def run_fitparams(weight=True): if weight: weighting = 'photometric' else: weighting = 'uniform' ir.delete('standards.transParams') ir.digiphot() ir.photcal() ir.unlearn('fitparams') ir.fitparams('standards.instMag', 'photcal_ukirt_faint.dat', 'standards.config', 'standards.transParams', weighting=weighting)
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 organizeFiles(): # Create files that are usually generated by mkimset. # We have more information about how to properly group our # files together. imsetsFilename = 'standards.imageSets' obsparFilename = 'standards.obsParams' shiftsFilename = 'standards.shifts' iMagniFilename = 'standards.instMag' imsets = open(imsetsFilename, 'w') obspar = open(obsparFilename, 'w') shifts = open(shiftsFilename, 'w') for star in stars: roots = [] for filter in filters: dir = star.lower() + '_' + filter.lower() if filter == 'Lp': dir += '2' images = glob.glob(dataDir + dir + '/c????.fits') imageRoots = [] for ii in images: fileRoot = ii.split('/')[-1] imageRoots.append(fileRoot.replace('.fits', '')) roots.append(imageRoots) print star, filter, len(imageRoots) longCount = np.array([len(r) for r in roots]).max() print 'Long Count for %s = %d' % (star, longCount) for ii in range(longCount): imsets.write('%5s : ' % star) for ff in range(len(roots)): if ii < len(roots[ff]): # Write obsParams ir.txdump(roots[ff][ii] + '.phot.mag', 'IMAGE,IFILTER,ITIME,XAIRMASS,OTIME', 'yes', Stdout=obspar) # Write shifts posInfo = ir.txdump(roots[ff][ii] + '.phot.mag', 'IMAGE,XCENTER,YCENTER', 'yes', Stdout=1) posFields = posInfo[0].split() shiftX = -1.0 * float(posFields[1]) shiftY = -1.0 * float(posFields[2]) shifts.write('%-10s %7.2f %7.2f\n' % (posFields[0], shiftX, shiftY)) # Write imageSet imsets.write('%-10s ' % (roots[ff][ii] + '.fits')) else: imsets.write('%-10s ' % ('INDEF')) imsets.write('\n') imsets.close() obspar.close() shifts.close() # Now run mknobsfile ir.delete(iMagniFilename) ir.digiphot() ir.photcal() ir.unlearn('mknobsfile') ir.mknobsfile.obsparams = obsparFilename ir.mknobsfile.shifts = shiftsFilename ir.mknobsfile('*.phot.mag', ','.join(filters), imsetsFilename, iMagniFilename, verbose='no', wrap='no') # Run mkconfig ir.delete('standards.config') ir.unlearn('mkconfig') ir.mkconfig('standards.config', 'fphotcal_ukirt_faint.dat', 'fstandards.instMag.dat', 'tphotcal_ukirt_faint.dat', check='no', edit='no')
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()
#! /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))
coordLines = coord.splitlines() 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")
import pyfits from types import * # Access to the iqutils from iqutils import * import iqpkg # Necessary packages iraf.images() iraf.immatch() iraf.noao() iraf.imred() iraf.ccdred() iraf.digiphot() iraf.daophot() iraf.photcal() iraf.images() iraf.imcoords() 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]
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
import pyfits from types import * # Access to the iqutils from iqutils import * import iqpkg # Necessary packages iraf.images() iraf.immatch() iraf.noao() iraf.imred() iraf.ccdred() iraf.digiphot() iraf.daophot() iraf.photcal() iraf.images() iraf.imcoords() 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]
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 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