From db1338f7fff3cfc0d33580996e7b749098ef724e Mon Sep 17 00:00:00 2001 From: alexandergagliano Date: Wed, 25 Oct 2023 17:49:22 -0400 Subject: [PATCH] Removing some unnecessary imports --- astro_ghost/DLR.py | 74 +- astro_ghost/ghostHelperFunctions.py | 8 +- astro_ghost/gwgc_good.csv | 47178 ++++++++++++++++++++++++++ setup.py | 2 +- 4 files changed, 47227 insertions(+), 35 deletions(-) create mode 100644 astro_ghost/gwgc_good.csv diff --git a/astro_ghost/DLR.py b/astro_ghost/DLR.py index 3eb99d9..48a4f29 100644 --- a/astro_ghost/DLR.py +++ b/astro_ghost/DLR.py @@ -9,9 +9,9 @@ from astropy.coordinates import SkyCoord, Angle, Distance from astropy.wcs import WCS from astropy.cosmology import FlatLambdaCDM, z_at_value -from astropy.utils.data import get_pkg_data_filename -from astroquery.vizier import Vizier - +#from astropy.utils.data import get_pkg_data_filename +#from astroquery.vizier import Vizier +from astroquery.ipac.ned import Ned def choose_band_SNR(host_df): """Gets the PS1 band (of grizy) with the highest SNR in PSF mag. @@ -376,7 +376,7 @@ def chooseByDLR(path, hosts, transients, fn, orig_dict, todo="s"): elif todo =="r": return hosts, dict_mod, noHosts, GA_SN -def chooseByGladeDLR(path, fn, snDF, verbose=False, todo='r'): +def chooseByGladeDLR(path, fn, snDF, verbose=False, todo='r', GWGC=None): """The wrapper function for selecting hosts by the DLR method (Gupta et al., 2013). Here, candidate hosts are taken from the GLADE (Dalya et al., 2021; arXiv:2110.06184) catalog. @@ -390,6 +390,8 @@ def chooseByGladeDLR(path, fn, snDF, verbose=False, todo='r'): :param todo: If todo == \\'s\\', save the dictionary and the list of remaining sources. If todo == \\'r\\', return them. :type todo: str + :param GWGC: DataFrame of local galaxies with shape information, from GWGC (White et al., 2011). + :type GWGC: Pandas DataFrame :return: The dataframe of properties for GLADE host galaxies found by DLR. :rtype: Pandas DataFrame :return: List of transients for which no reliable GLADE host galaxy was found. @@ -406,12 +408,22 @@ def chooseByGladeDLR(path, fn, snDF, verbose=False, todo='r'): foundHostDF = [] noGladeHosts = [] + if GWGC: + GWGC_coords = SkyCoord(GWGC['RAJ2000'].values, GWGC['DEJ200'].values, unit=(u.deg, u.deg)) + + #assume standard cosmology for distance estimates + cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=2.725) + for idx, row in snDF.iterrows(): name = str(row['Name']) ra_SN = float(row['RA']) dec_SN = float(row['DEC']) class_SN = str(row['Obj. Type']) + #broad cone search of GWGC galaxies: + seps = SkyCoord(ra=ra_SN, dec=dec_SN,unit=(u.deg, u.deg),frame='icrs').separation(GWGC_coords).deg + hosts = GWGC[seps < 0.2] #within 0.2 deg + # TODO fix this #query the glade catalog #Vizier.ROW_LIMIT = -1 @@ -421,40 +433,39 @@ def chooseByGladeDLR(path, fn, snDF, verbose=False, todo='r'): # hosts = result[0].to_pandas() #else: # hosts = pd.DataFrame({'a_b':[np.nan], 'maj':[np.nan], 'min':[np.nan]}) - + # query NED for GLADE sources and get their radius - GLADE_rad = hosts.dropna(subset=['a_b', 'maj', 'min']) - GLADE_norad = hosts[~hosts.index.isin(GLADE_rad.index)] - - from astroquery.ipac.ned import Ned - badRadCount = 0 - for idx, row in GLADE_norad.iterrows(): - try: - result_table = Ned.query_region(SkyCoord(ra=row.RAJ2000*u.degree, dec=row.DEJ2000*u.degree, frame='icrs'), radius=(2/3600)*u.deg, equinox='J2000.0') - diameters = Ned.get_table(result_table.to_pandas()['Object Name'].values[0], table='diameters') - tempDF = diameters.to_pandas() - tempMaj_arcsec = np.nanmedian(tempDF.loc[(tempDF['Major Axis Unit'] == 'arcsec') & (tempDF['Major Axis Flag'] == '(a)'), 'Major Axis']) - AxisRatio = np.nanmedian(tempDF.loc[(tempDF['Axis Ratio Flag'] == '(b/a)'), 'Axis Ratio']) - tempMin_arcsec = tempMaj_arcsec*AxisRatio - - GLADE_norad.loc[GLADE_norad.index == idx, 'maj'] = tempMaj_arcsec*2./60 - GLADE_norad.loc[GLADE_norad.index == idx, 'min'] = tempMin_arcsec*2./60 - GLADE_norad.loc[GLADE_norad.index == idx, 'a_b'] = 1/AxisRatio - except: - badRadCount +=1 + #GLADE_rad = hosts.dropna(subset=['a_b', 'maj', 'min']) + #GLADE_norad = hosts[~hosts.index.isin(GLADE_rad.index)] + + #badRadCount = 0 + #for idx, row in GLADE_norad.iterrows(): + # try: + # result_table = Ned.query_region(SkyCoord(ra=row.RAJ2000*u.degree, dec=row.DEJ2000*u.degree, frame='icrs'), radius=(2/3600)*u.deg, equinox='J2000.0') + # diameters = Ned.get_table(result_table.to_pandas()['Object Name'].values[0], table='diameters') + # tempDF = diameters.to_pandas() + # tempMaj_arcsec = np.nanmedian(tempDF.loc[(tempDF['Major Axis Unit'] == 'arcsec') & (tempDF['Major Axis Flag'] == '(a)'), 'Major Axis']) + # AxisRatio = np.nanmedian(tempDF.loc[(tempDF['Axis Ratio Flag'] == '(b/a)'), 'Axis Ratio']) + # tempMin_arcsec = tempMaj_arcsec*AxisRatio + + # GLADE_norad.loc[GLADE_norad.index == idx, 'maj'] = tempMaj_arcsec*2./60 + # GLADE_norad.loc[GLADE_norad.index == idx, 'min'] = tempMin_arcsec*2./60 + # GLADE_norad.loc[GLADE_norad.index == idx, 'a_b'] = 1/AxisRatio + # except: + # badRadCount +=1 #recombine - if verbose: - print("No NED radius found for %i GLADE galaxies."%badRadCount, file=f) + #if verbose: + # print("No NED radius found for %i GLADE galaxies."%badRadCount, file=f) - hosts = pd.concat([GLADE_rad, GLADE_norad], ignore_index=True) + #hosts = pd.concat([GLADE_rad, GLADE_norad], ignore_index=True) + #hosts.dropna(subset=['a_b', 'maj', 'min'], inplace=True) - hosts.dropna(subset=['a_b', 'maj', 'min'], inplace=True) if len(hosts)<1: noGladeHosts.append(name) continue - hosts['MajorRad'] = hosts['maj']*60./2 #in arcsec, to radius - hosts['MinorRad'] = hosts['min']*60./2 #in arcsec, to radius + hosts['MajorRad'] = hosts['maj']*60./2 # diameter to radius in arcsec + hosts['MinorRad'] = hosts['min']*60./2 # diameter to radius in arcsec #get names for the galaxies that match hosts.rename(columns={'RAJ2000':'raMean','DEJ2000':'decMean'}, inplace=True) @@ -516,9 +527,6 @@ def chooseByGladeDLR(path, fn, snDF, verbose=False, todo='r'): foundHostDF['GLADE_redshift'] = np.nan foundHostDF['GLADE_redshift_flag'] = '' - #assume standard cosmology - cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=2.725) - for idx, row in foundHostDF.iterrows(): if row['Dist'] == row['Dist']: dist = Distance(value=row['Dist'], unit=u.Mpc) diff --git a/astro_ghost/ghostHelperFunctions.py b/astro_ghost/ghostHelperFunctions.py index 6282505..7576290 100644 --- a/astro_ghost/ghostHelperFunctions.py +++ b/astro_ghost/ghostHelperFunctions.py @@ -693,8 +693,14 @@ def findNewHosts(transientName, snCoord, snClass, verbose=False, starcut='gentle #new low-z method (beta) - before we do anything else, find and associate with GLADE if GLADE: + # snag the GWGC, which we'll use to get radii + stream = importlib_resources.files(__name__).joinpath('gwgc_good.csv') + GWGC = pd.read_csv(stream) + if verbose: + print("Successfully read in GWGC catalog.\n") + fn_glade = "gladeDLR.txt" - foundGladeHosts, noGladeHosts = chooseByGladeDLR(path, fn_glade, snDF, verbose=verbose, todo="r") + foundGladeHosts, noGladeHosts = chooseByGladeDLR(path, fn_glade, snDF, verbose=verbose, todo="r", GWGC=GWGC) #open transients df and drop the transients already found in GLADE. 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classifiers=[