iraf.fits"
### create an MEF file that will contian the PSF(s)
import pyfits
fitsobj = pyfits.HDUList()
prihdu = pyfits.PrimaryHDU()
import re
prihdu.header.update("FILENAME", sexp, comment="CFHT Exposure Numebr")
prihdu.header.update("NEXTEND", NEXTEND, comment="number of extensions")
version = re.match(r"\$Rev.*: (\d*.\d*) \$", __Version__).group(1)
prihdu.header.update("MKPSF_V", float(version), comment="Version number of mkpsf")
fitsobj.append(prihdu)
if os.access(mef_psf, os.F_OK):
os.unlink(mef_psf)
fitsobj.writeto(mef_psf)
fitsobj.close()
outfits = pyfits.open(mef_psf, "append")
prihdr = outfits[0].header
import jjkmode
### Get my python routines
from pyraf import iraf
from pyraf.irafpar import IrafParList
### keep all the parameters locally cached.
iraf.set(uparm="./")
### Load the required IRAF packages
iraf.digiphot()
iraf.apphot()
iraf.daophot()
### temp file name hash.
tfile = {}
while current_ext <= NEXTEND:
### this is a psf SCRIPT so the showplots and interactive are off by force
print "Working on image section " + str(current_ext)
iraf.findpars.sharplo = 0
iraf.findpars.sharphi = 0.7
iraf.findpars.roundlo = -0.7
iraf.findpars.roundhi = 0.7
iraf.datapars.datamax = 20000
iraf.datapars.airmass = "AIRMASS"
iraf.datapars.filter = "FILTER"
iraf.datapars.obstime = "TIME-OBS"
iraf.datapars.exposure = "EXPTIME"
iraf.datapars.gain = "GAIN"
iraf.datapars.ccdread = "RDNOISE"
iraf.datapars.fwhmpsf = opt.fwhm
iraf.daopars.nclean = 2
iraf.daopars.psfrad = 5.0 * opt.fwhm
iraf.daopars.fitrad = 0.85 * opt.fwhm
iraf.daopars.function = "gauss"
iraf.centerpars.calgorithm = "centroid"
zero_mag = 26.19
iraf.photpars.zmag = zero_mag
iraf.photpars.apertures = int(0.85 * opt.fwhm)
iraf.fitskypars.annulus = 2 + int(opt.fwhm * 4.00)
iraf.fitskypars.dannulus = int(opt.fwhm * 2.0)
iraf.daophot.verbose = no
iraf.daophot.verify = no
iraf.daophot.update = no
iraf.psf.interactive = no
iraf.pstselect.interactive = no
iraf.datapars.saveParList()
iraf.fitskypars.saveParList()
iraf.centerpars.saveParList()
iraf.findpars.saveParList()
iraf.photpars.saveParList()
tfiles = [
"coo_bright",
"coo_ok",
"coo_faint",
"mag_all",
"mag_bright",
"mag_ok",
"mag_good",
"mag_best",
"pst_in",
"pst_out",
"pst_out2",
"prf",
"psg_org",
"psg",
"psf_1.fits",
"psf_2.fits",
"psf_final.fits",
"psf_3.fits",
"psf_4.fits",
"mag_pst",
"coo_pst",
"nst",
"nrj",
"seepsf.fits",
"sub.fits",
"fwhm",
"apcor",
]
for file in tfiles:
extname = "chip" + str(f[current_ext].header.get("IMAGEID", str(current_ext))).zfill(2)
tfile[file] = sexp + "_" + extname + "." + file
if os.access(tfile[file], os.F_OK):
os.unlink(tfile[file])
if EXTEND == "T":
this_image = opt.filename + "[" + extname + "]"
else:
this_image = opt.filename
gain = f[current_ext].header.get("GAIN", 1.0)
#### set sky/sigma parameters specific to this frame.
(skyvalue, sigma) = jjkmode.stats(f[current_ext].data)
import math
sigma = math.sqrt(skyvalue / gain)
datamin = float(skyvalue) - 8.0 * float(sigma)
print "Determined sky level to be " + str(skyvalue) + " +/-" + str(sigma)
iraf.datapars.datamin = datamin
iraf.datapars.sigma = float(sigma)
iraf.datapars.saveParList()
iraf.fitskypars.skyvalue = skyvalue
iraf.fitskypars.saveParList()
### find the bright stars in the image.
print "sextracting for stars in " + this_image
###iraf.daophot.daofind(image=this_image,
### output=tfile['coo_bright'],threshold=4.0)
os.system(
"sex -c /home/cadc/kavelaar/12kproc/config/default.sex -SATUR_LEVEL 25000 -CATALOG_NAME "
+ tfile["coo_bright"]
+ " "
+ this_image
)
### print "finding stellar locus in sround/ground space"
print "clipping using star_class > 0.85 "
fcoo = open(tfile["coo_bright"], "r")
lines = fcoo.readlines()
fcoo.close()
import numarray, math
fout = open(tfile["coo_ok"], "w")
for line in lines:
if re.match(r"^#", line) or re.search(r"INDEF", line):
continue
values = line.split()
star_class = float(values[2])
if star_class > 0.75:
fout.write(line)
fout.close()
print "Measuring photometry for psf candidate stars in " + tfile["coo_ok"]
iraf.daophot.phot(image=this_image, coords=tfile["coo_ok"], output=tfile["mag_bright"])
### do this selection in 2 steps because of the way IRAF handles INDEFs
print "Selecting stars that have good centroids and magnitudes "
iraf.pselect(
tfile["mag_bright"], tfile["mag_ok"], "(CIER==0)&&(PIER==0)&&(SIER==0)&&(MSKY>0)&&(MSKY<2e5)&&(MSKY!=INDEF)"
)
print "Selecting stars that have normal sky levels"
condition = "(abs(MSKY -" + str(skyvalue) + ") < 5.0*" + str(sigma) + ")"
iraf.pselect(tfile["mag_ok"], tfile["mag_good"], condition)
a = iraf.txdump(tfile["mag_good"], "SSKEW", iraf.yes, Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean ** 2)
limit = mean + 2 * stddev
os.unlink(tfile["mag_good"])
condition = condition + " && SSKEW < " + str(limit)
iraf.pselect(tfile["mag_ok"], tfile["mag_good"], condition)
print "Choosing the psf stars"
iraf.pstselect(image=this_image, photfile=tfile["mag_good"], pstfile=tfile["pst_in"], maxnpsf=25)
## construct an initial PSF image
print "computing psf with neighbor stars based on complete star list"
iraf.psf.mode = "a"
iraf.psf(
image=this_image,
photfile=tfile["mag_bright"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_1.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg_org"],
varorder=0,
)
try:
print "subtracting the psf neighbors and placing the results in " + tfile["sub.fits"]
iraf.daophot.nstar(
image=this_image,
groupfile=tfile["psg_org"],
psfimage=tfile["psf_1.fits"],
nstarfile=tfile["nst"],
rejfile=tfile["nrj"],
)
iraf.daophot.substar(
image=this_image,
photfile=tfile["nst"],
exfile=tfile["pst_in"],
psfimage=tfile["psf_1.fits"],
subimage=tfile["sub.fits"],
)
a = iraf.daophot.txdump(tfile["nst"], "chi", "yes", Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean ** 2)
limit = mean + 2.5 * stddev
print "Selecting those psf stars with CHI^2 <" + str(limit) + " after fitting with trial psf"
iraf.pselect(tfile["nst"], tfile["mag_best"], "CHI < " + str(limit))
os.unlink(tfile["pst_out"])
## os.unlink(tfile['psg'])
## rebuild the PSF file with the psf stars that fit well..
## using the neighbor subtracted image
print "Rebuilding the PSF"
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_2.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=0,
)
print "re-subtracting with rebuilt psf"
os.unlink(tfile["nst"])
os.unlink(tfile["nrj"])
iraf.daophot.nstar(
image=this_image,
groupfile=tfile["psg"],
psfimage=tfile["psf_2.fits"],
nstarfile=tfile["nst"],
rejfile=tfile["nrj"],
)
os.unlink(tfile["sub.fits"])
iraf.daophot.substar(
image=this_image,
photfile=tfile["nst"],
exfile=tfile["pst_in"],
psfimage=tfile["psf_2.fits"],
subimage=tfile["sub.fits"],
)
os.unlink(tfile["psg"])
os.unlink(tfile["pst_out"])
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_3.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=0,
)
os.unlink(tfile["nrj"])
os.unlink(tfile["nst"])
iraf.daophot.nstar(
image=this_image,
groupfile=tfile["psg"],
psfimage=tfile["psf_3.fits"],
nstarfile=tfile["nst"],
rejfile=tfile["nrj"],
)
a = iraf.daophot.txdump(tfile["nst"], "chi", "yes", Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean ** 2)
limit = mean + 2 * stddev
limit = 2.0
# print "Selecting those psf stars with CHI^2 < "+str(limit)+" after fit with GOOD psf"
os.unlink(tfile["mag_best"])
iraf.pselect(tfile["nst"], tfile["mag_best"], "CHI < " + str(limit))
print "Building final PSF.... "
os.unlink(tfile["sub.fits"])
iraf.daophot.substar(
image=this_image,
photfile=tfile["nst"],
exfile=tfile["pst_in"],
psfimage=tfile["psf_3.fits"],
subimage=tfile["sub.fits"],
)
os.unlink(tfile["psg"])
os.unlink(tfile["pst_out"])
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_final.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=0,
)
print "building an analytic psf for the FWHM calculations"
os.unlink(tfile["pst_out"])
os.unlink(tfile["psg"])
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_4.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=-1,
)
except:
print sys.exc_info()[1]
print "ERROR: Reverting to first pass psf"
tfile["psf_final.fits"] = tfile["psf_1.fits"]
iraf.daophot.psf(
image=this_image,
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_4.fits"],
opstfile=tfile["pst_out2"],
groupfile=tfile["psg"],
varorder=-1,
)
psf_ap = iraf.photpars.apertures
ap1 = int(psf_ap)
ap2 = int(4.0 * opt.fwhm)
apcor = "INDEF"
aperr = "INDEF"
if 0:
# try
### now that we have the psf use the output list of psf stars
### to compute the aperature correction
lines = iraf.txdump(tfile["pst_out"], "xcen,ycen,mag,id", iraf.yes, Stdout=tfile["coo_pst"])
## set the lower ap value for the COG (normally set to 2)
if ap1 < 3:
smallap = 1
else:
smallap = 2 - ap1 + 1
ap1 = 2
ap2 = int(math.floor(4.0 * opt.fwhm))
naperts = ap2 - ap1 + 1
iraf.photpars.apertures = str(ap1) + ":" + str(ap2) + ":1"
iraf.photpars.saveParList()
iraf.daophot.phot(image=this_image, coords=tfile["coo_pst"], output=tfile["mag_pst"])
iraf.photcal()
iraf.photcal.mkapfile(
tfile["mag_pst"],
naperts=naperts,
apercors=tfile["apcor"],
smallap=smallap,
verify="no",
gcommands="",
interactive=0,
)
fin = open(tfile["apcor"], "r")
lines = fin.readlines()
values = lines[2].split()
apcor = values[1]
aperr = values[2]
# except:
## compute the FWHM of the PSF image using the analytic PSF (VarOrd=-1)
psf_file = pyfits.open(tfile["psf_4.fits"])
fwhm = psf_file[0].header.get("PAR1", 99.0) + psf_file[0].header.get("PAR2", 99.0)
psf_file.close()
# ## Open the psf.fits
infits = pyfits.open(tfile["psf_final.fits"])
hdu = infits[0]
inhdu = hdu.header
inhdu.update("XTENSION", "IMAGE", before="SIMPLE")
inhdu.update("PCOUNT", 0, after="NAXIS2")
inhdu.update("GCOUNT", 1, after="PCOUNT")
del hdu.header["SIMPLE"]
del hdu.header["EXTEND"]
inhdu.update("EXTNAME", extname, comment="image extension identifier")
# inhdu.update("SLOW",slow,comment="SROUND low cutoff")
# inhdu.update("SIGH",sigh,comment="SROUND high cutoff")
inhdu.update("PFWHM", fwhm, comment="FWHM of stars based on PSF fitting")
inhdu.update("ZMAG", zero_mag, comment="ZMAG of PSF ")
inhdu.update("BCKG", skyvalue, comment="Mean sky level in counts")
inhdu.update("BCKG_STD", sigma, comment="standard deviation of sky in counts")
inhdu.update("AP1", psf_ap, comment="Apperture used for PSF flux")
inhdu.update("AP2", ap2, comment="Full Flux aperture")
inhdu.update("APCOR", apcor, comment="Apperture correction (ap1->ap2)")
inhdu.update("APERR", apcor, comment="Uncertainty in APCOR")
# ### append this psf to the output images....
print "Sticking this PSF onto the output file"
f[current_ext].header.update("PFWHM", fwhm, comment="FWHM of stars based on PSF fitting")
f[current_ext].header.update("BCKG", skyvalue, comment="Mean sky level in counts")
f[current_ext].header.update("BCKG_STD", sigma, comment="Standard deviation of sky in counts")
f.flush()
outfits.append(hdu)
outfits.flush()
infits.close()
### remove the temp file we used for this computation.
for tf in tfile.keys():
if os.access(tfile[tf], os.F_OK):
os.unlink(tfile[tf])
current_ext = current_ext + 1
outfits.close()
return mef_psf
def phot(fits_filename, x_in, y_in, aperture=15, sky=20, swidth=10, apcor=0.3,
maxcount=30000.0, exptime=1.0, zmag=None, extno=0, centroid=True):
"""
Compute the centroids and magnitudes of a bunch sources on fits image.
:rtype : astropy.table.Table
:param fits_filename: Name of fits image to measure source photometry on.
:type fits_filename: str
:param x_in: x location of source to measure
:type x_in: float, numpy.array
:param y_in: y location of source to measure
:type y_in: float, numpy.array
:param aperture: radius of circular aperture to use.
:type aperture: float
:param sky: radius of inner sky annulus
:type sky: float
:param swidth: width of the sky annulus
:type swidth: float
:param apcor: Aperture correction to take aperture flux to full flux.
:type apcor: float
:param maxcount: maximum linearity in the image.
:type maxcount: float
:param exptime: exposure time, relative to zmag supplied
:type exptime: float
:param zmag: zeropoint magnitude
:param extno: extension of fits_filename the x/y location refers to.
"""
if not hasattr(x_in, '__iter__'):
x_in = [x_in, ]
if not hasattr(y_in, '__iter__'):
y_in = [y_in, ]
if (not os.path.exists(fits_filename) and
not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
logger.debug(str(err))
raise TaskError("Failed to open input image: %s" % err.message)
# get the filter for this image
filter_name = input_hdulist[extno].header.get('FILTER', 'DEFAULT')
# Some nominal CFHT zeropoints that might be useful
zeropoints = {"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 32.0,
'r.MP9601': 31.9,
'gri.MP9603': 33.520}
if zmag is None:
logger.warning("No zmag supplied to daophot, looking for header or default values.")
zmag = input_hdulist[extno].header.get('PHOTZP', zeropoints[filter_name])
logger.warning("Setting zmag to: {}".format(zmag))
# check for magic 'zeropoint.used' files
for zpu_file in ["{}.zeropoint.used".format(os.path.splitext(fits_filename)[0]), "zeropoint.used"]:
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
logger.warning("Using file {} to set zmag to: {}".format(zpu_file, zmag))
break
photzp = input_hdulist[extno].header.get('PHOTZP', zeropoints.get(filter_name, zeropoints["DEFAULT"]))
if zmag != photzp:
logger.warning(("zmag sent to daophot: ({}) "
"doesn't match PHOTZP value in image header: ({})".format(zmag, photzp)))
# setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.photpars.apertures = aperture
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.sloclip = 5.0
iraf.fitskypars.shiclip = 5.0
if centroid:
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
else:
iraf.centerpars.calgori = "none"
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
for i in range(len(x_in)):
coofile.write("%f %f \n" % (x_in[i], y_in[i]))
coofile.flush()
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename+"[{}]".format(extno), coofile.name, magfile.name)
pdump_out = ascii.read(magfile.name, format='daophot')
logging.debug("PHOT FILE:\n"+str(pdump_out))
if not len(pdump_out) > 0:
mag_content = open(magfile.name).read()
raise TaskError("photometry failed. {}".format(mag_content))
# apply the aperture correction
pdump_out['MAG'] -= apcor
# if pdump_out['PIER'][0] != 0 or pdump_out['SIER'][0] != 0 or pdump_out['CIER'][0] != 0:
# raise ValueError("Photometry failed:\n {}".format(pdump_out))
# Clean up temporary files generated by IRAF
os.remove(coofile.name)
os.remove(magfile.name)
logger.debug("Computed aperture photometry on {} objects in {}".format(len(pdump_out), fits_filename))
del input_hdulist
return pdump_out
def photom(images,anillo,danillo,apertura,outfile=None,path=None,coords='coords.in'):
'''
Dada una lista de imágenes, realiza la fotometría de apertura con
la tarea phot.
INPUT
images : Lista de imágenes .in o archivo .fits
(coords) : Archivo con las coordenadas de los objetos, por defecto
es "coords.in"
anillo : radio annulus
danillo : radio dannulus
apertura : Apretura de los radios, ingresada como un string
ej: r1,r2:rn-1,rn
(outfile): Nombre de archivo(s) de salida. por defecto es el nombre de
images en extension phot
(path) : Línea que indica el camino al directorio de archivos.
------------------------------------------------------------------
Given a image list, performs aperture photometry via phot task.
INPUT
images : .in image list or .fits file.
coords : file with objects' coordinates.
anillo : ?
danillo : ??
(outfile): Name of the output file(s).
(path) : String that indicates the path to the files.
'''
if path is not None:
originalpath=aux.chdir(path,save=True)
iraf.noao()
iraf.digiphot()
iraf.apphot()
iraf.unlearn(iraf.datapars)
iraf.unlearn(iraf.fitskypars)
iraf.unlearn(iraf.photpars)
iraf.unlearn(iraf.phot) #eterno resplandor de una tarea sin recuerdos
# serie de comandos que indican el campo del header donde ir a buscar info
iraf.datapars.ccdread="rdnoise"
iraf.datapars.gain="gain"
iraf.datapars.exposure="exptime"
iraf.datapars.airmass="airmass"
iraf.datapars.filter="filtnew"
iraf.datapars.obstime="time-obs"
# setea parámetros de la fotometría
iraf.fitskypars.annulus=anillo # radio del anillo interior (cuentas obj - cuentas d cielo)
iraf.fitskypars.dannulus=danillo # radio del anillo exterior(cuentas cielo)
# iraf.photpars.apertures="0.5,1:30:1"
iraf.photpars.apertures= apertura # intervalo de radios a tomar
iraf.phot.interac='no'
iraf.phot.verify='no'
iraf.phot.verbose='no'
if (os.path.splitext(images)[-1] == '.fit' or
os.path.splitext(images)[-1] == '.fits'):
iraf.phot.image=images
iraf.phot.coords=coords
outfile=aux.default(outfile,os.path.splitext(images)[0]+'.phot',borrar=True)
iraf.phot.output=outfile
else:
iraf.phot.image='@'+images
iraf.phot.coords='@'+coords
outfile=aux.default(outfile,os.path.splitext(images)[0]+'_phots.in',borrar=True)
imagelista=np.genfromtxt(images,dtype=None)
f=open(outfile,'a+')
for im in imagelista :
f.write(os.path.splitext(im)[0]+'.phot'+'\n')
aux.rm(os.path.splitext(im)[0]+'.phot')
f.close()
iraf.phot.output='@'+outfile
iraf.phot()
#%%
# contador de cantidad de filas en un archivo -sin contar los comentarios-
# if (os.path.splitext(images)[-1] == '.fit' or
# os.path.splitext(images)[-1] == '.fits'):
# with open(coords) as fp:
# for curline in fp:
# check if the current line
# starts with "#"
# if curline.startswith("#"): #if it's a commented line
# continue #do nothing
# else:
# line = fp.readline() #initialize reading
# cnt = 1
# while line:
#print("Line {}: {}".format(cnt, line.strip()))
#eso puede servir para comprimir los dos for en
#uno solo, pero no sé como jeje... pronto pronto...
# line = fp.readline()
# cnt += 1 #count how many lines -stars- in coords file.
# fp.close()
if path is not None:
aux.chdir(originalpath)
def iraf_phot(f,x,y,zmag=26.5,apin=10,skyin=15,skywidth=10):
"""Compute the magnitude of the star at location x/y"""
import pyfits
import re
infits=pyfits.open(f,'update')
f=re.sub(r'.fits$','',f)
### Get my python routines
from pyraf import iraf
from pyraf.irafpar import IrafParList
### keep all the parameters locally cached.
iraf.set(uparm="./")
iraf.set(imtype="fits")
### Load the required IRAF packages
iraf.digiphot()
iraf.apphot()
iraf.daophot()
### temp file name hash.
tfile={}
iraf.datapars.datamax=60000
iraf.datapars.datamin=-1000
iraf.datapars.airmass='AIRMASS'
iraf.datapars.filter='FILTER'
iraf.datapars.obstime='TIME-OBS'
iraf.datapars.exposure='EXPTIME'
iraf.datapars.gain='GAIN'
iraf.datapars.ccdread='RDNOISE'
iraf.datapars.fwhmpsf=5.0
iraf.centerpars.calgorithm='centroid'
iraf.photpars.zmag=zmag
iraf.photpars.apertures=apin
iraf.fitskypars.annulus=skyin
iraf.fitskypars.dannulus=skywidth
iraf.daophot.verbose=iraf.no
iraf.daophot.verify=iraf.no
iraf.daophot.update=iraf.no
iraf.psf.interactive=iraf.no
iraf.pstselect.interactive=iraf.no
iraf.datapars.saveParList()
iraf.fitskypars.saveParList()
iraf.centerpars.saveParList()
iraf.findpars.saveParList()
iraf.photpars.saveParList()
tfiles = ['coo','mag']
for file in tfiles:
extname=f
tfile[file]=extname+"."+file
if ( os.access(tfile[file],os.F_OK) ):
os.unlink(tfile[file])
this_image=f
fd = open(tfile['coo'],'w')
fd.write('%f %f\n' % ( x, y) )
fd.close()
print "Measuring photometry psf star in "+tfile['coo']
iraf.daophot.phot(image=this_image,
coords=tfile['coo'],
output=tfile['mag'])
import string
a=iraf.txdump(tfile['mag'],"MAG,XCEN,YCEN",iraf.yes,Stdout=1)
(mag,x,y)=string.split(a[0])
inhdu=infits[0].header
inhdu.update("PSFMAG",float(mag),comment="PSF Magnitude")
inhdu.update("PSF_X",float(x),comment="PSF Magnitude")
inhdu.update("PSF_Y",float(y),comment="PSF Magnitude")
inhdu.update("ZMAG",zmag,comment="ZMAG of PSF ")
## now measure using a smaller aperture to get aper correction
iraf.photpars.apertures=apin*3.0
os.unlink(tfile['mag'])
iraf.daophot.phot(image=this_image,
coords=tfile['coo'],
output=tfile['mag'])
a=iraf.txdump(tfile['mag'],"MAG,XCEN,YCEN",iraf.yes,Stdout=1)
(magout,x,y)=string.split(a[0])
inhdu.update("APCOR",float(magout)-float(mag),comment="AP_OUT - AP_IN")
inhdu.update("AP_IN",apin*3.0,comment="Small aperature")
inhdu.update("AP_OUT",apin,comment="Large aperture")
# ### append this psf to the output images....
infits.close()
### remove the temp file we used for this computation.
#for tf in tfile.keys():
# if os.access(tfile[tf],os.F_OK):
# os.unlink(tfile[tf])
return 0
def plant_kbos(filename, psf, kbos, shifts, prefix):
"""
Add KBOs to an image
:param filename: name of the image to add KBOs to
:param psf: the Point Spread Function in IRAF/DAOPHOT format to be used by ADDSTAR
:param kbos: list of KBOs to add, has format as returned by KBOGenerator
:param shifts: dictionary with shifts to transfer to coordinates to reference frame.
:param prefix: an estimate FWHM of the image, used to determine trailing.
:return: None
"""
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.images()
if shifts['nmag'] < 4:
logging.warning("Mag shift based on fewer than 4 common stars.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift based on fewer than 4 common stars.")
fd.close()
if shifts['emag'] > 0.05:
logging.warning("Mag shift has large uncertainty.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift hsa large uncertainty.")
fd.close()
addstar = tempfile.NamedTemporaryFile(suffix=".add")
# transform KBO locations to this frame using the shifts provided.
w = get_wcs(shifts)
header = fits.open(filename)[0].header
# set the rate of motion in units of pixels/hour instead of ''/hour
scale = header['PIXSCAL1']
rate = kbos['sky_rate']/scale
# compute the location of the KBOs in the current frame.
# offset magnitudes from the reference frame to the current one.
mag = kbos['mag'] - shifts['dmag']
angle = radians(kbos['angle'])
# Move the x/y locations to account for the sky motion of the source.
x = kbos['x'] - rate*24.0*shifts['dmjd']*cos(angle)
y = kbos['y'] - rate*24.0*shifts['dmjd']*sin(angle)
x, y = w.wcs_world2pix(x, y, 1)
# Each source will be added as a series of PSFs so that a new PSF is added for each pixel the source moves.
itime = float(header['EXPTIME'])/3600.0
npsf = fabs(rint(rate * itime)) + 1
mag += 2.5*log10(npsf)
dt_per_psf = itime/npsf
# Build an addstar file to be used in the planting of source.
idx = 0
for record in transpose([x, y, mag, npsf, dt_per_psf, rate, angle]):
x = record[0]
y = record[1]
mag = record[2]
npsf = record[3]
dt = record[4]
rate = record[5]
angle = record[6]
for i in range(int(npsf)):
idx += 1
x += dt*rate*math.cos(angle)
y += dt*rate*math.sin(angle)
addstar.write("{} {} {} {}\n".format(x, y, mag, idx))
addstar.flush()
fk_image = prefix+filename
try:
os.unlink(fk_image)
except OSError as err:
if err.errno == errno.ENOENT:
pass
else:
raise
# add the sources to the image.
iraf.daophot.addstar(filename, addstar.name, psf, fk_image,
simple=True, verify=False, verbose=False)
# convert the image to short integers.
iraf.images.chpix(fk_image, fk_image, 'ushort')
def escut(image, pos_file, fwhm, peak):
# input image file name, file name with matched source positions, **np.array of fwhm measurements for each source
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from pyraf import iraf
# import sewpy
import os
from matplotlib.path import Path
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
iraf.unlearn(iraf.phot,iraf.datapars,iraf.photpars,iraf.centerpars,iraf.fitskypars)
iraf.apphot.phot.setParam('interactive',"no")
iraf.apphot.phot.setParam('verify',"no")
iraf.datapars.setParam('datamax',50000.)
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
# iraf.datapars.setParam('obstime',"date-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.photpars.setParam('zmag',0.)
iraf.centerpars.setParam('cbox',9.)
iraf.centerpars.setParam('maxshift',3.)
iraf.fitskypars.setParam('salgorithm',"median")
iraf.fitskypars.setParam('dannulus',10.)
# clean up the indefs so we can actually do stats, but reassign them to 99999 so we don't lose track of things
# keep a separate list without them to do the median (we need floats)
indefs = np.where(fwhm=='INDEF')
good = np.where(fwhm!='INDEF')
fwhm[indefs] = 99.999
fwhm = fwhm.astype(float)
fwhm_good = fwhm[good].astype(float)
indefs = np.where(peak=='INDEF')
peak[indefs] = -999.999
peak = peak.astype(float)
peak_good = peak[good].astype(float)
if not os.path.isfile(image[0:-5]+'.txdump'):
# findavgfwhm = sewpy.SEW(
# params = ["X_IMAGE", "Y_IMAGE", "FWHM_IMAGE", "FLAGS"],
# config = {"DETECT_THRESH":200.0},
# sexpath = "sex"
# )
#
# out = findavgfwhm(image)["table"]
#
# fwhms = out['FWHM_IMAGE'] # This is an astropy table.
# flags = out['FLAGS']
# get a really rough estimate of the stellar FWHM in the image to set apertures
# use the input fwhm measurement
# ap1x = fwhm_est
# xpos = datatable['X_IMAGE']
# ypos = datatable['Y_IMAGE']
# fwhm = datatable['FWHM_IMAGE']
# flags = datatable['FLAGS']
# idno = datatable['NUMBER']
ap1x = np.median(fwhm_good) # only use isolated detections of stars, this is the 1x aperture
# print ap1x
ap2x = 2.0*ap1x
# these = [ i for i,id in enumerate(idno) if (flags[i] == 0)]
# with open(image[0:-5]+'.escut.pos','w+') as f:
# for j in range(len(xpos)):
# print >> f, xpos[j], ypos[j], fwhm[j], idno[j]
iraf.datapars.setParam('fwhmpsf',ap1x)
iraf.photpars.setParam('apertures',repr(ap1x)+', '+repr(ap2x))
iraf.fitskypars.setParam('annulus',4.*ap1x)
iraf.apphot.phot(image=image, coords=pos_file, output=image[0:-5]+'.phot')
with open(image[0:-5]+'.txdump','w+') as txdump_out :
iraf.ptools.txdump(textfiles=image[0:-5]+'.phot', fields="id,mag,merr,msky,stdev,rapert,xcen,ycen,ifilter,xairmass,image", expr='MAG[1] != INDEF && MERR[1] != INDEF && MAG[2] != INDEF && MERR[2] != INDEF', headers='no', Stdout=txdump_out)
mag1x, mag2x = np.loadtxt(image[0:-5]+'.txdump', usecols=(1,2), unpack=True)
iraf_id = np.loadtxt(image[0:-5]+'.txdump', usecols=(0,), dtype=int, unpack=True)
# idno = np.loadtxt(image[0:-5]+'.escut.pos', usecols=(3,), dtype=int, unpack=True)
xpos, ypos = np.loadtxt(pos_file, usecols=(0,1), unpack=True)
keepIndex = iraf_id - 1
xpos, ypos, fwhm, peak = xpos[keepIndex], ypos[keepIndex], fwhm[keepIndex], peak[keepIndex]
# print idno.size, iraf_id.size, xpos.size
diff = mag2x - mag1x
diffCut = diff
magCut = mag2x
xCut = xpos#[good]
yCut = ypos#[good]
idCut = iraf_id
fwhmCut = fwhm#_good
peakCut = peak
print peakCut.size, magCut.size, diffCut.size
print diffCut.size, 0, np.median(diffCut), diffCut.std()
nRemoved = 1
# plt.clf()
# plt.scatter(peakCut, magCut, edgecolor='none')
# plt.savefig('peaktest.pdf')
plt.clf()
# plt.hlines(bin_edges, -2, 1, colors='red', linestyle='dashed')
plt.scatter(diff, mag2x, edgecolor='none', facecolor='black', s=4)
# plt.scatter(diffCut, magCut, edgecolor='none', facecolor='blue', s=4)
magdiff = zip(magCut.tolist(), diffCut.tolist(), peakCut.tolist(), idCut.tolist())
dtype = [('mag',float), ('diff', float), ('peak', float), ('id', int)]
magdiff = np.array(magdiff, dtype=dtype)
magSort = np.sort(magdiff, order='peak')
peakRange = (magSort['peak'] > 20000.0) & (magSort['peak'] < 40000.0)
peakVal = np.median((magSort['diff'])[np.where(peakRange)])
# peakVal = np.median(diffCut)
print peakVal
plt.scatter((magSort['diff'])[np.where(peakRange)], (magSort['mag'])[np.where(peakRange)], edgecolor='none', facecolor='blue', s=4)
while nRemoved != 0:
nBefore = diffCut.size
diffCheck = np.where(abs(peakVal-diffCut) < 2.5*diffCut.std())#[i for i,d in enumerate(diff) if (-0.5 < d < 0.0)]
#
diffCut = diffCut[diffCheck]
nRemoved = nBefore - diffCut.size
magCut = magCut[diffCheck]
xCut = xCut[diffCheck]
yCut = yCut[diffCheck]
idCut = idCut[diffCheck]
fwhmCut = fwhmCut[diffCheck]
print diffCut.size, nRemoved, np.median(diffCut), diffCut.std()
if 0.05 < diffCut.std() <0.06:
nRemoved=0
# plt.fill_betweenx(bin_centers, bin_meds+3.0*bin_stds, bin_meds-3.0*bin_stds, facecolor='red', edgecolor='none', alpha=0.4, label='2x RMS sigma clipping region')
# with open('escutSTD_i.pos','w+') as f:
# for i,blah in enumerate(xCut):
# print >> f, xCut[i], yCut[i], diffCut[i]
bin_meds, bin_edges, binnumber = stats.binned_statistic(magCut, diffCut, statistic='median', bins=24, range=(magCut.min(),magCut.max()))
bin_stds, bin_edges, binnumber = stats.binned_statistic(magCut, diffCut, statistic=np.std, bins=24, range=(magCut.min(),magCut.max()))
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width/2
# print bin_meds, bin_stds
left_edge = np.array(zip(peakVal-2.5*bin_stds, bin_centers))
right_edge = np.flipud(np.array(zip(peakVal+2.5*bin_stds, bin_centers)))
# print left_edge, right_edge
verts = np.vstack((left_edge, right_edge))
# print verts
# verts = np.delete(verts, np.array([0,1,2,22,23,24,25,45,46,47]), axis=0)
# print verts
esRegion = Path(verts)
sources = esRegion.contains_points(zip(diff,mag2x))
# print sources
with open('escutREG_i.pos','w+') as f:
for i,blah in enumerate(xpos[sources]):
print >> f, (xpos[sources])[i], (ypos[sources])[i], (diff[sources])[i]
magCut = mag2x[sources]
fwhmCut = fwhm[sources]
xCut = xpos[sources]
yCut = ypos[sources]
diffCut = diff[sources]
# find the sources that are in the std method but not the region method
# print idCut, idno[sources]
# extrasSTD = np.setdiff1d(idno[sources], idCut)
# print extrasSTD.size
# print extrasSTD
# with open('escutUNIQUE.pos','w+') as f:
# for i,blah in enumerate(extrasSTD):
# print >> f, xpos[blah-1], ypos[blah-1]
# fwhmcheck = np.loadtxt('testfwhmREG.log', usecols=(10,), unpack=True)
fwhmchk2 = np.where((magCut<-4) & (fwhmCut<90.0))
print np.median(fwhmCut[fwhmchk2]), np.std(fwhmCut[fwhmchk2])
fwchk = np.where(np.abs(fwhmCut-np.median(fwhmCut[fwhmchk2])) > 10.0*np.std(fwhmCut[fwhmchk2]))
drop = np.abs(fwhmCut-np.median(fwhmCut[fwhmchk2])) > 10.0*np.std(fwhmCut[fwhmchk2])
# fwmag = mag2x[sources]
with open('escutVBAD_i.pos','w+') as f:
for i,blah in enumerate(xCut[fwchk]):
print >> f, (xCut[fwchk])[i], (yCut[fwchk])[i]
with open('escut_r.pos','w+') as f:
for i,blah in enumerate(xCut):
if not drop[i]:
print >> f, xCut[i], yCut[i]
with open('escut_g.pos','w+') as f:
for i,blah in enumerate(xCut):
if not drop[i]:
print >> f, xCut[i], yCut[i]
plt.fill_betweenx(bin_centers, peakVal+2.5*bin_stds, peakVal-2.5*bin_stds, facecolor='red', edgecolor='none', alpha=0.4, label='2x RMS sigma clipping region')
plt.scatter(diffCut[fwchk], magCut[fwchk], edgecolor='none', facecolor='red', s=4)
plt.ylim(0,-12)
plt.xlabel('$m_{2x} - m_{1x}$')
plt.ylabel('$m_{2x}$')
plt.xlim(-2,1)
plt.savefig('testmagiraf.pdf')
plt.clf()
plt.scatter(magCut, fwhmCut, edgecolor='none', facecolor='black')
plt.scatter(magCut[fwchk], fwhmCut[fwchk], edgecolor='none', facecolor='red')
plt.hlines([np.median(fwhmCut)], -12, 0, colors='red', linestyle='dashed')
plt.hlines([np.median(fwhmCut)+fwhmCut.std(), np.median(fwhmCut)-fwhmCut.std()], -12, 0, colors='red', linestyle='dotted')
plt.ylim(0,20)
plt.xlim(-12,0)
plt.ylabel('fwhm')
plt.xlabel('$m_{2x}$')
plt.savefig('fwhmcheck.pdf')
return True
def get_app_phot_target(image, ra=None, dec=None, plot=True, store=True, wcsin="logical", fwhm=None, box=15, arcsecpix=0.394, app=2):
'''
coords: files:
wcsin: can be "world", "logic"
fwhm: in arcsec
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
impf = pf.open(image)
wcs = WCS(impf[0].header)
#Check that actually the object is within this frame.
if (ra is None or dec is None):
if (fitsutils.has_par(image, "OBJRA") and fitsutils.has_par(image, "OBJRA")):
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'OBJRA'), fitsutils.get_par(image, 'OBJDEC'))
else:
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'RA'), fitsutils.get_par(image, 'DEC'))
print "Assuming ra=%.5f, dec=%.5f"%(ra, dec)
pra, pdec = get_xy_coords(image, ra, dec)
else:
if("logic" in wcsin):
pra, pdec = ra, dec
else:
#Using new method to derive the X, Y pixel coordinates, as wcs module does not seem to be working well.
try:
#pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#print "Retrieved the pixel number"
pra, pdec = get_xy_coords(image, ra, dec)
except IndexError:
print "Error with astrometry.net. trying the rudimentary method."
pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#pra, pdec = wcs.wcs_sky2pix(np.array([ra, dec], ndmin=2), 1)[0]
shape = impf[0].data.shape
if (pra > 0) and (pra < shape[0]) and (pdec > 0) and (pdec < shape[1]):
pass
else:
print image, "ERROR! Object coordinates are outside this frame. Skipping any aperture photometry!!"
print pra, pdec, shape
return
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".objapp.mag")
if (not fwhm is None):
fwhm_value = fwhm
elif (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')
else:
#Put some default value for Palomar
fwhm_value=1.5
if (wcsin == 'logical'):
fwhm_value = fwhm_value / arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
if (not fitsutils.has_par(image, "EXPTIME")):
if (fitsutils.has_par(image, "ITIME") and fitsutils.has_par(image, "COADDS")):
exptime = fitsutils.get_par(image, "ITIME")*fitsutils.get_par(image, "COADDS")
fitsutils.update_par(image, "EXPTIME", exptime)
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
#print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*app) # Set aperture radius to two times the PSF radius
sky_rad= math.ceil(aperture_rad*app*2)
#print aperture_rad, sky_rad
print "Saving coodinates for the object in pixels",pra,pdec
coords = "/tmp/coords.dat"
np.savetxt("/tmp/coords.dat", np.array([[pra, pdec]]), fmt="%.4f %.4f")
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = "logical",
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), \
("stdev","<f4"), ("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
ma = np.array([ma])
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
insmag = np.round(ma['fit_mag'][0] , 3)
insmagerr = np.round(ma['fiterr'][0], 3)
if (fitsutils.has_par(image, "ZEROPT") and fitsutils.has_par(image, "ZEROPTU")):
mag = insmag + float(fitsutils.get_par(image, "ZEROPT"))
magerr = np.sqrt(insmagerr**2+ float(fitsutils.get_par(image, "ZEROPTU"))**2)
else:
mag = 0
magerr = 0
if np.isnan(mag):
mag, magerr = 0, 0
insmag, insmagerr = 0,0
fitsutils.update_par(image, "INSMAG", "%.3f"%insmag )
fitsutils.update_par(image, "INSMAGER", "%.3f"%insmagerr)
fitsutils.update_par(image, "APPMAG", np.round(mag, 3) )
fitsutils.update_par(image, "APPMAGER", np.round(magerr, 3))
if (plot):
#zmin, zmax = zscale.zscale(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50])
#zmin, zmax = zscale.zscale(impf[0].data)
#im = plt.imshow(impf[0].data, vmin=zmin, vmax=zmax, origin="bottom")
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
impf[0].data[np.isnan(impf[0].data)] = np.nanmedian(impf[0].data)
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
im = plt.imshow(impf[0].data, vmin=np.percentile(impf[0].data, 5), vmax=np.percentile(impf[0].data, 95), origin="bottom")
X = int(ma["X"][0])
Y = int(ma["Y"][0])
pra = int(pra)
pdec = int(pdec)
plt.scatter(X, Y, marker="o", s=100, facecolor="none", edgecolor="red")
plt.colorbar(im)
plt.savefig(os.path.join(plotdir, imname+".png"), dpi=200)
plt.clf()
zmin, zmax = zscale.zscale(impf[0].data.T[X-50:X+50,Y-50:Y+50].T)
im = plt.imshow(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50].T, vmin=zmin, vmax=zmax, interpolation="none", origin="bottom", extent=(-50,50,-50,50))
c1 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=aperture_rad, label="Initial position")
c11 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=sky_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=aperture_rad, label="Adjusted centroid")
c22 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
plt.gca().add_artist(c1)
plt.gca().add_artist(c11)
plt.gca().add_artist(c2)
plt.gca().add_artist(c22)
plt.colorbar(im)
myhandles = []
markers = ["o", "o"]
labels = ["Initial position", "Adjusted centroid"]
cols = ["k", "orange"]
for i in np.arange(len(markers)):
myhandles.append(mlines.Line2D([], [], mec=cols[i], mfc="none", marker=markers[i], ls="None", markersize=10, label=labels[i]))
plt.legend(handles=myhandles, loc="lower left", labelspacing=0.3, fontsize=11, numpoints=1, frameon=False, ncol=5, bbox_to_anchor=(0.0, 0.00), fancybox=False, shadow=True)
plt.title("MIN: %.0f MAX: %.0f"%(np.nanmin(impf[0].data.T[X-50:X+50,Y-50:Y+50]), np.nanmax(impf[0].data.T[X-50:X+50,Y-50:Y+50])))
plt.savefig(os.path.join(plotdir, imname+"_zoom.png"))
plt.clf()
def get_app_phot(coords, image, plot_only=False, store=True, wcsin="world", fwhm=2.5, plotdir=None, box=15, arcsecpix=0.394):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
from pyraf import iraf
if (not plot_only):
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
imdir = os.path.dirname(image)
imname = os.path.basename(image)
if (plotdir is None):
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".app.mag")
print "Will create output files", out_name, clean_name
# Read values from .ec file
fwhm_value = fwhm/arcsecpix
if (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')/arcsecpix
elif (fwhm is None):
fwhm_value=3.5/arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
noise = fitsutils.get_par(image, 'RDNOISE')
print "FWHM: %.1f pixels, %.1f arcsec"%(fwhm_value, fwhm_value*arcsecpix)
aperture_rad = math.ceil(float(fwhm_value)*1.5) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad)*4
if (not plot_only):
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime",\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
#fwhm = fwhm_value,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = wcsin,
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), ("stdev","<f4"),\
("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
hdulist = pf.open(image)
prihdr = hdulist[0].header
img = hdulist[0].data * 1.
nx, ny = img.shape
dimX = int(np.floor(np.sqrt(len(m))))
dimY = int(np.ceil(len(m)*1./dimX))
outerrad = sky_rad+10
cutrad = outerrad + 15
print "Cutrad %.1f"%cutrad
plt.suptitle("FWHM=%.2f arcsec. %d stars"%(fwhm_value*arcsecpix, len(m)))
for i in np.arange(dimX):
for j in np.arange(dimY):
if ( i*dimY + j < len(m)):
k = i*dimY + j
#print dimX, dimY, i, j, k
ax = plt.subplot2grid((dimX,dimY),(i, j))
y1, y2, x1, x2 = m[k]["X"]-cutrad, m[k]["X"]+cutrad, m[k]["Y"]-cutrad, m[k]["Y"]+cutrad
y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
y1 = np.maximum(y1, 0); y2=np.maximum(y2, 0); x1=np.maximum(x1, 0); x2 = np.maximum(x2, 0)
try:
zmin, zmax = zscale.zscale(img[x1:x2,y1:y2], nsamples=1000, contrast=0.25)
except ValueError:
print y1, y2, x1, x2
print img[x1:x2,y1:y2]
sh= img[x1:x2,y1:y2].shape
if sh[0]>0 and sh[1]>0:
zmin = np.nanmin(img[x1:x2,y1:y2])
zmax = np.nanmax(img[x1:x2,y1:y2])
ax.imshow(img[x1:x2,y1:y2], aspect="equal", extent=(-cutrad, cutrad, -cutrad, cutrad), origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
c1 = plt.Circle( (0, 0), edgecolor="r", facecolor="none", radius=aperture_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
c3 = plt.Circle( (0, 0), edgecolor="yellow", facecolor="none", radius=sky_rad+20)
plt.gca().add_artist(c1)
plt.gca().add_artist(c2)
plt.gca().add_artist(c3)
ax.set_xticks([])
ax.set_yticks([])
plt.text(+5, +5, "%d"%m[k]["id"])
plt.text(-cutrad, -cutrad, "%.2f$\pm$%.2f"%(m[k]["fit_mag"], m[k]["fiterr"]), color="b")
plt.tight_layout()
plt.savefig(os.path.join(plotdir, imname + "plot.png"))
plt.clf()
def get_fake_centroid_and_fluxcorr(filename, x, y, instrument, filt):
"""
Locate the center of a fake psf and determine the flux correction
needed to bring it into alignment with the hstsnphot zero points.
INPUTS: The fake-SN psf image in filename, the expected x,y position
of the center of the psf, the instrument and filter being modeled.
RETURNS: xcentroid, ycentroid, fluxcorr
"""
from pyraf import iraf
if instrument == 'WFC3_IR':
ZPT = ZPT_WFC3_IR_VEGA[filt]
elif instrument == 'WFC3_UVIS':
ZPT = ZPT_WFC3_UVIS_VEGA[filt]
elif instrument.startswith('ACS'):
ZPT = ZPT_ACS_VEGA[filt]
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn(iraf.apphot.phot)
iraf.unlearn(iraf.datapars)
iraf.unlearn(iraf.centerpars)
#Use the centroid algorithm right now as it seems more robust to geometric distortion.
iraf.centerpars.calgorithm = 'centroid'
iraf.centerpars.cbox = 5.0
iraf.unlearn(iraf.fitskypars)
iraf.unlearn(iraf.photpars)
photparams = {
'interac': False,
'radplot': False,
}
iraf.datapars.readnoise = 0.0
iraf.datapars.itime = 1.0
iraf.datapars.epadu = 1.0
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn(iraf.fitskypars)
iraf.fitskypars.salgorithm = 'constant'
iraf.fitskypars.skyvalue = 0.0
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = APDEFAULTSIZE[instrument] * PIXSIZE[
instrument] / NATIVE_PIXSIZE[instrument]
iraf.photpars.zmag = ZPT
iraf.photpars.mkapert = False
#Write the coordinate file starting as position x and y
coxyfile = 'centroid.xycoo'
coxy = open(coxyfile, 'w')
print >> coxy, "%10.2f %10.2f" % (x, y)
coxy.close()
if os.path.exists('centroid.mag'): os.remove('centroid.mag')
iraf.phot(image=filename,
skyfile='',
coords=coxyfile,
output='centroid.mag',
verify=False,
verbose=True,
Stdout=1,
**photparams)
f = open('centroid.mag', 'r')
maglines = f.readlines()
f.close()
xcentroid = float(maglines[76].split()[0])
ycentroid = float(maglines[76].split()[1])
#Calculate the aperture correction necessary to match the observed aperture correction.
meas_flux_frac = float(maglines[79].split()[1])
expected_flux_frac = 10.0**(
-0.4 * APCOR[filt][APSIZE.index('%1i' % APDEFAULTSIZE[instrument])])
flux_corr = expected_flux_frac / meas_flux_frac
return xcentroid, ycentroid, flux_corr
def phot(fits_filename,
x_in,
y_in,
aperture=15,
sky=20,
swidth=10,
apcor=0.3,
maxcount=30000.0,
exptime=1.0,
zmag=None):
"""
Compute the centroids and magnitudes of a bunch sources detected on
CFHT-MEGAPRIME images.
Args:
fits_filename: str
The name of the file containing the image to be processed.
Returns a MOPfiles data structure.
"""
if not hasattr(x_in, '__iter__'):
x_in = [
x_in,
]
if not hasattr(y_in, '__iter__'):
y_in = [
y_in,
]
if (not os.path.exists(fits_filename)
and not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
logger.debug(str(err))
raise TaskError("Failed to open input image: %s" % err.message)
## get the filter for this image
filter = input_hdulist[0].header.get('FILTER', 'DEFAULT')
### Some nominal CFHT zeropoints that might be useful
zeropoints = {
"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 26.4
}
if zmag is None:
zmag = input_hdulist[0].header.get('PHOTZP', zeropoints[filter])
### check for magic 'zeropoint.used' files
zpu_file = "zeropoint.used"
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
else:
zpu_file = "%s.zeropoint.used" % (fits_filename[0:-5])
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
photzp = input_hdulist[0].header.get(
'PHOTZP', zeropoints.get(filter, zeropoints["DEFAULT"]))
if zmag != photzp:
logger.warning(
"ZEROPOINT {} sent to DAOPHOT doesn't match PHOTZP {} in header".
format(zmag, photzp))
### setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.photpars.apertures = aperture
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.sloclip = 5.0
iraf.fitskypars.shiclip = 5.0
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
for i in range(len(x_in)):
coofile.write("%f %f \n" % (x_in[i], y_in[i]))
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename, coofile.name, magfile.name)
pdump_out = ascii.read(magfile.name, format='daophot')
if not len(pdump_out) > 0:
mag_content = open(magfile.name).read()
raise TaskError("photometry failed. {}".format(mag_content))
# Clean up temporary files generated by IRAF
os.remove(coofile.name)
os.remove(magfile.name)
return pdump_out
def phot(fits_filename,
x_in,
y_in,
aperture=15,
sky=20,
swidth=10,
apcor=0.3,
maxcount=30000.0,
exptime=1.0,
zmag=None,
extno=0,
centroid=True):
"""
Compute the centroids and magnitudes of a bunch sources on fits image.
:rtype : astropy.table.Table
:param fits_filename: Name of fits image to measure source photometry on.
:type fits_filename: str
:param x_in: x location of source to measure
:type x_in: float, numpy.array
:param y_in: y location of source to measure
:type y_in: float, numpy.array
:param aperture: radius of circular aperture to use.
:type aperture: float
:param sky: radius of inner sky annulus
:type sky: float
:param swidth: width of the sky annulus
:type swidth: float
:param apcor: Aperture correction to take aperture flux to full flux.
:type apcor: float
:param maxcount: maximum linearity in the image.
:type maxcount: float
:param exptime: exposure time, relative to zmag supplied
:type exptime: float
:param zmag: zeropoint magnitude
:param extno: extension of fits_filename the x/y location refers to.
"""
if not hasattr(x_in, '__iter__'):
x_in = [
x_in,
]
if not hasattr(y_in, '__iter__'):
y_in = [
y_in,
]
if (not os.path.exists(fits_filename)
and not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
logger.debug(str(err))
raise TaskError("Failed to open input image: %s" % err.message)
# get the filter for this image
filter_name = input_hdulist[extno].header.get('FILTER', 'DEFAULT')
# Some nominal CFHT zeropoints that might be useful
zeropoints = {
"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 32.0,
'r.MP9601': 31.9,
'gri.MP9603': 33.520
}
if zmag is None:
logger.warning(
"No zmag supplied to daophot, looking for header or default values."
)
zmag = input_hdulist[extno].header.get('PHOTZP',
zeropoints[filter_name])
logger.warning("Setting zmag to: {}".format(zmag))
# check for magic 'zeropoint.used' files
for zpu_file in [
"{}.zeropoint.used".format(os.path.splitext(fits_filename)[0]),
"zeropoint.used"
]:
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
logger.warning("Using file {} to set zmag to: {}".format(
zpu_file, zmag))
break
photzp = input_hdulist[extno].header.get(
'PHOTZP', zeropoints.get(filter_name, zeropoints["DEFAULT"]))
if zmag != photzp:
logger.warning(
("zmag sent to daophot: ({}) "
"doesn't match PHOTZP value in image header: ({})".format(
zmag, photzp)))
# setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.photpars.apertures = aperture
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.sloclip = 5.0
iraf.fitskypars.shiclip = 5.0
if centroid:
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
else:
iraf.centerpars.calgori = "none"
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
for i in range(len(x_in)):
coofile.write("%f %f \n" % (x_in[i], y_in[i]))
coofile.flush()
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the target_name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename + "[{}]".format(extno), coofile.name, magfile.name)
pdump_out = ascii.read(magfile.name, format='daophot')
logging.debug("PHOT FILE:\n" + str(pdump_out))
if not len(pdump_out) > 0:
mag_content = open(magfile.name).read()
raise TaskError("photometry failed. {}".format(mag_content))
# apply the aperture correction
pdump_out['MAG'] -= apcor
# if pdump_out['PIER'][0] != 0 or pdump_out['SIER'][0] != 0 or pdump_out['CIER'][0] != 0:
# raise ValueError("Photometry failed:\n {}".format(pdump_out))
# Clean up temporary files generated by IRAF
os.remove(coofile.name)
os.remove(magfile.name)
logger.debug("Computed aperture photometry on {} objects in {}".format(
len(pdump_out), fits_filename))
del input_hdulist
return pdump_out
def Photometry(path):
print "Starting Photometry..."
print "This can take a long time. Go drink some coffee!"
filename = path #raw_input("Path to the directory of the images: ")
os.chdir(filename)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
#Parameter settings
iraf.centerpars.setParam('calgorithm', 'centroid', check=0, exact=0)
iraf.centerpars.setParam('cbox', '10.0', check=0, exact=0)
iraf.centerpars.saveParList(filename="center.par")
iraf.centerpars.setParList(ParList="center.par")
iraf.fitskypars.setParam('salgorithm', 'mode', check=0, exact=0)
iraf.fitskypars.setParam('annulus', '7.0')
iraf.fitskypars.setParam('dannulus', '2.0', check=0, exact=0)
iraf.fitskypars.saveParList(filename='fits.par')
iraf.fitskypars.setParList(ParList='fits.par')
iraf.photpars.setParam('apertures', '4.5', check=0, exact=0)
iraf.photpars.saveParList(filename='phot.par')
iraf.photpars.setParList(ParList='phot.par')
iraf.datapars.setParam('fwhm', 'INDEF', check=0, exact=0)
iraf.datapars.setParam('sigma', 'INDEF', check=0, exact=0)
iraf.datapars.setParam('datamin', 'INDEF', check=0, exact=0)
iraf.datapars.setParam('datamax','INDEF', check=0, exact=0)
iraf.datapars.setParam('ccdread','RESPONSE', check=0, exact=0)
iraf.datapars.setParam('gain','EGAIN', check=0, exact=0)
iraf.datapars.setParam('exposur','EXPTIME', check=0, exact=0)
iraf.datapars.setParam('airmass','AIRMASS', check=0, exact=0)
#iraf.datapars.setParam('xairmas','1.', check=0, exact=0)
iraf.datapars.setParam('obstime','UTSHUT', check=0, exact=0)
iraf.datapars.setParam('filter','FILTER', check=0, exact=0)
iraf.datapars.saveParList(filename='data.par')
iraf.datapars.setParList(ParList='data.par')
#Takes the necessary parameters
iraf.centerpars.getParam('calgorithm', native=1, mode="h", exact=0, prompt=0)
iraf.centerpars.getParam('cbox', native=1, exact=0, prompt=0)
iraf.fitskypars.getParam('salgorithm', native=1, mode="h", exact=0, prompt=0)
iraf.fitskypars.getParam('annulus', native=1, exact=0, prompt=0)
iraf.fitskypars.getParam('dannulus', native=1, exact=0, prompt=0)
iraf.photpars.getParam('apertures', native=1, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('fwhm', native=0, mode='h', exact=0, prompt=0)
iraf.datapars.getParam('sigma', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('datamin', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('datamax', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('ccdread', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('gain', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('exposur', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('airmass', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('xairmas', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('obstime', native=0, mode="h", exact=0, prompt=0)
iraf.datapars.getParam('filter', native=0, mode="h", exact=0, prompt=0)
#Removing existing files
filelist1 = glob.glob("*.mag")
filelist2 = glob.glob("*.magnitude")
for f in filelist1:
os.remove(f)
for g in filelist2:
os.remove(g)
#Photometry
print "Extracting magnitude and magnitude error from the files created by Pyraf"
images = glob.glob("*.fits")
for i in images:
tr_coord = i + '.out.xy.done'
iraf.phot(image = i, output = i + '.mag', coords = tr_coord, interactive = 'no' , wcsin = 'tv', skyfile = '', verify='no', verbose='no' )
#uses IRAF's pdump to take MAG values from the PHOT's output file
photometry = glob.glob("*.mag")
for j in photometry:
iraf.pdump(j, 'MAG, MERR', 'yes', Stdout = j + 'nitude')
def main(argv):
home_root = os.environ['HOME']
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
for file_ in os.listdir("./"):
if file_.endswith("_i.fits"):
fits_file_i = file_
title_string = file_[0:9]
if file_.endswith("_g.fits"):
fits_file_g = file_
fits_h_i = fits.open(fits_file_i)
fits_h_g = fits.open(fits_file_g)
fwhm_i = fits_h_i[0].header['FWHMPSF']
fwhm_g = fits_h_g[0].header['FWHMPSF']
print 'Image FWHM :: g = {0:5.3f} : i = {1:5.3f}'.format(fwhm_g,fwhm_i)
# if not os.path.isdir('psf'): # make a psf subfolder if it doesn't exist
# os.mkdir('psf')
# # and then copy in the data files if they don't already exist as files
# if not os.path.isfile('psf/'+fits_file_i):
# iraf.images.imcopy(fits_file_i,'psf/'+fits_file_i,verbose="yes")
#
# if not os.path.isfile('psf/'+fits_file_g) :
# iraf.images.imcopy(fits_file_g,'psf/'+fits_file_g,verbose="yes")
#
# # also copy in the apcor star lists
# if not (os.path.isfile('psf/apcor_stars_i.txt') or os.path.islink('psf/apcor_stars_i.txt')) :
# shutil.copyfile('apcor_stars_i.txt','psf/apcor_stars_i.txt')
#
# if not (os.path.isfile('psf/apcor_stars_g.txt') or os.path.islink('psf/apcor_stars_g.txt')) :
# shutil.copyfile('apcor_stars_g.txt','psf/apcor_stars_g.txt')
#
# # and change to the psf folder
# os.chdir('psf')
iraf.unlearn(iraf.phot,iraf.datapars,iraf.photpars,iraf.centerpars,iraf.fitskypars)
iraf.apphot.phot.setParam('interactive',"no")
iraf.apphot.phot.setParam('verify',"no")
iraf.datapars.setParam('datamax',"INDEF")
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.photpars.setParam('zmag',0.)
iraf.centerpars.setParam('cbox',9.)
iraf.centerpars.setParam('maxshift',3.)
iraf.fitskypars.setParam('salgorithm',"median")
iraf.fitskypars.setParam('dannulus',10.)
iraf.datapars.setParam('fwhmpsf',fwhm_g)
iraf.photpars.setParam('apertures',iraf.nint(4.*fwhm_g))
iraf.fitskypars.setParam('annulus',(6.*fwhm_g))
iraf.apphot.phot(image=fits_file_g, coords='apcor_stars_g.txt', output="g_psfstars.mag.1")
iraf.datapars.setParam('fwhmpsf',fwhm_i)
iraf.photpars.setParam('apertures',iraf.nint(4.*fwhm_i))
iraf.fitskypars.setParam('annulus',(6.*fwhm_i))
iraf.apphot.phot(image=fits_file_i, coords='apcor_stars_i.txt', output="i_psfstars.mag.1")
with open("apcor_stars_i.txt") as foo:
lines = len(foo.readlines())
iraf.daophot(_doprint=0)
iraf.pstselect.setParam('image',fits_file_i)
iraf.pstselect.setParam('photfile',"i_psfstars.mag.1")
iraf.pstselect.setParam('pstfile',"default")
iraf.pstselect.setParam('maxnpsf',lines)
iraf.pstselect.setParam('plottype',"mesh")
iraf.daophot.pstselect(verify='no', mode='h')
with open("apcor_stars_g.txt") as foo:
lines = len(foo.readlines())
iraf.pstselect.setParam('image',fits_file_g)
iraf.pstselect.setParam('photfile',"g_psfstars.mag.1")
iraf.pstselect.setParam('pstfile',"default")
iraf.pstselect.setParam('maxnpsf',lines)
iraf.pstselect.setParam('plottype',"mesh")
iraf.daophot.pstselect()
iraf.datapars.setParam('datamax',"INDEF")
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.daopars.setParam('psfrad',iraf.nint(3.*fwhm_i))
iraf.daopars.setParam('fitrad',fwhm_i)
iraf.psf.setParam('image',fits_file_i)
iraf.psf.setParam('photfile',"i_psfstars.mag.1")
iraf.psf.setParam('pstfile',fits_file_i+".pst.1")
iraf.psf.setParam('interactive', 'no')
iraf.daophot.psf()
iraf.psf.setParam('image',fits_file_g)
iraf.psf.setParam('photfile',"g_psfstars.mag.1")
iraf.psf.setParam('pstfile',fits_file_g+".pst.1")
iraf.psf.setParam('interactive', 'no')
iraf.daophot.psf()
def phot(fits_filename, x_in, y_in, aperture=15, sky=20, swidth=10, apcor=0.3,
maxcount=30000.0, exptime=1.0):
"""
Compute the centroids and magnitudes of a bunch sources detected on
CFHT-MEGAPRIME images.
Args:
fits_filename: str
The name of the file containing the image to be processed.
Returns a MOPfiles data structure.
"""
if (not os.path.exists(fits_filename) and
not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
raise TaskError("Failed to open input image: %s" % err.message)
## get the filter for this image
filter = input_hdulist[0].header.get('FILTER', 'DEFAULT')
### Some CFHT zeropoints that might be useful
zeropoints = {"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 26.4
}
### load the
if not filter in zeropoints:
filter = "DEFAULT"
zmag = input_hdulist[0].header.get('PHOTZP', zeropoints[filter])
### setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
### check for the magical 'zeropoint.used' file
zpu_file = "zeropoint.used"
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
iraf.photpars.apertures = int(aperture)
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
#iraf.datapars.exposur="EXPTIME"
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
coofile.write("%f %f \n" % (x_in, y_in))
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename, coofile.name, magfile.name)
pdump_out = iraf.pdump(magfile.name, "XCENTER,YCENTER,MAG,MERR,ID,XSHIFT,YSHIFT,LID",
"MERR < 0.4 && MERR != INDEF && MAG != INDEF && PIER==0", header='no', parameters='yes',
Stdout=1)
os.remove(coofile.name)
os.remove(magfile.name)
### setup the mop output file structure
hdu = {}
hdu['header'] = {'image': input_hdulist,
'aper': aperture,
's_aper': sky,
'd_s_aper': swidth,
'aper_cor': apcor,
'zeropoint': zmag}
hdu['order'] = ['X', 'Y', 'MAG', 'MERR', 'ID', 'XSHIFT', 'YSHIFT', 'LID']
hdu['format'] = {'X': '%10.2f',
'Y': '%10.2f',
'MAG': '%10.2f',
'MERR': '%10.2f',
'ID': '%8d',
'XSHIFT': '%10.2f',
'YSHIFT': '%10.2f',
'LID': '%8d'}
hdu['data'] = {}
for col in hdu['order']:
hdu['data'][col] = []
for line in pdump_out:
values = line.split()
for col in hdu['order']:
if re.match('\%.*f', hdu['format'][col]):
if col == 'MAG':
values[0] = float(values[0]) - float(apcor)
hdu['data'][col].append(float(values.pop(0)))
elif re.match('\%.*d', hdu['format'][col]):
hdu['data'][col].append(int(values.pop(0)))
else:
hdu['data'][col].append(values.pop(0))
# Clean up temporary files generated by IRAF
os.remove("datistabe.par")
os.remove("datpdump.par")
return hdu
def get_app_phot_target(image, plot=False, store=True, wcsin="logical", fwhm=2, box=4, ra=None, dec=None):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
impf = pf.open(image)
wcs = pywcs.WCS(impf[0].header)
#Check that actually the object is within this frame.
if (ra is None or dec is None):
if (fitsutils.has_par(image, "OBJRA") and fitsutils.has_par(image, "OBJRA")):
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'OBJRA'), fitsutils.get_par(image, 'OBJDEC'))
else:
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'RA'), fitsutils.get_par(image, 'DEC'))
pra, pdec = get_xy_coords(image, ra, dec)
else:
if(wcsin == "logical"):
pra, pdec = ra, dec
else:
#Using new method to derive the X, Y pixel coordinates, as pywcs does not seem to be working well.
pra, pdec = get_xy_coords(image, ra, dec)
#pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#pra, pdec = wcs.wcs_sky2pix(np.array([ra, dec], ndmin=2), 1)[0]
shape = impf[0].data.shape
if (pra > 0) and (pra < shape[0]) and (pdec > 0) and (pdec < shape[1]):
pass
else:
print image, "ERROR! Object coordinates are outside this frame. Skipping any aperture photometry!!"
print pra, pdec, shape
return
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".objapp.mag")
fwhm_value = fwhm
nsrc, fwhm_value, ellip = sextractor.get_image_pars(image)
if np.isnan(fwhm_value):
fwhm_value=99
fitsutils.update_par(image, 'FWHM', fwhm_value)
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
#print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*3) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad*4)
#print aperture_rad, sky_rad
print "Saving coodinates for the object in pixels",pra,pdec
coords = "/tmp/coords.dat"
np.savetxt("/tmp/coords.dat", np.array([[pra, pdec]]), fmt="%.4f %.4f")
if (plot):
zmin, zmax = zscale.zscale(impf[0].data)
im = plt.imshow(impf[0].data, vmin=zmin, vmax=zmax, origin="bottom")
plt.scatter(pra, pdec, marker="o", s=100, facecolor="none")
plt.savefig(os.path.join(plotdir, imname+".png"))
plt.clf()
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 15. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = "logical",
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("ph_mag","<f4"), ("stdev","<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
if (ma.size==1):
ma = np.array([ma])
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
insmag = np.round(ma['fit_mag'][0] , 3)
insmagerr = np.round(ma['fiterr'][0], 3)
if (fitsutils.has_par(image, "ZEROPT")):
mag = insmag + float(fitsutils.get_par(image, "ZEROPT"))
magerr = np.sqrt(insmagerr**2+ float(fitsutils.get_par(image, "ZEROPTU"))**2)
if np.isnan(mag):
mag, magerr = 0, 0
insmag, insmagerr = 0,0
fitsutils.update_par(image, "INSMAG", "%.3f"%insmag )
fitsutils.update_par(image, "INSMAGER", "%.3f"%insmagerr)
fitsutils.update_par(image, "APPMAG", np.round(mag, 3) )
fitsutils.update_par(image, "APPMAGER", np.round(magerr, 3))
if (plot):
X = int(ma["X"][0])
Y = int(ma["Y"][0])
pra = int(pra)
pdec = int(pdec)
plt.scatter(X, Y, marker="o", s=100, facecolor="none", edgecolor="red")
plt.colorbar(im)
plt.savefig(os.path.join(plotdir, imname+".png"))
plt.clf()
zmin, zmax = zscale.zscale(impf[0].data.T[X-50:X+50,Y-50:Y+50].T)
im = plt.imshow(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50].T, vmin=zmin, vmax=zmax, interpolation="none", origin="bottom", extent=(-50,50,-50,50))
c1 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=aperture_rad, label="Initial position")
c11 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=sky_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=aperture_rad, label="Adjusted centroid")
c22 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
plt.gca().add_artist(c1)
plt.gca().add_artist(c11)
plt.gca().add_artist(c2)
plt.gca().add_artist(c22)
plt.colorbar(im)
myhandles = []
markers = ["o", "o"]
labels = ["Initial position", "Adjusted centroid"]
cols = ["k", "orange"]
for i in np.arange(len(markers)):
myhandles.append(mlines.Line2D([], [], mec=cols[i], mfc="none", marker=markers[i], ls="None", markersize=10, label=labels[i]))
plt.legend(handles=myhandles, loc="lower left", labelspacing=0.3, fontsize=11, numpoints=1, frameon=False, ncol=5, bbox_to_anchor=(0.0, 0.00), fancybox=False, shadow=True)
plt.title("MIN: %.0f MAX: %.0f"%(np.nanmin(impf[0].data.T[X-50:X+50,Y-50:Y+50]), np.nanmax(impf[0].data.T[X-50:X+50,Y-50:Y+50])))
plt.savefig(os.path.join(plotdir, imname+"_zoom.png"))
plt.clf()
#! /usr/local/bin/python
import os, sys
import numpy as np
from pyraf import iraf
from odi_calibrate import download_sdss, js_calibrate
from sdss_fit import getVabs
from collections import OrderedDict
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
def getHImass(object, dm):
# print object, mpc
uchvcdb = os.path.dirname(os.path.abspath(__file__))+'/predblist.sort.csv'
name, mass = np.loadtxt(uchvcdb, usecols=(1,6), dtype=str, delimiter=',', unpack=True)
# find the right row
coord = [i for i,this in enumerate(mass) if object.upper() in name[i]][0]
# print 'the HI mass of', name[coord], 'is', mass[coord], 'at 1 Mpc'
# mpc = mpc/1000.
mpc = pow(10,((dm + 5.)/5.))/1000000.
logm = float(mass[coord])
mass = mpc*mpc*10**logm # make sure to scale by the distance in Mpc^2
print '{:3.1f}'.format(np.log10(mass))
def build(f):
### is this an MEF file
current_ext = 0
NEXTEND = 0
EXTEND = f[0].header['EXTEND']
if (EXTEND == "T"):
NEXTEND = f[0].header['NEXTEND']
current_ext = 1
### create the name of the output MEF psf file
if not f[0].header.has_key('FILENAME'):
os.unlink(opt.filename)
sys.exit('The fits file ' + opt.filename + ' has no EXPNUM keyword\n')
sexp = f[0].header['FILENAME']
mef_psf = sexp + "p_psf_iraf.fits"
### create an MEF file that will contian the PSF(s)
import pyfits
fitsobj = pyfits.HDUList()
prihdu = pyfits.PrimaryHDU()
import re
prihdu.header.update('FILENAME', sexp, comment='CFHT Exposure Numebr')
prihdu.header.update('NEXTEND', NEXTEND, comment='number of extensions')
version = re.match(r'\$Rev.*: (\d*.\d*) \$', __Version__).group(1)
prihdu.header.update('MKPSF_V',
float(version),
comment="Version number of mkpsf")
fitsobj.append(prihdu)
if os.access(mef_psf, os.F_OK):
os.unlink(mef_psf)
fitsobj.writeto(mef_psf)
fitsobj.close()
outfits = pyfits.open(mef_psf, "append")
prihdr = outfits[0].header
import jjkmode
### Get my python routines
from pyraf import iraf
from pyraf.irafpar import IrafParList
### keep all the parameters locally cached.
iraf.set(uparm="./")
### Load the required IRAF packages
iraf.digiphot()
iraf.apphot()
iraf.daophot()
### temp file name hash.
tfile = {}
while (current_ext <= NEXTEND):
### this is a psf SCRIPT so the showplots and interactive are off by force
print "Working on image section " + str(current_ext)
iraf.findpars.sharplo = 0
iraf.findpars.sharphi = 0.7
iraf.findpars.roundlo = -0.7
iraf.findpars.roundhi = 0.7
iraf.datapars.datamax = 20000
iraf.datapars.airmass = 'AIRMASS'
iraf.datapars.filter = 'FILTER'
iraf.datapars.obstime = 'TIME-OBS'
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.gain = 'GAIN'
iraf.datapars.ccdread = 'RDNOISE'
iraf.datapars.fwhmpsf = opt.fwhm
iraf.daopars.nclean = 2
iraf.daopars.psfrad = 5.0 * opt.fwhm
iraf.daopars.fitrad = 0.85 * opt.fwhm
iraf.daopars.function = "gauss"
iraf.centerpars.calgorithm = 'centroid'
zero_mag = 26.19
iraf.photpars.zmag = zero_mag
iraf.photpars.apertures = int(0.85 * opt.fwhm)
iraf.fitskypars.annulus = 2 + int(opt.fwhm * 4.00)
iraf.fitskypars.dannulus = int(opt.fwhm * 2.0)
iraf.daophot.verbose = no
iraf.daophot.verify = no
iraf.daophot.update = no
iraf.psf.interactive = no
iraf.pstselect.interactive = no
iraf.datapars.saveParList()
iraf.fitskypars.saveParList()
iraf.centerpars.saveParList()
iraf.findpars.saveParList()
iraf.photpars.saveParList()
tfiles = [
'coo_bright', 'coo_ok', 'coo_faint', 'mag_all', 'mag_bright',
'mag_ok', 'mag_good', 'mag_best', 'pst_in', 'pst_out', 'pst_out2',
'prf', 'psg_org', 'psg', 'psf_1.fits', 'psf_2.fits',
'psf_final.fits', 'psf_3.fits', 'psf_4.fits', 'mag_pst', 'coo_pst',
'nst', 'nrj', 'seepsf.fits', 'sub.fits', 'fwhm', 'apcor'
]
for file in tfiles:
extname = "chip" + str(f[current_ext].header.get(
'IMAGEID', str(current_ext))).zfill(2)
tfile[file] = sexp + "_" + extname + "." + file
if (os.access(tfile[file], os.F_OK)):
os.unlink(tfile[file])
if (EXTEND == "T"):
this_image = opt.filename + "[" + extname + "]"
else:
this_image = opt.filename
gain = f[current_ext].header.get('GAIN', 1.0)
#### set sky/sigma parameters specific to this frame.
(skyvalue, sigma) = jjkmode.stats(f[current_ext].data)
import math
sigma = math.sqrt(skyvalue / gain)
datamin = float(skyvalue) - 8.0 * float(sigma)
print "Determined sky level to be " + str(skyvalue) + " +/-" + str(
sigma)
iraf.datapars.datamin = datamin
iraf.datapars.sigma = float(sigma)
iraf.datapars.saveParList()
iraf.fitskypars.skyvalue = skyvalue
iraf.fitskypars.saveParList()
### find the bright stars in the image.
print "sextracting for stars in " + this_image
###iraf.daophot.daofind(image=this_image,
### output=tfile['coo_bright'],threshold=4.0)
os.system(
"sex -c /home/cadc/kavelaar/12kproc/config/default.sex -SATUR_LEVEL 25000 -CATALOG_NAME "
+ tfile['coo_bright'] + " " + this_image)
### print "finding stellar locus in sround/ground space"
print "clipping using star_class > 0.85 "
fcoo = open(tfile['coo_bright'], 'r')
lines = fcoo.readlines()
fcoo.close()
import numarray, math
fout = open(tfile['coo_ok'], 'w')
for line in lines:
if re.match(r'^#', line) or re.search(r'INDEF', line):
continue
values = line.split()
star_class = float(values[2])
if star_class > 0.75:
fout.write(line)
fout.close()
print "Measuring photometry for psf candidate stars in " + tfile[
'coo_ok']
iraf.daophot.phot(image=this_image,
coords=tfile['coo_ok'],
output=tfile['mag_bright'])
### do this selection in 2 steps because of the way IRAF handles INDEFs
print "Selecting stars that have good centroids and magnitudes "
iraf.pselect(
tfile['mag_bright'], tfile['mag_ok'],
"(CIER==0)&&(PIER==0)&&(SIER==0)&&(MSKY>0)&&(MSKY<2e5)&&(MSKY!=INDEF)"
)
print "Selecting stars that have normal sky levels"
condition = "(abs(MSKY -" + str(skyvalue) + ") < 5.0*" + str(
sigma) + ")"
iraf.pselect(tfile['mag_ok'], tfile['mag_good'], condition)
a = iraf.txdump(tfile['mag_good'], "SSKEW", iraf.yes, Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean**2)
limit = mean + 2 * stddev
os.unlink(tfile['mag_good'])
condition = condition + " && SSKEW < " + str(limit)
iraf.pselect(tfile['mag_ok'], tfile['mag_good'], condition)
print "Choosing the psf stars"
iraf.pstselect(image=this_image,
photfile=tfile['mag_good'],
pstfile=tfile['pst_in'],
maxnpsf=25)
## construct an initial PSF image
print "computing psf with neighbor stars based on complete star list"
iraf.psf.mode = 'a'
iraf.psf(image=this_image,
photfile=tfile['mag_bright'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_1.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg_org'],
varorder=0)
try:
print "subtracting the psf neighbors and placing the results in " + tfile[
'sub.fits']
iraf.daophot.nstar(image=this_image,
groupfile=tfile['psg_org'],
psfimage=tfile['psf_1.fits'],
nstarfile=tfile['nst'],
rejfile=tfile['nrj'])
iraf.daophot.substar(image=this_image,
photfile=tfile['nst'],
exfile=tfile['pst_in'],
psfimage=tfile['psf_1.fits'],
subimage=tfile['sub.fits'])
a = iraf.daophot.txdump(tfile['nst'], 'chi', 'yes', Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean**2)
limit = mean + 2.5 * stddev
print "Selecting those psf stars with CHI^2 <" + str(
limit) + " after fitting with trial psf"
iraf.pselect(tfile['nst'], tfile['mag_best'],
"CHI < " + str(limit))
os.unlink(tfile['pst_out'])
## os.unlink(tfile['psg'])
## rebuild the PSF file with the psf stars that fit well..
## using the neighbor subtracted image
print "Rebuilding the PSF"
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_2.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=0)
print "re-subtracting with rebuilt psf"
os.unlink(tfile['nst'])
os.unlink(tfile['nrj'])
iraf.daophot.nstar(image=this_image,
groupfile=tfile['psg'],
psfimage=tfile['psf_2.fits'],
nstarfile=tfile['nst'],
rejfile=tfile['nrj'])
os.unlink(tfile['sub.fits'])
iraf.daophot.substar(image=this_image,
photfile=tfile['nst'],
exfile=tfile['pst_in'],
psfimage=tfile['psf_2.fits'],
subimage=tfile['sub.fits'])
os.unlink(tfile['psg'])
os.unlink(tfile['pst_out'])
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_3.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=0)
os.unlink(tfile['nrj'])
os.unlink(tfile['nst'])
iraf.daophot.nstar(image=this_image,
groupfile=tfile['psg'],
psfimage=tfile['psf_3.fits'],
nstarfile=tfile['nst'],
rejfile=tfile['nrj'])
a = iraf.daophot.txdump(tfile['nst'], 'chi', 'yes', Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean**2)
limit = mean + 2 * stddev
limit = 2.0
#print "Selecting those psf stars with CHI^2 < "+str(limit)+" after fit with GOOD psf"
os.unlink(tfile['mag_best'])
iraf.pselect(tfile['nst'], tfile['mag_best'],
"CHI < " + str(limit))
print "Building final PSF.... "
os.unlink(tfile['sub.fits'])
iraf.daophot.substar(image=this_image,
photfile=tfile['nst'],
exfile=tfile['pst_in'],
psfimage=tfile['psf_3.fits'],
subimage=tfile['sub.fits'])
os.unlink(tfile['psg'])
os.unlink(tfile['pst_out'])
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_final.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=0)
print "building an analytic psf for the FWHM calculations"
os.unlink(tfile['pst_out'])
os.unlink(tfile['psg'])
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_4.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=-1)
except:
print sys.exc_info()[1]
print "ERROR: Reverting to first pass psf"
tfile['psf_final.fits'] = tfile['psf_1.fits']
iraf.daophot.psf(image=this_image,
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_4.fits'],
opstfile=tfile['pst_out2'],
groupfile=tfile['psg'],
varorder=-1)
psf_ap = iraf.photpars.apertures
ap1 = int(psf_ap)
ap2 = int(4.0 * opt.fwhm)
apcor = "INDEF"
aperr = "INDEF"
if (0):
#try
### now that we have the psf use the output list of psf stars
### to compute the aperature correction
lines = iraf.txdump(tfile['pst_out'],
'xcen,ycen,mag,id',
iraf.yes,
Stdout=tfile['coo_pst'])
## set the lower ap value for the COG (normally set to 2)
if (ap1 < 3):
smallap = 1
else:
smallap = 2 - ap1 + 1
ap1 = 2
ap2 = int(math.floor(4.0 * opt.fwhm))
naperts = ap2 - ap1 + 1
iraf.photpars.apertures = str(ap1) + ":" + str(ap2) + ":1"
iraf.photpars.saveParList()
iraf.daophot.phot(image=this_image,
coords=tfile['coo_pst'],
output=tfile['mag_pst'])
iraf.photcal()
iraf.photcal.mkapfile(tfile['mag_pst'],
naperts=naperts,
apercors=tfile['apcor'],
smallap=smallap,
verify='no',
gcommands='',
interactive=0)
fin = open(tfile['apcor'], 'r')
lines = fin.readlines()
values = lines[2].split()
apcor = values[1]
aperr = values[2]
#except:
## compute the FWHM of the PSF image using the analytic PSF (VarOrd=-1)
psf_file = pyfits.open(tfile['psf_4.fits'])
fwhm = (psf_file[0].header.get('PAR1', 99.0) +
psf_file[0].header.get('PAR2', 99.0))
psf_file.close()
# ## Open the psf.fits
infits = pyfits.open(tfile['psf_final.fits'])
hdu = infits[0]
inhdu = hdu.header
inhdu.update('XTENSION', 'IMAGE', before='SIMPLE')
inhdu.update('PCOUNT', 0, after='NAXIS2')
inhdu.update('GCOUNT', 1, after='PCOUNT')
del hdu.header['SIMPLE']
del hdu.header['EXTEND']
inhdu.update("EXTNAME", extname, comment="image extension identifier")
#inhdu.update("SLOW",slow,comment="SROUND low cutoff")
#inhdu.update("SIGH",sigh,comment="SROUND high cutoff")
inhdu.update("PFWHM",
fwhm,
comment="FWHM of stars based on PSF fitting")
inhdu.update("ZMAG", zero_mag, comment="ZMAG of PSF ")
inhdu.update("BCKG", skyvalue, comment="Mean sky level in counts")
inhdu.update("BCKG_STD",
sigma,
comment="standard deviation of sky in counts")
inhdu.update("AP1", psf_ap, comment="Apperture used for PSF flux")
inhdu.update("AP2", ap2, comment="Full Flux aperture")
inhdu.update("APCOR", apcor, comment="Apperture correction (ap1->ap2)")
inhdu.update("APERR", apcor, comment="Uncertainty in APCOR")
# ### append this psf to the output images....
print "Sticking this PSF onto the output file"
f[current_ext].header.update(
"PFWHM", fwhm, comment="FWHM of stars based on PSF fitting")
f[current_ext].header.update("BCKG",
skyvalue,
comment="Mean sky level in counts")
f[current_ext].header.update(
"BCKG_STD", sigma, comment="Standard deviation of sky in counts")
f.flush()
outfits.append(hdu)
outfits.flush()
infits.close()
### remove the temp file we used for this computation.
for tf in tfile.keys():
if os.access(tfile[tf], os.F_OK):
os.unlink(tfile[tf])
current_ext = current_ext + 1
outfits.close()
return mef_psf
def runApphot(imfile, coofile=None, magfile=None):
"""
use iraf.digiphot.apphot
to collect aperture photometry
"""
import pyraf
from pyraf import iraf
import os
import pyfits
if not coofile:
coofile = imfile.replace('.fits', '.coo')
if not magfile:
magfile = imfile.replace('.fits', '.mag')
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
if os.path.exists(magfile):
os.remove(magfile)
hdr = pyfits.getheader(imfile)
if 'FILTER' in hdr:
filt = hdr['FILTER']
filtkey = 'FILTER'
elif 'FILTER1' in hdr:
filt = hdr['FILTER1']
filtkey = 'FILTER1'
if filt.startswith('CLEAR'):
filt = hdr['FILTER2']
filtkey = 'FILTER2'
if filt not in APCOR.keys():
return ([])
# iraf.digiphot.apphot.datapars :
iraf.datapars.scale = 1.0
iraf.datapars.fwhmpsf = 2.5
iraf.datapars.emission = True
iraf.datapars.sigma = 'INDEF'
iraf.datapars.datamin = 'INDEF'
iraf.datapars.datamax = 'INDEF'
iraf.datapars.noise = 'constant'
iraf.datapars.ccdread = ''
iraf.datapars.gain = ''
iraf.datapars.readnoise = 0.0
iraf.datapars.epadu = 1.0
iraf.datapars.exposure = ''
iraf.datapars.airmass = ''
iraf.datapars.filter = filt
iraf.datapars.obstime = ''
iraf.datapars.itime = 1.0
iraf.datapars.xairmass = 'INDEF'
iraf.datapars.ifilter = 'INDEF'
iraf.datapars.otime = 'INDEF'
# iraf.digiphot.apphot.centerpars :
iraf.unlearn(iraf.centerpars)
iraf.centerpars.calgorithm = 'centroid'
iraf.centerpars.cbox = 3.0
iraf.centerpars.cthreshold = 0.0
iraf.centerpars.minsnratio = 1.0
iraf.centerpars.cmaxiter = 10.0
iraf.centerpars.maxshift = 1.0
iraf.centerpars.clean = False
iraf.centerpars.rclean = 1.0
iraf.centerpars.rclip = 2.0
iraf.centerpars.kclean = 3.0
iraf.centerpars.mkcenter = False
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn(iraf.fitskypars)
iraf.fitskypars.salgorithm = 'median'
iraf.fitskypars.annulus = 25.0
iraf.fitskypars.dannulus = 40.0
iraf.fitskypars.skyvalue = 0.0
iraf.fitskypars.smaxiter = 10.0
iraf.fitskypars.sloclip = 0.0
iraf.fitskypars.shiclip = 0.0
iraf.fitskypars.snreject = 50.0
iraf.fitskypars.sloreject = 3.0
iraf.fitskypars.shireject = 3.0
iraf.fitskypars.khist = 3.0
iraf.fitskypars.binsize = 0.1
iraf.fitskypars.smooth = False
iraf.fitskypars.rgrow = 0.0
iraf.fitskypars.mksky = False
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = '2,3,4,5'
iraf.photpars.zmag = ZPT[filt]
iraf.photpars.mkapert = False
iraf.unlearn(iraf.phot)
iraf.gflush()
iraf.gflush()
iraf.flpr()
iraf.flpr()
iraf.flpr()
photparams = {
'interac': False,
'radplot': False,
}
outputstuff = iraf.phot(image=imfile,
skyfile='',
coords=coofile,
output=magfile,
verify=False,
verbose=True,
Stdout=1,
**photparams)
sourcelist = apphotOutput(magfile)
return (sourcelist)
def unisf(image,
fwhm,
sigma,
thold,
zmin='INDEF',
zmax='INDEF',
path=None,
outfile=None):
'''
Dada una lista de imágenes, identifica las estrellas de campo y
genera un archivo de coordenadas.
INPUT
image : Archivo .in con los nombres de las imágenes, o la imagen
simple.
fwmh : Ancho a mitad de intensidad máxima de las estrellas.
sigma : Desviación estándar del cielo.
thold : Amplitud mínima por encima del valor de fondo.
(zmin) : Valor mínimo de cuentas a considerar.
(zmax) : Valor máximo de cuentas a considerar.
(path) : String que indica el camino a la imagen.
(outfile) : Nombre del archivo de salida. Por defecto usa el nombre de
la imagen y agrega un .coo .
---------------------------------------
Given a images list, identifies field stars and generates a coordinates
file.
INPUT
image : .in file with the name of each image, or a single image.
fwmh : Full width half maximum of intensity of stars.
sigma : Standard deviation of the sky.
thold : Minimum amplitude above background level.
(zmin) : Minimum counts value to consider.
(zmax) : Maximum counts value to consider.
(path) : String that indicates the path do the images.
(outfile) : Name of the output file, including .coo. By default, uses
image name and adds .coo extension.
'''
if path is not None:
originalpath = aux.chdir(path, save=True)
iraf.noao() #loads noao
iraf.digiphot() #loads digiphot
iraf.apphot() #loads apphot
iraf.unlearn(iraf.daofind)
iraf.unlearn(iraf.datapars)
iraf.unlearn(iraf.findpars) #unlearns every task required
iraf.datapars.fwhmpsf = fwhm
iraf.datapars.sigma = sigma
iraf.datapars.datamin = zmin
iraf.datapars.datamax = zmax #sets meaningful parameters
iraf.findpars.thresho = thold #sets meaningful threshold
iraf.daofind.verify = "no" #avoid checking parameters given
aux.default(outfile, image + '.coo', borrar=True)
iraf.daofind.output = outfile
iraf.daofind.image = image
iraf.daofind()
#execute iraf task 'daofind'
if path is not None:
aux.chdir(originalpath)
def plant_kbos(filename, psf, kbos, shifts, prefix):
"""
Add KBOs to an image
:param filename: name of the image to add KBOs to
:param psf: Point Spread Function in IRAF/DAOPHOT format, used by ADDSTAR
:param kbos: list of KBOs to add, has format as returned by KBOGenerator
:param shifts: dictionary to transform coordinates to reference frame.
:param prefix: an estimate FWHM of the image, used to determine trailing.
:return: None
"""
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.images()
if shifts['nmag'] < 4:
logging.warning("Mag shift based on fewer than 4 common stars.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift based on fewer than 4 common stars.")
fd.close()
if shifts['emag'] > 0.05:
logging.warning("Mag shift has large uncertainty.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift hsa large uncertainty.")
fd.close()
addstar = tempfile.NamedTemporaryFile(suffix=".add", mode='w')
# transform KBO locations to this frame using the shifts provided.
w = get_wcs(shifts)
header = fits.open(filename)[0].header
# set the rate of motion in units of pixels/hour instead of ''/hour
scale = header['PIXSCAL1']
rate = kbos['sky_rate'] / scale
# compute the location of the KBOs in the current frame.
# offset magnitudes from the reference frame to the current one.
mag = kbos['mag'] - shifts['dmag']
angle = radians(kbos['angle'])
# Move the x/y locations to account for the sky motion of the source.
x = kbos['x'] - rate * 24.0 * shifts['dmjd'] * cos(angle)
y = kbos['y'] - rate * 24.0 * shifts['dmjd'] * sin(angle)
x, y = w.wcs_world2pix(x, y, 1)
# Each source will be added as a series of PSFs so that a new PSF is
# added for each pixel the source moves.
itime = float(header['EXPTIME']) / 3600.0
npsf = fabs(rint(rate * itime)) + 1
mag += 2.5 * log10(npsf)
dt_per_psf = itime / npsf
# Build an addstar file to be used in the planting of source.
idx = 0
for record in transpose([x, y, mag, npsf, dt_per_psf, rate, angle]):
x = record[0]
y = record[1]
mag = record[2]
npsf = record[3]
dt = record[4]
rate = record[5]
angle = record[6]
for i in range(int(npsf)):
idx += 1
x += dt * rate * math.cos(angle)
y += dt * rate * math.sin(angle)
addstar.write("{} {} {} {}\n".format(x, y, mag, idx))
addstar.flush()
fk_image = prefix + filename
try:
os.unlink(fk_image)
except OSError as err:
if err.errno == errno.ENOENT:
pass
else:
raise
# add the sources to the image.
if os.access(f'{fk_image}.art', os.R_OK):
os.unlink(f'{fk_image}.art')
iraf.daophot.addstar(filename,
addstar.name,
psf,
fk_image,
simple=True,
verify=False,
verbose=False)
# convert the image to short integers.
iraf.images.chpix(fk_image, fk_image, 'ushort')
def findStars(self):
# To look for the stars that exist in the image specified.
# It will:
# Utilise the IRAF package daofind
# be used for this image subtraction package
# Gives ouput of co-ords, "#imagename_daofind.coo"
output = self._Name.replace(".fits", "_daofind.coo")
try:
self.cleanOutputFiles(output)
except:
print "Error cleaning output files"
print sys.exc_info()[0]
# Load as in IRAF and neglect output:
# NOAO
# DIGIPHOT
# APPHOT
# DAOFIND
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
# Set the parameter list
# image: input image
# output: output co-ordinates
# verify: don't know
# threshold: sigma above which stars are determined (fixed in this script)
# datapar: point to a file with the user input
#
# ccdread: CCD readout noise (image header keyword)
# gain: CCD gain (image header keyword)
# readnoi: CCD readout noise in e
# epadu: gain in e- per count
#
# findpar: point to a file with the user input
#
# threshold: in units of sigma
#
# centerpars:
#
# Save the parameter list
# Pull information from the header of the FITS image
#self.loadHeader()
#datapars = iraf.datapars.getParList()
#findpars = iraf.findpars.getParList()
#ceneterpars = iraf.centerpars.getParList()
#for par in datapars:
#print par
## Print to user
#print "Information on images:"
#print "Gain: %s" % self.getHeader('RON')
#print "RON: %s" % self.getHeader('RON')
# datapar:
iraf.datapars.setParam('ccdread', 'RON')
iraf.datapars.setParam('gain', 'GAIN')
iraf.datapars.setParam('exposur','EXPTIME')
iraf.datapars.setParam('airmass','AIRMASS')
iraf.datapars.setParam('filter','SUBSET')
iraf.datapars.setParam('obstime','HIERARCH ESO OBS START')
iraf.datapars.setParam('datamin', '-100')
iraf.datapars.setParam('datamax', '60000')
# iraf.datapars.saveParList(filename="%s/uparm/datapars.par" % os.getcwd())
# findpar:
#threshold = 4.0
#iraf.findpars.setParam('threshold', str(threshold))
#iraf.findpars.saveParList(filename="uparm/findpars.par")
#print glob.glob(output)
# daofind:
iraf.daofind(image=self._Name,
output=output,
verify=0,
verbose=1,
sigma=self._skySTDEV,
nsigma=1,
scale=1,
fwhmpsf=self._MEDFWHM,
sharplo='0.0',
sharphi='1.0',
roundlo='-0.9',
roundhi='0.9',
thresho='6.0',
wcsout="logical",
Stdout=1,
mode='h'
)
# 1. Open the .coo file
# 2. Pasre the input
# 1. take input file
filename = "%s" % (self._Name.replace(".fits", "_daofind.coo"))
coords = open(filename)
coord = coords.readlines()
coords.close()
# 2. parse out the lines beginning with "#"
filename = "%s" % (self._Name.replace(".fits", "_daofind.reg"))
coordregfile = open(filename, "w")
coobject = []
for co in coord:
if co[0] != "#":
# 2b. parse each element
newCoordObject = imObject()
coitem = [i for i in co.replace("\n","").split(" ") if i!= ""]
newCoordObject._id = self.giveObjectID()
newCoordObject._pixelx = float(coitem[0])
newCoordObject._pixely = float(coitem[1])
newCoordObject._mag = float(coitem[2])
#newCoordObject.printInfo()
coordregfile.write("image; circle(%f,%f,4) # color = red\n" % (newCoordObject._pixelx, newCoordObject._pixely))
coobject.append(newCoordObject)
coordregfile.close()
self._ObjectList = coobject
fwhm = float(coordLines[9].split()[3])
sigma = float(coordLines[19].split()[3])
threshold = float(coordLines[27].split()[3])
coordFile.close()
print(objname, filter_, fwhm, sigma, threshold)
print('creating psf...')
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
iraf.daophot(_doprint=0)
iraf.tables(_doprint=0)
iraf.unlearn(iraf.ptools.pselect, iraf.tables.tcreate, iraf.tables.tmatch,
iraf.tables.tinfo, iraf.tables.tdump)
# first measure the psf for the image
iraf.unlearn(iraf.phot, iraf.datapars, iraf.photpars, iraf.centerpars,
iraf.fitskypars)
iraf.apphot.phot.setParam('interactive', "no")
iraf.apphot.phot.setParam('verify', "no")
iraf.datapars.setParam('datamax', "INDEF")
iraf.datapars.setParam('gain', "gain")
import pyfits
from types import *
from mx.DateTime import *
from iqpkg import *
import ephem
# Necessary packages
iraf.images()
iraf.immatch()
iraf.imfilter()
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.digiphot()
iraf.apphot()
yes=iraf.yes
no=iraf.no
INDEF=iraf.INDEF
hedit=iraf.hedit
imgets=iraf.imgets
imcombine=iraf.imcombine
pyrafdir="python/pyraf/"
pyrafdir_key='PYRAFPARS'
if os.environ.has_key(pyrafdir_key):
pardir=os.environ[pyrafdir_key]
else:
pardir=os.environ['HOME']+'/'+pyrafdir
def skySub(imageName):
#Set up iraf packages and variables for filenames
skyIm = imageName + '.sky'
outfile = 'mSky' + imageName + '.file'
avgValLog = 'mSkyLog' + imageName + '.file'
stDevLog = 'stDevLog' + imageName + '.file'
outputIm = imageName + '.sky'
iraf.apphot(_doprint=0)
#Delete exisiting files
for f in (skyIm, outfile, 'mSky.file', outputIm + '.fits', avgValLog):
silentDelete(f)
#Configure fitsky parameters
iraf.fitsky.coords = "coordslist"
iraf.fitsky.interactive = 'no'
iraf.fitsky.verify = 'no'
iraf.datapars.datamax = 150000
iraf.datapars.datamin = -200
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.annulus = 0
iraf.fitskypars.dannulus = 40
iraf.fitskypars.shireject = 2
iraf.fitskypars.sloreject = 2
iraf.fitsky.verbose = 'no'
#Call fitsky to compute the mean of the sky
iraf.fitsky(imageName, output=outfile)
#Use 'PDUMP" to extract only the mean sky values
sys.stdout = open(avgValLog, "w")
iraf.pdump(outfile, 'mSky', "yes")
sys.stdout = sys.__stdout__
#Use pdump to get the standard deviation from the sky
sys.stdout = open(stDevLog, "w")
iraf.pdump(outfile, 'stDev', "yes")
sys.stdout = sys.__stdout__
#Read in the mSky values file
skyValues = open(avgValLog, 'r')
valueList = skyValues.readlines()
#convert the value array to a numpy array
valueListNp = np.asfarray(valueList)
#compute average
average = np.mean(valueListNp)
#Print output
print(str(average) + " is the average")
#Read in the stDev log, take the average
stDevs = open(stDevLog, 'r')
stDevList = stDevs.readlines()
stDevListNp = np.asfarray(stDevList)
stDevAvg = np.mean(stDevListNp)
#Configure imarith
iraf.imarith.pixtype = "real"
iraf.imarith.calctype = "real"
#Use imarith to subtract the average sky value from the image
iraf.imarith(imageName, "-", average, outputIm)
#Add the average sky value and stDev to the image header
iraf.hedit(outputIm, "SKY", average, add='yes', verify='no')
iraf.hedit(outputIm, "SKYSIGMA", stDevAvg, add='yes', verify='no')
#Print an output image
print("Subtracted " + str(average) + " to create a new image called " +
outputIm)
#Delete acillary output files
silentDelete(outfile)
