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

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 [48]:
from __future__ import print_function, division

import numpy as np
from scipy import interpolate

from astropy import units as u
from astropy import constants as cnst
from astropy.time import Time
from astropy.table import Table
from astropy.coordinates import SkyCoord
from astropy.stats import sigma_clip
from astropy import modeling

import astroplan

In [3]:
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib import rcParams

rcParams['image.interpolation'] = 'none'
rcParams['figure.figsize'] = (16, 10)
plt.rcParams['image.cmap'] = 'viridis'

Find all of the sats that are in SAGA hosts


In [4]:
ls -lt SAGADropbox/data/*_spectra*


-rwxrw-rw- 1 erik erik 34596512 Sep 22 21:12 SAGADropbox/data/saga_spectra_clean.fits.gz*

In [5]:
t = table.Table.read('SAGADropbox/data/saga_spectra_clean.fits.gz')

In [6]:
t[(t['TELNAME']=='NSA')&(t['SATS']==1)]


Out[6]:
<Table masked=True length=130>
OBJIDRADECPHOTPTYPEPHOT_SGFLAGScleanSATURATEDBAD_COUNTS_ERRORBINNED1TOO_FEW_GOOD_DETECTIONSugrizu_errg_errr_erri_errz_errMODELMAGERR_UMODELMAGERR_GMODELMAGERR_RMODELMAGERR_IMODELMAGERR_ZEXTINCTION_UEXTINCTION_GEXTINCTION_REXTINCTION_IEXTINCTION_ZDERED_UDERED_GDERED_RDERED_IDERED_ZPETRORAD_UPETRORAD_GPETRORAD_RPETRORAD_IPETRORAD_ZPETRORADERR_UPETRORADERR_GPETRORADERR_RPETRORADERR_IPETRORADERR_ZDEVRAD_UDEVRADERR_UDEVRAD_GDEVRADERR_GDEVRAD_RDEVRADERR_RDEVRAD_IDEVRADERR_IDEVRAD_ZDEVRADERR_ZDEVAB_UDEVAB_GDEVAB_RDEVAB_IDEVAB_ZCMODELMAG_UCMODELMAGERR_UCMODELMAG_GCMODELMAGERR_GCMODELMAG_RCMODELMAGERR_RCMODELMAG_ICMODELMAGERR_ICMODELMAG_ZCMODELMAGERR_ZPSFMAG_UPSFMAGERR_UPSFMAG_GPSFMAGERR_GPSFMAG_RPSFMAGERR_RPSFMAG_IPSFMAGERR_IPSFMAG_ZPSFMAGERR_ZFIBERMAG_UFIBERMAGERR_UFIBERMAG_GFIBERMAGERR_GFIBERMAG_RFIBERMAGERR_RFIBERMAG_IFIBERMAGERR_IFIBERMAG_ZFIBERMAGERR_ZFRACDEV_UFRACDEV_GFRACDEV_RFRACDEV_IFRACDEV_ZQ_UU_UQ_GU_GQ_RU_RQ_IU_IQ_ZU_ZEXPAB_UEXPRAD_UEXPPHI_UEXPAB_GEXPRAD_GEXPPHI_GEXPAB_REXPRAD_REXPPHI_REXPAB_IEXPRAD_IEXPPHI_IEXPAB_ZEXPRAD_ZEXPPHI_ZFIBER2MAG_RFIBER2MAGERR_REXPMAG_REXPMAGERR_RPETROR50_RPETROR90_RPETROMAG_RSB_EXP_RSB_PETRO_RJJERRHHERRKKERRSPEC_ZSPEC_Z_ERRSPEC_Z_WARNPHOTOZPHOTOZ_ERRW1W1ERRW2W2ERRHOST_RAHOST_DECHOST_DISTHOST_VHOSTHOST_MKHOST_NSAIDHOST_FLAGHOST_SAGA_NAMERHOST_ARCMRHOST_KPCOBJ_NSAIDSATSPROBABILITY_CLASS1RESCALED_PROBABILITY_CLASS1REMOVETELNAMEMASKNAMEZQUALITYSPEC_CLASSSPECOBJIDSPEC_REPEAT
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In [7]:
sagamasks = ~((t['TELNAME']=='SDSS')|(t['TELNAME']=='NSA')|(t['TELNAME']=='GAMA'))
sagaids = np.array(np.unique(t['HOST_NSAID'][sagamasks]))
sagaspectra = t[np.in1d(t['HOST_NSAID'], sagaids)]

In [8]:
complete_hosts = [147100,166313,165536,61945,149781,33446,150887,132339,85746,140594,161174,13927,137625,129237,145729]
incompletes = sagaids[~np.in1d(sagaids, complete_hosts)]
complete_hosts_spectra = t[np.in1d(t['HOST_NSAID'], complete_hosts)]

incompletes


Out[8]:
array([ 63326, 150307, 153017])

In [9]:
sats = sagaspectra[sagaspectra['SATS']==1]
sats['COMPLETE_HOST'] = np.in1d(sats['HOST_NSAID'], complete_hosts)
sats.show_in_notebook()


Out[9]:
<Table masked=True length=49>
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291237662224092299404247.8400299520.18407581353GALAXY105553418387472100268435456015.327614.127813.472813.133812.84650.005455750.0008193470.0006615740.0006597630.001625990.1495660.05334530.05915940.1527870.2046250.3257110.2396550.1738180.1318010.093448321.362921.066820.923121.700720.6911.039021.092341.015230.959321.01090.1427080.3042540.1219490.1329240.2397270.1616041.20610.1617390.2077610.2433480.3113630.2402080.5938920.2438541.296220.0993550.09999710.09987290.0987640.099084721.70850.27448521.34110.13617321.0970.080596321.83720.16716920.77230.35091621.74380.23172421.43810.16409721.29310.20178322.04910.47337620.97490.3530420.15940.074548219.21680.033572518.62930.030535218.33770.029400717.9580.03910270.00.00.00.00.0-0.07041020.112537-0.03525270.0303944-0.02361430.0711803-0.01912340.04893270.05303790.1543010.1155590.26202990.03590.132720.24860580.72120.09946790.32395884.81480.09770780.31827490.51930.08316140.34456882.45719.46960.044167421.0970.08059630.5256160.99512221.226820.644859160121.82562136739999.09999.09999.09999.09999.09999.00.00855347-1.000.2486350.132598-1.0-1.0-1.0-1.0248.08719.826434.31992375.0-24.02773006871471000Odyssey25.5804231974255.3735115066856010.169138729211-1.0-1NSAsdss41.23766222409e+18NSATrue
301237662662147310256247.82589224120.21087877613GALAXY144150441435988240100268435456018.530217.486617.080816.799516.71620.05432090.007808880.008824940.02357970.03176340.06378830.01340110.01756350.02649010.07053780.326040.2398970.1739940.1319340.093542918.560217.565217.192616.975216.877110.351711.639112.261413.180143.77082.390140.6655120.5837940.710174-1000.016.65884.2503919.05911.0839520.50641.1640525.59172.1524929.67364.525620.3134470.4000560.5079040.4683190.44459819.08710.063477517.89790.013265117.35820.017644216.89870.027615316.53760.084696421.79420.11790221.2140.035488221.07070.038883320.87150.051748520.60950.13394721.48150.11357320.52650.020577720.23090.025300320.02980.030813919.75080.09931260.1386850.00.01236520.1906070.3082880.345641-0.07370610.225695-0.03433250.164382-0.02407690.138398-0.02662030.171156-0.06387380.3823055.91328205.3370.420637.02308194.0470.5150837.37528191.9590.5131128.16828186.5450.425489.44492193.49721.03480.035666717.36660.01756355.6780812.331417.367323.700895822923.13377443769999.09999.09999.09999.09999.09999.00.008224.25059e-0700.0425670.014763-1.0-1.0-1.0-1.0248.08719.826434.31992375.0-24.02773006871471000Odyssey27.3638674437273.17756075414708210.459613790263-1.0-1AAT/Spectra/Final/AAT/Odyssey_1_MG.mz4258.0AATTrue
311237662662147703369248.5807968519.72003490233GALAXY72163216142237968000268435456022.76919.85519.447818.695320.70141.575060.1219090.06461170.05184381.106761.575060.1219090.06461170.05184381.106760.3424490.2519710.182750.1385740.098250622.426619.603119.265118.556720.60312.969762.969722.96999.0792418.0202-1000.0-1000.0-1000.00.480341-1000.00.0006024352.0270610.71234.7774915.34654.5739123.82275.1207629.59320.0006782590.08181590.4499440.5061430.4391170.14998423.22950.58311819.92580.13349819.44790.065647318.53460.05117719.49192.2049222.90950.36332522.81670.13170522.4990.23973222.07830.21336422.48710.65403223.48720.79879321.970.088819521.4730.13470421.10460.15160521.86940.8030271.00.00.00.00.611217-0.9239010.222587-0.01980170.009047230.001878030.0229546-0.033030.0385256-0.481807-0.4141280.050.00759311140.8940.5999964.45402129.1520.5698945.29264122.1890.4938466.59374128.1420.099980535.4536146.59922.2940.18798919.44790.06564733.219345.3127219.787725.061677774524.32197762439999.09999.09999.09999.09999.09999.00.00825-1.0-1-9999.0-9999.0-1.0-1.0-1.0-1.0248.08719.826434.31992375.0-24.02773006871471000Odyssey28.6019909932285.537653364-110.0407410051245-1.0-1MMT/Spectra/Final/MMT/spHect-2013.0509_3.zlog3242.0MMTTrue
321237680192048857102336.048161241-3.482938690033GALAXY1301856395596120000268435456015.184214.15513.681413.432813.29870.01581390.00199190.003820910.002675060.01114060.03192440.003350780.003353150.003839530.01062370.3390450.2494660.1809340.1371960.09727416.354414.858814.328314.12714.042118.221242.628368.294239.505332.27692.196921.18145-1000.00.676790.91611829.03584.0753529.66330.33029529.66290.78351629.66520.95775129.6551.566830.4633360.9974950.8029110.9375850.80169916.83150.030284214.86610.0033004614.470.0034038914.21570.0039097514.21480.010854419.87750.10270819.20840.077905819.02990.11163618.65170.094042818.34010.037559820.50560.084821619.34040.080878118.83640.098885118.57890.086421718.38230.02996960.650890.00.2644680.2783170.323613-0.5249910.238041-0.03282630.0193736-0.01394090.0189475-0.01096890.0188772-0.05471230.02234080.46709612.643256.71880.7166621.343950.45730.64609417.782145.82930.67007617.05644.5460.61799914.466244.111719.64120.13514714.50910.0033531416.414636.192414.426822.754479427122.49838769319999.09999.09999.09999.09999.09999.00.00944989-1.000.0150250.008092-1.0-1.0-1.0-1.0335.913-3.4316739.17592828.0-23.83409491221497810OBrother8.6569385916798.65270037314979210.727741305486-1.0-1NSAned41.23768019205e+18NSATrue
331237679996084617517335.97997618-3.270548586313GALAXY35253360136208100268435456018.072216.776116.117615.798415.52780.03191830.005603850.005565670.007202080.01629310.03191830.005603850.005565670.007202080.01629310.389820.2868260.208030.1577430.11184217.682416.489315.909615.640615.4168.777229.5554510.035510.04059.342560.697770.9650881.022970.9438680.70075416.96332.3827517.91390.40857117.40710.38328717.15630.46567919.69931.105340.3258580.2999580.3439140.3499150.32682518.13240.031861516.78770.0055945816.11760.0055656715.80090.0072003915.53950.016290420.64940.067388819.57260.019829519.32330.017572119.16280.022486518.72360.037853420.48280.061148519.29070.010329918.66770.0093005718.35710.0095910918.09770.02659740.00.00.00.00.00.2282840.1466890.1930660.1529950.1891360.1535730.1808220.1526720.1918980.1415640.3348236.3487130.62690.316756.7632131.64290.3428916.763931.30080.3485256.7177331.85330.3481716.6585430.753719.46740.01317816.11760.005565674.540810.269916.136822.264042085821.417888094716.2620.18114.6569999.014.0929999.00.00951-1.0-10.0260250.010681-1.0-1.0-1.0-1.0335.913-3.4316739.17592828.0-23.83409491221497810OBrother10.4666191628119.275391561-110.460594315315-1.0-1AAT/Spectra/Final/AAT/OBrother_1_MG.mz4325.0AATTrue
341237680066954264778335.836305444-3.659802640843GALAXY1161187626717272000268435456017.004615.675315.044314.722114.49280.02885320.002827750.002423010.002919290.007552350.02106110.006945590.003535220.005377310.009031840.3386410.2491690.1807180.1370330.097158116.663915.430214.85414.581714.352920.550319.575919.90320.486518.01655.908523.455142.970362.863752.7643629.68031.1967629.67090.4291129.67250.29166329.66320.32261429.67280.9359280.2638790.236220.3002180.2780970.27132516.9810.021060815.69940.0069689315.03090.0035418114.71210.0053889114.46260.0090243120.3480.06407719.20050.027877918.76050.041322318.59080.055130818.15140.055418820.43340.0648619.13360.018801918.47980.035066218.16540.044338417.9370.04766280.00.00.01137740.01044580.00.182208-0.05694830.160366-0.07092050.151844-0.07560790.146496-0.07868040.136992-0.07615680.21509314.7725148.6490.22153513.8241149.3870.21348114.4369148.5070.21515914.4891148.6170.2178414.068148.19119.29790.049699915.03470.003535229.01321.367515.028122.827479307421.797898530316.370.21214.2839999.014.1499999.00.0102104-1.000.0249550.006209-1.0-1.0-1.0-1.0335.913-3.4316739.17592828.0-23.83409491221497810OBrother14.4370023033164.52081371514977410.676663312119-1.0-1NSAned41.23768006695e+18NSATrue
351237680066954330699335.953849494-3.701194818333GALAXY2416187539712100268435456020.907619.855219.613719.501719.64850.2667060.04020990.05319350.07436560.3058850.2667060.04020990.05319350.07436560.3058850.3236180.2381150.1727010.1309540.092847820.58419.617119.44119.370719.555611.31145.727925.911896.463984.10081-1000.00.3589410.7168176.314664.004321.58732.96749.894841.7467111.29833.1506112.32454.3967612.338717.90680.3001530.4955580.4441560.5157270.16002720.63290.26077619.84270.040176619.61370.061425719.36350.093943719.55770.32848922.74020.45035521.68250.066351821.66610.089456521.50520.12292221.54310.43450922.93410.50539621.70790.067169421.43940.083518621.38010.12850321.59950.518490.00.00.00.00.00.164441-0.2579370.2653110.13050.2600810.1252870.1782290.07240770.04581430.0802090.4467014.8375220.61920.5553223.4273120.33860.5024233.5092618.59390.536933.9174522.05090.6537123.41157182.40522.21110.11302919.61370.06142572.637384.9117819.695824.335192855223.79712842899999.09999.09999.09999.09999.09999.00.00936-1.0-10.087260.060236-1.0-1.0-1.0-1.0335.913-3.4316739.17592828.0-23.83409491221497810OBrother16.3554594917186.382962752-110.133487786004-1.0-1AAT/Spectra/Final/AAT/OBrother_1_MG.mz468.0AATTrue
361237679005021831220348.6438162584.49844349113GALAXY1319448581640280000268435456014.918813.641212.895312.524112.18010.004760490.002958110.0008638270.0009303940.001727330.00549750.00197020.001822550.001913940.002575640.3447360.2536530.183970.1394990.098906714.863613.504812.842712.561612.285235.080627.220623.238522.279319.30095.374535.471054.303773.966523.3577829.66650.61791429.67150.26164329.66460.22417827.31740.18575729.66780.3226670.5951110.3752920.3340030.3457790.37032514.77950.005211813.68330.0019851712.99240.0018468712.69730.0019433112.30750.0026368419.57760.042969717.67130.018002317.07310.017971416.6420.023785216.18630.015631218.98690.019894517.36970.0096024616.51740.008673816.07010.007963315.69360.00446050.00.175640.2363780.3114140.365622-0.316013-0.00719184-0.296181-0.00113476-0.3081080.00301164-0.3039490.00799961-0.3146980.01011140.34992220.971683.25360.34633315.283582.0830.33794714.128782.70480.37576512.32282.42840.36688511.581582.356917.30010.011510413.02660.0018225510.255930.898113.006820.772591431120.057109630712.8489999.012.169999.012.0060.0670.00891952-1.000.0216330.006931-1.0-1.0-1.0-1.0348.6834.5340637.0192689.0-24.51512341461508870Catch223.1715714425134.152715253115088210.704963341199-1.0-1NSAned41.23767900502e+18NSATrue
371237679005021831801348.6214885244.507170780543GALAXY35289867358552000268435456019.969818.593917.977617.728617.48850.1664130.0198340.02183950.02399210.07864580.1664130.0198340.02183950.02399210.07864580.3454860.2542050.1843710.1398030.099121919.624318.339717.793217.588817.389368.30199.0734810.458611.01685.06478-1000.00.4975470.4936441.037980.96712329.66739.3137713.52991.1415517.90321.6896122.01512.2406611.98774.876950.99030.756420.8692180.7905180.87408918.41080.17232518.7280.019699717.90660.022624217.56930.025064517.64460.07806522.75180.33479621.43910.045655121.32470.052745821.04840.063217920.57130.16390522.66720.32774521.22690.037675520.7160.04017120.52460.053135420.18930.1600031.00.00.06664850.07009740.00.188191-0.04765190.05434170.01268220.0638354-0.0133570.06635680.003479110.06624350.005823150.52791226.918384.69770.7852694.777674.312590.8276665.44512169.4540.8270235.790829.793240.8431734.55842160.29621.51470.056100917.97760.02183955.2494812.429217.951323.653106251623.54732343579999.09999.09999.09999.09999.09999.00.00862-1.0-10.0536370.028585-1.0-1.0-1.0-1.0348.6834.5340637.0192689.0-24.51512341461508870Catch224.0173962052843.2608816731-110.273166321675-1.0-1AAT/Spectra/Final/AAT/Catch22_2016_1_MG.mz3152.0AATTrue
381237678777399443498348.6966488914.639954870193GALAXY1319489383829520100268435456016.258915.411815.146115.15115.07110.01339540.003370960.003725280.005035850.0169960.01339540.003370960.003725280.005035850.0169960.3512280.2584310.1874350.1421270.1007715.907715.153414.958715.008914.97037.8567714.821616.972317.459216.1980.4160850.5923620.5326950.5836271.596426.140380.39312212.9450.14769716.55760.23083821.28750.42844219.27481.184880.5608360.6330220.6529680.6668180.64215616.92740.012595815.76630.0034433415.36670.0038493415.31860.0052521915.38940.017696118.56680.02926618.17650.016765118.01760.015761418.24270.037280618.26840.044452218.91260.020189418.39650.0081589618.27630.011205418.41890.033091218.27890.03297461.00.5979770.5429130.3309330.2788190.1410980.05806110.1534930.03300520.1379070.02165590.1381050.008222530.1176440.005158010.6091363.0603618.4440.6425875.920362.272560.661756.76683-0.1021340.6883677.1676177.6060.7122236.223860.76463419.02830.015108515.70490.00378127.1740220.272515.513921.852230002221.788190453615.789999.016.0410.22614.7069999.00.009556-1.0-10.0034870.006943-1.0-1.0-1.0-1.0348.6834.5340637.0192689.0-24.51512341461508870Catch226.405916584268.9813719874-110.71936968144-1.0-1MMT/Spectra/Final/MMT/spHect-2015.0918_1.zlog4130.0MMTTrue
391237678776862572690348.7769076364.3731970123GALAXY35287719870552000268435456017.492316.4115.793615.533315.31040.01654650.003722220.003444890.003773880.008789840.01654650.003722220.003444890.003773880.008789840.3442050.2532630.1836870.1392840.098754417.148116.156715.6115.39415.21167.620938.39588.646948.967638.067840.4536970.2998890.2334560.2244260.2567398.642910.6558249.174820.1546569.476760.15194110.35850.14884412.32240.4775520.6411160.6007250.6353760.6331740.57530317.45340.016863516.35840.0037892915.74220.0034972615.46080.0038404815.30940.0089538419.18390.039578918.27930.018440117.70890.018508817.55380.024288917.41820.023452219.63950.030297618.57690.0064787918.0070.0056222917.7780.0062707917.54370.01567310.2090680.1503570.1071630.103020.05343560.04621940.09529060.1205480.09261420.1114360.07191390.1188290.07120980.1347640.06511880.6731483.6883837.98260.5894484.3689418.34070.6298364.4032216.17590.6303884.4698616.95620.6242884.2350815.965818.80150.0079987215.79360.003444893.958929.487315.770421.007942276220.753706214915.8510.14715.350.19314.7870.1260.008904-1.0-10.0146810.005572-1.0-1.0-1.0-1.0348.6834.5340637.0192689.0-24.51512341461508870Catch2211.1674755393120.255527082-110.0725685128346-1.0-1MMT/Spectra/Final/MMT/spHect-2015.0918_1.zlog4216.0MMTTrue
401237678777399509170348.8741990974.613261474723GALAXY1231558551928920000268435456022.315920.624520.091919.815719.85340.3920780.03841790.03807880.04411040.1743270.3920780.03841790.03807880.04411040.1743270.369570.2719260.1972240.1495490.10603221.946320.352619.894719.666219.74741.957971.995652.013212.024831.81491.931420.08130150.09570390.1052370.3585330.2651893.54270.6423170.2048920.7247540.1696960.8110980.2973180.7602092.149110.050.3218010.3499640.4683590.90847822.4440.8879520.64470.038388820.09190.038078819.78840.045260319.81160.38438822.47420.43609620.92670.083239420.42370.081164220.20960.079704220.24210.22499621.89640.21242420.86850.06404720.330.063285220.04460.061354119.81390.1426791.00.00.00.00.0-0.120885-0.08557430.06680410.05706490.06037440.07412860.0356770.0451530.1388970.002169010.08523280.47200537.67510.4499650.83772421.67010.4572910.9124227.60230.5804930.90098223.61420.9997720.72012280.131521.16250.090823320.09190.03807881.149931.7662720.273721.888300434422.57251919469999.09999.09999.09999.09999.09999.00.009029-1.0-10.1066610.049037-1.0-1.0-1.0-1.0348.6834.5340637.0192689.0-24.51512341461508870Catch2212.3834987085133.350062655-110.0891206695302-1.0-1MMT/Spectra/Final/MMT/spHect-2015.0918_1.zlog3170.0MMTTrue
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461237648721248845970221.316115836-0.1599370825213GALAXY105587744575576000268435456016.577915.453414.999914.74814.61040.01320310.003077670.002449780.003118950.009105130.01466610.004199940.004391840.005590930.175220.1937760.1425790.103410.07841260.055595516.60315.478115.007314.839315.528114.884115.017517.015415.729513.17810.5666060.4154410.4415150.3674110.34717923.81721.1518225.3560.35036429.66320.52971329.66220.6945713.53569.472360.7159680.6293280.645020.596920.42239816.89170.014955515.67250.0041769115.11070.0043918414.9380.0055860215.41090.25396320.3940.057010419.64030.019483919.43350.020981419.15590.021743919.28750.056201620.13350.034768519.12350.0086102318.74720.0082244218.56380.010093918.44450.03239030.085050.00.00.00.00.0433737-0.01723280.0648738-0.006817280.0495119-0.008517070.0577374-0.009525590.0486287-0.01011980.7529517.38484146.2370.6500069.01963143.5820.65390810.0704140.2090.6498419.658141.9690.6051488.21325141.06819.60610.01220215.11070.004391847.9692418.936215.121122.1213968421.62366801329999.09999.09999.09999.09999.09999.00.005631.43632e-0500.0620950.033155-1.0-1.0-1.0-1.0221.546-0.22294225.39281659.36-23.59955296041655360Dune14.3220507622105.789036487257910.728516178787-1.0-1AAT/Spectra/Final/AAT/Dune_1_MG.mz4201.0AATTrue
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In [10]:
np.sum(complete_hosts_spectra['SATS']==1),np.sum(sagaspectra['SATS']==1)


Out[10]:
(49, 49)

In [11]:
np.sum(t[np.in1d(t['HOST_NSAID'], incompletes)]['SATS']==1)


Out[11]:
0

Looks like there's no difference between the "complete" and "regular" set

Functions/numbers useful for below


In [12]:
palomar_A = 1.76e5 * u.cm**2

In [13]:
# see the section at the bottom for how the relations were derived
u_fibmag_to_haflux = lambda mag: (10**(mag*-0.5447 - 3.4666))*u.erg/u.s * u.cm**-2
r_fibmag_to_haflux = lambda mag: (10**(mag*-0.5075 - 5.1159))*u.erg/u.s * u.cm**-2

u_haflux_sats = u_fibmag_to_haflux(sats['FIBERMAG_U'])
r_haflux_sats = r_fibmag_to_haflux(sats['FIBERMAG_R'])
haflux_sats = (u_haflux_sats + r_haflux_sats)/2

In [14]:
typical_ha_linewidth = 5*u.angstrom

CWI

From Jeremy:

Since you have spectra already, this should be pretty straight-forward. I use 20 minute exposures. Sometimes I combine two 20 minute exposures. The staff recommends sticking to this observing strategy. For the 20 minute exposures, the typical noise per pixel in a given spaxel is about 7x10^-18 erg/s/cm^2 (+/- 1x10^-18 erg/s/cm^2). This is pretty consistent across the entire bandwidth and across the CCD. I have found you can measure line centers down to a S/N of 5. I have not done mosaics but I know others have. The spatial resolution is about 2.7" by whatever your seeing is. You can rotate the FOV 90 degrees and co-add to get seeing limited data but rotation is not a simple task. The FOV is 60" by 40" but the edges of the field are a little wonky. The nod-and-shuffle mask limits the bandwidth range but is fine for H-alpha and both NII lines.

For a great detection, the SDSS h-alpha flux is 1.4e-13 [erg/s/cm^2].

For a typical detection, the SDSS h-alpha flux is 5.0e-15 [erg/s/cm^2].

For a so-so detection, the SDSS h-alpha flux is 1.0e-15 [erg/s/cm^2]. I would integrate for 40 min here or below.

For the smallest detection, the SDSS h-alpha flux is 2.0e-16 [erg/s/cm^2]. I don't really trust this.

I gave you the correct noise figure but let me make clear the units are "per pixel", meaning the typical noise is 7x10^-18 (+/-1x10^-18) erg/s/cm^2/Angstrom for an average galaxy in my sample in the emission line. I just wanted to be more specific.

The CWI web site says "Estimated Efficiency: ~10^-18 ergs/cm2 /s/arcsec2 after 3 hours on target and 3 hours on background"


In [15]:
plt.hist(np.log10(haflux_sats.value))


Out[15]:
(array([ 3.,  6.,  9.,  7.,  7.,  8.,  4.,  4.,  0.,  1.]),
 array([-16.51836014, -16.15350571, -15.78865128, -15.42379684,
        -15.05894241, -14.69408798, -14.32923355, -13.96437912,
        -13.59952469, -13.23467026, -12.86981583]),
 <a list of 10 Patch objects>)

In [16]:
cwi_noise_20min = 7e-18 * u.erg/u.s*u.cm**-2
cwi_noise_3hr = 1e-18 * u.erg/u.s*u.cm**-2

fib_to_cwi_spaxel = 2.5/(np.pi*3**2)  # area of a spaxel vs. area of SDSS fiber
fluxperspaxel = haflux_sats*fib_to_cwi_spaxel/4 # /4 for ~4 pixels width

In [17]:
# the *2 is because of the need to do on/off
timeto5 = (5*cwi_noise_20min/fluxperspaxel/2)**2 *(20*u.min) * 2 #/2 is Jeremy's correction

timeto5_2 = (5*cwi_noise_3hr/fluxperspaxel)**2 *(3*u.hr) * 2

np.sum(timeto5).to(u.hour), np.sum(timeto5_2).to(u.hour)


Out[17]:
(<Quantity 1064.2498697916667 h>, <Quantity 781.8978271484375 h>)

In [18]:
timeper = (np.max([timeto5_2.to(u.min), [40]*len(timeto5_2)*u.min],axis=0)*u.min).to(u.hour)
timeper


Out[18]:
$[150.7889,~0.66666667,~0.66666667,~3.5633982,~0.66666667,~63.98501,~334.06794,~5.5875707,~0.66666667,~10.636771,~1.7063151,~22.555245,~0.66666667,~0.66666667,~0.66666667,~0.66666667,~0.66666667,~0.66666667,~0.66666667,~19.728499,~0.66666667,~0.66666667,~0.66666667,~2.3453494,~34.632454,~0.66666667,~4.466747,~3.9715238,~0.66666667,~0.66666667,~1.0264648,~53.217863,~0.66666667,~0.66666667,~0.66666667,~27.637842,~0.66666667,~8.1363719,~0.66666667,~0.66666667,~2.0567083,~17.649266,~7.141865,~0.66666667,~1.2923238,~2.1816457,~0.66666667,~0.66666667,~0.66666667] \; \mathrm{h}$

Alternative Formulation using Jeremy's empirical approach


In [19]:
sats[np.argsort(haflux_sats)]['RA', 'DEC', 'ZQUALITY', 'FIBERMAG_R', 'r']


Out[19]:
<Table masked=True length=49>
RADECZQUALITYFIBERMAG_Rr
float64float64int64float32float32
149.68716886532.7202654623322.28320.6923
180.6364943061.95802947978422.649718.6728
353.7788052830.301058523211321.925421.3615
248.5807968519.7200349023321.47319.4478
248.1634268419.792207594421.750320.4952
335.953849494-3.70119481833421.439419.6137
39.5361613388-1.39669557972321.898120.1799
180.419076868-1.20276655787322.233921.8131
348.5545917394.91500326962421.821520.8844
20.328466612317.7539390491421.384219.8126
...............
123.24317320636.198359966418.66915.5962
348.7769076364.373197012418.00715.7936
158.0432463428.4830569538417.864715.2572
348.6966488914.63995487019418.276315.1461
180.29544216-1.2976838319418.121513.5133
248.39322569719.9461400824417.560915.8096
225.005401226-1.09130226734417.932214.9987
182.99066650920.0278486922417.153914.4876
348.6438162584.4984434911416.517412.8953
157.77827429828.7966447953415.541212.9873

In [20]:
# above 1e-15 we need 20 min, otherwise we take the flux ratio to be the S/N improvement needed 
snover20 = 1e-15*u.erg/(u.s*u.cm**2)/haflux_sats
snover20[snover20<1] = 1

timeper2 = (snover20**2 * 20*u.min* 2).to(u.hour)
np.sort(timeper2)


Out[20]:
$[0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.66666669,~0.84888351,~1.0494448,~1.2412006,~1.3207878,~2.2291374,~2.8064945,~3.7055449,~4.4664812,~4.7378039,~5.0933132,~7.7385063,~8.6248188,~9.7002792,~12.134331,~15.50974,~17.669472,~23.099499,~38.328289,~42.843693,~48.982426,~60.020119,~75.210091,~115.57136,~138.95398,~327.46292,~725.4837] \; \mathrm{h}$

DBSpec

Baseline numbers from DBSpec (for dr10 objid 1237678879396725010):

  • for conservative line: (online, offline, stdoffline, S/N) (16443.5, 2159.5, 2.589106988905634, 63.51031483233666)
  • 900s x 3
  • which has fibermag_r~20.1

In [21]:
fibfluxratios = 10**((sats['FIBERMAG_R']-20.1)/-2.5)
snr45=np.array(fibfluxratios*63)
timeper_dbsp = ((30/snr45)**2 * (900*u.s*3)).to(u.hour)
timeper_dbsp[timeper_dbsp<15*u.min] = 15*u.min
np.sort(timeper_dbsp)


Out[21]:
$[0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.25,~0.2522268,~0.25980732,~0.4169803,~0.46399614,~0.52894789,~0.57133687,~0.60339653,~0.73370689,~0.78595245,~1.2290394,~1.8111496,~2.0051212,~2.1330483,~3.5551956,~4.053647,~4.6678071,~4.9080462,~8.6651793,~9.4853239,~18.637209] \; \mathrm{h}$

In [22]:
np.sort(timeper2)/np.sort(timeper_dbsp)


Out[22]:
$[2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~2.6666667,~3.395534,~4.1977792,~4.9648023,~5.2831511,~8.9165497,~11.225978,~14.691321,~17.191515,~11.362177,~10.977059,~14.629997,~15.095856,~16.076126,~16.538389,~19.733686,~14.376652,~12.754053,~19.115198,~20.085665,~13.777702,~14.806449,~16.112511,~23.547325,~16.035904,~34.523113,~38.926628] \; \mathrm{}$

Below this is probably wrongish

Following http://www.astro.caltech.edu/~enk/dbsp_throughput/, but just assuming a straight ~20% throughput, which is good for ~Halpha for most gratings

As it currently stands this is wrong as it doesn't account for the sky background, but it also assumes the equivalent width is only ~5, so maybe those cancel...?


In [23]:
def m_to_nphot(mag, texp, dellamb, lamb, throughput=.2):
    num = texp * palomar_A * dellamb * (mag*u.ABmag).physical
    den = cnst.h * lamb
    return (throughput*num/den).decompose()
m_to_nphot(20, 1*u.second, 5*u.angstrom, 6563*u.angstrom)


Out[23]:
$1.4694482 \; \mathrm{}$

In [24]:
m_to_nphot(10, 1*u.second, 1*u.angstrom, 6563*u.angstrom)


Out[24]:
$2938.8963 \; \mathrm{}$

In [25]:
(0*u.ABmag).physical.to(u.erg*u.cm**-2)


Out[25]:
$3.6307805 \times 10^{-20} \; \mathrm{\frac{erg}{cm^{2}}}$

In [26]:
(cwi_noise_20min/(0*u.ABmag).physical/u.Hz).decompose()


Out[26]:
$192.79601 \; \mathrm{}$

In [27]:
# let's just say 2" is typical seeing
countrate = m_to_nphot(sats['FIBER2MAG_R'], 1*u.second, 1*u.angstrom, 6563*u.angstrom)/u.second

# assume we need ~100 counts per/angstrom for a decent centroid
time_needed = 100/ countrate
time_needed


Out[27]:
$[7527.8354,~666.25446,~215.03125,~927.83423,~558.86646,~3428.5591,~5340.5151,~1555.6124,~660.48151,~2494.8333,~969.76794,~3791.8398,~201.49609,~657.36633,~642.84979,~95.368851,~423.35196,~10.650342,~116.72738,~5751.8027,~274.92242,~232.68102,~107.15934,~1261.2764,~3053.5386,~435.76074,~1490.8419,~1600.1823,~69.731544,~208.75578,~882.53387,~2814.6228,~244.51656,~208.34956,~178.21991,~2607.6455,~28.303686,~1373.1394,~139.03751,~112.83287,~992.73224,~3507.2825,~1947.4664,~49.379429,~1216.761,~1417.7328,~236.72516,~301.72754,~610.71198] \; \mathrm{s}$

In [28]:
np.sum(time_needed).to(u.hour)


Out[28]:
$17.678227 \; \mathrm{h}$

observability


In [43]:
from astroplan import plots as planplots

In [34]:
pal = astroplan.Observer.at_site('palomar', timezone='US/Pacific')

In [42]:
targets = [astroplan.FixedTarget(SkyCoord(sat['RA'], sat['DEC'], unit=u.deg), str(sat['OBJID']))
          for sat in sats]

In [132]:
dbsp_thresh = 5*u.hour
any_thresh = 8*u.hour

for tcwi, tdbsp, targ in zip(timeper2, timeper_dbsp, targets):
    if tcwi<dbsp_thresh:
        targ.observable_by = 'CWI'
        targ.observing_time = tcwi
    elif tcwi>dbsp_thresh and tdbsp<any_thresh:
        targ.observable_by = 'DBSP'
        targ.observing_time = tdbsp
    else:
        targ.observable_by = None
        targ.observing_time = None
        

cwi_targets = [targ for targ in targets if targ.observable_by == 'CWI']
dbsp_targets = [targ for targ in targets if targ.observable_by == 'DBSP']
none_targets = [targ for targ in targets if targ.observable_by == None]

len(cwi_targets), len(dbsp_targets), len(none_targets)


Out[132]:
(32, 14, 3)

In [164]:
cons = [astroplan.AirmassConstraint(2.5)]
months = np.array(range(2, 13) + [1])

obstables = []
timeobservable = []
night_length = []
for monthnum in months:
    midmonth = Time('2017-{}-15'.format(monthnum))
    midnight = pal.midnight(midmonth, 'nearest')
    eve = pal.twilight_evening_astronomical(midnight, 'previous')
    morn = pal.twilight_morning_astronomical(midnight, 'next')
    
    tab = astroplan.observability_table(cons, pal, targets, 
                                        time_range=(eve, morn), time_grid_resolution=0.2*u.hour)
    obstables.append(tab)
    night_length.append((morn-eve).to(u.hour))
    timeobservable.append(tab['fraction of time observable']*night_length[-1])

timeobservable = u.Quantity(timeobservable)
for targ, tobs in zip(targets, timeobservable.T):
    targ.time_observable = tobs
    if targ.observing_time is None:
        targ.months_observable_bool = np.array([False]*12)
        targ.months_observable = []
    else:
        targ.months_observable_bool = np.array([t>=targ.observing_time for t in tobs])
        targ.months_observable = months[targ.months_observable_bool]

In [224]:
# individual target info
data = [[] for i in range(19)]
for targ in targets:
    data[0].append(targ.name)
    data[1].append(targ.observable_by)
    data[2].append(targ.observing_time.to(u.hour).value)
    for i, b in enumerate(targ.months_observable_bool):
        data[i+3].append(b)
    data[-4].append(np.sum(targ.months_observable_bool))
    data[-3].append(np.any(targ.months_observable_bool[:6]))
    data[-2].append(np.any(targ.months_observable_bool[-6:]))
    data[-1].append(targ.observing_time is None)
Table(names=['name', 'instrument', 'time'] + list(months) + ['nmonths', 'A', 'B', 'none_target'], data=data).show_in_notebook()


Out[224]:
<Table length=49>
idxnameinstrumenttime234567891011121nmonthsABnone_target
01237651735757259099NoneNoneFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse0FalseFalseTrue
11237657607497515484CWI1.04944479465 hTrueTrueTrueTrueFalseFalseFalseTrueTrueTrueTrueTrue9TrueTrueFalse
21237657607497318756CWI0.666666686535 hTrueTrueTrueTrueFalseFalseFalseTrueTrueTrueTrueTrue9TrueTrueFalse
31237666408439939282DBSP0.25 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
41237663277925204111CWI0.666666686535 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
51237666408439677694DBSP4.90804624557 hFalseFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalse4FalseTrueFalse
61237664338780029261NoneNoneFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse0FalseFalseTrue
71237678602387521791DBSP0.733706891537 hTrueFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
81237678602387456130CWI1.32078778744 hTrueFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
91237678602387456305DBSP1.81114959717 hFalseFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrue7TrueTrueFalse
101237679169841725673CWI3.70554494858 hFalseFalseFalseFalseFalseFalseTrueTrueTrueTrueTrueTrue6FalseTrueFalse
111237678881574814166DBSP4.6678071022 hFalseFalseFalseFalseFalseFalseFalseTrueTrueTrueTrueFalse4FalseTrueFalse
121237678881574944900CWI0.666666686535 hTrueFalseFalseFalseFalseFalseTrueTrueTrueTrueTrueTrue7TrueTrueFalse
131237667255616274560CWI0.848883509636 hTrueTrueTrueTrueTrueFalseFalseFalseTrueTrueTrueTrue9TrueTrueFalse
141237667255616143540CWI1.24120056629 hTrueTrueTrueTrueTrueFalseFalseFalseFalseTrueTrueTrue8TrueTrueFalse
151237667255616143515CWI0.666666686535 hTrueTrueTrueTrueTrueFalseFalseFalseTrueTrueTrueTrue9TrueTrueFalse
161237665367429677221CWI0.666666686535 hTrueTrueTrueTrueTrueFalseFalseFalseTrueTrueTrueTrue9TrueTrueFalse
171237667287812735027CWI0.666666686535 hTrueTrueTrueTrueTrueFalseFalseFalseTrueTrueTrueTrue9TrueTrueFalse
181237650372092690464CWI0.666666686535 hTrueTrueTrueTrueTrueFalseFalseFalseFalseTrueTrueTrue8TrueTrueFalse
191237674648855118180NoneNoneFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse0FalseFalseTrue
201237650762927308814CWI0.666666686535 hTrueTrueTrueTrueTrueFalseFalseFalseFalseTrueTrueTrue8TrueTrueFalse
211237655693015056396CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseFalseTrue8TrueTrueFalse
221237648702984683605CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseFalseTrue8TrueTrueFalse
231237662698115432783DBSP0.463996142149 hTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseTrue10TrueTrueFalse
241237662698115433445DBSP3.55519556999 hFalseTrueTrueTrueTrueTrueTrueFalseFalseFalseFalseFalse6TrueTrueFalse
251237662698115432544CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseTrue10TrueTrueFalse
261237662224092496776DBSP0.60339653492 hTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseTrue10TrueTrueFalse
271237662224092364842DBSP0.785952448845 hTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseTrue10TrueTrueFalse
281237662662147571761CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseTrue10TrueTrueFalse
291237662224092299404CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseTrue10TrueTrueFalse
301237662662147310256CWI2.22913742065 hTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseFalse8TrueTrueFalse
311237662662147703369DBSP2.13304829597 hTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseFalse8TrueTrueFalse
321237680192048857102CWI0.666666686535 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueFalse7TrueTrueFalse
331237679996084617517CWI0.666666686535 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueFalse7TrueTrueFalse
341237680066954264778CWI0.666666686535 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueFalse7TrueTrueFalse
351237680066954330699DBSP2.00512123108 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueFalse7TrueTrueFalse
361237679005021831220CWI0.666666686535 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
371237679005021831801DBSP0.528947889805 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
381237678777399443498CWI0.666666686535 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
391237678776862572690CWI0.666666686535 hFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueTrue8TrueTrueFalse
401237678777399509170CWI4.4664812088 hFalseFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalse4FalseTrueFalse
411237679005558702536DBSP4.05364704132 hFalseFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalse4FalseTrueFalse
421237668298203202132DBSP1.22903943062 hTrueTrueTrueTrueTrueFalseFalseFalseFalseTrueTrueTrue8TrueTrueFalse
431237668298203267092CWI0.666666686535 hTrueTrueTrueTrueTrueTrueFalseFalseFalseTrueTrueTrue9TrueTrueFalse
441237668298203070473CWI2.80649447441 hTrueTrueTrueTrueFalseFalseFalseFalseFalseFalseTrueTrue6TrueTrueFalse
451237668298740007188CWI4.737803936 hTrueTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseTrue5TrueTrueFalse
461237648721248845970CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseFalseTrue8TrueTrueFalse
471237665565541728490CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseTrue9TrueTrueFalse
481237665566078402826CWI0.666666686535 hTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseTrue9TrueTrueFalse

In [241]:
good_targets = [t for t in targets if t.observing_time is not None]
observed = [False]*len(good_targets)

months_to_consider = (3, 10)
monthidxs = [list(months).index(m) for m in months_to_consider]

total_hr = 0*u.hour
nights_needed = []
for monthnum in months_to_consider:    
    if monthnum == months_to_consider[0]:
        monthidx, monthidxother = monthidxs
    else:
        monthidxother, monthidx = monthidxs
        
    hr_needed_cwi = 0*u.hour
    hr_needed_dbsp = 0*u.hour
    for i, targ in enumerate(good_targets):
        if not observed[i] and targ.time_observable[monthidx] > targ.time_observable[monthidxother]:
            observed[i] = True
            total_hr += targ.observing_time
            if targ.observable_by == 'CWI':
                hr_needed_cwi += targ.observing_time
            elif targ.observable_by == 'DBSP':
                hr_needed_dbsp += targ.observing_time
    
    nights_needed.append(((hr_needed_cwi/night_length[monthidx]).value, 
                          (hr_needed_dbsp/night_length[monthidx]).value))
if not np.all(observed):
    print('Not all observed.  Missing:', [targ.name for targ, observed in zip(good_targets, observed)])
nights_needed, total_hr


Out[241]:
([(2.46617459824083, 0.9440027087930974),
  (1.50139210129116, 1.919853499913575)],
 <Quantity 65.46816685795784 h>)

for reference: middle of A semeseter


In [76]:
times = pal.midnight(Time('2017-5-1'))

planplots.plot_airmass(cwi_targets, pal, times, brightness_shading=False, 
                       style_kwargs=dict(color='b', alpha=.5))
planplots.plot_airmass(dbsp_targets, pal, times, brightness_shading=False, 
                       style_kwargs=dict(color='r', alpha=.5))
planplots.plot_airmass(none_targets, pal, times, brightness_shading=True, 
                       style_kwargs=dict(color='k', alpha=.5))
plt.ylim(2.5, 1)


Out[76]:
(2.5, 1)

for reference: middle of B semeseter


In [77]:
times = pal.midnight(Time('2017-11-1'))

planplots.plot_airmass(cwi_targets, pal, times, brightness_shading=False, 
                       style_kwargs=dict(color='b', alpha=.5))
planplots.plot_airmass(dbsp_targets, pal, times, brightness_shading=False, 
                       style_kwargs=dict(color='r', alpha=.5))
planplots.plot_airmass(none_targets, pal, times, brightness_shading=True, 
                       style_kwargs=dict(color='k', alpha=.5))
plt.ylim(2.5, 1)


Out[77]:
(2.5, 1)

Investigate H$\alpha$ vs. mags


In [144]:
nsa = table.Table(hosts.get_nsa())

In [289]:
bandtocolor = {'u':'b', 'g':'g', 'r':'r', 'i':'m', 'z':'k'}

fibmag_to_lhaflux_interp = {}
lmods = {'u':modeling.models.Linear1D(slope=-0.75, intercept=0),
         'r':modeling.models.Linear1D(slope=-0.75, intercept=0)}
lmod_ranges = {'u': (20, 22), 'r':(18,20)}

fibmags = {}
for i, bandnm in enumerate('FNugriz'):
    if bandnm in 'FN':
        continue
    fibmags[bandnm] = fibmag = 22.5-2.5*np.log10(nsa['FIBERFLUX'][:, i])
    flux = nsa['HAFLUX']*10**-17*u.erg/u.s* u.cm**-2
    
    bins_for_med = np.linspace(15,22,75)
    meds = []
    for b1, b2 in zip(bins_for_med[:-1], bins_for_med[1:]):
        msk = (b1<fibmag)&(fibmag<b2)
        lflux = np.log10(flux.value)
        if np.sum(msk&np.isfinite(lflux)) == 0:
            meds.append(None)
            stds.append(None)
        else:
            scvals = sigma_clip(lflux[msk&np.isfinite(lflux)],sigma=2)
            meds.append(np.median(scvals.compressed()))
            
    meds = np.array(meds, dtype=float)
            
    bincens = (bins_for_med[1:]+bins_for_med[:-1])/2
    fibmag_to_lhaflux_interp[bandnm] = interpolate.interp1d(bincens[np.isfinite(meds)], 
                                                    meds[np.isfinite(meds)], 
                                                    kind='quadratic')
    
    plt.figure()

    plt.hexbin(fibmag, np.log10(flux.value), extent=(15,23,-16.5,-13))
    
    plt.plot(bincens, meds, '-or')
    
    if bandnm in lmods:
        if bandnm in lmod_ranges:
            msk = (lmod_ranges[bandnm][0]<bincens)&(bincens<lmod_ranges[bandnm][1])
        else:
            msk = np.ones(len(bincens), dtype=bool)
        newmod = modeling.fitting.LevMarLSQFitter()(lmods[bandnm], bincens[msk], meds[msk])
        lmods[bandnm] = newmod
        x = np.linspace(15, 23, 100)
        plt.plot(x, newmod(x),'k')
    
    #plt.scatter(fibmag, np.log10(flux.value), lw=0, alpha=.04, s=3, c=bandtocolor[bandnm])
    plt.xlim(15,23)
    plt.xlabel(bandnm, fontsize=24)
    plt.ylabel(r'$\log({\rm H}\alpha)$ flux', fontsize=24)
    plt.ylim(-16.5,-13)


/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:12: RuntimeWarning: divide by zero encountered in log10
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:12: RuntimeWarning: invalid value encountered in log10
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:18: RuntimeWarning: invalid value encountered in greater
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:18: RuntimeWarning: invalid value encountered in less
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:19: RuntimeWarning: divide by zero encountered in log10
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:19: RuntimeWarning: invalid value encountered in log10
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:36: RuntimeWarning: divide by zero encountered in log10
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:36: RuntimeWarning: invalid value encountered in log10

Check that the two approaches give at least reasonably compatible-ish answers


In [299]:
uflux = 10**lmods['u'](fibmags['u'])
rflux = 10**lmods['r'](fibmags['r'])

plt.hexbin(fibmags['r'], (uflux - rflux)/uflux, extent=(15, 23, -1, 1))


/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: divide by zero encountered in true_divide
/Users/erik/miniconda3/envs/saga/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in true_divide
Out[299]:
<matplotlib.collections.PolyCollection at 0x1398cbe10>

In [257]:
lmods


Out[257]:
{'r': <Linear1D(slope=-0.5074828822006762, intercept=-5.115944337314049)>,
 'u': <Linear1D(slope=-0.544699254858693, intercept=-3.466572610994288)>}