Ejemplo n.º 1
0
    fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][0:nspec]
    fibermap['TARGETID'] = np.random.randint(sys.maxint, size=nspec)
    fibermap['TARGETCAT'] = np.zeros(nspec, dtype='|S20')
    fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32)*5400
    fibermap['TARGET_MASK0'] = np.zeros(nspec, dtype='i8')
    fibermap['RA_TARGET'] = ra
    fibermap['DEC_TARGET'] = dec
    fibermap['X_TARGET'] = x
    fibermap['Y_TARGET'] = y
    fibermap['X_FVCOBS'] = fibermap['X_TARGET']
    fibermap['Y_FVCOBS'] = fibermap['Y_TARGET']
    fibermap['X_FVCERR'] = np.zeros(nspec, dtype=np.float32)
    fibermap['Y_FVCERR'] = np.zeros(nspec, dtype=np.float32)
    fibermap['RA_OBS'] = fibermap['RA_TARGET']
    fibermap['DEC_OBS'] = fibermap['DEC_TARGET']
    fibermap['BRICKNAME'] = brick.brickname(ra, dec)
    
    return fibermap, truth


#-------------------------------------------------------------------------
#- Currently unused, but keep around for now
def sample_nz(objtype, n):
    """
    Given `objtype` = 'LRG', 'ELG', 'QSO', 'STAR', 'STD'
    return array of `n` redshifts that properly sample n(z)
    from $DESIMODEL/data/targets/nz*.dat
    """
    #- TODO: should this be in desimodel instead?

    #- Stars are at redshift 0 for now.  Could consider a velocity dispersion.
Ejemplo n.º 2
0
def get_targets(nspec, tileid=None):
    """
    Returns:
        fibermap
        truth table

    TODO (@moustakas): Deal with the random seed correctly. 
    
    TODO: document this better
    """
    if tileid is None:
        tile_ra, tile_dec = 0.0, 0.0
    else:
        tile_ra, tile_dec = io.get_tile_radec(tileid)

    # - Get distribution of target types
    true_objtype, target_objtype = sample_objtype(nspec)

    # - Get DESI wavelength coverage
    wavemin = desimodel.io.load_throughput("b").wavemin
    wavemax = desimodel.io.load_throughput("z").wavemax
    dw = 0.2
    wave = np.arange(round(wavemin, 1), wavemax, dw)
    nwave = len(wave)

    truth = dict()
    truth["FLUX"] = np.zeros((nspec, len(wave)))
    truth["REDSHIFT"] = np.zeros(nspec, dtype="f4")
    truth["TEMPLATEID"] = np.zeros(nspec, dtype="i4")
    truth["OIIFLUX"] = np.zeros(nspec, dtype="f4")
    truth["D4000"] = np.zeros(nspec, dtype="f4")
    truth["OBJTYPE"] = np.zeros(nspec, dtype="S10")
    # - Note: unlike other elements, first index of WAVE isn't spectrum index
    truth["WAVE"] = wave

    fibermap = empty_fibermap(nspec)

    for objtype in set(true_objtype):
        ii = np.where(true_objtype == objtype)[0]
        nobj = len(ii)

        fibermap["OBJTYPE"][ii] = target_objtype[ii]
        truth["OBJTYPE"][ii] = true_objtype[ii]

        # Simulate spectra
        if objtype == "SKY":
            continue

        elif objtype == "ELG":
            from desisim.templates import ELG

            elg = ELG(nmodel=nobj, wave=wave)
            simflux, wave1, meta = elg.make_templates()

        elif objtype == "LRG":
            from desisim.templates import LRG

            lrg = LRG(nmodel=nobj, wave=wave)
            simflux, wave1, meta = lrg.make_templates()

        elif objtype == "QSO":
            from desisim.templates import QSO

            qso = QSO(nmodel=nobj, wave=wave)
            simflux, wave1, meta = qso.make_templates()

        # For a "bad" QSO simulate a normal star without color cuts, which isn't
        # right. We need to apply the QSO color-cuts to the normal stars to pull
        # out the correct population of contaminating stars.
        elif objtype == "QSO_BAD":
            from desisim.templates import STAR

            star = STAR(nmodel=nobj, wave=wave)
            simflux, wave1, meta = star.make_templates()

        elif objtype == "STD":
            from desisim.templates import STAR

            star = STAR(nmodel=nobj, wave=wave, FSTD=True)
            simflux, wave1, meta = star.make_templates()

        truth["FLUX"][ii] = simflux
        truth["TEMPLATEID"][ii] = meta["TEMPLATEID"]
        truth["REDSHIFT"][ii] = meta["REDSHIFT"]

        # Pack in the photometry.  TODO: Include WISE.
        magg = meta["GMAG"]
        magr = meta["RMAG"]
        magz = meta["ZMAG"]
        fibermap["MAG"][ii, 0:3] = np.vstack([magg, magr, magz]).T
        fibermap["FILTER"][ii, 0:3] = ["DECAM_G", "DECAM_R", "DECAM_Z"]

        if objtype == "ELG":
            truth["OIIFLUX"][ii] = meta["OIIFLUX"]
            truth["D4000"][ii] = meta["D4000"]

        if objtype == "LRG":
            truth["D4000"][ii] = meta["D4000"]

    # - Load fiber -> positioner mapping and tile information
    fiberpos = desimodel.io.load_fiberpos()

    # - Where are these targets?  Centered on positioners for now.
    x = fiberpos["X"][0:nspec]
    y = fiberpos["Y"][0:nspec]
    fp = FocalPlane(tile_ra, tile_dec)
    ra = np.zeros(nspec)
    dec = np.zeros(nspec)
    for i in range(nspec):
        ra[i], dec[i] = fp.xy2radec(x[i], y[i])

    # - Fill in the rest of the fibermap structure
    fibermap["FIBER"] = np.arange(nspec, dtype="i4")
    fibermap["POSITIONER"] = fiberpos["POSITIONER"][0:nspec]
    fibermap["SPECTROID"] = fiberpos["SPECTROGRAPH"][0:nspec]
    fibermap["TARGETID"] = np.random.randint(sys.maxint, size=nspec)
    fibermap["TARGETCAT"] = np.zeros(nspec, dtype="|S20")
    fibermap["LAMBDAREF"] = np.ones(nspec, dtype=np.float32) * 5400
    fibermap["TARGET_MASK0"] = np.zeros(nspec, dtype="i8")
    fibermap["RA_TARGET"] = ra
    fibermap["DEC_TARGET"] = dec
    fibermap["X_TARGET"] = x
    fibermap["Y_TARGET"] = y
    fibermap["X_FVCOBS"] = fibermap["X_TARGET"]
    fibermap["Y_FVCOBS"] = fibermap["Y_TARGET"]
    fibermap["X_FVCERR"] = np.zeros(nspec, dtype=np.float32)
    fibermap["Y_FVCERR"] = np.zeros(nspec, dtype=np.float32)
    fibermap["RA_OBS"] = fibermap["RA_TARGET"]
    fibermap["DEC_OBS"] = fibermap["DEC_TARGET"]
    fibermap["BRICKNAME"] = brick.brickname(ra, dec)

    return fibermap, truth
Ejemplo n.º 3
0
def get_targets(nspec, tileid=None):
    """
    Returns:
        fibermap
        truth table

    TODO (@moustakas): Deal with the random seed correctly.

    TODO: document this better
    """
    if tileid is None:
        tile_ra, tile_dec = 0.0, 0.0
    else:
        tile_ra, tile_dec = io.get_tile_radec(tileid)

    #- Get distribution of target types
    true_objtype, target_objtype = sample_objtype(nspec)

    #- Get DESI wavelength coverage
    wavemin = desimodel.io.load_throughput('b').wavemin
    wavemax = desimodel.io.load_throughput('z').wavemax
    dw = 0.2
    wave = np.arange(round(wavemin, 1), wavemax, dw)
    nwave = len(wave)

    truth = dict()
    truth['FLUX'] = np.zeros((nspec, len(wave)))
    truth['REDSHIFT'] = np.zeros(nspec, dtype='f4')
    truth['TEMPLATEID'] = np.zeros(nspec, dtype='i4')
    truth['OIIFLUX'] = np.zeros(nspec, dtype='f4')
    truth['D4000'] = np.zeros(nspec, dtype='f4')
    truth['VDISP'] = np.zeros(nspec, dtype='f4')
    truth['OBJTYPE'] = np.zeros(nspec, dtype='S10')
    #- Note: unlike other elements, first index of WAVE isn't spectrum index
    truth['WAVE'] = wave

    fibermap = empty_fibermap(nspec)

    for objtype in set(true_objtype):
        ii = np.where(true_objtype == objtype)[0]
        nobj = len(ii)

        fibermap['OBJTYPE'][ii] = target_objtype[ii]
        truth['OBJTYPE'][ii] = true_objtype[ii]

        # Simulate spectra
        if objtype == 'SKY':
            continue

        elif objtype == 'ELG':
            from desisim.templates import ELG
            elg = ELG(wave=wave)
            simflux, wave1, meta = elg.make_templates(nmodel=nobj)

        elif objtype == 'LRG':
            from desisim.templates import LRG
            lrg = LRG(wave=wave)
            simflux, wave1, meta = lrg.make_templates(nmodel=nobj)

        elif objtype == 'QSO':
            from desisim.templates import QSO
            qso = QSO(wave=wave)
            simflux, wave1, meta = qso.make_templates(nmodel=nobj)

        # For a "bad" QSO simulate a normal star without color cuts, which isn't
        # right. We need to apply the QSO color-cuts to the normal stars to pull
        # out the correct population of contaminating stars.
        elif objtype == 'QSO_BAD':
            from desisim.templates import STAR
            star = STAR(wave=wave)
            simflux, wave1, meta = star.make_templates(nmodel=nobj)

        elif objtype == 'STD':
            from desisim.templates import STAR
            star = STAR(wave=wave, FSTD=True)
            simflux, wave1, meta = star.make_templates(nmodel=nobj)

        truth['FLUX'][ii] = 1e17 * simflux
        truth['UNITS'] = '1e-17 erg/s/cm2/A'
        truth['TEMPLATEID'][ii] = meta['TEMPLATEID']
        truth['REDSHIFT'][ii] = meta['REDSHIFT']

        # Pack in the photometry.  TODO: Include WISE.
        magg = meta['GMAG']
        magr = meta['RMAG']
        magz = meta['ZMAG']
        fibermap['MAG'][ii, 0:3] = np.vstack([magg, magr, magz]).T
        fibermap['FILTER'][ii, 0:3] = ['DECAM_G', 'DECAM_R', 'DECAM_Z']

        if objtype == 'ELG':
            truth['OIIFLUX'][ii] = meta['OIIFLUX']
            truth['D4000'][ii] = meta['D4000']
            truth['VDISP'][ii] = meta['VDISP']

        if objtype == 'LRG':
            truth['D4000'][ii] = meta['D4000']
            truth['VDISP'][ii] = meta['VDISP']

    #- Load fiber -> positioner mapping and tile information
    fiberpos = desimodel.io.load_fiberpos()

    #- Where are these targets?  Centered on positioners for now.
    x = fiberpos['X'][0:nspec]
    y = fiberpos['Y'][0:nspec]
    fp = FocalPlane(tile_ra, tile_dec)
    ra = np.zeros(nspec)
    dec = np.zeros(nspec)
    for i in range(nspec):
        ra[i], dec[i] = fp.xy2radec(x[i], y[i])

    #- Fill in the rest of the fibermap structure
    fibermap['FIBER'] = np.arange(nspec, dtype='i4')
    fibermap['POSITIONER'] = fiberpos['POSITIONER'][0:nspec]
    fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][0:nspec]
    fibermap['TARGETID'] = np.random.randint(sys.maxint, size=nspec)
    fibermap['TARGETCAT'] = np.zeros(nspec, dtype='|S20')
    fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32) * 5400
    fibermap['TARGET_MASK0'] = np.zeros(nspec, dtype='i8')
    fibermap['RA_TARGET'] = ra
    fibermap['DEC_TARGET'] = dec
    fibermap['X_TARGET'] = x
    fibermap['Y_TARGET'] = y
    fibermap['X_FVCOBS'] = fibermap['X_TARGET']
    fibermap['Y_FVCOBS'] = fibermap['Y_TARGET']
    fibermap['X_FVCERR'] = np.zeros(nspec, dtype=np.float32)
    fibermap['Y_FVCERR'] = np.zeros(nspec, dtype=np.float32)
    fibermap['RA_OBS'] = fibermap['RA_TARGET']
    fibermap['DEC_OBS'] = fibermap['DEC_TARGET']
    fibermap['BRICKNAME'] = brick.brickname(ra, dec)

    return fibermap, truth
Ejemplo n.º 4
0
    fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][0:nspec]
    fibermap['TARGETID'] = np.random.randint(sys.maxint, size=nspec)
    fibermap['TARGETCAT'] = np.zeros(nspec, dtype='|S20')
    fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32) * 5400
    fibermap['TARGET_MASK0'] = np.zeros(nspec, dtype='i8')
    fibermap['RA_TARGET'] = ra
    fibermap['DEC_TARGET'] = dec
    fibermap['X_TARGET'] = x
    fibermap['Y_TARGET'] = y
    fibermap['X_FVCOBS'] = fibermap['X_TARGET']
    fibermap['Y_FVCOBS'] = fibermap['Y_TARGET']
    fibermap['X_FVCERR'] = np.zeros(nspec, dtype=np.float32)
    fibermap['Y_FVCERR'] = np.zeros(nspec, dtype=np.float32)
    fibermap['RA_OBS'] = fibermap['RA_TARGET']
    fibermap['DEC_OBS'] = fibermap['DEC_TARGET']
    fibermap['BRICKNAME'] = brick.brickname(ra, dec)

    return fibermap, truth


#-------------------------------------------------------------------------
#- Currently unused, but keep around for now
def sample_nz(objtype, n):
    """
    Given `objtype` = 'LRG', 'ELG', 'QSO', 'STAR', 'STD'
    return array of `n` redshifts that properly sample n(z)
    from $DESIMODEL/data/targets/nz*.dat
    """
    #- TODO: should this be in desimodel instead?

    #- Stars are at redshift 0 for now.  Could consider a velocity dispersion.