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catquery_functions.py
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import numpy as np
import pandas as pd
from astropy.coordinates import SkyCoord
import astropy.units as u
from astropy.table import Table
#from astroquery.ned import Ned
from astroquery.ipac.ned import Ned
from astroquery.sdss import SDSS
import multiprocessing as mp
import time,logging
import itertools
####################################################
############## Convenience Functions ###############
####################################################
def freq_to_velo(freq):
return 299792.458*((1420405751.768 - freq)/freq)
#'''
###################################################
###################### NED ########################
###################################################
def nedqueryandcheck_df(name, ra,dec,velo):
#initial query
position = SkyCoord(ra = ra, dec = dec, unit=(u.deg, u.deg),frame='icrs')
output_table = Ned.query_region(position, radius=30*u.arcsec, equinox='J2000')
def_columns =['wallaby_id','delta_velocity_flag','delta_velocity','No.', 'Object Name','RA', 'DEC', 'Type', 'Velocity',\
'Redshift', 'Redshift Flag', 'Magnitude and Filter', 'Separation',\
'References', 'Notes', 'Photometry Points', 'Positions',\
'Redshift Points', 'Diameter Points', 'Associations']
#check for empty table and return "null" array with len default array
if len(output_table) < 1:
nomatch = -9999.99*np.ones((1,20))
nomatch = list(nomatch.astype(str))
nomatch[0][0] = name
nomatch[0][1] = 0
out = pd.DataFrame(data=nomatch,columns=def_columns)
out['No.'] = -9999
else:
#perform velocity check
output_df = Table.to_pandas(output_table)
#print(len(velo))
output_match = output_df[np.abs(output_df.Velocity - velo)< 150]
delta_v = output_df.Velocity - velo
#output_match['wallaby_id'] =
if len(output_match) < 1:
nomatch = -9999.99*np.ones((1,20))
nomatch = list(nomatch.astype(str))
nomatch[0][0] = name
nomatch[0][1] = 1
#output_match.insert(0, column='wallaby_id', value=name)
#output_match.insert(1, column='delta_velocity',value=delta_v)
out = pd.DataFrame(data=nomatch,columns=def_columns)
out['No.'] = -9999
else:
output_match.insert(0,column='wallaby_id',value=name)
output_match.insert(1, column='delta_velocity',value=delta_v)
output_match.insert(1, column='delta_velocity_flag', value=2)
out = output_match
return out
###The list method seemed to be counterintuitive given that the query region output was in the form
###an astropy Table. Deconstructing and reconstructing it doesn't necessarily make much sense. It
###is marginally faster though but it won't be use due to the simplicity of going from pandas to SQL
'''
def listappenddefault(lists,value):
for li in lists:
li.append(value)
return lists
def nedqueryandcheck_list(name, ra,dec,velo):
#initialize lists
wallaby_id = []
delta_velocity_flag = []
delta_velocity = []
No = []
ObjectName = []
RA = []
DEC = []
Type = []
Velocity = []
Redshift = []
RedshiftFlag = []
MagnitudeandFilter = []
Separation = []
References = []
Notes = []
PhotometryPoints = []
Positions = []
RedshiftPoints = []
DiameterPoints = []
Associations = []
#initial query
position = SkyCoord(ra = ra, dec = dec, unit=(u.deg, u.deg),frame='icrs')
output_table = Ned.query_region(position, radius=30*u.arcsec, equinox='J2000')
def_columns = [wallaby_id,delta_velocity_flag,delta_velocity,No, ObjectName,RA, DEC,\
Type, Velocity,Redshift, RedshiftFlag, MagnitudeandFilter, Separation,\
References, Notes, PhotometryPoints, Positions,RedshiftPoints,\
DiameterPoints, Associations]
names_columns = ['wallaby_id','delta_velocity_flag','delta_velocity','No.', 'Object Name','RA', 'DEC', 'Type', 'Velocity',\
'Redshift', 'Redshift Flag', 'Magnitude and Filter', 'Separation',\
'References', 'Notes', 'Photometry Points', 'Positions',\
'Redshift Points', 'Diameter Points', 'Associations']
#check for empty table and return "null" array with len default array
if len(output_table) < 1:
for i,col in enumerate(def_columns):
if i == 0:
col.append(name)
else:
col.append(-9999.99)
#print(f'No match for {name}')
else:
#perform velocity check
output_df = Table.to_pandas(output_table)
#print(output_df['Object Name'])
output_match = output_df[np.abs(output_df.Velocity - velo)< 150]
delta_v = output_df.Velocity - velo
#output_match['wallaby_id'] =
if len(output_match) < 1:
#print(f'No match within delV range for {name}')
for i,col in enumerate(def_columns):
if i == 0:
col.append(name)
elif i == 1:
col.append(1)
else:
col.append(-9999.99)
else:
#print(f'Match for {name}')
#print(output_match.loc[output_match.index[0]].to_list())
for i,col in enumerate(def_columns):
if i == 0:
col.append(name)
elif i == 1:
col.append(2)
elif i == 2:
if len(delta_v) > 1:
delta_v = delta_v.dropna().values[0]
col.append(delta_v)
else:
col.append(output_match[names_columns[i]].values[0])
#create output dataframe
#out = pd.DataFrame(data = def_columns, columns = str(def_columns))
return list(itertools.chain(*def_columns))
df_columns = ['wallaby_id','delta_velocity_flag','delta_velocity','No.', 'Object Name','RA', 'DEC', 'Type', 'Velocity',\
'Redshift', 'Redshift Flag', 'Magnitude and Filter', 'Separation',\
'References', 'Notes', 'Photometry Points', 'Positions',\
'Redshift Points', 'Diameter Points', 'Associations']
#'''
###################################################
###################### SDSS #######################
###################################################
def sdssqueryandcheck(name, wall_ra, wall_dec, velo):
wallaby_id = []
delta_velocity_flag = []
delta_velocity = []
objID = []
ra = []
dec = []
objtype = []
modelMag_u = []
modelMag_g = []
modelMag_r = []
modelMag_i = []
modelMag_z = []
modelMagErr_u = []
modelMagErr_g = []
modelMagErr_r = []
modelMagErr_i = []
modelMagErr_z = []
z = []
zErr = []
def_columns = [wallaby_id, delta_velocity_flag, delta_velocity, objID, ra, dec, objtype,\
modelMag_u, modelMag_g, modelMag_r, modelMag_i, modelMag_z, modelMagErr_u,\
modelMagErr_g, modelMagErr_r, modelMagErr_i, modelMagErr_z, z, zErr]
phot_columns = ['objID', 'ra', 'dec', 'objtype', 'modelMag_u', 'modelMag_g', 'modelMag_r',\
'modelMag_i', 'modelMag_z', 'modelMagErr_u', 'modelMagErr_g', 'modelMagErr_r',\
'modelMagErr_i', 'modelMagErr_z']
#phot_columns = ['objid', 'ra', 'dec', 'type', 'u', 'g', 'r',\
# 'i', 'z', 'err_u', 'err_g', 'err_r',\
# 'err_i', 'err_z']
spec_columns = ['z_best', 'zErr']
names_columns = phot_columns + spec_columns
total_columns = ['wallaby_id', 'delta_velocity_flag', 'delta_velocity'] + names_columns
position = SkyCoord(ra = wall_ra, dec = wall_dec, unit=(u.deg, u.deg),frame='icrs')
#try:
# output_table = SDSS.query_region(position,radius=60*u.arcsec,photoobj_fields=phot_columns,spectro=False) #specobj_fields=spec_columns
#except:
# print('No values')
# for i,col in enumerate(def_columns):
# if i == 0:
# col.append(name)
# else:
# col.append(-9999.99)
sql_query_string_1 = "select p.objid, p.ra, p.dec, p.u, p.err_u, p.g, p.err_g, p.r, p.err_r, p.i, "
sql_query_string_2 = "p.err_i, p.z, p.err_z, s.z as z_best, s.zErr from Galaxy p, specobj s,"
sql_query_string_3 = f"dbo.fgetNearByObjEq({wall_ra}, {wall_dec}, 1) n "
sql_query_string_4 = "where p.objid=s.bestobjid and p.objid=n.objid and s.bestobjid=n.objid"
sql_query_string = sql_query_string_1+sql_query_string_2+sql_query_string_3+ sql_query_string_4
#print(sql_query_string_3)
try:
output_table = SDSS.query_sql(sql_query_string)
except:
#if len(output_table) < 1:
#print('No match')
nomatch = -9999.99*np.ones((1,19))
nomatch = list(nomatch.astype(str))
nomatch[0][0] = name
nomatch[0][1] = 1
#output_match.insert(0, column='wallaby_id', value=name)
#output_match.insert(1, column='delta_velocity',value=delta_v)
out = pd.DataFrame(data=nomatch,columns=total_columns)
out['objID'] = -9999
else:
#perform velocity check
if output_table is None:
nomatch = -9999.99*np.ones((1,19))
nomatch = list(nomatch.astype(str))
nomatch[0][0] = name
nomatch[0][1] = 1
#output_match.insert(0, column='wallaby_id', value=name)
#output_match.insert(1, column='delta_velocity',value=delta_v)
out = pd.DataFrame(data=nomatch,columns=total_columns)
out['objID'] = -9999
#print(output_table['objid'])
else:
output_df = Table.to_pandas(output_table)
print(len(output_df['objid']))
output_match = output_df#[np.abs(output_df.z*299792.458 - velo)< 150]
delta_v = output_df.z*299792.458 - velo
#output_match['wallaby_id'] =
'''
if len(output_match) < 1:
#print(f'No match within delV range for {name}')
for i,col in enumerate(def_columns):
if i == 0:
col.append(name)
elif i == 1:
col.append(1)
else:
col.append(-9999.99)
else:
#print(f'Match for {name}')
#print(output_match.loc[output_match.index[0]].to_list())
for i,col in enumerate(def_columns):
if i == 0:
col.append(name)
elif i == 1:
col.append(2)
elif i == 2:
if len(delta_v) > 1:
delta_v = delta_v.dropna().values[0]
col.append(delta_v)
else:
print(output_match[names_columns[i]].values[0])
col.append(output_match[names_columns[i]].values[0])
'''
if len(output_match) < 1:
nomatch = -9999.99*np.ones((1,20))
nomatch = list(nomatch.astype(str))
nomatch[0][0] = name
nomatch[0][1] = 1
#output_match.insert(0, column='wallaby_id', value=name)
#output_match.insert(1, column='delta_velocity',value=delta_v)
out = pd.DataFrame(data=nomatch,columns=total_columns)
out['objID'] = -9999
else:
output_match.insert(0,column='wallaby_id',value=name)
output_match.insert(1, column='delta_velocity',value=delta_v)
output_match.insert(1, column='delta_velocity_flag', value=2)
out = output_match
# '''
return out
###################################################
############# Astropy Cross Matching ##############
###################################################
def cross_match_6df(sixd_df, wallaby_df):
sixd_positions = SkyCoord(ra = sixd_df._RAJ2000.to_numpy(), dec = sixd_df._DEJ2000.to_numpy(), unit='deg')
logging.info('SIXD positions extracted')
wallaby_positions = SkyCoord(ra = wallaby_df.ra.to_numpy(), dec = wallaby_df.dec.to_numpy(), unit='deg')
logging.info('WALLABY positions extracted')
print (sixd_positions)
#print(sixd_positions.shape)
sixd_idx, match_dist_deg, _ = wallaby_positions.match_to_catalog_sky(sixd_positions)
match_dist_arcsec = match_dist_deg.arcsecond
distance_mask = np.where(match_dist_arcsec < 30)
#print(distance_mask)
sixd_idx_masked = sixd_idx[distance_mask]
match_dist_arcsec_masked = match_dist_arcsec[distance_mask]
#print(match_dist_arcsec_masked)
wallaby_velo = freq_to_velo(wallaby_df.freq.to_numpy())[ ]
print(wallaby_velo)
print(sixd_df)
sixd_velo = sixd_df.v_best.to_numpy()[sixd_idx_masked]
delta_velo = np.abs(wallaby_velo - sixd_velo)
#print(delta_velo)
velo_mask = np.where(delta_velo < 150)
#print(velo_mask[0])
distance_velo_mask = distance_mask[0][velo_mask[0]]
#print(distance_velo_mask)
sixd_idx_masked_velo_masked = sixd_idx_masked[velo_mask[0]]
#print(sixd_idx_masked_velo_masked)
#print(len(distance_mask[0]))
#print(len(velo_mask[0]))
#print(len(distance_velo_mask))
#print(len(sixd_idx_masked_velo_masked))
wallaby_name_masked = wallaby_df.name.str.replace(r"\'",'',regex=True).iloc[distance_velo_mask]
#print(wallaby_name_masked)
delta_velo_masked = delta_velo[velo_mask]
sixd_df_matched = sixd_df.iloc[sixd_idx_masked_velo_masked].copy()
sixd_df_matched.insert(0, column='wallaby_id', value = wallaby_name_masked.to_list())
sixd_df_matched.insert(1, column='delta_velocity', value = delta_velo_masked)
sixd_df_matched.insert(2, column='angular_sep', value = match_dist_arcsec_masked[velo_mask])
return sixd_df_matched
def cross_match_gswlc(gswlc_df, wallaby_df):
gswlc_positions = SkyCoord(ra = gswlc_df.ra.to_numpy(), dec = gswlc_df.dec.to_numpy(), unit='deg')
wallaby_positions = SkyCoord(ra = wallaby_df.ra.to_numpy(), dec = wallaby_df.dec.to_numpy(), unit='deg')
gswlc_idx, match_dist_deg, _ = wallaby_positions.match_to_catalog_sky(gswlc_positions)
match_dist_arcsec = match_dist_deg.arcsecond
distance_mask = np.where(match_dist_arcsec < 30)
gswlc_idx_masked = gswlc_idx[distance_mask]
match_dist_arcsec_masked = match_dist_arcsec[distance_mask]
wallaby_velo = freq_to_velo(wallaby_df.freq.to_numpy())[distance_mask]
gswlc_velo = gswlc_df.velo.to_numpy()[gswlc_idx_masked]
delta_velo = np.abs(wallaby_velo - gswlc_velo)
velo_mask = np.where(delta_velo < 150)
distance_velo_mask = distance_mask[0][velo_mask[0]]
gswlc_idx_masked_velo_masked = gswlc_idx_masked[velo_mask[0]]
wallaby_name_masked = wallaby_df.name.str.replace(r"\'",'',regex=True).iloc[distance_velo_mask]
print(wallaby_name_masked)
delta_velo_masked = delta_velo[velo_mask]
gswlc_df_matched = gswlc_df.iloc[gswlc_idx_masked_velo_masked].copy()
gswlc_df_matched.insert(0, column='wallaby_id', value = wallaby_name_masked.to_list())
gswlc_df_matched.insert(1, column='delta_velocity', value = delta_velo_masked)
gswlc_df_matched.insert(2, column='angular_sep', value = match_dist_arcsec_masked)
return gswlc_df_matched