Пример #1
0
def reduce_science(args):
    for spec in args.specid:
        spec_ind_sci = np.where(args.sci_df['Specid'] == spec)[0]
        for amp in config.Amps:
            amp_ind_sci = np.where(args.sci_df['Amp'] == amp)[0]
            sci_sel = np.intersect1d(spec_ind_sci, amp_ind_sci) 
            for ind in sci_sel:
                if args.instr == "virus":
                    if not args.use_trace_ref:
                        ifucen = np.loadtxt(op.join(args.configdir, 
                                                    'IFUcen_files', 
                                                    args.ifucen_fn[amp][0]
                                                    + args.sci_df['Ifuid'][ind] 
                                                    + '.txt'), 
                                                    usecols=[0,1,2,4], 
                                               skiprows=args.ifucen_fn[amp][1])
                    else:
                        if args.sci_df['Ifuid'][ind] == '004':
                            ifucen = np.loadtxt(op.join(args.configdir,
                                                    'IFUcen_files',
                                                    'IFUcen_HETDEX_reverse_R.txt'),
                                                    usecols=[0,1,2,4],
                                               skiprows=args.ifucen_fn[amp][1])
                            ifucen[224:,:] = ifucen[-1:223:-1,:]

                        else:
                            ifucen = np.loadtxt(op.join(args.configdir,
                                                    'IFUcen_files',
                                                    'IFUcen_HETDEX.txt'),
                                                    usecols=[0,1,2,4],
                                               skiprows=args.ifucen_fn[amp][1])
                else:
                    ifucen = np.loadtxt(op.join(args.configdir, 'IFUcen_files', 
                                        args.ifucen_fn[amp][0]), 
                              usecols=[0,1,2,4], skiprows=args.ifucen_fn[amp][1])
                if args.debug:
                    print("Working on Sci/Twi for %s, %s" %(spec, amp)) 
                if args.check_if_twi_exists:
                    fn = op.join(args.twi_dir,'fiber_*_%s_%s_%s_%s.pkl' %(spec, 
                                                   args.sci_df['Ifuslot'][ind],
                                                     args.sci_df['Ifuid'][ind],
                                                                          amp))
                    calfiles = glob.glob(fn)
                    if not calfiles:
                        print("No cals found for %s,%s: %s"
                              %(spec, amp, args.sci_df['Files'][ind]))
                        print("If you want to produce cals include "
                              "--reduce_twi")
                              
                sci1 = Amplifier(args.sci_df['Files'][ind],
                                 args.sci_df['Output'][ind],
                                 calpath=args.twi_dir, skypath=args.sky_dir,
                                 debug=False, refit=False, 
                                 dark_mult=args.dark_mult[amp],
                                 darkpath=args.darkdir, biaspath=args.biasdir,
                                 virusconfig=args.configdir, 
                                 specname=args.specname[amp],
                                 use_pixelflat=(args.pixelflats<1),
                                 use_trace_ref=args.use_trace_ref,
                                 calculate_shift=args.adjust_trace,
                                 fiber_date=args.fiber_date,
                                 cont_smooth=args.cont_smooth,
                                 make_residual=False, do_cont_sub=False,
                                 make_skyframe=False)
                sci1.load_all_cal()
                wavelim=[4500,4600]
                xlim = np.interp([wavelim[0],wavelim[1]],
                                 np.linspace(args.wvl_dict[amp][0],
                                             args.wvl_dict[amp][1], sci1.D),
                                 np.arange(sci1.D))
                cols=np.arange(int(xlim[0])-10,int(xlim[1])+10)  
                sci1.fiberextract(cols=cols)
                sci1.sky_subtraction()
                sci2 = Amplifier(args.sci_df['Files'][ind].replace(amp, 
                                                      config.Amp_dict[amp][0]),
                                 args.sci_df['Output'][ind],
                                 calpath=args.twi_dir, skypath=args.sky_dir, 
                                 debug=False, refit=False, 
                             dark_mult=args.dark_mult[config.Amp_dict[amp][0]],
                                 darkpath=args.darkdir, biaspath=args.biasdir,
                                 virusconfig=args.configdir, 
                                 specname=args.specname[amp],
                                 use_pixelflat=(args.pixelflats<1),
                                 use_trace_ref=args.use_trace_ref,
                                 calculate_shift=args.adjust_trace,
                                 fiber_date=args.fiber_date,
                                 cont_smooth=args.cont_smooth,
                                 make_residual=False, do_cont_sub=False,
                                 make_skyframe=False)
                sci2.load_all_cal()
                sci2.fiberextract(cols=cols)                    
                sci2.sky_subtraction()
                Fe, FeS = recreate_fiberextract(sci1, sci2, 
                                                wavelim=wavelim, 
                                                disp=args.disp[amp])
                FE = [Fe, FeS]
                FEN = ['Fe', 'FeS']
                for f,n in zip(FE, FEN):
                    outname = op.join(args.sci_df['Output'][ind],
                                      '%s%s_%s_sci_%s.fits' %(n,
                              op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                       args.sci_df['Ifuslot'][ind], 
                                                          config.Amp_dict[amp][1]))
                    make_fiber_image(f, sci1.header, outname, args, amp)
                    
                    make_cube_file(args, outname, ifucen, args.cube_scale, 
                                   config.Amp_dict[amp][1])
                if args.save_sci_fibers:
                    sci1.save_fibers()
                    sci2.save_fibers()
                if args.save_sci_amplifier:
                    sci1.save()
                    sci2.save()
                if args.debug:
                    print("Finished working on Sci/Twi for %s, %s" %(spec, amp))        
Пример #2
0
def reduce_science(args):
    for spec in args.specid:
        spec_ind_sci = np.where(args.sci_df['Specid'] == spec)[0]
        for amp in config.Amps:
            amp_ind_sci = np.where(args.sci_df['Amp'] == amp)[0]
            sci_sel = np.intersect1d(spec_ind_sci, amp_ind_sci) 
            for ind in sci_sel:
                if args.instr == "virus":
                    if not args.use_trace_ref:
                        ifucen = np.loadtxt(op.join(args.configdir, 
                                                    'IFUcen_files', 
                                                    args.ifucen_fn[amp][0]
                                                    + args.sci_df['Ifuid'][ind] 
                                                    + '.txt'), 
                                                    usecols=[0,1,2,4], 
                                               skiprows=args.ifucen_fn[amp][1])
                        
                    else:
                        if args.sci_df['Ifuid'][ind] == '004':
                            ifucen = np.loadtxt(op.join(args.configdir,
                                                    'IFUcen_files',
                                                    'IFUcen_HETDEX_reverse_R.txt'),
                                                    usecols=[0,1,2,4],
                                               skiprows=args.ifucen_fn[amp][1])
                            ifucen[224:,:] = ifucen[-1:223:-1,:]
                        else:
                            ifucen = np.loadtxt(op.join(args.configdir,
                                                    'IFUcen_files',
                                                    'IFUcen_HETDEX.txt'),
                                                    usecols=[0,1,2,4],
                                               skiprows=args.ifucen_fn[amp][1])
                else:
                    ifucen = np.loadtxt(op.join(args.configdir, 'IFUcen_files', 
                                        args.ifucen_fn[amp][0]), 
                              usecols=[0,1,2], skiprows=args.ifucen_fn[amp][1])
                if args.debug:
                    print("Working on Sci for %s, %s" %(spec, amp)) 
                if args.check_if_twi_exists:
                    fn = op.join(args.twi_dir,'fiber_*_%s_%s_%s_%s.pkl' %(spec, 
                                                   args.sci_df['Ifuslot'][ind],
                                                     args.sci_df['Ifuid'][ind],
                                                                          amp))
                    calfiles = glob.glob(fn)
                    if not calfiles:
                        print("No cals found for %s,%s: %s"
                              %(spec, amp, args.sci_df['Files'][ind]))
                        print("If you want to produce cals include "
                              "--reduce_twi")
                  
                sci1 = Amplifier(args.sci_df['Files'][ind],
                                 args.sci_df['Output'][ind],
                                 calpath=args.twi_dir, skypath=args.sky_dir,
                                 debug=False, refit=False, 
                                 dark_mult=args.dark_mult[amp],
                                 darkpath=args.darkdir, biaspath=args.biasdir,
                                 virusconfig=args.configdir, 
                                 specname=args.specname[amp],
                                 use_pixelflat=(args.pixelflats<1),
                                 use_trace_ref=args.use_trace_ref,
                                 calculate_shift=args.adjust_trace,
                                 fiber_date=args.fiber_date,
                                 cont_smooth=args.cont_smooth)
                #sci1.load_fibers()
                #if sci1.fibers and not args.start_from_scratch:
                #    if sci1.fibers[0].spectrum is not None:
                #        sci1.prepare_image()
                #        sci1.sky_subtraction()
                #        sci1.clean_cosmics()
                #else:
                sci1.load_all_cal()
                if args.adjust_trace:
                    sci1.refit=True
                    sci1.get_trace()
                    sci1.refit=False
                sci1.fiberextract()
                if args.refit_fiber_to_fiber:
                    sci1.refit=True
                    sci1.get_fiber_to_fiber()
                    sci1.refit=False
                sci1.sky_subtraction()
                sci1.clean_cosmics()
                sci1.fiberextract()
                sci1.sky_subtraction()
                sci2 = Amplifier(args.sci_df['Files'][ind].replace(amp, 
                                                      config.Amp_dict[amp][0]),
                                 args.sci_df['Output'][ind],
                                 calpath=args.twi_dir, skypath=args.sky_dir, 
                                 debug=False, refit=False, 
                             dark_mult=args.dark_mult[config.Amp_dict[amp][0]],
                                 darkpath=args.darkdir, biaspath=args.biasdir,
                                 virusconfig=args.configdir, 
                                 specname=args.specname[amp],
                                 use_pixelflat=(args.pixelflats<1),
                                 use_trace_ref=args.use_trace_ref,
                                 calculate_shift=args.adjust_trace,
                                 fiber_date=args.fiber_date,
                                 cont_smooth=args.cont_smooth)
                #sci2.load_fibers()
                #if sci2.fibers and not args.start_from_scratch:
                #    if sci2.fibers[0].spectrum is not None:
                #        sci2.prepare_image()
                #        sci2.sky_subtraction()
                #        sci2.clean_cosmics()
                #else:
                sci2.load_all_cal()
                if args.adjust_trace:
                    sci2.refit=True
                    sci2.get_trace()
                    sci2.refit=False
                sci2.fiberextract()
                if args.refit_fiber_to_fiber:
                    sci2.refit=True
                    sci2.get_fiber_to_fiber()
                    sci2.refit=False
                sci2.sky_subtraction()
                sci2.clean_cosmics()
                sci2.fiberextract()
                sci2.sky_subtraction()
                outname = op.join(args.sci_df['Output'][ind],
                                  'S%s_%s_sci_%s.fits' %(
                          op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                   args.sci_df['Ifuslot'][ind], 
                                                      config.Amp_dict[amp][1]))
                make_spectrograph_image(sci1.clean_image, sci2.clean_image, 
                                        sci1.header, outname)
                make_spectrograph_image(sci1.error, sci2.error, 
                                        sci1.header, op.join(op.dirname(outname), 'ee.'+op.basename(outname)))                   
                make_error_frame(sci1.clean_image, sci2.clean_image, sci1.mask,
                                 sci2.mask, sci1.header, outname)
                outname = op.join(args.sci_df['Output'][ind],
                                  'cS%s_%s_sci_%s.fits' %(
                          op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                   args.sci_df['Ifuslot'][ind], 
                                                      config.Amp_dict[amp][1]))
                make_spectrograph_image(np.where(sci1.mask==0, 
                                                 sci1.clean_image, 0.0),
                                        np.where(sci2.mask==0, 
                                                 sci2.clean_image, 0.0),
                                        sci1.header, outname)
                make_error_frame(sci1.clean_image, sci2.clean_image, sci1.mask,
                                 sci2.mask, sci1.header, outname)
                outname = op.join(args.sci_df['Output'][ind],
                                  'CsS%s_%s_sci_%s.fits' %(
                          op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                   args.sci_df['Ifuslot'][ind], 
                                                      config.Amp_dict[amp][1]))
                make_spectrograph_image(sci1.continuum_sub, sci2.continuum_sub, 
                                        sci1.header, outname)
                make_error_frame(sci1.continuum_sub, sci2.continuum_sub, 
                                 sci1.mask, sci2.mask, sci1.header, outname)
                outname = op.join(args.sci_df['Output'][ind],
                                  'cCsS%s_%s_sci_%s.fits' %(
                          op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                   args.sci_df['Ifuslot'][ind], 
                                                      config.Amp_dict[amp][1]))
                make_spectrograph_image(np.where(sci1.mask==0, 
                                                 sci1.continuum_sub, 0.0),
                                        np.where(sci2.mask==0, 
                                                 sci2.continuum_sub, 0.0),
                                        sci1.header, outname)
                make_error_frame(sci1.continuum_sub, sci2.continuum_sub,
                                 sci1.mask, sci2.mask, sci1.header, outname)
                outname = op.join(args.sci_df['Output'][ind],
                                  'cCsS%s_%s_sci_%s_imstat.png' %(
                          op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                   args.sci_df['Ifuslot'][ind], 
                                                      config.Amp_dict[amp][1]))
                imstat(sci1.residual, sci2.residual, sci1.fibers,
                       sci2.fibers, outname)
                Fe, FeS = recreate_fiberextract(sci1, sci2, 
                                                wavelim=args.wvl_dict[amp], 
                                                disp=args.disp[amp])
                outname = op.join(args.sci_df['Output'][ind],
                                  'Fe%s_%s_sci_%s.fits' %(
                          op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                   args.sci_df['Ifuslot'][ind], 
                                                      config.Amp_dict[amp][1]))
                make_fiber_image(Fe, sci1.header, outname, args, amp)
                make_fiber_error(Fe, sci1.header, outname, args, amp)
                make_cube_file(args, outname, ifucen, args.cube_scale, 
                               config.Amp_dict[amp][1])
                outname = op.join(args.sci_df['Output'][ind],
                                  'FeS%s_%s_sci_%s.fits' %(
                          op.basename(args.sci_df['Files'][ind]).split('_')[0],
                                                   args.sci_df['Ifuslot'][ind], 
                                                      config.Amp_dict[amp][1]))
                make_fiber_image(FeS, sci1.header, outname, args, amp)
                make_fiber_error(FeS, sci1.header, outname, args, amp)
                make_cube_file(args, outname, ifucen, args.cube_scale, 
                               config.Amp_dict[amp][1])
                if args.save_sci_fibers:
                    sci1.save_fibers()
                    sci2.save_fibers()
                if args.save_sci_amplifier:
                    sci1.save()
                    sci2.save()
                if args.debug:
                    print("Finished working on Sci for %s, %s" %(spec, amp))