Plan: 1.5hr each

  • Alice: finish more from IMACS
  • Gilg: finish hatever is not in Hectospec
  • Ana K: do what can be
  • Aneid: start and finish if possible
  • Ulysses: start and finish if possible

In [1]:
# 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
import datetime

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 [39]:
from astropy.coordinates import ICRS, Galactic
from astropy.table import MaskedColumn
from astropy.time import Time

In [3]:
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 [6]:
hsd = hosts.get_saga_hosts_from_google('etollerud@gmail.com', passwd, useobservingsummary=True)
hsd = dict([(h.name, h) for h in hsd])

Checking out possible targets


In [473]:
alice = hsd['Alice in Wonderland']
ana = hsd['Ana Karenina']
uly = hsd['Ulysses']
gilg = hsd['Gilgamesh']
ane = hsd['Aeneid']

n356 = hosts.NSAHost(35664)

possibletargets = [alice, ana, uly, gilg, ane, n356]

In [211]:
for t in possibletargets:
    print '\n',t.name
    print t.dist,': r = 21 -> M_r =',21-t.distmod
    print t.physical_to_projected(300*u.kpc), t.projected_to_physical(1*u.deg)
    t.sdss_environs_query(dl=True)
    print len(targeting.select_targets(t, outercutrad=300,colorcuts=targeting.bossanova_color_cuts,faintlimit=21,galvsallcutoff=100))


Alice in Wonderland
22.9898107397 Mpc : r = 21 -> M_r = -10.8076769801
44.8600487805 arcmin 401.247891818 kpc
File catalogs/Alice in Wonderland_sdss.dat exists - not downloading anything.
Removing 256 objects at high z, keeping 1 (total of 46712 objects)
('Found host', 'Alice in Wonderland', 'in GAMA!')
('Removing', 989, 'GAMA objects')
3984

Ana Karenina
34.4525290948 Mpc : r = 21 -> M_r = -11.6861055407
29.9346392945 arcmin 601.310068343 kpc
File catalogs/NSA61945_sdss.dat exists - not downloading anything.
Removing 2220 objects at high z, keeping 21 (total of 62435 objects)
1606

Ulysses
41.7964913444 Mpc : r = 21 -> M_r = -12.1056991297
24.674894903 arcmin 729.486389741 kpc
File catalogs/NSA150340_sdss.dat exists - not downloading anything.
Could not find file "catalogs/Ulysses_sdss.dat" but did find "catalogs/NSA150340_sdss.dat" so using that.
Removing 1088 objects at high z, keeping 5 (total of 65277 objects)
953

Gilgamesh
27.7325222824 Mpc : r = 21 -> M_r = -11.214946853
37.1882521443 arcmin 484.023823711 kpc
File catalogs/Gilgamesh_sdss.dat exists - not downloading anything.
Removing 180 objects at high z, keeping 7 (total of 37151 objects)
2208

Aeneid
51.8862283573 Mpc : r = 21 -> M_r = -12.5752605135
19.8766428759 arcmin 905.585521277 kpc
File catalogs/NSA148734_sdss.dat exists - not downloading anything.
Could not find file "catalogs/Aeneid_sdss.dat" but did find "catalogs/NSA148734_sdss.dat" so using that.
Removing 0 objects at high z, keeping 0 (total of 19836 objects)
610

NSA35664
28.8911772071 Mpc : r = 21 -> M_r = -11.3038261904
35.6968504205 arcmin 504.246167042 kpc
File catalogs/NSA35664_sdss.dat exists - not downloading anything.
Removing 45 objects at high z, keeping 0 (total of 28928 objects)
935

Ben says MMT got successes down to 2.5 S/N/Ang, which the AAOmega ITC says takes ~84 min at V=21.3~r=21 (for g-r=1.5)


In [177]:
expRad_r=((10**((utargs21['sb_exp_r'] - utargs21['expMag_r'])/2.5))/2/np.pi-1e-20)**0.5
devRad_r=((10**((utargs21['sb_deV_r'] - utargs21['deVMag_r'])/2.5))/2/np.pi-1e-20)**0.5

plt.hist(expRad_r*1.67, histtype='step',bins=100,range=(0,5),color='b'); # FWHM for this one
#plt.hist(devRad_r*1.67, histtype='step',bins=100,range=(0,5),color='r');



In [180]:
utargs21 = targeting.select_targets(uly, outercutrad=300,colorcuts=targeting.bossanova_color_cuts,faintlimit=21,galvsallcutoff=100)
utargs20p5 = targeting.select_targets(uly, outercutrad=300,colorcuts=targeting.bossanova_color_cuts,faintlimit=20.5,galvsallcutoff=100)
utargs20 = targeting.select_targets(uly, outercutrad=300,colorcuts=targeting.bossanova_color_cuts,faintlimit=20,galvsallcutoff=100)
print len(utargs21),len(utargs20p5),len(utargs20)

plt.hist(utargs21['fiber2mag_r'],bins=100,histtype='step',range=(19,25),color='b',label='r<21')
plt.hist(utargs20p5['fiber2mag_r'],bins=100,histtype='step',range=(19,25),color='g',label='r<20.5')
plt.hist(utargs20['fiber2mag_r'],bins=100,histtype='step',range=(19,25),color='r',label='r<20')
plt.xlabel('fiber2mag')
plt.legend(loc=0);


Removing 1088 objects at high z, keeping 5 (total of 65277 objects)
Removing 1088 objects at high z, keeping 5 (total of 65277 objects)
Removing 1088 objects at high z, keeping 5 (total of 65277 objects)
953 598 376

Code for investigating possible hosts


In [123]:
"""
Yao made the following cuts:
- M_K between -23 and -24.5
- M_r < -18   (this removes strange things and selects for SDSS)
- distance between 20 and 45 Mpc
- galactic latitude > 25 or < -25
- outside Rvir of any halo whose mass > 5e12 (according to the 2MASS catalog)
"""
from masterlist import masterlist

ml = ascii.read('newmasterlist.csv')

dist = ml['distance'][:]
dist[dist.mask] = ml['vhelio'][dist.mask]/70.4
print dist.mask.sum()
dm = 5* np.log10(dist) + 25

M_K = ml['K'] - dm
ml.add_column(MaskedColumn(name='M_K', data=M_K))
M_r = ml['r'] - dm
ml.add_column(MaskedColumn(name='M_r', data=M_r))
c_gal = ICRS(ml['RA']*u.deg, ml['Dec']*u.deg).transform_to(Galactic)

msk = np.ones(len(ml), dtype=bool)
msk = msk & (-24.5 < M_K) & (M_K < -23)
msk = msk & (20 < dist) & (dist < 42)
#only mask those that have an r - allow those *not* in NSA for now:
#msk[~M_r.mask] = msk[~M_r.mask] & (M_r[~M_r.mask] < -18)
#this disallows all not in nsa
msk = msk & (M_r < -18)
msk = msk & ((-25*u.deg > c_gal.b) | (c_gal.b > 25*u.deg))

hl0 = ml[msk]
hl = masterlist.remove_from_list(hl0, 'host', erroronnomatch=False,tol=.3*u.arcsec)


0
Could not find matches for removelist indecies [ 6  7  9 12 13 14 15 18 19 20 23 24 25 26 27 30 31 32 33 35 36 37 39 40 41
 42 43 44 45 46 47 48 49 51].  Distance: [  4.43483726e+01   2.15423757e+04   1.37156816e+04   2.16917578e+04
   8.69778739e-01   2.73663972e+03   6.04635118e-01   1.21666503e+04
   2.74666737e+01   8.30768635e+01   4.04092104e+04   1.36181202e+04
   1.35954335e+04   6.30045233e-01   1.57620981e+01   9.69716648e-01
   7.02399904e-01   6.04635118e-01   3.31253100e+04   5.01414665e-01
   1.02017368e+00   1.23801625e+00   1.77789893e+04   4.18226825e-01
   4.14391408e-01   2.79164707e+03   1.10413234e+00   3.44966572e-01
   1.04124898e+00   2.78498890e+03   8.25555460e-01   9.40040814e-01
   1.47520679e+00   2.42799300e+02] arcsec, continuing anyway...

In [138]:
ramsk = (16*15 < hl['RA'])&(hl['RA'] < 22*15)
decmsk = hl['Dec'] < 10
print len(hl),np.sum(ramsk&decmsk)


232 1

In [139]:
urltempl='http://www.nsatlas.org/getAtlas.html?search=nsaid&nsaID={0}&submit_form=Submit'
url = urltempl.format(','.join([str(i) for i in hl[ramsk&decmsk]['NSAID']]))
print url


http://www.nsatlas.org/getAtlas.html?search=nsaid&nsaID=146933&submit_form=Submit

More detailed checking of specific targets


In [54]:
print (300*u.kpc*u.rad/ana.dist).to(u.arcmin)
print (300*u.kpc*u.rad/(34.4*u.Mpc)).to(u.arcmin)


29.9346392945 arcmin
29.9803497452 arcmin

In [59]:
anat = targeting.select_targets(ana, outercutrad=300*u.kpc,colorcuts={'g-r': (None, 1.2), 'r-i': (None, 0.7)},
                                     faintlimit=20.5,galvsallcutoff=100)
print ana.dist, ana.environsarcmin*u.arcmin
print len(anat)

anat2 = targeting.select_targets(ana, outercutrad=29.9803497452*u.arcmin,colorcuts={'g-r': (None, 1.2), 'r-i': (None, 0.7)},
                                     faintlimit=20.5,galvsallcutoff=100)
print len(anat2)


Removing 2220 objects at high z, keeping 21 (total of 62435 objects)
34.4525290948 Mpc 29.9346392945 arcmin
837
Removing 2220 objects at high z, keeping 21 (total of 62435 objects)
839

In [60]:
gilgt = targeting.select_targets(gilg,outercutrad=300*u.kpc, colorcuts={'g-r': (None, 1.2), 'r-i': (None, 0.7)},
                                 faintlimit=21,galvsallcutoff=100, removegalsathighz=False)
print gilg.environsarcmin, gilg.distmpc
len(gilgt)


37.1882521443 27.7325222824
Out[60]:
2009

Produce Test flds


In [91]:
reload(targeting)
reload(aat)


Out[91]:
<module 'aat' from 'aat.py'>

In [ ]:
ana.environsarcmin = 60
ana.usnob_environs_query(True)

Ana K


In [312]:
# note that SDSS catalog should go out to at least this far - when run it was 90 arcmin radius
anat =  targeting.select_targets(ana,outercutrad=1.2*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
anat = targeting.remove_targets_with_remlist(anat, 'AnaK')
len(anat)


Removing 2220 objects at high z, keeping 21 (total of 62435 objects)
Could not find a match for objid 1237663784194801800 of AnaK, closest is [ 2.12368631] arcsec
Could not find a match for objid 1237666408439873560 of AnaK, closest is [ 107.00869934] arcsec
Removed 13 objects for AnaK
Out[312]:
13556

In [204]:
guides = aat.select_guide_stars_sdss(ana.get_sdss_catalog())
calibs = aat.select_flux_stars(ana.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = aat.select_sky_positions(ana)

aat.produce_master_fld(ana, datetime.date(2014, 7, 4), anat, aat.prioritize_targets(anat),
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn='aat_targets/AnaK_master.fld', randomizeorder=True);


Out[204]:
['LABEL Ana Karenina base catalog',
 'UTDATE  2014 07 04',
 u'CENTRE  23 36 31.45 +0 17 50.15',
 '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'1237663784194671119 23 34 58.42 +0 03 53.74 P 5 22.49 0 magcol=fiber2mag_r, model_r=20.73',
 u'1237678617415123331 23 34 56.99 +1 14 37.88 P 1 22.15 0 magcol=fiber2mag_r, model_r=20.87',
 u'1237678617415188857 23 35 50.35 +1 23 17.05 P 4 21.68 0 magcol=fiber2mag_r, model_r=20.05',
 u'1237663783120994634 23 35 34.49 -0 42 19.66 P 2 21.73 0 magcol=fiber2mag_r, model_r=20.71',
 u'1237663277925466768 23 39 10.77 +0 25 55.47 P 2 22.48 0 magcol=fiber2mag_r, model_r=20.65',
 u'1237663277924876706 23 33 29.08 +0 32 05.31 P 1 21.82 0 magcol=fiber2mag_r, model_r=20.65',
 u'1237663462608667038 23 35 22.37 +0 49 25.27 P 3 21.81 0 magcol=fiber2mag_r, model_r=20.41',
 u'1237663278462337433 23 38 47.35 +1 02 31.11 P 3 21.41 0 magcol=fiber2mag_r, model_r=19.90',
 u'1237663463145668751 23 36 41.71 +1 11 13.16 P 4 19.95 0 magcol=fiber2mag_r, model_r=18.14',
 u'1237678617415123356 23 35 01.88 +1 11 50.25 P 4 21.50 0 magcol=fiber2mag_r, model_r=19.58',
 u'1237666407365804163 23 33 43.86 -0 25 43.96 P 4 20.48 0 magcol=fiber2mag_r, model_r=19.03',
 u'1237666408439349910 23 32 03.20 +0 19 56.64 P 4 22.05 0 magcol=fiber2mag_r, model_r=20.48',
 u'1237666407366394381 23 39 04.82 -0 31 19.29 P 1 21.74 0 magcol=fiber2mag_r, model_r=20.55',
 u'1237678617414992550 23 34 08.21 +1 15 34.40 P 2 22.40 0 magcol=fiber2mag_r, model_r=20.75',
 u'1237666408439940028 23 37 33.75 +0 17 57.61 P 8 21.78 0 magcol=fiber2mag_r, model_r=20.42',
 u'1237657190902465017 23 34 53.73 -0 04 25.24 P 2 22.38 0 magcol=fiber2mag_r, model_r=20.99',
 u'1237666408439808168 23 36 02.98 +0 17 28.78 P 8 20.37 0 magcol=fiber2mag_r, model_r=19.01',
 u'1237663462608666967 23 35 52.28 +0 48 05.44 P 4 21.02 0 magcol=fiber2mag_r, model_r=19.17',
 u'1237657190902661430 23 36 55.62 -0 00 35.03 P 8 21.40 0 magcol=fiber2mag_r, model_r=20.25',
 u'1237663783658128183 23 38 24.62 -0 14 21.39 P 4 21.75 0 magcol=fiber2mag_r, model_r=20.44',
 u'1237663463145538098 23 35 27.84 +1 11 25.72 P 3 22.21 0 magcol=fiber2mag_r, model_r=20.34',
 u'1237663277925139080 23 36 00.05 +0 33 42.98 P 5 22.16 0 magcol=fiber2mag_r, model_r=20.59',
 u'1237663783657931507 23 36 23.76 -0 15 46.23 P 2 21.75 0 magcol=fiber2mag_r, model_r=20.64',
 u'1237657190902792405 23 38 04.64 -0 08 30.36 P 4 19.60 0 magcol=fiber2mag_r, model_r=18.25',
 u'1237663783658389916 23 40 46.35 -0 14 07.53 P 3 21.68 0 magcol=fiber2mag_r, model_r=20.34',
 u'1237666408439939598 23 37 05.54 +0 18 36.45 P 7 20.60 0 magcol=fiber2mag_r, model_r=19.42',
 u'1237666407366394255 23 38 52.41 -0 35 17.80 P 1 22.15 0 magcol=fiber2mag_r, model_r=20.62',
 u'1237663277924811510 23 33 10.72 +0 27 19.15 P 2 22.76 0 magcol=fiber2mag_r, model_r=20.99',
 u'1237666407365935556 23 34 44.96 -0 28 46.40 P 3 22.19 0 magcol=fiber2mag_r, model_r=20.46',
 u'1237678617415189144 23 36 05.56 +1 19 36.87 P 3 21.63 0 magcol=fiber2mag_r, model_r=20.18',
 u'1237663463145472363 23 34 48.13 +1 06 30.41 P 4 21.41 0 magcol=fiber2mag_r, model_r=19.98',
 u'1237663783658127713 23 38 24.04 -0 14 25.89 P 1 22.21 0 magcol=fiber2mag_r, model_r=20.99',
 u'1237666408440201801 23 39 54.56 +0 13 01.66 P 2 22.04 0 magcol=fiber2mag_r, model_r=20.97',
 u'1237657190902858050 23 38 36.35 -0 11 32.08 P 4 21.72 0 magcol=fiber2mag_r, model_r=20.43',
 u'1237657190903054494 23 40 19.43 -0 01 03.45 P 4 19.06 0 magcol=fiber2mag_r, model_r=17.28',
 u'1237663278461747783 23 33 51.10 +0 57 29.45 P 3 20.56 0 magcol=fiber2mag_r, model_r=19.63',
 u'1237663784194605242 23 34 16.48 +0 10 43.61 P 4 19.83 0 magcol=fiber2mag_r, model_r=18.51',
 u'1237663784194736860 23 36 00.17 +0 05 56.77 P 7 22.67 0 magcol=fiber2mag_r, model_r=20.15',
 u'1237663462608339415 23 32 27.54 +0 45 29.07 P 2 21.49 0 magcol=fiber2mag_r, model_r=20.53',
 u'1237663783120994799 23 35 32.29 -0 39 05.91 P 4 20.65 0 magcol=fiber2mag_r, model_r=18.05',
 u'1237663277924746176 23 32 37.51 +0 33 29.40 P 3 21.43 0 magcol=fiber2mag_r, model_r=20.23',
 u'1237678617415123432 23 35 16.56 +1 15 31.93 P 3 20.97 0 magcol=fiber2mag_r, model_r=19.76',
 u'1237663783658128405 23 38 21.87 -0 15 24.19 P 4 23.27 0 magcol=fiber2mag_r, model_r=18.76',
 u'1237663463145275749 23 32 58.84 +1 03 51.35 P 1 22.03 0 magcol=fiber2mag_r, model_r=20.66',
 u'1237663783658193730 23 39 01.21 -0 17 04.57 P 3 23.03 0 magcol=fiber2mag_r, model_r=20.35',
 u'1237666407366001400 23 35 48.95 -0 31 52.87 P 4 21.25 0 magcol=fiber2mag_r, model_r=20.08',
 u'1237666408439415281 23 32 28.16 +0 19 40.39 P 4 21.04 0 magcol=fiber2mag_r, model_r=19.24',
 u'1237663783658193942 23 38 56.74 -0 16 14.97 P 1 23.09 0 magcol=fiber2mag_r, model_r=20.84',
 u'1237663278461616411 23 32 36.04 +0 58 17.49 P 4 21.20 0 magcol=fiber2mag_r, model_r=19.68',
 u'1237663784195129785 23 39 23.87 +0 07 35.67 P 4 21.14 0 magcol=fiber2mag_r, model_r=20.18',
 u'1237663783120929538 23 35 25.70 -0 38 44.49 P 2 22.45 0 magcol=fiber2mag_r, model_r=20.60',
 u'1237666408439874185 23 36 41.70 +0 24 04.51 P 6 21.67 0 magcol=fiber2mag_r, model_r=20.59',
 u'1237663783120994390 23 35 59.35 -0 45 06.31 P 2 22.21 0 magcol=fiber2mag_r, model_r=20.63',
 u'1237663462608797949 23 37 03.97 +0 38 05.08 P 8 21.20 0 magcol=fiber2mag_r, model_r=19.16',
 u'1237663463145538013 23 35 20.33 +1 08 05.17 P 2 21.87 0 magcol=fiber2mag_r, model_r=20.68',
 u'1237666408440267436 23 40 21.86 +0 19 19.12 P 3 21.42 0 magcol=fiber2mag_r, model_r=20.15',
 u'1237666408439415297 23 32 29.21 +0 13 20.10 P 4 21.36 0 magcol=fiber2mag_r, model_r=20.06',
 u'1237666407366132155 23 36 34.53 -0 37 32.05 P 2 22.12 0 magcol=fiber2mag_r, model_r=20.91',
 u'1237666407366460024 23 39 44.36 -0 28 36.09 P 1 21.78 0 magcol=fiber2mag_r, model_r=20.63',
 u'1237663277924745693 23 32 44.01 +0 37 17.42 P 4 20.88 0 magcol=fiber2mag_r, model_r=19.47',
 u'1237656906352492802 23 36 15.56 -0 52 43.69 P 4 21.84 0 magcol=fiber2mag_r, model_r=19.35',
 u'1237663462608339330 23 32 21.27 +0 40 06.96 P 4 21.40 0 magcol=fiber2mag_r, model_r=19.93',
 u'1237657191976665164 23 38 53.83 +0 48 12.47 P 4 19.45 0 magcol=fiber2mag_r, model_r=18.26',
 u'1237663784194343140 23 31 56.97 +0 09 13.65 P 4 21.34 0 magcol=fiber2mag_r, model_r=20.19',
 u'1237663278462468721 23 40 16.67 +0 53 01.22 P 3 21.99 0 magcol=fiber2mag_r, model_r=20.42',
 u'1237663783657734727 23 34 25.94 -0 21 54.06 P 4 20.91 0 magcol=fiber2mag_r, model_r=19.39',
 u'1237663277925204711 23 36 53.90 +0 28 53.02 P 7 21.66 0 magcol=fiber2mag_r, model_r=20.33',
 u'1237666408439874296 23 36 49.66 +0 21 33.57 P 6 21.81 0 magcol=fiber2mag_r, model_r=20.79',
 u'1237663463145603733 23 36 28.67 +1 07 04.34 P 1 22.59 0 magcol=fiber2mag_r, model_r=20.86',
 u'1237656906352624110 23 37 37.45 -0 52 11.99 P 3 21.43 0 magcol=fiber2mag_r, model_r=19.98',
 u'1237663278461944206 23 35 17.34 +0 56 44.14 P 2 21.51 0 magcol=fiber2mag_r, model_r=20.66',
 u'1237657191976730909 23 39 37.37 +0 40 23.07 P 1 21.90 0 magcol=fiber2mag_r, model_r=20.71',
 u'1237666408440070424 23 38 20.23 +0 13 45.45 P 7 20.67 0 magcol=fiber2mag_r, model_r=19.14',
 u'1237663783658128126 23 38 19.96 -0 17 10.28 P 4 21.31 0 magcol=fiber2mag_r, model_r=19.73',
 u'1237663277925401062 23 38 18.51 +0 36 12.98 P 3 21.74 0 magcol=fiber2mag_r, model_r=20.21',
 u'1237663784194998708 23 37 55.85 +0 08 13.52 P 7 21.71 0 magcol=fiber2mag_r, model_r=20.50',
 u'1237663784195326872 23 41 09.83 +0 07 36.36 P 2 22.47 0 magcol=fiber2mag_r, model_r=20.73',
 u'1237663783657538154 23 32 48.44 -0 25 04.54 P 4 23.07 0 magcol=fiber2mag_r, model_r=18.98',
 u'1237663783120929221 23 34 56.39 -0 48 43.62 P 4 21.81 0 magcol=fiber2mag_r, model_r=20.19',
 u'1237663783657800333 23 35 13.67 -0 18 49.67 P 1 21.68 0 magcol=fiber2mag_r, model_r=20.60',
 u'1237663277925728673 23 41 11.73 +0 28 44.20 P 3 20.66 0 magcol=fiber2mag_r, model_r=19.31',
 u'1237663783120929316 23 35 04.43 -0 40 07.83 P 2 22.25 0 magcol=fiber2mag_r, model_r=20.93',
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 u'1237663462608470433 23 33 36.47 +0 40 21.19 P 2 22.72 0 magcol=fiber2mag_r, model_r=20.50',
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 u'1237666407366460048 23 39 45.91 -0 34 51.89 P 2 22.21 0 magcol=fiber2mag_r, model_r=20.76',
 u'1237663784194999053 23 38 13.60 +0 10 20.01 P 7 21.70 0 magcol=fiber2mag_r, model_r=20.14',
 u'1237663783121060281 23 36 09.75 -0 46 50.70 P 4 20.53 0 magcol=fiber2mag_r, model_r=18.95',
 u'1237663783658258713 23 39 16.95 -0 15 57.81 P 2 22.04 0 magcol=fiber2mag_r, model_r=20.86',
 u'1237663784194540495 23 33 53.52 +0 03 44.53 P 2 23.23 0 magcol=fiber2mag_r, model_r=20.86',
 u'1237666408439677228 23 35 14.81 +0 19 41.92 P 8 20.79 0 magcol=fiber2mag_r, model_r=19.81',
 u'1237663462608667291 23 35 42.82 +0 40 37.55 P 6 22.15 0 magcol=fiber2mag_r, model_r=20.92',
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 u'1237657190902595808 23 36 10.57 -0 08 19.41 P 8 21.49 0 magcol=fiber2mag_r, model_r=20.47',
 u'1237666407366263085 23 38 01.06 -0 30 40.51 P 4 20.22 0 magcol=fiber2mag_r, model_r=18.64',
 u'1237666407366001173 23 35 27.35 -0 34 42.45 P 2 22.53 0 magcol=fiber2mag_r, model_r=20.79',
 u'1237666407365738859 23 33 19.14 -0 33 05.52 P 2 22.04 0 magcol=fiber2mag_r, model_r=20.76',
 u'1237666407366001101 23 35 21.21 -0 34 17.25 P 2 22.41 0 magcol=fiber2mag_r, model_r=20.88',
 u'1237663783657735010 23 34 50.74 -0 19 38.01 P 1 21.82 0 magcol=fiber2mag_r, model_r=20.84',
 u'1237666407366394422 23 39 08.44 -0 26 32.16 P 3 20.68 0 magcol=fiber2mag_r, model_r=19.56',
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 u'1237663783120929555 23 35 26.70 -0 43 44.27 P 1 22.23 0 magcol=fiber2mag_r, model_r=20.52',
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 u'1237663277925532224 23 39 40.80 +0 31 17.09 P 4 21.07 0 magcol=fiber2mag_r, model_r=19.89',
 u'1237663462608797941 23 37 01.19 +0 49 55.96 P 2 21.52 0 magcol=fiber2mag_r, model_r=20.64',
 u'1237666407366459748 23 39 44.79 -0 35 27.15 P 4 21.78 0 magcol=fiber2mag_r, model_r=20.07',
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 u'1237666408439480966 23 33 11.74 +0 24 18.02 P 1 22.45 0 magcol=fiber2mag_r, model_r=21.00',
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 u'1237663278462141066 23 37 28.02 +0 52 52.37 P 4 21.19 0 magcol=fiber2mag_r, model_r=19.81',
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 u'1237663783121257124 23 38 12.12 -0 48 36.57 P 2 22.16 0 magcol=fiber2mag_r, model_r=20.81',
 u'1237663784194999001 23 38 11.49 +0 03 33.76 P 6 21.84 0 magcol=fiber2mag_r, model_r=20.94',
 u'1237663784194409139 23 32 57.99 +0 03 32.60 P 2 21.87 0 magcol=fiber2mag_r, model_r=20.73',
 u'1237663783657734533 23 34 35.91 -0 20 56.32 P 4 20.97 0 magcol=fiber2mag_r, model_r=19.48',
 u'1237663783657603713 23 33 23.20 -0 12 43.40 P 1 21.79 0 magcol=fiber2mag_r, model_r=20.70',
 u'1237666407366000862 23 35 24.51 -0 36 05.15 P 3 21.72 0 magcol=fiber2mag_r, model_r=20.23',
 u'1237663277925007774 23 34 48.54 +0 26 36.91 P 8 20.73 0 magcol=fiber2mag_r, model_r=19.19',
 ...]

In [195]:
np.bincount(aat.prioritize_targets(anat))


Out[195]:
array([5567,  831, 1777, 1219, 2776,  172,  390,  272,  565])

In [209]:
aat.subsample_from_master_fld('aat_targets/AnaK_master.fld', 'aat_targets/test.fld',{8:300, 7:300,6:300},nflux=5, nguides=30)

Aeneid


In [223]:
ane.environsarcmin


Out[223]:
19.876642875889704

In [252]:
ane.environsarcmin = 60

In [244]:
ane.usnob_environs_query(True)


File catalogs/Aeneid_usnob.dat exists - not downloading anything.

In [245]:
ane.sdss_environs_query(dl=True)


File catalogs/Aeneid_sdss.dat exists - not downloading anything.

In [311]:
# note that SDSS catalog should go out to at least this far - when run it was 90 arcmin radius
anet =  targeting.select_targets(ane,outercutrad=1.2*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
anet = targeting.remove_targets_with_remlist(anet, ane)
len(anet)


Removing 12 objects at high z, keeping 4 (total of 171846 objects)
Removed 3 objects for Aeneid
Out[311]:
7650

In [273]:
guides = aat.select_guide_stars_sdss(ane.get_sdss_catalog())
calibs = aat.select_flux_stars(ane.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = aat.select_sky_positions(ane)

aat.produce_master_fld(ane, datetime.date(2014, 7, 4), anet, aat.prioritize_targets(anet),
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn='aat_targets/Aeneid_master.fld', randomizeorder=True);


Out[273]:
['LABEL Aeneid base catalog',
 'UTDATE  2014 07 04',
 u'CENTRE  20 38 54.48 -5 38 23.93',
 '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',
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 u'1237652600096688180 20 36 33.81 -5 25 05.59 P 2 21.78 0 magcol=fiber2mag_r, model_r=20.71',
 u'1237652935640221360 20 37 28.51 -5 15 44.68 P 2 22.12 0 magcol=fiber2mag_r, model_r=20.75',
 u'1237652936177223525 20 39 22.95 -4 54 14.61 P 4 22.11 0 magcol=fiber2mag_r, model_r=20.09',
 u'1237652599022945711 20 35 52.69 -6 09 35.42 P 4 20.56 0 magcol=fiber2mag_r, model_r=18.88',
 u'1237652934029739656 20 38 10.72 -6 21 48.03 P 2 22.13 0 magcol=fiber2mag_r, model_r=20.76',
 u'1237652935103414368 20 37 52.58 -5 39 42.77 P 8 22.32 0 magcol=fiber2mag_r, model_r=19.91',
 u'1237652600097342989 20 42 07.76 -5 33 40.21 P 2 21.70 0 magcol=fiber2mag_r, model_r=20.53',
 u'1237652934030000872 20 40 25.12 -6 32 03.65 P 4 19.52 0 magcol=fiber2mag_r, model_r=18.08',
 u'1237652936177484394 20 41 21.14 -4 57 56.90 P 4 18.09 0 magcol=fiber2mag_r, model_r=15.08',
 u'1237652935103613112 20 40 07.36 -5 45 07.96 P 5 21.96 0 magcol=fiber2mag_r, model_r=20.52',
 u'1237652600096621345 20 35 35.43 -5 21 14.88 P 2 22.32 0 magcol=fiber2mag_r, model_r=20.57',
 u'1237652934566283046 20 35 20.28 -5 58 46.81 P 3 21.01 0 magcol=fiber2mag_r, model_r=19.75',
 u'1237652935640549554 20 41 00.60 -5 14 00.21 P 3 21.15 0 magcol=fiber2mag_r, model_r=19.98',
 u'1237652599559882208 20 36 46.40 -5 41 58.10 P 4 21.70 0 magcol=fiber2mag_r, model_r=19.77',
 u'1237652934566937813 20 41 14.99 -6 11 14.03 P 3 20.30 0 magcol=fiber2mag_r, model_r=18.77',
 u'1237652599023535543 20 40 59.86 -6 23 26.56 P 1 22.43 0 magcol=fiber2mag_r, model_r=20.93',
 u'1237652936177222469 20 39 18.88 -4 47 00.06 P 3 19.63 0 magcol=fiber2mag_r, model_r=18.33',
 u'1237652934030002074 20 40 41.44 -6 32 09.08 P 4 21.36 0 magcol=fiber2mag_r, model_r=20.17',
 u'1237652600097277729 20 41 52.42 -5 30 05.91 P 2 21.87 0 magcol=fiber2mag_r, model_r=20.73',
 u'1237652599023142318 20 37 41.99 -6 15 52.58 P 4 21.39 0 magcol=fiber2mag_r, model_r=19.59',
 u'1237652935640418521 20 39 41.48 -5 18 52.21 P 1 22.22 0 magcol=fiber2mag_r, model_r=20.55',
 u'1237652935640614170 20 41 07.79 -5 13 34.68 P 4 20.83 0 magcol=fiber2mag_r, model_r=19.53',
 u'1237652598486336292 20 38 01.72 -6 35 25.55 P 4 20.64 0 magcol=fiber2mag_r, model_r=19.30',
 u'1237652599023142117 20 37 38.43 -6 12 58.94 P 4 20.93 0 magcol=fiber2mag_r, model_r=20.11',
 u'1237652934566610112 20 38 45.70 -6 01 21.62 P 4 21.40 0 magcol=fiber2mag_r, model_r=19.23',
 u'1237652935640286235 20 38 28.41 -5 06 36.83 P 4 21.61 0 magcol=fiber2mag_r, model_r=20.16',
 u'1237652600097014618 20 39 23.68 -5 21 54.41 P 8 19.87 0 magcol=fiber2mag_r, model_r=18.11',
 u'1237652935103480295 20 38 53.17 -5 42 29.79 P 7 21.36 0 magcol=fiber2mag_r, model_r=19.93',
 u'1237652599560340232 20 40 43.26 -6 00 27.20 P 4 19.19 0 magcol=fiber2mag_r, model_r=17.22',
 u'1237652599560079323 20 38 34.67 -5 47 37.12 P 7 21.19 0 magcol=fiber2mag_r, model_r=19.57',
 u'1237652599023404754 20 40 02.40 -6 22 23.41 P 4 20.91 0 magcol=fiber2mag_r, model_r=19.61',
 u'1237652600097015074 20 39 25.20 -5 27 58.37 P 7 20.08 0 magcol=fiber2mag_r, model_r=17.91',
 u'1237652598486337277 20 38 02.56 -6 33 25.79 P 2 22.00 0 magcol=fiber2mag_r, model_r=20.74',
 u'1237652599023076858 20 36 56.17 -6 13 54.95 P 2 22.07 0 magcol=fiber2mag_r, model_r=20.78',
 u'1237652600096753434 20 36 51.35 -5 22 32.01 P 4 21.15 0 magcol=fiber2mag_r, model_r=19.44',
 u'1237652935640680077 20 41 42.65 -5 20 25.47 P 4 21.10 0 magcol=fiber2mag_r, model_r=19.01',
 u'1237652935103611911 20 40 11.94 -5 35 06.97 P 8 20.53 0 magcol=fiber2mag_r, model_r=18.45',
 u'1237652600096622687 20 35 55.93 -5 22 47.39 P 4 20.97 0 magcol=fiber2mag_r, model_r=19.64',
 u'1237652934029871144 20 39 31.67 -6 28 21.15 P 4 21.13 0 magcol=fiber2mag_r, model_r=19.61',
 u'1237652599023468970 20 40 48.22 -6 24 22.78 P 3 19.64 0 magcol=fiber2mag_r, model_r=18.89',
 u'1237652599023207880 20 38 25.99 -6 13 13.62 P 4 21.32 0 magcol=fiber2mag_r, model_r=20.48',
 u'1237652935103742227 20 41 13.97 -5 43 12.01 P 4 21.28 0 magcol=fiber2mag_r, model_r=19.45',
 u'1237652600097408591 20 42 49.75 -5 34 16.28 P 1 21.88 0 magcol=fiber2mag_r, model_r=20.59',
 u'1237652935640221402 20 37 34.01 -5 09 19.97 P 2 22.11 0 magcol=fiber2mag_r, model_r=20.52',
 ...]

In [274]:
np.bincount(aat.prioritize_targets(anet))


Out[274]:
array([3505,  439,  878,  705, 1591,   73,  111,  119,  232])

In [256]:
aat.subsample_from_master_fld('aat_targets/Aeneid_master.fld', 'aat_targets/test2.fld',{8:300, 7:300,6:300, 4:100},nflux=5, nguides=30)

In [280]:
aat.imagelist_fld_targets('aat_targets/Aeneid_master.fld', ttype='prog8', n=25)


Out[280]:
'name ra dec\n1237652935103153032 308.917833333 -5.46376111111\n1237652599023075656 309.27775 -6.25160277778\n1237652600633559055 309.194083333 -4.95876944444\n1237652935640351638 309.766958333 -5.14728055556\n1237652935103154597 309.0075 -5.57546666667\n1237652935640287167 309.606416667 -5.09363333333\n1237652599023206531 309.539708333 -6.33797222222\n1237652935640549313 310.193708333 -5.28945\n1237652934566413108 309.184291667 -5.92925277778\n1237652934566348171 309.068208333 -6.04322777778\n1237652599560208574 309.909416667 -5.97499166667\n1237652600633557687 309.102708333 -4.95463333333\n1237652935103285067 309.213375 -5.59420555556\n1237652600096753822 309.3105 -5.39707222222\n1237652600096949659 309.623125 -5.40990555556\n1237652599023075657 309.277625 -6.25215555556\n1237652599560144585 309.776333333 -5.77063888889\n1237652600096688492 309.116541667 -5.37565\n1237652934566676071 309.760541667 -6.04174444444\n1237652935103154520 308.987125 -5.62606944444\n1237652935103349442 309.321791667 -5.675\n1237652598486337412 309.54975 -6.59216944444\n1237652600096948939 309.68225 -5.37445555556\n1237652935103482049 309.73875 -5.61116944444\n1237652935640418571 309.946833333 -5.23711111111'

Gilgamesh


In [422]:
gilg.environsarcmin = 60

In [431]:
# note that SDSS catalog should go out to at least this far - when run it was 90 arcmin radius
gilgt =  targeting.select_targets(gilg,outercutrad=1.2*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
gilgt = targeting.remove_targets_with_remlist(gilgt, gilg, 'TargetRemoveJul1_2014.csv')
len(gilgt)


Removing 180 objects at high z, keeping 7 (total of 37151 objects)
Removed 9 objects for Gilgamesh
Out[431]:
3388

In [432]:
gilgpris = aat.prioritize_targets(gilgt)

In [433]:
gilgtcoos = ICRS(gilgt['ra']*u.deg, gilgt['dec']*u.deg)
gilgsepsfrompossiblesat = gilgtcoos.separation(ICRS(234.52499 *u.deg, 17.022083 *u.deg))
possiblesatidx = np.argmin(gilgsepsfrompossiblesat)
print gilgtcoos[possiblesatidx], gilgt['objID'][possiblesatidx]
gilgpris[possiblesatidx] = 9


<ICRS RA=234.52499 deg, Dec=17.02208 deg> 1237668336857842054

In [434]:
gilg_observed = fits.getdata('mmthecto/configs_observed/saga_Gilgamesh.fits', 1)

#checked, and this is as reliable as coordinate-matching
oids = np.array([int(v) for v in gilg_observed['SDSS_OBJID']])
np.in1d(oids, gilgt['objID']).sum(), len(oids), np.in1d(gilgt['objID'], oids).sum(), len(gilgt)


Out[434]:
(1707, 2659, 1706, 3388)

In [435]:
alreadyobserved = np.in1d(gilgt['objID'], oids)

In [436]:
gilgt = gilgt[~alreadyobserved|(gilgpris>=9)]
gilgpris = gilgpris[~alreadyobserved|(gilgpris>=9)]

In [437]:
gilgt[gilgpris>=9]


Out[437]:
objIDradec...rhostrhost_kpc
1237668336857842054234.52498880217.0220833873...0.559882077949270.996264196

In [438]:
guides = aat.select_guide_stars_sdss(gilg.get_sdss_catalog())
calibs = aat.select_flux_stars(gilg.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = aat.select_sky_positions(gilg,
                                    usnocat=ane.get_usnob_catalog())  #REMOVE

aat.produce_master_fld(gilg, datetime.date(2014, 7, 4), gilgt, gilgpris,
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn='aat_targets/Gilgamesh_master.fld', randomizeorder=True);


Out[438]:
['LABEL Gilgamesh base catalog',
 'UTDATE  2014 07 04',
 u'CENTRE  15 36 31.68 +16 36 27.99',
 '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',
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 u'1237668336857842288 15 38 26.03 +16 59 53.54 P 8 21.51 0 magcol=fiber2mag_r, model_r=19.88',
 u'1237665566078337258 15 34 36.98 +16 58 37.60 P 6 22.20 0 magcol=fiber2mag_r, model_r=20.82',
 u'1237668336857907526 15 38 42.66 +16 54 04.93 P 7 20.33 0 magcol=fiber2mag_r, model_r=18.87',
 u'1237665565541532205 15 34 24.56 +16 36 14.43 P 5 22.31 0 magcol=fiber2mag_r, model_r=20.80',
 u'1237668335783772645 15 34 25.34 +16 24 10.88 P 8 21.39 0 magcol=fiber2mag_r, model_r=19.89',
 u'1237665565541860447 15 37 19.97 +16 14 53.57 P 8 23.32 0 magcol=fiber2mag_r, model_r=18.00',
 u'1237668335783970002 15 36 07.30 +16 14 45.08 P 5 23.04 0 magcol=fiber2mag_r, model_r=20.95',
 u'1237665565004726955 15 34 43.43 +16 11 21.22 P 8 22.74 0 magcol=fiber2mag_r, model_r=19.78',
 u'1237668336320643407 15 34 43.69 +16 50 58.76 P 7 20.78 0 magcol=fiber2mag_r, model_r=19.03',
 u'1237665565541925877 15 38 32.87 +16 20 59.91 P 6 22.12 0 magcol=fiber2mag_r, model_r=20.65',
 u'1237665566078599870 15 36 51.43 +16 49 36.48 P 5 21.84 0 magcol=fiber2mag_r, model_r=20.77',
 u'1237665565541991008 15 38 34.39 +16 15 43.65 P 5 22.10 0 magcol=fiber2mag_r, model_r=20.69',
 u'1237665566615535908 15 38 01.59 +17 06 22.07 P 8 20.44 0 magcol=fiber2mag_r, model_r=18.07',
 u'1237665565541860010 15 37 25.48 +16 21 43.99 P 6 22.24 0 magcol=fiber2mag_r, model_r=20.68',
 u'1237665566078337876 15 34 43.48 +17 01 45.12 P 6 22.08 0 magcol=fiber2mag_r, model_r=20.86',
 u'1237668336857580242 15 36 07.76 +17 13 04.57 P 6 22.09 0 magcol=fiber2mag_r, model_r=20.83',
 u'1237668336320577732 15 34 07.36 +16 49 11.59 P 7 19.75 0 magcol=fiber2mag_r, model_r=18.13',
 u'1237668336857776794 15 37 38.78 +17 04 16.39 P 5 22.21 0 magcol=fiber2mag_r, model_r=20.98',
 u'1237668335783772877 15 34 12.77 +16 24 22.50 P 6 21.90 0 magcol=fiber2mag_r, model_r=20.62',
 u'1237668335783837780 15 34 45.00 +16 18 27.76 P 5 21.87 0 magcol=fiber2mag_r, model_r=20.75',
 u'1237668336857776830 15 37 40.63 +17 03 05.85 P 5 21.99 0 magcol=fiber2mag_r, model_r=20.68',
 u'1237668336857580058 15 35 48.19 +17 11 13.47 P 6 22.34 0 magcol=fiber2mag_r, model_r=20.85',
 u'1237668336857776789 15 37 31.73 +16 58 23.53 P 7 21.45 0 magcol=fiber2mag_r, model_r=20.29',
 u'1237665565004924107 15 36 35.18 +16 03 15.12 P 7 23.38 0 magcol=fiber2mag_r, model_r=19.84',
 u'1237668367458894328 15 36 30.98 +15 59 33.69 P 8 20.33 0 magcol=fiber2mag_r, model_r=18.42',
 u'1237668336857646041 15 36 28.96 +16 58 27.94 P 7 23.27 0 magcol=fiber2mag_r, model_r=20.15',
 u'1237665565541532304 15 34 29.79 +16 33 51.07 P 8 23.40 0 magcol=fiber2mag_r, model_r=18.06',
 u'1237667783929037161 15 37 55.87 +16 05 30.00 P 7 21.40 0 magcol=fiber2mag_r, model_r=20.24',
 u'1237668367458894305 15 36 28.75 +16 01 21.74 P 8 20.50 0 magcol=fiber2mag_r, model_r=19.29',
 u'1237665565541598123 15 35 20.08 +16 25 58.54 P 8 23.38 0 magcol=fiber2mag_r, model_r=19.75',
 u'1237665566615405053 15 36 53.61 +17 11 01.42 P 7 21.24 0 magcol=fiber2mag_r, model_r=20.20',
 u'1237665565541990831 15 38 25.69 +16 13 39.21 P 8 21.15 0 magcol=fiber2mag_r, model_r=18.55',
 u'1237667783928906160 15 36 25.02 +16 10 46.35 P 7 20.65 0 magcol=fiber2mag_r, model_r=19.41',
 u'1237667783928906038 15 36 45.36 +16 09 56.50 P 7 20.06 0 magcol=fiber2mag_r, model_r=18.71',
 u'1237665566078730666 15 37 42.44 +16 39 28.02 P 8 23.22 0 magcol=fiber2mag_r, model_r=19.46',
 u'1237665566078599785 15 36 48.71 +16 54 36.02 P 5 22.06 0 magcol=fiber2mag_r, model_r=20.74',
 u'1237665566078403078 15 34 48.77 +16 56 03.93 P 5 22.19 0 magcol=fiber2mag_r, model_r=21.00',
 u'1237668336857776340 15 37 35.26 +17 03 40.42 P 6 21.61 0 magcol=fiber2mag_r, model_r=20.99',
 u'1237668367458894506 15 36 16.16 +16 01 12.31 P 8 21.15 0 magcol=fiber2mag_r, model_r=20.15',
 u'1237668336320905999 15 37 17.39 +16 31 38.27 P 7 21.86 0 magcol=fiber2mag_r, model_r=20.20',
 u'1237668367458960226 15 36 51.94 +15 59 43.41 P 6 22.18 0 magcol=fiber2mag_r, model_r=20.85',
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 u'1237668336320774938 15 36 00.78 +16 36 04.81 P 6 23.32 0 magcol=fiber2mag_r, model_r=20.86',
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 u'1237665565541990850 15 38 35.66 +16 16 23.00 P 5 21.80 0 magcol=fiber2mag_r, model_r=20.84',
 u'1237665565541467032 15 34 06.64 +16 32 47.97 P 6 21.92 0 magcol=fiber2mag_r, model_r=20.57',
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 u'1237667966962434725 15 36 25.16 +16 34 30.90 P 8 23.29 0 magcol=fiber2mag_r, model_r=17.82',
 u'1237665565004923240 15 36 44.82 +16 01 43.14 P 7 19.73 0 magcol=fiber2mag_r, model_r=18.22',
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 u'1237668336857907351 15 38 36.38 +16 55 21.75 P 7 19.46 0 magcol=fiber2mag_r, model_r=17.88',
 u'1237668336857711029 15 36 43.88 +17 00 19.14 P 7 21.38 0 magcol=fiber2mag_r, model_r=20.06',
 u'1237665565541925832 15 38 24.54 +16 18 04.87 P 5 21.85 0 magcol=fiber2mag_r, model_r=20.59',
 u'1237665565541467023 15 34 01.85 +16 30 12.57 P 5 21.78 0 magcol=fiber2mag_r, model_r=20.88',
 u'1237668335783772872 15 34 12.67 +16 24 33.40 P 6 21.94 0 magcol=fiber2mag_r, model_r=20.95',
 u'1237665566615405389 15 36 51.31 +17 13 10.62 P 6 22.16 0 magcol=fiber2mag_r, model_r=20.94',
 u'1237665565004923987 15 36 23.71 +16 04 22.57 P 8 23.39 0 magcol=fiber2mag_r, model_r=18.66',
 u'1237668367458894830 15 36 14.71 +16 01 13.39 P 6 22.26 0 magcol=fiber2mag_r, model_r=20.79',
 u'1237665566078599954 15 37 05.98 +16 54 01.05 P 5 22.21 0 magcol=fiber2mag_r, model_r=20.99',
 u'1237668336857841947 15 38 38.75 +16 57 48.74 P 7 20.33 0 magcol=fiber2mag_r, model_r=19.11',
 u'1237667783928972151 15 37 26.73 +16 02 42.49 P 6 22.23 0 magcol=fiber2mag_r, model_r=20.83',
 u'1237668336857777145 15 38 04.03 +17 03 42.01 P 5 21.62 0 magcol=fiber2mag_r, model_r=20.51',
 u'1237665566078862039 15 39 04.19 +16 36 13.35 P 7 21.84 0 magcol=fiber2mag_r, model_r=20.31',
 u'1237667966962434482 15 36 40.54 +16 41 16.07 P 6 22.54 0 magcol=fiber2mag_r, model_r=20.91',
 u'1237665566615470123 15 37 21.59 +17 10 49.92 P 6 22.25 0 magcol=fiber2mag_r, model_r=20.91',
 u'1237665566078599987 15 36 53.66 +16 43 17.31 P 7 21.65 0 magcol=fiber2mag_r, model_r=20.46',
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 u'1237665565541990963 15 38 30.02 +16 15 37.62 P 6 21.85 0 magcol=fiber2mag_r, model_r=20.73',
 u'1237665566078533702 15 36 15.83 +16 51 02.88 P 6 22.09 0 magcol=fiber2mag_r, model_r=20.78',
 '\n#Flux stars',
 '\n#Guide stars',
 u'Guide0 15 36 03.26 +16 40 19.78 F 9 13.58 0 id=1237668336320774249',
 u'Guide1 15 38 15.85 +16 48 23.85 F 9 13.50 0 id=1237665566078730329',
 u'Guide2 15 37 56.89 +16 10 41.27 F 9 13.66 0 id=1237668367995895917',
 u'Guide3 15 35 55.30 +17 11 01.13 F 9 13.86 0 id=1237668336857579562',
 u'Guide4 15 36 05.77 +16 57 27.84 F 9 13.89 0 id=1237665566078468356',
 u'Guide5 15 36 58.22 +16 55 21.44 F 9 13.51 0 id=1237665566078599219',
 u'Guide6 15 36 30.41 +17 10 38.66 F 9 13.62 0 id=1237668336857645069',
 u'Guide7 15 34 26.82 +16 33 12.77 F 9 13.76 0 id=1237665565541531730',
 u'Guide8 15 37 25.81 +16 15 26.89 F 9 13.71 0 id=1237665565541859405',
 u'Guide9 15 37 03.49 +16 51 18.97 F 9 13.84 0 id=1237665566078599262',
 u'Guide10 15 38 52.34 +16 38 04.75 F 9 13.99 0 id=1237665566078795932',
 u'Guide11 15 34 48.35 +16 41 57.64 F 9 13.89 0 id=1237668336320643143',
 u'Guide12 15 38 17.04 +16 33 30.90 F 9 13.89 0 id=1237668336321036313',
 u'Guide13 15 35 16.96 +16 11 16.83 F 9 13.70 0 id=1237668335783903323',
 u'Guide14 15 34 34.90 +16 44 48.06 F 9 13.79 0 id=1237668336320643113',
 u'Guide15 15 34 28.58 +16 37 20.42 F 9 13.81 0 id=1237665565541531701',
 u'Guide16 15 35 46.51 +16 58 35.19 F 9 13.90 0 id=1237665566078468225',
 u'Guide17 15 35 28.72 +16 39 02.54 F 9 13.77 0 id=1237668336320708717',
 u'Guide18 15 38 09.74 +16 41 34.84 F 9 13.52 0 id=1237665566078730340',
 u'Guide19 15 36 21.24 +16 42 34.72 F 9 13.85 0 id=1237667966962368624',
 u'Guide20 15 35 28.50 +16 31 48.97 F 9 13.65 0 id=1237665565541597342',
 u'Guide21 15 37 38.41 +16 17 32.62 F 9 13.81 0 id=1237665565541859425',
 u'Guide22 15 37 28.26 +16 36 46.80 F 9 13.61 0 id=1237668336320905325',
 u'Guide23 15 36 51.84 +16 45 18.15 F 9 13.90 0 id=1237665566078599256',
 u'Guide24 15 36 36.84 +17 07 42.04 F 9 13.68 0 id=1237668336857645113',
 u'Guide25 15 38 13.66 +17 02 35.83 F 9 13.56 0 id=1237668336857841671',
 u'Guide26 15 38 23.12 +16 35 19.22 F 9 13.94 0 id=1237668336321036325',
 u'Guide27 15 37 18.68 +16 25 47.25 F 9 13.64 0 id=1237665565541793915',
 u'Guide28 15 37 22.66 +16 07 55.39 F 9 13.88 0 id=1237667783928971370',
 u'Guide29 15 36 25.43 +16 02 43.31 F 9 13.55 0 id=1237668367458893916',
 u'Guide30 15 38 07.83 +16 36 11.43 F 9 13.65 0 id=1237668336320970984',
 u'Guide31 15 34 01.31 +16 39 13.89 F 9 13.83 0 id=1237665565541466213',
 u'Guide32 15 34 40.60 +16 52 34.61 F 9 13.62 0 id=1237668336320643103',
 u'Guide33 15 36 07.30 +16 39 29.94 F 9 13.93 0 id=1237668336320774267',
 u'Guide34 15 34 03.56 +16 36 15.50 F 9 13.58 0 id=1237665565541466239',
 '\n#Sky positions',
 u'Sky0 15 39 28.01 +16 18 01.77 S 9 20.00 0 sky',
 u'Sky1 15 36 19.11 +15 53 50.93 S 9 20.00 0 sky',
 u'Sky2 15 38 58.82 +15 57 19.16 S 9 20.00 0 sky',
 u'Sky3 15 33 48.85 +16 10 29.90 S 9 20.00 0 sky',
 u'Sky4 15 33 13.57 +16 45 49.50 S 9 20.00 0 sky',
 u'Sky5 15 36 22.50 +17 16 38.27 S 9 20.00 0 sky',
 u'Sky6 15 33 55.82 +16 44 59.04 S 9 20.00 0 sky',
 u'Sky7 15 38 26.28 +17 27 56.59 S 9 20.00 0 sky',
 u'Sky8 15 37 18.04 +17 33 36.89 S 9 20.00 0 sky',
 u'Sky9 15 38 50.66 +16 08 28.14 S 9 20.00 0 sky',
 u'Sky10 15 36 15.68 +15 59 58.24 S 9 20.00 0 sky',
 u'Sky11 15 33 17.81 +17 01 08.28 S 9 20.00 0 sky',
 u'Sky12 15 34 47.21 +15 50 56.51 S 9 20.00 0 sky',
 u'Sky13 15 38 56.21 +17 13 13.80 S 9 20.00 0 sky',
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 u'Sky130 15 34 42.26 +17 27 25.09 S 9 20.00 0 sky',
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 u'Sky134 15 39 30.71 +16 09 39.59 S 9 20.00 0 sky',
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 u'Sky152 15 36 30.09 +16 14 01.51 S 9 20.00 0 sky',
 u'Sky153 15 36 03.85 +17 26 45.38 S 9 20.00 0 sky',
 u'Sky154 15 33 00.42 +16 39 21.35 S 9 20.00 0 sky',
 u'Sky155 15 36 55.92 +17 27 07.99 S 9 20.00 0 sky',
 u'Sky156 15 35 56.48 +17 19 39.73 S 9 20.00 0 sky',
 u'Sky157 15 33 27.02 +16 39 19.17 S 9 20.00 0 sky',
 u'Sky158 15 37 20.75 +16 24 44.68 S 9 20.00 0 sky',
 u'Sky159 15 35 36.45 +16 10 45.37 S 9 20.00 0 sky',
 u'Sky160 15 34 50.22 +17 07 05.35 S 9 20.00 0 sky',
 u'Sky161 15 37 31.99 +17 28 57.20 S 9 20.00 0 sky',
 u'Sky162 15 39 33.77 +16 29 39.27 S 9 20.00 0 sky',
 u'Sky163 15 38 07.87 +16 50 42.67 S 9 20.00 0 sky',
 u'Sky164 15 35 20.78 +16 02 44.47 S 9 20.00 0 sky',
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 u'Sky172 15 34 13.58 +17 04 54.65 S 9 20.00 0 sky',
 u'Sky173 15 37 54.28 +16 42 05.46 S 9 20.00 0 sky',
 u'Sky174 15 37 55.25 +15 53 10.27 S 9 20.00 0 sky',
 u'Sky175 15 38 09.12 +16 56 09.38 S 9 20.00 0 sky',
 u'Sky176 15 40 12.93 +16 30 52.34 S 9 20.00 0 sky',
 u'Sky177 15 33 48.73 +16 55 25.10 S 9 20.00 0 sky',
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 u'Sky179 15 33 21.06 +16 50 02.01 S 9 20.00 0 sky',
 u'Sky180 15 33 52.66 +16 28 13.56 S 9 20.00 0 sky',
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 u'Sky182 15 34 11.47 +17 16 20.40 S 9 20.00 0 sky',
 u'Sky183 15 38 04.20 +16 00 57.79 S 9 20.00 0 sky',
 u'Sky184 15 34 56.73 +16 33 02.27 S 9 20.00 0 sky',
 u'Sky185 15 36 00.82 +15 55 09.79 S 9 20.00 0 sky',
 u'Sky186 15 35 21.87 +17 09 22.31 S 9 20.00 0 sky',
 u'Sky187 15 38 10.66 +15 58 47.22 S 9 20.00 0 sky',
 u'Sky188 15 38 52.03 +16 10 46.83 S 9 20.00 0 sky',
 u'Sky189 15 38 43.44 +17 22 44.33 S 9 20.00 0 sky',
 u'Sky190 15 33 20.22 +16 13 01.25 S 9 20.00 0 sky',
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 u'Sky192 15 38 42.51 +16 46 04.16 S 9 20.00 0 sky',
 u'Sky193 15 33 25.15 +16 37 19.76 S 9 20.00 0 sky',
 u'Sky194 15 40 15.01 +16 20 33.41 S 9 20.00 0 sky',
 u'Sky195 15 34 29.08 +17 04 25.88 S 9 20.00 0 sky',
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 u'Sky197 15 34 28.19 +17 09 21.51 S 9 20.00 0 sky',
 u'Sky198 15 38 32.02 +17 10 06.59 S 9 20.00 0 sky',
 u'Sky199 15 38 52.96 +16 31 14.41 S 9 20.00 0 sky',
 u'Sky200 15 35 40.03 +16 07 20.62 S 9 20.00 0 sky',
 u'Sky201 15 36 13.72 +16 42 53.71 S 9 20.00 0 sky',
 u'Sky202 15 38 31.83 +16 01 34.39 S 9 20.00 0 sky',
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 u'Sky213 15 38 01.08 +17 07 37.43 S 9 20.00 0 sky',
 u'Sky214 15 37 46.28 +15 55 17.52 S 9 20.00 0 sky',
 u'Sky215 15 38 31.87 +17 06 13.95 S 9 20.00 0 sky',
 u'Sky216 15 38 40.82 +15 58 44.32 S 9 20.00 0 sky',
 u'Sky217 15 38 00.34 +17 26 32.14 S 9 20.00 0 sky',
 u'Sky218 15 34 08.53 +16 59 59.27 S 9 20.00 0 sky',
 u'Sky219 15 39 20.68 +16 01 14.73 S 9 20.00 0 sky',
 u'Sky220 15 37 01.27 +15 36 57.78 S 9 20.00 0 sky',
 u'Sky221 15 33 58.84 +16 46 44.95 S 9 20.00 0 sky',
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 u'Sky223 15 35 27.72 +15 44 55.59 S 9 20.00 0 sky',
 u'Sky224 15 40 16.85 +16 51 49.38 S 9 20.00 0 sky',
 u'Sky225 15 39 13.52 +16 59 20.97 S 9 20.00 0 sky',
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 u'Sky228 15 34 08.56 +16 54 40.90 S 9 20.00 0 sky',
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 u'Sky232 15 36 49.18 +15 53 53.40 S 9 20.00 0 sky',
 u'Sky233 15 33 30.55 +17 04 26.41 S 9 20.00 0 sky',
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 u'Sky235 15 36 10.44 +15 51 04.30 S 9 20.00 0 sky',
 u'Sky236 15 35 38.88 +15 42 15.15 S 9 20.00 0 sky',
 u'Sky237 15 33 44.72 +17 09 59.29 S 9 20.00 0 sky',
 u'Sky238 15 34 12.06 +15 49 01.88 S 9 20.00 0 sky',
 u'Sky239 15 36 45.49 +15 38 06.92 S 9 20.00 0 sky',
 u'Sky240 15 39 22.01 +16 50 25.59 S 9 20.00 0 sky',
 u'Sky241 15 38 11.73 +15 56 05.72 S 9 20.00 0 sky',
 u'Sky242 15 33 48.03 +16 38 02.00 S 9 20.00 0 sky',
 u'Sky243 15 36 15.54 +15 54 08.18 S 9 20.00 0 sky',
 u'Sky244 15 35 03.08 +16 01 05.68 S 9 20.00 0 sky',
 u'Sky245 15 36 31.25 +16 39 34.39 S 9 20.00 0 sky',
 u'Sky246 15 37 33.41 +15 50 28.22 S 9 20.00 0 sky',
 u'Sky247 15 38 03.01 +16 25 12.61 S 9 20.00 0 sky',
 u'Sky248 15 38 47.88 +16 08 43.68 S 9 20.00 0 sky',
 u'Sky249 15 37 32.84 +15 45 00.96 S 9 20.00 0 sky']

In [439]:
np.bincount(gilgpris)


Out[439]:
array([1283,    0,    1,    0,    0,   80,   83,  139,   96,    1])

In [440]:
aat.subsample_from_master_fld('aat_targets/Gilgamesh_master.fld', 'aat_targets/test3.fld',{8:300, 7:300,6:300, 4:100},nflux=5, nguides=30)

For actual observing


In [168]:
targets = [alice, ana, uly, gilg, ane]
for t in targets:
    t.environsarcmin = 60

In [169]:
for t in targets:
    t.usnob_environs_query(dl=True)


Downloading USNO-B to catalogs/Alice in Wonderland_usnob.dat
Unknown Size
2691 kB downloaded
File catalogs/Ana Karenina_usnob.dat exists - not downloading anything.
File catalogs/Ulysses_usnob.dat exists - not downloading anything.
File catalogs/Gilgamesh_usnob.dat exists - not downloading anything.
File catalogs/Aeneid_usnob.dat exists - not downloading anything.

In [684]:
for t in targets:
    t.sdss_environs_query(dl=True)


File catalogs/Alice in Wonderland_sdss.dat exists - not downloading anything.
File catalogs/NSA61945_sdss.dat exists - not downloading anything.
File catalogs/NSA150340_sdss.dat exists - not downloading anything.
Downloading NSA ID166313 to catalogs/Gilgamesh_sdss.dat
Unknown Size
40353 kB downloaded
File catalogs/Aeneid_sdss.dat exists - not downloading anything.

In [474]:
#we show the queries for wise xmatch, as these may be too long to complete via the script, and should thus be in casjobs
for t in targets:
    print 'QUERY FOR:', t.name
    print t.sdss_environs_query(dl=False, xmatchwise=True, usecas=True)


QUERY FOR: Alice in Wonderland

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.w1mpro,-99) as w1, ISNULL(W.w1sigmpro, -99) as w1_err


INTO MyDB.Alice_in_Wonderland_wwise_environs
FROM fGetNearbyObjEq(180.09842901, -1.10007802185, 60.0) 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


QUERY FOR: Ana Karenina

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.w1mpro,-99) as w1, ISNULL(W.w1sigmpro, -99) as w1_err


INTO MyDB.Ana_Karenina_wwise_environs
FROM fGetNearbyObjEq(354.13105403, 0.297265046422, 60.0) 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


QUERY FOR: Ulysses

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.w1mpro,-99) as w1, ISNULL(W.w1sigmpro, -99) as w1_err


INTO MyDB.Ulysses_wwise_environs
FROM fGetNearbyObjEq(344.33144922, -1.04903473422, 60.0) 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


QUERY FOR: Gilgamesh

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.w1mpro,-99) as w1, ISNULL(W.w1sigmpro, -99) as w1_err


INTO MyDB.Gilgamesh_wwise_environs
FROM fGetNearbyObjEq(234.132011282, 16.6077737961, 60.0) 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


QUERY FOR: Aeneid

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.w1mpro,-99) as w1, ISNULL(W.w1sigmpro, -99) as w1_err


INTO MyDB.Aeneid_wwise_environs
FROM fGetNearbyObjEq(309.727020806, -5.63998013366, 60.0) 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



In [488]:
#Identify sky regions for each host and write out to separate files - 
from os.path import exists

for t in targets:
    outfn = 'aat_targets/' + t.name.replace(' ','_') + '_sky.dat'
    if exists(outfn):
        print outfn, 'exists, not overwriting'
    else:
        print 'Writing', outfn
        aat.select_sky_positions(t, nsky=100, outfn=outfn)


Writing aat_targets/Alice in Wonderland_sky.dat
Writing aat_targets/Ana Karenina_sky.dat
Writing aat_targets/Ulysses_sky.dat
Writing aat_targets/Gilgamesh_sky.dat
Writing aat_targets/Aeneid_sky.dat

In [597]:
#Now visually inspect them one at a time, and edit the file to remove any that are not good sky positions

In [497]:
aat.imagelist_fld_targets("aat_targets/Alice_in_Wonderland_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets/Alice_in_Wonderland_sky.dat"

In [498]:
aat.imagelist_fld_targets("aat_targets/Ana_Karenina_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets/Ana_Karenina_sky.dat"

In [499]:
aat.imagelist_fld_targets("aat_targets/Ulysses_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets/Ulysses_sky.dat"

In [500]:
aat.imagelist_fld_targets("aat_targets/Gilgamesh_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets/Gilgamesh_sky.dat"

In [501]:
aat.imagelist_fld_targets("aat_targets/Aeneid_sky.dat", ttype='sky', n=np.inf);
!subl "aat_targets/Aeneid_sky.dat"

First Night


In [7]:
alice = hsd['Alice in Wonderland']
ana = hsd['Ana Karenina']
gilg = hsd['Gilgamesh']
ane = hsd['Aeneid']
uly = hsd['Ulysses']

Alice


In [170]:
alicet =  targeting.select_targets(alice,outercutrad=1.0*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
alicet = targeting.remove_targets_with_remlist(alicet, 'Alice', 'TargetRemoveJul4_2014.csv')
len(alicet)


Removing 482 objects at high z, keeping 3 (total of 83307 objects)
Could not find a match for objid 1237674648854659334 of Alice, closest is [ 11.15808596] arcsec
Removed 8 objects for Alice
Out[170]:
12046

In [171]:
alicepris = aat.prioritize_targets(alicet)  #note that this one includes the WISE color categories

In [172]:
#now set priority=-1 for objects that have good readshifts from imacs.  Those then won't get written into the master list
alice_observed = ascii.read('imacs_data/Velocities_AliceInWonderland_full.dat')

#matching on objID is as reliable as (more?) than coordinate-matching
goodspectrum = alice_observed['Category'] < 3 #1 is good, 4 is bad
alice_to_remove = np.array([int(v) for v in alice_observed['Object'][goodspectrum] if v[0]!='A'])

print 'Matched',np.in1d(alice_to_remove, alicet['objID']).sum(), 'of', len(alice_to_remove), 'imacs targets that have good spec'
print 'Matched',np.in1d(alicet['objID'], alice_to_remove).sum(), 'in target list of', len(alicet)

alicepris[np.in1d(alicet['objID'], alice_to_remove)] = -1


Matched 566 of 567 imacs targets that have good spec
Matched 565 in target list of 12046

In [173]:
fnbase = 'aat_targets/Alice_in_Wonderland'
host = alice
targs = alicet
pris = alicepris

In [181]:
guides = aat.select_guide_stars_sdss(host.get_sdss_catalog(), magrng=(12.5, 14))
calibs = aat.select_flux_stars(host.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = fnbase + '_sky.dat'

aat.produce_master_fld(host, datetime.date(2014, 7, 4), targs, pris,
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn=fnbase+'_master.fld', fluxpri=8, 
                       randomizeorder=True)

#this counts up priorities directly from the master list
pricnt = {}
with open(fnbase+'_master.fld') as f:
    for l in f:
        ls = l.split(' ')
        if len(ls) > 8 and ls[8].isdigit():
            if ls[0].startswith('Sky') or ls[0].startswith('Guide') or ls[0].startswith('Flux'):
                continue
            pricnt.setdefault(ls[8], 0) 
            pricnt[ls[8]] += 1
pricnt


Out[181]:
{'1': 456, '2': 741, '3': 1503, '4': 1043, '5': 1, '6': 1538, '7': 107, '9': 1}

In [156]:
#No configurations because of the moon problem - but the above vars are used below in night 2

Gilgamesh


In [95]:
gilgt =  targeting.select_targets(gilg,outercutrad=1.0*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
gilgt = targeting.remove_targets_with_remlist(gilgt, gilg, 'TargetRemoveJul1_2014.csv')
len(gilgt)


Removing 558 objects at high z, keeping 9 (total of 99912 objects)
Removed 9 objects for Gilgamesh
Out[95]:
9244

In [96]:
gilgpris = aat.prioritize_targets(gilgt)

In [97]:
#identify the "candidate" satellite to later give it priority 9
gilgtcoos = ICRS(gilgt['ra']*u.deg, gilgt['dec']*u.deg)
gilgsepsfrompossiblesat = gilgtcoos.separation(ICRS(234.52499 *u.deg, 17.022083 *u.deg))
possiblesatidx = np.argmin(gilgsepsfrompossiblesat)
gilgtcoos[possiblesatidx], gilgt['objID'][possiblesatidx]


Out[97]:
(<ICRS RA=234.52499 deg, Dec=17.02208 deg>, 1237668336857842054)

In [109]:
gilg_observed = fits.getdata('mmthecto/configs_observed/saga_Gilgamesh.fits', 1)
goodqual = gilg_observed['ZQUALITY']>2
print np.sum(goodqual), len(goodqual)

#checked, and this is as reliable as coordinate-matching
oids = np.array([int(v) for v in gilg_observed['SDSS_OBJID']])
np.in1d(oids, gilgt['objID']).sum(), len(oids), np.in1d(gilgt['objID'], oids).sum(), len(gilgt)


2531 2659
Out[109]:
(1708, 2659, 1707, 9244)

In [99]:
#set priorities to -1 for already observed
alreadyobserved = np.in1d(gilgt['objID'], oids)
gilgpris[alreadyobserved] = -1
gilgpris[possiblesatidx] = 9

In [100]:
fnbase = 'aat_targets/Gilgamesh'
host = gilg
targs = gilgt
pris = gilgpris

In [692]:
guides = aat.select_guide_stars_sdss(host.get_sdss_catalog())
calibs = aat.select_flux_stars(host.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = fnbase + '_sky.dat'
aat.produce_master_fld(host, datetime.date(2014, 7, 4), targs, pris,
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn=fnbase+'_master.fld', randomizeorder=True,
                       fluxpri=8); #flux stars-> 8 b/c of possible satellite

#this counts up priorities directly from the master list
pricnt = {}
with open(fnbase+'_master.fld') as f:
    for l in f:
        ls = l.split(' ')
        if len(ls) > 8 and ls[8].isdigit():
            if ls[0].startswith('Sky') or ls[0].startswith('Guide') or ls[0].startswith('Flux'):
                continue
            pricnt.setdefault(ls[8], 0) 
            pricnt[ls[8]] += 1
pricnt


Out[692]:
{'1': 80, '2': 139, '3': 1361, '4': 83, '6': 96, '9': 2}

In [693]:
finum = 1
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:550, 4:100, 6:96, 9:2},
                              nflux=3, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now design config

#note: first one was done removing *all* targets, not just zqual<3

In [694]:
finum = 2
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:550, 4:100, 6:96, 9:2},
                              nflux=3, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now design config


Found 358 objects to remove in aat_targets/Gilgamesh_1.lis

Aeneid


In [187]:
anet =  targeting.select_targets(ane,outercutrad=1.0*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
anet = targeting.remove_targets_with_remlist(anet, ane, 'TargetRemoveJul4_2014.csv')
len(anet)


Removing 12 objects at high z, keeping 4 (total of 171846 objects)
Removed 3 objects for Aeneid
Out[187]:
7648

In [193]:
fnbase = 'aat_targets/Aeneid'
host = ane
targs = anet
pris = aat.prioritize_targets(anet)

In [194]:
guides = aat.select_guide_stars_sdss(host.get_sdss_catalog(), (12.5,14))
calibs = aat.select_flux_stars(host.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = fnbase + '_sky.dat'
aat.produce_master_fld(host, datetime.date(2014, 7, 4), targs, pris,
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn=fnbase+'_master.fld', randomizeorder=True,
                       fluxpri=8); #flux stars-> 8 b/c of possible satellite

#this counts up priorities directly from the master list
pricnt = {}
with open(fnbase+'_master.fld') as f:
    for l in f:
        ls = l.split(' ')
        if len(ls) > 8 and ls[8].isdigit():
            if ls[0].startswith('Sky') or ls[0].startswith('Guide') or ls[0].startswith('Flux'):
                continue
            pricnt.setdefault(ls[8], 0) 
            pricnt[ls[8]] += 1
pricnt


Out[194]:
{'1': 73, '2': 119, '3': 1587, '4': 111, '6': 232, '9': 1}

In [697]:
finum = 1
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:450, 4:np.inf, 6:np.inf, 9:np.inf},
                              nflux=3, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now design config

In [704]:
finum = 2
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:550, 4:100, 6:96, 9:2},
                              nflux=3, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now design config


Found 358 objects to remove in aat_targets/Aeneid_1.lis

In [706]:
finum = 3
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:550, 4:100, 6:96, 9:2},
                              nflux=3, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now design config


Found 358 objects to remove in aat_targets/Aeneid_1.lis
Found 358 objects to remove in aat_targets/Aeneid_2.lis

Ana K


In [133]:
anat =  targeting.select_targets(ana, outercutrad=1.0*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
anat = targeting.remove_targets_with_remlist(anat, 'AnaK', 'TargetRemoveJul4_2014.csv')
len(anat)


Removing 2220 objects at high z, keeping 21 (total of 62435 objects)
Could not find a match for objid 1237663784194801800 of AnaK, closest is [ 2.12368631] arcsec
Could not find a match for objid 1237666408439873560 of AnaK, closest is [ 107.00869934] arcsec
Removed 16 objects for AnaK
Out[133]:
9516

In [144]:
fnbase = 'aat_targets/Ana_Karenina'
host = ana
targs = anat
pris = aat.prioritize_targets(anat)

In [145]:
guides = aat.select_guide_stars_sdss(host.get_sdss_catalog(), magrng=(12.5, 14))
calibs = aat.select_flux_stars(host.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = fnbase + '_sky.dat'
aat.produce_master_fld(host, datetime.date(2014, 7, 4), targs, pris,
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn=fnbase+'_master.fld', randomizeorder=True,
                       fluxpri=8); #flux stars-> 8 b/c of possible satellite

#this counts up priorities directly from the master list
pricnt = {}
with open(fnbase+'_master.fld') as f:
    for l in f:
        ls = l.split(' ')
        if len(ls) > 8 and ls[8].isdigit():
            if ls[0].startswith('Sky') or ls[0].startswith('Guide') or ls[0].startswith('Flux'):
                continue
            pricnt.setdefault(ls[8], 0) 
            pricnt[ls[8]] += 1
pricnt


Out[145]:
{'1': 172, '2': 271, '3': 1777, '4': 387, '6': 558, '9': 1}

In [711]:
finum = 1
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:350, 4:50, 6:400, 9:np.inf},
                              nflux=3, nguides=30,
                              fieldname=str(finum), listorem=listorem)
#now design config
#this used what is now "Ana_Karenina_master_original.fld", with a couple remove list objects still in it

In [146]:
finum = 2
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:200, 4:400, 6:250},
                              nflux=3, nguides=30, guidemags=(13,13.5),
                              fieldname=str(finum), listorem=listorem)
#now design config


Found 357 objects to remove in aat_targets/Ana_Karenina_1.lis
Had 2 unmatched list file objects:
['1237663277925007626', '1237666408439939277']
Total remaining in each priority: {1: 172, 2: 271, 3: 1759, 4: 374, 5: 0, 6: 235, 7: 0, 8: 0, 9: 0}

In [ ]:
finum = 3
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:25,2:25, 3:200, 4:400, 6:250},
                              nflux=3, nguides=30, guidemags=(13,13.5),
                              fieldname=str(finum), listorem=listorem)
#now design config

Second Night

Aeneid


In [189]:
fnbase = 'aat_targets/Aeneid'
host = ane
targs = anet
pris = aat.prioritize_targets(anet)

In [195]:
finum = 2
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:100, 2:100, 3:400, 4:70, 6:60},
                              utcobsdate=datetime.date(2014, 7, 5),
                              nflux=3, nguides=30, guidemags=(13,13.5),
                              fieldname=str(finum), listorem=listorem)
#now design config


Found 358 objects to remove in aat_targets/Aeneid_1.lis
Total remaining in each priority: {1: 73, 2: 119, 3: 1451, 4: 66, 5: 0, 6: 56, 7: 0, 8: 0, 9: 0}

Alice


In [182]:
fnbase = 'aat_targets/Alice_in_Wonderland'
host = alice
targs = alicet
pris = alicepris

In [183]:
finum = 1
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:50, 2:50, 4:200, 5:1, 6:300, 7:110, 9:np.inf},
                              utcobsdate=datetime.date(2014, 7, 5),
                              nflux=3, nguides=30, guidemags=(13,13.5),
                              fieldname=str(finum), listorem=listorem)
#now design config


Total remaining in each priority: {1: 456, 2: 741, 3: 1503, 4: 1043, 5: 1, 6: 1538, 7: 107, 8: 0, 9: 1}

In [185]:
finum = 2
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              {1:50, 2:50, 4:300, 5:1, 6:400, 7:np.inf, 9:np.inf},
                              utcobsdate=datetime.date(2014, 7, 5),
                              nflux=3, nguides=30, guidemags=(13,13.5),
                              fieldname=str(finum), listorem=listorem)
#now design config


Found 358 objects to remove in aat_targets/Alice_in_Wonderland_1.lis
Total remaining in each priority: {1: 456, 2: 741, 3: 1503, 4: 1028, 5: 1, 6: 1299, 7: 4, 8: 0, 9: 0}

Gilgamesh


In [110]:
#re-doing the master catalog of this because we want those with failures from before

In [111]:
gilgt =  targeting.select_targets(gilg,outercutrad=1.0*u.deg, colorcuts=None, faintlimit=21,
                                 galvsallcutoff=100, removegalsathighz=True)
gilgt = targeting.remove_targets_with_remlist(gilgt, gilg, 'TargetRemoveJul1_2014.csv')
len(gilgt)


Removing 558 objects at high z, keeping 9 (total of 99912 objects)
Removed 9 objects for Gilgamesh
Out[111]:
9244

In [112]:
gilgpris = aat.prioritize_targets(gilgt)

In [113]:
#identify the "candidate" satellite to later give it priority 9
gilgtcoos = ICRS(gilgt['ra']*u.deg, gilgt['dec']*u.deg)
gilgsepsfrompossiblesat = gilgtcoos.separation(ICRS(234.52499 *u.deg, 17.022083 *u.deg))
possiblesatidx = np.argmin(gilgsepsfrompossiblesat)
gilgtcoos[possiblesatidx], gilgt['objID'][possiblesatidx]


Out[113]:
(<ICRS RA=234.52499 deg, Dec=17.02208 deg>, 1237668336857842054)

In [114]:
gilg_observed = fits.getdata('mmthecto/configs_observed/saga_Gilgamesh.fits', 1)
goodqual = gilg_observed['ZQUALITY']>2
print np.sum(goodqual), len(goodqual)

#checked, and this is as reliable as coordinate-matching
oids = np.array([int(v) for v in gilg_observed['SDSS_OBJID']])
np.in1d(oids, gilgt['objID']).sum(), len(oids), np.in1d(gilgt['objID'], oids).sum(), len(gilgt)


2531 2659
Out[114]:
(1708, 2659, 1707, 9244)

In [116]:
#set priorities to -1 for already observed *and sucessful*, and 7 for the unsucessful ones
alreadysuccessfulmsk = gilg_observed['ZQUALITY'] > 2
alreadysuccessful = np.in1d(gilgt['objID'], oids[alreadysuccessfulmsk])
alreadynotsuccessful = np.in1d(gilgt['objID'], oids[~alreadysuccessfulmsk])

print alreadysuccessful.sum(), alreadynotsuccessful.sum()

gilgpris[alreadysuccessful] = -1
gilgpris[alreadynotsuccessful] = 7
gilgpris[possiblesatidx] = 9


1581 126

In [127]:
fnbase = 'aat_targets/Gilgamesh'
host = gilg
targs = gilgt
pris = gilgpris

In [128]:
guides = aat.select_guide_stars_sdss(host.get_sdss_catalog(), (12, 14))
calibs = aat.select_flux_stars(host.get_sdss_catalog(), onlyoutside=300*u.kpc)
skyradec = fnbase + '_sky.dat'
aat.produce_master_fld(host, datetime.date(2014, 7, 4), targs, pris,
                       guidestars=guides, fluxstars=calibs,skyradec=skyradec,
                       outfn=fnbase+'_master.fld', randomizeorder=True,
                       fluxpri=8); #flux stars-> 8 b/c of possible satellite

#this counts up priorities directly from the master list
pricnt = {}
with open(fnbase+'_master.fld') as f:
    for l in f:
        ls = l.split(' ')
        if len(ls) > 8 and ls[8].isdigit():
            if ls[0].startswith('Sky') or ls[0].startswith('Guide') or ls[0].startswith('Flux'):
                continue
            pricnt.setdefault(ls[8], 0) 
            pricnt[ls[8]] += 1
pricnt


Out[128]:
{'1': 80, '2': 139, '3': 1361, '4': 83, '6': 96, '7': 125, '9': 2}

In [129]:
#finished 2 the first night
finum = 3
listorem = [fnbase + '_' + str(i) + '.lis' for i in range(1, finum)]
aat.subsample_from_master_fld(fnbase + '_master.fld', 
                              fnbase + '_{0}.fld'.format(finum),
                              utcobsdate=datetime.date(2014, 7, 5),
                              {1:25,2:25, 3:550, 4:np.inf, 6:np.inf, 7:np.inf, 9:np.inf},
                              nflux=3, nguides=30, guidemags=(13,13.5),
                              fieldname=str(finum), listorem=listorem, dontrempri=9)
#now design config

#note: first one was done removing *all* targets, not just zqual<3


Found 358 objects to remove in aat_targets/Gilgamesh_1.lis
Found 357 objects to remove in aat_targets/Gilgamesh_2.lis

Check moon for Alice


In [86]:
#from http://ssd.jpl.nasa.gov/horizons.cgi
jplmoon_header="""
*******************************************************************************
Revised: Mar 11, 1998             Moon / (Earth)                            301
 
PHYSICAL PROPERTIES:
 Radius, km            = 1737.53+-0.03   Mass, 10^20 kg        =  734.9
 Density, gm cm^-3     =    3.3437       Geometric albedo      =    0.12
 V(1,0)                =   +0.21         GM, km^3/s^2          = 4902.798+-.005
 Earth/Moon mass ratio = 81.300587       Surface gravity       =    1.62 m s^-2
 Nearside crust. thick.= 58+-8 km        Farside crust. thick. = ~80 - 90 km
 Heat flow, Apollo 15  = 3.1+-.6 mW/m^2  Heat flow, Apollo 17  = 2.2+-.5 mW/m^2
 Mean crustal density  = 2.97+-.07g/cm^3 k2                    = 0.0302+-.0012 
 Induced magnetic mom. = 4.23x10^22Gcm^3 Magnetometer moment   = 435+-15

DYNAMICAL CHARACTERISTICS:
 Mean angular diameter = 31'05.2"        Orbit period          = 27.321582 d
 Obliquity to orbit    = 6.67 deg        Eccentricity          = 0.05490
 Semi-major axis, a    = 384400 km       Inclination           = 5.145 deg
 Mean motion, rad/s    = 2.6616995x10^-6 Nodal period          = 6798.38 d
 Apsidal period        = 3231.50 d       Mom. of inertia C/MR^2= 0.3935+-.0011
 beta (C-A/B), x10^-4  = 6.31(72+-15)    gamma (B-A/C), x10^-4 = 2.278(8+-2)
*******************************************************************************
 
 
*******************************************************************************
Ephemeris / WWW_USER Thu Jul  3 22:58:10 2014 Pasadena, USA      / Horizons    
*******************************************************************************
Target body name: Moon (301)                      {source: DE-0431LE-0431}
Center body name: Earth (399)                     {source: DE-0431LE-0431}
Center-site name: Siding Spring Observatory
*******************************************************************************
Start time      : A.D. 2014-Jul-04 00:00:00.0000 UT      
Stop  time      : A.D. 2014-Jul-06 00:00:00.0000 UT      
Step-size       : 60 minutes
*******************************************************************************
Target pole/equ : IAU_MOON                        {East-longitude +}
Target radii    : 1737.4 x 1737.4 x 1737.4 km     {Equator, meridian, pole}    
Center geodetic : 149.066100,-31.277105,1.1723841 {E-lon(deg),Lat(deg),Alt(km)}
Center cylindric: 149.066100,5457.10593,-3292.799 {E-lon(deg),Dxy(km),Dz(km)}
Center pole/equ : High-precision EOP model        {East-longitude +}
Center radii    : 6378.1 x 6378.1 x 6356.8 km     {Equator, meridian, pole}    
Target primary  : Earth
Vis. interferer : MOON (R_eq= 1737.400) km        {source: DE-0431LE-0431}
Rel. light bend : Sun, EARTH                      {source: DE-0431LE-0431}
Rel. lght bnd GM: 1.3271E+11, 3.9860E+05 km^3/s^2                              
Atmos refraction: YES (Earth refraction model)
RA format       : DEG
Time format     : CAL 
EOP file        : eop.140703.p140924                                           
EOP coverage    : DATA-BASED 1962-JAN-20 TO 2014-JUL-03. PREDICTS-> 2014-SEP-23
Units conversion: 1 au= 149597870.700 km, c= 299792.458 km/s, 1 day= 86400.0 s 
Table cut-offs 1: Elevation (-90.0deg=NO ),Airmass (>38.000=NO), Daylight (NO )
Table cut-offs 2: Solar Elongation (  0.0,180.0=NO ),Local Hour Angle( 0.0=NO )
*******************************************************************************
 Date__(UT)__HR:MN     R.A._(J2000.0)_DEC. R.A._(r-appar)_DEC.  APmag  S-brt
****************************************************************************
"""
jplmoon_data="""
 2014-Jul-04 00:00 *   175.08980   0.18969 174.72808  -0.23458  -9.32   5.57
 2014-Jul-04 01:00 *m  175.58956   0.02693 175.39270  -0.28693  -9.35   5.56
 2014-Jul-04 02:00 *m  176.03949  -0.13704 176.16513  -0.25627  -9.38   5.55
 2014-Jul-04 03:00 *m  176.44071  -0.30212 176.59775  -0.40368  -9.40   5.54
 2014-Jul-04 04:00 *m  176.79765  -0.46809 176.96690  -0.56371  -9.43   5.53
 2014-Jul-04 05:00 *m  177.11782  -0.63460 177.29421  -0.72750  -9.45   5.53
 2014-Jul-04 06:00 *m  177.41138  -0.80120 177.59299  -0.89280  -9.46   5.52
 2014-Jul-04 07:00 *m  177.69046  -0.96739 177.87656  -1.05852  -9.48   5.52
 2014-Jul-04 08:00 Nm  177.96833  -1.13269 178.15884  -1.22404  -9.49   5.51
 2014-Jul-04 09:00  m  178.25837  -1.29667 178.45382  -1.38900  -9.50   5.50
 2014-Jul-04 10:00  m  178.57310  -1.45902 178.77499  -1.55352  -9.51   5.50
 2014-Jul-04 11:00  m  178.92330  -1.61959 179.13537  -1.71872  -9.52   5.49
 2014-Jul-04 12:00  m  179.31719  -1.77839 179.55165  -1.88965  -9.53   5.48
 2014-Jul-04 13:00  m  179.76005  -1.93560 180.10976  -2.11560  -9.54   5.48
 2014-Jul-04 14:00     180.25384  -2.09159 180.98925  -2.51594  -9.56   5.47
 2014-Jul-04 15:00     180.79731  -2.24685 181.51635  -2.69623  -9.57   5.46
 2014-Jul-04 16:00     181.38612  -2.40197 182.07139  -2.89599  -9.59   5.45
 2014-Jul-04 17:00     182.01326  -2.55760 182.63393  -3.11901  -9.61   5.43
 2014-Jul-04 18:00     182.66953  -2.71438 183.16996  -3.36147  -9.63   5.42
 2014-Jul-04 19:00     183.34412  -2.87293 183.64550  -3.59053  -9.66   5.41
 2014-Jul-04 20:00 A   184.02525  -3.03377 184.08541  -3.74880  -9.69   5.40
 2014-Jul-04 21:00 C   184.70077  -3.19731 184.56862  -3.84062  -9.72   5.39
 2014-Jul-04 22:00 *   185.35888  -3.36377 185.11166  -3.92387  -9.75   5.37
 2014-Jul-04 23:00 *   185.98876  -3.53318 185.67891  -4.02798  -9.78   5.36
 2014-Jul-05 00:00 *   186.58117  -3.70535 186.23765  -4.15596  -9.81   5.35
 2014-Jul-05 01:00 *   187.12908  -3.87983 186.76829  -4.30424  -9.84   5.34
 2014-Jul-05 02:00 *m  187.62814  -4.05596 187.69251  -4.21084  -9.86   5.33
 2014-Jul-05 03:00 *m  188.07707  -4.23286 188.22279  -4.33942  -9.89   5.32
 2014-Jul-05 04:00 *m  188.47790  -4.40950 188.64300  -4.50558  -9.91   5.32
 2014-Jul-05 05:00 *m  188.83591  -4.58473 189.01043  -4.67654  -9.93   5.31
 2014-Jul-05 06:00 *m  189.15946  -4.75744 189.34012  -4.84711  -9.95   5.30
 2014-Jul-05 07:00 *m  189.45941  -4.92656 189.64489  -5.01519  -9.97   5.30
 2014-Jul-05 08:00 Nm  189.74846  -5.09126 189.93827  -5.17955  -9.98   5.29
 2014-Jul-05 09:00  m  190.04015  -5.25097 190.23438  -5.33955  -9.99   5.29
 2014-Jul-05 10:00  m  190.34794  -5.40546 190.54723  -5.49510 -10.00   5.28
 2014-Jul-05 11:00  m  190.68413  -5.55490 190.89016  -5.64683 -10.01   5.27
 2014-Jul-05 12:00  m  191.05905  -5.69982 191.27602  -5.79674 -10.02   5.27
 2014-Jul-05 13:00  m  191.48033  -5.84106 191.72239  -5.95167 -10.03   5.26
 2014-Jul-05 14:00  m  191.95245  -5.97977 192.34674  -6.18204 -10.04   5.25
 2014-Jul-05 15:00     192.47659  -6.11724 193.21136  -6.55000 -10.05   5.24
 2014-Jul-05 16:00     193.05065  -6.25488 193.76042  -6.72328 -10.06   5.23
 2014-Jul-05 17:00     193.66952  -6.39407 194.33336  -6.91668 -10.08   5.22
 2014-Jul-05 18:00     194.32548  -6.53613 194.90748  -7.13148 -10.10   5.21
 2014-Jul-05 19:00     195.00875  -6.68219 195.45142  -7.35681 -10.12   5.20
 2014-Jul-05 20:00 A   195.70806  -6.83316 195.94770  -7.55597 -10.15   5.19
 2014-Jul-05 21:00 C   196.41130  -6.98967 196.43286  -7.69169 -10.17   5.17
 2014-Jul-05 22:00 *   197.10618  -7.15201 196.96072  -7.78300 -10.20   5.16
 2014-Jul-05 23:00 *   197.78090  -7.32009 197.53184  -7.87380 -10.23   5.15
 2014-Jul-06 00:00 *   198.42479  -7.49346 198.11583  -7.98456 -10.26   5.14
 2014-Jul-06 01:00 *   199.02895  -7.67124 198.68573  -8.11749 -10.28   5.13
 2014-Jul-06 02:00 *   199.58682  -7.85219 199.22465  -8.26962 -10.31   5.12
 2014-Jul-06 03:00 *m  200.09467  -8.03477 200.21468  -8.15314 -10.34   5.11
 2014-Jul-06 04:00 *m  200.55196  -8.21714 200.71146  -8.31255 -10.36   5.10
 2014-Jul-06 05:00 *m  200.96151  -8.39734 201.13501  -8.48549 -10.38   5.09
 2014-Jul-06 06:00 *m  201.32946  -8.57340 201.51082  -8.65817 -10.40   5.09
 2014-Jul-06 07:00 *m  201.66493  -8.74351 201.85186  -8.82650 -10.42   5.08
 2014-Jul-06 08:00 Nm  201.97951  -8.90616 202.17103  -8.98824 -10.43   5.08
 2014-Jul-06 09:00  m  202.28640  -9.06029 202.48221  -9.14209 -10.45   5.07
 2014-Jul-06 10:00  m  202.59949  -9.20541 202.79976  -9.28753 -10.46   5.06
 2014-Jul-06 11:00  m  202.93231  -9.34163 203.13780  -9.42485 -10.46   5.06
 2014-Jul-06 12:00  m  203.29703  -9.46969 203.50958  -9.55530 -10.47   5.05
 2014-Jul-06 13:00  m  203.70360  -9.59088 203.92780  -9.68178 -10.48   5.05
 2014-Jul-06 14:00  m  204.15912  -9.70694 204.41085  -9.81293 -10.49   5.04
 2014-Jul-06 15:00  m  204.66740  -9.81993 205.10822 -10.04099 -10.50   5.03
 2014-Jul-06 16:00     205.22890  -9.93205 205.96629 -10.37082 -10.51   5.02
 2014-Jul-06 17:00     205.84076 -10.04554 206.54448 -10.52863 -10.52   5.01
 2014-Jul-06 18:00     206.49715 -10.16249 207.14314 -10.70634 -10.54   5.00
 2014-Jul-06 19:00     207.18971 -10.28476 207.73888 -10.90236 -10.56   4.99
 2014-Jul-06 20:00 A   207.90807 -10.41387 208.30538 -11.10105 -10.58   4.98
 2014-Jul-06 21:00 C   208.64054 -10.55091 208.83769 -11.26833 -10.60   4.96
 2014-Jul-06 22:00 *   209.37466 -10.69648 209.37243 -11.38299 -10.62   4.95
 2014-Jul-06 23:00 *   210.09797 -10.85067 209.94486 -11.46784 -10.65   4.94
 2014-Jul-07 00:00 *   210.79860 -11.01303 210.54858 -11.55663 -10.68   4.93
"""[1:-1]

In [87]:
times = []
ras = []
decs = []

for l in jplmoon_data.split('\n'):
    times.append(Time(l[1:18].replace('Jul', '7'), format='iso', scale='utc'))
    radecpart = l[23:]
    ra, dec = radecpart.split()[:2]
    ras.append(float(ra))
    decs.append(float(dec))

mooncoords = ICRS(ras*u.deg, decs*u.deg)

dts = [(t - times[0]).jd*24 for t in times]

In [91]:
plt.plot(dts, alice.coords.separation(mooncoords).deg,'o-')
plt.axvline(8,c='k')
plt.axvline(11,c='k')
plt.axvline(8+24,c='k')
plt.axvline(11+24,c='k')
plt.axvline(8+48,c='k')
plt.axvline(11+48,c='k')
plt.axhline(1,c='r')
plt.ylim(0,26)


Out[91]:
(0, 26)

In [94]:
from matplotlib import patches

plt.scatter(mooncoords.ra.deg, mooncoords.dec.deg, c=dts, vmin=7, vmax=12)
plt.colorbar().set_label('dt (hours)')
plt.scatter(alice.coords.ra.deg, alice.coords.dec.deg,s=60,c='k')

twodfalicecirc = patches.Circle((alice.coords.ra.deg, alice.coords.dec.deg), 1, fc='none')
plt.gca().add_patch(twodfalicecirc)
plt.draw()

plt.xlim(178,182)
plt.ylim(-3,1)


Out[94]:
(-3, 1)

Moon just grazes Alice's 2dF field on the first night, so we'll have to skip it