INFO : AT.py : Setting {'basename': 'x', 'file': 'R-Cas_94052.nfs.fits'} for Ingest_AT
INFO : AT.py : Setting 'ppp' = True for CubeStats_AT
INFO : AT.py : Setting 'numsigma' = 4.0 for CubeSum_AT
INFO : AT.py : Setting 'sigma' = 99.0 for CubeSum_AT
INFO : AT.py : Setting 'numsigma' = 4.0 for SFind2D_AT
INFO : Admit.py : ADMIT run() called [flowcount 1]
INFO :
INFO :
INFO : Executing Ingest_AT - '' (V1.2.13)
INFO :
INFO :
INFO : Run using the following settings:
INFO : box : []
INFO : file : R-Cas_94052.nfs.fits
INFO : smooth : []
INFO : basename : x
INFO : mask : True
INFO : pb :
INFO : edge : []
INFO : restfreq : -1.0
INFO : vlsr : -999999.9
INFO : usepb : True
INFO :
TIMING : Ingest ADMIT [ 2.20422500e+00 1.64729392e+09]
TIMING : Ingest BEGIN [ 0. 0.]
INFO : Ingest_AT.py : OBJECT: R-Cas SHAPE: [ 97 97 2062]
INFO : Ingest_AT.py : basename=x
INFO : utils.py : OSTYPE: linux
TIMING : Ingest start [ 3.52903000e-01 3.60136986e-01 1.41040625e+03 2.11519531e+02]
TIMING : Ingest importfits [ 2.53356000e-01 6.31368876e-01 1.41110938e+03 2.13343750e+02]
WARNING : Ingest_AT.py : Adding dummy STOKES-I axis
TIMING : Ingest adddegaxes [ 1.24830000e-01 1.65058136e-01 1.42085938e+03 2.23851562e+02]
TIMING : Ingest summary-0 [ 1.41830000e-02 1.43029690e-02 1.42085938e+03 2.23851562e+02]
TIMING : Ingest mask [ 1.25860000e-01 1.35885954e-01 1.42103125e+03 2.24093750e+02]
TIMING : Ingest summary-1 [ 1.46840000e-02 1.86049938e-02 1.42103125e+03 2.24093750e+02]
TIMING : Ingest statistics [ 2.19766000e-01 2.20273018e-01 1.42393750e+03 2.27917969e+02]
INFO : Ingest_AT.py : COMMONBEAM[3] {'major': {'value': 17.2500012, 'unit': 'arcsec'}, 'pa': {'value': 0.0, 'unit': 'deg'}, 'minor': {'value': 17.2500012, 'unit': 'arcsec'}}
INFO : Ingest_AT.py : BASICS: [shape] npts min max: [ 97 97 2062 1] 15009298 -0.110412 20.113247
INFO : Ingest_AT.py : S/N (all data): 374.515083
INFO : Ingest_AT.py : GOOD PIXELS: 15009298/19401358 (77.362100% good or 22.637900% bad)
WARNING : Ingest_AT.py : MASKS: ['mask0']
REGRESSION : CUBE: -0.110412 20.1132 0.0537048 97 97 2062 22.637900
INFO : Ingest_AT.py : TELESCOPE: LMT
INFO : Ingest_AT.py : OBJECT: R-Cas
INFO : Ingest_AT.py : REFFREQTYPE: LSRK
INFO : Ingest_AT.py : RA Axis 1: 359.603600 -7.499999 48.000000
INFO : Ingest_AT.py : DEC Axis 2: 51.388800 7.499999 48.000000
INFO : Ingest_AT.py : VLSRv = 0.000000 (from source catalog)
INFO : Ingest_AT.py : VLSRz = 0.000000 +/- 0.000000 1 values: [ 0.]
INFO : Ingest_AT.py : Freq Orig Axis 3: 86.3372 -9.76562e-05 0
INFO : Ingest_AT.py : Cube Orig Axis 3: type=Frequency velocity increment=0.339465 km/s @ fc=86.236577 fw=-0.201367 GHz
INFO : Ingest_AT.py : RESTFREQ: 86.2434 86.2434 -1
INFO : Ingest_AT.py : VLSRc= 23.864173 VLSRf= 0.000000 VLSRv= 0.000000 VLSRz= 0.000000 WIDTH= 699.976211
INFO : Ingest_AT.py : VLSR = 23.864173 errs = 0.000000 0.000000 0.000000 width = 0.339465
TIMING : Ingest done [ 7.40151000e-01 7.46001959e-01 1.42693750e+03 2.31222656e+02]
TIMING : Ingest END [ 1.85805 2.30398798]
INFO : AT.py : BDP_OUT[0] = SpwCube_BDP x.im
INFO :
INFO :
INFO : Executing CubeStats_AT - '' (V1.2.3)
INFO :
INFO :
INFO : Run using the following settings:
INFO : ppp : True
INFO : psample : -1
INFO : robust : []
INFO : maxvrms : 2.0
INFO :
TIMING : CubeStats ADMIT [ 4.12372500e+00 1.64729392e+09]
TIMING : CubeStats BEGIN [ 0. 0.]
TIMING : CubeStats imval [ 6.24440000e-02 6.34551048e-02 1.42568750e+03 2.30054688e+02]
TIMING : CubeStats start [ 1.27510000e-02 1.27830505e-02 1.42568750e+03 2.30363281e+02]
TIMING : CubeStats imstat0 [ 9.97179000e-01 1.00031304e+00 1.44131250e+03 2.46160156e+02]
TIMING : CubeStats imstat1 [ 9.13816000e-01 9.16008949e-01 1.46439453e+03 2.69546875e+02]
INFO : CubeStats_AT.py : sigma varies from 0.005480 to 0.021662; 2062/2062 channels ok
WARNING : CubeStats_AT.py : sigma varies too much, going to clip to 0.0109603 (3.95282 > 2)
INFO : CubeStats_AT.py : Computing MaxPos for PeakPointPlot
TIMING : CubeStats ppp [ 2.450507 2.45916605 1427.48046875 232.734375 ]
INFO : CubeStats_AT.py : CubeMax: 20.113247 @ [ 47 48 1034 0]
INFO : CubeStats_AT.py : CubeMin: -0.110412 @ [ 47 48 824 0]
INFO : CubeStats_AT.py : CubeRMS: 0.010342
INFO : CubeStats_AT.py : RMS Sanity check 5.192881
WARNING : CubeStats_AT.py : RMS sanity check = 5.192881. Either bad sidelobes, lotsa signal, or both
REGRESSION : CST: 0.010342 5.192881
INFO : CubeStats_AT.py : mean,rms,S/N=0.000519 0.010342 1944.812201
INFO : CubeStats_AT.py : RMS BAD VARIATION RATIO: 2.245667 2.411395
TIMING : CubeStats plotting [ 2.441197 2.63597202 1455.08203125 258.33203125]
TIMING : CubeStats done [ 1.38220000e-02 1.38559341e-02 1.45508203e+03 2.58332031e+02]
TIMING : CubeStats summary [ 1.25920000e-02 1.26378536e-02 1.45508203e+03 2.58332031e+02]
TIMING : CubeStats END [ 6.916617 7.12654018]
INFO : AT.py : BDP_OUT[0] = CubeStats_BDP x.cst
INFO :
INFO :
INFO : Executing CubeSum_AT - '' (V1.2.2)
INFO :
INFO :
INFO : Run using the following settings:
INFO : linesum : True
INFO : numsigma : 4.0
INFO : zoom : 1
INFO : pad : 5
INFO : sigma : 99.0
INFO :
TIMING : CubeSum ADMIT [ 1.11004290e+01 1.64729393e+09]
TIMING : CubeSum BEGIN [ 0. 0.]
INFO : CubeSum_AT.py : Using constant sigma = 0.010342
TIMING : CubeSum start [ 5.31310000e-02 5.33258915e-02 1.45508203e+03 2.58332031e+02]
TIMING : CubeSum immoments [ 2.70702000e-01 2.99544096e-01 1.46075781e+03 2.64402344e+02]
TIMING : CubeSum statistics [ 2.06420000e-02 2.07269192e-02 1.46075781e+03 2.64402344e+02]
INFO : CubeSum_AT.py : Total flux: 170663.200072 (sum=3034.013417)
REGRESSION : CSM: [170663.20007179558, 3034.0134166050261]
TIMING : CubeSum implot [ 1.49264000e-01 6.07511115e+00 1.53276172e+03 2.64542969e+02]
TIMING : CubeSum getdata [ 2.32380000e-02 2.33099461e-02 1.53276172e+03 2.64542969e+02]
TIMING : CubeSum done [ 2.99230000e-01 3.19190025e-01 1.53276172e+03 2.64660156e+02]
TIMING : CubeSum END [ 0.830316 6.80535889]
INFO : AT.py : BDP_OUT[0] = Moment_BDP x.csm
INFO :
INFO :
INFO : Executing SFind2D_AT - 'csm' (V1.2.2)
INFO :
INFO :
INFO : Run using the following settings:
INFO : nmax : 30
INFO : numsigma : 4.0
INFO : snmax : 35.0
INFO : region :
INFO : zoom : 1
INFO : robust : ['hin', 1.5]
INFO : sigma : -1.0
INFO :
TIMING : SFind2D ADMIT [ 1.19938490e+01 1.64729394e+09]
TIMING : SFind2D BEGIN [ 0. 0.]
TIMING : SFind2D start [ 3.60030000e-02 3.61280441e-02 1.53276172e+03 2.64660156e+02]
TIMING : SFind2D imstat [ 4.36460000e-02 4.38709259e-02 1.53276172e+03 2.64660156e+02]
INFO : AT.py : Setting 'sigma' = 0.137818316672 for SFind2D_AT
INFO : SFind2D_AT.py : sigma, dmin, dmax, snmax, cutoff 0.137818 -0.448795 69.0754 35 0.0285714
WARNING : SFind2D_AT.py : Temporarely patching your K.km/s units to Jy/beam for ia.findsources()
TIMING : SFind2D findsources [ 9.51960000e-02 1.21773005e-01 1.53276172e+03 2.64828125e+02]
INFO : SFind2D_AT.py : Right Ascen. Declination X(pix) Y(pix) Peak Flux Major Minor PA SNR
INFO : SFind2D_AT.py : K.km/s Jy arcsec arcsec deg
INFO : SFind2D_AT.py : 23:58:25.526 +51.23.17.67 47.17 47.73 63.2 377 43.668 39.460 84.0 458.8
INFO : SFind2D_AT.py : Wrote ds9.reg
TIMING : SFind2D table [ 1.34512000e-01 1.38768911e-01 1.53276172e+03 2.65394531e+02]
REGRESSION : CONTFLUX: 1 377.218
INFO : SFind2D_AT.py : Fitted Gaussian size; NOT deconvolved source size.
INFO : SFind2D_AT.py : Restoring Beam: Major axis: 17.3 arcsec , Minor axis: 17.3 arcsec , PA: 0.0 deg
WARNING : SFind2D_AT.py : LogScaling applied
TIMING : SFind2D done [ 3.72170000e-01 3.91395092e-01 1.53251172e+03 2.65175781e+02]
TIMING : SFind2D END [ 0.696504 0.74694705]
INFO : AT.py : BDP_OUT[0] = SourceList_BDP x-csm.sl
TIMING : ADMITrun END [ 10.96478 17.70669007]
INFO : Admit.py : ADMIT run() finished [flowcount 1] [cpu 10.9648 17.7067 ]
INFO : AT.py : Setting 'csub' = [0, 0] for LineSegment_AT
INFO : Admit.py : ADMIT run() called [flowcount 1]
INFO :
INFO :
INFO : Executing CubeSpectrum_AT - '' (V1.2.5)
INFO :
INFO :
INFO : Run using the following settings:
INFO : xaxis :
INFO : sources : [0]
INFO : pos : []
INFO :
TIMING : CubeSpectrum ADMIT [ 1.29840620e+01 1.64729394e+09]
TIMING : CubeSpectrum BEGIN [ 0. 0.]
INFO : CubeSpectrum_AT.py : CubeStats::maxpos,val=[47, 48, 1034],20.113247
TIMING : CubeSpectrum CubeStats-pos [ 4.86140000e-02 4.87618446e-02 1.53251172e+03 2.65175781e+02]
INFO : CubeSpectrum_AT.py : CubeSum::maxpos,val=[47, 48],69.075394
TIMING : CubeSpectrum Moment-pos [ 4.29130000e-02 4.30481434e-02 1.53251172e+03 2.65175781e+02]
INFO : CubeSpectrum_AT.py : SourceList::maxpos,val=('23h58m25.526s', '+51d23m17.67s'),63.226089
TIMING : CubeSpectrum SourceList-pos [ 3.58450000e-02 3.59368324e-02 1.53251172e+03 2.65175781e+02]
TIMING : CubeSpectrum open [ 1.21650000e-02 1.21970177e-02 1.53251172e+03 2.65175781e+02]
TIMING : CubeSpectrum imval [ 5.49800000e-02 5.51919937e-02 1.53251172e+03 2.65609375e+02]
TIMING : CubeSpectrum imhead [ 4.23670000e-02 4.25090790e-02 1.53251172e+03 2.65609375e+02]
TIMING : CubeSpectrum imval [ 3.04377000e-01 3.23261976e-01 1.53251172e+03 2.65656250e+02]
REGRESSION : CSP: [20.113246917724609, 20.113246917724609]
INFO : CubeSpectrum_AT.py : Writing 2 testCubeSpectrum tables
TIMING : CubeSpectrum done [ 2.86785000e-01 3.05665016e-01 1.53251172e+03 2.65656250e+02]
TIMING : CubeSpectrum summary [ 1.24920000e-02 1.25238895e-02 1.53251172e+03 2.65656250e+02]
TIMING : CubeSpectrum END [ 0.852699 0.89128995]
INFO : AT.py : BDP_OUT[0] = CubeSpectrum_BDP x.csp
INFO :
INFO :
INFO : Executing LineSegment_AT - '' (V1.2.3)
INFO :
INFO :
INFO : Run using the following settings:
INFO : numsigma : 5.0
INFO : minchan : 4
INFO : edgechannels : 0
INFO : smooth : []
INFO : recalcnoise : False
INFO : maxgap : 3
INFO : csub : [0, 0]
INFO : iterate : True
INFO : segment : ADMIT
INFO :
TIMING : LineSegment ADMIT [ 1.38970470e+01 1.64729394e+09]
TIMING : LineSegment BEGIN [ 0. 0.]
TIMING : LineSegment start [ 2.44600000e-02 2.45239735e-02 1.53251172e+03 2.65656250e+02]
INFO : LineSegment_AT.py : Attempting Continuum Subtraction for Input Spectra
INFO : LineSegment_AT.py : Attempting Continuum Subtraction for Input CubeStats Spectra
TIMING : LineSegment getspectrum [ 57.045577 57.19616294 1532.51171875 266.046875 ]
INFO : LineSegment_AT.py : Detecting segments in CubeSpectrum based data
WARNING : specutil.py : 0 [127, 130]
WARNING : specutil.py : 1 [158, 165]
WARNING : specutil.py : 2 [223, 233]
WARNING : specutil.py : 3 [256, 259]
WARNING : specutil.py : 4 [383, 386]
WARNING : specutil.py : 5 [496, 500]
WARNING : specutil.py : 6 [561, 564]
WARNING : specutil.py : 7 [569, 572]
WARNING : specutil.py : 8 [751, 754]
WARNING : specutil.py : 9 [814, 829]
WARNING : specutil.py : 10 [928, 934]
WARNING : specutil.py : 11 [979, 982]
WARNING : specutil.py : 12 [990, 993]
WARNING : specutil.py : 13 [1018, 1060]
WARNING : specutil.py : 14 [1092, 1095]
WARNING : specutil.py : 15 [1195, 1198]
WARNING : specutil.py : 16 [1227, 1233]
WARNING : specutil.py : 17 [1263, 1266]
WARNING : specutil.py : 18 [1370, 1375]
WARNING : specutil.py : 19 [1479, 1482]
WARNING : specutil.py : 20 [1868, 1871]
WARNING : specutil.py : 21 [1899, 1904]
WARNING : specutil.py : 22 [55, 57]
WARNING : specutil.py : 0 [127, 130]
WARNING : specutil.py : 1 [158, 165]
WARNING : specutil.py : 2 [223, 233]
WARNING : specutil.py : 3 [256, 259]
WARNING : specutil.py : 4 [383, 386]
WARNING : specutil.py : 5 [496, 500]
WARNING : specutil.py : 6 [561, 564]
WARNING : specutil.py : 7 [569, 572]
WARNING : specutil.py : 8 [751, 754]
WARNING : specutil.py : 9 [814, 829]
WARNING : specutil.py : 10 [928, 934]
WARNING : specutil.py : 11 [979, 982]
WARNING : specutil.py : 12 [990, 993]
WARNING : specutil.py : 13 [1018, 1060]
WARNING : specutil.py : 14 [1092, 1095]
WARNING : specutil.py : 15 [1195, 1198]
WARNING : specutil.py : 16 [1227, 1233]
WARNING : specutil.py : 17 [1263, 1266]
WARNING : specutil.py : 18 [1370, 1375]
WARNING : specutil.py : 19 [1479, 1482]
WARNING : specutil.py : 20 [1868, 1871]
WARNING : specutil.py : 21 [1899, 1904]
WARNING : specutil.py : 22 [55, 57]
INFO : LineSegment_AT.py : Detecting segments in CubeStats based data
WARNING : specutil.py : 0 [508, 511]
WARNING : specutil.py : 1 [1019, 1059]
WARNING : specutil.py : 0 [822, 828]
WARNING : specutil.py : 1 [1926, 1931]
WARNING : specutil.py : 2 [1975, 1978]
TIMING : LineSegment segment finder [ 5.82368 5.84226608 1532.51171875 266.046875 ]
INFO : LineSegment_AT.py : Segment Coverage 164 / 2062 = 0.0795344
REGRESSION : LINESEG: [[86.287309239027692, 86.287602207747071], [86.233793619623114, 86.237699869214694], [86.256352211014459, 86.256938148453202], [86.148637378526757, 86.149125659725726], [86.144047535256675, 86.144340503976039], [86.324516266387448, 86.324809235106827], [86.321098297994823, 86.321781891673353], [86.314457673689148, 86.315434236087043], [86.311918611454615, 86.312211580173994], [86.299516269001373, 86.299809237720737], [86.288383457665375, 86.288774082624542], [86.282133458318867, 86.282426427038217], [86.281352208400548, 86.281645177119913], [86.263578772758876, 86.263871741478241], [86.256254554774671, 86.25771939837152], [86.24600064959678, 86.246586587035523], [86.241313150086896, 86.24160611880626], [86.240238931449213, 86.240531900168577], [86.233695963383326, 86.237797525454482], [86.230277994990686, 86.230570963710065], [86.220219402292386, 86.220512371011765], [86.216801433899761, 86.217387371338503], [86.213578777986712, 86.21387174670609], [86.202934247849669, 86.203422529048609], [86.192485030192202, 86.192777998911581], [86.154496752914142, 86.154789721633506], [86.151274097001078, 86.151762378200033], [86.331645171892077, 86.331840484371654], [86.324516266387448, 86.324809235106827], [86.321098297994823, 86.321781891673353], [86.314457673689148, 86.315434236087043], [86.311918611454615, 86.312211580173994], [86.299516269001373, 86.299809237720737], [86.288383457665375, 86.288774082624542], [86.282133458318867, 86.282426427038217], [86.281352208400548, 86.281645177119913], [86.263578772758876, 86.263871741478241], [86.256254554774671, 86.25771939837152], [86.24600064959678, 86.246586587035523], [86.241313150086896, 86.24160611880626], [86.240238931449213, 86.240531900168577], [86.233695963383326, 86.237797525454482], [86.230277994990686, 86.230570963710065], [86.220219402292386, 86.220512371011765], [86.216801433899761, 86.217387371338503], [86.213578777986712, 86.21387174670609], [86.202934247849669, 86.203422529048609], [86.192485030192202, 86.192777998911581], [86.154496752914142, 86.154789721633506], [86.151274097001078, 86.151762378200033], [86.331645171892077, 86.331840484371654]]
TIMING : LineSegment done [ 2.401836 2.51962686 1557.734375 290.98828125]
TIMING : LineSegment END [ 65.307958 65.59501696]
INFO : AT.py : BDP_OUT[0] = LineSegment_BDP x.lseg
TIMING : ADMITrun END [ 77.462305 84.58983707]
INFO : Admit.py : ADMIT run() finished [flowcount 1] [cpu 77.4623 84.5898 ]
INFO : Admit.py : ADMIT run() called [flowcount 1]
TIMING : ADMITrun END [ 77.569989 84.77585912]
INFO : Admit.py : ADMIT run() finished [flowcount 1] [cpu 77.57 84.7759 ]
INFO : AT.py : Setting 'csub' = [0, 0] for LineID_AT
INFO : AT.py : Setting 'references' = etc/tier1_lines.list for LineID_AT
INFO : Admit.py : ADMIT run() called [flowcount 1]
INFO :
INFO :
INFO : Executing LineID_AT - '' (V1.2.7)
INFO :
INFO :
INFO : Run using the following settings:
INFO : numsigma : 5.0
INFO : force : []
INFO : tier1width : 0.0
INFO : online : False
INFO : recomblevel : shallow
INFO : references : etc/tier1_lines.list
INFO : csub : [0, 0]
INFO : identifylines : True
INFO : iterate : True
INFO : segment : ADMIT
INFO : allowexotics : False
INFO : minchan : 4
INFO : pattern : AUTO
INFO : edgechannels : 0
INFO : smooth : []
INFO : recalcnoise : False
INFO : vlsr : -999999.99
INFO : maxgap : 3
INFO : reject : []
INFO : method : {'PeakFinder': {'thresh': 0.0}}
INFO : mode : ONE
INFO :
TIMING : LineID ADMIT [ 7.96135660e+01 1.64729401e+09]
TIMING : LineID BEGIN [ 0. 0.]
INFO : LineID_AT.py : Set vlsr = 23.86 for line identification.
INFO : LineID_AT.py : Identifylines = True
INFO : LineID_AT.py : Using vlsr = 23.8642
INFO : LineID_AT.py : Attempting Continuum Subtraction for Input Spectra
TIMING : LineID getspectrum-cubespecs [ 36.902285 37.02344084 1557.734375 291.16796875]
INFO : LineID_AT.py : Attempting Continuum Subtraction for Input CubeStats Spectra
TIMING : LineID getspectrum-cubestats [ 20.326885 20.40383315 1557.734375 291.16796875]
TIMING : LineID segment finder [ 1.47480000e-02 1.48119926e-02 1.55773438e+03 2.91167969e+02]
INFO : LineID_AT.py : Detecting segments in CubeSpectrum based data
WARNING : specutil.py : 0 [127, 130]
WARNING : specutil.py : 1 [158, 165]
WARNING : specutil.py : 2 [223, 233]
WARNING : specutil.py : 3 [256, 259]
WARNING : specutil.py : 4 [383, 386]
WARNING : specutil.py : 5 [496, 500]
WARNING : specutil.py : 6 [561, 564]
WARNING : specutil.py : 7 [569, 572]
WARNING : specutil.py : 8 [751, 754]
WARNING : specutil.py : 9 [814, 829]
WARNING : specutil.py : 10 [928, 934]
WARNING : specutil.py : 11 [979, 982]
WARNING : specutil.py : 12 [990, 993]
WARNING : specutil.py : 13 [1018, 1060]
WARNING : specutil.py : 14 [1092, 1095]
WARNING : specutil.py : 15 [1195, 1198]
WARNING : specutil.py : 16 [1227, 1233]
WARNING : specutil.py : 17 [1263, 1266]
WARNING : specutil.py : 18 [1370, 1375]
WARNING : specutil.py : 19 [1479, 1482]
WARNING : specutil.py : 20 [1868, 1871]
WARNING : specutil.py : 21 [1899, 1904]
WARNING : specutil.py : 22 [55, 57]
WARNING : specutil.py : 0 [127, 130]
WARNING : specutil.py : 1 [158, 165]
WARNING : specutil.py : 2 [223, 233]
WARNING : specutil.py : 3 [256, 259]
WARNING : specutil.py : 4 [383, 386]
WARNING : specutil.py : 5 [496, 500]
WARNING : specutil.py : 6 [561, 564]
WARNING : specutil.py : 7 [569, 572]
WARNING : specutil.py : 8 [751, 754]
WARNING : specutil.py : 9 [814, 829]
WARNING : specutil.py : 10 [928, 934]
WARNING : specutil.py : 11 [979, 982]
WARNING : specutil.py : 12 [990, 993]
WARNING : specutil.py : 13 [1018, 1060]
WARNING : specutil.py : 14 [1092, 1095]
WARNING : specutil.py : 15 [1195, 1198]
WARNING : specutil.py : 16 [1227, 1233]
WARNING : specutil.py : 17 [1263, 1266]
WARNING : specutil.py : 18 [1370, 1375]
WARNING : specutil.py : 19 [1479, 1482]
WARNING : specutil.py : 20 [1868, 1871]
WARNING : specutil.py : 21 [1899, 1904]
WARNING : specutil.py : 22 [55, 57]
INFO : LineID_AT.py : Detecting segments in CubeStats based data
WARNING : specutil.py : 0 [508, 511]
WARNING : specutil.py : 1 [1019, 1059]
WARNING : specutil.py : 0 [822, 828]
WARNING : specutil.py : 1 [1926, 1931]
WARNING : specutil.py : 2 [1975, 1978]
INFO : LineID_AT.py : Searching for spectral peaks with method: PeakFinder
INFO : LineID_AT.py : Too many peaks in CubeSpectrum for pattern finding to be useful, turning it off.[2]
INFO : LineID_AT.py : Found line: U_86.1511 @ 86.1511006747GHz, channels 1975 - 1978
REGRESSION : LINEID: U_86.1511 86.15110 1975 1978
INFO : LineID_AT.py : Found line: U_86.1559 @ 86.1558862114GHz, channels 1926 - 1931
REGRESSION : LINEID: U_86.1559 86.15589 1926 1931
INFO : LineID_AT.py : Found line: U_86.1585 @ 86.1585231398GHz, channels 1899 - 1904
REGRESSION : LINEID: U_86.1585 86.15852 1899 1904
INFO : LineID_AT.py : Found line: U_86.1615 @ 86.1614530602GHz, channels 1868 - 1871
REGRESSION : LINEID: U_86.1615 86.16145 1868 1871
INFO : LineID_AT.py : Found line: U_86.1995 @ 86.1995420257GHz, channels 1479 - 1482
REGRESSION : LINEID: U_86.1995 86.19954 1479 1482
INFO : LineID_AT.py : Found line: CH3OCHOv=0 7(4,4)-6(4,3)A @ 86.21008GHz, channels 1370 - 1375
REGRESSION : LINEID: CH3OCHOv=0 86.21008 1370 1375
INFO : LineID_AT.py : Found line: U_86.2205 @ 86.2205397887GHz, channels 1263 - 1266
REGRESSION : LINEID: U_86.2205 86.22054 1263 1266
INFO : LineID_AT.py : Found line: CH3OCH3 2(2,0)-2(1,1)AE @ 86.22378GHz, channels 1227 - 1233
REGRESSION : LINEID: CH3OCH3 86.22378 1227 1233
INFO : LineID_AT.py : Found line: U_86.2273 @ 86.2272786057GHz, channels 1195 - 1198
REGRESSION : LINEID: U_86.2273 86.22728 1195 1198
INFO : LineID_AT.py : Found line: SiO 2-1 @ 86.24337GHz, channels 928 - 1095
REGRESSION : LINEID: SiO 86.24337 928 1095
INFO : LineID_AT.py : Found line: (CH3)2COv=0 19(9,10)-19(8,11)EA @ 86.26354GHz, channels 819 - 829
REGRESSION : LINEID: (CH3)2COv=0 86.26354 819 829
INFO : LineID_AT.py : Found line: U_86.2645 @ 86.264488595GHz, channels 814 - 819
REGRESSION : LINEID: U_86.2645 86.26449 814 819
INFO : LineID_AT.py : Found line: U_86.2706 @ 86.2706414279GHz, channels 751 - 754
REGRESSION : LINEID: U_86.2706 86.27064 751 754
INFO : LineID_AT.py : Found line: (CH3)2COv=0 40(14,27)-39(15,24)AE @ 86.28835GHz, channels 569 - 572
REGRESSION : LINEID: (CH3)2COv=0 86.28835 569 572
INFO : LineID_AT.py : Found line: U_86.2891 @ 86.2890999266GHz, channels 561 - 564
REGRESSION : LINEID: U_86.2891 86.28910 561 564
INFO : LineID_AT.py : Found line: CH3CH2CHO 19(4,16)-18(5,13) @ 86.29444GHz, channels 508 - 511
REGRESSION : LINEID: CH3CH2CHO 86.29444 508 511
INFO : LineID_AT.py : Found line: U_86.2954 @ 86.2953504235GHz, channels 496 - 500
REGRESSION : LINEID: U_86.2954 86.29535 496 500
INFO : LineID_AT.py : Found line: U_86.3066 @ 86.3065817851GHz, channels 383 - 386
REGRESSION : LINEID: U_86.3066 86.30658 383 386
INFO : LineID_AT.py : Found line: U_86.3189 @ 86.3188874509GHz, channels 256 - 259
REGRESSION : LINEID: U_86.3189 86.31889 256 259
INFO : LineID_AT.py : Found line: g'Ga-(CH2OH)2 29(10,19)v=0-28(11,18)v=0 @ 86.3222GHz, channels 223 - 233
REGRESSION : LINEID: g'Ga-(CH2OH)2 86.32220 223 233
INFO : LineID_AT.py : Found line: H13CN J=1-0,F=2-1 @ 86.34016GHz, channels 55 - 165
REGRESSION : LINEID: H13CN 86.34016 55 165
INFO : LineID_AT.py : Line Coverage 380 / 2062 = 0.184287
TIMING : LineID done [ 20.914424 21.34397101 1667.84765625 402.609375 ]
TIMING : LineID END [ 78.174143 78.8018949]
INFO : AT.py : BDP_OUT[0] = LineList_BDP x.ll
TIMING : ADMITrun END [ 156.279354 164.18538404]
INFO : Admit.py : ADMIT run() finished [flowcount 1] [cpu 156.279 164.185 ]
INFO : Admit.py : ADMIT run() called [flowcount 1]
INFO :
INFO :
INFO : Executing LineCube_AT - '' (V1.2.2)
INFO :
INFO :
INFO : Run using the following settings:
INFO : equalize : False
INFO : pad : 5
INFO : fpad : -1.0
INFO :
TIMING : LineCube ADMIT [ 1.58275309e+02 1.64729409e+09]
TIMING : LineCube BEGIN [ 0. 0.]
TIMING : LineCube start [ 2.35901000e-01 2.37236023e-01 1.66784766e+03 4.02781250e+02]
TIMING : LineCube pad [ 1.53770000e-02 1.54249668e-02 1.66784766e+03 4.02781250e+02]
TIMING : LineCube trans-x.U_86.1511 [ 7.89020000e-02 1.47922039e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.1559 [ 7.50900000e-02 2.17674017e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.1585 [ 7.47880000e-02 2.47745991e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.1615 [ 7.48280000e-02 1.32544994e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.1995 [ 7.50070000e-02 1.21592045e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.CH3OCHO_86.21008 [ 7.54330000e-02 1.65746927e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.2205 [ 7.48870000e-02 1.39844179e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.CH3OCH3_86.22378 [ 7.57060000e-02 1.44678831e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.2273 [ 8.03400000e-02 3.06434155e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.SiO_86.24337 [ 1.03729000e-01 1.59991980e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.(CH3)2CO_86.26354 [ 7.59440000e-02 4.23909903e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.2645 [ 7.58410000e-02 1.76774979e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.2706 [ 7.85190000e-02 1.19657993e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.(CH3)2CO_86.28835 [ 8.12340000e-02 1.25626087e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.2891 [ 7.68530000e-02 1.54872894e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.CH3CH2CHO_86.29444 [ 7.91190000e-02 1.19405031e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.2954 [ 7.53780000e-02 1.19575977e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.3066 [ 7.54390000e-02 1.44236088e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.U_86.3189 [ 7.87610000e-02 1.30120993e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.(CH2OH)2_86.32220 [ 7.60800000e-02 1.36538982e-01 1.66784766e+03 4.02839844e+02]
TIMING : LineCube trans-x.H13CN_86.34016 [ 9.27290000e-02 1.58127069e-01 1.66784766e+03 4.02839844e+02]
REGRESSION : LC: [1970, 1983, 1921, 1936, 1894, 1909, 1863, 1876, 1474, 1487, 1365, 1380, 1258, 1271, 1222, 1238, 1190, 1203, 923, 1100, 814, 834, 809, 824, 746, 759, 564, 577, 556, 569, 503, 516, 491, 505, 378, 391, 251, 264, 218, 238, 50, 170]
TIMING : LineCube done [ 2.52590000e-02 2.53210068e-02 1.66784766e+03 4.02839844e+02]
TIMING : LineCube END [ 1.943583 3.88348103]
INFO : AT.py : BDP_OUT[0] = LineCube_BDP x.U_86.1511/lc.im
INFO : AT.py : BDP_OUT[1] = LineCube_BDP x.U_86.1559/lc.im
INFO : AT.py : BDP_OUT[2] = LineCube_BDP x.U_86.1585/lc.im
INFO : AT.py : BDP_OUT[3] = LineCube_BDP x.U_86.1615/lc.im
INFO : AT.py : BDP_OUT[4] = LineCube_BDP x.U_86.1995/lc.im
INFO : AT.py : BDP_OUT[5] = LineCube_BDP x.CH3OCHO_86.21008/lc.im
INFO : AT.py : BDP_OUT[6] = LineCube_BDP x.U_86.2205/lc.im
INFO : AT.py : BDP_OUT[7] = LineCube_BDP x.CH3OCH3_86.22378/lc.im
INFO : AT.py : BDP_OUT[8] = LineCube_BDP x.U_86.2273/lc.im
INFO : AT.py : BDP_OUT[9] = LineCube_BDP x.SiO_86.24337/lc.im
INFO : AT.py : BDP_OUT[10] = LineCube_BDP x.(CH3)2CO_86.26354/lc.im
INFO : AT.py : BDP_OUT[11] = LineCube_BDP x.U_86.2645/lc.im
INFO : AT.py : BDP_OUT[12] = LineCube_BDP x.U_86.2706/lc.im
INFO : AT.py : BDP_OUT[13] = LineCube_BDP x.(CH3)2CO_86.28835/lc.im
INFO : AT.py : BDP_OUT[14] = LineCube_BDP x.U_86.2891/lc.im
INFO : AT.py : BDP_OUT[15] = LineCube_BDP x.CH3CH2CHO_86.29444/lc.im
INFO : AT.py : BDP_OUT[16] = LineCube_BDP x.U_86.2954/lc.im
INFO : AT.py : BDP_OUT[17] = LineCube_BDP x.U_86.3066/lc.im
INFO : AT.py : BDP_OUT[18] = LineCube_BDP x.U_86.3189/lc.im
INFO : AT.py : BDP_OUT[19] = LineCube_BDP x.(CH2OH)2_86.32220/lc.im
INFO : AT.py : BDP_OUT[20] = LineCube_BDP x.H13CN_86.34016/lc.im