Run the BOG-Line, acoustic tracking test of NPS Spray glider.

Low solar charging, science sensors powered off between BOG Line runs to conserve power for acoustic testing.

First ~hour of data had low T, high S, high O2; CTD may not yet have been fully flushed, these data have been removed. First sample after science sensors turned back on is suspect and have been removed. T-vs-O2 relationship is not very tight including some relatively high-T, low-O2 data near M2; O2 data are ~25 umol/kg low compared to nearby M1, similar to previous deployment [20161031]...Use O2 data with caution.

pH data are within < 0.05 [total scale] of nearby M1, and not biased in either direction - very good. As in the previous deployment [20161031], pH is about 0.05 [total scale] low compared to that predicted from pCO2w.

All air temperature data are very low [0-7.5 C] and atmospheric pressure data after the sensors were turned back on are high [1030-1050 hPa]. All air T data have been removed. All atm press data after sensors were turned back on have been removed.

JS implemented 'BogLine' .csv file starting with this mission, generated in Matlab QC script, to ID data points taken along the BOG Line [C1 to M1 to M2]

--

  veh = 'wgTiny'
  dep = '20161212'
  # calibration/config
  CCUname = 'TinyToo'
  CCUnum = '1531399927' #Tiny = '794595959'; TinyToo = '1531399927'
  CTDserNum = '0164'
  O2serNum = '3233'
  O2calDate = '9/25/2015'
  O2cal = [0.00031556, -817.4, 1.41, -0.0038249, 0.00015244, -0.0000021156, 0.036]
  EPserNum = 'BB2FLMBC-1429'
  EPcalDate = '4/21/2016'
  EPcal = [[0.00001098, 52], [0.000003629, 40], [0.012, 35]] #EcoPuck factory cal
  pHconfig = ['N/A', 'N/A', 'N/A'] #cap adapter, electrode, housing
  pHcalDate = '4/20/2015'
  pHbatch = '136'
  pHcal = [-0.397897, 293.12]
  CO2id = 'WG1'
  CO2calDate = '5/5/2014'
  CO2stnd = 467.58
  CO2cal = [-30.27890, 0.43639, 1.09200, -0.00243]
  CO2cal_v2 = [-5.4872e-05, +3.0826e-02, -1.7203e+00, -9.9368e-08, +6.2298e-05, 
               -5.8365e-02, +5.2634e-06, -2.0836e-03, +1.7030e+00, -6.1803e-05, 
               +1.0173e+00, -1.1505e+01]

	tBeg = datetime.datetime(2016, 12, 12, 19, 40, 0, 0, timezone('UTC'))
	tEnd = datetime.datetime(2016, 12, 21, 22, 0, 0, 0, timezone('UTC'))

  # vehicle stats prior to beginning of this deployment - copied from website
  distCumul = 19153.5*1000 #km to m #ADJUSTED 12/12/16 USING LON/LAT FIXES INSTEAD OF LR DISTANCE OVER GROUND
  timeCumul = datetime.timedelta(days=465, hours=3, minutes=37)

--