""" This file uses easydict to set configurables which may be fetched in the module code """ from easydict import EasyDict as edict __C = edict() # Configurables may be fetched using cfg cfg = __C # imports import numpy as np import os #------------------------------------------------------------------------------ # Path variables #------------------------------------------------------------------------------ # Root directory of project __C.ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) # Working directory __C.W_DIR = os.path.join(__C.ROOT_DIR, 'qso_photometry', '') # Data directory __C.D_DIR = os.path.join(__C.ROOT_DIR, 'data', '') # Results directory. __C.RES_DIR = os.path.join(__C.W_DIR, 'res', '') # Path to thesis folder __C.THESIS_DIR = os.path.join(__C.ROOT_DIR, 'thesis_hrb', '') #------------------------------------------------------------------------------ # Configure environment variables #------------------------------------------------------------------------------ os.environ['MPLCONFIGDIR'] = __C.RES_DIR # Note, style sheets: # https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html #------------------------------------------------------------------------------ # User settings #------------------------------------------------------------------------------ __C.USER = edict() # Set below to True to use multiple cores during computationally intensive tasks. # Single core is not currently well supported, may cause errors when setting this to False. __C.USER.USE_MULTIPROCESSING = True # Choose how many cores to use __C.USER.N_CORES = 4 #------------------------------------------------------------------------------ # Data collection configurables #------------------------------------------------------------------------------ __C.COLLECTION = edict() # These dtypes should be checked against the datatypes provided when fetching lightcurve data # float32 - float # float64 - double __C.SURVEY_LABELS = {'sdss':'SDSS', 'ps':'Pan-STARRS', 'ztf':'ZTF', 'ssa':'SuperCOSMOS'} __C.SURVEY_LABELS_SHORT = {'sdss':'SDSS', 'ps':'PS', 'ztf':'ZTF', 'ssa':'SSS'} # sss = 3 # sdss = 5 # ps = 7 # ztf = 11 # __C.SURVEY_LABELS_PAIRS = {9 :'sss-sss', # 15 :'sss-sdss', # 21 :'sss-ps', # 33 :'sss-ztf', # 25 :'sdss-sdss', # 35 :'sdss-ps', # 55 :'sdss-ztf', # 49 :'ps-ps', # 77 :'ps-ztf', # 121:'ztf-ztf'} __C.COLLECTION.SDSS = edict() # Datatypes # mag converted from real[4] with similar precision # magerr converted from real[4] with similar precision __C.COLLECTION.SDSS.dtypes = { **{'uid' : np.uint32, 'uid_s' : np.uint32, 'objID' : np.uint64, 'mjd' : np.float32, 'ra' : np.float64, 'ra_ref' : np.float64, 'dec' : np.float64, 'dec_ref': np.float64, 'get_nearby_distance': np.float32}, **{band + 'psf' : np.float64 for band in 'ugriz'}, **{band + 'psferr': np.float64 for band in 'ugriz'}, } __C.COLLECTION.PS = edict() # Datatypes __C.COLLECTION.PS.dtypes = { 'objID' : np.uint64, 'obsTime' : np.float32, # converted from float[8] with reduced precision 'psfFlux' : np.float64, # converted from float[8] with similar precision. Since this is flux we use double precision. 'psfFluxErr': np.float64, # converted from float[8] with similar precision. 'mjd' : np.float32, 'mag' : np.float32, 'magerr' : np.float32, 'uid' : np.uint32, 'uid_s' : np.uint32 } __C.COLLECTION.ZTF = edict() # Datatypes __C.COLLECTION.ZTF.dtypes = { 'oid' : np.uint64, # note, uint32 is not large enough for ztf oids 'clrcoeff': np.float32, 'limitmag': np.float32, 'mjd' : np.float32, # reduced from float64 'mag' : np.float32, 'magerr' : np.float32, 'uid' : np.uint32, 'uid_s' : np.uint32 } __C.COLLECTION.CALIBSTAR_dtypes = { 'ra' : np.float64, 'dec' : np.float64, 'n_epochs': np.uint32, **{'mag_mean_'+b : np.float32 for b in 'gri'}, **{'mag_mean_err_'+b: np.float32 for b in 'gri'} } # 'uid': np.uint32, # 'uid_s':np.uint32, # 'catalogue': np.uint8, #------------------------------------------------------------------------------ # Preprocessing #------------------------------------------------------------------------------ __C.PREPROC = edict() # Datatypes __C.PREPROC.lc_dtypes = {'mjd' : np.float32, 'mag' : np.float32, 'mag_orig': np.float32, 'magerr' : np.float32, 'uid' : np.uint32, 'uid_s' : np.uint32, 'sid' : np.uint8} __C.PREPROC.stats_dtypes = {'n_tot': np.uint16, # Increase this to uint32 if we think we will have more than 2^16 (65,536) observations for a single object **{x:np.float32 for x in ['mjd_min','mjd_max','mjd_ptp', 'mag_min','mag_max','mag_mean','mag_med', 'mag_std', 'mag_mean_native','mag_med_native', 'mag_opt_mean','mag_opt_mean_flux','magerr_opt_std', 'magerr_max','magerr_mean','magerr_med']}} __C.PREPROC.dtdm_dtypes = {'uid' : np.uint32, 'uid_s' : np.uint32, 'dm' : np.float32, 'dm' : np.float32, 'de' : np.float32, 'dm2_de2': np.float32, 'dsid' : np.uint8} # maybe not needed as the types stay consistent __C.PREPROC.pairwise_dtypes = {'uid': np.uint32, 'dt' :np.float32, 'dm' :np.float32, 'de' :np.float32 } # Bounds to be applied in when running calculate_stats_looped __C.PREPROC.dtdm_bounds = {'qsos': {'dm': (-5, 5), 'de': (1e-10, 2)}, 'calibStars': {'dm': (-5, 5), 'de': (1e-10, 2)}, 'sim': {'dm': (-5, 5), 'de': (1e-10, 2)}} # Limiting magnitudes __C.PREPROC.LIMIT_MAG = edict() # https://www.sdss4.org/dr16/imaging/other_info/ # 5σ limiting magnitudes # I think these are single epoch?? SDSS doesn't really do much multiepoch imaging so it must be. # This is just taken from the website and doesn't seem to appear in a paper. How to reference? __C.PREPROC.LIMIT_MAG.SDSS = { 'u': 22.15, 'g': 23.13, 'r': 22.70, 'i': 22.20, 'z': 20.71 } # https://outerspace.stsci.edu/display/PANSTARRS/PS1+FAQ+-+Frequently+asked+questions # 5σ limiting magnitudes __C.PREPROC.LIMIT_MAG.PS = { # Below are the single epoch 5σ depths. Cite Chambers 2019 'g': 22.0, 'r': 21.8, 'i': 21.5, 'z': 20.9, 'y': 19.7 # Below are the 12 epochs stacked 5σ depths # 'g': 23.3, # 'r': 23.2, # 'i': 23.1, # 'z': 22.3, # 'y': 21.4 } # limitingmag can be fetched on a per-observation basis but below is an average # Note, using limitmag to filter out observations may be biased as we are selectively removing # dimmer observations. # 5σ limiting magnitudes __C.PREPROC.LIMIT_MAG.ZTF = { 'g': 20.8, 'r': 20.6, 'i': 19.9 } # https://arxiv.org/abs/1607.01189, peacock_ssa # 4σ limiting magnitudes, using the smaller of the two between UKST and POSS2. __C.PREPROC.LIMIT_MAG.SSA = { # 4σ 'g': 21.17, 'r': 20.30, 'i': 18.90 } # 5σ # 'g': 20.26, # 'r': 19.78, # 'i': 18.38 # Table from paper above. # Band | 5σ | 4σ | # --------|-------|-------| # UKST B | 20.79 | 21.19 | # UKST R | 19.95 | 20.30 | # UKST I | 18.56 | 19.94 | # POSS2 B | 20.26 | 21.17 | # POSS2 R | 19.78 | 20.35 | # POSS2 I | 18.38 | 18.90 | # Magnitude error threshold __C.PREPROC.MAG_ERR_THRESHOLD = 0.198 # Bounds to use on parse.filter_data in average_nightly_observations.py when removing bad data. __C.PREPROC.FILTER_BOUNDS = {'mag':(15,25),'magerr':(1e-10,2)} __C.PREPROC.SURVEY_IDS = {'ssa':3, 'sdss': 5, 'ps': 7, 'ztf': 11} __C.PREPROC.VAC_BOUNDS = {'z':(0,5), 'redshift':(0,5), 'Lbol':(44,48), 'Lbol_err':(0,1), 'MBH_HB':(6,12), 'MBH_HB_err':(0,1), 'MBH_MgII':(6,12), 'MBH_MgII_err':(0,1), 'MBH_CIV':(6,12), 'MBH_CIV_err':(0,1), 'MBH':(6,12), 'MBH_err':(0,1), 'nEdd':(-3,1), 'nEdd_err':(0,2), 'Mi:':(-30,-20), 'mag_mean':(15,23.5)} __C.PREPROC.MAX_DT = edict() # Max ∆t for quasars and stars in rest frame, rounded up to the nearest integer. # Calculated from mjd_ptp_rf.max() from clean/grouped_{band}.csv __C.PREPROC.MAX_DT['REST'] = {'qsos': {'g': 13794, 'r': 24765, 'i': 13056}, 'calibStars':{'g':np.nan, 'r':np.nan, 'i':np.nan}} # Do the same except with each black hole property # Max ∆t for quasars and stars in observer frame frame, rounded up to the nearest integer # Calculated from mjd_ptp.max() from clean/grouped_{band}.csv in grouped_analysis-NB.py __C.PREPROC.MAX_DT['OBS'] = {'qsos': {'g': 16513, 'r': 26702, 'i': 14698}, 'calibStars':{'g': 15122, 'r': 26062, 'i': 12440}, 'sim': {'g': 25550, 'r': 25550, 'i': 25550}} # Inner: using ∆m, ∆t pairs within surveys only. __C.PREPROC.MAX_DT_INNER = edict() __C.PREPROC.MAX_DT_INNER['REST'] = {'qsos': {'g': 5108, 'r': 16141, 'i': 6320}, 'calibStars':{'g':np.nan, 'r':np.nan, 'i':np.nan}} __C.PREPROC.MAX_DT_INNER['OBS'] = {'qsos': {'g': 6181, 'r': 18010, 'i': 7721}, 'calibStars':{'g': 4377, 'r': 17504, 'i': 6942}} __C.PREPROC.MAX_DT_COMBINED = {'outer':{'g': 15122, 'r': 26062, 'i': 13056}, 'inner':{'g': 5108, 'r': 17504, 'i': 6942}} # Max ∆t for quasars when splitting by black hole property, rounded up to the nearest integer __C.PREPROC.MAX_DT_VAC = {'Lbol': {'g': [12896, 12735, 13077, 12919, 11698, 11964, 11268, 10467], 'r': [23444, 22841, 23992, 22168, 22488, 21900, 19946, 18295], 'i': [10631, 10624, 9652, 9677, 9998, 11184, 10280, 8603 ]}, 'MBH': {'g': [12896, 12735, 13077, 12919, 11698, 11964, 11268, 10467], 'r': [23444, 22841, 23992, 22168, 22488, 21900, 19946, 18295], 'i': [10631, 10624, 9652, 9677, 9998, 11184, 10280, 8603 ]}, 'nEdd': {'g': [12896, 12735, 13077, 12919, 11698, 11964, 11268, 10467], 'r': [23444, 22841, 23992, 22168, 22488, 21900, 19946, 18295], 'i': [10631, 10624, 9652, 9677, 9998, 11184, 10280, 8603]}} # error = p[0] * mag + p[1] __C.PREPROC.SSA_ERROR_LINFIT = [0.0309916, -0.45708423] #------------------------------------------------------------------------------ # Colour transformations #------------------------------------------------------------------------------ __C.TRANSF = edict() __C.TRANSF.SSA = edict() # Form of transformation should be mag_native - mag_ref = c_n * color**n + ... + c_0 # where coefficients c are listed below # https://arxiv.org/abs/1607.01189, peacock_ssa # first coefficient is for highest order term __C.TRANSF.SSA.PEACOCK = {'g_north': ('g-r',[-0.134, +0.078]), 'g_south': ('g-r',[-0.102, +0.058]), 'r2_north':('g-r',[+0.054, -0.012]), 'r2_south':('g-r',[+0.022, +0.002]), 'i_north': ('r-i',[+0.024, -0.008]), 'i_south': ('r-i',[+0.092, -0.022])} # https://arxiv.org/abs/astro-ph/0701508, Ivezic2007_photometric_standardisation # first coefficient is for highest order term __C.TRANSF.SSA.IVEZIC = {'g_north': ('g-r', [+0.2628, -0.7952, +1.0544, +0.0268]), 'g_south': ('g-r', [+0.2628, -0.7952, +1.0544, +0.0268]), 'r1': ('r-i', [-0.0107, +0.0050, -0.2689, -0.1540]), 'r2_north': ('r-i', [-0.0107, +0.0050, -0.2689, -0.1540]), 'r2_south': ('r-i', [-0.0107, +0.0050, -0.2689, -0.1540]), 'i_north': ('r-i', [-0.0307, +0.1163, -0.3341, -0.3584]), 'i_south': ('r-i', [-0.0307, +0.1163, -0.3341, -0.3584])} # __C.TRANSF.SSA.HRB = {'g_south': ('g-r', [+0.23450781, +0.192816 ]), # 'r1': ('g-r', [+0.20054183, -0.33739011]), # 'r2_south': ('g-r', [-0.06210134, -0.17076644]), # 'i_south': ('r-i', [-0.07449726, -0.43398917]), # # ivezic # 'g_north': ('g-r', [+0.2628, -0.7952, +1.0544, +0.0268]), # 'r2_north': ('r-i', [-0.0107, +0.0050, -0.2689, -0.1540]), # 'i_north': ('r-i', [-0.0307, +0.1163, -0.3341, -0.3584])} ## Below is SSS -> SDSS # __C.TRANSF.SSA.OWN = {'g_south': ('g-r', [0.234126365664, 0.192988066258]), # 'r1': ('g-r', [0.223064113088, -0.348041603371]), # 'r2_south': ('g-r', [-0.06663700799, -0.168667410171]), # 'i_south': ('r-i', [-0.094850522924, -0.42802724224]), # # ivezic # 'g_north': ('g-r', [+0.2628, -0.7952, +1.0544, +0.0268]), # 'r2_north': ('r-i', [-0.0107, +0.0050, -0.2689, -0.1540]), # 'i_north': ('r-i', [-0.0307, +0.1163, -0.3341, -0.3584])} __C.TRANSF.SSA.OWN = {'g_south': ('g-r', [0.222067668122, 0.199906281913]), 'r1': ('g-r', [0.209817608231, -0.338134174909]), 'r2_south': ('g-r', [-0.091072654821, -0.150900424498]), 'i_south': ('r-i', [-0.125610015322, -0.435805256644]), 'g_north': ('g-r', [0.222067668122, 0.199906281913]), 'r2_north': ('g-r', [0.209817608231, -0.338134174909]), 'i_north': ('r-i', [-0.125610015322, -0.435805256644])} # ivezic # 'g_north': ('g-r', [+0.2628, -0.7952, +1.0544, +0.0268]), # 'r2_north': ('r-i', [-0.0107, +0.0050, -0.2689, -0.1540]), # 'i_north': ('r-i', [-0.0307, +0.1163, -0.3341, -0.3584])} #------------------------------------------------------------------------------ # Analysis and results #------------------------------------------------------------------------------ __C.RES = edict() #------------------------------------------------------------------------------ # Figures #------------------------------------------------------------------------------ __C.FIG = edict() # Path to style files. Empty string at end ensures trailing slash # This is no longer needed now that we have configured the MPLCONFIGDIR at the top of this file # __C.FIG.STYLE_DIR = os.path.join(__C.RES_DIR, 'styles', '') __C.FIG.COLORS = edict() # Colour palettes for plots __C.FIG.COLORS.PAIRED_BANDS = {'g':('#b2df8a', '#33a02c'), 'r':('#f98583', '#e31a1c'), 'i':('#c299D6', '#6a3d9a')} __C.FIG.COLORS.BANDS = {'g':'#33a02c', 'r':'#e31a1c', 'i':'#6a3d9a'} # __C.FIG.COLORS.SURVEYS = {'sdss':'#FBC599', # 'ps' :'#DB5375', # 'ztf' :'#75B2E3', # 'ssa' :'#9C6AD2'} # inverted __C.FIG.COLORS.SURVEYS = {'ssa' :'#FBC599', 'ztf' :'#DB5375', 'ps' :'#75B2E3', 'sdss':'#9C6AD2'} __C.FIG.LABELS = edict() __C.FIG.LABELS.PROP = {'Lbol':r'$\log_{10}( L_{\mathrm{bol}} \; [\mathrm{erg\;s}^{-1}])$', 'MBH' :r'$\log_{10}( M_{\mathrm{BH}} /M_\odot)$', 'nEdd':r'$\log_{10}( L_{\mathrm{bol}} /L_{\mathrm{Edd}})$', 'z':r'$z$'} __C.FIG.LABELS.PROPv2 = {'Lbol':r'$\log ( L_{\mathrm{bol}})$', 'MBH' :r'$\log ( M_{\mathrm{BH}}/M_\odot)$', 'nEdd':r'$\log ( n_{\mathrm{Edd}})$', 'z':r'$z$', 'wavelength':r'$\lambda$'}