def calibFlux(self, inIm, **kwargs): print "\nCALIBRATING FLUX IN " + inIm # determine which extinction file to use hdu = fits.open(inIm) if hdu[0].header["OBSERVAT"] == "Gemini-North": self.extDat = "gmos$calib/mkoextinct.dat" else: self.extDat = "onedstds$ctioextinct.dat" # determine which sensitivity function to use centWave = str(int(hdu[0].header["CENTWAVE"])) sFunc = self.sFunc.replace("X", centWave) hdu.close() # delete previous flux calibrated spectra and generate new one iraf.imdelete("c" + inIm) iraf.gscalibrate(inIm, sfunction=sFunc, extinction=self.extDat, **kwargs) # view the result print "Displaying calibrated data cube" self.viewCube("c" + inIm, version="1") return
def calibrate(scifiles, extfile, observatory): for f in scifiles: redorblue = getredorblue(f) iraf.unlearn(iraf.gscalibrate) iraf.gscalibrate('et' + f[:-4] + '.fits', sfunc='sens' + redorblue + '.fits', fl_ext=True, extinction=extfile, observatory=observatory) if os.path.exists('cet' + f[:-4] + '.fits'): iraf.unlearn(iraf.splot) iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def calibrate(scifiles, extfile, observatory): for f in scifiles: redorblue = getredorblue(f) iraf.unlearn(iraf.gscalibrate) iraf.gscalibrate('et' + f[:-4] + '.fits', sfunc='sens' + redorblue + '.fits', fl_ext=True, fl_vardq=dodq, extinction=extfile, observatory=observatory) if os.path.exists('cet' + f[:-4] + '.fits'): iraf.unlearn(iraf.splot) iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def reduce_science(rawdir, rundir, flat, arc, twilight, twilight_flat, sciimg, starimg, bias, overscan, vardq, observatory, lacos, apply_lacos, lacos_xorder, lacos_yorder, lacos_sigclip, lacos_objlim, bpm, instrument, slits, fl_gscrrej, wltrim_frac, grow_gap, cube_bit_mask): """ Reduction pipeline for standard star. Parameters ---------- rawdir: string Directory containing raw images. rundir: string Directory where processed files are saved. flat: string Names of the files containing flat field images. arc: string Arc images. twilight: string Twilight flat images. twilight_flat: string Flat field for twilight image. starimg: string Name of the file containing the image to be reduced. bias: string Bias images. grow_gap: number Number of pixels by which to grow the bad pixel mask around the chip gaps. """ iraf.set(stdimage='imtgmos') iraf.gemini() iraf.unlearn('gemini') iraf.gmos() iraf.unlearn('gmos') iraf.gemtools() iraf.unlearn('gemtools') # os.path.isfile('arquivo') iraf.unlearn('gemini') iraf.unlearn('gmos') iraf.task(lacos_spec=lacos) # set directories iraf.set(caldir=rawdir) # iraf.set(rawdir=rawdir) # raw files iraf.set(procdir=rundir) # processed files iraf.gmos.logfile = 'logfile.log' iraf.gfextract.verbose = 'no' iraf.cd('procdir') flat = flat.replace('.fits', '') twilight = twilight.replace('.fits', '') twilight_flat = twilight_flat.replace('.fits', '') arc = arc.replace('.fits', '') starimg = starimg.replace('.fits', '') sciimg = sciimg.replace('.fits', '') mdffile = 'mdf' + flat + '.fits' iraf.gfreduce.bias = 'caldir$' + bias iraf.gfreduce.fl_fulldq = 'yes' iraf.gfreduce.fl_fixgaps = 'yes' iraf.gfreduce.grow = grow_gap iraf.gireduce.bpm = 'rawdir$' + bpm iraf.gfextract.verbose = 'no' cal_reduction(rawdir=rawdir, rundir=rundir, flat=flat, arc=arc, twilight=twilight, bias=bias, bpm=bpm, overscan=overscan, vardq=vardq, instrument=instrument, slits=slits, twilight_flat=twilight_flat, grow_gap=grow_gap) # # Actually reduce science # image_name = 'rg' + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: iraf.gfreduce(sciimg, slits='header', rawpath='rawdir$', fl_inter='no', fl_addmdf='yes', key_mdf='MDF', mdffile=mdffile, weights='no', fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='no', recenter='no', fl_fulldq='yes', fl_flux='no', fl_gscrrej='no', fl_extract='no', fl_gsappwave='no', fl_wavtran='no', fl_novl='yes', fl_skysub='no', fl_vardq=vardq, mdfdir='procdir$') prefix = 'rg' # Gemfix image_name = 'p' + prefix + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: iraf.gemfix(prefix + sciimg, out='p' + prefix + sciimg, method='fit1d', bitmask=65535, axis=1) prefix = 'p' + prefix # LA Cosmic - cosmic ray removal if apply_lacos: image_name = 'l' + prefix + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: iraf.gemcrspec(prefix + sciimg, out='l' + prefix + sciimg, sigfrac=0.5, niter=4, fl_vardq=vardq, xorder=lacos_xorder, yorder=lacos_yorder, sigclip=lacos_sigclip, objlim=lacos_objlim) prefix = 'l' + prefix if fl_gscrrej: image_name = 'ex' + prefix + sciimg + '.fits' else: image_name = 'e' + prefix + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: iraf.gfreduce(prefix + sciimg, slits='header', rawpath='./', fl_inter='no', fl_addmdf='no', key_mdf='MDF', mdffile=mdffile, fl_over='no', fl_trim='no', fl_bias='no', trace='no', recenter='no', fl_flux='no', fl_gscrrej=fl_gscrrej, fl_extract='yes', fl_gsappwave='yes', fl_wavtran='no', fl_novl='no', fl_skysub='no', grow=grow_gap, reference='eprg' + flat, weights='no', wavtraname='eprg' + arc, response='eprg' + flat + '_response.fits', fl_vardq=vardq, fl_fulldq='yes', fl_fixgaps='yes') if fl_gscrrej: prefix = 'ex' + prefix else: prefix = 'e' + prefix # if wl2 > 7550.0: # wl2 = 7550.0 # # Apply wavelength transformation # wl1, wl2 = wl_lims(prefix + sciimg + '.fits', wltrim_frac) image_name = 't' + prefix + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: iraf.gftransform( prefix + sciimg, wavtraname='eprg' + arc, fl_vardq=vardq, w1=wl1, w2=wl2, ) prefix = 't' + prefix # # Sky subtraction # image_name = 's' + prefix + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: iraf.gfskysub( prefix + sciimg, expr='default', combine='median', reject='avsigclip', scale='none', zero='none', weight='none', sepslits='yes', fl_inter='no', lsigma=1, hsigma=1, ) prefix = 's' + prefix # # Apply flux calibration to galaxy # image_name = 'c' + prefix + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: iraf.gscalibrate(prefix + sciimg, sfuncti=starimg, extinct='onedstds$ctioextinct.dat', observatory=observatory, fluxsca=1, fl_vardq=vardq) prefix = 'c' + prefix # # Remove spurious data with PCA # image_name = 'x' + prefix + sciimg + '.fits' print(os.getcwd()) print(image_name) if os.path.isfile(image_name): pipe.skipwarn(image_name) else: t = pca.Tomography(prefix + sciimg + '.fits') t.decompose() t.remove_cosmic_rays(sigma_threshold=10.0) t.write(image_name) prefix = 'x' + prefix # # Create data cubes # image_name = 'd' + prefix + sciimg + '.fits' if os.path.isfile(image_name): pipe.skipwarn(image_name) else: data_cube = CubeBuilder(prefix + sciimg + '.fits') data_cube.build_cube() data_cube.fit_refraction_function() data_cube.fix_atmospheric_refraction() data_cube.write(image_name)
def reduce_stdstar(rawdir, rundir, caldir, starobj, stdstar, flat, arc, twilight, twilight_flat, starimg, bias, overscan, vardq, lacos, observatory, apply_lacos, lacos_xorder, lacos_yorder, lacos_objlim, lacos_sigclip, bpm, instrument, slits, fl_gscrrej, wltrim_frac=0.03, sens_order=6, sens_function='spline3', apsum_radius=1): """ Reduction pipeline for standard star. Parameters ---------- rawdir: string Directory containing raw images. rundi: string Directory where processed files are saved. caldir: string Directory containing standard star calibration files. starobj: string Object keyword for the star image. stdstar: string Star name in calibration file. flat: list Names of the files containing flat field images. arc: list Arc images. twilight: list Twilight flat images. starimg: string Name of the file containing the image to be reduced. bias: list Bias images. """ iraf.set(stdimage='imtgmos') iraf.task(lacos_spec=lacos) iraf.gemini() iraf.unlearn('gemini') iraf.gmos() iraf.unlearn('gmos') iraf.gemtools() iraf.unlearn('gemtools') iraf.gmos.logfile = 'logfile.log' iraf.gemtools.gloginit.logfile = 'logfile.log' iraf.gfextract.verbose = 'no' # set directories iraf.set(caldir=rawdir) # iraf.set(rawdir=rawdir) # raw files iraf.set(procdir=rundir) # processed files # os.path.isfile('arquivo') iraf.cd('procdir') flat = flat.strip('.fits') twilight = twilight.strip('.fits') twilight_flat = twilight_flat.strip('.fits') arc = arc.strip('.fits') starimg = starimg.strip('.fits') mdffile = 'mdf' + flat + '.fits' iraf.gfreduce.bias = 'rawdir$' + bias iraf.gfreduce.fl_fulldq = 'yes' iraf.gfreduce.fl_fixgaps = 'yes' iraf.gireduce.bpm = 'rawdir$' + bpm cal_reduction(rawdir=rawdir, rundir=rundir, flat=flat, arc=arc, twilight=twilight, bias=bias, bpm=bpm, overscan=overscan, vardq=vardq, instrument=instrument, slits=slits, twilight_flat=twilight_flat) # # Actually reduce star # imageName = 'rg' + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: imageName = 'g' + starimg + '.fits' if os.path.isfile(imageName): iraf.printlog( 'GIREDS: WARNING: Removing file {:s}'.format(imageName), 'logfile.log', 'yes') iraf.delete(imageName) iraf.gfreduce(starimg, slits='header', rawpath='rawdir$', fl_inter='no', fl_addmdf='yes', key_mdf='MDF', mdffile=mdffile, weights='no', fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='no', recenter='no', fl_flux='no', fl_gscrrej='no', fl_extract='no', fl_gsappwave='no', fl_wavtran='no', fl_novl='yes', fl_skysub='no', fl_vardq=vardq, mdfdir='procdir$') prefix = 'rg' # # Gemfix # imageName = 'p' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gemfix(prefix + starimg, out='p' + prefix + starimg, method='fit1d', bitmask=1, axis=1) prefix = 'p' + prefix # # LA Cosmic # if apply_lacos: imageName = 'l' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: if apply_lacos: iraf.gemcrspec(prefix + starimg, out='l' + prefix + starimg, sigfrac=0.32, niter=4, fl_vardq=vardq, xorder=lacos_xorder, yorder=lacos_yorder, objlim=lacos_objlim, sigclip=lacos_sigclip) prefix = 'l' + prefix # # Extraction and Gemini's comsmic ray rejection # if fl_gscrrej: imageName = 'x' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) fl_gscrrej = False else: imageName = 'ex' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gfreduce(prefix + starimg, slits='header', rawpath='./', fl_inter='no', fl_addmdf='no', key_mdf='MDF', mdffile=mdffile, fl_over='no', fl_trim='no', fl_bias='no', trace='no', recenter='no', fl_flux='no', fl_gscrrej=fl_gscrrej, fl_extract='yes', fl_gsappwave='yes', fl_wavtran='no', fl_novl='no', fl_skysub='no', reference='eprg' + flat, weights='no', wavtraname='eprg' + arc, response='eprg' + flat + '_response.fits', fl_vardq=vardq) prefix = 'ex' + prefix else: imageName = 'e' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gfreduce(prefix + starimg, slits='header', rawpath='./', fl_inter='no', fl_addmdf='no', key_mdf='MDF', mdffile=mdffile, fl_over='no', fl_trim='no', fl_bias='no', trace='no', recenter='no', fl_flux='no', fl_gscrrej=fl_gscrrej, fl_extract='yes', fl_gsappwave='yes', fl_wavtran='no', fl_novl='no', fl_skysub='no', reference='eprg' + flat, weights='no', wavtraname='eprg' + arc, response='eprg' + flat + '_response.fits', fl_vardq=vardq) prefix = 'e' + prefix # # Wavelength transform # wl1, wl2 = wl_lims(prefix + starimg + '.fits', wltrim_frac) imageName = 't' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gfreduce(prefix + starimg, slits='header', rawpath='./', fl_inter='no', fl_addmdf='no', key_mdf='MDF', mdffile=mdffile, fl_over='no', fl_trim='no', fl_bias='no', trace='no', recenter='no', fl_flux='no', fl_gscrrej='no', fl_extract='no', fl_gsappwave='no', fl_wavtran='yes', fl_novl='no', fl_skysub='no', reference='eprg' + flat, weights='no', wavtraname='eprg' + arc, response='eprg' + flat + '_response.fits', fl_vardq=vardq, w1=wl1, w2=wl2) prefix = 't' + prefix # # Sky subtraction # imageName = 's' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gfreduce(prefix + starimg, slits='header', rawpath='./', fl_inter='no', fl_addmdf='no', key_mdf='MDF', mdffile=mdffile, fl_over='no', fl_trim='no', fl_bias='no', trace='no', recenter='no', fl_flux='no', fl_gscrrej='no', fl_extract='no', fl_gsappwave='no', fl_wavtran='no', fl_novl='no', fl_skysub='yes', reference='eprg' + flat, weights='no', wavtraname='eprg' + arc, response='eprg' + flat + '_response.fits', fl_vardq=vardq, w1=wl1, w2=wl2) prefix = 's' + prefix # # Apsumming the stellar spectra # xinst = pf.getdata(prefix + starimg + '.fits', ext=1)['XINST'] if instrument == 'GMOS-N': x0 = np.average(xinst[xinst < 10]) elif instrument == 'GMOS-S': x0 = np.average(xinst[xinst > 10]) ap_expression = '((XINST-{:.2f})**2 + '\ '(YINST-2.45)**2)**0.5 < {:.2f}'.format(x0, apsum_radius) imageName = 'a' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gfapsum(prefix + starimg, fl_inter='no', lthreshold='INDEF', hthreshold='INDEF', reject='avsigclip', expr=ap_expression) # # Building sensibility function # if os.path.isfile('std' + starimg)\ and os.path.isfile('sens' + starimg + '.fits'): skipwarn('std{0:s} and sens{0:s}.fits'.format(starimg)) else: imageName = 'std' + starimg if os.path.isfile(imageName): iraf.printlog( 'GIREDS: WARNING: Removing file {:s}'.format(imageName), 'logfile.log', 'yes') iraf.delete(imageName) imageName = 'sens' + starimg + '.fits' if os.path.isfile(imageName): iraf.printlog( 'GIREDS: WARNING: Removing file {:s}'.format(imageName), 'logfile.log', 'yes') iraf.delete(imageName) iraf.gsstandard('a' + prefix + starimg, starname=stdstar, observatory=observatory, sfile='std' + starimg, sfunction='sens' + starimg, caldir=caldir, order=sens_order, function=sens_function) # # Apply flux calibration to star # imageName = 'c' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gscalibrate(prefix + starimg, sfuncti='sens' + starimg, extinct='onedstds$ctioextinct.dat', observatory=observatory, fluxsca=1, fl_vardq=vardq) # # Create data cubes # imageName = 'dc' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: data_cube = CubeBuilder('c' + prefix + starimg + '.fits') data_cube.build_cube() data_cube.fit_refraction_function() data_cube.fix_atmospheric_refraction() data_cube.write(imageName) # # Test calibration # iraf.cd(caldir) caldata = np.loadtxt(stdstar + '.dat', unpack=True) iraf.cd('procdir') calflux = mag2flux(caldata[0], caldata[1]) imageName = 'ca' + prefix + starimg + '.fits' if os.path.isfile(imageName): skipwarn(imageName) else: iraf.gscalibrate('a' + prefix + starimg, sfuncti='sens' + starimg, extinct='onedstds$ctioextinct.dat', observatory=observatory, fluxsca=1) sumflux = pf.getdata('ca' + prefix + starimg + '.fits', ext=2) sumhead = pf.getheader('ca' + prefix + starimg + '.fits', ext=2) sumwl = sumhead['crval1'] + np.arange( sumhead['naxis1']) * sumhead['cdelt1'] plt.close('all') plt.plot(sumwl, sumflux, 'b', lw=.5) plt.plot(caldata[0], calflux, 'r', lw=1.5) plt.xlim(sumwl[0] * .99, sumwl[-1] * 1.01) plt.ylim(min(calflux) * .8, max(calflux) * 1.2) plt.savefig('calib' + starimg + '.eps')
import glob, os import g0_init_cfg as ic # ----- Importing IRAF from the root directory ----- # current_dir = os.getcwd() os.chdir(ic.dir_iraf) from pyraf import iraf from pyraf.iraf import gemini, gmos os.chdir(current_dir) iraf.chdir(current_dir) # ----- Spectrophotometric calibration ----- # iraf.imdelete('cstxeqxbrg@'+ic.lst_sci, verify='no') for sci in iraf.type(ic.lst_sci, Stdout=1): sci = sci.strip() iraf.gscalibrate('stxeqxbrg'+sci, sfunction=ic.caldir+ic.sensfunc, obs=ic.obs_site, extinction=ic.extinction, fl_ext='yes', fl_vardq='yes') os.system('ds9 &') iraf.sleep(5.0) for sci in iraf.type(ic.lst_sci, Stdout=1): iraf.gfdisplay('cstxeqxbrg'+sci, 1, version=1) # Printing the running time print('--- %s seconds ---' %(time.time()-start_time))
vardq = 'yes' for i in gal: iraf.gsreduce(rawdir + i, rawpath=rawdir, bias=rawdir + bias, fl_fixpix='yes', flat='flat.fits', fl_over='no', fl_gscrrej='yes', fl_vardq=vardq, fl_fulldq=vardq) iraf.gstransform('gs' + i, wavtraname='gs' + arc.replace('.fits', ''), fl_vardq=vardq) iraf.gsskysub('tgs' + i, long_sample='800:900', fl_vardq=vardq) iraf.gscalibrate('stgs' + i, fl_vardq=vardq) a = iraf.hselect((3 * 'cstgs{:s}[0],').format(*gal), 'xoffset,yoffset', 'yes', Stdout=1) pixscale = 0.292 # arcsec/pix along the direction of the slit b = np.array([[float(j) for j in i.split('\t')] for i in a]) / pixscale np.savetxt('offsets.dat', b) iraf.imcombine((3 * 'cstgs{:s}[2],').format(*gal), 'ngc3081_545.fits', offsets='offsets.dat', combine='average', reject='sigclip')
iraf.gsskysub( 'tgs' + star, long_sample='150:200') iraf.gsextract( 'stgs' + star) iraf.gsstandard( 'estgs' + star, caldir='onedstds$ctionewcal/', starname='l3218') for i in gal: iraf.gsreduce( rawdir + i, rawpath=rawdir, bias=rawdir + bias, fl_fixpix='yes', flat='flat.fits', fl_over='no', fl_gscrrej='yes') iraf.gstransform( 'gs' + i, wavtraname='gs' + arc.replace('.fits', '')) iraf.gsskysub( 'tgs' + i, long_sample='800:900') iraf.gscalibrate( 'stgs' + i) a = iraf.hselect( (3*'cstgs{:s}[0],').format(*gal), 'xoffset,yoffset', 'yes', Stdout=1) pixscale = 0.292 # arcsec/pix along the direction of the slit b = np.array([[float(j) for j in i.split('\t')] for i in a]) / pixscale np.savetxt('offsets.dat', b) iraf.imcombine( (3*'cstgs{:s}[2],').format(*gal), 'ngc3081_545.fits', offsets='offsets.dat', combine='average', reject='sigclip')
iraf.gsstandard( 'astexlrg'+n, starname=stdstar[m], observatory=observatory[m], sfile='std'+str(m)+'_'+str(i), sfunction='sens'+str(m), caldir=caldir[m]) ''' # (B)Test calibration import matplotlib.pyplot as plt starflux = [locrep+starinfo[i]['flux'] for i in starinfo_idx] for m, i in enumerate(star): iraf.delete('castexlrg'+i+'.fits') iraf.gscalibrate( 'astexlrg'+i, sfuncti='sens'+str(m), observa=observatory[m], fluxsca=1) starcal_fl = pf.getdata('castexlrg'+i+'.fits', ext=2) starcal_he = pf.getheader('castexlrg'+i+'.fits', ext=2) starcal_wl = starcal_he['crval1'] + np.arange(starcal_he['naxis1'])*starcal_he['cdelt1'] starrep = np.loadtxt(starflux[m], unpack=True) # Need: baixar arquivos de todas estrelas com contagem em fluxo # ou converter o arquivo do gmos de mag>fluxo # Need: limitar range(x) do arquivo do repositorio # Need: limitar range(y) do plot. plt.close('all') plt.plot(starcal_wl, starcal_fl, 'b') plt.plot(starrep[0], starrep[1], 'r', lw=1.56) plt.xlim(starcal_wl[0], starcal_wl[-1])