def fiber_area_arcsec2(x, y): ''' Returns area of fibers at (x,y) in arcsec^2 ''' from desimodel.io import load_desiparams, load_platescale params = load_desiparams() fiber_dia = params['fibers']['diameter_um'] x = np.asarray(x) y = np.asarray(y) r = np.sqrt(x**2 + y**2) #- Platescales in um/arcsec ps = load_platescale() radial_scale = np.interp(r, ps['radius'], ps['radial_platescale']) az_scale = np.interp(r, ps['radius'], ps['az_platescale']) #- radial and azimuthal fiber radii in arcsec rr = 0.5 * fiber_dia / radial_scale raz = 0.5 * fiber_dia / az_scale fiber_area = (np.pi * rr * raz) return fiber_area
def __init__(self, wave, average_calib, atmospheric_extinction, seeing_term, \ pivot_airmass, pivot_seeing,\ atmospheric_extinction_uncertainty = None, seeing_term_uncertainty = None): """Lightweight wrapper object for average flux calibration data Args: wave : 1D[nwave] input wavelength (angstroms) average_calib: 1D[nwave] average calibration vector at pivot airmass and seeing ((electrons)/(1e-17 erg/cm^2)) atmospheric_extinction : 1D[nwave] extinction term, magnitude seeing_term : 1D[nwave], magnitude pivot_airmass : float, airmass value for average_calib pivot_seeing : float, seeing value for average_calib (same definition and unit as SEEING keyword in images) atmospheric_extinction_uncertainty : 1D[nwave] uncertainty on extinction term, magnitude seeing_term_uncertainty : 1D[nwave], uncertainty on seeing term magnitude All arguments become attributes, the calib vector should be in units of [electrons]/[1e-17 erg/cm^2]. The model is calib = average_calib*10**(-0.4*((seeing-pivot_seeing)*seeing_term + (airmass-pivot_airmass)*atmospheric_extinction)) """ assert wave.ndim == 1 assert average_calib.shape == wave.shape assert atmospheric_extinction.shape == wave.shape assert seeing_term.shape == wave.shape self.wave = wave self.average_calib = average_calib self.atmospheric_extinction = atmospheric_extinction self.seeing_term = seeing_term self.pivot_airmass = pivot_airmass self.pivot_seeing = pivot_seeing self.atmospheric_extinction_uncertainty = atmospheric_extinction_uncertainty self.seeing_term_uncertainty = seeing_term_uncertainty self.meta = dict(units='electrons/(1e-17 erg/cm^2)') self.desiparams = load_desiparams()
def __init__(self): self.output_type = "PER_FIBER" log = desiutil.log.get_logger() # load things we will use several times desiparams = load_desiparams() geometric_area_cm2 = 1e4 * desiparams["area"]["geometric_area"] # m2 self.thru_conversion_wavelength = 6000 # A energy_per_photon = ( constants.h * constants.c / (self.thru_conversion_wavelength * units.Angstrom)).to( units.erg).value #ergs self.thru_conversion_factor_ergs_per_cm2 = 1e17 * energy_per_photon / geometric_area_cm2 # ergs/cm2 # preload filters self.filters = dict() for photsys in ["N", "S"]: for band in ["G", "R", "Z", "W1", "W2"]: self.filters[band + photsys] = load_legacy_survey_filter( band=band, photsys=photsys) # define wavelength array min_filter_wave = 100000 max_filter_wave = 0 for k in self.filters.keys(): log.debug(self.filters[k].name) min_filter_wave = min(min_filter_wave, np.min(self.filters[k].wavelength)) - 0.1 max_filter_wave = max(max_filter_wave, np.max(self.filters[k].wavelength)) + 0.1 log.debug("min max wavelength for filters= {:d} , {:d}".format( int(min_filter_wave), int(max_filter_wave))) self.rwave = np.linspace(min_filter_wave, max_filter_wave, int(max_filter_wave - min_filter_wave))
def main(args): # Set up the logger if args.verbose: log = get_logger(DEBUG) else: log = get_logger() # Make sure all necessary environment variables are set DESI_SPECTRO_REDUX_DIR = "./quickGen" if 'DESI_SPECTRO_REDUX' not in os.environ: log.info('DESI_SPECTRO_REDUX environment is not set.') else: DESI_SPECTRO_REDUX_DIR = os.environ['DESI_SPECTRO_REDUX'] if os.path.exists(DESI_SPECTRO_REDUX_DIR): if not os.path.isdir(DESI_SPECTRO_REDUX_DIR): raise RuntimeError("Path %s Not a directory" % DESI_SPECTRO_REDUX_DIR) else: try: os.makedirs(DESI_SPECTRO_REDUX_DIR) except: raise SPECPROD_DIR = 'specprod' if 'SPECPROD' not in os.environ: log.info('SPECPROD environment is not set.') else: SPECPROD_DIR = os.environ['SPECPROD'] prod_Dir = specprod_root() if os.path.exists(prod_Dir): if not os.path.isdir(prod_Dir): raise RuntimeError("Path %s Not a directory" % prod_Dir) else: try: os.makedirs(prod_Dir) except: raise # Initialize random number generator to use. np.random.seed(args.seed) random_state = np.random.RandomState(args.seed) # Derive spectrograph number from nstart if needed if args.spectrograph is None: args.spectrograph = args.nstart / 500 # Read fibermapfile to get object type, night and expid if args.fibermap: log.info("Reading fibermap file {}".format(args.fibermap)) fibermap = read_fibermap(args.fibermap) objtype = get_source_types(fibermap) stdindx = np.where(objtype == 'STD') # match STD with STAR mwsindx = np.where(objtype == 'MWS_STAR') # match MWS_STAR with STAR bgsindx = np.where(objtype == 'BGS') # match BGS with LRG objtype[stdindx] = 'STAR' objtype[mwsindx] = 'STAR' objtype[bgsindx] = 'LRG' NIGHT = fibermap.meta['NIGHT'] EXPID = fibermap.meta['EXPID'] else: # Create a blank fake fibermap fibermap = empty_fibermap(args.nspec) targetids = random_state.randint(2**62, size=args.nspec) fibermap['TARGETID'] = targetids night = get_night() expid = 0 log.info("Initializing SpecSim with config {}".format(args.config)) desiparams = load_desiparams() qsim = get_simulator(args.config, num_fibers=1) if args.simspec: # Read the input file log.info('Reading input file {}'.format(args.simspec)) simspec = desisim.io.read_simspec(args.simspec) nspec = simspec.nspec if simspec.flavor == 'arc': log.warning("quickgen doesn't generate flavor=arc outputs") return else: wavelengths = simspec.wave spectra = simspec.flux if nspec < args.nspec: log.info("Only {} spectra in input file".format(nspec)) args.nspec = nspec else: # Initialize the output truth table. spectra = [] wavelengths = qsim.source.wavelength_out.to(u.Angstrom).value npix = len(wavelengths) truth = dict() meta = Table() truth['OBJTYPE'] = np.zeros(args.nspec, dtype=(str, 10)) truth['FLUX'] = np.zeros((args.nspec, npix)) truth['WAVE'] = wavelengths jj = list() for thisobj in set(true_objtype): ii = np.where(true_objtype == thisobj)[0] nobj = len(ii) truth['OBJTYPE'][ii] = thisobj log.info('Generating {} template'.format(thisobj)) # Generate the templates if thisobj == 'ELG': elg = desisim.templates.ELG(wave=wavelengths, add_SNeIa=args.add_SNeIa) flux, tmpwave, meta1 = elg.make_templates( nmodel=nobj, seed=args.seed, zrange=args.zrange_elg, sne_rfluxratiorange=args.sne_rfluxratiorange) elif thisobj == 'LRG': lrg = desisim.templates.LRG(wave=wavelengths, add_SNeIa=args.add_SNeIa) flux, tmpwave, meta1 = lrg.make_templates( nmodel=nobj, seed=args.seed, zrange=args.zrange_lrg, sne_rfluxratiorange=args.sne_rfluxratiorange) elif thisobj == 'QSO': qso = desisim.templates.QSO(wave=wavelengths) flux, tmpwave, meta1 = qso.make_templates( nmodel=nobj, seed=args.seed, zrange=args.zrange_qso) elif thisobj == 'BGS': bgs = desisim.templates.BGS(wave=wavelengths, add_SNeIa=args.add_SNeIa) flux, tmpwave, meta1 = bgs.make_templates( nmodel=nobj, seed=args.seed, zrange=args.zrange_bgs, rmagrange=args.rmagrange_bgs, sne_rfluxratiorange=args.sne_rfluxratiorange) elif thisobj == 'STD': std = desisim.templates.STD(wave=wavelengths) flux, tmpwave, meta1 = std.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'QSO_BAD': # use STAR template no color cuts star = desisim.templates.STAR(wave=wavelengths) flux, tmpwave, meta1 = star.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'MWS_STAR' or thisobj == 'MWS': mwsstar = desisim.templates.MWS_STAR(wave=wavelengths) flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'WD': wd = desisim.templates.WD(wave=wavelengths) flux, tmpwave, meta1 = wd.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'SKY': flux = np.zeros((nobj, npix)) meta1 = Table(dict(REDSHIFT=np.zeros(nobj, dtype=np.float32))) elif thisobj == 'TEST': flux = np.zeros((args.nspec, npix)) indx = np.where(wave > 5800.0 - 1E-6)[0][0] ref_integrated_flux = 1E-10 ref_cst_flux_density = 1E-17 single_line = (np.arange(args.nspec) % 2 == 0).astype( np.float32) continuum = (np.arange(args.nspec) % 2 == 1).astype(np.float32) for spec in range(args.nspec): flux[spec, indx] = single_line[ spec] * ref_integrated_flux / np.gradient(wavelengths)[ indx] # single line flux[spec] += continuum[ spec] * ref_cst_flux_density # flat continuum meta1 = Table( dict(REDSHIFT=np.zeros(args.nspec, dtype=np.float32), LINE=wave[indx] * np.ones(args.nspec, dtype=np.float32), LINEFLUX=single_line * ref_integrated_flux, CONSTFLUXDENSITY=continuum * ref_cst_flux_density)) else: log.fatal('Unknown object type {}'.format(thisobj)) sys.exit(1) # Pack it in. truth['FLUX'][ii] = flux meta = vstack([meta, meta1]) jj.append(ii.tolist()) # Sanity check on units; templates currently return ergs, not 1e-17 ergs... # assert (thisobj == 'SKY') or (np.max(truth['FLUX']) < 1e-6) # Sort the metadata table. jj = sum(jj, []) meta_new = Table() for k in range(args.nspec): index = int(np.where(np.array(jj) == k)[0]) meta_new = vstack([meta_new, meta[index]]) meta = meta_new # Add TARGETID and the true OBJTYPE to the metadata table. meta.add_column( Column(true_objtype, dtype=(str, 10), name='TRUE_OBJTYPE')) meta.add_column(Column(targetids, name='TARGETID')) # Rename REDSHIFT -> TRUEZ anticipating later table joins with zbest.Z meta.rename_column('REDSHIFT', 'TRUEZ') # explicitly set location on focal plane if needed to support airmass # variations when using specsim v0.5 if qsim.source.focal_xy is None: qsim.source.focal_xy = (u.Quantity(0, 'mm'), u.Quantity(100, 'mm')) # Set simulation parameters from the simspec header or desiparams bright_objects = ['bgs', 'mws', 'bright', 'BGS', 'MWS', 'BRIGHT_MIX'] gray_objects = ['gray', 'grey'] if args.simspec is None: object_type = objtype flavor = None elif simspec.flavor == 'science': object_type = None flavor = simspec.header['PROGRAM'] else: object_type = None flavor = simspec.flavor log.warning( 'Maybe using an outdated simspec file with flavor={}'.format( flavor)) # Set airmass if args.airmass is not None: qsim.atmosphere.airmass = args.airmass elif args.simspec and 'AIRMASS' in simspec.header: qsim.atmosphere.airmass = simspec.header['AIRMASS'] else: qsim.atmosphere.airmass = 1.25 # Science Req. Doc L3.3.2 # Set exptime if args.exptime is not None: qsim.observation.exposure_time = args.exptime * u.s elif args.simspec and 'EXPTIME' in simspec.header: qsim.observation.exposure_time = simspec.header['EXPTIME'] * u.s elif objtype in bright_objects: qsim.observation.exposure_time = desiparams['exptime_bright'] * u.s else: qsim.observation.exposure_time = desiparams['exptime_dark'] * u.s # Set Moon Phase if args.moon_phase is not None: qsim.atmosphere.moon.moon_phase = args.moon_phase elif args.simspec and 'MOONFRAC' in simspec.header: qsim.atmosphere.moon.moon_phase = simspec.header['MOONFRAC'] elif flavor in bright_objects or object_type in bright_objects: qsim.atmosphere.moon.moon_phase = 0.7 elif flavor in gray_objects: qsim.atmosphere.moon.moon_phase = 0.1 else: qsim.atmosphere.moon.moon_phase = 0.5 # Set Moon Zenith if args.moon_zenith is not None: qsim.atmosphere.moon.moon_zenith = args.moon_zenith * u.deg elif args.simspec and 'MOONALT' in simspec.header: qsim.atmosphere.moon.moon_zenith = simspec.header['MOONALT'] * u.deg elif flavor in bright_objects or object_type in bright_objects: qsim.atmosphere.moon.moon_zenith = 30 * u.deg elif flavor in gray_objects: qsim.atmosphere.moon.moon_zenith = 80 * u.deg else: qsim.atmosphere.moon.moon_zenith = 100 * u.deg # Set Moon - Object Angle if args.moon_angle is not None: qsim.atmosphere.moon.separation_angle = args.moon_angle * u.deg elif args.simspec and 'MOONSEP' in simspec.header: qsim.atmosphere.moon.separation_angle = simspec.header[ 'MOONSEP'] * u.deg elif flavor in bright_objects or object_type in bright_objects: qsim.atmosphere.moon.separation_angle = 50 * u.deg elif flavor in gray_objects: qsim.atmosphere.moon.separation_angle = 60 * u.deg else: qsim.atmosphere.moon.separation_angle = 60 * u.deg # Initialize per-camera output arrays that will be saved waves, trueflux, noisyflux, obsivar, resolution, sflux = {}, {}, {}, {}, {}, {} maxbin = 0 nmax = args.nspec for camera in qsim.instrument.cameras: # Lookup this camera's resolution matrix and convert to the sparse # format used in desispec. R = Resolution(camera.get_output_resolution_matrix()) resolution[camera.name] = np.tile(R.to_fits_array(), [args.nspec, 1, 1]) waves[camera.name] = (camera.output_wavelength.to( u.Angstrom).value.astype(np.float32)) nwave = len(waves[camera.name]) maxbin = max(maxbin, len(waves[camera.name])) nobj = np.zeros((nmax, 3, maxbin)) # object photons nsky = np.zeros((nmax, 3, maxbin)) # sky photons nivar = np.zeros((nmax, 3, maxbin)) # inverse variance (object+sky) cframe_observedflux = np.zeros( (nmax, 3, maxbin)) # calibrated object flux cframe_ivar = np.zeros( (nmax, 3, maxbin)) # inverse variance of calibrated object flux cframe_rand_noise = np.zeros( (nmax, 3, maxbin)) # random Gaussian noise to calibrated flux sky_ivar = np.zeros((nmax, 3, maxbin)) # inverse variance of sky sky_rand_noise = np.zeros( (nmax, 3, maxbin)) # random Gaussian noise to sky only frame_rand_noise = np.zeros( (nmax, 3, maxbin)) # random Gaussian noise to nobj+nsky trueflux[camera.name] = np.empty( (args.nspec, nwave)) # calibrated flux noisyflux[camera.name] = np.empty( (args.nspec, nwave)) # observed flux with noise obsivar[camera.name] = np.empty( (args.nspec, nwave)) # inverse variance of flux if args.simspec: for i in range(10): cn = camera.name + str(i) if cn in simspec.cameras: dw = np.gradient(simspec.cameras[cn].wave) break else: raise RuntimeError( 'Unable to find a {} camera in input simspec'.format( camera)) else: sflux = np.empty((args.nspec, npix)) #- Check if input simspec is for a continuum flat lamp instead of science #- This does not convolve to per-fiber resolution if args.simspec: if simspec.flavor == 'flat': log.info("Simulating flat lamp exposure") for i, camera in enumerate(qsim.instrument.cameras): channel = camera.name #- from simspec, b/r/z not b0/r1/z9 assert camera.output_wavelength.unit == u.Angstrom num_pixels = len(waves[channel]) phot = list() for j in range(10): cn = camera.name + str(j) if cn in simspec.cameras: camwave = simspec.cameras[cn].wave dw = np.gradient(camwave) phot.append(simspec.cameras[cn].phot) if len(phot) == 0: raise RuntimeError( 'Unable to find a {} camera in input simspec'.format( camera)) else: phot = np.vstack(phot) meanspec = resample_flux(waves[channel], camwave, np.average(phot / dw, axis=0)) fiberflat = random_state.normal(loc=1.0, scale=1.0 / np.sqrt(meanspec), size=(nspec, num_pixels)) ivar = np.tile(meanspec, [nspec, 1]) mask = np.zeros((simspec.nspec, num_pixels), dtype=np.uint32) for kk in range((args.nspec + args.nstart - 1) // 500 + 1): camera = channel + str(kk) outfile = desispec.io.findfile('fiberflat', NIGHT, EXPID, camera) start = max(500 * kk, args.nstart) end = min(500 * (kk + 1), nmax) if (args.spectrograph <= kk): log.info( "Writing files for channel:{}, spectrograph:{}, spectra:{} to {}" .format(channel, kk, start, end)) ff = FiberFlat(waves[channel], fiberflat[start:end, :], ivar[start:end, :], mask[start:end, :], meanspec, header=dict(CAMERA=camera)) write_fiberflat(outfile, ff) filePath = desispec.io.findfile("fiberflat", NIGHT, EXPID, camera) log.info("Wrote file {}".format(filePath)) sys.exit(0) # Repeat the simulation for all spectra fluxunits = 1e-17 * u.erg / (u.s * u.cm**2 * u.Angstrom) for j in range(args.nspec): thisobjtype = objtype[j] sys.stdout.flush() if flavor == 'arc': qsim.source.update_in('Quickgen source {0}'.format, 'perfect', wavelengths * u.Angstrom, spectra * fluxunits) else: qsim.source.update_in('Quickgen source {0}'.format(j), thisobjtype.lower(), wavelengths * u.Angstrom, spectra[j, :] * fluxunits) qsim.source.update_out() qsim.simulate() qsim.generate_random_noise(random_state) for i, output in enumerate(qsim.camera_output): assert output['observed_flux'].unit == 1e17 * fluxunits # Extract the simulation results needed to create our uncalibrated # frame output file. num_pixels = len(output) nobj[j, i, :num_pixels] = output['num_source_electrons'][:, 0] nsky[j, i, :num_pixels] = output['num_sky_electrons'][:, 0] nivar[j, i, :num_pixels] = 1.0 / output['variance_electrons'][:, 0] # Get results for our flux-calibrated output file. cframe_observedflux[ j, i, :num_pixels] = 1e17 * output['observed_flux'][:, 0] cframe_ivar[ j, i, :num_pixels] = 1e-34 * output['flux_inverse_variance'][:, 0] # Fill brick arrays from the results. camera = output.meta['name'] trueflux[camera][j][:] = 1e17 * output['observed_flux'][:, 0] noisyflux[camera][j][:] = 1e17 * ( output['observed_flux'][:, 0] + output['flux_calibration'][:, 0] * output['random_noise_electrons'][:, 0]) obsivar[camera][j][:] = 1e-34 * output['flux_inverse_variance'][:, 0] # Use the same noise realization in the cframe and frame, without any # additional noise from sky subtraction for now. frame_rand_noise[ j, i, :num_pixels] = output['random_noise_electrons'][:, 0] cframe_rand_noise[j, i, :num_pixels] = 1e17 * ( output['flux_calibration'][:, 0] * output['random_noise_electrons'][:, 0]) # The sky output file represents a model fit to ~40 sky fibers. # We reduce the variance by a factor of 25 to account for this and # give the sky an independent (Gaussian) noise realization. sky_ivar[ j, i, :num_pixels] = 25.0 / (output['variance_electrons'][:, 0] - output['num_source_electrons'][:, 0]) sky_rand_noise[j, i, :num_pixels] = random_state.normal( scale=1.0 / np.sqrt(sky_ivar[j, i, :num_pixels]), size=num_pixels) armName = {"b": 0, "r": 1, "z": 2} for channel in 'brz': #Before writing, convert from counts/bin to counts/A (as in Pixsim output) #Quicksim Default: #FLUX - input spectrum resampled to this binning; no noise added [1e-17 erg/s/cm2/s/Ang] #COUNTS_OBJ - object counts in 0.5 Ang bin #COUNTS_SKY - sky counts in 0.5 Ang bin num_pixels = len(waves[channel]) dwave = np.gradient(waves[channel]) nobj[:, armName[channel], :num_pixels] /= dwave frame_rand_noise[:, armName[channel], :num_pixels] /= dwave nivar[:, armName[channel], :num_pixels] *= dwave**2 nsky[:, armName[channel], :num_pixels] /= dwave sky_rand_noise[:, armName[channel], :num_pixels] /= dwave sky_ivar[:, armName[channel], :num_pixels] /= dwave**2 # Now write the outputs in DESI standard file system. None of the output file can have more than 500 spectra # Looping over spectrograph for ii in range((args.nspec + args.nstart - 1) // 500 + 1): start = max(500 * ii, args.nstart) # first spectrum for a given spectrograph end = min(500 * (ii + 1), nmax) # last spectrum for the spectrograph if (args.spectrograph <= ii): camera = "{}{}".format(channel, ii) log.info( "Writing files for channel:{}, spectrograph:{}, spectra:{} to {}" .format(channel, ii, start, end)) num_pixels = len(waves[channel]) # Write frame file framefileName = desispec.io.findfile("frame", NIGHT, EXPID, camera) frame_flux=nobj[start:end,armName[channel],:num_pixels]+ \ nsky[start:end,armName[channel],:num_pixels] + \ frame_rand_noise[start:end,armName[channel],:num_pixels] frame_ivar = nivar[start:end, armName[channel], :num_pixels] sh1 = frame_flux.shape[ 0] # required for slicing the resolution metric, resolusion matrix has (nspec,ndiag,wave) # for example if nstart =400, nspec=150: two spectrographs: # 400-499=> 0 spectrograph, 500-549 => 1 if (args.nstart == start): resol = resolution[channel][:sh1, :, :] else: resol = resolution[channel][-sh1:, :, :] # must create desispec.Frame object frame=Frame(waves[channel], frame_flux, frame_ivar,\ resolution_data=resol, spectrograph=ii, \ fibermap=fibermap[start:end], \ meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) ) desispec.io.write_frame(framefileName, frame) framefilePath = desispec.io.findfile("frame", NIGHT, EXPID, camera) log.info("Wrote file {}".format(framefilePath)) if args.frameonly or simspec.flavor == 'arc': continue # Write cframe file cframeFileName = desispec.io.findfile("cframe", NIGHT, EXPID, camera) cframeFlux = cframe_observedflux[ start:end, armName[channel], :num_pixels] + cframe_rand_noise[ start:end, armName[channel], :num_pixels] cframeIvar = cframe_ivar[start:end, armName[channel], :num_pixels] # must create desispec.Frame object cframe = Frame(waves[channel], cframeFlux, cframeIvar, \ resolution_data=resol, spectrograph=ii, fibermap=fibermap[start:end], meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) ) desispec.io.frame.write_frame(cframeFileName, cframe) cframefilePath = desispec.io.findfile("cframe", NIGHT, EXPID, camera) log.info("Wrote file {}".format(cframefilePath)) # Write sky file skyfileName = desispec.io.findfile("sky", NIGHT, EXPID, camera) skyflux=nsky[start:end,armName[channel],:num_pixels] + \ sky_rand_noise[start:end,armName[channel],:num_pixels] skyivar = sky_ivar[start:end, armName[channel], :num_pixels] skymask = np.zeros(skyflux.shape, dtype=np.uint32) # must create desispec.Sky object skymodel = SkyModel(waves[channel], skyflux, skyivar, skymask, header=dict(CAMERA=camera)) desispec.io.sky.write_sky(skyfileName, skymodel) skyfilePath = desispec.io.findfile("sky", NIGHT, EXPID, camera) log.info("Wrote file {}".format(skyfilePath)) # Write calib file calibVectorFile = desispec.io.findfile("calib", NIGHT, EXPID, camera) flux = cframe_observedflux[start:end, armName[channel], :num_pixels] phot = nobj[start:end, armName[channel], :num_pixels] calibration = np.zeros_like(phot) jj = (flux > 0) calibration[jj] = phot[jj] / flux[jj] #- TODO: what should calibivar be? #- For now, model it as the noise of combining ~10 spectra calibivar = 10 / cframe_ivar[start:end, armName[channel], :num_pixels] #mask=(1/calibivar>0).astype(int)?? mask = np.zeros(calibration.shape, dtype=np.uint32) # write flux calibration fluxcalib = FluxCalib(waves[channel], calibration, calibivar, mask) write_flux_calibration(calibVectorFile, fluxcalib) calibfilePath = desispec.io.findfile("calib", NIGHT, EXPID, camera) log.info("Wrote file {}".format(calibfilePath))
def new_exposure(program, nspec=5000, night=None, expid=None, tileid=None, nproc=None, seed=None, obsconditions=None, specify_targets=dict(), testslit=False, exptime=None, arc_lines_filename=None, flat_spectrum_filename=None, outdir=None, config='desi', telescope=None, overwrite=False): """ Create a new exposure and output input simulation files. Does not generate pixel-level simulations or noisy spectra. Args: program (str): 'arc', 'flat', 'bright', 'dark', 'bgs', 'mws', ... nspec (int, optional): number of spectra to simulate night (str, optional): YEARMMDD string expid (int, optional): positive integer exposure ID tileid (int, optional): integer tile ID nproc (object, optional): What does this do? seed (int, optional): random seed obsconditions (str or dict-like, optional): see options below specify_targets (dict of dicts, optional): Define target properties like magnitude and redshift for each target class. Each objtype has its own key,value pair see simspec.templates.specify_galparams_dict() or simsepc.templates.specify_starparams_dict() testslit (bool, optional): simulate test slit if True, default False; only for arc/flat exptime (float, optional): exposure time [seconds], overrides obsconditions['EXPTIME'] arc_lines_filename (str, optional): use alternate arc lines filename (used if program="arc") flat_spectrum_filename (str, optional): use alternate flat spectrum filename (used if program="flat") outdir (str, optional): output directory config (str, optional): the yaml configuration to load telescope (str, optional): the telescope used (i.e. 1m, 160mm) overwrite (bool, optional): optionally clobber existing files Returns: science: sim, fibermap, meta, obsconditions, objmeta Writes to outdir or $DESI_SPECTRO_SIM/$PIXPROD/{night}/ * fibermap-{expid}.fits * simspec-{expid}.fits input obsconditions can be a string 'dark', 'gray', 'bright', or dict-like observation metadata with keys SEEING (arcsec), EXPTIME (sec), AIRMASS, MOONFRAC (0-1), MOONALT (deg), MOONSEP (deg). Output obsconditions is is expanded dict-like structure. program is used to pick the sky brightness, and is propagated to desisim.targets.sample_objtype() to get the correct distribution of targets for a given program, e.g. ELGs, LRGs, QSOs for program='dark'. if program is 'arc' or 'flat', then `sim` is truth table with keys FLUX and WAVE; and meta=None and obsconditions=None. Also see simexp.simarc(), .simflat(), and .simscience(), the last of which simulates a science exposure given surveysim obsconditions input, fiber assignments, and pre-generated mock target spectra. """ if expid is None: expid = get_next_expid() if tileid is None: tileid = get_next_tileid() if night is None: #- simulation obs time = now, even if sun is up dateobs = time.gmtime() night = get_night(utc=dateobs) else: #- 10pm on night YEARMMDD night = str(night) #- just in case we got an integer instead of string dateobs = time.strptime(night + ':22', '%Y%m%d:%H') outsimspec = desisim.io.findfile('simspec', night, expid) outfibermap = desisim.io.findfile('simfibermap', night, expid) if outdir is not None: outsimspec = os.path.join(outdir, os.path.basename(outsimspec)) outfibermap = os.path.join(outdir, os.path.basename(outfibermap)) program = program.lower() log.debug('Generating {} targets'.format(nspec)) header = dict(NIGHT=night, EXPID=expid, PROGRAM=program) if program in ('arc', 'flat'): header['FLAVOR'] = program else: header['FLAVOR'] = 'science' #- ISO 8601 DATE-OBS year-mm-ddThh:mm:ss header['DATE-OBS'] = time.strftime('%FT%T', dateobs) if program == 'arc': if arc_lines_filename is None: infile = os.getenv( 'DESI_ROOT' ) + '/spectro/templates/calib/v0.4/arc-lines-average-in-vacuum-from-winlight-20170118.fits' else: infile = arc_lines_filename arcdata = fits.getdata(infile, 1) if exptime is None: exptime = 5 wave, phot, fibermap = desisim.simexp.simarc(arcdata, nspec=nspec, testslit=testslit) header['EXPTIME'] = exptime desisim.io.write_simspec_arc(outsimspec, wave, phot, header, fibermap=fibermap, overwrite=overwrite) fibermap.meta['NIGHT'] = night fibermap.meta['EXPID'] = expid desispec.io.write_fibermap(outfibermap, fibermap) truth = dict(WAVE=wave, PHOT=phot, UNITS='photon') return truth, fibermap, None, None, None elif program == 'flat': if flat_spectrum_filename is None: infile = os.getenv( 'DESI_ROOT' ) + '/spectro/templates/calib/v0.4/flat-3100K-quartz-iodine.fits' else: infile = flat_spectrum_filename if exptime is None: exptime = 10 sim, fibermap = desisim.simexp.simflat(infile, nspec=nspec, exptime=exptime, testslit=testslit, psfconvolve=False) header['EXPTIME'] = exptime header['FLAVOR'] = 'flat' desisim.io.write_simspec(sim, truth=None, fibermap=fibermap, obs=None, expid=expid, night=night, header=header, filename=outsimspec, overwrite=overwrite) fibermap.meta['NIGHT'] = night fibermap.meta['EXPID'] = expid desispec.io.write_fibermap(outfibermap, fibermap) # fluxunits = 1e-17 * u.erg / (u.s * u.cm**2 * u.Angstrom) fluxunits = '1e-17 erg/(s * cm2 * Angstrom)' flux = sim.simulated['source_flux'].to(fluxunits) wave = sim.simulated['wavelength'].to('Angstrom') truth = dict(WAVE=wave, FLUX=flux, UNITS=str(fluxunits)) return truth, fibermap, None, None, None #- all other programs fibermap, (flux, wave, meta, objmeta) = get_targets_parallel(nspec, program, tileid=tileid, nproc=nproc, seed=seed, specify_targets=specify_targets, config=config, telescope=telescope) if obsconditions is None: if program in ['dark', 'lrg', 'qso']: obsconditions = desisim.simexp.reference_conditions['DARK'] elif program in ['elg', 'gray', 'grey']: obsconditions = desisim.simexp.reference_conditions['GRAY'] elif program in ['mws', 'bgs', 'bright']: obsconditions = desisim.simexp.reference_conditions['BRIGHT'] else: raise ValueError('unknown program {}'.format(program)) elif isinstance(obsconditions, str): try: obsconditions = desisim.simexp.reference_conditions[ obsconditions.upper()] except KeyError: raise ValueError('obsconditions {} not in {}'.format( obsconditions.upper(), list(desisim.simexp.reference_conditions.keys()))) if exptime is not None: obsconditions['EXPTIME'] = exptime desiparams = load_desiparams(config=config, telescope=telescope) sim = simulate_spectra(wave, flux, fibermap=fibermap, obsconditions=obsconditions, psfconvolve=False, specsim_config_file=config, params=desiparams) #- Write fibermap telera, teledec = io.get_tile_radec(tileid) hdr = dict( NIGHT=(night, 'Night of observation YEARMMDD'), EXPID=(expid, 'DESI exposure ID'), TILEID=(tileid, 'DESI tile ID'), PROGRAM=(program, 'program [dark, bright, ...]'), FLAVOR=('science', 'Flavor [arc, flat, science, zero, ...]'), TELRA=(telera, 'Telescope pointing RA [degrees]'), TELDEC=(teledec, 'Telescope pointing dec [degrees]'), AIRMASS=(obsconditions['AIRMASS'], 'Airmass at middle of exposure'), EXPTIME=(obsconditions['EXPTIME'], 'Exposure time [sec]'), SEEING=(obsconditions['SEEING'], 'Seeing FWHM [arcsec]'), MOONFRAC=(obsconditions['MOONFRAC'], 'Moon illumination fraction 0-1; 1=full'), MOONALT=(obsconditions['MOONALT'], 'Moon altitude [degrees]'), MOONSEP=(obsconditions['MOONSEP'], 'Moon:tile separation angle [degrees]'), ) hdr['DATE-OBS'] = (time.strftime('%FT%T', dateobs), 'Start of exposure') simfile = io.write_simspec(sim, meta, fibermap, obsconditions, expid, night, objmeta=objmeta, header=hdr, filename=outsimspec, overwrite=overwrite) if not isinstance(fibermap, table.Table): fibermap = table.Table(fibermap) fibermap.meta.update(hdr) desispec.io.write_fibermap(outfibermap, fibermap) log.info('Wrote ' + outfibermap) update_obslog(obstype='science', program=program, expid=expid, dateobs=dateobs, tileid=tileid) return sim, fibermap, meta, obsconditions, objmeta
fits.open( '/global/cscratch1/sd/mjwilson/BGS/SV-ASSIGN/tiles/BGS_SV_30_3x_superset60_Sep2019.fits' )[1].data) utiles = np.unique(tiles['TILEID'].quantity) ## _bitdefs = load_mask_bits("sv1") desi_mask = BitMask('sv1_desi_mask', _bitdefs) bgs_mask = BitMask('sv1_bgs_mask', _bitdefs) types = bgs_mask.names() bits = [bgs_mask.bitnum(x) for x in types] ## L428 of https://github.com/desihub/desimodel/blob/master/py/desimodel/focalplane/geometry.py params = load_desiparams() fiber_dia = params['fibers']['diameter_um'] #- Platescales in um/arcsec ps = load_platescale() ## Add in GAMA labels. _fits = fits.open( '/global/cscratch1/sd/mjwilson/BGS/SV-ASSIGN/truth/legacy/ls-GAMA-south.fits' ) gama = Table(_fits[1].data) for tile in utiles: ## Scrape from the tile picker site. ## cmd = 'wget http://www.astro.utah.edu/~u6022465/SV/tiles/SV_BGS/fits_files/tile-{:06}.fits -O /global/cscratch1/sd/mjwilson/BGS/SV-ASSIGN/fiberassign/tile-{:06}.fits'.format(tile, tile) ## os.system(cmd)
def main(args): # Set up the logger if args.verbose: log = get_logger(DEBUG) else: log = get_logger() # Make sure all necessary environment variables are set DESI_SPECTRO_REDUX_DIR="./quickGen" if 'DESI_SPECTRO_REDUX' not in os.environ: log.info('DESI_SPECTRO_REDUX environment is not set.') else: DESI_SPECTRO_REDUX_DIR=os.environ['DESI_SPECTRO_REDUX'] if os.path.exists(DESI_SPECTRO_REDUX_DIR): if not os.path.isdir(DESI_SPECTRO_REDUX_DIR): raise RuntimeError("Path %s Not a directory"%DESI_SPECTRO_REDUX_DIR) else: try: os.makedirs(DESI_SPECTRO_REDUX_DIR) except: raise SPECPROD_DIR='specprod' if 'SPECPROD' not in os.environ: log.info('SPECPROD environment is not set.') else: SPECPROD_DIR=os.environ['SPECPROD'] prod_Dir=specprod_root() if os.path.exists(prod_Dir): if not os.path.isdir(prod_Dir): raise RuntimeError("Path %s Not a directory"%prod_Dir) else: try: os.makedirs(prod_Dir) except: raise # Initialize random number generator to use. np.random.seed(args.seed) random_state = np.random.RandomState(args.seed) # Derive spectrograph number from nstart if needed if args.spectrograph is None: args.spectrograph = args.nstart / 500 # Read fibermapfile to get object type, night and expid if args.fibermap: log.info("Reading fibermap file {}".format(args.fibermap)) fibermap=read_fibermap(args.fibermap) objtype = get_source_types(fibermap) stdindx=np.where(objtype=='STD') # match STD with STAR mwsindx=np.where(objtype=='MWS_STAR') # match MWS_STAR with STAR bgsindx=np.where(objtype=='BGS') # match BGS with LRG objtype[stdindx]='STAR' objtype[mwsindx]='STAR' objtype[bgsindx]='LRG' NIGHT=fibermap.meta['NIGHT'] EXPID=fibermap.meta['EXPID'] else: # Create a blank fake fibermap fibermap = empty_fibermap(args.nspec) targetids = random_state.randint(2**62, size=args.nspec) fibermap['TARGETID'] = targetids night = get_night() expid = 0 log.info("Initializing SpecSim with config {}".format(args.config)) desiparams = load_desiparams() qsim = get_simulator(args.config, num_fibers=1) if args.simspec: # Read the input file log.info('Reading input file {}'.format(args.simspec)) simspec = desisim.io.read_simspec(args.simspec) nspec = simspec.nspec if simspec.flavor == 'arc': log.warning("quickgen doesn't generate flavor=arc outputs") return else: wavelengths = simspec.wave spectra = simspec.flux if nspec < args.nspec: log.info("Only {} spectra in input file".format(nspec)) args.nspec = nspec else: # Initialize the output truth table. spectra = [] wavelengths = qsim.source.wavelength_out.to(u.Angstrom).value npix = len(wavelengths) truth = dict() meta = Table() truth['OBJTYPE'] = np.zeros(args.nspec, dtype=(str, 10)) truth['FLUX'] = np.zeros((args.nspec, npix)) truth['WAVE'] = wavelengths jj = list() for thisobj in set(true_objtype): ii = np.where(true_objtype == thisobj)[0] nobj = len(ii) truth['OBJTYPE'][ii] = thisobj log.info('Generating {} template'.format(thisobj)) # Generate the templates if thisobj == 'ELG': elg = desisim.templates.ELG(wave=wavelengths, add_SNeIa=args.add_SNeIa) flux, tmpwave, meta1 = elg.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_elg,sne_rfluxratiorange=args.sne_rfluxratiorange) elif thisobj == 'LRG': lrg = desisim.templates.LRG(wave=wavelengths, add_SNeIa=args.add_SNeIa) flux, tmpwave, meta1 = lrg.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_lrg,sne_rfluxratiorange=args.sne_rfluxratiorange) elif thisobj == 'QSO': qso = desisim.templates.QSO(wave=wavelengths) flux, tmpwave, meta1 = qso.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_qso) elif thisobj == 'BGS': bgs = desisim.templates.BGS(wave=wavelengths, add_SNeIa=args.add_SNeIa) flux, tmpwave, meta1 = bgs.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_bgs,rmagrange=args.rmagrange_bgs,sne_rfluxratiorange=args.sne_rfluxratiorange) elif thisobj =='STD': std = desisim.templates.STD(wave=wavelengths) flux, tmpwave, meta1 = std.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'QSO_BAD': # use STAR template no color cuts star = desisim.templates.STAR(wave=wavelengths) flux, tmpwave, meta1 = star.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'MWS_STAR' or thisobj == 'MWS': mwsstar = desisim.templates.MWS_STAR(wave=wavelengths) flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'WD': wd = desisim.templates.WD(wave=wavelengths) flux, tmpwave, meta1 = wd.make_templates(nmodel=nobj, seed=args.seed) elif thisobj == 'SKY': flux = np.zeros((nobj, npix)) meta1 = Table(dict(REDSHIFT=np.zeros(nobj, dtype=np.float32))) elif thisobj == 'TEST': flux = np.zeros((args.nspec, npix)) indx = np.where(wave>5800.0-1E-6)[0][0] ref_integrated_flux = 1E-10 ref_cst_flux_density = 1E-17 single_line = (np.arange(args.nspec)%2 == 0).astype(np.float32) continuum = (np.arange(args.nspec)%2 == 1).astype(np.float32) for spec in range(args.nspec) : flux[spec,indx] = single_line[spec]*ref_integrated_flux/np.gradient(wavelengths)[indx] # single line flux[spec] += continuum[spec]*ref_cst_flux_density # flat continuum meta1 = Table(dict(REDSHIFT=np.zeros(args.nspec, dtype=np.float32), LINE=wave[indx]*np.ones(args.nspec, dtype=np.float32), LINEFLUX=single_line*ref_integrated_flux, CONSTFLUXDENSITY=continuum*ref_cst_flux_density)) else: log.fatal('Unknown object type {}'.format(thisobj)) sys.exit(1) # Pack it in. truth['FLUX'][ii] = flux meta = vstack([meta, meta1]) jj.append(ii.tolist()) # Sanity check on units; templates currently return ergs, not 1e-17 ergs... # assert (thisobj == 'SKY') or (np.max(truth['FLUX']) < 1e-6) # Sort the metadata table. jj = sum(jj,[]) meta_new = Table() for k in range(args.nspec): index = int(np.where(np.array(jj) == k)[0]) meta_new = vstack([meta_new, meta[index]]) meta = meta_new # Add TARGETID and the true OBJTYPE to the metadata table. meta.add_column(Column(true_objtype, dtype=(str, 10), name='TRUE_OBJTYPE')) meta.add_column(Column(targetids, name='TARGETID')) # Rename REDSHIFT -> TRUEZ anticipating later table joins with zbest.Z meta.rename_column('REDSHIFT', 'TRUEZ') # explicitly set location on focal plane if needed to support airmass # variations when using specsim v0.5 if qsim.source.focal_xy is None: qsim.source.focal_xy = (u.Quantity(0, 'mm'), u.Quantity(100, 'mm')) # Set simulation parameters from the simspec header or desiparams bright_objects = ['bgs','mws','bright','BGS','MWS','BRIGHT_MIX'] gray_objects = ['gray','grey'] if args.simspec is None: object_type = objtype flavor = None elif simspec.flavor == 'science': object_type = None flavor = simspec.header['PROGRAM'] else: object_type = None flavor = simspec.flavor log.warning('Maybe using an outdated simspec file with flavor={}'.format(flavor)) # Set airmass if args.airmass is not None: qsim.atmosphere.airmass = args.airmass elif args.simspec and 'AIRMASS' in simspec.header: qsim.atmosphere.airmass = simspec.header['AIRMASS'] else: qsim.atmosphere.airmass = 1.25 # Science Req. Doc L3.3.2 # Set exptime if args.exptime is not None: qsim.observation.exposure_time = args.exptime * u.s elif args.simspec and 'EXPTIME' in simspec.header: qsim.observation.exposure_time = simspec.header['EXPTIME'] * u.s elif objtype in bright_objects: qsim.observation.exposure_time = desiparams['exptime_bright'] * u.s else: qsim.observation.exposure_time = desiparams['exptime_dark'] * u.s # Set Moon Phase if args.moon_phase is not None: qsim.atmosphere.moon.moon_phase = args.moon_phase elif args.simspec and 'MOONFRAC' in simspec.header: qsim.atmosphere.moon.moon_phase = simspec.header['MOONFRAC'] elif flavor in bright_objects or object_type in bright_objects: qsim.atmosphere.moon.moon_phase = 0.7 elif flavor in gray_objects: qsim.atmosphere.moon.moon_phase = 0.1 else: qsim.atmosphere.moon.moon_phase = 0.5 # Set Moon Zenith if args.moon_zenith is not None: qsim.atmosphere.moon.moon_zenith = args.moon_zenith * u.deg elif args.simspec and 'MOONALT' in simspec.header: qsim.atmosphere.moon.moon_zenith = simspec.header['MOONALT'] * u.deg elif flavor in bright_objects or object_type in bright_objects: qsim.atmosphere.moon.moon_zenith = 30 * u.deg elif flavor in gray_objects: qsim.atmosphere.moon.moon_zenith = 80 * u.deg else: qsim.atmosphere.moon.moon_zenith = 100 * u.deg # Set Moon - Object Angle if args.moon_angle is not None: qsim.atmosphere.moon.separation_angle = args.moon_angle * u.deg elif args.simspec and 'MOONSEP' in simspec.header: qsim.atmosphere.moon.separation_angle = simspec.header['MOONSEP'] * u.deg elif flavor in bright_objects or object_type in bright_objects: qsim.atmosphere.moon.separation_angle = 50 * u.deg elif flavor in gray_objects: qsim.atmosphere.moon.separation_angle = 60 * u.deg else: qsim.atmosphere.moon.separation_angle = 60 * u.deg # Initialize per-camera output arrays that will be saved waves, trueflux, noisyflux, obsivar, resolution, sflux = {}, {}, {}, {}, {}, {} maxbin = 0 nmax= args.nspec for camera in qsim.instrument.cameras: # Lookup this camera's resolution matrix and convert to the sparse # format used in desispec. R = Resolution(camera.get_output_resolution_matrix()) resolution[camera.name] = np.tile(R.to_fits_array(), [args.nspec, 1, 1]) waves[camera.name] = (camera.output_wavelength.to(u.Angstrom).value.astype(np.float32)) nwave = len(waves[camera.name]) maxbin = max(maxbin, len(waves[camera.name])) nobj = np.zeros((nmax,3,maxbin)) # object photons nsky = np.zeros((nmax,3,maxbin)) # sky photons nivar = np.zeros((nmax,3,maxbin)) # inverse variance (object+sky) cframe_observedflux = np.zeros((nmax,3,maxbin)) # calibrated object flux cframe_ivar = np.zeros((nmax,3,maxbin)) # inverse variance of calibrated object flux cframe_rand_noise = np.zeros((nmax,3,maxbin)) # random Gaussian noise to calibrated flux sky_ivar = np.zeros((nmax,3,maxbin)) # inverse variance of sky sky_rand_noise = np.zeros((nmax,3,maxbin)) # random Gaussian noise to sky only frame_rand_noise = np.zeros((nmax,3,maxbin)) # random Gaussian noise to nobj+nsky trueflux[camera.name] = np.empty((args.nspec, nwave)) # calibrated flux noisyflux[camera.name] = np.empty((args.nspec, nwave)) # observed flux with noise obsivar[camera.name] = np.empty((args.nspec, nwave)) # inverse variance of flux if args.simspec: for i in range(10): cn = camera.name + str(i) if cn in simspec.cameras: dw = np.gradient(simspec.cameras[cn].wave) break else: raise RuntimeError('Unable to find a {} camera in input simspec'.format(camera)) else: sflux = np.empty((args.nspec, npix)) #- Check if input simspec is for a continuum flat lamp instead of science #- This does not convolve to per-fiber resolution if args.simspec: if simspec.flavor == 'flat': log.info("Simulating flat lamp exposure") for i,camera in enumerate(qsim.instrument.cameras): channel = camera.name #- from simspec, b/r/z not b0/r1/z9 assert camera.output_wavelength.unit == u.Angstrom num_pixels = len(waves[channel]) phot = list() for j in range(10): cn = camera.name + str(j) if cn in simspec.cameras: camwave = simspec.cameras[cn].wave dw = np.gradient(camwave) phot.append(simspec.cameras[cn].phot) if len(phot) == 0: raise RuntimeError('Unable to find a {} camera in input simspec'.format(camera)) else: phot = np.vstack(phot) meanspec = resample_flux( waves[channel], camwave, np.average(phot/dw, axis=0)) fiberflat = random_state.normal(loc=1.0, scale=1.0 / np.sqrt(meanspec), size=(nspec, num_pixels)) ivar = np.tile(meanspec, [nspec, 1]) mask = np.zeros((simspec.nspec, num_pixels), dtype=np.uint32) for kk in range((args.nspec+args.nstart-1)//500+1): camera = channel+str(kk) outfile = desispec.io.findfile('fiberflat', NIGHT, EXPID, camera) start=max(500*kk,args.nstart) end=min(500*(kk+1),nmax) if (args.spectrograph <= kk): log.info("Writing files for channel:{}, spectrograph:{}, spectra:{} to {}".format(channel,kk,start,end)) ff = FiberFlat( waves[channel], fiberflat[start:end,:], ivar[start:end,:], mask[start:end,:], meanspec, header=dict(CAMERA=camera)) write_fiberflat(outfile, ff) filePath=desispec.io.findfile("fiberflat",NIGHT,EXPID,camera) log.info("Wrote file {}".format(filePath)) sys.exit(0) # Repeat the simulation for all spectra fluxunits = 1e-17 * u.erg / (u.s * u.cm ** 2 * u.Angstrom) for j in range(args.nspec): thisobjtype = objtype[j] sys.stdout.flush() if flavor == 'arc': qsim.source.update_in( 'Quickgen source {0}'.format, 'perfect', wavelengths * u.Angstrom, spectra * fluxunits) else: qsim.source.update_in( 'Quickgen source {0}'.format(j), thisobjtype.lower(), wavelengths * u.Angstrom, spectra[j, :] * fluxunits) qsim.source.update_out() qsim.simulate() qsim.generate_random_noise(random_state) for i, output in enumerate(qsim.camera_output): assert output['observed_flux'].unit == 1e17 * fluxunits # Extract the simulation results needed to create our uncalibrated # frame output file. num_pixels = len(output) nobj[j, i, :num_pixels] = output['num_source_electrons'][:,0] nsky[j, i, :num_pixels] = output['num_sky_electrons'][:,0] nivar[j, i, :num_pixels] = 1.0 / output['variance_electrons'][:,0] # Get results for our flux-calibrated output file. cframe_observedflux[j, i, :num_pixels] = 1e17 * output['observed_flux'][:,0] cframe_ivar[j, i, :num_pixels] = 1e-34 * output['flux_inverse_variance'][:,0] # Fill brick arrays from the results. camera = output.meta['name'] trueflux[camera][j][:] = 1e17 * output['observed_flux'][:,0] noisyflux[camera][j][:] = 1e17 * (output['observed_flux'][:,0] + output['flux_calibration'][:,0] * output['random_noise_electrons'][:,0]) obsivar[camera][j][:] = 1e-34 * output['flux_inverse_variance'][:,0] # Use the same noise realization in the cframe and frame, without any # additional noise from sky subtraction for now. frame_rand_noise[j, i, :num_pixels] = output['random_noise_electrons'][:,0] cframe_rand_noise[j, i, :num_pixels] = 1e17 * ( output['flux_calibration'][:,0] * output['random_noise_electrons'][:,0]) # The sky output file represents a model fit to ~40 sky fibers. # We reduce the variance by a factor of 25 to account for this and # give the sky an independent (Gaussian) noise realization. sky_ivar[j, i, :num_pixels] = 25.0 / ( output['variance_electrons'][:,0] - output['num_source_electrons'][:,0]) sky_rand_noise[j, i, :num_pixels] = random_state.normal( scale=1.0 / np.sqrt(sky_ivar[j,i,:num_pixels]),size=num_pixels) armName={"b":0,"r":1,"z":2} for channel in 'brz': #Before writing, convert from counts/bin to counts/A (as in Pixsim output) #Quicksim Default: #FLUX - input spectrum resampled to this binning; no noise added [1e-17 erg/s/cm2/s/Ang] #COUNTS_OBJ - object counts in 0.5 Ang bin #COUNTS_SKY - sky counts in 0.5 Ang bin num_pixels = len(waves[channel]) dwave=np.gradient(waves[channel]) nobj[:,armName[channel],:num_pixels]/=dwave frame_rand_noise[:,armName[channel],:num_pixels]/=dwave nivar[:,armName[channel],:num_pixels]*=dwave**2 nsky[:,armName[channel],:num_pixels]/=dwave sky_rand_noise[:,armName[channel],:num_pixels]/=dwave sky_ivar[:,armName[channel],:num_pixels]/=dwave**2 # Now write the outputs in DESI standard file system. None of the output file can have more than 500 spectra # Looping over spectrograph for ii in range((args.nspec+args.nstart-1)//500+1): start=max(500*ii,args.nstart) # first spectrum for a given spectrograph end=min(500*(ii+1),nmax) # last spectrum for the spectrograph if (args.spectrograph <= ii): camera = "{}{}".format(channel, ii) log.info("Writing files for channel:{}, spectrograph:{}, spectra:{} to {}".format(channel,ii,start,end)) num_pixels = len(waves[channel]) # Write frame file framefileName=desispec.io.findfile("frame",NIGHT,EXPID,camera) frame_flux=nobj[start:end,armName[channel],:num_pixels]+ \ nsky[start:end,armName[channel],:num_pixels] + \ frame_rand_noise[start:end,armName[channel],:num_pixels] frame_ivar=nivar[start:end,armName[channel],:num_pixels] sh1=frame_flux.shape[0] # required for slicing the resolution metric, resolusion matrix has (nspec,ndiag,wave) # for example if nstart =400, nspec=150: two spectrographs: # 400-499=> 0 spectrograph, 500-549 => 1 if (args.nstart==start): resol=resolution[channel][:sh1,:,:] else: resol=resolution[channel][-sh1:,:,:] # must create desispec.Frame object frame=Frame(waves[channel], frame_flux, frame_ivar,\ resolution_data=resol, spectrograph=ii, \ fibermap=fibermap[start:end], \ meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) ) desispec.io.write_frame(framefileName, frame) framefilePath=desispec.io.findfile("frame",NIGHT,EXPID,camera) log.info("Wrote file {}".format(framefilePath)) if args.frameonly or simspec.flavor == 'arc': continue # Write cframe file cframeFileName=desispec.io.findfile("cframe",NIGHT,EXPID,camera) cframeFlux=cframe_observedflux[start:end,armName[channel],:num_pixels]+cframe_rand_noise[start:end,armName[channel],:num_pixels] cframeIvar=cframe_ivar[start:end,armName[channel],:num_pixels] # must create desispec.Frame object cframe = Frame(waves[channel], cframeFlux, cframeIvar, \ resolution_data=resol, spectrograph=ii, fibermap=fibermap[start:end], meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) ) desispec.io.frame.write_frame(cframeFileName,cframe) cframefilePath=desispec.io.findfile("cframe",NIGHT,EXPID,camera) log.info("Wrote file {}".format(cframefilePath)) # Write sky file skyfileName=desispec.io.findfile("sky",NIGHT,EXPID,camera) skyflux=nsky[start:end,armName[channel],:num_pixels] + \ sky_rand_noise[start:end,armName[channel],:num_pixels] skyivar=sky_ivar[start:end,armName[channel],:num_pixels] skymask=np.zeros(skyflux.shape, dtype=np.uint32) # must create desispec.Sky object skymodel = SkyModel(waves[channel], skyflux, skyivar, skymask, header=dict(CAMERA=camera)) desispec.io.sky.write_sky(skyfileName, skymodel) skyfilePath=desispec.io.findfile("sky",NIGHT,EXPID,camera) log.info("Wrote file {}".format(skyfilePath)) # Write calib file calibVectorFile=desispec.io.findfile("calib",NIGHT,EXPID,camera) flux = cframe_observedflux[start:end,armName[channel],:num_pixels] phot = nobj[start:end,armName[channel],:num_pixels] calibration = np.zeros_like(phot) jj = (flux>0) calibration[jj] = phot[jj] / flux[jj] #- TODO: what should calibivar be? #- For now, model it as the noise of combining ~10 spectra calibivar=10/cframe_ivar[start:end,armName[channel],:num_pixels] #mask=(1/calibivar>0).astype(int)?? mask=np.zeros(calibration.shape, dtype=np.uint32) # write flux calibration fluxcalib = FluxCalib(waves[channel], calibration, calibivar, mask) write_flux_calibration(calibVectorFile, fluxcalib) calibfilePath=desispec.io.findfile("calib",NIGHT,EXPID,camera) log.info("Wrote file {}".format(calibfilePath))