In [2]:

    

from __future__ import print_function, division


    

In [3]:

    

# This changes the current directory to the base saga directory - make sure to run this first!
# This is necessary to be able to import the py files and use the right directories,
# while keeping all the notebooks in their own directory.
import os
import sys

if 'saga_base_dir' not in locals():
    saga_base_dir = os.path.abspath('..')
if saga_base_dir not in sys.path:
    os.chdir(saga_base_dir)


    

In [4]:

    

%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib import rcParams

rcParams['figure.figsize'] = (16, 10)
rcParams['image.interpolation'] = 'none'
rcParams['image.origin'] = 'lower'


    

In [5]:

    

import urllib2
import datetime

import numpy  as np

from astropy import units as u
from astropy.coordinates import SkyCoord, AltAz, EarthLocation
from astropy.time import Time
from astropy.utils.data import get_readable_fileobj
from astropy.io import fits
from astropy.table import Table, Column, MaskedColumn
from astropy import table


    

In [6]:

    

for module in ['hosts', 'targeting', 'aat']:
    if module in globals():
        reload(globals()[module])
    else:
        globals()[module] = __import__(module)
#g = targeting.get_gama() #re-caches the gama catalog


    

In [7]:

    

hsd = hosts.get_saga_hosts_from_google(clientsecretjsonorfn='client_secrets.json', useobservingsummary=False)
hsd = dict([(h.name, h) for h in hsd])


    

    




Using cached version of google hosts list from file "hosts_dl.pkl"

In [8]:

    

def catalog_to_sc(cat, hostordist = None):
    if hasattr(hostordist, 'dist'):
        distance = hostordist.dist
    else:
        distance = hostordist
        
    return SkyCoord(cat['ra']*u.deg, cat['dec']*u.deg, distance=distance)


    

In [ ]:

    

#if online
ufo = urllib2.urlopen('https://docs.google.com/spreadsheet/ccc?key=1b3k2eyFjHFDtmHce1xi6JKuj3ATOWYduTBFftx5oPp8&output=csv')
hosttab = QTable.read(ufo.read(), format='csv')
ufo.close()


    

In [207]:

    

#if offline
hosttab = Table.read('SAGADropbox/hosts/host_catalog_flag0.csv')


    

In [62]:

    

hostscs = SkyCoord(u.Quantity(hosttab['RA'], u.deg), 
                   u.Quantity(hosttab['Dec'], u.deg), 
                   distance=u.Quantity(hosttab['distance'], u.Mpc))


    

In [57]:

    

#UTC time from 8:35-19:35 is AAT 18 deg window
nighttimes = Time('2015-6-20 8:35:00') + np.arange(12)*u.hour
aao = EarthLocation(lon='149:3:57.9', lat='-31:16:37.3')
aao_frame = AltAz(obstime=nighttimes, location=aao)


    

In [85]:

    

seczs = []
for sc in hostscs:
    az = sc.transform_to(aao_frame)
    seczs.append(az.secz)
seczs = np.array(seczs)


    

In [128]:

    

hrsvis = np.sum((1<=seczs)&(seczs<1.75),axis=1)

visenough = hrsvis>2
for secz, nsaid in zip(seczs[visenough], hosttab['NSAID'][visenough]):
    msk = secz>0
    plt.plot(np.arange(len(secz))[msk], secz[msk], label=nsaid)
plt.legend(loc=0)
plt.ylim(0,5)


    

In [137]:

    

names = []
ras = []

with open('aat_targets/aattargs_iobserve.dat', 'w') as f:
    for host in hosttab[visenough]:
        name = 'NSA'+str(host['NSAID'])
        for nm, val in hsd.items():
            if val.nsaid == host['NSAID']:
                name = nm
                if nm.startswith('NSA'):
                    name = name+'_obsed'
                break
        f.write(name.replace(' ','_') + ' ')
        f.write(str(host['RA']) + ' ')
        f.write(str(host['Dec']) + '\n')
        
        names.append(name)
        ras.append(host['RA'])
    
names = np.array(names)
ras = np.array(ras)
earlymsk = (10<ras/15)&(ras/15<18)
print('Early targets')
for nm in names[earlymsk]:
    print(nm)
    
print('\nLater targets')
for nm in names[~earlymsk]:
    print(nm)


    

    




Early targets
NSA145729
Alice in Wonderland
NSA142722
NSA162580
Odyssey
NSA140458
NSA165082
NSA145398
Gilgamesh
NSA166141
NSA141465
NSA145879
Sopronos
Dune
NSA162547

Later targets
NSA150887
NSA150578
NSA127226
NSA149781
NSA150307
Ana Karenina
NSA149977

In [9]:

    

dune = hsd['Dune']
ody = hsd['Odyssey']
gilg = hsd['Gilgamesh']
aen = hsd['Aeneid']
aen.environsarcmin = 30.
ob = hsd['OBrother']
anak = hsd['AnaK']
hostsforrun = [dune, ody, gilg, aen, ob, anak]

#*possible* target
n150307 = hosts.NSAHost(150307)


    

In [10]:

    

[(h.name, h.environskpc, h.environsarcmin) for h in hostsforrun]


    

    
Out[10]:





[('Dune', 300.0, 41.056090595371984),
 ('Odyssey', 300.0, 30.327438140711887),
 ('Gilgamesh', 300.00000000000006, 37.18825214427636),
 ('Aeneid', 452.7927606385165, 30.0),
 ('OBrother', 300.0, 26.544825520097508),
 ('AnaK', 300.0, 29.934639294505377)]

In [11]:

    

#Casjobs queries.  But these don't have the UnWISE reduction, so we use different catalogs in the end
for h in hostsforrun:
    print(h.name, h.fnsdss)
    print(h.sdss_environs_query(dl=False, xmatchwise=True, usecas=True))
    print('\n\n')


    

    




Dune catalogs/Dune_sdss.dat

SELECT  p.objId  as objID,
p.ra, p.dec, p.type, p.flags, p.specObjID, dbo.fPhotoTypeN(p.type) as phot_sg,
p.modelMag_u as u, p.modelMag_g as g, p.modelMag_r as r,p.modelMag_i as i,p.modelMag_z as z,
p.modelMagErr_u as u_err, p.modelMagErr_g as g_err, p.modelMagErr_r as r_err,p.modelMagErr_i as i_err,p.modelMagErr_z as z_err,
p.psfMag_u as psf_u, p.psfMag_g as psf_g, p.psfMag_r as psf_r, p.psfMag_i as psf_i, p.psfMag_z as psf_z,
p.fibermag_r, p.fiber2mag_r,
p.petroMag_r + 2.5*log10(2*PI()*p.petroR50_r*p.petroR50_r) as sb_petro_r,
p.expMag_r, p.expMag_r + 2.5*log10(2*PI()*p.expRad_r*p.expRad_r + 1e-20) as sb_exp_r,
p.deVMag_r, p.deVMag_r + 2.5*log10(2*PI()*p.deVRad_r*p.deVRad_r + 1e-20) as sb_deV_r,
p.lnLExp_r, p.lnLDeV_r, p.lnLStar_r,
p.extinction_u as Au, p.extinction_g as Ag, p.extinction_r as Ar, p.extinction_i as Ai, p.extinction_z as Az,
ISNULL(s.z, -1) as spec_z, ISNULL(s.zErr, -1) as spec_z_err, ISNULL(s.zWarning, -1) as spec_z_warn, s.class as spec_class,
s.subclass as spec_subclass, ISNULL(pz.z,-1) as photz,ISNULL(pz.zerr,-1) as photz_err, 
ISNULL(w.j_m_2mass,9999) as J, ISNULL(w.j_msig_2mass,9999) as Jerr,
ISNULL(w.H_m_2mass,9999) as H, ISNULL(w.h_msig_2mass,9999) as Herr,
ISNULL(w.k_m_2mass,9999) as K, ISNULL(w.k_msig_2mass,9999) as Kerr,
ISNULL(w.w1mpro,9999) as w1, ISNULL(w.w1sigmpro,9999) as w1err,
ISNULL(w.w2mpro,9999) as w2, ISNULL(w.w2sigmpro,9999) as w2err,
ISNULL(w.w3mpro,9999) as w3, ISNULL(w.w3sigmpro,9999) as w3err,
ISNULL(w.w4mpro,9999) as w4, ISNULL(w.w4sigmpro,9999) as w4err


INTO MyDB.Dune_wwise_environs
FROM fGetNearbyObjEq(221.546363418, -0.222941918742, 41.0560905954) n, PhotoPrimary p
LEFT JOIN WISE_xmatch X ON p.objid = X.sdss_objid
LEFT JOIN wise_allsky W ON x.wise_cntr = W.cntr
LEFT JOIN SpecObj s ON p.specObjID = s.specObjID
LEFT JOIN PhotoZ pz ON p.ObjID = pz.ObjID
WHERE n.objID = p.objID





Odyssey catalogs/Odyssey_sdss.dat

SELECT  p.objId  as objID,
p.ra, p.dec, p.type, p.flags, p.specObjID, dbo.fPhotoTypeN(p.type) as phot_sg,
p.modelMag_u as u, p.modelMag_g as g, p.modelMag_r as r,p.modelMag_i as i,p.modelMag_z as z,
p.modelMagErr_u as u_err, p.modelMagErr_g as g_err, p.modelMagErr_r as r_err,p.modelMagErr_i as i_err,p.modelMagErr_z as z_err,
p.psfMag_u as psf_u, p.psfMag_g as psf_g, p.psfMag_r as psf_r, p.psfMag_i as psf_i, p.psfMag_z as psf_z,
p.fibermag_r, p.fiber2mag_r,
p.petroMag_r + 2.5*log10(2*PI()*p.petroR50_r*p.petroR50_r) as sb_petro_r,
p.expMag_r, p.expMag_r + 2.5*log10(2*PI()*p.expRad_r*p.expRad_r + 1e-20) as sb_exp_r,
p.deVMag_r, p.deVMag_r + 2.5*log10(2*PI()*p.deVRad_r*p.deVRad_r + 1e-20) as sb_deV_r,
p.lnLExp_r, p.lnLDeV_r, p.lnLStar_r,
p.extinction_u as Au, p.extinction_g as Ag, p.extinction_r as Ar, p.extinction_i as Ai, p.extinction_z as Az,
ISNULL(s.z, -1) as spec_z, ISNULL(s.zErr, -1) as spec_z_err, ISNULL(s.zWarning, -1) as spec_z_warn, s.class as spec_class,
s.subclass as spec_subclass, ISNULL(pz.z,-1) as photz,ISNULL(pz.zerr,-1) as photz_err, 
ISNULL(w.j_m_2mass,9999) as J, ISNULL(w.j_msig_2mass,9999) as Jerr,
ISNULL(w.H_m_2mass,9999) as H, ISNULL(w.h_msig_2mass,9999) as Herr,
ISNULL(w.k_m_2mass,9999) as K, ISNULL(w.k_msig_2mass,9999) as Kerr,
ISNULL(w.w1mpro,9999) as w1, ISNULL(w.w1sigmpro,9999) as w1err,
ISNULL(w.w2mpro,9999) as w2, ISNULL(w.w2sigmpro,9999) as w2err,
ISNULL(w.w3mpro,9999) as w3, ISNULL(w.w3sigmpro,9999) as w3err,
ISNULL(w.w4mpro,9999) as w4, ISNULL(w.w4sigmpro,9999) as w4err


INTO MyDB.Odyssey_wwise_environs
FROM fGetNearbyObjEq(248.087298875, 19.8264299356, 30.3274381407) n, PhotoPrimary p
LEFT JOIN WISE_xmatch X ON p.objid = X.sdss_objid
LEFT JOIN wise_allsky W ON x.wise_cntr = W.cntr
LEFT JOIN SpecObj s ON p.specObjID = s.specObjID
LEFT JOIN PhotoZ pz ON p.ObjID = pz.ObjID
WHERE n.objID = p.objID





Gilgamesh catalogs/Gilgamesh_sdss.dat

SELECT  p.objId  as objID,
p.ra, p.dec, p.type, p.flags, p.specObjID, dbo.fPhotoTypeN(p.type) as phot_sg,
p.modelMag_u as u, p.modelMag_g as g, p.modelMag_r as r,p.modelMag_i as i,p.modelMag_z as z,
p.modelMagErr_u as u_err, p.modelMagErr_g as g_err, p.modelMagErr_r as r_err,p.modelMagErr_i as i_err,p.modelMagErr_z as z_err,
p.psfMag_u as psf_u, p.psfMag_g as psf_g, p.psfMag_r as psf_r, p.psfMag_i as psf_i, p.psfMag_z as psf_z,
p.fibermag_r, p.fiber2mag_r,
p.petroMag_r + 2.5*log10(2*PI()*p.petroR50_r*p.petroR50_r) as sb_petro_r,
p.expMag_r, p.expMag_r + 2.5*log10(2*PI()*p.expRad_r*p.expRad_r + 1e-20) as sb_exp_r,
p.deVMag_r, p.deVMag_r + 2.5*log10(2*PI()*p.deVRad_r*p.deVRad_r + 1e-20) as sb_deV_r,
p.lnLExp_r, p.lnLDeV_r, p.lnLStar_r,
p.extinction_u as Au, p.extinction_g as Ag, p.extinction_r as Ar, p.extinction_i as Ai, p.extinction_z as Az,
ISNULL(s.z, -1) as spec_z, ISNULL(s.zErr, -1) as spec_z_err, ISNULL(s.zWarning, -1) as spec_z_warn, s.class as spec_class,
s.subclass as spec_subclass, ISNULL(pz.z,-1) as photz,ISNULL(pz.zerr,-1) as photz_err, 
ISNULL(w.j_m_2mass,9999) as J, ISNULL(w.j_msig_2mass,9999) as Jerr,
ISNULL(w.H_m_2mass,9999) as H, ISNULL(w.h_msig_2mass,9999) as Herr,
ISNULL(w.k_m_2mass,9999) as K, ISNULL(w.k_msig_2mass,9999) as Kerr,
ISNULL(w.w1mpro,9999) as w1, ISNULL(w.w1sigmpro,9999) as w1err,
ISNULL(w.w2mpro,9999) as w2, ISNULL(w.w2sigmpro,9999) as w2err,
ISNULL(w.w3mpro,9999) as w3, ISNULL(w.w3sigmpro,9999) as w3err,
ISNULL(w.w4mpro,9999) as w4, ISNULL(w.w4sigmpro,9999) as w4err


INTO MyDB.Gilgamesh_wwise_environs
FROM fGetNearbyObjEq(234.132011282, 16.6077737961, 37.1882521443) n, PhotoPrimary p
LEFT JOIN WISE_xmatch X ON p.objid = X.sdss_objid
LEFT JOIN wise_allsky W ON x.wise_cntr = W.cntr
LEFT JOIN SpecObj s ON p.specObjID = s.specObjID
LEFT JOIN PhotoZ pz ON p.ObjID = pz.ObjID
WHERE n.objID = p.objID





Aeneid catalogs/Aeneid_sdss.dat

SELECT  p.objId  as objID,
p.ra, p.dec, p.type, p.flags, p.specObjID, dbo.fPhotoTypeN(p.type) as phot_sg,
p.modelMag_u as u, p.modelMag_g as g, p.modelMag_r as r,p.modelMag_i as i,p.modelMag_z as z,
p.modelMagErr_u as u_err, p.modelMagErr_g as g_err, p.modelMagErr_r as r_err,p.modelMagErr_i as i_err,p.modelMagErr_z as z_err,
p.psfMag_u as psf_u, p.psfMag_g as psf_g, p.psfMag_r as psf_r, p.psfMag_i as psf_i, p.psfMag_z as psf_z,
p.fibermag_r, p.fiber2mag_r,
p.petroMag_r + 2.5*log10(2*PI()*p.petroR50_r*p.petroR50_r) as sb_petro_r,
p.expMag_r, p.expMag_r + 2.5*log10(2*PI()*p.expRad_r*p.expRad_r + 1e-20) as sb_exp_r,
p.deVMag_r, p.deVMag_r + 2.5*log10(2*PI()*p.deVRad_r*p.deVRad_r + 1e-20) as sb_deV_r,
p.lnLExp_r, p.lnLDeV_r, p.lnLStar_r,
p.extinction_u as Au, p.extinction_g as Ag, p.extinction_r as Ar, p.extinction_i as Ai, p.extinction_z as Az,
ISNULL(s.z, -1) as spec_z, ISNULL(s.zErr, -1) as spec_z_err, ISNULL(s.zWarning, -1) as spec_z_warn, s.class as spec_class,
s.subclass as spec_subclass, ISNULL(pz.z,-1) as photz,ISNULL(pz.zerr,-1) as photz_err, 
ISNULL(w.j_m_2mass,9999) as J, ISNULL(w.j_msig_2mass,9999) as Jerr,
ISNULL(w.H_m_2mass,9999) as H, ISNULL(w.h_msig_2mass,9999) as Herr,
ISNULL(w.k_m_2mass,9999) as K, ISNULL(w.k_msig_2mass,9999) as Kerr,
ISNULL(w.w1mpro,9999) as w1, ISNULL(w.w1sigmpro,9999) as w1err,
ISNULL(w.w2mpro,9999) as w2, ISNULL(w.w2sigmpro,9999) as w2err,
ISNULL(w.w3mpro,9999) as w3, ISNULL(w.w3sigmpro,9999) as w3err,
ISNULL(w.w4mpro,9999) as w4, ISNULL(w.w4sigmpro,9999) as w4err


INTO MyDB.Aeneid_wwise_environs
FROM fGetNearbyObjEq(309.727020806, -5.63998013366, 30.0) n, PhotoPrimary p
LEFT JOIN WISE_xmatch X ON p.objid = X.sdss_objid
LEFT JOIN wise_allsky W ON x.wise_cntr = W.cntr
LEFT JOIN SpecObj s ON p.specObjID = s.specObjID
LEFT JOIN PhotoZ pz ON p.ObjID = pz.ObjID
WHERE n.objID = p.objID





OBrother catalogs/OBrother_sdss.dat

SELECT  p.objId  as objID,
p.ra, p.dec, p.type, p.flags, p.specObjID, dbo.fPhotoTypeN(p.type) as phot_sg,
p.modelMag_u as u, p.modelMag_g as g, p.modelMag_r as r,p.modelMag_i as i,p.modelMag_z as z,
p.modelMagErr_u as u_err, p.modelMagErr_g as g_err, p.modelMagErr_r as r_err,p.modelMagErr_i as i_err,p.modelMagErr_z as z_err,
p.psfMag_u as psf_u, p.psfMag_g as psf_g, p.psfMag_r as psf_r, p.psfMag_i as psf_i, p.psfMag_z as psf_z,
p.fibermag_r, p.fiber2mag_r,
p.petroMag_r + 2.5*log10(2*PI()*p.petroR50_r*p.petroR50_r) as sb_petro_r,
p.expMag_r, p.expMag_r + 2.5*log10(2*PI()*p.expRad_r*p.expRad_r + 1e-20) as sb_exp_r,
p.deVMag_r, p.deVMag_r + 2.5*log10(2*PI()*p.deVRad_r*p.deVRad_r + 1e-20) as sb_deV_r,
p.lnLExp_r, p.lnLDeV_r, p.lnLStar_r,
p.extinction_u as Au, p.extinction_g as Ag, p.extinction_r as Ar, p.extinction_i as Ai, p.extinction_z as Az,
ISNULL(s.z, -1) as spec_z, ISNULL(s.zErr, -1) as spec_z_err, ISNULL(s.zWarning, -1) as spec_z_warn, s.class as spec_class,
s.subclass as spec_subclass, ISNULL(pz.z,-1) as photz,ISNULL(pz.zerr,-1) as photz_err, 
ISNULL(w.j_m_2mass,9999) as J, ISNULL(w.j_msig_2mass,9999) as Jerr,
ISNULL(w.H_m_2mass,9999) as H, ISNULL(w.h_msig_2mass,9999) as Herr,
ISNULL(w.k_m_2mass,9999) as K, ISNULL(w.k_msig_2mass,9999) as Kerr,
ISNULL(w.w1mpro,9999) as w1, ISNULL(w.w1sigmpro,9999) as w1err,
ISNULL(w.w2mpro,9999) as w2, ISNULL(w.w2sigmpro,9999) as w2err,
ISNULL(w.w3mpro,9999) as w3, ISNULL(w.w3sigmpro,9999) as w3err,
ISNULL(w.w4mpro,9999) as w4, ISNULL(w.w4sigmpro,9999) as w4err


INTO MyDB.OBrother_wwise_environs
FROM fGetNearbyObjEq(335.913164033, -3.43166554207, 26.5448255201) n, PhotoPrimary p
LEFT JOIN WISE_xmatch X ON p.objid = X.sdss_objid
LEFT JOIN wise_allsky W ON x.wise_cntr = W.cntr
LEFT JOIN SpecObj s ON p.specObjID = s.specObjID
LEFT JOIN PhotoZ pz ON p.ObjID = pz.ObjID
WHERE n.objID = p.objID





AnaK catalogs/AnaK_sdss.dat

SELECT  p.objId  as objID,
p.ra, p.dec, p.type, p.flags, p.specObjID, dbo.fPhotoTypeN(p.type) as phot_sg,
p.modelMag_u as u, p.modelMag_g as g, p.modelMag_r as r,p.modelMag_i as i,p.modelMag_z as z,
p.modelMagErr_u as u_err, p.modelMagErr_g as g_err, p.modelMagErr_r as r_err,p.modelMagErr_i as i_err,p.modelMagErr_z as z_err,
p.psfMag_u as psf_u, p.psfMag_g as psf_g, p.psfMag_r as psf_r, p.psfMag_i as psf_i, p.psfMag_z as psf_z,
p.fibermag_r, p.fiber2mag_r,
p.petroMag_r + 2.5*log10(2*PI()*p.petroR50_r*p.petroR50_r) as sb_petro_r,
p.expMag_r, p.expMag_r + 2.5*log10(2*PI()*p.expRad_r*p.expRad_r + 1e-20) as sb_exp_r,
p.deVMag_r, p.deVMag_r + 2.5*log10(2*PI()*p.deVRad_r*p.deVRad_r + 1e-20) as sb_deV_r,
p.lnLExp_r, p.lnLDeV_r, p.lnLStar_r,
p.extinction_u as Au, p.extinction_g as Ag, p.extinction_r as Ar, p.extinction_i as Ai, p.extinction_z as Az,
ISNULL(s.z, -1) as spec_z, ISNULL(s.zErr, -1) as spec_z_err, ISNULL(s.zWarning, -1) as spec_z_warn, s.class as spec_class,
s.subclass as spec_subclass, ISNULL(pz.z,-1) as photz,ISNULL(pz.zerr,-1) as photz_err, 
ISNULL(w.j_m_2mass,9999) as J, ISNULL(w.j_msig_2mass,9999) as Jerr,
ISNULL(w.H_m_2mass,9999) as H, ISNULL(w.h_msig_2mass,9999) as Herr,
ISNULL(w.k_m_2mass,9999) as K, ISNULL(w.k_msig_2mass,9999) as Kerr,
ISNULL(w.w1mpro,9999) as w1, ISNULL(w.w1sigmpro,9999) as w1err,
ISNULL(w.w2mpro,9999) as w2, ISNULL(w.w2sigmpro,9999) as w2err,
ISNULL(w.w3mpro,9999) as w3, ISNULL(w.w3sigmpro,9999) as w3err,
ISNULL(w.w4mpro,9999) as w4, ISNULL(w.w4sigmpro,9999) as w4err


INTO MyDB.AnaK_wwise_environs
FROM fGetNearbyObjEq(354.13105403, 0.297265046422, 29.9346392945) n, PhotoPrimary p
LEFT JOIN WISE_xmatch X ON p.objid = X.sdss_objid
LEFT JOIN wise_allsky W ON x.wise_cntr = W.cntr
LEFT JOIN SpecObj s ON p.specObjID = s.specObjID
LEFT JOIN PhotoZ pz ON p.ObjID = pz.ObjID
WHERE n.objID = p.objID

In [12]:

    

# this actually sets up the hosts to use the UnWISE-matched catalogs
for h in hostsforrun:
    h.altfnsdss.insert(0, h.fnsdss)
    h.fnsdss = 'catalogs/base_sql_nsa{0}.fits.gz'.format(h.nsaid)
    print('Loading catalog for', h.name, 'from', h.fnsdss)
    h._cached_sdss = None  # make sure not to used an old cached one if its present
    c = h.get_sdss_catalog()
    #we have to modify these catalogs because 'phot_sg' in the fits catalogs are 3/6 instead of 'STAR'/'GALAXY', and 'type' is missing
    c.add_column(MaskedColumn(name='type', data=c['phot_sg']))
    c.remove_column('phot_sg')
    phot_sg = np.select([c['type']==3,c['type']==6], ['GALAXY','STAR']).astype('a6')
    c['phot_sg'] = MaskedColumn(name='phot_sg', data=phot_sg)


    

    




Loading catalog for Dune from catalogs/base_sql_nsa165536.fits.gz
Loading catalog for Odyssey from catalogs/base_sql_nsa147100.fits.gz
Loading catalog for Gilgamesh from catalogs/base_sql_nsa166313.fits.gz
Loading catalog for Aeneid from catalogs/base_sql_nsa148734.fits.gz
Loading catalog for OBrother from catalogs/base_sql_nsa149781.fits.gz
Loading catalog for AnaK from catalogs/base_sql_nsa61945.fits.gz

In [13]:

    

for h in hostsforrun:
    c = h.get_sdss_catalog()
    csc = SkyCoord(ra=c['ra']*u.deg, dec=c['dec']*u.deg, distance=h.dist)
    hsc = SkyCoord(h.coords, distance=h.dist)
    print(h.name,'max',np.max(csc.separation_3d(hsc).to(u.kpc)))


    

    




Dune max 438.568485555 kpc
Odyssey max 593.673251912 kpc
Gilgamesh max 484.017895385 kpc
Aeneid max 905.568740605 kpc
OBrother max 678.186424926 kpc
AnaK max 601.324486953 kpc

In [14]:

    

gricolorcuts = {'g-r': (None, 0.8, 2),
                'r-i': (None, 0.5, 2)}
sagacolorcuts = gricolorcuts.copy()
sagacolorcuts['r-K'] = (None, 2.0, 2)
sagacolorcuts['r-w1'] = (None, 2.6, 2)

def uggrline_cut(cat, sl=1.5, inter=-0.2):
    gt = cat['u']-cat['g']+2*(cat['u_err']+cat['g_err'])
    lt = sl*(cat['g']-cat['r']-2*(cat['g_err']+cat['r_err'])) + inter
    return gt > lt
gricolorcuts['funcs'] = sagacolorcuts['funcs'] = [uggrline_cut]


    

In [15]:

    

allgoodspec = Table(fits.getdata('allgoodspec_v2_jun14_15.fits.gz'))
allgoodspecnorem = allgoodspec[allgoodspec['REMOVE']==-1]


    

In [266]:

    

allgoododydist = catalog_to_sc(allgoodspec, ody)


    

In [502]:

    

targs = targeting.select_targets(ody, colorcuts=sagacolorcuts, outercutrad=None, 
                                 galvsallcutoff=21, faintlimit=21, verbose=True,
                                 removespecstars=False, removegalsathighz =False) #do these because the fits catalogs don't have spec_class
len(targs)


    

    




Function 0 removed 3536 objects
Colorcut for g-r removed 30879 objects
Colorcut for r-K removed 5293 objects
Colorcut for r-i removed 30185 objects
Colorcut for r-w1 removed 57392 objects






    
Out[502]:





1671

In [315]:

    

targsc = catalog_to_sc(targs, ody)
idx, d2d, d3d = targsc.match_to_catalog_sky(allgoododydist)
alreadytargeted = d2d < 1*u.arcsec
np.sum(alreadytargeted), np.sum(~alreadytargeted)


    

    
Out[315]:





(332, 1339)

In [99]:

    

guides = aat.select_guide_stars_sdss(ody.get_sdss_catalog())
calibs = aat.select_flux_stars(ody.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = aat.select_sky_positions(ody)
aat.produce_master_fld(ody, datetime.date(2014, 6, 19), targs[~alreadytargeted], pris=None,
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn='aat_targets_jun2015/Odyssey_master_test.fld', randomizeorder=True)


    

    
Out[99]:





['LABEL Odyssey base catalog',
 'UTDATE  2014 06 19',
 u'CENTRE  16 32 20.95 +19 49 35.15',
 'EQUINOX J2000.0',
 '# End of Header',
 '',
 '# TargetName(unique for header) RA(h m s) Dec(d m s) TargetType(Program,Fiducial,Sky) Priority(9 is highest) Magnitude 0 TargetName',
 u'Odyssey 16 32 20.95 +19 49 35.15 P 9 12.15 0 host galaxy',
 u'1237662224092562213 16 33 24.33 +19 47 16.18 P 5 21.72 0 magcol=fiber2mag_r, model_r=20.89',
 u'1237662698115498710 16 32 47.82 +19 40 26.22 P 7 21.05 0 magcol=fiber2mag_r, model_r=20.23',
 u'1237662224092496801 16 32 55.93 +19 54 37.79 P 5 21.73 0 magcol=fiber2mag_r, model_r=20.89',
 u'1237662224092431082 16 32 30.37 +19 55 55.57 P 7 21.23 0 magcol=fiber2mag_r, model_r=20.42',
 u'1237662223555429474 16 30 16.70 +19 45 03.80 P 5 21.83 0 magcol=fiber2mag_r, model_r=21.00',
 u'1237662698115236834 16 30 52.38 +20 00 39.72 P 5 21.55 0 magcol=fiber2mag_r, model_r=20.71',
 u'1237662698115433118 16 32 33.99 +19 42 15.97 P 7 20.90 0 magcol=fiber2mag_r, model_r=20.08',
 u'1237662661610701596 16 32 41.09 +19 28 21.26 P 7 21.07 0 magcol=fiber2mag_r, model_r=20.25',
 u'1237662698652370053 16 34 13.38 +20 00 04.78 P 7 21.16 0 magcol=fiber2mag_r, model_r=20.35',
 u'1237662698652304053 16 33 21.85 +20 01 06.39 P 5 21.62 0 magcol=fiber2mag_r, model_r=20.80',
 u'1237662223555429328 16 30 20.47 +19 50 35.68 P 5 21.73 0 magcol=fiber2mag_r, model_r=20.94',
 u'1237662223555494678 16 30 40.77 +19 39 33.37 P 5 21.49 0 magcol=fiber2mag_r, model_r=20.69',
 u'1237662698115498966 16 33 03.56 +19 43 57.04 P 7 21.29 0 magcol=fiber2mag_r, model_r=20.46',
 u'1237662698115498778 16 33 10.05 +19 38 22.77 P 5 21.61 0 magcol=fiber2mag_r, model_r=20.78',
 u'1237662698115236494 16 30 27.26 +19 58 27.57 P 7 21.14 0 magcol=fiber2mag_r, model_r=20.31',
 u'1237662224092496398 16 32 43.45 +19 56 26.04 P 7 21.20 0 magcol=fiber2mag_r, model_r=20.39',
 u'1237662224092562131 16 33 09.84 +19 50 20.50 P 5 21.36 0 magcol=fiber2mag_r, model_r=20.55',
 u'1237662698115564220 16 33 08.29 +19 32 56.23 P 7 21.04 0 magcol=fiber2mag_r, model_r=20.21',
 u'1237662698652369306 16 34 15.15 +20 02 40.12 P 5 21.42 0 magcol=fiber2mag_r, model_r=20.79',
 u'1237662661610832923 16 33 33.39 +19 25 15.29 P 7 21.01 0 magcol=fiber2mag_r, model_r=20.21',
 u'1237662224092431017 16 32 10.49 +19 58 49.99 P 5 21.67 0 magcol=fiber2mag_r, model_r=20.85',
 u'1237662661610767472 16 33 08.03 +19 27 23.20 P 5 21.68 0 magcol=fiber2mag_r, model_r=20.87',
 u'1237662698115629994 16 34 10.75 +19 36 36.19 P 5 21.39 0 magcol=fiber2mag_r, model_r=20.57',
 u'1237662224092562190 16 33 23.97 +19 49 38.07 P 5 21.73 0 magcol=fiber2mag_r, model_r=20.91',
 u'1237662224092300016 16 31 04.13 +20 10 53.47 P 5 21.50 0 magcol=fiber2mag_r, model_r=20.67',
 u'1237662698115302363 16 31 12.87 +19 52 21.48 P 7 21.01 0 magcol=fiber2mag_r, model_r=20.19',
 u'1237662662147506933 16 33 00.87 +20 01 00.60 P 7 20.91 0 magcol=fiber2mag_r, model_r=20.09',
 u'1237662698115171110 16 30 33.62 +20 04 43.55 P 7 21.04 0 magcol=fiber2mag_r, model_r=20.22',
 u'1237662661610636053 16 31 47.73 +19 38 37.76 P 5 21.50 0 magcol=fiber2mag_r, model_r=20.70',
 u'1237662224092627765 16 34 05.94 +19 42 35.49 P 7 21.25 0 magcol=fiber2mag_r, model_r=20.44',
 u'1237662698652238530 16 33 00.76 +20 10 03.52 P 5 21.45 0 magcol=fiber2mag_r, model_r=20.62',
 u'1237662698115564293 16 33 33.54 +19 34 02.25 P 5 21.37 0 magcol=fiber2mag_r, model_r=20.54',
 u'1237662662147572338 16 33 28.28 +19 53 11.11 P 5 21.79 0 magcol=fiber2mag_r, model_r=20.97',
 u'1237662224092562218 16 33 34.75 +19 49 17.71 P 7 20.85 0 magcol=fiber2mag_r, model_r=20.01',
 u'1237662698652173232 16 32 09.68 +20 12 56.18 P 5 21.41 0 magcol=fiber2mag_r, model_r=20.58',
 u'1237662662147375461 16 31 59.99 +20 06 49.35 P 7 20.83 0 magcol=fiber2mag_r, model_r=20.03',
 u'1237662661610439750 16 30 33.97 +19 51 55.47 P 5 22.26 0 magcol=fiber2mag_r, model_r=20.81',
 u'1237662698115302413 16 31 26.58 +19 56 03.78 P 5 21.64 0 magcol=fiber2mag_r, model_r=20.81',
 u'1237662698115367516 16 32 01.05 +19 46 36.59 P 5 21.44 0 magcol=fiber2mag_r, model_r=20.62',
 u'1237662223555559521 16 31 13.89 +19 39 43.18 P 5 21.76 0 magcol=fiber2mag_r, model_r=20.97',
 u'1237662224092496500 16 33 06.42 +19 52 06.80 P 7 21.12 0 magcol=fiber2mag_r, model_r=20.32',
 u'1237662661610701598 16 32 48.80 +19 30 47.39 P 7 21.20 0 magcol=fiber2mag_r, model_r=20.39',
 u'1237662697578431277 16 30 40.17 +19 34 17.16 P 7 21.25 0 magcol=fiber2mag_r, model_r=20.41',
 u'1237662661610635846 16 31 56.64 +19 41 15.67 P 7 21.14 0 magcol=fiber2mag_r, model_r=20.31',
 u'1237662698115236504 16 30 34.99 +19 59 20.97 P 5 21.52 0 magcol=fiber2mag_r, model_r=20.68',
 u'1237662698115301500 16 31 31.72 +19 57 16.10 P 5 21.48 0 magcol=fiber2mag_r, model_r=20.69',
 u'1237662698652238505 16 32 43.11 +20 11 03.15 P 7 20.97 0 magcol=fiber2mag_r, model_r=20.15',
 u'1237662661610439386 16 30 30.24 +19 52 54.27 P 5 21.58 0 magcol=fiber2mag_r, model_r=20.76',
 u'1237662223555625738 16 31 59.99 +19 29 47.04 P 5 21.66 0 magcol=fiber2mag_r, model_r=20.83',
 u'1237662662147638540 16 34 13.32 +19 44 14.88 P 5 21.43 0 magcol=fiber2mag_r, model_r=20.57',
 u'1237662697578431766 16 30 33.75 +19 33 08.09 P 5 21.71 0 magcol=fiber2mag_r, model_r=20.88',
 u'1237662698115367925 16 31 53.51 +19 47 10.28 P 5 21.65 0 magcol=fiber2mag_r, model_r=20.82',
 u'1237662698652238535 16 32 59.66 +20 07 41.40 P 5 21.33 0 magcol=fiber2mag_r, model_r=20.51',
 u'1237662661610832646 16 33 28.02 +19 25 17.97 P 7 21.05 0 magcol=fiber2mag_r, model_r=20.27',
 u'1237662698652303806 16 33 40.26 +20 03 11.55 P 7 20.84 0 magcol=fiber2mag_r, model_r=20.02',
 u'1237662698115171637 16 30 34.51 +20 01 38.19 P 7 21.07 0 magcol=fiber2mag_r, model_r=20.25',
 u'1237662698652238711 16 32 41.35 +20 09 00.52 P 5 21.57 0 magcol=fiber2mag_r, model_r=20.75',
 u'1237662661610635874 16 32 01.44 +19 37 52.25 P 5 21.56 0 magcol=fiber2mag_r, model_r=20.73',
 u'1237662224092300183 16 30 56.75 +20 08 25.74 P 5 21.50 0 magcol=fiber2mag_r, model_r=20.69',
 u'1237662698115498993 16 33 07.82 +19 44 34.94 P 5 21.79 0 magcol=fiber2mag_r, model_r=20.94',
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 u'1237662662147637956 16 33 58.65 +19 47 24.12 P 7 21.09 0 magcol=fiber2mag_r, model_r=20.26',
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 u'1237662223555560311 16 31 05.18 +19 39 44.07 P 5 22.44 0 magcol=fiber2mag_r, model_r=20.87',
 u'1237662224092365239 16 31 51.67 +20 01 53.84 P 7 20.90 0 magcol=fiber2mag_r, model_r=20.09',
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 u'1237662661610832681 16 33 39.90 +19 26 08.51 P 5 21.67 0 magcol=fiber2mag_r, model_r=20.86',
 u'1237662698115237061 16 31 13.08 +19 59 41.93 P 7 21.30 0 magcol=fiber2mag_r, model_r=20.48',
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 u'1237662662147375693 16 31 53.71 +20 07 15.88 P 5 21.61 0 magcol=fiber2mag_r, model_r=20.79',
 u'1237662661610767460 16 33 07.39 +19 27 44.41 P 5 21.52 0 magcol=fiber2mag_r, model_r=20.71',
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 u'1237662698115367602 16 31 52.94 +19 45 57.42 P 5 21.60 0 magcol=fiber2mag_r, model_r=20.75',
 u'1237662661610767390 16 33 06.55 +19 29 54.69 P 5 21.72 0 magcol=fiber2mag_r, model_r=20.92',
 u'1237662698652303817 16 33 42.87 +20 04 06.62 P 7 21.28 0 magcol=fiber2mag_r, model_r=20.48',
 u'1237662698652304072 16 33 50.66 +20 07 44.54 P 7 20.84 0 magcol=fiber2mag_r, model_r=20.03',
 u'1237662697578562208 16 31 44.04 +19 27 05.73 P 5 21.53 0 magcol=fiber2mag_r, model_r=20.72',
 u'1237662223555494599 16 30 23.48 +19 42 58.52 P 7 20.86 0 magcol=fiber2mag_r, model_r=20.06',
 u'1237662223555691195 16 32 10.69 +19 30 27.22 P 5 21.54 0 magcol=fiber2mag_r, model_r=20.74',
 u'1237662698652369553 16 34 03.15 +19 55 12.94 P 5 21.80 0 magcol=fiber2mag_r, model_r=20.95',
 u'1237662698652369167 16 33 42.35 +19 59 03.27 P 7 21.14 0 magcol=fiber2mag_r, model_r=20.34',
 u'1237662661610438910 16 30 43.50 +19 50 57.82 P 5 21.73 0 magcol=fiber2mag_r, model_r=20.92',
 u'1237662223555691038 16 32 38.15 +19 27 19.41 P 7 21.25 0 magcol=fiber2mag_r, model_r=20.48',
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 u'1237662697578693630 16 32 41.70 +19 20 45.49 P 5 21.40 0 magcol=fiber2mag_r, model_r=20.59',
 u'1237662661610569779 16 31 28.04 +19 46 09.80 P 7 21.00 0 magcol=fiber2mag_r, model_r=20.22',
 u'1237662698115563610 16 33 40.91 +19 41 49.49 P 7 21.02 0 magcol=fiber2mag_r, model_r=20.22',
 u'1237662662147310344 16 31 41.88 +20 08 47.26 P 7 21.32 0 magcol=fiber2mag_r, model_r=20.50',
 u'1237662698115236549 16 31 12.02 +20 03 55.11 P 7 21.28 0 magcol=fiber2mag_r, model_r=20.45',
 u'1237662224092562212 16 33 23.63 +19 47 19.63 P 7 21.06 0 magcol=fiber2mag_r, model_r=20.25',
 u'1237662661610701605 16 32 48.03 +19 29 36.86 P 7 21.11 0 magcol=fiber2mag_r, model_r=20.29',
 u'1237662223555756684 16 32 43.55 +19 21 46.38 P 7 21.26 0 magcol=fiber2mag_r, model_r=20.43',
 u'1237662698652369339 16 34 12.62 +20 00 22.85 P 5 21.63 0 magcol=fiber2mag_r, model_r=20.80',
 u'1237662224092693301 16 34 17.46 +19 39 51.64 P 7 22.52 0 magcol=fiber2mag_r, model_r=20.18',
 u'1237662698652238533 16 33 05.35 +20 10 34.58 P 7 21.22 0 magcol=fiber2mag_r, model_r=20.40',
 u'1237662698652304009 16 33 11.45 +20 05 54.54 P 5 21.37 0 magcol=fiber2mag_r, model_r=20.54',
 u'1237662697578562213 16 31 41.47 +19 24 34.23 P 5 21.63 0 magcol=fiber2mag_r, model_r=20.82',
 u'1237662223555495253 16 31 00.10 +19 40 57.05 P 5 21.38 0 magcol=fiber2mag_r, model_r=20.58',
 u'1237662662147310318 16 31 33.45 +20 09 27.52 P 7 21.21 0 magcol=fiber2mag_r, model_r=20.38',
 u'1237662662147310268 16 31 19.69 +20 12 06.32 P 7 20.94 0 magcol=fiber2mag_r, model_r=20.12',
 u'1237662224092562150 16 33 16.26 +19 51 13.79 P 7 21.07 0 magcol=fiber2mag_r, model_r=20.25',
 u'1237662223555757282 16 32 50.48 +19 21 22.35 P 5 21.67 0 magcol=fiber2mag_r, model_r=20.85',
 u'1237662661610767660 16 33 10.99 +19 22 53.49 P 5 21.56 0 magcol=fiber2mag_r, model_r=20.75',
 u'1237662698115367093 16 32 04.69 +19 52 25.19 P 5 21.50 0 magcol=fiber2mag_r, model_r=20.65',
 u'1237662661610635956 16 32 16.89 +19 35 12.07 P 7 20.89 0 magcol=fiber2mag_r, model_r=20.09',
 u'1237662662147375857 16 31 55.16 +20 11 47.31 P 5 21.39 0 magcol=fiber2mag_r, model_r=20.56',
 u'1237662698652304069 16 33 31.81 +20 02 18.51 P 7 21.21 0 magcol=fiber2mag_r, model_r=20.39',
 u'1237662698652238545 16 32 57.81 +20 05 07.31 P 5 21.58 0 magcol=fiber2mag_r, model_r=20.76',
 u'1237662223555494902 16 30 39.91 +19 45 37.69 P 5 21.49 0 magcol=fiber2mag_r, model_r=20.67',
 u'1237662662147441075 16 32 39.63 +19 59 07.03 P 5 21.36 0 magcol=fiber2mag_r, model_r=20.55',
 u'1237662661610635978 16 32 26.63 +19 36 49.72 P 7 21.03 0 magcol=fiber2mag_r, model_r=20.22',
 u'1237662698652304394 16 33 27.55 +20 04 43.51 P 5 21.59 0 magcol=fiber2mag_r, model_r=20.76',
 u'1237662223555756278 16 32 49.63 +19 22 56.38 P 7 21.00 0 magcol=fiber2mag_r, model_r=20.19',
 u'1237662698652172967 16 32 29.92 +20 13 33.77 P 7 20.87 0 magcol=fiber2mag_r, model_r=20.04',
 u'1237662698652369644 16 33 52.69 +20 05 07.65 P 7 21.08 0 magcol=fiber2mag_r, model_r=20.27',
 u'1237662698115302057 16 31 34.85 +19 52 31.48 P 7 21.26 0 magcol=fiber2mag_r, model_r=20.43',
 u'1237662224092365784 16 31 46.07 +20 04 16.39 P 5 21.72 0 magcol=fiber2mag_r, model_r=20.92',
 u'1237662697578562497 16 31 30.58 +19 26 59.34 P 5 21.80 0 magcol=fiber2mag_r, model_r=20.97',
 u'1237662224092496975 16 32 58.01 +19 50 00.03 P 5 21.67 0 magcol=fiber2mag_r, model_r=20.85',
 u'1237662223555756524 16 32 56.22 +19 22 17.80 P 5 21.36 0 magcol=fiber2mag_r, model_r=20.55',
 u'1237662662147637936 16 33 51.05 +19 46 46.77 P 5 21.67 0 magcol=fiber2mag_r, model_r=20.84',
 u'1237662661610767454 16 33 04.67 +19 27 06.22 P 1 23.36 0 magcol=fiber2mag_r, model_r=20.63',
 u'1237662223555691278 16 32 34.00 +19 27 36.42 P 7 20.99 0 magcol=fiber2mag_r, model_r=20.18',
 u'1237662223555560211 16 31 25.21 +19 33 12.30 P 7 21.13 0 magcol=fiber2mag_r, model_r=20.31',
 u'1237662698115367558 16 31 48.32 +19 52 21.50 P 5 21.75 0 magcol=fiber2mag_r, model_r=20.92',
 u'1237662224092300038 16 31 05.93 +20 06 33.79 P 5 21.56 0 magcol=fiber2mag_r, model_r=20.74',
 u'1237662698652369551 16 34 13.86 +19 59 08.84 P 5 21.39 0 magcol=fiber2mag_r, model_r=20.58',
 u'1237662698652304026 16 33 36.29 +20 12 45.37 P 7 21.09 0 magcol=fiber2mag_r, model_r=20.29',
 u'1237662698652304370 16 33 25.66 +20 04 44.18 P 5 21.51 0 magcol=fiber2mag_r, model_r=20.69',
 u'1237662662147572191 16 33 50.02 +19 51 05.23 P 5 21.56 0 magcol=fiber2mag_r, model_r=20.75',
 u'1237662697578562181 16 31 36.59 +19 30 49.98 P 7 21.06 0 magcol=fiber2mag_r, model_r=20.23',
 '\n#Flux stars',
 '\n#Guide stars',
 u'Guide0 16 32 47.28 +20 00 24.49 F 9 12.68 0 id=1237662662147440828',
 u'Guide1 16 34 24.86 +19 47 25.82 F 9 12.83 0 id=1237662662147637429',
 u'Guide2 16 32 23.79 +19 22 21.89 F 9 13.89 0 id=1237662697578627406',
 u'Guide3 16 33 34.27 +20 04 56.64 F 9 13.93 0 id=1237662698652303483',
 u'Guide4 16 33 20.23 +19 27 25.06 F 9 13.10 0 id=1237662661610766583',
 u'Guide5 16 33 17.34 +19 32 39.61 F 9 12.97 0 id=1237662698115563637',
 u'Guide6 16 33 38.86 +19 46 58.99 F 9 13.04 0 id=1237662224092561580',
 u'Guide7 16 31 10.02 +19 30 51.95 F 9 12.98 0 id=1237662697578496151',
 u'Guide8 16 32 14.96 +20 15 21.55 F 9 13.30 0 id=1237662698652172362',
 u'Guide9 16 32 50.52 +20 12 41.90 F 9 13.65 0 id=1237662698652237882',
 u'Guide10 16 31 29.36 +19 34 06.87 F 9 13.64 0 id=1237662223555624979',
 u'Guide11 16 34 16.62 +19 52 52.07 F 9 13.88 0 id=1237662698652434533',
 u'Guide12 16 31 05.70 +20 02 15.55 F 9 13.94 0 id=1237662698115236019',
 u'Guide13 16 32 04.31 +20 05 00.02 F 9 13.77 0 id=1237662662147375262',
 u'Guide14 16 33 03.94 +19 46 48.74 F 9 13.88 0 id=1237662698115498030',
 u'Guide15 16 32 28.48 +20 09 43.75 F 9 13.56 0 id=1237662698652172468',
 u'Guide16 16 30 48.94 +20 07 53.78 F 9 13.29 0 id=1237662698115170597',
 u'Guide17 16 33 28.55 +20 13 46.83 F 9 13.43 0 id=1237662698652303384',
 u'Guide18 16 32 17.22 +19 40 55.00 F 9 13.28 0 id=1237662698115432579',
 u'Guide19 16 31 11.87 +20 13 02.63 F 9 13.93 0 id=1237662662147309610',
 u'Guide20 16 32 28.24 +19 41 23.86 F 9 13.74 0 id=1237662698115432616',
 u'Guide21 16 34 15.13 +19 44 01.95 F 9 13.27 0 id=1237662662147637441',
 u'Guide22 16 34 19.61 +19 57 39.88 F 9 13.45 0 id=1237662698652434472',
 u'Guide23 16 32 16.17 +19 42 51.59 F 9 13.48 0 id=1237662698115432559',
 u'Guide24 16 31 52.59 +19 43 02.15 F 9 13.95 0 id=1237662661610569906',
 u'Guide25 16 31 01.01 +19 54 12.74 F 9 13.90 0 id=1237662698115301379',
 u'Guide26 16 31 04.33 +19 51 58.09 F 9 13.98 0 id=1237662698115301426',
 u'Guide27 16 33 14.30 +19 37 42.74 F 9 12.64 0 id=1237662698115498192',
 u'Guide28 16 32 38.99 +20 04 48.99 F 9 13.91 0 id=1237662662147440728',
 u'Guide29 16 32 05.80 +19 26 57.26 F 9 13.11 0 id=1237662223555690586',
 u'Guide30 16 33 45.67 +19 29 29.92 F 9 13.91 0 id=1237662698115629079',
 u'Guide31 16 33 35.85 +19 27 02.57 F 9 13.82 0 id=1237662661610831934',
 u'Guide32 16 32 47.02 +19 58 29.66 F 9 12.92 0 id=1237662662147506186',
 u'Guide33 16 33 52.45 +19 59 04.20 F 9 13.84 0 id=1237662698652368974',
 u'Guide34 16 32 49.17 +20 07 36.30 F 9 13.15 0 id=1237662698652237932',
 u'Guide35 16 31 14.86 +19 56 13.65 F 9 13.15 0 id=1237662698115301395',
 u'Guide36 16 30 49.09 +19 55 55.69 F 9 13.10 0 id=1237662698115236034',
 u'Guide37 16 33 29.76 +19 37 15.02 F 9 13.25 0 id=1237662698115563623',
 u'Guide38 16 31 32.36 +20 09 09.86 F 9 13.79 0 id=1237662224092299515',
 u'Guide39 16 30 58.99 +19 59 39.69 F 9 13.94 0 id=1237662698115236024',
 u'Guide40 16 32 02.84 +19 30 16.89 F 9 13.74 0 id=1237662223555625223',
 u'Guide41 16 31 43.98 +19 58 17.42 F 9 13.90 0 id=1237662698115301544',
 u'Guide42 16 33 10.04 +20 00 24.61 F 9 13.50 0 id=1237662662147506234',
 u'Guide43 16 30 37.43 +19 37 42.49 F 9 13.67 0 id=1237662697578430658',
 u'Guide44 16 31 53.80 +19 33 32.66 F 9 13.29 0 id=1237662223555625138',
 u'Guide45 16 31 47.40 +19 25 27.55 F 9 12.82 0 id=1237662697578561728',
 u'Guide46 16 32 00.03 +20 03 14.50 F 9 13.17 0 id=1237662224092365004',
 u'Guide47 16 32 09.27 +19 21 44.34 F 9 12.64 0 id=1237662697578627334',
 u'Guide48 16 30 42.78 +19 44 38.91 F 9 13.20 0 id=1237662223555494005',
 u'Guide49 16 33 45.82 +19 42 45.33 F 9 13.30 0 id=1237662224092627034',
 u'Guide50 16 31 23.54 +19 30 58.67 F 9 13.44 0 id=1237662697578496253',
 u'Guide51 16 33 47.77 +19 50 57.05 F 9 13.74 0 id=1237662662147571880',
 u'Guide52 16 30 19.47 +19 42 24.10 F 9 13.75 0 id=1237662697578365041',
 u'Guide53 16 30 33.63 +19 41 31.65 F 9 13.78 0 id=1237662223555494019',
 u'Guide54 16 33 38.08 +20 02 21.73 F 9 13.54 0 id=1237662698652303561',
 u'Guide55 16 31 53.80 +19 20 56.81 F 9 13.73 0 id=1237662697578627213',
 u'Guide56 16 31 15.17 +20 09 00.64 F 9 13.54 0 id=1237662224092299371',
 u'Guide57 16 30 20.62 +19 42 37.32 F 9 13.23 0 id=1237662223555428527',
 u'Guide58 16 32 33.83 +19 23 32.81 F 9 12.97 0 id=1237662223555756059',
 u'Guide59 16 33 47.66 +20 08 41.25 F 9 12.81 0 id=1237662698652303491',
 u'Guide60 16 33 56.71 +19 54 47.71 F 9 13.38 0 id=1237662698652369031',
 u'Guide61 16 34 06.64 +19 45 06.20 F 9 13.62 0 id=1237662662147637406',
 u'Guide62 16 30 55.59 +19 27 38.44 F 9 13.80 0 id=1237662697578496114',
 u'Guide63 16 31 35.52 +19 37 26.20 F 9 13.04 0 id=1237662223555559691',
 u'Guide64 16 30 25.31 +19 57 16.49 F 9 12.90 0 id=1237662698115235854',
 u'Guide65 16 33 38.98 +19 29 46.17 F 9 13.04 0 id=1237662698115563793',
 u'Guide66 16 32 17.11 +20 14 19.16 F 9 13.06 0 id=1237662698652172389',
 u'Guide67 16 31 25.91 +20 16 59.35 F 9 13.73 0 id=1237662662147309625',
 u'Guide68 16 32 36.09 +20 10 45.45 F 9 13.58 0 id=1237662698652237846',
 u'Guide69 16 30 50.65 +20 01 20.53 F 9 13.05 0 id=1237662698115235915',
 u'Guide70 16 30 42.77 +19 51 07.21 F 9 13.44 0 id=1237662661610438907',
 u'Guide71 16 32 12.60 +19 29 28.39 F 9 13.80 0 id=1237662223555690577',
 '\n#Sky positions',
 u'Sky0 16 31 55.95 +19 58 24.07 S 9 20.00 0 sky',
 u'Sky1 16 31 39.90 +19 33 34.16 S 9 20.00 0 sky',
 u'Sky2 16 30 50.48 +20 06 11.61 S 9 20.00 0 sky',
 u'Sky3 16 30 49.71 +19 52 18.14 S 9 20.00 0 sky',
 u'Sky4 16 33 45.38 +20 09 59.25 S 9 20.00 0 sky',
 u'Sky5 16 31 38.37 +19 29 10.72 S 9 20.00 0 sky',
 u'Sky6 16 30 52.42 +20 02 35.35 S 9 20.00 0 sky',
 u'Sky7 16 34 14.28 +19 41 36.33 S 9 20.00 0 sky',
 u'Sky8 16 34 04.17 +19 37 15.42 S 9 20.00 0 sky',
 u'Sky9 16 31 11.53 +19 49 13.02 S 9 20.00 0 sky',
 u'Sky10 16 32 01.96 +20 02 14.58 S 9 20.00 0 sky',
 u'Sky11 16 30 54.33 +19 31 58.69 S 9 20.00 0 sky',
 u'Sky12 16 32 34.61 +20 13 57.00 S 9 20.00 0 sky',
 u'Sky13 16 33 29.90 +19 33 45.86 S 9 20.00 0 sky',
 u'Sky14 16 33 20.99 +19 23 44.58 S 9 20.00 0 sky',
 u'Sky15 16 30 33.03 +19 53 56.93 S 9 20.00 0 sky',
 u'Sky16 16 31 04.47 +20 00 48.77 S 9 20.00 0 sky',
 u'Sky17 16 32 26.31 +19 32 51.72 S 9 20.00 0 sky',
 u'Sky18 16 32 22.68 +19 50 02.41 S 9 20.00 0 sky',
 u'Sky19 16 32 25.22 +19 45 40.26 S 9 20.00 0 sky',
 u'Sky20 16 31 21.79 +19 35 24.51 S 9 20.00 0 sky',
 u'Sky21 16 31 27.77 +19 49 31.04 S 9 20.00 0 sky',
 u'Sky22 16 31 02.28 +20 00 27.21 S 9 20.00 0 sky',
 u'Sky23 16 31 08.29 +20 04 15.66 S 9 20.00 0 sky',
 u'Sky24 16 33 09.33 +19 25 13.79 S 9 20.00 0 sky',
 u'Sky25 16 31 07.66 +19 47 23.39 S 9 20.00 0 sky',
 u'Sky26 16 32 13.33 +19 25 57.92 S 9 20.00 0 sky',
 u'Sky27 16 31 43.03 +19 36 40.19 S 9 20.00 0 sky',
 u'Sky28 16 30 35.62 +19 52 57.00 S 9 20.00 0 sky',
 u'Sky29 16 32 58.62 +20 05 55.80 S 9 20.00 0 sky',
 u'Sky30 16 31 59.80 +19 20 54.83 S 9 20.00 0 sky',
 u'Sky31 16 31 11.40 +20 13 51.20 S 9 20.00 0 sky',
 u'Sky32 16 30 50.52 +19 55 37.40 S 9 20.00 0 sky',
 u'Sky33 16 32 48.30 +20 10 18.32 S 9 20.00 0 sky',
 u'Sky34 16 32 49.68 +19 50 13.98 S 9 20.00 0 sky',
 u'Sky35 16 34 10.43 +19 56 35.93 S 9 20.00 0 sky',
 u'Sky36 16 31 15.78 +19 33 27.99 S 9 20.00 0 sky',
 u'Sky37 16 31 46.16 +19 33 00.01 S 9 20.00 0 sky',
 u'Sky38 16 31 58.62 +19 42 02.02 S 9 20.00 0 sky',
 u'Sky39 16 32 03.78 +19 26 59.01 S 9 20.00 0 sky',
 u'Sky40 16 30 53.69 +20 03 07.20 S 9 20.00 0 sky',
 u'Sky41 16 31 25.04 +19 37 01.23 S 9 20.00 0 sky',
 u'Sky42 16 32 54.41 +19 50 05.15 S 9 20.00 0 sky',
 u'Sky43 16 32 14.43 +19 59 34.65 S 9 20.00 0 sky',
 u'Sky44 16 30 48.73 +20 01 38.04 S 9 20.00 0 sky',
 u'Sky45 16 31 58.73 +19 44 45.73 S 9 20.00 0 sky',
 u'Sky46 16 32 36.05 +20 09 51.73 S 9 20.00 0 sky',
 u'Sky47 16 32 44.97 +19 27 21.81 S 9 20.00 0 sky',
 u'Sky48 16 32 53.61 +19 54 53.90 S 9 20.00 0 sky',
 u'Sky49 16 33 26.87 +19 46 15.11 S 9 20.00 0 sky',
 u'Sky50 16 31 20.21 +19 40 06.28 S 9 20.00 0 sky',
 u'Sky51 16 32 34.51 +19 44 16.91 S 9 20.00 0 sky',
 u'Sky52 16 32 36.11 +19 45 13.50 S 9 20.00 0 sky',
 u'Sky53 16 31 38.51 +19 39 12.43 S 9 20.00 0 sky',
 u'Sky54 16 33 15.72 +19 33 35.69 S 9 20.00 0 sky',
 u'Sky55 16 33 11.77 +19 43 25.60 S 9 20.00 0 sky',
 u'Sky56 16 30 24.04 +19 49 00.06 S 9 20.00 0 sky',
 u'Sky57 16 30 35.29 +19 36 55.59 S 9 20.00 0 sky',
 u'Sky58 16 31 21.39 +19 47 23.53 S 9 20.00 0 sky',
 u'Sky59 16 31 43.77 +19 50 56.71 S 9 20.00 0 sky',
 u'Sky60 16 32 25.37 +19 48 19.09 S 9 20.00 0 sky',
 u'Sky61 16 34 03.31 +20 03 06.70 S 9 20.00 0 sky',
 u'Sky62 16 32 09.98 +19 48 47.84 S 9 20.00 0 sky',
 u'Sky63 16 32 02.70 +19 48 04.92 S 9 20.00 0 sky',
 u'Sky64 16 31 53.13 +19 55 46.49 S 9 20.00 0 sky',
 u'Sky65 16 33 08.08 +20 00 15.37 S 9 20.00 0 sky',
 u'Sky66 16 32 52.55 +19 28 41.06 S 9 20.00 0 sky',
 u'Sky67 16 32 18.41 +20 04 28.31 S 9 20.00 0 sky',
 u'Sky68 16 31 27.51 +19 54 00.27 S 9 20.00 0 sky',
 u'Sky69 16 31 54.19 +19 38 29.26 S 9 20.00 0 sky',
 u'Sky70 16 31 09.81 +19 41 19.49 S 9 20.00 0 sky',
 u'Sky71 16 32 39.67 +19 40 40.49 S 9 20.00 0 sky',
 u'Sky72 16 32 22.58 +19 49 45.89 S 9 20.00 0 sky',
 u'Sky73 16 31 58.13 +20 02 55.41 S 9 20.00 0 sky',
 u'Sky74 16 31 12.17 +20 07 31.80 S 9 20.00 0 sky',
 u'Sky75 16 32 18.50 +19 29 26.84 S 9 20.00 0 sky',
 u'Sky76 16 31 07.30 +19 35 15.93 S 9 20.00 0 sky',
 u'Sky77 16 32 54.93 +20 14 55.29 S 9 20.00 0 sky',
 u'Sky78 16 31 21.98 +20 11 45.73 S 9 20.00 0 sky',
 u'Sky79 16 33 09.88 +19 40 42.02 S 9 20.00 0 sky',
 u'Sky80 16 30 38.34 +19 58 01.97 S 9 20.00 0 sky',
 u'Sky81 16 33 44.04 +19 55 45.03 S 9 20.00 0 sky',
 u'Sky82 16 32 55.49 +20 00 48.27 S 9 20.00 0 sky',
 u'Sky83 16 33 02.98 +19 53 24.60 S 9 20.00 0 sky',
 u'Sky84 16 31 49.52 +19 57 34.23 S 9 20.00 0 sky',
 u'Sky85 16 31 12.67 +20 14 36.73 S 9 20.00 0 sky',
 u'Sky86 16 32 13.78 +19 48 12.82 S 9 20.00 0 sky',
 u'Sky87 16 32 12.64 +19 21 15.26 S 9 20.00 0 sky',
 u'Sky88 16 32 15.14 +19 52 04.43 S 9 20.00 0 sky',
 u'Sky89 16 30 38.55 +20 01 49.61 S 9 20.00 0 sky',
 u'Sky90 16 33 52.13 +20 05 24.21 S 9 20.00 0 sky',
 u'Sky91 16 30 42.13 +19 52 56.31 S 9 20.00 0 sky',
 u'Sky92 16 30 35.72 +19 36 53.23 S 9 20.00 0 sky',
 u'Sky93 16 33 25.88 +20 09 05.50 S 9 20.00 0 sky',
 u'Sky94 16 32 32.03 +19 56 01.03 S 9 20.00 0 sky',
 u'Sky95 16 30 56.77 +19 32 44.14 S 9 20.00 0 sky',
 u'Sky96 16 32 37.20 +19 20 15.49 S 9 20.00 0 sky',
 u'Sky97 16 31 21.16 +19 44 34.51 S 9 20.00 0 sky',
 u'Sky98 16 32 49.40 +20 13 23.26 S 9 20.00 0 sky',
 u'Sky99 16 33 16.11 +19 23 59.57 S 9 20.00 0 sky',
 u'Sky100 16 32 45.88 +20 03 28.41 S 9 20.00 0 sky',
 u'Sky101 16 32 04.97 +19 48 32.48 S 9 20.00 0 sky',
 u'Sky102 16 33 46.26 +19 53 48.46 S 9 20.00 0 sky',
 u'Sky103 16 31 05.64 +19 38 51.27 S 9 20.00 0 sky',
 u'Sky104 16 32 17.64 +20 16 32.97 S 9 20.00 0 sky',
 u'Sky105 16 33 15.69 +20 10 47.53 S 9 20.00 0 sky',
 u'Sky106 16 32 42.09 +19 55 38.73 S 9 20.00 0 sky',
 u'Sky107 16 31 45.53 +19 20 36.41 S 9 20.00 0 sky',
 u'Sky108 16 32 59.42 +19 46 29.39 S 9 20.00 0 sky',
 u'Sky109 16 33 48.20 +20 03 35.76 S 9 20.00 0 sky',
 u'Sky110 16 33 36.37 +19 58 11.00 S 9 20.00 0 sky',
 u'Sky111 16 33 16.69 +19 28 48.96 S 9 20.00 0 sky',
 u'Sky112 16 33 04.20 +19 47 13.08 S 9 20.00 0 sky',
 u'Sky113 16 31 06.25 +19 30 29.29 S 9 20.00 0 sky',
 u'Sky114 16 31 35.28 +20 02 45.33 S 9 20.00 0 sky',
 u'Sky115 16 30 40.73 +19 41 37.59 S 9 20.00 0 sky',
 u'Sky116 16 32 23.06 +19 40 07.38 S 9 20.00 0 sky',
 u'Sky117 16 31 59.53 +20 19 25.69 S 9 20.00 0 sky',
 u'Sky118 16 32 29.85 +20 05 04.92 S 9 20.00 0 sky',
 u'Sky119 16 32 20.76 +19 42 24.48 S 9 20.00 0 sky',
 u'Sky120 16 31 15.66 +19 59 23.65 S 9 20.00 0 sky',
 u'Sky121 16 32 20.68 +20 00 35.24 S 9 20.00 0 sky',
 u'Sky122 16 32 05.63 +19 48 26.14 S 9 20.00 0 sky',
 u'Sky123 16 31 06.12 +19 59 09.15 S 9 20.00 0 sky',
 u'Sky124 16 31 25.84 +20 14 19.73 S 9 20.00 0 sky',
 u'Sky125 16 31 13.48 +20 08 35.28 S 9 20.00 0 sky',
 u'Sky126 16 31 58.77 +19 36 38.82 S 9 20.00 0 sky',
 u'Sky127 16 32 41.20 +19 51 17.32 S 9 20.00 0 sky',
 u'Sky128 16 30 30.38 +19 51 47.96 S 9 20.00 0 sky',
 u'Sky129 16 33 09.69 +19 40 32.60 S 9 20.00 0 sky',
 u'Sky130 16 33 04.12 +19 37 20.15 S 9 20.00 0 sky',
 u'Sky131 16 33 45.95 +19 50 00.23 S 9 20.00 0 sky',
 u'Sky132 16 32 42.44 +20 08 35.73 S 9 20.00 0 sky',
 u'Sky133 16 33 15.06 +19 22 35.10 S 9 20.00 0 sky',
 u'Sky134 16 32 36.08 +19 35 51.32 S 9 20.00 0 sky',
 u'Sky135 16 33 40.61 +20 08 21.29 S 9 20.00 0 sky',
 u'Sky136 16 31 56.52 +19 31 44.68 S 9 20.00 0 sky',
 u'Sky137 16 31 48.31 +19 45 23.26 S 9 20.00 0 sky',
 u'Sky138 16 31 48.06 +20 07 00.70 S 9 20.00 0 sky',
 u'Sky139 16 33 18.44 +19 53 31.85 S 9 20.00 0 sky',
 u'Sky140 16 31 59.26 +19 39 31.31 S 9 20.00 0 sky',
 u'Sky141 16 32 07.49 +19 53 12.85 S 9 20.00 0 sky',
 u'Sky142 16 30 50.26 +19 59 25.81 S 9 20.00 0 sky',
 u'Sky143 16 32 41.94 +19 39 13.18 S 9 20.00 0 sky',
 u'Sky144 16 33 41.16 +19 30 32.87 S 9 20.00 0 sky',
 u'Sky145 16 30 39.64 +19 39 45.15 S 9 20.00 0 sky',
 u'Sky146 16 32 43.84 +19 32 57.26 S 9 20.00 0 sky',
 u'Sky147 16 32 58.14 +20 14 37.76 S 9 20.00 0 sky',
 u'Sky148 16 32 44.12 +19 36 43.63 S 9 20.00 0 sky',
 u'Sky149 16 30 31.33 +19 40 50.67 S 9 20.00 0 sky',
 u'Sky150 16 31 55.37 +19 31 23.78 S 9 20.00 0 sky',
 u'Sky151 16 31 30.73 +20 15 50.21 S 9 20.00 0 sky',
 u'Sky152 16 32 47.12 +19 46 04.78 S 9 20.00 0 sky',
 u'Sky153 16 32 35.14 +20 12 11.14 S 9 20.00 0 sky',
 u'Sky154 16 31 35.03 +19 38 13.94 S 9 20.00 0 sky',
 u'Sky155 16 32 02.12 +20 05 25.32 S 9 20.00 0 sky',
 u'Sky156 16 31 29.17 +19 47 19.82 S 9 20.00 0 sky',
 u'Sky157 16 32 51.03 +20 03 03.84 S 9 20.00 0 sky',
 u'Sky158 16 32 50.33 +19 56 31.93 S 9 20.00 0 sky',
 u'Sky159 16 32 33.05 +19 45 42.17 S 9 20.00 0 sky',
 u'Sky160 16 31 01.77 +19 39 31.52 S 9 20.00 0 sky',
 u'Sky161 16 32 20.47 +20 02 58.83 S 9 20.00 0 sky',
 u'Sky162 16 31 47.87 +20 11 18.47 S 9 20.00 0 sky',
 u'Sky163 16 31 56.32 +19 56 53.53 S 9 20.00 0 sky',
 u'Sky164 16 31 46.59 +19 40 24.40 S 9 20.00 0 sky',
 u'Sky165 16 32 33.62 +20 12 20.37 S 9 20.00 0 sky',
 u'Sky166 16 32 06.38 +19 25 09.16 S 9 20.00 0 sky',
 u'Sky167 16 33 15.62 +19 51 26.86 S 9 20.00 0 sky',
 u'Sky168 16 31 24.56 +20 12 18.53 S 9 20.00 0 sky',
 u'Sky169 16 30 23.69 +19 54 49.77 S 9 20.00 0 sky',
 u'Sky170 16 32 24.64 +19 43 11.66 S 9 20.00 0 sky',
 u'Sky171 16 32 37.51 +19 59 42.10 S 9 20.00 0 sky',
 u'Sky172 16 32 13.39 +19 51 14.02 S 9 20.00 0 sky',
 u'Sky173 16 31 35.77 +20 03 03.56 S 9 20.00 0 sky',
 u'Sky174 16 31 03.54 +20 09 13.04 S 9 20.00 0 sky',
 u'Sky175 16 32 51.52 +19 28 33.68 S 9 20.00 0 sky',
 u'Sky176 16 33 18.33 +20 08 47.20 S 9 20.00 0 sky',
 u'Sky177 16 32 55.89 +19 26 25.21 S 9 20.00 0 sky',
 u'Sky178 16 31 09.78 +19 33 50.53 S 9 20.00 0 sky',
 u'Sky179 16 33 20.58 +20 10 51.02 S 9 20.00 0 sky',
 u'Sky180 16 32 48.92 +20 06 49.05 S 9 20.00 0 sky',
 u'Sky181 16 32 20.19 +19 55 35.17 S 9 20.00 0 sky',
 u'Sky182 16 31 30.70 +19 45 57.77 S 9 20.00 0 sky',
 u'Sky183 16 31 50.05 +20 00 07.32 S 9 20.00 0 sky',
 u'Sky184 16 31 55.26 +19 50 30.78 S 9 20.00 0 sky',
 u'Sky185 16 33 27.06 +19 28 47.73 S 9 20.00 0 sky',
 u'Sky186 16 31 18.39 +20 10 38.37 S 9 20.00 0 sky',
 u'Sky187 16 31 49.48 +19 37 29.34 S 9 20.00 0 sky',
 u'Sky188 16 32 11.06 +19 48 41.89 S 9 20.00 0 sky',
 u'Sky189 16 34 07.75 +19 48 52.99 S 9 20.00 0 sky',
 u'Sky190 16 33 15.68 +19 53 08.60 S 9 20.00 0 sky',
 u'Sky191 16 30 45.42 +19 48 09.67 S 9 20.00 0 sky',
 u'Sky192 16 32 51.69 +19 51 48.61 S 9 20.00 0 sky',
 u'Sky193 16 32 26.01 +19 24 23.65 S 9 20.00 0 sky',
 u'Sky194 16 31 59.05 +20 09 43.65 S 9 20.00 0 sky',
 u'Sky195 16 33 51.87 +19 33 41.98 S 9 20.00 0 sky',
 u'Sky196 16 33 38.02 +19 43 47.34 S 9 20.00 0 sky',
 u'Sky197 16 32 51.09 +20 03 27.54 S 9 20.00 0 sky',
 u'Sky198 16 31 24.97 +20 06 19.41 S 9 20.00 0 sky',
 u'Sky199 16 30 53.83 +19 33 49.11 S 9 20.00 0 sky',
 u'Sky200 16 31 46.58 +19 36 35.89 S 9 20.00 0 sky',
 u'Sky201 16 32 47.95 +19 38 43.74 S 9 20.00 0 sky',
 u'Sky202 16 33 57.12 +20 04 03.41 S 9 20.00 0 sky',
 u'Sky203 16 32 17.44 +19 41 26.05 S 9 20.00 0 sky',
 u'Sky204 16 31 40.07 +19 40 13.72 S 9 20.00 0 sky',
 u'Sky205 16 32 33.68 +20 10 03.99 S 9 20.00 0 sky',
 u'Sky206 16 32 52.83 +19 54 38.64 S 9 20.00 0 sky',
 u'Sky207 16 31 49.12 +19 59 20.83 S 9 20.00 0 sky',
 u'Sky208 16 31 53.37 +19 21 36.68 S 9 20.00 0 sky',
 u'Sky209 16 32 16.30 +19 48 25.50 S 9 20.00 0 sky',
 u'Sky210 16 31 48.31 +19 50 53.54 S 9 20.00 0 sky',
 u'Sky211 16 32 12.80 +19 51 13.31 S 9 20.00 0 sky',
 u'Sky212 16 32 36.58 +19 39 11.47 S 9 20.00 0 sky',
 u'Sky213 16 33 16.01 +19 28 32.62 S 9 20.00 0 sky',
 u'Sky214 16 32 45.58 +20 09 50.37 S 9 20.00 0 sky',
 u'Sky215 16 32 34.08 +20 03 19.61 S 9 20.00 0 sky',
 u'Sky216 16 33 58.50 +19 44 27.38 S 9 20.00 0 sky',
 u'Sky217 16 32 33.90 +19 27 21.00 S 9 20.00 0 sky',
 u'Sky218 16 31 43.83 +20 17 42.60 S 9 20.00 0 sky',
 u'Sky219 16 33 21.59 +20 10 50.07 S 9 20.00 0 sky',
 u'Sky220 16 33 38.77 +19 58 34.89 S 9 20.00 0 sky',
 u'Sky221 16 33 59.99 +19 32 48.71 S 9 20.00 0 sky',
 u'Sky222 16 32 39.07 +20 15 02.43 S 9 20.00 0 sky',
 u'Sky223 16 33 16.79 +19 37 55.56 S 9 20.00 0 sky',
 u'Sky224 16 32 12.76 +19 23 09.90 S 9 20.00 0 sky',
 ...]

In [143]:

    

s = targs['phot_sg']=='STAR'
g = targs['phot_sg']=='GALAXY'
plt.scatter(targs['fibermag_r'][s],targs['sb_deV_r'][s],alpha=.5,s=5,edgecolor='none', facecolor='b')
plt.scatter(targs['fibermag_r'][g],targs['sb_deV_r'][g],alpha=.5,s=5,edgecolor='none', facecolor='r')


    

    
Out[143]:





<matplotlib.collections.PathCollection at 0x1380dc510>






    

In [150]:

    

#plt.scatter(targs['r'],targs['sb_exp_r'],alpha=.5,s=5,edgecolor='none', facecolor='b')
#plt.scatter(targs['r'],targs['sb_deV_r'],alpha=.5,s=5,edgecolor='none', facecolor='r')
plt.scatter(targs['r'],targs['sb_petro_r'],alpha=.5,s=5,edgecolor='none', facecolor='g')
plt.xlim(17.7, 21)
plt.ylim(20,30)


    

    
Out[150]:





(20, 30)






    

In [342]:

    

!cp aat_targets_jul2014/Gilgamesh_sky.dat aat_targets_jun2015/Gilgamesh_sky.dat
!cp aat_targets_jul2014/Aeneid_sky.dat aat_targets_jun2015/Aeneid_sky.dat
!cp aat_targets_jul2014/Ana_Karenina_sky.dat aat_targets_jun2015/AnaK_sky.dat


    

In [863]:

    

#Identify sky regions for each host and write out to separate files - 
from os.path import exists

for h in hostsforrun:
    outfn = 'aat_targets_jun2015/' + h.name.replace(' ','_') + '_sky.dat'
    if exists(outfn):
        print(outfn, 'exists, not overwriting')
    else:
        print('Writing', outfn)
        aat.select_sky_positions(h, nsky=100, outfn=outfn, rad=1*u.deg)


    

    




Writing aat_targets_jun2015/Dune_sky.dat
Writing aat_targets_jun2015/Odyssey_sky.dat
aat_targets_jun2015/Gilgamesh_sky.dat exists, not overwriting
aat_targets_jun2015/Aeneid_sky.dat exists, not overwriting
Writing aat_targets_jun2015/OBrother_sky.dat
aat_targets_jun2015/AnaK_sky.dat exists, not overwriting

In [865]:

    

aat.imagelist_fld_targets("aat_targets_jun2015/Dune_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets_jun2015/Dune_sky.dat"


    

In [866]:

    

aat.imagelist_fld_targets("aat_targets_jun2015/Odyssey_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets_jun2015/Odyssey_sky.dat"


    

In [867]:

    

aat.imagelist_fld_targets("aat_targets_jun2015/OBrother_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets_jun2015/OBrother_sky.dat"


    

In [16]:

    

#now organize the targets that should be manually forced to priority-9 no matter what
#these are Dune targets that look especially promising
dune9="""
221.04498      0.18304042
221.07614     0.034178416
"""

#and these are OBrother
ob9="""
334.99197      -3.0978879
336.67442      -3.0774462
335.36295      -4.1283350
335.97998      -3.2705486
"""

#this is the ambiguous one in Odyssey
ody9='\n247.82589 20.210879'

ras = []
decs = []
for l in (dune9+ob9+ody9).split('\n'):
    if l.strip() == '':
        continue
    ra, dec = l.strip().split()
    ras.append(float(ra))
    decs.append(float(dec))
    
toforcemanual = SkyCoord(ras*u.deg, decs*u.deg)


    

In [178]:

    

#use this to manually add particular lines to the target catalog.  Here we just add the DECALS AnaK object
manualtargetlinesdct = {'AnaK':['DECALS_target_1     23 37 07.90 +0 12 40.86 P 8 21.33 0 magcol=decals_fiber2mag_r, decals_r=18.5']}


    

In [17]:

    

# load the machine learning probabilities - these are used for pri 7 and 8
machine_learning_probs = Table.read('catalogs/machine_learning_june2015.csv.gz', format='csv')


    

In [19]:

    

for h in hostsforrun:
    print('Doing', h.name)
    
    allgooddist = catalog_to_sc(allgoodspec, h)
    targs = targeting.select_targets(h, colorcuts=gricolorcuts, outercutrad=None,
                                     galvsallcutoff=21, faintlimit=21.,
                                     removespecstars=False, removegalsathighz=False) #do these because the fits catalogs don't have spec_class
    targs = targeting.remove_targets_with_remlist(targs, h, 'TargetRemoveJun14_2015.csv')
    print('removing', np.sum(targs['REMOVE']!=-1),'REMOVE!=-1 objects')
    targs = targs[targs['REMOVE']==-1]

    targsc = catalog_to_sc(targs, h)
    idx, d2d, d3d = targsc.match_to_catalog_sky(allgooddist)
    alreadytargeted = d2d < 1*u.arcsec
    print('Already targeted', np.sum(alreadytargeted), 'leaving', np.sum(~alreadytargeted),'to target')
    
    #the targeting catalog *without* cuts applied, but de-duplicated
    rawdups = targeting.find_duplicate_objids(h.get_sdss_catalog())
    rawcatnodups = h.get_sdss_catalog()[~rawdups]
    print('Raw catalog has', np.sum(rawdups), 'duplicates')
    
    #for guide and flux, though, we need to use the *other* catalog, because the UnWISE matched one has a bright cut
    casjobs_cat = h._load_and_reprocess_sdss_catalog(h.altfnsdss[0])
    casjobs_cat = casjobs_cat[~targeting.find_duplicate_objids(casjobs_cat)]
    guides = aat.select_guide_stars_sdss(casjobs_cat)
    calibs = aat.select_flux_stars(casjobs_cat, onlyoutside=300*u.kpc)
    #skyradec = aat.select_sky_positions(h)
    skyradec = 'aat_targets_jun2015/{0}_sky.dat'.format(h.name)  # generated above
    
    targcat = targs[~alreadytargeted]
    #now look for duplicates and always take the first - usually means multiple WISE or 2MASS matches
    targdups = targeting.find_duplicate_objids(targcat)
    targcat = targcat[~targdups]
    print('Raw catalog has', np.sum(targdups), 'duplicates')
    
    #add column for extra notes
    #targcat.add_column(table.Column(name='extra_aat_notes', data=np.zeros(len(targcat), dtype='S25')))
    
    pris = aat.prioritize_targets(targcat, scheme='jun2015baseline')
    # these scheme doesn't include the SAGA color cuts yet - it just makes 1/2 and 3/4 on SB (1/2 is outside rvir)
    sagacolormsk = targeting.colorcut_mask(targcat, sagacolorcuts)
    #if you meet the saga cuts *and* are within rvir you get a boost to 5/6
    msk56 = sagacolormsk&(pris<5)&(pris>2)
    pris[msk56] += 2
    
    #now we set everything 
    
    #if you fail the saga cuts and are outside rvir you get thrown out... 
    #BUT WE DECIDED NOT TO DO THAT: the r-w1 cut seems to kick out things that we might want
    #outsidemsk = (~sagacolormsk)&(pris<3)
    #pris[outsidemsk] = 0
    
    # now the machine-learning forced priority 7/8 targets
    # everything with p_class1 > 0.05 goes into 7, and the 50 highest go into 8. Make those masks
    this_mlcat = machine_learning_probs[machine_learning_probs['HOST_NSAID']==h.nsaid]
    this_mlcat_scs = SkyCoord(this_mlcat['RA']*u.deg, this_mlcat['DEC']*u.deg)
    this_mlcat_withinrv = this_mlcat_scs.separation(h.coords) < h.environsarcmin*u.arcmin
    pri7_ml_msk = (this_mlcat['PROBABILITY_CLASS_1']>0.05)&this_mlcat_withinrv
    pri2_ml_msk = (this_mlcat['PROBABILITY_CLASS_1']>0.05)&~this_mlcat_withinrv
    pri8_ml_idx = np.argsort(this_mlcat['PROBABILITY_CLASS_1'])
    pri8_ml_idx = pri8_ml_idx[this_mlcat_withinrv[pri8_ml_idx]][-50:]
    #if they are in 8, don't put them in 2 or 7
    pri7_ml_msk[pri8_ml_idx] = False
    pri2_ml_msk[pri8_ml_idx] = False
    # now actually build the coordinate objects and match them to the base catalog
    targcat, pris = targeting.add_forced_targets(rawcatnodups, targcat, pris, this_mlcat['OBJID'][pri7_ml_msk], 7)
    targcat, pris = targeting.add_forced_targets(rawcatnodups, targcat, pris, this_mlcat['OBJID'][pri8_ml_idx], 8)
    pris2 = pris==2
    pris[pris2] = 1  # instead of 1/2 on SB, it's 2 for those with high ML-prob, and 1 for all else
    targcat, pris = targeting.add_forced_targets(rawcatnodups, targcat, pris, this_mlcat['OBJID'][pri2_ml_msk], 2)
    
    #and also set priorities for targets that by-hand should be forced to 8
    targcat, pris = targeting.add_forced_targets(rawcatnodups, targcat, pris, toforcemanual, 8)
    
    aat.produce_master_fld(h, datetime.date(2014, 6, 19), targcat, pris=pris, fluxpri=9,
                           guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                           outfn='aat_targets_jun2015/{0}_master.fld'.format(h.name), 
                           randomizeorder=True, manualtargetlines=manualtargetlinesdct.get(h.name, []))
    print('') #newline


    

    




Doing Dune
Found host Dune in GAMA!
Removing 934 GAMA objects
Could not find a match for objid 1237648703519981589 of Dune, closest is [ 17.38537812] arcsec
Could not find a match for objid 1237648703519981597 of Dune, closest is [ 3.98125226] arcsec
Could not find a match for objid 1237648720712237216 of Dune, closest is [ 89.2805649] arcsec
Could not find a match for objid 1237648704593592350 of Dune, closest is [ 2.00734335] arcsec
Removed 6 objects for Dune
removing 23 REMOVE!=-1 objects
Already targeted 1036 leaving 2532 to target
Raw catalog has 13 duplicates
Raw catalog has 0 duplicates
Found 61 objID matches in list of things to force
Removed 5 Forced doubles (things that were already possible targets *and* forced)
Found 50 objID matches in list of things to force
Removed 4 Forced doubles (things that were already possible targets *and* forced)
Found 114 objID matches in list of things to force
Removed 20 Forced doubles (things that were already possible targets *and* forced)
Found 2 coordinate matches in list of things to force
Removed 2 Forced doubles (things that were already possible targets *and* forced)

Doing Odyssey
No matches found for host "Odyssey" in remove list. Maybe you mis-typed something?
removing 14 REMOVE!=-1 objects
Already targeted 843 leaving 3056 to target
Raw catalog has 18 duplicates
Raw catalog has 1 duplicates
Found 12 objID matches in list of things to force
Found 50 objID matches in list of things to force
Found 215 objID matches in list of things to force
Removed 68 Forced doubles (things that were already possible targets *and* forced)
Found 1 coordinate matches in list of things to force

Doing Gilgamesh
Could not find a match for objid 1237667783928971283 of Gilgamesh, closest is [ 60.25321986] arcsec
Could not find a match for objid 1237668336320774200 of Gilgamesh, closest is [ 159.70427129] arcsec
Removed 11 objects for Gilgamesh
removing 14 REMOVE!=-1 objects
Already targeted 1696 leaving 2266 to target
Raw catalog has 11 duplicates
Raw catalog has 1 duplicates
Found 47 objID matches in list of things to force
Removed 1 Forced doubles (things that were already possible targets *and* forced)
Found 50 objID matches in list of things to force
Found 138 objID matches in list of things to force
Removed 31 Forced doubles (things that were already possible targets *and* forced)
Found NO coordinate matches in list of things to force!

Doing Aeneid
Removed 3 objects for Aeneid
removing 9 REMOVE!=-1 objects
Already targeted 345 leaving 3008 to target
Raw catalog has 12 duplicates
Raw catalog has 1 duplicates
Found 63 objID matches in list of things to force
Removed 8 Forced doubles (things that were already possible targets *and* forced)
Found 50 objID matches in list of things to force
Removed 3 Forced doubles (things that were already possible targets *and* forced)
Found 314 objID matches in list of things to force
Removed 63 Forced doubles (things that were already possible targets *and* forced)
Found NO coordinate matches in list of things to force!

Doing OBrother
Removed 3 objects for OBrother
removing 21 REMOVE!=-1 objects
Already targeted 9 leaving 4335 to target
Raw catalog has 20 duplicates
Raw catalog has 8 duplicates
Found NO objID matches in list of things to force!
Found 46 objID matches in list of things to force
Removed 22 Forced doubles (things that were already possible targets *and* forced)
Found 89 objID matches in list of things to force
Removed 50 Forced doubles (things that were already possible targets *and* forced)
Found 4 coordinate matches in list of things to force
Removed 4 Forced doubles (things that were already possible targets *and* forced)

Doing AnaK
Could not find a match for objid 1237663784194801797 of AnaK, closest is [ 6.31789107] arcsec
Could not find a match for objid 1237663784194801800 of AnaK, closest is [ 8.42559321] arcsec
Could not find a match for objid 1237666408439873560 of AnaK, closest is [ 128.75155346] arcsec
Could not find a match for objid 1237657191976599705 of AnaK, closest is [ 69.34198453] arcsec
Removed 14 objects for AnaK
removing 13 REMOVE!=-1 objects
Already targeted 988 leaving 3717 to target
Raw catalog has 9 duplicates
Raw catalog has 1 duplicates
Found NO objID matches in list of things to force!
Found 50 objID matches in list of things to force
Removed 2 Forced doubles (things that were already possible targets *and* forced)
Found 116 objID matches in list of things to force
Removed 35 Forced doubles (things that were already possible targets *and* forced)
Found NO coordinate matches in list of things to force!

In [21]:

    

scptarget = 'visitor3@aatlxa:configure/'


    

In [691]:

    

#note that this was done with a *different* master catalog: it does not have priorty 2 from the ML learning (instead they are all in 7)
h = dune
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 19 guides, and 92 skies not included): {1: 925, 2: 912, 3: 246, 4: 221, 5: 89, 6: 127, 7: 175, 8: 50, 9: 1}
Dune_1.fld                                    100%  121KB 121.0KB/s   00:00    

In [860]:

    

h = ody
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 94 skies not included): {1: 2898, 2: 215, 3: 10, 4: 11, 5: 10, 6: 69, 7: 12, 8: 51, 9: 1}
Odyssey_1.fld                                 100%   57KB  57.0KB/s   00:00    

In [894]:

    

h = aen
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 88 skies not included): {1: 408, 2: 0, 3: 16, 4: 17, 5: 55, 6: 92, 7: 63, 8: 50, 9: 1}
Aeneid_1.fld                                  100%   50KB  49.9KB/s   00:00    

In [24]:

    

h = ob
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:150, 4:150, 5:200, 6:200, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 95 skies not included): {1: 3426, 2: 86, 3: 260, 4: 193, 5: 135, 6: 241, 7: 0, 8: 49, 9: 1}
OBrother_1.fld                                100%   92KB  92.1KB/s   00:00    

In [21]:

    

scptarget = 'visitor3@aatlxa:configure/'


    

In [33]:

    

h = dune
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 20),
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 94 skies not included): {1: 1827, 2: 114, 3: 245, 4: 221, 5: 86, 6: 124, 7: 61, 8: 50, 9: 1}
Dune_1.fld                                    100%  104KB 103.6KB/s   00:00    

In [159]:

    

h = dune
fnbase = 'aat_targets_jun2015/' + h.name

finum = 2
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]

!scp {scptarget}{h.name}*.lis aat_targets_jun2015/
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 20),
                              fieldname=str(finum), listorem=listorem)
!scp $fnconfig $scptarget


    

    




Dune_1.lis                                    100%   45KB  44.9KB/s   00:00    
Dune_2.lis                                    100%   45KB  44.9KB/s   00:00    
Found 355 objects to remove in aat_targets_jun2015/Dune_1.lis
Total remaining in each priority (5 fluxes, 30 guides, and 94 skies not included): {1: 1827, 2: 114, 3: 245, 4: 168, 5: 9, 6: 8, 7: 2, 8: 1, 9: 0}
Dune_2.fld                                    100%   72KB  72.4KB/s   00:00    

In [76]:

    

h = ody
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:300, 2:300, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 20),
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 92 skies not included): {1: 2888, 2: 215, 3: 16, 4: 30, 5: 4, 6: 49, 7: 12, 8: 51, 9: 1}
Odyssey_1.fld                                 100%   67KB  67.0KB/s   00:00    

In [117]:

    

h = aen
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:300, 2:300, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 20),
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 88 skies not included): {1: 2753, 2: 314, 3: 41, 4: 52, 5: 23, 6: 64, 7: 63, 8: 50, 9: 1}
Aeneid_1.fld                                  100%   85KB  85.0KB/s   00:00    

In [118]:

    

h = ob
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:300, 2:300, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 20),
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 95 skies not included): {1: 3426, 2: 86, 3: 260, 4: 193, 5: 135, 6: 241, 7: 0, 8: 49, 9: 1}
OBrother_1.fld                                100%  118KB 118.0KB/s   00:00    

In [179]:

    

h = anak
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)

listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:300, 2:300, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 20),
                              fieldname=str(finum), listorem=listorem)
#now scp to the aat machines to design config
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 96 skies not included): {1: 3253, 2: 116, 3: 90, 4: 118, 5: 64, 6: 154, 7: 0, 8: 51, 9: 1}
AnaK_1.fld                                    100%   85KB  85.4KB/s   00:00    

In [21]:

    

scptarget = 'visitor3@aatlxa:configure/'


    

In [160]:

    

h = dune
fnbase = 'aat_targets_jun2015/' + h.name

finum = 3
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]

!scp {scptarget}{h.name}*.lis aat_targets_jun2015/
aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 21),
                              fieldname=str(finum), listorem=listorem)
!scp $fnconfig $scptarget


    

    




Dune_1.lis                                    100%   45KB  44.9KB/s   00:00    
Dune_2.lis                                    100%   45KB  44.9KB/s   00:00    
Found 355 objects to remove in aat_targets_jun2015/Dune_1.lis
Found 354 objects to remove in aat_targets_jun2015/Dune_2.lis
Total remaining in each priority (5 fluxes, 30 guides, and 94 skies not included): {1: 1825, 2: 94, 3: 88, 4: 11, 5: 1, 6: 0, 7: 1, 8: 0, 9: 0}
Dune_3.fld                                    100%   41KB  41.5KB/s   00:00    

In [181]:

    

h = gilg
fnbase = 'aat_targets_jun2015/' + h.name

finum = 1
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]

aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 21),
                              fieldname=str(finum), listorem=listorem)
!scp $fnconfig $scptarget


    

    




Total remaining in each priority (5 fluxes, 30 guides, and 98 skies not included): {1: 2061, 2: 138, 3: 24, 4: 66, 5: 5, 6: 77, 7: 47, 8: 50, 9: 1}
Gilgamesh_1.fld                               100%   61KB  61.1KB/s   00:00    

In [219]:

    

h = aen
fnbase = 'aat_targets_jun2015/' + h.name

finum = 2
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)
listorem = ['aat_targets_jun2015/Aeneid_1.lis']
zlogfns = ['aat_targets_jun2015/Aeneid_1.zlog']

aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 21),
                              fieldname=str(finum), listorem=listorem,
                              zlogfns=zlogfns)
!scp $fnconfig $scptarget


    

    




Skipped the following fibers due to low zqual: [1, 2, 4, 5, 6, 8, 11, 12, 13, 14, 19, 20, 23, 31, 36, 37, 38, 40, 41, 42, 45, 46, 48, 49, 52, 57, 58, 60, 63, 64, 66, 67, 68, 72, 73, 78, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 101, 102, 103, 104, 107, 108, 110, 115, 118, 119, 122, 124, 130, 134, 139, 140, 142, 144, 145, 146, 147, 149, 153, 154, 156, 159, 162, 163, 169, 171, 172, 173, 174, 176, 177, 180, 185, 187, 188, 189, 190, 191, 194, 195, 201, 202, 205, 207, 209, 211, 215, 216, 221, 222, 224, 226, 227, 228, 230, 231, 232, 233, 234, 235, 236, 240, 241, 247, 248, 251, 252, 256, 257, 259, 260, 261, 263, 265, 266, 267, 268, 270, 273, 274, 277, 278, 279, 282, 283, 284, 290, 292, 293, 294, 302, 304, 306, 307, 310, 314, 317, 318, 319, 320, 321, 322, 323, 328, 329, 331, 336, 340, 341, 344, 347, 348, 349, 352, 354, 357, 358, 359, 360, 364, 369, 370, 371, 373, 374, 376, 378, 379, 381, 383, 384, 385, 389, 391, 392, 395, 396, 397, 398, 399]
Found 159 objects to remove in aat_targets_jun2015/Aeneid_1.lis
Total remaining in each priority (5 fluxes, 30 guides, and 88 skies not included): {1: 2753, 2: 256, 3: 33, 4: 40, 5: 17, 6: 49, 7: 35, 8: 19, 9: 0}
Aeneid_2.fld                                  100%   59KB  58.6KB/s   00:00    

In [438]:

    

h = ob
fnbase = 'aat_targets_jun2015/' + h.name

finum = 2
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)
listorem = ['aat_targets_jun2015/OBrother_1.lis']
zlogfns = ['aat_targets_jun2015/OBrother_1slow.zlog']

def zltabkeepfunc(entry):
    if entry['zqual']<3:
        return True
    elif entry['zqual']==3 and entry['z']<.02:
        return True
    return False

aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 21),
                              fieldname=str(finum), listorem=listorem,
                              zlogfns=zlogfns, zltabkeepfunc=zltabkeepfunc)
!scp $fnconfig $scptarget


    

    




Kept the following fibers in due to zltabkeepfunc: [7, 8, 10, 11, 12, 13, 16, 17, 19, 20, 23, 24, 25, 37, 44, 52, 53, 60, 62, 63, 72, 79, 81, 82, 85, 86, 94, 96, 98, 99, 102, 106, 107, 108, 110, 113, 118, 119, 120, 122, 127, 131, 134, 138, 139, 142, 152, 155, 158, 160, 169, 170, 172, 174, 175, 177, 178, 180, 182, 188, 190, 193, 194, 197, 199, 201, 203, 212, 216, 217, 218, 227, 228, 239, 240, 241, 245, 246, 248, 249, 252, 253, 255, 258, 266, 275, 283, 288, 289, 290, 291, 294, 295, 297, 301, 302, 303, 305, 307, 309, 311, 319, 320, 324, 326, 329, 330, 331, 332, 333, 346, 348, 349, 353, 356, 358, 359, 360, 363, 365, 366, 369, 370, 373, 378, 379, 382, 385, 386, 387, 388, 389, 394, 395, 396, 397]
Found 219 objects to remove in aat_targets_jun2015/OBrother_1.lis
Total remaining in each priority (5 fluxes, 30 guides, and 95 skies not included): {1: 3426, 2: 86, 3: 259, 4: 176, 5: 67, 6: 145, 7: 0, 8: 13, 9: 0}
OBrother_2.fld                                100%   90KB  89.9KB/s   00:00    

In [443]:

    

h = anak
fnbase = 'aat_targets_jun2015/' + h.name

finum = 2
fnmaster = fnbase + '_master.fld'
fnconfig = fnbase + '_{0}.fld'.format(finum)
listorem = ['aat_targets_jun2015/AnaK_1.lis']
zlogfns = ['aat_targets_jun2015/AnaK_1.zlog']

def zltabkeepfunc(entry):
    if entry['zqual']<3:
        return True
    elif entry['zqual']==3 and entry['z']<.02:
        return True
    return False

aat.subsample_from_master_fld(fnmaster, fnconfig,
                              {1:200, 2:200, 3:300, 4:300, 5:np.inf, 6:np.inf, 7:np.inf, 8:np.inf, 9:np.inf}, #8 reserved for flux
                              nflux=5, nguides=30,
                              utcobsdate=datetime.date(2015, 6, 21),
                              fieldname=str(finum), listorem=listorem,
                              zlogfns=zlogfns, zltabkeepfunc=zltabkeepfunc)
!scp $fnconfig $scptarget


    

    




Kept the following fibers in due to zltabkeepfunc: [1, 2, 4, 9, 11, 12, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45, 46, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 67, 68, 70, 71, 73, 74, 75, 77, 78, 79, 80, 83, 84, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 103, 104, 105, 106, 107, 108, 109, 110, 113, 114, 117, 119, 120, 121, 122, 124, 125, 127, 128, 130, 131, 134, 135, 137, 142, 145, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 167, 169, 170, 171, 172, 173, 174, 176, 177, 184, 186, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 202, 203, 204, 207, 209, 213, 214, 215, 216, 217, 221, 222, 225, 226, 228, 230, 231, 232, 234, 236, 237, 240, 241, 242, 243, 244, 246, 247, 249, 251, 252, 253, 255, 259, 265, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 286, 287, 288, 289, 290, 291, 292, 293, 294, 296, 297, 299, 301, 302, 303, 304, 305, 308, 309, 311, 312, 313, 314, 316, 318, 319, 322, 323, 324, 325, 326, 327, 331, 332, 333, 334, 335, 337, 340, 341, 343, 344, 346, 348, 349, 351, 355, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 369, 370, 371, 372, 373, 374, 375, 376, 379, 380, 382, 383, 384, 385, 386, 387, 388, 389, 391, 394, 395, 396, 397, 398]
Found 90 objects to remove in aat_targets_jun2015/AnaK_1.lis
Total remaining in each priority (5 fluxes, 30 guides, and 96 skies not included): {1: 3253, 2: 115, 3: 80, 4: 106, 5: 46, 6: 132, 7: 0, 8: 25, 9: 0}
AnaK_2.fld                                    100%   69KB  68.7KB/s   00:00    

In [115]:

    

tab, sc, info = aat.load_lis_file('aat_targets_jun2015/Dune_2.lis')
print(info)


    

    




LABEL Dune 2
UTDATE 2015 06 20
CENTRE 14 46 11.130  -00 13 22.59
EQUINOX 2000.0
PROPER_MOTIONS

In [116]:

    

targeting.sampled_imagelist(sc[tab['codes'] == 'F'], None, names=tab[tab['codes'] == 'F']['fibnums'])


    

    
Out[116]:





'name ra dec\n50 221.786833333 -0.250763888889\n100 221.499291667 -0.638013888889\n150 220.916125 -0.769791666667\n200 221.025791667 -0.123305555556\n250 221.371041667 0.0571527777778\n300 221.529666667 0.579811111111\n350 222.121625 0.351430555556\n400 221.995 -0.237725'

In [695]:

    

h, h.ra, np.mean(targs['ra']), len(targcat), len(pris)


    

    
Out[695]:





(<hosts.NSAHost object w/ name 'AnaK' AKA: ['NGC7716', 'NSA61945']>,
 354.13105403011235,
 354.1165307355015,
 3849,
 3849)

In [121]:

    

ml_probs = Table.read('catalogs/machine_learning_june2015.csv.gz', format='csv')
ml_probs


    

    
Out[121]:




<Table masked=True length=81435>

OBJID
RA
DEC
DERED_R
HOST_NSAID
HOST_SAGA_NAME
PROBABILITY_CLASS_1
BEST_GUESS_CLASS
int64
float64
float64
float64
int64
string72
float64
int64
1237663277925204111
354.19530129
0.623404150085
15.9147
61945
AnaK
0.996579788789
1
1237648721248845970
221.315874582
-0.159530792095
15.0073
165536
Dune
0.996377840975
1
1237665566078402826
233.786715266
16.8710451043
15.6201
166313
Gilgamesh
0.98105981917
1
1237648702983176377
221.584835212
-1.08275118047
16.1253
165536
Dune
0.952071687678
1
1237662698115432544
248.051339587
19.6957396993
16.6417
147100
Odyssey
0.913708567144
1
1237668367458697416
233.57500272
16.1723997442
17.3436
166313
Gilgamesh
0.902231518307
1
1237648720711712783
220.698695953
-0.498377007486
16.9863
165536
Dune
0.890465657544
1
1237648704593592352
221.044983596
0.183040423156
20.3064
165536
Dune
0.864805674055
1
1237665566078599491
234.269565649
16.861385789
17.7395
166313
Gilgamesh
0.852158358331
1
1237663277924941998
353.561716312
0.485428556245
18.0167
61945
AnaK
0.841546230955
1
...
...
...
...
...
...
...
...
1237652934566871527
310.293684383
-6.102436338
16.5496
148734
Aeneid
0.0
0
1237652934566871644
310.160649788
-6.10686442748
17.6383
148734
Aeneid
0.0
0
1237652934566871531
310.30837202
-6.04619272541
16.3693
148734
Aeneid
0.0
0
1237652934566871533
310.285877202
-6.20442412887
15.676
148734
Aeneid
0.0
0
1237652934566871612
310.155724051
-6.06601920952
17.5208
148734
Aeneid
0.0
0
1237652934566871624
310.13824956
-6.1977786657
18.8829
148734
Aeneid
0.0
0
1237652934566871632
310.167581717
-6.03496570653
17.302
148734
Aeneid
0.0
0
1237652934566871633
310.166551533
-6.03550014492
18.6466
148734
Aeneid
0.0
0
1237652934566871639
310.149302761
-6.16153947564
18.1636
148734
Aeneid
0.0
0
1237652598486204879
309.265342298
-6.52365084604
16.8463
148734
Aeneid
0.0
0

In [697]:

    

plt.hist(ml_probs['PROBABILITY_CLASS_1'], bins=1000,log=True,histtype='step')
plt.axvline(0.05, c='k')
plt.tight_layout()


    

In [698]:

    

this_mlcat = ml_probs[ml_probs['HOST_NSAID']==h.nsaid]
len(this_mlcat)


    

    
Out[698]:





7322

In [699]:

    

np.sum(np.in1d(targcat['objID'], this_mlcat['OBJID'])), len(targcat)


    

    
Out[699]:





(866, 3849)

In [700]:

    

np.sum(np.in1d(targcat['objID'][pris>2], this_mlcat['OBJID'])), np.sum(pris>2)


    

    
Out[700]:





(221, 590)

In [701]:

    

np.sum(this_mlcat['PROBABILITY_CLASS_1']>0.05), len(this_mlcat)


    

    
Out[701]:





(164, 7322)

In [702]:

    

#now check that they're all at least in the base catalog
rawcat = h.get_sdss_catalog()
np.sum(np.in1d(this_mlcat['OBJID'], rawcat['objID'])), len(this_mlcat)


    

    
Out[702]:





(7322, 7322)

In [703]:

    

this_mlcat['PROBABILITY_CLASS_1'][np.argsort(this_mlcat['PROBABILITY_CLASS_1'])[-50:]]


    

    
Out[703]:




<MaskedColumn name='PROBABILITY_CLASS_1' dtype='float64' length=50>

0.10072598065
0.100874380638
0.102701993496
0.102743960392
0.102798716401
0.103814898732
0.113723864084
0.113907976758
0.11525451039
0.116189603623
0.11619600644
0.116302813896
...
0.187695412055
0.216389819992
0.218254404019
0.256371433396
0.27357788686
0.274635549882
0.446392384831
0.507761288042
0.615943893104
0.644379489119
0.841546230955
0.996579788789

In [763]:

    

ml_scs = SkyCoord(u.Quantity(ml_probs['RA'],u.deg),u.Quantity(ml_probs['DEC'],u.deg))
targeting.sampled_imagelist(ml_scs[ml_probs['PROBABILITY_CLASS_1']>.5], None, None);


    

In [764]:

    

ml_scs = SkyCoord(u.Quantity(ml_probs['RA'],u.deg),u.Quantity(ml_probs['DEC'],u.deg))
targeting.sampled_imagelist(ml_scs[ml_probs['PROBABILITY_CLASS_1']>.1], None, None);


    

In [143]:

    

omlps = ml_probs[ml_probs['HOST_SAGA_NAME']=='Odyssey']
omlps['OBJID'].name = 'objID'
ocat = ody.get_sdss_catalog()
joined = table.join(ody.get_sdss_catalog(), omlps, keys='objID')
dra = joined['ra']-joined['RA']
ddec = joined['dec']-joined['DEC']
plt.hist(np.hypot(dra*np.cos(ody.coords.dec), ddec)*3600)
plt.tight_layout()


    

In [150]:

    

# random sampling from the master list:
# 1237662697578561899 16 31 48.49 +19 28 27.25 P 8 20.14 0 magcol=fiber2mag_r, model_r=16.62r
match = omlps[omlps['objID']== 1237662697578561899 ]
SkyCoord(match['RA'], match['DEC'], unit=u.deg).to_string('hmsdms')


    

    
Out[150]:





[u'16h31m48.4901s +19d28m27.2516s']

In [892]:

    

for h in hostsforrun:
    rawcat = fits.getdata(h.fnsdss)
    rawcat2 = Table.read(h.altfnsdss[0], format='csv', guess=False)
    stars = rawcat['phot_sg']==6
    stars2 = rawcat2['type']==6
    
    plt.figure()
    plt.hist(rawcat[stars]['r'],bins=100, range=(10,21.2), histtype='step', label='r_fitscat')
    plt.hist(rawcat2[stars2]['r'],bins=100, range=(10,21.2), histtype='step', label='modelMag_r')
    plt.hist(rawcat2[stars2]['psf_r'],bins=100, range=(10,21.2), histtype='step', label='psf_r')
    plt.title(h.name)
    plt.legend(loc='upper left')
    plt.savefig('/Users/erik/tmp/stars'+h.name+'.png')


    

In [451]:

    

h = aen
zlfn = 'aat_targets_jun2015/Aeneid_1.zlog'
lisfn = 'aat_targets_jun2015/Aeneid_1.lis'


    

In [483]:

    

h = ob
zlfn = 'aat_targets_jun2015/OBrother_1slow.zlog'
lisfn = 'aat_targets_jun2015/OBrother_1.lis'


    

In [444]:

    

h = anak
zlfn = 'aat_targets_jun2015/AnaK_1.zlog'
lisfn = 'aat_targets_jun2015/AnaK_1.lis'


    

In [484]:

    

zltab = Table.read(zlfn, format='ascii', names=aat.zlogcolnames)
zltab


    

    
Out[484]:




<Table masked=False length=400>

name
ra
dec
mag
z
sn
zqual
idx
sat?
star
unknown
string144
float64
float64
float64
float64
float64
int64
int64
int64
int64
string32
OBrother_1slow_002
336.196228027
-3.442491531
21.96
0.392299
6.298147
3
1
0
0
none
OBrother_1slow_003
336.333770752
-3.51373601
21.81
0.252784
13.29357
4
2
0
0
none
OBrother_1slow_004
336.216949463
-3.489738703
19.75
0.029733
14.629863
4
3
0
0
none
OBrother_1slow_005
336.340301514
-3.482774734
20.83
0.038745
13.679986
4
4
0
0
none
OBrother_1slow_006
336.31741333
-3.43503046
21.62
0.10548
9.385761
4
5
0
0
none
OBrother_1slow_007
336.059967041
-3.409421921
22.53
0.008336
6.152685
0
6
0
0
none
OBrother_1slow_008
336.265014648
-3.454447031
21.98
0.382301
4.232491
0
7
0
0
none
OBrother_1slow_009
336.127319336
-3.411916494
22.78
0.066502
10.580534
4
8
0
0
none
OBrother_1slow_010
336.006347656
-3.427905321
22.98
0.211224
4.527804
0
9
0
0
none
OBrother_1slow_011
336.212615967
-3.51639986
23.22
-0.000481
3.402294
0
10
0
0
none
...
...
...
...
...
...
...
...
...
...
...
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none
noid
0.0
0.0
0.0
0.0
0.0
0
0
0
0
none

In [485]:

    

plt.hist(zltab[zltab['zqual']>2]['z'], bins=100, range=(-.01, h.zspec*2), histtype='step')
plt.tight_layout()


    

In [486]:

    

listab, lissc, lisinfo = aat.load_lis_file(lisfn)
listab


    

    
Out[486]:




<Table masked=False length=392>

fibnums
ids
ras
decs
codes
pris
mags
comments
int64
string152
string96
string96
string8
int64
float64
string264
1
Sky29
22:25:37.960
-03:26:07.85
S
9
20.0
sky
2
1237679996084683619
22:24:47.100
-03:26:32.97
P
4
21.96
magcol=fiber2mag_r, model_r=20.53
3
1237680192048988574
22:25:20.110
-03:30:49.45
P
6
21.81
magcol=fiber2mag_r, model_r=20.31
4
1237680192048922851
22:24:52.070
-03:29:23.06
P
8
19.75
magcol=fiber2mag_r, model_r=18.33
5
1237680192048988479
22:25:21.680
-03:28:57.99
P
8
20.83
magcol=fiber2mag_r, model_r=19.32
6
1237679996084749076
22:25:16.180
-03:26:06.11
P
6
21.62
magcol=fiber2mag_r, model_r=18.96
7
1237679996084683389
22:24:14.400
-03:24:33.92
P
8
22.53
magcol=fiber2mag_r, model_r=21.75
8
1237680192048923276
22:25:03.610
-03:27:16.01
P
6
21.98
magcol=fiber2mag_r, model_r=20.77
9
1237679996084683480
22:24:30.560
-03:24:42.90
P
6
22.78
magcol=fiber2mag_r, model_r=20.85
10
1237679996084618333
22:24:01.530
-03:25:40.46
P
6
22.98
magcol=fiber2mag_r, model_r=20.77
...
...
...
...
...
...
...
...
391
1237679996084748550
22:25:02.640
-03:23:05.59
P
6
21.99
magcol=fiber2mag_r, model_r=20.58
392
1237679996084682944
22:24:26.320
-03:21:18.01
P
5
19.9
magcol=fiber2mag_r, model_r=16.58
393
1237679996084749138
22:25:24.040
-03:23:22.28
P
5
21.67
magcol=fiber2mag_r, model_r=20.36
394
1237679996084748999
22:25:07.640
-03:24:17.42
P
6
22.14
magcol=fiber2mag_r, model_r=20.23
395
1237680192048988517
22:25:13.420
-03:30:37.24
P
4
22.68
magcol=fiber2mag_r, model_r=20.68
396
1237679996084749393
22:25:15.160
-03:25:05.38
P
6
23.31
magcol=fiber2mag_r, model_r=20.33
397
1237680192048923280
22:25:03.700
-03:30:16.79
P
4
22.2
magcol=fiber2mag_r, model_r=20.99
398
Sky97
22:25:42.100
-03:25:32.15
S
9
20.0
sky
399
1237680192048857626
22:24:21.030
-03:29:19.58
P
5
21.72
magcol=fiber2mag_r, model_r=20.55
400
Guide29
22:27:21.430
-03:29:53.99
F
9
13.79
id=1237680192049184894

In [487]:

    

fibnums = [int(nm.split('_')[-1]) if nm != 'noid' else -1 for nm in zltab['name']]
objids = []
matchpris = []
for fibnum in fibnums:
    if fibnum == -1:
        objids.append(-1)
        matchpris.append(-1)
    else:
        matchid = listab['ids'][fibnum==listab['fibnums']][0]
        if matchid.startswith('Flux') or matchid.startswith('DECALS'):
            objids.append(-2)
            matchpris.append(-2)
        elif matchid == h.name:
            objids.append(-3)
            matchpris.append(-3)
        else:
            objids.append(int(matchid))
            matchpris.append(listab['pris'][fibnum==listab['fibnums']][0])
            
cat = h.get_sdss_catalog()
sg = np.array([objid if objid < 0 else cat['phot_sg'][objid==cat['objID']][0]  for objid in objids])
matchpris = np.array(matchpris)


    

In [488]:

    

galwithlowz = (sg=='GALAXY')&(zltab['zqual']>2)&(np.abs(zltab['z'])<0.005)
starwithhighz = (sg=='STAR')&(zltab['zqual']>2)&(np.abs(zltab['z'])>0.005)

galwithhighz = (sg=='GALAXY')&(zltab['zqual']>2)&(np.abs(zltab['z'])>0.005)
starwithlowz = (sg=='STAR')&(zltab['zqual']>2)&(np.abs(zltab['z'])<0.005)

np.sum(galwithlowz), np.sum(starwithhighz), np.sum(galwithhighz), np.sum(starwithlowz)


    

    
Out[488]:





(2, 4, 206, 6)

In [492]:

    

names = np.array(['{3}:{0}wpri{1}z={2:.2g}'.format(sgi, pi, zi, nmi.split('_')[-1]) for sgi, pi, zi, nmi in zip(sg, matchpris, zltab['z'], zltab['name'])])

#all things w/ stars
targeting.sampled_imagelist(zltab[sg=='STAR'], None, None, names=names[sg=='STAR'], posttoimglist=0.1)

#weird things
targeting.sampled_imagelist(zltab[galwithlowz], None, None, names=names[galwithlowz], posttoimglist=0.1)
targeting.sampled_imagelist(zltab[starwithhighz], None, None, names=names[starwithhighz], posttoimglist=0.1)

#expected things
targeting.sampled_imagelist(zltab[galwithhighz], None, None, names=names[galwithhighz], posttoimglist=0.1)
targeting.sampled_imagelist(zltab[starwithlowz], None, None, names=names[starwithlowz], posttoimglist=0.1)
h.zspec


    

    
Out[492]:





0.0094332071

In [499]:

    

for h in hostsforrun:
    print(h.name, h.coords.galactic.b.deg)