Full desisurvey API Reference¶

Define bit masks used in BOSS data and support symbolic operations on masks.

The SDSS bitmasks are documented at http://www.sdss3.org/dr10/algorithms/bitmasks.php. The authoritative definition of the bit masks is the file http://www.sdss3.org/svn/repo/idlutils/trunk/data/sdss/sdssMaskbits.par. A copy of this file is included in this package’s top-level directory and was used to automatically generate the bitmask definitions in this file with the extract_sdss_bitmasks() function.

class bossdata.bits.ANCILLARY_TARGET1[source]¶

BOSS survey target flags for ancillary programs

BRIGHTGAL¶: int – (1<<21) defined in bright_gal_v3.descr

MTEMP¶: int – (1<<63) defined in blake-transient-v3.fits

CXOGRIZ¶: int – (1<<59) defined in brandt-xray.par

BLAZGX¶: int – (1<<52) defined in anderson-blazar.par

RQSS_SFC¶: int – (1<<33) defined in rqss090630.descr

OTBAL¶: int – (1<<18) defined in master-BAL-targets.descr

RQSS_SF¶: int – (1<<32) defined in rqss090630.descr

FBQSBAL¶: int – (1<<15) defined in master-BAL-targets.descr

BLAZGXQSO¶: int – (1<<53) defined in anderson-blazar.par

QSO_RADIO¶: int – (1<<25) defined in qsoals_v2.descr

SN_LOC¶: int – (1<<39) defined in ancillary_supernova_hosts_v5.descr

QSO_HIZ¶: int – (1<<30) defined in sdss3_fan.descr

LBQSBAL¶: int – (1<<16) defined in master-BAL-targets.descr

LOW_MET¶: int – (1<<4) defined in blake_boss_v2.descr

BLAZGRFLAT¶: int – (1<<50) defined in anderson-blazar.par

QSO_GRI¶: int – (1<<29) defined in sdss3_fan.descr

VARBAL¶: int – (1<<20) defined in master-BAL-targets.descr

BLAZXRVAR¶: int – (1<<10) defined in brandtxmm-andersonblazar-merged.descr

RQSS_STM¶: int – (1<<34) defined in rqss090630.descr

FAINTERM¶: int – (1<<47) defined in sd3targets_final.descr

BLAZGRQSO¶: int – (1<<51) defined in anderson-blazar.par

RED_KG¶: int – (1<<48) defined in redkg.descr

SN_GAL1¶: int – (1<<36) defined in ancillary_supernova_hosts_v5.descr

PREVBAL¶: int – (1<<19) defined in master-BAL-targets.descr

BLAZXRSAM¶: int – (1<<9) defined in brandtxmm-andersonblazar-merged.descr

RVTEST¶: int – (1<<49) defined in redkg.descr

VARS¶: int – (1<<5) defined in blake_boss_v2.descr

BLAZXR¶: int – (1<<8) defined in brandtxmm-andersonblazar-merged.descr

GAL_NEAR_QSO¶: int – (1<<62) defined in weiner-qso-sightline.fits

QSO_NOAALS¶: int – (1<<28) defined in qsoals_v2.descr

QSO_RADIO_IAL¶: int – (1<<27) defined in qsoals_v2.descr

ODDBAL¶: int – (1<<17) defined in master-BAL-targets.descr

FLARE2¶: int – (1<<2) defined in blake_boss_v2.descr

BRIGHTERM¶: int – (1<<45) defined in sd3targets_final.descr

CXOBRIGHT¶: int – (1<<58) defined in brandt-xray.par

QSO_AALS¶: int – (1<<23) defined in qsoals_v2.descr

BLAZR¶: int – (1<<7) defined in brandtxmm-andersonblazar-merged.descr

WHITEDWARF_NEW¶: int – (1<<42) defined in WDv5_eisenste_fixed.descr

XMMGRIZ¶: int – (1<<12) defined in brandtxmm-andersonblazar-merged.descr

BLAZGXR¶: int – (1<<54) defined in anderson-blazar.par

BLUE_RADIO¶: int – (1<<56) defined in tremonti-blue-radio.fits.gz

BLAZGVAR¶: int – (1<<6) defined in brandtxmm-andersonblazar-merged.descr

FLARE1¶: int – (1<<1) defined in blake_boss_v2.descr

BRIGHTERL¶: int – (1<<44) defined in sd3targets_final.descr

AMC¶: int – (1<<0) defined in blake_boss_v2.descr

QSO_AAL¶: int – (1<<22) defined in qsoals_v2.descr

SN_GAL2¶: int – (1<<37) defined in ancillary_supernova_hosts_v5.descr

SPEC_SN¶: int – (1<<40) defined in ancillary_supernova_hosts_v5.descr

HPM¶: int – (1<<3) defined in blake_boss_v2.descr

RQSS_STMC¶: int – (1<<35) defined in rqss090630.descr

CHANDRAV1¶: int – (1<<57) defined in haggard-sf-accrete.fits

QSO_RADIO_AAL¶: int – (1<<26) defined in qsoals_v2.descr

FAINTERL¶: int – (1<<46) defined in sd3targets_final.descr

ELG¶: int – (1<<61) defined in kneib-cfht-elg.fits

XMMBRIGHT¶: int – (1<<11) defined in brandtxmm-andersonblazar-merged.descr

QSO_IAL¶: int – (1<<24) defined in qsoals_v2.descr

QSO_RIZ¶: int – (1<<31) defined in sdss3_fan.descr

SN_GAL3¶: int – (1<<38) defined in ancillary_supernova_hosts_v5.descr

XMMRED¶: int – (1<<14) defined in brandtxmm-andersonblazar-merged.descr

SPOKE¶: int – (1<<41) defined in BOSS_slowpokes_v2.descr

WHITEDWARF_SDSS¶: int – (1<<43) defined in WDv5_eisenste_fixed.descr

CXORED¶: int – (1<<60) defined in brandt-xray.par

XMMHR¶: int – (1<<13) defined in brandtxmm-andersonblazar-merged.descr

class bossdata.bits.ANCILLARY_TARGET2[source]¶

additional BOSS survey target flags for ancillary programs

QSO_EBOSS_W3_ADM¶: int – (1<<31) defined in myers_eboss_qso_w3.descr

LRG_ROUND3¶: int – (1<<22) defined in newman.descr

KOE2068BSTAR¶: int – (1<<46) defined in knapp_ngc2068.descr

QSO_VAR_FPG¶: int – (1<<4) defined in nathalie-ancillary3.par

QSO_VAR_LF¶: int – (1<<27) defined in palanque_str82.descr

SEQUELS_ELG_LOWP¶: int – (1<<39) defined in sequels_elg.descr

CLUSTER_MEMBER¶: int – (1<<16) defined in finoguenov_auxBOSS.descr

KOE2068_STAR¶: int – (1<<44) defined in knapp_ngc2068.descr

KQSO_BOSS¶: int – (1<<2) defined in mcmahon-ukidss.fits

KOEKAP_STAR¶: int – (1<<42) defined in knapp_kappaori.descr

RM_TILE1¶: int – (1<<54) reverberation mapping, high priority

FAINT_ELG¶: int – (1<<18) defined in comparat.descr

TDSS_PILOT_SNHOST¶: int – (1<<29) defined in TDSS_SPIDERS_MD03.descr

KOE2023_STAR¶: int – (1<<43) defined in knapp_ngc2023.descr

KOEKAPBSTAR¶: int – (1<<47) defined in knapp_kappaori.descr

PTF_GAL¶: int – (1<<19) defined in kasliwal.descr

SEQUELS_ELG¶: int – (1<<34) defined in sequels_elg.descr

_25ORI_WISE_W3¶: int – (1<<41) defined in knapp_25ori.descr

GES¶: int – (1<<35) defined in rockosi_ges_segue.descr

TDSS_SPIDERS_PILOT¶: int – (1<<26) defined in GreenMerloni_MD01.descr

STRIPE82BCG¶: int – (1<<6) defined in alexie-bcgs.fits

COROTGES¶: int – (1<<49) defined in rocksi_ges_segue.descr

HIZQSO82¶: int – (1<<0) defined in mcgreer-hizqso.fits

KOE2023BSTAR¶: int – (1<<45) defined in knapp_ngc2023.descr

SEGUE2¶: int – (1<<37) defined in rockosi_ges_segue.descr

HIZ_LRG¶: int – (1<<21) defined in newman.descr

QSO_XD_KDE_PAIR¶: int – (1<<15) defined in myers.descr

SEQUELS_TARGET¶: int – (1<<53) any target in SEQUELS darktime program

FAINT_HIZ_LRG¶: int – (1<<30) defined in newman_lrg_w3.descr

RM_TILE2¶: int – (1<<55) reverberation mapping, low priority

XMM_SECOND¶: int – (1<<33) defined in georgekaksi_xmmxll.descr

COROTGESAPOG¶: int – (1<<48) defined in rocksi_ges_segue.descr

QSO_VAR¶: int – (1<<3) defined in butler-variable.fits.gz

QSO_SUPPZ¶: int – (1<<7) defined in qso_suppz.descr

WISE_BOSS_QSO¶: int – (1<<14) defined in ross_wisebossqso.descr

QSO_STD¶: int – (1<<20) defined in margala.descr

IAMASERS¶: int – (1<<12) defined in zaw.descr

SDSSFILLER¶: int – (1<<38) defined in rockosi_ges_segue.descr

TAU_STAR¶: int – (1<<52) defined in knapp_taurus.descr

SEGUE1¶: int – (1<<36) defined in rockosi_ges_segue.descr

XMMSDSS¶: int – (1<<11) defined in georgakakis.descr

WISE_COMPLETE¶: int – (1<<23) defined in weiner_wise.descr

TDSS_PILOT¶: int – (1<<24) defined in GreenMerloni_MD01.descr

ELAIS_N1_JVLA¶: int – (1<<62) LOFAR-selected target

QSO_VAR_SDSS¶: int – (1<<8) defined in VARQSO.descr

RADIO_2LOBE_QSO¶: int – (1<<5) defined in kimball-radio-2lobe-qso.fits.gz

SPIDERS_PILOT¶: int – (1<<25) defined in GreenMerloni_MD01.descr

ELAIS_N1_FIRST¶: int – (1<<59) LOFAR-selected target

SPOKE2¶: int – (1<<17) defined in dhital.descr

APOGEE¶: int – (1<<50) defined in rocksi_ges_segue.descr

QSO_DEEP¶: int – (1<<56) DEEP QSO described in QSO_DEEP_LBG.descr

LBG¶: int – (1<<57) LBG described in QSO_DEEP_LBG.descr

_2MASSFILL¶: int – (1<<51) defined in rocksi_ges_segue.descr

ELAIS_N1_LOFAR¶: int – (1<<58) LOFAR-selected target

DISKEMITTER_REPEAT¶: int – (1<<13) defined in shen.descr

QSO_WISE_SUPP¶: int – (1<<9) defined in BOSS_QSO_targets_July_WISE.descr

XMM_PRIME¶: int – (1<<32) defined in georgekaksi_xmmxll.descr

TDSS_PILOT_PM¶: int – (1<<28) defined in TDSS_SPIDERS_MD03.descr

ELAIS_N1_GMRT_TAYLOR¶: int – (1<<61) LOFAR-selected target

QSO_WISE_FULL_SKY¶: int – (1<<10) defined in none

_25ORI_WISE¶: int – (1<<40) defined in knapp_25ori.descr

HIZQSOIR¶: int – (1<<1) defined in mcgreer-hizqso.fits

ELAIS_N1_GMRT_GARN¶: int – (1<<60) LOFAR-selected target

class bossdata.bits.APOGEE2_TARGET1[source]¶

APOGEE2 primary target bits

APOGEE2_NORMAL_SAMPLE¶: int – (1<<14) Selected as part of the random sample

APOGEE2_APOKASC_GIANT¶: int – (1<<27) Selected as part of APOKASC giant sample

APOGEE2_NO_DERED¶: int – (1<<6) Selected with no dereddening

APOGEE2_LONG¶: int – (1<<13) Selected as part of a long cohort

APOGEE2_STREAM_CANDIDATE¶: int – (1<<19) Selected as potential halo tidal stream member (based on photometry)

APOGEE2_TWOBIN_0_5_TO_0_8¶: int – (1<<1) Selected in blue 0.5 < (J-Ks)o < 0.8 color bin

APOGEE2_SCI_CLUSTER¶: int – (1<<9) Science cluster candidate member

APOGEE2_MEDIUM¶: int – (1<<12) Selected as part of a medium cohort

APOGEE2_DSPH_CANDIDATE¶: int – (1<<21) Selected as potential dSph member (non Sgr) (based on photometry)

APOGEE2_ONEBIN_GT_0_3¶: int – (1<<16) Selected in single (J-Ks)o > 0.3 color bin

APOGEE2_WISE_DERED¶: int – (1<<4) Selected with RJCE-WISE dereddening

APOGEE2_FAINT_EXTRA¶: int – (1<<29) Faint star (fainter than cohort limit; not required to reach survey S/N requirement)

APOGEE2_STREAM_MEMBER¶: int – (1<<18) Selected as confirmed halo tidal stream member

APOGEE2_APOKASC_DWARF¶: int – (1<<28) Selected as part of APOKASC dwarf sample

APOGEE2_SHORT¶: int – (1<<11) Selected as part of a short cohort

APOGEE2_ONEBIN_GT_0_5¶: int – (1<<0) Selected in single (J-Ks)o > 0.5 color bin

APOGEE2_APOKASC¶: int – (1<<30) Selected as part of the APOKASC program (incl. seismic/gyro targets and others)

APOGEE2_IRAC_DERED¶: int – (1<<3) Selected with RJCE-IRAC dereddening

APOGEE2_MANGA_LED¶: int – (1<<15) Star on a shared MaNGA-led design

APOGEE2_SGR_DSPH¶: int – (1<<26) Selected as confirmed Sgr core/stream member

APOGEE2_WASH_DWARF¶: int – (1<<8) Selected as Wash+DDO51 photometric dwarf

APOGEE2_RRLYR¶: int – (1<<24) Selected as a bulge RR Lyrae star

APOGEE2_WASH_NOCLASS¶: int – (1<<17) Selected because it has no W+D classification

APOGEE2_DSPH_MEMBER¶: int – (1<<20) Selected as confirmed dSph member (non Sgr)

APOGEE2_MAGCLOUD_MEMBER¶: int – (1<<22) Selected as confirmed Mag Cloud member

APOGEE2_WASH_GIANT¶: int – (1<<7) Selected as Wash+DDO51 photometric giant

APOGEE2_TWOBIN_GT_0_8¶: int – (1<<2) Selected in red (J-Ks)o > 0.8 color bin

APOGEE2_SFD_DERED¶: int – (1<<5) Selected with SFD_EBV dereddening

APOGEE2_MAGCLOUD_CANDIDATE¶: int – (1<<23) Selected as potential Mag Cloud member (based on photometry)

class bossdata.bits.APOGEE2_TARGET2[source]¶

APOGEE2 secondary target bits

APOGEE2_ARGOS_OVERLAP¶: int – (1<<15) Overlap with ARGOS

APOGEE2_RV_STANDARD¶: int – (1<<3) Stellar RV standard

APOGEE2_GAIA_OVERLAP¶: int – (1<<16) Overlap with Gaia

APOGEE2_INTERNAL_CALIB¶: int – (1<<6) Internal survey calibration target (observed in at least 2 of: APOGEE-1, -2N, -2S)

APOGEE2_1M_TARGET¶: int – (1<<22) Selected as a 1-m target

APOGEE2_OBJECT¶: int – (1<<30) This object is an APOGEE-2 target

APOGEE2_LITERATURE_CALIB¶: int – (1<<13) Overlap with high-resolution literature studies

APOGEE2_STANDARD_STAR¶: int – (1<<2) Stellar parameters/abundance standard

APOGEE2_EXTERNAL_CALIB¶: int – (1<<5) External survey calibration target (generic flag; others below dedicated to specific surveys)

APOGEE2_CALIB_CLUSTER¶: int – (1<<10) Selected as calibration cluster member

APOGEE2_GES_OVERLAP¶: int – (1<<14) Overlap with Gaia-ESO

APOGEE2_GALAH_OVERLAP¶: int – (1<<17) Overlap with GALAH

APOGEE2_SKY¶: int – (1<<4) Sky fiber

APOGEE2_RAVE_OVERLAP¶: int – (1<<18) Overlap with RAVE

APOGEE2_TELLURIC¶: int – (1<<9) Telluric calibrator target

class bossdata.bits.APOGEE2_TARGET3[source]¶

APOGEE2 trinary target bits

APOGEE2_SUBSTELLAR_COMPANIONS¶: int – (1<<4) Selected as part of the substellar companion search

APOGEE2_ANCILLARY¶: int – (1<<8) Selected as an ancillary target

APOGEE2_MDWARF¶: int – (1<<3) Selected as part of the M dwarf study

APOGEE2_KOI_CONTROL¶: int – (1<<2) Selected as part of the long cadence KOI control sample

APOGEE2_KOI¶: int – (1<<0) Selected as part of the long cadence KOI study

APOGEE2_EB¶: int – (1<<1) Selected as part of the EB program

APOGEE2_MASSIVE_STAR¶: int – (1<<9) Selected as part of the Massive Star program

APOGEE2_YOUNG_CLUSTER¶: int – (1<<5) Selected as part of the young cluster study (IN-SYNC)

class bossdata.bits.APOGEE_ASPCAPFLAG[source]¶

APOGEE ASPCAP mask bits

LOGG_BAD¶: int – (1<<17) BAD log g (see PARAMFLAG[1] for details)

STAR_BAD¶: int – (1<<23) BAD overall for star: set if any of TEFF, LOGG, CHI2, COLORTE, ROTATION, SN error are set, or any parameter is near grid edge (GRIDEDGE_BAD is set in any PARAMFLAG)

NFE_WARN¶: int – (1<<6) WARNING on [N/Fe] (see PARAMFLAG[6] for details)

COLORTE_WARN¶: int – (1<<9) effective temperature more than 500K from photometric temperature for dereddened color (WARN)

ROTATION_WARN¶: int – (1<<10) Spectrum has broad lines, with possible bad effects: FWHM of cross-correlation of spectrum with best RV template relative to auto-correltion of template > 1.5 (WARN)

STAR_WARN¶: int – (1<<7) WARNING overall for star: set if any of TEFF, LOGG, CHI2, COLORTE, ROTATION, SN warn are set

CFE_WARN¶: int – (1<<5) WARNING on [C/Fe] (see PARAMFLAG[5] for details)

SN_BAD¶: int – (1<<27) S/N<50 (BAD)

METALS_BAD¶: int – (1<<19) BAD metals (see PARAMFLAG[3] for details)

NO_ASPCAP_RESULT¶: int – (1<<31) No result

VMICRO_BAD¶: int – (1<<18) BAD vmicro (see PARAMFLAG[2] for details)

ALPHAFE_WARN¶: int – (1<<4) WARNING on [alpha/Fe] (see PARAMFLAG[4] for details)

SN_WARN¶: int – (1<<11) S/N<70 (WARN)

CFE_BAD¶: int – (1<<21) BAD [C/Fe] (see PARAMFLAG[5] for details)

ALPHAFE_BAD¶: int – (1<<20) BAD [alpha/Fe] (see PARAMFLAG[4] for details)

ROTATION_BAD¶: int – (1<<26) Spectrum has broad lines, with possible bad effects: FWHM of cross-correlation of spectrum with best RV template relative to auto-correltion of template > 2 (BAD)

CHI2_BAD¶: int – (1<<24) high chi^2 (> 5*median at ASPCAP temperature (BAD)

TEFF_WARN¶: int – (1<<0) WARNING on effective temperature (see PARAMFLAG[0] for details)

NFE_BAD¶: int – (1<<22) BAD [N/Fe] (see PARAMFLAG[6] for details)

VMICRO_WARN¶: int – (1<<2) WARNING on vmicro (see PARAMFLAG[2] for details)

COLORTE_BAD¶: int – (1<<25) effective temperature more than 1000K from photometric temperature for dereddened color (BAD)

TEFF_BAD¶: int – (1<<16) BAD effective temperature (see PARAMFLAG[0] for details)

METALS_WARN¶: int – (1<<3) WARNING on metals (see PARAMFLAG[3] for details)

LOGG_WARN¶: int – (1<<1) WARNING on log g (see PARAMFLAG[1] for details)

CHI2_WARN¶: int – (1<<8) high chi^2 (> 2*median at ASPCAP temperature (WARN)

class bossdata.bits.APOGEE_EXTRATARG[source]¶

APOGEE pixel level mask bits

APO1M¶: int – (1<<3) APO1M + APOGEE observation

DUPLICATE¶: int – (1<<4) Duplicate observation of star

NOT_MAIN¶: int – (1<<0) Not main survey target

TELLURIC¶: int – (1<<2) Telluric target

COMMISSIONING¶: int – (1<<1) Commissioning data

class bossdata.bits.APOGEE_PARAMFLAG[source]¶

APOGEE parameter mask bits (set for each stellar parameter in ASPCAP fit)

GRIDEDGE_WARN¶: int – (1<<8) Parameter within 1/2 grid spacing of grid edge

OTHER_WARN¶: int – (1<<10) Other warning condition

GRIDEDGE_BAD¶: int – (1<<0) Parameter within 1/8 grid spacing of grid edge

OTHER_BAD¶: int – (1<<2) Other error condition

PARAM_FIXED¶: int – (1<<16) Parameter set at fixed value, not fit

CALRANGE_BAD¶: int – (1<<1) Parameter outside valid range of calibration determination

CALRANGE_WARN¶: int – (1<<9) Parameter in possibly unreliable range of calibration determination

class bossdata.bits.APOGEE_PIXMASK[source]¶

APOGEE extra targeting bits

BADDARK¶: int – (1<<4) Pixel marked as bad as determined from dark frame

UNFIXABLE¶: int – (1<<3) Pixel marked as unfixable in ap3d

BADERR¶: int – (1<<6) Pixel set to have very high error (not used)

CRPIX¶: int – (1<<1) Pixel marked as cosmic ray in ap3d

PERSIST_MED¶: int – (1<<10) Pixel falls in medium persistence region, may be affected

PERSIST_LOW¶: int – (1<<11) Pixel falls in low persistence region, may be affected

PERSIST_HIGH¶: int – (1<<9) Pixel falls in high persistence region, may be affected

SATPIX¶: int – (1<<2) Pixel marked as saturated in ap3d

BADPIX¶: int – (1<<0) Pixel marked as BAD in bad pixel mask

SIG_SKYLINE¶: int – (1<<12) Pixel falls near sky line that has significant flux compared with object

BADFLAT¶: int – (1<<5) Pixel marked as bad as determined from flat frame

SIG_TELLURIC¶: int – (1<<13) Pixel falls near telluric line that has significant absorption

LITTROW_GHOST¶: int – (1<<8) Pixel falls in Littrow ghost, may be affected

NOSKY¶: int – (1<<7) No sky available for this pixel from sky fibers

class bossdata.bits.APOGEE_STARFLAG[source]¶

APOGEE star-level mask bits

PERSIST_MED¶: int – (1<<10) Spectrum has significant number (>20%) of pixels in medium persistence region: WARN

PERSIST_LOW¶: int – (1<<11) Spectrum has significant number (>20%) of pixels in low persistence region: WARN

PERSIST_HIGH¶: int – (1<<9) Spectrum has significant number (>20%) of pixels in high persistence region: WARN

BRIGHT_NEIGHBOR¶: int – (1<<2) Star has neighbor more than 10 times brighter: WARN

SUSPECT_RV_COMBINATION¶: int – (1<<16) WARNING: RVs from synthetic template differ significantly from those from combined template

BAD_PIXELS¶: int – (1<<0) Spectrum has many bad pixels (>40%): BAD

VERY_BRIGHT_NEIGHBOR¶: int – (1<<3) Star has neighbor more than 100 times brighter: BAD

COMMISSIONING¶: int – (1<<1) Commissioning data (MJD<55761), non-standard configuration, poor LSF: WARN

PERSIST_JUMP_POS¶: int – (1<<12) Spectrum show obvious positive jump in blue chip: WARN

LOW_SNR¶: int – (1<<4) Spectrum has low S/N (S/N<5): BAD

SUSPECT_BROAD_LINES¶: int – (1<<17) WARNING: cross-correlation peak with template significantly broader than autocorrelation of template

PERSIST_JUMP_NEG¶: int – (1<<13) Spectrum show obvious negative jump in blue chip: WARN

class bossdata.bits.APOGEE_TARGET1[source]¶

APOGEE primary target bits

APOGEE_MDWARF¶: int – (1<<19) M dwarfs selected for RV program (ancillary)

APOGEE_LONG¶: int – (1<<13) Long cohort target

APOGEE_HIRES¶: int – (1<<20) Star with optical hi-res spectra (ancillary)

APOGEE_SGR_DSPH¶: int – (1<<26) Sagittarius dwarf spheroidal member

APOGEE_CHECKED¶: int – (1<<31) This target has been checked

APOGEE_OLD_STAR¶: int – (1<<21) Selected as old star (ancillary)

APOGEE_INTERMEDIATE¶: int – (1<<12) Intermediate cohort target

APOGEE_EXTENDED¶: int – (1<<10) Extended object

APOGEE_FAINT_EXTRA¶: int – (1<<29) Selected as faint target for low target-density field

APOGEE_SCI_CLUSTER¶: int – (1<<9) Probable cluster member

APOGEE_FIRST_LIGHT¶: int – (1<<16) First list plate target

APOGEE_NO_DERED¶: int – (1<<6) Selected using no dereddening

APOGEE_MASSIVE_STAR¶: int – (1<<25) Selected as massive star (ancillary)

APOGEE_SEGUE_OVERLAP¶: int – (1<<30) Selected because of overlap with SEGUE survey

APOGEE_SERENDIPITOUS¶: int – (1<<15) Serendipitously interesting target to reobserve

APOGEE_KEPLER_HOST¶: int – (1<<28) Kepler planet-host program target

APOGEE_KEPLER_SEISMO¶: int – (1<<27) Kepler asteroseismology program target

APOGEE_DISK_RED_GIANT¶: int – (1<<22) Disk red giant (ancillary)

APOGEE_WASH_GIANT¶: int – (1<<7) Selected as giant in Washington photometry

APOGEE_WISE_DERED¶: int – (1<<4) Selected using RJCE-WISE dereddening

APOGEE_M31_CLUSTER¶: int – (1<<18) M31 cluster target (ancillary)

APOGEE_IRAC_DERED¶: int – (1<<3) Selected using RJCE-IRAC dereddening

APOGEE_BRIGHT¶: int – (1<<2) Selected in bright bin of cohort

APOGEE_SHORT¶: int – (1<<11) Short cohort target

APOGEE_MEDIUM¶: int – (1<<1) Selected in medium bin of cohort

APOGEE_ANCILLARY¶: int – (1<<17) An ancillary program

APOGEE_KEPLER_EB¶: int – (1<<23) Eclipsing binary from Kepler (ancillary)

APOGEE_GC_PAL1¶: int – (1<<24) Star in globular cluster (ancillary)

APOGEE_WASH_DWARF¶: int – (1<<8) Selected as dwarf in Washington photometry

APOGEE_DO_NOT_OBSERVE¶: int – (1<<14) Do not observe (again)

APOGEE_SFD_DERED¶: int – (1<<5) Selected using SFD E(B-V) dereddening

APOGEE_FAINT¶: int – (1<<0) Selected in faint bin of cohort

class bossdata.bits.APOGEE_TARGET2[source]¶

APOGEE secondary target bits

APOGEE_CHECKED¶: int – (1<<31) This target has been checked

APOGEE_GC_GIANT¶: int – (1<<11) Probable giant in Galactic Center

APOGEE_EMISSION_STAR¶: int – (1<<15) Emission-line star (ancillary)

APOGEE_KEPLER_COOLDWARF¶: int – (1<<16) Kepler cool dwarf/subgiant (ancillary)

APOGEE_MIRCLUSTER_STAR¶: int – (1<<17) Candidate MIR-detected cluster member (ancillary)

APOGEE_RV_STANDARD¶: int – (1<<3) Radial velocity standard

APOGEE_FLUX_STANDARD¶: int – (1<<1) Flux standard

BUNDLE_HOLE¶: int – (1<<7) Bundle hole

APOGEE_LONGBAR¶: int – (1<<14) Probable RC star in long bar (ancillary)

APOGEE_STANDARD_STAR¶: int – (1<<2) Stellar abundance, parameters standard

APOGEE_TELLURIC_BAD¶: int – (1<<8) Selected as telluric standard but identified as bad (via SIMBAD or observation)

GUIDE_STAR¶: int – (1<<6) Guide star

APOGEE_GC_SUPER_GIANT¶: int – (1<<12) Probable supergiant in Galactic Center

APOGEE_TELLURIC¶: int – (1<<9) Hot (telluric) standard

SKY_BAD¶: int – (1<<5) Selected as sky but identified as bad (via visual exam or observation)

SKY¶: int – (1<<4) Sky

LIGHT_TRAP¶: int – (1<<0) Light trap

APOGEE_EMBEDDEDCLUSTER_STAR¶: int – (1<<13) Young embedded clusters (ancillary)

APOGEE_CALIB_CLUSTER¶: int – (1<<10) Known calibration cluster member

class bossdata.bits.BOSSTILE_STATUS[source]¶

BOSS tiling code status bits

DUPLICATE¶: int – (1<<17) trimmed as a duplicate object (only checked if not trimmed for any other reason)

REPEAT¶: int – (1<<10) included on more than one tile

MIDLEVEL_PRIORITY¶: int – (1<<23) targets (from ancillary list) tiled between gals and ancillaries

BOSSTARGET¶: int – (1<<2) in the high priority set of targets

CENTERPOST¶: int – (1<<9) 92 arcsec collision with center post

TOOFAINT¶: int – (1<<20) trimmed because it was fainter than the ifiber2mag limit

FILLER¶: int – (1<<11) was a filler (not normal repeat)

OUT_OF_BOUNDS¶: int – (1<<13) outside bounds for this sort of target (for restricted QSO geometry, e.g.)

TILED¶: int – (1<<0) assigned a fiber

KNOWN_OBJECT¶: int – (1<<16) galaxy has known redshift

ANCILLARY_ROUND2¶: int – (1<<22) new ancillaries added June 2012 (tiled after old ancillaries)

BAD_CALIB_STATUS¶: int – (1<<14) bad CALIB_STATUS

DUPLICATE_TILED¶: int – (1<<19) trimmed as a duplicate object, and its primary was tiled

TOOBRIGHT¶: int – (1<<7) fibermag too bright

DECOLLIDED¶: int – (1<<3) in the decollided set of high priority

POSSIBLE_KNOCKOUT¶: int – (1<<5) knocked out of at least one tile by BOSSTARGET

BLUEFIBER¶: int – (1<<8) allocate this object a blue fiber

PREVIOUS_CHUNK¶: int – (1<<15) included because not tiled in previous overlapping chunk

NAKED¶: int – (1<<1) not in area covered by tiles

DUPLICATE_PRIMARY¶: int – (1<<18) has associated duplicate object that were trimmed (but this one is kept)

NOT_TILED_TARGET¶: int – (1<<12) though in input file, not a tiled target

IGNORE_PRIORITY¶: int – (1<<6) priority exceeds max (ANCILLARY only)

SUPPLEMENTARY¶: int – (1<<21) supplementary targets tiles after the ancillaries (subset of ancillaries)

ANCILLARY¶: int – (1<<4) in the lower priority, ancillary set

class bossdata.bits.BOSS_TARGET1[source]¶

BOSS survey primary target selection flags

QSO_KDE¶: int – (1<<19) selected by kde+chi2

GAL_IFIBER2_FAINT¶: int – (1<<8) ifiber2 > 21.5, extinction corrected. Used after Nov 2010

GAL_CMASS_ALL¶: int – (1<<7) GAL_CMASS and the entire sparsely sampled region

QSO_BONUS¶: int – (1<<11) permissive qso selection: commissioning only

QSO_KNOWN_MIDZ¶: int – (1<<12) known qso between [2.2,9.99]

QSO_KNOWN_LOHIZ¶: int – (1<<13) known qso outside of miz range. never target

SDSS_KNOWN¶: int – (1<<6) Matches a known SDSS spectra

TEMPLATE_GAL_PHOTO¶: int – (1<<32) galaxy templates

STD_QSO¶: int – (1<<22) qso

GAL_CMASS_SPARSE¶: int – (1<<3) GAL_CMASS_COMM & (!GAL_CMASS) & (i < 19.9) sparsely sampled

QSO_UKIDSS¶: int – (1<<15) UKIDSS stars that match sweeps/pass flag cuts

TEMPLATE_STAR_SPECTRO¶: int – (1<<35) stellar templates (spectroscopically known)

TEMPLATE_QSO_SDSS1¶: int – (1<<33) QSO templates

GAL_LODPERP_DEPRECATED¶: int – (1<<5) (DEPRECATED) Same as hiz but between dperp00 and dperp0

QSO_KDE_COADD¶: int – (1<<16) kde targets from the stripe82 coadd

QSO_CORE¶: int – (1<<10) restrictive qso selection: commissioning only

QSO_BONUS_MAIN¶: int – (1<<41) Main survey bonus sample

GAL_LOZ¶: int – (1<<0) low-z lrgs

TEMPLATE_STAR_PHOTO¶: int – (1<<34) stellar templates

GAL_CMASS¶: int – (1<<1) dperp > 0.55, color-mag cut

GAL_CMASS_COMM¶: int – (1<<2) dperp > 0.55, commissioning color-mag cut

QSO_FIRST_BOSS¶: int – (1<<18) FIRST radio match

QSO_CORE_ED¶: int – (1<<42) Extreme Deconvolution in Core

QSO_CORE_MAIN¶: int – (1<<40) Main survey core sample

QSO_CORE_LIKE¶: int – (1<<43) Likelihood that make it into core

QSO_KNOWN_SUPPZ¶: int – (1<<44) known qso between [1.8,2.15]

STD_FSTAR¶: int – (1<<20) standard f-stars

QSO_LIKE¶: int – (1<<17) likelihood method

QSO_NN¶: int – (1<<14) Neural Net that match to sweeps/pass cuts

STD_WD¶: int – (1<<21) white dwarfs

class bossdata.bits.CALIB_STATUS[source]¶

Calibration status for an SDSS image

PS1_LOW_RMS¶: int – (1<<14) Low RMS in comparison with PS1

INCREMENT_CALIB¶: int – (1<<5) Incrementally calibrated by considering overlaps with ubercalibrated data

UNPHOT_DISJOINT¶: int – (1<<4) Data is disjoint from the rest of the survey (even though conditions may be photometric), the calibration is suspect

UNPHOT_EXTRAP_CLEAR¶: int – (1<<2) Extrapolate the solution from the clear part of a night (that was ubercalibrated) to the cloudy part

NO_UBERCAL¶: int – (1<<11) not uber-calibrated

DEFAULT¶: int – (1<<10) a default calibration used

ASTROMBAD¶: int – (1<<12) catastrophically bad astrometry

PS1_CONTRAIL¶: int – (1<<7) Comparison to PS1 reveals possible contrail

UNPHOT_OVERLAP¶: int – (1<<1) Unphotometric observations, calibrated based on overlaps with clear, ubercalibrated data; this is done on a field-by-field basis

PS1_PCOMP_MODEL¶: int – (1<<13) Enough information for PS1-based principal component flat model

PHOTOMETRIC¶: int – (1<<0) Photometric observations

PS1_UNPHOT¶: int – (1<<6) Comparison to PS1 reveals unphotometric conditions

PT_CLEAR¶: int – (1<<8) PT calibration for clear data

PT_CLOUDY¶: int – (1<<9) PT calibration for cloudy data

UNPHOT_EXTRAP_CLOUDY¶: int – (1<<3) The solution here is based on fitting the a-term to cloudy data.

class bossdata.bits.EBOSS_TARGET0[source]¶

targeting bitmask for SEQUELS (eBOSS precursor)

TDSS_FES_MGII¶: int – (1<<34) TDSS Few epoch spectroscopy

TDSS_FES_VARBAL¶: int – (1<<35) TDSS Few epoch spectroscopy

QSO_EBOSS_CORE¶: int – (1<<10) QSOs in XDQSOz+WISE selection for clustering

SPIDERS_RASS_CLUS¶: int – (1<<21) RASS Cluster sources

TDSS_FES_NQHISN¶: int – (1<<33) TDSS Few epoch spectroscopy

TDSS_FES_DE¶: int – (1<<31) TDSS Few epoch spectroscopy

SPIDERS_ERASS_CLUS¶: int – (1<<23) ERASS Cluster sources

QSO_PTF¶: int – (1<<11) QSOs with variability in PTF imaging

QSO_EBOSS_KDE¶: int – (1<<13) KDE-selected QSOs (sequels only)

QSO_SDSS_TARGET¶: int – (1<<17) Known TARGETS from SDSS with spectra

SPIDERS_RASS_AGN¶: int – (1<<20) RASS AGN sources

SPIDERS_ERASS_AGN¶: int – (1<<22) ERASS AGN sources

TDSS_A¶: int – (1<<30) Main PanSTARRS selection for TDSS

SEQUELS_PTF_VARIABLE¶: int – (1<<40) Variability objects from PTF

TDSS_FES_DWARFC¶: int – (1<<32) TDSS Few epoch spectroscopy

QSO_REOBS¶: int – (1<<12) QSOs from BOSS to be reobserved

DO_NOT_OBSERVE¶: int – (1<<0) Don’t put a fiber on this object

QSO_BOSS_TARGET¶: int – (1<<16) Known TARGETS from BOSS with spectra

QSO_EBOSS_FIRST¶: int – (1<<14) Objects with FIRST radio matches

QSO_KNOWN¶: int – (1<<18) Known QSOs from previous surveys

DR9_CALIB_TARGET¶: int – (1<<19) Target found in DR9-calibrated imaging

LRG_IZW¶: int – (1<<1) LRG selection in i/z/W plane

SEQUELS_COLLIDED¶: int – (1<<41) Collided galaxies from BOSS

QSO_BAD_BOSS¶: int – (1<<15) QSOs from BOSS with bad spectra

LRG_RIW¶: int – (1<<2) LRG selection in r/i/W plan with (i-z) cut

class bossdata.bits.EBOSS_TARGET1[source]¶

targeting bitmask for eBOSS

QSO1_EBOSS_KDE¶: int – (1<<13) KDE-selected QSOs (sequels only)

DO_NOT_OBSERVE¶: int – (1<<0) Don’t put a fiber on this object

QSO1_EBOSS_CORE¶: int – (1<<10) QSOs in XDQSOz+WISE selection for clustering

QSO_BOSS_TARGET¶: int – (1<<16) Known TARGETS from BOSS with spectra

QSO1_EBOSS_FIRST¶: int – (1<<14) Ojbects with FIRST radio matches

QSO1_BAD_BOSS¶: int – (1<<15) QSOs from BOSS with bad spectra

LRG_KNOWN¶: int – (1<<3) LRG selection in r/i/W plan with (i-z) cut

LRG1_IDROP¶: int – (1<<2) LRG selection in r/i/W plan with (i-z) cut

QSO1_REOBS¶: int – (1<<12) QSOs from BOSS to be reobserved

SPIDERS_TARGET¶: int – (1<<31) Target for SPIDERS (subclass found in eboss_target2)

QSO1_PTF¶: int – (1<<11) QSOs with variability in PTF imaging

QSO_SDSS_TARGET¶: int – (1<<17) Known TARGETS from SDSS with spectra

STD_WD¶: int – (1<<51) white dwarfs

QSO1_VAR_S82¶: int – (1<<9) Variability-selected QSOs in the repeated Stripe 82 imaging

TDSS_TARGET¶: int – (1<<30) Target for TDSS (subclass found in eboss_target2)

ELG_TEST1¶: int – (1<<40) Test targets for ELG selection

STD_FSTAR¶: int – (1<<50) standard f-stars

STD_QSO¶: int – (1<<52) qso

LRG1_WISE¶: int – (1<<1) LRG selection in i/z/W plane

QSO_KNOWN¶: int – (1<<18) Known QSOs from previous surveys

class bossdata.bits.EBOSS_TARGET2[source]¶

targeting bitmask for eBOSS

TDSS_FES_MGII¶: int – (1<<24) TDSS Few epoch spectroscopy

TDSS_FES_VARBAL¶: int – (1<<25) TDSS Few epoch spectroscopy

SPIDERS_RASS_CLUS¶: int – (1<<1) RASS Cluster sources

TDSS_FES_NQHISN¶: int – (1<<23) TDSS Few epoch spectroscopy

TDSS_FES_DE¶: int – (1<<21) TDSS Few epoch spectroscopy

SPIDERS_ERASS_CLUS¶: int – (1<<3) ERASS Cluster sources

TDSS_FES_ACTSTAR¶: int – (1<<30) TDSS Few epoch spectroscopy

TDSS_CP¶: int – (1<<31) TDSS in common with CORE/PTF

SPIDERS_ERASS_AGN¶: int – (1<<2) ERASS AGN sources

TDSS_B¶: int – (1<<26) Main TDSS SES version B

SPIDERS_RASS_AGN¶: int – (1<<0) RASS AGN sources

ELG_SCUSS_TEST1¶: int – (1<<40) SCUSS selection for test1 ELG plates

TDSS_A¶: int – (1<<20) Main PanSTARRS selection for TDSS

SPIDERS_XMMSL_AGN¶: int – (1<<4) XMM Slew survey

ELG_GRIW_TEST1¶: int – (1<<46) WISE selection for test1 ELG plates

ELG_UGRIZW_TEST1¶: int – (1<<44) WISE selection for test1 ELG plates

TDSS_FES_DWARFC¶: int – (1<<22) TDSS Few epoch spectroscopy

ELG_DESI_TEST1¶: int – (1<<42) DESI selection for test1 ELG plates

TDSS_FES_HYPSTAR¶: int – (1<<28) TDSS Few epoch spectroscopy

ELG_SDSS_TEST1¶: int – (1<<43) SDSS-only selection for test1 ELG plates

SPIDERS_XCLASS_CLUS¶: int – (1<<5) XMM serendipitous clusters

TDSS_FES_WDDM¶: int – (1<<29) TDSS Few epoch spectroscopy

ELG_DES_TEST1¶: int – (1<<41) DES selection for test1 ELG plates

TDSS_FES_HYPQSO¶: int – (1<<27) TDSS Few epoch spectroscopy

ELG_UGRIZWbright_TEST1¶: int – (1<<45) WISE selection for test1 ELG plates

class bossdata.bits.FLUXMATCH_STATUS[source]¶

Flags from flux-based matching to SDSS photometry

ORIGINAL_FLUXMATCH¶: int – (1<<0) used the original positional match (which exists)

FIBER_FLUXMATCH¶: int – (1<<1) flagged due to fiberflux/aperflux issue

NOPARENT_FLUXMATCH¶: int – (1<<3) no overlapping parent in primary field

PARENT_FLUXMATCH¶: int – (1<<4) overlapping parent has no children, so used it

NONMATCH_FLUXMATCH¶: int – (1<<2) flagged due to non-match

BRIGHTEST_FLUXMATCH¶: int – (1<<5) picked the brightest child

class bossdata.bits.IMAGE_STATUS[source]¶

Sky and instrument conditions of SDSS image

CLOUDY¶: int – (1<<1) Cloudy skies (unphotometric)

BAD_ROTATOR¶: int – (1<<3) Rotator problems (set score=0)

FF_PETALS¶: int – (1<<7) Flat-field petals out of place (unphotometric)

BAD_ASTROM¶: int – (1<<4) Astrometry problems (set score=0)

UNKNOWN¶: int – (1<<2) Sky conditions unknown (unphotometric)

DEAD_CCD¶: int – (1<<8) CCD bad (unphotometric)

CLEAR¶: int – (1<<0) Clear skies

NOISY_CCD¶: int – (1<<9) CCD noisy (unphotometric)

SHUTTERS¶: int – (1<<6) Shutter out of place (set score=0)

BAD_FOCUS¶: int – (1<<5) Focus bad (set score=0)

class bossdata.bits.MANGA_DAPQUAL[source]¶

Mask bits for MaNGA DAP quality flags

VALIDFILE¶: int – (1<<0) File is valid

class bossdata.bits.MANGA_DRP2PIXMASK[source]¶

Mask bits per fiber or pixel for 2d MaNGA spectra.

COMBINEREJ¶: int – (1<<26) Rejected in combine B-spline

BADTRACE¶: int – (1<<1) Bad trace

NEARBADPIXEL¶: int – (1<<17) Bad pixel within 3 pixels of trace.

NOPLUG¶: int – (1<<0) Fiber not listed in plugmap file

BADSKYFIBER¶: int – (1<<7) Sky fiber shows extreme residuals

LOWFLAT¶: int – (1<<18) Flat field less than 0.5

BADSKYCHI¶: int – (1<<28) Relative chi^2 > 3 in sky residuals at this wavelength

WHOPPER¶: int – (1<<9) Whopping fiber, with a very bright source.

MANYREJECTED¶: int – (1<<5) More than 10% of pixels are rejected in extraction

BADPIX¶: int – (1<<15) Pixel flagged in badpix reference file.

NODATA¶: int – (1<<25) No data available in combine B-spline (INVVAR=0)

SMEARHIGHSN¶: int – (1<<11) S/N sufficient for full smear fit

NEARWHOPPER¶: int – (1<<8) Within 2 fibers of a whopping fiber (exclusive)

BRIGHTSKY¶: int – (1<<24) Sky level > flux + 10*(flux_err) AND sky > 1.25 * median(sky,99 pixels)

CROSSTALK¶: int – (1<<22) Cross-talk significant

PARTIALREJECT¶: int – (1<<20) Some pixels rejected in extraction model fit

REDMONSTER¶: int – (1<<29) Contiguous region of bad chi^2 in sky residuals (with threshhold of relative chi^2 > 3).

MANYBADCOLUMNS¶: int – (1<<4) More than 10% of pixels are bad columns

SMEARIMAGE¶: int – (1<<10) Smear available for red and blue cameras

LARGESHIFT¶: int – (1<<6) Large spatial shift between flat and object position

COSMIC¶: int – (1<<16) Pixel flagged as cosmic ray.

_3DREJECT¶: int – (1<<30) Used in RSS file, indicates should be rejected when making 3D cube

FULLREJECT¶: int – (1<<19) Pixel fully rejected in extraction model fit (INVVAR=0)

BADFLUXFACTOR¶: int – (1<<27) Low flux-calibration or flux-correction factor

SMEARMEDSN¶: int – (1<<12) S/N only sufficient for scaled median fit

BADFLAT¶: int – (1<<2) Low counts in fiberflat

DEADFIBER¶: int – (1<<13) Broken fiber according to metrology files

SCATTEREDLIGHT¶: int – (1<<21) Scattered light significant

NOSKY¶: int – (1<<23) Sky level unknown at this wavelength (INVVAR=0)

BADARC¶: int – (1<<3) Bad arc solution

class bossdata.bits.MANGA_DRP2QUAL[source]¶

Mask bits for MaNGA DRP-2d quality flags

SKYSUBFAIL¶: int – (1<<11) Failed sky subtraction

HIGHSCAT¶: int – (1<<5) High scattered light levels

SCATFAIL¶: int – (1<<6) Failure to correct high scattered light levels

RAMPAGINGBUNNY¶: int – (1<<9) Rampaging dust bunnies in IFU flats

FULLCLOUD¶: int – (1<<12) Completely cloudy exposure

EXTRACTBRIGHT¶: int – (1<<2) Extracted spectra abnormally bright

BADIFU¶: int – (1<<4) One or more IFUs missing/bad in this frame

EXTRACTBAD¶: int – (1<<1) Many bad values in extracted frame

ARCFOCUS¶: int – (1<<8) Bad focus on arc frames

VALIDFILE¶: int – (1<<0) File is valid

BADDITHER¶: int – (1<<7) Bad dither location information

LOWEXPTIME¶: int – (1<<3) Exposure time less than 10 minutes

SKYSUBBAD¶: int – (1<<10) Bad sky subtraction

class bossdata.bits.MANGA_DRP3PIXMASK[source]¶

Mask bits per spaxel for a MaNGA data cube.

DEADFIBER¶: int – (1<<2) Major contributing fiber is dead

FORESTAR¶: int – (1<<3) Foreground star

DONOTUSE¶: int – (1<<10) Do not use this spaxel for science

NOCOV¶: int – (1<<0) No coverage in cube

LOWCOV¶: int – (1<<1) Low coverage depth in cube

class bossdata.bits.MANGA_DRP3QUAL[source]¶

Mask bits for MaNGA DRP-3d quality flags

BADFLUX¶: int – (1<<8) Bad flux calibration

HIGHSCAT¶: int – (1<<3) High scattered light in one or more frames

CRITICAL¶: int – (1<<30) Critical failure in one or more frames

VALIDFILE¶: int – (1<<0) File is valid

BADSET¶: int – (1<<7) One or more sets are bad

BADASTROM¶: int – (1<<4) Bad astrometry in one or more frames

SKYSUBBAD¶: int – (1<<2) Bad sky subtraction in one or more frames

BADOMEGA¶: int – (1<<6) Omega greater than threshhold in one or more sets

VARIABLELSF¶: int – (1<<5) LSF varies signif. between component spectra

BADDEPTH¶: int – (1<<1) IFU does not reach target depth

class bossdata.bits.MANGA_TARGET1[source]¶

Mask bits identifying galaxy samples.

PRIMARY_PLUS_COM¶: int – (1<<1) March 2014 commissioning

COLOR_ENHANCED_v1_1_0¶: int – (1<<6) First tag, August 2014 plates

NONE¶: int – (1<<0)

SECONDARY_COM2¶: int – (1<<8) July 2014 commissioning

SECONDARY_v1_1_0¶: int – (1<<5) First tag, August 2014 plates

FILLER¶: int – (1<<13) Filler targets

SECONDARY_v1_2_0¶: int – (1<<11)

COLOR_ENHANCED_v1_2_0¶: int – (1<<12)

PRIMARY_v1_2_0¶: int – (1<<10)

PRIMARY_COM2¶: int – (1<<7) July 2014 commissioning

COLOR_ENHANCED_COM¶: int – (1<<3) March 2014 commissioning

PRIMARY_v1_1_0¶: int – (1<<4) First tag, August 2014 plates

ANCILLARY¶: int – (1<<14) Ancillary program targets

COLOR_ENHANCED_COM2¶: int – (1<<9) July 2014 commissioning

SECONDARY_COM¶: int – (1<<2) March 2014 commissioning

class bossdata.bits.MANGA_TARGET2[source]¶

Mask bits identifying non-galaxy samples.

STD_FSTAR_COM¶: int – (1<<20)

NONE¶: int – (1<<0)

STD_STD_COM¶: int – (1<<22)

STD_FSTAR¶: int – (1<<23)

STELLIB_COM_mar2015¶: int – (1<<5) Commissioning selection in March 2015

STD_APASS_COM¶: int – (1<<25) Commissioning selection of stds using APASS photometry

STELLIB_SDSS_COM¶: int – (1<<2) Commissioning selection using SDSS photometry

STD_WD_COM¶: int – (1<<21)

STELLIB_KNOWN_COM¶: int – (1<<4) Commissioning selection of known parameter stars

STELLIB_2MASS_COM¶: int – (1<<3) Commissioning selection using 2MASS photometry

SKY¶: int – (1<<1)

STD_WD¶: int – (1<<24)

class bossdata.bits.MANGA_TARGET3[source]¶

Mask bits identifying ancillary samples.

ANGST¶: int – (1<<18)

AGN_PALOMAR¶: int – (1<<4)

AGN_BAT¶: int – (1<<1)

PAIR_RECENTER¶: int – (1<<8)

NONE¶: int – (1<<0)

AGN_WISE¶: int – (1<<3)

AGN_OIII¶: int – (1<<2)

VOID¶: int – (1<<5)

PAIR_SIM¶: int – (1<<9)

DEEP_COMA¶: int – (1<<19)

DWARF¶: int – (1<<14)

LETTERS¶: int – (1<<11)

DISKMASS¶: int – (1<<16)

RADIO_JETS¶: int – (1<<15)

BCG¶: int – (1<<17)

MASSIVE¶: int – (1<<12)

PAIR_ENLARGE¶: int – (1<<7)

MWA¶: int – (1<<13)

EDGE_ON_WINDS¶: int – (1<<6)

PAIR_2IFU¶: int – (1<<10)

class bossdata.bits.M_EYEBALL[source]¶

Eyeball flags for mergers in VAGC

MIXED_REDBLUE¶: int – (1<<11)

BEFORE¶: int – (1<<13)

NOT_MERGER¶: int – (1<<1)

MAJOR¶: int – (1<<7)

SHELLS¶: int – (1<<5)

DURING¶: int – (1<<14)

RING¶: int – (1<<6)

REPEAT¶: int – (1<<12)

TIDAL_TAILS¶: int – (1<<4)

ALL_RED¶: int – (1<<9)

DRY¶: int – (1<<3)

AFTER¶: int – (1<<15)

ALL_BLUE¶: int – (1<<10)

QUESTIONABLE¶: int – (1<<2)

DONE¶: int – (1<<0)

MULTIPLE¶: int – (1<<8)

class bossdata.bits.OBJECT1[source]¶

Object flags from photo reductions for SDSS (first 32)

BINNED1¶: int – (1<<28) The object was detected in an unbinned image.

MOVED¶: int – (1<<31) The object appears to have moved during the exposure. Such objects are candidates to be deblended as moving objects.

NOPROFILE¶: int – (1<<7) Frames couldn’t extract a radial profile.

SUBTRACTED¶: int – (1<<20) Object (presumably a star) had wings subtracted.

PEAKCENTER¶: int – (1<<5) Given center is position of peak pixel, as attempts to determine a better centroid failed.

NOTCHECKED¶: int – (1<<19) Object includes pixels that were not checked for peaks, for example the unsmoothed edges of frames, and the cores of subtracted or saturated stars.

SATUR¶: int – (1<<18) The object contains saturated pixels; INTERP is also set.

DEBLENDED_AS_PSF¶: int – (1<<25) When deblending an object, in this band this child was treated as a PSF.

PETROFAINT¶: int – (1<<23) At least one candidate Petrosian radius occured at an unacceptably low surface brightness.

BINNED4¶: int – (1<<30) The object was detected in a 4x4 binned image. The objects detected in the 2x2 binned image are not removed before doing this.

NOSTOKES¶: int – (1<<21) Object has no measured Stokes parameters.

EDGE¶: int – (1<<2) Object is too close to edge of frame in this band.

BINNED2¶: int – (1<<29) The object was detected in a 2x2 binned image after all unbinned detections have been replaced by the background level.

DEBLEND_TOO_MANY_PEAKS¶: int – (1<<11) The object had the OBJECT1_DEBLEND flag set, but it contained too many candidate children to be fully deblended. This flag is only set in the parent, i.e. the object with too many peaks.

CANONICAL_CENTER¶: int – (1<<0) The quantities (psf counts, model fits and likelihoods) that are usually determined at an object’s center as determined band-by-band were in fact determined at the canonical center (suitably transformed). This is due to the object being to close to the edge to extract a profile at the local center, and OBJECT1_EDGE is also set.

BLENDED¶: int – (1<<3) Object was determined to be a blend. The flag is set if: more than one peak is detected within an object in a single band together; distinct peaks are found when merging different colours of one object together; or distinct peaks result when merging different objects together.

ELLIPFAINT¶: int – (1<<27) No isophotal fits were performed.

MANYR50¶: int – (1<<13) More than one radius was found to contain 50% of the Petrosian flux. (For this to happen part of the radial profile must be negative).

BADSKY¶: int – (1<<22) The estimated sky level is so bad that the central value of the radial profile is crazily negative; this is usually the result of the subtraction of the wings of bright stars failing.

NODEBLEND¶: int – (1<<6) Although this object was marked as a blend, no deblending was attempted.

CR¶: int – (1<<12) Object contains at least one pixel which was contaminated by a cosmic ray. The OBJECT1_INTERP flag is also set. This flag does not mean that this object is a cosmic ray; rather it means that a cosmic ray has been removed.

DEBLEND_PRUNED¶: int – (1<<26) When solving for the weights to be assigned to each child the deblender encountered a nearly singular matrix, and therefore deleted at least one of them.

BRIGHT¶: int – (1<<1) Indicates that the object was detected as a bright object. Since these are typically remeasured as faint objects, most users can ignore BRIGHT objects.

TOO_LARGE¶: int – (1<<24) The object is (as it says) too large. Either the object is still detectable at the outermost point of the extracted radial profile (a radius of approximately 260 arcsec), or when attempting to deblend an object, at least one child is larger than half a frame (in either row or column).

NOPETRO_BIG¶: int – (1<<10) The Petrosian ratio has not fallen to the value at which the Petrosian radius is defined at the outermost point of the extracted radial profile. NOPETRO is set, and the Petrosian radius is set to the outermost point in the profile.

BAD_RADIAL¶: int – (1<<15) Measured profile includes points with a S/N <= 0. In practice this flag is essentially meaningless.

INTERP¶: int – (1<<17) The object contains interpolated pixels (e.g. cosmic rays or bad columns).

MANYPETRO¶: int – (1<<9) Object has more than one possible Petrosian radius.

NOPETRO¶: int – (1<<8) No Petrosian radius or other Petrosian quanties could be measured.

CHILD¶: int – (1<<4) Object is a child, created by the deblender.

MANYR90¶: int – (1<<14) More than one radius was found to contain 90% of the Petrosian flux. (For this to happen part of the radial profile must be negative).

INCOMPLETE_PROFILE¶: int – (1<<16) A circle, centerd on the object, of radius the canonical Petrosian radius extends beyond the edge of the frame. The radial profile is still measured from those parts of the object that do lie on the frame.

class bossdata.bits.OBJECT2[source]¶

Object flags from photo reductions for SDSS (second 32)

NODEBLEND_MOVING¶: int – (1<<1) The object has the MOVED flag set, but was not deblended as a moving object.

DEBLENDED_AS_MOVING¶: int – (1<<0) The object has the MOVED flag set, and was deblended on the assumption that it was moving.

CENTER_OFF_AIMAGE¶: int – (1<<17) At least one peak’s center lay off the atlas image in some band. This can happen when the object’s being deblended as moving, or if the astrometry is badly confused.

AMOMENT_UNWEIGHTED¶: int – (1<<21) `Adaptive’ moments are actually unweighted.

INTERP_CENTER¶: int – (1<<12) An object’s center is very close to at least one interpolated pixel.

AMOMENT_MAXITER¶: int – (1<<23) Too many iterations while determining adaptive moments.

BINNED_CENTER¶: int – (1<<6) When centroiding the object the object’s size is larger than the (PSF) filter used to smooth the image.

SPARE1¶: int – (1<<31)

DEBLENDED_AT_EDGE¶: int – (1<<13) An object so close to the edge of the frame that it would not ordinarily be deblended has been deblended anyway. Only set for objects large enough to be EDGE in all fields/strips.

DEBLEND_NOPEAK¶: int – (1<<14) A child had no detected peak in a given band, but we centroided it anyway and set the BINNED1

DEBLEND_UNASSIGNED_FLUX¶: int – (1<<10) After deblending, the fraction of flux assigned to none of the children was too large (this flux is then shared out as described elsewhere).

DEBLEND_PEEPHOLE¶: int – (1<<28) The deblend was modified by the optimizer

PEAKS_TOO_CLOSE¶: int – (1<<5) Peaks in object were too close (set only in parent objects).

SPARE3¶: int – (1<<29)

BAD_COUNTS_ERROR¶: int – (1<<8) An object containing interpolated pixels had too few good pixels to form a reliable estimate of its error

BAD_MOVING_FIT_CHILD¶: int – (1<<9) A putative moving child’s velocity fit was too poor, so it was discarded, and the parent was not deblended as moving

STATIONARY¶: int – (1<<4) A moving objects velocity is consistent with zero

TOO_FEW_DETECTIONS¶: int – (1<<2) The object has the MOVED flag set, but has too few detection to be deblended as moving.

HAS_SATUR_DN¶: int – (1<<27) This object is saturated in this band and the bleed trail doesn’t touch the edge of the frame, we we’ve made an attempt to add up all the flux in the bleed trails, and to include it in the object’s photometry.

DEBLEND_DEGENERATE¶: int – (1<<18) At least one potential child has been pruned because its template was too similar to some other child’s template.

BAD_MOVING_FIT¶: int – (1<<3) The fit to the object as a moving object is too bad to be believed.

MAYBE_EGHOST¶: int – (1<<25) Object appears in the right place to be an electronics ghost.

TOO_FEW_GOOD_DETECTIONS¶: int – (1<<16) A child of this object had too few good detections to be deblended as moving.

SPARE2¶: int – (1<<30)

SATUR_CENTER¶: int – (1<<11) An object’s center is very close to at least one saturated pixel; the object may well be causing the saturation.

LOCAL_EDGE¶: int – (1<<7) The object’s center in some band was too close to the edge of the frame to extract a profile.

AMOMENT_SHIFT¶: int – (1<<22) Object’s center moved too far while determining adaptive moments. In this case, the M_e1 and M_e2 give the (row, column) shift, not the object’s shape.

PSF_FLUX_INTERP¶: int – (1<<15) The fraction of light actually detected (as opposed to guessed at by the interpolator) was less than some number (currently 80%) of the total.

BRIGHTEST_GALAXY_CHILD¶: int – (1<<19) This is the brightest child galaxy in a blend.

CANONICAL_BAND¶: int – (1<<20) This band was the canonical band. This is the band used to measure the Petrosian radius used to calculate the Petrosian counts in each band, and to define the model used to calculate model colors; it has no effect upon the coordinate system used for the OBJC center.

MAYBE_CR¶: int – (1<<24) This object may be a cosmic ray. This bit can get set in the cores of bright stars, and is quite likely to be set for the cores of saturated stars.

NOTCHECKED_CENTER¶: int – (1<<26) Center of object lies in a NOTCHECKED region. The object is almost certainly bogus.

class bossdata.bits.Q_EYEBALL[source]¶

Quality eyeball flags from VAGC

POSSIBLE_LENS¶: int – (1<<21)

NEED_BIGGER_IMAGE¶: int – (1<<3)

INTERNAL_REFLECTION¶: int – (1<<13)

BAD_SPEC_CLASS¶: int – (1<<14)

BAD_SPECTRUM¶: int – (1<<20)

FLECK¶: int – (1<<5)

USE_PARENT¶: int – (1<<15)

USE_ANYWAY¶: int – (1<<8)

PLANETARY_NEBULA¶: int – (1<<24)

STAR_ON_GALAXY¶: int – (1<<17)

SATELLITE¶: int – (1<<10)

BAD_Z¶: int – (1<<12)

IS_STAR¶: int – (1<<22)

EDGE¶: int – (1<<9)

OTHER¶: int – (1<<1)

BAD_DEBLEND¶: int – (1<<4)

GOOD_Z¶: int – (1<<26)

DOUBLE_STAR¶: int – (1<<6)

USE_CHILD_IMAGE¶: int – (1<<27)

HII¶: int – (1<<7)

USE_CHILD_SPECTRUM¶: int – (1<<28)

BAD_PARENT_CENTER¶: int – (1<<25)

UNCLASSIFIABLE¶: int – (1<<2)

NEGATIVE_QSO_FIT¶: int – (1<<19)

DOUBLE_Z¶: int – (1<<23)

IN_HUGE_OBJECT¶: int – (1<<16)

DONE¶: int – (1<<0)

QSO_ON_GALAXY¶: int – (1<<18)

PLANE¶: int – (1<<11)

class bossdata.bits.RESOLVE_STATUS[source]¶

Resolve status for an SDSS catalog entry. Only one of bits RUN_PRIMARY, RUN_RAMP, RUN_OVERLAPONLY, RUN_IGNORE, and RUN_DUPLICATE can be set. RUN_EDGE can be set for any object. To get a unique set of objects across the whole survey, search for objects with SURVEY_PRIMARY set. To get a unique set of objects within a run, search for objects with RUN_PRIMARY set.

SURVEY_PRIMARY¶: int – (1<<8) Primary observation within the full survey, where it appears in the primary observation of this part of the sky

RUN_RAMP¶: int – (1<<1) in what would be the overlap area of a field, but with no neighboring field

RUN_OVERLAPONLY¶: int – (1<<2) only appears in the overlap between two fields

RUN_EDGE¶: int – (1<<4) near lowest or highest column

RUN_DUPLICATE¶: int – (1<<5) duplicate measurement of same pixels in two different fields

RUN_IGNORE¶: int – (1<<3) bright or parent object that should be ignored

SURVEY_EDGE¶: int – (1<<12) Not kept as secondary because it is RUN_RAMP or RUN_EDGE object

SURVEY_BADFIELD¶: int – (1<<11) In field with score=0

RUN_PRIMARY¶: int – (1<<0) primary within the objects own run (but not necessarily for the survey as a whole)

SURVEY_SECONDARY¶: int – (1<<10) Repeat (independent) observation of an object that has a different primary or best observation

SURVEY_BEST¶: int – (1<<9) Best observation within the full survey, but it does not appear in the primary observation of this part of the sky

class bossdata.bits.SEGUE1_TARGET[source]¶

SEGUE-1 primary target bits

SEGUE1_MAN¶: int – (1<<24) manual selection

SEGUE1_MPMSTO¶: int – (1<<20) metal-poor main sequence turn-off

SEGUE1_KD¶: int – (1<<15) K-dwarfs

SEGUE1_SDM¶: int – (1<<22) M sub-dwarfs

SEGUE1_KG¶: int – (1<<14) K-giants (l and red)

SEG1LOW_AGB¶: int – (1<<27) low latitude selection of AGB stars

SEGUE1_FG¶: int – (1<<9) F and G stars, based on g-r color (0.2<g-r<0.48 and 14<g<20.2)

SEGUE1_BD¶: int – (1<<21) brown dwarfs

SEGUE1_BHB¶: int – (1<<13) blue horizontal branch star

SEGUE1_WD¶: int – (1<<19) white dwarf

SEGUE1_CWD¶: int – (1<<17) cool white dwarf

SEG1LOW_KG¶: int – (1<<10) low latitude selection of K-giant stars

SEGUE1_LM¶: int – (1<<16) low metallicity star

SEG1LOW_TO¶: int – (1<<11) low latitude selection of bluetip stars

SEGUE1_AGB¶: int – (1<<23) asympototic giant branch stars

SEGUE1_GD¶: int – (1<<18) G-dwarf

SEGUE1_CHECKED¶: int – (1<<31) was a checked object

SEGUE1_MSWD¶: int – (1<<12) main-sequence, white dwarf pair

class bossdata.bits.SEGUE1_TARGET2[source]¶

SEGUE-1 secondary target bits

REDDEN_STD¶: int – (1<<1) reddening standard star

SPECTROPHOTO_STD¶: int – (1<<5) spectrophotometry standard (typically an F-star)

SEGUE1_SCIENCE¶: int – (1<<30) SEGUE-1 science target

SEGUE1_QA¶: int – (1<<3) QA Duplicate Observations (unused)

SKY¶: int – (1<<4) sky target

SEGUE1_TEST¶: int – (1<<31) SEGUE-1 test target

class bossdata.bits.SEGUE2_TARGET1[source]¶

SEGUE-2 primary target bits

SEGUE2_CHECKED¶: int – (1<<31) was a checked object

SEGUE2_HVS¶: int – (1<<5) hyper velocity candidate

SEGUE2_MSTO¶: int – (1<<0) Main-sequence turnoff

SEGUE2_LKG¶: int – (1<<2) K-giant star identified by l-color

SEGUE2_MII¶: int – (1<<7) M giant

SEGUE2_CWD¶: int – (1<<17) Cool white dwarf

SEGUE2_HHV¶: int – (1<<8) High-velocity halo star candidate

SEGUE2_LM¶: int – (1<<4) Low metallicity

SEGUE2_REDKG¶: int – (1<<1) Red K-giant stars

SEGUE2_XDM¶: int – (1<<6) extreme sdM star

SEGUE2_PMKG¶: int – (1<<3) K-giant star identified by proper motions

SEGUE2_BHB¶: int – (1<<13) Blue horizontal branch star

class bossdata.bits.SEGUE2_TARGET2[source]¶

SEGUE-2 secondary target bits

SEGUE2_CHECKED¶: int – (1<<31) was a checked object

SEGUE2_REDDENING¶: int – (1<<1) reddening standard

SEGUE2_STETSON¶: int – (1<<11) Stetson standard target

BUNDLE_HOLE¶: int – (1<<7) bundle hole

HOT_STD¶: int – (1<<9) hot standard

QUALITY_HOLE¶: int – (1<<8) quality hole

GUIDE_STAR¶: int – (1<<6) guide star

SEGUE2_QA¶: int – (1<<3) repeat target across plates

SEGUE2_TEST¶: int – (1<<2) test target

SEGUE2_SPECPHOTO¶: int – (1<<5) spectrophotometric star

SKY¶: int – (1<<4) empty area for sky-subtraction

LIGHT_TRAP¶: int – (1<<0) light trap hole

SEGUE2_CLUSTER¶: int – (1<<10) SEGUE-2 stellar cluster target

class bossdata.bits.SPECIAL_TARGET1[source]¶

SDSS special program target bits

TAURUS_STAR¶: int – (1<<35) star on taurus or reddening plate

ALLPSF_STELLAR¶: int – (1<<33) i<19.1 point sources on stellar locus

COMMISSIONING_STAR¶: int – (1<<3) star in commissioning

VARIABLE_HIPRI¶: int – (1<<29) high priority variable

PERSEUS¶: int – (1<<37) galaxy in perseus-pisces

FAINT_QSO¶: int – (1<<26) faint QSO in south

U_PRIORITY¶: int – (1<<22) priority u-band target

PREMARVELS¶: int – (1<<19) pre-MARVELS stellar target

SPECIAL_FILLER¶: int – (1<<15) filler from completeTile, check primtarget for details

ORION_BD¶: int – (1<<12) Brown dwarf in Orion

BENT_RADIO¶: int – (1<<27) bent double-lobed radio source

PREBOSS_QSO¶: int – (1<<17) QSO for pre-BOSS observations

ORION_MSTAR_LATE¶: int – (1<<14) Late-type M-star (M4-) in Orion

VARIABLE_LOPRI¶: int – (1<<30) low priority variable

PREBOSS_LRG¶: int – (1<<18) QSO for pre-BOSS observations

MSTURNOFF¶: int – (1<<11) main sequence turnoff

PHOTOZ_GALAXY¶: int – (1<<16) test galaxy for photometric redshifts

SOUTHERN_EXTENDED¶: int – (1<<20) simple extension of southern targets

DEEP_GALAXY_RED¶: int – (1<<8) deep LRG

SOUTHERN_COMPLETE¶: int – (1<<21) completion in south of main targets

LOWZ_ANNIS¶: int – (1<<1) low-redshift cluster galaxy

LOWZ_GALAXY¶: int – (1<<7) low-redshift galaxy

ORION_MSTAR_EARLY¶: int – (1<<13) Early-type M-star (M0-3) in Orion

QSO_M31¶: int – (1<<2) QSO in M31

TAURUS_GALAXY¶: int – (1<<36) galaxy on taurus or reddening plate

STRAIGHT_RADIO¶: int – (1<<28) straight double-lobed radio source

BCG¶: int – (1<<10) brightest cluster galaxy

ALLPSF¶: int – (1<<31) i<19.1 point sources

U_EXTRA¶: int – (1<<23) extra u-band target

DEEP_GALAXY_RED_II¶: int – (1<<9) deep LRG

HIPM¶: int – (1<<34) high proper motion

FSTAR¶: int – (1<<5) F-stars

DISKSTAR¶: int – (1<<4) thin/thick disk star

U_EXTRA2¶: int – (1<<24) extra u-band target

FAINT_LRG¶: int – (1<<25) faint LRG in south

ALLPSF_NONSTELLAR¶: int – (1<<32) i<19.1 point sources off stellar locus

HYADES_MSTAR¶: int – (1<<6) M-star in Hyades

APBIAS¶: int – (1<<0) aperture bias target

LOWZ_LOVEDAY¶: int – (1<<38) low redshift galaxy selected by Loveday

class bossdata.bits.SPPIXMASK[source]¶

Mask bits for an SDSS spectrum. 0-15 refer to each fiber, 16-31 refer to each pixel in a spectrum.

COMBINEREJ¶: int – (1<<25) Rejected in combine B-spline

BADTRACE¶: int – (1<<1) Bad trace from routine TRACE320CRUDE

NEARBADPIXEL¶: int – (1<<16) Bad pixel within 3 pixels of trace.

NOPLUG¶: int – (1<<0) Fiber not listed in plugmap file

BADSKYFIBER¶: int – (1<<7) Sky fiber shows extreme residuals

LOWFLAT¶: int – (1<<17) Flat field less than 0.5

BADSKYCHI¶: int – (1<<27) Relative chi^2 > 3 in sky residuals at this wavelength

WHOPPER¶: int – (1<<9) Whopping fiber, with a very bright source.

MANYREJECTED¶: int – (1<<5) More than 10% of pixels are rejected in extraction

NODATA¶: int – (1<<24) No data available in combine B-spline (INVVAR=0)

SMEARHIGHSN¶: int – (1<<11) S/N sufficient for full smear fit

NEARWHOPPER¶: int – (1<<8) Within 2 fibers of a whopping fiber (exclusive)

BRIGHTSKY¶: int – (1<<23) Sky level > flux + 10*(flux_err) AND sky > 1.25 * median(sky,99 pixels)

NOSKY¶: int – (1<<22) Sky level unknown at this wavelength (INVVAR=0)

REDMONSTER¶: int – (1<<28) Contiguous region of bad chi^2 in sky residuals (with threshhold of relative chi^2 > 3).

MANYBADCOLUMNS¶: int – (1<<4) More than 10% of pixels are bad columns

SMEARIMAGE¶: int – (1<<10) Smear available for red and blue cameras

LARGESHIFT¶: int – (1<<6) Large spatial shift between flat and object position

CROSSTALK¶: int – (1<<21) Cross-talk significant

FULLREJECT¶: int – (1<<18) Pixel fully rejected in extraction (INVVAR=0)

BADFLUXFACTOR¶: int – (1<<26) Low flux-calibration or flux-correction factor

SMEARMEDSN¶: int – (1<<12) S/N only sufficient for scaled median fit

BADFLAT¶: int – (1<<2) Low counts in fiberflat

SCATTEREDLIGHT¶: int – (1<<20) Scattered light significant

PARTIALREJECT¶: int – (1<<19) Some pixels rejected in extraction

BADARC¶: int – (1<<3) Bad arc solution

class bossdata.bits.TARGET[source]¶

Primary target mask bits in SDSS-I, -II (for LEGACY_TARGET1 or PRIMTARGET).

SOUTHERN_SURVEY¶: int – (1<<31) Set in primtarget if this is a special program target

SERENDIP_RED¶: int – (1<<22) lying outside the stellar locus in color space

GALAXY_RED¶: int – (1<<5) Luminous Red Galaxy target (any criteria)

SERENDIP_FIRST¶: int – (1<<21) coincident with FIRST sources but fainter than the equivalent in quasar target selection (also includes non-PSF sources

GALAXY_BIG¶: int – (1<<7) Low-surface brightness main sample galaxy (mu50>23 in r-band)

QSO_FIRST_SKIRT¶: int – (1<<4) FIRST source with stellar colors at low Galactic latitude

SERENDIP_BLUE¶: int – (1<<20) lying outside the stellar locus in color space

STAR_BHB¶: int – (1<<13) blue horizontal-branch stars

ROSAT_A¶: int – (1<<9) ROSAT All-Sky Survey match, also a radio source

QSO_MAG_OUTLIER¶: int – (1<<25) Stellar outlier; too faint or too bright to be targeted

QSO_HIZ¶: int – (1<<0) High-redshift (griz) QSO target

STAR_CATY_VAR¶: int – (1<<17) cataclysmic variables

SERENDIP_MANUAL¶: int – (1<<24) manual serendipity flag

ROSAT_B¶: int – (1<<10) ROSAT All-Sky Survey match, have SDSS colors of AGNs or quasars

STAR_WHITE_DWARF¶: int – (1<<19) hot white dwarfs

SERENDIP_DISTANT¶: int – (1<<23) lying outside the stellar locus in color space

STAR_SUB_DWARF¶: int – (1<<16) low-luminosity subdwarfs

STAR_PN¶: int – (1<<28) central stars of planetary nebulae

GALAXY_RED_II¶: int – (1<<26) Luminous Red Galaxy target (Cut II criteria)

QSO_CAP¶: int – (1<<1) ugri-selected quasar at high Galactic latitude

QSO_SKIRT¶: int – (1<<2) ugri-selected quasar at low Galactic latitude

GALAXY_BRIGHT_CORE¶: int – (1<<8) Galaxy targets who fail all the surface brightness selection limits but have r-band fiber magnitudes brighter than 19

STAR_BROWN_DWARF¶: int – (1<<15) brown dwarfs (note this sample is tiled)

QSO_REJECT¶: int – (1<<29) Object in explicitly excluded region of color space, therefore not targeted at QSO

ROSAT_E¶: int – (1<<27) ROSAT All-Sky Survey match, but too faint or too bright for SDSS spectroscopy

GALAXY¶: int – (1<<6) Main sample galaxy

QSO_FIRST_CAP¶: int – (1<<3) FIRST source with stellar colors at high Galactic latitude

STAR_CARBON¶: int – (1<<14) dwarf and giant carbon stars

ROSAT_C¶: int – (1<<11) ROSAT All-Sky Survey match, fall in a broad intermediate category that includes stars that are bright, moderately blue, or both

ROSAT_D¶: int – (1<<12) ROSAT All-Sky Survey match, are otherwise bright enough for SDSS spectroscopy

STAR_RED_DWARF¶: int – (1<<18) red dwarfs

class bossdata.bits.TTARGET[source]¶

Secondary target mask bits in SDSS-I, -II (for LEGACY_TARGET2, SPECIAL_TARGET2 or SECTARGET).

QUALITY_HOLE¶: int – (1<<8) hole drilled for plate shop quality measurements

REDDEN_STD¶: int – (1<<1) reddening standard star

SPECTROPHOTO_STD¶: int – (1<<5) spectrophotometry standard (typically an F-star)

GUIDE_STAR¶: int – (1<<6) guide star hole

HOT_STD¶: int – (1<<9) hot standard star

TEST_TARGET¶: int – (1<<2) a test target

SOUTHERN_SURVEY¶: int – (1<<31) a segue or southern survey target

BUNDLE_HOLE¶: int – (1<<7) fiber bundle hole

SKY¶: int – (1<<4) sky target

LIGHT_TRAP¶: int – (1<<0) hole drilled for bright star, to avoid scattered light

QA¶: int – (1<<3) quality assurance target

class bossdata.bits.T_EYEBALL[source]¶

Type eyeball flags from VAGC

PITCH_0¶: int – (1<<8) tightly wound

HII_REGIONS¶: int – (1<<15)

S0¶: int – (1<<7)

PSF¶: int – (1<<13)

DISK¶: int – (1<<4)

PITCH_1¶: int – (1<<9)

WARPED_DISK¶: int – (1<<23)

PITCH_2¶: int – (1<<10)

RING¶: int – (1<<19)

DUST_ASYMMETRY¶: int – (1<<24)

ELLIPTICAL¶: int – (1<<3)

OUTFLOW¶: int – (1<<27)

BAR¶: int – (1<<18)

SPIRAL_STRUCTURE¶: int – (1<<16)

PITCH_4¶: int – (1<<12) openly wound

OTHER¶: int – (1<<1)

UNUSED_0¶: int – (1<<6)

IRREGULAR¶: int – (1<<5)

SHELLS¶: int – (1<<21)

MERGER¶: int – (1<<26)

NEAR_NEIGHBORS¶: int – (1<<25)

UNCLASSIFIABLE¶: int – (1<<2)

TIDAL_TAILS¶: int – (1<<20)

BLUE_CORE¶: int – (1<<22)

PITCH_3¶: int – (1<<11)

DONE¶: int – (1<<0)

DUST_LANE¶: int – (1<<17)

ASYMMETRIC¶: int – (1<<14)

class bossdata.bits.VAGC_SELECT[source]¶

Selection flags for Main VAGC sample

TILED¶: int – (1<<0) selected because near a tiled target

PLATEHOLE¶: int – (1<<1) selected because near a hole on an SDSS plate

MAIN¶: int – (1<<2) selected according to slightly adjusted Main sample criteria

class bossdata.bits.ZWARNING[source]¶

Warnings for SDSS spectra.

SMALL_DELTA_CHI2¶: int – (1<<2) chi-squared of best fit is too close to that of second best (<0.01 in reduced chi-sqaured)

LITTLE_COVERAGE¶: int – (1<<1) too little wavelength coverage (WCOVERAGE < 0.18)

Z_FITLIMIT¶: int – (1<<5) chi-squared minimum at edge of the redshift fitting range (Z_ERR set to -1)

BAD_TARGET¶: int – (1<<8) catastrophically bad targeting data (e.g. ASTROMBAD in CALIB_STATUS)

NODATA¶: int – (1<<9) No data for this fiber, e.g. because spectrograph was broken during this exposure (ivar=0 for all pixels)

NEGATIVE_EMISSION¶: int – (1<<6) a QSO line exhibits negative emission, triggered only in QSO spectra, if C_IV, C_III, Mg_II, H_beta, or H_alpha has LINEAREA + 3 * LINEAREA_ERR < 0

SKY¶: int – (1<<0) sky fiber

NEGATIVE_MODEL¶: int – (1<<3) synthetic spectrum is negative (only set for stars and QSOs)

MANY_OUTLIERS¶: int – (1<<4) fraction of points more than 5 sigma away from best model is too large (>0.05)

UNPLUGGED¶: int – (1<<7) the fiber was unplugged, so no spectrum obtained

bossdata.bits.bitmask_from_text(mask, text)[source]¶

Initialize a bitmask from text.

Builds an integer value from text containing bit names that should be set. The complement of decode_bitmask(). For example:

>>> COLORS = define_bitmask('COLORS','Primary colors',RED=0,BLUE=1,GREEN=4)
>>> '{0:b}'.format(bitmask_from_text(COLORS,'GREEN|BLUE'))
'10010'

Parameters:	mask – A bitmask type, normally created with `create_bitmask()`, that defines the symbolic bit names that are allowed. text – A list of bit names separated by ‘\|’.
Returns:	Integer with bits set for each bit name appearing in the text.
Return type:	int
Raises:	`ValueError` – invalid text specification.

bossdata.bits.decode_bitmask(mask, value, strict=True)[source]¶

Decode a integer value into its symbolic bit names.

Use this function to convert a bitmask value into a list of symbolic bit names, for example:

>>> COLORS = define_bitmask('COLORS','Primary colors',RED=0,BLUE=1,GREEN=4)
>>> decode_bitmask(COLORS,COLORS.RED|COLORS.BLUE)
('RED', 'BLUE')

For pretty printing, try:

>>> print('|'.join(decode_bitmask(COLORS,COLORS.RED|COLORS.BLUE)))
RED|BLUE

Parameters:	mask – A bitmask type, normally created with `create_bitmask()`, that defines the symbolic bit names to use for the decoding. value (int) – The integral value to decode. strict (bool) – If set, then all bits set in value must be defined in the bitmask type definition.
Returns:	A tuple of symbolic bit names in order of increasing bit offset. If strict is False, then any bits without corresponding symbolic names will appear as ‘1<<n’ for offset n.
Return type:	tuple
Raises:	`AttributeError` – mask does not have the attributes necessary to define a bitmask. `ValueError` – value has a bit set that has no symbolic name defined and strict is True.

bossdata.bits.define_bitmask(mask_name, mask_description, **bits)[source]¶

Define a new type for a bitmask with specified symbolic bit names.

After defining a bitmask type, its bit names are accessible as class-level attributes of the returned type and can be used as integer values, for example:

>>> COLORS = define_bitmask('COLORS','Primary Colors',RED=0,BLUE=1,GREEN=4)
>>> COLORS.BLUE
2
>>> '{0:b}'.format(COLORS.RED|COLORS.GREEN)
'10001'

The decode_bitmask() function is useful for converting an integral value back to a list of symbolic bit names.

Parameters:	mask_name (str) – The type name for this mask. By convention, this name is upper case and matches the name assigned to this function’s return value, as in the examples above. mask_description (str) – A description of this bit mask that will be available as the docstring of the new defined type. bits (dict) – A dictionary of name,definition pairs that define the mapping from symbolic bit names to bit offsets and optional comments. Although this argument can be passed as a dictionary, the dictionary is usually implicitly defined by the argument list, as in the examples above. By convention, bit names are all upper case. Each bit definition can either be specified as an integer offset >= 0 or else an (offset,description) tuple.
Returns:	A new type with the specified name that has class-level attributes for each named bit (see the examples above). The type also defines a reverse map that is normally accessed via `decode_bitmask()`.
Return type:	type
Raises:	`TypeError` – missing name and/or description args. `ValueError` – bit definition is invalid or an offset is repeated.

bossdata.bits.extract_sdss_bitmasks(filename='sdssMaskbits.par', indent=' ')[source]¶

Scan the parfile defining SDSS bitmasks and print code to define these types for bossdata.bits.

This function is intended to be run by hand with the output pasted into this module, to bootstrap or update the official SDSS bitmask definitions defined here. The generated code is printed directly to the standard output. This function should normally be run from the package top-level directory as:

python bossdata/bits.py > bitdefs.py

and will read sdssMaskBits.par from the same directory. The contents of bitdefs.py is then pasted directly into this file, replacing any previous pasted version.

Parameters:	filename (str) – Path of the parfile to read. indent (str) – Indentation to use in the generated output.
Raises:	`RuntimeError` – Parse error while reading the input file.

bossdata.bits.summarize_bitmask_values(mask, values, strict=True)[source]¶

Summarize an array of bitmask values.

Parameters:

mask – A bitmask type, normally created with create_bitmask(), that defines the symbolic bit names to summarize.
values (numpy.ndarray) – An array of values that will be decoded and summarized.

Returns:

A dictionary with bit names as keys and the number of values in which each: bit is set as values. Any bit that is never set will not appear in the list of keys.

Return type:

dict

Support for querying the metadata associated with BOSS observations.

class bossdata.meta.Database(finder=None, mirror=None, lite=True, quasar_catalog=False, quasar_catalog_name=None, platelist=False, verbose=False)[source]¶

Initialize a searchable database of BOSS observation metadata.

Parameters:

finder (bossdata.path.Finder) – Object used to find the names of BOSS data files. If not specified, the default Finder constructor is used.
mirror (bossdata.remote.Manager) – Object used to interact with the local mirror of BOSS data. If not specified, the default Manager constructor is used.
lite (bool) – Use the “lite” metadata format, which is considerably faster but only provides a subset of the most commonly accessed fields. Ignored if either quasar_catalog or platelist is True.
quasar_catalog (bool) – Initialize database using the BOSS quasar catalog instead of spAll.
quasar_catalog_name (str) – The name of the BOSS quasar catalog to use, or use the default if this is None.
platelist (bool) – Initialize the database use the platelist catalog instead of spAll.

prepare_columns(column_names)[source]¶

Validate column names and lookup their types.

Parameters:	column_names (str) – Comma-separated list of column names or the special value ‘*’ to indicate all available columns.
Returns:	Tuple (names,dtypes) of lists of column names and corresponding numpy data types. Use `zip()` to convert the return value into a recarray dtype.
Return type:	tuple
Raises:	`ValueError` – Invalid column name.

select_all(what='*', where=None, sort=None, max_rows=100000)[source]¶

Fetch all results of an SQL select query.

Since this method loads all the results into memory, it is not suitable for queries that are expected to return a large number of rows. Instead, use select_each() for large queries.

Parameters:	what (str) – Comma separated list of column names to return or ‘’ to return all columns. where* (str) – SQL selection clause or None for no filtering. Reserved column names such as PRIMARY must be escaped with backticks in this clause. max_rows (int) – Maximum number of rows that will be returned.
Returns:	Table of results with column names matching those in the database, and column types inferred automatically. Returns None if no rows are selected.
Return type:	`astropy.table.Table`
Raises:	`RuntimeError` – failed to execute query.

select_each(what='*', where=None)[source]¶

Iterate over the results of an SQL select query.

This method is normally used as an iterator, e.g.

for row in select(…):

# each row is a tuple of values …

Since this method does not load all the results of a large query into memory, it is suitable for queries that are expected to return a large number of rows. For smaller queries, the select_all() method might be more convenient.

Parameters:	what (str) – Comma separated list of column names to return or ‘’ to return all columns. where* (str) – SQL selection clause or None for no filtering. Reserved column names such as PRIMARY must be escaped with backticks in this clause.
Raises:	`sqlite3.OperationalError` – failed to execute query.

bossdata.meta.create_meta_full(catalog_path, db_path, verbose=True, primary_key='(PLATE, MJD, FIBER)')[source]¶

Create the “full” meta database from a locally mirrored catalog file.

The created database renames FIBERID to FIBER and has a composite primary index on the (PLATE,MJD,FIBER) columns. Sub-array columns are also unrolled: see sql_create_table() for details. The conversion takes about 24 minutes on a laptop with sufficient memory (~4 Gb). During the conversion, the file being written has the extension .building appended, then this extension is removed (and the file is made read only) once the conversion successfully completes. This means that if the conversion is interrupted for any reason, it will be restarted the next time this function is called and you are unlikely to end up with an invalid database file.

Parameters:	catalog_path (str) – Absolute local path of the “full” catalog file, which is expected to be a FITS file. db_path (str) – Local path where the corresponding sqlite3 database will be written.

bossdata.meta.create_meta_lite(sp_all_path, db_path, verbose=True)[source]¶

Create the “lite” meta database from a locally mirrored spAll file.

The created database has a composite primary index on the (PLATE,MJD,FIBER) columns and the input columns MODELFLUX0..4 are renamed MODELFLUX_0..4 to be consistent with their names in the full database after sub-array un-rolling.

The DR12 spAll lite file is ~115Mb and converts to a ~470Mb SQL database file. The conversion takes about 3 minutes on a laptop with sufficient memory (~4 Gb). During the conversion, the file being written has the extension .building appended, then this extension is removed (and the file is made read only) once the conversion successfully completes. This means that if the conversion is interrupted for any reason, it will be restarted the next time this function is called and you are unlikely to end up with an invalid database file.

Parameters:	sp_all_path (str) – Absolute local path of the “lite” spAll file, which is expected to be a gzipped ASCII data file. db_path (str) – Local path where the corresponding sqlite3 database will be written.

bossdata.meta.get_plate_mjd_list(plate, finder=None, mirror=None)[source]¶

Return the list of MJD values when a plate was observed.

Uses a query of the platelist, so this file will be automatically downloaded if necessary. Only MJD values for which the observation data quality is marked “good” will be returned.

Parameters:

plate (int) – Plate number.
finder (bossdata.path.Finder) – Object used to find the names of BOSS data files. If not specified, the default Finder constructor is used.
mirror (bossdata.remote.Manager) – Object used to interact with the local mirror of BOSS data. If not specified, the default Manager constructor is used.

Returns:

A list of MJD values when this plate was observed. The list will: be empty if this plate has never been observed.

Return type:

list

bossdata.meta.sql_create_table(table_name, recarray_dtype, renaming_rules={}, primary_key=None)[source]¶

Prepare an SQL statement to create a database for a numpy structured array.

Any columns in the structured array data type that are themselves arrays will be unrolled to a list of scalar columns with names COLNAME_I for element [i] of a 1D array and COLNAME_I_J for element [i,j] of a 2D array, etc, with indices I,J,… starting from zero.

Parameters:	table_name (str) – Name to give the new table. recarray_dtype – Numpy structured array data type that defines the columns to create. renaming_rules (dict) – Dictionary of rules for renaming columns. There are no explicit checks that these rules do not create duplicate column names or that all rules are applied. primary_key (str) – Column name(s) to use as the primary key, after apply renaming rules. No index is created if this argument is None.
Returns:	Tuple (sql,num_cols) where sql is an executable SQL statement to create the database and num_cols is the number of columns created.
Return type:	tuple
Raises:	`ValueError` – Cannot map data type to SQL.

Generate paths to BOSS data files.

The path module provides convenience methods for building the paths of frequently used data files. Most scripts will create a single Finder object using the default constructor for this purpose:

import bossdata
finder = bossdata.path.Finder()

This finder object is normally configured by the $BOSS_SAS_PATH and $BOSS_REDUX_VERSION environment variables and no other modules uses these variables, except through a a Finder object. These parameters can also be set by Finder constructor arguments. When neither the environment variables nor the constructor arguments are set, defaults appropriate for the most recent public data release (DR12) are used.

Finder objects never interact with any local or remote filesystems: use the bossdata.remote module to download data files and access them locally. See API Usage for recommendations on using the bossdata.path and bossdata.remote modules together.

class bossdata.path.Finder(sas_path=None, redux_version=None, verbose=True)[source]¶

Initialize a path finder object.

When the constructor is called with no arguments, it will raise a ValueError if either BOSS_SAS_PATH or BOSS_REDUX_VERSION is not set.

Parameters:	sas_path (str) – Location of the SAS root path to use, e.g., /sas/dr12. Will use the value of the BOSS_SAS_PATH environment variable if this is not set. redux_version (str) – String tag specifying the BOSS spectro reduction version to use, e.g., v5_7_0. Will use the value of the BOSS_REDUX_VERSION environment variable if this is not set.
Raises:	`ValueError` – No SAS root or redux version specified on the command line or via environment variables.

get_plate_path(plate, filename=None)[source]¶

Get the path to the specified plate directory or file.

The returned path contains files that include all targets on the plate. Use the get_spec_path() method for the path of a single spectrum file.

This method only performs minimal checks that the requested plate number is valid.

Parameters:	plate (int) – Plate number, which must be positive. filename (str) – Name of a file within the plate directory to append to the returned path.
Returns:	Full path to the specified plate directory or file within this directory.
Return type:	str
Raises:	`ValueError` – Invalid plate number must be > 0.

get_plate_plan_path(plate, mjd, combined=True)[source]¶

Get the path to the specified plate plan file.

A combined plan may span several nearby MJDs, in which case the last MJD is the one used to identify the plan.

Parameters:	plate (int) – Plate number, which must be positive. mjd (int) – Modified Julian date of the observation, which must be > 45000. combined (bool) – Specifies the combined plan, which spans all MJDs associated with a coadd, but does not include calibration frames (arcs,flats) for a specific MJD.
Returns:	Full path to the requested plan file.
Return type:	str
Raises:	`ValueError` – Invalid plate or mjd inputs.

get_plate_spec_path(plate, mjd)[source]¶

Get the path to the file containing combined spectra for a whole plate.

Combined spectra for all exposures of a plate are packaged in spPlate files. As of DR12, these files are about 110Mb for 1000 spectra.

Parameters:	plate (int) – Plate number, which must be positive. mjd (int) – Modified Julian date of the observation, which must be > 45000.
Returns:	Full path to the requested plan file.
Return type:	str
Raises:	`ValueError` – Invalid plate or mjd inputs.

get_platelist_path()[source]¶

Get the location of the platelist summary file.

The platelist contains one row per observation (PLATE-MJD), unlike most other sources of metadata which contain one row per target (PLATE-MJD-FIBER).

get_quasar_catalog_path(catalog_name=None)[source]¶

Get the location of the quasar catalog file.

The quasar catalog is documented at http://www.sdss.org/dr12/algorithms/boss-dr12-quasar-catalog/. As of DR12, the file size is about 513Mb.

Parameters:	catalog_name (str) – BOSS quasar catalog name. Will use the `get_default_quasar_catalog_name()` method if this is not set.

get_raw_path(mjd, camera, exposure)[source]¶

Get the location of the raw data for the specified camera and exposure.

The DR12 data model for BOSS raw data in the sdR format is at http://data.sdss3.org/datamodel/files/BOSS_SPECTRO_DATA/MJD/sdR.html

Parameters:	mjd (int) – Modified Julian date of the observation, which must be > 45000. camara (str) – One of b1, b2, r1, r2. exposure (int) – Exposure number to return.
Raises:	`ValueError` – Invalid camera or exposure number.

get_sp_all_path(lite=True)[source]¶

Get the location of the metadata summary file.

The spAll file provides extensive metadata for all survey targets as a FITS file. There is also a smaller “lite” version containing a subset of this metadata in compressed text format. As of DR12, the full file size is about 10Gb and the lite file is about 115Mb.

Parameters:	lite (bool) – Specifies the “lite” version which contains all rows but only the most commonly used subset of columns. The lite version is a compressed (.gz) text data file, while the full version is a FITS file.

get_spec_path(plate, mjd, fiber, lite=True, mock=False, compressed=False)[source]¶

Get the location of the spectrum file for the specified observation.

The DR12 data model for the returned files is at http://dr12.sdss3.org/datamodel/files/BOSS_SPECTRO_REDUX/RUN2D/spectra/PLATE4/spec.html but only HDUs 0-3 are included in the (default) lite format. Each lite (full) file is approximately 0.2Mb (1.7Mb) in size.

Use the get_plate_path() method for the path to files that include all targets on a plate.

This method only performs minimal checks that the requested plate-mjd-fiber are valid.

Parameters:	plate (int) – Plate number, which must be positive. mjd (int) – Modified Julian date of the observation, which must be > 45000. fiber (int) – Fiber number of the target on this plate, which must be in the range 1-1000 (or 1-640 for plate < 3510). lite (bool) – Specifies the “lite” version which contains only HDUs 0-3, so no per-exposure data is included.
Returns:	Full path to the spectrum file for the specified observation.
Return type:	str
Raises:	`ValueError` – Invalid plate, mjd or fiber inputs.

Access BOSS plate data products.

class bossdata.plate.FrameFile(path, index=None, calibrated=None)[source]¶

A BOSS frame file containing a single exposure of one spectrograph (half plate).

This class supports both types of frame data files: the uncalibrated spFrame and the calibrated spCFrame. Use get_valid_data() to access this plate’s data and the plug_map attribute to access this plate’s plug map.

BOSS spectrographs read out 500 fibers each. SDSS-I/II spectrographs (plate < 3510) read out 320 fibers each. The plate, camera and exposure_id attributes provide the basic metadata for this exposure. The complete HDU0 header is available as the header attribute.

This class is only intended for reading the BOSS frame file format, so generic operations on spectroscopic data (redshifting, resampling, etc) are intentionally not included here, but are instead provided in the speclite package.

Parameters:

path (str) – Local path of the frame FITS file to use. This should normally be obtained via Plan.get_exposure_name() and can be automatically mirrored via bossdata.remote.Manager.get() or using the bossfetch script. The file is opened in read-only mode so you do not need write privileges.
index (int) – Identifies if this is the first (1) or second (2) spectrograph, which determines whether it has spectra for fibers 1-500 (1-320) or 501-1000 (321-640). You should normally obtain this value using Plan.get_spectrograph_index(). As of v0.2.7, this argument is optional and will be inferred from the file header when not provided, or checked against the file header when provided.
calibrated (bool) – Identifies whether this is a calibrated (spCFrame) or un-calibrated (spFrame) frame file. As of v0.2.7, this argument is optional and will be inferred from the file header when not provided, or checked against the file header when provided.

get_fiber_offsets(fiber)[source]¶

Convert fiber numbers to array offsets.

Parameters:	fibers (numpy.ndarray) – Numpy array of fiber numbers 1-1000 (or 1-640 for plate < 3510). All fibers must be in the appropriate range 1-500 (1-320) or 501-1000 (321-640) for this frame’s spectograph. Fibers do not need to be sorted and repetitions are ok.
Returns:	Numpy array of offsets 0-499 (or 0-319 for plate < 3510).
Return type:	numpy.ndarray
Raises:	`ValueError` – Fiber number is out of the valid range for this spectrograph.

get_pixel_masks(fibers)[source]¶

Get the pixel masks for specified fibers.

The entire mask is returned for each fiber, including any pixels with zero inverse variance.

Parameters:	fibers (numpy.ndarray) – Numpy array of fiber numbers 1-1000 (or 1-640 for plate < 3510). All fibers must be in the appropriate range 1-500 (1-320) or 501-1000 (321-640) for this frame’s spectograph. Fibers do not need to be sorted and repetitions are ok.
Returns:	Integer numpy array of shape (nfibers,npixels) where (i,j) encodes the mask bits defined in `bossdata.bits.SPPIXMASK` (see also http://www.sdss3.org/dr10/algorithms/bitmask_sppixmask.php) for pixel-j of the fiber with index fibers[i].
Return type:	numpy.ndarray

get_valid_data(fibers, pixel_quality_mask=None, include_wdisp=False, include_sky=False, use_ivar=False, use_loglam=False)[source]¶

Get the valid for the specified fibers.

Parameters:

fibers (numpy.ndarray) – Numpy array of fiber numbers 1-1000 (or 1-640 for plate < 3510). All fibers must be in the appropriate range 1-500 (1-320) or 501-1000 (321-640) for this frame’s spectograph. Fibers do not need to be sorted and repetitions are ok.
pixel_quality_mask (int) – An integer value interpreted as a bit pattern using the bits defined in bossdata.bits.SPPIXMASK (see also http://www.sdss3.org/dr10/algorithms/bitmask_sppixmask.php). Any bits set in this mask are considered harmless and the corresponding spectrum pixels are assumed to contain valid data.
include_wdisp – Include a wavelength dispersion column in the returned data.
include_sky – Include a sky flux column in the returned data.
use_ivar – Replace dflux with ivar (inverse variance) in the returned data.
use_loglam – Replace wavelength with loglam (log10(wavelength)) in the returned data.

Returns:

Masked array of shape (nfibers,npixels). Pixels with no: valid data are included but masked. The record for each pixel has at least the following named fields: wavelength in Angstroms, flux and dflux in 1e-17 ergs/s/cm2/Angstrom. Wavelength values are strictly increasing and dflux is calculated as ivar**-0.5 for pixels with valid data. Optional fields are wdisp in constant-log10-lambda pixels and sky in 1e-17 ergs/s/cm2/Angstrom. The wavelength (or loglam) field is never masked and all other fields are masked when ivar is zero or a pipeline flag is set (and not allowed by pixel_quality_mask).

Return type:

numpy.ma.MaskedArray

class bossdata.plate.Plan(path)[source]¶

The plan file for configuring the BOSS pipeline to combine exposures of a single plate.

Combined plan files are small text files that list the per-spectrograph (b1,b2,r1,r2) exposures used as input to a single coadd. Use the exposure_table attribute to access this information. Note that bossdata.spec.SpecFile has a similar exposures attribute which only includes exposures actually used in the final co-add, so is generally a subset of the planned exposures.

Parameters:	path (str) – The local path to a plan file.

get_exposure_name(sequence_number, band, fiber, ftype='spCFrame')[source]¶

Get the file name of a single science exposure data product.

Use the exposure name to locate FITS data files associated with individual exposures. The supported file types are: spCFrame, spFrame, spFluxcalib and spFluxcorr. Note that this method returns None when the requested exposure is not present in the plan, so the return value should always be checked.

Parameters:	sequence_number (int) – Science exposure sequence number, counting from zero. Must be less than our num_science_exposures attribute. fiber (int) – Fiber number to identify which spectrograph to use, which must be in the range 1-1000 (or 1-640 for plate < 3510). band (str) – Must be ‘blue’ or ‘red’. ftype (str) – Type of exposure file whose name to return. Must be one of spCFrame, spFrame, spFluxcalib, spFluxcorr. An spCFrame is assumed to be uncompressed, and all other files are assumed to be compressed.
Returns:	Exposure name of the form [ftype]-[cc]-[eeeeeeee].[ext] where [cc] identifies the spectrograph (one of b1,r1,b2,r2) and [eeeeeeee] is the zero-padded exposure number. The extension [ext] is “fits” for spCFrame files and “fits.gz” for all other file types. Returns None if the name is unknown for this band and fiber combination.
Return type:	str
Raises:	`ValueError` – one of the inputs is invalid.

get_spectrograph_index(fiber)[source]¶

Get the spectrograph index 1,2 for the specified fiber.

Parameters:	fiber (int) – Fiber number to identify which spectrograph to use, which must be in the range 1-1000 (or 1-640 for plate < 3510).
Returns:	Value of 1 if fiber is read out by the first spectrograph 1-500 (1-320), or else 2 for the second spectrograph.
Return type:	int
Raises:	`ValueError` – fiber is outside the allowed range 1-1000 (1-640) for this plate.

class bossdata.plate.PlateFile(path)[source]¶

A BOSS plate file containing combined exposures for a whole plate.

This class provides an interface to the spPlate data product, containing all co-added spectra for a single observation. To instead read individual co-added spectra, use bossdata.spec.SpecFile. To access individual exposures of a half-plate use FrameFile.

Use get_valid_data() to access this plate’s data, or the exposures attribute for a list of exposures used in the coadd. The num_exposures attribute gives the number of science exposures used for this target’s co-added spectrum (counting a blue+red pair as one exposure). The plug_map attribute records this plate’s plug map.

This class is only intended for reading the BOSS plate file format, so generic operations on spectroscopic data (redshifting, resampling, etc) are intentionally not included here, but are instead provided in the speclite package.

Parameters:	path (str) – Local path of the plate FITS file to use. This should normally be obtained via `bossdata.path.Finder.get_plate_spec_path()` and can be automatically mirrored via `bossdata.remote.Manager.get()` or using the bossfetch script. The file is opened in read-only mode so you do not need write privileges.

get_fiber_offsets(fiber)[source]¶

Convert fiber numbers to array offsets.

Parameters:	fibers (numpy.ndarray) – Numpy array of fiber numbers 1-1000 (or 1-640 for plate < 3510). Fibers do not need to be sorted and repetitions are ok.
Returns:	Numpy array of offsets 0-999.
Return type:	numpy.ndarray
Raises:	`ValueError` – Fiber number is out of the valid range for this plate.

get_pixel_masks(fibers)[source]¶

Get the pixel masks for specified fibers.

The entire mask is returned for each fiber, including any pixels with zero inverse variance. Returns the ‘and_mask’ and ignores the ‘or_mask’.

Parameters:	fibers (numpy.ndarray) – Numpy array of fiber numbers 1-1000 (or 1-640 for plate < 3510). Fibers do not need to be sorted and repetitions are ok.
Returns:	Integer numpy array of shape (nfibers,npixels) where (i,j) encodes the mask bits defined in `bossdata.bits.SPPIXMASK` (see also http://www.sdss3.org/dr10/algorithms/bitmask_sppixmask.php) for pixel-j of the fiber with index fibers[i].
Return type:	numpy.ndarray

get_valid_data(fibers, pixel_quality_mask=None, include_wdisp=False, include_sky=False, use_ivar=False, use_loglam=False, fiducial_grid=False)[source]¶

Get the valid for the specified fibers.

Parameters:

fibers (numpy.ndarray) – Numpy array of fiber numbers 1-1000 (or 1-640 for plate < 3510). Fibers do not need to be sorted and repetitions are ok.
pixel_quality_mask (int) – An integer value interpreted as a bit pattern using the bits defined in bossdata.bits.SPPIXMASK (see also http://www.sdss3.org/dr10/algorithms/bitmask_sppixmask.php). Any bits set in this mask are considered harmless and the corresponding spectrum pixels are assumed to contain valid data. This mask is applied to the AND of the masks for each individual exposure. No mask is applied if this value is None.
include_wdisp – Include a wavelength dispersion column in the returned data.
include_sky – Include a sky flux column in the returned data.
use_ivar – Replace dflux with ivar (inverse variance) in the returned data.
use_loglam – Replace wavelength with loglam (log10(wavelength)) in the returned data.
fiducial_grid – Return co-added data using the fiducial wavelength grid. If False, the returned array uses the native grid of the SpecFile, which generally trims pixels on both ends that have zero inverse variance. Set this value True to ensure that all co-added spectra use aligned wavelength grids when this matters.

Returns:

Masked array of shape (nfibers,npixels). Pixels with no: valid data are included but masked. The record for each pixel has at least the following named fields: wavelength in Angstroms (or loglam), flux and dflux in 1e-17 ergs/s/cm2/Angstrom (or flux and ivar). Wavelength values are strictly increasing and dflux is calculated as ivar**-0.5 for pixels with valid data. Optional fields are wdisp in constant-log10-lambda pixels and sky in 1e-17 ergs/s/cm2/Angstrom. The wavelength (or loglam) field is never masked and all other fields are masked when ivar is zero or a pipeline flag is set (and not allowed by pixel_quality_mask).

Return type:

numpy.ma.MaskedArray

class bossdata.plate.TraceSet(hdu)[source]¶

A set of interpolating functions along each trace of a half plate.

TraceSets use the terminology that x is the pixel coordinate along the nominal wavelength direction and y is some quantity to be interpolated as a function of x. This implementation is based on the original SDSS IDL code: https://trac.sdss3.org/browser/repo/idlutils/trunk/pro/trace/traceset2xy.pro

Note that red and blue CCDs are handled differently, as described here:

The plan is to switch from 1-phase to 2-phase readout on
the red CCDs in summer 2010. This will effectively make
the pixels more uniform, and the flat-fields much better.

A problem introduced will be that the central two rows will
each be taller by 1/6 pix. That will flat-field, but there
will be a discontinuity of 1/3 pix across this point.
Technically, the PSF will also be different for those pixels,
and the resulting resolution function.

Parameters:	hdu – fitsio HDU containing the trace set data as a binary table.
Raises:	`ValueError` – Unable to initialize a trace set with this HDU.

get_y(xpos=None, ignore_jump=False)[source]¶

Evaluate the interpolating function for each trace.

Parameters:

xpos (numpy.ndarray) – Numpy array of shape (ntrace,nx) with x-pixel coordinates along each trace where y(x) should be evaluated. For BOSS, ntrace = 500 and for SDSS-I/II (plate < 3510), ntrace = 320. The value of ntrace is available as self.ntrace. If this argument is not set, self.default_xpos will be used, which consists of num_fibers identical traces with x-pixel coordinates at each integer pixel value covering the full allowed range.
ignore_jump (bool) – Include a jump when this is set and this is a 2-phase readout. There is probably no good reason to set this False, but it is included for compatibility with the original IDL code.

Returns:

Numpy array y with shape (ntrace,nx) that matches the input: xpos or else the default self.default_xpos. ypos[[i,x]] gives the value of the interpolated y(x) with x equal to xpos[[i,x]].

Return type:

numpy.ndarray

bossdata.plate.get_num_fibers(plate)[source]¶

Return the number of fiber holes for a given plate number.

Plate numbers 3510 or larger are (e)BOSS plates with 1000 fibers. Smaller plate numbers are assumed to be SDSS-I/II with 640 fibers.

Parameters:	plate (int) – Plate number.
Returns:	The value 640 or 1000.
Return type:	int

Support for plotting BOSS spectscopic data in different formats.

These functions use the optional matplotlib dependency so will raise an ImportError if this is not installed. Functions do not create figures or call matplotlib.pyplot.show() before exiting, to provide the maximum flexibility. To display a single plot, you can use the following wrapper:

plt.figure(figsize=(11,8.5))
# ... call one of the plot functions ...
plt.tight_layout()
plt.show()

See the Examples for details.

bossdata.plot.by_fiber(data, mask=None, subsets={}, percentile_cut=0.0, plot_label=None, data_label=None, default_options={'marker': 'o', 'lw': 0.5, 's': 60})[source]¶

Plot per-fiber data values in fiber order.

This is a useful plot to show any dependence of the data value on a fiber’s position on the CCD and slithead. Both spectrographs are superimposed on the same plot. The points for each fiber are color-coded according to their associated data value using the same scheme as focal_plane().

Parameters:

data (numpy.ndarray) – A 1D array of data values to plot, where the array index matches the fiber number and all fibers are included.
mask (numpy.ndarray) – An optional 1D array of boolean values with True values used to mask out values in the data array. Masked values will not be plotted and will not be used to calculate the plot data range.
subsets (dict) – A dictionary of fiber subsets that will be separately identified in the plot. Each dictionary must define values for two keys: ‘options’ and ‘fibers’. The options are a dictionary of arguments passed to matplotlib.pyplot.scatter() and used to style the subset. The fibers value is used to index the data array to pick out the subset’s data values.
percentile_cut (float) – Data will be clipped to this percentile value on both sides of its distribution. Use a value of zero (the default) for no clipping.
plot_label (str) – A label identifying this plot that will be displayed in the top-left corner.
data_label (str) – A label identifying the data values that will be used to label the y axis.
default_options (dict) – A dictionary of options passed to matplotlib.pyplot.scatter() that is used to draw data points. Options in a subset dictionary override any values here. Fibers not in any subset are drawn using these default options.

bossdata.plot.focal_plane(xfocal, yfocal, data, mask=None, subsets={}, background=None, numbered=None, percentile_cut=0.0, mesh_refinement=0, plot_label=None, data_label=None, show_background_mesh=False, number_color='red', default_options={'marker': 'o', 's': 60, 'lw': 0.5, 'edgecolors': 'k'}, rmax=350.0)[source]¶

Plot per-fiber data values using focal-plane positions.

This is a useful plot to show any dependence of the data value on a fiber’s position in the focal plane. The points for each fiber are color-coded according to their associated data value using the same scheme as by_fiber().

Parameters:

xfocal (numpy.ndarray) – A 1D array of x focal-plane positions, where the array index matches the fiber number and all fibers are included.
yfocal (numpy.ndarray) – A 1D array of y focal-plane positions, where the array index matches the fiber number and all fibers are included.
data (numpy.ndarray) – A 1D array of data values to plot, where the array index matches the fiber number and all fibers are included.
mask (numpy.ndarray) – An optional 1D array of boolean values with True values used to mask out values in the data array. Masked values will not be plotted and will not be used to calculate the plot data range.
subsets (dict) – A dictionary of fiber subsets that will be separately identified in the plot. Each dictionary must define values for two keys: ‘options’ and ‘fibers’. The options are a dictionary of arguments passed to matplotlib.pyplot.scatter() and used to style the subset. The fibers value is used to index the data array to pick out the subset’s data values.
background (numpy.ndarray) – An optional subset of fibers whose data values are used to fill the background using interpolation. The resulting background fill will only cover the convex hull of the subset, where interpolation is possible.
numbered (numpy.ndarray) – An optional subset of fibers that will be numbered in the generated plot.
percentile_cut (float) – Data will be clipped to this percentile value on both sides of its distribution. Use a value of zero (the default) for no clipping.
mesh_refinement (int) – Smoothness of background fill interpolation to use. A value of zero (the default) corresponds to linear interpolation.
plot_label (str) – A label identifying this plot that will be displayed in the top-left corner.
data_label (str) – A label identifying the data values that will be used to label the y axis.
show_background_mesh (bool) – Draw the triangulation used for the background fill when this is True.
number_color (str) – Matplotlib color used to draw fiber numbers.
default_options (dict) – A dictionary of options passed to matplotlib.pyplot.scatter() that is used to draw data points. Options in a subset dictionary override any values here. Fibers not in any subset are drawn using these default options.

Work with raw spectroscopic science and calibration images.

class bossdata.raw.RawImageFile(path)[source]¶

Wrapper for a raw science or calibration sdR image format.

Each camera (b1,b2,r1,r2) has its own raw image file for a given exposure ID. See the sdR datamodel for details. The FITS file is opened, read, and closed by the constructor.

The raw files used in a co-add can be located and opened using bossdata.spec.SpecFile.get_raw_image().

Parameters:	path (str) – Local path of the FITS image file to use.

header¶: dict – Dictionary of FITS header keyword-value pairs.

plate¶: int – Plate number used to record this raw data.

exposure_id¶: int – Exposure ID for this raw data.

camera¶: str – One of b1, b2, r1, r2.

flavor¶: str – One of science, arc, flat.

data¶: numpy.ndarray – 2D array of raw pixel values.

get_amplifier_bias(amplifier_index, percentile_cut=1)[source]¶

Estimate the amplifier bias.

Estimate the bias in one amplifier (quadrant) using the truncated mean of pixel values in its overscan region.

Parameters:	amplifier_index (int) – Amplifier’s are indexed 0-3. percentile_cut (float) – Percentage of outliers to ignore on both sides of the distribution.
Returns:	Estimated bias.
Return type:	float

get_amplifier_region(amplifier_index, region_type)[source]¶

Get overscan and data regions for one amplifier.

Region definitions are taken from the sdR datamodel. Each amplifier reads out one quadrant of the sensor. Amplfiers 0-3 have outside corner pixel indices [0, 0], [0, -1], [-1, 0], [-1, -1], respectively. Results are only valid for MJD >= 55113 (9-Oct-2009).

Parameters:

amplifier_index (int) – Amplifier’s are indexed 0-3.
region_type (str) – One of data, overscan.

Returns:

Tuple (rows, cols) of pixel slices that define the requested: region. The return value can be used to create a view of our raw data as self.data[rows, cols].

Return type:

tuple

get_data(bias_subtracted=True, percentile_cut=1, bias_point=100)[source]¶

Get the data region of this raw image.

The data region is the union of the four amplifier data regions.

Parameters:

bias_subtracted (bool) – Subtract bias from each amplifier quadrant, estimated using get_amplifier_bias() (rounded to the nearest integer value).
percentile_cut (float) – Percentage of outliers to ignore on both sides of the distribution for estimating bias (when bias_subtracted is True).
bias_point (int) – When bias_subtracted is True, raw pixel values will be offset so that the bias value is shifted to the specified value. Must be between 0 - 0xffff and smaller than the estimated bias value.

Returns:

2D array of pixel values as unsigned 16-bit integers.: The return value is a copy (rather than a view) of the raw data array in the file. When bias_subtracted is True, saturated values (0xffff) will remain saturated, and values that underflow (<0) after bias subtraction will be set to zero.

Return type:

numpy.ndarray

read_plug_map(speclog_path=None)[source]¶

Read the plug map associated with this plate and exposure.

Plug maps are not stored under the same SAS tree as other data products so reading a plug map requires that a separate directory containing plug map files is already available on the local disk. To download the set of plug map files used by the pipeline, use the SDSS public SVN repository to make a local checkout of the speclog product:

svn co https://svn.sdss.org/public/data/sdss/speclog/trunk speclog

This will take a while to run (about 15 minutes) and will copy about 25 Gb of data into the newly created speclog sub-directory. Pass the full name of this directory to this method.

You can create your speclog directory anywhere, but you should avoid putting it under $BOSS_SAS_PATH in your $BOSS_LOCAL_ROOT since plug map files do not originate from SAS. Note that ~11Gb of the files in speclog are guidermon files and not very useful. You cannot avoid downloading them with the svn checkout, but you can delete them after the checkout using:

cd speclog
find . -name 'guiderMon-*.par' -delete

The plug maps are yanny parameter files. See the plug map datamodel for details.

Parameters:	speclog_path (str) – The local path to a directory containing plPlugMapM files organized in subdirectories by observation MJD. If None is specified, the value of the BOSS_SPECLOG environment variable will be used, if available.
Returns:	Object wrapper around the plug map yanny file. The PLUGMAPOBJ attribute contains the table of plugging info.
Return type:	pydl.pydlutils.yanny.yanny

Download BOSS data files from a remote server.

The remote module is responsible for downloading data files into a local filesystem using a directory layout that mirrors the remote data source. Most scripts will create a single Manager object using the default constructor for this purpose:

import bossdata
mirror = bossdata.remote.Manager()

This mirror object is normally configured by the $BOSS_DATA_URL and $BOSS_LOCAL_ROOT environment variables and no other modules uses these variables, except through a a Manager object. These parameters can also be set by Manager constructor arguments. When neither the environment variables nor the constructor arguments are set, a default data URL appropriate for the most recent public data release (DR12) is used, and a temporary directory is created and used for the local root.

Manager objects have no knowledge of how data files are organized or named: use the bossdata.path module to build the paths of frequently used data files. See API Usage for recommendations on using the bossdata.path and bossdata.remote modules together.

class bossdata.remote.Manager(data_url=None, local_root=None, verbose=True)[source]¶

Manage downloads of BOSS data via HTTP.

The default mapping from remote to local filenames is to mirror the remote file hierarchy on the local disk. The normal mode of operation is to establish the local root for the mirror using the BOSS_LOCAL_ROOT environment variable. When the constructor is called with no arguments, it will raise a ValueError if either BOSS_DATA_URL or BOSS_LOCAL_ROOT is not set.

Parameters:

data_url (str) – Base URL of all BOSS data files. A trailing / on the URL is optional. If this arg is None, then the value of the BOSS_DATA_URL environment variable we be used instead.
local_root (str) – Local path to use as the root of the locally mirrored file hierarchy. If this arg is None, then the value of the BOSS_LOCAL_ROOT environment variable, if any, will be used instead. If a value is provided, it should identify an existing writeable directory.

Raises:

ValueError – No such directory local_root or missing data_url.

download(remote_path, local_path, chunk_size=4096, progress_min_size=10)[source]¶

Download a single BOSS data file.

Downloads are streamed so that the memory requirements are independent of the file size. During the download, the file is written to its final location but with ‘.downloading’ appended to the file name. This means than any download that is interrupted or fails will normally not lead to an incomplete file being returned by a subsequent call to get(). Instead, the file will be re-downloaded. Tere is no facility for resuming a previous partial download. After a successful download, the file is renamed to its final location and has its permission bits set to read only (to prevent accidental modifications of files that are supposed to exactly mirror the remote file system).

Parameters:	remote_path (str) – The full path to the remote file relative to the remote server root, which should normally be obtained using `bossdata.path` methods. local_path (str) – The (absolute or relative) path of the local file to write. chunk_size (int) – Size of data chunks to use for the streaming download. Larger sizes will potentially download faster but also require more memory. progress_min_size (int) – Display a text progress bar for any downloads whose size in Mb exceeds this value. No progress bar will ever be shown if this value is None.
Returns:	Absolute local path of the downloaded file.
Return type:	str
Raises:	`ValueError` – local_path directory does not exist. `RuntimeError` – HTTP request returned an error status.

get(remote_path, progress_min_size=10, auto_download=True, local_paths=None)[source]¶

Get a local file that mirrors a remote file, downloading the file if necessary.

Parameters:	remote_path (str,list) – This arg will normally be a single string but can optionally be a list of strings for some advanced functionality. Strings give the full path to a remote file and should normally be obtained using `bossdata.path` methods. When passing an iterable, the first item specifies the desired file and subsequent items specify acceptable substitutes. If the desired file is not already available locally but at least one substitute file is locally available, this method immediately returns the first substitute without downloading the desired file. If no substitute is available, the desired file is downloaded and returned. progress_min_size (int) – Display a text progress bar for any downloads whose size in Mb exceeds this value. No progress bar will ever be shown if this value is None. auto_download (bool) – Automatically download the file to the local mirror if necessary. If this is not set and the file is not already mirrored, then a RuntimeError occurs. local_paths (list) – When this arg is not None, the local paths corresponding to each input remote path are stored to this arg, resulting in a list of the same size as the input remote_path (or length 1 if remote_path is a single string). This enables the following pattern for detecting when a substitution has ocurred: mirror = bossdata.remote.Manager() remote_paths = [the_preferred_path, a_backup_path] local_paths = [] local_path = mirror.get(remote_paths, local_paths=local_paths) if local_path != local_paths[0]: print('substituted {} for {}.'.format(local_path, local_paths[0]))
Returns:	Absolute local path of the local file that mirrors the remote file.
Return type:	str
Raises:	`RuntimeError` – File is not already mirrored and auto_download is False.

local_path(remote_path, suffix=None, new_suffix=None)[source]¶

Get the local path corresponding to a remote path.

Does not check that the file or its parent directory exists. Use get() to ensure that the file exists, downloading it if necessary.

Parameters:	remote_path (str) – The full path to the remote file relative to the remote server root, which should normally be obtained using `bossdata.path` methods. suffix (str) – The expected suffix of the returned local path. A RuntimeError is raised when the local path does not have this suffix according to `str.endswith()`, unless this parameter is None. new_suffix (str) – Replace suffix with this value. No change is performed when this parameter is None, and `suffix` must also be set with this parameter is not None.
Returns:	Absolute local path of the local file that mirrors the remote file, with a possible suffix replacement.
Return type:	str
Raises:	`ValueError` – The `new_suffix` parameter is set but `suffix` is None. `RuntimeError` – The local path does not have the expected suffix.

Access spectroscopic data for a single BOSS target.

class bossdata.spec.Exposures(header)[source]¶

Table of exposure info extracted from FITS header keywords.

Parse the NEXP and EXPIDnn keywords that are present in the header of HDU0 in spPlate and spec FITS files.

The constructor initializes the table attribute with column names offset, camera, science, flat and arc, and creates one row for each keyword EXPIDnn, where offset equals the keyword sequence number nn, camera is one of b1, b2, r1, r2, and the remaining columns record the science and calibration exposure numbers.

Use get_info() to retrieve the n-th exposure for a particular camera (b1, b2, r1, r2). Note that when this class is initialized from a spec file header, it will only describe the two cameras of a single spectrograph (b1+r1 or b2+r2). The num_by_camera attribute is a dictionary of ints indexed by camera that records the number of science exposures available for that camera.

Parameters:	header (dict) – dictionary of FITS header keyword, value pairs.

Returns:

get_exposure_name(exposure_index, camera, ftype='spCFrame')[source]¶

Get the file name of a single science or calibration exposure data product.

Use the exposure name to locate FITS data files associated with individual exposures. The supported file types are: spCFrame, spFrame, spFluxcalib spFluxcorr, spArc, spFlat. This method is analogous to bossdata.plate.Plan.get_exposure_name(), but operates for a single target and only knows about exposures actually used in the final co-add (including the associated arc and flat exposures).

Parameters:	exposure_index (int) – The sequence number for the requested camera exposure, in the range 0 - (num_exposures[camera]-1). camera (str) – One of b1,b2,r1,r2. ftype (str) – Type of exposure file whose name to return. Must be one of spCFrame, spFrame, spFluxcalib, spFluxcorr, spArc, spFlat. An spCFrame is assumed to be uncompressed, and all other files are assumed to be compressed. When a calibration is requested (spArc, spFlat) results from the calibration exposure used to analyze the specified science exposure is returned.
Returns:	Exposure name of the form [ftype]-[cc]-[eeeeeeee].[ext] where [cc] identifies the camera (one of b1,r1,b2,r2) and [eeeeeeee] is the zero-padded arc/flat/science exposure number. The extension [ext] is “fits” for spCFrame files and “fits.gz” for all other file types.
Return type:	str
Raises:	`ValueError` – one of the inputs is invalid.

get_info(exposure_index, camera)[source]¶

Get information about a single camera exposure.

Parameters:	exposure_index (int) – The sequence number for the requested camera exposure, in the range 0 - (num_exposures[camera]-1). camera (str) – One of b1,b2,r1,r2.
Returns:	A structured array with information about the requested exposure, corresponding to one row of our `table` attribute.
Raises:	`ValueError` – Invalid exposure_index or camera. `RuntimeError` – Exposure not present.

get_raw_image(plate, mjd, exposure_index, camera, flavor='science', finder=None, mirror=None)[source]¶

Get the raw image file associated with an exposure.

Parameters:	plate (int) – Plate number, which must be positive. mjd (int) – Modified Julian date of the observation, which must be > 45000. exposure_index (int) – The sequence number for the requested camera exposure, in the range 0 - (num_exposures[camera]-1). camera (str) – One of b1,b2,r1,r2. flavor (str) – One of science, arc, flat. finder (bossdata.path.Finder) – Object used to find the names of BOSS data files. If not specified, the default Finder constructor is used. mirror (bossdata.remote.Manager) – Object used to interact with the local mirror of BOSS data. If not specified, the default Manager constructor is used.
Returns:	requested raw image file.
Return type:	bossdata.raw.RawImageFile
Raises:	`ValueError` – one of the inputs is invalid.

class bossdata.spec.SpecFile(path)[source]¶

A BOSS spec file containing summary data for a single target.

A spec file contains co-added spectra for a single target of an observation. This class supports the full version described in the data model as well as a lite version that does not contain the per-exposure HDUs with indices >= 4. Use the lite attribute to detect which version an object represents.

To read all co-added spectra of an observation use bossdata.plate.PlateFile. Individual exposures of a half-plate can be read using bossdata.plate.FrameFile.

The plate, mjd and fiber attributes specify the target observation. The info attribute contains this target’s row from spAll as a structured numpy array, so its metadata can be accessed as info['OBJTYPE'], etc.

Use get_valid_data() to access this target’s spectra, or the exposures attribute for a list of exposures used in the coadd (see bossdata.plate.Plan for alternative information about the exposures used in a coadd.) The num_exposures attribute gives the number of science exposures used for this target’s co-added spectrum (counting a blue+red pair as one exposure). Use get_exposure_name() to locate files associated the individual exposures used for this co-added spectrum.

This class is only intended for reading the BOSS spec file format, so generic operations on spectroscopic data (redshifting, resampling, etc) are intentionally not included here, but are instead provided in the speclite package.

Parameters:	path (str) – Local path of the spec FITS file to use. This should normally be obtained via `bossdata.path.Finder.get_spec_path()` and can be automatically mirrored via `bossdata.remote.Manager.get()` or using the bossfetch script. The file is opened in read-only mode so you do not need write privileges.

get_exposure_hdu(exposure_index, camera)[source]¶

Lookup the HDU for one exposure.

This method will not work on “lite” files, which do not include individual exposures.

Parameters:	exposure_index (int) – Individual exposure to use, specified as a sequence number starting from zero, for the first exposure, and increasing up to self.num_exposures-1. camera (str) – Which camera to use. Must be one of b1,b2,r1,r2.
Returns:	The HDU containing data for the requested exposure.
Return type:	hdu
Raises:	`RuntimeError` – individual exposures not available in lite file.

get_exposure_name(sequence_number, band, ftype='spCFrame')[source]¶

Get the file name of a single science exposure data product.

Use the exposure name to locate FITS data files associated with individual exposures. The supported file types are: spCFrame, spFrame, spFluxcalib and spFluxcorr. This method is analogous to bossdata.plate.Plan.get_exposure_name(), but operates for a single target and only knows about exposures actually used in the final co-add.

Parameters:	sequence_number (int) – Science exposure sequence number, counting from zero. Must be less than our num_exposures attribute. band (str) – Must be ‘blue’ or ‘red’. ftype (str) – Type of exposure file whose name to return. Must be one of spCFrame, spFrame, spFluxcalib, spFluxcorr. An spCFrame is assumed to be uncompressed, and all other files are assumed to be compressed.
Returns:	Exposure name of the form [ftype]-[cc]-[eeeeeeee].[ext] where [cc] identifies the camera (one of b1,r1,b2,r2) and [eeeeeeee] is the zero-padded exposure number. The extension [ext] is “fits” for spCFrame files and “fits.gz” for all other file types.
Return type:	str
Raises:	`ValueError` – one of the inputs is invalid.

get_pixel_mask(exposure_index=None, camera=None)[source]¶

Get the pixel mask for a specified exposure or the combined coadd.

Returns the and_mask for coadded spectra. The entire mask is returned, including any pixels with zero inverse variance.

Parameters:

exposure_index (int) – Individual exposure to use, specified as a sequence number starting from zero, for the first exposure, and increasing up to self.num_exposures-1. Uses the co-added spectrum when the value is None.
camera (str) – Which camera to use. Must be either ‘b1’, ‘b2’ (blue) or ‘r1’, ‘r2’ (red) unless exposure_index is None, in which case this argument is ignored.

Returns:

Array of integers, one per pixel, encoding the mask bits defined: in bossdata.bits.SPPIXMASK (see also http://www.sdss3.org/dr10/algorithms/bitmask_sppixmask.php).

Return type:

numpy.ndarray

get_raw_image(sequence_number, band, flavor='science', finder=None, mirror=None)[source]¶

Get a raw image file associated with one of this coadd’s exposures.

Parameters:	sequence_number (int) – The sequence number for the requested camera exposure, in the range 0 - (num_exposures[camera]-1). band (str) – Must be ‘blue’ or ‘red’. flavor (str) – One of science, arc, flat. finder (bossdata.path.Finder) – Object used to find the names of BOSS data files. If not specified, the default Finder constructor is used. mirror (bossdata.remote.Manager) – Object used to interact with the local mirror of BOSS data. If not specified, the default Manager constructor is used.
Returns:	requested raw image file.
Return type:	bossdata.raw.RawImageFile
Raises:	`ValueError` – one of the inputs is invalid.

get_valid_data(exposure_index=None, camera=None, pixel_quality_mask=None, include_wdisp=False, include_sky=False, use_ivar=False, use_loglam=False, fiducial_grid=False)[source]¶

Get the valid data for a specified exposure or the combined coadd.

You will probably find yourself using this idiom often:

data = spec.get_valid_data(...)
wlen,flux,dflux = data['wavelength'][:],data['flux'][:],data['dflux'][:]

Parameters:	exposure_index (int) – Individual exposure to use, specified as a sequence number starting from zero, for the first exposure, and increasing up to self.num_exposures-1. Uses the co-added spectrum when the value is None. camera (str) – Which camera to use. Must be either ‘b1’, ‘b2’ (blue) or ‘r1’, ‘r2’ (red) unless exposure_index is None, in which case this argument is ignored. pixel_quality_mask (int) – An integer value interpreted as a bit pattern using the bits defined in `bossdata.bits.SPPIXMASK` (see also http://www.sdss3.org/dr10/algorithms/bitmask_sppixmask.php). Any bits set in this mask are considered harmless and the corresponding spectrum pixels are assumed to contain valid data. When accessing the coadded spectrum, this mask is applied to the AND of the masks for each individual exposure. No mask is applied if this value is None. include_wdisp – Include a wavelength dispersion column in the returned data. include_sky – Include a sky flux column in the returned data. use_ivar – Replace `dflux` with `ivar` (inverse variance) in the returned data. use_loglam – Replace `wavelength` with `loglam` (`log10(wavelength)`) in the returned data. fiducial_grid – Return co-added data using the `fiducial wavelength grid`. If False, the returned array uses the native grid of the SpecFile, which generally trims pixels on both ends that have zero inverse variance. Set this value True to ensure that all co-added spectra use aligned wavelength grids when this matters.
Returns:	Masked array of per-pixel records. Pixels with no valid data are included but masked. The record for each pixel has at least the following named fields: wavelength in Angstroms (or loglam), flux and dflux in 1e-17 ergs/s/cm2/Angstrom (or flux and ivar). Wavelength values are strictly increasing and dflux is calculated as ivar**-0.5 for pixels with valid data. Optional fields are wdisp in constant-log10-lambda pixels and sky in 1e-17 ergs/s/cm2/Angstrom. The wavelength (or loglam) field is never masked and all other fields are masked when ivar is zero or a pipeline flag is set (and not allowed by `pixel_quality_mask`).
Return type:	numpy.ma.MaskedArray
Raises:	`ValueError` – fiducial grid is not supported for individual exposures. `RuntimeError` – co-added wavelength grid is not aligned with the fiducial grid.

bossdata.spec.get_fiducial_pixel_index(wavelength)[source]¶

Convert a wavelength to a fiducial pixel index.

The fiducial wavelength grid used by all SDSS co-added spectra is logarithmically spaced:

wavelength = wavelength0 * 10**(coef * index)

The value coef = 1e-4 is encoded in the FITS HDU headers of SDSS coadded data files with the keyword CD1_1 (and sometimes also COEFF1). The value of wavelength0 defines index = 0 and is similarly encoded as CRVAL1 (and sometimes also COEFF0). However, its value is not constant between different SDSS co-added spectra because varying amounts of invalid data are trimmed. This function adopts the constant value 3500.26 Angstrom corresponding to index = 0:

>>> get_fiducial_pixel_index(3500.26)
0.0

Note that the return value is a float so that wavelengths not on the fiducial grid can be converted and detected:

>>> get_fiducial_pixel_index(3500.5)
0.29776960129179741

The calculation is automatically broadcast over an input wavelength array:

>>> wlen = np.arange(4000,4400,100)
>>> get_fiducial_pixel_index(wlen)
array([ 579.596863  ,  686.83551692,  791.4898537 ,  893.68150552])

Use fiducial_pixel_index_range for an index range that covers all SDSS spectra and fiducial_loglam to covert integer indices to wavelengths.

Parameters:	wavelength (float) – Input wavelength in Angstroms.
Returns:	Array of floating-point indices relative to the fiducial wavelength grid.
Return type:	numpy.ndarray