matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
# import seaborn as sns
import aplpy
# sns.set_style('whitegrid')
# plt.close('all')
# sns.set_context('talk')
# v = Vizier(columns=['_RAJ2000', '_DEJ2000',
# 'RA_ICRS', 'e_RA_ICRS', 'DE_ICRS', 'e_DE_ICRS', 'Source', '<Gmag>'],
# column_filters={'<Gmag>': '<22'})
v = Vizier()
v.ROW_LIMIT = -1
v.TIMEOUT = 3600
def load_sta_eop(_inp, _date, station_name='KP-VLBA'):
const = constants()
''' load cats '''
_, sta, eops = load_cats(_inp, 'DUMMY', 'S', [station_name], _date)
''' calculate site positions in geodetic coordinate frame
+ transformation matrix VW from VEN to the Earth-fixed coordinate frame '''
for ii, st in enumerate(sta):
sta[ii].geodetic(const)
return sta, eops
def sta_compute_position(sta, eops, _date):
result_table['Type'] = ['Gal' if x=='Gx' else 'OpC' if x=='OC' else 'GlC' if x=='Gb' else 'PN' if x=='Pl' else 'Str' if x=='*' or x=='D*' or x=='***' else '-' if x == '' or x=='-' or x=='?' else x for x in result_table['Type']]
coords = SkyCoord(result_table['_RAJ2000'],result_table['_DEJ2000'],unit="deg")
const = coords.get_constellation()
const_abr = coords.get_constellation(short_name = "True")
result_table.add_column(const,name="Constellation", index = 2)
otype = result_table['Type']
internal_id = [otype[i]+'_'+const_abr[i]+'_'+str(i+1) if otype[i]!='-' else 'notype'+'_'+const_abr[i]+'_'+str(i+1) for i in range(len(const))]
result_table.add_column(internal_id, name = "Internal ID Number", index = 0)
result_table.write("NGC.csv",format="csv",overwrite="True")
#cross catalogue for NGC
v = Vizier(columns = ['Object','Name'])
v.ROW_LIMIT = -1
v.TIMEOUT = 1000
cross_catalog = v.get_catalogs('VII/118/names')[0]
cross_catalog['Name'] = ['IC '+x.split()[1] if len(x.split())==2 and x.split()[0]=="I" else 'IC'+x.split()[0][1:] if len(x.split())==1 and x.split()[0]=="I" else 'NGC '+x for x in cross_catalog['Name']]
cross_catalog.write("DsCrossCatalog.csv",format='csv',overwrite="True")
ccat = pd.read_csv('DsCrossCatalog.csv')
ccat = pd.DataFrame(ccat)
ccat = ccat.loc[~ccat.Name.duplicated(keep='first')]
ntable= pd.read_csv('NGC.csv')
ntable = pd.DataFrame(ntable)
ntable.insert(2,"Common Name",np.zeros(len(ntable['_RAJ2000'])))
ntable['Common Name'] = ntable.Name.map(ccat.set_index('Name').Object,na_action="ignore")
ntable.to_csv('NGC.csv')
ntable = Table.read('NGC.csv')
ntable.remove_columns(['col0'])
ntable.write('NGC.csv',format='csv',overwrite="True")
def download_vizier(inputfile,outputfile):
import splat
import time
import numpy
import numpy as np
import pandas as pd
from astroquery.vizier import Vizier
from astropy import coordinates
from astropy.coordinates import SkyCoord
from astropy.coordinates import Angle
from astropy import units as u
from astropy.table import Column, Table, join
#Your input file needs these columns: INDEX, RA (deg), Dec (deg)
#full = pd.read_csv('/Users/daniella/Python/Thesis/targetlist/Input_Lists/all_lists_combined.csv')
#folder = '/Users/daniella/Python/Thesis/Targetlist/Input_Lists/'
folder = '/Users/daniella/Research/M7L5Sample/'
# full = pd.read_csv(folder+inputfile)
# print(full)
# full['INDEX'] = np.arange(len(full))
# full.columns
# print(full['ra_deg'])
fullfull = pd.read_csv(folder+inputfile)
full = fullfull.ix[1000:]
#try these catalogs:
#2MASS: JHK
#SDSS: riz
#SSSPM: RI
#WISE: W1W2W3
#ULAS: YJHK
#PPMXL: PMRA, PMDEC
#GAIA: G, parallax
# VIZIERdf = pd.DataFrame(index=np.arange(len(full)),columns=['INDEX','2MASS_RA','2MASS_DEC','2MASS_NAME','2MASS_J',\
VIZIERdf = pd.DataFrame(index=np.arange(len(full))+1000,columns=['INDEX','2MASS_RA','2MASS_DEC','2MASS_NAME','2MASS_J',\
'2MASS_J_E','2MASS_H','2MASS_H_E','2MASS_KS','2MASS_KS_E','2MASS_QFLG','2MASS_CNTR','2MASS_OBSDATE',\
'2MASS_OBSJD','2MASS_Rmag','2MASS_MATCHES','SDSS_RA','SDSS_DEC','SDSS_NAME','SDSS_cl','SDSS_OBSDATE',\
'SDSS_QFLG','SDSS_r','SDSS_r_E','SDSS_i','SDSS_i_E','SDSS_z','SDSS_z_E','SDSS_ObjID','SDSS_SPSHAPE','SDSS_SPCLASS',\
'SDSS_SPT','SDSS_pmRA','SDSS_pmRA_E','SDSS_pmDE','SDSS_pmDE_E','SDSS_MATCHES','WISE_RA','WISE_DEC',\
'WISE_NAME','WISE_W1','WISE_W1_E','WISE_W2','WISE_W2_E','WISE_W3','WISE_W3_E','WISE_2M_JMAG',\
'WISE_2M_JERR','WISE_2M_HMAG','WISE_2M_HERR','WISE_2M_KMAG','WISE_2M_KERR','WISE_pmRA','WISE_pmRA_E',
'WISE_pmDE','WISE_pmDE_E','WISE_2M','WISE_2M_sep','WISE_MATCHES','ULAS_RA','ULAS_DEC','ULAS_NAME',\
'ULAS_Y','ULAS_Y_E','ULAS_J','ULAS_J_E','ULAS_H','ULAS_H_E','ULAS_K','ULAS_K_E','ULAS_EPOCH',\
'ULAS_cl','ULAS_pmRA','ULAS_pmRA_E','ULAS_pmDE','ULAS_pmDE_E','ULAS_MATCHES','PPMXL_RA','PPMXL_DEC',
'PPMXL_pmRA','PPMXL_pmRA_E','PPMXL_pmDE','PPMXL_pmDE_E','PPMXL_epochRA','PPMXL_epochDE','PPMXL_J',
'PPMXL_J_E','PPMXL_H','PPMXL_H_E','PPMXL_K','PPMXL_K_E','PPMXL_USNO_R2','PPMXL_USNO_I','PPMXL_MATCHES',
'GAIA_G','GAIA_plx','GAIA_plx_e','GAIA_pmRA','GAIA_pmDE','GAIA_pmRA_e','GAIA_pmDE_e'])
VIZIERdf['INDEX'] = full['INDEX'].values
# now do a VIZIER search for sources based on coordinates
print('\nVIZIER search')
v = Vizier()
v.TIMEOUT = 5000
vizier_radius = 15*u.arcsec
start = time.time()
# for i,des in enumerate(range(len(full))):
for i in np.arange(len(full))+1000:
des = i
c = SkyCoord(full['RA (deg)'][i],full['Dec (deg)'][i],unit=(u.deg,u.deg),frame='icrs')
# c = SkyCoord(full['ra_head'][i],full['dec_head'][i],unit=(u.hour,u.deg),frame='icrs')
result = Vizier.query_region(c,radius=vizier_radius, catalog=['SDSS9','2MASS','AllWISE','ULAS9','PPMXL','GAIA'])
if 'II/246/out' in result.keys():
tmass = result['II/246/out']
else:
tmass = np.nan
if 'V/139/sdss9' in result.keys():
sdss = result['V/139/sdss9']
else:
sdss = np.nan
if 'II/328/allwise' in result.keys():
wise = result['II/328/allwise']
else:
wise = np.nan
if 'II/319/las9' in result.keys():
ulas = result['II/319/las9']
else:
ulas = np.nan
if 'I/317/sample' in result.keys():
ppmxl = result['I/317/sample']
else:
ppmxl = np.nan
if 'I/337/gaia' in result.keys():
gaia = result['I/337/gaia']
else:
gaia = np.nan
# VIZIERdf['INDEX'][i] = full['INDEX'][i]
if isinstance(tmass,Table):
print('\nSource {} Designation = {} {} match(es) in 2MASS'.format(i+1,des,len(tmass)))
#many sources found
n_tmass = len(tmass)
if len(tmass) > 1: # take the closest position
sep = [c.separation(SkyCoord(tmass['RAJ2000'][lp],tmass['DEJ2000'][lp],unit=(u.deg,u.deg))).arcsecond for lp in numpy.arange(len(tmass))]
tmass['sep'] = sep
tmass.sort('sep')
while len(tmass)>1:
tmass.remove_row(1)
#one source found
else:
tmass['sep'] = [c.separation(SkyCoord(tmass['RAJ2000'][0],tmass['DEJ2000'][0],unit=(u.deg,u.deg))).arcsecond]
print(tmass)
VIZIERdf['2MASS_RA'][i] = tmass['RAJ2000'][0]
VIZIERdf['2MASS_DEC'][i] = tmass['DEJ2000'][0]
VIZIERdf['2MASS_NAME'][i] = 'J{}'.format(tmass['_2MASS'][0].decode())
VIZIERdf['2MASS_J'][i] = tmass['Jmag'][0]
VIZIERdf['2MASS_J_E'][i] = tmass['e_Jmag'][0]
VIZIERdf['2MASS_H'][i] = tmass['Hmag'][0]
VIZIERdf['2MASS_H_E'][i] = tmass['e_Hmag'][0]
VIZIERdf['2MASS_KS'][i] = tmass['Kmag'][0]
VIZIERdf['2MASS_KS_E'][i] = tmass['e_Kmag'][0]
VIZIERdf['2MASS_QFLG'][i] = tmass['Qflg'][0].decode()
VIZIERdf['2MASS_CNTR'][i] = tmass['Cntr'][0]
VIZIERdf['2MASS_OBSDATE'][i] = tmass['Date'][0].decode()
VIZIERdf['2MASS_OBSJD'][i] = tmass['JD'][0]
VIZIERdf['2MASS_Rmag'][i] = tmass['Rmag'][0]
VIZIERdf['2MASS_MATCHES'][i] = n_tmass
if isinstance(sdss,Table):
print('\nSource {} Designation = {} {} match(es) in SDSS9'.format(i+1,des,len(sdss)))
#many sources found
n_sdss = len(sdss)
if len(sdss) > 1: # take the closest position
sep = [c.separation(SkyCoord(sdss['RAJ2000'][lp],sdss['DEJ2000'][lp],unit=(u.deg,u.deg))).arcsecond for lp in numpy.arange(len(sdss))]
sdss['sep'] = sep
sdss.sort('sep')
while len(sdss)>1:
sdss.remove_row(1)
#one source found
else:
sdss['sep'] = [c.separation(SkyCoord(sdss['RAJ2000'][0],sdss['DEJ2000'][0],unit=(u.deg,u.deg))).arcsecond]
print(sdss)
VIZIERdf['SDSS_RA'][i] = sdss['RAJ2000'][0]
VIZIERdf['SDSS_DEC'][i] = sdss['DEJ2000'][0]
VIZIERdf['SDSS_NAME'][i] = sdss['SDSS9'][0].decode()
VIZIERdf['SDSS_cl'][i] = sdss['cl'][0] #classification by shape: 3=galaxy, 6=star
VIZIERdf['SDSS_OBSDATE'][i] = sdss['ObsDate'][0]
VIZIERdf['SDSS_QFLG'][i] = sdss['Q'][0] #(0=unknown): 1=bad 2=acceptable 3=good 4=missing 5=hole
VIZIERdf['SDSS_r'][i] = sdss['rpmag'][0] #PSF magnitude
VIZIERdf['SDSS_r_E'][i] = sdss['e_rpmag'][0]
VIZIERdf['SDSS_i'][i] = sdss['ipmag'][0]
VIZIERdf['SDSS_i_E'][i] = sdss['e_ipmag'][0]
VIZIERdf['SDSS_z'][i] = sdss['zpmag'][0]
VIZIERdf['SDSS_z_E'][i] = sdss['e_zpmag'][0]
VIZIERdf['SDSS_ObjID'][i] = sdss['objID'][0]
VIZIERdf['SDSS_SPSHAPE'][i] = sdss['spType'][0].decode()
VIZIERdf['SDSS_SPCLASS'][i] = sdss['spCl'][0].decode()
VIZIERdf['SDSS_SPT'][i] = sdss['subClass'][0].decode()
VIZIERdf['SDSS_pmRA'][i] = sdss['pmRA'][0]
VIZIERdf['SDSS_pmRA_E'][i] = sdss['e_pmRA'][0]
VIZIERdf['SDSS_pmDE'][i] = sdss['pmDE'][0]
VIZIERdf['SDSS_pmDE_E'][i] = sdss['e_pmDE'][0]
VIZIERdf['SDSS_MATCHES'][i] = n_sdss
if isinstance(wise,Table):
print('\nSource {} Designation = {} {} match(es) in AllWISE'.format(i+1,des,len(wise)))
#many sources found
n_wise = len(wise)
if len(wise) > 1: # take the closest position
sep = [c.separation(SkyCoord(wise['RAJ2000'][lp],wise['DEJ2000'][lp],unit=(u.deg,u.deg))).arcsecond for lp in numpy.arange(len(wise))]
wise['sep'] = sep
wise.sort('sep')
while len(wise)>1:
wise.remove_row(1)
#one source found
else:
wise['sep'] = [c.separation(SkyCoord(wise['RAJ2000'][0],wise['DEJ2000'][0],unit=(u.deg,u.deg))).arcsecond]
print(wise)
VIZIERdf['WISE_RA'][i] = wise['RAJ2000'][0]
VIZIERdf['WISE_DEC'][i] = wise['DEJ2000'][0]
VIZIERdf['WISE_NAME'][i] = wise['AllWISE'][0].decode()
VIZIERdf['WISE_W1'][i] = wise['W1mag'][0]
VIZIERdf['WISE_W1_E'][i] = wise['e_W1mag'][0]
VIZIERdf['WISE_W2'][i] = wise['W2mag'][0]
VIZIERdf['WISE_W2_E'][i] = wise['e_W2mag'][0]
VIZIERdf['WISE_W3'][i] = wise['W3mag'][0]
VIZIERdf['WISE_W3_E'][i] = wise['e_W3mag'][0]
VIZIERdf['WISE_2M_JMAG'][i] = wise['Jmag'][0]
VIZIERdf['WISE_2M_JERR'][i] = wise['e_Jmag'][0]
VIZIERdf['WISE_2M_HMAG'][i] = wise['Hmag'][0]
VIZIERdf['WISE_2M_HERR'][i] = wise['e_Hmag'][0]
VIZIERdf['WISE_2M_KMAG'][i] = wise['Kmag'][0]
VIZIERdf['WISE_2M_KERR'][i] = wise['e_Kmag'][0]
VIZIERdf['WISE_pmRA'][i] = wise['pmRA'][0]
VIZIERdf['WISE_pmRA_E'][i] = wise['e_pmRA'][0]
VIZIERdf['WISE_pmDE'][i] = wise['pmDE'][0]
VIZIERdf['WISE_pmDE_E'][i] = wise['e_pmDE'][0]
VIZIERdf['WISE_2M'][i] = wise['_2Mkey'][0]
VIZIERdf['WISE_2M_sep'][i] = wise['d2M'][0] #arcsec
VIZIERdf['WISE_MATCHES'][i] = n_wise
if isinstance(ulas,Table):
print('\nSource {} Designation = {} {} match(es) in ULAS9'.format(i+1,des,len(ulas)))
#many sources found
n_ulas = len(ulas)
if len(ulas) > 1: # take the closest position
sep = [c.separation(SkyCoord(ulas['RAJ2000'][lp],ulas['DEJ2000'][lp],unit=(u.deg,u.deg))).arcsecond for lp in numpy.arange(len(ulas))]
ulas['sep'] = sep
ulas.sort('sep')
while len(ulas)>1:
ulas.remove_row(1)
#one source found
else:
ulas['sep'] = [c.separation(SkyCoord(ulas['RAJ2000'][0],ulas['DEJ2000'][0],unit=(u.deg,u.deg))).arcsecond]
print(ulas)
VIZIERdf['ULAS_RA'][i] = ulas['RAJ2000'][0]
VIZIERdf['ULAS_DEC'][i] = ulas['DEJ2000'][0]
VIZIERdf['ULAS_NAME'][i] = ulas['ULAS'][0].decode()
VIZIERdf['ULAS_Y'][i] = ulas['Ymag'][0]
VIZIERdf['ULAS_Y_E'][i] = ulas['e_Ymag'][0]
VIZIERdf['ULAS_J'][i] = ulas['Jmag2'][0]
VIZIERdf['ULAS_J_E'][i] = ulas['e_Jmag2'][0]
VIZIERdf['ULAS_H'][i] = ulas['Hmag'][0]
VIZIERdf['ULAS_H_E'][i] = ulas['e_Hmag'][0]
VIZIERdf['ULAS_K'][i] = ulas['Kmag'][0]
VIZIERdf['ULAS_K_E'][i] = ulas['e_Kmag'][0]
VIZIERdf['ULAS_EPOCH'][i] = ulas['Epoch'][0]
VIZIERdf['ULAS_cl'][i] = ulas['cl'][0] #-2=galaxy,-1=star
VIZIERdf['ULAS_pmRA'][i] = ulas['pmRA'][0]
VIZIERdf['ULAS_pmRA_E'][i] = ulas['e_pmRA'][0]
VIZIERdf['ULAS_pmDE'][i] = ulas['pmDE'][0]
VIZIERdf['ULAS_pmDE_E'][i] = ulas['e_pmDE'][0]
VIZIERdf['ULAS_MATCHES'][i] = n_ulas
if isinstance(ppmxl,Table):
print('\nSource {} Designation = {} {} match(es) in PPMXL'.format(i+1,des,len(ppmxl)))
#many sources found
n_ppmxl = len(ppmxl)
if len(ppmxl) > 1: # take the closest position
sep = [c.separation(SkyCoord(ppmxl['RAJ2000'][lp],ppmxl['DEJ2000'][lp],unit=(u.deg,u.deg))).arcsecond for lp in numpy.arange(len(ppmxl))]
ppmxl['sep'] = sep
ppmxl.sort('sep')
while len(ppmxl)>1:
ppmxl.remove_row(1)
#one source found
else:
ppmxl['sep'] = [c.separation(SkyCoord(ppmxl['RAJ2000'][0],ppmxl['DEJ2000'][0],unit=(u.deg,u.deg))).arcsecond]
print(ppmxl)
VIZIERdf['PPMXL_RA'][i] = ppmxl['RAJ2000'][0]
VIZIERdf['PPMXL_DEC'][i] = ppmxl['DEJ2000'][0]
VIZIERdf['PPMXL_pmRA'][i] = ppmxl['pmRA'][0]
VIZIERdf['PPMXL_pmRA_E'][i] = ppmxl['e_pmRA'][0]
VIZIERdf['PPMXL_pmDE'][i] = ppmxl['pmDE'][0]
VIZIERdf['PPMXL_pmDE_E'][i] = ppmxl['e_pmDE'][0]
VIZIERdf['PPMXL_epochRA'][i] = ppmxl['epRA'][0]
VIZIERdf['PPMXL_epochDE'][i] = ppmxl['epDE'][0]
VIZIERdf['PPMXL_J'][i] = ppmxl['Jmag'][0]
VIZIERdf['PPMXL_J_E'][i] = ppmxl['e_Jmag'][0]
VIZIERdf['PPMXL_H'][i] = ppmxl['Hmag'][0]
VIZIERdf['PPMXL_H_E'][i] = ppmxl['e_Hmag'][0]
VIZIERdf['PPMXL_K'][i] = ppmxl['Kmag'][0]
VIZIERdf['PPMXL_K_E'][i] = ppmxl['e_Kmag'][0]
VIZIERdf['PPMXL_USNO_R2'][i] = ppmxl['r2mag'][0]
VIZIERdf['PPMXL_USNO_I'][i] = ppmxl['imag'][0]
VIZIERdf['PPMXL_MATCHES'][i] = n_ppmxl
if isinstance(gaia,Table):
print('\nSource {} Designation = {} {} match(es) in GAIA'.format(i+1,des,len(gaia)))
#many sources found
n_gaia = len(gaia)
if len(gaia) > 1: # take the closest position
sep = [c.separation(SkyCoord(gaia['_RAJ2000'][lp],gaia['_DEJ2000'][lp],unit=(u.deg,u.deg))).arcsecond for lp in np.arange(len(gaia))]
gaia['sep'] = sep
gaia.sort('sep')
while len(gaia)>1:
gaia.remove_row(1)
#one source found
else:
gaia['sep'] = [c.separation(SkyCoord(gaia['_RAJ2000'][0],gaia['_DEJ2000'][0],unit=(u.deg,u.deg))).arcsecond]
print(gaia)
VIZIERdf['GAIA_G'][i] = gaia['__Gmag_'][0]
VIZIERdf['GAIA_plx'][i] = gaia['Plx'][0]
VIZIERdf['GAIA_plx_e'][i] = gaia['e_Plx'][0]
VIZIERdf['GAIA_pmRA'][i] = gaia['pmRA'][0]
VIZIERdf['GAIA_pmDE'][i] = gaia['pmDE'][0]
VIZIERdf['GAIA_pmRA_e'][i] = gaia['e_pmRA'][0]
VIZIERdf['GAIA_pmDE_e'][i] = gaia['e_pmDE'][0]
end = time.time()
print(end-start)
VIZIERdf.to_csv('VIZIERinfo_'+outputfile)
print('Open file at '+folder+'VIZIERinfo_'+outputfile)
